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This is to certify that the
dissertation entitled

DIVERSITY OF BACTERIA ASSOCIATED WITH THE HOUSE FLY
(DIPT.: MUSCA DOMESTICA L) AND HORIZONTAL GENE
TRANSFER AMONG BACTERIA IN THE HOUSE FLY GUT.

presented by

Michael Theodore Petridis

has been accepted towards fulfillment

 

 

 

 

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PhD. degree in Entomology
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DIVERSITY OF BACTERIA ASSOCIATED WITH THE HOUSE FLY
(DIPT.: MUSCA DOMESTICA L.) AND HORIZONTAL GENE TRANSFER AMONG
BACTERIA IN THE HOUSE FLY GUT
By

Michael Theodore Petridis

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Entomology

2004

ABSTRACT
DIVERSITY OF BACTERIA ASSOCIATED WITH THE HOUSE FLY
(DIPT.: MUSCA DOMESTICA L.) AND HORIZONTAL GENE TRANSFER AMONG
BACTERIA IN THE HOUSE FLY GUT
By

Michael Theodore Petridis

The microbial diversity of the gastrointestinal track in animals is determined by
commensal and symbiotic relationships and interactions between microbes, their animal
hosts, and the external environment. Although the significance of house flies as vectors
of pathogens has been acknowledged, an intensive study of the bacterial flora associated
with the house fly gut environment has heretofore been lacking. Quantifying gut
bacterial biodiversity is of particular interest for an insect vector of pathogens associated
with food home diseases. Furthermore, the significance of the house fly gut as a
potential site for the emergence of new bacterial pathogens through genetic exchange
mechanisms has not been explored.

This study determined that horizontal gene transfer among strains of Escherichia
coli can occur in the fly gut. Plasmid-bom antibiotic resistance genes and bacteriophage-
bom encoded Shiga toxin genes moved horizontally between donor and recipient strains
in this environment. These findings suggest that the house fly gastrointestinal tract is a
favorable environment for the evolution and emergence of new pathogens, when
acquisition of genes contributing to virulence is a component of the process.

The bacterial diversity of the house fly gut was studied by using two culture
independent approaches. A Terminal Fragment Length Polymorphism (T-RF LP)

analysis was used to quantify diversity and compare the microbial community found in

the gut and exoskeleton of house flies. House fly gut harbored a complex microbiota,
whereas the cxoskeleton was less diverse and more variable, reflecting environmental
pressures. There was no evidence fi'om this analysis to justify that use of antibiotics in
dairy farms where flies were sampled had a significant effect on house fly bacterial
community structures in the gut or exterior surface. A comparative analysis involving
construction of bacterial 16S rDNA‘ sequence libraries of the house fly gut and cow fecal
bacterial communities showed that house fly gut community was more diverse (55
genera) than the cow fecal community (27 genera), but forty percent of the clones
classified to genus (16 genera) were common to both communities. The genera
Pseudomonas, Janthinobacterium, Clostridium, and Acinetobacter were especially
common in both communities. Within the house fly gut, several potentially pathogenic
bacteria] groups were evident, including Enterobacter, Enterococcus, Shigella, and
Pantoea. However, both communities were highly uneven with a few dominant taxa and
many uncommon taxa; a richness estimator predicted 36 and 79 genera for the fecal and

house fly gut communities, respectively.

Copyright by
Michael Theodore Petridis
2004

I dedicate this work to Magda, Sylvana, Konstantinos Petridis

and to Sofia Merkouris for her unconstrained love

ACKNOWLEDGEMENTS

I would like to thank and acknowledge the Institution of State Scholarships
(I.K.Y.) in Greece and the Hutson Endowment in the Department of Entomology for
financial support during my studies at Michigan State University, East Lansing, MI,
USA. This dissertation could not been undertaken or completed without the assistance of
the following:

I am indebted to my major advisors Dr. Edward Walker and Dr. Michael
Bagdaserian for their support, encouragement and guidance during my studies and to the
members of my advisory committee, Drs. Michael Kaufinan and Paul Bartlett for
guidance as committee members. I would like to acknowledge Dr. Michael Kron for
allowing me to participate in his research project and introduce me to the world of tRNA
synthetases and Dr. Terry Marsh fer sharing with me his expertise in Microbial Ecology
and genetic fingerprinting.

I thank Mr. Blair Bullard and Mr. William Morgan for their invaluable assistance,
technical support and inventive humor. I extend my special thanks to Eric Poster for all
of those interesting discussions and his altruistic spirit. He was helpful when I needed his
assistance. Many thanks to my colleagues Shahnaz Maknojia and Fred Arnimo for all of
those good times we spend together. I am fortunate that I met my friend Dylo Pemba
during the first two years of my studies at MSU. He is a talented, independent spirit,

innovative fellow with pure philosophical views for everyday issues.

vi

I would like also to thank those people who have surrounded my life Drs. Garry
Stein, Youli Milev and Ms. Sharon Scholley at Department of Medicine, for their

support, interest to my research and friendliness. I offer to you all my heartfelt gratitude.

vii

TABLE OF CONTENTS

Page
LIST OF TABLES ............................................................................................... xii
LIST OF FIGURES ............................................................................................. xiv
CHAPTER I
LITERATURE REVIEW .................................................................................... 1
1. HOUSE FLY BIOLOGY ......................................................................... 1
Ecology ........................................................................................ 2
Nutrition ...................................................................................... 3
Genetic variation ......................................................................... 4
Dispersal ...................................................................................... 5
Temperature ................................................................................ 8
Diapause ...................................................................................... 9
Competition ................................................................................. 10
Predation-Parasitism .................................................................... 10
House flies as vectors of pathogens ............................................ 11
Control measures ............................................................. ~...-...~. ..... 14
House flies and adulteration of food ........................................... 14
Direct effects of house flies in foods ........................................... 15
Allergic reactions ........................................................................ 1.5. .
House fly gut diversity ................................................................ 15
Conclusion ................................................................................... 16
2. APPROACHING MICROBIAL DIVERSITY ......................................... 17
Culture dependent methods ......................................................... 17
Culture independent methods ...................................................... 18
Genetic probing ........................................................................... 18
Genetic fingerprinting ................................................................. 19
Restriction Enzyme Analysis (REA) ........................................... 20
Restriction Fragment Length Polymorphism (RF LP) with
hybridization . ................................................................................ 21
T-RFLP ........................................................................................ 22
Amplified rDNA Restriction Analysis (ARDRA) ...................... 22
PCR-DGGE analysis ................................................................... 23
Ribosomal DNA (rDNA) analysis ............................................... 24
Microarrays ................................................................................. 25
3. PHYLOGENY AND HORIZONTAL GENE TRANSFER (HGT) ........ 27
HGT and Evolution of bacterial genomes ................................... 27

viii

4. ' OBJECT IVES ..........................................................................................
5. REFERENCES ........................................................................................

CHAPTER II
TRANSFER OF SHIGA TOXIN AND ANT [BIOTIC RESISTANSE GENES
AMONG ESCHERICHIA COLI STRAINS IN THE HOUSE FLY GUT ........

1. ABSTRACT .............................................................................................
2. INTRODUCTION ...................................................................................

3. MATERIALS AND METHODS .............................................................
Bacterial strains, plasmids and culture conditions ......................
Confirmation of phage transfer by PCR ......................................
Flies .............................................................................................
Experimental in viva gene transfer ..............................................
Study area and sample collection ................................................
Antibiotic Susceptibility ..............................................................
Detection and isolation of E. coli 0157:H7
in farm-caught flies .....................................................................

4. RESULTS ................................................................................................
Plasmid transfer experiments ......................................................
Detection of antibiotic resistant bacteria
from house fly guts ......................................................................
Escherichia coli transduction in vitro .........................................
Escherichia coli transduction in vivo ..........................................
Detection of E. coli 0157zH7
and related genes from field caught flies ....................................

5. DISCUSSION ..........................................................................................

6. REFERENCES ........................................................................................
CHAPTER III
COMPARATIVE INTERNAL AND EXTERNAL BACTERIAL COMMUNITY
STRUCTURE OF FARM-CAUGHT HOUSE FLIES (MUSCA DOMESTIC/1 L.,
DIPTERA: MUSCIDAE), USING TERMINAL RESTRICTION LENGTH
POLYMORPHISM ANALYSIS ........................................................................

1. ABSTRACT .............................................................................................

2. INTRODUCTION ...................................................................................

3. MATERIALS AND METHODS .............................................................

ix

31

32

43

43

47
47
SO
50
51
52
53
53

55
55

58

60
62

65

71

77
77
78

81

Preparation of house fly samples ................................................ 81

DNA isolation ............................................................................. 81
Optimization of PCR to amplify bacterial 16S rDNA ................ 82
T-RFLP ........................................................................................ 82
Data analysis ............................................................................... 84
4. RESULTS ................ . ............................................................................... 86
Pandemics .................................................................................... 89
Endemics ..................................................................................... 90
Cluster analysis ........................................................................... 91
Similarity index analysis ............................................................. 94
Multinomial analysis ................................................................... 94
T-RFLP Analysis Program (TAP) ............................................... 98
5. DISCUSSION .......................................................................................... 101
Diversity analysis ........................................................................ 101
Electropherograms ...................................................................... 103
Cluster analysis ........................................................................... 104
Similarity index analysis ............................................................. 105
Multinomial analysis ................................................................... 105
T-RFLP Analysis Program (TAP) ............................................... 107
6. REFERENCES ........................................................................................ 109
CHAPTER IV

BACTERIAL COMMUNITY COMPOSITION OF THE HOUSE FLY AND
A COMPARATIVE ANALYSIS WITH COW FECES USING

168 RIBOSOMAL DNA SEQUENCE ANALYSIS ......................................... 115
1. ABSTRACT ............................................................................................. 115
2. INTRODUCTION ................................................................................... 1 17
3. MATERIALS AND METHODS .................................................... 120

Study area and sampling ............................................................. 120
DNA extraction, amplification, cloning, and sequencing ........... 121
Classification and phylogenetic placement of sequences ........... ‘ 122
Nucleotide sequence accession numbers ..................................... 125
4. RESULTS ................................................................................................ 126
' Classification of sequences ......................................................... 126
Comparisons of sequence libraries . ............................................. 130
Phylogenetic analysis .................................................................. 134
Actinobacteria ................................................................. 134
Bacteroidetes, Flavobacteria,
Actinobacteria and Alphaproteobacteria ......................... 139

Betaproteobacteria and gammaproteobacteria ................
Gammaproteobacteria .....................................................
Clostridia ..........................................................................
Clostridia and Bacilli .......................................................
Bacilli .............................................................

Diversity Indices ..........................................................................

Estimat

5. DISCUSSION

eS ......................................................................................

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

Comparison of libraries ................................................................
Diversity indices ...........................................................................
Estimation of Genera ....................................................................

6. REFERENCES

0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

xi

142
146
149
149
153
156
157

161
169
171
172

174

LIST OF TABLES
Table Page

2.] Strains of E. coli, their chromosomal markers,
antibiotic resistance phenotypes, associated plasmids,
and bacteriophages used in experiments reported here ........................... 48

2.2 Recovery results (cfu/ml) of Cm ' , Rif' and Cm ' + Rifr
from the crop and gut sans crop of house flies and their
transfer frequency per donor cell. Each replication was
derived from the homogenization of three crops or '
gut sans crop (values in meaniSE, N= 4) ............................................... 5 7

2.3 In vitro results of the logarithmic transfer frequency
(means 3: SE, N= 3) of the H-19B::Ap1 bacterial phage
using MC4100 H-19B::Ap1 as donor strain and MP001
as the recipient ......................................................................................... 61

2.4 Recovery results in cfu/ml (mean :E SE) of AmpR, RifR
and AmpR-l-RifR from the gut of house flies and their
transfer frequency. Each replication was derived from
the homogenization of ten guts (N= 4, except C1 N= 3).
Transfer of bacteriophage H-19B::Apl between E. coli
strains in the crop and gut of the fly ........................................................ l 63 .

3.1 Number of 5’ terminal restriction fragments (T-RFs)
generated from Hha I or Msp I digestion of 168 rDNA
fi‘om bacteria from the exoskeleton and gut of house flies.
Pandemic fragments by convention are those found in more
than 75% of all electropherograrns. Endemic fiagments are
pandemics found in more than 75% of electropherograms of
one insect structure and less than 25% in the other insect
structure. Sample size is the number of farms
sampled for each category ....................................................................... 88

3.2 Similarity indices for the bacterial communities associated
with house fly exoskeleton and gut, based on T-RF analysis
and Hh'a I and Msp I digestions. Index ranges from 0
(completely dissimilar) to 1 (completely similar) ................................... 96

xii

LIST OF TABLES (Continued)
Table Page

3.3 Chi-square analysis using fragment length and abundance
data generated from Hha I and Msp I digestions.
Values represent chi-square f value, degrees of freedom and p values .. 97

3.4 Nearest match of pandemic 5’ Terminal Restriction
Fragments (5’ T-RFs) generated by capillary electrophoresis
and predicted 5 ’ T-RFs generated with computer simulated
digestion of 16S rDNA sequences in the Ribosomal
~ Database Project H (Maidak et a1. 2001) ............................................... 99

xiii

Figure
2.1

3.1

3.2

4.1

LIST OF FIGURES

A. Number of colony forming units of bacteria

fiom pools of guts of field-caught house flies

plated onto TSA media containing no antibiotics
(CONTROL), or chlorarnphenicol (CLM, 100 ug/ml),
tetracycline (TETR, 25 ug/ml), or streptomycin

(STR, 100 ug/ml). B. The proportion of antibiotic
resistant bacteria recovered from the same house fly guts.
House flies were collected from farms that either practice

the conventional or the organic system ..........................................

Electropherograms of the Hha I digestion of 16S rDNA
from four representative bacterial communities found

in the gut (G) and on the exoskeleton (E) of pooled
house flies, sampled in conventional (C) and organic (O)
farms from Winsconsin. The frequency of 5’ terminal
restriction fragments is represented in the y axis as

relative fluorescence units ..............................................................

Electropherograrns of the Hha I digestion of 16S rDNA
analysis of terminal restriction fragments, frequency
data from Hha I and Msp I digestions of 168 rDNA
sequences of bacteria associated with the house fly gut
or external surfaces. Individual farms and fly samples

were considered as the Operational Taxonomic Units (OTUs).

The dcndrograms were generated with PAUP using the
maximum likelihood method. A — C, Hha I digestions.

D — F, MsP I digestions. OTU abbreviations: E, exoskeleton;

G, gut; C, conventional farm practice; 0, organic farm practice;

H, Hha I; M, Msp 1. Example: ECl 10H was an exoskeleton fly
sample fi'orn conventional farm 110, and the bacterial l6S rDNA ......

Pie chart showing the bacterial community composition
of cow fecal samples at the generic level, as determined
by 16S rDNA sequence classification With RDP 11.
Percentages represent the percentage of the total number

of sequences (N = 501) that were classified to the indicated

taxon. The smaller pie chart shows those sequences
classified to genera each of which made up less than 1%

of the total sequences ......................................................................

xiv

Page

59

87

92

128

Figure
4.2

4.3

4.4

4.5

LIST OF FIGURES (Continued)

Pie chart showing the bacterial community composition

of house fly gut samples at the generic level, as determined
by 16S rDNA sequence classification with RDP 11.
Percentages represent the percentage of the total number
of sequences (N = 704) that were classified to the indicated
taxon. The smaller pie chart shows those sequences
classified to genera each of which made up less than 1%

of the total sequences ..........................................................................

Results of comparison by LIBSHUFF of bacterial

16S rDNA sequence libraries house fly gut (F G) and
from cow fecal (CF) samples fiom dairy farms.
Homologous (open squares) and heterologous

(solid triangles) coverage curves for 16S rRNA gene
sequence libraries are shown. Solid lines indicate values
0f (CFG-CFG/CF)2 (panel A) 01‘ (CCF-CCFIFG)2 (panel B)

for the original samples at each value of evolutionary
distance (D). Broken lines indicate the 950th value

(or p=0.05) of corresponding (CFC-CFG/CF)2 or

(CCPCCp/FG)2 values for the randomized samples ..............................

Homologous (open squares) and heterologous

(solid triangles) coverage curves for 168 rDNA
sequence libraries from dairy farms are shown.

Solid lines indicate the value of (Cx-ny)2 or
(Cy-ny)2 for the original samples at each value

of Evolutionary Distance. Broken lines indicate

the 950‘“ value (or p=0.05) of (Cx-ny)2 or (Cy-ny)2
for the randomized samples. Comparison sequence:

FCvsFO (panels A,B), GCvsGO (panels C,D) . ..................................

Homologous (open squares) and heterologous (solid triangles)
coverage curves for 16S rRNA gene sequence libraries

from dairy farms are shown. Solid lines indicate the value

of (Cx-ny)2 or (Cy-ny)2 for the original samples at each
value of Distance. Broken lines indicate the 950th value

(or p= 0.05) of (Cx-ny)2 for the randomized samples.
Comparison sequence: F OvsGO (panels A,B),

FCvsGC (panels C,D) . ........................................................................

XV

Page

129

132

133

137

Figure
4.6

4.7

4.8

4.9

4.10

LIST OF FIGURES (Continued)

Phylogenetic tree demonstrating relationships within

the Actinobacteria class, as determined by Neighbor

Joining method of 16S rDNA sequences. The Sequence

Associated Information (SAI) was based on 258 valid

columns. The red color bar represents 10% estimated

sequence divergence. Vertical bars depict sequences from

a single source (purple) or multiple sources (green) ............................

Phylogenetic tree demonstrating relationships within

the Bacteroidetes, F lavobacteria, Actinobacteria and
Alphaproteobacteria classes, as determined by Neighbor

Joining method of 16S rDNA sequences. The Sequence

Associated Information (SAI) was based on 275 valid

columns. The red color bar represents 10% estimated

sequence divergence. Vertical bars depict sequences from

a single source (purple) or multiple sources (green) ............................

Phylogenetic tree demonstrating relationships within the
Betaproteobacteria and Gammaproteobacteria classes, as

determined by Neighbor Joining method of 16S rDNA

sequences. The Sequence Associated Information (SAI)

was based on 310 valid columns. The red color bar

represents 10% estimated sequence divergence. Vertical

bars depict sequences from a single source (purple) or

multiple sources (green) .......................................................................

Phylogenetic tree demonstrating relationships within the
Gammaproteobacteria class, as determined by Neighbor

Joining method of 168 rDNA sequences. The Sequence

Associated Information (SAI) was based on 363 valid columns.

The red color bar represents 10% estimated sequence

divergence. Vertical bars depict sequences from

a single source (purple) or multiple sources (green) ............................

Phylogenetic tree demonstrating relationships within
the Clostridia class, as determined by Neighbor Joining

. method of 168 rDNA sequences. The Sequence Associated

Information (SAI) was based on 269 valid columns. The red

color bar represents 10% estimated sequence divergence.

Vertical bars depict sequences from a single source (purple)

or multiple sources (green) ...................................................................

xvi

Page

138

141

145

148

151

LIST OF FIGURES (Continued)
Figure

4.11Phylogenetic tree demonstrating relationships within
the Clostridia and Actinobacteria classes, as determined
by Neighbor Joining method of 16S rDNA sequences.
The Sequence Associated Information (SAI) was based
on 278 valid columns. The red color bar represents 10%
estimated sequence divergence. Vertical bars depict sequences
fiom a single source (purple) or multiple sources (green) ...................

4.12 Phylogenetic tree demonstrating relationships within
the Bacilli class, as determined by Neighbor Joining
method of 16S rDNA sequences. The Sequence
Associated Information (SAI) was based on 285 valid
columns. The red color bar represents 10% estimated
sequence divergence. Vertical bars depict sequences from
a single source (purple) or multiple sources (green) ............................

4.13 A rank abundance plot showing the diversity of
house fly gut (solid triangles) and cow fecal '
(open squares) bacterial communities ..................................................

4.14 Chaol estimates of house fly gut (O) and cow fecal (0)
bacterial genera richness as a function of the sample size.

Bars represent 95% CIs and calculated with the variance
derived by Chao (1987) . .......................................................................

xvii

Page

152

155

158

160

CHAPTER I

LITERATURE REVIEW

HOUSE FLY BIOLOGY

The house fly (Diptera: Musca domestica L.) is one of the most common insects
associated with the humans. The word ‘synanthropy’ was first used by Aristotle to
indicate in a simple way that flies are more abundant in man’s living environment than
they are elsewhere (Herman 1965). They have been considered pests of humans and
livestock since the first signs of modern human civilization and have been the subject of
basic and applied research. Extensive research of insecticide resistance has been done
since 1945 (Kciding 1999). In the last 20 years, we have entered into a new era in
medical entomology that encompasses other research areas such as molecular biology
and microbiology. This new era in entomological progress has revealed significant
aspects of the house fly’s biological role, and initiated a process for further research.

House flies belong to the Class Insecta, order Diptera, family Muscidae (Kciding
1986). This family includes other medically and economically important flies such as the
Musca autumnalis De Geer, Stomoxys calcitrans, Haematobia irritans (L.), Fannia
canicularis and Muscina stabulans. Taxonomic classification at the level of suborder has
been changed from the old Cyclorrhapha (Keiding 1986) to the modern Brachycera
(Borror et a1. 1989) based mainly on genetic evidence (nucleotide and amino acid

sequences). The ratio of width of frons over the width of head was a character for its

taxonomic classification to four subspecies (M. domestica domestica, M. d. vicina, M. d.
nebula, and M. d. curviforceps) (Keiding 1986).

The following is a general review of the biotic and abiotic factors affecting the
distribution and abundance of the house fly. The aim is to discuss ecological adaptations
and constraints including temperature, nutrition, competition, predation and parasitism.
Population dynamics such as genetic variation, competition and dispersal are also

discussed.

Ecology. Abiotic factors such as weather conditions affect populations irrespective of
the density of individuals while the effects of biotic factors such as competition among
individuals for limited resources increase with population density (Samways 1994). The
ecological adaptive properties of house flies present a heterogeneous habitat range; They
can be found anywhere in the world from tropical to temperate climates. Their '
abundance and niche breadth may display their ecological success (Sterelny and Griffiths
1999). House fly eggs are layed in animal manure, human excrement, garbage and
decayed organic matter with high humidity (>90%) and temperature ranging from 13-40
°C (Greenberg 1973, Keiding 1986). Favored sites for oviposition and breeding include
decomposing vegetable and animal matter or manure. Females lay 100-150 eggs in
batches, with the eggs hatching into the first instar larva within 8-12 hours. The larvae
molt through three successive instars and then pupate. The entire life cycle can be
completed in 12 days (Siew 1978). Late third instar larvae stop feeding and move to

cooler and drier places to pupate. The time of development from the egg stage to the

adult is dependent on nutrition, moisture and temperature of the medium (Hedges 1990,

Keiding 1986).

Nutrition. House flies require a diet balanced in carbohydrates, protein and water in
order to develop or reproduce. As previously mentioned, vegetable materials, preferably
enriched with dung or manure compose ideal conditions for breeding house fly larvae
(Oldroyd 1964). The house fly and face fly (Musca autumnalis) are closely related
species and it would be rational to postulate that both species have similar or very similar
ecological niches. Early studies with M. autumnalis support that water content is
important for its survival in animal dropping (Mohr 1943), but later studies showed
water content in experimental droppings did not increased the mortality of face flies

(V aliela 1969). On the basis of indirect evidence, we may claim that moisture content of
larval house fly media may not effect larval survival, but Fay (1939) suggested that under
high temperatures (43-46 °C), moisture can be essential to larval survival. It is believed
that insects occurring in decaying organic matter are feeding on microfauna and
microflora along with the substrate itself (V aliela 1969). Filtering is a general way of
feeding in higher muscoid flies (Dowding 1967) and pupal size is an accurate index of
competition or food shortage (Nicholson 1954). Chemoreceptors are located in tarsi, the
terminal segments of its. legs which allow the house fly to taste. Upon stimulation by
food, the proboscis extends and secretes digestive enzymes to render the food in a liquid
form enabling the house fly absorb to it by using its labella (Siew 1978). House flies are
also able to smell by olfactory receptors located in the antennae. These receptors are

stimulated by airborne particles that direct the house fly to the source of food or breeding

site (Siew 1978). Adult house flies, when provided with an incomplete diet of sugar and

water, had curtailed ovarian development (Sacca and Benetti 1960).

Genetic Variation. Very early the house fly became a species for testing resistance to
insecticides and a subject for the study of genetic variations. High levels of genetic
variation are present in most natural populations of flies. This is thought to be an
evolutionary process to adapt to environmental changes (Samways 1994).

House fly suitability for genetic studies is based upon it’s developmental cycle,
high fertility and ease of handing (Milani 1967). Milani in his work (1967), reports
about the great genetic variability, resulting in morphological diversity. Color patterns,
width of fi‘onts in males and chetae are characters of genetic heterogeneity.
Physiological, behavioral and life history traits can also be expressions of this variation
(Brakefield 1991). Morphological differentiation of house fly populations is a reflection
of genetic differentiation, which under suitable environmental conditions is expressed by
their phenology (Milani 1967). These genetically inherited characters, such as widening
of fronts and darkening of abdomen can change in the course of adaptation to laboratory
conditions.

Geographic differentiation of distinct taxa resulted in the adoption of specific
names (such as Musca domestica domestica, M. domestica vicina, M. domestica nebula,
M. domestica curvifarceps). Human activity sometimes acts as a causal factor for the
breakdown of isolating mechanisms leading to hybridization (Paterson 1956) or
' sometimes leads to fiagmentation making insect populations vulnerable to adverse

enviromnental effects (Samways 1994).

DNA technology has not been implemented to a satisfactory degree for cryptic
polymorphism. Techniques like this can give information related to gene flow between
populations and consequently about fragmentation. Large population size, movement of
individuals between local populations and environmental heterogeneity can minimize
loss of genetic diversity (Brakefield 1991). Information like this can become very useful
tools from an anthropocentric point of view, but further research is required in genetic
monitoring.

Variation in morphogenic traits among localities has an adaptive genetic basis
according to Bryant (1985). Other studies support that environmental rather than genetic
factors govern the size of the adult house fly and morphometric analysis of geographic
variation is irrelevant to the quantification of genotypic adaptation (Black and Krafsur
1985). This means that larval density determines adult size and geographic variations are

confounded by seasonal effects (Black and Krafsur 1985).

