University of California Riverside

UNIVERSITY OF CALIFORNIA RIVERSIDE

Selective Association Between the Free-Living Nematode Acrobeloides maximus and Soil Bacteria

A Thesis submitted in partial satisfaction of the requirements for the degree of

Master of Science

Genetics, Genomics and Bioinformatics

Sammy Farid Sedky

June 2013

Thesis Committee: Dr. Paul De Ley, Chairperson Dr. Paul Orwin Dr. David Crowley

The Thesis of Sammy Farid Sedky is approved:

Committee Chairperson

University of California, Riverside

ACKNOWLEDGMENTS

This master thesis would not have been possible without the help of several individuals who contributed their time, assistance, and expertise to the completion of this work:

• To my advisor, Dr. Paul De Ley: Thank you for giving me a lab I could call home, where I was welcome and where I could grow as a researcher and scholar. Your assistance in navigating the labyrinth of challenges involved in the writing and completion of this document has been invaluable. Thank you for your guidance and support.

• To Dr. Paul Orwin: I will always be grateful for the kind tutelage you provided during your sabbatical stay at UCR. This study could not have been completed without your vigilance.

• To Dr. David Crowley: Thank you for allowing us to use Science Lab I 308 to perform most of the wet lab work and for your assistance as a committee member in the review and editing of this thesis.

• To my colleagues, Dr. JP Baquiran and Brian Thater: It was a blast working with you both. Thanks for the laughs and good times.

UCR DEDICATIONS

• To my friend and protégé, Rosalynn Duong: I could not have chosen a better undergraduate assistant. Thank you for all your help, the late hours you put in taking care of the nematodes and bacterial cultures, your assistance with molecular work, and for most of all sticking with me on this journey.

• To Dr. Irma Tandingan De Ley: Thank you for always being generous with your time and for your help and technical expertise in the lab.

• To Dr. Cecilia Ritzinger: Thanks for showing me that sometimes we just need to step back and take a coffee break! Obrigado!

• To the wonderful GGB graduate students: Amanda Hollowell, Yifan Lii,

Stephanie Coffman, Keira Lucas, Hagop Atamian, Patrick Schacht,

Meenakshi Kagda, Tusar Saha, Divya Sain, Ritu Chaudhary, Venkatesh

Sivanandam: I wish you all nothing but the best in your future endeavors! It’s

been a pleasure being your social committee chair these last two years.

• To Dr. Michael Fugate, Dr. John Oross, Dr. Laura Abbot, Dr. Brian

Brady, Dr. Gregory Filatov, Devon Duron Ehnes-Zepeda: Being a TA was one

of the highlights of my graduate career. It was a pleasure to work alongside you

while developing my pedagogical skills.

• To Tiago Pereira and Ran Sun: I always enjoyed your visits to the lab.

Thanks for taking time out of your busy schedules to chat with me. All the best!

DEDICATIONS TO FAMILY AND FRIENDS

• To my loving parents, Soha and Mohamed: Thank you for your endless

encouragement, love, and support. This one’s for you!

• To my aunt and uncle, Siham and Fathy: Thanks for always believing in me and for your love, encouragement, and support.

• To my sister, Sally: You are never far from my thoughts. Your strength and

courage has always been a source of inspiration.

• To the friends that cheered me on, encouraged me, and believed in me:

Jorge Flores, Ryan Lee, Johnny Tang, Arif Mosavi, Samuel Koo, Deep

Shah, Anand Panchal, Mrunal Patel, Jason Pico, Edgar Alvarado, David

Benitez, Max Flores, Jasmine Flores, Dynasty Carfangnia, Jose Hernandez,

Stephen Clawson: Thank you !!!

TABLE OF CONTENTS

GENERAL INTRODUCTION IMPORTANCE OF NEMATODE- BACTERIA SYMBIOSIS 1 RESEARCH RECENT STUDIES OF NEMATODE-BACTERIAL ASSOCIATIONS 4 IN SOIL MICROBIAL COMMUNITIES Radopholus similis and Wolbachia 4 Entomopathogenic nematodes and γ-Proteobacteria 5 Steinernema carpocapsae and Xenorhabdus 6 nematophila Heterorhabditis bacteriophora and Photorhabdus 6 luminescens Bacillus thuringiensis Cry5B crystal proteins and free living 7 nematodes Caenorhabditis elegans 7 Pristionchus pacificus 8 SYNOPSIS OF THIS THESIS 8 REFERENCES 10

CHAPTER ONE: Survey of the microbiota associated with A. maximus in soil microcosm ABSTRACT 15 INTRODUCTION 16 MATERIALS AND METHODS 18 Nematode cultivation and harvest 18 Rhizosphere soil collection and exposure 19 Long-term culture of A. maximus 20 Isolation of associated bacteria 20 DNA extraction and PCR analysis 21 16S rRNA gene library construction, sequencing and analysis 22 Preparation for Fluorescence In Situ Hybridization (FISH) 23 FISH Probes 24 FISH- Hybridization, Mounting, Microscopy 24 RESULTS 25 16S rDNA analysis of extracts 25 Co-cultures 26 Isolation and culture 26 Fluorescence In Situ Hybridization 27 DISCUSSION 27 REFERENCES 31 FIGURES AND TABLES 34

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CHAPTER TWO: Phylogenetic Analysis of Bacterial Genera Detected At Significant Levels Within The Microbiota Associated With A. maximus ABSTRACT 41 INTRODUCTION 42 MATERIALS AND METHODS 43 Data Preparation 43 Multiple sequence alignments: T-Coffee, MAFFT, Kalign, and 44 WebPRANK Phylogenetic inference tools: Maximum Likelihood, PhyML, 45 Mr. Bayes LALIGN 45 RESULTS 46 Ochrobactrum sp. clones 46 Chitinophaga sp. clones 46 Pedobacter sp. clones 47 LALIGN plots 47 DISCUSSION 48 Comparison of multiple sequence alignment algorithms 48 Comparison of phylogenetic reconstruction methods 49 Ochrobactrum sp. clones 50 Chitinophaga sp. clones 51 Pedobacter sp. clones 53 LALIGN analysis 54 Requirements for describing a new bacterial species 55 Genotypic approaches 55 Phenotypic approaches 56 Chemotaxonomic approaches 56 FINAL THOUGHTS 57 REFERENCES 58 FIGURES AND TABLES 62

CONCLUDING COMMENTS BACTERIAL COMMUNITY IDENTIFICATION: 16S rRNA GENE 88 MARKER SURVEY VERSUS SHOTGUN METAGENOMICS 16S rRNA Gene Marker Surveys 88 Shotgun Metagenomics 90 PCR-amplified 16S rRNA and Pyrosequencing 91 Sequencing Technologies and Future Considerations 92 REFERENCES 95

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LIST OF FIGURES

CHAPTER ONE

Figure 1. Soil exposed A. maximus populations were found to 34 be associated with a consistent bacterial population Figure 2A. Ochrobactrum is shown to be the predominate 35 alpha-proteobacteria within A. maximus Figure 2B. Pedobacter is shown to be the primary Spingobacteria 36 within A. maximus, along with a consistent representation of Chitinophaga at a lower level Figure 3A-C. Long term culture experiments appear to show stability 37 at the phylum level (A) but reveal shifts in population as the worm population ages at the genus level (B,C) Figure 4A-F. Fluorescence in situ hybridization was used to identify 38 the presence of both Ochrobactrum sp. (green) and Pedobacter sp. (red) in the gut of formaldehyde fixed A. maximus after recovery from soil microcosm

CHAPTER TWO

Figure 1. Ochrobactrum: T-Coffee-PhyML Phylogram 67-68 Figure 2. Ochrobactrum: MAFFT-ML Phylogram 69-70 Figure 3. Ochrobactrum: Kalign-MrBayes Phylogram 71-72 Figure 4. Chitinophaga: T-Coffee-ML Phylogram 73-74 Figure 5. Chitinophaga: MAFFT-ML Phylogram 75-76 Figure 6. Chitinophaga: Kalign-ML Phylogram 77-78 Figure 7. Pedobacter : T-Coffee-PhyML Phylogram 79-80 Figure 8. Pedobacter : MAFFT-PhyML Phylogram 81-82 Figure 9. Pedobacter : Kalign-PhyML Phylogram 83-84 Figure 10A. LALIGN Plot: Ochrobactrum sp. clone Acro231 85 compared against the consensus sequence of Ochrobactrum clones Figure 10B. LALIGN Plot: Chitinophaga sp. clone Acro210 compared 86 against the consensus sequence of Chitinophaga clones Figure 10C. LALIGN Plot: of Pedobacter sp. clone Acro279 compared 87 against the consensus sequence of Pedobacter clones

LIST OF TABLES

CHAPTER ONE

Table 1. List Of Sequences Deposited Into GenBank 39

Table 2. Shannon-Wiener Population Statistics for Microcosm 39 Samples

Table 3. Identified Isolates from association from A. maximus 40 microcosm and long term culture

