—Z nmmmm

31293 017141437

This is to certify that the

dissertation entitled

Assessing the Potential for Creating Biased
Rhizospheres Based on Inositol Rhizopines

presented by

Brian B. McSpadden Gardener

has been accepted towards fulﬁllment
of the requirements for

Ph . D . degree in Botany and
Plant Pathology

/

ajor professor

 

Date M

MSU is an Afﬁrmative Action/Equal Opportunity Institution 0-12771

 

 

Mlchlgan State

LIBRARY
Unlversity

 

 

PLACE IN RETURN BOX
to remove this checkout from your record.
TO AVOID FINE return on or before date due.

 

MTE DUE

MTE DUE

DATE DUE

 

|
Ilml 12'19'99

 

“I
1')
”I

F A 1
J l

n
U

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1/93 clamor-mu

ASSESSING THE POTENTIAL OF CREATING BIASED
RHIZOSPHERES BASED ON IN OSITOL RHIZOPINES

By
Brian B. McSpadden Gardener

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Botany and Plant Pathology
1998

ABSTRACT

ASSESSING THE POTENTIAL OF CREATING BIASED RHIZOSPHERES
BASED ON INOSITOL RHIZOPINES

By

Brian B. McSpadden Gardener

Particular microbial populations may be selectively enhanced by the introduction of speciﬁc
organic nutrients into the rhizosphere environment. The initial objective of this thesis was to evaluate
this "biased rhizosphere" hypothesis in systems where inositol rhizopines were to be provided as the
selective organic nutrient. When it became apparent that a substitute for rhizopines was required to
continue this assessment, myo-inositol was selected as a surrogate compound, and the impact of
exogenous applications of this organic nutrient on rhizosphere bacterial populations was investigated.
Thus, several lines of investigation were pursued to assess the potential of creating biased
rhizospheres systems and evaluating their usefulness to speciﬁcally stimulate target populations of
rhizosphere bacteria.

The rhizopine synthesis (mos) genes from Sinorhizobium meliloti 15-30 were used in efforts
to generate rhizopine producing transgenic plants. Rhizopines were not observed to accumulate in
transformed plant tissues, and molecular analyses indicated that the cloned mos genes were not all
properly expressed An analysis of the cloned mos genes indicated that they were not sufﬁcient for
rhizopineaccmnulation in nodules. More information regarding the nature of rhizopine synthesis is
required before a biased rhizosrrhere system based on rhiZOpine-synthesizing plants can be created.

The abundance and source of rhizopine catabolic activity in the soil and rhizosphere

environment was investigated. The number of culturable bacteria capable of utilizing these

compounds for growth was one to ten percent of the number growing on complex media. A diverse
set of novel rhizopine-catabolizing bacteria were isolated and characterized Different bacteria capable
of catabolizing the proposed nutritional mediators are present in the environment and may compete for
these orpnic nutrients in situ.

The impact of myo—inositol amendment on soil and rhizosphere bacterial populations was
examined in growth chamber and ﬁeld experiments. Application of the nutrient resulted in increases
in the numbers of culturable bacteria ARDRA and FT-ARDRA analyses indicated that the abundance
and composition of the bacterial communities changed in response to the amendment. Increases in
the abundance and activity of S. meliloti were observed. These results indicate that nutrient
amendments Can be used to promote the growth and activities of targeted bacterial pOpulations in the
rhizosphere.

ACKNOWLEDGEMENTS

There are many people that have contributed to the rich experience of my graduate career, and
Iﬁnd it difﬁcult to ﬁnd words that can adequately convey my deeply felt appreciation for all of their
contributions. The discussions and interactions with the diverse group of scientists with whom I
have had the pleasure of working these past ﬁve years have taught me more than I had ever expected
to learn in graduate school. Therefore, here I would simply like to extend my heartfelt thanks to all of
those who have each directly contributed in their own way to my scientiﬁc success: B. Abbot, V.
Benedict, L. Besaw, U. Bloom, G. Bloomberg, F. de Bruijn, M. Cameron, R. Chen, S. Clement,
C. Cotton, L. Danhoff, M.E. Davey, J. Doherty, J. van Elsas, A. Engleberts, S. Fujimoto, O.
Folkerts, D. Gilliland, P. Green, D. Hamburger, S-Y. He, A. Jullien, P. Kapranov, B. Kasiborski,
J. Klug, A. Lilley, F. Louws, J. Mattei, I. MacSpadden, T. McSpadden, F. Michel, A. Millcamps,
M. Molinari, K. Nadler, B. Nault, K. Nielsen, B. Niemera, J. Ohlrogge, E. Paul, K. Poff, A.
Pinaev, J. Rademaker, D. Ragatz, K. Ritalahti, J. Roll, W. Roll, K. Roll Gardener, A. Rosado, G.
Rosado, S. Rossbach, U. Rossbach, O. Schabenberger, M. Schneider, M.L Schultz, B. Sears, D.
Silver, C. Somerville, Snyder, K Stepnitz, J. Stoltzfus, S. Stoltzfus, P. Stnrfﬁ, K. Szczyglowski, J.
Tiedje, S. Tjugum-Holland, A. Unge, C. Vriezen, J. Walton, J. Whitmarsh, M. Wilson, J.
Wolpreis, A. Wolters, R. Zelinski, G. de Zoeten, and all the others who I have accidently forgotten
to list. Additionally, to those who, known and unknown to me, have worked to establish and
maintain the open and dynamic research environment I have known here at Michigan State University

and elsewhere, I would like to express my sincere appreciation.

iv

”The rain drops are still falling, large, heavy, and few," said
Doctor Manette. "It comes slowly. "

”It comes surely, " said Carton.

They spoke low, as people watching and waiting mostly do;
as people in a dark room, watching and waiting for lightning, always do.

- Charles Dickens

TABLE OF CONTENTS

List of Tables
List of Figures

Chapter 1: General Introduction and Project Summary
The rhizosphere environment
Orpnic nutrients and niche expansion in the rhizosphere
The biased rhizosphere hypothesis
Rhizopines and inositols
Project summary

References

Chapter 2: Efforts to Construct Transgenic Plants That Produce Rhizopine
Abstract
Introduction
Materials and Methods
Bacterial strains and culture media
Molecular methods
Plant transformation
Assays for rhizopine synthesis and catabolism
Results
Rhizopine synthesis and the absence of catabolism in plants
Initial construction of pMOS plant expression vectors
Lotus and Nicotiana transformations and analysis

Evaluation of the lack of hygromycin resistant plants

vi

Construction of new vectors using PCR-ampliﬁed mos ORFs
Expression of the ampliﬁed mos genes in E. coli
Construction of pMOS7Bl
Arabidopsis transformation and analysis of transgenic tissues
Evaluation of the functionality of the ampliﬁed mos genes
Discussion ; A
References
Chapter 3: Evaluation of the Abunthnce of Culturable Bacteria Capable of Utilizing Potential
Nutritional Mediators, and Investigation into the Impact of Glucosamine Amendment on
Culturable Soil Bacteria
Abstract
Introduction
Materials and Methods
Enumeration of soil bacteria
Glucosamine amendment experiment
Results
Enumeration of culturable bacteria utilizing different substrates for growth

Impact of glucosamine amendment on indigenous and inoculant
populations of soil bacteria

Discussion

References

Chapter 4: Detection and Isolation of Novel Rhizopine-Catabolizing Bacteria from the
Environment

Abstract
Introduction
Materials and Methods
Soil and rhizosphere sampling
Preparation of nodule extracts and catabolism assays

vii

70
71
71
72
72

Results

Discussion

Isolation and maintenance of bacteria
Characterization of bacterial isolates

Statistics

Detection and quantiﬁcation of Moc activity in the environment
Isolation and characterization of Moct bacteria

Acknowledgements

References

Chapter 5: Examination of Soil and Rhizosphere Bacterial Communities Using a Modiﬁed
T-RFLP Analysis of Ampliﬁed 168 rDNA

Abstract

Introduction

Mata'ials and Methods

Results

Discussion

Model bacterial communities
Soil and rhizosphere samples

Flourescent-tag ampliﬁed ribosomal DNA restriction analysis
(FT-ARDRA)

Analysis of model bacterial communities
Examination of soil and rhizosphere bacterial communities

Acknowledgements

References

viii

3 836$

$8838

88

106
110
110

Chapter 6: Impact of myo-Inositol Amendment on Sinorhizobium meliloti and Other Bacterial

Populations in the Rhizosphere of Medicago sativa 113
Abstract 114
Introduction 114
MaterialsandMethods 116

In vitro competition experiments 116
Growth chamber experiments 117
Field experiments 118

Flourescently-tagged ampliﬁed ribosomal DNA restriction analysis
(FT-ARDRA) 1 19

Statistics 120
Results 120
Impact on Sinorhizobium meliloti under gnotobiotic conditions 120
Impact on culturable rhizosphere bacteria in ﬁeld soil 122

Impact on bacterial populations recovered in soil and
rhizosphere washes 129
Impact on nodulation of Medicago sativa 131
Discussion 133
Acknowledgements 136
References 136
Appendix: Analysis of Microbial Field Release Data 140
Introduction 141
Overview 142
Inoculantcharacterization 142
Site characterization 142
Preliminary assessments of data variation 144
Experimental design considerations 145

Statistical analysis of the data 146

ix

Nonparametric statistics
Presentation of results
Procedures
Getting started
Smnmary statistics
Making cornpari sons
Quantifying differences
Describing functional relationships
Examples
Making cornpari sons
Describing functioml relationships
Statistical Tables
Acknowledgements

References

146
148
149
149
150
152
154
155
156
157
161
165
173
174

LIST OF TABLES

Table 1.1: Substances detected in plant root exudates.
Table 2.1: Strains and plasmids used in this work.
Table 2.2: Oligonucleotide linkers and PCR primers used in this work.

Table 3.1: Log culturable counts per gram of soil on media containing different sources of
carbon and nitrogen as substrates for growth.

Table 3.2: Relative increases in CFUs on various media due to glucosamine amendment.

Table 3.3: The proportion of CFUs growing on GN media that displayed the white-convex
morphotype.

Table 4.1: Characterization of isolates capable of growing on the SI mix as sole carbon and
nitrogen source by partial sequencing of their 16 rRNA genes.

Table 5.1: FT-ARDRA data of isolated strains.

Table 6.1: The median number of culture forming units (CFUs) from rhizospheres of
Medicagosativa .

Table 6.2: Changes in FT-ARDRA proﬁles of rhizosphere washes following
myo-inositol amendment in the ﬁeld

Table 63: Increases in nodulation of Medicago sativa following myo-inositol amendment
in the ﬁeld

Table A.l: Information used in site descriptions.

Table A.2: Commonly used summary statistics of sample populations.

Table A3: Binomial tail probabilities.

Table A4: Critical values for the Mann-Whitney U statistic.

Table A5: Critical values for the signed rank test using the Wilcoxon T statistic.

Table A6: Critical values for the x2 distribution, which approximates the critical
values for the H and S statistics.

Table A.7: Standard normal distribution tail probabilities, to be used in Dunn's procedure
for multiple pair-wise comparisons.

Table A8: Critical values for the K statistic based on Kendall's rank conelation coefﬁcient.
Table A9: Critical values for Speamran's rank correlation coefﬁcient, r,

30

i

81

122

131

133
144
151
166
167
169

170

171
172
173

LIST OF FIGURES

Figure 1.1: Factors determining the nature of the rhizosphere environment.

Figure 1.2: A model for a biased rhizosphere system based on 3-O-methyl-
scyllo-inosamine (M81) as a nutritional mediator.

Figure 1.3: Common stereoisomers of inositol and the several classes of
inositol-containing compounds discussed in the text.

Figure 2.1: Accumulation of rhizopine in nodules of a heterologorrs symbiotic system.
Figure 2.2: Initial strategy for inserting the mos ORFs into pPCV9l.

Figure 2.3: Flow chart of PCR-based mos gene cloning strategy and applications of
various constructs.

Figure 2.4: PCR-based cloning of mos ORFs.
Figure 2.5: Cloning of PCR-ampliﬁed mos ORFs into bacterial expression vectors.

Figure 2.6: SDS-PAGE gel of protein extracts from E. coli containing different mos gene
constructs.

Figure 2.7: Construction of pMOS7A and pMOS7Bl.

Figure 2.8: Phenotypes of Arabidopsisthaliana plants transformed with pBINI9 or
pMOS7Bl.

Figure 2.9: I-IVPE analysis of extracts of transgenic Arabidopsis thaliana plants.
Figure 2.10: Analysis of mos gene expression in transgenic Arabidopsis thaliana plants.

Figure 2.11: Reconstruction of a mos operon using PCR-ampliﬁed mos open-reading
frames.

Figure 3.1: General model for the impact of nutritional mediators introduced into the soil
and rhizosphere environment

Figure 3.2: BOX genomic ﬁngerprints of bacteria isolated from glucosarnine amended soil
samples.

Figure 4.1: Detection of Moc activity in the environment
Figure 4.2: Colony hybridization of bacterial isolates with mac gene probes.

Figure 43: BOX-PCR generated genomic ﬁngerprints of isolates capable of growing
on SI as sole carbon and nitrogen source.

Figure 4.4: Deterrrrination of the Moc phenotype of isolates.

xii

ll

36

39

41

43

49

51

Figure 4.5: Southern blot analysis of D1, R3, and L5-30 for the presence of nod gene
sequences.

Figure 5.1: Fluorescent gel and corresponding FT-ARDRA proﬁles of model
communities containing ﬁve phylogenetically diverse strains.

Figure 5.2: Dilution extinction of FT-ARDRA proﬁles using different quantities of model
community templates.

Figrue 53: Changes in the FT—ARDRA proﬁles due to changes in the relative abrmdance
of component bacteria

Figure 5.4: FT-ARDRA proﬁles of natural communities.

Figure 5.5: Changes in FT—ARDRA proﬁles of natural communities due to myo-inositol
amendment.

Figure 5.6: Comparison of washes and cultures of the same samples.

Figure 6.1: Impact of myo-inositol amendment on the relative abundance of Mic
S. meliloti in the rhizosphere of M. sativa.

Figure 6.2: ARDRA gels displaying RFLPs in the ampliﬁed I6S rRNA sequences from
cultures obtained in the growth chamber experiment.

Figure 6.3: FT-ARDRA proﬁles of the terminal MA cultures obtained in the growth
chamber experiment.

Figure 6.4: FT-ARDRA proﬁles of rhizosphere cultures obtained from ﬁeld test I.
Figure 6.5: FT-ARDRA proﬁles of rhizosphere washes generated with Mspl.

Figure 6.6: Increases in S. meliloti-like signals due to myo-inositol amendment in the
growth chamber experiment.

Figure A.1: General methodology for microbial ﬁeld release experiments.

. Figure A.2: Example monotonic regression curve.

xiii

85

100

101
103

104
105

121

124

125
127
I30

132
143
I64

Chapter 1
General Introduction and Project Summary

The rhizosphere environment

The rhizosphere has been simply deﬁned as the space in and around plant roots (40). Yet
this simple deﬁnition belies the complex and dynamic nature of this environment (See Figure 1.1). In
soil, roots constantly interact with a variety of abiotic and biotic factors which aﬁect growth and
activities. The structure and activities of plant roots vary according to the prevailing environmental
conditions of climate and soil type, as well as to the plant's genetic characteristics (62). Ecological
interactions of all types also play a major role in determining the rhizosphere environmental
conditions (19, 28, 36. 42). While the disparity in scale between the macroscopic observer and the
microscopic heterogeneity of relevant below ground structures and activities challenges our abilities
of synthesis, we have learned much and our understanding of the rhizosphere environment is
constantly being reﬁned (9, 15, 39, 41, 43, 62, 66, 75).

Because my thesis work focused on the potential management of microbial populations in the
rhizosphere, I will restrict this general introduction to the rhizosphere enviromnent of crop plants
grown in agricrrltural settings. Additionally, I will make some generalizations regarding the structural
components of the rhizosphere and their activities, so as to underscore the basis of my research.

The environment into which a root emerges is already managed, in part, by agronomic
practice (62). Generally, the soil matrix has been restructured by tillage, fertilization, and planting.
The soil environment may be further modiﬁed by the application of inigation, fertilizers, pesticides,
and, on occasion, industrial wastes. In some instances, biological amendments (e.g. rhizobial
inoculants) are also introduced for the purpose of improving the harvestable yield. In total, these
agronomic practices are aimed at providing a suitable environment for plant growth and development;
however, each practice Will substantially impact the nature of the emerging rhizosphere somewhat
differently.

The plant itself plays a central role in deﬁning the physical, chemical, and biological
characteristics of the below ground environment. Plant growth stimulates and directs the bulk ﬂow

of water and dissolved nutrients into plant tissues, primarily by evapotranspiration (5, I7). Roots

  
    
   

Climatic

      
   
   

light
moisture
, temperature
Animal atmosphere
herbivory
pollination Plant
defecation
genotype
movement ontogeny
nutrient status
‘—)

l

1 a7

Microbial
Soil /
texture

 

diversity
activities
distribution
structure mteracuons
composition
chemistry

Figure 1.1: Factors determining the nature of the rhizosphere environment.
Arrows represent dynamic interactions between different factors and plant roots.

consequently actively alter the soil environment through which they grow and subsequently senesoe
(27). Below ground, plants provide the greatest proportion of ﬁxed carbon and nitrogen that sustains
a diversity of rhizosphere organisms (40, 77). During growth the soil matrix is altered by the
exudation and secretion of various molecules by the plant, particularly around active root tips (15,
28). Mature root tissues also undergo ontogenic and stochastic changes which can alter the
composition of the associated microbial communities (15). During senescence, the root biomass that
is not reallocated to other portions of the plant becomes available for consumption by saprophytes and
detritovores. Taken together, the structures and activities of plant roots can be described as the
foundation upon which rhizosphere microbial communities establish themselves.

In terms of total energy and biomass, rhizosphere microbes primarily rely on plants for ﬁxed
carbon. The occunence, composition, and fate of plant-derived carbon compounds in the soil and
rhizosphere have been well studied (15, 62, 7‘7). In living roots, an abundance of complex
polysaccharides are secreted from actively growing root tips and associated root border cells (28, 61).
The components of this mucigel are synthesized intracellularly in the Golgi and secreted via
membrane bound vesicles. The presumed function of this muci gel is to protect the meristematic loci
from injury (e.g. physical abrasion, pathogen attack). A number of different organic compounds,
including sugars, carboxylates, amino acids, nucleotides, vitamins, and ﬂavonoids, have been found
in root washes and leachates of various plants (see Table 1.1)(15). Environmental factors and plant
genotype both affect the composition of these exudates; however, the extent to which these
compounds are actively or passively exuded from the root remains unclear (15). It is estimated that,
on average, about five to thirty percent of the carbon transloeated to the roots is released from the
roots (75). The presence of these organic compounds in the rhizosphere stimulates the growth and
activity of a variety of microbes (18).

The microbial communities that inhabit the rhizosphere are diverse (15). Some of the
bacteria that have been cultured from root samples include members of the genera Agrobacterium,
Achromobacter, Arthrobacter, Azospirillum, Azotobacter, Bacillus, Enterobacter, F lavobacterium.

Pseudomonas, Rhizobium, and Streptomyces. Of the fungi, members of the genera F usan'um,

Table 1.1: Substances detected in plant root exudates (after ref. 15).

 

 

class of compound exudate components plants studied

sugars glucose, fructose, sucrose, maltose, Fbose, T. aestivum, H.
galactose, xylose, arabinose, rhamnose, vulgare, P. vulgaris,
rafﬁnose, oligosaccharides Pinus spp.

amino compounds asparagine, alanine, glutamine, aspanate, T. aestivum, Z. mays,
glutamate, leucine, isoleucine, serine, P. sativum, A. sativa,
aminobutyrate, glycine, cysteine, methionine, Trifolium spp., M.
proline, phenylalanine, tyrosine, threonine, sativa, 0. sativa, L.
tryptophan, arginine, homoserine, cystathionine, esculentum, Pinus spp.
glycine betaines -

organic acids tartarate, oxalate, citrate, malate, propionate, T. aestivum, Z. mays,
butyrate, succinate, fumarate, malonate, P. vulgaris, L
glycolate, valerate esculentum, Pinus spp.

fatty acids and sterols palmitic, stearic, oleic, linoleic, linolenic, P. vulgaris, A.
earnpesterol, sitosterol, campesterol - hypogaea

nulceotidesand adenine, guanine, thiamine, uridine, cytidine, T. aestivum, P.

macromolecules DNA, RNA, protease, phosphatase, sativum, T rtfolium
polygalacturonase, amylase, spp., Z. mays

secmdarymetabolites flavones, ﬂavonoids, glycoloids, hydrocyanic Medicago spp., P.
acid, saponins, organic phosphates, auxins, sativum. Tnfolium
biotin, inositol, thiamine, niacin, pantothenate, spp., L. esculentum,
choline, p-amino benzoic acid, nicotinic acid, Z. mays
bioactive compounds

Gigaspora, Glomus, Penicillium, Phytophthora, Pythium, and Trichoderma are well known to
inhabit the rhizosphere. Not surprisingly, the pathogens and mutualists have been characterized in
the most detail. Microscopic examinations of root surfaces extracted from soil have indicated that
microbial populations

occur in patches, presumably in regions of high nutrient availability (20). Attempts to
comprehensively catalog the diversity of microbial populations are probably incomplete due to
practical andtechnical limitations (12, 34). Only a small percent of the total microbial population is
culturable on any given medium (12) and recent studies using molecular methods based on 16S
rRNA genes have resulted in the identiﬁcation of phylogenetically novel rhizosphere inhabitants (6,
35). It is not clear what proportion of rhizosphere-inhabiting microorganisms signiﬁcantly impacts
plant growth and development. However, it is known that the abundance and/or activity of these

microbes varies spatially and temporally according to a number of factors, including plant genotype

and ontogeny, soil type, soil water potential, climate, and agricultural management (15, 62).

Organic nutrients and niche expansion in the rhizosphere

Away from plant roots, soil microorganisms are thought to be carbon limited (77). The
application of organic amendments to soil generally results in measurable increases in microbial
respiration and biomass (23). The presence of ﬁxed carbon in root tissues and exudates stimulates
microbial activity and colonization of the rhizosphere (7). The rhizosphere effect is largely
nonspeciﬁc because many diﬂerent organisms are known to utilize the organic substrates released by
the plant into the rhizosphere (8). In a several instances, though, connections between speciﬁc plant-
derived compounds and particular microorganisms have been made. Flavonoids are known to
stimulate rhizobial activity, resulting in increased growth rate, chemotactic migration to the root,
production of Nod factors, and subsequent infection (74). The synthesis and catabolism of
rhizopines by some rhizobial strains has been associated with nodule competitiveness (26). Opines
synthesized in plant galls and hairy roots as a result of infection by strains of Agrobacterium can be
utilized as growth substrates and can promote conjugal transfer of large plasmids carrying
pathogenesis-related genes (16). It has also been shown that organic pesticides and industrial
pollutants can enrich for organisms capable of utilizing them as carbon and/or energy sources (3).
Additionally, the presence of auxotrophic bacteria in the rhizosphere indicates that numerous
microbes may rely on speciﬁc components in root exudates for active growth (15). These
observations indicate that organic nutrients may be useful to selectively manage the microbial
populations in the soil and rhizosphere environments.

Several authors have suggested that beneﬁcial microorganisms may be selectively managed
in agricultural settings by the introduction of novel organic nutrients (7, 13, 14, 50, 60, 65). From
an ecological standpoint, the presence or absence of such an organic nutrient can be used to deﬁne the
niche boundaries between organisms that can or cannot utilize that nutrient. If the nutrient is novel

with respect to the environment into which it is introduwd and if some organisms present in that

environment can utilize it as a resource, then one can say that the niche space of those organisms has
been expanded or, alternatively, that new niche space has been created. The direct result of this niche
space creation is predicted to be stimulation of the metabolic activities and abundance of organisms
capable of catabolizing the introduced nutrient. Indirect effects may include a variety of shifts in the
population structures and activities of the communities into which the nutrient is introduced,
depending on the amount of the nutrient, the relative abundance of catabolizers and noncatabolizers,
and the ecological relationships between them.

To establish a practical system of nutritional mediation (i.e. nutrient-based niche space
creation) and to validate it as an operational mechanism, several criteria must be met. First, the
organic nutrient must be available to and utilized by the target microorganisms in situ. Second, the
stimulation of the targeted microbial populations must result in increases in the desired activities (e.g.
increased nodulation or biological control of a plant pathogen). Concomitantly, the introduced
nutrient should be unavailable to organisms that would inhibit the growth and activity of the beneﬁcial
target microorganisms or that would otherwise compromise agronomic production. Correlations
should be observed between the size of the created niche space (i.e. the amount of nutrient), the
relative abundance of target microorganisms, and the amount of beneﬁcial activity. In this work, I
will describe several experiments that establish that nutrient amendment-mediated niche expansion can
be used to promote the growth and activities of beneﬁcial bacteria in the rhizosphere (see Chapter 6).

The biased rhizosphere hypothesis

Rhizosphere microbial populations vary from plant to plant (15). And it has been suggested
that plants may be bred with respect to the colonizing microﬂora to improve crop yield (7, 50).
Indeed, selection for plant resistance to microbial pathogens is a common element of plant breeding
programs (1). Additionally, improvements have been made in the mutualistic plant-microbe
relationship between legumes and ririzobia (10, 31, 49, 54). Such breeding programs rely on rapid
assays that are dependable predictors of agronomic performance (1, 29, 63). The lack of such assays

for beneﬁcial plant-microbe interactions has limited progress towards more comprehensively

managing rhizosphere microbial populations. Our understanding of the abundance, distribution,
physiology, and ecology of the majority of rhizosphere-colonizing microorganisms in different soils
and their responses to agricultural practice Ins yet to be synthesized into a genuinely useful paradigm.
In the absence of such a synthesis, efforts have focused on the responses of particular microbial
populations (e.g. the rhizobia, mycorrhizae) to particular agronomic practices, and conelations are
being established that are paving the way for a more mechanistic understanding of plant-microbe
interactions.

One of these correlations involves the relationship between speciﬁc plant-derived organic
nutrients and microorganisms capable of catabolizing them. In Agrobacteriwn, such a conelation
exists between opines and virulent strains, and it is believed that these novel amino-acid derivatives
promote the selective propagation of the inducing pathogen (16). This phenomenon is referred to as
the ”opine concept" (16). An analogous "rhizopine concept” is believed to describe the impact of
novel nitrogen-containing inositol derivatives (i.e. rhizopines) on catabolizing Rhizobium (48, 74).
A variety of other novel plant—derived secondary compounds (e.g. calystegin, mimosine, stachydrine,
trigonelline) may also be used in situ as organic nutrients by rhizosphere bacteria (24, 70). The genes
encoding for thecatabolism of these compounds have been reported to occur on symbiotic plasmids
in Rhizobium (25, 45, 76). Furthermore, the catabolism of rhizopines and betaines have been
correlated with improved competitiveness for nodule occupancy when compared to near-isogenic
transposon mutants unable to catabolize these compounds (25, 26). Thus, it appears that the ability
to catabolize certain organic nutrients may play a signiﬁcant role in the symbiotic success of
rhizosphere bacteria.

Recently it has been shown that the presence of certain organic compounds can increase the
number of rhizosphere bacteria capable of catabolizing them. Salicylate provided in irrigation water
has been shown to enrich for a salicylate-utilizing inoculant bacteria in growth chamber and ﬁeld
experiments (13, 14). Increased numbers of culturable opine-catabolizing bacteria have been
observed in the rhizosphere of plants genetically engineered to produce mannityl-opines (51, 52).

Furthermore, the use of nutrient amended granules has been shown to increase the numbers of

 

 

 

 

I = “3+ bacteria W

normal rhizosphere I biased rhizosphere

 

 

 

 

 

 

 

 

 

Figure 1.2: A model for a biased rhizosphere system based on 3-0-
methyl-scyllo-inosamine (MS!) as a nutritional mediator (after ref. 50).
The rhizopine is synthesized in planta as a result of mos gene expression. The
compound becomes available to rhizosphere bacteria in root exudates,
providing a novel source of ﬁxed carbon and nitrogen for bacterial growth.
Only bacteria harboring the mac genes are able to utilize the novel organic
nutrient. Populations of rhizopine catabolizing bacteria increase in response to
the presence of the rhizopine. Arrows indicate: gene expression (a), synthesis
(b), release into the rhizosphere (c), uptake and catabolism (d).

culturable rhizosphere-colonizing Bradyrhizobium present in the inoculant formulation (22). These
studies indicate that despite the relative abundance of organic nutrients in the rhizosphere,
subpopulations of rhizosphere bacteria capable of utilizing introduced nutrients can be stimulated.
This correlation provides the basis of the "biased rhizosphere" hypothesis.

This hypothesis states that increasing the concentration of a speciﬁc organic nutrient in the
rhizosphere will result in an increase in the abundance and activities of microorganisms capable of
utilizing it (50, 60) (See Figure 1.2). Several predictions can be made from this hypothesis. Frrst, a
monotonic correlation should exist between the amount of the organic nutrient added to the
rhizosphere and the population size and activities of organisms that can catabolize it. Second, the
short-term impactof adding an organic nutrient to a system will be most pronounced for organisms
that utilize it as a carbon and energy source. And, third, the presence of nontarget catabolizing

organisms will reduce the effectiveness with which a nutritional mediator will stimulate targeted

microorganisms.

Rhizopines and inositols

My thesis research focused on assessing the potential for creating biased rhizospheres based
using rhizopines and myo-inositol as nutritional mediators. In this section, several classes of
inositol-containing compounds will be discussed with regards to the potential use of rhizopines and
myo—inositol as nutritional mediators in the rhizosphere environment. Representative structures of
these different types of compounds are presented in Figure 1.3.

Rhizopines are inositol derivatives, the structures of which have been reported to be 3-0-
methyl-scyllo-inosamine (M81) and scyllo-inosamine (SI) (48). This class of compounds was that
identiﬁed in die nodules of Medicago sativa infected with Sinorhizobium meliloti 15-30 (73) and has
since been observed in the nodules of several other legumes infected with particular strains of S.
meliloti and Rhizobium legwninosarum (76). Little is known about the biochemistry of rhizopines,
although genes required for their biosynthesis and catabolism have been identiﬁed and characterized
(45, 46, 47, 58, 59, 64). It has been proposed that, like opines (16), rhizopines may act as

10

 

D-cln'ro mo

 

 

 

streptomycin

 

Figure 1.3: Common stereoisomers of inositol and the several
classes of inositol-containing compounds discussed in the text.

11

”proprietary growth substrates" which can be utilized only by the bacterial strains that synthesize
them in planta (48). Two lines of evidence support this hypothesis. First, genes for synthesis and
catabolism are closely linked in the genome of rhizobia (45) and the large majority of strains that have
been reported to catabolize rhizopines also synthesize them in the nodule (76). Additionally, wild-
type strains capable of utilizing rhizopines out-compete near-isogenic mutants for nodule occupancy
(26). However, Gordon et. al. have reported that rhizopine catabolism alone may not completely
explain such increases in nodulation (26). They reported that the abundance of the wild-type
inoculum did not increase over time relative to a mutant incapable of utilizing rhizopine despite the
factthat rhizopine was being produced in the rhizosphere (i.e. in infected nodules). However, one
could argue that such a trend does appear in their published data over the course of a single growing
season. So, while rhizopines may act to some degree as nutritional mediators in nature, their role in
the rhizobia-legume symbiosis is not completely understood.

Closely related to rhizopines in structure are methylated inositols. These compounds occur
widely in plants, particularly in the Leguminosae (37). Pinitol (3-O-methyl-chiro-inositol) is found
throughout the plant (69), and it has been used as a quantitative marker for legume abundance in
mixed forage systems (68). Different methylated inositols, including pinitol, ononitol (4—O-methyl-
myo-inositol) and O-methyl-scyllo—inositol, have also been identiﬁed in nodules of various legume
plants (33, 67, 71, 72). In pea, the concentration of these compounds in roots and nodules has been
shown to vary depending on the identity of the infecting strain of Rhizobium (33). Methylated-
inositols are also believed to play a role in osmoregulation in plants because they increase in
concentration following osmotic stress (21). By analogy, rhizopines may play some role in
osmotolerance of rhizobial strains that synthesize them in the nodule environment.

Phosphate-containing derivatives of inositols occur in several forms in plants,
microorganisms, and soil. Phosphotidyl inositol-containing lipids are ubiquitous in cell membranes
(38). In plants, mono-, di-, and tri-phosphate derivatives also occur free in the cytosol and play a
major role in calcium regulation (57). Phytate (inositol-hemphosphate) is a major storage form of
phosphorus in plants, representing 50 to 90 percent of the total phosphate in seeds and 10 to 15

12

percent of the phosphate in root and shoot tissues (1 l, 30, 55). Phytate and its partially
dephosphorylated breakdown products also represent a signiﬁcant proportion of soil phosphorous
(4). Mycorrhirae lave been reported to preferentially utilize this pool of organic phosphorous when
associated with their plant symbionts (2). The introduction of additional inositol-containing
compounds into the rhizosphere, then, has the potential to impact this mutualistic plant-microbe
interaction.

Potentially toxic inositol-containing compounds are also likely to be present in the soil and
rhizosphere environment. Several naturally occurring microbial antibiotics (e.g. streptomycin,
gentamicin, kanamycin, and spectinomycin) contain inositol moieties (22b). While resistance to these
antibiotics often involves catabolism of these molecules, the enzymatic attack of the inositol moieties
are seldom implicated in such mechanisms (22b). The degree to which these compounds might act as
nutrient sources for naturally resistant or insensitive bacteria is unknown.

Inositols represent a signiﬁcant fraction of the total soluble carbohydrates in legume nodules
and may play a role in the ecological success of infecting strains. There are approximately 2 mg of
uncharged inositols per g fresh weight of nodule tissue, which represents 30 to 60 percent of the
soluble carbohydrates (21, 67, 71, 72). For comparison, estimates of rhizopine concentration in
alfalfa nodules infected with Sinorhizobium meliloti L5-30 are on the order of 0.5 mg per g (see
Chapter 4). In soybeans, myo-inositol is the most abundant cyclitol, accounting for over 25 percent
of the total extractable carbohydrate (71 ). In alfalfa, pinitol has been reported to be the most abundant
form (21). In peas, ononitol and O—methyl-scyllo—inositol were reported to be the most abundant
carbohydrates (67). The largest fraction of these compounds can be found in the cytosolic fraction of
the plant cells as opposed to the bacteroids; however, the relative abundance does not seem to differ
between the two compartments (72). Additionally, the ﬂux of inositols between these two
compartments may not be substantial (56). Nonetheless, this large pool of inositol may be available
to rhizobia for utilization upon nodule senescence. In Rhizobium, cellulase activity is induced by
myo-inositol as well as by a variety of polysaccharides (44), and most strains are capable of

accumulating substantial intracellular carbon reserves (32, 53). Thus, it is quite possible that

13

infecting rhizobia utilize the large pool of inositol present in the nodule both as a signal and as a
nutrient reserve to successfully migrate out of a senescing nodule to reinfect a new and conducive

root segment.

Project summary

In this thesis I will assess the potential of creating biased rhizospheres in several ways.
First, efforts were made to generate rhizopine-producing transgenic plants so as to directly test the
biased rhizosphere hypothesis using rhizopines as the nutritional mediator. Second, the source and
abundance of rhizopine catabolizing activity in the soil and rhizosphere was investigated to evaluate
the potential effectiveness of rhizopines as selective agents. Lastly, the biased rhizosphere hypothesis
was evaluated in grth chamber and ﬁeld experiments using myo-inositol as the nutritional
mediator. The impact of this nutrient amendment on different rhizosphere microbial populations was
assessed with particular emphasis on the symbiosis between rhizobia and legumes.

In the wurse of conducting my research it became apparent that an alternative to rhizopine
was required to continue the assessments of the biased rhizosphere hypothesis because neither
rhizopine producing plants noran adequate supply of pure rhizopine would be available. The work
proceeded after ﬁnding an alternative compound that might adequately model a rhizopine-based
biased rhizosphere. The criteria used for selecting myo-inositol as an analog for rhizopines were: (a)
its structural relatedness, (b) its comparable speciﬁcin as a utilizable substrate, and (c) apparent
functional relationships in catabolism of both compounds in the Rhizobium-legume symbiosis.

Chapter 2 describes efforts to construct rhizopine-producing plants. It was determined that
rhizopine synthesis might be possible in plants because cosmids containing the rhizopine synthesis
(mos) locus from Sinorhizobium meliloti L5-30 were functional in the bacteroids of a heterologous
system composed of Rhizobium 1011' and Lotus corniculatus. Additionally, rhizopine catabolic activity
was not detected in root and leaf extracts of several different plant species, indicating that the
accumulation of rhizopines was not precluded in other plant tissues. A variety of plasmids containing

the mos genes were constructed in an effort to generate rhizopine-producing transgenic plants.

14

Rhizopine was not observed to accumulate in any of the transformed plant lines. Transgenic
Arabidopsr's plants were obtained that accumulated transcripts corresponding to all three genes;
however, the mosA transcripts were signiﬁcantly shorter than expected. In these transgenic plants,
proteins of the expected molecular weight were detected using anti-MosB antibodies, but a full-sized
MosA protein was not detected. None of the transgenic lines were observed to accumulate
rhizopines; however, the introduction of these constructs resulted in a distinct mutant phenotype. Tire
occurrence of this phenotype may reflect the disruption of inositol metabolism in planta by the
introduced mos genes. The functionality of the cloned mos ORFs was tested by reconstructing the
mos operon on a broad-host range bacterial expression vector called pMOS27. Medicago sativa
nodules induced by S. meliloti 1021 harboring this construct were not observed to accumulate
rhizopines. From these experiments it was concluded that more work needs to be carried out to
understand the nature of rhizopine synthesis in S. meliloti before rhizopine synthesizing plants can be
created.