Dispersal. For the first half of 20th Century studies of house fly geographic distribution
have been limited to studying fly dispersal. There have not been any integrated studies to
indicate geographical distribution of a common species in households such as the house
fly (Musca domestica L.). Literature up to 1949 has shown an historical interest on this
species; fi'om 1950-1959, there was an emphasis on insecticides,resistance, insecticide
synergism and new groups of insecticides such as organophosphorus and carbamates in
relation to house fly populations (West and Peters 1973). After 1960, research has
focused on complex biochemical and physiological phenomena, integrated control

practices, and flies as vectors of pathogens (West and Peters 1973).

The importance of the house fly as a vector of diseases was responsible for
attempts to determine its dispersal. The house fly’s connection with primitive systems of
sanitation and waste disposal has been studied, while passive transportation with garbage
vehicles and vegetable trucks has been supported (Greenberg 1973). Early studies
(Parker 1916) for different species of flies showed the maximum distance from the
release point that house flies spread is 13 miles in less than 24 hours. Maximum distance
can be achieved with the desire to reach food or shelter (Parker 1916). Wind determined
the direction of fly distribution (Parker 1916) or flies were carried by the wind (Hodge
1913). Olfactory cues might be the stimulus for dispersal, or flies just move in the
direction of air currents. Releases of radioactive adult flies showed that adult flies
orientate to wind-bom odors from farmyards and migrate from one farmstead to another
in sub-optimal weather conditions for flight (Hanec 1956). Dispersal behavior under city
or town conditions may differ from that under open country conditions (Parker 1916),
factors that determine the radius or direction of dispersion are sometimes conflicting.
Hodge (l913).believed that flies travelled with the wind as opposed to Hindle (1914);
Pickens (1967) supported the flight against or across the wind, and Bishopp (1921)
supported both cases. Sanitation and chemical control of breeding sites can also be
limiting factors of fly population dispersal (Pickens et a1. 1967, Bishopp 1921).
Specimens from Nova Scotia at the US. National Museum initially labeled as Musca
autumnalis were later identified as a dark Musca domestica, This indicates the presence
of the house fly in northern USA, but also that M. autumnalis, was an imported species
from Europe since there were not any reported cases of this species in North America

(Sabrosky 1961). The two most common subspecies M. domestica domestica and M. d.

vicina have been reported worldwide (N earctic, Neoqopical, Australian, Palearctic,
Ethiopian, Oriental geographic regions) (West 1951).

Gill, in 1955, reported that house flies were not present among other filth feeding
diptera in Central Alaska. The same author states that similar studies in some cases
reported the presence of the house fly. Vockeroth (1978) finally reported the presence
and economic importance of house flies in the arctic and high arctic of the insect fauna of
Canada. Humans can carry insects to parts of the world where they did not formerly
exist. Domestic cockroaches of temperate North America and Europe arrived fi'om
various parts of tropics and subtropics (Evans 1984). The fiequency of air travel might
have increased the possibilities of developing house flies as serious pests as a
consequence of their arrival without any natural enemies (Evans 1984).

According to Price (1975), all species have the potential to increase their fitness.
In order to succeed it, they use different strategies in the most energy efficient way.
Dispersal permits exchange of genetic material and promotes in the long run better
adjustment to the environment. Favorable conditions promote fly survival, but
intolerable conditions promote dispersal. The house fly’s ability to reproduce in very
large numbers, its opportunistic colonization ability, high developmental rate and high
dispersability designate the house fly as an r-strategist.

The ecological role of chlorinated insecticides has been extensively examined.
According to Price (1975), insecticide application over a long period of time may have
lead to rapid population expansion due to the house fly population responding to severe

mortality by rapidly increasing birth rates. Reduction of natural enemies, competition for

food sources and improved food quality drives the resurgence of non-target organisms

(Price 1975).

Temperature. According to Hoffman and Blows (1994) house flies are not restricted to
geographic ranges due to geographic barriers, only extreme ranges of abiotic (climatic
conditions) factors prevent them from expanding their range even more. In this case, we
are able to associate population dynamics with environmental variables, and then we will
be able to predict future distributions under global climatic changes.

The house fly’s ability to overwinter in every possible stage makes them a highly
versatile insect (Oldroyd 1964). Synanthropic flies inhabiting a specific biotope,
undergo seasonal and daily changes in relation to temperature changes within a season, as
well as a 24 hour period (Sychevskaya 1962). In a two-year study (Schoof and Savage
1955) monitoring fly populations in five states across the USA, differences in
temperature affected house fly abundance by progressively reducing population density
from southwest to northeast. April and May represented a key period for maximum fly
production since lack of moisture at this time is very important for initial house fly
production (Schoof and Savage 1955). Other related species such as Phaenicia sericata
showed that their abundance increased from southwest to northwest states indicating that
lower temperatures are more favorable for its reproduction.

Temperature can be a limiting factor for the house fly like it is in other organisms.
House fly larvae die below —4 °C while eggs can survive at —-8 °C for one hour (V aliela
1969). Fcldman-Muhsan (1944) showed that there is a lower developmental threshold at

about 12 °C while they can hatch up to 43 °C (Melvin 1934). Experimental evidence

(Hafez 1941; Larsen 1943) support the sensitivity of younger larvae to higher
temperatures, with higher tolerance (60 °C) in some tropical subspecies such as M.
domestica carvina (Roubaud 1911). Laboratory constant temperatures may not have the
same results as fluctuating temperatures, or the duration of exposure to a certain
temperature have particular significance (V aliela 1969). Global warming can also be an
issue to take under consideration; It can be a causal reason for habitat expansion and new
niche fulfillment.

Interactions of mortality factors could provide a more realistic and better
understanding of house fly contemporary ecology. Hoffman and Blows (1994) suggested
an approach looking at the geographic limitation factors by using density estimates and
fitness-related traits of marginal and central populations. Supportive evidence suggests
that marginal populations display a progressive decrease or a sudden drop possibly due to
environmental conditions and resource availability, respectively. In this case, reliable

estimates of density are largely dependent on immigration from central population.

Diapause. Depending on the mechanisms initiating diapause, it can be a facultative or
obligatory diapause. In the facultative diapause, extrinsic factors dominate; while in the
obligate diapause intrinsic events dominate regardless of the environmental conditions
(Dethier 1976). Phormia regina (blowfly), a temperate-zone fly, is a representative
species'of true facultative diapause (Calabrese and Stoffolano 1974). Stoffolano (1968)
showed that before Musca autumnalis enters diapause tarsal acceptance, thresholds to
proteins are elevated in correspondence to reduced protein synthesis. This is a common

behavior for insects entering diapause as a mechanism of cold tolerance and survival

(Hall 1967). The arrival of spring and rise in temperatures depletes energy stores,

acceptance thresholds are lowered and flies are attracted again to protein (Dethier 1976).

Competition. An important aspect related to house fly survival is competition among
individuals within a species and between species in determining the fitness of individuals
in populations. In the case of M. autumnalis, lower densities increased mortality which
might indicate that a critical minimal density is required for liquification of the substrate
(V aliela 1969). Competition between species may eliminate one species. The effects of
population density on the growth of house fly individuals should be examined in relation
to larval size and survival. Also, it would be interesting to seehow mixed species can

affect species and individual survival.

Predation—Parasitism. Among the large range of symbiotic organisms, there is a large
number of arthropods that adversely affect house flies. Staphylinid beetles (Jones 1967;
White and Legner 1966) and macrochelid mites (Axtel 1963) have been shown to
reduce house fly populations. Aleachara taeniata Erichson, a staphylinid parasite, was
introduced in California from Jamaica for biological control purposes (White and Legner
1966). Adult mites of Macrocheles muscaedomesticae (Scopoli) and adults of
Fuscurapada vegetans (DeGeer) are significant mortality factors of eggs and first instar
house fly larvae (O’Donnell and Axtell 1965). Although predator mites seem to occupy
a particular niche and normally one replaces the other over time, predators and prey do
not often occur together in the field (Peck and Anderson 1969). Predation response to

availability of prey would be elucidating for the determination of house fly population

10

limitations. House fly larvae are found in wet manure, possibly due in part to a co-
evolutionary process and the adaptive traits house flies have developed during their
evolutionary history to avoid predation.

According to Pimentel (1955) ants are an important factor in suppressing fly
populations in Puerto Rico. He also stated that the Fire ant (Salenopsis geminata (F abr.)
is the most aggressive species that attacks the adults and larvae of house flies. There are
no known competitors that are able to compete for the same niches, or predators who will
be able to suppress the house fly population and be effective biological control agents.
The overall rate of parasitism of house fly larvae and pupae in dairy farms in Denmark
demostrated that even when nine parasitoids were collected, the rate of parasitism was
low (Skovgard and J espersen 2000), unless combinations of parasitoid species with
different manure moisture preference were released (Geden 1999). The bacterial species
Staphylococcus muscae and the fungus Empusa muscae have also been reported to be

natural enemies of the house fly (Siew 1978).

House files as vectors of pathogens. There is significant evidence that house flies
acquire and transmit pathogens under natural and experimental conditions. House flies
can acquire and transmit pathogens through mechanical dislodgrnent from the
exoskeleton, fecal deposition and regurgitation (Greenberg 1973, Rosef and Kapperud
1983). Pathogens attached to the house fly’s legs and/or surface of proboscis can be
deposited onto human food (Siew 1978). Interestingly, the pulvillus of house flies is

coated with a sticky substance that facilitates attachment of many different pathogens

11

when they land on surfaces (Hedges 1990); those pathogens can also originate from a
different part of the exoskeleton during house fly grooming (Graczyk et al. 2001).
Bacterial pathogens, including members of the family Enterabacteriaceae, have been
identified as a cause of economical and major health problems for humans.
Enterabacteriaceae previously reported as part of the natural fauna of other medically
important insects including house flies, are associated with food home epidemics (Straif
et al. 1998, Bidawid and Edeson 1978). Environmental conditions or human
intervention can be determinants of food borne epidemics. For example, the incidence of
diarreal is high during the summer months (Echeverria et al. 1983). Also, indiscriminate
disposal of human and animal excretions, or areas that lack specific hygiene measures,
increased the incidence of diarrhea in residential areas (Khalil et al. 1994). Latrine
trenches are the ‘environmental reservoirs’ where flies get contaminated from fecal
material (Cohen et al. 1991). When the house fly population was controlled by
insecticides, the decrease of the fly population resulted in a decrease of diarrhea
incidence because it mitigated bacterial numbers. The use of DDT successfully reduced
the incidence of shigellosis in United States in 19403 (Watt and Lindsay 1948, Lindsay
et al. 1953). Therefore, the indirect association of the diarreal incidence and house fly
population has been supported (Bidawid and Edeson 1978, Echeverria et al. 1983). A
mere awareness of the importance of hygiene is not always enough (Henderson 1995)
but an integrate plan taking under consideration the control of the house fly might be
necessary (Urban and Broce 1998).

Direct association of house flies and diarrhea cases has also been tested. Infantile

gastroenteritis caused by members of Enterabacteriaceae has been associated with

12

unsanitary conditions and the isolation of enteropathogenic bacteria from batches of flies
(Bidawid and Edeson 1978). Yersinia pseudotuberculosis (Pfeiffer) has been isolated
from the intestinal tract of house flies after adults were allowed to acquire the pathogen
(Zurek et al. 2001). The fact that the pathogen did not replicate in the house fly gut as
well as the competition for the same niche from other Enterobacteriaceae (part of the
natural nricrobiota of house flies) suggests that only a certain microbial community is
favorable in house fly gut and new microorganisms are eliminated by competitive
exclusion (Zurek et al. 2001). Shigellosis has been characterized by the low number of
bacterial cells that can cause an infection and facilitate dissemination (Pickering et al.
1986). House flies along with Musca sorbens could potentially be vectors of infantile
trachoma (Chlamydia trachomatis) and responsible for blindness because of the frequent
contact with children’s eyes in Gambia (Emerson et al. 2000).

The significant role of house flies in dissemination of equine lymphangitis (Addo
1983) and porcine-transmissible gastroenteritis (Cough and Jorgenson 1983) has been
also documented. AnOther pathogen causing enterocolitis in humans and domestic
animals was found to be transmitted by house flies. This pathogen has been isolated fiom
the chicken causing liver changes and depressed egg production (Peckrnan 1958) while
consumption of chicken has caused carnpylobacteriosis in humans (Brouwer et al. 1979).
Campylobacterjejuni was successfully transmitted to pathogen free chicken when house
flies contaminated with the pathogen came in contact with chicken (Shane at al. 1984).
House flies have been responsible for contaminating raw meat that was used to feed
racing dogs in Kansas, resulting in dog high morbidity and mortality due to intestinal

infections (Urban and Broce 1998).

13

Finally, bacterial pathogens found in a hospital environment have been associated
with house flies. These pathogens have been identified as Streptococcus aureus,
Escherichia coli, Klebsiella spp., Enterobacter spp., Bacillus spp., Proteus spp.,
Pseudamonas aeruginasa and Enteracoccusfaecalis (Foredar et al. 1992) as well as
anthropozoonotic enteropathogens such as Campylabacterjejuni (Wright 1983, Rosef
and Kapperud 1983) and Yersinia enterocolitica (Fukushima et al. 1979).

The biology and ecology of house flies makes them an ideal vector for protozoan
parasites and bacteria (Graczyk 2001). Adult house flies acquire and transmit oocysts of
T axaplasma gandii and Cryptosporidium parvum eggs (Graczyk et al. 1999, 2001).
Larvae contaminated with T. gandii oocysts do not transmit the oocysts to adults
(Wallace 1970) due to a complete change in the digestive system during pupation

(Graczyk 2001).

Control measures. Other than the use of insecticides, a ‘bait and trap’ strategy has been
implemented in studies (Cohen et al. 1991) which is practical, effective and'an economic
method for less developed countries to control mechanically transmitted pathogens by
house flies. The use of ventilated improved pit latrines (‘VIP’) and it targets to limit

house fly access to human feces (Mara 1984).
House flies and adulteration of food. There is very little documentation of food

adulteration by house flies. Food can be unfit for consumption if it has been previously

contaminated.

l4

Direct effects of house flies in foods. Efforts to associate house flies with nutritional
benefits have been reported from time to time in introductory Entomology books but the
significance becomes limited due to inability of the human digestive system to digest
chitin. In most cases the unfavorable side of house flies is presented. Dyspepsia is
caused fi'om ingestion of house fly larvae (Gorham 1979). Dissemination of parasites
and pathogens is a problem especially for those foods that have not been heat treated or

are subject to insect invasion (Gorham 1979).

Allergic reactions. After an inquiry by the US. Department of Agriculture, the National
Institute of Occupational Safety and Health (NIOSH) reported many cases where allergic
reactions were discovered involving employees working in facilities where entomOlogical
research was conducted. This is the first to our knowledge reported allergic reaction
involving the house fly. Symptoms included nasal irritation, congestion, cough, episodes
of shortness of breath and elevated serum immunoglobulin E level (CDC 1984).

Allergic reactions of employees working in entomological research are similar to that of
the public when people come in contact with an increased number of insects (Etkind et al.
1982). In order to prevent allergic reactions fiom reoccurring the CDC recommends

' measures that target reducing the potential, for contact with airborne allergens (CDC

1984)

House fly gut diversity. A number of bacterial species have been reported in the
digestive tract of field collected house fly larvae. Zurek et al. (2000), showed that house

fly larval gut diversity reflected environmental bacterial diversity; this suggested house

15

fly larvae were not selective in the ingestion of bacteria and that house fly gut bacterial
flora was metabolizing organic substrates and providing nutrients for larval development.
Serratia marcescens (Bizio), Pravidencia rettgeri, Providencia stuartii (Ewing), and
Morganella marganii are representative species of the house fly larvae digestive tract
diversity (Zurek et al. 2000). Supporting evidence of the significance of the bacterial
species for the emergence of house flies in a artificial environment, was the variation in
the pupation and emergence rates in different laboratory cultures of bacteria
(Shmidtrnann and Martin 1992). House flies reared in bacterial cultures from bacteria
isolated from the house fly environment suggested that those isolates were improper for
larval development or that isolates produced substances that inhibited larval grth

(Zurek et al. 2000).

Conclusion. The house fly has been generally accepted as a ubiquitous species. Its wide
range is partly due to a wide range of food adaptations, facultative diapause, absence of
serious predators and absence of interespecific and intraspecific competition. This is the
reason why studies of geographic boundaries are missing. House flies constitute an
important link in the energy-matter flow of an ecosystem as primary decomposers. Their
genetic variation has been studied because of their adaptation to survive under extreme

environmental conditions and their response when they are managed by humans.

16

APROACHING MICROBIAL DIVERSITY.

The most important purposes of ecological studies are the identification and enumeration
of microorganisms. Very ofien microbial studies are lacking the appropriate methods to
accomplish this because of the specific ecological niches required by the microbes.
Traditional microbiology studied microorganisms in culture media that provided
information of their physiology and grth requirements. Modern microbiology has
relied on a combination of culture-dependent methods and culture independent methods
which followed the advancement in molecular biology by the discovery of the double
helix structure of DNA by Francis Crick and James Watson in 1953, and later by the

development of rules that govern DNA replication.

Culture dependent methods. Some authors claim that culture-independent methods,
rather than culture-dependent methods are considered more appropriate for estimating
bacterial diversity (Ovreas and Torsvik 1998, Wilson and Blitchington 1996). This is
because cultured microorganisms represent only a small portion of the microbial
community, hence misrepresenting the microbial diversity (Friedrich et al. 1997). Thus,
culture dependent methods underestimate bacterial diversity of natural populations
(Wilson and Blitchington 1996). However, eventlrough culture-independent methods do
reveal greater complexity plate counts, culture-dependent methods are more appropriate
for determining the effect of heave-metal-contamination on soil (Ellis et al. 2003).
Classical identifications rely on phenotypic characterization, growth

requirements, fermentation profiles, protein fingerprinting, electrophoretic mobility and

17

the most recent Fatty Acid Methyl Ester (FAME) analysis (Klein et al. 1998, McCartney
2002). The discriminatory ability for closely related taxa is limited with the above
methods. The poor reproducibility and ambiguity of some phenotypic methods
necessitated the use of culture independent identification methodologies. The combined
use of both culture dependent and independent methods is applied to overcome
limitations of each method (McCartney 2002). Differentially plating methodologies are
useful for the isolation and enumeration of probiotic organisms in mixed bacterial
populations (Charteris et al. 1997). Soil samples homogenized in low saline solution
were plated on high nutrient concentration Tryptic Soy Broth Agar (T SBA) and lower
nutrient R2A medium for heterotrophs (Ellis et al. {2003). Although the plating
technique provides an inside to predominant culturable microbes, it does not provide a
view of the diversity dynamics in the community (McCartney 2002).

Fatty Acid Methyl Ester (FAME) is used to identify isolates, but the low
similarity indices and other limitations of fatty acid profiling results in data frOm the
isolates being grouped in broad taxonomic groups (Ellis et al. 2003). This is especially
truefor lactic acid producing bacteria, where FAME was not reliable when DNA-DNA

homology was used as a reference method (Klein et al. 1998).

Culture independent methods.

Genetic probing. This method is based on the hybridization of synthetically-prepared
oligonucleotides to target specific bacterial DNA (McCartney 2002). A number of
probes target regions in ribosomal genes, thus they can be universal probes targeting

highly conserved regions or taxa specific probes within highly variable regions

18

(McCartney 2002, Langendijk et al. 1995). Gens other than ribosomal genes (e. g.
enzyme genes) can be used that can provide a better discrimination within species
(O’Sullivan 1999). Probes can be used in colony, dot-blot and in situ hybridizations
(Belzl et al. 1990, Charteris et al. 1997, O’Sullivan 1999, Kroes et al. 1999).

F luorescent-labeled probes are increasingly used for gut microbiology community
characterizations. Hybridization conditions have to be optimized by applying probes at
different stringencies to samples (e. g. optimize formarnide and NaCl concentrations)

(J uretschko et al. 2002). Probes have been designed to investigate variation of specific
bacterial populations in the human flora by targeting group—specific l6S rDNA regions
(Franks et al. 1998). A comparative analysis of quantitative fluorescence in situ
hybridization with a culture dependent method showed that the culture-dependent method

overestimated intestinal microflora by 10 fold (Langendijk et al. 1995).

Genetic fingerprinting. DNA fingerprinting methods are not equally effective; every
method has its own assets and limitations (8011 2000). Although a method oan resolve
differences between isolates, because the method has not adequately characterized them,
it can not be used to resolve genetic distance (8011 2000). To classify an isolate to a
specific species we must have a fingerprinting method to perform the function. The need
for a sensitive method becomes important when identifing- a pathogen from a commensal
flora due to potential threat to the general population. The method must be reproducible
and quantitative.

DNA stability is also important when we generate data by fingerprinting and

require little recombination within a species (8011 2000). If the frequency of

19

recombination is high, the data are difficult to be interpreted. Finally fingerprinting data
should be amenable to automated computer-assisted analysis (e. g. identification of bands
and lanes, density-scan the bands in a pattern, normalize patterns to universal standards,
generation of phylogenetic trees etc.) (8011 2000).

DNA fingerprint similarity is a sensitive estimation of relative levels of
population homozygosity (Lynch 1990). The species diversity is represented not only by
differences in DNA homology which underestimate the number of species, but also the
occupation of different niches by environmental partitioning (Dykhuizen 1997).
Estimations of the actual population diversity show that it is by ten-fold underestimated
based on bacterial DNA rehybridization results (T orsvik et al. 1990a., 1990 b.). Thus, it
is easier to measure differences between different commtmities than to estimate the
species richness within a community (Dykhuizen 1997). Community DNA
hybridization using radiolabeled DNA from a different community as a probe or from the
same community for normalization purposes has been used to measure species
composition and relative diversity (Dykhuizen 1997). Genetic fingerprinting is a
discriminatory method of differentiating bacterial isolates deriving from diverse bacterial

populations (McCartney 2002).

A technique that facilitates discrimination at the species level is the Restriction
Enzyme Analysis (REA). REA is the digestion of chromosomal DNA with restriction
endonucleases and separation of the fiagrnents either by Conventional Gel
Electrophoresis (CGE) or by Pulsed-Field Gel Electrophoresis (PFGE) (Wesley et al.

p 1991, Charteris et al. 1997). Gels are visualized by staining in ethidium bromide.

20

Separation depends upon the percentage of agarose in the gel, the voltage and the
particular endonuclease employed. The REA method is also named by some authors (e. g.
S011 2000) as Restriction fragment length polymorphism without hybridization or
simply PFGE (McCartney et al. 1996). Differentiation of isolates is based on changes
in restriction site sequences, deletion of recognition sites, or insertions in the sequences
between recognition sites (S011 2000). The initial limitation of the method for
microorganism isolation was overcome by the application of polymerase chain reaction
(PCR) (Charteris et al. 1997, McCartney 2002). RFLP has been used also with analysis
of bacterial protein genes to detect genetic variability within Barrelia burgdafi-eri
(Masuzawa et a1. 1997). Thus, RFLP is used as an cpidiomiological tool that can target

genes other than rRNA.

A similar technique to REA which also reveals polymorphisms between strains with high
homology, is the Restriction Fragment Length Polymorphism (RFLP) with
hybridization. In RFLP, DNA from different isolates is digested with restriction
endonucleases. DNA fiagments are then transferred to a membrane and labeled
oligonucleotide probes hybridize to the DNA fragments (Southern blot) ($011 2000,
Charteris et al. 1997). When rDNA is used as a probe, complex fingerprint patterns are
generated and DNA digests will contain multiple fragments of different sizes of rDNA
sequences (8011 2000). Ribotype is a specific pattern of bands where each band has been
digested and probed using the southern blot method (N g et al. 1999). Probes can be

radiolabeled or biotinylated and recognize specific fragments as a result of sequence

21

homology. Varying the salt concentration or temperature can control the stringency of

hybridization.

Terminal Restriction Fragment Length Polymorphism (T-RFLP). T—RFLP is a
method used to estimate phylogenetic diversity between related or unrelated bacterial
communities. This signature method is a mean to assess changes in microbial community
structure based on temporal or spatial changes in the environment. Culture dependent or
independent studies have shown that some bacterial divisions are cosmopolitan in certain
environments while others are restricted to certain habitats (Hugenholtz et al. 1998). In
both cases the number of unique Terminal Restriction Fragments (T-Rfs) is an
underestimation of the actual community structure due to the generation of identical size
T-RFs fiom phylogenetically related organisms (Totsch et al. 1995). In order to generate
the optimal unique terminal fiagrnents (fiom 5’ terminus), PCR products from all
samples are subject to a combination of different endonuclease treatments which
increases the number of unique terminal fragments (Liu et al. 1997). The capillary
electrophoresis system used for the TRFLP method is advantageous over gel resolution
for the discrimination of diverse bacterial communities and there is a limited variation on
the T-RFs observed even when the same PCR product is digested twice. This might be
associated with the automated sequencer error or reagents used during T-RF detection.
The latter can be a reason to justify a less conservative interpretation and use of the data

generated.

22

A variation of the RF LP is the Amplified rDNA Restriction Analysis (ARDRA).
ARDRA has been used successfully to discriminate bacterial isolates from a variety of
environments (Segonds et al. 1999, Ovreas and Torsvik 1998). Bacterial isolates are
lysed, DNA is extracted and rDNA is amplified. PCR products are then digested with a
set of different restriction endonucleases (Segonds et al. 1999, Ovreas and Torsvik
1998). Restricted DNA fiagments are analyzed with CGE and visualized under UV light
afler being stained with ethidium bromide. Fragment patterns can be used based on
similarities of band positions to cluster the different genotypes and construct a
dendograrn, or to differentiate pathogenic from nonpathogenic strains (Segonds et al.

1999, Ovreas and Torsvik 1998).

PCR-DGGE analysis. Denaturing Gradient Gel Electrophoresis (DGGE) is another
method to discriminate bacteria or archeal species from a microbial community. DGGE
is a molecular method for culture-dependent microbiological investigations used to
identify and subsequently isolate microorganisms from bacterial communities or co-
cultures (Kane et al. 1993). A region of 16S rDNA is amplified by PCR by using
bacterial (or archeal) specific primers. PCR products are loaded to polyacrylamide gels,
which are prepared with a gradient denaturant (urea and forrnamide). After
electrophoresis, gels are stained with SYBR Green I nucleic acid stain and are visualized
on a UV transillumination table (Ovreas and Torsvik 1998). PCR products of identical
length can be separated on the basis of primary sequence and base composition (Muyzer
et al. 1 1993). Thus, closely related organisms differing by a few oligonucleotides can be

discriminated. Important issues with this method are the selectivity of PCR primers and

23

the resolution of the DGGE (Teske 1996). DGGE patterns of natural samples can be very
complex because of the presence of uncultivable bacteria (Teske 1996). Thus, in order
to simplify the DGGE pattern, 168 rDNA primers are designed or other functional genes
of a selected bacterial group can be used (W awer and Muyzer 1995). DGGE is also
reported to be a more effective method of discriminating different soil samples from

cultured media than direct amplification of rRNA genes from the soil (Ellis et al. 2003).

