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THESIS

’2/ LIBRARY
Michigan State
2007 Umverslty

This is to certify that the
dissertation entitled

BACTERIAL DIVERSITY AND NUTRITIONAL
SIGNIFICANCE OF THE SURFACE MICROLAYER IN
ANOPHELES GAMBIAE (DIPTERA: CULICIDAE) LARVAL
HABITATS

presented by

Shahnaz Rahim Maknojia

has been accepted towards fulfillment
of the requirements for the

Doctoral degree in Entomology

 

 

 

 

(gal/hat 9), W
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Date

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BACTERIAL DIVERSITY AND NUTRITIONAL SIGNIFICANCE OF THE
SURFACE MICROLAYER IN ANOPHELES GAMBIAE (DIPTERA:
CULICIDAE) LARVAL HABITATS

By

Shahnaz Rahim Maknojia

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Department of Entomology

2006

ABSTRACT
BACTERIAL DIVERSITY AND NUTRITIONAL SIGNIFICANCE OF THE
SURFACE MICROLAYER IN ANOPHELES GAMBIAE (DIPTERA: CULICIDAE)
LARVAL HABITATS
By
Shahnaz Rahim Maknojia

Anopheles gambiae and Anopheles fimestus are well known vectors of human
malaria in subSaharan Africa. The abundance of the adult stages of these mosquitoes
necessarily depends on the number and productivity of the larval habitats. Even though
these habitats are the source of these competent vectors, little is known about the
productivity for larva habitat. Larvae are specialized gathering-filtering feeders and feed
on materials in the surface microlayer which is enriched with microorganisms and other
materials relative to subsurface zones of the water column.

In this study the bacterial composition of the surface microlayer, and its
significance to larval nutrition and growth was examined. Removal of the surface
microlayer at regular intervals resulted in decreased survival of larvae, prolonged
developmental time to pupation, and produced adults with lower body mass.
Supplementations of the surface microlayer from habitats with no larval grazing
improved larval growth, shortened larval developmental time, and produced adults with
higher total mass. Importance of heterotrophic bacteria in relation to larval nutrition was
studied and it was observed that larvae grew, molted, and achieve metamorphosis to

pupation when heterotrophic bacterial grth was enhanced by addition of glucose, but

larval survival rate and total adult emergence was very low compared to sunlit treatments

rich in algae. Glucose addition to sterilized habitats resulted in complete growth failure of
larvae.

Effects of larval grazing pressure on bacterial communities was studied with two
different soil types using 16S rDNA sequence library construction and Terminal
Fragment Length Polymorphism (TRFLP) analysis. Community shifts were observed
either by presence or absence of certain taxa, or changes in the fi‘equencies of certain
taxa, as represented by the sequence data.

Finally, a culture-independent survey of bacteria present in the surface microlayer
of natural An. gambiae and An. funestus larval habitats in western Kenya was undertaken.
Overall both An. gambiae and An. fimestus larval habitats were very diverse and revealed
few dominant and many uncommon taxa. LIBSHUFF analysis revealed that these
communities were statistically different, but Principal Component Analysis (PCA) on the
sequence data and TRF LP analysis did not show any significant clustering of specific
habitats. Therefore, there was no clear evidence supporting habitat segregation based on

bacterial community structure.

ACKNOWLEDGEMENTS

I would like to thank and acknowledge the NIH grant (A150703) for funding this
project. I am grateful to the Department of Entomology at Michigan State University for
all the support and resources.

I thank my major advisor Dr. Edward Walker for his encouragement, support, and
patience and for always being available when needed throughout my dissertation
research. I would also like to thank the members of my advisory committee, Dr. James
Miller for his advice not just pertaining to my dissertation but also surviving graduate
school, Dr. Michael Bagdasan'an for his comforting conversations, Dr. Orlando Sarnelle
for guiding my thoughts in the right direction, and Dr. Frank Dazzo for being critical
about every small detail. My deep thanks to Dr. Michael Kaufman for his guidance in
setting up experiments, help in analyzing data specially PCA analysis and for always
being available for help. I would also like to acknowledge Dr. Terry Marsh for his
guidance on TRFLP analysis. I express thanks to Dr. Karim Maredia and Dr. Mywish
Maredia for their immense support right fiom the beginning and for all the
encouragement.

I thank Mr. William Morgan and Mr. Blair Bullard for their assistance whenever
it was needed. I thank John Wertz for his enormous help in learning ARB, Kristin
Huizinga for sharing her experience with DOTUR, Lane LaFleur Frasier and Max Foster
for their help with the microscopic analysis. I also thank Shicheng Chen for being a
wonderful laboratory companion. Thanks to Susannah Cooper, Alicia Bray and Kirsten

Pelz-Stelinski for being great friends.

iv

I would like to extend my earnest and heartfelt gratitude to many people to whom
I owe this dissertation. This dissertation would have not been possible without them. I
thank my husband Rahim Hasanali Maknojia for his love and encouragement. While
working on my dissertation I was blessed with two beautiful children, Zoe Rahim
Maknojia and Izaan Rahim Maknojia, fulfilling and enriching my life.

I offer my heartfelt gratitude to my father-in-law Hasanali Raje Maknojia and my
grandparents Mr. Hasanali and Mrs. Kulsum Hasanali Maredia, for what I am today I
owe it to them. I thank my parents Mr. Dilawar and Mrs. ShahSultana Prasla, my uncles
Nooruddin, Pyarali and Imtiyaz Maredia, and my aunt Mrs. Mumtaz Dhuka, for all their
encouragement and for always being proud of me. I specially thank my mother for taking
care of my daughter, while I was working on my dissertation. I would also like to

remember my dear friend Rachana Bheda for believing in me and being there for me.

TABLE OF CONTENTS

LIST OF TABLES ...............................................................
LIST OF FIGURES ..............................................................

CHAPTER I
LITERATURE REVIEW .......................................................

The malaria problem ...................................................
Biology of mosquitoes ...................................................
Ecology of Anopheles mosquitoes of Africa ...........................
Nutrition ..................................................................
Temperature .............................................................
Aquatic Microbial Communities:

Factors Affecting Structure and Dynamics ........................
Methodological Approaches to

Aquatic Microbial Community Structure .........................

PURPOSE/SCOPE OF THE PROJECT ....................................
REFERENCES ...................................................................

CHAPTER II

CONTRIBUTION OF THE SURFACE MICROLAYER AND ITS
ALGAL AND BACTERIAL BIOMASS TO GROWTH OF
ANOPHELES GAMBME LARVAE (DIPTERA: CULICIDAE) ......

ABSTRACT ........................................................................
INTRODUCTION ................................................................
METHODS AND MATERIALS ..............................................

Experiment 1. Contribution of surface microlayer to larval Anopheles
gambiae growth.
Habitat setup ..............................................................
Microbial and Chemical analysis .............................. ‘ ......
Statistical Analysis .......................................................
Experiment 2. Contribution of bacteria to larval Anophelese gambiae
nutrition
Habitat setup ..............................................................
Bacterial counts ...........................................................
Bacterial productivity ...................................................

vi

Page

y—A

GOQfil—A

14

19

22

27

27

28

34

34
35
36

36
37
38

Statistical Analysis ......................................................

RESULTS ..........................................................................

Experiment 1. Contribution of surface microlayer to larval Anopheles
gambiae growth ...................................................................
Microbial and Chemical analysis ....................................

Experiment 2. Contribution of bacteria to larval Anophelese gambiae
nutrition ............................................................................

DISCUSSION .....................................................................
REFERENCES ...................................................................

CHAPTER III

EFFECTS OF LARVAL ANOPHELES GAMBIAE GRAZING ON
THE BACTERIAL COMMUNITY STRUCTURE OF LARVAL
HABITATS ........................................................................

ABSTRACT ........................................................................
INTRODUCTION ................................................................
METHODS AND MATERIALS ..............................................

Habitat array ..............................................................
Nucleic Acid Extraction, Amplification, Cloning

and Sequencing ..........................................................
Classification of sequences .............................................
Statistical comparisons ...................................................
Principal Component Analysis ........................................
Terminal Restriction Fragment Length Polymorphism
Analysis (TRFLP) ........................................................
Nucleotide sequence accession numbers .............................

RESULTS ...........................................................................

Classification of sequences .............................................
Analysis of treatment effects

Effects of larval presence/absence ....................................
Efi'ects of soil type ........................................................
Principal Component Analysis ........................................
Terminal Restriction Fragment Length Polymorphism
Analysis (T RFLP) ........................................................

vii

38

39

39
43

45

49

53

56
56
58
65
65
66
67
69
69

70
71

72
72
73
87
89

93

DISCUSSION .....................................................................
REFERENCES ...................................................................

CHAPTER IV

BACTERIAL DIVERSITY OF THE SURFACE MICROLAYER OF
AN. GAMBIAE AND AN. F WESTUS HABITATS USING 16S rDNA
SEQUENCE LIBRARY CONSTRUCTION ...............................

ABSTRACT ........................................................................
INTRODUCTION ................................................................
METHODS AND MATERIALS ..............................................

Sampling sites .............................................................
Bacterial and Algal density ...........................................
Chlorophyll a Analysis ................................................
Nucleic Acid Extraction, Amplification, Cloning

and Sequencing ..........................................................
Classification of sequences .............................................
Statistical comparisons ...................................................
Principal Component Analysis ........................................
Terminal Restriction Fragment Length Polymorphism
Analysis (TRF LP) ........................................................
Nucleotide sequence accession numbers ............................

RESULTS ...........................................................................
Microbial and Chemical Analysis .....................................
Classification of sequences .............................................
Community comparsions ...............................................
Principal Component Analysis ........................................
Terminal Restriction Fragment Length Polymorphism
Analysis (T RFLP) ........................................................

DISCUSSION .....................................................................

REFERENCES ...................................................................

SUMMARY .......................................................................

APPENDIX A .....................................................................

APPENDIX B .....................................................................

viii

95

103

108
108
109
112
112
112
112
113
114
115
116

116
117

118
118
118
126
126
132
134
139
142
146

162

LIST OF TABLES
Tables Page

3.1. Bacterial community composition of surface microlayer
from microcosms with Red Soil having larval grazing
pressure (RL) based on 168 rDNA sequences
classification with RDP II. The numbers of sequences
observed per taxon are in parenthesis ........................... 76

3.2. Bacterial community composition of surface microlayer
from microcosms with Red Soil with no larval (RN L)
grazing pressure based on 16S rDNA sequences
classification with RDP II. The number of sequences
observed per taxon are in parenethesis .......................... 77

3.3. Bacterial community composition of surface microlayer
from microcosms with Black Soil with larval grazing
pressure (BL) based on 16S rDNA sequences
classification with RDP II. The number of sequences
observed per taxon are in parenethesis ......................... 82

3.4. Bacterial community composition of surface microlayer
from microcosms with Black Soil with no larval (BN L)
grazing pressure based on 16S rDNA sequences
classification with RDP II. The number of sequences
observed per taxon are in parenethesis .......................... 83

4.1. Bacterial community composition of surface microlayer
from An. gambiae habitats based on 168 rDNA
sequences classification with RDP II. The number
of sequences observed per taxon are in
parenethesis ........................................................... 120

4.2. Bacterial community composition of surface microlayer
from An. funestus habitats based on 16S rDNA
sequences classification with RDP II. The number
of sequences observed per taxon are in
parenethesis ........................................................... 123

ix

Figure

2.1

2.2

2.3

2.4

2.5

2.6

LIST OF FIGURES

Mean number of pupae produced in the four
treatments. T1, Surface harvested; T2, Surface
microlayer added from habitats with LARVAE;
T3, Surface microlayer added from source habitats;
T4 Control (no harvesting or supplementing).N =6

replications per treatment ..............................................

Average female developmental time in different
treatments. T1, Surface harvested; T2, Surface
microlayer added from habitats with LARVAE;
T3, Surface microlayer added from source
habitats; T4 Control (no harvesting or

supplementing).N=6 replications per treatment ....................

Mean total dry mass produced in different treatments.

T1, Surface harvested; T2, Surface microlayer
added from habitats with LARVAE; T3, Surface
microlayer added from source habitats;

T4 Control (no harvesting or supplementing) ......................

Bacterial and Algal counts of the surface microlayer
from different treatments. T1, Surface harvested;
T2, Surface microlayer added from habitats with
LARVAE; T3, Surface microlayer added from
source habitats; T4 Control (no harvesting or

supplementing) ............................................................

Average number of pupae produced from the
respective treatments in two different set of
experiments (refer to text). A. Habitats shaded

or not, and glucose supplemented in some shaded
habitats but not others. B. Habitats sterile or
nonsterile, and glucose added to all. Fish food was
added to some nonsterile ones as a control. A. N = 6

and B. N =5 replications per treatment ...............................

Bacterial productivity measured as mean pg of
carbon/Uday in glucose amended and non amended

microcosms. N=6 replications per treatment .......................

Page

41

42

42

44

47

48

3.1.

3.2.

3.3.

3.4.

3.5.

3.6

Comparison of bacterial 16S rDNA sequence libraries

of red soil with larval grazing pressure (RL) to Red Soil

with no larval grazing pressure (RN L) using LIBSHUFF.
Homologous (open triangles) and heterlogous (solid triangles)
coverage curvzes are shown. Solid lines indicatze values of
(Cm-Cmmm)2 (panel A) or of (CRNL'CRNL/RL)2 (panel B)

at each value of evolutionary distance (D). Broken lines
indicate the 950"I value (or p=0.05) of corresponding
(Cm-CWLY or (Cm-emu“? for the randomized

samples .......................................................................

Rank abundance plot showing diversity of red soil with

larvae (RL) and red soil with no larvae (RN L)

bacterial communities at evolutionary distance

of 0.03 .........................................................................

Comparison of bacterial l6S rDNA sequence libraries

of black soil with larval grazing pressure (BL) to

black soil with no larval grazing pressure (BNL)

using LIBSHUFF. Homologous (open triangles)

and heterlogous (solid triangles) coverage curves

are shown. Solid lines indicate2 values of (CBL-CBUBNL)2

(panel A) or of (CBNL-CBNUBL)2 (panel B) at each value

of evolutionary distance (D). Broken lines indicate the

950tll value (or p=0. 05) of corresponding (CBL‘CBUBNL)2

or (Cam-Comma: for the randomized samples ......................

Rank abundance plot showing diversity of black
soil with larvae (BL) and black soil with no larvae

(BNL) bacterial communities at evolutionary
distance of 0.03 ..............................................................

Rarefaction curves generated from the OTUs at 3%
distance using DOTUR for all four treatments
(RL, RNL, BL, BNL) .......................................................

Homologous (open triangles) and Heterologous

(solid triangles) coverage curves for 168 rDN A

sequence libraries from Black Soil and Red Soil

with larvae and without larvae are shown. Solid

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

for the original samples at each evolutionary distance.

Broken lines indicate the 950th value (or p=0. 05).

The above comparisons are BLvsRL (Panel A, B)

and BNLvsRNL (Panel C,D) ............................................

xi

78

79

85

86

88

3.7

3.8

3.9

3.10

3.11

3.12

Principal component analysis of the 16S rDNA sequence

data from Red Soil with/ without larvae and Black

Soil with! without larvae. OTUs determined by DOTUR
analysis at 3% distance, A; OTUs obtained by RDP 11
classification at order level, B; 46.27% of the variance

was explained by PC1-2 for DOTUR OTU data and 59.41%

of the variance was explained by PC1-2 for RDP

classification data ..........................................................

Phylogentic analysis of 16S rDNA gene sequences from

the unclassified group from various treatments. The
dendogram was generated by Neighbor Joining Method

using a filter of 241 bases and Jukes cantor correction

using ARB software. The scale bar represents 10%

sequence divergence. Number of sequence within each

group are in parenthesis. RL=Red Soil/ larvae;

RNL=Red Soil/no larvae; BSNL=Black Soil/no

larvae .........................................................................

TRFLP Cluster Analysis using Paup for presence/absence

of the terminal fragements obtained for Hhal and MspI
restriction enzyme digestions. (RL, Red Soil with larvae;

RNL, Red Soil with no larvae; BL, Black Soil with larvae;
BNL, Black Soil with no larvae) .........................................

Phylogenetic analysis of 16S rDNA gene sequence

obtained from Red Soil/larvae. The dendogram was

generated by Neighbor Joining Method using a filter of

265 bases and Jukes cantor correction using ARB

software. The scale bar represents 10% sequence

divergence. Labels RLl and RL2 refer to clones retrieved

from two different replicates of same treatment. Number

of sequences within each group are in parenthesis ..................

Phylogenetic analysis of 168 rDNA gene sequence

obtained from Red Soil/no larvae. The dendogram was
generated by Neighbor Joining Method using a filter of

265 bases and J ukes cantor correction using ARB

software. The scale bar represents 10% sequence

divergence. Labels RNLl and RNL2 refer to clones retrieved
from two different replicates of same treatment. Number

of sequences within each group are in parenthesis ........ _ ..........

Phylogenetic analysis of 16S rDN A gene sequence

obtained from Black Soil/larvae. The dendogram was
generated by Neighbor Joining Method using a filter of

xii

90

92

94

147

148

3.13

4.1

4.2

4.3

4.4

4.5

265 bases and Jukes cantor correction using ARB

software. The scale bar represents 10% sequence

divergence. Labels BSLl and BSL2 refer to clones retrieved

from two different replicates of same treatment. Number

of sequences within each group are in parenthesis .................. 149

Phylogenetic analysis of 16S rDNA gene sequence

obtained from Black Soil/no larvae. The dendogram was

generated by Neighbor Joining Method using a filter of

265 bases and Jukes cantor correction using ARB

software. The scale bar represents 10% sequence

divergence. Labels ESL] and BSL2 refer to clones retrieved

from two different replicates of same treatment. Number

of sequences within each group are in parenthesis .................. 150

Comparison of bacterial 16S rDNA sequence libraries of

An. funestus habitats to An. gambiae using LIBSHUFF.

Homologous (open triangles) and heterlogous

(solid triangles) coverage curves are shown. Solid lines

indicate values of (CF-ch)2 (panel A) or of (Cc-Can?)2

(panel B) at each value of evolutionary distance (D).

Broken lines indicate the 950‘” value (or p=0.05) of

corresponding (Cr-Cr/c)zor (Cc-CG/F)2 for the

randomized samples ....................................................... 128

Rank abundance plot showing diversity of An. gambiae

and An. funestus bacterial communities at evolutionary

distance of 0.03. Relative abundance is plotted on

logarithmic scale against species rank from most

abundant to least abundant .............................................. 129

Rarefaction curves generated from the OTUs at 0.03
distance (97% similarity) using DOTUR for
An. gambiae (A) and An. funestus (B) habitats ....................... 130

Principal component analysis of the 16S rDN A

sequence data from An. gambiae and An. funestus

habitats. OTUs determined by DOTUR analysis at

0.03 distance (97 % similarity), A; OTUs obtained by

RDP 11 classification at order level, B; 32.71% of the

variance was explained by PC 1-2 for DOTUR OTU

data and 50.75% of the variance was explained by

PC 1-2 for RDP classification data ...................................... 131

TRFLP Cluster Analysis using Paup for presence/absence
of the terminal fragements obtained for Hhal (A) and

xiii

4.6

4.7

4.8

4.9

4.10

4.11

Mspl (B) restriction enzyme digestions. (G, An. gambiae

habitats; F, An. funestus habitats) ......................................

Phylogenetic analysis of 16S rDNA gene sequence
obtained from An. gambiae habitats (G1). The dendogram
was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence
divergence. Number of sequences within each group

are in parenthesis .........................................................

Phylogenetic analysis of 16S rDNA gene sequence
obtained from An. gambiae habitats (G3). The dendogram
was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence
divergence. Number of sequences within each group

are in parenthesis .........................................................

Phylogenetic analysis of 16S rDN A gene sequence
obtained from An. gambiae habitats (G6). The dendogram
was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence
divergence. Number of sequences within each group

are in parenthesis .........................................................

Phylogenetic analysis of 16S rDNA gene sequence
obtained from An. gambiae habitats (G7). The dendogram
was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence
divergence. Number of sequences within each group

are in parenthesis .........................................................

Phylogenetic analysis of 16S rDNA gene sequence

obtained from An. gambiae habitats (G13). The dendogram
was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence
divergence. Number of sequences within each group

are in parenthesis .........................................................

Phylogenetic analysis of 16S rDN A gene sequence
obtained from An. funestus habitats (F l). The dendogram
was generated by Neighbor Joining Method using a

xiv

133 .

151

152

153

154

155

4.12

4.13

4.14

4.15

4.16

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 156

Phylogenetic analysis of 16S rDNA gene sequence

obtained from An. funestus habitats (F2). The dendogram

was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 157

Phylogenetic analysis of 16S rDNA gene sequence

obtained from An. funestus habitats (F3). The dendogram

was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 158

Phylogenetic analysis of 16S rDNA gene sequence

obtained from An. funestus habitats (F4). The dendogram

was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 159

Phylogenetic analysis of 16S rDNA gene sequence

obtained from An. funestus habitats (F5). The dendogram

was generated by Neighbor Joining Method using a

filter of 200 bases and Jukes cantor correction using

ARB software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 160

Phylogenetic analysis of 16S rDNA gene sequence

obtained from the unclassified group obtained from

An. gambiae (G3, G13) and An. funestus habitats

(F1, F2, F3, F4, F5). The dendogram was generated

by Neighbor Joining Method using a filter of 245

bases and Jukes cantor correction using ARB

software. The scale bar represents 10% sequence

divergence. Number of sequences within each group

are in parenthesis ......................................................... 161

XV

CHAPTER I

Literature Review

The Malaria Problem

The World Health Organization estimates that, yearly, 300-500 million cases of
malaria occur and more than 1 million people die of malaria (Breman et a1. 2001). About
1,200 cases of malaria are diagnosed in the United States each year. Most cases in the
United States are in immigrants and travelers returning from malaria-risk areas, mostly
from sub-Saharan Afi’ica and the Indian subcontinent. Malaria occurs in over 100
countries and territories. More than 40% of the people in the world are at risk. Large
areas of North, Central and South America, Hispaniola, Afi'ica, the Indian subcontinent,
Europe, South Asia, the Middle East, and Oceania are considered malaria-risk areas,
although malaria was formerly a health problem in most of these regions, including
temperate areas such as Michigan.

Malaria is a disease syndrome caused by infection with protozoan parasites of the
genus Plasmodium. Four species of Plasmodium can produce the disease: Plasmodium
falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodz'um malaria. P. falciparum is
the most widespread and dangerous of the four; untreated it can lead to fatal cerebral
malaria. Unfortunately, it is also the most common in Africa. Malaria parasites are
transmitted by bites of the female Anopheles mosquitoes. The discovery of this
relationship was accomplished by Sir Ronald Ross in 1899, an accomplishment for which

he was awarded the Nobel Prize in 1902. Giovanni Battista Grassi simultaneously

T?-

L:

-Kl

discovered the biological role of Anopheles mosquitoes as “vectors” (i.e., biological hosts
capable of transmission), but ironically he did not share in the award. Infective
sporozoite stages are transferred to the human circulatory system when the mosquito
salivates into the skin, prior to when it begins to imbibe blood. There are about 760
species of anopheline mosquito, but only 60 or so are competent to transmit the parasite
(Budiansky 2002). Their competency derives from innate genetic attributes and
biological associations such as host selection that enhance population capacity for
transmission. Malaria kills over one million people each year, most of whom are children
under 5, and almost 90% of whom live in Afiica, south of the Sahara (WHO 1998). So
important is malaria that it has become a medical, research and public health specialty.

Malaria is such an enormous public health problem with clear negative affects on
individual human health and on socioeconomic conditions that there are active attempts
to suppress it. In general, vaccines and drugs are important in prevention and treatment
of an illness. So far development of malaria vaccine has achieved little success and there
are no malaria vaccines available. There is a wide spectrum of antimalarial drugs (e.g.;
chloroquine) used for treatment and prophylaxis; but, these parasites rapidly evolve
resistance to them. Drugs such as quinine, sulfadoxine pyrimetharnine (Fansidar) and
artemether are prescribed for populations but they are expensive and can be toxic if dose
is not carefully controlled.

As it has long been said, prevention is better than cure; for malaria, protection can
be afforded by minimizing mosquito bites. Mosquito bites can be reduced in frequency
by using measures to avoid exposure during peak mosquito activity, using window

screens and pyrethroid impregnated bed nets. Mosquitoes can be repelled using devices

or chemical (DEBT) repellents. These above efforts should be coupled with measures to
lower the mosquito densities to obtain effective malaria control. Mosquito densities can
be controlled using either chemical or biological control agents targeted against larvae or
adults. For adults insecticides (e. g.; DDT) are applied to the resting surfaces and these
surfaces may retain their toxicity for days to months. Other insecticides include
organophosphates, carbamates, pyrethrins, etc. But again development of resistance to
insecticides (Hemingway et al. 2002) and change in adult feeding and resting behavior
may render these chemicals ineffective. The rich and often dramatic history of organized
mosquito control to control malaria has been well summarized (Bruce-Chwatt 1988).

Larval control (source reduction) can be obtained by habitat modification
preventing oviposition, hatching or larval development. Biological control can be
obtained by use of predators (e.g.; fish) or pathogens (fungal, viral and bacterial) but they
are still under development or have limited effectiveness, with the exception of Bacillus
thuringiensis israelensi (Bti). Synthetic chemicals are used as larvicides and these include
organophosphates, juvenile hormone mimics. Adult resting and feeding places may in
some cases be altered for control, but overall suppressing production of adults itself is an
element of control since production of adult AnOpheles from larval habitats is a key factor
in malaria risk.

The world-wide attempt to eradicate malaria by indoor house residual spraying of
insecticides showed dramatic results initially, but it lacked sustainability as it relied
solely on one method of control. It has been realized and known from failures of
eradication programs that only one method cannot be employed for effective malaria

control but a more realistic control program which is integrated pest management using

combination of methods to reduce mosquito abundance and disease prevalence is
required. For example, in the Indian subcontinent and in Sri Lanka, malaria programs
were so successful that malaria cases were brought to levels below detection by public
health surveillance; but when the programs were eliminated owing to shifts in funding
priorities, epidemics ensued and malaria quickly re-established (Collins and Paskewitz
1995). It is clear that unless socioeconomic changes including increased standard of
living and improved health care accompany malaria control programs, then the conditions
for transmission remain and programs are not ultimately viable. Economic losses due to
malaria in human populations are such that an easy argument can be made to control
malaria on the basis of economic productivity alone. However, given that poor and
underprivileged populations are often the beneficiaries of malaria control, the political

will to direct scarce resources to these populations is ofien lacking.

Biology of mosquitoes

The biology and public health significance of mosquitoes has recently been
reviewed (Foster and Walker 2002). Mosquitoes are a highly diverse group, with some
3,000 species worldwide among 38 genera distributed into 3 subfamilies and 10 tribes.
Their higher classification places them in the Nematocera, the most primitive suborder of
the Diptera or true flies. Their geographic distribution ranges fiom the high arctic to the
tropics. Most species occur in the tropics, but mosquito abundance can be extremely high
in arctic settings and temperate latitudes.

The generalized mosquito life cycle involves both aquatic and terrestrial

environments and is one of complete metamorphosis with four separate and distinct

stages - egg, larva, pupa, and adult. The adults are terrestrial whereas the egg, larva, and
pupa stages are strictly aquatic, but their habitats are highly variable. The larval stage is
characterized by four instars each separated by a molt followed by a period of feeding
and growth. The final molt yields the pupa which is a motile but non-feeding stage of
short duration. Those of the anophelines of interest mentioned above are typically water
habitats, generally soil or mud in substrate, and are often small and transient.

Unlike other mosquito larvae, Anopheles larvae do not have a respiratory siphon
but instead have one pair of spiracles located at the terminal abdominal segment. Thus
the larvae position themselves parallel to the surface of water to breathe.
Correspondingly, Anopheles larvae feed at the air water interface and are specialized
gathering-filtering feeders, rotating the head 180° degrees from the normal position and
directing the rapidly beating mouthparts to the air-water interface (Merritt et a1. 1992).
This mode of feeding differs substantially from that of Aedes (Merritt et al. 1992, Walker
and Merritt 1991) in that it is largely restricted to a single zone of the water column. The
air-water interface (termed by limnologists the ‘surface microlayer’) of Anopheles
habitats is a zone that is enriched with microorganisms and other materials relative to
subsurface zones of the water column (Walker and Merritt 1993). The specialized
surface feeding behavior of these larvae upon the surface microlayer — exhibited by some
other aquatic invertebrates as well might very well be an adaptation to exploit this food-
rich region.

Each mosquito species has particular environmental requirements for the
maintenance of its life cycle, and these requirements define the larval habitat. The adult

female mosquitoes show distinct preferences for oviposition sites (i.e.; egg-laying sites), '

and this preference may be a major determinant of larval mosquito distribution in nature.
Thus, mosquitoes of different species may show habitat overlap or habitat segregation,
sometimes along both temporal and spatial scales. In Kenya, studies have shown that the
Anophelines differ in their breeding, feeding and resting habits (Gimnig et al. 2001 ,

Mutero and Birley 1987).