Chapter 3 brieﬂy describes two sets of experiments that were instrumental in redirecting my
research program. The ﬁrst was an assessment of the abundance of culturable bacteria capable of
utilizing different organic nutrients as sole carbon and/or nitrogen sources. This work indicated that
the numbers of bacteria capable of catabolizing myooinositol and rhizopine were roughly equivalent
and represented a small fraction of the total number of culturable bacteria. The second experiment
examined the impact of glucosamine amendment on inoculant and indigenous soil bacteria. It was
obseved that this nutrient amendment preferentially increased the abundance of bacteria capable of
growing on media containing glucosamine as sole carbon and nitrogen source. The fraction of
glucosamine-utilizing bacteria represented by a single morphotype of indigenous soil bacteria
increased signiﬁcantly over time. These observations were consistent with the predictions of the
biased rhizosphere hypothesis. Interestingly, the impact of the glucosamine amendment was greatest
on the indigenous bacteria. The measured increases in the number of inoculant bacteria capable of
catabolizing the compound were srmller and more transitory. This observation indicated that nutrient

15

amendment might have a relatively plastic effect on the microbial populations in the soil and
rhizosphere environments.

In Chapter 4, the detection and isolation of novel rhizopine-catabolizing bacteria are
described. This represents the ﬁrst direct evaluation of the potential speciﬁcity of rhizopines as
nutritional mediators. Rhizopine catabolism was detected and enumerated in soil and rhizosphere
washes. A diverse set of bacteria were isolated using scyIIo-inosamine (SI) containing media. None
of the isolates contained sequences similar to the known moc genes as assayed by colony
hybridization, indicating that they contain novel rhizopine catabolism genes. Twenty-one different
strains were observed to grow on SI as sole carbon and nitrogen source and were further
characterized. Six of these were found to be capable of catabolizing 3-O-methyl-scyllo—inosamine
(MSI). Two of these were identiﬁed as S. meliloti strains based on their 16S rDNA sequences;
however, they did not contain the highly conserved nodBC gene sequences and were incapable of
nodulating alfalfa. It was concluded that diverse indigenous rhizopine-catabolizing microorganisms
were present in the experimental soil and could compete for the nutritional mediator if introduced.

Chapter 5 describes a molecular technique called ﬂuorescent-tag ampliﬁed rDNA restriction
analysis (FT-ARDRA) that was deveIOped for the examination of bacterial communities in soil and
rhizosphere environments. The method is a modiﬁed T-RFLP analysis of amplified 168 rDNA
sequences using serial dilutions of soil and rhizosphere washes. The use of different restriction
enzymes results in the generation of unique community proﬁles that can be used to monitor changes
in the dominant bacterial members. The technique was evaluated using deﬁned mixtures of diverse
isolates obtained from soil and rhizosphere samples. Examination of natural communities using this
method revealed distinct differences in soil and rhizosphere samples and their response to myo-
inositol amendment. Comparison of the FT-ARDRA proﬁles also indicated that some organisms
respond differently to the organic nutrient amendment depending on their localization in the soil or
rhizosphere. Thus, it was shown that this method could be usefully applied to the evaluation of the

impact of nutritional amendments in the rhizosphere environment.

16

In Chapter 6, the biased rhizosphere hypothesis is evaluated with respect to the impact of
myo-inositol amendment on bacterial populations in the rhizosphere. The relative ability of two S.
meliloti strains to colonize the roots of Medicago sativa was compared under gnotobiotic conditions.
In these experiments, a wild-type strain outcompeted a near-isogenic mutant unable to utilize myo-
inositol for rhizosphere colonization only when the compound was added to the growth medium.
From this it was concluded that myo-inositol can be used to expand the niche space of catabolizing
bacteria in the rhizosphere. In experiments using non-sterile agricultural soil, it was observed that the
nutrient amendment increased the number of culturable microorganisms present in the rhizosphere of
alfalfa. ARDRA and FT-ARDRA of the most abundant culturable microorganisms indicated
quantitative shifts in the composition of these fast-growing bacterial populations. The observed
increases were greatest for organisms that could utilize myo-inositol as sole carbon source to support
growth. Because Sinorhizobium meliloti strains are known to be generally capable of utilizing myo-
inositol for growth (32, 51), it was hypothesized that this compound may be used as a nutritional
mediator to stimulate populations of this beneﬁcial soil bacteria in an agricultural setting. FT-ARDRA
proﬁles of rhizosphere samples indicated that there was a strong correlation between the amount of
myo-inositol amendment and ﬂuorescent signals corresponding to those predicted for S. meliloti one
week after germination of the planted M. sativa. The amount of applied organic nutrient was likewise
strongly correlated with nodulation of M. sativa in both growth chamber and ﬁeld experiments.
Therefore, myo-inositol amendment was able to stimulate the abundance and activities of a targeted
microbial population. Additional changes in the FT -ARDRA proﬁles indicated that the structure of
the rhizosphere microbial communities was altered by the nutrient amendment. Given the complex
natureof soil and rhizosphere microbial communities, it is not possible to conclusively demonstrate
the mechanism of nutritional mediation for any particular organism in nattual settings, however, the
results of these experiments are largely consistent with it in the case of myo—inositol-mediated
increases in S. meliloti abundance and nodulation activity.

In conclusion, evidence supporting the biased rhizosphere hypothesis is presented, and the
potential for creating practical systems based on nutritional mediation is indicated. Although it is

17

uncertain if systems based on rhizopines as the nutritional mediator can be successfully engineered, it
should be possible to ﬁnd alternative compounds (e.g. myo-inositol) with equivalent speciﬁcity. It
might be most desirable to alter the composition of plant root exudates to deliver the organic nutrient
into the rhizosphere, both in terms of the amount that may be supplied and the efﬁciency with which
it is delivered. However, it seems that exogenous amendments can be used to effectively alter
rhizosphere microbial communities. The prediction that a nutritional mediator can speciﬁcally
increase the abundance and activities of microorganisms capable of utilizing it in the rhizosphere is
supported by both the glucosamine and inositol amendment studies. Indeed, the results of the ﬁeld
tests indicate that practical applications of biased rhizosphere systems may be found because targeted
populations of beneﬁcial microorganisms can be stimulated. Alteration of the nutrient balance will
undoubtedly impact the microbial communities which inlnbit the rhizosphere, but the extent to which
such alterations impact plant growth and yield potential still needs to be investigated. The
development of new experimental tools, such as FT—ARDRA, should facilitate the extensive analyses
that need to be done to evaluate the dynamic nature of rhizosphere microbial communities and their
responses to various agricultural practices. With the information derived from such investigations,
the management of rhizosphere microbial communities may play an even greater role in the

development of more productive and sustainable agricultural systems.

References
l. Agrios GN (1997) Plant Pathology. 4th Ed. Academic Press: San Diego.

2. Allen MF, Sexton JC, Moore TS Jr, Christensen M (1981) Inﬂuence of phophate
source on vesicular-arbuscular mycorrhizae of Bouteloua gracilis. New Phytologist 8 7: 687-694.

3. Anderson TA, Coats JR (1995) An overview of microbial degradation in the rhizosphere and
its implications for bioremediation. pp. 135-143 In Bioremediation: Science and Applications. Soil
Science Society of America: Madison, WI.

4. Appiah MR, Thomas RL (1982) Inositol phosphate and organic phosphorus contents and
phosphatase activity of some Canadian and Ghanian soils. Canadian J. Soil Sci. 62: 31-38

5. Barber SA (1984) Soil Nutrient Bioavailability. J. Wiley: New York.

18

6. Borneman J, Skroch PW, O’Sullivan KM, Palus JA, Rumjanek NG, Jensen JL,
Nienhuis J, Triplett EW (1996) Molecular microbial diversity of an agricultural soil in
Wisconsin. Appl. Environ. Microbiol. 62: 1935-1943.

7. Bowen GD (1980) Misconceptions, concepts and approaches in rhizosphere biology. pp. 283-
304. In Contemporary Microbial Ecology. Elwood DG ed. Academic Press: London.

8. Bowen GD, Rovira AD (1976) Microbial colonization of plant roots. Ann. Rev.
Phytopathology 14: 121-144.

9. Bowen GD, Rovira AD (1993) The rhizosphere: The hidden halfof the hidden half. pp. 641-
669 In Plant roots: the hidden half. Waisel Y, Eshe A, Kafkaﬁ U eds. Marcel Dekker. New York.

10. Battery BR, Park SJ, Hume DJ (1992) Potential for increasing nitrogen ﬁxation in grain
legumes. Can. J. Plant Sci. 72: 323-349.

11. Campbell M, Dunn R, Ditterline R, Pickett S, Raboy V (1991) Phytic acid represents
10 to 15% d total phosphorus in alfalfa root and crown. J. Plant Nutrition 1 4: 925-937.

12. Campbell R, Greaves MP (1990) Anatomy and community structure of the rhizosphere. pp
11-34 In The Rhizosphere. Lynch IM ed. .1. Wiley & Sons: New York.

13. Colbert SF, Isakeit T, Ferri M, Weinhold AR, Hendson M, Schroth MN (1993)
Use of an exotic carbon source to selectively increase metabolic activity and growth of Pseudomonas
putida in soil. Appl. Environ. Microbiol. 59: 2056-2063.

14. Colbert SF, Schroth MN, Weinhold AR, Hendson M (1993) Enhancement of
p0pulation densities of Pseudomonas putida PpG7 in agricultural ecosystems by selective feeding
with the carbon source salicylate. Appl. Environ. Microbiol. 5 9: 20642070.

15. Carl EA, Truelove B (1986)Therhizosphere. Springer-Veﬂag: Benin.

16. Dessaux Y, Petit A, Tempe J (1993) Chemistry and biochemistry of opines, chemical
mediators of parasitism. Phytochemistry 3 4: 31 -38.

17. Drew MC (1990) Root function, development, growth and mineral nutrition. pp. 35-58 In The
Rhizosphere. Lynch JM ed. J. Wiley & Sons: New York

18. Elsas JD van, van Overbeek LS (1993) Bacterial responses to soil stimuli. pp. 55-79 In
Starvation in bacteria. Kjelleberg S ed. Plenum: New York

19. Fitter All, Garbaye J (1994) Interactions between mycorrhizal fungi and other soil
organisms. Plant and Soil 159. 123-132.

20. Foster RC, Rovira AD, Cock TW (1983) Ultrastructure of the root-soil interface.
American Phytopathological Society: St. Paul, MN.

21. Fougere F, Le Rudulier D, Streeter JG (1991) Effects of salt stress on amino acid,
organic acid, and carbohydrate composition of roots, bacteroids, and cytosol of alfalfa (Medicago
sativa 1..) Plant Physiol. 96:1228-1236.

22. Foullleux G, Revellin C, Hartmann, A, Catroux G (1996) Increase of

Bradyrhizobiran japonicum numbers in soils and enhance nodulation of soybean (Glycine max (L)
merr.) using granular incoulants amended with nutrients. FEMS Microb. Ecol. 2 0: 173-183.

19

22b. Gale EF, CundIiffe E, Reynolds PE, Richmond MH, Waring MJ (1980) Bacterial
resistance to antibiotics. pp. 548-593 In The molecular basis of antibiotic action. J. Wiley & Sons:
London.

23. Glass DJ (1995) Biotic effects of soil microbial amendments. pp. 251-303 In Soil
amendments. Rechci g] I ed. CRC Press: Boca Raton.

24. Goldmann A, Boivin C, Fleury V, Message B, Lecoeur L, Maille M, Tepfer D
(1991) Betaine use by rhizosphere bacteria genes essential for trigonelline, stachydrine, and camitine
catabolism in Rhizobium meliloti are located on pSym in the symbiotic region. Molec. Plant-Microb.
Interact. 4: 571 -578.

25. Goldman A, Lecoeur L, Message M, Delarue M, Schoonejans E, Tepfer D
(1994) Symbiotic plasmid genes essential to the catabolism of proline betaine, or stachydrine, are also
required for efficient nodulation by Rhizobium meliloti. FEMS Microbiol. Lett. 1 l 5: 305-312.

26. Gordon DM, Ryder MB, Heinrich [(11, Murphy PJ (1996) An experimental test of the
rhizopine concept in Rhizobiran meliloti. Appl. Environ. Microbiol. 6 2: 3991 -3996.

27. Goes MJ (1991) Consequences ofthe activityofroots on soil. pp. 171-186 In Plant root
growth: an ecological perSpective. Atkinson D ed. Blackwell Scientiﬁc: Oxford.

28. Hawes MC, Brigham LA (1992) Impact of root border cells on microbial populations in the
rhizosphere. Adv. Plant Pathol. 8: 119-148.

29. Herridge DF, Rupela OP, Serraj R, Beck DP (1993) Screening techniques and
improved biological nitrogen ﬁxation in cool season legumes. Euphytica 7 3: 95-108.

30. Horbowicz M, Obendorf RL, McKersie BD, Viands DR (1995) Soluble saccharides
and cyclitols in alfalfa (Medicago sativa L) somatic embryos, leaflets, and mature seeds. Plant Sci.
109: 191-198.

31. Imsande J (1992) Agronomic characteristics that identify high yield, high protein soybean
genotypes. Agronomy J. 84: 409-414.

32. Jordan DC (1984) Family III: Rhizobiaceae. pp. 235-254 In Bergey's manual of systematic
bacteriology. Tansill B ed. Williams&Wilkings: Baltimore.

33. Lafontaine PJ, Lafreniere C, Chaifour FP, Dion P, Antoun H (1989) Carbohydrate
and organic acid composition of effective and ineffective root nodules of Phaseolus vulgaris.
Physiologia. Plantarum 7 6: 507-513.

34. Liesack W, Jansen PH, Rainey FA, Ward-Rainey NL, Stackebrandt E (1997)
Microbial diversity in soil: The need for a combined approach using molecular and cultivation
techniques. pp. 375—440. In Modern Soil Biology. J.D. van Elsas, J.T. Trevors, E.M.H.
Wellington eds. Marcel Dekker. New York.

35. Liesack W, Stackebrandt E (1992) Occurrence of novel groups of the domain Bacrena as
revealed by analysis of genetic material isolate from an Austrailian terrestial environment. I .
Bacterial. l7 4: 5072-5078.

36. Lindernran RG (1988) Mycorrhizal interactions with the rhizosphere microﬂora: the
mycorrhizosphere effect. Phytopathology 7 8: 366-371.

20

37. Loewus FA (1990) Structure and occurrence of inositols in plants. pp. 1-12 In Inositol
metabolism in plants. Morre DJ, Boss WF, Loewus FA eds. Wiley-Liss: New York.

38. Loewus FA, Dickinson DB (1983) Cyclitols. pp. [93-208 In Plant carbohydrates 1.
Encyclopedia of Plant Physiology NSl3A. Loewus FA, Tanner W eds. Springer-Verlaag: New
York

39. Lynch JM (1990) Introduction: Some consequences of microbial rhizosphere competence for
plant and soil. pp. 1-10 In The Rhizosphere. Lynch JM ed. J. Wiley & Sons: New York.

40. Lynch JM (1995) Root architecture and plant productivity. Plant Physiol. 109: 7-13.
41. Lynch JM ed. (1990) The rhizosphere. J. Wiley & Sons: New York.

42. Martin JK (1971) Inﬂuence of plant species and plant age on the rhizosphere microflora. Aust.
J. Biol. Sci. 24: 1143-1150.

43. McCuIly M (1995) How do real roots work? Plant Physiol. 10% 1-6.

44. Morales VM, Martinez-Molina E, Hubbell DH (1984) Cellulase production by
rhizobium. Plant Soi180: 407-415.

45. Murphy PJ, Heycke N, Banfalvi Z, Tate ME, deBruijn F, Kondorusl A, Tempe
J, Schell J (1987) Genes for the catabolism and synthesis of an opine-like compound in R.
melilotr' are closely linked and on the Sym plasmid Proc. Nat. Acad. Sci (USA) 84: 495-497.

46. Murphy PJ, Heycke N, Trenz SP, Tate ME, deBruiin F, Schell J (1988) Synthesis
of an opine-like compound, a rhizopine, in alfalfa nodules is symbiotically regulated Proc. Nat.
Acad. Sci. (USA) 85: 9133-9137.

47. Murphy PJ, Trenz SP, Grzemski W, de Bruijn FJ, Schell J (1993) The Rhizobiunr
melilotrhizopine mos locus is a mosaic structure facilitating its symbiotic regulation. J. Bacteriol.
175: 5193-5204.

48. Murphy PJ, Wexler w, Grzemski w, Rao JP, Gordon D (1995) Rhizopines-their
role in symbiosis and competition. Soil Biol. Biochem. 2 7: 525-529.

49. Nutman PS (1983) Physiology and the legume symbiosis. pp. 1-18 In Temperate legumes:
physiology, genetics, and nodulation. Jones DG, Davies DR eds. Pitrnan: Boston.

50. O'Connel KP, Goodman RM, Handelsman J (1996) Engineering the rhizosphere:
expressing a bias. Trend. Biotech. 1 4: 83-88.

51. Oger P (1995) Ph.D. thesis: Etudes su la possibilite de favoriser speciﬁquement la croissance
de bacteries de la rhizosphere-Le cas des plantes transgeniques productrices d' ines. Universite de
Paris sud centre d'Orsay: Paris.

52. Oger P, Petit A, Dessaux Y (1997) Genetically engineered plants producing opines alter
their biological environment. Nature Biotechnology 15: 369-372.

53. Padmanabhan S, Hirtz RD, Broughton WJ (1990) Rhizobia in tropical legumes: culttual
characteristics ofBradyrhizobiwn and Rhizobium sp. Soil Biol. Biochem. 2 2: 23-28.

21

54. Pereira PAA, Miranda BD, Attewells JR, Kmieclk KA, Bliss FA (1993) Selection
for increased nodule number in common bean (Phaseolus vulgaris L). Plant Soil 1 42: 203-209.

55. Reddy NR, Sathe SK, Salunkhe DK(1982) Phytates in legumes and cereals. Adv. Food
Res. 28: 1-92.

56. Reibach PH, Streeter JG (1984) Evaluation of active versus passive uptake of metaboliztes
by Rhizobiranjaponicran bacteroids. J. Bacteriol. 159: 47-52.

57. Rincon M, Boss WF (1990) Second-messenger role of phosphoinosities. pp. 173 -200 In
Inositol metabolism in plants. Mone DJ, Boss WF, Loewus FA eds. Wiley-Liss: New York.

58. Rossbach S, Kulpa DA, Rossbach U, de Bruijn FJ (1994) Molecular and genetic
characterization of the rhizopine catabolism (mocABRC) genes of Rhizobium meliloti L5-30. Mol.
Gen. Genet. 245: 11-24.

59. Rossbach S, Rasul G, Schneider M, Eardly B, de Bruijn FJ (1995) Structural and
functional conservation of the rhizopine catabolism (moc) locus is limited to selcted Rhizobium
meliloti strains and unrelated to their geographical origin. Molec. Plant Microb. Interact. 8: 549-559.

60. Rossbach SR, McSpadden B, Kulpa D, de Bruijn FJ (1994) Rhizopine synthesis and
catabolism genes for the creation of "biased rhizospheres” and as marker system to detect (genetically
modiﬁed) microorganisms in the soil. pp. 223-249 In Biotechnology Risk Assessment: Proceedings
of the biotechnology risk assessment symposium, June 22-24, 1994, College Park, MD. Levin M,
Grim C, Angle JS, eds.

61. Rougier M (1981) Secretory activity of the root cap. pp. 542-547 In Plant carbohydratesll,
Extracellular carbohydrates: Encyclopedia of Plant Physiology NSI3B Tanner W, Leowus FA eds.
Springer-Vedag: Beriin.

62. Russel RS (1977) Plant root systems: their function and interaction with the soil. McGraw-
Hill: Londond.

63. Rydberg I (1992) A new approach for screening peas (Pisum sativum L.) for yield capacity.
Plant Breeding 1 08: 69-74.

64. Saint CJ, Wexler M, Murphy PJ, Tempe J, Tate ME, Murphy PJ (1993)
Characterization of genes for synthesis and catabolism of a new rhizopine induced in nodules by
Rhizobium meliloti Rm220-3: extension of the rhizopine concept. J. Bacteriol. l 75: 5205-5215.

65. Savka MA, Farrand SK (1997) Modiﬁcation of rhizobacterial populations by engineering
bacterium utilizations of a novel plant-produced resource. Nature Biotechnology 15: 363-368.

66. Schiefelbein JW, Benfey PN (1991) The development of plant roots: new approaches to
underground problems. Plant Cell 3: 1147-1 154.

67. Shot L, Egsgaard H (1984) Identiﬁcation of ononitol and O-methyl-scyllo-inositol in pea
root nodules. Plants 1 6 1: 32-36.

68. Smith AE (1982) Determining the legume-fraction of a grass-legume mixture by pinitol
analysis. Agronomy J. 74: 157-159.

69. Smith AE, Phillips DV (1980) Occurrence of pinitol in folage of several forage legume
species. Crop Sci. 20: 75—77.

22

70. Soedarjo M, Hemscheidt TK, Borthakur D (1994) Mimosine, a toxin present in
leguminous trees (Leucaena spp.), induces a mimosine-degrading enzyme activity in some
Rhizobium strains. Appl. Environ. Microbiol. 60: 4268-4272.

71. Streeter JG (1980) Carbohydrates in soybean nodules. Plant Physiol. 66: 471-476.

72. Streeter JG (1987) Carbohydrate, organic acid, and amino acid composition of bacteroids and
cytosol from soybean nodules. Plant Physiol 85: 768-773.

73. Tempe J, Petit A, Bannerot H (1982) Presence de substances sembles a des opines dans
des nodosites de luzeme. CR. Acad. Sc. Paris Serie III 295: 413-416.

74. Vlassak KM, Vanderleyden J (1997) Factors inﬂuencing nodule occupancy by inoculant
rhizobia. Crit. Rev. Plant Sci. 16: 163-229.

75. Waisel Y, Eshe A, Kalkafi U eds. (1993) Plant roots: the hidden half. Marcel Dekker:
New York.

76. Wexler M, Gordon D, Murphy PJ (1995) The distribution of inositol rhizopine genes in
Rhizobium populations. Soil Biol. Biochem. 27: 531-537.

77. Whipps JM (1990) Carbon economy. pp. 59-98 In The Rhizosphere. Lynch JM ed. J. Wiley
& Sons: New York.

Chapter 2
Efforts to Construct Transgenic Plants 'lhat Produce Rhizopine

Abstract

Rhizopine synthesis (mos) genes from Sinorhizobium meliloti L5-30 were used in attempts
to produce genetically-engineered plants that accumulate rhizopine. It was hypothesized that this
approach might be feasible beacause the mos genes functioned in a heterologous system, and plant
tissues were not observed to contain rhizopine-catabolizing activity. The initial efforts to transform
plants with the mos genes indicated that hygromycin-based selection, DNA-transfer of the pPCV 91-
derived vectors into the plant genome, and/or the structure of the constructs were inadequate to
generate rhizopine producing plants. An alternative cloning strategy dependent on PCR amplification
of the three predicted mos open reading frames, mosA, mosB, and mosC, was subsequently carried
out. Ampliﬁed DNA corresponding to the mos genes were introduced into bacterial over-expression
vectors, and proteins corresponding to MosA and MosB, but not MosC, were isolated. MosA and
MosB speciﬁc antisera were generated and used to examine the expression of these genes in
transformed plants. The three ampliﬁed mos open reading frames were inserted into a plant
expression vector called pMOS7Bl. Plants transformed with pMOS7Bl displayed a visibly altered
growth phenotype characterized by enlarged leaves and suppression of ﬂowering. Northern blot
analyses indicated that all three mos open reading frames were transcribed in several transformed
lines, but the mosA transcripts were smaller than expected. Western blot analysis indicated that
MosB but not MosA proteins accumulated in these transformed lines. High-voltage paper
electrophoresis analyses of extracts of transgenic tissues failed to detect any accumulation of
rhizopines. Thefunctionality of the cloned mos ORFs was tested by reconstructing the mos operon
on a pTE3-derived vector under the control of the up promoter. Medicago sativa nodules induced by
S. meliloti 1021 harboring this reconstructed operon were not observed to accumulate rhizopines.

Introduction
The biased rhizosphere hypothesis predicts that the introduction of a nutritional mediator into
the rhizosphere will result in an increase in microbial populations capable of catabolizing it (26). A

spatial and temporal association between the compound and the organism which can utilize it are

required for the phenomenon of a biased rhizosphere to occur. One means of creating such an
association is to genetically engineer the plant to produce the nutritional mediator.

Recently, several repons have substantiated the biased rhizosphere hypothesis in systems
using transgenic plants engineered to produce opines. It has been reported that such transgenic
plants can synthesize signiﬁcant quantities of opines, up to 150 mg per g dry weight of tissue (29).
These plants can also support a greater fraction of genetically engineered opine-catabolizing bacteria
under gnotobiotic conditions when co-inoculated with near-isogenic non-catabolizing strains (8, 30).
Perhaps most signiﬁcantly, it has been shown that opine-producing plants can speciﬁcally increase
the populations of culturable opine-utilizing bacteria in non-sterile soil (20, 21). These results
suggest that plant-produced nutritional mediators can be used to effectively bias rhizosphere microbial
populations.

Interest in rhizopine as a nutritional mediator (16, 26) prompted me to try to genetically-
engineer plants to produce and accumulate rhizopines using the mos genes from Sinorhizobium
meliloti L5-30. These genes are required for the synthesis of 3-O-methyI-scyllo-inosamine (M81) in
nodules infected with S. meliloti (14, 17). An analysis of the DNA sequence of this region identiﬁed
four open reading frames (ORFs), three of which are thought to be indispensable for rhizopine
synthesis (17). No studies have been done on the biochemistry of these proteins, and the only
available information is based on DNA sequence analysis. It has been suggested that the mosA
product is involved in the methylation of scyllo—inosamine (SI), because SI accumulates in nodules
infected by a strain of S. meliloti lacking mosA sequence in its otherwise homologous mos locus
(27). The deduced mosB product contains a putative DNA-binding motif, which may indicate some
regulatory function. The deduced mosC product has been predicted to be a membrane spanning
protein, possibly involved in either importing a precursor or exporting rhizopine. The mosA, B, and
C genes are likely expressed from a common nrfA-dependent promoter (15, 17). It was postulated
that the introduction of these genes into the plant genome would result in rhizopine production in
plant tissues and such rhizopine-producing plants could be used to test the predictions of the biased
rhizosphere hypothesis (26).

Materials and Methods
Bacterial strains andculture media

The strains used in this work are listed in Table 2.1. Escherichia coli were cultured on LB
media supplemented with either ampicillin (100 ug/ml), carbenicillin (100 ug/ml), kanamycin (50
uglrrrl) or tetracycline (10 ug/ml) as needed to maintain introduced plasmids. Agrobacterium strains
were maintained on either YEB or LB media supplemented with rifampicin (100 ug/ml) and either
carbenicillin (100 ug/ml), gentamycin (25 ug/ml), and/or kanamycin (50 ug/ml) to maintain
introduced plasmids. Sinorhizobium meliloti and Mesorhizobium loti strains were maintained on TY

media supplemented with streptomycin (100 ug/ml) and/or tetracycline (10 ug/ml).

Molecularmethods

Molecular clonings were carried out according to standard methods (28). Plasmids were
isolated using alkaline-lysis based mini-preps and maxi-preps of DNA. CaClz-mediated
transformation or electroporation was used to introduce the DNA constructs into E. coli. Plasmids
were introduced into Agrobacteriwn strains by electroporation or tri-parental mating using pRK2013
as the helper plasmid. Restriction mapping was used to verify the structure of recombinant
plasmids.

The polymerase chain reaction (PCR) was also used to clone the mos open-reading frames
(ORFs). Primers were designed to contain speciﬁc restriction sites so that the PCR products could be
conveniently cloned into three different vectors. The primers used and their DNA sequences are
listed in Table 2.2. Ampliﬁcation conditions were as follows. Each 50 ul reaction mix included 1x
Gitchier brrffer (24), 160 ug/ml BSA, 200 uM dNTPs, 5% DMSO, 50 pmoles of each primer, and
100 ng of linearized template plasmid (pAG8523, pAG8525, or pAG8527). Two units of Ampli-Taq
Polymerase were added after pre-heating sample mixtures to 95°C. Reactions were cycled 15 times;
94°C for 1 minute, 50°C for 1 minute, and 68°C for 2 minutes. After the ﬁfteenth cycle, a 5 minute
extension cycle at 72°C was included, after which samples were cooled and stored at 4°C. The

ampliﬁed products were digested and gel-puriﬁed prior to cloning.

27

Table 2.1: Strains and plasmids used in this work.

 

 

strain 4‘ species plasmids mos genes
Testing of mos genes in Rhizobium loti
8544 E coli / H8101 pPM1071 ABC
8545 E coli / H8101 pPM1062 ABC
8547 E coli / H8101 pPMIOBI
7011 E coli / H8101 pRK2013
6023 Rhizoblum lotl IPN184 -
6078 Rhlzoblum lotl / PN184 pPM1031
6079 Rhizobium loti / PN184 pPM1062 ABC
6080 Rhizoblum lotl / PN184 pPM1071 ABC
Initial constructs used for plant transformation
' 7011 E coli / H8101 pRK2013
7137 E coli IHBIOI pBS
7139 E coli I HBlOl pPCV9l
8523 E. coli / H8101 pAG8523 A
8525 E coli / H8101 pA68525 B
8527 E coli / H8101 pA08527 C
8558 E coli / DHSalpha pBS-mosA(s) A
8559 E coli / DH5alpha pBS-mosB(n) B
8561 E coli / DHSalpha pPCV9l-C / pMOS3 C
8562 E coli / DH5alpha pPCV91-AC / pMOSS A, C
8563 E. coli / DI-ISalpha pPCV9I-BC / pMOS6 B, C
8564 E coli / DHSalpha pPCV91-ABC / pMOS7 A, B, C
4062 A. rlrizogenes / GV3101CT pRi 15834
4063 A. tumefaciens / GV3101CT pTiBoS42
4600 A. rhlmgenes / GV3101CT pRll5834, pPCV91
4601 A. rhizogenee / GV3101CT pR115834, pMOS7 A, B, C
4602 A. rhlaogenee / GV3101CT pRl15834, pMOS6 B, C
4604 A. tumefaclens / GV3101 CT pTlBoS42, pPCV91
4605 A. tumefaelena / GV3101CT pT180542, pMOS7 A, B, C
PCR-based cloning and plant transformation
7011 E coli / H8101 pRK2013
7137 E coli / H8101 pBS
7139 E coli I H8101 pPCV91
8624 E coli lHBIOI pBS-A‘ A‘
8625 E coli / H8101 pBS-B’bl 8‘
8627 E coli / H8101 pBS-C" C*
8628 E coli / H8101 p8S-C*249 C“
. 8630 E coli / H8101 pBS-A*?.44 A"
8633 E coli / H8101 pBS-deltaBN
8634 E coli / H8101 pBS-delta8N258
8637 E coli l HB101 pBS-A'C*244 A', C*
8640 E coli I H8101 pPP

 

Table 2.1:

 

 

strain 4‘ species plasmids mos genes

8646 E coli / H8101 pPP-0" C"

8647 E coli I H8101 pPP-C‘A" A“, C‘

8648 E coli / H8101 pPP-C’A‘B’ A’, 8", C‘

8649 E coli / DHSalpha p81N19-PPC‘A‘B‘IpMOS781 A‘, 8*, C"

8650 E coli I H8101 pPCV91-C'I C"

8651 E coli I HBlOl " pPCV91-C‘A" A", C‘

8652 E coli / DHSalpha pPCV9I-C‘A‘B‘IpMOS7A

4542 A. tumefaciens GV3101 pMP90RK

4070 A. tumefaciens GV3101 pMP90RK, pPCV91

4071 A. tumefaciens GV3101 pMP90RK, pMOS7A A", 8", C“

4072 A. tumefaciens I C58C1 pMP90

4073 A. tumefaciens I C58C1 pMP90, pBINl9

4074 A. tumefaciens I C58C1 pMP90, pMOS7Bl A‘, B‘, C‘
Overexpression of Mos proteins

7148 E coli / DI-ISalpha pETle

8653 E coli l H8101 pETle-A“ A"

8654 E coli I H8101 pEI‘le-B" B'

8636 E coli I H8101 pETle—C“

8655 E. coli I BL21(IDE3)pLYS S pETISb

8656 E. coli / BL21(IDE3)pLYS S pETle-A‘ A"

8657 E. coli / BL21(IDE3)pLYS S pETISb-B‘ B‘

8658 E. coli I BL21(|DE3)pLYS S pETISb-C‘ 0'
Test of reconstructed operon

8642 E coli I H8101 pTB-245

8643 E coli I H8101 p88-A‘B'C’244/ pMOSl7

8644 E coli I H8101 pMOSZ7

8645 E coli I H8101 pMOS72

3169 S. melllotl 1021 pPM1093 ABC

3247 S. melllotl 1021 pTE3-245

3249 S. melllotl 1021 pMOS72 C‘B‘A‘(-)

3250 S. melllotl 1021 pMOS27 A'B'C‘

 

Table 2.2: Oligonucleotide linkers and PCR primers used in this work.

 

 

oﬂo # description sequence
Linkers
D8130 ss adaptor Pst1->Notl CAG CGG CCG CTG TGC A
N881071 ds Baml-II CGG GAT CCC G
NE81148 ds Sall CGG TCG ACC G
D8244 ss adaptor SpeI->Sacl CT A GCG AGC TCG
D8245 ss adaptor PstI->Sacl GGA OCT CCT GCA

D8249 ss adaptor 8amHIx->SacII GAT CAC CGC GGT

PCR primers
D8238 mosA‘forward
D8239 mosA’reverse
D8240 mosB‘forward
D8241 mosB‘reverse
D8242 mosC‘forward
D8203 mosC‘reverse

CAG GCC TCT AGA TCT CCA TCC ACA ATI‘ C

GCG TGT CT Cr AGA TCT AGA CGG CAC CT G

CGG GAT GCA TAT GCG GCC GCT GGT CIT ACA G
GGA GCA CAT ATG CGG CCG CIT GTC CT G GTC
CGT CAT CCC GCG GTC GAC GAA ACC AAG CGG AG
CGC CAA GGA TCC GCG CAT 006 ATG

 

30

The preparation of antisera followed standard protocols (28). The induction and isolation of
the over-expressed Mos proteins from E. coli were performed according to the protocols provided by
Novagen (Madison, WI). The expressed His-tagged proteins were isolated from E. coli using a Ni-
binding column. Protein puriﬁcation was monitored by SDS-PAGE and Coomassie staining of
various fractions. Rabbits were maintained, immunized, and bled by employees of the Michigan State
University Animal Care Facilities.

The molecular analyses of plant tissues were carried out using standard protocols (28). For
Northern blot analyses, total RNA was isolated using a hot-phenol-based method (33), except that the
buffer of Hall et al. was used (9). RNA was separated on forrnaldehyde-containing gels and
transferred onto MagnaGraph nylon membranes (MSI Scientiﬁc, Westborough, MA). Random-
primed DIG-labeled probes were prepared with the Genius I Kit (Boehringer Mannheim
. Biotechnologies, Indianapolis, IN) according to the manufacturers instructions. The hybridization
solution contained 45% deionized formarnide, 4.5x SSC, 45 mM NaPO4, 1.8% blocking reagent
(BMB, Indianapolis, IN), and 1.8% SDS, pH 7.0. Hybridizations were performed overnight at
68°C and blots were subjected to two high stringency washes (0.1x SSC, 0.1% SDS, a 37°C).
Detection of bound hybrids involved alkaline-phosphatase mediated staining following the
manufacturer's instructions. For Western blot analyses, proteins were isolated by precipitation with
70% acetone. The protein concentration was estimated using the Bio-Rad assay (Hercules, CA).
Proteins were separated on SDS-PAGE gels and transferred onto nitrocellulose membranes.
Membranes were treated with Blotto to prevent non-speciﬁc binding, and incubated with 1:500
dilutions of crude anti-sera. Detection of bound anti-Mos anti-bodies relied on alkaline-phosphatase-
conjugamd secondary antibodies and colorimetric detection using NBT and BCIP.

Plantn'arrsfonnation
The vectors used for plant transformation were based on pPCV9l or pBINl9. The vector
pPCV9l was obtained from Drs. C. Koncz and N. Strizhov (Max-Planck-Institute, Koln, Germany).

This plasmid contains three different promoters followed by unique restriction sites and thus allows

31

for the concurrent expression of mosA, mosB, and mosC. The promoters on pPCV91 are an
enhanced 358 promoter of the cauliﬂower mosaic virus and the 1’ and 2’ promoters from the
mannopine synthase (nras) genes (34). The mas 1’ and 2’ promoters have been shown to direct
expression of genes predominantly in the basal parts of plants (13, 31). In addition, pPCV91
contains the hpt gene (7), encoding hygromycin phosphotransferase, as a plant selectable marker and
the B-lactamase gene as a selectable marker in bacteria (28). It also contains the left and right border
of the T-DNA of A. tumefaciens, thus enabling the transfer of DNA between the two borders into the
plant genome, when the virulence functions are provided in trans. The plasmids pBINl9 and its
derivative pBIB have been described elsewhere (2, 3).

Plant transformations were mediated by Agrobacterium using standard protocols.
Transformation of Lotus corniculatus was accomplished by Agrobacteriron-mediated hairy root
induction of infected hypocotyls (23). For Nicotiana tabacum co-cultivation of Agrobacterium
tumefaciens with leaf explants was used to generate transformed callus from which plants were
regenerated (19). Arabidopsis thaliana var. Columbia was transformed via vacuum inﬁltration of
Agrobacterium tumefaciens (1, 1]). Selection of transfonnants was based on resistance to the
antibiotics hygromycin B or kanamycin, depending on the construct. All plants and tissues were
incubated in growth chambers.