Ribosomal DNA (rDNA) analysis. A new approach using phylogenetic-based
taxonomy fi'om non-isolated bacteria estimates the total diversity without relying on
morphological, physiological and biochemical characters (Frostegard et al. 1999). While
several cell components are informative, small subunit (SSU) rRNA genes are highly
conserved among organisms and make them the best macromolecular descriptors for
phylogenetic relationships of microbes in ecological studies (Britschgi et al. 1991 ,
Wilson and Blitchington 1996). The use of oligodeoxynucleotide primers to access 16S
rDNA sequences has been successfully attempted increasing the simplicity to screen a
large number of organisms (Lane et al. 2003). These genes are reliable phylogenetic
markers used to assess natural relations between isolated and uncultured prokaryotes
using modern PCR and sequencing technologies (Friedrich et al. 1997). However,
intracellular symbiots that can not be cultured outside of their hosts give us a significant
insight into their evolutionary histories and specific adaptations to symbiosis (Moran and
Telang 1998). A portion (<500 bp) of the 168 rDNA is usually sufficient to resolve a
phylogenetic issue if a close relative sequence is known but it can be misleading in case

of novel sequences that require a longer sequence (Hugenholtz et al. 1998).

24

However, this method has its own weakness due to differential amplification of template
DNA in a mixtured-template reaction. The G+C (Guanine plus Cytocine) content of the
template can not explain all cases of PCR bias, instead the secondary structure affecting
the availability of the priming sites during amplification has been proposed (Suzuki and
Giovannoni. 1996). This becomes important, especially when more than one set of
primer pairs are used to amplify regions that possibly do not have the same accessibility.
In mixed-template reactions the amplification is inhibited when templates with high
initial concentrations reach inhibitory concentrations, while other templates continue to
amplify resulting in underestimation of the PCR product of the most concentrated
templates (Suzuki and Giovannoni. 1996). The genome size and the number of rm
genes of different bacterial species influence the amounts of PCR amplification products
(F arrelly et al. 1995). Therefore, if information from these two parameters is lacking,
quantification of microbial communities is not possible fi'om the 16S rDNA clone

libraries (Farrelly et al. 1995).

Microarrays. Advances in molecular genetics include the development of Microarrays.
Microarrays can be used to identify and quantify microbial diversity of communities
based on presence or absence of specific genes using a single test (Stine et al. 2003).
This test can also be used to discriminate closely-related sequences or sequences with
very high homology by implementing multiple genes or probes to detect each desired
taxa (Stine et al. 2003). The advantages of Microarrays are a high throughput screening
of different microbial communities in very short time and a very comprehensive picture

of spatiotemporal changes in a single bacterial community. The major disadvantages of

25

Microarrays are the incomplete development of the technology, the high cost of testing

and equipment to perform the analysis (Stine et al. 2003).

26

PHYLOGENY AND HORIZONTAL GENE TRANSFER (HGT)

Kurlan et al. (2003) questions: a. that eukaryotic nuclear genome derived from archea
and bacteria, b. HGT is faster than tinkering preexisting sequences for rapid adaptation
and c. HGT will replace the classical rRNA phylogenetic tree with a jumbled netwOrk.
According to Kurlan et al. (2003), HGT has been inflated and it is not the ‘essence’ of
phylogeny. BLAST-based estimates for HGT can be misleading when identifying similar
homolog sequences in pairwise comparisons of the three domains or when defining
organisms as the sum of their genes to explain evolutionary relationships (Doolittle et a1.
1996, Rivera et al. 1998). Rather than creating phylogenies based on sequence identity
for the sum of orgarrismal genes, phylogeny can be generated by using ortholog genes
(Huynen et al. 1999). There is no single case demonstrating that HGT generated an
ambiguity to the phylogeny based on rRNA family gene (Kurland 2000, Woese 1987).
Because of rRNA genes ubiquitousness and conservetion among species, rRNA genes are

the universal reference for systematics and phylogeny.

HGT and Evolution of bacterial genomes. Diversity studies have revealed novel forms
of bacteria that have revolutionized environmental microbiology. For example,
Rhodopsin a bacterial protein, a light driven proton pump, was functionally expressed in
E. coli and shared the highest similarity with rhodopsins in archea (Bej a et al. 2000).
Studies of the evolution conclude that genes are not transferred only between closely

related species but between even more distant species (Saltzberg 2001 ).

27

The comparative study of genome sequences can give us information of the
similarities or differences between genomes, the presence or absence of genes and an
understanding of substitution patterns in noncoding regions (Eisen and Fraser 2003).
Genomes and genes are transmitted vertically when they are inherited by offsprings from
parents. The fate of genes is determined by mutational changes, and rearrangement due
to homologous recombination (N g et al. 1999, Milkrnan 1997, Vulic et al. 1999).
However lateral transfer of genes fiom one phylogenetic lineage to another is of
significant interest (Lawrence 1999, Eisen and Frazer 2003, Tettelin 2001, Lawrence
1998, Saltzberg 2001, Martin 2003) because it is the process by which bacteria become
rapidly adapted to novel environments (Lawrence 1998).

The horizontally transferred DNA is expressed by transformation, conjugation
and transduction (Bushman 2002). Bacteriophages are an important element of the
horizontal DNA transfer. During their lysogenic stage their DNA becomes integrated
with the bacterial DNA and when lysogenic bacterial cells are transduced, the bacterial
DNA can be packaged into phage capsules. This can lead to transfer of bacterial'DNA' by
bacteriophages to newly infected bacteria species (Desiere et al. 2001). In some cases
the bacterial DNA can contain pathogenic genes (e. g. toxin genes) and bacteriophages
can help in the dissemination of the pathogenic genes. Two thirds of the published
gammaptoteobacteria genomes contained identifiable prophages (Canchaya et al. 2003).
The conversion of non-pathogenic strains to virulent —such as antibiotic resistance and
toxin genes - is caused by acquisition of sequences rather than by point mutations
(F alkow et al. 1971). Three pathogenicity islands were identified in Neisseria

meningitidis after complete sequencing of its genome and they were designated as

28

putative islands of horizontally transferred DNA (Tettelin et al. 2000). Forty human
genes were identified as candidates for possible lateral transfer from bacteria (Saltzberg
2001). Explanations given for the existence of genes shared by humans and prokaryotes
but missing in non-vertebrates include, the evolutionary rate of variation, small sample of
non-vertebrate genomes and gene loss in non-vertebrate lineages (Saltzberg 2001).
Genes that encode beneficial function -not already present in the recipient — can persist
under weak or transient selection (Lawrence 1999). Horizontal transfer has been
observed not only to operons that confer nonessential metabolic functions but to genes
that confer essential functions (Lawrence 1999). Genes involved in pyridoxine
biosynthesis are believed to have been transferred between Steptacaccus pneumonia and
Haemophilus influenzae pathogens (T ettelin et al. 2001). Althought plausible rates of
mutations have been estimated under laboratory conditions, the rate of horizontal transfer
is hard to be assessed (Lawrence 1999). The rate of horizontal transfer is dependent on
the availability of foreign DNA, the rate of introduction of the DNA, the successful
integration into bacterial chromosome and the benefit that it confers to the recipient
(Lawrence 1999). Bacterial strains Yestrinia pestis and Yersinia pseudotuberculosis
possess a common pathogenisity island found at the end of different asparaginyl tRNA
genes (Hare et al. 1999). The virulence of pathogenicity islands (PAIs) is due to iron
uptake (Bearden et a1. 1997) and the acquisition mechanism is consistant with
bacteriophage-mediated integration (Cheetham and Katz 1995). Bacteriophage
attachment sites on PAIs which are homologous to phage integrase genes indicate that
these genetic elements are spread among bacterial pOpulation by horizontal transfer

(Hacker 1996). Although point mutations lead to incremental evolution of metabolic

29

novelty (Lawrence 1999), horizontal transfer introduces fully frmctional metabolic
capabilities after integration allowing exploitation of novel environments required for
microbial diversification and speciation (Lawrence 1998). Kurlan et al. (2003) report
that alien sequences imported to the genome purge older imports because they don’t
improve the fitness of their hosts. If a cell acquires a sequence that lowers the functional
rate or increases the mass investment of the growth network, the mutant will lower the
cell fitness (Kurlan et al. 2003). In the case of bacteria that acquire alien sequences that
provide an adaptive antibiotic resistance phenotype, these populations will be found in
patches within large populations whereas in the absence of a selection, the new
phenotypes will be purged by random mutation (Kurlan 2000, Berg and Kurland 2002).
Commensal flora constitutes a reservoir of antibiotic resistance genes for
pathOgenic bacteria due to the selectidn pressure caused by the use of antimicrobial
agents as growth promoters (Van de Bogaard and Stobberingh 2000). There are two
mechanisms for the emergence of antibiotic-resistant bacteria: either by the direct
selection of resistant mutants within the population of pathogenic bacteria or initial
selection of antibiotic resistant commensal flora followed by horizontal transfer to
pathogenic species (Andremont 2003). The major strategy to reduce antibiotic resistance

remains the reduced use of antibiotics (Andremont 2003).

30

OBJECTIVES

The objectives of this study are,

a. to investigate house fly gut bacterial community based on results of a culture-
independent study generating l6S rDNA libraries from the house fly gut bacterial fauna
and a comparative approach with the cow fecal community. This study aims to detect
previously unknown bacteria found in the house fly gut and establishes its bacterial
phylogenetic diversity. Quantifying gut biodiversity is of particular interest for an insect
vector of pathogens and foodbome diseases. Furthermore, the increased bacterial
diversity in the house fly gut is a result of DNA transfer that drives evolution of the
microorganisms.

b. to compare the diversity and taxonomic structure of the bacterial communities
of house fly intestines and exoskeletons from populations isolated from farms practicing
either conventional or organic management regimes.

c. to examine whether the fly gut may serve as a permissive environment for gene
recombination through conjugation and phage induction. We detected the transmission
of plasmids carrying antibiotic resistance genes and transmission of bacteriophages

encoding sth, the principal virulence factor of stx producing E. coli.

31

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40

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41

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42

CHAPTER 11
Transfer of Shiga Toxin and Antibiotic Resistance Genes among

Escherichia coli Strains in the House Fly Gut

ABSTRACT

The role of house flies (Musca domestica L.) in the evolution of antibiotic resistant
bacteria is not well known. House flies can acquire, harbor, and transmit enteric bacteria
fiom a persistent bacterial flora in their alimentary canal. This study determined that
horizontal gene transfer between strains of Escherichia coli occurs in the fly gut. In an
experimental infection of houseflies by E. coli, .we found transfer of both plasmid-bom
antibiotic resistance genes and bacteriophage-bom Shiga toxin genes in the fly gut.
These findings suggest that genes encoding antibiotic resistance traits or toxin traits will
move horizontally among bacteria in the house fly gut via plasmid transfer or phage
transduction. House flies may be a favorable environment for the evolution and

emergence of new pathogens.

43

INTRODUCTION

Lateral gene transfer is increasingly recognized as an important process that
promotes emergence of bacterial pathogens, through acquisition and accumulation of
pathogenicity islands, virulence factors, and antibiotic resistance traits in gene recipients
(Cheetham and Katz 1995, Whittam and Bumbaugh 2002). For example, ancestral
Escherichia coli strains apparently acquired the locus of enterocyte effacement virulence
factor island, Shiga toxin (stx) genes, and enterohemolysin and plarnsid-associated
virulence factors through lateral gene transfer processes in distant and recent evolutionary
time (F eng et a1 1998, Reid et al. 2000, Donnenberg and Whittam 2001). The bacterial
pathogen Escherichia coli 0157:H7 secretes Shiga toxins similar to the cytotoxin of
_ Shigella dysenteriae type 1 (O’Brien and Holmes 1987) and are important contributors
to the virulence of the non-invasive E. coli strains (STEC), mucosa-invasive Shigella
dysenteriae type 1, and several other enteric bacteria (Paton and Paton 1995, Acheson
and Keusch 1999, Wagner et al. 2001). The stx genes are encoded in bacteriophages that
are integrated into the genomes of bacteria, and are widely disseminated in natural E. coli
populations (Newland and Neil 1988). Bacteriophages likely mediated transfer of stx

genes in this process (Cheetham and Katz 1995).

Plasmids commonly bear genes encoding antibiotic resistance traits and virulence
factors (Ambrozic et al. 1998). Plasmid transfer by conjugation is an important
mechanism for gene exchange amongst bacteria (Yin and Stotzky 1997; Ande and

Andersen 1999). The ability of some plasmids, particularly self-transmissible and

promiscuous ones, to be transferred between unrelated bacterial species increases the
probability of genetic recombination (Mazodier and Davis 1991, Morales et al. 1991).
Ever since the demonstration of conjugative transfer of antibiotic resistance genes, this
mode of transfer of antibiotic resistance genes has become widely recognized (Watanabc
and Fukasawa 1961). Recently, it was reported that plasnrid-mediated resistance to
streptomycin was detected in a strain of Yersinia pestis isolated fiom a human case of
bubonic plague that occurred in Madagascar in 1995 (Guiyoule et al. 2001). The authors
of this report suggested that the strain may have arisen in the midgut of the flea vector

when Y. pestis acquired antibiotic resistance genes borne on promiscuous plasmids.

House flies (Musca domestica L.) and other synantlrropic, filth-associated flies
ingest bacteria, harbor them on their bodies or in their guts, and contaminate surfaces by
their excreta and by regurgitation (Herman 1965, Greenberg 1971, Grubel et al. 1997).
House flies have been implicated as mechanical or biological vectors of bacterial
pathogens including species and strains of Salmonella, Escherichia, Proteus, Shigella,
Chlamydia, and Campylobacter (Bidawid et al. 1978, Echeverria et a1. 1983, Shane et al.
1985, Khalil et al. 1994, Urban and Broce 1998). The closely-related fly Musca sorbens
has been confirmed as a vector of Chlamydia trachomatis, causative agent of trachoma in
humans in Gambia, owing to its behavioral association with both human feces and human
eyes (Emerson et al. 2001). The control of house flies was correlated with a reduction in
prevalence of Shigella infections in humans (Cohen et al. 1991). An epidemic of E. coli
0157:H7 infection among Japanese school children was attributed to transmission by

house flies (Kobayashi et al. 1999, Moriya et al. 1999, Iwasa et a1. 1999, Mutsuo et al.

45

1999). These scenarios suggest that pathogenic bacteria and flies commonly meet in their
respective environments. However, the role of house flies as hosts within which can
occur lateral transfer of genes encoding virulence factors or antibiotic resistance traits
amongst bacteria has not heretofore been investigated. If previously ingested bacteria
survive, divide and exchange such genetic material in their guts, then flies would
contribute not only to the spread, but also to the evolution of pathogenic microorganisms.
Therefore, in this study, we eXamined whether the fly gut may serve as a permissive
environment for gene recombination through conjugation and phage induction. We
detected the transmission of plasmids carrying antibiotic resistance genes and
transmission of bacteriophages encoding stx] , the principal virulence factor of stx

producing E. coli.

46

MATERIALS AND METHODS

Bacterial strains, plasmids and culture conditions. Bacterial strains, plasmids and
bacteriophages used in this study are listed in Table 2.1. Bacteria were cultured in Luria-
Bertarri (LB) medium at 37° on rotary shakers. The media were supplemented, as
necessary, with antibiotics at the following concentrations: ampicillin, 100 ug/ml;
chlorarnphenicol, 25 ug/ml; tetracycline, 10 ug/ml; rifarnpicin, 30 jig/ml.

For plasmid transfer experiments, cultures of donor and recipient strains were
grown separately overnight at 37 °C in 2 ml of LB medium supplemented with
appropriate antibiotics. The cultures were centrifuged for 5 rrrinutes at 5,000 rpm,
washed once with flash LB without antibiotics, and re-suspendedin 1.0 ml of nrilk-sugar
solution (MS) containing 1.56 g powdered milk and 1.67 g commercial sucrose dissolved .
in 93 ml of autoclaved, distilled water. This experiment was necessary because flies did
not feed well on LB medium, thus a diluent that flies would imbibe was needed, but one
that would also support bacterial survival and plasmid transfer. To measure the rate of
plasmid transfer from donors to recipients, in culture tubes containing different diluents, 1
ml of donor cells (either CB167 or CB405) and 1 ml recipient cells (CB566) fi'om these
tubes were re-suspended in either LB, 0.9% NaCl saline solution, in a sugar solution, or
in a milk-and-sugar (MS) solution, and incubated at 37°C for 1 h. This experiment was
necessary because flies were reluctant to imbibe LB broth, and so we had to find an

alternative diluent that would support conjugation in vitro and would be accepted by flies.

47

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We determined the number of donor, recipient, and transconjugant cells by plating serial
dilutions of these cell suspensions onto LB agar containing the appropriate antibiotics,
counting colony forming turits (ch1) after 12-14 h incubation (37 °C), and converting
counts to number of bacteria per ml of original culture using a standard formula
(Gerhardt et al. 1994). If selective plates had fewer than 25 colonies, then as a standard
method the plate count results were recorded as less than 25 and bacterial concentrations
as <25 x 1/dilution ratio. The transfer frequency of plasmids from donor cells to
recipient cells was calculated as the ratio of the number of transconjugants (i.e., those
with dual antibiotic resistance phenotypes, corresponding to donor and recipient
phenotypes) to the number of donors in the reaction vessel after a predetermined interval
(Andrup and Andersen 1999).

For bacteriophage transfer experiments, cultures of donor and recipient strains
were grown separately, overnight, at 37°C in 2 ml of LB medium. To measure the rate of
phage transfer fiom donors to recipients, 200u1 of donor cells were resuspended in LB, or
until OD650= 0.1. Then, 200 111 of the donor suspension were mixed with 200ul of
recipient (OD650= 0.3-0.35), and incubated at 378C for 1 or 2h. When rnitomycin C was
used to initiate phage induction donor bacteria (OD650= O. 1) were treated with either
lug/ml or 2ug/ml for either 0.5 h or 1h induction time. When experiments involved
house flies, donor bacteria (OD650= 0.1) were treated with 2ug/ml nritomycin C,
centrifuged at 5,000rpm for 5min, the supernatant was discarded, and resuspended in
40u1 of milk and sugar solution. Recipient strains (OD650= 0.3-0.35) were centrifuged at
5,000 rpm for 5 min, the supernatant was discarded and resuspended in the same fashion

as the donor bacteria in milk-and-sugar solution. We determined the number of donor,

49

recipient, and transductant cells by plating serial dilutions of these cell suspensions, or 10
homogenized house fly guts in 0.9%NaCl, onto LB agar containing the appropriate

antibiotics. Then, colony forming units (cfu) were counted after a 12-14 h incubation (37
°C), and the counts were converted to the estimated number of bacteria per ml of original

culture using a standard formula (Gerhardt et al. 1994). \

Confirmation of phage transfer by PCR. Ampicillin resistant (Amp'), rifampicin
resistant (Rif) colonies, isolated fiom the fly gut after feeding of both donor, MC4100 H-
19B::Ap1, and recipient, MPOOI, strains to the flies, were checked for the presence of
stx] genes by performing PCR with the forward primer 5’-TGT AAC TGG AAA GGT
GGA GTA TAG A-3 ’ and reverse primer 5’-GCT ATT CT G AGT CAA CGA AAA
ATA AC-3’ which are designed to amplify a 210 bp fiagrnent of stx] . The PCR
conditions used to amplify the 210 bp fragment were 15 sec at 95 °C for template
denaturation, 30 sec at 57 °C for primer annealing and 30 sec at 72 °C for extension.

This amplification ran for 30‘ cycles and the PCR product was visualized on a 1.5 %

agarose gel.

Flies. House flies were obtained fiom a commercial source (SC Johnson, Inc., Racine,
WI) and maintained through multiple generations as follows. Adults were held in
screened cages and were provided water and a 1:1 mixture of powdered milk and sucrose.
Eggs were collected on 20 g of larval medium placed in a dish inside the cage. Larvae
were reared in plastic dishes with tight-fitting lids with a plastic screen to allow air

exchange. The dishes were provisioned with 170 g of dry larval fly chow made fiom

50

alfalfa mash (PMI, Nutrition International, Inc., St. Louis, M0) to which was added a
mixture of 380 ml deionized water, 5 g commercial sucrose and 2.5 g dry yeast. The
medium was stirred until the water was fully absorbed. To start a new cohort of flies,
oviposition diShes were placed for 24 h in the adult cage, then material fi'om the dish,
including eggs, was transferred to the larval rearing container. Cages holding adult flies
and larval rearing containers were kept at 25-30 °C and >60% relative humidity.

The crop (i.e., foregut storage organ) and midgut (i.e, digestive organ) plus
hindgut (i.e., water retention and defecation organ) of adult flies were dissected as
follows. The ventral abdomen was opened with fine scissors, the crop separated fi'om the
midgut and hindgut, and then the crop, and midgut/hindgut posterior to the crOp were
transferred separately to Eppendorf tubes containing 0.5 ml of sterile saline solution.
Each tube was provisioned with 3-5 organs. The organs were triturated with tight-fitting
pestles, tubes were vortexed, 0.5 ml of saline added, and tubes vortexed again. Then,
serial dilutions of the tritmated organs were prepared in saline, and 100 11.1 volumes were
spread onto LB agar supplemented with appropriate antibiotics as indicated, using aseptic

technique.

Experimental in viva gene transfer. To accomplish force feeding of bacterial
suspensions to flies, individual flies were placed on a cold plate to immobilize them. A
drop of Instant All Purpose Krazy Glue (Elmer’s Products, Columbus, OH) was applied
to the scutum, and the fly was fixed upside-down to the bottom of a polystyrene dish (35
x 10 mm, Conring, Corning, NY). Fly survival was high with this treatment, allowing

experiments that lasted at least 3 hours. Suspensions of bacteria were prepared as

51

follows. First, primary cultures were washed in fresh LB, centrifuged to concentrate the
bacteria, the supernatant poured off, and the pellet resuspended in 50 ul of the milk and
sugar solution. This solution was delivered per as to individual flies using a micropipette
(Pipetteman, 1-10 11] capacity, Oxford®, BenchMateTM, Japan) fitted with a fine tip
allowing precise delivery of In] of liquid suspension. Flies readily grasped the tip with
their tarsi when it was presented to their heads, and they extended the proboscis so that
the labellum contacted the opening of the tip. The flies sucked the liquid from the tip and
it was easily possible to see the liquid disappear from the tip as the fly drank; i.e., the
experimenter did not expel the liquid from the pipette tip mechanically. In this manner,
house flies were first fed a donor strain, then a recipient strain. Flies that did not imbibe
the droplets, or flies in which the droplet contaminated their outer surface, were
discarded. After force-feeding, individual flies were held for 1 hour (37°C, high
humidity) to allow time for plasmid exchange or bacteriophage mediated cell lysis and
stx gene exchange. Some flies were fed milk and sugar solution only, and other flies

were fed only donor or recipient strains in suspensions, as controls.

Study area and sample collection. This test was designed to screen bacterial
communities found in the house fly gut from house fly pools. House flies collected from
dairy farms in Winsconsin by using a entomological sweep net and a vacuum device to
facilitate the capture and preservation of the specimens. Specimens were shipped alive
overnight to our lab preserved in a cooling container and they were processed next day.
Twenty farms were screened, half of which are practicing the organic farming system and

half the conventional system. Individual flies were dissected, intestines were removed

52

and placed in individual eppendorf tubes and triturated using a pestle in 0.5 ml of Luria-
Bertani (LB) medium. The remaining material (house fly sans gut) was triturated in the
same manner. Eppendorf tubes were filled to lml and were vortexed. Each fly was
ground in LB broth and 0.7 ml of the broth was added to 0.3 mL glycerol. Aliquots (100
111) fiom each tube were pooled according to the house fly collection site. Samples were

frozen at —70 °C until they were processed.

Antibiotic Susceptibility. Samples from the field study were thaw and vortexed before
being processed. Lauryl Sulfate Broth (LSB) tubes were inoculated with 50 ul from the
gut pooled samples and incubated for 24 hrs at 35 'C. Diluted samples in 0.9%NaCl
were plated to Trypticase soy Agar (TSA) amended without or with the appropriate
antibiotics (chloramphenicol, tetracycline and streptomycin). Plates were incubated for
24 hours at 35 °C and colony forming units (ch1) counted. The proportion of the
antibiotic resistant cfu to total counts was estimated. ANOVA analysis was performed by -:
h using data fiom individual farms grouped according to their farming system (organic or
conventional). Bacterial isolates from each farm pool were grown for 24 hours in Luria-
Bertani (LB) broth at 35 'C. After growth, cultures were, plated on TSA media selecting

for tetracycline, chloromphenicol, streptomycin and a control for the total counts.

Detection and isolation of E. coli 0157:H7 in farm-caught flies. To detect genes from
E. coli 0157:H7 in flies collected on farms and processed as described above, PCR
reactions were conducted as follows. Oligonucleotide primers were designed that flank

the following genes: Shiga toxin 2 (stx2, 484 bp fragment), hemolysin (h1y933, 166 bp

53

fiagrnent), the 0157 somatic group (259 bp fragment), and the H7 flagellar antigen
(fliCh7 626 bp fiagrnent). Primer sequences were fiom Fratamico et al. (2000) and Paton
and Paton (1997). The GeneReleaser (Bioventures, Inc., Murfreesboro, TN) cell lysis
and DNA extraction system was used prior to amplification. Amplification was
performed using F ailsafeTM PCR kit from Epicentre Technologies (Madison, WI) and
PCR conditions were optimized using the provided premix E. House fly samples were
collected from dairy farms in Wisconsin. Samples were prepared and preserved the same
way described for the detection of antibiotic resistant. PCR reaction samples were
heated at 94 °C for 2 min (hot start), and then were subjected to 30 cycles of denaturation
at 94 °C for l min, annealing at 56 °C for 40 sec, and extension at 72 _°C for 40 sec with a
final extension for 7 nrin.