Ecology of Anopheles mosquitoes of Africa

The anophelines of the world include nearly 760 species of which 60 or so can
transmit malaria. Many of the species in Africa are highly efficient vectors of human
malaria, in particular species of the Anopheles gambiae and An. funestus complexes.
Within the gambiae complex, An. gambiae and An. arabiensis have the widest
distribution in sub-Saharan Afiica (Coluzzi 1984). An. gambiae and An. arabiensis breed
in fresh water and are associated with small habitats often created by man or animal
activity, such as foot or hoof prints, burrow pits, roadside puddles formed by tire tracks,
and irrigation ditches. An. gambiae habitats are turbid and persist for short periods and
lack aquatic vegetation (Gimnig et al. 2001, Gilles and Coetzee 1987). An. fimestus on
the other hand breed in large, semi-permanent bodies of water, characterized by emergent
vegetation, such as swamps, river edges and ditches. In addition An. funestus is one of the
most ‘domestic’ of Afiican anophelines, preferring to feed and rest inside human houses
for most of its adult life. Even in human dwellings shared with cattle, most female An.
funestus bite humans and congregate in sections occupied by humans. The biogeography
and habitat associations of these species are currently rather poorly known and are under

intensive study.

The An. gambiae complex includes 7 members: An. gambiae, An. arabiensis, An.
quadriannulatu species A and species B, An. merus, An. melas, and An. bwambae (White
1974, Coluzzi 1984). All the species of the so called “gambiae complex” are
morphologically indistinguishable, but can be distinguished through chromosomal
analysis and with molecular methods. The discovery of this complex of species is
fascinating because at one time all were considered to be the same species, Anopheles
gambiae (Giles) sensu lato. However, institution of the malaria eradication campaign in
the 19508 and early 19603 revealed subtle variations in ecology and behavior, especially
that related to the relative extent to which local populations fed upon humans
(anthropophily) or cattle (zoophily) and the location of larval habitats (brackish water,
flesh water, or mineral springs). Simultaneously with these observations was the
development and application of chromosomal banding and cytology methods to discern
so-called chromosomal forms with characteristic inversion morphologies which would
create genetic barriers to fertilization, and so function as post-mating isolation barriers.
More recent studies have revealed population genetic substructuring within An. gambiae
s.s. in which chromosomal ecoforms (Toure et al. 1998) and so-called M and S
mitochondrial forms have been elucidated in west Africa (della Torre et al. 2001, 2002).
Overall, the history of the discovery of closely-related species in the An. gambiae
complex represents a story in evolutionary biology suggestive of on-going speciation
through adaptive radiation to blood hosts and larval environments. The latter context is
provided by the human living environment. In Kenya, studies have shown that two of

the sibling species that commonly co-occur, An. gambiae s.s. and An. arabiensis

(Koenraadt et al. 2004), differ (sometimes subtly) in their breeding, feeding and resting
habits (Gimnig 2001, Mutero et al. 1987).

A similar story is emerging for the other important Afiican malaria vector,
Anopheles firnestus. Although originally thought to be a single species, it is now clear
that it represents a species complex comprised of at least nine members which also show
variations in host utilization, larval habitat utilization, and comparative vectorial capacity
for malaria (Kamau et al. 2002). The members of this complex include An. fimestus, An.
vaneedeni, An. parensis, An. aruni, An. confuses, An. leesom', and An. brucei. Of the nine
species in the complex, An. funestus has the widest distribution. It is a highly efficient
vector of malaria owing to its dependency on humans for blood and the strong
predilection of males and females to rest indoors.

An. gambiae predominate in humid areas with larval production occurring during
the rainy periods, whereas An. arabiensis are also found in arid areas and are likely to
reproduce year round (Takken et al.1998). An. gambiae are usually associated with
human dwellings whereas An. arabiensis are found in habitats near cattle (Gimnig et al.
2001). An. arabiensis will feed on animals when available and feed on man when
alternative hosts are not available. These two species may occur within the same habitat
though survival rates of An. gambiae are higher compared to An. arabiensis, suggesting
interspecific competition between these two closely related species (Schneider et al.
2000). The latter study was confined to laboratory conditions, however, thus the
significance of interspecific competition in field populations is poorly known. Gimni g et
al. (2002) demonstrated intraspecific competition processes with An. gambiae s.s. in

artificial field habitats where larval densities were similar to those found in nature.

Observations of cannibalism amongst larvae of this species indicate that competition for

food resources may be extreme (Koenraadt and Takken 2003).

Nutrition

Knowledge about mosquito nutrition and diet come fi'om development of fully
chemically defined diets developed for Culex pipiens and Aedes aegypti larvae. Culex
pipiens larvae could be reared to adults with these defined diets with 90% or more
survival and at growth rates comparable to those obtained by unrefined diets (Clements
1992). The ten amino acids (arginine, histidine, isoleucine, leucine, lysine, methionine,
phenylalanine, threonine, tryptophan and valine) essential for growth of other insects and
many vertebrates are also required by mosquitoes although omission of proline in Aedes
aegypti diet even though a non-essential amino acid retarded larval development (Dadd
1985). Culex pipiens requires asparagine in addition to the ten essential amino acids
(Dadd 1978).

All insects require C20 polyunsaturated fatty acids for various body functions such
as use of fat soluble vitamins, as constituents of membrane glycerophospholipids, and as
precursors for prostaglandin synthesis. Nearly all insects can synthesize C20 from C13
polyunsaturated fatty acids but mosquitoes are different than other insects in this regard.
They cannot synthesize C20 from C”; polyunsaturated fatty acids, because they lack the
necessary enzyme systems to do so. In mosquitoes, the lack of C20 polyunsaturated fatty
acids in their diets renders the adults unable to fly (Clements 1992). Inclusion in the

mosquito diet of arachidonic acid or other fatty acids (such as eicosapentanoic acid)

which have a series of four double bonds of cis configuration, terminating at the n'6
position in a C20 or C22 positions, rernediates this problem.

Vitamins are required by all insects including mosquitoes. Vitamin B6 complex
are required by Ae. aegypti for survival of first instars and also for pupation. This is true
also for vitamin A, though its absence does not have detrimental effects on Ae. aegypti;
its absence in diet causes abnormalities in the receptor cells of the adult eye. Choline is
required by all insects and also mosquitoes because it is a neurotransmitter and a
component of some phospholipids, thus it is essential in their diets. Nucleotides are
required for producing nucleic acids in all insects. Addition of only bases in their diet
does not allow growth. Nucleotides for ‘a’, ‘g’, ‘c’ and ‘t/u’ are required, though
omission of ‘g’ does not alter the growth of the larvae. All insects including mosquitoes

lack the ability to synthesize sterols and require an exogenous source of sterols such as

cholesterol or various phytosterols (Clements 1992).

Temperature

The influences of temperature on the adult and aquatic stages of several species,
including An. gambiae have been studied. The rates at which the new individuals are
produced determine the growth rate of insect populations. Population growth rate
critically depends on growth characteristics of immature stages. Growth rates can be
influenced by temperature where food is not limiting. Larvae of An. gambiae develop
into adults at temperature ranging from 16 to 34°C with reduced survival at temperatures
between 38-40°C and 14-20°C (Bayoh and Lindsay 2004). Interestingly, the production

of adult mosquitoes is not directly proportional to the rate of development of the aquatic

10

.’|
I

stages since at temperatures resulting in faster rate of larval development fewer adults
were produced. An. gambiae developmental rate increased with temperatures between 22-
28°C then declined. Even though adult developmental rate was highest between 28-32°C

adult emergence was highest between 22-26°C (Bayoh and Lindsay 2003).

Aquatic Microbial Communities: Factors Affecting Structure and Dynamics

Larvae of the Anopheles species reviewed above have some obvious and
characteristic habitat features and qualities, but the nutrient and microbial facets of these
habitats that contribute to primary and secondary (including mosquito) production and to
habitat differences among these Anopheles species are poorly known. My research
proposal aims to address some of the aspects of these qualities and features, as detailed
below. It is informed by studies of aquatic microbial community ecology in general,
about which much is known and there exists an enormous literature ranging from pelagic
ocean to stream and tree hole ecosystems. In general, autotrophic freshwater communities
are thought to be organized around three predominant, shaping forces: the intensity of
nutrient inputs (“bottom up effects”), the intensity of predation (“top down effects”), and
the availability of sun light (“insolation”) all of which dictate the flow of energy and
structure of the food web (Cohen et al. 2003). These three interacting and sometimes
counteracting forces will influence energy flow and biomass accumulation at each trophic
level, and are determinants of the extent of primary and secondary production. Indeed,
mesocosm studies and whole lake manipulation studies have suggested that aquatic
management schemes could be developed on the basis of these ecological concepts.

However, little of this research has been applied conceptually to an understanding of

11

mosquito aquatic habitat production; container habitats are a notable exception to this
statement (Kaufman et al. 2002). In particular, there is currently a poor understanding of
the interactions of nutrients, energy inputs, microorganisms and mosquito production.

An overall theme that emerges from studies of aquatic communities, however, is that
strong effects of predation on microbial communities limit secondary production. How

relevant is this theme to mud puddle habitats of Anopheles mosquitoes in Kenya?

In shallow, turbid, mud—bottomed habitats of the kind occupied by Anopheles
larvae in Kenya, Cooper et al. (1998) have suggested that algal production and abundance
of zooplankton and insects is limited by phosphorus availability. This provides evidence
that P could limit algal production, and in turn mosquito production, in Kenyan mud-
lined pools of water. A bottom-up effect was clearly identified in these cases. Evidence
for top-down effects is provided by more recent studies including some of my own,
outlined below. In field microcosm experiments, Gimnig et a1. (2002) showed that the
most likely important food source for larval An. gambiae was algae, which were
significantly reduced in the presence of larvae as measured by chlorophyll a in surface
water samples and by counts of algae in sedimentation chambers, compared to when
larvae were absent. In their first experiment, mosquito feeding reduced dry algal biomass
by 100 ug/ml, or a total of 7 mg per habitat surface layer. Based upon total adult
production of 2.8 and 7.5 mg in treatments with 20 and 60 larvae respectively, the 4.8 mg
decline in algal biomass they measured as an indicator of larval grazing pressure on algae
explains much larval production. Bacteria may have supplemented or supplanted algae
under such feeding pressure as it may have formed a secondary food source, but bacterial

densities were not affected significantly by the presence of larvae so the extent of grazing

12

mosquito aquatic habitat production; container habitats are a notable exception to this
statement (Kaufman et al. 2002). In particular, there is currently a poor understanding of
the interactions of nutrients, energy inputs, microorganisms and mosquito production.
An overall theme that emerges from studies of aquatic communities, however, is that
strong effects of predation on microbial communities limit secondary production. How

relevant is this theme to mud puddle habitats of Anopheles mosquitoes in Kenya?

In shallow, turbid, mud-bottomed habitats of the kind occupied by Anopheles
larvae in Kenya, Cooper et al. (1998) have suggested that algal production and abundance
of zooplankton and insects is limited by phosphorus availability. This provides evidence
that P could limit algal production, and in turn mosquito production, in Kenyan mud-
lined pools of water. A bottom-up effect was clearly identified in these cases. Evidence
for top-down effects is provided by more recent studies including some of my own,
outlined below. In field microcosm experiments, Gimnig et al. (2002) showed that the
most likely important food source for larval An. gambiae was algae, which were
significantly reduced in the presence of larvae as measured by chlorophyll a in surface
water samples and by counts of algae in sedimentation chambers, compared to when
larvae were absent. In their first experiment, mosquito feeding reduced dry algal biomass
by 100 ug/ml, or a total of 7 mg per habitat surface layer. Based upon total adult
production of 2.8 and 7.5 mg in treatments with 20 and 60 larvae respectively, the 4.8 mg
decline in algal biomass they measured as an indicator of larval grazing pressure on algae
explains much larval production. Bacteria may have supplemented or supplanted algae
under such feeding pressure as it may have formed a secondary food source, but bacterial

densities were not affected significantly by the presence of larvae so the extent of grazing

12

on them was uncertain. The levels of nitrogen and phosphorus in the habitats were not
affected by the presence of larvae although there was evidence for decreasing nitrogen
levels with increasing larval densities suggesting that nitrogen may be a limiting resource
in the larval environment. These trends find parallels in studies of other aquatic systems.
For example, a series of studies in lake ecosystems in northern Europe has revealed
marked predator-prey relationships, as follows. Daphnia fed upon both bacteria and their
protozoan predators having the general effect of reducing abundance of both groups and
severely dampening bottom-up nutrient effects. A fraction of the bacteria may have
possessed some kind of grazing resistance that would buffer seasonal fluctuations and
maintain high densities or the main bacteriovores might be top-down controlled. Due to
rapid compensation of bacterial grazing losses by the development of resistant forms, the
total bacterial biomass may remain less affected and could be maintained on a relatively
high level (Jurgens 1994, J urgens and Gude 1994). The planktonic bacterial communities
in these studies responded both phenotypically and genotypically to strong grazing
pressure by metazooplankton (J urgens and Matz 2002), interacted strongly in their
population dynamics to carbon and phosphorus limitations (Matz and Jurgens 2003), and
were shaped by combined protozoan and metazoan predation (Langenheder and J urgens
2001). These studies showed how microcosm and mesocosm experiments can support
whole ecosystem studies and represented a model for long-term, integrated analysis of

these relationships.

13

Methodological Approaches to Aquatic Microbial Community Structure

Estimates of microbial diversity within an environment can be divided into two
broad categories: culture-dependent or independent methods. Culture-dependent methods
involving isolation and cultivations are ideal, but they are not representative of the
community and are slow and laborious. Very few (0.1%-1%) bacteria in the natural
environment have been cultured as pure isolates in the laboratory. Possible reasons why
naturally-occurring bacteria and other microbes have been proved difficult to grow in the
laboratory include the following: many microbes lack the ability to adapt fi'om severe
oligotrophy to the high nutrient concentration of culture media. Many bacteria exist in
consortia in natural assemblages, where inter and intra population interactions (including
quorum sensing) are important. Our poor understanding of the basic physiology of many
microbes makes it difficult to design appropriate culture media and thus it is clear that
current isolation procedures will fail to adequately investigate the microbial diversity
extant in natural environments (Zoetendal et a1. 2004, Nubel et al. 1999). Also classical
identification relies heavily on phenotypic characterization, including morphology,
growth requirements and characteristics, fermentation profiles, cell wall protein analysis,
serology and more recent fatty acid methyl ester (FAME) analysis. From the phenotypic
characterization of certain lactic acid bacteria (cell wall profiling) and some species of
Bifidobacterium (cell wall peptidoglycan analysis), there is general awareness that
observation of a similar phenotype does not always relate to similar, closely-related,
genotype (McCartney 2002). Also these methods have weaknesses like poor

reproducibility, ambiguity in some techniques and poor discriminatory power.

14

A method for analysis of microbial diversity should involve evaluation of the total
population-those that grow on laboratory media plus those that don’t, those that have
been described and those that never will be. Since the simple task to assess microbial
diversity is insurmountable, a reasonable compromise to achieve this ideal objective
would be to use a surrogate to assess microbial diversity. This surrogate indicator of
microbial diversity would have to occur only in the living cells, vary in a meaningful way
in relationship to the overall microbial diversity in the system, and be sufficiently
variable that groups could be constructed based on similarity. While several cell
components are informative, SSU rRNA genes are highly conserved among organisms
and make them the best for studies of phylogenetic relationships of microbes in
ecological studies (Hugenholtz et al. 1998). Molecular techniques therefore provide main
source of information on the genetic and physiological diversity of environmental
populations. While sequencing of the gene for the small subunit ribosomal RNA (168)
from natural assemblages is now routine, the 16S sequences in databases rarely match
culturable microbes, instead they usually have the closet affinity with sequences from
other uncultured organisms (Lu et al. 2003). The delineation of species on basis of
morphologies, even though, the most common practice does not necessarily result in
evolutionarily and ecologically coherent entities, particularly when applied to
microorganisms. Microbial phylogeneticists in particular depend on molecular sequence
characters, because prokaryotes offer relatively little in the way of complex morphology
and behavior (Doolittle 1999). Numerous limitations inherent in the various
methodologies currently available, microbial diversity has not yet been convincingly

reported or it would be at present impossible (Nubel et al. 1999). The determination of

15

prokaryotic species richness and diversity in nature is impracticable. Depending on the
research objectives, it may be more fiuitful to take into account the organisms specific
identities and their ecologically relevant properties. However molecular techniques are
not without limitations, thus a polyphasic approach might often be necessary and is
perhaps the most desirable among the range of options (Hugenholtz et al. 1998).

Current molecular techniques used to study microbial ecosystems can be
separated on the bases of their wide use. Clone libraries can be sequenced to identify the
composition of microbiota down to the species level. Microbial community structure can
be analyzed via fingerprinting techniques, while FISH and dot blot hybridization can be
used to measure abundance of a particular taxa. Also there are approaches based on
functional genes and their expression and the use of stable isotopes and biomarkers that
are being optimized to study metabolic activities of groups or individual organisms in
situ. (Zoetendal et al. 2004). 16S rDNA sequencing relies on sequences of rRNA genes
obtained by cloning directly from environmental DNA or, as in the majority of studies,
after amplification by the PCR (Hugenholtz et al. 1998). Although 16S rDNA analysis
represents a very useful technique for culture-independent analysis of complex microbial
communities, and it gives significant information about the identity of uncultured
bacteria, the clone fiequencies in the clone libraries do not reflect the in situ quantities of
the respective microorganisms (Eschenhagen et al. 2003). This approach is more
qualitative than quantitative. Possible reasons are differences in the numbers of rRNA
operons in different microbes, different efficacies of cell lyses and DNA extraction, or
shifts due to PCR amplification. Sequencing of SSU rRNA genes has become a standard

procedure in the identification of isolates. Currently, >79,000 16S rRNA sequences are

16

available in DNA databases, which is far greater than for any other gene (Zoetendal et al.
2004). Studies have shown that many of the 168 rDNA sequences exhibit low sequence
similarity to genes of known cultured bacterial genera (Lu et al. 2003). This method has
also helped in developing cultivation strategies for many previously unknown or
uncultivated bacteria (butyrate-producing, cellobiose-degrading bacteria) (Zoetendal et
al. 2004).

Denaturing gradient gel electrophoresis (DGGE) and terminal-restriction
fragment length polymorphism (T-RFLP) are community fingerprinting techniques which
are widely used to monitor communities over time or in response to dietary treatments
(Juck et al. 2000; Duatre et al. 2001). DGGE is PCR-based and generates profiles
representing the sequence diversity within the selected ecosystem. The general principle
of DGGE/TGGE is separation of individual rRNA genes based on differences in
chemical stability or melting temperature of these genes (McCartney 2002). The total
number of bands visualized in a DGGE gel also provides an estimate of the genetic
diversity within a given environment. T-RFLP is based on specific target site for
restriction enzymes (Eschenhagen et al. 2003). Profiling is based on the banding patterns
obtained from DNA restriction digests. The method shows good sensitivity and has been
employed for environmental studies including bacterial soil communities and
comparative community analysis (McCartney 2002). For T-RF LP, 16S rRNA sequences
have been extensively used as markers. Non-16S rRNA profiling approaches based on
cellular fatty acid composition or G + C content of DNA have also been used
successfully to monitor shifts in bacterial communities in an ecosystem. But the

disadvantage to these methods is the lack of phylogenetic databases.

17

Although TRFLP fingerprints can show the dominant groups and the ones which are rare
it provides only semi quantitative information about the abundance of the respective
groups in the community. To estimate the abundance of particular taxa a direct approach
is more appropriate. Fluorescent in situ hybridization (FISH) is used to quantify bacterial
cells in the environmental samples. This method involves use of fluorescent-labeled
oligonucleotide probes targeted towards SSU rRNA and visualization using
epiflourescent light microscopy. Several probes have been developed to quantify bacteria
of various domain and genera. This method is widely used to quantify bacterial cells in an
ecosystem like marine arctic sediments (Ravenschlag et al. 2001), for characterizing
nitrifying bacteria in biofilms (Gieseke et al. 2001). Limitations of this method are that it
is dependent on SSU rDNA sequences available in the databases, and that only a few
probes can be used per analysis (Zoetendal et al. 2004). This method also depends on the
permeability of the bacterial cells, accessibility of the target and number of ribosomes per
cell. This technique is reliable and relatively easy to use, but probe design and detection
limits are the main disadvantages. Such protocols are useful for bacterial groups or
predominant genera but an extensive list of probes would be necessary for diversity
studies. Dot-blot hybridization is used to quantify specific 16S rRNA in a mixture
relative to the total rRNA. It involves probing DNA or RNA extracts from bacterial
isolates or environmental samples. Total RNA is isolated from the sample, bound to a
filter, and hybridized with labeled oligonucleotide probes. The relative concentration is
estimated by dividing the concentration of specific probe by the concentration of the

universal probe, however it is important to note that such quantification are only relative.

l8

Purpose/Scope of the Project

Adult An. gambiae density depends on the number and collective productivity of
the larval habitats. Even though these habitats are the source of these competent vectors,
little is known about the larval biology of these important insects, in particular, what
forms the basis for production of insect biomass in them. According to the study
conducted by Gimnig et. al. (2002), the most likely important food source for larval An.
gambiae seem to be algae, which were significantly reduced in presence of larvae as
measured by chlorophyll a in the surface water samples and by counts of algae in
sedimentation chambers, compared to with when larvae were absent. Other experiments
support this study and report that presence of algae does improve the growth and
development of the larvae, with shorten developmental time, and increased survival over
that when algae were absent, a condition obtained by shading the habitats (Kaufman et a1.
2006). But this ignores algal turnover rates, larval consumption and other nutritional
sources (i.e.; bacteria and non cellular organics in the surface layer). Algae probably play
a key role as food resources-in An. gambiae habitats and bacteria may likely form a
secondary food source. Studies on larval gut analysis reveal that bacteria form the bulk of
the food bolus, along with organic particulates, algae, and small invertebrate parts
(Walker et al. 1988). Of interest is that larval mosquito feeding on bacteria in their
habitats has been estimated to account for production of a large fraction of the insect
biomass (Kaufinan et a1. 2001), but simultaneously, larval mosquito feeding reduces
microbial abundance, alters the microbial community qualitatively and quantitatively,
and diminishes the quality of the microbes as food (Kaufman et al. 2000, 2002; Xu et al.

in preparation). Thus it is essential to conduct studies to determine the contribution of

19

bacteria to Anopheles larval nutrition. Bacteria along with other organisms that occur in
larval habitats may form the basis for production of adults, by transforming inorganic
nutrients through autotrophic and heterotrophic processes into assimilable forms, by
providing food and specific larval nutritional demands (Walker et a1 1988, Walker and
Merritt 1993, Kaufman et a1. 2000, 2001, 2002).

Further, microbial communities in larval mosquito habitats have been postulated
to influence ovipositional site selection by mosquitoes owing to volatile organic
compounds emitted from these communities, and owing to microbially-derived flavors in
water (Trexler et al. 2003). This research proposal cleaves to that hypothesis, and applies
it to the An. gambiae and An. funestus habitats currently under study in western Kenya by
my advisor and colleagues (Gimnig et al. 2001, 2002). Location and selection of an
ovipositional site is an essential part of the life history of all mosquito species. The
location and selection of an ovipostional site involves visual, olfactory, and tactile
responses. Intensive field studies have shown that mosquitoes are quite discriminating in
selecting sites for egg deposition and considerable evidence points to this site
discriminating larval distribution (Bentley and Day 1989). Oviposition site selection is
the net result of the interaction of a complex array of both chemical and physical factors.
These attracting substances may, in some cases be pheromones produced by the larvae,
but, studies have shown that concurrent presence of A. gambiae larvae reduced
oviposition, while turbid water from natural breeding sites increased oviposition
selectivity. Thus other possible attractants such as microbial metabolites must also be

considered keeping in mind that mosquito biting on the human body is also mediated by

20

microbes. An. gambiae, a human biter, prefers to bite human feet and is attracted to odors
emitted by bacteria from feet, such as isovaleric acid (Braks et al. 1999).

Notably, An. gambiae and An. fimestus habitats are segregated and distinct in
Kenya (Gimnig et al. 2001). The bacterial communities are postulated to be distinct
between these habitat types. These differences may be crucial in differential ovipositional
site selection by females of these two species, and the differences may account for habitat
production for adult mosquitoes. Since very little information exists on the bacterial
community, it is essential to study it and also to determine what comprises the larval
food. By conducting a comparative analysis, one can also determine what makes one
habitat more favorable for one species (An. gambiae and An. arabiensz's) and the other
habitat for other species (An. funestus). Further, experimentation in which the surface
microlayer and its microbial components are intentionally stripped away may reveal the
importance of those components to mosquito feeding and growth. Therefore, the
objectives of my study ar'eul) to analyZe the effects of removal of the surface microlayer
on mosquito growth. 2) to study the contribution of bacteria to larval nutrition and 3) to
determine the bacterial diversity of the surface microlayer in presence and absence of

larval feeding pressure, and 4) to determine the bacterial diversity of An. gambiae and

An. fimestus habitats using 16S rDNA sequence library construction.

21

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26

CHAPTER II
Contribution of the surface microlayer and its algal and bacterial biomass to growth

of Anopheles gambiae larvae (Diptera: Culicidae)

ABSTRACT

The importance of the aquatic surface microlayer and its components on the growth and
development of larval An. gambiae was studied in two experiments in field and
greenhouse microcosms. Removal of surface microlayer at regular intervals in the first
experiment decreased survival of larvae, prolonged developmental time to pupation, and
lowered body mass. Supplementations of the surface microlayer from sources with no
larval grazing improved larval growth. Larval developmental time was shortened and
total mass was significantly higher, compared to microcosms from which surface
microlayer was deliberately removed. In the second experiment, the importance of
bacteria as a food source was studied using a method in which heterotrophic bacterial
production was stimulated with addition of glucose as a carbon source, in microcosms
that were intentionally shaded to reduce autotrophic algal production. Glucose addition to
sterilized habitats did not stimulate growth. In nonsterilized habitats to which glucose
was added, larvae grew, molted, and achieved metamorphosis under these conditions, but

larval survival rate and total adult emergence was very low compared to sunlit treatments.

27

INTRODUCTION

Anopheles larvae, unlike mosquito larvae in most other genera in the Culicidae, position
their bodies parallel to the air-water interface, allowing them to breathe and feed. These
larvae do not have a respiratory siphon but rather have one pair of spiracles located at the
terminal abdominal segments. Larvae feed at the air-water interface and feed as
specialized collector-gatherer, whereby they rotate the head 180° from the normal
position and direct the rapidly beating labral brushes to the air-water interface (Merritt et
al. 1992a). This mode of feeding in mosquitoes differs substantially fi'om that of larvae in
genera where a respiratory siphon is present (Merritt et al. 1992 a, b, Walker and Merritt
1991) in that Anopheles feeding is largely restricted to a single zone of the water column.
This feeding mode has been studied in detail mainly in Anopheles quadrimaculatus, a
species in a different subgenus from Anopheles gambiae. Nevertheless, feeding modes
are likely so similar that comparisons are useful. In An. quadrimaculatus larvae,
magnified visual observations of feeding larvae (Renn 1943) and high speed
microcinematography of larval feeding (Merritt et al. 1992a, b, Merritt et al. 1996)
showed that the lateral palatal brushes beat 7 times per second at room temperature,
generating currents that were both linear and curvilinear around the head of the larvae
and extending to about one body length away. Particles were entrained in a zone of the
surface microlayer equal to the depth of the larval head, ca. 1 mm, and rapidly
approached the anterior of the larval head in distinct starts and stops with no inertia,
indicating that low Reynolds numbers governed entrained particle movements. As

particles neared the anterior of the larval head, they accelerated and entered the preoral

28

cavity. A plume of uningested materials formed and entered the water column vertically
at a 90 degree angle from the original direction of particle movement horizontally. It
appeared to originate at the region of the maxillary pads in the larval preoral cavity, a set
of structures which has been postulated to form a particle capture mechanism in all
mosquito larvae in general (Dahl et al. 1988). Although particle movement could not be
traced thereafter in those studies, formation of a food bolus at a rate of ca. one bolus
every 4.4 seconds in the pharynx was quantified; indicating that rate of particle
entrainment by the lateral palatal brushes was decoupled from rate of food bolus
formation and swallowing. Renn (1943) and Schremmer (1949) postulated that the
mandibles and maxillae interact to pack the pharynx with food particles, while Dahl et al.
(1988) suggest that the pharynx itself functions as an organ which by contraction sucks
entrained particles into the true mouth (i.e., the opening of the pharynx itself, which is the
most anterior section of the foregut). However, this model of particle capture does not
account for the simultaneous role of the pharynx in food bolus formation, nor the
mechanism by which excess water is expelled as the bolus is formed. How the pharynx
can simultaneously suck in water with food particles, pack the food particles into a bolus,
swallow that bolus into the posterior section of the foregut, and expel excess water is a
currently unresolved set of contradictory functions, all attributed to it (Dahl et al. 1988,
Merritt et al. 1992b, Merritt et a1. 1996). Head movements by larvae indicate that not all
particles are acceptable; some are examined by the mandibles and are crushed and then
swallowed; or are rejected through a 180 degree or less head turn to the normal position
after mandibular manipulation (Merritt et al. 1996). This brief review of larval

Anopheles feeding shows that much remains to be determined about feeding mechanisms

29

and food gathering processes. For example, later stages of An. gambiae larvae are known
to capture and ingest neonate larvae and eggs, such that cannibalism has been proposed
as a supplementary feeding strategy (Koenraadt and Takken 2003, Juan Huang et al.
unpublished). Yet all studies support the notion that these larvae are highly adapted to a
particle capture feeding mode in a specialized surface feeding posture.