CHAPTER TWO

Table 1A. Ochrobactrum Sequences Used In Phylogenetic 62 Analyses

Table 1B. Pedobacter Sequences Used In Phylogenetic Analyses 62

Table 1C. Chitinophaga Sequences Used In Phylogenetic Analyses 63

Table 2. Pedobacter Settings For Phylogenetic Trees 64

Table 3. Ochrobactrum Settings For Phylogenetic Trees 65

Table 4. Chitinophaga Settings For Phylogenetic Trees 66

GENERAL INTRODUCTION

“The soils of our yards, gardens, and fields swarm with thousands of kinds of minute animals and plants of which we know little or nothing. We depend on the soil for our very existence, and it may seem that this fact should have caused us long ago to make ourselves thoroughly acquainted with it and all its inhabitants; yet the truth is otherwise. Here beneath our very feet are microbes, protozoa, fungi, and many other kinds of small organisms, thousands of species, of which we know hardly the first thing beyond the mere fact of their existence. . . . Inhabiting the soil in myriads, hidden behind this veil of ignorance, there is a group of organisms known as nematodes.” (6) Nathan A. Cobb, 1914

IMPORTANCE OF NEMATODE-BACTERIA SYMBIOSIS RESEARCH

Nematodes are among the most abundant metazoans on Earth, inhabiting terrestrial, marine, and freshwater ecosystems (7). They can be found in mountains, grasslands, forests, rivers, lakes, oceans and even in inhospitable environments such as arid deserts and icy tundras. Some parasitize animals and plants, making their homes within these unsuspecting organisms (25, 28, 44).

Nematodes have evolved a myriad of fascinating survival strategies, allowing them to adapt to virtually all environments to increase their chances of survival, especially under adverse conditions. For example, nematodes temporarily enter different quiescent states in response to environmental stresses, such as anhydrobiosis in response to desiccation and cryobiosis in response to low temperatures (27). Furthermore they also utilize several reproductive strategies ranging from dioecous to protandric hermaphroditism to parthenogenesis (9).

Some species are easily culturable bacterial feeders and are therefore highly suitable for eukaryotic-prokaryotic association studies.

Bacteria are the most abundant and ubiquitous of all organisms found in nature. Together with nematodes they make up two of the most prominent groups of organisms in the biosphere. Bacteria and nematodes have long interacted and evolved alongside one another, resulting in the creation of many different types of associations, along a continuum ranging from mutualistic and beneficial to antagonistic and detrimental through for example competition, pathogenesis, and parasitism (29). There are many free-living nematode species which obtain their energy entirely or partially from the consumption of bacteria (54). These nematodes can be non-selective in the bacteria they ingest, such as with laboratory grown Caenorhabditis elegans, or they can be particular in the bacteria they choose, allowing them to differentiate between bacteria with different nutritional qualities and to select for specific genera or species of bacteria to consume or cultivate (18, 19, 31, 48). This selectivity allows nematodes to avoid dangerous pathogens while picking up bacteria that promote development, fecundity, and survival (20, 35, 41). Investigating nematode- bacteria symbioses can pave the way for comparative analyses with other eukaryotic host-bacteria associations and will enhance our understanding in the areas of developmental and evolutionary biology while also leading to improvements in agriculture, ecology, and health. For example, it will allow for new insights into the genes implicated in the evolution of symbiosis and those

involved in horizontal transfer between bacteria and eukaryotes (11, 12, 53).

Furthermore, it will aid in elucidating the metabolic pathways and chemical

interactions between symbiont and host and can thus lead to the creation of new

drug targets for invasive pathogens (32, 41).

Genetic and molecular tools are being applied to provide new insights into

nematode-bacterial interactions. Metagenomics, the direct sequencing of

environmental samples, has allowed for simultaneous sequencing of symbionts

and their hosts (24). Utilizing the power of the latest genome deep sequencing

platforms, these studies can now be performed on a genome-wide level as

opposed to more traditional gene-centric approaches focusing for example on

16S ribosomal RNA (16S rRNA) based molecular methods. Such high-

throughput molecular tools allow for comprehensive taxonomic and phylogenetic

classification of the diverse bacteria nematodes associate with in soil

communities (45, 46).

The remainder of this discussion will focus on some particularly interesting recent studies of soil nematode-bacterial associations in which the participating organisms engage in specific and persistent relationships. Understanding the mechanisms involved in nematode-bacterial recognition may lead to functional

studies and practical applications through genetic manipulation of these

interactions (38).

RECENT STUDIES OF NEMATODE-BACTERIAL ASSOCIATIONS IN SOIL

MICROBIAL COMMUNITIES

Radopholus similis and Wolbachia

Wolbachia are a group of gram negative intracellular α-Proteobacteria in the order Rickettsiales, the group from which mitochondria and similar organelles are thought to have originated under the endosymbiotic theory. The latter postulates symbioses between separate single-celled organisms leading to the formation of organelles within eukaryotic organisms (50). In 2009, Haegeman et al. confirmed the presence of a Wolbachia endosymbiont in Radopholus similis, a burrowing plant-parasitic nematode which attacks the roots of banana plants

(21). This is the first and so far only reported occurrence of Wolbachia being found in a non-zooparasitic soil-dwelling nematode.

The role Wolbachia plays in R. similis is not yet known. In filarial nematodes however, Wolbachia is known to be an obligatory mutualist; eradication of Wolbachia from the host via the antibiotic tetracycline has been shown to cause adverse effects such as abnormal growth, eventually leading to death of the nematodes (5, 14, 15, 22, 23). Conversely in arthropods, Wolbachia takes on the role of a reproductive parasite, imparting a selective advantage to infected females via feminization, parthenogenesis, male killing, or cytoplasmic incompatibility, all leading to a decrease of the uninfected portion of the host population while the endosymbionts are vertically transmitted to the offspring of

the infected portion (2, 47, 52). Due to the high infection rate of Wolbachia in R.

similis, it is believed the endosymbiont also provides the nematode with essential

metabolites and is necessary for survival (21), although no studies have as yet

been published documenting the effects on Radopholus of targeted Wolbachia

eradication.

Finding Wolbachia inside R. similis raises many unanswered questions. Do

other plant-parasitic nematodes carry Wolbachia endosymbionts? Can it be

found in nematode species or genera that are easy to culture and maintain in a

laboratory setting? What experiments can be performed to determine the role the

endosymbiont plays inside Radopholus similis?

Entomopathogenic nematodes and γ-Proteobacteria

Entomopathogenic nematodes are soil-dwelling insect parasites that work in concert with insect-pathogenic bacteria to cause rapid insect death, usually within 48 hours of infection (10). Model systems are being developed in which entomopathogenic nematodes and their bacterial symbionts are used as environmentally-friendly biological control agents against insect pests, providing an alternative to the spraying of ecologically harmful chemical pesticides into the environment (8, 4).

Steinernema carpocapsae and Xenorhabdus nematophila

Xenorhabdus are gram-negative γ-Proteobacteria in the order

Enterobacteriales (49). The bacterium Xenorhabdus nematophila is a mutualist of

the entomopathogenic nematode Steinernema carpocapsae and a pathogen of

many types of insects including weevils, caterpillars, moths, beetles, fleas, flies,

crickets, and ants (19).

S. carpocapsae ambush and infect highly mobile insects near the soil

surface (3). Once inside, the nematodes ingest the insect’s body liquid and release X. nematophila into the hemocoel, where together they suppress the insect’s immune system, leading to its eventual death (40). X. nematophila will

continue to grow and multiply in the insect cadaver, converting the cadaver into

substrates capable of supporting S. carpocapsae reproduction; the resulting new nematode juveniles will ingest and store the endosymbiont in their digestive tract, allowing the cycle to repeat (19).

Heterorhabditis bacteriophora and Photorhabdus luminescens

Like Xenorhabdus, Photorhabdus are also gram-negative γ-Proteobacteria

in the order Enterobacteriales (1, 16, 34). The bacterium Photorhabdus

luminescens is an endosymbiont of the entomopathogenic nematode

Heterorhabditis bacteriophora and a pathogen of many insect species (33).

H. bacteriophora typically infects insects that reside deep within the soil

(3). The cycle and mechanism of infection is similar to that utilized by S.

carpocapsae and X. nematophila; infected H. bacteriophora invade unsuspecting

insects and release P. luminescens within the insect, causing its death within a

few days (17).

Bacillus thuringiensis Cry5B crystal proteins and free living nematodes

Bacillus thuringiensis (Bt) is a gram positive soil-dwelling bacterium in the

order Bacillales known for producing crystal toxin proteins which are used as a

natural biological pesticide against insects in crop fields (39). Recent studies

have been undertaken to learn about the roles which these bacteria and their

crystal proteins play in the presence of nematodes and in soil microbial

communities (36, 37, 51). The Bt crystal protein, Cry5B has been shown to be toxic towards Caenorhabditis elegans and Pristionchus pacificus (37). Cry5B along with other nematicidal Bt crystal proteins have the potential to be utilized as biological control agents against nematodes which parasitize plants and

animals (26, 51).