House fly pooled samples were grown in lactose, Lauryl sulfate broth (LSB) and
modified Tryptic soy broth (LSB) supplemented with bile salts no 3 and novobiocin as
described by Lehmacher et al. (1998).

To isolate E. coli strains, samples were processed as follows. Flies were processed
as described above and pooled by farm. Aliquots of fly suspensions in LB broth were
plated onto vancomycin-cefixirne-cefsulodin blood agar (Hornitzky et al. 2001) or
MacConkey sorbitol agar and characteristic colonies (in particular, sorbitol negative
colonies) picked and subjected to immunomagnetic separation (IMS) using anti-0157

antibodies in as described in Eldor et al. (2000).

54

RESULTS

Plasmid transfer experiments. To determine whether antibiotic resistant bacteria were
present in the house fly alimentary canal prior to force feeding the flies with bacterial
suspensions, some flies were fed 1 ul of a sterile milk and sugar solution, and guts and
crops were then dissected, triturated together, and plated onto media containing
chloramphenicol, rifampicin, or both antibiotics. Results showed growth of bacteria on
media containing one or the other antibiotic, indicating presence of bacteria in the gut
with reduced susceptibility to these antibiotics. The means t SE were 2.1x103 :1: 4.0x103
and 9.5x 103 21: 2.3x103 cfu/ml for chloromphenicol and rifampicin, respectively, with 9
flies dissected and 3 organs pooled into single tubes and triturated for plating. However,
there was no bacterial growth on these media from plating of crop material for any
combination of antibiotic, nor was there recovery of bacteria fiom gut samples on media
containing both antibiotics. In a second study to determine whether tetracycline resistant
bacteria were present in the fly gut, flies (N = 9) were removed from the laboratory
colony and dissected without first feeding them a sterile'milk and sugar solution. Their
guts were removed, pooled as above, triturated, and suspensions plated onto media
containing tetracycline, tetracycline and rifampicin, or both antibiotics. Results showed
that there were 1.8x105 :1: 1.4x105 cfu/ml on media with tetracycline, 1.2x104 :1: 5.5x103
cfu/ml on media with rifampicin, and 6.7x102 i 4.7x102 cfu/ml on media with
tetracycline and rifampicin.

We estimated the number of bacteria in concentrated suspensions that were force

fed to house flies by serial plate counting of the bacteria contained in 1 ul of feeding

55

solution, in parallel to the bacterial recovery fi'om the fly crop or gut sans crop, discussed
below. The estimate for donor strain CB405 was 7.3 x 106/ul and for recipient strain
CB566 was 7.3 x 107/ul. After dissection, trituration, and plating of gut and crop
suspensions, bacterial colonies were recovered on media containing rifampicin only,
chloramphenicol only, or both antibiotics (Table 2.2), thus documenting that conjugation
occurred in the crop and the gut of the house flies within 1 h after the flies were fed.
Conjugation occurred consistently in the gut but not in the crop; the number of
transconjugants and the transfer frequency was significantly higher in the gut than in the
Crop (Table 2.2; Kruskal-Wallis nonparametric ranking test, H=5.33, df = 3, P = 0.021;
same statistical result for both tests). Recovery of bacterial strains fi'om house flies fed
only the donor or recipient strains did not result in any transconjugants, although bacteria
resistant to rifampicin (donor strain) or chloramphenicol (recipient strain) were recovered
on the appropriate media (Table 2.2). There were no bacterial colonies on media
containing both antibiotics fi‘om flies that were not fed either strain.

Results of this experiment permitted estimations of plasmid transfer frequencies.
Low concentrations of both strains in some cases did not produce any transconjugants in
some triturated crops, whereas transconjugants were recovered from all gut samples. The
estimated transfer frequency was accordingly higher in the gut (4.5x10‘2 t 3.4x10'2) than
in the crop (5.1x10'3 :1: 6.6x10’3) by an order of magnitude (Table 2.2). Comparison of
transfer frequency between the two E. coli (CB405, CB566) bacterial strains in vitro in
milk and sugar (MS) solution with transfer fiequency in the house fly gut showed that
gene transfer was significantly higher in the latter (Kruskal-Wallis test, H= 5.33 df=1 p=

0.021).

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Results of the experiment to determine whether there were differences in plasmid transfer
frequencies in standard LB medium compared to diluents used for preparations of
bacterial suspensions that were fed to flies were as follows. There were four replications
in each of the four treatments (N= 4) per group. There were significantly higher transfer
frequencies in LB medium (mean = 0.21, range = 0.14 to 0.31) compared to 0.9% NaCl
saline solution (mean = 2.7x10'2, range = 1.8x10'2 to 3.6 x10'2), sugar (mean = 7.8x10‘4,
range = 7.1x10" to. 9.0x10'4), and MS solutions (mean = 2.9 x10‘3, range = 1.0 x10'3 to

3.6 x103) (Kruskal-Wallis test, H = 14.12, or: 3, P = 0.003).

Detection of antibiotic resistant bacteria from house fly guts. Bacteria resistant to
tetracycline (TETR), chloramphenicol (CLM), and streptomycin (STR) were recovered at
different frequencies from guts of flies collected on both farm types (Figure 2.1). The
proportion of ARB for all three antibiotics was ~ 40% of the total cultured bacterial fauna
harbored in the house fly gut. There were no significant differences between farming
systems in the proportion of ARB recovered from the house fly intestines within each
type of antibiotic (CLM: F = 0.07, P = 0.789; TETR: F = 0.03, P = 0.872; STR: F = 1.12,
P = 0.305). However, there were significant differences of the proportion of different
ARB within the conventional system (CLM-TETR: F = 20.67, P = 0.00; TETR-STR: F =
4.77, P = 0.043; CLM-STR: F = 11, P = 0.004) and only between CLM-ARB and TETR-
ARB in the organic system but not for the other two combinations (CLM-TETR: F =

5.99, P = 0.025; TETR-STR: F = 3.42, P = 0.081; CLM-STR: F = 2.66, P = 0.120).

58

 

 

 

 

   

 

 

 

 

IOrganic
IConventional
2500000 -
ED 3 2000000 4 Mean+/-SE,N=10 filrmsper group
5’ 2 1500000 -
E
\ §- 1000000 ~
3
E " 500000 l
0 " ‘ I I
CONTROL CLM TETR STR
A Antibiotic amendement
I 3 .
IConventioml

 

 

9.6.69.0
I—NUAUI
Ligl

 

O

 

Proportion of 'Resistant'
bacterla

Antibiotic amendement

 

 

 

Figure 2.1 A. Number of colony forming Units of bacteria from pools of guts of field-
caught house flies plated onto TSA media containing no antibiotics (CONTROL), or
cthramphenicol (CLM, 100 ug/ml), tetracycline (TETR, 25 ug/ml), or streptomycin
(STR, 100 ug/ml). B. The proportion of antibiotic resistant bacteria recovered fi'om the
same house fly guts. House flies were collected from farms that either practice the
conventional or the organic system.

59

Escherichia coli transduction in vitro. Phage H-19B::Apl is a derivative of the stx]-
~ encoding phage H-19B that contains a bla insertion in the A subunit ofsth . The bla
gene encodes resistance to ampicillin, and thereby provides a convenient genetic marker
for the detection of phage transfer (Acheson et a1. 1998). In order to optimize the
experimental conditions for the in vivo experiments, we determined the phage transfer
frequency in vitro- for the same bacterial strains on which flies were later fed. Transfer
fiequency data were normalized by using the logarithmic values (Table 2.3). Bacterial
phages were induced from donor bacteria (MC4100) and lysogenized recipient bacteria
(MP001) in the presence or absence of mitomycin C. Significant differences occurred
among all three doses of mitomycin C (F=34.84, p<0.001), between 0.5 and 1 hour A
induction times (F=30.04, p<0.001), incubation for 1 and 2 hours (F=21.61, p = 0.001)
and for the interaction of dose with induction time (F=0.006, p= 10.16). No significant
differences were observed for the interaction of dose with incubation (F=1.62, p=
0.2191), induction and incubation (F =2.6, p= 0.1201), dose, induction time and

incubation (F=1.57, p= 0.2287).

60

 

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61

Escherichia coli transduction in vivo. To check whether pathogenicity genes encoded
on bacteriophages are transferred between strains in house fly gut, house flies were force
fed with the same bacterial strains used for the in vitro experiments. Transductant
bacteria (Ampr Rif) were also recovered from triturated house fly guts whether donor
bacteria were treated with mitomycin C or not (Table 2.4). Colonies recovered from the
guts on Amp-Rif plates and suspected to contain the transferred bacteriophages H-
19B::Apl were checked by PCR and found indeed to contain the stx] gene.

.Statistical analysis of phage logarithmic transfer frequency in the house fly gut showed
significant differences when donor bacteria were treated with mitomycin C, either when
donor bacteria given to flies first (F = 36.55, p= 0.001), or after the recipient (F= 22.65,
p= 0.003) compared to transfer frequency without any treatment of donor bacteria.
Interestingly, there was a marginal difference in transfer fi'equency when flies fed first on
donor and later on recipient (F = 5.98, p = 0.05). Additionally, no significant differences
found in the transduction ratio between in vivo and in vitro experiments when donor
bacteria were treated with mitomycin C (F =0.02, p= 0.888) or not treated with mitomycin
C (F= 3.06, p= 0.14).

As shown in Table 2.4, transduction of the H-l9Bzzi6lp1 bacteriophages may take
place in the fly gut at a low, but detectable fiequency. This fi'equency is increased
dramatically under conditions that induce bacteriophage excision. The addition of
Mitomycin C to the donor before feeding to flies increased the frequency of

bacteriophage transfer by 4 orders of magnitude.

62

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63

Detection of E. coli 0157:H7 and related genes from field caught flies. In a total
number of 22 samples, the work reported here demonstrates the detection of the flagellar
antigen H7 (fliCh7) in four samples (house fly collection sites) in 1 sample the plasmid
encoded hemolysin (hly933) in 2 samples the shiga toxin 2 (stx-2) and in 2 samples the
detection of the somatic antigen 0157. Interestingly, in one sample were detected the
0157, stx-2 and fliCh7 genes which are accessory genes of the E. coli 0157:H7, without
necessarily to be the case. In an effort to test the reproducibility of the method and the
efficiency of culture media for the detection of the 0157, H7 antigens, 100ul of the '
pooled fly samples were grown in mTSB, LSB and lactose. Seven, eight and one
samples were found positive for the fliCh7 when they were grown in mTSB, LSB and
lactose respectively. Only three, two and none were found positive for 0157 from the
respective culture media. The results indicate that the detection by PCR of 01 57, H7
antigens is more advantageous when LSB and mTSB are used. In two fly samples
positives were detected for both 0157 and H7 antigens when fly samples were incubated
in LSB or mTSB. Isolates of E. coli 0157:H7 were obtained from culture and [MS from

4 pools of flies fi'om 2 farms, one conventional and the other organic practice.

64

DISCUSSION

The importance of gut microbes is essential to insect nutrition and consequently
its survival (Eutick et al. 1978, Lilbum et al. 2001). Very often, mutual relationships are
a common phenomenon not only between insects and gut microbes, but among gut
microbes (Leadbetter et al. 1999).

Intestinal bacteria community is considered essential for the larval development
of houseflies and is associated with the environment where larvae are growing (Zurek
2000). The potential for dissemination of pathogens such as Campylabacterjejuni
(Jones), Salmonella sp., and Shigella sp. have been reported (Greenberg 1971, Shane et
al. 1985). Bacterial shed in heifer feces can potentially be a source for housefly
contamination. Their high concentrations (2x102 to 8.7x104 cfu/gr) and high survival
after shed by domestic animals (Mutsuo et al. 1999) can indicate a potential for gene
transfer not only in the animal intestine (Nikolich 1994), but in the house fly gut. This
makes adult houseflies an important vehicle of bacterial dissemination and a site for gene
exchange.

Antibiotic resistance is a hot topic in the scientific community due to our limited
available Options for treatment of pathogenic bacteria. There is a conception in our
society that the development of antibiotic resistant bacteria is the result only from the use
of antibiotics for human therapy and prophylaxis. Unfortunately this is not the only case, .
there are uses for antibiotics in animal husbandry for chemotherapy, prophylaxis and
especially as growth promoters. There are scientists who support the idea that antibiotics

are losing effectiveness (Cetron et al. 1995,.Lee at al. 1994); while others believe the

65

multi-resistant strains are extremely low compared to pathogens that are still treatable
with current antimicrobials (Walker and Thomsberry 1998). The above divergence can
be resolved by the use of a uniform susceptibility testing procedure (Walker and
Thomsberry 1998).

Antibiotic resistance arises by mutations, acquisition of antibiotic resistance genes and/or
is already there and becomes evident when a selective pressure is used (Gould 1999).
Antibiotic resistance can be encoded on the chromosome or on a transmissible plasmid
(Datta 1984). In the first case resistance is transferred to the progeny, whereas in the
second case it is transferred to other bacteria species (Piddock 1996).

Results of this study -where the antibiotic resistance is encoded on a transmissible
plasmid- show a close association between number of transconjugants and acclimation
time. This is in agreement with the time required for synthesis of complimentary DNA,
substance aggregation and other transfer fimctions (Andrup and Andersen 1999). Thus,
a lag period is required for conjugation not only in vitro test, but inside the crop of a
house fly--especially when bacterial numbers were reduced compared to the initial
concentration of each E. coli strain in the milk and sugar suspension after 1 hour of
incubation. The size of the plasmid transfer can be inversely proportional to the plasmid
size and maximal at high recipient concentrations to achieve donor saturation (Andrup
and Andersen 1999). Variation of the bacterial concentration at a certain time can be
expected due to parameters that govern the physiology of flies such as the bacterial
transfer to mid or hind gut of the intestine as a natural way of excretion or through

regurgitation.

66

Low numbers of transconjugants in the gut sans crop and high numbers in crop, or the
reverse, can be an indication of the bacterial kinetics in the house fly digestive system.
The dissemination of surfaces is primarily with transconjugants derived viable from their
excreta (Mutsuo et al. 1999).

Gut sans crop harbored bacteria that were not present in the crop since we
recovered resistant bacteria only fi'om the former. This might be associated with the
functional role of these bacteria and plausible mutualistic relation with the house fly.
Our work demonstrates that the house fly’s gut is plausible site for conjugation, and since
it has been generally accepted as a ubiquitous species, with this new information its role
in evolutionary history of bacteria becomes important. This is coming in contrast to the
findings by Thomas and colleagues (2000) for the low transfer ratio of pXOl6:: Tn5401 a
normally highly potent plasmid and lack 'of transfer of pBC16 between Bacillus
thuringiensis in the gut of Aedes aegypti larvae.

Mouthparts in a house fly can serve as a site of bacterial proliferation (Kobayshi
_ 1999), and/or enhance the potential of pathogen dissemination during the first 24 h
(Sasaki 2000),.but their significance for plasmid-mediated transfer is unknown.
Evolutionary homologous genes have been found in phylo genetically distinct bacterial
lineages suggesting horizontal transfer of the virulence genes among bacterial hosts
(Moran 2001). Plasmids harbored by E.coli bacteria encode not only antibiotic
resistance but also various virulence determinants (Ambrozic et a1. 1998).
Recombinations of virulence genes are responsible for epidemics and food poisoning
(Faith et al. 1996). Recent results indicate that E. coli 0157:H7, a food-poisoning agent,

acquired phage-encoded Shiga toxins and haemolycin, a pathogenicity island involved in

67

intestinal adhesion (Sean et al. 2000). The model which has been proposed by Feng and
colleagues (1998) that led to the emergence of 0157:H7 includes the acquisition of the
EHEC plasmid from stx-2 producing 0552H7 strains.

The dissemination of antibiotic resistant bacteria (ARB) becomes a global issue.
Strains can be disseminated before their presence is recognized and measures to stop
dissemination can be many times impractical or inadequate (Okeke and Edelrnan 2001).
An indication of the degree of ARB dissemination is the relatedness of Enterococcus
faecium and Salmonella enteritica was higher between strains isolated from humans in
different counties than humans and animals in the same country (Seyfarth et al. 1997,
Quednau et al. 1999). Dissemination of antibiotic resistant bacteria becomes important
not only for pathogenic strains but also for nonpathogenic because they can serve as
reservoirs of resistance genes (Okeke and Edelman 2001). The increasing number of
short term travelers (Cetron et a1. 1995), the high population densities (Hamburg 1998),
immigration from countries where the pathogen is endemic (Kenyon et al. 1999) are
common routes and causes of rapid spread of ARB by humans. Importation of
agricultural products thatlhave been contaminated (Klopp 1999, Rasrinaul et al. 1988)
aggregate the problem. At the same time somebody might thing that wild life is AR free
while wild life act as AR reservoirs (Gilliver et al. 1999, Souza et al. 1999) and
contaminated water (Sokari et al. 1988). Over prescription of antibiotics (Watson et al.
1999), increased use of disinfectants (Guillemot 1999), veterinary medication or growth
production in animal husbandry (W egener et al. 1999) constitute major selection
pressures for ARB in industrialized countries. On the other side the use of

subtherapeutic doses of antimicrobial agents, improperly treated or untreated infections,

68

absence of sanitation in developing countries generates countries that act as reservoirs of
ARB (Okeke and Edelman 2001).

Our model system for the house fly intestinal colonization suggests that E. coli
strains such as MC4100 produce infectious virions from the H-l9B lysogen. Recovery of
transductants of the recipient strain MPOOI indicates the transfer of bacterial phages into
E. coli hosts. One hour post-feeding of both bacterial strains, four transductants per 10‘5
donor cells were recovered from the intestine (Table 2.4). These results indicate that
MC4100 transduction accounted for the house fly intestinal transmission of the Ampr
marker. Because the H—19B phage has been integrated into the bacterial chromosome
stx-l , production is dependent on the presence of transcriptional regulators upstream of
the open reading fiarne (ORF), in the same fashion as the ctx gene in CTX<D (Lazar, S.
and MK. Waldor 1998). There are cases that toxin genes exist in the form of replicative
plasmids such as the CTX<D where CT production is independent of transcriptional
regulators and toxin production is plausible under non-permissive conditions such as
- elevated PH (Lazar, S. and MK. Waldor 1998). In the assumption that induction
constitutes a mechanism for virulence gene regulation, then a toxin gene encoded on a
‘ plasmid leads to a different evolutionary pathway by increasing toxin production.

Our results suggest that a house fly gut can potentially be a site for emergence or
horizontal transfer of bacterial toxin genes encoded by bacteriophages. It is also known
that horizontal DNA transfer can be regulated by host factors unique to the environment
in viva (Mel and Mekalanos 1996). The mice model for DNA transfer, either as
transformation by Griffith in 1928 or as conjugation by Schneider et al. (1961), has been

used many times since its inception. Today, the mammalian intestine has been proposed

69

for providing the necessary signals for the in vivo transduction of CTX<D of the Vibrio
cholerae (Mel and Mekalanos 1996). Although, the transduction ratio in vivo was not
significantly different than the experiments, the potential of dissemination due to
ubiquity of house flies, their high reproductive rate and the potential of toxigenic strains
to arise within their intestine from non-pathogenic bacterial strains makes them an

important element in the bacterial evolution.

70

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76

CHAPTER III

Comparative Internal and External Bacterial Community Structure of Farm-
Caught House Flies (Musca domestica L., Diptera: Muscidae), using Terminal

Restriction Fragment Length Polymorphism Analysis
ABSTRACT

Terminal Fragment Length Polymorphism (T-RFLP) analysis was used to estimate
diversity and compare the bacterial community structures found in the gut and on the
exoskeleton of house flies (Diptera: Musca domestica L.) collected on dairy farms.
Terminal Restriction Fragments (5’ T-RFs) were generated with the digestion of bacterial
community 16S rDNA with the use of Hha I and Msp I endonucleases. Overall, the
bacterial diversity of the gut was higher than the exoskeleton community, although there
were cosmopolitan 5’ T-RFs, i.e., ones common to both communities. The dairy farms
that were sampled adhered to a strict organic practice or to more conventional practices.
A multinomial chi-square analysis revealed no differences in bacterial community
structure of either the fly exoskeleton or fly gut with regard to type of farm practice. A
maximum likelihood cluster analysis showed that the bacterial community of the
exoskeleton was more variable than that of the house fly gut, suggesting that the gut
community is more stable but still complex. The house fly gut presents a complex,
stable and endemic microbiota in a close association with its host whereas the

exoskeleton is a less diverse, variable, reflecting environmental pressures.

77

INTRODUCTION

Microorganisms interact with insects in ways that affect growth and development
of insects through nutritional and symbiotic interactions, influence survival of insects
through pathogenic relationships, and establish the status of insects as vectors of
pathogens of humans and other animals (Cazrnier, et al. 1997, Kaufman et al. 2000).
Synanthropic flies are one group of insects in which microorganisms likely fit into all
three of these aspects of the microbe/insect interaction (Grazcyk et al. 2001). However,
the breadth of microbial associations with flies remains poorly known, despite abundant
evidence for their role as vectors of human pathogens. Indeed, the biology and ecology
of house flies makes them an ideal vector for protozoan parasites and bacteria (Graczyk
et al. 2001). Nosocomial bacterial pathogens such as Straphylococcus aureus,
Escherichia coli, Klebsiella spp., Enterobacter spp., Bacillus spp., Proteus spp.,
Pseudomonas aeruginasa and Enterococcusfaecalis (Foredar et al. 1992) and
anthropozoonotic enteropathogens such as Campylabacterjejuni (Wright 1983, Rosef
and Kapperud .1983) Yersinia enterocolitica (Fuushima et al. 1979) and
Cryptosporidium parvum eggs (Graczyk et al. “1999, 2001), have all been reported as
significantly important pathogens associated with house flies. Interestingly, the pulvillus
of house flies is coated with a sticky substance that allows many different pathogens to
attach and be transported when house flies land on different surfaces (Hedges 1990);
originate from a different part of the exoskeleton while they are grooming (Graczyk et al.
2001). As an example of these mechanisms is that adult house flies acquire and transmit

oocysts of Toxoplasma gondii, while larvae contaminated with T. gondii oocysts have a

78

complete change in the digestive system during pupation (Graczyk 2001), do not
transmit the oocysts to adults (Wallace 1970). Aside from transporting microbial
pathogens on the exterior surfaces or exoskeleton, flies are also implicated as biological
vectors of pathogens harbored in the fly gut (Sasaki et a1. 2000).

The farm environment represents one type of landscape in which house flies exist
and interact intimately in their different life stages with microorganisms (Zurek et al.
2000). For example, differential intensity of use of antibiotics in animal feed can select
for resistant bacterial populations (Piddock 1996, Van den Bogaad et al. 2000, Bager et
a1. 1997, Aarestrup et al. 2001, Witte 1998). Theoretically this practice could lead to a
predominance of bacterial taxa resistant to antibiotics in these settings. Further, house
flies resident in these settings could acquire, harbor, and transmit antibiotic-resistant
bacteria either mechanically (i.e., on external surfaces) or biologically (i.e., after
ingestion and egestion). The house fly gut and exoskeleton bacterial communities would
obviously be influenced by the surrounding environment and the diverse microbial
communities present there in various microenvironments such as animal feed, animal
feces, water, and so forth. However, these interactions are poorly known and deserve
study. I am unaware of any study which assessed the microorganisms associated with
house flies in this kind of ecological setting.

Here, I have compared the bacterial community of the house fly intestine with the
community on the exoskeleton in the context where flies were sampled from farm
environments, using a culture-independent approach that targets 16S rRNA genes (Pace
eta1., Marsh 1999, Stackebrandt and Rainey 1995). Terminal Restriction Fragment

Length Polymorphism (T-RFLP) is a technique for assessing diversity within a bacterial

79

community, as well as comparative distribution among communities (Marsh 1999). The
analysis is based on the digestion by restriction endonucleases with 4-bp recognition
sites, of the fluorescently labeled PCR products. Previously identified conserved
sequence domains within the bacterial domain (Amann et al. 1995) can be used to design
universal primers to amplify the 16S rDNA bacterial genes and subsequently
amplification products be digested by restriction endonucleases. This method provides
information of the number and abundance of the 5’ terminal restriction fragments (5 ’ T-
RFs) (Liu et al. 1997). The degree of realism of the method is dependent on its
reproducibility, its limits of detection, and any biases inherent in the PCR amplification
approach such as preferential annealing to particular primer pairs (Osborn et al. 2000).
The advantages of the method include the precise determination of 5’ T-RFs (high
resolution) and high throughput due to the automated DNA sequencer. Clearly, any
study which aims to assess microbial diversity relative to insect ecology will be
dependent upon the methods that are utilized.

The purpose of this study was two-fold, as follows. Overall, I aimed to assess and
compare the diversity of the bacterial communities of the house fly gut and exoskeleton
using the T-RFLP methodology. Secondly, I aimed to determine if differential organic
and conventional farming practices affected the bacterial community composition as
reflected by this methodology, where the house fly is the sampler of the bacteria on those

farm environments.

80

MATERIALS AND METHODS

Preparation of house fly samples. House flies were collected from twenty dairy farms
in Wisconsin; ten were practicing an organic farming system and the other ten used a
conventional farming system (Sato et al. 2002). Under the organic farming system, no
antibiotics were used for any of the calves, heifers, or milking cows for at least 3 years
(mean 8 years) in the herds. Data comparing management practices are forthcoming
(Sato et al. 2002). House flies were sampled by sweeping a net over the backs and sides
of cows, and by sweeping the net over feed and resting sites. Flies were retrieved from
the net, placed into sterile tubes, shipped alive by overnight courier in a cold container,
and were processed immediately upon receipt. Fly species other than M. domestica were
discarded. Each house fly was dissected, and the alimentary canal (i.e., gut) was
separated fi’orn the carcass. The gut and carcass from each fly were placed separately
into 0.5 ml of Luria-Bertani (LB) medium held in 1.5 ml Eppendorf tubes, triturated by
hand with a pestle, and then vortexed for 10 seconds. A total of 5 flies from each farm
were treated this way, and 100 111 aliquots of the suspension from each tube were pooled
into cryovials, glycerol was added to a final concentration of 30%, vials were labeled,

and stored at ~80 °C. The sampling unit for this study was the individual farm.

DNA isolation. DNA from suspensions was extracted from thawed samples using the
UltraCleanTM Soil DNA Kit from MO-BIO (Cat. # 12800-50 MO-BIO Laboratories inc.,
California, USA) according to the manufacturer’s instructions. The concentration of the

purified DNA was determined on a diode array spectrophotometer (Hewlett Packard) and

81

the presence of DNA was confirmed on 1% agarose gels and TBE buffer. DNA

preparations were stored at -20°C.