The surface microlayer is a zone of Anopheles habitats enriched with
microorganisms and other materials compared to subsurface zones of the water column
(Walker and Merritt 1993). The specialized surface feeding behavior of these larvae upon
the surface microlayer might very well be an adaptation to exploit this food-rich region.
The surface microlayer varies in thickness (or depth) depending upon the way it is
sampled. Generally, it consists of an approximately 1 mm deep portion of the uppermost
column of any natural, lentic body of water, small or large. It is perhaps best conceived of
as an organic film with accumulation of hydrophobic lipids and lipoproteins at the
surface. This region of the air-water interface is enriched in both particulate organic
matter and microorganisms, the latter comprising the microbial ‘neuston’ of its living
components (Clements 1992). The surface microlayer is highly enriched in chemicals,
particles and biota (algae and bacteria). A high surface tension created by hydrostatic
forces and the hydrophobic nature of the composition prevents particles and biota from
sinking. An epineustonic community of invertebrates exists above the surface microlayer
and may in some ways be considered to be part of it, and floating and moving on it. Other
materials may deposit from the atmosphere and form part of the nonliving epineustonic

matrix and may interact with the surface microlayer and its components.

30

Experimental ecology requires manipulation of particular factors under conditions
that have sufficient realism to allow generalized conclusions about the phenomenon
under study. One approach has been to develop and utilize experimental microcosms as
suitable models of natural habitats. Recently, development of experimental, field
microcosms designed to simulate natural habitats was accomplished for larval Anopheles
gambiae habitats, in which density-dependent larval grth (suggesting food limitation)
and utilization of microbial food was demonstrated for larval growth cohorts (Gimnig et
al. 2002). It is a novel use of microcosms and is developed further in my studies here. In
the field microcosms, a surface microlayer developed well in the absence of actively
feeding larvae, and it could be sampled with a syringe and fine gauge needle (Gimnig et
a1. 2002). An array of such habitats was established at a secure study site in western
Kenya, and similar habitats were established in greenhouses at the Michigan State
University campus.

According to the study conducted by Gimnig et al. (2002), the most likely
important food source for larval An. gambiae was likely algae. Other experiments also
reveal that presence of algae improved the growth and development of the larvae with
shorter developmental time and greater survival as compared to when algae were absent,
a condition established by shading the habitats without reducing water temperature
(Kaufman et. a1 2006). But this finding ignores algal turnover rates, larval consumption,
their digestibility and other nutritional sources (i.e.; bacteria and non cellular organics in
the surface layer).

Bacteria are an integral component of planktonic food webs and mediate key

pathways in global biogeochemical cycles (Jurgens and Matz 2002, Cole et al. 1988).

31

Bacterial biomass increases in proportion to bacterial production but there is another
phenomenon which not explained by the current models that bacterioplankton show
relatively small seasonal fluctuations within a given system (J urgens and Matz 2002). In
contrast phytoplankton biomass vary between blooms and collapses even within one
season. One study revealed that phytoplankton did not bloom in all microcosms enriched
with N and P and when C was added, the phytoplanktonic groups could not compete with
mineral nutrient-lirnited bacteria. In mesocosm set where no silicate was added,
enrichment with phytoplankton nutrients and glucose led to replacement of diatoms not
by other algae, but by heterotrophic bacteria (Havskum et al. 2003).

Studies reveal that around 75% of ingested algae pass through the gut of mosquito
larvae (Clements 1992, Wotton et al. 1997) and larval gut analyses reveal that bacteria
form the bulk of the food bolus, along with organic particulates, algae, and small
invertebrate parts, when direct counts but not biovolume were used as the data collection
method (Walker et al. 1988). In marine environments, gelatinous zooplankton
(larvaceans, salps, doliolids) are potentially important bacterivores although their general
impact on bacterial communities is poorly studied (J urgens and Matz. 2002). Algae might
be playing a key role as food resources in An. gambiae habitats but bacteria may indeed
form a secondary food source. This aspect of the feeding biology of Anopheles larvae is
poorly understood. Thus, here I propose to determine the contribution of bacteria to larval
nutrition. A controlled greenhouse experiment was conducted to study the contribution
of bacteria to larval nutrition. A set of artificial microhabitats was shaded in order to
reduce algal production and to determine if non-photosynthetic microorganism biomass

can compensate for the absence of algae. I used the field habitat microcosms described by

32

Gimnig et al. (2002) and Kaufman et a1. (2006) to conduct studies on the effect of surface
microlayer removal, transfer, and supplementation on larval mosquito growth. Because I
specifically postulate that formation of the surface microlayer simultaneously provides
larvae with food, but larval grazing reduces their food, I conducted an experiment in
which this. layer was manipulated to reduce or increase its availability to larvae
volumetrically. Additionally, I conducted an experiment in which shade was used to
reduce experimentally algal biomass, and glucose was added to stimulate bacterial
production, to determine if bacterial biomass could compensate for lack of algal biomass

and could promote larval growth.

33

METHODS AND MATERIALS

Experiment 1. Contribution of surface microlayer to larval Anopheles gambiae
growth.

Habitat setup Microcosms were designed to simulate natural habitats of larval
Anopheles gambiae (Gimnig et al. 2002). An array of such habitats was established in the
green house at Michigan State University. Plastic basins (30cm in diameter) were
provided with organically enriched soil (green house potting soil) so as to fill 1/2 of the
depth of the basin. 1.5 liters of distilled water was added to fill up the basins. Water
levels were maintained throughout the experiment with distilled water. The microcosms
were covered with insect nets so as to prevent invasion by other insects and oviposition
by local mosquitoes. After the habitats (plastic basins) with soil were filled with water,
they were allowed to develop microbial growth in full sun for 3 days, and then were
stocked with 40 first instar An. gambiae larvae (KISUMU strain) obtained from the
colony maintained in our laboratory at Michigan State University (Huang et al. 2005).
The experimental design was comprised of four treatments spread in a randomized
complete block design with a total of 24 experimental units. Each treatment was
replicated 6 times. The treatments were as follows: (1) The surface layer was
purposefully removed every other day (a treatment abbreviated Tl ), (2) The composite
surface layer was added from habitats T1 containing larvae (a treatment abbreviated T2),
(3) The composite surface layer was added from source habitats without larvae (a
treatment abbreviated T3), and (4) An experimental control in which there were no

additions or removals of surface microlayer material, but the microcosms contained

34

larvae (a treatment abbreviated T4). A set of 6 habitats (abbreviated T5) containing no
larvae was used as the source of surface layer material added to microcosms assigned to
T3, see above. Temperature of the water was air was recorded at 30 minute intervals
throughout the experiment. Manipulations were done on day 2 and continued every other
day. Twelve ml of the surface microlayer material was harvested from each replicate of
T1 (surface harvested) and from the “no larvae” source habitats (T5) using a syringe and
16 gauge needle and was pooled by source. Ten ml of the composite was added to the T2
and T3 microcosms, respectively. On days 5 and 9 of the experiment , 3 ml of surface
microlayer was preserved in formalin (10% final cone.) for direct counts of bacteria (see
below), 3 ml in Lugol’s preservative (few drops) for microscopic counts of algae, and 5
ml filtered for quantification of chlorophyll a. The above samples were obtained as a
composite by sampling 2 ml from each of the 6 replicate habitats.

Each habitat was monitored daily by visual inspection to collect pupae. The date
of pupation was recorded, and individual pupae were held in cups and adults were
allowed to emerge. The adults were separated by sex and stored at -80° C. The frozen
mosquitoes were later lyophilized and their dry mass (mg) determined by a rnicrobalance.
The growth responses were summarized by microcosm as the proportion surviving, mean

individual mass of males and females, and development time of males and females.

Microbial and Chemical analysis Direct bacterial counts of formalin preserved samples
were performed using 4'6-diamidino-2-phenylindole (DAPI) stain and epifluoresence
microscopy as described in Walker and Merritt (1993). Algae were counted using settling

chambers and inverted microscopy in the laboratory of Dr. Orlando Samelle at Michigan

35

State University. Counts were converted with standard formulae to numbers of cells per
ml of original sample. Chlorophyll a content of filtered particulates was determined
fluorimetrically by extraction overnight in 95% ethanol (Welschmeyer 1994), yielding

chlorophyll a in micrograms per liter of original sample.

Statistical Analysis Analysis of variance (ANOVA) was performed using SAS for the

growth data. Data were transformed as needed to meet the assumptions of ANOVA.

Experiment 2. Contribution of bacteria to larval Anopheles gambiae nutrition
Habitat setup: Microcosms were designed similar to Experiment 1, to simulate natural
habitats for Anopheles gambiae larvae. Twenty-seven such microcosms were set up and
24 of them were shaded. The shading was accomplished by using aluminum foil-covered,
perforated boxes which were placed over each microcosm in such a way as to facilitate
air circulation but greatly reduce light. Air over the treatments was well circulated with
fans to maintain even temperatures at ambient levels. All the treatments were covered
with insect nets to prevent predation and oviposition by other insects. The experimental
design was a randomized complete block design with 24 total experimental units. To
stimulate heterotrophic microbial growth, 12 of the 24 shaded habitats were
supplemented with glucose to obtain 30 ppm of final concentration. Glucose was added
beginning two days prior to introduction of the larvae and continuing every 3 days until
most of the larvae pupated. The concentration of glucose added to the treatments was
determined by conducting a pilot assay with different concentrations of glucose to obtain

maximum bacterial productivity. Twelve habitats did not receive any glucose, as controls.

36

Out of 12 treatments with glucose and no glucose, 6 treatments did not receive any
larvae. Thus, 6 habitats with glucose and 6 without glucose did not receive any larvae to
determine bacterial counts in absence of the larvae. Microwsms with specific treatments
were stocked with 10 larvae four days after the habitats were setup. Each treatment had 6
replications. Three habitats were left in bright sunlight and stocked with larvae to allow
normal algal grth to occur and to confirm normal development of the larvae. Pupae
collections and data acquisition and summarization were done as in the first experiment.
To ascertain that glucose did not have any direct effect on the growth responses in
the above experiment a different experiment was setup. Fifteen experimental units were
setup similar to Experiment 1 with the exception that the plastic basins used were 15 cm
in diameter containing 250 gm of soil, 600 ml of sterile distilled water and five larvae
added to each unit. Five replicates of each of the following three treatments were setup.
Treatments included sterile soil with glucose, non sterile soil with glucose and control.
Fish food (used in maintaining the laboratory colony) was added to the control units.
Glucose was added every other day to all the treatments receiving glucose to final

concentration of 30 ppm. Sterile treatments received filter sterilized glucose.

Bacterial counts: 3 ml sample of surface microlayer were collected from the habitats one
and three days before the larvae were added for bacterial counts. After the introduction of
larvae, the bacterial counts were carried out on days 12 and 18). The first pupa from the
glucose treatment was harvested on day 15. The samples were preserved in 4% formalin
and stored at 4° C until counts were performed. Counts were done using 4'6-diamidino-2-

phenylindole (DAPI) stain and epifluoresence microscopy.

37

Bacterial productivity: 2 ml samples of the surface microlayer were collected on day 6
(three days after larvae added) from each shaded glucose and no glucose habitat to
determine the bacterial productivity within those habitats. The productivity assay was
performed by incubating sub-samples with 3H-leucine and measuring the 3H—leucine
incorporation into protein using liquid scintillation (Kirchman 2001, Kaufman et al.

2002).

Statistical Analysis: A non-pararnetric statistical test, the Wilcoxon/Kruskal-Wallis rank

sum test, was performed on the grth data.

38

RESULTS

Experiment 1. Contribution of surface microlayer to larval Anopheles gambiae
growth.
Pupal productivity was greatest from microcosms receiving surface microlayer (T3)
material from source habitats containing no larvae (70.8% mean survival to pupation; 170
out of 240 pupae harvested); this productivity was higher than that in habitats in which
surface microlayer was removed (Tl) (42.9% mean survival to pupation; 103/240 pupae
harvested). Pupal production was lowest in habitats to which surface microlayer material
was added from habitats containing larvae (T2) (34.2% mean survival to pupation;
82/240 total pupae harvested) (Fig 2.1). In control habitats (T4) with no additions or
removals of surface microlayer 47.5% larvae survived to pupation and 114 from 240
pupae were harvested). The average number of pupae developing from each individual
unit was 17.1 (d: 7.4) fi'om T1, 13.6 (a: 5.6) from T2, 28.3 (i 2.7) from T3, and 19 (:1: 9.6)
from T4.

The total adults that emerged from the pupae collected were 86 (83.5 %) for T1,
47 (57.32 %) for T2, 143 (84.18 %) for T3, and 85 (74.56%) for T4. The average number
of adults that emerged from each unit were 14.3 (i 8), 7.8 (:l: 4.9), 23.8 (i 4.1), and 14.2
(:i: 9.2). The above results indicate that treatment T3, in which composite surface layer
was added from source habitats, had the highest survival rate and yielded the most pupae
compared to other treatments, (F = 5.12, P = 0.0086) as well as adults (F = 5.43, P =
0.0068). Supplementing T3 microcosms with food from source habitats improved the

production efficiency of the habitat but the addition of food source from microcosm with

39

'Q

.3 1'83:

‘ ’t
«r l

A'u ..4

7L,-

a
- uni

-

‘51. o
l l

- .HIL,

larvae did not improve the production instead it had lowered mnnber of pupae and adults
coming out of this microcosm (T2). There were significant difference inthe female
development time (F: 5.29, p= 0.0075) between the treatments (Fig 2.2). Females from
T3 treatment had the shortest development time of 18.9 i1.9 days compared to 23.3 i 2.7
in T1, 24.7 i 2.23 in T2 and 23.3 i 3.6 in T4. The male development time (F=2.26, p=
0.1163) was not affected by the treatments. The total mass of adults emerging fromT3
was higher significantly higher (F = 4.85, p=0.01) than T1 and T4 (Fig 2.3). Adults from
T2 had significantly lower adult dry weights than the rest of the treatments. Similar
results were observed for total male mass (F: 4.44, p= 0.0151) and total female mass (F:

3.71 , p= 0.0286) between treatments.

40

Mean number of pupae
per treatment
_l --| N N (A)
0 UI O 01 O
U

01
r

 

 

   

O
r

 

Harvested Transferred Supplemented Control

Treatments

Figure 2.1: Mean number of pupae produced in the four treatments in the Experiment 1. T1, Surface
harvested; T2, Surface microlayer added from habitats with LARVAE; T3, Surface
microlayer added from source habitats; T4 Control (no harvesting or supplementing).

N = 6 replications per treatment

41

 

g 25

3 a

o l

E 20 1

a 15 «-

-o

.2

a E

g 10 1

h—

. 5i

E

0 0.0 . ‘00 0 .
Harvested Transferred Supplemented Control
Treatments

Figure 2.2: Average female development time in different treatments. T1, Surface harvested; T2,
Surface microlayer added from habitats with LARVAE; T3, Surface nricrolayer added
from source habitats; T4 Control (no harvesting or supplementing). N = 6 replications
per treatment

Mean total mass (mg)
(.0

 

   

Harvested Transferred Supplemented Control
Treatment

Figure 2.3: Mean total dry mass produced in different treatments. T1, Surface harvested; T2,
Surface microlayer added from habitats with LARVAE; T3, Surface microlayer added
from source habitats; T4 Control (no harvesting or supplementing). N=6

42

Chemical and Microbial analysis Chlorophyll a, a measure of algae was estimated on
day 9. The chlorophyll a measure in T1 was 23.74 ug/L, T2 9.29 ug/L (After the
manipulation was performed, 9.29 rig/L), T3 23.74 ug/L (after manipulation was
performed 53.68 ug/L), T4 20.65 ug/L, T5 609.05 ug/L. The bacterial counts on day 5
did not vary much between the treatments and ranged from 1.8 x 106 to 2.2 x 106 cells/ml
in all the four treatments (Fig 2.4). The bacterial count of the source habitats on day 5
was 2.9 x 107 cells/ml. On day 9 the bacterial counts remained low in treatments T1 and
T2 being 6.2 x 106 and 9.3 x 106 cells/ml respectively. Bacterial numbers in T3, T4 and
T5 were higher (by one order of magnitude from T1 and T2), but fairly similar between
them and they were 2 x 107, 1.7 x 107, and 1.9 x 107 cells/ml respectively. The above
counts represent the bacterial numbers before the manipulations were done on that day.
Algal counts on day 5 were 1.1 x 104 cells/ml for T1; 1.3 x 103 cells/ml for T2; 4.4 x 102
cells/ml for T3; 5.4 x 103 cells/ml for T4 and 2.4 x 105 cells/ml for T5. On day 9 algal
counts in T1 was 3.6 x 104 cells/ml, in T2 was 2.2 x 104 cells/ml (after manipulation, 2.8
x 104 cells/ml), in T3 was 6.3 x 104 cells/ml (after manipulation 8.4 x 104 cells/ml), T4

was 5.6 x 104 cells/ml and T5 was 1.6 x 105 cells/m1.

43

lDay 5
lDay 9

 

Bacterial counts *10'6 cells/ml

T1 T2 TB T4 T5
Treatments
_ 300
E
Q 250
0
0
go 200
9.
:9 150
§ 100
0
* 5° \ s
or
2 OLNN '_ 0,
T1 T2 T3 T4 T5
Treatments

Figure 2.4: Bacterial and Algal counts of the surface microlayer from different treatments. T1,
Surface harvested; T2, Surface microlayer added from habitats with LARVAE; T3,
Surface microlayer added from source habitats; T4 Control (no harvesting or
supplementing)

44

Experiment 2. Contribution of bacteria to larval Anopheles gambiae nutrition.

No adults were produced in the shaded treatments without glucose amendment during the
entire duration of the experiment (25 days). The larvae did not develop beyond late
second or early third instar. The glucose amended treatments produced pupae that also
emerged as adults. A total of 21 out of 60 larvae (35%) pupae developed from glucose
amended treatment with an average of 3.5 i 1.3 per unit (Fig 2.5 A). On the other hand a
total of 26/30 (86%) pupae developed from sunlit treatments which were added to the
experiment as a control. The average number of pupae per unit was 8.6 $0.33 in sunlit
treatments. Of the pupae produced from glucose treatment 57.14% emerged as adults the
remaining died while emerging or prior to emergence. The emergence rate for the pupae
collected from the sunlit treatment was very high up to 96.15%. The average
development time for Anopheles larvae was 7.76 (i 0.03) days in sunlit treatment and
15.38 (i 8)‘ days in glucose treatment. Non-parametric statistical test, Wilcoxon/Kruskal-
Wallis test (rank sum) was performed for number of pupae and the probability (Z stat)
was 0.0284. The adults produced in glucose treatment had lower body weight with an
average of 0.16 mg (:t 0.026) than the treatments in open sun which had an average adult
dry mass of 0.59 mg (:I: 0.11). Similar outcome was observed in sterile and non sterile
treatments containing glucose (Fig 2.5 B); pupae were produced in non sterile treatments
containing glucose (36.7%). No pupae were produced in the sterile treatments containing
glucose, the larvae did not grow beyond first or second instar. Larvae developed normally

with 86.7 % pupae produced in control treatments containing fish food.

45

Bacterial counts were performed at different time points before and afier first
instar larvae were added to the experimental units. The bacterial counts performed at the
beginning of the experiment prior to glucose addition in all the treatment were in the
range of 5.7 x 106 to 43.5 x 106 cells/ml. In sun treatments the bacterial numbers ranged
from 3 x 106 to 7.7 x 106 cells/ml. Also counts were performed for unidentified algae in
sunlit treatments, which ranged from 4 x 105 to 4 x 106 cells/ml. No such algae were
observed in shaded treatments. One day after the first addition of glucose in the
respective treatments, the counts in treatments without glucose ranged from 9.8 x 106 to
28.2 x 10‘5 cells/m1 whereas in treatments with glucose the numbers ranged from 2.6 x 106
to 22 x 106 cells/ml. Three days prior to pupation the bacterial counts in treatments with
glucose/larvae were 4.02 x 106 cells/ml and in glucose/no larvae 1.9 x 107 cells/m1. In
treatments with no glucose/larvae the counts were 5.06 x 106 cells/ml and in treatments
with no glucose/no larvae the counts were 5.1 x 107 cells/ml.

Bacterial productivity determined after two glucose additions, by measuring the
3H—leucine incorporation into bacterial proteins was estimated and was 105.27 pg C/ L
May (SEM = 10.0) in the glucose-amended treatments and 49.16 pg C/L/day (SEM:

2.33) in non amended ones (Fig 2.6).

46

 

 

 

 

 

 

1O —
9 _
8 a
8
a. 7 ~
3
“a 6 ‘
g 5‘
a 4‘
é’ 3*
2 _
1 _
O - 7
Shade/Glucose Shade/No glucose
Treatment
B
5 -
o 4 e
a
5’:
a. 3 -
e’
E
:1 2 —
E
1 _
0 T fl 1
Fish food Non-sterile soil + Sterile soil + glucose
glucose
Treatment

Figure2.5: Average number of pupae produced from the respective treatments in two different set of
experiments (refer to text). A. Habitats shaded or not, and glucose supplemented in some
shaded habitats but not others. B. Habitats sterile or nonsterile, and glucose added to all.
Fish food was added to some nonsterile ones as a control. A. N = 6 and B. N=5
replications per treatment.

47

140 -
120 ~
100 -
80 4
60 4
40 «
20 4

Mean ug Carbon/Liday

 

   

 

Glucose No glucose

Treatment

Figure 2.6: Bacterial productivity measured as mean pg of carbon/Uday in glucose amended and
non amended microcosms. N = 6 replications per treatment.

48

DISCUSSION

Larval growth processes have strong effects on adult success and, ultimately,
determine the adult population abundance. A wide range of factors influence larval
success and adult emergence, a primary one being larval mortality which result from food
limitation, competition, predation, disease, or physical destruction of the habitat (Olson
and Olson 1989). Competition for nutrients when food supply is limited or when larvae
are crowded can affect An. gambiae by slowing larval development and reducing
pupation success (Gimni g et al. 2002). Observations of cannibalism amongst larvae also
indicate that competition for food resources may be extreme (Koenraadt and Takken
2003). Also, factors like temperature have a critical effect in larval developmental and
adult success when other factors such as food are not limiting (Bayoh and Linsay 2003,
2004). Results of this study (Experiment 1) indicate that removal of surface microlayer
results in lengthened developmental time of An. gambiae larvae, and correspondingly
smaller adults at emergence. Supplementing the larval microcosms with a food source not
previously utilized, by contrast, shortened larval development time, increased larval
survival, and produced larger adults. Deprivation of food by harvesting the surface
microlayer or addition of surface microlayer from microcosm grazed by larvae does not
show any positive effect on the larval growth, in fact the opposite was observed. The
treatments supplemented with surface microlayer that had larval grazing pressure had the
least number of pupae and adults and the total mass was also minimal. The chlorophyll a
levels as well as algal counts were higher in treatments that received the non-utilized food

than that in the other three treatments. This result suggests that algae are indeed a key

49

nutrient required for the success of these larvae. Bacteria were high in density and similar
in density for all treatments except for the harvested treatment and in the treatments
receiving previously utilized food. That bacterial counts were lower in these treatments
suggests that bacteria were fed upon, and could therefore compensate for a deficiency in
algae. Measurements of biovolume instead of cell count would have been a better
approach in interpreting these data, but it was not done.

The classic method of assessing the importance of food limitation is to manipulate
food supply through enrichment or reduction (Olson and Olson 1989). The glucose
amendment study (Experiment 2) aimed to enrich the heterotrophic bacteria in the
habitats to compensate for lack of sufficient algae. Bacteria apparently provided some
larvae the nutrition required for growth, but few adults were produced even though the
density of larvae per microcosm was intentionally kept low so as to reduce competition.
Addition of glucose and subsequent increase in bacterial growth rates enhanced pupal
production above that seen in non-amended microcosms. Direct effects of glucose on
larval growth were ruled out since no larvae grew in treatments with sterile soil and
amendments of glucose. No studies on Anopheles larvae or other invertebrate larvae
have indicated that the larvae can subsist on dissolved organic matter (DOM) alone
(Olson and Olson 1989, Clements 1992). This is consistent with the results observed
here. Manahan et al. (1989) found that growth of molluscan larvae on the defined nutrient
mixture alone was significantly greater than that of larvae which were starved, but not as
high as that fed with algae. By 8 days the larvae fed on nutrient mixture showed tissue
loss and began dying. Merritt et al. (1996) provided experimental evidence that hatching

and survival of An. quadrimaculatus was higher with addition of surface microlayer

50

alone and with combination of DOM but not with DOM alone (Merritt et al.1996, Wotton
et al. 1997).

Studies have revealed that Anopheles gambiae larvae are commonly associated
with algae in the surface microlayer (Gimnig et al. 2002), and that algal biomass
measured as chlorophyll a and algal counts were significantly reduced in surface water
samples in presence of larvae. Other studies reveal that presence of algae improved the
growth and development of the larvae with shorter developmental time and greater
survival as compared to when algae were absent, a condition established by shading the
habitats (Kaufman et a1. 2006). The microbial components and nutrients available as food
for Anopheles larvae found within the surface microlayer and in gut contents have been
documented in few studies (Walker et al. 1988, Walker and Merritt 1993). Larval gut
analysis revealed that bacteria form the bulk of the food bolus, along with organic
particulates, algae, and small invertebrate parts (Walker et al. 1988, Wallace and Merritt
2004). There is no doubt that algae are an important component of An. gambiae diet but
bacteria can also serve as a food source, although they might not serve as a sole source in
natural settings. Addition of glucose in Experiment 2 with subsequent increase in
heterotrophic bacterial growth rates (bacterial productivity was enhanced 2-3X) enhanced
larval development compared to that seen in treatments without glucose. However the
pupation rates were lower than in sunlit treatments. Adult emergence was much lower
with either dead pupae or death of pupae while emerging. Bacteria predominate
numerically in the surface microlayer (Walker and Merritt 1993), but their biomass may
not be able to compensate for biomass produced by primary producers. Even though there

is evidence that bacteria can provide nutrition for survival and pupation it is inadequate

51

food resource by itself in natural habitats. Also, algae can provide certain essential
nutrients that the bacteria cannot provide, in particular long chain, polyunsaturated fatty
acids required by all mosquitoes in their larval diet (Clements 1992). Lack of the above in
the diet causes an inability in the adults to fly. Wotton et al. (1997) showed that surface
microlayer bacteria aided growth and survival of An. gambiae larvae but they did not
study the pupation of the larvae. Overall, my study demonstrates that bacteria in the
surface microlayer bacteria promote growth of A n. gambiae larvae, but are likely to be

insufficient for typical cohorts in nature.

52

REFERENCES

Bayoh M. N ., Linsay S. W., 2003. Effects of temperature on the development of the
aquatic stages of Anopheles gambiae sensu stricto (Diptera: Culicidae). Bulletin of
Entomology Research. 93. 375-381.

Bayoh M. N., Linsay S. W., 2004. Temperature-related duration of aquatic stages of the
Afrotropical malaria vector mosquito Anpheles gambiae in the laboratory. Medical and
Veterinary Entomology. 18. 174-179.

Clements A. N. 1992. Larval feeding. Pg74-99 In eds. Chapman & Hall. The biology of
mosquitoes. University press, Cambridge. v2.

Cole, J. J., Findlay S., Pace M. L. 1988. Bacterial production in flesh and saltwater
ecOsystems: a cross overview. Marine Ecology Progress Series. 43: -10.

Dahl C., Widahl L., Nilsson C. 1988. Functional Analysis of the Suspension Feeding
System in Mosquitoes (Diptera: Culicidae). Annals of the Entomological Society of
America. 81(1): 105-127.

Gimnig, J. E., Ombok M., Kamau L., Hawley W.A. 2001. Characteristics of larval
anopheline (Diptera: Culicidae) habitats in western Kenya. Journal of Medical
Entomology. 38: 282-288.

Gimnig, J. E., Ombok M., Otieno S., Kaufman M. G., Vulule J. M., and Walker E.
D. 2002. Density-dependent development of Anopheles gambiae in simulated natural
habitats. Journal of Medical Entomology. 39:162-172.

Havskum H., Thingstad T. F., Scharek R., Peters F., Berdalet E., Sala M. M.,
Alcaraz M., Bangsholtz J. C., Zweifel U. L., Hagstrom A., Perez M., and Dolan J. R.,
2003. Silicate and labile DOC interfere in structuring the microbial food web via algal—
bacterial competition for mineral nutrients: Results of a mesocosm experiment.
Limnology and Oceanography. 48(1): 129-140.

Huang J., Walker E . D., Giroux P. Y., Vulule J. 2005. Ovipositional site selection by
Anopheles gambiae : influences of substrate moisture and texture. Medical and
Veterinary Entomology. 1 9: 442 — 450.

Huang J., Miller R. R., Chen Shi-Cheng., Vulule J. M., and Walker E. D. 2006.

Anopheles gambiae (Diptera: Culicidae) Oviposition in response to agarose media ad
cultured bacterial volatiles. Journal of Medical Entomology. 43(3): 498-504.

Jurgens K., Matz C. 2002. Predation as a shaping force for the phenotypic and
genotypic compostion of planktonic bacteria. Antonie van Leeuwenhoek 81 : 413-434.

53

Kaufman, M.G., Goodfriend W., Kohler-Garrigan A., Walker E.D., & Klug MJ.
2002. Soluble nutrient effects on microbial communities and mosquito production in
Ochlerotatus triseriatus (Say) habitats. Aquatic Microbial Ecology 29: 7388.

Kaufman M. G., Wanja E., Maknojia S. R., Bayoh N. M., Vulule J. M., Walker E.
D. 2006. The importance of algal biomass to the growth and development of Anopheles
gambiae larvae. Journal of Medical Entomology. 43: 669-676.