Assays for rhizopine synthesis and catabolism

Preparation and analysis of plant extracts followed a published prowdure for detection of
rhizopine from nodule tissues (25). Brieﬂy, plant material was crushed in sterile distilled water,
centrifuged to remove debris, and then dried in a Speed-Vac. Samples were resuspended to a ﬁnal
concentration of 1 mg fresh weight per ul in sterile distilled water. Samples were stored at -20°C
prior to analysis. Five to twenty microliters of each sample was loaded on 3MM Whatrnan paper for
high-voltage paper electrophoresis (HVPE). Electrophoresis was performed at 3000 V in 1.1 M
acetic acid/0.7 M formic acid buffer. Visualization of a-diol-containing compounds, including MSI,
resulted from staining with alkaline AgNO; following Dessaux et. al. (4). MSI was detected as a

32

dark spot running at a characteristic distance relative to orange G (Roms. G = - 0.9). Extracts from
nodules containing the MSI rhizopine were used as positive controls.

For the rhizopine catabolism assays, approximately 100 mg (fresh weight) of plant tissue
was crushed with a plastic pestle in a tube containing 1 ml of sterile distilled water. Plant debris was
removed by centrifugation. 100 ul of supernatant was incubated with 900 ul of nodule extract
containing MSI rhizopine for 3 days at 28°C. The assay mix was then concentrated, stored, and
analyzed as described above.

Results
Rhizopine synthesis and the absence of catabolism in plants

Rhizopine synthesis is known to occur only in legume nodules infected by rhizobia
containing the mos genes (18). To determine whether rhizopine synthesis could occur in other
plants, such as Lotus comiculatus, cosmids containing the mas and moc loci from Sinorhizobium
meliloti 1530 were introduced into Mesorhizobium loti PN184 by tri-parental mating. The presence
of the constructs in the putative transconjugants was conﬁrmed by reisolating the cosmids and
subsequent restriction analysis (data not shown). All of the strains nodulated Lotus comiculatus
plants under gnotobiotic conditions. Nodules infected by M. loti transconjugants harboring the mos
locus, alone or in combination with the mac locus, accumulated the rhizopine, 3-O—methyl-scyllo-
inosarnine (MSI) (see Figure 2.1).

Different plant tissues were screened for activity that would degrade rhizopines. Extracts
from Arabidopsis thaliana, Nicotiana tabacum, Medicago sativa, and Lchpersicon esculentum were
examined using the standard assay for rhizopine catabolism. No catabolism of the the MSI rhizopine

was detected in extracts from any of the plant species tested (data not shown).
Initial construction of pMOS plant expression vectors
Restriction fragments containing the mos genes, required for the production of 3-0-MSI in

nodules by S. meliloti, were initially cloned from pPM1093 (A. Gufstafson, S. Rossbach, F.J. de

33

** r 4':

 

     

Figure 2.2: Accumulation of rhizopine in nodules of a heterologous symbiotic
system. Rhizobium loti PN184 containing plasmids containing the mos and/or moc loci
from Sinorhizobium meliloti L5-30 were inoculated onto L. comiculatus plants. Extracts
from nodules induced by PN184(pPM1071) (mos, mac) (1), PN184(pPM1062) (mos) (2),
or PN184(pPM103 l)(moc) (3) were examined by HVPE analysis. Extracts from nodules
induced by PN184 were used as a negative control (4). Extracts of M. sativa nodules
induced by L5-30 are presented as a positive control (L). Uncharged a—diol-containing
compounds are visible at the origin (**). The positively staining rhizopine migrates
toward the negative electrode (*).

34

Bnrijn, unpublished data). The mos-containing inserts of pA68523, pAG8525, and pAG8527 were
modiﬁed by linker ligations so that the fragments containing the three open-reading frames could be
inserted into the plant expression vector pPCV91 (see Figure 2.2). A double-stranded
Oligonucleotide (NE81148) containing a Sall site was ligated to the Sall-Xbal fragment from
pAG8523, digested with Sall, and gel puriﬁed. A single-shanded Oligonucleotide linker, D8130
(PstI->NotI), was used to modify pAG8525. The newly generated plasnrid, pAG8525(n), was
digested with NotI, and the 1.84 kb fragment containing mosB was gel puriﬁed. The mosC
containing fragment was excised from pAG8527 with BamHI and EcoRV. A double-stranded (ds)
BamHI linker (NEBIO'II) was ligated to the insert and was subsequently digested with BarnHI
before gel puriﬁcation. Fragments carrying the three mos genes were inuoduced sequentially into the
plant expression vector pPCV9l: mosC, mosA, then mosB. The ﬁnal construct, pMOS7, contained
all threemos genes. The plasmid pMOS 6, containing only mosB and mosC, was constructed in a

sinrilar fashion.

Lotus and Nicotiana transformations and analysis

The plasmids pPCV91, pMOS6, and pMOS7 were introduced separately into two diﬁerent
strains of Agrobacterium. These strains have isogenic chromosomal backgrounds, GV3101CT,
containing helper functions necessary for the maintenance of pPCV91-derived plasmids in
Agrobacterium (12). However, the strains differ in their plasmid content; strain 4062 contains
pR115824 (22), while strain 4063 contains pTi80542 (10). Both strains grow well on YEB agar
containing rifampicin (100 ug/ml), and are sensitive to carbenicillin (a 75 ug/ml), but not ampicillin
(5 250 ug/ml). It was found that pPCV91 derived vectors could be adequately maintained in both
strains in the presence of carbenicillin at a level of 100 ugIml.

Transformation of Lotus comiculatus was performed via Agrobacteriwn-mediated hairy root
induction. Hypocotyls were infected with strain 4062 harboring the three different plasmid
constructs. Hairy roots were generated and removed, however shoots could not be regenerated in the

presence of hygromycin, even at low concentrations (10 ug/ml). Because hygromycin selection has

35

lkb

:5 E
mosB mosC
.. a. . - ~ pPM 1093

 

EcoRl
_ incll
— -— — —Seol

 

 

 

I I
I |
A
I I I
I .e ,5? I I
I C I
I rm in ends and eubclone into Smal site or pBlueocript KSII+ I

 

 

[ engineer polylinkera of eubclonee for single-alto lneertlon into pPCV91 J

 

 

 

 

l l l

Sal I site Notl site BamHI site

in ii 4'» i i "D" ii i. %

     

Figure 2.2: Initial strategy for inserting the mos ORFs into pPCV91.

36

not been used in any published report of Lotus transformation, and the fact that hairy roots are
themselves transformed (32), regeneration of plants proceeded in the absence of selection.

Transformation of Nicotiana tabacum was mediated by Agrobacterium-induced callus
formation. Leaves were infected with strain 4063 harboring either pPCV9l or pMOS7. Hygrornycin
tolerant calli were generated; however, no hygromycin resistant shoots were obtained from these
calli.

During the course of the transformation procedures, samples of calli and hairy roots were
taken for HVPE analysis. No difference between control and experimental tissues was observed (data
not shown). Regenerated L comiculatus were also screened after approximately six months of
greenhouse growth. In wild-type plants, no stainable compound that co-migrated with rhizopine was
observed in extracts prepared from root, leaf, or ﬂower tissue. Similarly, no rhizopine-like signal
was observed in extracts of any of the plants regenerated from hairy roots.

Evaluation of the lack of hygronrycin resistant plants

Hygromycin-resistant plants were not obtained in the experiments described above. Two
control experiments were carried out to evaluate the cause(s) of this unexpected result. In the ﬁrst
experiment, strain 4063(pPCV91) and LBA4404(pBIB) were used to transform N. tabacum.
Hygromycin-resistant tobacco plants could only be regenerated from the latter combination.
Subsequently, pPCV9l was introduced into A. tumefaciens GV3101- (pMP90RK). However, none
of the seeds obtained from plants inﬁltrated with this strain germinated in the presence of
hygromycin. It was concluded that the rate of effective plasmid transfer and/or the expression of the
hpt gene in planta was signiﬁcantly reduced in the case of the pPCV91 vector system.

Construction of new vectors using PCR-ampliﬁed mos ORF s

The strategy for cloning and expressing the mos genes was reevaluated after the initial
screens of primary transforrnants were found to be negative in terms of rhizopine production. A
review of the DNA sequence data of the mos locus revealed the presence of a number of potential

37

translational start sites upstream of the coding regions of all three mos ORFs. Several of these ATG
codons are contained within the fragments used to generate pMOS7, are out of frame with the coding
sequences, and may preclude the functional expression of the mos genes in planta (17). Since no
convenient restriction sites exist between these out of frame ATG codons and the correct translational
start, a PCR-based cloning procedure was pursued. Figure 2.3 outlines the cloning strategy used to
prepare the different types of vectors described below.

Ampliﬁcation of the mos ORFs using mosA-, mosB-, or mosC-speciﬁc primers produced
products of the expected sizes (data not shown). These PCR products were inserted separately into
pBluescript 11 SK (p88) vectors (see Figure 2.4). The mosA PCR product was digested with Xbal
and inserted into the polylinker of the vector in both orientations (pBM8623 and pBM8624). The
mosB PCR product was treated with the Klenow fragment of DNA polymerase and T4
polynucleotide kinase prior to being inserted into the SmaI site of the polylinker (pBM8625 and
pBM8626). The mosC PCR product was digested with SacH and BamHI and inserted directionally
into the polylinker of p88 (pBM8627). These plasmids provided the mos gene fragments for all
further molecular cloning.

Expression of the ampliﬁed mos genes in E. coli

The three mos ORFs were inserted separately into the bacterial expression vector pET 15b
(see Figure 2.5). The forward primers used in the PCR reactions contained restriction sites that
allowed for the generation of in-frarne gene-fusions between the polyhistidine encoding leader and
each of the mos genes. The BglIl-Xbal fragment from pBM8624 containing mosA was inserted into
the BamHI site of p51" 15b to yield p8M8653. Likewise, the mosB gene from pBM8625 was
inserted into the Mid site to yield pBM8654, and the Sall-Xhol fragment from containing mosC
from pBM8628 was inserted into the Sall site to yield p8M8636. These constructs were introduced
into E. coli BL21(hDE3)(pLysS) for induction and isolation. Induction of gene expression by the
addition of IPTG resulted in the production of proteins of the expected size for two of the three genes
(see Figure 2.6). Both the putative MosA and MosB proteins accumulated to a signiﬁcant percentage

38

amplify the mos ORFs by PCR

I

modify pBluescript vectors
pET-15b pPP p81uescript*
I I I
over-express proteins pBIN l9 pTEB
I I I
generate antisera transform plants test in bacteria

Figure 2.3: Flow chart of PCR-based mos gene cloning
strategy and applications of various constructs.

39

 

pPM 1093

 

 

 

 

 

ﬂll In ends and subclone into Smal site of pBIuescrlpt II

I

amplify open reading frames using PCR

 

 

 

 

 

 

C'no

B.

t — ..— - _,n _.
3'. ._J v . .

subclone PCR products Into pBluescript II SK

 

 

 

 

— —= =_ ;=_
a; A' £75 33 mo £33

_______J_.L—L_LL_.__

Figure 2.4: PCR-based cloning of mos ORFs.

 

clone amplified mos ORFs into pET15b

 

 

 

I

j T7tenn

 

 

 

 

transtorm into E. coli BL21(ADE3)

 

 

I

 

Isolate protein from IPTG induced E. oolicelis

 

 

 

Figure 2.5: Cloning of PCR-amplified mos ORFs into bacterial
expression vectors.

41

of the total cellular protein, and were present exclusively in the cell pellet fraction. The putative mosC
protein, predicted to run at 46 kDa, was was not observed to accumulate. The purified MosA and
MosB proteins were injected into rabbits and antisera were obtained. Western blot analyses of
proteins extracted from nodules infected with S. meliloti 15-30 indicated that the antibodies detected
the wild-type MosA and MosB proteins (see Figure 2.10).

Construction of pMOS 731

Because of the problems regarding selection of hygromycin resistant plants mentioned
above, I decided to create a hybrid vector containing the three promoter and terminator combinations
from pPCV9l (for expression of the mos ORFs) and the nptlI gene from pBINl9 (for selection of
transformed plants based on resistance to kanamycin) (see Figure 2.7). First, a modiﬁed pBluescript
SK vector was constructed, lacking the BamHI, Natl, and Sall sites from its polylinker. The vector
was digested with BamHI and Natl, treated with the Klenow fragment to fill in the overhanging
ends, and religated to form pBM8633. The Sall site of this vector was changed to a Sad site by
inserting the oligo linker D8258, resulting in pBM8634. Then, the 3.5 kb EcoRI fragment from
pPCV9l, containing the three unique cloning sites with their associated promoters and tenninators,
was inserted into pBM8634 to generate pPlantPrep.

The three mos open reading frames were sequentially inserted into pPlantPrep. The 1.28 kb
mosC-containing insert from pBM8628 was inserted in the proper orientation to generate pPPC.
Then the 1.06 kb BglII-Xbal mosA-containing fragment from pBM8630 was inserted to yield
pPPCA. Subsequently, the 1.60 kb mosB containing NotI fragment from pBM8626 was inserted to
yield pPPCAB. Similar constructions were undertaken in parallel using pPCV9l, giving rise to
pMOS7A

To complete the construction of the plant transfonnation vector, the 7.5 kb Bssl-[II fragment
containing the three mos genes was rendered blunt-ended with mung bean nuclease, gel puriﬁed, and
inserted into the Smal site of pBINl9. This construct is referred to as pMOS7Bl.

42

 

I “Uighur. 'u A ”I ”u r

 

pET15b pET15b-A* pET15b-B* pET15b-C*

Figure 2.6: SDS-PAGE gel of protein extracts from E. coli containing different mos
gene constructs. Cells were either uninduced (U) or induced (I) with lmM IPTG to
express the plasmid encoded proteins. Arrows indicate expressed MosA and MosB
proteins.

 

insert amplified mos ORFs into pPCV91

 

 

 

bla

 

 

éon'pBR

“'LB

 

pMOS7A

 

 

Insert EcoRI fragment containing unique cloning sites into pBS-deltaBN258

 

 

—BseH ll

iiili-ﬁi:

—BesH II

 

 

pPP

 

 

 

 

 

 

 

 

 

 

 
  

E- .B. 3% C‘ {Q A' E g nptII

. r . u .
5 ''''''''''' :"o’u'

 

pMOS781

Figure 2.7 : Construction of pMOS7A(a) and pMOS7B l(b).

Arabidopsis traanonnation and analysis of transgenic tissues

The plasmids pBIN 19 and pMOS7B] were introduced into A. tumefaciens C58Cl.
Restriction analysis of the plasmids reisolated from the transconjugants indicated that their structure
was stabily maintained in these strains (data not shown). Arabidopsis plants were infected with these
strains by vacuum infiltration, and T1 seed, representing at least twelve independent lines, was
obtained from separate plants. Selection of transformed plants was made on the basis of resistance to
kanamycin (100 ug/ml). Notably, plants infected with C58C1(pMOS7Bl) produced much less seed
than those infected with the control strain C58C1(pBINl9) (data not shown).

Plants transformed with the pMOS7Bl construct generally displayed an altered growth
phenotype (see Figure 2. 8). Under normal light conditions (16 hr light: 8 hr dark), these transgenic
plants displayed enlarged and more numerous rosette leaves. Furthermore ﬂowering was generally
delayed or entirely suppressed. This contrasted with wild-type plants and those transformed with
pBIN19 that initiated flowering two to three weeks after germination under the same light conditions.
The aberrant phenotype was observed to be partially suppressed under 24 hour light conditions.
Under such conditions, the plants transformed with pMOS7B] ﬂowered, but the emerging bolts were
generally fewer and greatly enlarged and the plants retained the enlarged rosette leaves. The initial
seed pads were greatly enlarged and contained no viable seed; however, more apical pods gave rise to
viable seed.

Molecular and biochemical analyses indicated that the mos construct, pMOS7Bl, was
insuﬂicient for the production of rhizopine in planta. Leaf tissues of T1 plants and whole seedlings
of T2 plants were assayed for the accumulation of rhizopine using the HVPE assay, but no rhizopine
(La. 5 30 ppm) could be detected in these tissue extracts (see Figure 2.9). Northern and Westem
blot analyses were performed on select lines displaying variations in their growth phenotype (see
Figure 2.10). Transcripts conesponding to all three mos genes were detected in three of the four
selected lines that had been transformed with pMOS7Bl. Interestingly, only the lines displaying a
visibly mutant phenotype were observed to contain the mos transcripts. The sizes of the hybridizing
bands were estimated relative to the migration pattern of the 288 and 188 rRNA. The mosB and

45

Figure 2.8: Phenotypes of Arabidopsis thaliana plants transformed with
pBIN19 (l) or pMOS7B] (2). Plants are shown after 3 weeks of incubation under
normal light conditions (panel a) and after 6 weeks under 24 hour light conditions (panel b).

 

1 2
Figure 2.8

47

 

NE12345678SI910N
1.0 1.0 0.

ENE
60.

3

Figure 2.9: HVPE analysis of extracts of transgenic Arabidopsis thaliana plants. The
independent lines of plants displayed were transformed with either pMOS7B (lanes 1 -
8) or pBINl9 (lanes 9 - 10). Nodule extracts containing MSI (NE) and synthesized SI
(SI) were included as positive controls.

a b

5‘93 3’: 'rt’Rl’ﬁlymtxii’ '2‘"! -",€;‘}".f2 »'« f’ p".4“;"'."'.".;'-fl Immaﬁ‘IS'Et‘tﬂtlﬁ‘F'ﬁ‘d 9’ 591"?" ’03,. 123'." §{~‘&ir."'-"it
& ' ‘.- aw' . ' . ’ ' I

.
a:
,

‘3“ u-

 

 

 

 

 

 

12345 L12345

Figure 2.10: Analysis of mos gene expression in transgenic Arabidopsis thaliana
plants. (a) Northern blot analysis using DIG-labeled probes containing mosA, mosB,
and mosC DNA sequences. (b) Western blot analysis using anti-MosA and anti-MosB
antisera. The independent lines of plants displayed were transformed with either
pMOS7B (lanes 1 - 4) or pBIN19 (lane 5). Total protein from nodules infected with S.
meliloti L5-3O (L) were included as a positive control.

49

mosC transcripts were observed to be 1.79 and 1.49 kb respectively, approximately 190 bp longer
than the conesponding open reading-frames. The increased length of these mRNAs probably results
from the presesence of 3' untranslated sequence in the nos terminator regions adjoining each insertion
site in the vector. In contrast the detected mosA transcript was observed to be 1.03 kb in length,
approximately 220 bp shorter than expected The anti‘MosA antisera could detect proteins of the size
expected for MosA in nodules infected with S. meliloti L5—30, however, no protein conesponding to
MosA was detected in extracts of the transformed plants. Two bands, approximately 60 kDa in size,
similar to the size predicted for M058, were detected both in L5—30 infected nodules and transgenic
plant tissues using the antisenrrn speciﬁc for MosB. At least one comigrating band was observed to
be faintly present in samples of control plants transformed with pBIN 19. Neither of these signals
was detected in replicate blots hybridized with pre-immune sera (data not shown). Line 4 which

displayed the greatest amount of mosB transcript also contained the greatest amount of MosB protein.

Evaluation of the frarctionality of the ampliﬁed mos genes

To investigate whether or not the PCR-ampliﬁed mos genes were sufficient for rhizopine
synthesis, the three ORFs were reorganized into an operon structure similar to that of the wild-type
mos locus (see Figure 2.11). First, the plasmid pBM8627 was modiﬁed using D8249 (BamI-II-
>SacII), resulting in pBM8628. In parallel, plasmid pBM8624 was modiﬁed with the linker DBZ44
(SpeI->Sacl), resulting in pBM8630. The mosC-containing SacII fragment of pBM8628 was
inserted into the corresponding unique site in pBM8630 to yield pBM8637. The mosB-containing
NotI fragment from pBM8626 was subsequently inserted into the unique site of pBM8637 producing
pMOSl7. The Sad insert of pMOSl7, containing all three open reading frames (in the order mosA,
mosB, mosC), was then cloned into the unique SacI site of pTB-245. The broad host-range
plasmid pTE3 contains the trp promoter 5‘ to the unique cloning site, and expression from this
promoter has been suggested to be constitutive in S. meliloti (6). The mos operons constructed are
referred to as pMOSZ7 and pMOS72, with the inserts in the (+) and (-) orientations respectively.

 

 

insert amplified mos ORFs into a modified pBS vector

I

Aar- 3* car- c§

pBS SK 245::mosA'B‘C'l pM0817

 

 

Sacl

 

 

insert Sac 1 fragment into a modified broad host-range vector

I

B*
“ _;-:-?;‘?.§:?Iia~ié and -—- =i=::i=:":=

¢ pTE3245::mosA'B'C'/ pMOSZT

 

 

   
   

 

 

introduce into E. coli and assay for rhizopine production

I

introduce into R. meliloti and assay for rhizopine production

 

 

 

 

 

 

Figure 2.11: Reconstruction of 3 mos operon using PCR-amplified
mos open-reading frames.

51

These reconstructed operons were used to determine the functionality of the ampliﬁed mos
ORFs in nodules. The three pTE3-derived constructs and pPM1093 were mobilized separately into
S. meliloti 1021 by tri-parental mating, giving rise to strains 3247, 3249, 3250, and 3169 (see Table
2.1). M. sativa were inoculated separately with each of these strains under gnotobiotic conditions.
Nodules induced by these strains were all found to contain tetracycline-resistant rhizobia indicating
that the plasmids were maintained in the cells during infection and nodulation. However, rhizopine
was only detected in extracts of nodules induced by the positive control strain, 3169 (data not

shown).

Discussion

The feasibility of creating rhizopine-producing transgenic plants was investigated. Previous
work had indicated that the 5.3 kb fragment of pPM1093 containing the mos genes from S. meliloti
L5-30 was sufficient for the production of the rhizopine M81 in nodules of M. saliva induced by an
otherwise Mos strain of S. meliloti (14). Here the same cosmid, and others containing the same mos
locus, were found to be sufﬁcient for the accumulation of rhizopine in nodules of L. comiculatus (see
Figure 2.2). Additionally, no rhizopine speciﬁc catabolic activity was detected in extracts of roots or
leaves of three different plant species. These data indicate that there is no a priori limitation to
rhizopine accumulation in planta, and that it may be feasible to create transgenic plants that produce 3-
O-MSI.

However, the goal of producing measurable quantities of rhizopine in planta was not
achieved The initial efforts used pPCV9l—derived vectors in Agrobacterr'um GV3101 CT. Contrary
to expectations, the hairy roots and calli induwd were generally hygromycin-sensitive, and tissues
regenerated from them were not observed to accumulate rhizopines. It is possible that the mos
sequences were not transfened into the genome of the infected plant tissues or that the hpt gene was
not functional in these constructs. In a control test, hygromycin-resistant transformed plants were
obtained with LBA4404(pBIB) but not GV3101(pPCV9l). Also, attempts to transform Arabidopsis
plants by vacuum inﬁltration with GV3101(pPCV91) failed to result in the generation of hygromycin

52

resistant seed. The presence of putative translational start sequences upstream of the predicted ORFs
may also have prevented the proper expression of the Mos proteins. Because rhizopine accumulation
was not observed, an alternative PCR-based molecular cloning strategy was pursued which also
allowed for the use of an alternative selection strategy based on kanamycin resistance. While the mos
sequences present on pMOS7Bl were clearly mobilized into the plant genome, as evidenced by
hybridization signals observed in the Northern blot analyses (see Figure 2.10), they were not
sufﬁcient for rhizopine accumulation in any of the transformed lines of Athaliana (see Figure 2.9).
There are several possible explanations for why rhizopine accumulation was not observed in
the transformed Arabidopsis plants. First, the mosA gene did not appear to be properly expressed at
the mRNA level and the MosA protein did not accumulate; this would preclude MSI synthesis,
though it might still allow for SI accumulation. Second, the MosC protein may not have
accumulated; if so, neither rhizopine would have been synthesized. It is also possible that signiﬁcant
point mutations were introduced during the PCR ampliﬁcation of one or more of the mos ORFs.
While it is known that occasional DNA sequence changes in PCR-ampliﬁed fragments do occur (5),
several other functional genes were successfully cloned using the same ampliﬁcation protocol (S.
Rossbach, unpublished data). Both the expression of the MosA and MosB proteins of the expected
sizes in E. coli (see Figure 2.6) and the positive cross-reaction of the anti-MosA and anti-MosB
antisera with the wild-type proteins from nodules infected with S. meliloti L5—30 (see Figure 2.10)
indicate that at least these two genes were cloned without major errors. Nonetheless, an alteration in
the coding sequence of one or more of the ampliﬁed sequences may have occuned. Indeed, assays
of the functionality of the reconstructed operon failed to verify the ability of the cloned mos genes to
operate in concert to produce the rhizopines. It is also possible that the three deduced ORFs are
necessary but not srfﬁcient for the synthesis of rhizopines in plant cells or heterologous bacteria.
Since rhizopine synthesis has only been reported in nodules infected with rhizobia containing mos
sequences, other rhizobial genes and/or plant genes with nodule speciﬁc expression may be required
for its synthesis. Lastly, the observed phenotypic changes in the transformed plants indicate that one
or more of the mos genes can signiﬁeantly affect plant development. If this is the case, the practical

S3

production of rhizopine in planta may not be tenable using the known mos genes. I conclude that
further investigation of mos gene expression in rlrizobium will be required before any more fnritful
attempts at creating biased rhizospheres based on rhizopine synthesis in transgenic plants can be
made.

References

l Bechtold N, Ellis J, Pelletier G (1993) In planta Agrobacterium mediated gene
transfer by inﬁltration of adult Arabidopsis plants. C. R. Acad. Sci. Paris, Life Sci. 316: 1194-
1199.

2 Becker D (1990) Binary vectors which allow the exchange of plant selectable markers and
reporter genes. Nuc. Acids Res. 1 8: 203.

3 Bevan M (1984) Binary Agrobacterium vectors for plant transformation. Nuc. Acids Res.
1 2: 871 1-8721.

4 Dessaux Y, Petit A, Tempe J (1992) Opines in Agrobacterium biology. pp109-l36. In
Molecular signals in plant-microbe communications. CRC Press: Boca Raton.

5 Echols H, Goodman MF(1991) Fidelity mechanisms in DNA replication. Ann. Rev.
Biochem. 60; 477-511.

6 Egelhoff TT, Long SR (1985) Rhizobium meliloti nodulation genes: identiﬁcation of
nodDABC gene products, puriﬁcation of nodA protein, and expression of nodA in Rhizobium
meliloti.. J. Bacteriol. 164: 591-599.

7 Elzen I’JM van den, Townsend J, Lee KY, Bedbrook JR (1985) A chimaeric
hygromycin resistance gene as a selectable marker in plant cells. Plant Molec. Biol. 5: 299-302.

8 Guyon P, Petit A, Tempe J, and Dessaux Y (1993) Transformed plants producing
opines speciﬁcally promote growth of opine-degrading Agrobacteria. MPMI 6: 92-98.

9 Hall TC, Ma Y, Buchbinder BU, Pyne JW, Sun SM, Bliss FA (1978)
Messenger RNA for G] protein of French bean seeds: cell-free translation and product
characterization. Proc. Natl. Acad. Sci. (USA) 35: 3196-3200.

10 Hood EE, Fraley FR, Chilton M-D (1987) Virulence of Agrobacterium tumefaciens
strain A281 on legumes. Plant Physiol. 83: 529-534.

1 1 Roof A van, Green PJ (1996) Premature nonsense codons decrease the stability of
phytohemagglutinin mRNA in a position-dependent manner. Plant J. 1 0: 415-424.

1 2 Koncz C, Schell J (1986) The promoter of TL-DNA gene 5 controls the tissue-speciﬁc
expression of chimaeric genes carried by a novel type of Agrobacterium binary vector. Mol. Gen.
Genet 204: 383-396.

l3 Langridge WEIR, Fitzgerald KJ, Koncz C, Schell J, Szalay AA (1989) Dual
promoter of Agrobacteriurn tumefaciens mannopine synthase genes is regulated by plant growth
hormones. Proc. Nat Acad. Sci. (USA) 86: 3219-3223.

1 4 Murphy PJ, Heycke N, Banfalvi Z, Tate ME, de Bruijn F, Kondorosi A,
Tempe J, Schell J (1987) Genes for the catabolism and synthesis of an opine-like compound in
R. meliloti are closely linked and on the Sym plasmid. Proc. Nat Acad. Sci. (USA) 84: 495-497.

15 Murphy PJ, Heycke N, Trenz SP, Tate ME, de Bruijn F, Schell J (1988)
. Synthesis of an opine-like compound, a rhizopine, in alfalfa nodules rs symbiotically regulated,
Proc. Nat. Acad. Sci. (USA)85: 9133-9137.

1 6 Murphy PJ, Saint CP (1992) Rhizopines in the legume-Rhizobium symbiosis. pp. 377-
390 In Molecular signals in plant-microbe communications. CRC Press: Boca Raton.

17 Murphy PJ, Trenz SP, Grzemski W, de Bruijn FJ, Schell J (1993) The
Rhizobium melilot rhizopine mos locus is a mosaic structure facilitating its symbiotic regulation. J.
Bacteriol. 175: 5193-5204.

18 Murphy PJ, Wexler W, Grzemski W, Rao JP, Gordon D (1995) Rhizopines-their
role in symbiosis and competition. Soil Biol. Biochem. 27: 525-529.

19 Newman TC, Ohme-Takagi M, Taylor CB, Green PJ ( 1993) DST sequences,
highly conserved among plant SAUR genes, target reporter transcripts for rapid decay in tobacco.
Plant Cell 5: 701 -714.

20 Oger P (1995) Ph.D. thesis; Etudes su 1a possibilite de favoriser speciﬁquement la
croissance de bacteries de la rhizosphere-Le cas des plantes transgeniques productrices d'opines.
Universite de Paris sud centre d'Orsay: Paris.

2 l Oger P, Petit A, Dessaux Y (1997) Genetically engineered plants producing opines alter
their biological environment. Nature Biotechnology 15: 369-372.

22 Petit A, David C, Dahl GA, Ellis JG, Guyon P (1983) Further extension of the
opine concept: plasmids in Agrobacterium rhizogenes cooperate for opine degradation. 1 90: 204-
214.

23 Petit A, Stougaard J, Kuhle A, Marcker KA, Tempe J (1987) Transformation and
regeneration of the legume Lotus comiculatus: a system for molecular studies of symbiotic nitrogen
ﬁxation. Molec. Gen. Genet. 207: 245-250.

24 Rademaker JLW, de Bruiin FJ (1997) Characterization and classiﬁcation of microbes
by rep-PCR genomic ﬁngerprinting and computer assisted pattern analysis. in DNA markers:
protocols, applications and overviews. Caeteno-Anolles G, Gresshoff PM eds. J. Willey &
Sons:New York. Chapter 10. pp. 151-171.

25 Rossbach S, Kulpa DA, Rossbach U, de Bruijn FJ (1994) Molecular and genetic
characterization of the rhizopine catabolism (mocABRC) genes of Rhizobium meliloti L5-30. M01.
Gen. Genet. 245: 11-24.

26 Rossbach SR, McSpadden B, Kulpa D, de Bruiin FJ (1994) Rhizopine synthesis

and catabolism genes for the creation of ”biased rhizospheres" and as marker system to detect
(genetically modiﬁed) microorganisms in the soil. pp. 223-249 In Biotechnology Risk Assessment:

55

Proceedings of the biotechnology risk assessment symposium, June 22-24, 1994, College Park,
MD. Levin M, Grim C, Angle JS, eds.

27 Saint CJ, Wexler M, Murphy PJ, Tempe J. Tate ME, Murphy PJ (1993)
Characterization of genes for synthesis and catabolism of a new rhizopine induced in nodules by
Rhizobium meliloti Rm220-3: extension of the rhizopine concept. J. Bacteriol. l 7 5: 5205-5215.

28 Sambrook J, Fritsclr EF, Maniatis T (1989) Molecular cloning: a laboratory manual.
Cold Spring Harbor Laboratory: Cold Spring Harbor, NY.

29 Savka MA, Farrand SK (1992) Mannityl opine accumulation and exudation by
transgenic tobacco. Plant Physiol. 98: 784-789.

30 Savka MA, Farrand SK (1997) Modiﬁcation of rhizobacterial populations by
engineering bacterium utilizations of a novel plant-produced resource. Nature Biotechnology 15 :
363-368.

3 1 Stevanov I, Fekete S, Bogre L, Pauk J, Feher A, Dudits D (1994) Differential
activityof the mannopine synthase and the CaMV 35S promoters during development of transgenic
rapeseed plants. Plant Sci. 95: 175-186.

32 Tempe J, Casse-Delbart F (1989) Plant gene vectors and genetic transformation :
Agrobacteriwn Ri Plasmids. pp. 2549 In Cell culture and somatic cell genetics of plants. Vasil 1K
ed. vol. 6 of Molecular biology of plant nuclear genes. Vasil 1K, Schell J eds. Academic Press:
New York.

33 Verwoerd TC, Dekker BMM, Hoekema A (1989) A small-seale procedure for the
rapid isolation of plant RNAs. Nucl. Acid. Res. 1 7: 2362.

34 Walden R, Koncz C, Schell J (1990) The use of gene vectors in plant molecular
biology. pp. 175-194 In Methods in Molecular and Cellular Biology. J. Wiley & Sons: New York.

Chapter 3

Evaluation of the Abundance of Culturable Bacteria Capable of
Utilizing Potential Nutritional Mediators, and
Investigation into the Impact of Glucosamine Amendment on
Culturable Soil Bacteria

Abstract

The abundance of crrlturable soil microorganisms capable of growing on different organic
substrates was examined. The numbers of CFUs were greatest on mixed media and were interpreted
as a baseline estimate of the number of culturable heterotrophic bacteria in the soil. 0f the tested
substrates, scyllo-inosamine and myo-inositol were among the most selective, indicating that they are
good candidates for nutritional mediators in a biased rhizosphere system. Additionally, the impact of
glucosamine amendment on the relative abundance of different bacterial populations was investigated
in the ﬁeld. The nutrient amendment increased the numbers of inoculant and indigenous bacteria
cultured as expected under conditions of nutrient limitation. However, the observed impact on the
inoculant populations was small and transient when compared to the changes in the indigenous
glucosamine-catabolizing bacterial papulations. Amongst the glucosamine-utilizing bacteria that were
isolated, a single morphotype was found to increase two- to three-fold in relative abundance over the
course of the experiment. Partial sequencing of 168 rDNA ampliﬁed from this morphotype indicated
that it is a strain of Arthrobacter globiformus. The implications of these results for the creation of

biased rhizospheres are discussed.

Introduction

The ability of various microorganisms to utilize a speciﬁc organic nutrient will partially
determine its effectiveness as a nutritional mediators in a biased rhizosphere system. The more
commonly a compound can be used as a nutrient source, the less likely it is to directly impact the
abundance and activities of the target microbial populations. Therefore, compounds that can be used
by relatively few bacteria are good candidates for nutritional mediators (l3). Opines and rhizopines
have been hypothesized to be nutritional mediators in nature (3, 4, 10) and may be useful in creating
biased rhizosphere systems (17, 18). Bacteria other than members of the genera Agrobacteriwn that
can catabolize opines have been isolated (11, 12), but the enumeration of these organisms in the
environment has only recently been reported (14, 15). A direct examination of the diversity and
abundance of rhizopine-utilizing bacteria in the environment has not been previously reported (see

Chapter 4). Beeause the proposed biased rhizosphere systems based on these two organic nutrients
depend on the use of inoculant strains, it was considered worthwhile to investigate the relative
abundance of potential microbial competitors, i.e. indigenous microorganisms capable of utilizing
different potential nutritional mediators.

A simpliﬁed model describing the impact of a nutrient amendment on microbial populations
in an agricultural setting is diagrarnmed in Figure 3.1. For any compound used as an organic
nutrient, there is likely to be a set of indigenous microorganisms eapable of catabolizing it in the soil.
The addition of a prescribed amount of an organic nutrient is predicted to increase the abundance
and/or activities of those organisms to some degree (vector 1) for some period of time (vector 2). 1n
the case of inoculant microorganisms, the pOpulation size is commonly observed to decline
logarithmically after introduction into the soil (19). Nonetheless, one would expect that inoculants
capable of utilizing that prescribed nutrient would also respond positively to its addition (vectors 3
and 4). While several reports have indicated the response of particular bacterial populations to
nutrient amendment, none of these have compared the responses of inoculant versus indigenous
microbial populations (5, 6, 16, 20).

The goals of the experiments described below were to (a) evaluate the relative speciﬁcity of
several compounds that might be used as nutrient amendments in ﬁeld plots, and (b) to determine the
impact of a nutrient amendment in the ﬁeld in terms of the model described above. The results of
these experiments were instrumental in the design of the subsequent experiments (see Chapter 4, 5,
and 6), and they provided some additional insight into the nature of indigenous and inoculated
bacterial populations and the way in which they respond to organic nutrients

Materials and Methods
Enumeration of soil bacteria

The soil used in these experiments was a Michigan sandy-loam that was obtained from the
MSU Crop and Soil Sciences Research Farm, East Lansing, MI. Samples were diluted to 0.1 g per

ml with sterile distilled water in 50 ml conical tubes. Bacteria were dislodged by alternating 10

abundance / activity

 

 

time

Figure 3.1: General model for the impact of nutritional
mediators introduced into the soil and rhizosphere environment.
Changes in inoculant (I) and indigenous (N) microbial populations
capable of utilizing the nutrient are indicated in the presence (solid
lines) and absence (dashed lines) of the amendment.

second treatments: vortex, sonication (tube placed in Ultrasonik cleaning bath, NEY Inc.,
Bloomﬁeld, CT), vortex, sonication, and ﬁnal vortex. Aliquots were then serially diluted in distilled
water, and three 20 ul aliquots were spot inoculated onto deﬁned media. Colonies were enumerated
after 2 and 4 days of incubation at room temperature.