Caenorhabditis elegans

Caenorhabditis elegans, is a free-living rhabditid commonly used as a

model organism for genetics research because of its ease to cultivate and

maintain under laboratory conditions. Cry5B has been shown to have adverse

effects on C. elegans; when ingested the Bt toxin causes extensive damage to

the gut, a decrease in progeny production, and leads to death (26).

Pristionchus pacificus

Pristionchus pacificus, a free-living diplogastrid and a necromenic associate of beetles is emerging alongside the popular model C. elegans to

become a commonly used tool in studying evolutionary biology, ecology, and

population genetics (42). Chemosensory studies have shown that P. pacificus

avoids Bt and other insect pathogenic bacteria (36, 37). P. pacificus is

susceptible to the Bt Cry5B crystal protein; when force fed E. coli expressing the

Bt toxin, the nematodes were unable to develop past the L1 larvae stage and

their intestinal tract exhibited signs of thinning, constriction, and degeneration

(51).

SYNOPSIS OF THIS THESIS

In the following chapters we will discuss our work investigating the soil

microbial community associated with the nematode Acrobeloides maximus, a

nonpathogenic bacterial-feeding cephalobid. Cephalobids occur in almost all

soils and are phylogenetically much closer to plant parasitic nematodes than the

model nematodes C. elegans and P. pacificus (7). Moreover, cephalobids have

developed ecological adaptations to desiccation and have slower generation

times at room temperature when compared with C. elegans and P. pacificus (30).

A sealed plate of A. maximus can last in culture well over six months whereas

faster growing species like C. elegans and P. pacificus require subculturing every

few weeks to avoid population collapse (13, 43). These differences make

8 cephalobids like A. maximus a much more suitable model for interaction studies over C. elegans and P. pacificus. Chapter one discusses the results of our 16S rRNA gene survey of soil microbiota associated with A. maximus while chapter two takes a closer phylogenetic look at the bacterial genera detected at significant levels within the nematode.

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CHAPTER ONE:

Survey of the microbiota associated with A. maximus in soil microcosm

ABSTRACT

Microbial populations observed in the microbiome of Acrobeloides maximus consistently comprised a set of different microorganisms compared to the microbiome of Caenorhabditis elegans or the metagenome of the soil sample to which nematodes were exposed. Using 16S rRNA gene amplification, the bacterial genera Ochrobactrum, Pedobacter, and Chitinophaga were identified at significant levels within the A. maximus communities only and were found to be consistently present during each of the three sampling periods over six months.

We were able to isolate and grow colonies of Ochrobactrum and Pedobacter from the microbiome of the nematode on Miller’s Lysogeny Broth-Miller (LB) and

Yeast Extract (YE) agar plates. We were unable to isolate the associate from the genus Chitinophaga. The results obtained suggest a possible specific and persistent association between A. maximus and the two bacterial isolates we were able to culture.

Using fluorescence in situ hybridization (FISH) with probes specific for

Ochrobactrum and Pedobacter, we were able to stain these bacterial cells and show that they reside within the intestinal tract of A. maximus. It is possible that these gut bacteria may be mutualistic by providing A. maximus with essential metabolites, by aiding the nematode in the digestion of other bacteria, or by providing it with protection against pathogens.

INTRODUCTION

Microbes are ubiquitous within metazoans. Understanding the relationship

between a multicellular organism and its microbiome will allow for a better

understanding of how these interactions have evolved over time and how

conserved these associations are across diverse populations. The biological

interactions that occur within these host-microbe symbioses are complex; the abundance and diversity of microbes present and the totality of their interactions between one another and as they relate to their host and environment are often

poorly understood. These host-bacterial associations can be neutral and facultative, positive in the form of for example mutualistic cooperation or commensalism, or negative involving for example competition, pathogenesis or parasitism. Mutualistic microbes provide increased fitness benefits to their host, whereas those that are pathogenic compromise the well-being of the host and decrease its fitness. Commensal relationships occur when microbes use their metazoan host for shelter or as a vehicle for transportation; some microbes even linger around waiting for the death of their host to use the remains to their advantage (3, 5, 32, 33).

Studying these networks of interactions will allow for a better understanding of rhizosphere ecology in which bacteria, fungi, nematodes, arthropods, and plants associate intimately with one another. Past studies often lacked analytical focus on the distribution and abundance of organisms that are

16 part of these microbiomes. 16S rRNA based metagenomic studies are beginning to elucidate the population composition of these microbiomes and the functions they perform. This is not just important for our scientific understanding, but also allows for practical applications such as the creation of microbial inoculants and soil amendments to promote plant health & growth, and the utilization of nematodes that promote soil nutrient cycling (14, 15, 23). It can also allow for improvements in the prevention and treatment of microbial disease, for example by enabling us to identify the mechanisms used by bacterial symbionts to protect their eukaryotic host. This may in turn allow us to develop molecular tools and inoculate soils, plants or animals with suppressive organisms to target those microbes that might otherwise be harmful and pathogenic.

Nematodes are used as laboratory model systems to study microbial associations. Several important and intricate microbial associations have been studied among entomopathogenic and filarial nematodes. For example, the insect-pathogenic bacteria Photorhabdus and Xenorhabdus are necessary partners for the worm's life cycle in the entomopathogenic nematodes

Heterorhabditis and Steinernema nematodes, respectively (24). In a different form of mutualism, treatment with the antibiotic tetracycline will eliminate

Wolbachia, an obligatory endosymbiont that provides essential nutrients and metabolites for filarial nematodes such as Brugia malayi and Litomosoides sigmodontis (11, 18).

In this study our efforts focus on the microbiome of Acrobeloides maximus,

a true soil inhabiting member of the nematode family Cephalobidae and a non-

pathogenic free-living bacterivore common in sandy soils that are subject to heat

and protracted desiccation. Using microcosms, we perform a 16S rRNA gene

survey of bacteria of our soil sample and compare that to the bacteria in the

microbiome of A. maximus extracted after exposure to a soil slurry for 24h. A

similar cultivation, exposure, and extraction was performed using monoxenically

grown cultures of Caenorhabditis elegans N2, a free-living bacterivorous rhabditid specialized in rapid colonization of decomposing fruits and commonly used as a model organism for molecular genetics research (10 ). For both species, the nematodes extracted from the soil slurry were maintained on agar plates supplemented with cholesterol and sampled over a period of two, four, and six months. These samples were also analyzed using 16S rRNA amplification and sequencing methods to monitor for changes in the number of species and abundance of microbes present in the population over time.

MATERIALS AND METHODS

Nematode cultivation and harvest

15 mm and 90 mm petri dishes containing 1.5% nutrient-free agar supplemented with cholesterol (1 µg/ml) were prepared and seeded with

Escherichia coli S17-1, which contains a constitutive red fluorescent protein

(RFP) expression plasmid for easy detection with fluorescence microscopy.

Exogenous cholesterol is necessary for nematode survival; most nematodes are assumed to be incapable of de novo cholesterol biosynthesis and omission of cholesterol has been shown to have adverse effects on growth, development, molting, and brood size in C. elegans (9, 17, 25). A. maximus was grown in a

28°C incubator for a period of at least two weeks and C. elegans was cultivated at room temperature for up to a week until the nematode populations reached high density. Worm populations were counted directly and diluted to 104 worms/ml for subsequent soil exposure.

Rhizosphere soil collection and exposure

Rhizospheric soil samples were collected from wild sunflowers at the

California State University San Bernardino (CSUSB) Natural Preserve (San

Bernardino, CA) and placed in 50 ml sterile conical tubes, weighed, and stored at

4o C until processing. A worm slurry was created by rinsing with an adequate amount of sterile water the agar plates on which nematodes were growing. For every gram of soil, 1ml of the worm slurry was added to each of three conical tubes after filling them with approximately 5g of soil each. In the same manner, we also exposed C. elegans to the same soil in triplicate, and as control three more tubes received 1 ml of sterile water without nematodes. Samples were allowed to incubate for 24h at room temperature prior to worm extraction.

Employing a density gradient approach, we recovered the nematodes from the soil samples: MgSO4 heptahydrate was added to the samples to a

concentration of 409 g/L followed by centrifugation at 1800g for 3 minutes (30).

The soil pelleted to the bottom and the above supernatant containing nematodes

was removed and then vacuum filtered using Millipore’s Stericup and Steritop

system through a 0.22 micron polyvinylidene difluoride (PVDF) membrane to

remove external bacterial contaminants. Nematodes were rinsed off the

membrane and collected in 3 ml of sterile water. The remaining soil pellets were

washed twice in sterile water and aliquots were frozen at -20 o C for later DNA

extraction and analysis for our long term culture investigation.

Long-term culture of A. maximus

Recovered nematodes were grown on 1.5% nutrient-free agar

supplemented with cholesterol (1 µg/ml) with no other food sources. The

nematodes were collected for analysis and subcultured at two, four, and six

months. Material from the plates was collected using sterile 10x phosphate buffer

saline (PBS) and washed as previously described to remove free-living bacteria.