Kirchman, D. 2001. Measuring bacterial biomass production and growth rates fi'om
leucine incorporation in natural aquatic environments. Methods in Microbiology. 30:
227-237.

Koenraadt C. J. M., Takken W. 2003. Cannibalism and predation among larvae of the
Anopheles gambiae complex. Medical and veterinary Entomology. 17: 61 -66

Manahan DT, Jaeckle WB, Nourizadeh S. 1989. Ontogenetic changes in the rates of
amino acid transport from seawater by marine invertebrate larvae (Echinodermata,
Echiura, Mollusca). Biological Bulletin. 176: 161—168.

Merritt, R.W., Craig D.A., Walker E.D., Vanderploeg H.A., Wotton R.S. 1992a.
Interfacial feeding behavior and particle flow patterns of Anopheles quadrimaculatus
larvae (Diptera: Culicidae). Journal of Insect Behavior 5: 741 -761.

Merritt, R. W., Dadd R. H., Walker E. D. 1992b. Feeding behavior, natural food, and
nutritional relationships of larval mosquitoes. Annual Reviews in Entomology. 37: 349—
76

Merritt R. W., Craig D. A., Wotton R. S., WalkerE. D. 1996. Feeding behaviour of
aquatic insects: Case studies on blackfly and mosquito larvae. Invertebrate Biology.
115(3). 206-217.

Olson R. R. and Olson M. H. 1989. Food limitation of planktotrophic marine
invertebrate larvae: Does it control recruitment success? Annual Reviews in Ecology and
Systematics. 20:225-247

Renn C. E. 1943. Emergent vegetation, mechanical properties of water surface, and
distribution of Anopheles larvae. National Malaria society journal.2: 47-52

Schremmer F. 1949. Morphologische und funktionelle Analyse der Mundteile und des
Pharynx der Larve von Anopheles maculipennis Meig. Osterreichische Zoologische
Zeitschrifi. 2: 173-222.

Walker E. D., Olds E. J., Merritt R. W. 1988. Gut content analysis of mosquito larvae

(Diptera: Culicidae) using DAPI stain and Epifluorescence microscopy. Journal of
Medical Entomology. 25(6): 551-554.

54

Walker E. D., Merritt R.W. 1991. Behavior of Aedes triseriatus (Diptera: Culicidae)
larvae. Journal of Medical Entomology. 28: 581-589.

Walker E. D., Merritt R. W. 1993. Bacterial enrichment in the surface microlayer of an
Anopheles quadrimaculatus (Diptera: Culicidae) larval habitat. Journal of Medical
Entomology. 30:1050-1052.

Wallace J. R., Merritt R. W. 2004. Die] feeding periodicity of larval anopheline
mosquitoes on microorganisms and microinvertebrates: a spatial and temporal

comparison of Anopheles quadrimaculatus (Diptera: Culicidae) diets in a Michigan pond.
Journal of Medical Entomology. 41(5):853-860.

Welschmeyer, N. A. 1994. F luorometric analysis of chlorophyll a in the presence of
chlorophyll b pheopigments. Limnology and Oceanography. 39:1985-1992

Wotton, R. S., Chaloner D.T., Yardley C.A., Merritt R.W. 1997. Growth of

Anopheles mosquito larvae on dietary microbiota in aquatic surface microlayers. Medical
and Veterinary Entomology. 11: 65-70.

55

CHAPTER III

Effects of larval Anopheles gambiae grazing on the bacterial community structure of
larval habitats ~

ABSTRACT

A culture-independent molecular phylogenetic survey was carried out for the bacterial
community in microcosms simulating natural habitats of larvae of the mosquito,
Anopheles gambiae s.s. Two different local (Kisumu, Kenya) soil types, Red Soil and
Black Soil, were added to microcosms, some of which received larvae and some did not
resulting in a 2 x 2 factorial experiment. A total of 732 sequences were obtained. These
sequences were classified using the Ribosomal Database Project II bacterial classification
system. The analysis tool called DOTUR was used to define operational taxonomic units
and to calculate diversity indices. Overall, the bacterial communities were highly
diverse, Rank abundance curves revealed few common and many rare OTUs, a pattern
typical of bacterial communities in general. Presence of larvae had strong effects on
bacterial community composition. Comparisons using the LIBSHUFF procedure and
principal components analysis indicated that the bacterial communities in larvae/no
larvae treatments of both soil types (Red Soil with larvae/Black Soil with larvae, and Red
Soil without larvae/ Black Soil without larvae) were significantly different. There were a
large number of unclassified sequences in no larvae treatments in both soil types, and
they were closest to sequences of phototrophic primary producers, likely representing
chloroplast rDNA sequences or unclassified cyanobacterial rDNA sequences.

Community shifts were observed either by presence or absence of certain taxa, or

56

changes in the frequencies of certain taxa, as represented by the sequence data. These
community shifts could explain variation in productivity of habitats in natural settings

and also habitat segregation of Anopheles species due to ovipositional site selection.

57

INTRODUCTION

The thin, liquid zone at the air-water interface (termed by limnologists the
‘surface microlayer’) of Anopheles larval habitats is a zone enriched with
microorganisms and other materials compared to subsurface zones of the water column
(Walker and Merritt 1993, and see Chapter 2). These larvae position themselves parallel
to the surface of the water to breathe, and they feed at the air-water interface. They are
specialized collector-gatherer or gatherer-filter feeders, whereby they rotate the head
180° degrees from the normal position and direct the rapidly beating lateral palatal
brushes to the air-water interface (Merritt et al. 1992, Merritt et al. 1996). The resultant
currents generated by the extension and flexion of the lateral palatal brushes entrain
particles within the surface microlayer, drawing them to the anterior of the larval head,
where they are processed by a series of mouthpart movements leading to ingestion into
the pharynx, followed by food bolus formation and swallowing (Merritt et al. 1992,
Merritt et al. 1996). Enrichment of the surface microlayer in both freshwater and
saltwater environments with microorganisms, lipids, and other materials is well known
(Chapter 1). In an investigation of a larval habitat of Anopheles quadrimaculatus larvae,
Walker and Merritt (1993) found that the total numbers of bacteria ranged from 18.8 x
106/ml to 65.4 x 106/ml in surface microlayer samples, and hem 3.8 x 106/ml to 14.3 x
106/ml in subsurface samples. These results suggest that microbial food items of
Anopheles larvae are more concentrated in this feeding zone, thus their feeding strategy is

adaptive in taking advantage of a food rich zone of the water column.

58

Various studies involving larval gut analysis reveal that bacteria comprise a
substantial portion of the food bolus, along with organic particulates, algae, and small
invertebrate parts (Walker et a1. 1988). Bacteria could account for production of a large
fi‘action of the mosquito biomass produced from tree hole environments (Kaufinan et a1.
2001), but simultaneously, larval mosquito feeding reduces microbial abundance, alters
the microbial community quantitatively, and diminishes the quality of the microbes as
food (Kaufman et a1. 2000, 2002; Xu et al. in preparation). Bacteria along with other
organisms that occur in larval habitats may form the basis for production of adults, by
transforming inorganic nutrients through autotrophic and heterotrophic processes into
assimilable forms, by providing food, and specific larval nutritional demands (Walker et
al. 1988, Walker and Merritt 1993, Kaufinan et al. 2000, 2001, 2002).

Adult mosquito density depends on the number and productivity of the larval
habitats; larval habitat productivity in turn depends upon nutrient mobilization into the
microbial food base for these larvae (Peck and Walton 2005, Peck and Walton 2006,
Kaufman and Walker 2006, Kaufinan et al. 2006). In water-filled tree holes, nitrogen
supplementation resulted in a cascade of effects, in which fungal biomass and metabolic
activity increased, thereby increasing rate of leaf decay and availability of larval
mosquito food (Kaufman and Walker 2006). Although the amount of dissolved
carbohydrate (DOC) increased as well, bacterial production on leaf surfaces did not
increase; thus the role of bacteria in supporting mosquito production was diminished
compared to that of fungi. Peck and Walton (2005, 2006) showed that variation in ratios
of carbon, nitrogen, and phosphorus, or variation in phosphorus content in bacteria,

differentially affected growth responses of larvae of two species of Culex. Gimni g et al.

59

(2001) showed in field microcosm experiments that the most likely food source for larval
An. gambiae was algal biomass, which was significantly reduced in the presence of
larvae as measured by chlorophyll a in surface water samples and by counts of algae in
sedimentation chambers, compared to when larvae were absent. Mosquito feeding
reduced dry algal biomass by 100 pg/ml, or a total of 7 mg per habitat surface layer.
Based upon total adult production of 2.8 and 7.5 mg in treatments with 20 and 60 larvae
respectively, the decline in algal biomass documented effects of larval grazing pressure
on algae, and also explained much of the observed larval production. Bacteria may have
supplemented or supplanted algae under such feeding pressure as it may have formed a
secondary food source, as bacterial densities were not affected significantly by the
presence of larvae. The levels of nitrogen and phosphorus in these soil-lined habitats
(mud puddles) were not affected by the presence of larvae, although there was evidence
for decreasing nitrogen levels with increasing larval densities, suggesting that nitrogen
may be a limiting resource in the larval environment. Kaufinan et al. (2006) extended on
these findings in a set of experiments in which habitats were shaded or left in full sun,
such that primary production was nearly eliminated, or was left to occur normally. Under
shaded conditions, Kaufman et al. (2006) found that primary production as measured by
algal biomass was greatly reduced and An. gambiae larval growth was stalled, consistent
with the hypothesis that algal biomass is crucial to larval growth. As shown in Chapter 2,
bacterial biomass did support some larva growth but could not compensate for the lack of
algal biomass in fiirthering complete development of larval cohorts in those microcosms.
Bacteria rich in phosphorus promoted growth of Culex quinquefasciatus but, by contrast,

Cu. tarsalis larvae grew better on bacteria low in concentration of this nutrient (Peck and

60

Walton 2006). Collectively, these studies suggest that the structure of the microbial
communities in a range of larval mosquito habitats (tree holes, mud puddles, waste water
lagoons, constructed wetlands) affect larval mosquito growth in positive and negative
ways, and that nutrient dynamics, microbial community dynamics, mosquito feeding, and
mosquito production interact with each other.

One of the ways in which mosquito growth could be limited is that larval
mosquito feeding itself reduces microbial biomass, microbial density, and alters
microbial community structure, such that the quality and quantity of microbial food
becomes unsupportive for growth. This idea finds parallels in studies of other aquatic
systems where invertebrate grazing on microorganisms is a key trophic process. For
example, a series of studies in lake ecosystems in northern Europe revealed marked
predator-prey relationships, as follows. Planktonic bacterial communities in these studies
responded both phenotypically and genotypically to strong grazing pressure by
metazooplankton (Jurgens and Matz 2002), interacted strongly in their population
dynamics to carbon and phosphorus limitations (Matz and J urgens 2003), and were
shaped by combined protozoan and metazoan predation (Langenheder and Jurgens 2001).
Daphnia fed upon both bacteria and their protozoan predators, having the general effect
of reducing abundance of both groups and severely dampening bottom-up nutrient
effects. Due to rapid compensation of bacterial grazing losses by the development of
resistant forms, the total bacterial biomass may remain less affected and could be
maintained on a relatively high level (Jurgens 1994, J urgens and Gude 1994). These
studies show how mesocosm experiments can support whole ecosystem studies, and can

model long-term, integrated analysis of these relationships. These observations represent

61

in their broadest sense an extension of the analysis of predator—prey interactions, which
have been considered as a driving force in population dynamics since the beginning of
ecological studies (May 1974).

In this study, I examined bacterial community structure and diversity associated
with presence and absence of larval grazing pressure. The second experimental context
related to nutrient sources stemming fi'om underlying soil type. The overall hypothesis
was that bacterial community structure in the surface microlayer will vary with intensity
of mosquito feeding, where the Anopheles larvae are predators, and will vary with the
nutrient regime as determined by underlying soil type. As shown elsewhere (Kaufman et
al. 2006), the dark, alluvial, clay, top soil prevalent in the Lake Victoria basin (locally
called black cotton soil, Ranteng, or Anyuong) and herein referred to as Black Soil) was
higher in phosphorus content compared to the other locally prevalent soil, which is a red,
sandy soil used commonly in road and house wall construction (locally called Luala
makuoyo and herein referred to as Red Soil). This observation is confirmed by soil
analyses in this region, and variation in soil nutrients has been correlated with agricultural
practices and agricultural productivity (Mango 1999). Anopheles gambiae larvae have

been found in habitats formed in each soil type.

In this study, I utilized molecular methods to study bacterial diversity in these
environments. There is virtually no information on the bacterial community structure of
An. gambiae larval habitats, with the exception of the recent study by Huang et al.
(2006), in which 61 bacterial isolates from a single habitat were identified to genus using
a 16S rDNA sequencing method, in preparation for oviposition studies. Yet, bacteria

may be important as larval food at least in supplementing algal biomass (Gimnig et al.

62

2002, Kaufman 2006), may be sources of odors influencing oviposition decisions by
gravid females (Huang et al. 2006), and may have a role as mediators of habitat
segregation between molecular forms of An. gambiae (Edillo et al. 2006). Of interest is
that Huang et al. (2006) showed that bacterial cultures were typically repellent to
ovipositing females, suggesting that those mosquitoes were sensitive to bacterial-derived
odors. Accordingly, an investigation of the community structure of bacteria in An.
gambiae habitats relative to larval feeding pressure and underlying soil structure is highly
relevant to elucidate an important missing feature of the biology of this highly dangerous

malaria vector.

It has become axiomatic that the majority of bacteria in any given environment
are uncultivable for a variety of reasons (Hugenholtz et al. 1998), thus making
alternatives to culture-based methods important for studies of bacterial diversity.
According to Zwart et al. 2002, on the basis of cultivation techniques, Rheinheimer
(1980) concluded that bacteria found in groundwater, spring water and‘streams also
commonly occur in soils, and that there was no clear separation between soil bacteria and '
aquatic bacteria when soil was in close association with the overlying water. These
observations call into question the existence of a unique freshwater bacterial flora (Zwart
et al. 2002). The advent of molecular techniques and especially the polymerase chain
reaction has made it possible to obtain information on microbial community composition
directly, without cultivation (Giovannoni et al. 1990). An environmental sample can be
inventoried for taxa present by direct nucleic acid isolation, followed by amplification of
particular marker genes and analysis of the sequence of base pairs. The most widely used

marker gene is the small subunit rRNA gene (16S rDNA), and the recent application of

63

mac
his g

Show

1.,
21::

gm

molecular techniques in a variety of habitats has produced a large set of sequences from
this gene (Maidak et al. 1999, Cole et al. 2005). This growing body of information has
shown that the diversity of the bacterial domain is much larger than was thought before
the application of molecular methods (Pace 1997, Hugenholtz et al. 1998). For the
purpose of this study I have used the small subunit rRNA gene (16S rDNA) as a marker

gene for community fingerprinting and sequencing analysis.

64

METHODS AND MATERIALS

Habitat array In 2002, an array of artificial habitats/field microcosms was designed to
simulate natural habitats in which density dependent larval growth (suggesting food
limitation) and utilization of microbial food was demonstrated for larval growth cohorts
(Gimnig et a1. 2002, Kaufinan et al. 2006). In these field microcosms, a surface
microlayer developed well in the absence of actively feeding larvae, and it could be
sampled with a syringe and 16 gauge needle (Gimnig et al. 2002). An array of such
habitats was established at a secure study site at the Kenya Medical Research Institute
(KEMRI), Vector Biology and Control Research Center in Kisian, Kenya to study growth
and development of Anopheles gambiae larvae (Kaufinan et al. 2006). I utilized this array
of naturalistic habitats for my studies on the effects of mosquito feeding and soil type on
bacterial community diversity. The artificial habitats were constructed fiom plastic
washtubs (approx. 40 cm diam.) lined with black plastic and set into the ground.
Approximately 2000 grams of Black Soil or Red Soil (as described above) were added to
randomly assigned habitats within the array of 44 individual treatments. These soils are
associated with crops in the area, and there is local knowledge of variation in agricultural
productivity and nutrient depletion, as well as scientific data on soil fertility (Mango
1999). Anopheles gambiae larvae have been found in habitats formed in each soil type
(Gimnig et al. 2001, Mutuku et al. 2006). Four liters of rainwater collected from roof run-
off at the facility was added to each habitat 2 days prior to the addition of 40 newly
hatched first instar larvae, to allow sufficient time for the formation of surface

microlayer. Larvae were obtained from a laboratory colony of An. gambiae at the Kisumu

65

facility. Some habitats were left without larvae. All habitats were covered with aluminum
window screen to keep out debris and prevent oviposition by native mosquitoes. Within
each soil treatment, half of the habitats were randomly assigned a shade or sun treatment
to complete a full factorial design with 8 replicates of each treatment combination. Water
levels were maintained with additions of rainwater, as needed. Three habitats (one
shaded, one sun plus Black Soil, and one in sun with Red Soil) received temperature
recording probes. This experimental setup was used to collect samples to evaluate the
changes in bacterial community structure in presence or absence of larval grazing
pressure, four habitats of each larval density (larvae, no larvae) x soil type treatment
combination were randomly selected for sampling. These habitats were the ones exposed
to sunlight. Thirteen ml of surface microlayer was collected on day 5 (larvae added on
day 0), and stored on ice until taken to the lab for further analysis. Surface microlayer
samples (13 ml) were collected using a syringe and 16 gauge needles, as described in
Chapter 2. At the lab, 10 ml was utilized for DNA extraction and 3 ml was preserved in

4% formalin for direct counts of bacteria, using methods described in Chapter 2.

Nucleic Acid Extraction, Amplification, Cloning and Sequencing Surface microlayer
samples from the habitats were centrifuged at 6000 rpm for 30 minutes and the pellet was
retained for nucleic acid (DNA) extraction. The DNA was extracted using Ultraclean Soil
DNA kits (Cat. # 12800-50, MO BIO Laboratories Inc., Carlsbad, California) following
the manufacturer’s instructions. The presence of DNA was confirmed on 1% agarose gels
in TBE buffer. Extractions from each sample were stored at -20° C. An approximately

1.3 kb region of a consensus 16S rRNA gene of bacteria was amplified by PCR

66

amplification using forward primer 63f 5' -CAG GCC TAA CAC ATG CAA GTC- 3'
and reverse primer 1387r 5' — GGG CGG WGT GTA CAA GGC- 3' (Marchesi et al.
1998). The PCR reactions conditions were carried out as per the Taq DNA polymerase
instruction manual (New England Biolabs). PCR conditions consisted of initial
denaturing at 94°C for 2 min followed by 25 cycles of denaturing at 94°C for 45 sec,
annealing at 55°C for 30 sec, extension at 72°C for 1.3 min, and final extension at 72°C
for 7 min. The resulting PCR products were subjected to low melting agarose gel
electrophoresis; bands were then excised and purified by sodium acetate and ethanol
precipitation. Purified 16S rDNA fi'agments were cloned into pGEM-T Easy vector
(Promega, Madison, Wisconsin) and transformed clones were picked and purified. The
plasmid DNA from transformed clones was extracted using Wizard Plus SV Miniprep kit
(Promega). After confirmation that the plasmids contained an insert of the expected size,
these plasmids (or clones for some samples) were then subjected to high throughput
sequencing using dideoxy dye terminator chemistry, at the Genomic Technology Support
Facility, Michigan State University. The 519R 5'- G(AT)ATTACCGCGGC(GT)GCTG-
3' sequencing primer (Lane et al. 1985) which reads the 16S rDNA was used to obtain

partial 16S rDNA sequences.

Classification of sequences Sequence data from each clone were examined for chimera
formation using the CHECK_CHIMERA program offered through Ribosomal Database
Project II (RDP 11; Release 8.1, May 18, 2001 (Maidak et al. 1999) following procedures
outlined in Lilbum et a1. (1999). Possible chimeras and sequences with fewer than 250

base pair in length were excluded from any further analysis. The 16S rDNA sequences

67

were then classified to named, operational taxonomic units (OTUs) using the Ribosomal
Database Project II (RDP 11; Release 9.38, April 03, 2006, http://rdpcmemsuedu). The
RDP II contained 210,976 aligned 16S rRNA sequences as of April 03, 2006 and this
database formed the reference library for the classifications here. The hierarchy model
used by the naive Bayesian rRNA classifier in RDP 11 comes from the bacterial
classification proposed in release 6.0 of the Bergey’s Manual of Systematic Bacteriology
(htLp://bergevsoutline.com). The classifier calculates the joint probability of finding eight
base subsequences (“words”) in the query. When a query sequence is submitted, the joint
probability of observing all the words in the query can be calculated separately for each
genus from the training set probability values. Using the naive Bayesian assumption, the
query is most likely a member of the genus with the highest probability, given the
limitations of the available database (Cole et al. 2005). This classification process puts a
name on each sequence.

Sequences from all 8 libraries (2 replicates from each treatment combinations)
were aligned separately with existing bacterial sequences in ARB 16S rDNA database
(http://www.arb-home.de) and phylogenetic trees were constructed using the ARB
software package (Strunk and Ludwig 1997, Lilbum et al. 1999). The selection of the
reference sequence for initial sample sequence alignment was based on the RDP 11
classification of the samples to genus level. A mask was generated by the filtering
method in ARB to produce valid columns for comparison of sequences. Sequences with
short lengths were excluded so as to generate at least 265 valid columns. Distance

matrices were generated using the above filter and Jukes-cantor correction. From these

68

libraries, 4 new libraries were created, as follows: (1) Black Soil/Larvae Present; (2)

Black Soil/Larvae Absent; (3) Red Soil/Larvae Present; and (4) Red Soil/Larvae Absent.

Statistical comparisons Sequence libraries were compared between clone libraries using
the LIBSHUFF program, (http://www.arches.uga.edu/~whitman/libshuff.html, Singleton
et al. 2001). The program estimates differences between homologous coverage curves

C X(D), and heterologous coverage curves CXy(D), by a Cramér-von Mises-type statistic,
AC xy= 2(CX-nyf. The input for these two-way comparisons was the distance matrices
of aligned sequences generated in ARB. The distance matrices derived from nucleotide
sequence differences generated in ARB were also used as input for diversity analysis
using the DOTUR program (Distance based 9E and Richness determination;
http://www.plantpath.wisc.edu/fac/ioh/dotur.html). A 97% similarity value was used to
determine operational taxonomic units (OTUs), (Schloss and Handelsman 2005). Using
these new OTU assignments, which were un-named in contrast to the classification in
RDP II, DOTUR constructed rarefaction curves for sampling intensity, richness
estimators, and diversity indices. To address relative evenness of the bacterial
communities reflected by sequence libraries, rank abundance graphs were generated
where the abundance (i.e., frequency) of each OTU was plotted on a logarithmic scale

against the rank from most abundant to least abundant (Magurran 2004).

Principal Component Analysis (PCA) PCA is a technique for simplifying a dataset
where the data are transformed to a new coordinate system such that the greatest variance
by any projection of the data lies on the first coordinate (called the first principal

component), the second greatest variance on the second coordinate, and so on. PCA can

69

be used for dimensionality reduction in a dataset while retaining those characteristics of
the dataset that contribute most to its variance. PCA analysis was invoked for the
sequence dataset with RDP classification as well as OTUs obtained by DOTUR analysis.
Factorial ANOVA was performed on the PCA scores and the sequences that explained

most of the variance were identified through the PCA loading values.

Terminal Restriction Fragment Length Polymorphism Analysis (TRFLP) The DNA
extraction procedure and the PCR condition were the same as above with the exception
that the forward 63f primer was labeled with 6-FAM (Integrated DNA Technologies,
Coralville IA) at the 5' end. Three 100 pl PCR reactions were performed for each sample
to assure maximum coverage of the diversity of 168 rDNA present in the sample. These
reactions were combined and purified using Qiaquick PCR purification kit (Qiagen
Valencia, CA). To generate TRFLP fiagments, restriction enzyme Hhal and Mspl (New
England Biolabs Inc.) were selected since they offer decent number of restriction
fragment and discrimination between different species (Marsh 1999).

The digestions were carried out in a two 10 pl aliquots (Liu et al. 1997, Marsh et
al. 2000). 400-600 ng of purified PCR product was digested for 3 hours with either Hhal
or Mspl at 37° C. The reaction mixture contained approximately 400 ng (1-5 pl) of DNA,
2 p1 of 10x restriction enzyme buffer, 1.5 pl of the one of the above restriction enzymes.
The reaction mixture was brought to 10 pl with nuclease free water. After 3 hrs of
digestion, the reactions were stopped at 60° C for 20 minutes. The T-RF lengths from the
digest were determined on an automated DNA sequencer (ABI PRISM 310, Foster City,
California) in GeneScan mode. The fragments were separated by electrophoresis using

capillary electrophoresis system. The fragment sizes were determined with ABI

70

Genescan Analysis Software (Applied Biosystems) and the alignment of the community
profiles were done in Genotyper software (Genotyper 2.5, Applied Biosystems). The
peaks ranging between 50-950 bp were used for further analysis. In case of Red Soil with
larvae and without larvae two different samples with similar treatments were amplified
by PCR (each 3 x 50 pi) and later pooled before the digestion was performed (same was
done for Black Soil larvae and without larvae). Cluster analysis of TRFLP profiles was

done using PAUP* 4.0 Orttp://paup.csit.fsu.edu/).

Nucleotide sequence accession numbers The sequences obtained during this study were
deposited to GenBank. The accession numbers are as follows: EF139885 to EF140054
for clones obtained from Red Soil/larvae; EF140055 to EF 140206 for clones obtained
from Red Soil/no larvae; EF 140207 to EF140406 for clones obtained from Black
Soil/larvae; EF 140407 to EF 140586 for clones obtained from Black Soil/no larvae.

FIGURES IN THIS CHAPTER ARE PRESENTED IN COLOR.

7l

RESULTS

Classification of sequences A total of 732 sequences was obtained. Twenty three
sequences fi'om Red Soil and 7 sequences from Black Soil libraries were excluded from
the analysis as chimeras or sequences with short lengths (< 250 bp). A total of 170 and
152 16S rDNA clones was retained for further analysis from microcosms with Red Soil
with larvae (RL) and no larvae (RNL), respectively. Similarly, a total of 200 and 180
sequences was retained from microcosms with Black Soil with larvae (BL) and no larvae
(BNL), respectively. The number of base pairs per sequence used for the analysis ranged
from 255-520 bp.

Classifications based on the RDP II for each of the four treatment combinations
are summarized in Tables 3.1, 3.2, 3.3, and 3.4. The classification process assigned the
sequences to 4 phyla, namely, Actinobacteria, Bacteroidetes, Cyanobacteria, and
Proteobacteria. The most abundant phylum was Proteobacteria, representing 62.9% in
RL, 65.8% RNL, 65.5% in BL and 45.5% in BNL. The second most abundant phylum
was Bacteroidetes (RL, 22.9%; RN L, 15.1%; BL, 32.5%; and BNL, 24.4%). The
remaining sequences were classified into Actinobacteria (RL, 11.7%; RNL, 1.3%; BL,
2%; BNL, 1.1%) and Cyanobacteria (RL, 0.6%, RNL, 5.9, BL 0.0%, and BNL 1.7%). In
RN L and BNL libraries, there was a significant percent of unclasssified sequences
(11.8% and 27.2%, respectively). There were a total of 14 and 19 generic classifications
observed for RL and BL, and 19 and 14 classifications for RNL and BNL, respectively.

Each of the sequences in the clone libraries was placed into a phylogenetic tree

using ARB, using the RDP II classifier as a guide for initial placement. For the sake of

72

brevity, these phylogenetic trees are shown in the Appendix A, with the exception of a set

of sequences which were left “unclassified” by RDP 11 (see below).

Analysis of treatment effects

Effects of larval presence/absence The most abundant sequences obtained fiom
Red Soil with larvae (RL) at the class level are in the order as follows: Betaproteobacteria
(51.76%), Sphingobacteria (15.29%), Actinobacteria (11.18%), Alphaproteobacteria
(9.41%), Flavobacteria (5.88%), and Cyanobacteria (0.59%) (Table 3.1). Interestingly,
there were no gammproteobacteria in this library. Within the Betaproteobacteria class,
most of the sequences belonged to one order, the Burkholderiales. The most dominant
genus was Polynucleobacter (family Burkholderiaceae) along with unclassified
sequences belonging to Alcaligenaceae (matching closest to Achromobacter) and
Comamonadaceae. Within Sphingobacteria the most dominant genus was Pedobacter
(family Sphingobacteriaceae). The sequences in Actinobacteria all belonged to the order
Actinomycetales, and could not be classified to lower taxon than order but RDP 11 found
the sequences closest to genus Arsenicicoccus. Within the Alphaproteobacteria, the
dominant genus was Novosphingobium (family Spingomonadaceae) and sequences
belonging to unclassified Rhizobiales, and within the class Flavobacteria, the genus
F lavobacterim (family Flavobacteriaceae) was the most dominant.

In sequences obtained from Red Soil without larvae (RNL), the most dominant
classes observed were Betaproteobacteria (45.39%), F lavobacteria (13.16%),
Garnrnaproteobacteria (11.84%) and Cyanobacteria (5.92%). They comprised up to

76.3% of the total sequences. The other classes Alphaproteobacteria, Deltaproteobacteria

73

and Actinobacteria represented < 5% each (Table 3.2). Within the Betaproteobacteria
class, the most common genera were Limnobacter (family Burkholderiaceae),
unclassified Incertae sedis 5, and unclassified Comamonadaceae. Genus Methylophilus
(farme Methylophilaceae) was also present in this class. The genus F lavobacterium
(family Flavobacteriaceae) was most abundant in the class Flavobacteria. Within
Gammaproteobacteria, the genera Aeromonas' (family Aeromonadaceae) and
Pseudomonas (family Pseudomonanadaceae) were the most abundant. All the
Cyanobacteria fell into unclassified cyanobacterial groups without assignment to lower
taxa.