Optimization of PCR to amplify bacterial l6S rDNA. In order to optimize conditions
for the polymerase chain reactions, a portion of the 168 rRNA genes was amplified by
PCR fiom community DNA using universal primers for- the bacterial domain, as follow.
Reactions (100 pl) contained 400 ng of template DNA, 2x FailSafe PCR Premix E
(Epicentre Technologies), 0.2 to 1.0 uM of forward and reverse primer, and 1.25 units of
FailSafe PCR Enzyme Mix (Epicentre Technologies). The primers used for the
amplification were 27-Forward (5’-AGAGTTTGATCCTGGCTCAG-3’) and 1100-
Reverse (5’-AGGGTTGCGCTCG'I‘TG-3’). A 2400 GeneAmp PCR system (Perkin
Elmer) thermocycler was used to incubate reactions through an initial 3-minute
denaturation step at 94°C, followed by 30 cycles of denaturation at 94°C for l min,
primer annealing at 60°C for 0.45 min and primer extension at 72°C for 2 min.
Optimization of the PCR reaction for environmental samples was done by adjusting the
annealing temperature and by choosing the FailSafe PCR premix that yielded the best
band signal by visual inspection. The PCR products were purified using the Wizard PCR
Preps DNA Purification System (Cat. # A7170, Promega Corporation, WI USA)

according to the manufacturer's instructions.
T-RFLP. To generate T-RFLPs, the PCR conditions were the same as in the

optimization study described above, except that the forward primer was labeled at the 5’

end with 6-FAM (Operon Technologies, Alameda CA). For each sample, 3 x 100 ul

82

PCR reactions were performed to maximize the coverage of the diversity of 168 rDNA in
the sample. Reaction products were combined and purified with the Wizard PCR Preps
and the concentration was determined spectrophotometrically.

Restriction fragments were generated from PCR amplicons as follows:
Approximately 400 ng of cleaned and purified PCR product were digested for three hours
at 37°C with either Hhal or Mspl (New England Biolabs, Cambridge MA) restriction
endonucleases in 15 ul reaction mixtures. The specific endonucleases were chosen
because they provide excellent discrimination of different species (Marsh 1999). The
reaction mixtures contained 1.5 ul of 10 X restriction enzyme buffer (New England
Biolabs), 1.2 ul of restriction enzyme (New England Biolabs), 1-5 111 of DNA template,
and ultra pure water to the final volume of 15 ul. Reactions were stopped at 65 0C for 10
minutes and samples were stored at —20 0C until electrophoresis. Digests were run on an
automated sequencer (PRISM 3100 Genetic Analyzer, Applied Biosystems, Foster City
CA). The digest product was mixed with a DNA standard labeled with a dye, and
fragments were separated by electrophoresis using a capillary electrophoresis genetic
analyzer yielding electropherograms. Although the peak height in an electropherogram
provides a measure of the relative frequency of individual restriction fragment
phylotypes, bias can result from preferential annealing to particular primer pairs (Suzuki
and Giovannoni 1996), which in turn would bias cluster analysis. To reduce variability,
the parameters for PCR were optimized and maintained throughout the experiments,
including template concentrations, number of PCR cycles, annealing temperatures, and

source of Taq polymerase (Osbom et al. 2000). '

83

The fragment sizes were determined with ABI Genescan Analysis Software
(Applied Biosystems) and the alignment of community profiles was performed with
Genotyper software (Genotyper 2.5, Applied Biosystems). A level of 100 fluorescence
units was imposed as a minimum threshold value for all peaks in the analysed size range
of 50-900 bp. For each enzyme, duplicates from selected farms were run as a means of
confirming the reproducibility of the method. In the case of house fly gut analysis, 20 fly
gut pools — ten organic and ten conventional- were digested. There were 20 pooled
samples of fly guts (10 from each farm type), and 13 samples from the fly carcasses (7

from organic and 6 from conventional farms).

Data analysis. Cluster analysis of T-RFLP profiles was done using PAUP (Swofford
2000) with bacterial communities represented of different combinations of farm
practicing regimes and house fly structures as operational taxonomic units (OTUs). The
robustness of the cluster topology was estimated using Neighbor J oirring (NJ) and
Maximum Likelihood (ML) methods. We chose the ‘standard distances’ option as a NJ
distance analysis method and the ‘base frequencies’ option with a Heuristic search for
ML.

To test for differences in frequencies of the restriction fragments by farm practice
and insect body structure, a multinomial Chi-square analysis was done with a
contingency table. Similarity of the different experimental groups or OTUs was
estimated by calculating pair-wise similarity indices for the treatments. The index
divides the total number n of common 5’ T-RFs exhibited by profiles x and y (i.e., nxy) by

the average number of 5’ T-RFs of both profiles [(nx+ny)/2] (Lynch 1990). The index

84

equals 1, for two completely similar communities, and the index equals to 0 for two
completely different communities that do not share common fragments.

Pandemic 5’ T-RFs generated by DNA capillary electrophoresis and 5’ T-RFs
predicted by a computer simulated digestion of the RDP 11 database, were pairwise
compared. The T-RF LP Analysis Program (TAP)
(http://www.cme.msu.edu/RDP/html/a_rglyses.html) allowed the identification of unique
or numerically dominant strains (Marsh et a1. 2000) in both house fly commrmities as

one of the culture independent approaches to get an inside of the community structure.

85

RESULTS

The community fingerprint revealed by the T-RFLP method is a composition of
unique fragments and their amplitude represented by the size and height of each peak in
the electropherograrn. Electropherograrns depict both the degree of complexity and
community structure of both house fly exoskeleton and gut communities. FIGURES IN
THIS CHAPTER ARE PRESENTED IN COLOR. Four representative profiles of 13
from exoskeleton communities (E) (Figure 3.1, bottom four profiles) and four of 20 gut
communities (G) (Figure 3.1, top four profiles) illustrate 5’ T-RFLP patterns. Each
panel contains two representative electropherograms from either conventional (C) (codes:
112GC, 113GC, 112EC, 113EC) and organic farms (0) (codes: 105GO, 119GO,
105EO, 119EO), respectively. The most obvious visual differences in the
eletropherograrn profiles are between gut and exoskeleton samples, where 5’T-RFs of 60
(i 15) bp, 225 (21:.15)bp, 375 (i 15) bp, 600 (i 15) bp, were prevalent in the exoskeleton
community while 5’T-RFs of 55 (i 15) bp, 195 (i 15) bp, 345 (:1: 15) bp, and 555 (i 15)
bp, dominated in the gut community.

Data generated with GeneScan (amplitude and size of peaks in
electropherograms) were analyzed by comparing between farm practice, insect body
structure, and endonuclease used to generate the fragments. A simple way to find the
relative diversity between the two communities is to compare the number of unique 5’T-
RFs generated by both digestions. Both endonucleases generated a large number of 5’ T-
RFs. Exoskeleton and gut communities combined generated a high number of unique 5 ’

T-RFs, 222 and 213 from Hha I and Msp I digestions respectively (Table 3.1).

86

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Table 3.1 Number of 5’ terminal restriction fragments (T-RFs) generated from Hha l or Msp I digestion of 16S rDNA from bacteria from the
exoskeleton and gut of house flies. Pandemic fragments by convention are those found in more than 75% of all electropherograms.
Endemic fragments are pandemics found in more than 75% of electropliei'Ograms of one insect structure and less than 25% in the
other insect structure. Sample size is the number of farms sampled for each category.

 

 

 

 

 

 

Organic Conventional Unique T—RFs without Unique T—RFs
Famis Farms regard to farm practice without regard to
farm practice or
insect structure
Gut Exoskeleton Gut Exoskeleton Gut Exoskeleton
Hhal 135 84 142 88 187 113 222
(*meaniSE) (34i4) (30i6) (403:5) (41i3) (37i5) (35i4)
Pandemic 11 4 1 l 19 11 4 3
(¥A) (32%) (13%) (28%) (46%) (30%) (11%)
Endemic 1 0 2 4 l O —
(TB) (9%) (0%) (18%) (21%) (9%) (0%)
Mspl 118 88 113 84 173 117 213
(meaniSE) (34i3) (27i6) (37i3) (40i2) (34d 2) (335:3)
Pandemic 6 4 10 15 6 4 1
(¥A) (18%) (15%) (27%) (38%) (18%) (12%)
Endemic 0 0 1 1 O 0 -
(TB) (0%) (0%) (10%) (7%) (0%) (0%)
Sample Size 10 7 10 6 20 13 33

 

*mean number of TRF3 per profile, ¥ Percentage of Pandemics per mean number of peaks, T Percentage of Endemics per number of pandemics

88

 

 

 

 

These results suggest that the gut community was more diverse (i.e., higher species
richness) than the exoskeleton, because of the higher total unique fragments generated
from gut samples (187 fiagments from Hha I digestion, 173 fragments from Msp I
digestion), compared to exoskeleton samples (113 from Hha I digestion, 117 from Msp I
digestion). However the average number of fragments per profile (e. g. each individual
farm) from gut and exoskeleton profiles were similar. The mean (iSE) number of
fragments per profile for the gut and community was 37(15) and 35(;|:4) for the
exoskeleton community generated by the Hha I digestion. Similarily, a mean of 34(i2)
and 33(:t3) unique fragments were generated from the gut and exoskeleton communities

by the Msp I digestion.

Pandemics. A number of pandemic fragments (i.e., fiagments that were present in more
than 75% of all electropherograms within each farming system and insect structure) have
been identified. Results from the Hha I digestion showed the fi'action of pandemics
identified in gut remained constant in both farming practices . In the gut structure, 32%
of the T-RFs were identified as pandemics in the organic system and 28% in the
conventional system. In general, 30% of the fragments within the gut without regard to -
practicing system were identified as pandemics. However, in the exoskeleton structure
the fraction of pandemics appeared to be more variable. In the organic practicing system
13% identified as pandemic and 30% in the conventional system. Thus, there was a
significant range variation depending on farming system within the exoskeleton

community. Without regard to practicing system, 30% of the 5’T-RFs identified as

89

pandemics in the gut and 11% in the exoskeleton by using the Hha I digestion. T-RFs
such as 55.7, 235.7 and 568.7 bp were identified as pandemic while others such as 214.4,
366.0, 591.0 and 811.0 were frequently found from the Hha I digestion. .

Results from the Msp I digestion were very similar to Hha I digestion. About
18% of the 5’ T-RFs were identified as pandemics from the gut structure in the organic
system and 27% in the conventional system. In the exoskeleton, 15% were identified as
pandemics in the organic system and 38% in the conventional. Without regard to
practicing system, 18% of the 5’ T-RFs identified as pandemics in the gut and 12% in the
exoskeleton. Digestion with Msp I generated one pandemic fragment that were present in
gut and exoskeleton, and both farming systems. The pandemic fragment was identified
as 67.8 bp while other T-RFs frequently found 5’ T—RFs were 160.0, 188.7, 191.7, 521.0

and 564.2 bp.

Endemics. Additionally, endemic 5’ T-RFs (i.e. are pandemics found in more than 75%
of elecropherograms of one insect structure and less than 25% in the other insect
structure) within each insect structure independent of practicing system were also
identified. In the gut structure, 9 % of the 5’ T-RFs identified as endemics and none in
the exoskeleton by using the Hha I digestion without regard to practicing system. When
the farming system was considered the range of the endemic fragments found in the gut
was 9 % for the organic to 18 % for the conventional system. The range of endemic
fiagments found on the exoskeleton was 0% for the organic to 21 % for the conventional

system.

90

There were not any T-RFs indentified as endemics using the Msp I digestion in both body
structures. The range of endemic fragments in the gut for the organic system was 0% to
10% for the conventional. The range of endemic fragments on the exoskeleton was 0%
for the organic to 7% for the conventional system.

Consequently, there was only one case that a pandemic fragments found in the gut
was later characterized as endemic. For example, one out of eleven 5’ T-RFs identified
in the gut community was present in more than 75% of all gut electropherograms and less
than 25% of the exoskeleton electropherograms (Table 3.1). The size of the endemic

fragment found from Hha I and regardless of practicing system was 140 bp.

Cluster analysis. Bacterial communities found in different house fly structures and farm
practicing systems generated a fingerprint (pattern) of presence or absence of 5’ T-RFs.
This fingerprint was used to infer relationships between gut and exoskeleton bacterial
communities or organic and conventional practicing systems. Thus, a phylogenetic
analysis was performed were each Operational Taxonomic Unit (OTU) was 5’ T-RF
fingerprints from bacterial communities found on different house fly structures from
farms practicing either the organic or conventional farming system. Maximum
Likelihood and Neighbor Joining methods (implemented in PAUP) were applied to data
generated by Genotyper. The two methods revealed similar topologies so only results

from Maximum Likelihood are shown (Figure 3.2).

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Cladograms showing phylogenetic consensuses from analysis of terminal restriction fragment,

frequency data from Hha 1 and Msp I digestions of 16S rDNA sequences ofbacteria associated
with the house fly gut or external surfaces. Individual farms and fly samples were considered
as the Operational Taxonomic Units (OTUs). The dendrograms were generated with PAUP
using the maximum likelihood method. A ~ C, Hha l digestions. D ~ F, MSP I digestions.
OTU abbreviations: E, exoskeleton; G, gut; C, conventional farm practice; 0, organic farm
practice; H, Hha l; M, Msp 1. Example: ECl 10H was an exoskeleton fly sample from
conventional farm 110, and the bacterial 16S rDNA.

 

 

 

Fingerprints of bacterial communities digested by either Hha I or Msp I endonucleases
and found on the exoskeleton of house flies, were not able to differentiate practicing
systems (Figure 3.2, panels A and D). Specifically, fingerprints from the organic farms
116 and 111 (E0116H, E0116M, E011 1H, E011 1M) were more closely related to
' fingerprints from other conventional farms rather than the remaining organic farms.
Three to five of the organic farms (43%-71%) clustered together depending on the
endonuclease used. The conventional farms consistently clustered together. Analysis
from the exoskeleton community DNA from E0114H OTU was amplified and digested
twice and results showed identical fingerprints indicating reproducibility of the method
(Figure 3.2, panel A).

Similarly, fingerprints of house fly gut bacterial communities digested by either
Hha I or Msp I endonucleases, were not able to differentiate practicing systems. Four
subtypes of fingerprints were observed from the Hha I digestion of the house fly gut
community (Figure 3.2, panel B) whereas no clear subtypes were observed from the Msp
I digestion (Figure 2.2, panel B). Fingerprints of the organic farms 117, 111 and 103
(G0117H, GO] 1 l, G0103) were more closely related to fingerprints from conventional
gut bacterial communities rather than other organic gut communities. In support of the
Hha I digestion, the phylogenetic analysis by using Msp Ireveals all organic farms being
distributed within conventional farms without any indication of clustering based on

farming system (Figure 3.2, panel E).

93

When a phylogenetic analysis was performed using fingerprints from both house fly
structures, then fingerprints from fly gut were clustered together and exoskeleton
fingerprints formed two subtypes on either side of the gut cluster (Figure 3.2, panels C,
F). Interestingly, the exoskeleton fingerprint from the farm 117 (E0117H, E0117M)
was more closely related to other fingerprints from the gut community rather than
fingerprints from other exoskeleton communities (Figure 3.2, panels C, F). The

topologies of farm fingerprints remained similar for both digestions.

Similarity index analysis. Results of similarity analyses (Table 3.2) showed that
overall, house fly gut community fi'om house flies sampled in organic farms was more
similar to the house fly gut community from house flies sampled in conventional farms.
The similarity index was 0.65 and 0.62 for the Hha I and Msp I digestions respectively.
Both organic and conventional gut communities were less similar than either one from
the exoskeleton communities. At the same time house fly exoskeleton community from
organic farms was more similar to the house fly exoskeleton community fiom
conventional farms than either one of the two house fly gut communities. Fingerprint
similarity between organic and conventional communities is higher (0.68, 0.65) than
house fly gut and exoskeleton fingerprints (0.52, 0.54) for the Hha I and Msp I digestions

respectively.

Multinomial analysis. The above results were supported using a chi-square test. Data

from every distinct community was represented in the form of a histogram where the X

94

axis depicted the fragment length and the Y axis represented the fragment frequency in
each community. Chi-square analysis compares the histograms whether they have the
same distribution. Similar histograms or community fingerprints are depicted with larger
than 0.05 values while more distant ones have values <0.001 (Table 3.3). Results
coincide with those fi'om the phylogenetic and similarity index analysis. Conventional
and organic system communities were not significantly different within either the
exoskeleton or the gut community. Gut community was significantly different than the
exoskeleton within Organic or Conventional systems from T-RF’s generated with Hha 1,

whereas Msp I shown only in case of exoskeleton organic and gut conventional.

95

Table 3.2 Similarity indices for the bacterial communities associated with house fly
exoskeleton and gut, based on T-RF analysis and Hha I and Msp I digestions.
Index ranges from 0 (completely dissimilar) to 1 (completely similar).

 

 

Insect Structure and Gut, Exoskeleton,
. . . Exoskeleton,
Farrmng System Conventional Orgamc .
Conventional Farm
Farm Farm
Endonuclease Hha I Msp I Hhal Msp I Hha I Msp I
Gut Organic 0.65 0.62 0.48 0.52 0.51 0.50
Gut Conventional 0.51 0.48 0.52 0.52
Exoskeleton 0.69 0.64
Organic

 

96

Table 3.3 Chi-square analysis using fiagment length and abundance data generated from
Hha I and Msp I digestions. Values represent chi-square f value, degrees of
fi'eedom and p values.

 

Insect Structure
and Farming Gut Exoskeleton Exoskeleton
System Organic Conventional Organic

 

Endonuclease Hha I Msp I Hha I Msp I Hha I Msp I

 

Gut 2.26 9.46 111.15: 8.61 90.27 13.68
Conventional 6 6 6 6 6 6

0.8940 0.1490 <0.0001 0.1966 <0.0001 0.0333

Gut l 14.50 6.93 96.07 6.89
Organic 6 6 6 6
<0.0001 0.3271 <0.0001 0.3303
3.16 3.16
Exoskeleton 6 . ' 6
Conventional 0.7877 0.7877

 

97

T-RFLP Analysis Program (TAP). The results of an effort to match the predicted T-RF
are generated by using a computer-simulated digestion of the RDP II and the observed T-
RF’s are shown in Table 3.4. The RDP 11 online analyses has 72,626 aligned and
annotated bacterial 16S rRNA sequences updated at 06/25/03. Sequences appear in the
RDP H Preview Release approximately one month after they are released in GenBank
(http://rdpcmemsuedu/html, Marsh et al. 2000, Maidak 2001).

For the observed sizes on both house fly structures of 214.4 and 191.7 bp from the
Hha I and Msp I digestions respectively, the predicted matches were the blaclr water
bioreactor bacterium and Xanthomonas sp. The blackwater bacterium BW3 belongs to
the class of betaproteobacteria, family Alcaligenaceae (Morgan et al. 2002). This strain
had a 99% identity with the Strenotrophomonas nitriducens of the Xanthomonas group,
which includes ubiquitous plant pathogenic species (The prokaryotes 2003).
Xanthomonas sp. belongs to class of gammaproteobacteria, family Xanthomonadaceae
(Ganity et al. 2001). Members of this group are also phytopathogenic bacteria (The
prokaryotes 2003). The class betaproteobacteria is well represented by cultivated and
uncultivated organisms with a'wide range of habitats cosmopolitan species (Hugenholtz

et al. 1998).

98

 

 

 

Table 3.4 Nearest match of pandemic 5’ Terminal Restriction Fragments (5’ T-RFs) generated by capillary electrophoresis
and predicted 5’ T—RFs generated with computer simulated digestion of 16S rDNA sequences in the Ribosomal
Database Project II (Maidak et a1. 2001).

 

Observed Mean 5’

Predicted RDP II 5’

 

 

 

 

 

 

 

T—RFs T—RFs
Number Genebank
Hha I Msp I Hha I Msp I Nearest match in RDP oftaxa accession References
found numbers
214.4 191.7 213 192 Blackwater bioreactor bacterium 1 AF 394168 Morgan, C.A. et a1. 2002
214.4 191.7 213 192 Xanthomonas sp. oral 1 AF3 85546 Paster, B]. et a1. Unpublished
235.7 521 234 523 Clostridium botulinum 1 X68317 Hutson, RA. et a1. 1993
235.7 521 234 523 Clostridium perfringens 2 M69264 Garnier, T. et al. 1991
366 67.8 365 65 Uncultured bacterium 1 AF388339 Sessitsch, A. et a1. 2001
366 67.8 365 69 Uncultured sludge bacterium 1 AF234754 Juretschko, S. et a1. 2002
366 160 365 159 Kineococcus—like str./bacterium 6 AFO95334 Garrity, GM. and DB. Searles 1998
366 160 365 159 Streptomyces bikiniensis l X79851 Mehling, A. et al. 1995
366 160 365 159 Mycobacz‘erz‘um sp. 28 AY215266, Hall, L. et al. 2003
366 AY012577, Schinsky, M.P. et a1. Unpublished
367 AF498661, Coleman, N.V. et a1. 2002
AY163338, Le Dantec et al. Unpublished
X55594, Pitulle, C. et a1. 1992
X53896 Kazda, J. et a1. 1990
M29569 Stahl, DA. and J.W. Urbance. 1990
366 160 365 160 Unidentified firmicute 1 AB010619 Hiraishi, A. et a1. Unpublished

 

99

 

 

 

The observed 235.7 and 521 bp 5’ T-RFs generated from the Hha I and Msp I digestions
respectively, match Clostridium botulinum and Clostridium perfi'ingens pathogens. Both
species belong to phylum F irmicutes, class Clostridia, order Clostridiales, family
Clostridiaceae (Garrity et a1. 2001). The habitat of the first species is soil whereas for
the second can vary from soil to intestines of humans, animals and birds (The
Prokaryotes. 2003).

The observed 366 bp 5’ T-RF from the Hha I digestion and 67.8 bp 5’ T-RF from
the Msp I digestion, generated two matches with uncultured bacteria from environmental
samples. Since both stains are uncultivable little is known about their general properties.
The first strain remains unclassified whereas the second belongs to the phylum
Actinobacteria nov., class Actinobacteria. This class is well, represented by a high G+C
content cultivated organisms described in a wide range of habitats but dominating in soil
and waste water habitats (Hugenholtz et al. 1998).

The 366 bp from the Hha I digestion and 160 bp from the Msp 1, generated with the RDP
11 matches with taxa within the class Actinobacteria. The class of Actinobacteria is
represented by the families of Kineosporiaceae, Streptomycetaceae and
Mycobacteriaceae. Members of the Streptomycetaceae found in soil habitats are
quantitatively and qualitatively dependent on a number of different soil environmental

components (The prokaryotes. 2003).

100

DISCUSSION

Diversity analysis. DNA fingerprint similarity is a sensitive estimation of relative levels
of population homozygosity (Lynch 1990). The species diversity is represented not only
by differences in DNA homology which underestimate the number of species, but also
the occupation of different niches by environmental partitioning (Dykhuizen 1997). For
example, experiments with Escherichia coli grown in a glucose limited environment
evolved in a coexistence of three strains with different metabolic phenotypes (Helling et
al. 1987). Estimations of the actual p0pulation diversity show that it is by ten fold
underestimated based on bacterial DNA rehybridization results (T orsvik et al. 1990a,
1990 b.). Thus, it is easier to measure differences between different communities than to
estimate the species richness within a community (Dykhuizen 1997). Dukhuizen (1997)
claims that methods, such as cloning and sequencing, are inadequate to provide a good
estimate of species diversity but they can give information on the most common species
in a community.

T-RFLP is method that provides an estimation of community structure and
diversity (Moyer et al. 1996) but also estimates phylogenetic diversity between related or
unrelated bacterial communities. In order to generate the optimal combination of unique
terminal fragments (5’ T-RFs), PCR products from all samples were subject to Hha I and
Msp I digestions; each endonuclease treatment generates a large number of unique
terminal fiagments that aim to detect and differentiate the maximum number of O.T.U.s
with the minimal effort. As the number of different endonucleases increases the resulting

5’ T-RFs from each digestion will tentatively identify experimentally determined

101

phylotypes from cognate phylotypes in the RDP II database (Marsh et a1. 2000). Thus,
this method can eliminate a large number of phylotypes and identifies phylotypes that
meet the criteria set (length of 5’ T-RFs) from both digestions. The number of unique 5’
T-RFs is an underestimation of the actual community structure due to the production of
identical size 5’ T-RFs from phylogenetically related organisms (Totsch et al. 1995).
Peaks present in low intensity in electropherograms are an additional reason for
underestimating species richness and community diversity.

Particular interest shows the gastrointestinal microbiota due to physiological,
immunological and biochemical features that are considered intrinsic characteristics but
they are actually host responses to presence and metabolic activities of the normal
microbiota (Tannock 1997b).

The house fly gut appears to constitute an enormous reservoir of bacterial
diversity. Here, it is shown that the house fly gut bacterial community is more diverse
and distinct than the exoskeleton community. It is numerically more abundant than
exoskeleton community based on 5’ T-RFs generated by PCR-amplified 16S rDNAs.
This outcome was expected due to co-evolutionary adaptations that microbes have
developed between with their host and their significance in food utilization, metabolism,
and pathogenesis. No significant differences in diversity were found in house fly gut
from flies sampled in different farming regiments. This can be justified due to the limited
use of antibiotics used in the conventional farms which can not induce a significant
difference in the house fly gut bacterial diversity.

Although the fraction of 5’ T-RFs identified in the gut as pandemics remained

stable in both farming practices, the fi'action of pandemics in the exoskeleton varied

102

significantly. This indicates that exoskeleton community is prone to temporal or spatial
practices implemented in the farm environment or in the habitat range of the house flies.
A significant fraction though of the pandemics was predominantly found in both body
structures. In general, the number of pandemics was higher in the exoskeleton structure
but the percentage of endemics was higher in the house fly gut. Thus, the house fly gut
presents a complex, stable and endemic microbiota in a close association with its host and
unaffected by the use of antimicrobials whereas the exoskeleton is a less diverse,

variable, reflecting environmental pressures.