All of the deﬁned media contained 1x BGTS mineral salts (25 mM KH2P04 (pH 7.3), 10
mM NaCl, 25 ppm MgSO4- 7H20, 1.25 ppm CaClg, 0.27 ppm FeCl; - 6 H20, 0.242 ppm Na2M004
- 2 H20, 3 ppm H3B03, 1.83 ppm NaSO4- H20, 0.287 ppm ZnSO..- 7 H20, 0.125 ppm CuSO4 - 5
H20, 0.119 ppm CoSO4- 6 H20) and 1% SeaKem LE agarose (FMC, Rockland, ME). Individual
organic nutrients were prepared as ﬁlter-sterilized stock solutions (50 mg/ml) and added as sole
carbon and nitrogen source to a ﬁnal concentration of 2 g/l. The media were named based on the
nutrients tested: glucosamine (GN), glutamate (E), proline (P), glycine (G), threonine (T),
mannopine, nopaline, octopine, myo-inositol (M1), or scyllo-inosamine (SI). In the case of myo-
inositol-containing media, 20% w/v (N114)ZSO4 was also added to the media to give a ﬁnal
concentration of 2 gll. Two deﬁned mixtures of carbon and nitrogen sources were also used: MCN 1
(0.35 g/l glucose, 0.35 g/l sodium succinate, 0.125 g/l pyruvate, 0.126 g/l glycerol, 0.05 g/l
NaHCO3, 0.05 gll (NH4)2804, 0.1 g/l NH4N03, 0.1 g/l NaN03, pH 7.2) and MCN 2 (0.4 g/l
glucose, 0.3 g/l fructose, 0.1 gll sodium citrate, 0.05 g/l leucine, 0.05 g/l glutamate, 0.05 gll
asparagine, 0.2 g/l (NH4)ZSO4, 0.25 gll NaNO3, 0.025 gll glycerol, 0.025 gll pyruvate, 0.025 g/l
sodium acetate, 0.025 gll sodium bicarbonate, 0.025 gll sodium succinate, pH 7.2).

Glucosamine amendment experiment

The ﬁeld experiment was conducted late in the summer of 1996. Microplots were deﬁned by
bottomless 50 ml plastic tubes inserted into unplanted plowed soil to a depth of ten centimeters one
week prior to the start of the experiment. These plots were set up in a replicated block design over an
area of one square meter. Each microplot was amended with 10 mls of sterile distilled water or 0.1%

glucosamine :t. inoculant bacteria.

61

Two strains, 111 and IV, were used individually as inoculants at two different concentrations.
They are rifampicin-resistant variants of R2 and S3, two bacterial strains isolated from the same ﬁeld
plot in a screen for rhizopine-utilizing bacteria (see Chapter 4). These strains were grown on the
mineral salts medium described above supplemented with glucosamine as sole carbon and nitrogen
source. To prepare each inoculant, overnight cultures were diluted to a concentration of
approximately 105 cells per m1, and 100 ul were spread onto solid media four hours prior to
inoculation in the ﬁeld At the ﬁeld site, cells were washed off of the plates with one ml of sterile
distilled waterand diluted 1: 2000 or 1:20000 in either sterile distilled water or 0.1% glucosamine.

Two replicate samples of each of the ten different treatments were taken after 1, 4 and 17
days of incubation in the ﬁeld. Whole soil cores deﬁned by the bottomless plastic tubes were
removed from the ﬁeld and returned to the laboratory for processing. For each sample, soil was
removed from a tube, mixed, and weighed. Bacteria were enumerated as described above on four

different media: 0.1xTSA and glucosamine-containing minimal media (GN) _+_ rifampicin (50 mg/l).

Results
Enumeration of culturable bacteria utilizing diﬁ’erent substrates for growth

The abundance of culturable soil bacteria capable of utilizing various substrates as sole
earbon and/or nitrogen source was investigated (see Table 3.1). Of the compounds examined, three
classes were deﬁned based on the number of CFUs appearing on the different media after two days
and four days of incubation. Class A represents the least speciﬁc substrates (i.e. allowing the
greatest number of bacteria to form colonies), while Class C represents the most speciﬁc substrates
(i.e. allowing for the fewest number of bacteria to form colonies). In all cases, mixtures of nutrients
gave rise to the greatest numbers of CFUs on the mineral salts media tested. Individual compounds,
with the exception of glucosamine, were relatively more selective. Notably, myo-inositol and the
rhizopines were among the most selective compounds. Mannopine, nopaline, and threonine were
found to be equivalently selective substrates as the rhizopines in terms of the number of CFUs
observed.

62

 

days of inggbag'on

 

C and N source 2d 4d class
MCN 1 7.2 - 7.7 7.7 - 8.7 A
MCN 2 7.5 - 8.3 7.5 - 8.3 A

glucosamine 6.7 - 7.7 7.5 - 8.7 A
glutamate 5.0 - 6.5 7.7 - 83 B
proline 5.0 - 6.5 7.7 - 83 B
glycine 4.0 - 5.0 7.3 - 7.7 B

' <40 63-10 C
mannopine 5.3 - 6.5 6.0 - 7.0 C
nopaline 4.0 - 55 6.0 - 7.5 C
octopine 4.0 - 5.0 6.7 - 7.7 BC
MI + NH, 5.5 - 7.0 5.5 - 7.0 C
SI 4.5 - 6.0 5.5 - 7.3 C

 

Table 3.1: Log culturable counts per gram of soil on media containing different
sources of carbon and nitrogen as substrates for growth. Class deﬁnitions are based on
the log median CFUs: 2 7 after 2d (A), < 7 after 2d and >7 after4 d (B), < 7 after4d (C).

Impact of glucosamine amendment on indigenous and inoculant populations of soil bacteria

The two bacterial strains used as inoculants in this ﬁeld experiment were rifampicin-resistant
isolates of rhizopine-catabolizing bacteria that had been isolated from soil samples taken from the
same research plot the previous autumn. Both of these strains were observed to be capable of
utilizing glucosamine or scyllo-inosamine as sole carbon and nitrogen source for growth. The
organisms were grown on glucosamine-containing media and introduced in aqueous solutions
containing either distilled water or 1000 ppm of glucosamine. The number of CFUs growing on the
rifampicin-containing media were approximately 1 to 3 percent of the numbers growing on GN media
from the same sample (data not shown). The number of CFUs found on GN media numbered from
roughly 10 to 30 percent of the numbers observed on the MCN media (data not shown). The impact
of the glucosanrine amendment on different populations of culturable bacteria is summarized in Table
3.2.

 

MCN GN Rif

 

 

sampleday 2d 4d 2d 4d 23 4d
1 1.3 1.3 0.9 0.9 1.6 1.4
4 2.4 3.1 25.0 2.7 2.1 2.0
17 2.7 2.1 7.8 2.5 1.7 0.7

 

Table 3.2: Relative increases in CFUs on various media due to glucosamine
amendment. The ratio of (CFUs mended ,0“ / CFUs mended ,0“) are tabulated after 2 and 4 days of
incubation on various media. The media used consisted of 1x BGTS basal salt mixture
supplemented with mixed C and N sources (MCN), glucosamine (GN), or rifampicin to select for
inoculant strains (Rif). Rifampicin-containing media were found to speciﬁcally select for inoculant
organisms. Signiﬁcant differences (P < 0.05) are highlighted in bold.

 

 

sample day urrarnended soil glucosanfrne amended scir
l 0.21 0.00 - 0.03 0.24 0.20 - 0.50
4 0.28 0.17 - 033 0.45 0.25 - 0.66
17 0.20 0.11 - 0.33 0.60 0.50 - 0.75

 

Table 3.3: The proportion of CFUs growing on GN media that displayed the white-
convex (WC) morphotype. Medians and their associated interquartile range are tabulated.
Signiﬁcant differences due to the amendment (P< 0.05) are highlighted in bold.

A small but signiﬁcant increase in the numbers of inoculant organisms due to the
glucosamine amendment was detected after one day of incubation in the ﬁeld, but the effect was small
and not statistically signiﬁcant in the later samplings. The impact on the indigenous catabolizers was
much more striking. Large increases in the proportion of culturable catabolizers isolated from the
glucosamine amended plots as compared to the unarnended plots were noted at both 4 and 17 days
post-inoculation. These increases were generally more pronounced after two days, as compared to
four days, of incubation on the laboratory media. Additionally, there appeared to be an enrichment
for a particular white convex (WC) morphotype over the time-course examined (see Table 33).
Multiple isolates of this morphotype had very similar genomic ﬁngerprint patterns, indicating that
they are closely related phylogenetically (see Figure 3.2). Partial sequencing of the PCR-ampliﬁed
16S rRNA sequences indicated that the WC morphotypes were strains of Arthrobacter globiformis
(data not shown).

Discussion

The effectiveness of a biased rhizosphere system will be partially dependent on the relative
speciﬁcityof the organic nutrient used as a nutritional mediator. Here, several compounds were
examined for their ability to promote the growth of culturable soil bacteria. 01' these, scyllo-
inosamine and myo-inositol were found to be among the most selective (i.e. allowed for the fewest
number of organisms to be cultured). While the numbers of CFUs utilizing the most selective types
of compounds ( i.e. Class C compounds) were roughly equivalent, it is not known to what extent the
diversity of the cultured isolates differ because the identity of the cultured organisms was not
examined.

As expected, the use of glucosamine as an applied nutrient amendment resulted in increases
in the numbers of indigenous and inoculant bacteria, as diagrarnmed in Figure 3.1. Under conditions
of nutrient limitation, one would expect that an applied substrate would most signiﬁcantly impact the
populations of bacteria capable of utilizing it as a nutrient The application of glucosamine resulted in
signiﬁcant increases in the numbers of CFUs growing on all three media, but the greatest differences

65

III I IV WC isolates

O 17 0 17 17 17 17 17

 

. ..
- ~ I9:
=

' .‘ j, .3; {25:

fl; '1...- i... it.“ 8“

.Weavrm- emulate“.

3'38 bop.
“““mmWwMMwma-

w A
g at. I

 

Figure 3.2: BOX genomic fingerprints of bacteria isolated from glucosamine
amended soil samples. The recovered inoculant microorganisms (strains IH and IV)
retain their fingerprint pattern. A selection of fifteen isolates displaying the WC
morphotype isolated in this experiment display similar genomic ﬁngerprint patterns.

were observed on GN medium (see Table 3.2). The data indicate that the addition of the organic
nutrient promoted the growth and culturability of both the inoculant and indigenous bacteria in situ.
Similar responses of soil bacterial populations to nutrient amendments have been reported before in
experiments using various organic substrates. In such studies, the impact of simple organic
substrates has been noted to vary according to the concentration and identity of the applied substrate
as well as totlre identity of the targeted populations (1, 2, 6, 7, 8, 16, 20). However, comparisons
between inoculant and indigenous bacteria are geneme lacking.

In this study, the greatest change in relative abundance of CFUs resulting from the nutrient
amendment occuned on the ON media. This is logical since one would expect that the glucosamine-
utilizing bacteria have the ﬁrst opportunity to acquire the nutritional beneﬁt of the substrate. Increases
were also observed on the MCN media, which might indicate that at least some of the carbon,
nitrogen, and/or energy present in the glucosamine had ﬁltered into other bacterial populations. The
noted increases in the bacterial populations were long-lasting, indicating that nutrient amendment at
planting could substantially impact indigenous microﬂora well into the growing season. In contrast,
the observed increases in the relative abrmdance of the inoculant microorganisms were relatively small
and transient compared to the changes on the indigenous soil bacterial populations. It is uncertain
whether this is due to an inability of the inoculant to readapt to the soil environment from which it
was ﬁrst isolated or due to a decreased ability to effectively compete for the nutrient amendment In
any case, the application of the nutrient amendment preferentially beneﬁted indigenous
microorganisms that were preadapted to living in the soil environment and capable of utilizing the
amendment as a nutrient source.

Because only a small percentage of soil bacteria are cultured on any prescribed media (9), it
is not possible to comprehensively assess the utility of any particular substrate using the method
described here. Thus, these results can only be interpreted as a ﬁrst estimate of the potential

effectiveness of a proposed nutritional mediator and its impact on soil and rhizosphere bacterial
populations.

67

References

1 Acea MJ, Alexander M (1988) Growth and survival of bacteria introduced into carbon-
amended soil. Soil Biol. Biochem. 20: 703-709.

2 Colbert SF, Schroth MN, Weinhold AR, Hendson M (1993) Enhancement of
population densities of Pseudomonas putida PpG7 in agricultural ecosystems by selective feeding
with the carbon source salicylate. Appl. Environ. Microbiol. 5 9: 2064-2070.

3 Dessaux Y, Petit A, Tempe J (1992) Opines in Agrobacterium biology. pp109-136. In
Molecular signals in plant-microbe communications. CRC Press: Boca Raton.

4 Desmux Y, Petit A, Tempe J (1993) Chemistry and biochemistry of opines, chemical
mediators of parasitism. Phytochemistry 3 4: 31-38

5 Fouilleux G, Revellin C, Hartmann, A, Catroux G (1996) Increase of
Bradyrhizobium japonicum numbers in soils and enhance nodulation of soybean (Glycine max (L)
men.) using granular inoculants amended with nutrients. FEMS Microb. Ecol. 20: 173-183.

6 Germida JJ (1988) Growth of indigenous Rhizobium leguminosarum and Rhizobium
meliloti in soils amended with organic nutrients. Appl. Environ. Microb. 5 4: 257-263.

7 Ka JO, Burauel P, Bronsons JA, [10an WE, Tiedje JM (1995) DNA probe
analysis of microbial community selected in the ﬁeld by long-term 2,4,-D application. Soil Sci. Soc.
Am. J. 59. 1581-1587.

8 Ka J0, Holben WE, Tiedje JM (1994) Analysis of competition in soil among 2,4—
dichlorophenoxyacetic acid-degrading bacteria. Appl. Environ. Microbiol. 6 0: 1 121-1 128.

9 Liesack W, Jansen PH, Rainey FA, Ward-Rainey NL, Stackebrandt E (1997)
Microbial diversity in soil: The need for a combined approach using molecular and cultivation
techniques. pp. 375-440 In Modern Soil Biology. J.D. van Elsas, J.T. Trevors, E.M.H. Wellington
eds. Marcel Dekker. New York.

1 0 Murphy PJ, Saint CP (1992) Rhizopines in the legume-Rhizobium symbiosis. pp. 377-
390 In Molecular Signals in Plant-Microbe Communications. CRC Press; Boca Raton.

11 Nautiyal CS, Dion P, Chilton WS (1991) Mannopine and mannopinic acid as
substrates for Arthrobacter sp. strain MBA209 and Pseudomonas putida NA513. J. Bacteriol.
17 3:2833-2841.

l 2 Nautiyal SC, Dion P (1990) Characterizationof the opine-utilizing microflora associated
with samples of soil and plants. Appl. Environ. Microbiol. 56: 2576—2579.

1 3 O'Connel KP, Goodman RM, Handelsman J (1996) Engineering the rhizosphere:
expressing a bias. Trend. Biotech. 1 4: 83-88.

14 Oger P (1995) Ph.D. thesis: Etudes su 1a possibilite de favoriser speciﬁquement la
croissance de bacteries de la rhizosphere-Le cas des plantes transgeniques productrices d'opines.
Universite de Paris sud centre d'Orsay: Paris.

1 5 Oger P, Petit A, Dessaux Y (1997) Genetically engineered plants producing opines alter
their biological environment Nature Biotechnology 15: 369-372.

68

16 Pena-Cabriales JJ, Alexander M (1983) Growth of Rhizobium in soil amended with
organic matter. Soil Sci. Soc. Amer. J. 47: 241-245.

17 Rowbach SR, McSpadden B, Kulpa D, de Bruiju FJ (1994) Rhizopine synthesis
and catabolism genes for the creation of ”biased rhizospheres" and as marker system to detect
(genetically modiﬁed) microorganisms in the soil. pp. 223-249 In Biotechnology Risk Assessment:
Proceedings of the biotechnology risk assessment symposium, June 22-24, 1994, College Park,
MD. Levin M, Grim C, Angle JS, eds.

18 Savka MA, Farrand SK (1997) Modiﬁcation of rhizobacterial populations by
engineering Meterium utilizations of a novel plant-produced resource. Nature Biotechnology 15:
363-368.

19 Veen A van, van Overbeek LS, van Elsas JD (1997) Fate and activity of
- microorganisms introduced into soil. Microb. Molec. Biol. Rev. 6 1: 121-135.

20 Viteri SE, Schmidt EL (1987) Ecology of indigenous soil rhizobia: response of

Bradyrhizobium japonicum to readily available substrates. Appl. Environ. Microbiol. 5 3: 1872-
1875.

69

Chapter 4

Detection and Isolation of Novel Rhizopine-Catabolizing Bacteria from
the Environment

McSpadden Gardener B, de Bruijn FJ (1998) Detection and Isolation of Novel Rhizopine-
catabolizing bacteria from the environment. Appl. Environ. Microbiol. in preparation

70

Abstract

Microbial rhizopine-catabolizing (Moe) activity was detected in serial dilutions of soil and
rhizosphere washes. The activity observed generally ranged between 106 and 107 catabolic units
(CUs) per gram, and the numbers of nonspeciﬁc culture-forming units (CFUs) were found to be
approximately 10 times higher. A diverse set of thirty-seven isolates was obtained by enrichment on
scyllo-inosamine (SD-containing media. However, none of the bacteria that were isolated were
found to contain DNA sequences homologous to the known mocA, mocB, and mocC genes of
Sinorhizobium meliloti. Twenty-one of the isolates could utilize an SI preparation as sole carbon and
nitrogen source for growth. Partial sequencing of 16S rDNA ampliﬁed from these strains indicated
that ﬁve distinct bacterial genera (Arthrobacter, Sinorhizobium, Pseudomonas, Aeromonas, and
Alcaligenes) were represented in this set Only six of these twenty-one isolates could catabolize 3-0-
methyl-scyllo-inosamine under standard assay conditions. Two of these, strains D1 and R3, were
found to have 168 sequences very similar to Sinorhizobium meliloti. However, these strains are not
symbiotically effective at Medicago sativa, and DNA sequences homologous to the nodBC genes
were not detected in D1 and R3 by Southern hybridization analysis.

Introduction

Rhizopines are inositol derivatives synthesized in legume nodules induced by speciﬁc
members of the Rhizobiaceae (14, 31). The ﬁrst rhizopine was isolated from alfalfa nodules infected
with Sinorhizobium meliloti L530 (28). The structure of this compound was determined to be 3-0-
mdhyI-scyllo-inosamine (M81) (13). Genes on the large symbiotic plasmid of L5-30 were
determined to be involved in the synthesis and catabolism of this compound (12). Transposon
mutagenesis and subsequent DNA sequence analysis of the identiﬁed rhizopine catabolism (mac)
locus from this strain revealed four open-reading frames (ORFs) involved in the catabolism of M81
(24). Three of these ORFs, mocA, mocB, and mocC, have been found to be sufﬁcient to confer Mcc

activity onto otherwise Moc strains of Sinorhizobium meliloti. (22). DNA sequences homologous to

71

these moc genes were not observed in a broad screen of known soil and rhizosphere bacteria (24),
andonly members of the Rhizobiaceae have been reported tobe Moc+ (14).

Some organic molecules may be used to speciﬁcally promote the growth and metabolic
activities of soil and rhizosphere bacteria capable of utilizing them as growth substrates (5, 6, 13, 18,
23, 24, 25). Several recent reports have shown that these speciﬁc ”nutritional mediators” can enrich
for bacteria capable of utilizing them as growth substrates in soil and on plant leaves and roots (3, 4,
7, 10, 20, 27, 32, 33, 34). However, the potential of nutritional mediators to promote the activities
of target microbial populations could be limited by a variety of factors, including the relative
abrmdance of non-target microbes capable of catabolizing the compound While several reports have
indicated that other proposed nutritional mediators can be catabolized by a variety of indigenous
bacteria (5, 15, 16, 19), no previous study has directly investigated rhizopine catabolism in the
environment. Here we report the detection, isolation, and enumeration of previously unknown
rhizopine-catabolizing bacteria from the environment and discuss the implications of their existence
for the use of rhizopine as a selective nutritional mediator.

Materials and Methods
Soil and rhizosphere sampling

Samples of a Michigan sandy-loam were obtained from a fallowed plot on the MSU Crop
and Soil Sciences Research Farm, East Lansing, w. The plot had been left fallow for ﬁve years
prior to these experiments and was covered by a variety of annual and perennial plants, including
nodrrlated alfalfa. In September of 1995, approximately 50 kg was removed from the top 12 inches
of the soil and transported to a research greenhouse for mixing and storage. Rocks and wooden
debris were removed as the soil was homogenized in a rotary mixer. This soil was allowed to air dry
and stored at room temperatrue until use.

For the initial detection and quantiﬁcation of catabolic activities, aliquots of this soil were
placed in plastic pots moistened, planted with Medicago sativa var. Cardinal, and incubated in a
growth chamber (12:12 LD, 22°C). Soil and rhizosphere samples (< 100 mg each) were taken after 1

72

and 3 weeks of incubation. Samples were placed in 5 ml of distilled water in 15 ml Sarstedt tubes.
Bacteria were dislodged by alternating 10 second treatments: vortex, sonication (tube placed in
Ultrasonik cleaning bath, NEY, Inc., Bloomﬁeld, CT), vortex, sonication, and ﬁnal vortex. They
were serially diluted in distilled water, and 50 ul aliquots were inoculated into one ml of the

catabolism assay mixtures.

Preparation of nodule extracts and caton ism assays

Extracts containing 3-O-methyl-scyllo-inosamine (MSI) were obtained from alfalfa nodules
induced by Sinorhizobium meliloti L5-30 under gnotobiotic conditions as described previously (23).
Five grams of nodules were crushed with a mortar and pestle in 25 ml of distilled water. The
suspension was centrifuged at 20,000 xg for 20 minutes to clarify the solution. The supernatant was
ﬁlter-sterilized, aliquoted, and kept frozen at -20°C. The total solute concentration of these 1x
extracts was determined gravimetrically to be 0.5% (WM and the concentration of M81 in the extracts
was estimated to be 0.01% (w/v) by HVPE analysis (see below). Preparations containing synthetic
scyllo-inosamine (SI) were provided by Dr. R. Hollingsworth (Michigan State University
Department of Biochemistry). Myo-inositol was obtained commercially (Sigma Chemical Co., St.
Louis, MO, USA).

The rhizopine catabolism assay used to deﬁne Moe activity was similar to that described
previously (23). Assay mixtures contained 03x nodule extract in 1x BGTS ( 25 mM KH2P04 (pH
7.3), 10 mM NaCl, 25 ppm MgSO4 - 7H20, 1.25 ppm CaClz, 0.27 ppm FeCl3- 6 H20, 0.242 ppm
Na2M<JO4 : 2 H20, 3 ppm H3303, 1.83 ppm NaSO4 . H20, 0.287 ppm ZnSO4 - 7 H20, 0.125 ppm
CuSO4- 5 H20, 0.119 ppm C0804 ' 6 H20). Assay mixtures were incubated for 5 days in a rotary
incubator at 28°C and 200 RPM. The mixtures were centrifuged for 2 minutes at 13,000 xg to
remove cell debris. The decanted supematants were concentrated 10-fold by evaporation in a rotary
Speed-Vac and resuspended in small volumes of sterile distilled water. Samples were stored at -20°C
prior to analysis by high-voltage paper electrophoresis (HVPE). Ten microliters of each sample was
loaded onto 3MM Whatrnan paperand air dried. Electrophoresis was performed at 3000 V in 1.1 M

acetic acid/0.7 M formic acid buffer. Visualization of a-diol-containing compounds, including MSI,
resulted from staining with alkaline AgN03 (5). The rhizopine was detected as a dark spot running at
acharacteristic distance relative to the reference dye orange G (Rorange G = - 0.9). Catabolism of SI
and myo-inositol was scored as growth in similar assay mixtures, where either 0.2% 81 or 0.2%
myo-inositol + 0.1% (NH4)2$O4 were added in lieu of the nodule extract. Growth was scored by
visual inspection for turbidity.

Isolation andmaintenance of bacteria

To increase the diversity of isolates obtained, bacteria were isolated from the soil described
above in several different ways. For the liquid enrichment cultures, samples included: (a) air-dried
soil (Soil D), (b) soil that had been saturated and kept at approximately ﬁeld capacity for just over 5
months (Soil W), and (c) the crown rhizospheres of two separate 5 month-old alfalfa plants grown in
this saturated soil (Rhizospheres R and S). Samples (_<_ 5 g each) were placed in 25 ml of distilled
water, vigorously shaken for 15 seconds, then diluted to the equivalent of 0.01 g/rnl (w/v) in sterile
distilled water. Seventy-ﬁve microliters of these mixtrues were inoculated into 1 ml of SI-containing
media (0.05% S1 in 1x BGTS) and incubated at 28°C. After 3 days, the cultures were diluted and
spread onto 0.1 x TSA for isolation of individual colonies. Seventeen isolates representing several
distinct morphotypes from each of the samples were selected for further analysis. Isolates were also
obtained by selection of organisms growing on solid media containing 81. Two soil samples (Soil
W2a and W2b) were obtained from the same pots as described in (b) above two months later.
Additionally, four soil samples (Soils Aa, Ab, Ba and Bb) were obtained in late April, 1996 from
within ﬁve meters of the original sampling site in the ﬁeld plot described above. Samples (_<_ 4 g
each) were diluted 0.1 g/ml in 1x BGTS, mixed vigorously, serially diluted, and plated onto solid
media( 1% agarose i 0.2% SI i 0.1% (NH4)2804, or 0.1x TSA). Plate counts were determined
after 2, 4, and 8 days of incubation at 28°C. At the four day time-point, twenty strains representing

distinct morphotypes of the most abundant colonies were selected from each of the SI-containing

74

plates and puriﬁed on 0.1x TSA media. For all subsequent work, cultures were grown 28°C on 0.1x
TSB or 0.1x TSA. Frozen stocks of puriﬁed isolates were kept in 15% glycerol at -70°C.

Characterization ofbacterial isolates

MagnaGraph nylon membranes (MSI Scientiﬁc, Westboro, MA) and random-primed DIG-
labeled probes prepared with the Genius 1 Kit (Boehringer Mannheim Biotechnologies, Indianapolis,
IN) were used in all hybridization experiments according to the manrrfacturers' instructions. When
screening the isolates for mac-like sequences, ten microliters of overnight cultures were spot-
inoculated onto 0.1x TSA and incubated for 24 hours at 28°C prior to colony lifts. Probe templates
consisted of the full-length ORFs of the mocA, mocB, and mocC genes from S. meliloti L5-30 (23).
For the characterization of strains D1, R3, and L5-30, genomic DNA was isolated, digested, and
transferred to nylon membranes following standard procedures (26). Hybridizations were performed
overnight at 68°C and blots were subjected to two 10 minute high stringency washes (0.1x SSC,
0.1% SDS, _>_ 37°C). Genomic ﬁngerprinting using the BOX primer was performed using whole
cells as described elsewhere (21 ).

In the nodulation assays, large test tubes containing 15 ml of a nitrogen-free mineral salts
media (1) and wicks of 3MM Whatrnan ﬁlter paper were autoclaved and allowed to cool to room
temperature. Seeds of Medicago sativa var. Cardinal were surface sterilized by immersion in 3%
hydrogen peroxide for 10 minutes followed by thorough rinsing in sterile distilled water. Two seeds
were asceptically transfened onto the wick in each tube. Liquid cultures of the three bacterial strains
were grown to saturation, pelleted by centrifugation, and washed twice with sterile-distilled water.
150 ul (containing a 107 cells) of these washed cultures was used to inoculate each tube. Four

replicates tubes were prepared for each strain and for the uninoculated control.
Statistics
The nonparametric sign test (17) was used for all comparisons. Differences in relative

population sizes were conservatively estimated by ﬁnding the maximum factor which still yielded

75

signiﬁcant results in each of the comparisons. Tests were performed using SchoolStat software
(David Darby, WhiteAnt Occasional Publishing, Victoria, Australia, ddarby@ariel.ucs.unimelb.
edu.au). All P-values less than or equal to 0.15 are reported.

Results
Detection and quantiﬁcation of Mac activity in the environment

erizopine-catabolizing (Moe) activity was detected in serial dilutions of soil and rhizosphere
washes after ﬁve days of incubation in standard assay mixtures (see Figure 4.1). Since it could not be
assumed that the breakdown of 3-0-methy1-scyllo—inosamine (MSI) in these assays was due to the
activities of individual organisms, the enumeration of Moo activity in these assays is referred to in
terms of catabolic units (CUs) analogous to culture-forming units (CFUs). The median Moe activity
was observed to be 106 to 107 CUs per gram (:1 = 6) in both soil and rhizosphere samples. The
median myo-inositol-catabolizing (Mic) activity was observed to be approximately ﬁve-fold higher,
but this difference was not statistically signiﬁcant These data contrast with the median bacterial
growth in these assays (P < 0.04) which was observed to be approximately 108 CFUs per gram.
Bacterial growth in the assay mixtures increased with time and correlated well with the disappearance
of uncharged organic compounds from the nodule extract as visualized by HVPE analysis.
Catabolism of the uncharged compounds generally preceded catabolism of MSI (data not shown).

The number of bacteria growing on the crude nodule extract was estimated to be just under
10 times that growing on myo-inositol (MI) as sole carbon source (P < 0.11). Likewise, the number
of CFUs was approximately 10 times the number of units of Moc activity present in the assay
mixtures (P < 0.11), though this ratio was measured to be 2100 in half of the assays. Similarly,
the number of colonies growing on scyllo-inosamine (SI) containing medium was less than the
number growing on 0.1x TSA (P < 0.005). When the SI medium was supplemented with 0.1%
(w/v) (NH4)2SO4, the number of colonies observed was intermediate between the numbers observed
on SI medium and 0.1x TSA medium (P < 0.04). Again, the number of bacteria growing on SI-

76

**

 

345678C

Figure 4.1: Detection of Moc activity in the environment. MSI in assay mixtures is
detected as a positively-staining spot (*) with a deﬁned electrophoretic mobility relative
to orange G. Uncharged a-diol-containing organic compounds remain at the origin (**).
Moe activity is scored as the absence of a detectable signal co-rrrigrating with MSI after 5
days of incubation in the standard assay mix. To quantitate the observed activity, 10-fold
serial dilutions (3 - 8) of soil and rhizosphere samples were assayed by comparison to an
uninoculated control (C). In this example, 104 catabolic units (CUs) per gram of Moc
activity were detected.

containing media was estimated to be approximately 10-fold less than the number growing on
0.1xTSA (P < 0.11).

Isolation and characterization of Moc+ baaeria

Liquid and solid media containing 81 were used to enrich for Moc+ bacteria from soil and
rhizosphere samples. A total of thirty-seven isolates were selected for further investigation based on
the type of enrichment used, the sample source, and colony morphology of the isolate. All of these
isolates were screened by colony hybridization for the presence of DNA sequences homologous to
three of the known moc genes from Sinorhizobium meliloti L5-30 (see Figure 4.2). However, no
hybridization was observed for any of the isolates.

Twenty-one of the isolates were found to be capable of growing on minimal medium
containing the SI preparation as sole carbon and nitrogen source (data not shown). Each of these
strains was characterized by partial 16S rDNA sequencing. DNA sequence analysis revealed that
several distinct phylogenetic groups were represented, including Gram-negative a, p, and y
Proteobacteria and a Gram-positive phylum (see Table 4.1). Ampliﬁed rDNA restriction analysis
(ARDRA) of representatives of each of these groups,
using Mspl and Rsal in single restriction digests, conﬁrmed these designations (data not shown).
The 16S rDNA sequences of strains that were determined to belong to the same genus were found to
be a 98% identical over the region analyzed. However, rep-PCR genomic ﬁngerprinting of these
strains using the BOX primer revealed that most of the isolates were genotypically distinct (See
Figure 4.3). Seventeen distinct patterns were observed in the set of twenty-one genomic ﬁngerprints.
Seven strains, all belonging to the y proteobacteria based on their 16S sequence, were found to fall
into three distinct groups based on their BOX-PCR generated genomic ﬁngerprints: (D2, W2), (2,6),
and (7,14,18). Additionally, some similarities were noted in the patterns generated from two sets of
putative Arthrobacter strains (i.e. (R4,S3) and (1,1N)). Interestingly, the putative S. meliloti strains
D1 and R3 had distinct BOX-genomic ﬁngerprints from S. meliloti L5-30 (L).

Figure 4.2: Colony hybridization of bacterial isolates with mac gene probes.
DIG-labeled probes corresponding to the mocA, mocB. and mocC genes of S. meliloti L5
30 were hybridized to blots of colonies lifted from replica plates such as those shown. All
thirty-seven of the isolates obtained from liquid (a) and solid (b) media containing SI were
examined. L5-30 (L) was included twice on all of the plates as a positive control.

79

 

Figure 4.2

 

enrichment source' strain bp highest seguence match“ Sal:

Liquid SoilD D1 332 Rhizobium meliloti 0.855
medium D2 381 Azospirillum sp. 0.921
Soil W WI 345 Azospirillum sp. 0.924

W2 337 Azospirillum sp. 0.914

W4 316 Pseudomonas putida mt-2 0.956

W5 382 Alcaligenes eutrophus 335 0.890

Rhizosphere R R1 394 Azospirillum sp. 0.939

R2 314 Arthrobacter globiformus‘ 0.538

R3 333 Rhizobium meliloti 0922

R4 346 Arthrobacter globiformus 0:812

Rhizosphere S 81 335 Azospirillum sp. 0.904
S3 317 Arthrobacter globiformus 0.886

Solid Soil W2a 1 315 Arthrobacter globiformus 0.990
medium 1N 273 Arthrobacter globiformus 0.982
Soil W2b 2 372 Azospirillum sp. 0.962

Soil Bb 6 380 Pseudomonas putida mt-2 0.916

7 382 Azospirillum sp. 0.948

Soil Ba 1 1 388 Aeromonas media , 0.923

Soil Ab 14 384 Azospirillum sp. 0.924

16 350 Azospirillum sp. 0.940

Soil As 18 372 Azospirillum sp. 0.763

20 383 Azospirillum sp. 0.943

 

aSee Materral' s and Methods of W

l’Highest match using SIM_ software.

cThe low similarity value of this strain is due to a large number of ambiguous bases in the obtained
sequence.

Table 4.1: Characterization of isolates capable of growing on the SI mix as sole
carbon and nitrogen source by partial sequencing of their 16 rRNA genes. Strains
found capable of catabolizing MSI are highlighted in bold. Sequence differences, not counting
ambiguous base calls, were < 2% over 300 bp of aligned sequence for isolates with the same

phylogenetic match.

81

 

11N2671114161820 L

D1 D2 W1 W2 W4 W5 R2 R3 H4 S1 83

Figure 4.3: BOX-PCR generated genomic fingerprints of isolates capable of
growing on SI as sole carbon and nitrogen source. Seventeen distinct patterns can be
observed in the set of twenty-one genomic ﬁngerprint obtained from the rhizopine-
catabolizing isolates. Three sets of strains with very similar patterns were observed: (D2,
W2), (2,6), and (7,14,18). Additionally, strains D1 and R3 had distinct BOX-genomic

fingerprints from S. meliloti L5-30 (L).

Of the twenty-one isolates, only six were designated Moc+ based on their performance in the
standard catabolism assay (see Figure 4.4). Four of these isolates, R4, 83, 1, and 1N, appear to
belong to the genus Arthrobacter, based on their 16S rDNA sequences. The other two strains, D1
and R3, appear to be most closely related to S. meliloti, the species from which the mac genes were
initially isolated (12, 23).

Because the mac gene probes did not hybridize to genomic DNA from strains D1 and R3, we
further analyzed these two putative S. meliloti strains. Attempts to amplify portions of the nif HDK
and nodBC loci using conserved primers were unsuccessful despite ampliﬁcation of appropriately
sized fragments from S. meliloti L5-30 (data not shown). Additionally, no hybridization was
observed to a nodBC probe generated from the 1530 sequence (see Figure 4.5). Strians D1 and R3
also failed to nodulate alfalfa under gnotobiotic conditions.

Discussion

We have detected and characterized rhizopine-catabolizing activity in soil and rhizosphere
environments, and we isolated a collection of novel rhizopine-catabolizing bacteria Twenty-one
bacterial strains capable of growing on synthetic SI were identiﬁed, six of which were found to be
able to catabolize MSI under standard assay conditions. The abundance of two of these Moct strains,
1 and 1N, approximated the total amount of rhizopine-catabolizing activity in the soil. Thus, while it
is likely that we only isolated a subset of the entire diversity of rhizopine-catabolizing microbes from
these samples, we have identiﬁed at least some of the more abundant strains.

DNA sequences similar to the known moc genes were not detected in any of the isolated
organisms. This was particularly surprising with regards to isolates D1 and R3 which were identiﬁed
as S. meliloti based on the sequence of their 16S rRNA genes. While it has been shown that the
known moc genes are located on the large symbiotic plasmid in S. meliloti (12), D1 and R3 may be
lacking that plasmid, as indicated by their non-symbiotic phenotype. Additionally, previous reports

have indicated that Moc+ S. meliloti strains contained moc genes that were very similar to those from

  
    

 

1.64») may 1"

D1 D2 W1 W2 W4 W5 E R2R3 R481 S3

M11N 2 s 7 E 1114161820 L

Figure 4.4: Determination of the Moc phenotype of isolates. Twenty-one isolates
capable of growth on SI were obtained from liquid (a) and solid (b) media and assayed
for their ability to catabolize MSI (*) in nodule extracts. L5-30 (L) was included as a
positive control.

 

111th“?

in I2.

 

R3LD1R3LD1
E B E B

 

 

Figure 4.5: Southern blot analysis of D1, R3, and L5-30 for the presence of nod gene
sequences. (a) Ethidium-stained agarose gel containing 1 mg of genomic DNA from
each of the three strains digested with either EcoRI (E) or BamHI (B). (b) Southern blot
of the same gel hybridized with a DIG-labeled probe speciﬁc to 1 kb spanning the nodBC
genes of S. meliloti.

1530(24, 30, 31). However, our colony hybridization results with D1 and R3 lead us to suggest
that alternative genes responsible for MSI catabolism may be present in some strains of rhizobia.