Comparing both RL and RNL communities, the number of unclassified bacteria
was higher in RNL (11.8%) as compared to RL (2.4%) treatments. In the RNL
treatments, 5% of the total classified sequences were Cyanobacteria, whereas only 0.6%
in RL belonged to this class. Notably, there were no sequences from the class
Garnrnaproteobacteria and Deltaproteobacteria in RL, whereas there were no sequences
from class Sphingobacteria in RNL. Pedobacter was present and there were increased
numbers of Polynucleobacter in RL. Pedobacter was absent in RNL treatments. A
comparison of libraries of surface microlayer from RL and RNL indicated that the two
libraries were significantly different from each other (Amt/RNL: 1.344, p= 0.001;
ACRNURLz 1.992, p= 0.001) (Fig 3.1).

Red Soil/no larvae treatments exhibited higher bacterial diversity estimated by
diversity indices. The Simpson diversity index (l/D) for RL determined by DOTUR was
13.93 and that for RNL was 27.39. Note that the value of the index increases with

increasing diversity. The Shannon diversity index was 3.09 for RL and 3.52 for RNL.

74

The OTU data obtained by DOTUR analysis was used to plot rank/abundance graph
where the relative abundance of each OTU was plotted against the OTU (0.03 %
dissimilarity) ranked from most abundant to the least abundant (Fig 3.2). The density of
bacteria estimated by DAPI ranged from 2.6 x 106/ml to 6.1 x 106/ml in the surface

microlayer of RL, and 9.7 x 107/ml to 38 x 107/ml in RNL.

75

Table 3.1. Bacterial community composition of surface microlayer from microcosms with Red Soil
having larval grazing pressure (RL) based on 168 rDN A sequences classification with RDP
11. The numbers of sequences observed per taxon are in parenthesis.

 

Phylum Class Order Family Genus

 

Actinobacteria (l9) Actinobacteria (l9) Actinomycetales (l9) Microbacteriaceae (l) unclassified
Microbacteriaceae (1)
unclassified
Actinomycetales (l 8)

Bacteroidetes (39) Flavobacteria (10) Flavobacteriales (10) F lavobacteriaceae (10) Flavobacterium (6)

unclassified
Flavobacteriaceae (4)

Sphingobacteria (26) Sphingobacteriales (26) Sphigobacteriaceae (25) Pedobacter(l9)
unclassified

Sphingobacteriaceae (6)
Crenotrichaceae (l) Chitinophaga (l)
unclassified
Bacteroidetes (3)

Cyanobacteria (l) Cyanobacteria (l) unclassified
Cyanobacteria (1 )

Proteobacteria) Alphaproteobacteria Rhodobacterales (2) Rhodobacteraceae (2) Rhodobacter (l)
(107 ' (16)
unclassified
Rhodobacteraceae (l )
Sphingomonadales (6) Sphingomonadaceae (6) phingomonas (l)
Sphingopyxis (l )

Novosphingobium (4)
Rhizobiales (8) unclassified
Rhizobiales (8)

Betaproteobacteria Burkholderiales (85) Comamonadaceae (l 9) Rhodoferax (l)

(88) Polaromonas (4)
Curvibacter (l)
unclassified

Comamonadaceae (l 3)
Oxalobacteraccae (2) Herbaspirillum(2)
Alcaligenaceae (l3) unclassified

lcaligenaceae (l 3)
Burkholderiaceae (44) Limnobacter (l)

Polynucleobacter (42)
unclassified
Burkholderiaceae (l )
lncertae sedis 5 (2) Roseateles (2)
unclassified
Burkholderiales (5)

unclassified
Betaproteobacteria (3)
unclassified
Proteobacteria (3)
unclassified Bacteria (4)

Total Bacteria 170

76

Table 3.2. Bacterial community composition of surface microlayer from microcosms with Red Soil
with no larval (RNL) grazing pressure based on 16S rDNA sequences classification with
RDP II. The number of sequences observed per taxon are in parenthesis.

 

 

{Phylum Class Order Family Genus
Actinobacteria (2) Actinobacteria (2) Actinomycetales (2) Microbacteriaceae (l) unclassified
Microbacteriaceae (l )
unclassified
Actinomycetales (l)
Bacteroidetes (23) Flavobacteria (20) Flavobacteriales (20) Flavobacteriaceae (20) Flavobacterium (18)
unclassified
Flavobacteriaceae (2)
unclassified
Bacteroidetes (3)
Cyanobacteria (9) Cyanobacteria (9) unclassified
Cyanobacteria (9)
Proteobacteria Alphaproteobacteria Rhodobacterales (4) Rhodobacteraceae (4) Rhodobacter (2)
(100) (7) unclassified
Rhodobacteraceae (2)
Sphingomonadales (2) Sphingomonadaccae (2) Novosphingobium (l)
Unclassified
Sphingomonadaceaefl )
Rhizobiales (1) unclassified
Rhizobiales (l)
Betaproteobacteria Methylophilales (4) Methylophilaceae (4) Methylophilus (3)
(69) unclassified
Methylophilaceae (l)
Burkholderiales (61) Comamonadaceae (l8) Polaromonas (l)
Curvibacter (2)
Ramlibacter (l)
Hydrogenophaga (2)
Acidovorax (l)
unclassified
Comamonadaceae( l l )
Oxalobacteraceae (2) unclassified
Oxalobacteraceae (2)
Burkholderiaceae (l 7) Limnobacter (l l)
Polynucleobacter (6)
lncertae sedis 5 (l6) Roseateles (2)
Aquabacterium (l)
unclassified
lncertae sedis 5 (l3)
unclassified
Burkholderiales (8)
Neisseriales (l) Neisseriaceae (l) Vogesella (l)
unclassified
Betaproteobacteria (3)
Gammaproteobacteria(l 8) Xanthomonadales (2) Xanthomonadaceae (2) Nevskia (2)
Aeromonadales (7) Aeromonadaceae (7) Aeromonas (7)
Pseudomonanadales (6) Pseudomonanadaceae (6) Pseudomonas (3)
unclassified
Pseudomonadacean)
Legionellales (l) Coxiellaceae (l) Aquicella (l)
unclassified
Gammaproteobacteria (2)
Deltaproteobacteria (6) Bdellovibrionales (3) Bdellovibrionaceae (3) Bdellovibrio (3)
unclassified

Deltaproteobacteria (3)
unclassified Bacteria (18)

Total Bacteria 152

 

 

1.2

 

' 0.25

.p 02

4 0.15

«l 0.1

 

~ 0.05

 

 

 

 

0.5

 

 

 

 

 

 

 

O A
v o
a X.
a 0
g 3i
O ‘55
U 1.2 0.25
1.
.t 02
0.0 -
a 0.15
0.6 .
4* 0.1
0.4 4
. 0.05
0.2 4
0 ......r.Y.'§'.‘1‘ast:%4%‘#efir4fi.4..1fi W.Tfi T44 fi.r .T4.Tw. 0
0 0.1 0.2 0.3 0.4 0.5

 

Evolutionary Distance (D)

 

 

Figure 3.1. Comparison of bacterial 16S rDNA sequence libraries of Red Soil with larval grazing
pressure (RL) to Red Soil with no larval grazing pressure (RN L) using LIBSHUFF.
Homologous (open triangles) and heterlogous (solid triangles) coverage curves are
shown. Solid lines indicate values of (CRL'CRURNL)2 (panel A) or of (CRNL'CRNURL)2
(panel B) at each value of evolutionary distance (D). Broken lines indicate the 950‘"

value (or p=0.05) of corresponding (Cm,—Cm,,m..l,)2 or (CRNL-Cmmf for the randomized
samples.

78

 

 

 

1 .000

 

 

 

-D—RNL

 

0.100 .

Abundance

 

0.010 4
IIJ_ITIII‘I IiI II‘I‘I I I II I I III I'I‘I I
_ fifififififi L—fi-*&~—u~d—‘
v v v v v a v v v I v v v v vvvvv I c v

 

 

 

0.001
41 51

 

OTU

 

 

Figure 3.2. Rank abundance plot showing diversity of Red Soil with larvae (RL) and Red Soil with
no larvae (RNL) bacterial communities at evolutionary distance of 0.03

 

the

2:.

l

1W»-
a..\. v

Bu:

\l‘é'

“C

No

0b:

(10}

Ga

Hit

59c

{\S

The most dominant sequences obtained from Black Soil with larvae (BL) were from
the class Betaproteobacteria (31.5%), Flavobacteria (28.5%), Alphaproteobacteria (24%),
and Garnrnaproteobacteria (9%) (Table 3.3). The remaining sequences belonged to
Sphingobacteria and Deltaproteobacteria comprising less than 5% of each class. There
were no sequences assigned to the unclassified bacteria category in BL treatments.
Within the Betaproteobacteria class, most of the sequences were assigned to the order
Burkholderiales and almost one third remained unclassified. Of the rest the most common
were unclassified Comamonadaceae. Most of the sequences from the class Flavobacteria
were classified to the genus F lavobacterium, or were classified to the family
Flavobacteriaceae but not assigned to genus. The most dominant genera in the class
Alphaproteobacteria were Rhodobacter (family Rhodobacteraceae) and
Novosphingobium (family Sphingomonadaccae). Aeromonas was the only genus
observed within Garnrnaproteobacteria to which sequences were classified.

Within sequences obtained from Black Soil without larvae (BNL), the most
dominant classes were Flavobacteria (17.8%), Betaproteobacteria (16.1%),
Garnrnaproteobacteria (16.1%) and Alphaproteobacteria (13.3%) (Table 3.4). Together
they comprised 63.33% of the total sequences, while 27.2% of the total sequences were
unclassified bacteria, and the remainder Sphingobacteria and Cyanobacteria represented
less than 5% of the total each. Sequences assigned to the genus F lavobacterium were the
most dominant in the class Flavobacteria. Unclassified sequences assigned to lncertae
sedis 5 were the most common sequences in the family Betaproteobacteria, A eromonas
and Pseudomonas were the most common in the Gammaproteobacteria, and sequences

assigned to Rhodobacter were the most common in the class Alphaproteobacteria.

80

A comparison of libraries of surface microlayer from BL and BNL treatments indicated
that the two libraries were significantly different from each other (ACBUBNE 0.327, p=
0.001; ACBNUBL= 2.934, p= 0.001) (Fig 3.3). The Simpson diversity index (l/D) for BL
obtained by DOTUR analysis was 11.88 and for BNL 12.29. The Shannon index was
2.83 and 3.19, respectively. Bacterial diversity in BNL treatments was higher than BL
indicated by the diversity indices. The OTU data obtained by DOTUR were used to plot
rank/abundance graph where the relative abundance of each OTU was plotted against the
OTU (0.03 % dissimilarity) ranked from most abundant to the least abundant (Fig 3.4).
The total number of bacteria ranged from 7.7 x 106/ml to 16 x 106/ml in the surface
microlayer of BL treatments, and the sole sample available fiom BNL had count of 1.9 x
107/ml. The rarefaction curves indicated that all four sequence libraries were
undersampled, as the curves did not reach an asymptote given the number of sequences in

each clone libraries (Fig. 3.5).

81

Table 3.3. Bacterial community composition of surface microlayer from microcosms with Black Soil
with larval grazing pressure (BL) based on 16S rDNA sequences classification with RDP

II. The number of sequences observed per taxon are in parenethesis.

 

 

l Phylum Class Order Family Genus I
Actinobacteria (4) Actinobacteria (4) Actinomycetales (4) Microbacteriaceae (3) unclassified
Microbacteriaceae (3)
unclassified
Actinomycetales (l)

Bacteroidetes (65)

Proteobacteria (131)

Total Bacteria

F lavobacteria (57)

Sphingobacteria (4)
unclassified
Bacteroidetes (4)

Alphaproteobacteria
(48)

Betaproteobacteria
(63)

F lavobacteriales (57)

Sphingobacteriales (4)

Rhodobacterales (30)

Sphingomonadales (l4)
Rhizobiales (4)

Burkholderiales (60)

Neisseriales (l)
unclassified
Betaproteobacteria (2)

Gammaproteobacteria Aeromonadales (1 8)

(18)

Deltaproteobacteria (l) Bdellovibrionales (l )

unclassified
Proteobacteria (1)

200

82

F lavobacteriaceae (57)

Sphingobacteriaceae (4)

Rhodobacteraceae (30)

Sphingomonadaccae (l4)

unclassified
Rhizobiales (4)
Comamonadaceae (l 7)

Oxalobacteraceae (7)

Alcaligenaceae (8)

Burkholderiaceae (3)
lncertae sedis 5 (l)

unclassified
Burkholderiales (24)
Neisseriaceae (1)

Aeromonadaceae (18)

Bdellovibrionaceae (1)

F lavobacterium (2)
unclassified
Flavobacteriaceae (55)
Pedobacter(4)

Rhodobacter (21)
unclassified
Rhodobacteraceae (9)
Sphingopyxis (l)
Novosphingobium (l 3)

Polaromonas (l)
unclassified
Comamonadaceae (l6)
Herbaspirillum(3)
unclassified
Oxalobacteraceae (4)
unclassified
Alcaligenaceae (8)
Polynucleobacter (3)
unclassified
lncertae sedis 5 (l)

Aquaspirillum (l)

Aeromonas (18)

Bdellovibrio (1)

Table 3.4. Bacterial community composition of surface microlayer from microcosms with Black Soil
with no larval (BNL) grazing pressure based on 16S rDNA sequences classification with
RDP II. The number of sequences observed per taxon are in parenethesis.

 

 

I Phylum Class Order Family Genus
Actinobacteria (2) Actinobacteria (2) Actinomycetales (l) Microbacteriaceae (1) unclassified
Microbacteriaceae (l)
unclassified
Actinobacteria (l)
Bacteroidetes (4) Flavobacteria (32) Flavobacteriales (32) F lavobacteriaceae (32) F lavobacterium (l3)

unclassified
Flavobacteriaceae (l9)
Sphingobacteria (5) Sphingobacteriales (5) Sphigobacteriaceae (5) Pedobacter(3)

unclassified
Sphingobacteriaceae (2)
unclassified
Bacteroidetes (7)
Cyanobacteria (3) Cyanobacteria (3) unclassified
Cyanobacteria (3)
Proteobacteria (82) Alphaproteobacteria Rhodobacterales (21) Rhodobacteraceae (21) Rhodobacter (9)
(24)
unclassified
Rhodobacteraceaefl 2)
Rhizobiales (2) Rhizobiaceae (l) unclassified
Rhizobiaceae (l)
unclassified
Rhizobiales (l)
unclassified
Alphaproteobacteria (l)

Betaproteobacteria (29) Burkholderiales (27) Comamonadaceae (6) Polaromonas (l)

Caenibacterium (l)
Hydrogenophaga (l)
Acidovorax (l)

unclassified

Comamonadaceae (2)

lncertae sedis 5 (12) unclassified
lncertae sedis 5 (12)

unclassified
Burkholderiales (9)
Neisseriales (2) Neisseriaceae (2) Aquaspirillum (2)
Gammaproteobacteria Xanthomonadales (5) Xanthomonadaceae (5) Silanimonas (1)
(29) Nevskia (3)

unclassified

Xanthomonadaceae (l)

Aeromonadales (l l) Aeromonadaceae (l 1) Aeromonas (1 1)
Pseudomonanadales Pseudomonanadaceae Pseudomonas (4)

(1 3) (9)
Cellvibrio (1)
F lavimonas (l)
unclassified
Pseudomonadaceae(3)
unclassified

Pseudomonadales (4)

unclassified Bacteria (49)
Total Bacteria 180

83

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Figure 3.3. Comparison of bacterial 16S rDNA sequence libraries of Black Soil with larval grazing
pressure (BL) to Black Soil with no larval grazing pressure (BNL) using LIBSHUFF.
Homologous (open triangles) and heterlogous (solid triangles) coverage curves are ‘
shown. Solid lines indicate values of (CBL'CBUBNL)2 (panel A) or of (CBNL-CBNUBLY (panel
B) at each value of evolutionary distance (D). Broken lines indicate the 950‘” value (or
p=0.05) of corresponding (CBL'CBUBNL)2 or (CBNL'CBNUBL)2 for the randomized samples.

84

 

 

1000

 

+BL

 

 

 

Abundance

 

 

 

 

0.001
1 11 21 31 41 51

OTU

 

 

 

Figure 3.4. Rank abundance plot showing diversity of Black Soil with larvae (BL) and Black Soil
with no larvae (BNL) bacterial communities at evolutionary distance of 0.03

85

 

 
    
     
  
        
 
 

    

 
   

   
 

           

  
   

           

  

      
    
   
  

   

 

 

 

   

 

 

   
   

 
     

   

 

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Figure 3.5. Rarefaction curve generated from the OTUs at 3% distance using DOTUR for all four
treatments (RL, RNL, BL, BNL).

86

Effects of soil type LIBSHUFF analysis was also performed to determine if the
libraries fi'om larvae/no larvae treatments from both soil types were different statistically.
Distance matrices from Red Soil with larvae and Black Soil with larvae were compared
(ACBURL= 1.203, p= 0.001; ACRUBL= 0.856, p= 0.001), and similarly Red Soil and Black
Soil without larvae were compared (ACBNURNL= 1.752, p= 0.001; ACRNUBNL= 1.411, p=
0.001), indicating all four libraries were statistically different from one another and that

none of the libraries were subsets of any other library (Fig. 3.5).

87

 

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Evolutionary Distance

Figure 3.6. Homologous (open triangles) and Heterologous (solid triangles) coverage curves for 16S

rDNA sequence libraries from Black Soil and Red Soil with larvae and without larvae
are shown. Solid lines indicate the value of (Cx-ny)z or (Cy-C“): for the original
samples at each evolutionary distance. Broken lines indicate the 950'“ value (or p=0.05).
The above comparisons are BLvsRL (Panel A, B) and BNLvsRNL (Panel C,D)

88

 

 

Principal Components Analysis PCA was invoked to identify a pattern with the OTU
dataset (0.03 dissimilarity distances) between the larvae/no larvae treatments and to
distinguish treatment effects as well. The OTU data obtained from DOTUR using a 0.03
distance criterion was used to perform PCA analysis. 46.17% variance was explained by
PCl-2. Factorial ANOVA of PCA Scores for OTUs on PC I showed significant larvae/no
larvae treatment effects (Fig. 3.7 A) (Larvae, F = 59.3, P = 0.0015; Soil, F = 0.01, P =
0.91; and Soil*Larvae, F = 2.89, P = 0.16). On PC 11 ANOVA showed significant soil
effects (Larvae, F = 0.14, P = 0.73; Soil F = 63.68, P = 0.0013; and Soil*Larvae F = 2.42,
P = 0.19). Using the factor loadings it was possible to explain which sequences caused
this variation and in PC 1, and those sequences belonged to unclassified Alcaligenaceae
(closest matching genus was Achromobacter) fiom order Burkholderiales,
F lavobacterium and unclassified Burkholderiales (closest matching genus was
Herbaspirillum) present in Black Soil larvae treatments. The same analysis was
performed on RDP 11 classification data at the order level for comparison. PCA on
taxonomic classification by RDP 11 classification data indicated significant larvae/ no
larvae effect (Fig. 3.7 B). 59.41% variance was explained by PC] and PC2. Factorial
ANOVA of PCA Scores for PC I for taxa from RDP 11 classification was significant for
larvae/ no larvae treatment but not significant for soil neither in PC I nor PC II (Larvae
=24.3976, p= 0.0078, Soil F=0.1819, p=0.6917and Soil*Larvae F=0.1895, p=0.6858).
Unclassified bacteria, Pseudomonanadales, Actinomycetales (class Actinobacteria),
Sphingobacteriales (class Sphingobacteria) and Sphingomonadales (Alphaproteobacteria)

contributed to most of the explained variation.

89

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Fig 3.7. A, B: Principal component analysis of the 16S rDNA sequence data from Red Soil with!
without larvae and Black Soil with] without larvae. OTUs determined by DOTUR
analysis at 3% distance, A; OTUs obtained by RDP 11 classification at order level, B;
46.27% of the variance was explained by PC 1-2 for DOTUR OTU data and 59.41% of
the variance was explained by PCl-Z for RDP classification data.

90

RDP classification revealed that there were a total of 75 sequences that fell either in
unclassified bacteria or unclassified Cyanobacteria category. A blastn search of these
sequences indicated that these sequences bear a close resemblance to chloroplast
sequences fiom algae. Phylogenetic analysis was performed to place these sequences in
an evolutionary tree with chloroplast sequences (Fig. 3.8). Notably the Red Soil
sequences clustered separately from the Black Soil sequences indicating that there were

differences in the community composition of these two soil types.

91

BSNL I _37
RNL2_I6
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RNL l_28

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Figure 3.8. Phylogentic analysis of 168 rDNA gene sequences from the unclassified group from
various treatments. The dendogram was generated by Neighbor Joining Method using
a filter of 241 bases and Jukes cantor correction using ARB software. The scale bar
represents 10% sequence divergence. Number of sequence within each group are in
parenthesis. RL=Red Soil/ larvae; RN L=Red Soil/no larvae; BSNL=Black Soil/no larvae

£92

Terminal Restriction Fragment Length Polymorphism Analysis (TRFLP) Results of
cluster analysis of TRF LP data are shown in F i g 3.8. The total number of unique
differently sized terminal fragments obtained from Hhal digestion for all four treatments
was 124. BL produced 36, BNL 50 terminal fiagments individually. Sixty-six terminal
fragments were obtained in combined BL and BNL profiles, with 16 unique in BL and 30
in BNL. The rest 20 terminals fragments were common in both the treatments. Ninety-
seven fragments were obtained in RL and RNL profiles combined, with 20 unique in RL
and 46 unique in RN L. Thirty-one fragments were common in both treatment types.

The total number of 65 uniquely sized terminal fragments was obtained from
Mspl digests of all four treatments. BL produced 21, BNL 32 individually. Thirty-eight
terminal fragments were obtained in combined BL and BNL profiles, with 6 unique in
BL and 17 in BNL. The remaining 15 terminals fi'agments were common in both the
treatments. Similarly RL produced 31 and RNL 27 fragments individually. Forty-three
fragements were obtained in both RL and RNL profiles combined, with 16 unique in RL
and 12 unique in RN L. 5 fragments were common in both treatment types.

Cluster analysis based on the UPGMA showed that samples typically segregated
by larvae/no larvae treatments within soil types along the trees generated by the PAUP
program, for the Mspl restriction fragments (Fig. 3.9). A similar result was observed
Hhal restriction fragments for Black Soil, however the Red Soil no larvae treatments

formed their own branch separately from the other three treatment combinations.

93

 

BL

 

 

 

BL

 

 

 

 

 

RL

 

 

 

 

RL

 

RNL

 

 

 

 

RNL

 

BL

 

 

BL

 

RNL

 

 

 

i——— BNL

RL

 

 

RL

 

 

RNL

 

 

 

RNL

Figure 3.9. TRFLP Cluster Analysis using Paup for presence/absence of the terminal fragements
obtained for Hhal and Mspl restriction enzyme digestions. (RL, Red Soil with larvae;
RNL, Red Soil with no larvae; BL, Black Soil with larvae; BNL, Black Soil with no
larvae).

94

DISCUSSION

Overall, the results of this study demonstrate that the bacterial community was highly
diverse in the habitats that were constructed for this study and that experimental
treatment effects on community composition were evident. These microcosms very likely
replicated the natural habitats of the An. gambiae for the following reasons: (1) natural
rain water was used; (2) local soil was used; (3) they were established in a natural setting.
Accordingly, results fiom this study would be expected to be similar to experiments done
in entirely natural settings. The high diversity here, with a large number of bacterial taxa
and some 16S rDNA sequences that were unclassified at various levels of taxonomic
group, is typical of investigation of bacterial communities in most environments that have
been studied to date (Hugenholtz et a1. 1998, Suzuki et al. 1997, Giovannoni et al. 1990).
Further, the pattern of diversity as reflected by the shape of rank abundance curves is also
typical, with few common and many rare taxa (Magurran 2004, Hughes et al. 2001).
Despite this high diversity in this study system there were discernable changes in the
bacterial community attributable to the experimentally imposed treatment effects (i.e.,
predation by mosquito larvae; underlying soil type).

Various methods have been employed to study bacterial community diversity in a
wide range of environments. Any given method will influence the way one interprets this
diversity. No method is without limitations and depending on the research question, one
has to use the best method available to address the relevant hypothesis. In this study, I
used two molecular approaches (168 rDNA sequence library construction, and 16S rDNA

TRFLP analysis) to generate genotypic data, followed by several statistical procedures

95

and summary analyses to test the hypotheses stemming fi'om the factorial experimental
design. Analysis of 16S rDNA sequence data, or TRF LP fiequency data, is daunting due
to the tremendous diversity encountered by such methods, but currently they represent
the most powerful tools for studies of this kind, particularly when culturing is not
feasible. These are genotypic-based methods and are rooted in phylogenetic analysis.
Other methods that could be used include culture-based methods on enriched or minimal
essential media; direct count methods using various stains and genotypic probes; and
phenotypic analyses such as BIOLOG (a system that identifies carbon substrates utilized
in the system); and cellular fatty acid analysis (which generates chromatographic
signatures representing bacterial community composition). Chapter 1 reviews methods
and approaches and indicates their advantages and disadvantages. For example, clone
sequence libraries do not provide data on abundance of organisms (Zoetendal et al.
2004); however, one can make inferences about relative abundance of different taxa on
the basis of frequency of clones recovered, even with the well known problem of operon
copy number variation leading to biased estimates (F arrelly 1995, Klappenbach et a1.
2000).

The second challenge in analysis of bacterial community diversity is the choice of
statistical analyses of the genotypic data, both for purposes of summarization,
comparison, and to test hypotheses in an experimental context. For this reason, I chose
several different methods to analyze the sequence libraries in particular, with the aim of
determining if these different methods supported a set of general conclusions, which
could lead to a strong inference. Here, the use of analysis of variance on factor scores

generated by principle components analysis allows such an inference, and the problem

96

was separately approached with the LIBSHUFF procedure. Both methods provide
statistical tests. The principle components analysis per se, and the dendrogram generated
fiom the TRF LP data, are data reduction methods which provide support for these
general conclusions but do not provide statistical tests. Similarly, diversity, richness, and
evenness indices, and rank abundance curves, provide summary statistics or graphical
representations.

Despite the high bacterial community diversity, there were clear experimental
treatment effects that allow the following general conclusions. First, presence of actively
feeding and growing An. gambiae larvae resulted in a change in community composition,
compared to those microcosms where larvae were absent. This effect is consistent with
the phenomenon of “top down” effects of predation on lower trophic level community
structure observed in other aquatic ecosystems (Carpenter et al. 1985), and it indicates
that larval mosquito feeding within the surface microlayer has a dramatic effect on
composition of that layer, a finding consistent with recent studies on algal biomass
(Gimnig et al. 2002, Kaufinan et al. 2006). This effect was mediated both by changes in
frequencies of certain taxa represented by the sequence data, and by presence or absence
of certain taxa. In particular, several groups were either absent or decreased in
frequency, possibly due to larval grazing. Garnrnaproteobacteria were completely absent
in treatments with larvae and Red Soil and were in reduced frequency in treatments with
larvae in Black Soil compared with other combinations. These finding are consistent with
Kaufman et al. (1999) in which cultivated pseudomonads decreased in frequency under
feeding pressure by larvae of the mosquito Ochlerotatus triseriatus, in contrast with

cultured Enterobacteriaceae which increased in relative proportion. The number of

97

sequences belonging to group of unclassified Bacteria, genus F lavobacterium,
unclassified Incerta sedis 5, genus Limnobacter, unclassified Cyanobacteria, unclassified
Burkholderiales, and genus Aeromonas decreased in Red Soil treatments with larvae. By
contrast there was a trend towards increase in frequency of sequences belonging to
Betaproteobacteria and Alphaproteobacteria in both soil treatments in presence of larvae.
Also there was an increase in frequency of sequences of Polynucleobacter, unclassified
Actinomycetales, unclassified Rhizobiales and unclassified Sphingobacteriaceae.
Pedobacter and unclassified Alcaligenaceae sequences were present only in samples
from Black Soil with larvae. These results also indicate that apart from a marked larval
effect, there were differences in bacterial community composition in the soil types.

PCA identified different taxa changing in frequency under larval feeding
pressure, depending upon whether RDP II or DOTUR were used to classify the sequence
data. However, the overall conclusion that bacterial community shifts occurred is upheld.
The differences between RDP II and DOTUR are likely related to the algorithms used to
classify sequences and the robustness of the sequence library in RDP II. It is unlikely that
variation in ingestibility explains these patterns since the mosquito larvae are generalist
collector gatherers and would ingest all the bacteria present in the surface microlayer,
given the size range of bacteria (Merritt et al. 1992).