Electropherograrns. The four major groups of peaks observed in the electropherograms
illustrate the complexity and structure of both exoskeleton and gut microbial
communities. Evidently, at least four dominant peaks reflect equal number of dominant
' taxa that fill up niches of the morphological and physicochemical properties of the house
fly gut and exoskeleton. At the same time new microniches are created due to microbe-
microbe interactions in the house fly gut due to the mutual dependence in almost any
anoxic environment (Dolfing and Gottschal 1997). Although the presence of the same
number of taxonomically distinct groups, the size of the dominant 5’ T-RFs generated are
distinctively different between house fly structure communities. .-

It has been suggested the use of antimicrobials in animal food suppresses the
normal microbial populations in mammals by affecting obligate anaerobic populations by
regulating populations of potential pathogens (Tannock 1997a). Although thisiis true for

the animal husbandry where antimicrobials are used as growth promoters, there are not

enough evidence to suggest that the implementation of antimicrobials in the farming

103

industry has any effect on either the microbial community composition found on the
exoskeleton or in the gut of house flies. Due to growth rate cost that antibiotic resistance
confers to bacteria it would be expected in an antibiotic free environment (organic farms),
that counter-selection of antibiotic resistance taxa would be the case for the bacterial
community found on the exoskeleton of house flies. Alternatively, the use of
antimicrobials has been closely associated with selection for resistant phenotypes (Bates
et a1. 1994, Piddock 1996), it does not reflect species displacement and change in the
community diversity in both house fly structures, instead house fly gut might serves as a
reservoir for the dissemination of resistant phenotypes or a site for antibiotic transfer
between and within bacterial species. The conventional farms were not selected
randomly, but they were selected to be in close proximity to the selected organic dairy
farms that agreed to participate. Certified organic farms maintained the records that
demonstrate compliance with the organic association and we assumed that the farners

complied with them.

Cluster analysis. Restriction site variation among SSU rDNAs make them useful for
phylogenetic reconstructions (Moyer et al. 1996). Although, Neighbor-Joining (NJ)
phylogenetic analysis does not always find the smallest overall distance (as a parminony
method does), allows unequal rates of evolutionary changes to be incorporated in the
distance analysis (Avise 1994), and uses less computational time than maximum
likelihood (ML). On the other hand, ML generates a large number of trees and
maximizes the probability of finding the tree that best describes the data (Hall 2001).

Thus, the implementation of ML along with the choice of terminal restriction

104

endonucleases (TREs) that detect and differentiate bacteria taxa, they can reduce the
complexity of the community profile and estimates of community diversity can be done
easily. When datasets of exoskeleton and gut communities were combined, the
maximum likelihood showed a distinct cluster of all gut communities in the center of the
cladogram (Fig. 2C). However, there was high similarity between bacterial communities
from the same body structure and different farming regiments. This is an indication that
exoskeleton communities reflect a variable environmental bacterial community rather
than within the gut community, which is a complex and stable commensal fauna that
possibly reflects in part some mutualistic relationships satisying the nutritional

requirements of the house fly.

Similarity index analysis. Similarity analysis supports a significant overlap between gut
communities from the organic and conventional practicing system which is based merely
on the presence or absence of common 5’ T-RFs. Approximately the same overlap was
observed between exoskeleton communities from different practicing systems. Only in
the case of communities from different body structures the community’s structure overlap

was less evident than before.

Multinomial analysis. Results from Chi- square analysis indicate that data generated by
Msp I digestion, are inadequate to differentiate communities based on fingerprints
generated. Although Msp I generates a high number of restriction sites per taxon, the

frequency of T-RFs is observed in less than 200 bp classes (Moyer et al. 1996). On the

105

other side, the other tetrameric restriction endonuclease Hha I yields the highest
frequency in the 250-550 bp range (Mayer et a1. 1996).

Discriminatory results of chi-square analysis support that bacterial diversity in the house
fly exoskeleton is independent of the specific site where flies were sampled. The
hypothesis that the use of antibiotics in the conventional farms might reflect a less diverse
bacterial community on the surface of the house fly body is not supported. This can be
explained from the fact that house flies have a wide flight range and thus they are not
limited to bacteria found in the sampling location. House fly connection with primitive
systems of sanitation and waste disposal has been studied, while passive transportation
with garbage vehicles and vegetable trucks has been supported (Greenberg 1973). Early
studies (Parker 1916) for different species of flies showed the maximum distance from
the release point that house flies spread is 13 miles in less than 24 hours. Maximum
distance can be achieved with the desire to reach food or shelter (Parker 1916). Olfactory
cues might be the stimulus for dispersal, or flies just move in the direction of air currents.
Releases of radioactive adult flies showed that adult flies orientate to wind-bom odors
from farmyards and rrrigrate from one farmstead to another in suboptimal weather
conditions for flight (Hanec 1956).

The house fly gut environment promotes bacterial fauna very important for the
organic matter metabolism at the larval stage (Zurek et a1. 2000) and possibly during the
adult stage for nutrient utilization. Undoubtedly the gut fauna is dominated by species
that are primarily involved in food utilization but also it is a long co-evolutionary

adaptation to morphological, physicocherrrical conditions found in the gut habitat.

106

T-RFLP Analysis Program (TAP). In an effort to provide insight to community
structure I attempted to identify dominant bacterial groups found in common on the
exoskeleton and house fly gut. The application of a web based tool in the RDP II allows
the identification numerically dominant taxa by comparing the 5’ TRFs generated from
the experimental digestions and the predicted digestions from in silico digestion of RDP
II. The predicted match as black water bioreactor bacterium has been used previously
isolated from substrate-limiting conditions of a biomass-recycle bioreactor that was
inoculated with activated sludge from municipal wastewater (Morgan et al. 2002).
Members of black water bioreactor bacterium are ubiquitous species and belong to the
class betaproteobacteria and family Alcaligenaceae. The second predicted match is a
member of the class gammaproteobacteria, family Xanthomonadaceae (Paster et al.
Unpublished). This member has been isolated fiom purified crevicular epithelial cells.
Members of the class Clostridia, family .Clostr'idiales are dominant species of the
gastroidestinal microbiota. Clostridia species are gram positive, obligatory rely on an
anaerobic energy metabolism with a low G+C content (Woese 1987). They are
considered one of early colonizers in animals along with Bacteroides and Lactobacilli
(Moughan et al. 1992). Clostridia have been previously isolated because of the interest
to study their ability to degrade organic material to acids when in many-cases the
production of butyrate is associated with the genus as well hydrogen accumulation during
fermentation in the hindgut of termites (Schmitt-Wagner et al. 2003).
Identified matches as: uncultured sludge bacterium, Mycobacterium sp. , Streptomyces
bikiniensis and Kineococcus like str., belong to the class Actinobacteria. Actinobacteria

is a cosmopolitan division composed of cultivated and uncultivated organisms

107

(Hugenholtz et al. 1998). The identified Mycobacterium sp. has been isolated from sites
contaminated with chlorinated solvents and it has been associated with degradation of
vinyl chloride (Coleman et al. 2002). The uncultured bacterium was isolated from soil
(Sessitsch et al. 2001). Further information for the remaining predicted matches are
limited. In conclusion, the experimental 5’ T-RFs matched with universally found
bacterial strains and groups and it is not surprising that they were also associated to the
house fly gut or exoskeleton community.

Neglecting amplification bias of the 16S rDNA from bacterial communities
(Suzuki and Giovannoni. 1996), and chimera formation which can confound diversity
estimates, we implemented a culture independent method to reveal community
polymorphisms and compare closely associated bacterial communities. Community
analysis by T-RFLP of bacterial 16S rDNA showed that some bacterial divisions are
cosmopolitan to the house fly while others are restricted to the exoskeleton or gut
habitats. Fanning practices seem to have no significant effect to the house fly microbial
diversity and microbial communities from the same body structure but different farming
practices are very similar. House fly gut is a very diverse, and stable community sharing
bacterial stains or groups found on a less diverse, variant exoskeleton structure. Thus,
our results demonstrate that gut microbial diversity is site specific association and it is not
affected by extemal factors such as the use of antibiotics in farm practices. The
exoskeleton community structure will help us understand the medical importance of
house fly as vector or a habitat for foodbom pathogens. A better understanding of the

role of emergence of new pathogens is of great importance.

108

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114

CHAPTER IV

Bacterial community composition of the house fly gut and a comparative analysis

with cow feces using 16S rDNA sequence analysis.
ABSTRACT

House fly adults and freshly shed cow fecal samples were obtained from conventional
and organic practice dairy farms in Wisconsin, USA. A total of 892 and 745 bacterial
16S rDNA clones were sequenced from house fly gut and feces respectively, and were
classified using a naive Bayesian classification algorithm provided by the Ribosomal
Database Project H. Results showed that the bacterial community of the house fly gut
was more diverse (55 genera) than the fecal community (27 genera), but forty percent of
the clones classified to genus (16 genera) were common to both communities. The
genera Pseudomonas, Janthinobacterium, Clostridium, and Acinetobacter were
especially common in both communities. However, both communities were highly
uneven with a few dominant taxa and many uncommon taxa; a richness estimator
predicted 36 and 79 genera for the fecal and house fly gut communities, respectively.
Phylogenetic placement of a subset of sequences using the ARB program revealed novel
taxa within the classes Bacilli, Clostridia and Actinobacteria. It also revealed that the
dominant Pseudomonas taxa in both fly gut and feces were close phylogenetically to the
fluorescent pseudomonads. Within the house fly gut, several potentially pathogenic

bacterial groups were evident, including Enterobacter, Enterococcus, Shigella, and

115

Pantoea. Comparisons using the LIBSHUFF procedure indicated that the house fly
library was significantly different in bacterial taxa composition than the fecal library, but

libraries between organic and standard practice dairy farms (whether from fly guts or

fecal samples) did not differ substantially.

116

INTRODUCTION

Bacteria commonly occur in the alimentary canal and feeding apparatus of
invertebrates, either as commensal organisms, associated with ingested food, as
pathogens, as symbiotes (Cazemeier et al. 1997, Lilbum et a1. 1999, Kaufman et al.
2000). Investigations of the phylogenetic diversity of the bacterial assemblages can
reveal the functional role of microorganisms in the invertebrate gut, and in turn the role
of the invertebrate as a host of the microbes. High densities of microorganisms within
the insect alimentary canal suggest that microbes can make a significant contribution to
the digestive process whereas in other documented examples with very low numbers of
microbes suggest a lesser role (Cazemier et al. 1997). Interestingly, diversification of
the insect gut resulted in a series of physiocherrrical adaptations reflected by bacterial
diversity and coadaptations (Schmitt-Wagner et al. 2003).

House flies constitute an important link of an ecosystem as indirect primary
decomposers. It is accomplished by the bacterial flora harbored in house fly alimentary
canal or the bacteria in the ingested food, which together decompose the ingested organic
matter and facilitate digestion. Although, gut bacterial diversity has been studied for
termites (Schmitt-Wagrrer et a1. 2003), beetle larvae (Egert et al. 2003), honeybees
(J eyaprakash et al. 2003), lepidoptera (Broderick et al. 2004), house fly larvae (Zurek et
al. 2000), and many other arthropods (Kaufman et al. 2000), diversity of bacteria in the
adult house fly gut has not been extensively examined. The bacterial associations with

the house fly gut becomes even more significant due to the association of house flies with

117

pathogens and food-bome diseases (Greenberg 1973, Bidawid and Edeson 1978,
Graczyk 2001).

Cultured microorganisms typically represent only a small portion of the microbial
community, hence the microbial diversity (Friedrich et al. 1997). Therefore, estimates
based only on the use of culture dependent methods tend to underestimate actual
microbial diversity (Zoetendal et a1. 2004). A phylogenetic based taxonomy based on
non-culturing methods that estimate total bacterial diversity without relying on
morphological, physiological and biochemical characters is the new approach (Frostegard
et a1. 1999). While several cell components are phylogenetically informative, small
subunit ribosomal RNA genes, such as the 168 rRNA genes in bacteria,are highly
conserved among all organisms and this quality makes them well-suited macromolecules
for inferring phylogenetic relationships of microbes in ecological studies (Britschgi et al.
1991). The successful use of oligodeoxynucleotide primers to amplify 16S rRNA gene
sequences allows construction of sequence libraries that can be studied for bacterial
diversity. It further offers simplicity because it applies to a biological samples of a wide
range of origin and with widely divergent bacterial composition. (Lane et al. 1985).
These genes are reliable phylogenetic markers used to assess natural relations of
prokaryotes using modern PCR and sequencing technologies (Friedrich et al. 1997)
while in the case of intracellular symbiots that can not be cultured outside of their hosts
provides insight into their evolutionary histories and specific adaptations to symbiosis
(Moran and Telang 1998). There is sufficient information at the 5’ end of 16S rRNA '
genes to construct phylogenies and resolve phylogenetic placements (Hugenholtz et al.

1998)

118

Quantifying gut biodiversity is of particular interest for an insect vector of
pathogens and foodbome diseases. Particular bacterial species and strains, known to be
pathogenic to humans and domesticated animals, have been isolated from the house fly
gut and some have been demonstrated to transmissible by either biological or mechanical
means (Graczyk et al. 2001). However, lacking are studies of the diversity of bacteria in
the house fly gut, under conditions where flies from natural populations are under study
and when modern molecular methods are utilized. Therefore, the purpose of this study
was to quantify the diversity of bacteria in the house fly gut enviromnent using these
molecular methods. Overall, this study presents aculture-independent analysis of the
bacterial diversity of the house fly gut and compares it to bacterial diversity of cow feces,

on dairy farms utilizing conventional or organic practices. '

119

MATERIALS AND METHODS

Study area and sampling. House fly adults were sampled on dairy farms in Wisconsin
with a sweep net and a battery-powered aspirator to capture flies and secure them. Flies
were transferred to sterile collection tubes, shipped alive overnight in a cold box
surrounded by frozen blue ice blocks, and upon arrival at the laboratory they were
processed within 24 hours as described below. Fly mortality was very low (<1%) during
shipping. Twenty farms representing a network of study farms in southwestern
Wisconsin (Sato et al. 2002) were sampled, thus the farm was the experimental unit in
this study. Fecal samples and house flies were sampled from 10 organic and 10
conventional farms. The organic farms were certified by a state agency and had not used
antibiotics for at least 3 years. All farms were visited once in September of 2001.

Adult house flies were sorted from other flies in samples using identification procedures
for the Diptera (Hall and Gerhardt 2002 and references therein), and were retained for
studies here. They were dissected using aseptic technique with sterile forceps and
minuten needles. The guts were removed, placed in individual eppendorf tubes (5 per
tube) and triturated with a pestle in 0.5 ml of Luria-Bertani (LB) medium. The remaining
carcass of the fly was triturated in the same manner for studies reported elsewhere
(Chapter HI). Aliquots (100 111) from each tube were pooled by farm. Samples were
preserved with 30% glycerol and fiozen at —70 °C for later study.

Feces were sampled fiom five lactating cows on each farm, by walking among the cows
and collecting freshly-voided feces at the moment of defecation. Samples were excluded

if the cows showed symptoms of diarrhea. Approximately five grams of fecal samples

120

were transferred into a Cary-Blair Transport Media tube. Tubes were held on ice and
shipped ovenright to the laboratory, where they were pooled (5 samples per tube) and

fiozen at —70 °C.

DNA extraction, amplification, cloning, and sequencing. Pooled fly gut and fecal
samples were thawed at room temperature and were vortexed for 30 sec. DNA was
extracted from them using the UltraClean Soil DNA Kit (Catalog # 12800-50, Mo Bio
Laboratories Inc., Solana Beach, CA) according to the manufacturer’s protocol. DNA
from each sample was eluted in 50 ul elution buffer (10 mM Tris) and temporarily stored
at -20 °C. This DNA was used as template in the polymerase chain reaction using the
FailSafeTM PCR System (Epicentre®, Madison, WI) with premix E. A 1.3 kb fragment
of the 16S rDNA was amplified using the universal primers 63F 5’-CAG GCC TAA
CAC ATG CAA GTC -3’ and 1387R 5’-GGG CGG WGT GTA CAA GGC -3’
(Marchesi et al. 1998). The PCR conditions consisted of an initial holding step at 80 °C
for l min, followed by 30 cycles of denaturing at 95 °C for l min, annealing at 55 °C for
1 nrin and extension at 72 °C for 1.5 min. For each 100 ul PCR reaction we used 10 uM
of each primer and 2 ul (ca. 20 ng) of DNA template. The PCR product integrity was
visualized on an agarose gel before cloning (see below). The PCR product was purified
from the gel using the QIAquick Gel Extraction Kit (Cat. # 28704, Qiagen, Valencia,
CA) according to the manufacturer’s protocol, concentrated using a speed vacuum and
suspended in 10 ul of DNAase free water.

The PCR products were cloned using the pGEM®-T easy vector system II (Cat #

A1380, Promega Corp., Madison, WI). A minimum 3:1 ratio of insertzvector was used to

121

optimize the ligation reaction and maximize the number of transfonnants. High
efficiency competent cells JM109 provided in the kit were used for the transformation
and they were compatible with a black/white screening protocol on S-Galm/LB Agar
(Cat # C-4478, Promega) plates amended with 100 ug/ml arnpicillin. Candidate white
colonies were passed to fresh S-Gal agar plates for confirmation, and incubated for more
than 24 hours and colonies inspected for color to verify the presence of the insert. Single
colonies were picked and inoculated into 96 well plates containing LB freezing buffer
and arnpicillin (100ug/ml), covered, and incubated to permit growth for 24 hours at 37
°C. DNA from bacterial cultures were purified at the Genomics Technology Support
Facility, Michigan State University, using a Qiagen 3000 robot following the
manufacturer’s protocol. Sequencing of the cloned inserts was done with high
throughput sequencing using dideoxy dye terminator chemistry at the Genomic
Technology Support Facility, Michigan State University, on an ABI 3730 Genetic
Analyzer or ABI Prism 3700 DNA Analyzer. Partial sequences of the 5’ end of all rDNA
clones were obtained using the general 16S rDNA sequencing primer 519R (5’-
G(AT)ATTACCGCGGC(GT)GCTG-3’) (Lane et al 1985) and the M13 forward primer

(5’-TGTAAAACGACGGCCAGT 3’).

Classification and phylogenetic placement of sequences. Sequence data were
subjected to a series of analyses to obtain phylogenetic placement of each sequence; and
to compare composition of the sequence libraries among the experimental categories
(feces and fly gut from the two farm types). Each sequence was analyzed for the

possibility of chimera formation using procedures described in the Ribosomal Database

122

Project II (i.e., RDP II; Release 8.0, June 1, 2000, http://rdpcmemsuedu/html, Maidak
2001) and following Lilbum et al. (1999). Possible chimeras were excluded from
subsequent analyses. For purposes of classification, a naive Bayesian classifier provided
by the RDP II was invoked (Cole et al. 2003). It calculates the joint probability of
finding eight-base subsequences (“words") in the query and the query is then assigned to
a genus and higher order taxon with the highest probability of being correct (Wang et al.
2004).

A subset of sequences from both bacterial communities was aligned with existing
’ bacterial sequences in the ARB 16S rDNA database (June 2002 version) (Strunk and
Ludwig 1997). Classified sequences from the naive Bayesian classifier in RDP II were
initially grouped to genus level prior to importation into ARB for initial alignments. The
following restrictions were employed for phylogenetic placements in ARB: (1)
Sequences less than 260 base pairs in length were excluded due to the low number (less
than 250) of valid colurrms generated by the filtering method in ARB. (2) The base
frequency filter used was defined. Columns containing gaps, columns with Ns and
columns without data were excluded and minimal similarity for every column was set to
15. (3) Short sequences with longer duplicates after sequence alignment were also
’ excluded to increase the number of valid columns in the generated filter for each
phylogenetic tree.

All 16S rRNA gene sequences that were placed phylogenetically in ARB and
having similarity of more than 97% with each other were grouped into Operational
' Taxonomic Units (OTUs) (J uretschko et al. 2002). The bacterial nomenclature proposed

in the taxonomic outline (release 1, April 2001) of the second edition of Bergey’s

123

manual of systematic bacteriology (http://www.cme.msu.edu/bergeys/) was used to
taxonomically identify sequences placed in ARB.

A statistical test provided by the LIBSHUFF program (Singleton et al. 2001) was
used to determine whether significant differences between sequence libraries existed.
Here, those libraries were (1) fly gut (FG)/organic farm, (2) fly gut/conventional farm, (3)
feces (CF)/organic farm, and (4) feces/conventional farm. The analysis was
accomplished by accessing an on-line computer program in Perl script
{http://www.inrchesuggdu/ ~whitm_a_in/libs_huff.html). The program estimates
differences between homologous (Cx(D)), and heterologous (ny(D)) coverage curves and
tests for the difference with the Cramer-von Mises test statistic, Any = 2‘. (Cx-ny)2. The
Simpson and Shannon diversity indices (Magurran 1988) were estimated in order to
compare bacterial genus diversity between the cow fecal and house fly gut sequence
libraries. To address the question of relative evenness of the bacterial communities
reflected by these sequence libraries, the data were represented as rank/abundance graphs
where the abundance of each species is plotted on a logarithmic scale against the species’
rank fiorn the most to least abundant (Magurran 1988). The form of the data distribution
was estimated that could be plausibly described by either the geometric series model or
by the logarithmic series (May 1975). The program Estimates was used to generate
nonparametric statistical estimators of minimal true genera richness or otherwise
aSWII'lptotic richness (S) (Colwell, 1997, Gotelli and Colwell 2001,). Some of the.
methods are based on incidence data (presence/absence) whereas others are based on

relative abundance data (Chazdon et al. 1998).

124

Nucleotide sequence accession numbers. The environmental sample sequences used in
this study were deposited in the NCBI database under the accession numbers AY510741
to AY511110 for the Fecal Conventional clones, AY51 1 l 1 l to AY511451 for the Fecal
Organic, AY511452 to AY511908 for House fly gut Conventional and AY511909 to

AY512333 for the House fly gut Organic libraries.

125

RESULTS

Classification of sequences.

A total of 745 and 892 16S rDNA clones were sequenced from feces and house
fly gut respectively. The number of base pairs per sequence ranged from 115-892 bp for
fecal samples and 120-466 bp for house fly gut samples. Thirty-three sequences fi‘om the
cow fecal community and 18 from the house fly gut community were excluded as
chimeric artifacts and sequences with fewer than 260 bp were also excluded. A total of
501 and 704 sequences fi'om fecal and house fly gut respectively remained for
classification, phylogenetic placement, and firrther analysis. Most of these sequences
were 97% similar to genera in the RDP H (Cole et al. 2003, Wang et al. 2004) and
therefore qualified as acceptable matches by the pragmatic criteria of Stackebrandt and
Goebel (1994) and McCaig et al. (1999); however, sequences indicative of novel
organisms were also found (see Phylogenetic Analysis, below).

Classifications were distributed among 4 phyla and 8 classes, and included a
small set of unclassified sequences. The Proteobacteria was the most dominant phylum
in both habitats, representing 51% and 70% of the fecal and house fly sequences,
YBSpectively. The Firmicutes was the second most dominant phylum, comprising 44%
and 20% of fecal and house fly gut sequences. The remaining sequences were classified
into the phyla Actinobacteria (fecal, 2.6%; gut, 9.0%), Bacteriodetes (fecal, 2.2%; gut,
1-6%), or were unclassified sequences (“Incertae Sedis,” fecal, 0.3%; gut, 0.2%). The
Gammaproteobacteria was the dominant class, representing 45.6% and 58.8% of the
fecal and house fly gut sequences, respectively. The Clostridia was the second most

dominant class, representing 42.5% and 10.3% of the Fecal and house fly gut sequences.

126

The classes Betaproteobacteria, Alphaproteobacteria, Bacilli, and Actinobacteria each
represented less than 6% of the total sequences in the fecal samples, whereas the Bacilli,
Actinobacteria and Betaproteobacteria represented 8-10% of the house fly gut sequences
while the Alphaproteobacteria represented 3.1% of the gut sequences. There were no
sequences from the class F lavobacteria in fecal samples and 0.1% of sequences from the
house fly gut were in this class.

FIGURES IN THIS CHAPTER ARE PRESENTED IN COLOR. There were a
total of 27 generic classifications to the RDP II for fecal samples and 55 generic
classifications for house fly gut samples (Figures 4.1, 4.2). Seven genera comprised 92%
of the total sequences in fecal samples matched to genera in the RDPII, namely:
Pseudomonas, Clostridium, Janthinobacterium, Acinetobacter, Bacillus, T uricibacter and
Psychrobacter; the remaining 20 genera each represented <1 % of the sequences. Twelve
genera represented 88% of the classified sequences in the house fly gut, namely:
Pseudomonas, Janthinobacterium, Clostridium, Acinetobacter, Corynebacterium,
Lactobacillus, Enterobacter, Enterococcus, Bzfidobacterium, Weissella, Pediococcus, and
Vagococcus; the remaining 43 genera each comprised less than 1% of the total ntunber of

sequences from fly guts.

127

 

.—- L—J 1M7 I Eubacteriwn El Mogibacterium El Anaeravorax
[ova El Camobactefiwn I Citrobacter - Ralstonia I Comamonas
.Mesorhizobiwn Dillagnetospifilliwn DBuerlderia DAquabacterium
EIPhyuobacteriwn ElMicrobacterflan Elszackia DCorynebacteriwn
DAnhrobacter IBifidobacterium DEggmheua IOIseneIla
I Eigoribacta'iwn
Pseudomonas (60%)

 

   
   
  

Jmthinobacterium (6%)
‘ Others (8%)

CIostfidium (19%)

 

Psychmbacter (1%) _| I ‘ L____ Ila-icibacter (2%)

Acinetobacter (3%) Bacillus (2%)

 

Figure 4.1. Pie chart showing the bacterial community composition of cow fecal samples at the generic
level, as determined by 168 rDNA sequence classification with RDP 11. Percentages represent
the percentage of the total number of sequences (N= 501) that were classified to the indicated taxon.

The smaller pie chart shows those sequences classified to genera each of which made up less
than 1% of the total sequences.

 

 

m1

 

 

 

 

6ZI

 

 

   
  
  
  
 
 
 
 

El 1M7 I Mogibacterium El Staphylococcus I Leuconostoc
D Macrococcus I Anaeroglobus I Aerococcus I Ila-Icibacter
.Riemerella .Raoltella UPravidencia DShigeIla
El Pragia U Pantoea U Kluyvera El Citrobacter
El Halmonas D Psychrobacter I Schinera Acetobacter
I Asaia Legionella U Paracoccus I Ralstonia
I Habaspirilliwn I Sphingomonas I Del/ha - Devosia
I Methylobacten'wn I Commands I Mesorhizobz‘mn El Burkholderz‘a
.Rhodobacter .Rhizobium .Microbacteritm Collinsella
.Atopobium ERhodococcus .Brachybacten‘um Olsenella
.Brevibaclen‘um lDietza DLeucobacter UNocm'dioides
Pseudomonas (56%)
Janthinobacterium (7%)
Corynebacterium (6%)

Bifidobacterium (1%)

  

/
/

/
L Clostridia»: (4%)
LI— Weissella(1%)

Pediococcus (1%)
Lactobacillus (3%)
Vagococcus ( 1%)

Figure 4.2. Pie chart showing the bacterial community composition of house fly gut samples at
the generic level, as determined by RDP II. Percentages represent the percentage
of the total number of sequences (N= 704) that were classified to the indicated taxon.