Continuing cultures were seeded with 50µl of this washed worm suspension. The

remaining suspension was frozen at -20 o C for subsequent DNA analysis.

Isolation of associated bacteria

Individual nematodes from the initial culture of worms recovered from the soil slurry were transferred onto petri plates containing 1.5% agar with 5g/L of yeast extract (YE). The worms were allowed to wander about on the plates for up

to 24h, they were then removed and any released bacteria were allowed to grow.

All bacterial isolates were subsequently identified by PCR amplification and

sequencing of their 16s rRNA genes.

DNA extraction and PCR analysis

Using the FastDNA Spin Kit for Soil (Qbiogene), we extracted DNA from

the original rhizosphere soil as well as from soils exposed to A. maximus or C.

elegans and from the long term cultures. With the GenomiPhi V2 DNA

Amplification Kit (GE Healthcare Life Sciences) we individually amplified DNA

from all the samples, which we then used for PCR analysis. Utilizing the 27F (5'-

AGAGTTTGATCMTGGCTCAG-3') and 1492R (5'-GGTTACCTTGTTACGACTT-3') universal

bacteria primers, we next amplified near full length 16S rRNA gene fragments

from our samples (19, 29).

PCR reactions were performed in 50 µl reactions consisting of 25 µl of 1X

concentration of GoTaq Green PCR Master Mix (Promega), 0.5 µm of both 27F and 1492R primers, and at least 20 ng of template DNA. Thermal cycling

conditions consisted of 5 minutes at 95˚C followed by 30 cycles of 30 sec at

94˚C, 30 sec at 55˚C, 1 min at 72˚C, followed by a final extension step for 10 min

at 72˚C. Gel electrophoresis was performed using a 1% agarose gel stained with

0.5µg/ml 1% ethidium bromide. PCR products were visualized using the BioRad

Gel Doc XR+ System.

16S rRNA gene library construction, sequencing and analysis

PCR products were purified using the Wizard SV Gel and PCR Clean-up

System (Promega) and subsequently ligated into the pCR 2.1-TOPO vector and

cloned with the TOPO TA Cloning Kit using One Shot TOP10 Electrocompetent

E. coli. Sets of 94 clones from each of the A. maximus triplicates, 94 clones from

the C. elegans triplicates, and 94 clones from the triplicate controls without

nematodes underwent rolling circle amplification in which the bacterial DNA

inserts within these recombinant plasmids were first amplified then sequenced

(Laragen, CA). In addition, 94 clones from the two, four, and six month long-term culture studies were also sequenced in one direction, using the M13 forward primer site in pCR2.1.

Sequences were compiled into FASTA formatted files and aligned against the prokaryotic multiple sequence alignment (prokMSA) 16S rRNA gene sequence database by applying the near alignment space termination (NAST) algorithm (7). The Bellerophon program was used to detect and filter 16S rRNA gene chimeras (13). The aligned set was then compared against the Ribosomal

Database Project (RDP) phylogeny database (rdp.cme.msu.edu) at 97% similarity using the classifier on the Greengenes website (greengenes.lbl.gov) (4,

8). The 97% cutoff level is commonly used to differentiate bacterial organisms at the species level and to distinguish between organisms that may represent different strains or subgroups (16). To determine operational taxonomic units

(OTUs), SimRank, a general similarity measure was used to find the nearest

22 neighbors to our sequences and calculate divergence (8). Using online calculators at Chang Bioscience (changbioscience.com), the Shannon-Wiener diversity index and Shannon-Wiener evenness were calculated based on the

Greengenes assigned OTUs. The sample A. maximus 2 was later resequenced in the reverse direction to verify error rates. All of our newly generated 16S rRNA sequences have been deposited into Genbank (Table 1).

Refer to Chapter 2 for phylogenetic analysis and discussion regarding the identities of the three most interesting bacterial genera found: Ochrobactrum,

Pedobacter, and Chitinophaga.

Preparation for Fluorescence In Situ Hybridization (FISH)

The previously described rhizosphere soil collection and exposure methods were repeated for fluorescence in situ hybridization (FISH) probing of exposed worms: worms were extracted after exposure to a soil slurry for 24h, along with worms collected from agar plates without soil exposure. The FISH protocol used was largely adopted from the work of Vandekerckhove et al. (31):

Preparation of samples was performed in triplicates. Samples were rinsed twice in sterile water, fixed for 10 minutes in a 1:1 mixture of glacial acetic acid and ethanol, rinsed twice for 5 minutes in pure ethanol, transferred to a mixture a 1:1 mixture of methanol and PBS w/Tween 20 (PBT; 50 mM NaCl, 10 mM Na3PO4,

0.1% (wt/vol) Tween 20 [pH 7.4]) for 10 minutes, and fixed for 30 minutes in a

PBT solution containing 1% (vol/vol) paraformaldehyde.

FISH Probes

The oligonucleotide probes used were based on probeBase’s 16S rRNA data (microbial-ecology.net/probebase) and were designed to specifically anneal to either Pedobacter or Ochrobactrum (21). The probe specific to the Pedobacter sp., PED672 (5'-GCTACTTGTCACATTCCG-3') (12) was linked with sulfoindocyanine chromophore Cy3, which fluoresces green and the probe specific to the

Ochrobactrum sp., Ochro3 (5'-AACTCCCCAACGGCTAACAT-3') (6) was linked to Cy5, which fluoresces red. Each probe was dissolved in TE buffer (10 mM Tris-HCl, 1 mM EDTA [pH 7.4]) to a concentration of 10 μM where 10 μl was used for each

100 μl of the hybridization mixture.

FISH- Hybridization, Mounting, Microscopy

The hybridization mixture consisted of 20 mM Tris-HCl (pH 7.4), 0.02%

(wt/vol) sodium dodecyl sulfate (SDS), 0.9 M NaCl, 5 mM EDTA, 60% (vol/vol) formamide, and a 1 μM concentration of the two fluorochrome-labeled probes.

Each of the fixed paraformaldehyde samples were added to this hybridization buffer in sealed conical tubes at 42°C for 3 hours followed by two 30 minute washes at 42°C in a hybridization buffer containing no formamide. Nematodes were pipetted out of this wash onto microscope slides. DIC and fluorescence images from the samples were collected using a Zeiss Axioplan2 equipped with a

CoolSnapFX cooled CCD camera and ImageProExpress 2.0 capture software.

RESULTS

16S rDNA analysis of extracts

Nematodes were recovered from soil samples after 24h using a density gradient extraction, washed to remove free-living bacteria, and examined using culture-independent 16S rRNA sequencing to taxonomically analyze the bacteria comprising their microbiomes (Figure 1). Analysis revealed that the A. maximus

triplicates were dominated by α-Proteobacteria and Sphingobacteria, while C.

elegans associates showed a more broad distribution across taxa with no clear

dominant clade. The soil sample to which no nematodes were exposed had a

much higher percentage of bacilli compared to the other samples. Shannon-

Wiener diversity index calculations and analysis of population structures in the

samples showed marked differences among the samples (Table 2). The

α-Proteobacteria clade of the A. maximus triplicates was dominated by the genus

Ochrobactrum, while the Sphingobacteria clade was dominated by the genus

Pedobacter ; these two genera were not detected in the soil samples (data not shown) and the samples from C. elegans gave no clear indication of any dominant genera within these two clades (Figure 2). In subsequent experiments,

we were unable to recover transmitted E. coli S17-1 from the initial inoculants of our samples, and were also unable to observe its red fluorescence via fluorescence and confocal microscopy (data not shown).

Co-cultures

For a period of six months we maintained co-cultures with bacteria from

the soil, with bimonthly collecting and subculturing. Utilizing 16S rRNA methods

we examined the microbiome of a three week old co-culture which showed a

stable population similar in composition to the samples recovered from soil

exposure (Figure 3). Another co-culture was analyzed in the same way, which

again yielded a similar population makeup (data not shown). Over the six month

period we observed a shift away from the initial population structure however

(Fig 3A). Pedobacter and Chitinophaga persisted in the population for up to six

months, with Chitinophaga peaking at four months (Fig 3B) while Ochrobactrum

persisted in the population for up to four months only (Fig 3C). Throughout the

six months, the bacterial population displayed noticeable increases in the

abundance of Sphingobium and a decrease in Brevundimonas.

Isolation and culture

Bacterial isolates were obtained during various time points from the co- cultures by inoculation onto different kinds of agar media or by isolating individual nematodes on different media. Applying 16S rRNA gene sequencing we identified these bacterial isolates (Table 3). Pedobacter was isolated by

transferring nematodes onto fresh agar plates lacking nutrients for bacterial

growth and isolating the microcolonies that formed, while Ochrobactrum isolates

were recovered by transferring individual nematodes onto YE agar. Both

Pedobacter and Ochrobactrum isolates were cultivated axenically at 37°C and had aliquots frozen at -80°C. Attempts to isolate Chitinophaga isolates using colloidal chitin media were unsuccessful.