The soil effects on the bacterial community composition were less pronounced
than were the larval feeding effects, but they were present and detected by LIBSHUFF
and PCA. LIBSHUFF analysis revealed significant differences in clone libraries from RL
and RNL; the same was true for BL and BNL. Furthermore, there were significant

differences in clone libraries RL and BL and similarly RNL and BNL were different

98

indicating all four libraries were different than one another. Thus, the larval effect was
apparent and strong in both soil types and there were differences between the soil types
as well. LIBSHUF F did not point out specific shifts in the community but PCA was
useful to resolve this question, indicating the groups associated with the variations.
Indeed, results obtained by PCA clearly indicated effects of larval presence in both soil
types. TRFLP analysis supports the same conclusion by separating larvae/no larvae
treatments within the Red Soil cluster. However, Kaufman et al. (2006) showed that
different soil types did not affect the productivity of these habitats, even though nutrient
regime was affected.

Shannon and Simpson diversity indices indicated that diversity in no larvae
treatments (RN L and BNL) community was higher than that in the treatments with larvae
(RL and BL). However, it should be noted that the diversity was not completely assessed, '
as indicated by the rarefaction curve generated in DOTUR. This finding limits the power
of various methods used with regards to the sequence libraries. The diversity revealed
here was much higher than that in a single cultivation study published recently (Huang et
al. 2006), suggesting that cultivation methods for An. gambiae habitats will under-
represent the bacterial diversity present. It is important to realize that interpretation is not
always simple, and one must bear in mind that a particular analysis provides only a
minimum estimate of the diversity (Fuhrman 2002). Methods affect results and different
methods used in microbial ecology and bacterial diversity studies should be used to test
or examine the same hypothesis. PCA supported by ANOVA on factor scores is rarely

used, but looks to be a powerfirl adjunct tool for analysis of data from sequence libraries,

99

both when a taxonomic classification is achieved (here, RDP II) or when it is not but
OTUs can be reliably derived using a specified similarity value (here, DOTUR).

Sequences that were unclassified by RDP II, when subjected to Blast search
revealed that most of the sequences matched close to Cyanobacteria (Cylindrospermum,
T olypothrix), or chloroplasts of unicellular green algae (Chlamydomonas, Dunaliella),
diatoms (Nitzchia, Skeletonema), colonial algae (Scenedesmus), and Euglena. These
sequences detected in the treatments without larvae in both soil types support the
conclusion of few others studies that these primary producers might be important food for
the Anopheles mosquito larvae. Absences of these sequences in the treatments with larvae
also support findings regarding changes in algal abundance and biomass with larvae
present (Kaufman et al. 2006, Gimnig et al. 2001). Also it has been noted in many aquatic
ecology studies that predator mediated top down effects shift bacterial communities and
make bacteria less useful to the predators as food items (Jurgens and Gude 1994). Thus,
changes in the bacterial community in current study might reflect a similar top down
effect of the larvae grazing on acceptable forms and shifting community to unacceptable
forms (Kaufman et al. 2000, 2002, Xu et al. in preparation). The changes in bacterial
community composition in treatments with larvae might also be an indirect effect of the
absence of algae. Algal and bacterial productivity are usually tightly linked in freshwater
systems and absence of algae would limit bacterial growth (Cole et al. 1988) and
planktonic community structure can determine bacterial production (Pace et a1. 1990).
Increase in particular groups of bacteria may also be due the removal of grazing pressures
of other organisms feeding on bacteria. Grazing experiments revealed that Pedobacter

was preferred as prey by the ciliate over Brevundimonas by a factor of four (Becks et al

100

2005). Thus for instance if ciliates are removed by Anopheles larvae, bacteria like
Pedobacter might increase in numbers. Thus the concept of cascading trophic
interactions (Carpenter et a1. 1985) might also be applicable for these mosquito habitats.
The shifts in the community might also be an effect of predation by other predators like
protozoans and zooplanktons that might be present in these habitats. There is increase in
number of reports on appearance of grazing resistant bacteria in marine and fieshwater
habitats aquatic ecosystems (Jurgens and Matz 2002, Langenheder and Jurgens 2001,

J urgens 1994). The importance of graze resistant bacteria increases with increasing
grazing pressure exerted by protozoans, whereas decreases with increasing top down
control of protozoans by zooplankton. This might reduce the productivity of planktonic
systems through decrease in trophic transfer efficiencies and reduced regeneration of
bacteria bound nutrients (J urgens and Gude 1994).

It is essential to be aware of bottom up effects along with top down control of
bacterial populations. Nutrient conditions alone can result in differences in the structure
of bacterial community and under grazing pressure different graze resistant bacteria can
develop under different nutrient conditions (Matz and Jurgens 2003). In their study Matz
and Jurgens (2003) noticed that bacteria developed different survival mechanism, highly
motile bacteria developed under C limitation and were dominated by filamentous forms
in P limitation. In the current study, the underlying soil had effects on P concentrations,
with Black Soil releasing higher amounts than the Red Soil (Kaufman et al. 2006). In any
of the above cases where the habitat might become less productive for the Anopheles
larvae possibly resulting in segregation of larval habitats in natural setting and may also

eXplain oviposition selection by the female Anopheles mosquito. Further studies on these

101

interactions might be more useful on understanding the food web in these habitats, in
particular, studies which examine nutrient depletion from soil and allow successive

cohorts of larvae to establish, feed and grow.

102

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CHAPTER IV

Bacterial diversity of the surface microlayer of Anopheles gambiae and Anopheles
funestus habitats using 16S rDNA sequence library construction

ABSTRACT

A descriptive analysis of bacterial diversity of Anopheles gambiae and Anopheles
fimestus habitats was carried out using 16R rDNA sequencing and TRFLP analysis. A
total of 644 rDNA sequences from An. gambiae habitats and 642 sequences from An.
fimestus habitats obtained were classified using Ribosomal Database project 11. Both
communities were highly diverse with few dominant and many rare taxa. LIBSHUFF
analysis revealed that these communities were statistically different but principal
component analysis and TRFLP analysis did not show any significant clustering of these
habitats. There were a large number of unclassified bacteria and a few other groups like
Actinomycetales and Rhodobacterales which were found in high frequencies in An.
gambiae habitats. Alteromonadales was found in high frequency in An. funestus habitats.
There was no evidence that would support habitat segregation based on bacterial
community structure within this habitat that might play a role in oviposition selection by

gravid females.

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INTRODUCTION

Many of the Anopheles species in Afiica are highly efficient vectors of human malaria, in
particular species of the Anopheles gambiae and the An. funestus complex. Within the
gambiae complex, An. gambiae and An. arabiensis have the widest distribution in sub-
Saharan Afiica (Coluzzi 1984). An. gambiae and An. arabiensis breed in fresh water and
are associated with small habitats often created by man or animal activity, such as foot or
hoof prints, burrow pits, roadside puddles formed by tire tracks, and irrigation ditches.
Their habitats are turbid and persist for short periods and lack aquatic vegetation. An.
funestus on the other hand is distinguished from the gambiae complex by breeding in
large, semi-permanent bodies of water, characterized by emergent vegetation, such as
swamps, river edges and ditches.

Larvae of the Anopheles species reviewed above have some obvious and
characteristic habitat features and qualities, but the nutrient and microbial facets of these
habitats which contribute to primary and secondary (including mosquito) production and
to habitat differences among these Anopheles species are poorly known. The adult
mosquito density depends on the number and collective productivity of the larval
habitats. Even though these habitats are the source of these competent vectors, little is
known about the larval biology of these important insects, in particular, what forms the
basis for production of insect biomass in them. According to the study conducted by
Gimnig et al. 2002 the most likely important food source for larval An. gambiae seem to
be algae, which were significantly reduced in presence of larvae as measured by

chlorophyll a in the surface water samples and by counts of algae in sedimentation

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chambers, compared to with when larvae were absent. Other experiments support Gimnig
et a1. study and report that presence of algae does improve the growth and development
of the larvae with shorter developmental time and greater survival as compared to when
algae were absent, a condition obtained by shading the habitats (Kaufinan et al. 2006).
But this ignores algal turnover rates, larval consumption and other nutritional sources
(i.e.; bacteria and non cellular organics in the surface layer). Algae probably play a key
role as food resources in An. gambiae habitats and bacteria may likely form a secondary
food source. Studies on larval gut analysis reveal that bacteria form the bulk of the food
bolus, along with organic particulates, algae, and small invertebrate parts (Walker et a1.
1988). Of interest is that larval mosquito feeding on bacteria in their habitats has been
estimated to account for production of a large fraction of the insect biomass (Kaufman et
al. 2001), but simultaneously, larval mosquito feeding reduces. microbial abundance,
alters the microbial community qualitatively and quantitatively, and diminishes the
quality of the microbes as food (Kaufman et a1. 2000, 2002; Xu et al. in preparation).
Further, microbial communities in larval mosquito habitats have been
hypothesized to influence oviposition site selection by mosquitoes owing to volatile
organic compounds emitted from these communities, and owing to microbially-derived
flavors in water (Trexler et a1. 2003). Location and selection of an oviposition site is an
essential part of the life history of all mosquito species. The location and selection of an
ovipostional site involves visual, olfactory, and tactile responses. Intensive field studies
have shown that mosquitoes are quite discriminating in selecting sites for egg deposition
and considerable evidence points to this site discriminating larval distribution (Bentley

and Day 1989). Oviposition site selection is the net result of the interaction of a complex

110

array of both chemical and physical factors. These attracting substances may, in some
cases be pheromones produced by the larvae. Studies have shown that concurrent
presence of A. gambiae larvae reduced oviposition, while turbid water from natural
breeding sites increased oviposition selectivity. Thus other possible attractants such as
microbial metabolites must also be considered keeping in mind that mosquito biting on
the human body is also mediated by microbes. An. gambiae, a human biter, prefers to bite
human feet and is attracted to odors emitted by bacteria from feet, such as isovaleric acid
(Braks et a1 1999).

Notably, An. gambiae and An. fimestus habitats are segregated and distinct in
Kenya (Gimnig et al. 2001). The bacterial communities are postulated to be distinct
between these habitat types. These differences may be crucial in differential oviposition
site selection by females of these two species, and the differences may account for habitat
production for adult mosquitoes. Since very little information exists on the bacterial
community, it is essential to study it and also to determine what comprises the bacterial
community within these habitats. By conducting a comparative analysis one can also
determine what makes one habitat more favorable for one species (An. gambiae and An.

arabiensis) and the other habitat for other species (An. fimestus).

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METHODS AND MATERIALS

Sampling the Sites: Samples were obtained from habitats characteristic of An. gambiae
and An. funestus and were identical to sites used in previous studies (Gimnig et al. 2001,
Walker unpublished) from the Asembo District near Kisumu, western Kenya. Five
habitats of An. gambiae and 5 habitats of An. fimestus were sampled and were designated
as G1, G3, G6, G7, G13 and F1 through F5, respectively. The surface microlayer from
each habitat was sampled (25ml) using a needle and syringe as previously described
(Gimnig et al. 2002). These samples were returned to the laboratory and preserved at -20°
C until processed. The surface microlayer sample was divided and preserved for different
analysis. A 3 m1 aliquot of surface microlayer sample from An. gambiae and An. funestus
habitats were separated and preserved with Lugol’s solution for algal counts, 5 ml filtered
for chlorophyll a analysis, and 3 ml for direct bacterial counts. The remaining 14 ml was

used for nucleic acid extraction.

Bacterial and Algal density: Direct bacterial counts of 3 ml formalin preserved samples
were performed using 4'6-diamidino-2-phenylindole (DAPI) stain and epifluoresence
microscopy. Algae were counted using settling chambers and inverted microscopy.
Counts were converted with standard formulae to numbers of cells per ml of original

sample.

Chlorophyll a Analysis: Surface microlayer samples for chlorophyll a analyses were

collected from the central region of each habitat using a syringe and 16 gauge needle.

112

Five ml of the surface microlayer were filtered on site through glass fiber filters (nominal
pore size 1 11M). Filters were then kept frozen until analysis. Chlorophyll a, a measure of
algal biomass in ug per ml of sample, was estimated by fluorometric analysis
(Welschmeyer, 1994). Chlorophyll a was extracted overnight in 95% ethanol and the

samples were read against a 95% ethanol blank. The chlorophyll a was calculated in

rig/L-

Nucleic Acid Extraction, Amplification, Cloning and Sequencing Surface microlayer
samples (14 m1) from each of the above mentioned habitats were centrifuged at 6000 rpm
for 30 minutes and the pellet was retained for nucleic acid (DNA) extraction. The DNA
was extracted using Ultraclean Soil DNA kits (Cat. # 12800-50, MO BIO Laboratories
Inc., Carlsbad, California) following the manufacturer’s instructions. The presence of
DNA was confirmed on 1% agarose gels in TBE buffer. Extractions from each sample
were stored at -20° C. An approximately 1.3 kb region of a consensus 16S rRNA gene of
bacteria was amplified by PCR amplification using forward primer 63f 5' -CAG GCC
TAA CAC ATG CAA GTC- 3' and reverse primer 1387r 5' — GGG CGG WGT GTA
CAA GGC- 3' (Marchesi et al. 1998). The PCR reactions conditions were carried out as
per the Taq DNA polymerase instruction manual (New England Biolabs). PCR
conditions consisted of initial denaturing at 94°C for 2 min followed by 25 cycles of
denaturing at 94°C for 45 sec, annealing at 55°C for 30 sec, extension at 72°C for 1.3
min, and final extension at 72°Cgfor 7min. The resultant PCR products were purified
using low melting agarose gel electrophoresis, by cutting out the bands and purifying it

by sodium acetate and ethanol precipitation. Purified 16S rDNA fragments were cloned

113

into the pGEM-T Easy vector (Promega, Madison, Wisconsin) and transformed clones
were picked and purified. The plasmid DNA from transformed clones was extracted
using Wizard Plus SV Miniprep kit (Promega). After confirmation that the plasmids
contained an insert of the expected size, these plasmids (or clones for some samples)
were then subjected to high throughput sequencing using dideoxy dye terminator
chemistry, at the Genomic Technology Support Facility, Michigan State University. The
519R 5'- G(AT)ATTACCGCGGC(GT)GCTG- 3' sequencing primer (Lane et a1 1985)

was used to obtain partial 16S rDNA sequences.

Classification of sequences Sequence data from each of the 10 clone libraries (5 An.
gambiae and 5 An. funestus habitats) was examined for possible chimeras using the
CHECK_CHIMERA program offered through Ribosomal Database Project II (RDP 11;
Release 8.1, May 18, 2001 (Maidak et al. 1999) following procedures outlined in Lilburn
et al. (1999). Possible chimeras along with sequences with fewer than 250 base pair in
length were excluded from any further analysis. The 16S rDNA sequences were then
classified to named, operational taxonomic units (OTUs) using the Ribosomal Database
Project II (RDP 11; Release 9.38, April 03, 2006, http://rdp.cme.msu.edu). The RDP 11
contains 210,976 aligned 16S rRNA sequences as of April 03, 2006, and this database
formed the reference library for the classifications here. The hierarchy model used by the
naive Bayesian rRNA classifier in RDP II comes from the bacterial classification
proposed in release 6.0 of the Bergey’s Manual of Systematic Bacteriology
(http://beggevsoutlinecom). The classifier calculates the joint probability of finding eight

base subsequences (“words”) in the query. When a query sequence is submitted, the joint

114

probability of observing all the words in the query can be calculated separately for each
genus from the training set probability values. Using the naive Bayesian assumption, the
query is most likely a member of the genus with the highest probability, given the
limitations of the available database (Cole et al. 2005). This classification process puts a
name on each sequence.

Sequences from all 10 libraries were aligned with existing bacterial sequences in
ARB 16S rDNA database http://www.arb-home.de and phylogenetic trees were
constructed using the ARB software package (Strunk and Ludwig 1997, Lilburn et a1.
1999). The RDP 11 classification was used as a guide for initial alignment since RDP
places the sequences to genus level which was used select reference sequences for
alignments. A mask was generated by the filtering method in ARB to produce valid
columns for comparison of sequences. Sequences with short lengths were excluded so as
to generate at least 200 valid columns. Distance matrices were generated using the above

filter and Jukes-cantor correction.

Statistical comparisons Sequence libraries were compared between clone libraries using
the LIBSHUFF program, (http://wwwarchesgg.edu/~whitman/libshuff.html, Singleton
et al. 2001). The program estimates differences between homologous coverage curves
CX(D), and heterologous coverage curves ny(D), by a Cramér-von Mises-type statistic,
Any= 2(CX-ny)2. The input for these two-way comparisons was the distance matrices
of aligned sequences generated in ARB. The distance matrices derived from nucleotide
sequence differences generated in ARB were also used as input for diversity analysis

using the DOTUR program (Distance based OTU and Richness determination;

115

http://www.plantp2_1th.wisc.edu/fac/ioh/dotur.html). A 97% similarity value was used to
determine operational taxonomic units (OTUs) (Schloss and Handelsman 2005). Using
these new OTU assignments, which were un-named in contrast to the classification in
RDP II, DOTUR constructed rarefaction curves for sampling intensity, richness
estimators, and diversity indices. To address relative evenness of the bacterial
communities reflected by sequence libraries, rank abundance graphs were generated
where the abundance (i.e., frequency) of each OTU was plotted on a logarithmic scale

against the rank from most abundant to least abundant (Magurran 2004).

Principal Component Analysis (PCA) PCA is a technique for simplifying a dataset
where the data are transformed to a new coordinate system such that the greatest variance
by any projection of the lies on the first coordinate (called the first principal component),
the second greatest variance on the second coordinate, and so on. PCA can be used for
dimensionality reduction in a dataset while retaining those characteristics of the dataset
that contribute most to its variance. PCA analysis was invoked for the sequence dataset
with RDP classification as well as OTUs obtained by DOTUR analysis. Factorial
ANOVA was performed on the PCA scores and the sequences that explained most of the

variance were identified through the PCA loading values.

Terminal Restriction Fragment Length Polymorphism Analysis (T RFLP) The DNA
extraction procedure and the PCR condition were the same as above with the exception
that the forward 63f primer was labeled with 6-FAM (Integrated DNA Technologies,
Coralville IA) at the 5' end. Three 100 111 PCR reactions were performed for each sample

to assure maximum coverage of the diversity of 16S rDNA present in the sample. These

116

reactions were combined and purified using Qiaquick PCR purification kit (Qiagen
Valencia, CA). To generate TRFLP fragments, restriction enzyme Hhal and Mspl (New
England Biolabs Inc.) were selected since they offer decent number of restriction
fragment and discrimination between different species (Marsh 1999).

The digestions were carried out in a two 10 111 aliquots (Liu et a1. 1997, Marsh et
al. 2000). 400-600 ng of purified PCR product was digested for 3 hours with either Hhal
or Mspl at 37° C. The reaction mixture contained approximately 400 ng (1-5 ul) of DNA,
2 111 of 10x restriction enzyme buffer, 1.5 111 of the one of the above restriction enzyme.
The reaction mixture was brought to 10 ul with nuclease free water. After 3 hrs of
digestion, the reactions were stopped at 60° C for 20 minutes. The T-RF lengths from the
digest were determined on an automated DNA sequencer (ABI PRISM 310, Foster City,
California) in GeneScan mode. The fragments were separated by electrophoresis using
capillary electrophoresis system. The fragment sizes were determined with ABI
Genescan Analysis Software (Applied Biosystems) and the alignment of the community
profiles were done in Genotyper software (Genotyper 2.5, Applied Biosystems). The
peaks ranging between 50-950 bp were used for further analysis. Cluster analysis of

TRFLP profiles was done using PAUP (http://paup.csit.fsu.edu/).

 

Nucleotide sequence accession numbers The sequences obtained during this study were
deposited to GenBank under the accession numbers EF149017 to EF149660 for clones
obtained from An. gambiae habitats and EF149661 to EF150302 for clones from An.
funestus habitats.

FIGURES IN THIS CHAPTER ARE PRESENTED IN COLOR.

117

RESULTS

Microbial and Chemical Analysis The total number of bacteria in the surface
microlayer from An. gambiae habitats ranged from 0.76 x 106/n11t0 2.3 x 106/ml and in
and An. funestus habitats the bacterial counts ranged from 1.6 x 106/m1 to 37 x 106/ml.
The algal counts in An. gambiae ranged from 3.5 x 103/ml to 8.6 x 103/ml and in An.
funestus habitats the counts ranged from1.8 x 103/ml to 15 x 103/ml. Mean :1: SE
chlorophyll a in the surface microlayer of An. gambiae and An. funestus habitats was

43.89 a: 15.35 fig/L and 42.7 :1: 12.52 [Ag/L.

Classification of sequences: A total of 1362 sequences were obtained for An. gambiae
and non-gambiae (An. funestus) habitats. Individually 673 sequences fiom An. gambiae
and 689 sequences from An. funestus habitats were obtained. Sequences with short
lengths (< 250bp) and chimeras were removed from each library reducing the library to
644 An. gambiae and 642 An. funestus sequences which were then classified using RDP
II.

Classifications based on the RDP II for An. gambiae and An. funestus sequences
libraries are summarized in Tables 4.1 and 4.2 respectively. The classification scheme
assigned the sequences from both libraries to 8 phyla, namely, Actinobacteria,
Bacteroidetes, Finnicutes, Nitrospira, Cyanobacteria, Proteobacteria, Verrucomicrobia
and Acidobacteria. Sequences belonging to Nitrospira, Verrucomicrobia and
Acidobacteria were absent in An. gambiae habitats. The most dominant Phylum in both

libraries was Proteobacteria (72.8% in An. gambiae habitats and 70.9 % in An. funestus

118

habitats). The second most common Phylum in An. gambiae library was Actinobacteria
(11.8%) and Bacteriodetes (9.8%). Within Proteobacteria sequences belonging to class
Alphaproteobacteria (22.7%), Betaproteobacteria (27.3%) and Garnrnaproteobacteria
(21.3%) were present in almost equal frequencies. Rhodobacterales and
Sphingomonadales were most abundant in class Alphaproteobacteria, Burkholderiales
were abundant in class Betaproteobacteria, and within Gammaproteobacteria,
Pseudomonadales and Alteromonadales were abundant. The second most abundant phyla
in An. fimestus library were unclassified bacteria (12.6%), Cyanobacteria (7.16%) and
Bacteriodetes (6.38%). Within Proteobacteria sequences belonging to class
Garnrnaproteobacteria (38.8%) and Betaproteobacteria (23.4%) were most abundant.

In An. gambiae library within class Garnrnaproteobacteria genus Acinetobacter
(Alteromonadaceae) and Alishwanella (Alteromonadaceae) were most common. Within
Betaproteobacteria genus Rhodobacter (Rhodobacteraceae) and Polynucleobacter
(Burkholderiaceae) was most common. Within Actniobacteria the sequences remained
unclassified at the family level (Microbacteriaceae). In An. funestus library within class
Gammaproteobacteria genus Alishwanella (Alteromondaceae) was dominant and
Cellvibrio was common in order Pseudomonadales.

The most evident difference between An. gambiae and An. funestus libraries was
as follows. Higher frequencies of clones around 11.65% from order Actinomycetales
(Actinobacteria) and 13.2% Rhodobacterales (Alphaproteobacteria ) in An. gambiae
library as compared to 2.17% and 1.55% of the same in An. funestus library. On the other
hand, in An. funestus library Alteromonadales (Gammaproteobacteria) were observed

with high frequency, around 16.3% while only 4.97% in An. gambiae library.

119

Table 4.1. Bacterial community composition of surface microlayer from An. gambiae habitats based
on 16S rDNA sequences classification with RDP II. The numbers of sequences observed
per taxon are in parenthesis.

 

 

l Phylum 1 Class I Order I Family I Genus J
Actinobacteria Actinobacteria (76) Actinomycetales (75) Microbacteriaceae Unclassified
(76) (47) Microbacteriacead47)
Unclassified

Bacteroidetes
(63)

C yanobacteria

(6)

Firmicutes (4)

Proteobacteria
(469)

Flavobacteria (32)

Sphingobacteria (20)

Bacteroidetes (4)

Unclassified
Bacteroidetes (7)
Cyanobacteria (6)

Bacilli (1)
Clostridia (3)

Alphaproteobacteria
( l 46)

Unclassified
Actinobacteria (1)
Flavobacteriales (32)

Sphingobacteriales (20)

Bacteroidales (4)

subsection
Subsection 4 (1)
Unclassified
Cyanobacteria (5)
Bacillales (l)
Clostridiales (3)

Rhodobacterales (85)

Sphingomonadales (36)

Rhizobiales (l4)

Rhodospirillales (3)

Caulobacterales (l)

12f)

Actinomycetales (28)

Flavobacteriaceae Flavobacterium (6)

(32)
Riernerella (1)
Unclassified
Flavobacteriaceae
(25)
Sphigobacteriaceae Pedobacter(2)
(9)
Unclassified
Sphingobacteriaceae
(7)
Flexibacteraceae (1) Unclassified
F lexibacteraceae (1)
Crenotrichaceae (3) Chitinophaga (3)
Unclassified
Sphingobacteriales (7)

Prevotellaceae (4) Prevotella (4)

Family 4.1 (1) Anabena (l)

Bacillaceae (l) Exiguobacterium (l)

Clostridiaceae (2) Acetivibrio (2)
Peptococcaceae (1) Unclassified
Peptococcaceae (1 )
Rhodobacteraceae Rhodobacter (20)
(85)
Unclassified
Rhodobacteraceae
(65)
Sphingomonadaccae Porphyrobacter (4)

(36)

Novosphingobium
(] 8)

Unclassified
Sphingomonadaccae
(14)

Unclassified
Beijerinckiaceae (1)

Beijcrinckiaceae (1 )

Unclassified
Rhizobiales (13)
Rhodospirillaceae
(3)
Caulobacteraceae

(1)

Magnetospirillum (3)

Caulobacter (1)

 

Dhylum I Class I Order I Family I Genus
Unclassified
Alphaproteobacteria (7)
Betaproteobacteria Burkholderiales (135) Comamonadaceae Polaromonas (2)
(176) (48)
Curvibacter (4)
Comamonas (2)
Hydrogenophaga
(1 1)
Acidovorax (1)
Unclassified
Comamonadaceae
(28)
Oxalobacteraceae Unclassified
(2) Oxalobacteraceae (2)
Alcaligenaceae (l) Unclassified
Alcaligenaceae (l)
Burkholderiaceae Polynucleobacter
(63) (61)
Unclassified
Burkholderiaceae (2)
lncertae sedis 5 (l4) Roseateles (l)
Leptothrix (l)
Aquabacterium (3)
ldeonella (2)
Unclassified
lncertae sedis 5 (7)
Unclassified
Burkholderiales (7)
Neisseriales (15) Neisseriaceae (15) Vogesella (3)
Chitinibacter (1)
Aquaspirillum (8)
Unclassified
Neisseriaceae ( 3)
Rhodocyclales (14) Rhodocyclaceae Dechoromonas (1)
(l4)
Unclassified
Rhodocyclaceae (l 3)
Unclassified
Betaproteobacteria ( 12)
Gammaproteobacteria Xanthomonadales (5) Xanthomonadaceae Pseudoxanthomonas
(137) (5) (2)
Unclassified
Xanthomonadaceae
(3)
Aeromonadales (l 1) Aeromonadaceae Aeromonas (l l)
(l 1)
Pseudomonanadales Pseudomonadaceae Pseudomonas (6)
(63) (2|)

Alteromonadales (32)

121

Moraxellaceae (42)
lncertae sedis 7 (31)
Shewanellaceae (1)

Cellvibrio (2)
Unclassified
Pseudomonadaceae

(1 3)
Acinetobacter (42)

Alishewanella (31)
Shewanella (l)

Phylum Class I Order I Family I Genus

Chromatiales (3) Chromatiaceae (3) Rheinheimera (3)
Enterobacteriales (4) Enterobacteriaceac Escherichia (l)
(4)
Klebsiella (l)
Unclassified
Enterobacteriaceae
(2)
Unclassified
Gammaproteobacteria (19)
Deltaproteobacteria Desulfobacterales (l) Desulfobacteraceae Unclassified
(1 ) (1 ) Desulfobacteraceae
(1)
Epsilonbacteria (6) Campylobacterales (6) Campylobacteraceae Arcobacter (6)
(6)
Unclassified
Proteobacteria (3)
Unclassified
Bacteria (26)

Total Bacteria 644

122

Table 4.2 Bacterial community composition of surface microlayer from An. funestus habitats based
on 16S rDNA sequences classification with RDP II. The numbers of sequences observed
per taxon are in parenthesis.