One of the motivations for performing this study was to explore the potential impact of
indigenous catabolizers on a "biased rhizosphere" system based on rhizopine as the nutritional
mediator. Rhizopines were hypothesized to be good candidates for rhizosphere nutritional mediation
because only beneﬁcial soil bacteria (i.e. nitrogen-ﬁxing rhizobia) were known to catabolize these
compounds (13, 23, 25). Additionally, some evidence indicated that catabolism of MSI may play a
role in competitiveness of certain strains for nodule occupancy (9). Other compounds present in the
root exudate of legumes may also act as nutritional mediators stimulating nodulation by Rhizobium
(8, 11). That nutritional mediators can stimulate other beneﬁcial plant-microbe interactions has also
been shown. The biological control of plant pathogens has been enhanced by the concurrent
application of salicylate and bacteria capable of catabolising it (2, 35). Thus, it seems possible to
promote the beneﬁcial activities of plant-associated microbes capable of catabolizing nutritional
mediators.

It is reasonable to assume that nontarget microbial populations capable of catabolizing any
given nutrient exist, and their response to that nutrient may complicate the predicted outcome of
efforts to bias microbial communities with nutritional mediators. Ideally, the abundance and diversity
of microbes capable of using a selected nutritional mediator should be minimal. Some evidence
indicates that opine-catabolizing bacteria represent a relatively small fraction of the total number of
culturable bacterial heterotrophs in soil and rhizosphere environments (20). However, we have
observed that the numbers of soil bacteria capable of growing on mannopine, nopaline, or octopine as
sole carbon and nitrogen source was similar to those reported here for SI and myo-inositol +
(N H0280... (data not shown). This discrepancy may reflect differences in media composition and/or
differences in the abundance of indigenous catabolizers in different soils. Diverse types of bacteria
have been reported to catabolize mannityl opines, including members of Arthrobacter and
Pseudomonas (15, 16, 19). The coincidental isolation of rhizopine catabolizers from these same

genera likely reﬂects the metabolic diversity that is known to be present in these two genera. Of

course, plant-pathogenic Agrobacterium can also catabolize opines (5, 6). Because of this, opine-
based nutritional mediators may not be suitable for biasing rhizosphere microbial populations in
agricultural settings (19).

Other factors may also play signiﬁcant roles in determining the effectiveness of nutritional
mediators in the complex milieu of the rhizosphere environment. The relative number of target and
nontarget catabolizers, their relative efﬁciency in utilizing the nutritional mediator, and the
consequences of their enrichment on microbial ecology in and around the plant roots may all be
factors. In our catabolism assays, we have noticed that the catabolism of MSI appears to follow
catabolism of other a-diol-containing compounds present in the nodule extracts, both in the serial
dilutions of environmental samples and in the cases of the Mac isolates (data not shown). This may
indicate that MSI is not a prderred metabolic substrate for the microorganisms capable ofcatabolizing
it. Additionally, the growth rate of the Moc+ isolates generally exceeded that of [.530 both in the
nodule extracts and in 0.1x TSB. To what extent these observations would relate to a rhizopine-
based "biased rhizosphere" in the ﬁeld remains an open question.

Acknowledgements

We would like to thank Josiemeer Mattel and Baptiste Nault for their technical assistance in
these experiments and related work. We would also like to thank Dr. Mark Wilson and Dr. Silvia
Rossbach for their critical review of this manuscript and many helpful discussions. This work was
supported in part by USDA NRICGP #9501182 awarded to M. Wilson, Auburn U and by a STAR
graduate fellowship granted by the US. Environmental Protection Agency to B.M.G.

References

1 Brown CM, Dilworth MJ (1975) Ammonia assimilation by Rhizobium cultures and
bacteroids. J. Gen. Microbiol. 86: 39-48.

2 Colbert SF, Hendson M, Ferri M, Schroth MN (1993) Enhanced growth and
activity of biocontrol bacterium genetically engineered to utilize salicylate. Appl. Environ. Microbiol.
59: 2071-2076.

3 Colbert SF, Isakeit T, Ferri M, Weinhold AR, Bendson M, Schroth MN

(1993) Use of an exotic carbon source to selectively increase metabolic activity and growth of
Pseudomonasputida in soil. Appl. Environ. Microbiol. 59: 2056-2063.

87

4 Colbert SF, Schroth MN, Weinhold AR, Hendson M (1993) Enhancement of
papulation densities of Pseudomonas putida PpG7 in agriculutral ecosystems by selective feeding
with the carbon source salicylate. Appl. Environ. Microbiol. 5 9: 2064-2070.

5 Dessaux Y, Petit A, Tempe J (1992) Opines in Agrobacterium biology. pp109-136. In
Molecular signals in plant-microbe communications. CRC Press: Boca Raton.

6 Dessaux Y, Petit A, Tempe J (1993) Chemistry and biochemistry of opines, chemical
mediators of parasitism. Phytochemistry 3 4: 31-38.

7 Goldmann A, Boivin C, Fleury V, Message B, Lecoeur L, Maille M, and
Tepfer D (1991) Betaine use by rhizosphere bacteria: genes essential for trigonelline, stachydrine,
and camititen catabolism in Rhizobium meliloti are located on pSym in the symbiotic region. Mol.
Plant-Microbe Interact. 6: 571-578.

8 Goldmann, Lecoeur L, Menage B, Delarue M, Schoonejans E, and Tepfer D
(1994) Syrrrbiotic plasmid genes essential to the catabolisms of proline betaine, or stachydrine, are
also required for efﬁcient nodulation by Rhizobium meliloti. FEMS Microbiol. Lett. 1 55: 305-312.

9 Gordon DM, Ryder MH, Heinrich KH, Murphy PJ (1996) An experimental test of
the rhizopine concept in Rhizobium meliloti. Appl. Environ. Microbiol. 6 2: 3991-3996.

1 0 Guyon P, Petit A, Tembe J, Dessaux Y (1993) Transformed plants producing opines
speciﬁcally promote growth of opine-degrading agrobacteria. Moi. Plant-Microbe Interact. 6: 92-98.

11 McSpadden Gardener B, Cotton C, de Bruijn FJ (1998) Impact of myo-inositol
amendment on Sinorhizobium meliloti and other bacterial papulations in the rhizosphere of Medicago
sativa. Appl. Environ. Microbiol. inpreparation

1 2 Murphy PJ, Heycke N, Banfalvi Z, Tate ME, deBruijn F, Kondorosi A,
Tempe J, Schell J (1987) Genes for the catabolism and synthesis of an opine-like compound in
R. meliloti are closely linked and on the Sym plasmid. PNAS-USA 84:495-497.

1 3 Murphy PJ, Saint CP (1992) Rhizopines in the legume-Rhizobium symbiosis. pp. 377-
390. In Molecular signals in plant-microbe communication. DPS Verma ed. CRC Press: Boca
Raton.

1 4 Murphy PJ, Wexler W, Grzemski W, Rao JP, Gordon D (1995) Rhizopines-their
role in symbiosis and competition. Soil Biol. Biochem. 27: 525-529.

15 Nautiyal CS, Dion P, Chilton WS (1991) Mannopine and mannopinic acid as
substrates for Arthrobacter sp. strain MBA209 and Pseudomonas putida NA513. J. Bacteriol.
17 3:2833-2841.

l 6 Nautiyal SC, Dion P ( 1990) Clmacterization of the opine-utilizing microﬂora sassociated
. with samples of soil and plants. Appl. Environ. Microbiol. 56: 2576-2579.

1 7 Neave HR, Worthington PL (1993) Distribution-free Tests. Chapman and Hall: New
York.

1 8 O'Connel KP, Goodman RM, Handelsman J (1996) Engineering the rhizosphere:
expressing a bias. Trend. Biotech. 1 4: 83-88.

19 Oger P (1995) Etudes sur la possibilite de favoriser speciﬁquement la croissance de
bacteries de la rhizosphere-Le cas des plantes transgeniques productrices d'opines (Ph. D. Thesis).
Universite de Paris sud Centre D'Orsay.

20 Oger P, Petit A, Dessaux Y (1997) Genetically engineered plants producing opines alter
their biological environment. Nature Biotechnology 15: 369-372.

2 1 Rademaker JLW, de Bruijn FJ (1997) Characterization and classiﬁcation of microbes
by rep-PCR genomic ﬁngerprinting and computer assisted pattern analysis. pp. 151- 171 In DNA
markers: protocols, applications and overviews. Caeteno-Anolles G, Gresshoff PM eds. J. Willey &
Sons: New York.

22 Rossbach S (1998) Western Michigan University, Kalamazoo, MI, personal
communication.

23 Rossbach S, Kulpa DA, Rossbach U, de Bruijn FJ ( 1994) Molecular and genetic
characterization of the rhizopine catabolism (mocABRC) genes of Rhizobium meliloti L5-30. Mol.
Gen. Genet 245: 11-24.

24 Rossbach S, Rasul G, Schneider M, Eardly B, de Bruijn FJ (1995) Structural
and functional conservation of the rhizopine catabolism (moc) locus is limited to selcted Rhizobium
meliloti strains and unrelated to their geographical origin. Molec. Plant-Microbe Interact 8: 549-559.

25 Rossbach SR, McSpadden B, Kulpa D, de Bruijn FJ (1994) Rhizopine synthesis
and catabolism genes for the creation of ”biased rhizospheres" and as marker system to detect
(genetically modiﬁed) microorganisms inthe soil. pp. 223-249 In Biotechnology Risk Assessment:
Proceedings of the biotechnology risk assessment symposium, June 22-24, 1994, College Park,
MD. Levin M, Grim C, Angle JS, eds.

26 Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual.
Cold Spring Harbor Laboratory: Cold Spring Harbor, NY.

27 Savka MA, Farrand SK (1997) Modiﬁcation of rhizobacterial populations by
engineering bacterium utilizations of a novel plant-produced resource. Nature Biotechnology 15:
363-368.

2 8 Tempe J, Petit A, Bannerot H ( 1982) Presence de substances semblaables a des opines
dans des nodosites de luzeme. CR. Acad. Sc. Paris Serie [B 295: 413-416.

29 Tepfer D, Goldmann A, Pamboukdjian N, Maille M, Lepingle A, Chevalier
D, Denarie J, Rosenberg C (1988) A plasmid or Rhizobium meliloti 41 encodes catabolism of
two compounds from root exudate of Calysegium sepium. J Bacteriol. 170: 1153-1161.

30 Wexler M, Gordon D, Murphy PJ (1995) The distribuiton of inositol rhizopine genes
in Rhizobium populations. Soil Biol. Biochem. 27: 531-537.

3 l Wexler AM, Gordon DM, Murphy PJ (1996) Genetic relationships among rhizopine-
producing Rhizobium strains. Microbiology 142: 1059-1066.

32 Wilson M, Lindow SE (1994) Coexistence among epiphytic bacterial populations
mediated through nutritional resource partitioning. Appl. Environ. Microbiol. 6 0: 4468-4477.

33 Wilson M, Lindow SE (1995) Enhanced epiphytic coexistence of near-isogenic
salicylate-catabolizing and non-salicylate-mtabolizing Pseudomonas putica strains after exogenous
salicylate application. Appl. Environ. Microbiol. 61: 1073-1076.

34 Wilson M, Savka MA, Whang I, Farrand SK, Lindow SE (1995) Altered
epiphytic colonization of mannityl opine-producing transgenic tobacco plants by a mannityl opine-
catabolizing strain of Pseudomonas syringae. Appl. Environ. Microbiol. 6 1: 2151-2158.

3 5 Wilson M (1998) Auburn University, Auburn, AL, personal communication.

Chapter 5

Examination of Soil and Rhizosphere Bacterial Communities Using a
Modiﬁed T-RFLP Analysis of Ampliﬁed 16S rDN A

McSpadden Gardener B, de Bruijn FJ (1998) Examination of soil and rhizosphere bacterial
communities using a modiﬁed T-RFLP analysis of ampliﬁed 168 rDNA. Appl. Environ. Microbiol.
in preparation

91

Abstract

Soil and rhizosphere bacterial populations are described using a rapid PCR-based technique
which, when combined with culture-based methods, provided unique insights into the structure of
these microbial communities and their responses to environmental changes. Serial dilutions of soil
and rhizosphere washes were used directly for PCR ampliﬁcation of 168 rDNA using the conserved
eubacterial primers 8F-HEX and 1492R. Subsequent restriction analysis of the ﬂuorescently labelled
products (FT-ARDRA) provided a reproducible baseline community proﬁle that was used to monitor
changes in the dominant bacterial members of microbial communities and to tentatively identify them.
When applied to model communities of phylogenetically diverse environmental isolates, differences
in the absolute abundance of unique organisms was found to be conelated with signal intensity, but
comparisons between the relative abundance of different organisms could not be made quantitatively.
Distinct differences between soil and rhizosphere communities were detected. When serial dilutions
of soil and rhizosphere washes were subsequently incubated in liquid media, the FT-ARDRA proﬁles
indicated that some, but not all, of the numerically dominant populations were cultured. The
combination of PCR- and culture-based techniques also revealed that certain bacterial populations
reSpond differentially to nutrient amendment depending on their localization in the soil or rhizosphere.

Introduction

The soil environment harbors diverse microbial communities (3, 17, 30, 32). To understand
the nature of microorganisms in situ , information on the identity, abundance, and activities of these
organisms is required Much effort has been applied to describing these microbial communities as a
whole, and a variety of useful techniques have been developed (1 ). Proﬁles based on carbon-source
utilization (10), extractable lipids (e.g. PLFA, FAME)(4, 11), exuactable nucleic acids (e.g. GC-
content of DNA)(13), and PCR-ampliﬁed genes (e.g. ribotyping, DGGE, TGGE)(12, 23, 26) have
been used to display differences in the structure of soil and rhizosphere communities (e.g. 3, 4, 6,
11, 12, 16, 36). While limited, each in its own way, these methods have proved usd‘ul complements
to culture-based approaches in the study of microbial communities from a variety of habitats (21).

The data obtained by these various methods often allow for the identiﬁcation of differences in
microbial communities, and conelations with associated environmental and empirical variables can be
made. However, the ability to identify and quantify those differences in terms of speciﬁc bacterial
populations is essential to dissecting the mechanisms responsible for the different patterns. In this
regard, molecular methods based on the ampliﬁcation of 16S ribosomal RNA genes by PCR have
greatly expanded our knowledge of microbial diversity (24, 33). Data useful for the elucidation of
bacterial phylogeny have been obtained through the (optional) cloning and sequencing of ampliﬁed
products and then compared to established databases (e. g. GenBank, RDP) to make inferences about
the microbial populations present in different environmental samples (31). Alternatively, restriction
fragment length polymorphisms of ampliﬁed 16S rDNA (ARDRA) have been used to distinguish
bacterial communities and identify novel community components (14, 18, 20, 34).

Here we describe soil and rhizosphere bacterial populations using a high throughput method
involving multiple replicate proﬁles of the terminal restriction fragment length polymorphisms (T-
RFLPs) of ampliﬁed 16S rDNA sequences. We call this approach ﬂuorescent-tag ampliﬁed rDNA
restriction analysis (FT-ARDRA). This methodology was applied to serial dilutions of soil and
rhizosphere washes as well as to cultures of those samples to reveal new information about bacterial

populations in these environments and their response to nutrient amendment.

Materials and Methods
Model bacterial communities

Five of the strains (numbered 1 - 5) used for the creation of model communiu'es were isolated
from bulk soil and the ririzospheres of alfalfa plants grown in a Michigan sandy-loam (MSU Crop
and Soil Sciences Research Farm, East Lansing, MI). Full-length 16S rRNA genes from each strain
were ampliﬁed using the conserved eubacterial primers 8F and 1492R (35). The identities of these
strains, presented in Table 5.1, was based on the direct partial sequencing of these ampliﬁed
products. Greater than 250 bp of 16S rDNA sequence was generated from each sample using the
primer 519R (15) and compared to the RDP data base using the SIM_RANK program (19).

Additional strains (numbered 6 - 9) were obtained from a laboratory collection of rhizosphere isolates
(J. Stolzfus and F.J. de Bruijn, unpublished data). For preparation of the model community
mixtures, individual strains were grown on 0.1x TSA media at 28° C. Individual colonies were
removed from the plates and resuspended in 1 ml distilled water. Cells were pelleted by
centrifugation (30 sec, 6000 xg) and resuspended in 0.3 ml of distilled water. The cell density was
measured and adjusted so that all cultures had a ﬁnal absorbance, at 600 nm, of 0.1 per ml.
Subsequent direct microscopic cormts of these cultures indicated that this absorbance corresponded to
approximately 1.5 x 108 cells per ml for most cultures. Notably, strain 5 was characterized by much
larger cells and the adjusted culture contained only 0.2 x 108 cells per ml. Mixture G (containing all
10 strains) was prepared by mixing 10 ul from each individual culture. Mixtures A, B, C, D, E, and
F contained only ﬁve strains (1 -5) and were prepared in a similar manner except that one strain was
added in excess. For mixtures A, C, and B, an additional 80 ul of strain 1,2, and 4 were used,
respectively. For mixtures B, D, and F, an additional 30 ul of strain 1,2, and 4 were used,
respectively. Two sets of each mixture were prepared Samples were frozen at -20°C overnight and
subsequently thawed at room temperature to be used as templates for PCR ampliﬁcation.

Soil and rhizosphere samples

Several kilograms of a Michigan sandy loam were air dried, mixed, and aliquoted into
separate potting units containing approximately 50 g. Each unit was moistened with either 5 ml of
2% myo-inositol (+) or an equal volume of distilled water (-). Three to ﬁve alfalfa seeds
(NitraginGold pre—inoculated, PH1310277, Idaho Crop Improvement Association, Boise, ID) were
spread on top of each unit of soil and imbibed with 100 ul of distilled water to promote rapid
gemrination. Soil and plants were incubated in a growth chamber (12:12 LD, 22°C) for one week.

Samples were processed sequentially, alternating (+) and (-) treatments. Soil samples
(approxinrately 150 mg each) were taken from the surface at least 1 cm away from alfalfa shoots with
a clean spatula. Rhizosphere samples (approximately 25 mg of root tissue + approximately 30 mg of
attached soil) were obtained by gently separating the roots from surrounding soil and cutting off the

attendant shoot Samples were promptly placed in 5 ml of distilled water in 15 ml Sarstedt tubes.
Bacteria were dislodged by alternating 15 second treatments: vortex, sonication (tube placed in
Ultrasonik cleaning bath, NEY, Inc.), vortex, sonication, and ﬁnal vortex. A 50 ul aliquot was
transferred to a preﬁlled (200 ul distilled water per well) 96-well microtiter plate for serial dilution.
Twenty microliters of each well-mixed dilution was then inoculated into replieate microtiter plates
containing 200 ul per well ofeither 0.1x TSB medium or MA medium ( 25 mM KH2P04 (pH 73),
10 mM NaCl, 25 ppm Mg804 - 7H20, 1.25 ppm CaClz, 0.27 ppm FeCl3 - 6 H20, 0.242 ppm
NazMoO4- 2 H20, 3 ppm H3B03, 1.83 ppm NaSO4- H20, 0.287 ppm ZnSO4 ~ 7 H20, 0.125 ppm
CuSO4- 5 H20, 0.119 ppm CoSO4- 6 H20, 1 gll myo—inositol, 1 g/l (NI-102804). This procedure
was repeated until all soil and rhizosphere samples had been processed. Roots were then rinsed in
distilled water and transfened to a 1.5 ul tube containing 750 ul of distilled water to be crushed with a
sterile pestle. A 50 ul aliquot from each was transfened to a nricrotiter plate for serial dilution as
described above. Dilution plates were stored at -20°C. Culture plates were incubated at room

temperature in the dark for 18 hours before sampling for PCR.

Fluorescent-tag ampliﬁed ribosomal DNA restriction analysis (FT-ARDRA)

HPLC-puriﬁed primers 8F-I-IEX (5’- *AGA GTT TGA TCC TGG CTC AG -3’) and 1492R
(5’- ACG GCT ACC TTG ’ITA CGA CIT -3’) were designed according Weisburg et al. (35) and
were synthesized by PEApplied Biosystems (Foster City, CA). The polymerase chain reaction was
performed according to conditions similar to those that have been described before for whole cell rep-
PCR (25). Master mixes were prepared while template samples were thawed at room temperature.
The master mix contents for 30 reactions was: 150 ul of 5x Gitschier buffer (83 mM (NH4)ZSO4, 335
mM Tris-HCI, pH 8.8, 33.5 mM MgC12, 33.5 uM EDTA, 150 mM B-mercaptoethanol), 6 ul BSA
(20 mg/ml), 75 ul DMSO, 256 ul sterile distilled water, 37.5 ul dNTPs (2.5 mM, mixed 1:1:1 :1), 30
ul each primer (25 pmol per ul), 3.75 ul RNAse (2.5 mg/ml), 10 ul Taq DNA polymerase. Twenty
microliter aliquots of the master mix were distributed into 8—strip PCR tubes. Five microliters of

thawed template (containing 104 to 106 bacteria) were mixed in thoroughly by micropipetting.

95

Negative control reactions were performed for each ampliﬁed set. Reactions were processed in either
PTC100 or PTC200 cyclers (MJ Research, Inc., Watertown, MA). Cycling conditions: 95°C for 7
min, then 35 x (94°C 1 min, 50°C 1 min, 65°C 8 min), then 65 °C for 16 min, followed by 4 °C
storage.

Restriction digestions were performed in 96-well microtiter plates. Each reaction consisted
of 7 ul of a PCR reaction in a total reaction volume of 40 111 and 8 U of either Rsal or MspI
(Boehringer-Mannheim, Indianapolis, IN). Reactions were incubated at 37°C for two to four hours.
Reactions were then stored at -20°C. For standard ARDRAfguI‘ aliquots were loaded onto 1.5%
agarose gels and electrophoresed in 0.5x TAE buffer. For FT-ARDRA, 10 ul of each sample were
submitted to the Michigan State University DNA Sequencing Facility (East Lansing, M1) for
electrophoresis and imaging. Brieﬂy, 2 ul of each sample were mixed‘with 2 ui or G2500-TAMRA
and loaded onto 6% urea—containing polyacrylamide gels (24 WTR plates). Electrophoresis was
performed on an ABB73 sequencer (PEApplied Biosystems, Foster City, CA) and run for 14 hr at
1680 V. Data was collected automatically using the ‘B-ﬁlter md—ahalyied :uSing GeneScan 2.1
Software (PEApplied Biosystems, Foster City, CA ).

Results
Analysis of model bacterial communities

Phylogenetically diverse strains of soil and rhizosphere bacteria were examined using an
improved T-RFLP analysis of ampliﬁed 16S rRNA gene sequences (FT-ARDRA). The 165 rDNA
gene was ampliﬁed from all of the tested strains using the conserved eubacterial primers 8F-HEX and
1492R Digestion and electrophoretic separation of the ampliﬁed products gave rise to distinct
banding patterns that were generally in good agreement with the patterns predicted by the RDP
database sequences of phylogenetically related strains (see Table 5.1 ).

Examination of the digested products on ethidium bromide-stained gels revealed that the 168
rDNA of some strains was ampliﬁed better than others (data not shown). This qualitative observation

was conﬁrmed quantitatively by measuring the amount of ﬂuorescence of the HEX-labeled 5’

fragment (see Table 5.1). When averaging four measurements, the amount of ﬂuorescence,

 

 

whim Whom
Strain Putative Identitya Expected Observed Aren° Expected Observed Aimc
l Pseudomnasﬂuorescencs 860 878 l 18 490 497 153
2 Artlrrobactermacroides 478 458 103 68 68, 70 78
3 Rhizobium meliloti 71101-148)" 832 78 406 402 99
4 Bacillus macroides 455 458 45 150 154 B
5 Alcaligenes eutrgrhus 470 476 76 440 433 92

 

‘ Asdeterminedbypartial 168 rDNA sequencing.

b Mean size ofmajor peak(s), n=4.

c Meanareaofmajorﬂmscempeakrmitsofﬂuorescence/1000,n=4.
dThededucedsizesofthei'ustandseoondfragrnentiiarelisted

Table 1: FF-ARDRA data of isolated strains. The 168 rDNA sequences were ampliﬁed from
each of the ﬁve strains, digested with either Rsal or Mspl, and the resulting fragments were separated
by PAGE. The observed sizes of the 5'-HEX-labelled fragments were determined by GeneScan
software. The expected sizes were deduced from correSponding 16S rDNA sequences present in the
RDP database. The ﬂuorescent signals of different digests of the same strain varied by less than two-
fold. The ﬂuorescent signals of different strains varied by up to 13-fold, indicating differential
ampliﬁcation efﬁciency.

measured as the total area of the major peaks, varied from strain to strain by up to 6.7-fold. This is
signiﬁcantly greater than the variation between replicates of the same strain, which differed by less
than 2—fold. For individual reactions, the amount of ﬂuorescence varied from strain to strain by up to
10-fold and the variation between replicates of the same strain differed by no more than 4—fold.
Mixtures of cultured strains were used to evaluate the ability of the FT-ARDRA technique to
detect the phylogenetically distinct components of complex communities known to inhabit the soil and
rhizosphere environments (see Figure 5.1). In mixtures where ﬁve distinct strains were present in
equal proportions, all of the strains could be readily detected. However, as expected, the ﬂuorescent
signals ascribed to each of the strains were not equally intense. In more complex mixtures containing

10 strains in roughly equal proportions, most but not all of the strains could be unequivocally

F; I so 'ou :50 are 25c sco ass use as: so: 550 5:: ssa fan 759 sec as: be:
sa . i.

A___4

_M_12345 me 3 5 1a b

 

IC

 

 

3
on
F‘“

..n
U
,—

 

 

 

 

 

400: c
sou.
zoo-
'00

o a.

r

:00) 24‘ d
sea:
200 1a
109:: 1b 3

0 A‘
coo e

 

Figure 5.1: Fluorescent gel and corresponding FT-ARDRA profiles of model
communities containing five phylogenetically diverse strains. Fluorescent gel data (a) is
analyzed with GeneScan software to generate the corresponding proﬁles (b - d). Composites of
individual strains (b & d) are compared to mixtures (M) of all ﬁve strains ampliﬁed together (c & e).
Peaks corresponding to individual strains are numbered according to Table l.

detected (see Figure 5.2). Independently ampliﬁed replicates of these mixtures showed that the FT-
ARDRA proﬁles were very reproducable.

Serial dilution of the model communities prior to ampliﬁcation showed that the general
character of the proﬁle was maintained while the total signal decreased as a function of template
abundance (see Figure 5.2). Template abundance was conelated with total ﬂuorescent signal of the
proﬁle, and the overall pattern was generally maintained over the dilution series. The rank order of
peak intensities did not change signiﬁcantly over the range of 10° to 8 x103 templates. Thus, the
technique is robust with regards to sample size where reactions containing 104 to 10° bacteria are
used. In mixtures where one strain represented a dominant fraction of the whole (69% or 43%), the
ﬂuorescent signal for that strain was increased, though not proportionately (see Figure 5.3). The
amount of increase in signal intensity was dependent on the identity of the dominant strain. For
example, there was a 150% increase in signal intensity for a 350% increase in the total number of
cells of strain 1 (mixture B). In contrast the signal corresponding to strain 5 increased by over
1000% (Mixture D) when it was present as the single numerically dominant strain in the model
community. In all cases the overall ampliﬁcation efﬁciency was not greatly affected when one strain
was present in greaterabundance than theothers.

Examination of soil and rhizosphere bacterial communities

FT-ARDRA was used to examine the bacterial communities of soil and rhizosphere
environments and their response to nutrient amendment (Figure 5.4). Each proﬁle was found to
contain approximately ten majorﬂuorescent signal bands, most of which were observed in both soil
and rhizosphere samples. Overall, both soil and rhizosphere proﬁles could be readily distinguished
from one another. Most strikingly, the proﬁles of the rhizosphere samples contain major signals
absent or much reduced in the proﬁles of the nearby bulk soil. These rhizosphere-enhanced signals
are most easily observed at350 bp and between 490 and 510 bp in the MspI proﬁles and at 475 bp,
around 600 bp and between 820 and 900 bp in the Rsal proﬁles (see Figure 5.4). The major signal
peaks from both the soil and rhizosphere samples occurred in size ranges expected for known soil

so 100 ‘50 20° 25° 30° 33° ‘00 45° “0 53° .00 .50 70° 75° .00 .50 .00
W

 

 

 

 

 

 

 

 

 

 

000.: 4.0 X 10‘
eoo1
ace1
0 A 4. A - A_.
m 8.0 x 103

.00
400

 

 

 

Figure 5.2: Dilution extinction of FT-ARDRA profiles using different quantities of
model community templates. Ten strains were mixed together in equal proportions, and
serial dilutions of this mixture were ampliﬁed separately and digested with MspI. Note
that template abundance is correlated with total ﬂuorescent signal of the proﬁle, and that
the overall pattern is generally maintained over the dilution series.

100

50 ‘00 ‘50 20° 25° 300 350 ‘00 45° 500 550 .00 .50 70° 75° .00 .50 .00
W

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5.3: Changes in the FT-ARDRA proﬁles due to changes in the relative
abundance of component bacteria. All mixtures contain strains 1, 2, 3, 4, and 5.
Dominant strains represent 69% (a, c, e) or 43% (b, d, e) of the total number of bacteria
present in the template mixture. Peaks are labeled by strain number and dominant strains
are indicated by an (*) in the proﬁle of each mixture. Proﬁles are those generated by
MspI digestion.

101

and rhizosphere bacterial genera (compare to Table 5.1). Occasionally, a distinctly novel band
appeared in one of the replicated proﬁles (e. g. the 270 bp band in one of the Mspl digests of the soil
sample, see Figure 5.4). Because such bands were not repeatedly observed, little can be said
conclusively about their signiﬁcance (see Discussion).

Application of a nutrient amendment (i.e. myo-inositol) to the soil caused the community
proﬁles of both the soil and rhizosphere to change dramatically. Comparison of multiple proﬁles
indicated that the most apparent change could be ascribed to organisms with a unique 168 rDNA
restriction pattern (indicated by an ”*" in Figure 5.5). This signal consisted of an ~670 bp 5’RsaI
fragment and an ~500 bp Mspl fragment and appeared in both soil and rhizosphere samples after
application of the nutrient amendment. This signal is referred to as the 670/500 ribotype.
Comparison of this pattern to theoretical digests of known sequences present in the RDP database
indicated that this ribotype may correspond to a strain of Pseudomonas putida. Additional
quantitative changes in certain signal peaks were also noted in the chromatograrns, indicating that
several additional ribotypes responded to nutrient amendment.

Ff-ARDRA of cultures inoculated from soil and rhizosphere washes was used to further
investigate the nature of the bacterial communities (see Figure 5.6). After 18 hours of incubation in
liquid media, the banding patterns of the ampliﬁed samples were generally similar to those derived
from the direct washes. Because the samples were diluted for culturing, ﬂuorescent bands indicated
bacterial growth in the media. Major signals representative of the 670/500 ribotype (*) appeared in
the culture proﬁles of the amended (+) as well as the unarnended (-) rhizosphere samples. Thus, cells
represented by the 670/500 ribotype could be easily cultured from the rhizosphere regardless of the
nutrient treatment. Because the amount of ﬂuorescent signal in the rhizosphere cultures was similar,
no increase in the abundance of this ribotype due to the nutrient amendment was evident. This
contrasted directly with the rhizosphere washes where a dramatic increase was observed in the ~670
bp ﬂuorescent signal due to the nutrient amendment. This indicates that the nutrient amendment
affected the physiological status as well as the abundance of the bacterial population represented by
the 670/500 ribotypes.

102

Map1 Raa1

u 1'1“2”.““OSNONOMMINWMM1MIUMM

         
    

 

 

 

 

 

 

 

 

 

 

S
1 AK
R
e
CH
O A L _ n: .x A .L 1L.

 

 

 

 

 

 

Figure 5.4: FT-ARDRA proﬁles of natural communities. Proﬁles were generated by
digesting ampliﬁed products with Mspl or Rsal. Chromatographic traces from four
independent samples were used for each sample to display the reproducibility of the
technique. S, soil; R, rhizosphere; CR, crushed rhizosphere.

103

  

 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 

.35}!

 

 

 

Figure 5.5: Changes in FT-ARDRA profiles of natural communities due to myo-
inositol amendment. Proﬁles are analogous to those in Figure 4. Soil (S), rhizosphere
(R), and crushed rhizosphere (CR) samples were taken from four separate pots that had
been saturated once with a 2% solution of myo—inositol.

104

 

 

536

490 ,‘g.
470 r ' :u

361

286

238

186 .

 

116 ,

Figure 5.6: Comparison of washes and cultures of the same samples. Fluorescent gels
of direct sample washes (W) and cultures of the same samples incubated for 18 hr at room
temperature in two different media (MA or 0.1x TSB). Multiple soil (S) and rhizosphere (R) samples
are shown from both amended (+) and unamended (-) treatments. Ampliﬁed products were digested
with Rsal.

105

These data support the hypothesis that the nutrient amendment causes a physiological change
in the 670/500 ribotype that results in metabolic activity and growth. This change results in a
dramatic increase in the observed ﬂuorescent peak and, by inference, in the apparent numerical
abundance of the ribotype in the washes. However, analysis of the cultured communities suggests
that very similar numbers of the ribotype are present, because the signal intensities are similar in both
(+) and (-) treatments. The best explanation for these results is that some large proportion of 670/500
type organisms are inaccessible to detection by PCR (see Discussion). Similarly, changes in the
abundance of a 1 19 base pair band (**) also indicate this type of physiological switching. However,
in this case, myo-inositol appears to repress the growth and activity of this ribotype in the
rhizosphere. The application of myo-inositol reduced the abundance of this signal in the rhiZOSphere
washes, but this had little effect on the amount of 120 bp ﬂuorescent signal observed in the cultures

of those washes.

Discussion

The utility of PCR to examine mixed microbial populations has become widely recognized
(17, 24, 31, 33). Methods based on ampliﬁcation of the 16S ribosomal RNA and DNA have ﬁgured
prominently in the recent explosion of data on bacterial communities (24). However, many of the
published protocols are complicated by the isolation of DNA from environmental samples prior to
ampliﬁcation, puriﬁcation of ampliﬁed products, and/or the analysis of clone banks from the
ampliﬁed products (31). The costs involved in these steps can quickly make comprehensive studies
prohibitively expensive. Additionally, studies which rely on the analysis of clone banks may be
subject to unintended bias if a non-representative ampliﬁcation event occurs (see Figure 5.4). In
contrast, the FT-ARDRA method described in this paper allows for high throughput of multiple
independent samples by direct ampliﬁcation of small samples and direct digestion of ampliﬁcation
products prior to electrophoresis.

Valid concerns have been raised regarding potential artifacts associated with ampliﬁcation of

16S rDNA from mixed templates (27, 34). To address these concerns, we evaluated our technique

106

using mixtures of phylogenetically distinct bacteria isolated from soil and rhizosphere environments.
Representatives from both Gram(+) and Gram(-) genera were used. 16S rRNA gene products were
ampliﬁed from all of the individually tested strains, indicating that the combined stresses of the
freeze-thaw treatment and PCR cycling were sufﬁcient for cell lysis (see Figure 5.1). There was
some variation in the efficiency of ampliﬁcation from strain to strain, but whether this was due to
incomplete cell lysis or differences in priming efﬁciency is not known. Furthermore, one might
predict that only a single ﬂuorescent band should be observed for each strain. In fact, more than one
major ﬂuorescent band was observed in a few instances (see Figure 5.2). Additionally, several
minor ﬂuorescent bands were also observed in some cases. These bands most likely represent
intragenomic variation of the rDNA genes (8). Given the reproducibility of the distinct band sizes
from independent applications of the technique, it is unlikely that these multiple signals are due to
stochastic production of chimeras or sequence mutations that result in altered digestion patterns. One
of the advantages of performing FT-ARDRA as described is that the complexity of the template is
likely reduced by serial dilution, reducing the chances for chimeric artifacts to occur. This is, of
course, also a limitation because only the most abundant bacteria that can be recognized by the primer
set will be detected. The remainder of the bacterial diversity is thereby ignored

In applying this technique, we recommend that comparisons be based on full proﬁles
(spanning 50 to 950 base pairs) using two different restriction enzymes. We routinely use Mspl and
Rsal because they efficiently resolve different sets of known soil genera (see Figure 5.1). The use of
two different enzymes is essential for making inferences about the abundance and identity of
individual ribotypes. Conelations in the peak areas of complementary band sizes (e.g. in the case of
the 670/500 ribotype, see Figure 55) can be used to make inferences about increases and decreases
in the abundance of individual ribotypes. Visualization of bands smaller than 50 base pairs is not
generally useful because of the presence of excess labeled primers and associated artifacts.
Sometimes these artifacts spilled over into the range of 50 to 150 base pairs and interfered with the
detection and analysis of signals in that region of the proﬁle (e.g. compare Figures 5.1 and 5.2).

107

While detection of bands greater than 950 base pairs in size is possible, we have not generally
observed signiﬁcant signals above this size limit.

When applying this technique to complex environmental samples, one needs to be cautious
about evaluating the large amount of quantitative and qualitative information generated by T-RFLP
analysis (18). In general, different phylogenetic groups will have different restriction patterns of their
ampliﬁed 16S rDNA (18, 22). It has been estimated that ARDRA using three well chosen restriction
enzymes is sufficient to identify organisms at the genus level (22). Because FT-ARDRA provides
additional information regarding the identity of the 5’ restriction fragment, one would expect that
fewer digests would be required to make identiﬁcations with the same degree of accuracy. From our
experience, combining the information on the ﬂuorescent gels and ethidium stained gels was
sufficient to conﬁrm the classification of the isolated strains that had been previously established by
partial DNA sequencing (B. McSpadden Gardener, J. Stolzfus, and F. J. de Bruijn, unpublished
data). However, any identiﬁcation made in the absence of culturing must be viewed as preliminary
(9). With this in mind, we note that the major signals observed in the FT-ARDRA proﬁles of both
soil and rhizosphere samples fall into size classes similar to those expected for genera known to
inhabit these environments. This observation leads us to suggest that the majority of bacteria present
in these samples are similar to known bacterial genera based on the similarity of their 168 rRNA
sequences.