The smaller pie chart shows those sequences classified to genera each of which made up
less than 1% of the total sequences.

 

 

Acinetabacter (4%) _ __.|

Enterobacter (2%)

Enterococcus (2%)

 

 

Sixteen genera were found in both communities including Pseudomonas,
Janthinobacterium, Clostridium, T uricibacter, Psychrobacter, Acinetobacter,
Mesorhizobium, C itrobacter, Microbacterium, Bifidobacterium, Mogibacterium,
Ralstonia, Comamonas, Corynebacterium, Olsenella and the unclassified sequences
(Insertae setis ‘TM7’). Bacterial sequences found only in the fecal community were
classified to genus as follows: Bacillus, Carnobcaterium, Phyllobacterium, Arthrobacter,
F rigoribacterium, E ubacterium, Magnotospirillium, Burkholderia, Slackia, Eggerthella,
Anaerovorax and Aquabacterium. Finally, sequences found only in the house fly gut
community were classified to the following genera: Enterobacter, Enterococcus,
Vagococcus, Lactobacillus, Pediococcus, Weissella, Macrococcus, Riemerella, Pragia,
Halmomonas, Asaia, Herbaspirillium, Methylobacterium, Rhodobacter, Atopobium,
Brevibacterium, Ana'eroglobus, Raoltella, Pantoea, Legionella, Sphigomonas,
Rhizobium, Rhodococcus, Dietza, Staphylococcus, Aerococcus, Providencia, Kluyvera,
Shinera, Paracoccus, Delfia, Brachybacterium, Leucobacter, Leuconostoc, Shigella,

Acetobacter, Devosia, Burkholderia, Collinsella, and Nocardioides.

Comparisons of sequence libraries.

Results fiom implementation of the LIBSHUFF procedure showed that sequence
libraries from the fecal (CF) and house fly gut (F G) libraries were significantly different
from each other, i.e., they were highly unlikely to be from a common community
(ACFG/CF = 0.989, P = 0.001; ACcp/m = 0.583, P = 0.001). At low evolutionary distance

(D<0.2), the actual values exceeded the comparable values (at a cutoff value of P = 0.05)

130

afier reshuffling the two libraries 999 times (Figure 4.3). In the case of the homologous
cow fecal coverage with the heterologous house fly gut (Figure 4.3, panel B), the (Cc):-
CCF/Fg)2 values became very close or just above the P critical values when D = 0.05 to
0.1, suggesting that many more distant phylogenetic groups were found in common for
both libraries. Interestingly, for D > 0.2, no differences were found in higher level
phylogenetic groups.

I also compared environmental clone libraries based on farming system used at
the sampling site and the habitat. Therefore I compared clones from the cow fecal habitat
sampled in dairy farms that practice the conventional system (FC), versus clones from the
cow fecal habitat from farms practicing the organic system (F 0) (Figure 4.4). ACFC/Fo
and ACFo/FC values were not significant (0.009 and 0.004) and were well below the 950th
value (PFC/F0 = 0.763, PFC/FC = 0.991) suggesting that the farm practicing system does not
affect the cow fecal bacterial composition. Results from the house fly gut library from
organic farms (GO) vs house fly gut clones fiom conventional farms (GC) gave a
ACGo/Gc = 0.843 and P-value of 0.001. However, the GCvsGO comparison had a
ACGc/Go = 0.191 and a P-value of 0.283. The results support the conclusion that GO and
GC are not significantly different communities because the ACGc/Go was not significant,
which suggests that all taxa present in GC were also present in GO; and because the

reciprocal value ACGo/Gc was significant.

131

 

 

 

 

1.2 1— 0.14

0.12

 

 

 

 

0 0.1 0.2 0.3 0.4 0.5
Evolutionary Distance

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4.3

Results of comparison by LIBSHUFF of bacterial l6S rDNA sequence
libraries house fly gut (PG) and from cow fecal (CF) samples fi-orn dairy
farms. Homologous (open squares) and heterologous (solid triangles)
coverage curves for 16S rRNA gene 2sequence libraries are shown. Solid
lines indicate values of (Cm-lecp)2 (panel A) or (Ccp-CCF/FG)2 (panel B)
for the original samples at each value of evolutionary distance (D). Broken
lines indicate the 950“1 value (or p=0. 05) of corresponding (Cm-Cm/cp)2 or
(CCPCCF/FG)2 values for the randomized samples.

132

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

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1.2 0.025 1.2 ~—~—- —~~~ W” - - _-_ ~ 0.025
~ 0.02 r 0.02
3a ~ 0.015 as; :5 > 0.015 ”a
E " E
0 Q o 9
s a 5 G
U . 0.01 - 9 ~ 0.01 .
«» 0005 —» 0.005
0 r A1‘/I\ r .I-‘.A‘I 1 r 0 '— 0
0.0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5
Evolutionary Distance B Evolutionary Distance
A. O
1.2 0.35 1.2 0.35
l . 0.3
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C .Evolutionary Distance D Evolutionary Distance

 

 

 

 

Figure 4.4 Homologous (open squares) and heterologous (solid triangles) coverage
curves for 16S rDNA sequence libraries from dairy farms are shown. Solid

lines indicate the value of (Cx-ny)2 or (Cy-ny)2 for the original

samples

at each value of Evolutionary Distance. Broken lines indicate the 950
value (or p=0.05) of (Cx-ny)2 or (Cy-ny)2 for the randomized samples.
Comparison sequence: F CvsF 0 (panels A,B), GCvsGO (panels C,D).

133

 

 

 

Therefore, the GC vs GO comparison suggests that GC is a subset of GO (Singleton et al.
2001). Results depicted in figure 4.4 (panel C) show that taxa from GC library for
evolutionary distances of D<0.1 were not represented in GO but there were taxa

represented in both libraries when D>0.1.

The same reasoning can justify the significant and high ACpo/Go = 0.899 for a P-
value of 0.001 (Figure 4.5, panel A) and a ACGo/Fo = 0.826 for a P-value of 0.001
(Figure 4.5, panel B). The high ACpo/Go and ACGo/po values indicate significantly

different libraries. Finally, the FC vs GC comparison had ACpc/Gc = 0.553 for a P-value

of 0.001 and the reciprocal ACGC/Fc.= 1.194 for a P-value of 0.001 (Figure 4.5, panels A

and B). Both statistical tests support that F C and GC libraries are different.

Phylogenetic analysis.

The following section represents phylogenetic placement of a subset of sequences
using the ARB sofiware and database. The placements are presented sequentially by
class and are supported with figures showing resultant phylogenetic trees. It is inferred
that highly homologous phylogenetic relationships of unknown sequences with known
bacterial species reflect similar physiological properties and evolutionary histories. By

extension, novel sequences represent poorly known bacterial species, likely undescribed
ones, with unknown physiological properties and lesser known evolutionary histories.
Actinobacteria. Phylogenetic placement of sequences in ARB showed that 43 sequences
were in the Gram positive, high G+C class Actinobacteria (W oese 1987). Thirty-five of
these sequences Were from house fly gut samples, suggesting that Actinobacteria were

represented more frequently in the house fly gut than in cow feces (Figure 4.6).

134

In detail, two house fly sequences (GC112.41, GC112.42) represented two OTUs.
GC 112.42 had a 94% homology with Olsenella uli. Two house fly secluences
(GC112.13, GC112.21) representing one OTU had an 89% homology with Collinsella
aerofaciens. C. aerofaciens is an obligate anaerobic, non-sporeforrning coccus
previously isolated from human feces (Kageyama et a1. 1999). Two fecal sequences
(FC112.34, FOl 1 1.11) represented two OTUs. F0111.11 had an 86% homology with
Eggerthella lenta. The genera Collinsella and Eggerthella belong to family
Con'obacterinaceae, order Coriobacteriales. Five sequences from both communities
(G01 17.02, GOl 17.19, F0111.14, GC113.10 and GC113.12) represented three OTUs.
GC113.12 had a 90% homology with Bifidobacterium thermophilum. Although most
bifidobacteria are mainly isolated from the intestinal flora, B. thermophilum was
previously isolated from an anaerobic digester for the treatment of waste water (Dong et
al. 2000). Two fecal sequences (FC112.06, F 01 17.15) represented one OTU. F0117.15
and Arthrobacter ureafaciens had a 88% homology. The sequence GC1 13.40 had a 92%
homology with Sanguibacter suarezii. Two house fly sequences (GC106.44, GOl 19.30)
represented two OTUs. GOl 19.30 and Microbacterium kitamiense had a 91% homology.
FC115.34 had a 94% homology with Microbacteriumflavescens. Members of the genera
Microbacterium, Arthrobacter and Sanguibacter belong to the order Actinomycetales.
Four house fly sequences (GC115.36, GC112.43, GC115.02 and GC115.10) represented
three OTUs. GC115.10 had a 95% homology with Rhodococcusfascians. This was the
highest homology with a species in the ARB database of all sequences presented in the
figure 4.6. R. fascians is plant pathogen that encodes fas virulence genes found on a

plasmid and induces the formation of either leafy galls or fasciations in many plant

135

species (T emmerrnan et a1. 2000). Towards the bottom of the tree in figure 4.6 can be
observed seven sequences from both communities represented five OTUs. GC106.23
had a 94% homology with Corynebacterium simulans. Eight sequences from the house
fly gut representing one OTU had a 94% homology with Corynebacterium simulans. C.
simulans is a non motile, facultative anaerobe showing a diphtheroid arrangement; it has
been previously isolated from human clinical samples as have many other corynebacteria
(Wattiau et al. 2000). Taxa in the genus Corynebacterium, a subdivision of the family I
Corynebacteriaceae, order Actinomycetales in the class Actinobacteria, were previously

identified in both communities using the RDP II analysis (Figures 4.1, 4.2).

136

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.2 ~~ ~-—— - — 0.35 1.2 0.35
, 0.3
r 0.25
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A. Evolutionary Distance B. Evolutionary Distance
1.2 0.35 1.2 0.35
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0 0.1 0.2 0.3 0.4 0.5 o 0.1 0.2 0.3 0.4 0.5.
C_ Evolutionary Distance 1) Evolutionary Distance

 

 

 

 

 

 

 

 

 

Figure 4.5 Homologous (open squares) and heterologous (solid triangles) coverage

curves for 16S rRNA gene sequence libraries from

' farms are shown.

Solid lines indicate the value of (ex-cm)2 or (Cy-ny) for the original
samples at each value of Distance. Broken lines indicate the 950'h value (or
p= 0.05) of (Cx-ny)2 for the randomized samples. Comparison sequence:

FOvsGO (panels A,B), FCvsGC (panels C,D).

137

 

 

 

  
 
 
 
 
 
  
  
 
 
  
   
  
  
 
   
  
    
  
  

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R ’zoa’ococcus fasciuns
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Rhodococcus percolalm
Rhudococc'us sp.
Tsukumztrellu paurrmzelabola
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Aclinualloteic/ms (j‘tlizogriseus
Streptomyces megasporus
Sireplomyces gnseus
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Figure 4.6 Phylogenetic tree demonstrating relationships within the Actinobacteria class.
as determined by Neighbor Joining method of 168 rDNA sequences.
The Sequence Associated Information (SAI) was based on 258 valid columns.
The red color bar represents 10% estimated sequence divergence. Vertical bars
depict sequences from a single source (purple) or multiple sources (green).

 

Bacteroidetes, Flavobacteria, Actinobacteria and Alphaproteobacteria. Phylogenetic
placement in ARB showed that 33 sequences were positioned with known species,
uncharacterized strains and yet not-cultivated strains fi'om the four classes Bacteroidetes,
Flavobacteria, Actinobacteria and Alphaproteobacteria (Figure 4.7). Twenty five of
those sequences were from the house fly gut samples and three out of four classes were
more frequently found in the house fly gut than in cow feces.

Five sequences (F0114.20, F0117.27, GC120.16, GC112.14 and GOl 19.02)
fiom both communities represented five OTUs. The sequence F0114.20 had an 80%
homology with Rhodospirillum rubrum. Three house fly sequences (GC115.39,
GC115.30 and GC115.34) represented two OTUs. GC115.39 had a 92% homology with
Devosia riboflavina. D. riboflavina, previously named as Pseudomonas riboflavina, is a
soil aerobic bacterium that oxidizes riboflavin to lumichrome (Nakagawa et al. 1996).
Three house fly sequences (GC115.29, GOl 19.54 and GC115.03) formed three OTUs.
The OTU GC119.54 had a 91% homology with Rhizobium huautlense. R. huautlense is a
nitrogen-fixing rhizobial symbiont of Serbania herbacea (Wang et a1. 1998).
Rhodospirillum belongs to family Rhodospirillaceae, order Rhodospirillales. Rhizobium
belongs to family Rhizobiaceae, while Devosia to family Hyphomicrobz‘aceae and both to
order Rhizobiales. All three genera belong to the class Alphaproteobacteria.
Due to limited representation in ARB database of taxa within the classes of
Actinobacteria and Flavobacteria, sequences from both communities remained
unresolved; The order Actinomycetales within the class Actinobacteria includes 38

families. A number of those families are either represented by sequences from both

139

communities or they are novel (previously unknown) sequences representing undescribed
taxa. Seventeen sequences representing sixteen OTUs were positioned between species
of Actinomycetales and Cytophaga sp. The OTU GOl 17.39 and Actinomycetales had a
75% homology. The OTU F0107 .02 had a 75% homology with Cytophaga sp. and
Bacteroidesfiagilis. Two house fly sequences representing two OTUs clustered within
Flavobacteria. The OTU GC120.30 had 83% homology with Cellulophaga lytica. C.
lytica characterized for the endocellulase activity was previously isolated from marine
environment (J ohansen et al. 1999). Taxa in the genus F lavobacterium belong to family
Flavobacteriaceae, order Flavobacteriales, class Flavobacteria. A set of three fecal
sequences (F C1 10.45, FC108.32 and F01 14.31) representing three OTUs clustered
within the Prevotella-Bacteroides group. The OTU FOl 14.31 had a 78% homology with
Bacteroidesfiagilis. Both classes of Bacteroidetes and Flavobacteria belong to the

phylum of Bacteroidetes phyl. nov.

140

 

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Figure 4.7 Phylogenetic tree demonstrating relationships within the Bacteroidetes,
Flavobacteria, Actinobacteria and Alphaproteobacteria classes,
as determined by Neighbor Joining method of 16S rDNA sequences.
The Sequence Associated Information (SAI) was based on 275 valid columns.
The red color bar represents 10% estimated sequence divergence.
Vertical bars depict sequences from a single source (purple) or multiple sources (green).

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Betaproteobacteria and gammaproteobacteria. Sixty four sequences from both
communities were clustered in the heterogeneous classes of Betaproteobacteria and
Gammaproteobacteria (Figure 4.8). Gammaproteobacteria is the largest group of all
proteobacteria including 13 orders and 20 families. Twenty one sequences from both
communities were clustered within the Gammaproteobacteria class. Moraxellaceae
forms a distinct monophyletic group within Gammaproteobacteria and includes members
of opportunistic agents that can cause eye infections and chronic bronchitis, but they are
also part of the normal airway flora (Pettersson et al. 1998). The long lineage of
Moraxe'lla atlantae represents the second group within Moraxellaceae that grows with
particularly small colonies, displays highly fimbn'ated phenotypes and pronounced
twitching motility (Pettersson et al. 1998). Four house fly sequences representing two
OTUs were a sister group to the distant Moraxella atlantae. No Moraxella sp. was
identified from the house fly gut using the RDP H database, however. Two fecal
sequences forming two OTUs were a sister group of Psychrobacter immobilis. Isolates
from under seawater that were similar 99% with P. immobilis were psychotrophic,
broadly halotolerant (growth in 0-15% NaCl) and able to form acid from carbohydrates
(Bowman et al. 1997). Psychrobacter species represented 1% of the total fecal
sequences while they were also represented in the house fly gut community according to
RDP II results. A house fly gut sequence was a sister group of Schineria larvae strain
which was previously detected in larvae of the obligate parasitic fly Wohlfarhrtia
magnified (Diptera: Sarcophagidae) (Toth et al. 2001). Thirteen sequences from both

habitats formed a monophyletic group within Acinetobacter genus. The genus

142

 

Acinetobacter are ubiquitous in the natural environment, and are implicated in the
biodegration of hydrocarbons and in causation of some human diseases (Y amamoto and
Harayama 1996). Two house fly sequences (GC113.35, GC113.37) representing one
OTU had 96% homology with Acinetobacter haemoliticus. A. haemolyticus has been
used for the biodegration of phenol pollutants in synergistic relationship with the green
algae Chlorella sorokim'ana (Borde et al. 2003). The fecal sequence F01 1.21 had a 93%
homology with Acinetobcaterjuniz‘ and FC1 12.07 had a 95% homology with
Acinetobacterjohnsonii. Eleven out of those sequences were from the house fly gut.
The genus‘Acinetobacter was identified by the RDP II as a dominant genus in both
communities representing 3% and 4% of total sequences for the fecal and house fly gut
respectively.

The Betaproteobacteria class, a descendent line of gammaproteobacteria, includes
chemolitho-autotrophs, ammonia oxidizing bacteria and plant, human, animal pathogens
(Kowalchuk and Stephen 2001, Coeny et al. 2000, Wen et a1. 1999). A small number
of house fly sequences formed sister groups with genera of Burkholderia, Delftia and
Comamonas. Each genus represents less than 1% of the bacterial sequences classified
according to the RDP 11 method. One house fly sequence formed a monophyletic group
with Burkholderia gladioli. B. gladioli contains strains that show antagonistic properties
to plant pathogens and therefore they could be used for biocontrol purposes, but also
contains strains that cause infections to compromised human hosts (Coeny et al. 2000).
The genus Burkholderia belongs to the family Burkholderiaceae, while the genera Delflia
and Comamonas belong to family Comamonadaceae. Comamonadaceae is a coherent

group of 16 genera. Both genera have been previously isolated from soil, fresh water,

143

marine water, clinical samples and activated sludge (Wen et al. 1999). While
intergeneric relationships remain to be resolved within the genus Comamonas, cells in
genus Deftia are described as strictly aerobic, nonfermentative, chemo-organotrophic
rods (Wen et al. 1999). Lineages of Defiia acidovorans, GC115.14 and GC115.4O
formed one OTU. Lineages of GC120.17, GC120.34 formed two distinct OTUs and they
were a sister group of Comamonas terrigena. Both families of Burkholderiaceae and
Comamonadaceae belong to the order Burkholderiales of the class Betaproteobacteria.
Twenty-two sequences from both communities representing 3 OTUs formed a
monophyletic group with Janthinobacterium,agaricidamnosum. J. agaricidamnosum is
a causative agent of soft rot disease of mushrooms (Agaricus bispoms) and is highly
similar (99% 168 rDNA sequence similarity) to the non pathogenic strain J. lividum
(Lincoln et al. 1999). The sequence GC113.39 had a 97% homology with Pseudomonas
mephitica and J. agaricidamnosum. Oxalobacter and the two species of
Janthinobacterium have more than 95% sequence similarity (Lincoln et al. 1999).
Oxalobacterfonnigenes is considered a important inhabitant of rumen and large bowel
because contributes to the degration oxalic acid (Allison et al. 1985). A set of 12
sequences derived from both communities representing three OTUs where positioned
between 0. formigenes and J. agaricidamnosum. The sequence G01 19.24 had an 87%
homology with J. agaricidamnosum. The sequence F0107.14 had an 87% homology
with Oxalobacterfonnigenes. Janthinobacterium and Oxalobacter taxonomically belong

to family Oxalobacteraceae and class of Betaproteobacteria.

144

 

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Figure 4.8 Phylogenetic tree demonstrating relationships within
the Betaproteobacteria and Gammaproteobacteria classes, as determined
by Neighbor Joining method of 168 rDNA sequences. The Sequence
Associated Information (SAI) was based on 310 valid columns.

The red color bar represents 10% estimated sequence divergence.
Vertical bars depict sequences from a single source (purple) or

multiple sources (green).

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Gammaproteobacteria. The over-representation of gammaproteobacteria sequences
fi'om both communities was evident with the phylogenetic analysis. The sequence
60119.52 from the house fly gut had a 92% homology to Morganella morganii. M.
morgam’i, a member of the family Enterobacteriaceae, which has high intraspecies
heterogeneity and ability to ferment trehalose (Jensen et al. 1992). The house fly
sequence GC106.01 had a 95% homology with Serratia marcescens, another member of
the family Enterobacteriaceae. S. marcescens has been previously isolated from a water
treatment tank (Ajithkumar et al. 2003). Five house fly sequences reflecting two OTUs
formed a monophyletic group with Shigella sonnei and Shigella flexneri . The sequence
GC112.02 had a 96% homology with Shigella sonnei. Shigella spp. were identified from
the house fly gut community by the RDP II method and represented less than 1% of the
total bacterial sequences. Two house fly sequences (G0119.15, 60119.56) representing
one OTU formed a monophyletic group with species of the genera Enterobacter and
Kluyvera. Enterobacter pyrinus is an organism associated with brown leaf spot disease
of pear trees and distantly related to three house fly sequences (Chung et al. 1993). Both
genera were also identified by the RDP 11. Three more house fly gut sequences
(GC112.32, GC113.09 and 60119.46) representing three OTUs were a sister group of
Enterobacter, Klebsiella and Pantoea. Although, the genus Enterobacter represented 2%
and the genus Kluyvera less than 1% of the house fly sequences, the genera Klebsiella
and Pantoea were not identified by the RDP II. The genera Enterobacter, Kluyvera,
Klebsiella, Pantoea and Shigella taxonomically belong to the family Enterobacteriaceae,

order Enterobacteriales and class Gammaproteobacteria.

146

The over-representation of Pseudomonas spp. was also evident with ARB analysis. F ifiy
two sequences representing two OTUs from both communities generated a monophyletic
group with Pseudomonas tolaasii. The house fly sequence GC106.12 had a 96%
homology with P. tolaassi. P. tolaassi although has similar physiological pr0perties with
P. fluorescens and other fluorescent pseudomonas is readily distinguishable from P.
fluorescens using Restriction Fragment Length Polymorphisms (RF LP) (Godfrey et al.
2001). P. tolaasii produces tolaassin, an extracellular toxin responsible for the brown
blotch disease in commercial mushrooms (Brodey et al. 1991). Pseudomonas is the most
dominant genus in both communities representing 60% and 56% of the fecal and house
fly gut bacterial sequences respectively according to RDP H classification. Pseudomonas

species belong to the family Pseudomonaceae, order Pseudomonales.

147

 

 

 

8’71

 

 

Bacillus t‘ereus

 

 

 

 

Figure 4.9 Phylogenetic tree demonstrating relationships
within the Gammaproteobacteria class, as
determined by Neighbor Joining method of
168 rDNA sequences. The Sequence Associated
Information (SAI) was based on 363 valid columns.
The red color bar represents 10% estimated sequence
divergence. Vertical bars depict sequences from

a single source (purple) or multiple sources (green).

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Clostridia. Clostridia class is a very diverse group of gram-positive species with a low
G+C content (Woese 1987). Three fecal sequences (FC110.31, F0109.31 and F011.47)
representing three OTUs formed a monophyletic group with other Clostridium and
Eubacterium species. The fecal sequence FC110.31 had an 82% homology with
Anaerovorax odorimutans.

Thirty two fecal sequences reflecting ll OTUs formed a sister group of
Clostridium bifermentans and Eubacterium tenue (Figure 4.10). The fecal sequence
FC110.36 had a 93% homology with Clostridium bifermentans and Eubacterium tenue.
C. bifermentans is a strictly anaerobic, motile and spore-forming bacterium, and has been
previously isolated from olive mill wastewaters on cinnamic acid (Chamkha et a1. 2001).
Three more fecal sequences clustered with other Clostridium and Eubacterium species.
Taxa within the genus Eubacterium belong to family Eubacteriaceae while taxa within
the genus Clostridia»: belong to the family Clostridiaceae. Both families belong to the

order Clostridiales.

Clostridia and Bacilli. Forty eight sequences from both bacterial sources were clustered
within the classes of Clostridia and Bacilli (Figure 4.11). Novel sequences from both
communities were clustered within other Clostridia species. Four sequences (GC 106.17,
FC110.33, G0119.23 and G0119.42) from both communities formed three OTUs. The
sequences FC110.33 and 60119.42 had a 90% homology with Clostridium
acetoutylicum. Four house fly sequences (GC112.01, GC112.‘20, GC112.50, G01 19.08)
sampled from four different farms formed one OTU which showed 86% homology with

Bacillus lentusi while two fecal sequences (FOlO9.15, F0109.23) showed 87%

149

 

homology with Bacillus fumarioli. B. fumarioli, an aerobic endospore-forming
bacterium, was isolated from active fumarioles in Antartica and from Candelemas island
in South Sandwich archipelago (Logan et al. 2000). Four fecal sequences (FC110.15,
FC110.37, F0111.17 and F0111.44) represented 2 OTUs. The sequence F0111.44 and
Planococcus citreus had 86% homology. The genus Planococcus belongs to the family
Planococcaceae and in the order Bacillales. The genus Bacillus belongs to family
Bacillaceae and in the order Bacillales. According to RDP H results, members of the
genus Bacillus but not for the genus Planococcus were identified in the fecal community.
Twenty eight sequences representing thirteen OTUs fi'om the house fly gut formed a
monophyletic group with other species in the genera of Enterococcus, Abiotrophia,
Granulicatella and Facklamia. The sequence GC106.09 had 89% homology with
Enterococcusfaecium and GC113.18 had 89% homology with Enterococcus
moraviensis. Enterococci have been previously isolated from soil, plants, insects, wild
animals, water, and became increasingly interesting because of their clinical significance
in acquiring antibiotic resistant genes (Svec et al. 2001). Enterococci comprised a 2% of
the total housefly gut diversity based on RDP 11 results. These results indicate a very
diverse genus within the house fly gut. The genus Enterococcus belongs to family

Enterococcaceae and the order Lactobacillales.