 

 

I Phylum I Class I Order I Family I Genus
Actinobacteria (15) Actinobacteria (15) Actinomycetales Microbacteriaceae Unclassified
( l 4) ( 10) Microbacteriaceae
(10)
Unclassified
Actinomycetales (4)
Unclassified
Actinobacteria (1)
Bacteroidetes (41) F lavobacteria (15) Flavobacteriales Flavobacteriaceae F lavobacterium (6)
' (15) (14)
Unclassified
Flavobacteriaceae (8)
Unclassified

Cyanobacteria (46)

Firmicutes (l)

Nitrospira (l)

Proteobacteria
(455)

Sphingobacteria (3)

Bacteroidetes (3)

Unclassified
Bacteroidetes (20)

Cyanobacteria (46)

Bacilli (1)

Nitrospira (l)

Alphaproteobacteria (50)

Sphingobacteriales

(3)

Bacteroidales (3)

subsection
Subsection 3 (1)
subsection
Subsection 4 (2)
Unclassified
Cyanobacteria (43)

Bacillales (1)

Nitospirales (1)

Rhodobacterales
( 1 0)

Sphingomonadales
(l4)

Rhizobiales (7)

123

F lavobacteriales (1)
Flexibacteraceae (3)

Porphyromonadaceae

(1)

Unclassified
Bacteroidales (2)

Family 3.1 (1)

Family 4.1 (2)

Bacillaceae (1)

Nitrospiraceae (1 )

Rhodobacteraceae
( l 0)

Sphingomonadaccae
( 14)

Rhizobiaceae (2)

Hyphomicrobiaceae

(l)

Flectobacillus (2)

Unclassified

F lexibacteraceae (l )
Unclassified
Porphyromonadaceae

(1)

Oscillatoria (1)
Nodularia (l)

Unclassified
Family 4.1 (l)

Unclassified
Bacillaceae ( l)

Nitrospira (1 )

Rhodobacter (3)

Unclassified
Rhodobacteraceae
(7)

Sphingomonas (1)

Porphyrobacter (1 )
Novosphingobium
(3)

Unclassified
Sphingomonadaccae
(9)

Unclassified
Rhizobiaceae (2)
Devosia ( 1)

 

 

I Phylum I Class I Order LFamily I Genus
Unclassified
Rhizobiales (4)
Rhodospirillales (8) Rhodospirillaceae (8) Azospirillum (l)
Unclassified
Rhodospirillaceae (7)
Unclassified
Alphaproteobacteria
(1 1)

Betaproteobacteria (150) Methylophilales (l) Methylophilaceae (l) Methylophilus (1)
Burkholderiales Comamonadaceae Rhodoferax (5)
(130) (34)

Curvibacter (7)
Hydrogenophaga (7)
Acidovorax (2)
Unclassified
Comamonadaceae
(13)
Alcaligenaceae (l) Unclassified
Alcaligenaceae (l)
Burkholderiaceae Limnobacter (2)
(16)
Polynucleobacter
(10)
Wautersia (l)
Unclassified
Burkholderiaceae (3)
lncertae sedis 5 (51) Roseateles (5)
Leptothrix (l)
Aquabacterium (3)
ldeonella (1)
Unclassified
lncertae sedis 5 (41)
Unclassified
Burkholderiales (28)
Neisseriales (l) Neisseriaceae (l) Aquaspirillum (1)
Rhodocyclales (12) Rhodocyclaceae (12) Unclassified
Rhodocyclaceae (12)
Unclassified
Betaproteobacteria
(6)
Gammaproteobacteria(249) Xanthomonadales Xanthomonadaceae Nevskia (1)
(S) (5)
Xanthomonas (1)
Unclassified
Xanthomonadaceae
(3)
Methylococcales M ethylococcaceae Methylosarcina (l)
(l) (1)
Aeromonadales (7) Aeromonadaceae (7) Aeromonas (7)
Pseudomonanadales Pseudomonanadaceae Pseudomonas (3)
(62) (33)

124

Alkanindiges (7)
Cellvibrio (22)
Unclassified
Pseudomonadaceae

(8)

 

I Phylum IClass I Order LFamilL I Genus

 

Unclassified
Pseudomonadales (1)
Moraxellaceae (28) Acinetobacter (20)

Unclassified
Moraxellaceae (l)
Alteromonadales lncertae sedis 7 (105) Alishewanella (105)
(105)
Enterobacteriales Enterobacteriaceae Enterobacter (1 )
(4) (4)
Unclassified
Enterobacteriaceae
(3)
Unclassified
Gammaproteobacteria (65)
Deltaproteobacteria (l) Bdellovibrionales Bdellovibrionaceae Bdellovibrio (1)
(l) (1)
Unclassified
Proteobacteria (5)
Acidobacteria (l) Acidobacteria (1) Acidobacteriales (1) Acidobacteriaceae (1) Acidobacterium (1)
Verrucomicrobia Verrucomicrobia (1) Verrucomicrobiales Unclassified
(1) (l) Verrucomicrobiales (1)
Unclassified
Bacteria (81)
Total Bacteria 642

125

Community Comparison To determine if the An. gambiae and An. fimestus libraries
were different statistically distance matrices obtained from ARB were compared using
LIBSHUFF and it revealed that both communities were significantly different from each
other (ACF/G= 0.242, p= 0.001; AC5”: 0.610, p= 0.001). The comparisons indicate that
the communities differed greatly at high levels of relatedness but shared many deep
phylogenetic taxa at low levels of relatedness (Fig 4.1). Sequences were classified into
Operational Taxonomic Units (OTUs) and diversity indices were determined using
DOTUR. The Simpson diversity index (1/D) for An. gambiae library determined by
DOTUR was 63.51 and that for An. funestus library was 53.17. Note that the value of the
index increases with increasing diversity. The Shannon diversity index was 4.57 for An.
gambiae library and 4.8 for An. funestus library. The OTUs obtained by DOTUR analysis
were used to plot rank/abundance graph where the relative abundance was plotted on
logarithmic scale against the rank of each OTU (97 % similarity), ranked from most
abundant to the least abundant (Fig 4.2). The rarefaction curves indicated that the
diversity estimates obtained above might be an underestimation of the total diversity, as
the curves do not reach an asymptote given the number of sequences in each clone

libraries (Fig 4.3).

Principal Components Analysis PCA was invoked to identify a pattern and to see how
different libraries fi'om An. gambiae and An. funestus habitats clustered. The OTUs
obtained from DOTUR using a 0.03 distance (97% similarity) criterion was used to
perform PCA analysis (Fig 4.4 A). Similar analysis was performed on RDP 11

classification data at the order level for comparison (Fig 4.4 B). Analysis of variance

126

performed on PCA scores for OTU data (PC I: F: 0.72, P: 0.4197 and PC 2: F= 0.66, P:
0.4401) was not significant for libraries from An. gambiae and An. funestus habitats.
Similarly ANOVA on PCA scores for RDP classification data (PC I: F: 2.2, P: 0.1756
and PC 2:F= 1.37, P: 0.2749) was not significant for An. gambiae and An. funestus

libraries.

127

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1.2 0.07
1 '* 0.06
‘* 0.05
0.84
" 0.04
0.6 1
r 0.03
0.4 *
r 0.02
0'2“ -~ 0.01
A o rr.i.r”r.i.1"1'n‘—.r-'T.i.r“r-i111111IIrrirIIT—rfififirrfifrrrrrrrirrrr 0
9. o 0.1 0.2 0.3 0.4 0.5 a...
s a;
N
3 1.2 0.16 x
> e
8
“P 0.14
r 0.12
‘* 0.1
r 0.08
‘* 0.06
r 0.04
t 0.02
o .I-.I.I.1-'I-I-f-IA‘I‘I‘AI-T‘I‘llIITITIIIIIIiIIIIIllIITTTTTTTITIIIlII o
0 0.1 0.2 0.3 0.4 0.5
Evolutionary Distance (D)

Figure 4.1. Comparison of bacterial 16S rDNA sequence libraries of An. funestus habitats to An.
gambiae using LIBSHUFF. Homologous (open triangles) and heterologous (solid
triangles) coverage curves are shown. Solid lines indicate values of (Cf-CWG)2 (panel A)
or of (Cc-Cm)2 (panel B) at each value of evolutionary distance (D). Broken lines

indicate the 950"I value (or p=0.05) of corresponding (Cr-Cncfor (Cc-Cc”)z for the
randomized samples.

128

 

Abundance

 

 

OJ

 

 

 

 

 

 

 

 

—o— Gambiae
-a— Funestus
0011
0001
1 26 51 76 101 126 151 176 201 226

 

Figure 4.2. Rank abundance plot showing diversity of An. gambiae and An. fimestus bacterial
communities at evolutionary distance of 0.03. Relative abundance is plotted on
logarithmic scale against species rank from most abundant to least abundant.

129

 

 

300 -

250 r

200 r

U

l- 150-

100 1

 

50-
A

 

 

1 31 61 91 121 151 181 211 241 271 301 331 361 391 421 451 481 511 541 571
#Sequences

 

 

 

 

300 I

250 r

200 -

150

OTU

100 ‘

 

50+

 

 

0
1 35 69 103 137 171 205 239 273 307 341 375 409 443 477 511 545 579

# Sequences

 

 

 

Fig 4.3. Rarefaction curves generated from the OTUs at 0.03 distance (97% similarity) using DOTUR
for An. gambiae (A) and An. funestus (B) habitats.

130

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n g- i:
in ~1- m N
lllllllllllllllilllll

4.5 ‘

-‘
01

l

-‘

lllllllll‘lll

.6
UI

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II

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O
I I

 

 

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I O

IIIIIIIIIIIIIIIIII

-‘L5 -1 «051
PC1

 

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II III IIII III IIII III

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1.5 .1

 

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.Mt‘mm
0111.93111be

 

Fig 4.4. A, B: Principal component analysis of the 16S rDN A sequence data from An. gambiae and
An. funestus habitats. OTUs determined by DOTUR analysis at 0.03 distance (97%
similarity), A; OTUs obtained by RDP II classification at order level, B; 32.71 % of the
variance was explained by PC 1-2 for DOTUR OTU data and 50.75% of the variance was
explained by PC 1-2 for RDP classification data.

131

 

Terminal Restriction Fragment Length Polymorphism Analysis (T RFLP) Results of
cluster analysis of TRF LP data are shown in Fig 4.5. The total number of unique
differently sized terminal fiagments obtained from Hhal digestion for both An. gambiae
and An. fimestus combined were 203. 152 terminal fiagments were observed in gambiae
profiles and 121 fragments in funestus profiles. Eighty fragments were unique in gambiae
profiles which were absent in funestus profiles, similarly 51 fragments were unique in
funestus profiles.

The total number of unique differently sized terminal fiagments obtained from
Mspl digestion for both An. gambiae and An. funestus combined was 178. 103 terminal
fragments were observed in An. gambiae habitat profiles and 114 fi'agrnents in An.
funestus habitat profiles. Sixty fragments were unique in gambiae profiles which were
absent in funestus profiles, similarly 70 fragments were unique in funestus profiles.

Cluster analysis based on the UPGMA algorithm did not show any segregation of
An. gambiae and An. funestus habitats along the trees generated by the PAUP program
(Fig. 4.5 A, B). Thus fingerprints of bacterial communities of An. gambiae and An.
funestus habitats obtained by either Hhal or Mspl endonucleases digestion were not able

to separate An. gambiae from An. funestus habitats.

132

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A

 

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b

 

 

 

 

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8»

 

 

 

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or

 

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0)

 

 

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01

 

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01

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UIN

 

nmfihr

Figure 4.5. TRF LP Cluster Analysis using Paup for presence/absence of the terminal fragements
obtained for Hhal (A) and Mspl (B) restriction enzyme digestions. (G, An. gambiae

habitats; F, An. funestus habitats).

133

DISCUSSION

In this study, the bacterial community composition of the surface microlayer from
a discrete set of previously characterized An. gambiae and An. firnestus habitats was
assessed. Overall, the diversity was high in both the communities, as reflected by the
values of the diversity indices, the shape of the rank abundance curves, and the fact that
the rarefaction curves indicated that these communities were markedly under sampled for
assessment of “true” diversity, which is typical of many microbial diversity studies in a
wide range of environments (Hughes et al. 2001, Magurran 2004). The latter point was
true, even though the sequence libraries generated from the process were generously
large, compared with many studies (Singleton et al. 2001). LIBSHUFF analysis indicated
that both of these communities were statistically significantly different in composition;
however, the PCA and TRFLP analyses did not show any clear trends towards habitat
segregation on the basis of bacterial community composition. These differences are very
likely due to the differences of the procedures, LIBSHUFF used sequence data directly
(as generated from a distance matrix in ARB), which would maximize the amount of
diversity reflected in the sequence library at the various levels of similarity encompassed
by the LIBSHUFF program (see Fig. 4.1). LIBSHUFF is more likely to detect differences
when there are more operational taxonomic units represented by the original data. By
contrast, TRFLP analyses used derivative variables (bins of 16S rDNA amplicon
fragments sizes, with frequencies of fragments per bin as the actual variable) that reduce
the variation to higher orders compared to LIBSHUFF, thus any true differences would

be masked.

134

As in the research reported in Chapter 3, PCA was utilized here as a tool for reduction
of sequence library data, where points representing the individual habitats were displayed in
a bivariate plane defined by principle components 1 and 2. Two approaches were used, one
in which the input data for PCA were fiequencies of OTUs generated by DOTUR. Sequences
showing 97% or higher sequence similarity were considered to be the same OTU. Other
input data for PCA was the taxonomic classifications of sequences based upon the RDP II at
the order level, regardless of the percent similarity of those matches. Results differed
between these approaches, with the DOTUR classification system explaining more variation
in PCA (ca. 50%) than RDP classification (ca. 33%). These results indicate that, from the
standpoint of interpretation, the more precisely one attempts an OTU classification, the more
variation will be explained or the more likely a difference in communities will be found, as
was the case with the LIBSHUFF comparisons. I am unaware of any study in which the
sensitivity, specificity, and error rate of LIBSHUFF have been estimated for sequence
libraries of different sizes and compositions (Singleton et al. 2001), so the “alpha error” rate
for LIBSHUFF is currently unknown. The LIBSHUFF website
(http://whitmanmywebuga.edu/libshuffihtml) indicates as follows:

“In simulations, the sensitivity of LIBSHUFF increases with the number

of sequences in the library. For instance, when the library size n is 50, the

introduction of 10-20 novel sequences into one library is frequently

sufficient to allow LIBSHUFF to distinguish between the libraries

(p=0.05). Similarly, when n = 100, the introduction of 10-20 novel

sequences into one library is also frequently sufficient to allow

LIBSHU FF to distinguish between the libraries (p=0.05).”

From this statement, one can see that both the sample size of the libraries being compared

(i.e., total number of sequences) and the number of novel (i.e., unique) sequences will

affect the sensitivity of the statistical test. The libraries compared here had both a large

135

number of sequences and many novel sequences, indicating that there was a sensitive
test.

As discussed in Chapter 3, the use of PCA with classified 16S rDNA sequence
data offers several advantages, in particular the use of analysis of variance on factor
scores as a means of identifying particular groups contributing to differences in sequence
library composition. However, such an approach is limited if a large number of
sequences remain unclassified by the reference database, which was true for RDP II; a
large fraction of sequences were unclassified here. Here, the ANOVAs did not identify
significant factor scores when either RDP II or DOTUR were used as classification
systems, although the An. funestus habitat samples did form clusters in PCA for the
DOTUR classification, whereas An. gambiae habitat samples did not (Fig. 4.4).

Despite these findings, in this study there were no strong bacterial community
composition differences that would allow inferences about habitat segregation of these
mosquito species. Even though LIBSHUFF analysis performed on combined data from
An. gambiae and An. fimestus habitats showed that these habitats were statistically
different not such indication was given by PCA and TRF LP analysis performed with data
on individual habitat samples. Differences that were noted by the LIBSHUFF were at the
higher taxonomic level and while communities shared deep taxa at lower taxononmic
level. Notable difference between combined data that were observed by RDP
classification was presence of several unclassified Cyanobacteria (7.16%) and
unclassified bacteria (13.6%) in An. funestus habitats as opposed to 0.93 % and 4.03%
respectively, in An. gambiae habitats. Higher frequencies of Actinomycetales and

Rhodobacterales in An. gambiae habitats and higher frequencies of Alteromonadales in

136

An. fimestus habitats, in particular a set of 105 sequences branching very closely to
Alishewanellafetalis, an organism detected in a human fetus (V ogel et al. 2000) (see
Appendix A for ARB trees and phylogenetic placement). These groups were present in
most of the individual habitat samples and only 31 were found in An. gambiae habitats.

One possible explanation for substantial overlap in bacterial community
composition of An. gambiae and An. funestus habitats within close proximity to each
other, as here, is that heavy rains result in inundations and movement of water across the
landscape, causing mixing of soil, water, and microorganisms into any location where
water would settle after the rain subsided. The result would be that many common
bacterial sequences would thus occur in both types of habitats.

Intensive field studies have shown that mosquitoes are quite discriminating in
selecting sites for egg deposition and considerable evidence points to this site
discriminating larval distribution (Bentley and Day 1989, Trexler et al. 2003). True larval
habitat segregation in these species may be due to differences in female behavior with
regard to egg laying (oviposition), unrelated to bacterial diversity in habitats. For
example, Huang et al. (2006) showed that some bacteria were repellent or were not
attractive to gravid An. gambiae females, whereas contrast, darkness, and substrate
moisture were much strong elicitors of egg laying by this species. Boyd and Foot 1928
found negative association between Anopheline larvae and unicellular Cyanophyceace.
No positive correlation found to exist between the distribution of larvae and the
distribution of this plankton, due to the fact that these forms were also found in places

that had not been utilized as breeding places.

137

McCrae (1984) also showed that An. gambiae preferred turbid water rather than
clean water to oviposit their eggs. Maybe turbid waters for these species might be an
indication of freshly formed habitats not previously utilized by cohorts and fi'ee of
predators. In their review Bentley and Day 1989 have noted that concurrent presence of
An. gambiae larvae reduced oviposition. This maybe either be pheromones produced by
larvae or maybe due to fecal material present or microbial metabolites. An funestus on the
other hand prefer clean water with partial coverage of aquatic vegetation and were not
found in open waters (Giminig et a1. 2001). For An. fimestus, there is apparently little or
nothing known about oviposition behavior, in part because of the lack of availability of
laboratory colonies for research.

It is also possible that microbes other than bacteria might be important in
oviposition selection by gravid females An. gambiae. Bond et al. 2005 showed two kinds
of algae Spirogyra majuscula and Cladophora glomerata influenced oviposition site
selection by An. pseudopunctipennis in a positive manner. Geetha et al. (2003)
demonstrated that secondary metabolites produced by T richoderma viride, a fungus,
showed remarkable attractancy to oviposition by gravid females of Culex
quinquefasciatus mosquito. It might thus, be fi'uitful to evaluate microbes, otherthan

bacteria as oviposition attractants for gravid An. gambiae females.

138

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oviposition. Annual reviews in Entomology. 34:401-421

Bond J. G., Arredondo-Jimenez J. 1., Rodriguez M. H., Humberto Quiroz-Martinez
Q., Williams T. 2005. Oviposition habitat selection for a predator refuge and food source
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Braks, M.A.H., Anderson, R.A., Knols, B.G.J. 1999. Infochemicals in Mosquito Host
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Geetha 1., Paily K.P., Padmanaban V., Balaraman K. 2003. Oviposition Response of
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Gimnig J., Ombok M., Kamau L., Hawley w. 2001. Characteristics of larval
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Gimnig, J .E., M. Ombok, S. Otieno, M. Kaufman, J. Vulule, & E. D. Walker. 2002.
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Huang J ., Miller J. R., Chen Shi-Cheng., Vulule J. M., Walker E. D. 2006. Anopheles
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Hughes J. B., Hellmann J. J, Ricketts T. H., Bohannan B. J. M. 2001. Counting the

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Kaufman, M.G., E.D. Walker, D.A. Odelson, & M.J. Klug. 2000. Microbial
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Kaufman, M.G., S.J. Bland, M.E. Worthen, E.D. Walker, & M.J. Klug. 2001.
Bacterial and fungal contributions to growth of larval Aedes triseriatus (Diptera:
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Kaufman, M.G., Goodfriend W., Kohler-Garrigan A., Walker E.D., & Klug M.J.
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Kaufman M. G., Wanja E., Maknojia S. R., Bayoh N. M., Vulule J. M., Walker E.
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Lilburn T. G., Schmidt T. M., Breznak J. A. 1999. Phylogenetic diversity of termite
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Maidak B. L., C. J. R., Parker C. T., Garrity G. M., Larsen N., Li B., Lilburn T. G.,
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Marchesi J. R., Sato T., Weightman A. J., Martin T. A., Fry J. C., Hiom S. J., Wade
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Marsh T.L. 1999. Terminal restriction fragment length polymorphism (T-RFLP): an
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McCrae A. W. R. 1984. Oviposition by Afiican malaria vector mosquitoes 11. Effects of
site tone, water type and conspecific immatures on target selection by freshwater

Anohpeles gambiae Giles sensu lato. Annals Tropical Medicine and Parasitology. 78:
3 07-318.

Singleton D. R., Furlong M. A., Rathbun S. L., Whitman W. B. 2001. Quantitative

comparisons of 16S rRNA gene sequence libraries from environmental samples. Applied
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Strunk O. and Ludwig W. 1995. ARB- a software environment for sequence data.
Department of Microbiology, Technical University of Munich, Germany.

Trexler, J.D., Apperson C. S., Zurek L., Gemeno C., Schal C., Kaufman M., Walker
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responses of Aedes albopz’ctus (Diptera: Culicidae). Journal of Medical Entomology.
40(6): 841—848.

Trexler, J.D., Apperson C. S., Zurek L., Gemeno C., Schal C., Kaufman M., Walker
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40(6): 841—848.

Vogel B. F., Venkateswaran K., Christensen H., Falsen E., Christiansen G,. Gram L.
2000. Polyphasic taxonomic approach in the description of Alishewanella fetalis gen.

nov., sp. nov., isolated from a human foetus. International Journal of Systematic and
Evolutionary Microbiology. 50: 1133—1142

Walker E. D., Olds E. J., Merritt R. W. 1988. Gut content analysis of mosquito larvae

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Medical Entomology. 25(6): 551-554. '

Welschmeyer, N. A. 1994. Fluorometric analysis of chlorophyll a in the presence of
chlorophyll b pheopigrnents. Limnol. Oceanogr. 39:1985-1992

141

SUMMARY

The objectives underlying this research project were to analyze the effects of
removal of the surface microlayer on mosquito growth and to study the contribution of
bacteria to larval nutrition. Along with the above objectives, I also wanted to determine
the bacterial diversity of the surface microlayer of An. gambiae habitats in presence and
absence of larval feeding pressure and also the bacterial diversity of naturally occuring

A n. gambiae and An. funestus habitats using 16S rDNA sequence library construction.
Although there is lot of research that has been undertaken with the adults of these species,
very little is known about the larval biology and the habitats in which these larvae
develop.

Chapter II of this dissertation attempts to address the first two objectives. Results
indicate that removal of surface microlayer at regular intervals resulted in decreased
survival of An. gambiae larvae, prolonged developmental time to pupation, and resultant
adults with lower body mass. Supplementations of the surface microlayer from sources
with no larval grazing improved larval growth. Larval developmental time in these
microcosms was shortened and total mass was significantly greater, compared to
microcosms fi'om which surface microlayer was deliberately removed. An. gambiae
habitats are hydrologically unstable and dry quickly, thus larvae must feed and develop
before habitats dry up. Food limitation can affect this critical life history parameter
(developmental time) which was the most sensitive variable in the above case. In another
experiment, the importance of bacteria as a food source was studied. Heterotrophic

bacterial production was stimulated with addition of glucose as a carbon source, in

142

rnicrocosms that were intentionally shaded to reduce autotrophic algal production. It was
observed that larvae grew poorly, few molted, and few achieved metamorphosis to
pupation under these conditions, and that larval survival rate and total adult emergence
was very low compared to sunlit treatments which were rich in algae. Thus, bacterial
biomass does not compensate for algal biomass in larval growth, but it may be important
as a larval food supplement under conditions where algae are grazed. Bacteria supported
some larval growth but probably did not provide the broad suite of micronutrients
required by mosquito larvae for full metamorphosis.

To determine whether larval grazing had any effect on bacterial community
compostion in An. gambiae habitats, the bacterial diversity within these habitats with
presence and/or absence of larvae within two different soil types was studied. Overall, the
results in (Chapter III) demonstrate that presence of larvae has strong effects on bacterial
community composition and that these bacterial communities were significantly different.
There were a large number of unclassified sequences in no larvae treatments in both soil
types, and they were closest to sequences of phototrophic primary producers, likely
representing chloroplast rDNA sequences or unclassified cyanobacterial rDNA

sequences. Community shifts were observed either by presence or absence of certain
taxa, or changes in the frequencies of certain taxa, as represented by the sequence data.
These community shifts may explain patterns of variation in productivity of habitats in
natural settings and also habitat segregation of Anopheles species due to oviposition site
selection. However, the results in Chapter IV indicate that the bacterial communities in

natural An. gambiae and An. fimestus habitats seem to have no specific bacterial

143

signatures that might explain habitat segregation and selective oviposition site selection

among these species.

144

APPENDIX

145

APPENDIX A

Phylogenetic trees

146

 

 

 

 

     
    
    
 
 
 
    

RLI (25) RL2 (3)

RL2 (9)

PolymtcleobacZer necessarius
RL2 (5)
RL2C10neSl

RLI (2) RL2 (1 1)

Achromobacler
RL2C10ne1 1

KL] (3)

RL 1 Clone24

RL2C10ne4l

Herbaspirl/lzzm

RL 1 C10ne26

RL2C10ne88

RLI Clone68

L171
7::
E
O
5
:3
(D
U!
DJ

 

 

H d RL2C/lo
y m 76170 raga
. Ril thne92
1 Rhodoferax r’nrlal‘cfic'us
I RL2C10neS9
I 5112117012171]er azovoranx
RLl Clone8

RL2C10ne46
RL2 (4)
N01’().sp/1ing0/>i1mz almnaliohwruns
S 9171'17001770/7as

lClone64
RL2C10ne37

RLl Clone67
Rhodobacter
RL2C10ne49
Rhizobizmr
RL 1 C e

 

 

 
 

 

 

RL 1 Clone7]

 

 
   
  

RLl (6)
RL2C10ne85
RL2C10ne67
A ctinomvcclc
RLI C lone l 1
A 611770121} ’cela/cs

RL1(24)RL2(1)

 

    
      
    

Pedo/mclcr
l'lm’ubaclc/‘Imn 1111:11/1111

{7 RLl (4)

 

 

RLl CloneZl

RL 1 Clone7

 

Chitinophaga pinensis
lClone8

 

 

L

1‘— RL2C10ne54

L RL2C10ne66
RL2C10ne86
RLl Clone5 0

Escherichia col i

 

 

0.10

 

Fig 3.10. Phylogenetic analysis of 16S rDNA gene sequences obtained from Red Soil/larvae. The
dendogram was generated by Neighbour Joining Method uaing a filter of 265 bases and Jukes
cantor correction using ARB software. The scale bar represents 100/o sequence divergence.

Labels RLl and RL2 refer to clones retreived from two different replicates of same
treatment. Number of sequences within each group are in parenthesis.

 

 

 

E==r

RNL2 (9)
RNL2Clone39
RNL2Clon

Ram/ibczcster fataouinen si s
AC1 ovorax S
RNLZClone 5

RN L2 (3)
Rzodoferax fermentans
[-[KIdrOéenophagaa

RNL2Clone24
Col ne43
RoseatelLeSS 5p .

 
     
    
   

Rt thrix S
RNL2%100ne20 p

RNLI (1)RNL2 (IO)
RNL2CloneIO

RNLI (l) RNL2 (4)

RNLI (1) RNL2 (3)

AC/uabaC renrizstm parvum
RI\II{L2Clone

  
  
  
   

8171

 

  
    

 

_:—— RNL2Clonell
RNL2Clone90
RNLl (3)
Methylophilus mar/71701101717115
RNLI C10 66
RNL2Clone93

RNLlClone4
NeiSSC'r ia 517

_rRNL1ClonCIO
RNL2Clon e44
RNLI (6)

 

  

 

 

 

 

Polvnuc legbac lei I1€CCXS scu [HS
RNLlClon
RNLI (2) RNL2 (l7)
F/ariolmrxlw‘iunz L‘()/l//IIII(II’(‘
[Karin/1c:'fC’I'iI/In (alumna/2'
RNLlClontigN

11C on r1563

' RRNL LlCl on n6€4

11C one 23

 

qcfll7r)liliCell\I()
RNL2C10 ne28
NLlClone29
RNLI (3) RNL2 (7)
RNL2Clone4O

RNLlClonCBO
RNLl (I) RNL2 (3)
RNL1C10n§66

 
 
  
 

 

 

 

RNL2Clo
F—C’ 0 )[aCCz/s
F—'—— P10C11/()iaC"'.()CCus SI)
I fl RNLI (6)

 

%‘ RNLl (4)

RNLIClone2O
FleClone59
RNLlClone44
—— anel/ovibrio Sp
RNLIClo
RNLIClllonel7
RNLI (2) RNL2 (2)
I ,J L 4
‘— Rhodobacter
NR0NW)L¥j/71mg0bmm capsularum

RIRNLZClone61

 

 

 

 

 

ENLICIone e42

. Rhizobium sp

Nevskia ramo sa
RNL2Clon 682
RNLZIClone38
RNL2Clon n65
RNL2 Conl

RNL1(5)RNL2(1)

Pseudomonas Sp
A eromona

 

 

 

 

 

0.10

Fig 3.11. Phylogenetic analysis of 16S rDNA gene sequences obtained from Red Soil/no larvae. The
dendogram was generated by Neighbour Joining Method uaing a filter of 265 bases and J ukes
cantor correction using ARB software. The scale bar represents 10% sequence divergence.
Labels RNLl and RNL2 refer to clones retreived from two different replicates of same
treatment. Number of sequences within each group are in parenthesis.

 

6171

 

 

l—FVBSLI (‘19)BSL2 (22)
F/avobaclcrium

 

   
 

 

 

 

 

 

 

 

 

 

  
 
 
   
  
 
 
  
 
 
  
 
 
 

 

 

 

B SL1 (13)
BSL 1 Clone4l
BSLlClone45
BSL2Clon627
F/avobaclcrirm2 aqua/i/C
B ne
B SLS 1 Clone l 6
C10 ne86
BSLB2Clone34
BSLlClone2
BSL 1 Clone 14
PCc/Obac/Cr
BSLlClone72
BSL2CloneS8
BSL2Clone6
BSL 1 Clone3 8
ALII.IIIUII£ILLIL
BSL1(18)BSL2(1)
BSLlClone77
. SLlClone78
BSL2Clon681
BSLI Clonel l3
BSL2Clone38
R/rodobacrcr
BSLlClone75
Paracoccus Sp.
BSLI (5)
BSL1(10)BSL2(1)
BSLI Clone63
NOVOS lioingobium CI/mmliiC i1 m (1le
BSLl
BSlLlCloneS
BSLI (3)
Rhodop/aues
4gzsriISfEiliH/mgsiicz5011171"
Cl60ne
BSL2Clone74
___:—— BSLlClone4
Bdellovibrio Sp.