Differences in experimental samples were detected using Ff-ARDRA as changes in the areas
of ﬂuorescent peaks. Here, we report the occrn'rence of distinct differences between soil and
rhizosphere samples as indicated by comparing replicate FTvARDRA proﬁles generated using two
different restriction enzymes. These differences generally conespond to rhizosphere-speciﬁc
increases in the signal intensity of several different size classes of 5' terminal restriction fragments
(see Figures 5.4 and 5.5). This observation is consistent with the ”rhizosphere eﬁect" ascribed to the
relatively nutrient-rich below—ground niche sunounding plant roots (29).

Because different strains amplify with different efficiencies, especially when present in

mixtures, the relative abundance of different organisms cannot always be directly compared (see

108

Figure 5.1). Nonetheless, changes in peak area of a designated size class can be clearly correlated
with monotonic changes in the abundance for individual strains which produce bands of that size
class in the absence of comigrating signals from other organisms. For example, an increase in the
relative abundance of strain 5 can be easily observed using Mspl but not Rsal because in the latter
digest the 5’ fragment comigrates with the 5’ fragment of strain 3 which ampliﬁes much more
eﬁiciently (see Figure 5.1). Despite these limitations, we think that this method can provide valuable
new information about bacterial communities in the environment, especially when sample washes and
subsequent cultures are analyzed in concert with each other.

FT-ARDRA of cultures complements the analysis of samples used to inoculate them.
Because physiologically active and numerically dominant bacterial populations will have a
reproductive advantage in liquid media, we hypothesize that their relative abundance will be enhanced
in short term enrichment cultures as those described here. From this we deduce that dominant
signals, present in both the liquid cultures and direct washes, represent abundant and physiologically
active bacterial populations. We observed that a carbon nutrient amendment, myo-inositol, promoted
a proliﬁc rise in a 670/500 ribotype in both the soil and rhizosphere environments (compare Figures
5.4 and 5.5). This ribotype was detected in the washes of the amended samples only; however, it
was also detected in the cultures of the unamended rhizospheres (see Figure 5.6). This indicates that
the rhizosphere environment (and by analogy the nutrient amendment) alters the physiological state
of this ribotype allowing it to grow readily in the liquid media. We hypothesize that some large
proportion of 670/500 type organisms are present in the unamended rhizospheres but escape
detection. This may be due to responses of this ribotype to the nutrient starved environment of the
soil (7). In bacteria, starvation may result in physiological conditions that make bacteria more
resistant to lysis (2, 28) or minimize chromosomal copy number (5, 8). However, when these
bacteria are presented with favorable conditions, modeled by the liquid media used here, they quickly
respond in such a way that they become detectable by this PCR-based approach. The absence of the
670/500 signal in both the washes and cultures of the unamended soil samples leads us to suggest

that the nutrient amendment stimulates the activation and subsequent growth of speciﬁc bacterial

109

populations capable of catabolizing it. This is consistent with the idea that soil bacteria represent a
microbial "seed bank" of metabolic potential that "germinates" in response to favorable environmental

conditions.

Acknowledgements

We would like to thank Shari Tjugum-Holland and Juliane Dougherty for their technical assistance in
making and running numerous gels. We would also like to thank M.E. Davey, Dr. I... Fomey, Dr.
A. Milcamps, J. Stoltzfus, and Dr. J.D. van Elsas for their critical reading of the manuscript and
many helpful comments. This work was funded by a STAR Graduate Fellowship granted to B.M.G.
by the US. Environmental Protection Agency.

References

l Akkermanns ADL, van Elsas JD, de Bruijn FJ eds. (1995) Molecular Microbial
Ecology Manual. Kluwer: Amsterdam.

2 Baklren LR (1997) Culturable and nonculturable bacteria in soil. pp. 47-61. In Modern
Soil Microbiology. van Elsas JD, Trevors JT, Wellington EMH eds. Marcel Dekker: New York.

3 Borneman J, Skroch PW, O’Sullivan KM, Pains JA, Rumjanek NG, Jansen
JL, Nienhuls J, Triplett EW (1996) Molecular microbial diversity of an agricultural soil in
Wisconsin. Appl. Environ. Microbiol. 62: 1935-1943.

4 Cavigelli MA, Robertson GP, King MJ (1995) Fatty acid methyl ester (FAME)
proﬁles as measures of soil microbial community structure. Plant Soil 1 7 0: 99.113.

5 Condon C, Squires C, Squires CL (1995) Control of rRNA transcription in
Escherichiacolr‘. Microbiol. Rev. 59: 623-645.

6 Ellis RJ, Thompson 11’, Bailey MJ (1995) Metabolic proﬁling as a means of
characterizing plant-associated microbial communities. FEMS Microbiol Ecol. l 6: 9-18.

7 Ekas JD van, van Overbeek LS (1993) Bacterial responses to soil stimuli. pp. 55-79.
In Starvation in Bacteria. Kjelleberg S ed. Plenum Press: New York.

8 Family V, Rainey FA, Stackebrandt E (1995) Effect of genome size and mr gene
copy number on PCR ampliﬁcation of 168 rRNA genes from a mixture of bacterial species. Appl.
Environ. Microb. 61: 2798-2801.

9 Fox GE, Wisotzkey JD. Jurtshrrk Jr. P (1992) How close is close: 168 rRNA

sequence identity may not be sufficient to guarantee species identity. Int. J. Syst. Bacteriol. 4 2: 166-
170.

110

10 Garland JL, Mills AL (1991) Classiﬁcation and characterization of heterotrophic
microbial communities on the basis of patterns of community-level sole-carbon source utilization.
Appl. Environ. Microbiol. 5 7: 2351-2359.

1 l Guckert J B, Antworth CP, Nichols PD, White DC (1985) Phospholipid, ester-
linked fatty acid proﬁles as reproducible assays for changes in prokaryotic community structure of
estuarine sediments. FEMS Microbiol. Ecol. 3 1: 147-158.

1 2 Hofle MG (1992) Bacterioplankton community structure and dynamics after large-scale
release of nonindigenous bacteria as revealed by low-molecular-weight-RNA analysis. Appl.
Environ. Microb. 5 8: 3387-3394.

13 110an WE, Harris D (1995) DNA-based monitoring of total bacterial community
structure in environmental samples. Molec. Ecol. 4: 627-631.

14 Laguerre G, Allard M, Fevoy F, Amarger N (1994) Rapid identiﬁcation of rhizobia
by restriction fragment length polymorphism analysis of PCR-ampliﬁed 16S rRNA gens. Appl.
Envrion. Microbial. 60:56-63.

15 Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR (1985) Rapid
determination of 168 ribosomal RNA sequences for phylogenetic analyses. Proc. Nat. Acad. Sci.
USA 8 2: 6955-6959.

16 Liesack W, Jansen PH, Rainey FA, Ward-Rainey NL, Stackebrandt E (1997)
Microbial diversity in soil: The need for a combined approach using molecular and cultivation
techniques. pp. 375—440. In Modern Soil Biology. J.D. van Elsas, J.T. Trevors, E.M.H.
Wellington eds. Marcel Dekker. New York.

1 7 Liesack W, Stackebrandt E (1992) Occurrence of novel groups of the domain Baaeria
as revealed by analysis of genetic material isolate from an Austrailian tenestial environment. J.
Bacteriol. 174: 5072-5078.

18 Liu W, Marsh TL, Cheng H, Forney LJ (1997) Characterization of microbial
diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S
rRNA. Appl. Environ.Microb. 63: 4516-4522.

19 Maidak BL, Olsen GJ, Larsen N, Overbeek R, McCaughey MJ, Woese CR
(1996) The Ribosomal Database Project (RDP). Nuc. Acid. Res. 24: 82-85.

20 Massol-Deya A, Odelsou DA, Hickey RF, Tiedje JM(1995) Bacterial community
ﬁngerprinting of ampliﬁed 16 S and 16—238 ribosomal DNA gene sequences and restriction
endonuclease analysis (ARDRA). In Molecular Microbial Ecology Manual. Akkennanns ADI. van
Elsas JD, and de Bruijn FJ eds. Kluwer: Amsterdam. 3.3.2: 1-8.

21 Mclneruey MJ, Stahl DA (1997) Molecular approaches for the measurement of density,
diversity, and phylogeny. pp. 27 - 28 In Manual of Environmental Microbiology. Hurst CJ ed. in
chief. Knudsen GU, Mclnemey MJ, Stetzenbach LD, Walter MV eds. ASM Press: Washington DC.

22 Moyer CL, Tiedje JM, Dobbs FC, Karl DM (1996) A computer-simulated
restriction fragment length polymorphism analysis of bacterial small-subunit rRNA genes: Efﬁcacy of
selected tetrameric restriction enzymes for studies of microbial diversity in nature.
Appl.Environ.Microb. 62: 2501-2507.

111

23 Muyzer G, de Waal EC, Uitterlinden A (1993) Proﬁling of complex microbial
populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-
ampliﬁed genes coding for 168 rRNA. Appl. Environ. Microbiol. 5 9: 695-700.

24 Pace NR (1997) A molecular view of microbial diversity and the biosphere. Science 27 6:
734-740.

25 Rademaker JLW, de Bruijn FJ (1997) Characterization and classiﬁcation of microbes
by rep-PCR genomic ﬁngerprinting and computer assisted pattern analysis. pp. 151-171. In DNA
markers: protocols, applications and overviews. Caeteno-Anolles G, Gresshoff PM eds. J. Wiley &
Sons:New York.

26 Reisner D, Heuco K, Steger G (1991) Temperature-gradient gel electrophoresis: A
method for the analysis of conformational transitions and mutations in nucleic acids and proteins.
Advaneas in Hectrophoresis 4: 169-250.

27 Reysneback AL, Giver LJ, Wickham GS, Pace NR (1992) Differential
ampliﬁcation of rRNA genes by polymerase chain reaction. Appl. Environ. Microbiol. 58: 3417-
3418.

28 Roszak DB, Colwell RR (1987) Survival strategies of bacteria in the natural
environment. Microbiol. Rev. 5 1: 365-379.

29 Soreusen J (1997) The rhizosphere as a habitat for soil microorganisms. pp. 21-45 In
Modern Soil Microbiology. van Elsas JD, Trevors JT, Wellington EMH eds. Marcel Dekker: New
York.

30 Stackebrandt E, Liesack W, Goebel BM (1993) Bacterial diversity in a soil sample
from a subtropical Australian errvironmentas determined by 168 rDNA analysis. FASEB J. 7: 232-
236.

3 1 Stahl DA (1997) Molecular approaches for the measurement of density, diversity, and
phylogeny. in Manual of Environmental Microbiology. pp. 102-114. Hurst CJ ed in chief. Knudsen
GU, Mclnemey MJ, Stetzenbach LD, Walter MV eds. ASM Press: Washington DC.

32 Torsevick V, Goksoyl' J. Dane FL (1990) High diversity of soil bacteria. Appl.
Environ. Microbiol. 56: 782-787.

33 Ward DM, Bateson MM, Weller R, Ruff-Roberts AL (1992) Ribosomal RNA
analysis of microorganisms as they occur in nature. Adv. Microb. Ecol. l 2: 219-286.

34 Weidner S, Arnold W, Puhler A (1996) Diversity of uncultured microorganisms
associated with the seagras Halophr’lia stiulacea estimated by restriction fragment length
polymorphism analysis of PCR-amplified 16S rRNA genes. Appl. Environ. Microbiol. 62: 766-771.

35 Weisburg WG, Barns SM, Pelletier DA, Lane DJ (1991) 16S ribosomal DNA
ampliﬁcation for phylogenetic study. J. Bacteriol. 1 7 3: 697-71B.

36 Zak JC, Willig MR, Moorhead DL, Wildman HG (1994) Functional diversity of
microbial communities: a quantitative approach. Soil Biol. Biochem. 2 6: 1101 -1 108.

112

Chapter 6

Impact of myo-Inositol Amendment on Sinorhizobium meliloti
and Other Bacterial Populations in the Rhizosphere '
of Medicago sativa

McSpadden Gardener B, Cotton C, de Bruijn FJ (1998) Impact of myo-inositol amendment on
Sinorhizobium meliloti and other bacterial populations in the rhizosphere of Medicago sativa. Appl.
Environ. Microbiol. in preparation

113

Abstract

Application of the nutrient amendment myo-inositol stimulated speciﬁc changes in bacterial
populations in the rhizosphere of Medicago sativa. Under gnotobiotic conditions, a strain of
Sinorhizobium meliloti capable of catabolizing myo-inositol (Mic+) outcompeted a near-isogenic non-
catabolizing (Mic) strain for rhizosphere colonization when myo-inositol was added to the growth
medium. For plants grown in non-sterile soil, the median number of Mic+ culture forming units
(CFUs) was observed to be higher from samples amended with myo-inositol at planting. ARDRA
and FT-ARDRA of the most abrmdant Mic CFUs indicated quantitative shifts in the composition of
the bacterial populations reaching saturating growth after 5 days of incubation. FT-ARDRA of soil
and rhizosphere washes also detected quantitative differences between amended and unamended
rhizospheres. In both laboratory and ﬁeld experiments, T-RFLPs of the ampliﬁed 16 rDNA
corresponding to S. meliloti were more abundant in samples that had been amended with myo-
inositol. Nodulation of M. sativa was greatest in the amended ﬁeld plots, and increases in nodulation
conelated with increases in the amount of myo-inositol applied at planting. These data indicate that
the nutrient amendment myo-inositol can promote the growth of bacteria capable of catabolizing it in
the rhizosphere environment and that it promotes the growth and activity of S. meliloti in the
rhizosphere of M. sativa.

Introduction

Biological nitrogen ﬁxation plays a major role in the productivity and sustainability of
agricultural systems (11, 28). The intimate symbiosis between leguminous plants and rhizobia
provides the largest proportion of ﬁxed nitrogen in agricultural systems world-wide (11, 28). Efforts
to increase agricultural yields of legumes and subsequent crops in rotations has led to the widespread
application of rhizobial inoculants with a high capacity for nitrogen ﬁxation (1, 22). However, the
effectiveness of these treatments are often undermined by the presence of indigenous rhizobia which
outcompete the inoculant strains for nodule occupancy (37, 44).

114

Several alternative approaches have been pursued to improve the effectiveness of this
nitrogen-ﬁxing symbiosis (43). Increasing inoculum levels has shown limited success, primarily in
soils with low indigenous populations of rhizobia (22). Laboratory selection of strains with traits that
improve their competitiveness and/or enhance nitrogen ﬁxation capabilities has also led to improved
effectiveness of inoculant strains (13, 22). Some studies have also examined the possibility of
promoting the activity of rhizobia by supplementing the soil and/or inoculant formulation with
nutrients (9, 12, 27, 45). More recently, efforts to improve the symbiosis have included genetic
engineering of rhizobia and/or their conesponding plant host (2, 25, 33, 36). In this regard, it has
been proposed that the rhizosphere environment may be speciﬁcally managed so as to promote the
activities of beneﬁcial microorganisms (23, 24).

Nutritional mediators can be deﬁned as organic substrates that alter ecological relationships
between microorganisms so as to promote the growth of catabolizers over non-catabolizers. In the
diverse milieu of soil microbial communities (39), it is likely that numerous organisms will have the
abilityto catabolize any given substrate. So, if the goal is to promote a particular subpopulation of
bacteria, the ability to catabolize a nutritional mediator should relatively rare (21, 33). Several recent
reports indicate that nutritional mediation can occur in the soil and rhizosphere environments (4, 5, 6,
9, 15, 24, 35). In some instances, the application of the nutritional mediator has been shown to
promote plant health by promoting the growth and/or activity of beneﬁcial microorganisms (4, 9, 46).

In a screen of potential nutritional mediators, we have observed that a relatively small
proportion of soil and rhizosphere bacteria are capable of utilizing myo-inositol as sole carbon source
to support growth in vitro (see Chapter 3). Thus, myo-inositol may be useful as a selective
nuuitional mediator of competition between catabolizing (Mict) and non-catabolizing (Mic) bacteria.
Because S. meliloti are capable of utilizing this relatively speciﬁc substrate, we hypothesized that the
application of myo-inositol would stimulate the growth and/or activity of S. meliloti in the
rhizosphere of M. sativa. Here we present the results of several experiments which consistently
support these hypotheses and provide additional information on the impact of myo-inositol
amendment on other bacterial populations in the soil and rhizosphere.

115

Materials and Methods
In vitro competition experiments

Two strains of S. meliloti differing only in their ability to catabolize myo-inositol were used;
strain 1021 (Sm’, Mic+) and its near-isogenic relative (1021 (idh::Tn5)) called IM56 (Strep', Kan',
Mic) (10). Fresh single colonies were obtained from streaks on YMA media supplemented with
either 200 ug/ml of streptomycin (for 1021) or 100 ug/ml of kanamycin (for IM56), inoculated
separately into 3 ml of YMB media, and incubated in a rotary shaker overnight at 28°C and 200 rpm.
The following morning, seeds of Medicago sativa were surface sterilized by incubation in 3%
hydrogen peroxide for 10 minutes and then washed repeatedly in sterile distilled water. The seeds
wete placed onto large petri plates containing 3&1) medium (3) or 3&1) amended with 500 PPM of
myo-inositol. The cells in one milliliter of each S. meliloti culture were pelleted by centrifugation,
washed in sterile distilled water, pelleted again, and resuspended in sterile distilled water. The cell
density was measured and adjusted so that the cultures had a ﬁnal absorbance, at 600 nm, equal to
0.1 per ml. The two cultures were then mixed in a 1:1 ratio. Four microliters of the S. meliloti
mixture was pipetted onto each seed. Plates were then sealed and placed into a growth chamber (12
hr light: 12 hr dark, 22°C) for incubation. Eight replicates of each condition were used.

The relative abundance of Mic+ and Mic rhizobia was determined as follows. From each
plate, samples of 5 seeds were collected after 1 day of incubation. Subsequently, rhizospheres from
5 seedlings were sampled after 0.5, 1, and 2 weeks of incubation. Root samples were promptly
placed in 5 mls of distilled water in 15 ml Sarstedt tubes (Numbrecht, Germany). Bacteria were
dislodged by alternating 15 second treatments: vortexing, sonication (tube placed in Ultrasonik
cleaning bath, NEY, Inc.), vortexing, sonication, and a ﬁnal vortexing. A 50 ul aliquot was
u'ansferred to a 96-well microtiter plate, preﬁlled with 200 ul distilled water per well, for serial
dilution. For the 0 timepoint, an aliquot of the S. meliloti mixture was serially diluted directly.
Twenty microliters of each well-mixed dilution was then spot-inoculated, in triplicate, onto YMB +
streptomycin (200 ug/ml) for total rhizobial counts and onto YMB + kanamycin (100 ug/ml) for Mic-

116

mutant rhizobial counts. Plates were incubated at 28°C for three days prior to counting. The value of
(l - [(counts on YMB + Kan) / (counts on YMB + Strep)] ) was used to calculate the proporu'on of
W rhizobia.

Growth chamber experiments

The soil usedwasasandyloamobtainedfrom the same MSU research plot used in theﬁeld
experiments described below. Several kilograms of this Michigan sandy loam were air dried, mixed,
and aliquoted into separate potting units containing approximately 50 g of soil. Each unit was
moistened with 5 ml of distilled water or a solution of myo-inositol at a concentration of 0.002%,
0.02 %, 0.2%, or 2% (w/v). Several seeds of Medicago sativa (NitraginGold pre—inoculated,
PHIBIUZT7, Idaho Crop Improvement Association, Boise, ID) were planted in each unit. Soil and
plants were incubated in a growth chamber (12:12 LD, 22°C).

Soil samples (approximately 150 mg each) were taken from the surface away from alfalfa
shoots with a clean spatula at l, 4, and 7 days post-germination. Rhizosphere samples
(approximame 25 mg f.w. root + 30 mg soil) were deﬁned by gently separating the roots of three
plants from sunounding soil and cutting off the attendant shoots. Samples were promptly placed in 5
mls of distilled water in 15 ml, and bacteria were dislodged as described above. A 50 ul aliquot was
transferred to a 96-well microtiter plate, preﬁlled with 200 ul distilled water per well, for serial
dilution. For the one week time point, twenty microliters of each well-mixed dilution was then
inoculated into replicate microtiter plates containing 200 it! per well of either MA (a modiﬁed GTS
mineral salts medium containing myo-inositol as sole carbon source) (20) or 0.1x TSB medium.
This procedure was repeated, alternating samples from the different treatments, until all soil and
rhizosphere samples had been processed. Dilution plates were stored at -20°C. Culture plates were
incubated at room temperature in the dark. After ﬁve days of incubation, a 100 I11 aliquot of the
terminal serial dilution of each sample which displayed saturating growth was saved for further

investigation by mixing in 100 ul 35% sterile glycerol and freezing at -70°C.

117

Field experiments

Two ﬁeld studies were undertaken in the summer of 1997 at a research plot at Michigan State
University. The soil was tested by the MSU Soil Testing Laboratory using standard procedures, and
the analysis is presented in Table 6.1. After tillage and raking, the soil was planted to Medicago
sativa in 6 x 150 foot strips running north to south. The eastern strip (designated A) contained pre-
inoculated seed (NitraginGold pre-inoculated, Apron coated, PHIBIOQTL Idaho Crop Improvement
Association, Boise, ID) and the western strip (designated B) was planted with seed from the same lot
that had been washed, surface sterilized, and air dried in the laboratory the day before planting. After
planting the ﬁeld was marked and treated as shown in Figure 6.1. In ﬁeld test 1, treatments were
applied as surface sprays of either distilled water or solutions of 1000 mg/l myo—inositol at a rate of
1.61/m2. In ﬁeld test 11, treatments were similarly applied as solutions of 0, 50, 100, 200, 500, and
10“) mg/l myo-inositol at a rate of 3.2 l/m2. Germination followed the ﬁrst signiﬁcant rain, one week
post-planting. Ten days post-planting, germination rate was measured by counting the number of M.
sativa seedlings proximal to replicate 1.5 m transects of each treatment block.

Sampling of the ﬁeld plots for was conducted 1, 2, 4, and 5 weeks post-germination. Soil
and air temperature were taken at the time of each sampling. The sampling procedure for the ﬁrst
three time points was as follows: Samples were placed individually into pre-labeled Ziploc plastic
bags, partially sealed to prevent desiccation, and carefully transported to the laboratory for immediate
processing. For ﬁeld test I A, two plants per treatment block were carefully dug up with the
surrounding soil so as to minimally disturb the structure of the rhizosphere sample. For ﬁeld test II A
and II B, two sets of ﬁve closely spaced plants were sampled from each treatment block. In the
laboratory, rhizosphere samples were processed sequentially, alternating treatments. Either one (ﬁeld
test I) orﬁve (ﬁeld test 11) rhizospheres per sample were gently removed from surrounding soil and
placed into 25 mls of sterile distilled water. Bacteria were dislodged from the rhizospheres and
serially diluted as described above. Cultures were inoculated as described above for ﬁeld test I
samples only and incubated up to three week at room temperature in the dark. Bacterial growth was
scored by visual inspection of turbidity. Nodules were counted using a dissecting microscope. At

118

ﬁve weeks post-germination, an additional sampling was undertaken to adequately determine the

extent of nodulation in test I A and I B (n a 50 plants per treatment).

F luorescently-tagged ampliﬁed ribosomal DNA restriction analysis ( FT ARDRA )

The analysis of terminal restriction fragment length polymorphisms (T-RFLPs (18)) of
ampliﬁed l6S rRNA genes was performed as described previously (20). Brieﬂy, conserved
eubacterial primers 8F-HEX (5’- *AGA G'I'I‘ TGA TCC TGG CTC AG -3’) and 1492R (5’- ACG
GCI‘ ACC TTG 'I'I‘A CGA C'I'I‘ -3’) were used to directly amplify l6S rDNA sequences from a
broad cross-section of the microbial communities present in soil and rhizosphere washes. Master
mixes were prepared and aliquoted to 8—strip PCR tubes while template samples were thawed at room
temperature. The ﬁrst or second dilution (containing roughly 104 to 10° bacteria) were mixed
thoroughly by micropipetting. Negative control reactions were performed for each ampliﬁed set.
Reactions were processed in either P'I‘CIOO or PTC200 cyclers (MJ Research, Inc., Watertown,
MA). Cycling conditions: 95°C for 7 min, then 35 x (94°C 1 min, 52°C 1 min, 65°C 8 min), then 65
°C for 16 min, followed by 4 °C storage.

Restriction digests were performed in 96-well microtiter plates. Each reaction consisted of 7
ul of a PCR reaction in a total reaction volume of 40 ul and 8 U of either Rsal or Mspl (Boehringer-
Mannheim, Indianapolis, IN). Reactions were incubated at 37°C for at least 2 hours. Reactions were
then stored at -20°C. For standard ARDRA, 8 ul aliquots were loaded onto 1.5% agarose gels and
electrophoresed following published protocols (34). For FT-ARDRA, 10 ul of each sample were
submitted to the Michigan State University DNA Sequencing Facility (East Lansing, M1) for
electrophoresis and imaging. Brieﬂy, 2 ul of each sample were mixed with 2 ul of G2500-TAMRA
and loaded onto 6% urea-containing polyacrylamide gels (24 WTR plates). Electrophoresis was
performed on an ABB73 sequencer (PEApplied Biosystems, Foster City, CA) run for 14 hrs at
1680 V. Data was collected automatically using the B ﬁlter and analyzed using GeneScan 2.1
Software (PEApplied Biosystems, Foster City, CA ).

119

Statistics

Because non-normal distributions of the sample data were observed, nonparametric statistics
were applied. Mann—Whitney U statistics were calculated using SchoolStat software (David Darby,
WhiteAnt Occasional Publishing, Victoria, Australia, ddarby@ariel.ucs. unimelb.edu.au) for making
comparisons between treatments. Kendall's correlation coefﬁcient was calculated following the
method described by Fisher (7) when determining the relationship between the amount of myo-
inositol added and ﬂuorescent peak area or__the nrnnbers of nodules. AMP-values less than or equal to

0.15are reported

Results
Impact on Sinorhizobium meliloti under gnotobiotic conditions

We hypothesized that myo-inositol amendment could positively impact the growth and
activities of myo-inositol catabolizing(Mic+) bacteria in the rhizosphere. This hypothesis was tested
by examining the impact of myo-inositol on S. meliloti populations under gnotobiotic conditions.
Two strains, S. meliloti 1021 (Mic) and IM56 (Mic), differing only in their ability to catabolize
myo-inositol were co-inoculated in a 1:1 proportion onto seeds of Medicago sativa and the
abundance of each was followed over time. The combined results of eight independent tests are
shown in Figure 6.1. Over a two week time course, the Mic+ and Mic populations remained in
approximately equal proportions in the absence of the myo-inositol amendment. In contrast, the
addition of myo-inositol to the medium resulted in an increase in the relative abundance of Mic+ strain
(see Figure 6.1). This difference in the relative amount of Mict and Mic bacteria was noticeable after
one day of incubation and persisted throughout the course of the experiment (P < 0.10 for all pair-
wise comparisons except for day 7). Thus, the addition of myo-inositol can mediate a proportional
increase in Mic+ over Mic bacteria in the rhizosphere.

The only observed phenotypic difference in the plants observed under these gnotobiotic

conditions was an increase in nodulation on the amended plates. The fraction of nodulated plants on

120

0.8

 

Ino MI I plus MI

 

proportion Mic+

 

 

days post-inoculation

Figure 6.1: Impact of m y o-inositol amendment on the relative abundance of
Mic+ S. meliloti in the rhizosphere of M. sativa. Surface-sterilized seeds were
placed onto B&D agarose supplemented with a 500 PPM myo-inositol solution (plus MI)
or an equal volume of water (no MI). Wild-type strain 1021 (Mic+) and its near isogenic
mutant IM56 (Mic) were inoculated onto the seeds in equal proportions.

121

each plate was signiﬁcantly increased in samples that had been amended with myo—inositol (37% vs.
13%; P < 0.10). We subsequently detemrined that myo-inositol was not speciﬁcally promoting this
increase since similar results were obtained when sucrose was used as the amendment in the medium

(data not shown).

Impact on culturable rhizosphere bacteria inﬁeld soil

If the rhizosphere of plants grown in natrual soil is a carbon-limited environment, one would
expect that the number of culturable bacteria would increase in response to carbon amendment.
Furthermore, the application of a particular carbon source (e.g. myo-inositol) should primarily beneﬁt
bacteria which candirectly catabolize it (e.g. Mic+ bacteria). Indeed, the application of myo-inositol
to a natural soil at planting resulted in distinct increases in the number of bacteria cultured from
rhizospheres of M. sativa (see Table 6.1). The observed increases were most pronounced for

 

 

 

-. ----------- ﬁeld test I A (16) ------------ &c. test --
media timeII cond.“ 1 wk“ 2 wkc 4 witc l wk°
MA 0.5 - 1.3 x 105 2.7 x 105 7.0x 105 1.7 x 105

+ 1.8 x 105 3.8 x 105 5.7x 105 3.9 x 105

3 - 5.5 x 105 7.4 x 105 2.2x 106 n.d.

+ 6.9 x 105 8.8 x 105 1.8x106 n.d.
0.1x TSB 0.5 .. 1.9 x 10‘ 1.9 x 10‘ 3.0 x 10‘ 1.3 x 106
+ 2.2 x 10‘ 3.0 x 10‘ 3.3 x 10‘ 8.7x 105

3 - 6.7x 106 1.5x107 9.2x 10‘5 n.d.

+ 5.0 x 106 7.4 x 106 6.3 x 106 n.d.

 

3Weeks of incubation in liquid media

bPresence (+) or absence (-) ofmyo-inositol amendment at planting.

°Sampling time point in weeks (wk) post-germination.

Table 6.1 : The median number of culture forming units (CFUs) from rhizospheres

of Medicago sativa.

122

Increases conelated with myo—inositol amendment are highlighted in bold. cultures grown
on MA media, where myo-inositol was provided as sole-carbon source, one week after germination.
The numbers of fast growing (0.5 week incubation) and slow growing (3 week incubation) Mic+
bacteria from the inositol-amended rhizospheres were higher in both growth chamber and ﬁeld
studies. Similar increases were noted for a ﬁeld test involving myoinositol amendment to plots
planted to soybean (data not shown). In contrast, only the fast growing fraction of bacteria cultured
on 0.1x TSB increased following myo-inositol amendment in the ﬁeld, and no such increase was
observed in the growth chamber.

Since the abundance of culturable bacteria increased in response to the nutrient amendment,
we hypothesized that the diversity of these populations were altered as well. To determine if myo-
inositol amendment changed the composition of the cultured fraction of bacteria at one week post-
gennination, samples containing the most abundant, fast growing, culturable bacteria were analyzed
using ARDRA and FT-ARDRA. In the growth chamber experiments, distinct changes in both types
of proﬁles were associated with myo—inositol amendment in both the soil and rhizosphere samples,
indicating that these cultures are qualitatively different to some degree (we Figures 6.2 and 6.3). The
changes were most obvious in the soil samples, where the myo—inositol amendment consistently
resulted in the appearance of several major bands in the digests of the MA cultures. Notably, there
was an increase in the number of times that the S. meliloti-like signals (occurring at ~400 bp in the
Mspl proﬁles and ~830 bp in the Rsal proﬁles) were observed in the MA cultures of samples that had
been amended with myo-inositol (see Figure 6.3). In the ﬁeld experiment, the changes in the
ARDRA proﬁles appeared to be somewhat different depending on the treatment, however, no
discernible pattern could be recognized (data not shown). These differences appeared as occasional
novel peaks and quantitative differences in some common peaks in FT-ARDRA proﬁles generated
using Mspl and Rsal (see Figure 6.4). These data indicate that the application of myo-inositol in the
ﬁeld promoted the growth of different culturable bacteria

123

 

 

Msp1

l

l

.1
1:11:11”le l

 

Rsa1

 

Figure 6.2: ARDRA gels displaying RFLPs in the ampliﬁed 16S rRNA sequences
from cultures obtained in the growth chamber experiment. Aliquots of the terminal
dilution cultures in MA media (a) or 0.1x TSB media (b) displaying saturating growth
were used as templates for PCR. Ethidium-stained agarose gels display the MspI and
RsaI restriction digests of ampliﬁed sequences from cultures of four independent samples
take from soil (S) or rhizosphere (R) that had been amended with myo—inositol (+) or an
equal volume of distilled water (-).

Figure 6.3: FT-ARDRA profiles of the terminal MA cultures obtained in the
growth chamber experiment. The size of ﬂuorescent signals is given in base pairs on
the x-axis. Fluorescence intensity is diplayed on the y-axis. Overlaid chromatographic traces
from the four independent samples are displayed for each condition. These proﬁles were
generated using the samples displayed in Figure 6. I (a).

125

 

 

 

 

 

 

m m

 

 

 

hgnn ,.oo

‘1
'11; '~ .
11M“:

lit+

 

 

 

 

 

 

 

Rsa" 50 100 150 20" 250 300 350 400 450 500 550

 

‘V
can use one

 

 

 

 

 

 

 

 

 

 

Figure 6.3

126

Figure 6.4: FT-ARDRA profiles of rhizosphere cultures obtained from field
test I. Aliquots of the terminal MA and 0.1x TSB cultures of amended (+) and unamended
(-) rhizosphere samples were used for PCR ampliﬁcation. Overlaid chromatographic traces
are presented as in F1 gure 2 except that data from eight independent samples are displayed for
each condition.

127

 

 

Msp1 150 ‘00 450 600 700 750

 

Dlll ﬁtii roe
irate;
hit at;

. . 400
watt“: sou

Rsa“ too 150 400 450

Figure 6.4

Impact on bacterial populations recovered in soil and rhizosphere washes

FT-ARDRA was used to directly examine the impact of myo—inositol amendment on bacterial
populations in the soil and rhizosphere without prior culturing. Previously, we reported that the
proﬁles of these "wash" samples differed markedly in a growth chamber experiment (20). In
contrast, the proﬁles of unamended and amended rhizosphere samples from the ﬁeld experiments
were remarkably similar over the entire time-course (see Figure 6.5). Nonetheless, quantitative
changes in several signals were observed repeatedly in both ﬁeld experiments at 1, 2, and 4 weeks
post-inoculation (see Table 6.2). These changes may represent a lasting impact of myo—inositol
amendment on the corresponding rhizosphere bacterial populations.

Since wild-type S. meliloti can utilize myo-inositol as a growth substrate, we hypothesized
that the application of myo-inositol would stimulate the growth and/or activity of these beneficial
bacteria. In the growth chamber experiment, distinct quantitative increases in signals corresponding
to S. meliloti (an ~400 bp Msp1 signal and an ~830 bp RsaI signal) were observed in amended
samples (see Figure 6.6). The ﬂuorescence of the 400 bp MspI signal is signiﬁcantly increased in
soil and rhizosphere samples amended with myo-inositol (P < 0.10 and P < 0.05, respectively). The
~830 bp RsaI signal did not generally appear to be rnlique and unobstructed by nearby signals, so the
quantiﬁcation of the peak area was not possible. Nonetheless, comparison of the amended and
unamended samples indicate a distinct increase the intensity of this signal (see Figure 6.6). In the
ﬁeld experiments, quantitative increases in the S. meliloti-like Msp1 signal were also observed,
particularly at one and two weeks post-germination (see Figure 6.5). In ﬁeld test 11, where a titration
of myo-inositol from 0 to 32 kg per ha was applied, a signiﬁcant positive correlation was found to
exist between the amount of the nutrient amendment and the intensity of the S. meliloti-like Msp1
signal (I = 0.600, P < 0.10). At two weeks post-germination, this conelation was weaker (1: =
0.467, P < 0.15), and it was absent four weeks after germination (1: = 0.067, NS). These data
indicate that the number of ampliﬁable S. meliloti-like 16S rDNA targets had increased in response to

myo—inositol amendment.

129

§o 300 350 goo ?50 poo p50 300 550 §oo §50 900 950 zoo 250 000 850 900

200. i 1 wk -
150‘ i [W
100..l ’

 

 

 

 

zooq .’ 1 wk 4-

 

 

 

 

200. L, 2 Wk '

 

 

2“: 5‘ 2wk+

 

 

 

 

2004 ' 4wk-

 

 

200F— 4wk+

 

 

 

Figure 6.5: FT-ARDRA profiles of rhizosphere washes generated with Mspl. The
profiles of eight independent rhizosphere samples from each condition are overlaid for
each time point (1, 2, and 4 weeks post-germination) in these results from ﬁeld test I.
Plots were amended with 16 kg/ha of myo-inositol (+) or an equal volume of water (-).

130

 

ﬂuorescent signals (bp) showing a change in intensity

 

—C

enzyme digest increase ainconsistentchagnge

Msp1 400, 404, 487 75, 96, 141, 150
495,505, 545, 153,300,345,
635 400, 404, 408,
424, 440, 465,
475, 500, 518
615, 800, 820

RsaI 476, 479 121, 173, 308, 119
. 312, 317, 427,
456, 464, 470,
485, 490, 494,
610, 640, 830
840, 860, 880

 

aBandsizeathatchangeinonlyonefieldexperimentandonlyatonetimepointareincludedhere,alongwith
thosethatwerenotobservedtobeaffectedbytheamendmeminbothﬁeldexperimems.

Table 6.2: Changes in FT-ARDRA proﬁles of rhizosphere washes following myo-
inositol amendment in the ﬁeld. Theﬂuorescent peakareasof eight independent samples from
each treatment were compared at 1, 2, and 4 weeks post-germination in two different ﬁeld

experiments. Major signal peaks are highlighted in bold.

Impact on nodulation of Medicago sativa

The impact of myo-inositol amendment on nodulation of M. sativa was also investigated. In
both ﬁeld tests, nodulation was signiﬁcantly increased (see Table 6.3). The observed increases were
between 12 and 36 percent in plots amended with 16 kg of myo—inositol per ha. It is interesting to
note that the observed increases in nodulation were greatest for uninoculated seed, generally greater
than ﬁve times the increase observed for plants grown from seed preinoculated with S. meliloti. In
ﬁeld test 11, the correlation between nodulation (nodules/m g rhizosphere) and myo-inositol
amendment (kg/ha) was higher for the uninoculated plots (1 = 0.87, P < 0.01) than the inoculated
plots (1: = 0.47, P < 0.15). In the growth chamber studies this correlation between nodulation and
the nutrient amendment was highly signiﬁcant (1: = 0.91, P < 0.005). These results provide further

evidence for the amendment-induced stimulation of S. meliloti in the rhizosphere of M. sativa.