Fluorescence In Situ Hybridization

After 24h of soil exposure, nematodes were removed from the soil, and using FISH, were probed for the presence of Ochrobactrum and Pedobacter.

FISH signals corresponding to both genera were simultaneously identified in the guts of the nematodes (Fig 4 A -D). Control experiments using cultured strains of the two bacterial isolates showed no cross reactivity of the probes under fixation and hybridization conditions and no background staining was observed in worms fed only E. coli S17-1 (Fig 4E & F).

DISCUSSION

Bacterial isolates from Acrobeloides maximus recovered from soil microcosms consistently yielded DNA sequences most closely matching with known sequences of Ochrobactrum, Pedobacter, and Chitinophaga suggesting that these are clade specific associations. The sequences belonging to these genera were largely enriched in all A. maximus samples compared to similar sampling from Caenorhabditis elegans exposed to soil and to direct sampling from the soil, which showed differing association patterns. Furthermore, these three bacterial genera persisted in long term co-culture with the nematodes on

agar plates where no external nutrients were provided to the nematodes or the

soil microbiota. These associations were adequately stable at the phylum and

subphylum level, but at the class level (alphaproteobacteria and

sphingobacteriia) the populations shifted substantially over time, most likely due

to nutrient consumption and dissipation. Bacterial isolates of Ochrobactrum and

Pedobacter were shown via FISH to colonize the gut of A. maximus.

Other studies describing the microbiota of other nematodes have provided

similar results to our own. Ochrobactrum and Pedobacter were both identified as genera associated with wild C. elegans (28). In another study, these two genera were also identified as associates of the soybean root cyst nematode Heterodera glycines, and those investigators were able to isolate and culture Pedobacter

from soybean cysts (22). Pedobacter was also recently identified as a bacterial

associate of the plant parasitic nematode Bursaphelenchus mucronatus (27).

These investigations along with our own suggest a broad association of

Pedobacter with many nematodes, and that its occurrence is not just specific to

only A. maximus. The replicates we performed during our investigation show that

a quick selection process takes place in the A. maximus microbiome when the

nematodes are introduced to the soil, relative to the soil microbial community

structure. Conversely, it appears from our work and others that laboratory bred C.

elegans are usually unselective in its microbiota, consuming an array of

phylogenetically diverse bacteria which it isolates from the soil (1, 26). It is

important to note the E. coli strain S17-1, used to monoxenically farm both A.

28 maximus and C. elegans before soil inoculation, was quickly displaced by soil bacteria as a result of nematode exposure to the soil.

The bacterial associations we observed in A. maximus were consistent and reproducible; however, they were also prone to population shifts in our long term culture analysis. In our culture based and culture independent tests we were able to repeatedly recover Ochrobactrum and Pedobacter in association with A. maximus, and using FISH were able to show that these two genera are localized primarily in the gut of the nematode. We were unable to find an appropriate FISH probe to test for the presence of Chitinophaga, and were also unable to culture it from our enrichments.

Our observations suggest a symbiotic nematode-microbe interaction between A. maximus and Ochrobactrum and Pedobacter, perhaps involving mutualism or a phoretic association. These bacteria may for example act as mutualists by aiding the nematode in the digestion of other microbes and toxins through secretion of digestive enzymes or they may play a role in protecting the nematode against attacks by pathogenic bacteria by producing antibiotics.

Research has shown various strains of Pedobacter to be associated with antifungals and probiotics acting against pathogenic bacteria and fungi (20) and there have been reports of Ochrobactrum strains protecting entomopathogenic nematodes from harmful microbes (2). Thus, these two bacterial genera appear to have potentially widespread associations with a number of taxonomically different nematode species, suggesting a possibly important evolutionary

29 relationship that warrants further study to elucidate the occurrence and role of symbiosis in the behavior and ecology of soil nematodes.

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FIGURES AND TABLES

Figure 1. Soil exposed A. maximus populations were found to be associated with a consistent bacterial population. After 24h soil exposure, the A. maximus samples were dominated by α-Proteobacteria and Sphingobacteria, while C. elegans sample was shown to be associated with a broader bacterial distribution. A high percentage of Bacilli is shown to be associated with the bacteria identified from the soil sample.

Figure 2A. Within the dominant subgroups the A. maximus populations were consistent and limited compared to the C. elegans associated bacteria which were shown to be more diverse at the genus level. Ochrobactrum is shown to be the predominate alpha-proteobacteria within A. maximus.

Figure 2B. Within the dominant subgroups the A. maximus populations were consistent and limited compared to the C. elegans associated bacteria which were shown to be more diverse at the genus level. Pedobacter is shown to be the primary Spingobacteria within A. maximus, along with a consistent representation of Chitinophaga at a lower level.

B C

Figure 3. Long term culture experiments appear to show stability at the phylum level (A) but reveal shifts in population as the worm population ages at the genus level (B,C). At 6 months post exposure, the initially dominant clades have been supplanted in both the Sphingobacteria (B) and Alpha-Proteobacteria (C).

A B

C D

E F

Figure 4. Fluorescence in situ hybridization was used to identify the presence of both Ochrobactrum sp. (green) and Pedobacter sp. (red) in the gut of formaldehyde fixed A. maximus after recovery from soil microcosm. Samples from soil microcosms were imaged by DIC (A,C,E) and epifluorescence (B,D,F). A. maximus recovered from soil microcosms (A-D) are compared with A. maximus grown on a monoxenic culture on E. coli DH5α (E,F) to show the specificity of the FISH probes. Scale bar = 10 microns.

Table 1. List Of Sequences Deposited Into GenBank

Accession

Pedobacter KC110983, KC110977, KC110967, KC110963, KC110947, KC110933, KC110921, KC110919, KC110917, KC110913, KC110909, KC110905, KC110895, KC110984, KC110978, KC110968, KC110966, KC110958, KC110934, KC110904 Ochrobactrum KC110896, KC110901, KC110903, KC110910, KC110914, KC110916, KC110918, KC110923, KC110926, KC110929, KC110931, KC110932, KC110935, KC110936, KC110939, KC110940, KC110941, KC110944, KC110949, KC110951, KC110956, KC110957, KC110960, KC110961, KC110962, KC110970, KC110973, KC110975, KC110976, KC110980, KC110985 Chitinophaga KC110981, KC110971, KC110965, KC110955, KC110943, KC110915, KC110907, KC110897, KC110950, KC110908

Sphingobacteriales bacterium KC110912, KC110922, KC110946

Niastella KC110902, KC110979

Other Genera KC110924, KC110969, KC110945, KC110952, KC110959, KC110920, KC110953, KC110982, KC110954, KC110972, KC110898, KC110938, KC110974, KC110906, KC110948, KC110987, KC110899, KC110900, KC110925, KC110930, KC110937, KC110928, KC110911, KC110927, KC110942, KC110964, KC110986

Table 2. Shannon-Wiener Population Statistics for Microcosm Samples A. maximus 1 A. maximus 2 A. maximus 3 C. elegans Soil

SW Diversity 1.78 2.26 1.96 3.41 1.31 Index

SW- 0.63 0.71 0.64 0.9 0.53 Evenness

Table 3. Identified Isolates from association from A. maximus microcosm and long term culture

Accession Isolate name OTUa Source(s) KC687078 Ochrobactrum sp. 1108 Nematode long term culture W2P and isolation on YE KC687079 Pseudomonas sp. 18131396 Nematode long term culture JPB-PO4 Massilia sp. PO12 KC687080 Bacillus sp. JPB-PO2 758 Dead worm in long term culture KC687081 Pedobacter sp. API 534 Nematode long term culture, oligotrophic medium KC687082 Brevundimonas sp. 1066 Acrobeloides stock plate JPunk3-2 KC687083 Salmonella sp. 1607 Nematode associated isolation JPunk1-2 on YE KC687084 Achromobacter sp. 1317 Nematode associated isolation JPB-PO7 on YE KC687085 Pseudomonas sp. 1813 Nematode associated isolation JPunk3-1 on YE KC687086 Pseudomonas sp. 1813 Nematode associated isolation PO7 on YE KC687087 Bacillus sp. JPB-PO3 758 Dead worm long term culture Nematode associated isolation on YE KC687088 Achromobacter sp. 1317 Nematode associated isolation JPB-PO7 on YE a OTU assigned using RDP phylogeny. Isolates assigned to the same OTU were verified to be distinct using direct sequence comparison (bl2seq)

CHAPTER TWO: Phylogenetic Analysis of Bacterial Genera Detected At Significant Levels Within The Microbiota Associated With A. maximus

ABSTRACT

Using 16S rRNA amplification and sequencing, three bacterial strains

belonging to the genera Ochrobactrum, Pedobacter, and Chitinophaga were detected at significant levels within cultured Acrobeloides maximus populations exposed to soil for 24 hours before extraction and re-inoculation on agar. All three strains were found to persist in re-inoculated A. maximus cultures over a period of six months. For each strain, sequences from our A. maximus associated clones along with relevant 16S rRNA sequences from congeners or related genera available in GenBank were subjected to comprehensive phylogenetic analyses. Sequences were aligned using the multiple sequence alignment algorithms implemented in T-Coffee, MAFFT, Kalign, and WebPRANK, and phylogenetic trees were constructed using Bayesian and maximum- likelihood methods. Similarities and differences among the different alignment and tree-building methods suggest tree building methods with T-Coffee performed more poorly while maximum-likelihood methods with MAFFT and

Kalign produced more mutually consistent topologies that correspond better to recognized genus boundaries. Inferences are made about the known species

that are phylogenetically closest to each of our three strains. Our Ochrobactrum strains appear to be most closely related to Ochrobactrum tritici, while our

Chitinophaga strains are most similar to a bacterium previously identified as

“Flexibacter cf. sancti.” Our Pedobacter strains seem to be a previously undescribed sphingobacterium within the genus.