150

 

191

 

 

 

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Figure 4.10 Phylogenetic tree demonstrating relationships within the Clostridia FOIIHS
class, as determined by Neighbor Joining method of 16S rDNA
sequences. The Sequence Associated Information (SAI) was based
on 269 valid columns. The red color bar represents 10% estimated
sequence divergence. Vertical bars depict sequences from a single F0107 ,0
source (purple) or multiple sources (green). — .-

 

 

 

 

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Figure 4.1 1 Phylogenetic tree demonstrating relationships within GC110.22
the Clostridia and Bacilli classes, as determined (1311047
by Neighbor Joining method of 16S rDNA sequences. GC.‘ 10"“
The Sequence Associated Information (SAI) was based GC110.23
on 278 valid columns. The red color bar represents 10% GC1 10.34 '
estimated sequence divergence. Vertical bars depict (1911009

sequences from a single source (purple) or multiple sources (green). GC110.14
GC110.46

 

 

Bacilli. Four sequences from the house fly gut formed a monophyletic group with
Weissella species (Figure 4.12). Weissella represented 1% of the house fly bacterial
community sequences based on results from RDP II analysis. Two house fly sequences
(GC112.10, GC119.26) formed one OTU which was 81% homologous to Lactococcus
lactis. There were not any fecal sequences classified to the genus Lactococcus by the
RDP II analysis. Bacterium $24-10 formed a monophyletic group with six fecal
sequences representing four OTUs. $24-10 was previously identified as a new
uncultured bacterium isolated from mouse intestine (Salzrnan et al. 2002) with a
nucleotide NCBI accession number of AJ 400262. This monophyletic group was a sister
group of the genus Gemella but there were not any fecal sequences classified to genus
Gemella by the RDP H analysis. Five house fly sequences (GC120.40, GC110.17,
GC110.33, GC112.15 and GC112.25) representing 5 OTUs formed a monophyletic group
with two Lactobacillus species. The sequence GC112.25 had a 83% homology with
Lactobacillusjensenii. Four house fly sequences representing three OTUs clustered
within the Weissella genus. The sequence GC110.43 had a 92% homology with
Weissella hellenica. W. hellenica and W. viridescense are fermentation strains isolated
from sausages and the former constitutes a type species for the new genus Weissella,
formerly of the Leuconostoc paramesenteroides group of species (Collins et al. 1993).
The sequence G0119.05 had a 93% homology with Weissella viridescence and
00119.01 had a 90% homology with Weissella viridescence.

The genus Lactococcus belongs to the family Streptococcaceae, the genus

Weissella to family Leuconostocaceae and the genus Lactobacillus to the family

153

 

Lactobacillaceae. All three families belong to order Lactobacillales. Tthe genus Gemella

belongs to family Staphylococcaceae and order Bacillales.

154

 

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Figure 4.12 Phylogenetic tree demonstrating relationships within the Bacilli class,
as determined by Neighbor Joining method of 16S rDNA sequences.
The Sequence Associated Information (SAI) was based on 285 valid columns.
The red color bar represents 10% estimated sequence divergence. Vertical bars
depict sequences from a single source (purple) or multiple sources (green).

 

 

Diversity Indices. Diversity indices are used to characterize diversity of a sample or a
community based on a single, relative number. Simpson’s index was calculated as 3.06
with a standard deviation of 0.14 for the house fly gut and 2.52 for the cow fecal
community with a standard deviation of 0.01 . The value of the index increases with
increasing diversity. Therefore, Simpson’s index supports the more diverse house fly
bacterial community compared to cow fecal community.

The Shannon indices were 2.09 for the house fly gut and 1.52 for the fecal
community. Given the higher value of house fly gut index than the fecal, we estimated
evenness, variance and invoked a t-test to compare the diversity of the two communities.
Evenness values were 0.518 and 0.456 for the house fly gut and fecal respectively.
Variances were estimated as 0.005 for both communities. The t-test gave a t value of
5.76 with df = 1179 when the critical t 0.001,.» value was 3.291. Therefore, the t-test
supports the conclusion that the two communities are significantly different (p<0.001) in
terms of bacterial diversity and that the house fly gut is more diverse than the fecal
community.

The observation that in a community sample only a small number of species are
very abundant while most species are represented by a few individuals, led to the
development of species abundance models (Magurran 1988). I have examined the
logarithmic series and geometric series as plausible models that can describe community
data from this study. In both cases I used data from the RDP II genus classification
results for both communities. Data initially were plotted on a rank/abundance graph

(Figure 4.14) where the abundance of each species is plotted on a logarithmic scale

156

against the genera ranked from most abundant to least abundant. Data distribution was
estimated that could be plausibly described by either the geometric series model or by the
logarithmic series (May 1975) model due to the steep gradient when plotted on a
rank/abundance graph. Overall, the distributions do not justify being described by the
broken stick or logarithmic normal models due to low evenness. A chi-square ()8) test
was performed as a goodness of fit test for both communities. The x2 for the fecal
community was 875.6 when the critical value was £005.27 = 40.11 and for the house fly
gut x2 = 2664 when the x2005” = 43.77. Therefore the geometric model was not able to
predict bacterial genera distribution in both habitats. For the log series model, the xz fit
test for the fecal community gave a )8 value of 18.18 and for the house fly gut 92.51
when the critical value for both was 3120.023 = 18.1. Therefore the log series model was not

a good model to predict bacterial genera in both habitats.

Estimates. Estimates version 6.0 b1 was successfully used to compute a non-parametric
estimator of true genera richness. Chaol and Abundance-based Coverage Estimator
(ACE) are the only estimators used that require relative abundance data, while all others
are incidence-based estimators (Chazdon et al. 1998).

Observed genera richness inevitably underestimates true richness of each sampled
habitat. Samples were added to the analysis afier 50 randomizations, without replacement
(each sample was selected only once). Chaol estimator demonstrated the least
dependence on sample size in the fecal habitat. Chaol became stable after 423

individuals with a slight decrease in its value (iSD) from 36.3 (i9.6) to 35.7 (i6.9).

157

 

 

1000

100 *

Abundance

10a

 

1 , III-II...

 

 

 

 

60

 

Figure 4.13 A rank abundance plot showing the diversity of house fly gut (solid
triangles) and cow fecal (open squares) bacterial communities.

158

 

I also used a log transformation to calculate confidence intervals for Chaol because the
distribution of estimates was not normal (Chao 1987). The estimated number of genera
for the house fly gut community after 704 clones was Chaolpg= 79 and the 95% CI was
[67, 102], while for the fecal community after 501 clones was Chaolcp= 36 and the 95%
CI was [31, 49] (Figure 4.14). The precision of the estimate was very high because was
very close to the CI range. Significant differences between the two estimates (CIs did not
overlap) were observed only after 450 fecal clones were sampled. Since the CIs of both
communities do not overlap, I can claim that the genera richness is significantly different
in the two communities but I can not address how close the estimates are to the true
genera richness or if they are representative of other house fly gut communities or fecal

communities (Hughes et al. 2001).

159

 

 

 

 

 

 

 

‘1

 

 

 

 

 

 

 

 

 

 

 

 

 

 

140
120-
100-
80-
60-

800

700

600

500

400

300

200

100

Number of clones 331

Figure 4.14 Chaol estimates of house fly gut (O) and cow fecal (0) bacterial genera

richness as a function of the sample size. Bars represent 95% CIs and

calculated with the variance derived by Chao (1987).

160

DISCUSSION

The aim of this study was to investigate bacterial community diversity in the
house fly gut and cow feces. I have assumed that biases in DNA isolation, PCR
amplification and cloning operated uniformly in samples from both communities and
therefore communities can be compared. Although, the physical and chemical conditions
in the gm of different animals may differ, the conditions are relatively constant in a single
species and on a given diet (Mackie 2002). Therefore, the bacterial diversity in the cow
gastrointestinal tract —a foregut fermentor- should not differ over time given the intake of
food varies little with the time. Since bacterial isolates fiom house fly larvae were
capable of fermantation (Zurek et a1. 2000) and all animals carry some fermentative
activity in their hindgut (Stewart 1997), then house flies can possibly be considered
hindgut fermentors. Furthermore, a selection pressure and enlarged fermentation
chambers are missing from the house fly alimentary canal. However the microbial
diversity in the house fly gut can be temperature dependent (Mackie 2002) since house
flies are poikilothennic animals and physical and chemical conditions in the alimentary
canal may be affected. Diet can have a significant impact on insect midgut and hindgut
microbial diversity (Kaufinan et al. 2000) and this effect has been demonstrated by
culture independent methods in the gypsy moth larval gut (Broderick et a1. 2004).

A number of bacterial species have been previously reported in the digestive tract
of field collected house. fly larvae including Serratia marcescens, Providencia rettgeri
and Morganella morganii (Zurek et al. 2000). Our results based on RDP II classification

and phylogenetic analysis provide a comprehensive identification of both adult house fly

161

gut bacterial community and the cow fecal community. This is the first study to our
knowledge to investigate bacterial diversity from field caught, adult house flies. We have
also statistically compared clone libraries, determined minimal genera richness and
community evenness and compared community diversities by using diversity indices.
This microbial ecological study used a neighbor joining phylogenetic approach to
infer relationships of our cloned 16S rRNA gene sequences. In addition we used RDP 11
because it implements a na'r've Bayesian algorithm to assign a sequence to a genus with
the highest probability. Both RDP II and ARB analysis results revealed a highly diverse
house fly gut community which coincides with results from our T-RFLP analysis
(Petridis et al. Unpublished; see Chapter III). Classification with the RDP 11 suggests
that cow fecal and house fly gut microbial communities differ in both qualitative and '
quantitative composition, assuming that the amplification efficiencies of the DNA
fragments are the same for all templates. This is the case when all templates are equally
accessible to primer hybridization, primer-template hybrids form with equal efficiencies,
the efficiency of DNA polymerase is the same for all templates, and substrate exhaustion
equivalently affect the extension of all templates (Friedrich et al. 1997). The greater
diversity of bacterial sequences in the house fly gut than the fecal community might be
due to greater diversity of microniches or available resources that support the microbial
community. Although the vertebrate gastrointestinal tract contains a large number of
microniches in the stratified, squamous epithelium (Tannock 1997), those niches are not
reflected in the cow fecal habitat. Therefore, the bacterial diversity in cow feces is
expected to be lower than in the vertebrate gastrointestinal tract. Cows are grazers whose

diet is largely fresh forage; their rumen is a pre-gastric fermentation chamber filled with

162

carbohydrate polymers indigestible to most animals but hydrolysable by some
microorganisms (Johns 1955, Krause et al. 2003). Large rurnens with a high throughput
processing would be expected to be dominated by microbes that improve fiber utilization.
We have identified genera common for both habitats which implies that there were
common microniches in both habitats. A large number of those sequences may not have
been previously reported or specific genera not identified from the house fly gut. Rumen
bacteria in the low G+C content, Gram positive group and Cytophaga-Flavobacter-
Bacteroides group represent 44% and 43% respectively of the fiber-associated
community (Koike et al. 2003, Krause and Russell 1996). Some strains, such as
Butyrivibriofibrisolvens demostrate cellolytic and xylanolytic activity while other strains
without cellulytic activity can enhance cellulose degradation in co-culture with cellulytic
strains (Koike et a1. 2003). However, fecal clones reflect the constant bacterial diversity
of the caeco-colonic (hindgut) environment provided with undigested dietary
polysaccharides, tissues and endogenous secretions (Mackie 2002). Hindgut may serve
as an accessory site of fermentation for ruminants by supplying a significant amount of
amino acids to the bloodstream (Stewart 1997).

The dominance of Gammaproteobacteria, especially taxa within the Pseudomonas
genus from both communities is evident with both RDP II and phylogenetic analyses.
The source and physiologic role of these organisms in house fly gut and cow feces are
uncertain. Taxa within the Pseudomonas genus from both communities, might reflect
environmental bacterial diversity that unselectively was digested as it has been previously
reported for the house fly larvae (Zurek et al. 2000). The postsarnpling time until

preservation of the specimens and storage can be critical for community structure and

163

relative abundances. When samples were taken from anaerobic marine sediments, there
were significantly higher numbers of Betaproteobacteria and Gammaproteobacteria
compared to Alphaproteobacteria, thought to be an artifact due to enrichment of a
specific group during sample storage and before freezing (Rochelle et al. 1994). Furlong
et al. (2002) stated that members of Pseudomonas genus were amplified in the gut of
earthworm (Lumbricus rubellus) and postulated that their presence was due to resistance
to antibiotics produced by actinobacteria, and to the higher levels of moisture, organic
carbon, nitrogen in the alimentary canal compared to the soil habitat. Further study might
be required to reveal diversity within this group by designing genus specific primers. We
still need to know if these species serve as energy source as they are egested or if they are

excreted again back to the environment. The higher genera richness of house fly

' . community than the fecal community is reflected within the Gammaproteobacteria class.

The low numbers of Enterobacteriaceae in the fecal community could possibly be a result
of highly fragmented nucleic acids during lysis (Friedrich et al. 1997) but this was
unlikely to be the case because we used the same method for the house fly microbial
community where members of the family were observed. Also, fragmentation of DNA
leads to formation of chimericmolecules during PCR (Friedrich et a1. 1997). The low
percentage of possible chimeric sequences does not support the possibility of
fragmentation. Enterobacteriaceae are considered one of earliest gut colonizers of
newborn humans (Favier et a1. 2002, Conway 1997, Stark and Lee 1982) and they then
create a reduced environment favorable for anaerobes such as the Clostridia, the later

colonizers.

164

Both communities contained sequences of the Phylum Firmicutes, particularly
bacteria of the class Clostridiales. Clostridia species are obligate anaerobes, gram
positive, with low G+C content (W oese 1987). A number of Clostridia clones are
represented in both libraries. They are considered one of early colonizers in animals
along with Bacteroides and Lactobacilli (Moughan et al. 1992). Clostridia have been
previously isolated because of the interest to study their ability to degrade organic
material to acids. In many cases the production of butyrate is associated with the genus
as well hydrogen accumulation during fermentation in the hindgut of termites (Schmitt-
Wagner et al. 2003). A number of Clostridia species have been recognized as important
toxin producers such as the alpha-toxin by Clostridium perfringens, toxins A and B by
Clostridia»: dificile, and Clostridium botulinum toxin C2 (Stevens et al. 1987, Knoop et
al.. 1993, Aktories et al. 1986). A factor leading to the relatively high numbers of clones
identified as Clostridia can be due to the high number of rrn operon copies identified in
cloned 16S rRNA genes (Rainey et al. 1996). Also, templates with a low G+C content
denature with a higher efficiency than high G+C content templates and therefore leading
to the preferential amplification of templates with low G+C content such as Clostridia
(Friedrich et al. 1997). The best approach for a quantitative estimation of prokaryotes
within a community, is the use of specific rRNA-targeted oligonucleotide probes for
fluorescence in situ hybridization (FISH) (Amann et al. 1995, J uretschko et al. 2002,
Franks et al. 1998). The human fecal rrricroflora bacterial population showed stability
overtime and in response to diet variation (Stark and Lee, 1982). Rumen animals and gut
bacteria have evolved into a long time relationship that would be unexpected to be

affected by temporarily changes is regiments. This can explain why Clostridium

165

proteoclasticum, a proteolytic strain, population in New Zealand cows was unresponsive
to changes in different dietary regiments (Reilly and Attwood 1997).

Bacilli, the second class within F irmicutes identified from both communities,
showed significantly different community structures in the two communities. Clones
from the cow fecal community found to be closely related to members of the genus
Bacillus and members of $24-10 (NCBI Acc. # AJ400262) and Gemella all members of
the order Bacillales. On the other hand, clones from the house fly gut found to be closely
related to members of the genera Lactobacillus, Enterococcus, Pediococcus, Vagococcus,
Lactococcus and Weissella, all members of the order Lactobacillales. Especially
interesting is the cluster of house fly clones within the Enterococcus genus. This genus is
considered part of the bacterial gut fauna of humans and studies have been contacted for
the intrinsic or acquired antibiotic resistance of species within the genus (Murray 1990,
Fontana et al. 1996).

A clone from the house fly gut was a sister species of Schineria larvae (Figure
4.8). Species of S. larvae were identified and classified as close relatives to Xylella

fastidiosa within the class of Gammaproteobacteria. These obligate anaerobes utilize
chitin and their significance was speculated to the metamorphosis of the parasitic fly
(Toth et al. 2001). The identified clone from this study may have had a similar role in
the house fly metamorphosis and being adapted to new dietary regimens.

Another genus within Actinobacteria class was found in both habitats. Corynebacteria
belong to Gram-positive bacteria with a high G+C content (Stackebrandt and Woese
1981). The genus Corynebacterium is very diverse, containing both aerobes and

facultative anaerobes of medically importance, or they are part of the commensal flora of

166

 

 

humans (W attiau et al. 2000). The genus Corynebacteria»: includes fermentative and
oxidative species (Wattiau et al. 2000) that might be important for the food utilization
and host survival. Corynebacteria] species including species of Corynebacterium bovis
have been previously isolated fi'om bovine mammary glands exhibiting symptoms of .
bovine mastitis (Watts et a1. 2001). House flies can be either a significant vector for the
transmission of the pathogen from cow to cow or a pathogen reservoir. A corynebacteria]
common property is pleomorphism (Barksdale 1981). This can explain a number of
strains being endemic only into the house fly gut and others being present in both
communities.

Sequences from both communities were found to be related to the genus
Microbacterium. Species of the genus Microbacterium have been previously found in
milk, dairy equipment, cheese but also in soil (Topping 1937) and frozen vegetables
(Splittstoesser et al. 1967). Micrabacterium imperiale was isolated from the alimentary
tract of the imperial moth Eacles imperialis (Steinhaus 1941) and Microbacterium
saperdae fi'om dead larvae of elm borer Saperda caracharias (Lysenko 1959) and gut of
the termite Zootemopsis angusticollis (W enzel et al. 2002).

Clones from both communities clustered or they were classified by the RDP 11
within the genus Bifidobacterium, a member of the class Actinobacteria. The genus
Bifidobacterium, belongs to gram positive, high G+C content, pleomorphic Eubacteria
(Woese 1987) and includes bacteria of the human, animal, arthropod intestinal fauna
(Moughan et a1. 1992, J eyaprakash et al. 2003). Bifidobacteria are successive colonizers
of gastrointestinal tract after coliforms and streptococci (Moughan et al. 1992). The

microbial ecological succession in the gastrointestinal tract ends when a stable climax

167

microbiota develops (Rolfe 1997). The existence of adhesion sites in the house fly gut
can plausibly be one of the reasons why Bifidobacteria were identified in the house fly
gut but were less abundant in the fecal community. The reduced environment in house
fly gut and cow rumen may be a prerequisite for Bifidobacterium colonization as it has
reported for the colonization of infant gut (Favier et al. 2002) when at the same time the
number of Enterobacteriaceae was decreased (Y oshioka et al. 1983). Colonization
resistance is regulated by factors related to the host environment, host, diet and factors
inherited to microorganisms (Rolfe 1997). Diet, host endogenous nutrients, host
physiology, microbial adhesive features, resistance to acids, ability to degrade complex
carbohydrates can restrict the microbial flora (The prokaryotes).

Many members of the class Bacteroides have been associated with plant fiber
degradation and protein degradation (Avgustin et al. 1997, Manz, et al. 1996). A highly
diverse Bacteroides group was identified in the mouse gut (Salzrnan et al. 2002) and
Bacteroides forsythus was isolated from patients with advanced periodontitis (Gersdorf et
a1. 1993). This comes in agreement with our observations when clones from the fecal
habitat clustered with species of Prevotella and Bacteroides (Figure 4.7). Eubacterium
is the second most numerous species after Bacteroidetes in quantitative studies of the
human flora (F inegold et a1. 1983). Eubacterium clones have been only identified in the
fecal community by the RDP 11.

House fly gut clones were also clustered within the Alphaproteobacteria class.
Taxa of the genera Rhizobium, Devosia and Phodospirillum were found to be the closest
genera to house fly clones. Rhizobacteria form a subgroup along with agrobacteria and

rickettsias within Alphaproteobacteria class and they are endosymbionts of plant tissues

168

contributing to nitrogen fixation (W oese 1987). The identification of Rhizobium species
can be incidental as a result of an unselective feeding behavior by house flies since
members of this genus have been described for the intimate or intracellular associations
with eukaryotic cells (W oese 1987). Information about the habitat of Devosia taxa is
lacking while members of Rhodospirillum are very diverse and heterogeneous group both

genotypically and phenotypically (Kawasaki et al. 1993).

Comparison of libraries. We investigated bacterial community diversity in cow
excrement samples and house fly guts from organic and conventional dairy farms.
Significant differences in clone libraries were found only between house fly gut and fecal
communities. No substantial differences were detected between farming systems.
Differences in cow fecal and house fly gut microbial communities were striking and
suggest that flies do not feed exclusively on cow feces, which might influence directly
the similarity in bacterial composition of the two environments. Overall, the differences
in bacterial diversity as reflected by the sequence analysis presented here are likely due to
the differential feeding behavior and food sources of flies vs. cows, the differential
anatomy and digestive processes in the alimentary canal, and differential survival of
bacteria in the alimentary canal of these very different animals. Obviously, differences
might be due to the high fermentative activity in the rumen versus in the house fly gut.
Cows are very spatially limited and purposefully fed on fresh forage and grains by
farmers, whereas flies range widely and can locate food sources in a variety of locations
close to and distant from cow feed and cow feces. Ruminants are adapted to digest the

cellulose and hemicellulose from their fibrous foodstuffs in large chambers or rumens

169

where bacterial activity favors fermentation (Friedrich et al. 1997, Stewart 1997). By
contrast, house fly adults do not have a defined fermentation chamber as part of their
alimentary canal, in contrast with some other insects (Kaufman et a1. 2000), but this
anatomical limitation does not rule out the possibility that fermentation supplements
catabolic digestion. House flies more likely exploit soluble carbohydrate sources in
decomposing organic material allows them to get the nutrients for their survival. The
ideal house fly breeding sites are decomposed vegetable materials enriched with dung or
manure, where adult house flies visit, lay eggs, and feed (Oldroyd 1964). House flies
feeding habits are closely related to their habitat range which in turn should influence the
bacterial community apparent in the gut. The utilization of microorganisms in the adult
house fly gut as food is more generally assumed than experimentally verified, but
facultative anaerobes are supported in the larval bacterial community (Zurek et al. 2001).
The results here suggest the need for detailed studies on the digestive physiology of the
adult house fly relative to differential digestibility versus survivability of bacteria in the
house fly gut.

Farm practicing system seemed to have no effect in fecal and house fly gut
community composition. The high ACGo/Gc and low ACac/Go values in case of house gut
libraries based on farming system might be an indication of dependence on sample size
because the organic house fly gut library comprised as many as half of the clones of the
corresponding fecal samples. Apparently, the naturally high variation in the house fly gut

community among flies was independent of the farm practice system.

170

Diversity indices. Diversity indices are simultaneously estimators of species richness,
i.e., the number of species; and evenness, i.e., the relative species abundance (Magurran
1988). The Shannon index assumes that individuals are randomly sampled from an
indefinitely large population and all species are represented in the sample (Pielou 197 5).
The higher diversity indices are indications of the higher species richness and evenness in
the house fly gut compared to the cow fecal habitat. Shannon index can discriminate
communities when species richness (S) and total number of individuals (N) are identical
but evenness varies (Magurran 1988). The first impression from the figure 4.14 is that
both communities have the same number of dominant genera but they vary in genera
richness. Our effort to describe the microbial community of both habitats using the log
series and geometrical models was unsuccessful suggesting that both communities are
influenced by a number of unknown factors and not only by one spatial or seasonal factor
that also determines the number of habitat niches. The last outcome could also be
explained by a number of biases that are involved in cloning and sequencing methods
(Zoetendal et al. 2004). For example, many short length sequences resulting from the
PCR could not be classified to genus. The methods used here may have been inadequate
to provide a good estimate of total species diversity but they can provide information of
the most common species in each habitat (Dykhuizen 1997).

Estimation of Genera. In both microbial communities, the number of genera observed
increased with the sampling effort or cumulative number of individuals. The relative
observed genera richness could be visualized in figure 4.15. The concave shape of the
‘observed’ curves is a first indication of how diverse both communities were and how

well they have been sampled (Hughes et a1. 2001). The ideal case for adequate

171

description of community richness is when the curve reaches an asymptote, reflecting
how well a community has been sampled, or conversely, if it was undersampled.
Although there are limitations of different genera estimators in predicting true genera
richness among samples, Chaol supported that genera richness is significantly different
between the two habitats and the estimated richness is 36 and 79 genera for the fecal and
house fly gut respectively. The Chaol non-parametric estimator used in this study, can
estimate total community richness from a sample (Hughes et al. 2001). However genera
estimators are not able to estimate the true genera richness, or if samples are
representatives of the communities (Hughes et a1. 2001), they predict the true ordering of
richness among samples. However, bacterial diversity can be insect species dependent.
For example, the microbial diversity in the gypsy moth (Lymantia dispar (L.)) midgut
was rather low, containing 7-15 phylotypes (Broderick et al. 2004), whereas the
microbial diversity of different xylophagous insects is very complex (Breznak 1982).
Therefore, it is necessary to conduct the kind of analysis here in order to make estimates
of true species richness.

As in the rank-abundance curve where genera are ordered from the most to the
least abundant, it is evident that there is a small number of dominant genera in both
communities but a high number of ‘rare’ genera producing the long right-hand tail
(Figure 4.13). The use of 16S rDNA clones to estimate bacteria diversity from
environmental samples is a valid method with a high probability of detecting rare taxa
that have had a low probability being detected by other methods. Both RDP H and ARB
analysis gave a comprehensive assessment of comrmmity composition of both

communities. Identical sequences (<3% mismatch) were retrieved from both

172

communities in the sister group of Janthinobacterium agaricidamnosum
(Betaproteobacteria and Gammaproteobacteria tree) and the sister group of Pseudomonas
tolasii (Gammaproteobacteria tree). The observation of identical sequences and the
presence of the nonparametric asymptotic curves, is a indication of the high library
coverage. Discrepancies came fi'om the relative small length of the sequenced ribosomal
fragments. In detail, analysis could be done if the maximum plausible fragment was
obtained. Then, we could determine biodiversity within monophyletic groups by using

group specific primers or using 168 rRNA-targeted probes (Franks et al. 1998).

173

 

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