 

 
  
 
 
  
  
 
    
 
 
 
 
   
  

BSL2 (4)
BSL2Clone15

HerbaSpiri/lz/m Sp.

BSL2 (5)

BSL2C10ne70

BSL2 (10)

BSLI (9) BSL2 (5)
BSL2C10nel l
BSL2Clone23

Xena) iii/in E:1: 0190111!“
BSL Clon

Aquabac IC/ iii/en S.[)
Aquabaclcr rum pa] 1 um
BSL

on
BSLlClonel7
BSL2Clon655
BSL2Clone49
Polglrgc/C507bachI IYCCCSACII ius
BSLI
.Meth lobaci/lz/S

 

 

 

 

Ac/vromobacter

 

 

 

BSLI (1) BSL2 (9)
BSL l Clone90 (7)
BSL2C101’1682
Aeromonas

Escherichia col i

0.10

 

Fig 3.12. Phylogenetic analysis of 16S rDNA gene sequences obtained from Black Soil/larvae. The
dendogram was generated by Neighbour Joining Method uaing a filter of 265 bases and
Jukes cantor correction using ARB software. The scale bar represents 10% sequence
divergence. Labels BSLl and BSL2 refer to clones retreived from two different repliates of
same treatment. Number of sequences within each group are in parenthesis.

 

 

 

 

BSNL1(42) BSNL2 (1)

SNLlClonel 0
BNS NLlCloneZS
l€38NL2Clon€t78 .4 .
CIIIIuIII [JG/I 0
ii / BSNLT Clone37
Chlore a 1655' C11
BSNL2Clone7
I BSNLlClone4
SNLlClone64
SNL1C1§DC79

B SNL2Clon628

 

      
 
 

 

 

 

 

A I7(Ib(ICn(I

BSNLlCloneS2 ‘
v ' BSNLl (l2)
BSNLlClone24
BSNLlCloneS7
BSNLlCloen76791
BSNLlClon
. '7 BSNL2 (7)
BSNLlCloneEZ

 

 

 
 
    
 
  
 

0S1

 

BSNL2 (7)

Pedobaclcr

BSNLlC I e2 ‘
BSNL1C1058
[\IC‘K\SI\ICIHCII1III()SCI
‘ BSNL2C10neS6
BSNL2Clon687

BSNL2C10nelS
BSNL] (5) BSNL2 (11)

P(II"7'(IC(1((IIS
R/7I1d()/)(I(ICI Sp
BSN

 

 

   
  
  
 
 
   
 
    
  
  
  
 
 
 
 

L2C1031’12630
BSNL2Clone3
lg/mdo/mc (CI

BSNL2Clone85

R/II': 0/)izIm S

ONVO. SCIJ2I71II7QOI7IU/71 (1I()I77uII( l\ ()I am
BSNL2C10n

BSNL2C10116124

B L2Clone20

AqZICIBS‘gl‘INIi—IIIUH? IICI‘SUHII

L2Clon657

BSN 2Clone7
J—C' 11;;ch ()<>’()I7()_/)'/767ga

 

._ Ll..’.(l| (I, (III\

BSNL]N Clone88
BS NLl Clone26
Acid/0110mm [(ICi/iS
PS CII(I'0I17I)I7(IS 9])
lClo ne4

/ICICIOVOI"(IX S1).
BSNL2 (5)

BSNL l Clone92
BSN

DgfifmC/la C/OIIC
BS one30
[fer/)(IS )iril/um

BSNL2C10ne43
BSNL2 (7)

LGfilOI/H‘Ix (iiSI;I,)/)/II)I'(7
BS CloneZ7
BSNL2Clone8
BSNL2C10nel9
AfilaI)CICI(Z/éilll77 [76H vum
S LlCl 36

 
  
  
  

 

 

 

 

Bdellowbrio

 

BSNL2 (4)
BSNL2C10ne12
Pseudomonas putida
BSNL2
BSNL2Clon623
Cellvibrio
Pseudomonas
BSNLl (4)

BSNL2 (10)

A 6FOI710I’ICIS
BSNL2C10ne67

 

 

Escherichia coii
0.1 0

Fig 3.13. Phylogenetic analysis of 16S rDNA gene sequences obtained from Black Soil/no larvae. The
dendogram was generated by Neighbour Joining Method uaing a filter of 265 bases and J ukes
cantor correction using ARB software. The scale bar represents 10% sequence divergence.
Labels BSNLl and BSNL2 refer to clones retreived from two different repliates of same
treatment. Number of sequences within each group are in parenthesis.

 

 

 

G1 (21)

1- G1c1011e61

Glclone80

G1C10n07 6
GlclonCSZ
G101011e22
Glclonel 19
G1010110102
AcinetobactCrS Sp.
Acinetobacter calcoacez‘icuS

 

 

GlClOflCS
_| Glclone67

G1 (7)
Pseudomonas szIida
GlClOIlC109

01 (7)
HydIogCnophaga S
Ac;

[-
IIIIIIIIII (’1‘ (161/?

 

  
 
  
   
  
  
  
  
  
     
 

191

Rhoda/CI ax am‘m Czic us
G 1 clone
G 1 c1011059

G 1c10nel29
G] (7)
G1 01011030

V0 CSCl/(Ioindl 0 C10
g G1011C7Sf

onG 1010110105

GlcloneS

GPO?}I7LICI€0I)046IC/l I7CCGSSCI} izIS
concl

Glclone39

Comamonas Sp.

G1010ncll7

G1 (6)

DCCh/oromanas Sp.
G1c1011e133

G 1C10n6124

G 1 01011679

 

G1 (5)

F/avobauei III/II (quIaIi/C
G lclonc9

Flavobactei izIm S.p
FiavobacICI mm Sp

G1 (4)

  
   
 
 
   
 

Pl‘eleC’I/(I (I/[7CI1SIS
Prevotella I'lIIIIiIIiCO/(I
G 1 clone 1 2
G 1 c1011c93
C/IiIiImp/mga pinCIISI'S

 

 

 

 

(31(6)

 
  
  

ACliIIIImyCCIC _
C IIUSII ILIIIIIIII S‘D.

 

G1 ClOHCl 3 1

G 1C1011€63

G1c10nc28

Glclo11885

G 1 clone89

Novosphingobium (II‘OmalicivoranS
01011845

 

 

G1 (3)
Magnetospirillum Sp.
G1 (4)
Arcobaczer butzleri
G1c10ne2

 

 

I: Alishewanellafetalis
G 1 0101164

 
   

G 1c10ne3 5
Enterobacter S .
Escherichia c0 i

0.10

 

Figure 4.6. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. gambiae habitat
(G1). The dendogram was generated by Neighbor Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents 100/o
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

 

ZSI

 

G3 (5)

G3Clonc58
Rhodobacter Sp.

G3Clo11056
G3C1011c94
POI hwobacICI Sp.
G3C 011679

Caulobacter Sp.
G3C1011cl 5
G3C1011c96
G3C1011065
G3Clo11067

  
    
 
 
 
   
   

 

  

F/avoblaC ICI iIIIII SI).
C13C 104C)
C06]7()l’llia (marina
|—-—-——— G3C1011c46
Exi LIObaCtCI 161m? (ICCIv/iczim
53C] Clone7
G3C1011c95
G3C1011022
G3C1011C39
G3Clonc9

 

 

G3Clo11c7

 

4——————U G3 (5)
Bdellavibria Sp.

Prochlorococcus Sp.

  
 
   
 
 
  
  
  

 

 

 

 

 

 

 
 
  
  
   
  
  
  
  
   
 
 
 

 

 

 

G3C1011C45
G3C1011053
A uabacterium parvum
G5C1011029
A1 .1, ‘ iIIIIIiIII graci/C
G3C1011028
G3C1011c42
LCpIoI/II 1x discop/IOI a
ROSCaIC/CS 5.1)
G3C1011CS4
G3C1011c36
Leptothrix Sp.
G3 (3)
V0é€3561icl3117d100jel (I
C1011c3 1
G3C10n099
G3C1011c91
Acinetobacter Sp.
G3ClOIl€50
G3C1011c27
. Acinetobacter Sp.
G3 (4)
G3C1011C43
Pseudomonas SIuIzCri
lone
G3 (3)
T G3Clo11e98
LG3C10ne34
G3C1011e88
G3C10n086
G3C1011066
A6101770170Sjandael
G3Clone37
Shewanel/afia’C/ia
G3C1011e73
Shewanella
G3 (7)
G3C1011055
Rheinheimera
G3C1011682
G3C1011087
G3C10ne84
G3C1011c92
G3C1011e68
G3C10n018
G3 (15)
Alishewanellafetalis

 

""— G3 C101165
G3 C10n024
Enterobactei s

Escherichia COl

0.10

 

Figure 4.7. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. gambiae habitat
(G3). The dendogram was generated by Neighbor Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents lO‘Vo
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

 

 

  
  
  
  
 

66(25)

AcIiIIOIIIyCCI‘C
G6C10110109

G6C 1011026

(36001107

  
 

(3600110122

 

G6C1011085
0100110110

___:_—_ (360011036
(360011059

 

 

  
  
 
 
 
 
    
 
   

 

(36 ( 1111

Pca’obucwr Sp.

F [C.Yibaclcr CIIIIcIcI'CIISiS
(36001101113

(36(51011047

F/avobacICI'IIIm Sp.

(160011056

(36(‘1011018

 

 

 

€91

 

 

 

(160011054
RhodobacICI' Sp.
(716(1 101101

 

Rhizobiiim Sp.

(36(‘1111101 15
(360011050
..G60011010
G6C1011019
G60011051
(360011057
(360011064
POIp/Iyrobactcr Sp.

$117— 11. 191
PO/ynucleolmcter HGCC’SSCII'iHS

G6 (6)

G60011062
1 66131
360011029

G(IC10110 7‘)
(160011046

   
  
 
 
 

 

 

 

 

  
  
 
 
   
  
 
 
  
 

 

 

G 6 13 1

G60011045

G6C10n'052

010011098

Rubrivivm gC/aIiIIOSIIS
A quabacierium Sp.

XCIIop/Ii/IIS azovoraIIS

G6001101 12

RamiibacICI‘ IaIaOIIiIICnSiS

 

G6C101103

G6C1011041
_:_:_-— Z00g/0Ca ramigera
G6C10n084

G600n033

 

 

 

 

 

 

061131

     
   
  
 
 

G6C10nc63
(3600110104
Alishewanellafetalis
Aeromonas Sp.
Aeromonas Sp.
G6C10n0106
G6C10n625
Cellvibrio Sp.
G6C10n034

 

 

Escherichia coii

0.10

 

Figure 4.8. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. gambiae habitat

(G6). The dendogram was generated by Neighbor Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents 10‘Vo
. sequence divergence. Number of sequences within each group are in parenthesis.
\

 

 

 

# . . . ”..., “..-!

r——j7 <17 (1 1 1
F/avm’mclm‘iiirII .11).
(170011036
(170011013
(17(‘1011067

(17 ( 7 1

G7(‘101106(1

G‘7C‘101106‘)

Pedobacter heparimIIIz

(17(8)

 

  
   
  
 
  
 
  
 
 

 

 

 

 

Riemerella ahatipCiner
(17(‘10110103

 

(37(161

 
 

(170011048
Actinomycete

L—C707191

(3700110124
Rhodobacter Sp.
Rhodobacter capSIIlIIIIIs
Rhizobium Sp.

1 .. (17(‘1011002
1 Novosphingobium ammaticivoram
(370011033

(170011076
(170011097

1791

 

 

 

 

 

   
 
 
  
  
   
   
  
  
   

(17 (34)

(1700110113

Polynucleobacter necessarius
(17 ( 7)

(1700110104

(17001103

(17(‘1011031

(17(‘1011072

(37(‘10110110
Rubrivivax gelatinosus

Xenophilus azavorans
Ramlibacier Iataauinensis

Acidovorax Sp.

(17(‘1011079

(1700110121

Herbaspirillum Sp.

(17(‘10110911

(17001101 (1

I (170011075

(17(‘1011077

_—_I_ (1700110111

(37(‘10110133

Cellvibrio sp.
l Cellvibrio sp.
(37C 1011046

I _ 07010.1,ch

Escherichia coli

 

 

 

 

 

 

 

 

 

0.10

 

Figure 4.9. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. gambiae habitat
(G7). The dendogram was generated by Neighbor Joining Method uaing a filter of 200

bases and Jukes cantor correction using ARB software. The scale bar represents 10%
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

 

 
  
  
  
  
  
 
 
  
   
  
  

G13 (81
010110124

G13C10n0103

C113C1 11064
Rhodobacter Sp.
G1317)
113C1011C‘162
G-1 C1C11074
G13C101105
C113C101107

G 3C1011051
C11 3C 1011C 71

 

 

C111 (7)
G13C‘1C111015

 

991
J_

G13C101

 

....
....O

  
       
 
  
 
 
   
 
 
 
    
  
 

G 3C10nC‘1—18
G13C1011C‘41
G13C1011c )3
N0v0S Sphinoobium aiomaticivorans
C113C 0110 7
(313C10n031
G13C‘10110135
G13C1011C126
G13C101’1C117
G13C‘1C111011()
G13C‘1011Cl()8 _
G13C10n050
G C10110122
-‘ 13(‘1011085
, 3C 10110106
G1314)

G13C1011C‘C-7 -
jl 1( 1011c - 5

 

 

3C 101102 8
)

 

1C71111C1 101
~ClC1nC‘90

O-

7113(‘1011081 s

(,1 1 J ( J 1

Polynucleobacter IICCCSSCIriIIS
C1 1- 7 1 5 1

AquabacteI I'um Sp.

uabacrerium parvum

 

 

 

0%

O1]
C1171C1011C‘l29

1 13(‘ 10110
_ C313C‘1011C‘44
C113C’101101 7
(113C1011C‘(1U

(1 13(11111001
(1 13C1011C| ()7
(313(1011C1‘)
(113(‘1011C161

(3131101

 

 

 

 

 

 

 
 
  
  

011C181)
141

Actmomg cete
13C10110 12

 

 

Anabaena circinalis

C313C10110121
__L—l7 (313 (3'1
Alishewanella feta/1's

eromonas Sp.

eromorzas S.p

G13C10n0154

G 13C115 6

Cheryseomonas luteola
(313C1011076

  
  
 
 
 
  
 
 

 

G l 3C10nel 5 3
Klebsiella pneumoniae
Escherichia co 1

0.10

Figure 4.10. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. gambiae habitat
(G13). The dendogram was generated by Neighbor Joining Method uaing a filter of 200

bases and Jukes cantor correction using ARB software. The scale bar represents 10%
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

991

 

    
  
 

F 5)
H Idrogeno ha 70 avg
‘ 15.1% ’30

F10010110131
F1010n0113

  
 

F4111)

 
  
  
 
 
  

Acidovorax S .
F 10101109 3
Acidovorax Sp.

F1 131
F101011053
Flcloncl 19
F1010110125

Rhodoferax am‘aI CfiCI/S
1010110 16

  
 
  
 
  
  
 
  
 
 

 

F‘ 1 01011077

F 101011038

Lepfoth [X (1’1 soap/701 a

F101011051)

' F10101105l

F101011085

F 10101109

Aquabacrer iI/m Sp
1‘ 10 011058

F10101101110

F101011069

'— F101011099

BFI/rkholdeI [a planfai 1i

BI/I kholderia Sp

F113)

I F1010110121
‘——— Nitrospira Sp.

   
  
 
  
  
 
  

 

F101011047
Bdellovibrio baclei iovorI/S

C 0111: 3
1110101101

Fz’qvobacferimn £I(/ll£Ifi/C’
‘ F 10101101 11
11101011012/

191010110130
1 101011084
Flavobaclei iIIII 112w c/II (Ip/Ii/I/m
F101011C-11

 

1" 101011014
(‘1 ’..‘ .h ’ 1010/111171I/Iu/pnp/Ii/I/III
Bacillus ceieuS ‘
F 10101108
F1010110123
F1010110124
BCIJCI INC/116! INC/[CG
F1010110111
L:- ~ 11'...“ l ‘ )(‘fll‘l/ITI

F101011011)5 77°
11101011094
’ 31 ’fel‘Sphael‘OideS
F101011010
7' F118)

Anabaena Sp.
CyanobacteI'II/m Smmerl

 

 

 

F101011015

  
 
  

F 101011040
Acinetobactel calcoacetlcus
F1010110103

F 101011068

__L—7Fl (31
PSeIIdomonas Sp.

 

 

 

Alishewanellafetalis

F1 (3)
Aeromonas salmonicida subsp smithia

F 1 clone]
Fl 01011021

 
 
 
  

 

 

E nterobacter Sp
F1001n0117
Escherichia coli
F1010n013

0.10

Figure 4.11. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. funestus habitat
(F1). The dendogram was generated by Neighbour Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents 100/o
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

 

 

F2 (51)

F2 (7)
unidentified cyanobacterium

——-—|7 F2 (9)

 
 
 
 
 
   
 
 

 

Prochlorococcz/S Sp.
Synechococcus Sp.
Synechococcus elongatus
Anabaena Sp.
Geitlerinema PC C 71 05
.S’pirulina Sp.

F2 ( 7)
F

  
 
 
 
  

Aquabacterimn Sp.
Bur/(holderia Sp.
F2010nel30

F2010ne78
Polynucleobacter necessariz/S

‘ Azovibrio reStricn/S
-———— F2clone83

F2 (6)

Cellvibrio Sp.
132510116144
Acinetobacter Sp.
Acinetobacter Sp.

LSI

 

 
  
  
  

  
  
  
   
  
 
  
 

F2 (4)

Rhodobacter capsu/atus
Rhodobacter Sphaeroides
Rhodobacter Sp.
Pai‘acoccus Sp.

132010116159

 

FZCloneS?

Rhizobium leguminosarum
F2clone79

Methylosimls Sp.

n F2010ne38
F 2clone48
F2clonel24

 

 

 
  
 
 
 
  

2(3)

F2c10ne46

F lavobaclcrium calunmare
132010116166

Fflclonel 18

F2c1011092

FZClonel 11
F2clone97
F2010116120
Verrucomicrobium Sp.
Clostridium Sp.

F2 (5)

Actinomycetaceae

   
 
  

 

 

 

 

Actinomycere

 

l —— Alteromonas Sp.
Vibrio vulnificus

F2 (33)

 

Alishewanellafetalis
FZClone127
Escherichia coli
F2010nel45
Plesiomonas Shigelloides

0.10

 

Figure 4.12. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. funestus habitat
(F2). The dendogram was generated by Neighbour Joining Method uaing a filter of 200
bases and J ukes cantor correction using ARB software. The scale bar represents 10%
sequence divergence. Number of sequences within each group are in parenthesis.

 

F3 (12)

F3 Clonel41
Oscillatorfa Sp.

F 3Clon66

F 3Clonel43

F3 Clone47

 
 
    
 
  
  

 

F3 (8)

 

AL 1 1.1/um} L can)“
AL ill/1U!!!) e um en
F3C10n6118
Rhodobacter Sp.

F3 Clone 1 30
SphingomonaS chlorophenolica

 

 

 

F3 Clone 1 01

 
  
     
  
 
 
 

   

 

 

  
 
 

 

 

 

 

 

 

 

 

 

 

F- F3Clonel 33
,_. F3Clone62
$ F[(7w)/)acre/‘iz//21 CO/UHHILII'C
F3C10nel O7
F3Clone97
F3ClonelO8
F 3Clonel 15
F 3Clone39
‘ F3C10ne 1 38
F/eclobaci/lz/S Ina/01‘
{ 10ne24
‘ F3C10n623
‘ F3Clone64
l F3Clonel34
E .___|7 F3Clone31
I Helicobacter Sp.
F F3 (4)
F F 3Clonel 11

Lepml/N'ix discnp/mm
F3 Clone68
Aquabacterium parvum

 

 
 
 
 

F3 (3)

   
 
  
 
 
  
  
  

F3C10n6105
F 3Clon65 7
ROSGCIIC/ES Sp.

F3 (6)

Acido vorax Sp.
F 3Clone48
F3 Clonel 19
F3C10n6123
F3C10ne 1 3 l

  
 
  
 
  

F3 CloneESl
Methylophilus leiSinge/‘ii
F3Clone32

F3Clone3
F3Clone73

 

 

 

P'" F3ClonelS
Xanthomonas Sp.

F3 (11)

    

F3Clon625
F3Clone43
Cellvibrio Sp.

____..: F3Clonel6
F3Clone34

F3 (40)

 

 

Alishewanellafetalis
F3ClonelO3
Escherichia coli

 

 

0.10

 

Figure 4.13. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. funestus habitat
(F3). The dendogram was generated by Neighbour Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents 10%
sequence divergence. Number of sequences within each group are in parenthesis.

 

 

691

 

 

F4 (4)

Bur/(I10Ide1‘ia
CloneS4
F4C10ne73
J Achlnnmhm‘m 5p
'-- F4Clon627
F4C10ne8

RosealeIeS
F4 (4)

F4C10n62
Hvdrogenophagaflava

 
   
  
  
  
 
  
 

"rj
4:.

F4 (5)

Polymcheobacter 11eceSSa1‘1'11S

—l‘_l:_7' F418)
1 Zoogloea 1'a1111'gera

F4 (4)
Cellvibrio
F4Clone82

F4Clonel7
__1_—'r— F4Clone71
Acinetobacter

-———Xa11I/10111011aS

F 4:7'm)

F4Clone62
—_‘|—_ES[)_III?§0C11100110SS Ierrae
AZOS 111111 1111 (11110201161156
ev0S1a11'b0fIa11111a

F4C10ne90
F4CIOH652

F4C10n653
F Clone

F4Clon677
_r——'I— F4Clone81
F4Clone22

l—E F4Cl one35
Nevskia 1111110511
'— F4Cl onel2

 

 

 

 

 

   
 
  

   

 

 

  
    

 

 

  
  

F4 (6)

Prochlorococcus

 

:7 F4 (6)
F4Clone76
F4Clone96
Actinonn‘cere
F4Clon629
F4Clone31
F4Clone47
F4C10ne78
F Iavobacterium 60/1111111111‘6
F4Clone48
F4 (3)
Alishewanel Ila fetal 1S
Esche1ich1a (3011'

0.10

 

 

 

Figure 4.14. Phylogenetic analysis of 16S rDNA gene sequences obtained from An.
funestus (F4) habitat. The dendogram was generated by Neighbour Joining Method
uaing a filter of 265 bases and Jukes cantor correction using ARB software. The scale
bar represents 10% sequence divergence. Number of sequences within each group are
in parenthesis.

 

 

 

..u-u...-4 ~.v.~. \.s\~ \Assq s. \ \\\\\.\q|~ \~.x\.\\\.\.flqu.~\ ‘\./
.12 N: e. C. I: It; :2 22:22:frithxx.

as? _ DEC—tum;

 

091

 

 

 

   
 
  
   

F5 Clone 144
Sphingomonas a10111atzc1v011c111s
Nova 5 phi/11 obium a110111cztici\1011a17S
F5C1011e1
F 5 C10116126
F5C1011612 8
F5 C1011682
F5 Cl one 1 04
F5 Clonegfl/ _ 1
1111 00111011 as 6111116

p F5C1011e123

l1 5C101169

 

 

  
  
  

Nada/[171151 Sp
F5 (3)

F5Clonel36
F5Clonel3'1 1

 

F5C1011€58
RhodobacteI Sp.
F5C1011Cl45 1
F5Clonel34
F5C101IC72
F5C10ne77

F5Clo11cl42

Rhizobium h11a1.11/811Se
F5ClonC.(3
F5Clonél6 v .
FJC 1011C l ‘)
F 5C 1011659
1:](l1’()/)LIC 1121111111 ( (1/1111111111‘ c
I 5C1011C8‘
F5C1011C76
115(31011071
F5C‘1011C130

F 5C101_1C1()()

F SC
I 5C 1011C l .31
11‘5C‘1011C130
Prevotella infer/11111711
Prevolc’l/u Sp.

C [0.S1‘1'id1'1/111 Sp.

 
 
 
  
 
   
 
 
  

 

 

 

 

 

 

 

  
 
 
 

dado/1111 [UH/111 U 13
F5C 1(31 311013
1 5C 1011C l4

 
 

   
 
 
  
  
 

F5C1011047
NGVS/(FZLI I‘CIIHOSCI
I 5C 1011021
135 (3)
F‘5Clone 57
Lepz‘olhrix OIISC'Op/ZOI (1
F5C1011688
4F105Clo nelO7
F5Clonel4

4qilabacrm 121m par 1111117

5C101‘
13.5 C 10111-396
F5Clonc40

F 5Clo
Rhoda3 911161.11 61111181115111.3'

   
 
  
  
  
   
  
 

   

F 5C101165
F5C10ne30

l1 5C101165 3

F5C10ne31

F 5C10nc47

 

Dee/11.7111 .... Sf).
F5C10ne361;
X . .-.

 

 

  

F5 (1 1)
Acinetobacter Sp.

 

 

F5 (8)

Cellvib11io4sp.
F5Clone74
F5C10116105
F5C10116106

F5 (4)

 
 
   
 
 

l__E _; F5 (27)
mAliShevzpnellafetaliS

 

:Escherichiaw 0011'

0.10

F5C10nelZl4n

Figure 4.15. Phylogenetic analysis of 16S rDNA gene sequences obtained from An. funestus habitat
(F5). The dendogram was generated by Neighbour Joining Method uaing a filter of 200
bases and Jukes cantor correction using ARB software. The scale bar represents 10%
sequence divergence. Number of sequences within each group are in parenthesis.

 

[I : 132 (51) + unclassified C 111111011chva
(3 1

G 3 5 9

Shae—[011611111 605111121112

S/(elefonema COSZCIIZIIII
11‘37

  
   

 

 

I91

 

 

4_37
Odolue/la 3111611115 P/u.
C1340
(13:17
uncultured 6111151111016

Spirogyra maxima

OSCi/lmoria SCIIIC’ICI

F3_143

 

    
 

TrichodeSmimu
Planktothrix 111021g€0111
F3 141

 

 

 

F 3_6
F5 123
15‘9
Anabaena 5p.
(3 l 3 _l 2 l
L: Cryptomonas phi Pia.
Pyrenomonas sal 111a Pia.

 

F3_47
I _____.—————--"""’ 0131131

Cosmarium botrytis
F5_1 36

 

 

 

Escherichia coli

 

 

Figure 4.16. Phylogenetic analysis of 16S rDNA gene sequences from the unclassfied group obtained
from An. gambiae (G1, G3, G6, G7 and G13) and An. funestus (F1, F2, F3, F4 and F5)
(habitat. The dendogram was generated by Neighbour Joining Method uaing a filter of
245 bases and Jukes cantor correction using ARB software. The scale bar represents
10% sequence divergence. Number of sequences within each group are in parenthesis.

 

APPENDIX B

Record of Deposition of Voucher Specimens

162

 

I;

 

Appendix 8
Record of Deposition of Voucher Specimens’
The specimens listed on the following sheet(s) have been deposited in the named museum(s) as
samples of those species or other taxa, which were used in this research. Voucher recognition
labels bearing the Voucher No. have been attached or included in fluid-preserved specimens.
Voucher No.: 2006-01
Title of thesis or dissertation (or other research projects):

BACTERIAL DIVERSITY AND NUTRITIONAL SIGNIFICANCE OF THE SURFACE
MICROLAYER IN AN OPHELES GAMBIAE (DIPTERA: CULICIDAE) LARVAL HABITATS

Museum(s) where deposited and abbreviations for table on following sheets:

Entomology Museum, Michigan State University (MSU)

Other Museums:

Investigators Name(s) (typed)

Shahnaz Rahim Maknoiia

Date 03/23/06

*Referencez. Yoshimoto, C. M. 1978. Voucher Specimens for Entomology in North America.
Bull. Entomol. Soc. Amer. 24: 141 -42.

Deposit as follows:
Original: Include as Appendix B in ribbon copy of thesis or dissertation.

Copies: Include as Appendix B in copies of thesis or dissertation.
Museum(s) files.
Research project files.

This form is available from and the Voucher No. is assigned by the Curator, Michigan State
University Entomology Museum.

163

 

 

 

Voucher Specimen Data

Pages

___of

Page

 

 

 

 

 

ooonQm 2mm.

 

£8me 529”. 3:595

 

.8 952.6on 8%: 93¢ 05 uozmoom

 

Forooom .oz ..m:o:o>

$595 30562 32339:.
$338: a £35. .9520? $3

 

.__8 9.8me .w
8.5.2-8

393 2:53: 23.3 93
39.50 :92 .. 0523 3mm.

164

 

 

 

 

 

 

 

 

 

0.. or cm do 895:. 59:25. 3.553 mochoz<
m m 8 0+ m e 9.8% cm
W. m m a P5 m m m a s a u . c v coxfl 550 5 36on
u m w. .m m m nuv v. m % new: .6 3628 9.958 m .8 Emu .33
M w d O A A P N L E

 

 

 

no 52532