INTRODUCTION

During the last decades of the 20th century, Carl Woese pioneered the idea

of using small subunit ribosomal RNAs (SSU rRNA) as reliable molecular

markers to indicate relatedness among different organisms (45). SSU rRNAs are

thought to be reliable for discerning phylogenetic relatedness because they can

easily be amplified and sequenced directly and are one of the few genes which

can be found within all uni- or multicellular organisms. Furthermore, they encode

for highly conserved ribosomal structures yet usually exhibit sufficient nucleotide

sequence variation to allow for distinction between different species (36).

Sequencing of 16S rRNA has become an established method for identifying

bacteria and a standard tool for discerning their phylogenetic relationships (20,

34).

As discussed in Chapter 1, we used microcosms and performed a 16S

rRNA gene survey of bacteria of our soil sample, and then compared that to the

bacteria in the microbiome of Acrobeloides maximus extracted after 24h of exposure to soil. Clones from our A. maximus sample were sequenced and

generated 16S rRNA sequences from dominant strains belonging among others

to the genera Ochrobactrum, Pedobacter, and Chitinophaga. In this study, a

phylogenetic analysis is undertaken to examine in greater detail how sequences

from our clones of these three bacterial genera relate to known species, and

particularly to clarify whether they most likely represent previously named

species versus as yet unidentified species.

MATERIALS AND METHODS

Data Preparation

Our 16S rRNA amplification via PCR and direct sequencing methods are

described in Chapter 1. Relevant 16S rRNA sequences for each bacterial genus

were gathered from GenBank using NCBI’s Reference Sequence (RefSeq)

database collection as well as the International Nucleotide Sequence Databases

(INSD) and compiled into a FASTA file along with sequences belonging to our

own clones (Table 1 A-C). For purposes of rooting trees and gauging quality of

formal identifications for previously published 16S sequences, outgroup

sequences were also included from genera closely related to each of our three

associates of A. maximus : sequences from the genus Brucella were used for the

outgroup for Ochrobactrum; sequences from Chitinophagaceae and

Sphingobacteriales along with sequences from the genera Segetibacter,

Parasegitibacter, and Niastella were used for the outgroup for Chitinophaga; and

sequences from the genera Sphingobacterium and Mucilaginibacter were used

for the outgroup for Pedobacter (Table 1 A-C).

Multiple sequence alignments: T-Coffee, MAFFT, Kalign, and WebPRANK

The following multiple sequence alignment (MSA) tools located on EMBL-

EBI (www.ebi.ac.uk/Tools/msa/) were used to align 16S rRNA sequences for each genus: T-Coffee, MAFFT, Kalign, and WebPRANK. T-Coffee is an algorithm that attempts to lessen the faults of other popular progressive alignment methods by using a consistency based approach (32). By contrast, MAFFT utilizes fast

Fourier transforms to perform rapid alignments and when using more than 60 sequences is shown to be 100 times faster than T-Coffee without sacrificing accuracy (19). Kalign uses the Wu-Manber string-matching algorithm to perform quick and accurate sequence alignments (26). WebPRANK is a relatively new tool which employs a phylogeny-aware progressive algorithm to perform its sequence alignments (29). Each alignment algorithm ran at its respective default settings on EMBL-EBI for all alignments.

Phylogenetic inference tools- Maximum Likelihood, PhyML, Mr. Bayes

Alignment files generated from the MSA tools were examined in MEGA 5, a software program for molecular evolutionary genetic analysis (38). Maximum likelihood (ML) analyses were performed in MEGA 5. The sequence alignment, assembly and analysis software Geneious 6.0.5 (Biomatters Ltd.) incorporates a

MrBayes plugin which was used for Bayesian phylogenetic analyses (16). PhyML analyses were carried out via the interface of the HIV database website

(hiv.lanl.gov) (13). The settings for substitution models, rate variation, gamma categories, and branch support methods are presented in Tables 2-4.

LALIGN

LALIGN, a tool on the EMBL-EBI website (www.ebi.ac.uk/Tools/psa/lalign/) for identifying regions of sequence similarity between two sequences (15) was used to compare our clones which had noticeably longer branch lengths against a consensus sequence of the other clones in their respective genus generated using Consensus Maker v2.0.0 with Kalign on the HIV database website

(hiv.lanl.gov).

RESULTS

Ochrobactrum sp. clones

ML, PhyML, and MrBayes trees generated with T-Coffee and WebPRANK alignments generally did not group the Ochrobactrum clones together; furthermore, sequences from Ochrobactrum tritici strains did not form a

monophyletic group and were dispersed throughout the trees (Figure 1). The

trees produced using MAFFT and Kalign alignments yielded better results,

clustering the Ochrobactrum clones and the Ochrobactrum tritici sequences

together in their own respective clades, which jointly comprise a monophyletic

group (Figure 2, 3). The ML tree created with MAFFT shows a 99% bootstrap

support value for the monophylum of these two clades (Figure 2). In all trees,

Ochrobactrum sp. clone Acro231 (designated with a blue asterisk) is placed on a

noticeably longer branch than the other Ochrobactrum clones (Figures 1-3).

Chitinophaga sp. clones

Trees constructed with T-Coffee alignments were poorly resolved and

show low branch support values (Figure 4). Trees generated with the other three

sequence alignment tools produced better and similarly comparable results. The

Chitinophaga clones clustered together in their own monophyletic group along

with Flexibacter cf. sancti with 99-100% bootstrap support in the ML and PhyML

trees generated using MAFFT, Kalign, and WebPRANK alignments. ML trees

created with MAFFT and Kalign alignments show identical topology to one

another (Figures 5, 6). In a majority of the trees, the Chitinophaga sp. clone

Acro210 (designated with an orange asterisk) has a significantly longer branch

than the other clones (Figures 5, 6). Our Sphingobacteriales clones Acro262 and

Acro263 cluster together in the Segetibacter clade, while our Sphingobacteriales

clone Acro264 is placed in a Niastella clade along with known Niastella

sequences, as well as our Niastella clones Acro224 and 225 (Figures 5, 6).

Pedobacter sp. clones

In all trees, the Pedobacter clones grouped together and formed a sister

clade to Pedobacter africanus strains K1/K2 and NBRC 10065. PhyML trees

were the only ones to separate the Pedobacter strains and the Sphingobacterium

outgroup into distinct clades (Figures 7-9). The topology of the generated

Pedobacter trees is poorly resolved, disallowing clear distinctions or well

supported groupings at the species level. Pedobacter sp. clone Acro279

(designated with a green asterisk) represents a longer branch than the rest of the

Pedobacter clones in nearly all trees (Figures 7-9).

LALIGN plots

Analyses were performed for comparing Ochrobactrum sp. clone Acro231

(blue asterisk), Chitinophaga sp. clone Acro210 (orange asterisk), and

Pedobacter sp. clone Acro279 (green asterisk) against a generated consensus sequences of their respective clones because they showed longer branch

lengths that their counterparts in the phylogenetic trees. A "dot-plot" like graph of these sequence alignments is reported (Figure 10A-C).

DISCUSSION

Comparison of multiple sequence alignment algorithms

Many factors influence the performance of alignment algorithms, including the number of sequences used, the length of the sequences, the occurrence and lengths of insertions & deletions, and the degree of similarity among the sequences (31). Benchmark criteria have been developed to compare the performance of MSA algorithms against one another; such as for example

BAliBASE (Benchmark Alignment dataBASE) (bips.ustrasbg.fr/en/Products/

Databases/BAliBASE/) which examines problems commonly associated with

MSAs such as small numbers of sequences, unequal phylogenetic distributions, internal insertions, repeats, inverted regions and so forth (39, 40, 41). An alignment benchmark investigation performed by Thompson et al. in 2011 revealed key differences between some of the more popular alignment algorithms: MAFFT and T-Coffee performed better with more divergent sequences, whereas Kalign performed better with sequences containing multiple conserved regions (41). Fletcher and Yang in 2010 performed their own investigation and showed that WebPRANK consistently outperforms MAFFT in their branch site test analysis (10).