131

Msp1 Rsa1

4300 I350 I400 I450 750 800 850 900

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

M

Figure 6.6: Increases in S. meliloti-like signals due to myo-inositol amendment in the
growth chamber experiment. Portions of the FT-ARDRA proﬁles of soil (S) and
rhizosphere (R) washes are displayed. Changes in signals corresponding to those
expected for S. meliloti ( MspI 404 bp, Rsal 830 bp) are discussed in the text.

 

 

132

 

ﬁeld test' % changeb 1’c

IA (16) 2.2 NS
IB(16) 14 NS
Ialldata(l6) 12 0.166
11 A (16) 18 NS
II B (16) 99 NS
Hall data(l6) 36 0.074
I] A(32) 12 NS
II B (32) 106 NS
11 all data (32) 30 0.092

 

‘ Field tests are distinguished Med on the use of seed pre-imculated with S. meliloti (A) or uninoculated
seed (B). The number in parentheses refers to the amount of myo-inositol applied (kg/ha).

bClnrngeinthemediannumberofnodules permgofrhizoshpere.

c P-value ohainedfromcomparingamendedandlmmerxiedsamples usingthe Mam-Whitneytest

Table 6.3: Increases in nodulation of M. sativa following In yo-inositol amendment
in the ﬁeld.

Discussion

In our laboratory, we have been examining the potential of using certain compounds to
promote the growth of speciﬁc bacterial populations able to utilize them in the soil and rhizosphere
environments (31, 32, 33). In the course of these investigations, we discovered that glucosamine
amendment signiﬁcantly increased the number of culturable glucosamine-utilizing bacteria in the ﬁeld
(see Chapter 3). While this amendment increased the number of inoculant bacteria in situ, a more
signiﬁcant and longer lasting affect was noted for the indigenous soil bacteria (see Chapter 3).
Furthermore, analysis of the colonies cultured on glucosamine-containing medium indicated that a
single morphotype was enriched in situ over the course of two and a half weeks after the amendment
was applied (see Chapter 3). These observations led us to hypothesize that a nutrient amendment
may be found which could speciﬁcally increase the numbers and activities of beneﬁcial soil

microorganisms when applied at planting.

133

We undertook this study to examine whether myo-inositol could serve as a nutritional
mediator promoting the growth and activities of the nitrogen-ﬁxing symbiont of Medicago sativa, S.
meliloti. Myo-inositol was chosen for several reasons. First of all, members of the Rhizobiaceae,
including S. meliloti, can utilize it as a carbon source for growth in culture (16, 26, 40). Since a
relatively small number of other soil and rhizosphere bacteria living in the tested soil can utilize the
compound as sole-carbon source in culture (19), we expected that a greater proportion of the
nutritional beneﬁt would be directed to the target populations of S. meliloti with this compound.
Also, large quantities of pure myo-inositol can be obtained commercially, making amendment
experiments feasible. Finally, a near-isogenic mutant unable to catabolize this compound was
available for doing competition studies (10).

There is some evidence that myo-inositol may act as a nutritional mediator of rhizobial
activity in nature. Inositol substantially accumulates in the nodules of several legume species;
however, little is known about its role in the nitrogenﬁxing symbiosis (8, 17, 38, 41, 42). The
compound is structurally similar to 3-O-methyl-scyllo-inosamine, a compound implicated in
promoting competitiveness of some S. meliloti strains for nodule occupancy (14, 21). While the
catabolic pathway for myo-inositol is inducible, it does not seem to be active in the nodule (29, 30).
Thus, if the accumulated inositol is not metabolized while the nodule is functioning, it may play a role
in maintaining rhizobia after nodule senescence.

In this work, we have shown that myo-inositol amendment can promote the growth of
culturable Mic+ bacteria in the rhizosphere of Medicago sativa. Under gnotobiotic conditions, myo-
inositol amendment was found to promote the growth and activity of strain 1021, a wild-type Mic+ S.
meliloti, over IM59, its near-isogenic Mic competitor, in the rhizosphere of Medicago sativa (see
Figure 6.1). In the absence of the amendment, no difference in the ability of the wild-type and
mutant to colonize the rhizosphere was observed; the ratio of wild-type to mutant remained
approximately 1:1. In contrast, the proportion of Mic+ bacteria was signiﬁcantly greater in the
presence of the amendment; the ratio of Mict to Mic strains was approximately 2:1 for the ﬁrst week

134

of the experiment and approximately 3:1 after two weeks. Thus, the amendment contributed to a
proportional increase of Mic+ over Mic bacteria in the rhizosphere.

Because wild-type S. meliloti can utilize myo-inositol as a growth substrate, we
hypothesized that this nutrient amendment would result in a measurable increase in the number and
activity of indigenous populations of this organism. FT-ARDRA was used to monitor changes in S.
meliloti populations in soil and rhizosphere washes and cultures inoculated from them. In the growth
chamber experiments, signals indicative of s. meliloti increased signiﬁcantly following the addition
of myo-inositol (see Figure 6.6). These S. meliloti-like signals were also more abundant in MA
cultures of myo-inositol amended samples (see Figures 6.3 and 6.4). In the ﬁeld experiments,
increases in the S. meliloti-like signals of the Msp1 proﬁles were observed up to four weeks after
germination. Additionally, increases in the amplitude of this «400 bp signal correlated with increases
in the concentration of the applied myo-inositol amendment in ﬁeld test 11. These increases may be
due to an increase in the number of S. meliloti, their chromosome copy number, or a reﬂection of
increased susceptibility to lysis and ampliﬁcation; all indications of a larger and/or more active
population. We also observed an increase in nodulation (see Table 6.3) due to myo-inositol
amendment. This increase in nodulation was signiﬁcantly correlated with the amount of myo-inositol
applied in the ﬁeld These data indicate that the mutualistic symbiosis of M. sativa and S. meliloti is
promoted by myo—inositol amendment.

This work describes some intriguing correlations between the application of a nutrient
amendment, subsequent increases in a beneﬁcial rhizosphere microorganism, and a stimulation of a
beneﬁcial symbiosis. Unfortunately, we cannot causally relate the increases in nodulation with the
apparent increases in S. meliloti, nor can we conclusively state that the nutrient amendment directly
stimulated the growth and activity of S. meliloti in the natural environment. This is partly because
other bacteria respond to the amendment and may alter the relationships between the symbiotic
partners (see Table 6.2). Thus, it may be that the observed changes in the rhizosphere bacterial
communities indirectly stimulate one or both of the symbionts so as to produce the observed results.

The complexity of microbial communities greatly complicates the search for mechanistic

135

understanding of below-ground ecological relationships. However, by examining these systems with
a variety of complementary tests, we may slowly accumulate a preponderance of evidence supporting

the mechanistic explanation provided by the biased rhizosphere hypothesis.

Acknowledgements _

We would like to thank Brian Glaf and Joe Paling of the MSU. Crop and Soil Sciences
Department for their assistance in establishing and maintaining the ﬁeld plots and many helpful
discussions. This work was supported by a STAR graduate fellowship granted by the US
Environmental Protection Agency to B.M.G.

References

l Bohlool B (1988) Rhizobium technology: application for international agricultural
development in the tropics. pp. 759-764 In Nitrogen ﬁxation: hundred years after. Proceedings of
the 7th International Congress on nitrogen ﬁxation. Bothe H, de Bruijn FJ, Newton WE eds. Gustav
Fischer. New York.

2 Bosworth AR, Williams MK, Albrecht KA, Kwiatokowski R, Benyon J,
Hankinson TR, Ronson CW, Cannon F, Wacek TJ, Triplett EW (1994) Alfalfa yield
response to inoculation with recombinant strains of Rhizobium meliloti with an extra copy of dctABC
and/or modified nifA expression. Appl. Environ. Microbiol 6 0: 3815-3832.

3 Brown CM, Dilworth MJ (1975) Ammonia assimilation by Rhizobium cultures and
bacteroids. J. Gen. Microbiol. 86: 39-48.

4 Colbert SF, Hendson M, Ferri M, Schroth MN (1993) Enhanced growth and
activity of biocontrol bacterium genetically engineered to utilize salicylate. Apple. Environ. Microb.
59: 2071-2076.

5 Colbert SF, Isakeit T, Ferri M, Weinhold AR, Hendson M, Schroth MN
(1993) Use of an exotic carbon source to selectively increase metabolic activity and growth of
Pseudomonasputida in soil. Apple. Environ. Microb. 59: 2056-2063.

6 Colbert SF, Schroth MN, Weinhold AR, Hendson M (1993) Enhancement of
population densities of Pseudomonas putida PpG7 in agricultural ecosystems by selective feeding
with the carbon source salicylate. Apple. Environ. Microb. 59: 2064-2070.

7 Fisher LD, Belle G van (1993) Biostatistics: a methodology for the health sciences. J.
Wiley and Sons: New York.

8 Fougere F, Le Rudulier D, Streeter JG (1991) Effects of salt stress on amino acid,

organic acid, and carbohydrate composition of roots, bacteroids, and cytosol of alfalfa (Medicago
sativa L) Plant Physiol. 96:1228-1236.

136

9 Fouilleux G, Revellin C, Hartmann, A, Catroux G (1996) Increase of
Bradyrhizobium japonicum numbers in soils and enhance nodulation of soybean (Glycine max (L)
merr.) using granular inoculants amended with nutrients. FEMS Microb. Ecol. 2 0: 173-183.

10 Galbraith MP, Feng SF, Borneman J, Triplett EW, de Bruijn FJ, Rossbach
SR (1998) A functional myo—inositol catabolism pathway is essential for rhizopine catabolism by
Sinorhizobium meliloti. Microbiology submitted.

1 1 Gardner FP, Pearce RB, Mitchell RL (1985) Biological nitrogen ﬁxation. pp. 132-
155 In Physiology of crop plants. Iowa State Univ. Press: Ames.

1 2 Germida JJ (1988) Growth of indigenous Rhizobium leguminosarum and Rhizobium
meliloti in soils amended with organic nutrients. Appl. Environ. Microb. 5 4: 257-263.

13 Glass DJ (1995) Biotic effects of soil microbial amendments. pp. 251-303 In Soil
amendments. Rechcigl 1 ed. CRC Press: Boca Raton.

1 4 Gordon DM, Ryder MH, Heinrich KB, Murphy PJ (1996) An experimental test of
the rhizopine concept in Rhizobium meliloti. Appl. Environ. Microbiol. 6 2: 3991-3996.

1 5 Guyon P, Petit A, Tempe J, and Dessaux Y (1993) Transformed plants producing
poines specifically promote growth of pine-degrading Agrobacteria. MPMI 6: 92-98.

1 6 Jordan DC (1984) Family 111: Rhizobiaceae. pp. 235-254 In Bergey's manual of systemati
bacteriology. Tansill Bed. Williams&Wilkings: Baltimore.

17 Lafontaine PJ, Lafreniere C, Chaifour FP, Dion P, Antoun H (1989)
Carbohydrate and organic acid composition of effective and ineffective root nodules of Phaseolus
vulgaris. Physiologia. Plantarum 7 6: 507-513.

18 Liu W, Marsh TL, Cheng H, Forney LJ (1997) Characterization of microbial
diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S
rRNA. Appl. Environ. Microbiol. 63: 4516-4522.

19 McSpadden Gardener B, de Bruijn FJ (1998) Detection and isolation of novel
rhizopine-catabolizing bacteria present in the environment. Appl. Environ. Microbiol. in preparation

20 McSpadden Gardener B, de Bruijn FJ (1998) Examination of soil and rhizosphere
bacterial communities using a modiﬁed T—RFLP analysis of ampliﬁed 16S rDNA. Appl. Environ.
Microbiol. inpreparation

21 Murphy PJ, Saint CP (1992) Rhizopines in the legume-Rhizobium symbiosis. pp. 377-
21330. In Molecular signals in plant—microbe communication. DPS Verma ed. CRC Press: Boca
ton.

22 Newbould P (1983) The application of inoculation in agriculture. In Temperate legumes:
physiology, genetics, and nodulation. Jones DG, Davies DR eds. Pitrnan Advanced Publishing
Program: Boston. 442 pp. pp. 417-442.

23 O'Connell KP, Goodman RM, Handelsman J (1996) Engineering the rhizosphere:
expressing a bias. Trend. Biotech. 1 4: 83-88.

137

24 Oger P (1995) Etudes sur la possibilite de favoriser speciﬁquement la croissance de
bacteries de la rhizosphere-Le cas des plantes transgeniques productrices d'opines (Ph.D. Thesis).
Universite de Paris sud Centre D'Orsay.

25 Oger P, Petit A, Dessaux Y (1997) Genetically engineered plants producing opines alter
their biological environment. Nature Biotechnology 15: 369-372.

26 Padmanabhan S, 11th RD, Broughton WJ (1990) Rhizobia in tropical legumes:
cultural characteristics of Bradyrhizobium and Rhizobium sp. Soil Biol. Biochem. 2 2: 23-28.

‘27 Pena-Cabriales J], Alexander M (1983) Growth of Rhizobium in soil amended with
organic matter. Soil Sci. Soc. Amer. J. 47: 241 -245.

28 Peoples MB, Craswell ET (1992) Biological nitrogen ﬁxation: investments,
expectations and actual contributios to agriculture. Plant Soil 1 4 1: 13-39.

29 Poole PS, Blyth A, Reid CJ, Walters K (1994) Myo-inositol catabolism and
catabolite regulation in Rhizobium leguminosarum bv. viciae. Microbiology 140: 2787-2795.

30 Reibach PH, Streeter JG (1984) Evaluation of active versus passive uptake of
metaboliztes by Rhizobium japonicum bacteroids. J. Bacteriol. l 59: 47-52.

3 1 Rossbach S, Kulpa DA, Rossbach U, do Bruijn FJ (1994) Molecularand genetic
characterization of the rhizopine catabolism (mocABRC) genes of Rhizobium meliloti 1.5-30. Mol.
Gen. Genet. 245: 11-24.

32 Rossbach S, Rasul G, Schneider M, Eardly B, de Bruijn FJ (1995) Structural
and functional conservation of the rhizopine catabolism (moc) locus is limited to selcted Rhizobium
meliloti strains and unrelated to their geographical origin. Molec. Plant-Microbe Interact. 8: 549-559.

33 Rossbach SR, McSpadden B, Kulpa D, de Bruijn F J (1994) Rhizopine synthesis
and catabolism genes for the creation of "biased rhizospheres" and as marker system to detect
(genetically modiﬁed) microorganisms in the 'soil. pp. 223-249 In Biotechnology Risk Assessment:
Proceedings of the biotechnology risk assessment symposium, June 22-24, 1994, College Park,
MD. Levin M, Grim C, Angle JS, eds.

34 Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual.
Cold Spring Harbor Laboratory: Cold Spring Harbor, NY.

35 Savka MA, Farrand SK (1997) Modiﬁcation of rhizobacterial populations by
engineering bacterium utilizations of a novel plant-produced resource. Nature Biotechnology 15:
363-368.

36 Scupham AJ, Bosworth AG, Ellis WR, Wacek TJ, Albrecht KA, Triplett EW
(1996) Inoculation with Sinorhizobium meliloti RMBPC-2 increases alfalfa yiled compared with
inoculation with a nonengineered wild-type strain. Appl. Environ. Microbiol. 6 2: 4260-4262.

37 Singleton PW, Tavares JW (1986) Inoculation response of legumes in relation to the
number and effectiveness of indigenous Rhizobium populations. Appl. Environ. Microbiol. 5 1:
1013-1018.

38 Skot L, Egsgaard H (1984) Identiﬁcation of ononitol and O-methyl-scyllo—inositol in
pear root nodules. Planta 1 6 1: 32-36.

138

39 Stotzky G (1997) Soil as an environment for microbial life. pp. 1-20 In Modern Soil
Microbiology. van Elsas JD, Trevors JT, Wellington EMH eds. Marcel Dekker: New York.

40 Stowers MD (1985) Carbon metabolism in Rhizobium species. Annu. Rev. Microbiol. 3 9:
89-108.

41 Streeter JG (1980) Carbohydrates in soybean nodules. Plant Physiol. 6 6: 471 -476.

42 Streeter JG (1987) Carbohydrate, organix acid, and amino acid composition of bacteroids
and cytosol from soybean nodules. Plant Physiol 85: 768-773.

43 Streeter JG (1994) Failure of inoculant rhizobia to overcome the dominance of indigenous
strains for nodule formation. Can. J. Microbiol. 40: 513-522.

44 Thies J E Singleton PW, Bohlool BB (1991) Modeling symbiotic performance of
introduced rhizobia 1n the ﬁeld by use of indices of indigenous population size and nitrogen status of
soil. Appl. Environ. Microb. 57: 29-37.

.J

45 Viteri SE, Schmidt EL (1987) Ecology of indigenous soil rhizobia: response of
Bradyrhizobium japonicum to readily available substrates. Appl. Environ. Microbiol. 53:1872-
1875.

46 Wilson M (1998) Auburn University, Auburn, AL, personal communication.

139

Appendix ‘
Analysis of Microbial Field Release Data

McSpadden Gardener B, de Bruijn FJ (1997) Analysis of Microbial Field release data.
Chapter 7.X.X. In Molecular Microbial Ecology Manual. Kluwer: Amesterdam. submitted

140

Introduction

Recent advances in microbial ecology promise to contribute signiﬁcantly to the
development of many useful technologies, such as bioremediation of polluted sites and
microbe-mediated plant growth promotion. These technologies often depend on the use of
large scale releases of microbes into the environment. Because of this, much interest has
been generated over the safety and efﬁcacy of such releases. To properly assess the impact
of ﬁeld releases, precise quantitative descriptions of microbial populations, biomass, and
activity are required.

Numerous methods have been developed to measure ecologically relevant
quantities, many of which are described in this volume and elsewhere (e.g. 26). Each has
certain strengths and weaknesses with regards to addressing the broader questions of safety
and efﬁcacy of microbial inoculants. Because of the complex nature of the ﬁeld
environment, these critical questions require the use of complementary methods and
suitable statistical analyses to draw valid conclusions from ﬁeld studies.

In this chapter we describe an experimental approach that will provide a basic
statistical assessment of microbial ﬁeld release data. Because of the diversity of potential
inoculants, it is impossible to list which sets of methods are most appropriate for all
applications. Therefore, we will provide an empirical framework that is independent of the
nature of the microbial inoculum or its intended application. The prerequisites for and
presentation of basic statistical analyses will be described. Because of the nature of the data
obtained from the ﬁeld release of microbes, nonparametric statistical methods will be
detailed. At the end of the chapter, we will present examples to assist the reader in
understanding the types of questions that can be easily addressed with each of the described

methods. An appendix containing all of the necessary statistical tables is also included.

141

Overview (Figure A.1)

Thorough methodological and statistical analyses are essential to interpret ﬁeld data.
In the complex external environment, multiple techniques are required to quantify the
relationships between inoculant populations, activity, and environmental impact.
However, all such relationships are confounded by natural variation in the quantities
measured. Characterization of both the inoculant organism and the site of release is
prerequisite to the rational design of an experimental study. Determining the extent of
variation present in an experimental system will indicate the degree of replication required
for statistically signiﬁcant results to be obtained. Because the extent of such variation is
often unknown prior to a ﬁeld release, small scale pilot studies can be effective in reﬁning
the experimental design. The experimental objectives and the structure of the data obtained
will determine the extent of sampling and the type of statistical analysis required.

Inoculant chw'acterizmion

The qualitative characteristics of the microbe’s physiology and ecology should be
relatively well understood prior to large scale ﬁeld releases (24). The identity of the
organism should be reasonably stable with regards to the assay used to monitor its presence
in the environment. Some attention must also be paid to the relative strengths and
weaknesses of various molecular markers used to track microbes in the environment (8).
The survival characteristicsof the organism need to be investigated (e.g. whether or not it
forms resting spores). Combined, this qualitative knowledge will allow for more accurate

quantiﬁcation of inoculant abundance and activity.

Site characterization
To assess the environmental impact of an inoculant, a site must be characterized
prior to inoculation. A variety of standardized tests can be used to describe a site (23).

Some of these tests are listed in Table A.1. While it may not be possible to perform all of

142

 

Cieﬁnition of question <

l

ghamggg'zaiign gt system
inoculant

target
environment

1

(small scale ﬁeld experimentation)

l

error variation / noise
phenomenological variation / signal, response

1

. no
QM. emergng

yes {

Wan

focus question
balance resources

1

C large-scale ﬁeld experimentation )

i

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure A.1: General methodology for microbial ﬁeld release experiments.

143

Table A.l: Information used in site descriptions.

 

General description
Location and topography
Climatic data for the experimental period
Vegetation
Management history

Soil composition
Structure (sand: silt: clay)
Soil moisture and pH
Cation exchange capacity
Soil organic matter
Nutrient status (concentrations of N, P, K, and micronutrients)

Study-speciﬁc daa
Biota relevant to expected impact of inoculant, e.g. pathogen abrmdance
Chemical constituents relevant to inoculant activity, e . g. organic pollutants

 

these tests, more complete characterizations will allow for more thorough comparisons with
other studies.

Proper sampling of the test site is required to determine the extent of variation of the
study area. Several studies have attempted to describe the appropriate scale for studying
various microbiological processes (e.g. 11, 17, 20). However, the extent of sampling
required will depend on the scale of the ﬁeld study and the degree of heterogeneity at the
site. Consequently, sample size and sampling intensity must be established empirically for

each application.

Preliminary assessment of datavariation

The sources of data variation should be identiﬁed and characterized in preliminary
control experiments prior to performing a full scale ﬁeld study. Variation can arise from
many sources, natural and methodological. The degree of natural variation can be measured
by performing controlled replicates of homogeneous, independent units. For any given

method, perform control experiments to determine the effects of sample handling and

144

processing time on data variation. This information is needed to describe the quantitative
precision of the method and to determine a practical sample size. A measure of dispersion,
such as the standard deviation, can be used to describe the degree of variation present in the
data.

For any given experiment, the reproducibility of data is dependent on the degree of
natural variation, the precision of the quantitative method, and the number of times the
method is applied. Imprecise measurement or inadequate replication will lead to
irreproducible results and a bias against detecting differences in the system being studied.
Alternatively, excessive precision or replication will lead to the detection of differences
which are of questionable importance. The challenge for researchers is to identify
phenomenologically signiﬁcant patterns. Statistics, if properly applied, can help

characterize those patterns.

Experimental design considerations

Once preliminary studies have been completed, a careful review of the data is
necessary. The assumptions for performing statistical analyses should be tested. In
particular, the sampling scheme should be random with regard to the measured variable.
Evidence of systematic bias will appear in a test of randomness on the data. One may
characterize the distribution of the measured variable to determine the type of statistical
test(s) that can be properly applied. Because the distribution may change over time and
space, differences should be viewed cautiously. Care should be taken not to extrapolate
beyond the scope of the tested experimental conditions. Finally, the time, labor, and
material expense required to expand the study should guide the reﬁnement of the scientiﬁc

questions that can be fruitfully addressed given the constraints of the experimental system.

145

Statistical analysis of the data

Statistical analysis will complement the experimental approaches used by
summarizing the numerical relationships present in the data. Consideration of which
statistics will be used in the data analysis will help determine the experimental design. A
core set of tests useful for making comparisons and describing functional relationships is
detailed below. Readers familiar with parametric statistics (e.g. Student’s t and Pearson’s r)
might wonder why these cormnon statistical tools are not described. The reason is that
nonparametric statistics will generally offer greater versatility when dealing with microbial
ﬁeld release data.

The patchiness of the ﬁeld environment, both spatially and temporally, is likely to
give rise to complex distributions of measured variables. Additionally, the apparent
distributions of microbial counts and activity (or any other variable) will depend on the
scale at which they are sampled (3,19). With such complexities, statistics dependent on
well-deﬁned distribution curves are unlikely to be applicable in as many situations as
analogous nonparametric statistics which have no such assumption. Since microbial
numbers and activity do not remain constant over time (e.g. I), researchers must be
cautious in making any assumptions regarding the underlying distributions of sample data.

Because nonparametric methods are not always taught in introductory statistics
courses, a brief introduction follows. More detailed discussion of nonparametric statistical
tests can be found elsewhere( 3, 15, 21, 25).

Nonparametric statistics

Statistics summarize sample data by reducing the information into a single number.
Measures of central tendency (e.g. mean, median) and dispersion (e.g. standard deviation,
variance) calculated from the sample data are used to describe the populations from which
they are obtained. However, to make quantitative comparisons between data sets or to

describe functional relationships between measured variables, statistical tests are required.

146

The nature of the underlying distributions will determine the type of statistical tests
needed to analyze the data. In this regard, nonparametric tests and their parametric analogs
differ. Nonparametric tests do not require the underlying distribution of data values to be
precisely deﬁned. In contrast, parametric tests assume that the distribution is known and
can be described mathematically. In general, the assumption is that the data follow a
“normal” (i.e. Gaussian) distribution, or that they can be made to conform to this
distribution by transformation of the raw data. If parametric tests are applied to data sets
whose distributions are not normal, then their validity is undermined. While several
reports have indicated that microbial counts and their biochemical functions can be
described by a log-normal distribution (5, 13, I6), exceptions to this pattern have been
noted in the literatu1e(10, 11). Therefore, one cannot assrnne a priori that ﬁeld data from
microbial releases will conform to a deﬁnable distribution. While one may assess the
distribution of an experimental variable, such a study requires a relatively large data set
(e. g. a 30 independent samples) which may not be practical. In general, nonparametric
statistics should be used if the distribution of the sampled variable is not, or cannot be,
ascertained with reasonable certainty.

Additionally, nonparametric statistics have other useful properties. For instance,
they can be used to analyze ordinal data. This type of data differs from interval data (e.g.
microbial counts, rates of nitrogen ﬁxation) in that the units do not follow a uniformly
divisible unit scale. As a practical example, data relating to disease severity induced by
plant pathogens is commonly reported on ordinal scales. These ratings often progress from
low (few symptoms) to high (plant death) on an arbitrarily deﬁned scale. Such data can
best be analyzed by nonparametric tests. Nonparametric statistical tests are also easy to
perform, since they do not require sophisticated calculation. While most statistical
packages will perform a basic battery of nonparametric tests, none perform all of the
methods described here. Computer spreadsheet programs that can order and sum may
speed up some of the initial data handling but are not essential. In this regard, most

147

graphing programs have such options and so will nicely complement the requirements for
data presentation discussed below.

The properties of nonparametric statistics make them uniquely suited to analyzing
ﬁeld data of microbial releases. In this chapter a core set of these statistical tools will be
described. Detailed examples of each method will be provided as a model for better

understanding the types of research questions to which these methods can be applied.

Presentation of results

Because statistics condense data into more easily understood bits of information, it
is critical that the mathematical tools used be fully described. The sample size, number of
replicates, test performed, and level of signiﬁcance of the test statistic form the backbone of
any statistical analysis; they should be presented together for clarity. In graphical
presentations, error bars should indicate a speciﬁed conﬁdence interval for the plotted
points. All of these quantities are deﬁned in the next section. Often, presentation of the raw
data can be a valuable aid in the judicious evaluation of results; however, this may be
impractical if a study is particularly large. The goals of clarity and completeness must be
balanced with, but not subservient to, conciseness.

The quantitative characteristics of data analyzed using statistics may be confounded
by several variables. Sometimes it is relatively straightforward to control for these
differences. For instance, in the case of immunofluorescence assays, cross-reactivity to
non-target cells can be tested in uninoculated controls. However, other potential variables
may not be easily identiﬁed. For example, the amount of antigen binding to inoculum cells
recovered may change because of differential adaptation to the soil, treatment, and/or
sampling procedure. Therefore, the assumption that such potential confounding variables
are of little consequence must either be directly tested or clearly stated in the discussion of

the results.

148

Procedures
Getting started

The following tests are presented in their most basic format and require the use of
the statistical tables included as appendices at the end of this chapter. Althought there are a
number of ways to organize the calculations of the test statistics, the methodology
described below is designed for a manual or computer-assisted approach.

The basic procedure for all statistical tests begins with the statement of a hypothesis
to be tested. This null hypothesis is generally that there are no quantiﬁable differences
between sample populations, though, other null hypotheses can be speciﬁed (see below
(4) Quantifying differences). The raw data for a given empirical test (i.e. the measured
variable) are then tabulated for processing. Generally, nonparametric statistical procedures
require that each raw data value be ranked with respect to all others in the data to be
analyzed. Ranks proceed from 1 (lowest value) to N (highest value) and are tabulated in an
adjacent column. Equal sample values are assigned a corresponding average rank value.
The sum of the ranks for each treatment group is then used in a formula speciﬁc to each
test. The calculated test statistic is then compared to one of the tables included at the end of
this chapter to determine the probability, P, that the test statistic would be as extreme as the
calculated value if the null hypothesis were true. The P value is equivalent to the level of
signiﬁcance, or, which corresponds to the probability of unjustly disproving the null
hypothesis. It is this value that one uses in making statements about the statistical
signiﬁcance of a particular result. Note that failure to disprove the null hypothesis is not
equivalent to proof of it. A relatively high P value simply indicates that there is insufficient

evidence to disprove the null hypothesis.

149

The algebraic conventions used in the procedures described below are as follows: -

Ho{ } = the null hypothesis being tested
H,{ } = the alternative hypothesis accepted if there is a sufﬁciently low P value
for the calculated test statistic
x 2 sample value
n 2 sample size for an individual sample population
k
N = ns , the total sample size
r-l
k = the number of sample populations, i .e. qualitatively different treatments
or conditions
7(x) = the rank of a sample value
R,- _ E r,(x) , the rank sum for an individual sample population
r-l
P = probability that the data support the null hypothesis (equivalent to (1)
df = degrees of freedom with which the test statistic may be calculated

Most of the statistical tests described below assume that the measured variable is
randomly sampled. Failure to validate this assumption can cause bias in the conclusions
drawn from the statistical analysis. Validation is generally inherent in the experimental
design. Hence, the experimental setup may involve random assignment of treatments to a
ﬁeld, or conversely, random sampling within the ﬁeld. , Tests of the randomness
assumption (e.g. a runs test, see 25) are generally not reported in the literature, but they

should be performed when the data sets are sufﬁciently large (e.g. n _>_ 12).

Summary statistics

Complex data sets are often simpliﬁed by summarizing many observations with
statistics that describe the central tendency and dispersion of the data (see Table A2).
Measures of central tendency are the most commonly reported. However, such numbers
are not particularly meaningful without a corresponding measure of dispersion (e.g. the
standard deviation). Generally speaking, the most informative descriptions are made using
the mean or median and their corresponding conﬁdence intervals. A conﬁdence interval
describes the range of values within which the central measure of the population from

which the sample was drawn truly exists, given a speciﬁed probability. Sample means

150

Table A.2: Commonly used summary statistics of sample populations.

 

Central tendency of the data

Mean: f-(Sx‘ﬂn

Median: for odd 11 Value of ranked observation numbered: (n+1) / 2
for even n The mean of the ranked values numbered: n / 2 and (n+2) / 2

Dispersion of the data
Variance shim—row (n—l)
r-l
Standard deviation 5 - J?
Coefﬁcient of variation Y, - s/ f

 

with nonoverlapping conﬁdence intervals can, at ﬁrst glance, be considered signiﬁcantly
different. However, one should note that sample means with overlapping conﬁdence
intervals may still be signiﬁcantly different by the criteria of comparison tests such as those
described in the next section. The mean is perhaps the most commonly used measure of
central tendency; however, it is sensitive to outlying values. Nevertheless, a relatively
conservative nonparametric conﬁdence interval for the mean can be calculated. The median
is a better descriptor of data that come from unknown distributions, but it is only useful if

the sample size is sufﬁciently large (e.g. n 2. 7).

(l) Describing the conﬁdence interval (CI) for:

a) A sample mean (using Chebyshev’s inequality (3)):
i) Specify a P value (generally 0.05 or 0.1).
ii) Calculate: CIM - f a: (w)( s) where l/w2 = P

b) A sample median (using the sign test( 15)):
i) List the sample values of a single experimental treatrnentin column 1 .
ii) Rank the samples values in column 2.
iii) Go to Table A3. Find the value,V, approximating a chosen P

151

(e.g. 0.05 or 0.1) and n of the sample.
iv) The CImd,an is the range described by the sample values with 10:) = (V)
or l(x) = (n-V) .
Making Comparisons

Because the conﬁdence intervals described above are large for unknown
distributions, they are not likely to detect signiﬁcant differences between data sets with
small sample sizes. In such instances, statistical tests using ranked data are much more
powerful. The following procedures allow comparisons to be made between data sets
without relying on summary statistics such as the mean or median. They test for
differences in location on the scale of the measured variable. Following this section, a
method to quantitate such differences between sample populations will be presented.

One must choose a procedure that is speciﬁc to the number of conditions tested and
the independence of the sample measurements. Dependent measurements from the same
experimental unit must be analyzed differently than measurements taken from replicate
independent units. The investigator decides what constitutes an independent sample unit
(e.g. a single leaf, multiple leaves from a single plant, or multiple leaves from multiple

plants).

(1) Comparing two sample populations based on:

a) Independent observations (using the W (14)): Samples sizes may be
different, although the test is somewhat more powerful when the two samples sizes are
approximately equal for any given N. Ho{the two populations are similar}, H,{the two
populations differ}.

i) List values for the two samples, A and B, in two different columns.
ii) Considering the entire data set, rank the data from low to high. If two or more
values are the same, assign an average rank value to all equivalent values.

iii) Sum the ranks for each group: 2r, - RA and Zr, - R, . One can verify the

1
math with the following identity: RA + RB = (nA + nBXnA + n3 + 1) l2
iv) Calculate: U, = R, - [nA(nA+l) I 2] and U3 =R3 - [n3(n3+l)/2]
v) Identify U which is the greater of U A and U3.

vi) Compare U to Table AA to determine its P value.
vii) If for the given sample sizes U is _<_ the tabled value, then reject H0.

152

b) Dependent (paired) observations from the same eXperimental units (using the Wilco com-

W (27)): H°{the two populations are similar}, H {the two populations
differ}.

i) List sample values by group in two different columns.
ii) Tabulate the row by row differences of the two columns in column 3 .
iii) Rank the absolute value of each difference in column 4.
iv) Sign the ranks in column 5, following the designations in column 3.
v) Sum the ranks of the (+) and (-) differences respectively:
We) 't -)

Rm and Er 'Ru
t-l

One can verify the math with the following identity: Rm + RM 2 (n)(n + 1)/2
v) Tis the lesser of R(+ and Rw
vi) Compare T to Table A5 to determine rts P value.
vii) If T rs _<_ the tabled value using the given value of n, then reject Ho.

(2) Comparing three or more sample populations, based on:

a) Independent observations (using the MW (12)): This test performs well
even for small samples (N z 9), but special tables are required for determining the level of
signiﬁcance. These can be found in many statistical texts but are too lengthy to include
here. However, the test statistic can be fairly evaluated using the 35’ table presented in the
appendix, whenever N > 20 and n.- _>_ 4 for all samples. H°{all k populations are similar},
H1 {one or more populations differ}.

i) List all data values in a k (columns) by n (rows) matrix.
ii) Considering the entire data set, rank the data from low to high. If two or more
values are the same, assign an average rank value to all equivalent values.

m) Calculate H-{lZ/[N(N+l)]2(R, /n,)}— -3(N+l)

iv) Compare H to TableA. 6 using (k- 1) df to deterrnineits P value.
v) If H 1s > the tabled value, then rejectHo.

To subsequently identify which sample population(s) are signiﬁcantly different, perform
W (2) for multiple pair-wise comparisons as follows:

i) Calculate the mean ranks for each group: R - R, / n,
ii) For each comparison, calculatezz = IE - ii, I / [N(N+1)(l/n,- +1/n,-)/ 12] “2
iii) Read the one-sided P value for the calculatedz using Table A.7. Call it P1 .
iv) Calculate: P = P,[k(k -1)]
v) Optional: Tabulate all the P values in a k by (k —1) matrix. Highlight signiﬁcant
differences (i.e. low P values).

b) Dependent (blocked) observations from the same experimental units (using W5
get (4)): As for the H statistic, special tables are required for determining the precise level
of signiﬁcance for Friedman’s S statistic. In the absence of more lengthy tables, the test
statistic can be reasonably assessed using the 36 table in the appendix when k z 4. Ho{all k
populations are similar}, H,{one or more populations differ}. The formulas here assume
equal sample sizes (i .e. all n are equal).

1) List all data values in a k (columns) by n (rows) matrix.

153

ii) Considering the data row by row, rank the data from low to high. If two or more
values in the same row have the same value, assign an average rank value to all
equivalent values.

iii) Calculate: s- -{12/[nk(k+1)12m 3n(k+l)

iv) Compare S to TableA. 6 with (k -1) df to determineits P value.
v) If S rs > the critical value rejectHo.

To subsequently identify which sample population(s) are signiﬁcantly different, perform
W (2) for multiple pair-wise comparisons as follows:

i) Calculate the rank sum, R, , for each group.

ii) For each comparison, calculate. z = l R- R l/ [nk(k+l) /6] “2

iii) Read the one-sided P value for the calculatedz using Table A .7 Call rt P,.

iv) Calculate: P: P, [k(k— 1)]

v) Optional: Tabulate all the P values in a k by (k-l) matrix. Highlight signiﬁcant
differences (i.e. low P values).

Note: Multiple pair-wise comparisons should be performed only if a signiﬁcant difference
between sample populations has been detected using the H or S statistic.

Quantifying diﬁerences

The identiﬁcation of population differences and the calculation of the statistical
signiﬁcance of those differences is relatively straightforward. However, it is most often
desirable to quantify those differences. One nonparametric method has been described by
Hodges and Lehmann (6), but it requires numerous calculations and specialized tables.
Another involves modifying the null hypothesis in terms of some quantitative factors. Both
methods provide good, though not necessarily identical, estimates of quantitative

differences between treatments.