Comparison of phylogenetic reconstruction methods

Computing time continues to be a major factor when comparing different phylogenetic tree building tools. Standard maximum likelihood analyses with bootstrapping suffer from the burden of lengthy computational time. Bayesian inference methods, like MrBayes, tend to perform computationally faster than standard maximum likelihood methods while still having comparable phylogenetic accuracy (16, 25). PhyML utilizes approximate likelihood ratio test branch support methods based on a Shimodaira-Hasegawa-like procedure (aLRT SH-like branch support methods), making it extremely fast compared to standard maximum likelihood bootstrapping methods: 1000 bootstrap iterations were used in each of our standard ML analyses, whereas PhyML-aLRTs only required a single iteration per analysis (2, 13).

Studies undertaken to compare Bayesian posterior probabilities against maximum likelihood bootstrap support values show that both are overly supportive in their measure of phylogenetic accuracy, with Bayesian support values tending to be higher than values obtained from bootstrapping (6, 9, 27, 43,

44).

Ochrobactrum sp. clones

The genus Ochrobactrum is comprised of six species: O. anthropi, O. intermedium, O. tritici, O. grignonense, O. gallinifaecis, and O. ciceri, which can be found in plants and their rhizospheres, soil, wastewater, or animal feces (4).

The genus Brucella consists of highly pathogenic strains which infect humans and livestock (e.g. cattle, sheep, goats, pigs) and is the closest relative to

Ochrobactrum among those available from published sequence databases; it was therefore used as the outgroup in our phylogenetic analyses (Table 1).

ML, PhyML, and MrBayes trees generated with MAFFT and Kalign converge on a consensus placing our Ochrobactrum clones and Ochrobactrum tritici sequences together in their own monophylum (Figure 2 and 3), suggesting our Ochrobactrum clones are closely related to, or are a new variant of

Ochrobactrum tritici. O. tritici was first found and isolated over a decade ago from the rhizoplane of wheat (28) and isolates of O. tritici have also been recovered from wastewater (11).

Some of the O. tritici sequences are very divergent from the others within the monophylum; most divergent are O. tritici AS1 and the clade comprised of O. tritici strains TA 93, W19-1, W19-2, A4, and W49-1 (Figures 2 and 3). From our trees it is reasonable for us to identify our Ochrobactrum clones as being the same species; it however draws into question whether all the sequences gathered from GenBank as O. tritici are really one and the same organism.

In order to decide whether or not two bacterial strains are the same

species, microbiologists first use 16S rRNA sequence-based phylogeny to check

if bacteria are similar enough to be assigned the same name. Having greater

than 97% 16S rRNA sequence identity similarity is the recommended cut-off for species delineation (8, 12, 21, 22). There are drawbacks to using 16S rRNA sequencing to delineate species. The >97% threshold is not universally accepted and by itself is not sufficient enough for accurate species determination, moreover 16S rRNA sequencing has poor resolving power for discerning differences at and below the species level (17). In addition, 16S rRNA genes can be subject to horizontal gene transfer; this raises a slew of hotbed questions regarding species concept and phylogenetic reconstruction, including questioning the validity of 16S rRNA constructed phylogenetic trees (3, 5, 14).

Chitinophaga sp. clones

The genus Chitinophaga was first proposed in 1981 by Sankhobol and

Skerman, and contained only a single species, Chitinophaga pinensis (35). In

2006, Kämpfer et al. proposed reclassifying Cytophaga arvensicola, Flexibacter

filiformis, Flexibacter japonensis, and Flexibacter sancti into the genus

Chitinophaga (18). Thus, the genus now consists of six species: C. pinensis, C.

arvensicola, C. filiformis, C. japonensis, C. sancti, and C. skermanii. Members of

this genus are of special interest because of their ability to degrade the

polysaccharide chitin, a β-1,4-glycosidic linked homopolymer of N-acetyl-D-

51 glucosamine know to be the main component of arthropod exoskeletons and fungal cell walls (7).

ML and PhyML trees generated using MAFFT, Kalign, and WebPRANK cluster our Chitinophaga clones and an organism previously identified as

“Flexibacter cf. sancti” (Accession AF181568) together in their own monophylum

(Figure 5 and 6), suggesting our clones are novel strains of this species. From our trees, it appears that "Flexibacter cf. sancti" is clearly a different organism from the clade of virtually identical sancti sequences that are presumably genuine and correctly identified (C. sancti strains NBRC 16033, IFO 15057,

NBRC 15057 and F. sancti strains IFO 16033, IFO 15057, ATCC 23092) (Figure

5 and 6). Most likely "Flexibacter cf. sancti" is an earlier discovery of the same as yet unnamed Chitinophaga in our experiments to be an associate of A. maximus.

The soil bacterium “Flexibacter cf. sancti” (reclassified as Chitinophaga cf. sancti) was first discovered as part of a consortium of bacteria comprising a biofilm that degrades diclofop-methyl, an active ingredient found in many commercial herbicides, although by itself "Flexibacter cf. sanct" is not known to be a diclofop- methyl degrader (24).

The trees also group our Sphingobacteriales bacterium clones Acro262 and Acro263 in a Segetibacter specific clade, suggesting the sequences represent this genus (Figure 5 and 6). The trees moreover group all the Niastella sequences together into their own clade, which includes our Niastella sp. clones

Acro224 and Acro225. Furthermore, our Sphingobacteriales bacterium clone

Acro264 also falls into this Niastella clade, suggesting it too might be a member of this genus. Parasegetibacter luojiensis RHYL-37 (Accession NR044576) is placed into our Niastella clade as well, which is not surprising as it was first classified as being a Niastella sp. in 2008 before being reclassified as

Parasegetibacter in 2009 (46) (Figure 5 and 6).

Pedobacter sp. clones.

The genus Pedobacter was proposed in 1998 and consisted initially of four species: P. heparinus (formerly Sphingobacterium heparinurn), P. piscium

(formerly S. piscium), P. africanus, and P. saltans (37). As of date, there are no fewer than 42 named species within the genus: P. africanus, P. agri, P. alluvionis, P. aquatilis, P. arcticus, P. borealis, P. boryungensis, P. caeni, P. composti, P. cryoconitis, P. daechungensis, P. daejeonensis, P. duraquae, P. ginsengisoli, P. glucosidilyticus, P. hartonius, P. heparinus, P. himalayensis, P. insulae, P. jejuensis, P. koreensis, P. kribbensis, P. kwangyangensis, P. lentus,

P. metabolipauper, P. nyackensis, P. oryzae, P. panaciterrae, P. piscium, P. rhizospharae, P. roseus, P. saltans, P. sandarakinus, P. soli, P. steynii, P. suwonensis, P. terrae, P. terricola, P. wanjuense, P. westerhofensis, P. xinjiangensis, and P. yonginense. This large number of Pedobacter species makes 16S rRNA phylogenetic analysis difficult and is probably one of the reasons why the topologies of our Pedobacter trees provide little support for clear distinctions and groupings at the species level (Figures 7-9). In all the trees our

Pedobacter clones cluster together and usually formed sister clades or monophyla with Pedobacter africanus strains K1/K2 and NBRC 10065 (Figures

7-9). Pedobacter africanus is known to occur in soil and activated sludge in dry regions, particularly within South Africa (37). There is evidence to suggest that our Pedobacter clones may be a novel species, but further work will be required to prove or disprove this assumption.

LALIGN analysis

LALIGN analysis of Ochrobactrum sp. clone Acro231 compared against the consensus sequence of Ochrobactrum clones shows a 98.6% similarity among the sequences (Figure 10A). This is above the 97% cutoff and suggests the longer branch of Ochrobactrum sp. clone Acro231 seen in our trees (Figures

1-3) may be an artifact due to pitfalls intrinsic to the different tree-building methods used (1).

LALIGN analysis of Chitinophaga sp. clone Acro210 compared against the consensus sequence of Chitinophaga clones shows a 94.3% similarity (Figure

10B) and the analysis of Pedobacter sp. clone Acro279 compared against the consensus sequence of Pedobacter clones shows a 94.0% similarity among the sequences (Figure 10C). Both of these fall slightly below the 97% cuttoff indicating they are dissimilar from the rest of their respective clones. Further protocols need to be implemented (discussed below) to determine if they are truly dissimilar.

Requirements for describing a new bacterial species

A combination of phenotypic, genotypic, and chemotaxonomic approaches is required for a formal description of a new bacterial species. In addition, the rules for nomenclature and guidelines for publication discussed in the

Bacteriological Code (1990 Revision) (23) must be followed. A multitude of different diagnostic approaches is required in order to have sufficient descriptive information to allow for characterization of a novel species.

Genotypic approaches

Use 16S rRNA gene sequencing information and phylogenetic analysis is the first step to determine the phylogenetic placement of an organism down to at least the family and genus level. This is commonly followed by a DNA-DNA hybridization test in order to decide whether two isolates should be considered as similar or different species. If their 16S rRNA sequences show > 97% identity and if their genomes show >70% DNA-DNA hybridization the two strains are said to belong to the same species (14). Multilocus sequence analysis can also be carried out in which internal fragments of several housekeeping genes are analyzed to characterize the species (30). Additionally, if the whole genomes of reference strains are available, then whole genome sequencing of the isolate can be undertaken. Examining the % G+C content of DNA might be helpful as well; deviations from expected values may help in elucidating information about the strain being studied.