(1) To calculate the factor by which two sample groups differ:

i) Calculate the sample means for each k (sample population).
ii) Estimate the factor, C1, by which two treatments differ signiﬁcantly. As a ﬁrst
best estimate use the ratio coefﬁcient: C, - (0.75)( SE, ISEB)where 3:" > i, .
iii) Multiply the raw data for the treatment with a lower mean value by C1.
iv) Rerun the nonparametric statistical test appropriate to the data set (U, T, H, or S).
v)Repeat ii) through iv) iteratively to ﬁnd the highest coefﬁcient that still yields a
statistically signiﬁcant result (e.g. with a chosen P 5 0.10).
vi) Estimate the factor, C2, by which the treatments differ signiﬁcantly. As a ﬁrst
best estimate use the ratio coefﬁcient: C2 - (0.75X1't’A I353) where EF,, > if, .
vii) Multiply the raw data for the treatment with a upper mean value by (l / C 2) .
viii) Rerun the nonparametric statistical test appropriate to the data set as in step iv).

154

ix)Repeat vi) through viii) iteratively to ﬁnd the highest coefﬁcient that still yields a
statistically signiﬁcant result.
x) The estimated ratio of the two population means lies between C1 and C2,

Note: In cases where three or more samples are compared, rigorous estimates( i.e. with an
easily deﬁned P value) can only be made when treatment groups separate into two distinct
classes as determined by Dunn’s multiple range test. In such instances, the data from all
samples not considered signiﬁcantly different should be multiplied by the factors C1 and C 2
when required in steps iii) and vii).

Describing ﬁmctional relationships

A central goal of ﬁeld studies is to establish whether or not the application of an
inoculant is related to a particular measured response. While correlation does not establish
causality, it can provide the basis for establishing a mechanistic explanation for the
phenomena being investigated. To determine the relationship between variables, the
formulas for two rank-based correlation coefﬁcients are presented below along with a
graphical method for describing the monotonic relationship in a data set. In a monotonic
relationship, the values of two measured variables (e.g. cell counts and disease resistance)
will generally increase or decrease together. The values of the correlation coefﬁcients
described below will range from -1 to 1, where higher absolute values indicate stronger

relationships between the measured variables.

(1) Monotonic correlation:

a) For discrete data, use W (9) as follows: H0{the two variables are not
correlated}, H,{the two variables are correlated}. Note that the correlation coefﬁcient 1 =
{[4K / n (n -1)] - 1} and thatTable A8 is given in terms of K.

i) List the data values for the ﬁrst variable (x) from low to high in Column 1.
ii) List the corresponding values for the second variable (y) in Column 2.

iii) Place the rank of each y value next to it in Column 3. The next two calculations
are made from the rankings in this column.

iv) Calculate Q, by taking each ranking in turn and counting how many values listed
below it have a higher ranking. Thus, Q, equals the number of values in rows 2
through n which have a higher value than the ranking in row 1, plus the number of
values in rows 3 through n which have a higher value than the ranking in row 2,
plus ........ plus 1 if the value in row n is higher value than the ranking in
row (n - 1).

v) In a similar manner, calculate Q2 by taking each ranking in turn and counting
how many values listed below it have a lower ranking.

155

vi) Considering the raw data, calculate: N, - (t, — l)/ 2

where t is the number of tied observations for eachx or y value, m, that has more
than one observation.
vii) Calculate: K = [2|(Q, - Q2)l + n(n -l)] /4 + N ,
viii) Compare this value to Table A8 to determine its P value.
ix) If K 2 the tabled value, then reject Ho.
x) If K is signiﬁcant, calculate 1:.

b) For data from continuous distributions, use W (22) as follows:
Ho{the two variables are not correlated}, H, {the two variables are correlated}.

i) For the ﬁrst variable, tabulate the x values in column 1, and r(x) in column 2.

ii) For the second variable, tabulate the corresponding y values in column 3 and the
r(y) in column 4.

v) Calculate the differences: (1,- = r(x,-) - r(y,-) and tabulate in column 5.

vi) Calculate: r, - 1 -[(6 .. df)/(n3 -n)]

vi) Compare this value to Table A9 to determine its P value.
vii) If r, _>_ the tabled value, then reject H0.

(2) Monotonic regression:

3) Graph the observed monotonic relationship between variables (using the method of m
W (7)): This procedure results in a jagged regression line on a scatter plot of the
raw data. This line can be used as a starting point to describe the mathematical relationship
(e.g. linear, logarithmic, discontinuous) between two variables. If the ﬁrst variable, x, is
controlled by the investigator, one needs only to plot a single regression line. If neither
variable is controlled, the procedure should be repeated, switching the designation of the
variables, to give both regression lines.

i) In a table, listx, r(x), y, and r(y) in four separate columns.
ii) Graph the points of r(y) versus y. Connect all data points sequentially.
iii) Calculate Spearman’s correlation coefﬁcient, r, (see above).
iv) Calculate and tabulate in Column 5 the eXpected rank of each y value using the
equation: E[r(y)] = r, r(x) + (n +1)(1 - r,)l2
v) Using ii) and iv), calculatean expected value of y , EU), for each rank value of 1:.
Place these values in Column 6.
vi) Plot E(y) versus x and connect the points.
vii) On the same graph, plot y versus 1: for a visual reference.

Examples
The following examples use hypothetical data to illustrate the proper application of
statistical tests to various research topics of current interest to microbial ecologists. They

are provided solely as a training guide to the statistical procedures described above.

156

Making comparisons
(1) Comparing two groups of independent measurements:

A bacterial suspension is inoculated into two qualitatively different ﬁeld soils. Soil
A is a loamy sand and soil B is a silt loam. Approximately ten independent soil cores of
equal size are collected randomly from each ﬁeld. The cores are placed in plastic bags,
stored on ice, and brought to the laboratory for analysis. The inoculated bacteria can be
selectively enumerated by virtue of a fluorescence-based assay. The following data

represent direct counts of bacteria obtained from the same dilution (10'3 per gram).

 

Soil A: 148, 98, 123, 45, 80, 96, 32, 165, 117, 130
Soil B: 186, 208, 95, 152, 48, 174, 214, 230, 215

 

Because the two soils are qualitatively different, the data are compared using the
Mann-Whitney U statistics. Here, n = 10 and n9 = 9, giving R, = 71 and R, = 119.
Using the procedure outlined above U A = 16 and U3 2 74. Using the latter value, the level
of signiﬁcance is found in Table AA to equal 0.02. In other words, the probability that the
population size of the inoculant in the two soils is the same given the above data is quite
small. The observed ratio of the mean counts is 1.6, but this overestimates the ratio to
which we can subscribe a ﬁrm P value. By iteratively rerunning the analysis using the
method described above, the counts were found to differ by a factor of about 1.25 at the
chosen level of signiﬁcance (or = 0.10). Qualitatively, the number of cells detected differed
in the two soils (P 5 0.02). Quantitatively, the counts of the inoculum in soil B were about
25 percent higher than in soil A (P 5, 0.10). The underlying assumptions are that the

population distributions, sampling, and assay conditions are the same.

(2) Comparing two groups of dependent/paired measurements:
A fungicide is applied to legume seed and researchers are interested in examining its
effects on nodulation. While the viable counts of rhizobia recovered from seed treated with

fungicide do not differ from that of the control, data is lacking on the impact on nodulation.

157

Therefore an experiment is set up using ten plants each of seven different cultivars. These
seven cultivars represent distinct genetic lines selected for growth in different geographic
locations. The seeds are sown into fourteen separate ﬂats and grown in the greenhouse.
After six weeks, the plants are uprooted and brought to the lab. From each set of plants,
one hundred nodules are independently removed, surface sterilized, crushed, and plated on
YMA agar with and without antibiotics selective for the inoculant. After incubation, plates

are scored for growth. The number of nodules occupied by the inoculant rhizobia are

 

 

 

presented below.
cultivar: l 2 3 4 5 6 7
- fungicide: 98 98 98 97 93 90 95
+ fungicide: 96 98 64 94 97 93 94

 

The two treatments are compared using the Wilcoxon signed rank test because the
data are likely to be cultivardependent. For these data, RM = 10.5 and RH = 17.5. Using
the lesser of these, the test statistic, T, is compared to Table A.5. The value is not
signiﬁcant (P >> 0.10) so the null hypothesis cannot be rejected. Thus, there is
insufﬁcient evidence that the application of the fungicide signiﬁcantly affects nodule
occupancy of this species by the inoculant. Nonetheless, nodulation of cultivar3 appears to
be greatly affected by the fungicide treatment, and further investigation of this observation

is warranted.

(3) Comparing three or more groups of independent observations:

Four different bacterial strains are compared for their colonization of the roots of a
single cultivar of wheat. lnoculants are mixed into the soil of four separate pots _
immediately prior to sowing the seeds. Three seeds are sown in each pot, and after two
weeks seedlings are thinned to one plant per pot. After four weeks, plants are uprooted

and rhizosphere samples are prepared by sonication of whole root systems in 50 mls of

158

distilled water. This wash is then serially diluted and spot plated onto selective media. The

colony forming units (CFU) of the inoculant were counted after three days of incubation.

 

 

inoculant: A B T: D
plantl 86 147 225 222
plant 2 52 128 192 203
plant3 38 77 184 157
plant4 24 33 147 53

 

Here there are four qualitatively different treatments (i.e. k = 4). Because each
plant can be considered an independent unit, the Kruskal-Wallis procedure is appropriate.
The rank sums (R A = 15, R,3 = 25.5, RC = 50.5, RD = 45) suggest that there are differences
between treatment groups. More formally, one calculates H = 9.1 which is signiﬁcant, (P
< 0.05). This means that one or more of the inoculant strains differs from the others in
rhizosphere abundance. To discover which strains differ signiﬁcantly, Dunn’s multiple
comparison procedure is performed. Strain A is found to differ from strain C (P 5 0.05)
and, arguably, strain D (P g 0.16). If one performs multiple comparisons using Dunn's
procedure qﬁer a signiﬁcant H value has been established for a given data set, one can
reasonably acceptP values up to 0.20. No other combination of strains was found to differ
signiﬁcantly. Because strain B does not differ signiﬁcantly from any other strain, no
rigorous estimate of the quantitative differences can be made using the method given above.

At this point, we run into the problem of deﬁning the experiment. If strain B is
dropped from the analysis, a quantitative estimate of population differences can be made.
However, one must be careful in making such post hoc decisions, because they are bound
to have repercussions on the estimated P values of any calculated statistic. In the spirit of
exploratory data analysis, the practice is ﬁne as a starting point when the treatments are
qualitatively different, since the patterns are suggestive of signiﬁcant differences.
However, if the treatments are qualitatively the same (e.g. different concentrations of cells
of a single strain), one cannot honestly omit treatments if the research questions focuses on

the purely quantitative differences of the treatments. To reiterate the recommendation

159

above, all experimental comparisons should be repeated independently to verify the
observed patterns. The calculated test statistics for each replicate should be reported with
their corresponding levels of signiﬁcance.

Multiple comparison tests should only be performed aﬂer a signiﬁcant H (or S)
statistic has been established for a given data set. For example, if one were to remove
strain A from the analysis above, no signiﬁcant differences would be detected using the
procedure of Kruskall and Wallis. Despite this, Dunn's procedure would still detect
differences in the counts of strains B and C (P g 0.12). This latter result, however, would
not be acceptable since the original null hypothesis (that the counts of strains B, C, and D
are equal) could not be rejected.

(4) Comparison of three or more groups of dependent/blocked observations:

In preparing for commercialization of a new product, a company is attempting to
determine which formulation will give the most consistent yield increases. Ten sites,
spread throughout the company’s market area, are tested. At each site, four treatments are
used: three formulations and one uninoculated control. Two of the formulations contain a
single strain (A or B), while the third contains both strains. Before the exact yield tonnage
was determined, an ordering of lowest (1) to highest (4) yield of the four treatments at each

of the sites was made.

 

 

 

formulation: A B ASLB no inoculant

1 3.? i 33 1
2 3 1.5 4 1.5
3 4 2 3 1
4 2 4 3 1
5 3 l 4 2
6 3 2 4 1
7 3.5 2 3.5 1
8 3 2 4 l
9 4 1 3 2
10 3 _5 1.5 3.5 1.5

A, 32.? 19 33.5 E

 

160

Since the yields are likely to be dependent on the site at which the plants are grown,
Friedman’s test is the appropriate choice. As the yields are already ranked on a per site
basis, no further ordering is required. The rank sums are included in the above table for
reference. Calculation of the test statistic shows that S = 20.8 which has, when compared
to Table A6, a corresponding P < 0.001. Dunn’s procedure is used to identify which of
the treatments differ. Formulation A is signiﬁcantly different from B (P _<_ 0.12) and the
control (P 5 0.005), but it is not signiﬁcantly different from the mixed A&B formulation.
The mixed formulation is also different from B (P g 0.026) and the uninoculated control (P
_<_ 0.001 ). Formulation B is not signiﬁcantly different from the control. Formulations A
and A&B give consistently higher yields across the entire market area.

The decisions to be made based on these data depend on the marketing strategy and
production costs. Without doubt, Formulation A&B is the most effective product.
However, the differences in A and A&B may not warrant the extra costs of producing the
mixed formulation. To make such a decision, the precise yield data (e.g. in tons per
hectare) must be evaluated. The interval scale data may also be useful in determining if
there are signiﬁcant site to site yield variations (as assumed above). To perform such an
analysis, the raw data would have to be ranked by column instead of by row, with k =10

andn =4.

Describing ﬁmctional relationships
( 1) Monotonic conelation:

The relationship between the phyllosphere population size of a biological control
strain and observed disease resistance is investigated. Population sizes were determined by
colony counts coming from washes of healthy leaf material harvested late in the season
from different plants in a single ﬁeld. Each plant was scored for disease control on an
ordinal scale of 1 (no protection / most severe symptoms) to 5 (greatest protection / no

disease symptoms).

161

 

CFU er disease control CFU .2515 disease control

 

 

. e l 4.2e4 4
3.8e5 l 2.3e6 4
7.4e4 2 7.4e6 4
2.3e4 2 9.5e5 5
3.7e6 2 2.5e6 5
9.2e5 3 3.8e6 5
4.2e5 3 3.9e6 5
l .4e5 4 4.2e6 5

 

Since the disease control scale is discrete, the use of the K statistic based on
Kendall’s correlation coefﬁcientis the appropriate method. The value of Q, = 77 and Q2 =
22. There are no tied values in the CPU data, but the disease control data has numerous
ties, and N , = 5.5. Using these values, it can be shown that K = 93. By comparing this
value to Table A8, the correlation was found to be highly signiﬁcant (P < 0.005). The
corresponding coefficient r = 0.55, a value which indicates a moderate monotonic
correlation. Thus, these data strongly suggest that a positive functional relationship exists
between inoculant population size and disease control. However, that relationship is most
likely confounded by other variables, a conclusion arising from the moderate coefﬁcient

value (1 < 0.7).

2) and 3) Monotonic regression:

A researcher wishes to investigate the effect of nutrient addition on biodegradation
of a pollutant in a contaminated soil. The nutrient solution and inoculant strain are mixed
into soil microcosms containing 200 PPM of the pollutant. After two weeks of incubation
the remaining pollutant is extracted from the soil and quantiﬁed. The addition of the
nutrient solution alone indicated that no degradation of the pollutant occurred in absence of
the inoculant (data not shown). The experimental data for microcosms inoclulated with a

standard concentration of inoculant(10" cells per gram of soil) are tabulated below.

162

 

PPM nutrient PPM pollutant E(y)

 

1 195 199
2 184 194
5 196 189
7 198 187
10 167 177
20 140 162
so 115 138
70 181 115
100 64 105
200 104 71
500 52 59
700 14 54
1000 57 40
2000 20 26
3000 37 18

 

A scatter plot of the data shows a ”noisy” downward trend, with the data becoming
highly variable in the region where most of the decrease occurs (see Figure A.2). It would
be improper to deﬁne the relationship a priori, because no single relationship seems to
describe the entire data set. Since the relationship between nutrient amendment and
degradation may not be constant over the range of applied nutrient concentrations, a
monotonic regression procedure would be useful in identifying a range of values within
which particular relationships (e.g. linear, logarithmic) exist. Thus, the procedure of [man
and Conover is an exploratory tool used to identify and describe monotonic patterns in
data. In this instance, Spearman's correlation coefﬁcient is rs = - 0.91, which is highly
signiﬁcant (P < 0.01). This indicates that there is, in fact, a strong negative trend in the
data. Using this rs value and a plot of ﬂy) versus y, the expected y values are calculated
and tabulated next to the observed values (see above). These values are then plotted against
the nutrient concentration to generate the regression line in Figure A2. The curve appears
multiphasic over the range of nutrient concentrations tested, but it is observed to be roughly
linear above 5 PPM of added nutrient. This result could be used to justify a range of values

that should be examined in farther investigations.

163

 

 

 

PPM pollutant

220

200

180

160

140

120

100

80

60

40

20

O raw data

0 regression

 

10 100 1000
PPM nutrient

10000

 

Figure A.2: Example monotonic regression curve.

164

 

Statistical Tables

The following tables were assembled following those presented in Biostatistics: A
Methodology for the Health Sciences by L.D. Fisher and G. van Belle (3). More
comprehensive tables for the tests described in this chapter can be found in other statistical

texts (e.g. 25) or as separate volumes (e.g. 18).

165

Table A.3: Binomial tail probabilities. A P value is chosen to specify the

conﬁdence interval for the median of a sample population using the binomial sign test.

 

 

 

n v P n v P n v P
2 6 5.2% 8 3 0.3633 iii—(T717666? 13 me
1 >>>> 4 >>>> 1 0.0032 14 0.0001
2 0.2500 5 0.3633 2 0.0193
6 0.1445 3 0.0730 15 0 0.0000
3 0 0.1250 7 0.0352 4 0.1938 1 0.0005
1 >>>> 8 0.0039 5 0.3872 2 0.0037
2 >>>> 6 >>>> 3 0.0176
3 0.1250 9 0 0.0020 7 0.3872 4 0.0592
1 0.0195 8 0.1938 5 0.1509
4 0 0.0625 2 0.0898 9 0.0730 6 0.3036
1 0.3125 3 0.2539 10 0.0193 7 >>>>
2 >>>> 4 >>>> 11 0.0032 8 >>>>
3 0.3125 5 >>>> 12 0.0002 9 0.3036
4 0.0625 6 0.2539 . 10 0.1509
7 0.0898 13 0 0.0001 11 0.0592
5 0 0.0313 8 0.0195 1 0.0017 12 0.0176
1 0.1875 9 0.0020 2 0.0112 13 0.0037
2 >>>> 3 0.0461 14 0.0005
3 >>>> 10 0 0.0010 4 0.1334 15 0.0000
4 0. 1875 1 0.0107 5 0.2905
5 0.0313 2 0.0547 6 >>>> 16 0 0.0000
3 0.1719 7 >>>> 1 0.0003
6 0 0.0156 4 0.3770 8 0.2905 2 0.0021
1 0.1094 5 >>>> 9 0.1334 3 0.0106
2 0.3438 6 0.3770 10 0.0461 4 0.0384
3 >>>> 7 0.1719 11 0.0112 5 0.1051
4 0.3438 8 0.0547 12 0.0017 6 0.2272
5 0.1094 9 0.0107 13 0.0001 7 0.4018
6 0.0156 10 0.0010 8 >>>>
l4 0 0. 0001 9 0.4018
7 0 0.0078 11 0 0.0005 1 0.0009 10 0.2272
1 0.0625 1 0.0059 2 0.0065 11 0.1051
2 0. 2266 2 0.0327 3 0.0287 12 0.0384
3 >>>> 3 0.1133 4 0.0898 13 0.0106
4 >>>> 4 0.2744 5 0. 2120 14 0.0021
5 0.2266 5 >>>> 6 0. 3953 15 0.0003
6 0.0625 6 >>>> 7 >>>> 16 0.0000
7 0.0078 7 0.2744 8 0.3953
8 0.1133 9 0.2120
0 0.0039 9 0.0327 10 0.0898
1 0.0352 10 0.0059 11 0.0287
2 0.1445 11 0.0005 12 0.0065

 

166

Table AA: Critical values for the Mann-Whitney U statistic.

 

P . P
na nb 0.20 0.10 0.05 0.02 0.01 na nb 0.20 0.10 0.05 0.02 0.01
T7 6 16 T? 19 20
3 3 8 9 10 3 24 26 27 29 30
10 4 30 33 35 37 38
4 2 8 10 5 37 39 42 44 46
4 3 ll 12 10 6 43 46 49 52 54
4 4 13 15 16 10 7 49 53 56 59 61
10 8 56 60 63 67 69
5 2 9 10 10 9 62 66 70 74 77
5 3 13 14 15 10 10 68 73 77 81 84
5 4 16 18 19 20
5 5 20 21 23 24 25 1 l 2 19 21 22
1 l 3 26 28 3O 32 33
6 2 1 1 12 ll 4 33 36 38 40 42
6 3 15 16 17 l l 5 40 43 46 48 50
6 4 19 21 22 23 24 ll 6 47 50 53 57 59
6 5 23 25 27 28 29 1 l 7 54 58 61 65 67
6 6 27 29 31 33 34 11 8 61 65 69 73 76
ll 9 68 72 76 81 83
7 2 13 14 1 1 10 74 79 84 88 92
7 3 17 19 20 21 1 1 ll 81 87 91 96 100
7 4 22 24 25 27 28
7 5 27 29 30 32 24 12 2 20 22 23
7 6 31 34 36 38 39 12 3 28 31 32 34 35
7 7 36 38 41 43 45 12 4 36 39 41 43 45
12 S 43 47 49 52 54
8 2 14 15 16 12 6 51 55 58 61 63
8 3 19 21 22 24 12 7 58 63 66 70 72
8 4 25 27 28 30 31 12 8 66 70 74 79 81
8 5 30 32 34 36 38 12 9 73 78 82 87 9O
8 6 35 38 40 42 44 12 10 81 86 91 96 99
8 7 40 43 46 49 50 12 ll 88 94 99 104 108
8 8 45 49 51 55 57 12 12 95 102 107 113 117
9 2 16 17 18 13 2 22 24 25 26
9 3 22 23 25 26 27 13 3 30 33 35 37 38
9 4 27 30 32 33 35 13 4 39 42 44 47 49
9 5 33 36 38 40 42 13 5 47 50 53 56 58
9 6 39 42 44 47 49 13 6 55 59 62 66 68
9 7 45 48 51 54 56 13 7 63 67 71 75 78
9 8 50 54 57 61 63 13 8 71 76 80 84 87
9 9 56 60 64 67 7O 13 9 79 84 89 94 97
13 10 87 93 97 103 106

 

 

 

167

Table A.4 (continued)

 

P P
na ab “6.20 0.10 0.05—W .01 na nb We .1 0.05 o. .1

 

 

13 11 95 161 106 112 116 16 11 115—‘7—‘2'9—‘35—12 1 1 140
13 12 103 109 115 121 125 16 12 125 132 139 146 151
13 13 111 118 124 130 135 16 13 134 143 149 157 163

16 14 144 153 160 168 174

14 10 93 99 104 110 114 17
14 11 102 1% 114 120 124 17 91 97 102 1% 112
14 12 110 117 123 130 134 17 101 1% 114 120 124
l4 13 119 126 132 139 144 17 10 112 119 125 132 136
14 14 127 135 141 149 154 17 11 122 130 136 143 148

17 12 132 140 147 155 160

81 86 91 96 1%

l4 2 23 25 27 28 16 15 154 163 170 179 185
14 3 32 35 37 40 41 16 16 163 173 181 190 196
14 4 41 45 47 50 52
14 5 50 54 57 60 63 17 2 28 31 32 34
14 6 59 63 67 71 73 17 3 39 42 45 47 49
14 7 67 72 76 81 83 17 4 50 53 57 60 62
14 8 76 81 86 90 94 17 5 60 65 68 72 76
14 9 85 90 95 100 104 17 6 71 76 80 84 87
7
8
9

15 2 25 27 29 30 17 13 142 151 158 166 172
15 3 35 38 40 42 43 l7 14 153 161 169 178 184
15 4 44 48 50 53 55 17 15 163 172 180 189 196
15 5 53 57 61 64 67 17 16 173 1% 191 201 207
15 6 63 67 71 75 78 17 17 183 193 202 212 219
15 7 72 77 81 86 89
15 8 81 87 91 96 100 18 2 20 32 34 36
15 9 90 96 101 107 111 18 3 41 45 47 50 52
15 10 99 106 111 117 121 18 4 52 56 60 63 66
15 11 1% 115 121 128 132 18 5 63 68 72 76 79
15 12 117 125 131 138 143 18 6 74 80 84 89 92
15 13 127 134 141 148 153 18 7 85 91 96 102 105
15 14 136 144 151 159 164 18 8 96 103 1% 114 118
15 15 145 153 161 169 174 18 9 107 114 120 126 131
18 10 118 125 132 139 143
16 2 27 29 31 32 18 11 129 137 143 151 156
16 3 37 40 42 45 46 18 12 139 148 155 163 169
16 4 47 50 53 57 59 18 13 150 159 167 175 181
16 5 57 61 65 68 71 18 14 161 170 178 187 194
16 6 67 71 75 80 83 18 15 172 182 190 200 206
16 7 76 82 86 91 94 18 16 182 193 202 212 218
16 8 86 92 97 102 105 18 17 193 204 213 224 231
16 9 96 102 107 113 117 18 18 204 215 225 236 243
16 10 106 112 118 124 129

 

168

Table A.5: Critical values for the signed rank test using the Wilcoxon T
statistic.

 

 

P P

n 0.10 0.05 0.02 0.01 n 0.10 0.05 0.02 0.01
5 1 30 152 137 120 109
6 2 1 31 163 148 130 118
7 4 2 0 32 175 159 141 128
8 6 4 2 0 33 188 171 151 138
9 8 6 3 2 34 201 1% 162 149
10 11 8 5 3 35 214 195 174 160
11 14 11 7 5 36 228 2% 186 171
12 17 14 10 7 37 242 222 198 1%
13 21 17 13 10 38 256 235 211 195
14 26 21 16 13 39 271 250 224 2%
15 30 25 20 16 40 287 264 238 221
16 36 30 24 19 41 303 279 252 234
17 41 35 28 23 42 319 295 267 248
18 47 40 33 28 43 336 311 281 262
19 54 46 38 32 44 353 327 297 277
20 60 52 43 37 45 371 344 313 292
21 68 59 49 43 46 389 361 329 307
22 75 66 56 49 47 4% 379 345 323
23 % 73 62 55 48 427 397 362 339
24 92 81 69 61 49 446 415 380 356
25 101 90 77 68 50 466 434 398 373
26 110 98 85 76

27 120 107 93 84

28 130 117 102 92

29 141 127 111 100

 

169

Table A.6: Critical values for the 3:1 distribution, which approximates the
critical values for the H and S statistics.

 

P
df (I10 0.05 0.025 0.010 0.001

p—s
p—s
'o

Ut-bUJN

ssocoqax

11
12
13
14
15

16
17
18
19
20

21

24

 

4.61
6.25
7.78
9.24

10.64
12.02
13.36
14.58
15.99

17.27
18.55
19.81
21.06
22.31

23.54
24.77
25.99
27.20
28.41

29.62
30.81
32.01
33.20
34.38

5.99

7.82

9.49
l 1.1

12.59
14.07
15.51
16.92
18.31

19.68
21 .03
22.36
23.68
25.00

26.30
27.59
28.87
30.14
31.41

32.67
33.92
35.17
36.42
36.65

 

P
df 0.10 0.05 0.025 0.010 6.1251

 

7.38
9.35
11.14
12.%

14.45
16.01
17.53
19.02
20.48

21.92
23.34
24.74
26.12
27.49

28.85
30.19
31.53
32.85
34.17

35.48
36.78
38%
39.36
40.65

6.63
9.21
1 1.34
13.28
15.09

16.81
18.47
20.09
21.67
23.21

24.72
26.22
27.69
29.14
30.58

32.00
33.41
34.81
36.19
37.57

38.93
40.29
41.64
42.98
44.31

10.%
13.82
16.27
18.47
20.52

22.46
24.32
26.12
27.88
29.59

31.26
32.91
34.53
36.12
37.70

39.25
40.79
42.3 1
43.82
45.3 1

46.80
48.27
49.73
51.18
52.62

27
28
29
30

35
4o
45
50
55

60
65
70
75
80

85
90
95

170

36.74
37.92
39.09
40.26

46.06
51.81
57.61
63.17
68.80

74.40
79.97
85.53
91 .06
96.58

40.1 1
41.34
42.56
43 .77

49.80
55.76
61.66
67.50
73 .31

79.%
84.82
90.53
96.22

43 . 19
44.46
45.72
46.98

53.20
59.34
65.41
71 .42
77.38

%.30
89.18
95.02

46.96
48.28
49.59
50.89

57.34
63.69
69.96
76. 15
82.29

88.38
94.42
100.43

100.84 106.39
101.88 106.63 112.33

102.% 107.52 112.39 118.24
107.57 113.15 118.14 124.12
113.04 118.75 123.86 129.97
100 118.50 124.34 129.56 135.81

26 35.56 38.89 41.92 3.34- 5405

55.45
56.89
58.30
59.70

66.62
73.40
80.%
86.66
93.17

99.61

105.99
112.32
118.60
124.84

131.04
137.21
143.34
149.45

Table A.7: Standard normal distribution tail probabilities, to he used in
Dunn's procedure for multiple pair-wise comparisons.

 

Z

 

 

0. 60
0.65
0.70
0.75
0.80

0.85
0.90
0.95
1.00
1.05

1.10
1.15
1.20
1.25
1.30

1.35
1.40
1.45
1.50
1.55

1.60
1.65
1 .70
1 .75
1 .80

1.85
1.90
1.95
2.00
2.05

 

 

P P
two-sided one-sided z two-sided one-sided
03285—1577. 43 2.10 0.035‘7—'—15"79—0. 1
0.5157 0.2578 2.15 0.0316 0.0158
0.4839 0.2420 2.20 0.0278 0.0139
0.4543 0.2266 2.25 0.0244 0.0122
0.4237 0.2119 2.30 0.0214 0.0107
0.3953 0.1977 2. 35 0.0188 0.0094
0.3681 0.1841 2. 40 0.0164 0.0%2
0.3421 0.171 1 2. 45 0.0143 0.0071
0.3173 0.1587 2. 50 0.0124 0.0062
0.2937 0.1469 2.55 0.01% 0.0054
0.2713 0.1537 2. 60 0.0093 0.0047
0.2501 0.1251 2. 65 0.0%0 0. 0040
0.2301 0.1151 2. 70 0.0069 0. 0035
0.2113 0.1056 2.75 0.0060 0.0030
0. 1936 0.0968 2.80 0.0051 0.0026
0.1770 0.0885 2. 85 0.0044 0.0022
0.1615 0.08% 2. 90 0.0037 0.0019
0.1471 0.0735 2. 95 0.0032 0.0016
0.1336 0.0668 3.00 0.0027 0.0013
0.121 1 0.0606 3 .05 0.0023 0.001 1
0.1096 0.0548 3.10 0.0019 0.0010
0.0989 0.0495 3.15 0.0016 0.00%
0.0891 0.0446 3.20 0.0014 0.0007
0.%01 0.0401 3.25 0.0012 0. 0006
0.0719 0.0359 3.30 0.0010 0. 0005
0.0643 0.0322 3.35 0.00% 0.0004
0.0574 0.0287 3.40 0. 0007 0. 0003
0.0512 0.0256 3.50 0. 0005 0. 0002
0.0455 0.0228 3.60 0. 0003 0.0002
0.0404 0.0202 3 7.0 0. 0002 0.0001

 

171

Table A.8: Critical values for the K statistic based on Kendall's rank
correlation coefficient.

 

 

 

P
11 T20 0.15 15.10 0.05 0.01 door—Ciro. 1
3 3
4 5 6 6 6
5 8 8 9 9 1o
6 1 1 11 12 13 14 15
7 14 15 16 17 18 20 21
8 18 19 20 22 23 25 26
9 23 24 25 27 28 31 33
1o 28 29 31 33 34 37 4o
11 34 35 37 39 41 44 47
12 4o 42 43 46 48 52 55
13 47 49 51 53 56 61 64
14 54 56 58 62 64 69 73
15 62 64 67 7o 73 79 83
16 7o 73 75 79 83 89 94
17 79 82 85 89 93 100 105

18 89 91 95 99 103 111 117
19 99 101 105 110 114 123 129
20 109 112 116 121 126 135 142

21 120 123 127 133 138 148 156
22 132 135 139 146 151 161 170
23 144 147 152 159 164 176 184
24 156 160 165 172 178 190 200
25 169 173 179 I86 193 205 216

172

 

 

 

 

 

 

Table A.9: Critical Table A.9: Critical values for Spearman's rank
correlation coefficient, r .
P P

n 0.10 0.03 0.01 0.10 0.05 0.01
5 0.900 20 0.377 0.450 0.5‘ 70
6 0.829 0.886 21 0.368 0.438 0.556
7 0.714 0.786 0.929 22 0.359 0.428 0.544
8 0.643 0.738 0.881 23 0.351 0.418 0.532
9 0.600 0.700 0.%3 24 0.343 0.409 0.521
10 0.564 0.648 0.794 25 0.336 0.400 0.51 1
1 1 0.536 0.618 0.755 26 0.329 0.392 0.501
12 0.497 0.591 0.727 27 0.323 0.385 0.491
13 0.475 0.566 0.703 28 0.3 17 0.377 0.4%
14 0.457 0.545 0.675 29 0.31 1 0.370 0.475
15 0.441 0.525 0.654 30 0.305 0.364 0.467
16 0.425 0.507 0.635
17 0.412 0.490 0.615
18 0.399 0.476 0.600
19 0.388 0.462 0.584

Acknowledgements

We would like to thank D. Gilliland, D. Ragatz, O. Schabenberger for constructive
criticisms of earlier versions of this manuscript. Thanks are also due to K. Roll Gardener,
J. Rademaker, and K. Szczyglowski for their suggestions for making this manuscript more
accessible to a broader audience. The work presented here was supported by the U.S .
Department of Energy (by grant DE-F602—91 ER20021) and the U.S. Environmental
Protection Agency (through a graduate fellowship to B.B.M.G.).

173

References

l Bahme JB, Schroth MN (1987) Spatial-temporal colonization patterns of a
rhizobacterium on underground organs of potato. PhytOpathology 77: 1093-1100.

2 Drum OJ (1964) Multiple comparisons using rank sums. Technometrics 6: 241-
252.

3 Fisher LD, Belle G van (1993) Biostatistics: a methodology for the health
sciences. J. Wiley and Sons: New York.

4 Friedman M (1937) The use of ranks to avoid the assumption of normality
implicitin the analysis of variance. .1 Am Stat Soc 32: 675-701.

S Hirano SS, Nordheim EV, Arny DC, Upper CD (1982) Lognormal
distribution of epiphytic bacterial populations of leaf surfaces. Appl Environ Microbiol 44:
695-700.

6 Hodges JL Jr, Lehmann EL (1963) Estimates of location based on rank tests.
Ann Math Statist 41 :783-801.

7 Inmu RL, Conover WJ (1979) The use of the rank transform in regression.
Technometrics 21: 499-509.

8 Jansonn JR (1995) Tracking genetically engineered microorganisms in nature.
C urr Opin Biotech 6: 275-2% .

9 Kendall MG (1938) A new measure of rank correlation. Biometrika 30: 81-93.
10 Kessel C van, Pennock DJ, Farrel RE (1993) Seasonal variations in
denitriﬁcation and nitrous oxide evolution at the landscape scale. Soil Sci Soc Am J 57:
988-995.

11 Kinkel LL, Wilson M, Lindow SE (1995) Effect of sampling scale on the
assessment of epiphytic bacterial populations. Microbial Ecol 29: 2%-297 .

12 Kruskal WH, Wallis WA (1952) Use of ranks in a one criterion variance
analysis. J Am Stat Assoc 47:5%-621.

13 Loper J, Suslow TV, Schroth MN (1984) Lognorrnal distribution of bacterial
populations in the rhizoshpere. Phytopathology 74: 1454-1460.

14 Mann HB, Whitney DR (1947) On a test of whether one of two random
variables is stochastically largerthan the other. Ann Math Statist18z50-60.

15 Neave HR, Worthington PL (1988) Distribution-ﬂee Tests. Unwin Hyman:
London. 1988.

16 Parkin TB, Robinson JA (1989) Stochastic models of soil denitriﬁcation. Appl
Environ Microbiol 55: 72-77.

17 Parkin TB, Starr JL, Meisinger JJ (1987) Influence of sample size on
measurement of soil denitriﬁcation. Soil Sci Soc Am J 51: 1492-1501.

174

18 Powell FC (1982) Statistical tables for the social, biological, and physical
sciences. Cambridge U. Press: Cambridge.

19 Robertson GP, Gross KL ( 1994) Assessing the heterogeneity of below ground
resources: quantifying pattern and scale. in Exploitation of Environmental Heterogeneity in
Plants. Academic Press: New York. pp. 237-253.

20 Robertson GP, Crum JR, Ellis BG (1993) The spatial variability of soil
resources following long-terrn disturbance. Oceologia 96: 451 -456.

21 Rosner BA (1990) Fundamentals of Biostatistics 3rd ed. PWS-Kent. Boston.
1990.

22 Spearman C (1940) The proof and measurement of association between two
things. Am J Psychol 15: 72-101.

23 Tan K (1996) Soil sampling, Preparation, and Analysis. Dekker: New York.

24 Tiedje JM, Colwell RK, Grossman YL, Hodson RE, Lenski RE,
Mack RN, Regal PJ (1989) The planned introduction of genetically engineered
organisms: ecological considerations and recommendations. Ecology 70: 298-315.

25 Wall FJ (1986)StatisticalDataAnalysis Handbook. McGraw-Hill: New York.

26 Weaver RW ed. (1994) Methods of Soil Analysis, Part 2. Microbiological and
Biochemical Properties. Soil Science Society of America Book Series No. 5.

27 Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics 1:
80-83.

175

"I11111111111111.1111Es