Phenotypic approaches

Microscopy is the oldest method for obtaining information about the

morphological features of bacteria. Light microscopy may be sufficient for

visualizing and describing the cell shape, size, pigment color, and staining

characteristics. Utilizing different differential staining dyes and techniques such

as Gram staining, can aid in the characterization of the novel species. The

presence and/or absence of certain characteristics structures like flagella,

capsules, polyphosphate granules, and spores also can aid in the description

and identification of a new species. Notes regarding the motility and speed (or lack thereof) of the bacterium should be recorded as it may be of significance.

Furthermore, it may be necessary to utilize scanning electron microscopy to show morphology of whole cells or transmission electron microscopy to view intracellular structures (42).

Physiological characteristics such as pH and temperature range for optimal growth and its effect on phenotype should be recorded. Cells should be cultured using various media in nutrient broths and on agar plates to determine carbon and nitrogen source utilization.

Chemotaxonomic approaches

Chemotaxonomy typically deals with using chemical approaches to study the composition of different cellular components, such as the cell membrane, outer cellular layers, and the different organelles residing in the cytoplasm (42). It

is common to start with a commercially available cell-based phenotypic assay kits

such as those available from Biolog (Biolog, Inc.) or API (bioMérieux). Common

tests include the oxidase test (to detect cytochrome c oxidases), catalase test (to

determine presence of catalase enzymes using hydrogen peroxide), testing the

strains relationship to oxygen (is the strain aerobic and/or anaerobic?), testing

the strains relationship to carbon dioxide and hydrogen (via manometric

methods), and tests to determine the fatty acid and polar lipid profile of the strain

(33).

FINAL THOUGHTS

Our phylogenetic analysis suggests that two of the three most common

and persistent bacterial associates of A. maximus are unnamed species:

"Flexibacter cf. sancti" appears to be an earlier discovery of the same as yet unnamed Chitinophaga from our experiments while our Pedobacter strains

appear to be a novel species within the genus. Our Ochrobactrum strains also

appear to be a new variant of Ochrobactrum tritici.

As discussed in the last chapter, the Pedobacter associate of A. maximus

is available in pure culture. This allows for the opportunity for the Pedobacter sp.

to be potentially named and characterized using the discussed phenotypic,

genotypic, and chemotaxonomic approaches.

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FIGURES AND TABLES

Table 1A. Ochrobactrum Sequences Used In Phylogenetic Analyses

Clone Sequences Relevant Sequences from GenBank KC110896, KC110901, KC110903, AB091758, AB681865, AF508089, KC110910, KC110914, KC110916, AF508090, AF508091, AF508092, KC110918, KC110923, KC110926, AJ242579, AJ864999, AJ865000, KC110929, KC110931, KC110932, AM114403, AM490634, AM490635, KC110935, KC110936, KC110939, AM490636, AY429607, DQ647056, KC110940, KC110941, KC110944, EU999218, GU171382, GU345782, KC110949, KC110951, KC110956, JX010950, JX393006, JX393007, KC110957, KC110960, KC110961, JX393011, NR028902, NR026039, KC110962, KC110970, KC110973, NR042447, U88440 KC110975, KC110976, KC110980, KC110985 Outgroup sequences: L26166, L26168, L37584, NR042460, NR042462, NR042463, X13695

Table 1B. Pedobacter Sequences Used In Phylogenetic Analyses Clone Sequences Relevant Sequences from GenBank KC110895, KC110904, KC110905, AB365796, AB680215, AB680732, AB680733, KC110909, KC110913, KC110917, AB680841, AB681143, AB681144, AB681397, KC110919, KC110921, KC110933, AB682400, AJ438170, AM237384, AM279214, KC110934, KC110947, KC110958, AM279215, AM279217, AM279218, AM491368, KC110963, KC110966, KC110967, EF660750, EF660752, EF693742, EU169155, KC110968, KC110977, KC110978, EU734803, EU834277, FJ263019, GQ225687, KC110983, KC110984, KC119214, GU391547, HM051286, HQ824869, JF915326, KC157040 JQ236820, JQ342863, JX122142, JX122157, JX133186, JX401449, JX827217, JX869971, JX971539, JX971546, JX971550, JX971552, NR025534, NR025535, NR025536, NR028977, NR041371, NR041374, NR041506, NR041507, NR042133, NR042204, NR042323, NR042446, NR042601, NR042602, NR042603, NR042604, NR042605, NR043538, NR043543, NR043555, NR043665, NR044005, NR044083, NR044218, NR044219, NR044339, NR044380, NR044381, NR044382

Outgroup sequences: AJ459411, NR025537, NR042134, NR042135, NR044404

Table 1C. Chitinophaga Sequences Used In Phylogenetic Analyses

Clone Sequences Relevant Sequences from GenBank KC110897, KC110907, KC110908, AB078049, AB078066, AB078067, AB078068, KC110915, KC110943, KC110950, AB267723, AB267724, AB278570, AB680728, KC110955, KC110965, KC110971, AB680761, AB680762, AB681008, AB681009, KC110981 AB681041, AB681043, AB681047, AB681048, AB681049, AB681050, AB681051, AB681052, AB681053, AB682428, AB682429, AF181568, AM237311, AM237312, AM237313, AM237314, AM237315, JQ659521, M62795, NR028638, NR028720, NR040909, NR040917, NR041375, NR041486, NR042512, NR043516, NR044559, NR044560,

Outgroup sequences: AB682366, GQ421847, KC110912, KC110922, NR041501, FN665659, FN665660, FN665661, KC110902, KC110979, NR044576, KC110946, AB682649, AB682408, AB682409, NR043672, NR043673

Table 2 Pedobacter Settings For Phylogenetic Trees

Multiple Tree Sequence Substitution Rates Gamma Branch Building Alignment Model Variation Categories Support Algorithm Tool Gamma Bootstrap Maximum T-Coffee K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- T-Coffee PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap T-Coffee Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates) Gamma Bootstrap Maximum MAFFT K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- MAFFT PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap MAFFT Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates) Gamma Bootstrap Maximum Kalign K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- Kalign PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap Kalign Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates) Gamma Bootstrap Maximum WebPRANK K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- WebPRANK PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap WebPRANK Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates)

Table 3. Ochrobactrum Settings For Phylogenetic Trees

Multiple Tree Sequence Substitution Rates Gamma Branch Building Alignment Model Variation Categories Support Algorithm Tool Bootstrap Maximum Uniform T-Coffee GTR 5 (1000 Likelihood Rates replicates) aLRT (SH- Uniform T-Coffee PhyML GTR 4 like Rates supports) Bootstrap Uniform T-Coffee Mr. Bayes GTR 5 (1000 Rates replicates) Bootstrap Maximum Gamma MAFFT K80 5 (1000 Likelihood Distributed replicates) aLRT (SH- Uniform MAFFT PhyML K80 4 like Rates supports) Bootstrap Gamma MAFFT Mr. Bayes HKY85 5 (1000 Distributed replicates) Bootstrap Maximum Gamma Kalign HKY85 5 (1000 Likelihood Distributed replicates) aLRT (SH- Uniform Kalign PhyML HKY85 4 like Rates supports) Bootstrap Gamma Kalign Mr. Bayes HKY85 5 (1000 Distributed replicates) Bootstrap Maximum Gamma WebPRANK K80 5 (1000 Likelihood Distributed replicates) aLRT (SH- Uniform WebPRANK PhyML K80 4 like Rates supports) Bootstrap Gamma WebPRANK Mr. Bayes HKY85 5 (1000 Distributed replicates)

Table 4. Chitinophaga Settings For Phylogenetic Trees

Multiple Tree Sequence Substitution Rates Gamma Branch Building Alignment Model Variation Categories Support Algorithm Tool Bootstrap Maximum T-Coffee GTR Uniform Rates 5 (1000 Likelihood replicates) aLRT (SH- T-Coffee PhyML GTR Uniform Rates 4 like supports) Bootstrap T-Coffee Mr. Bayes GTR Uniform Rates 5 (1000 replicates) Gamma Bootstrap Maximum MAFFT K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- MAFFT PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap MAFFT Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates) Gamma Bootstrap Maximum Kalign K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- Kalign PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap Kalign Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates) Gamma Bootstrap Maximum WebPRANK K80 Distributed with 5 (1000 Likelihood Invariant Sites replicates) aLRT (SH- WebPRANK PhyML K80 Uniform Rates 4 like supports) Gamma Bootstrap WebPRANK Mr. Bayes HKY85 Distributed with 5 (1000 Invariant Sites replicates)

Figure 1. Ochrobactrum: T-Coffee-PhyML Phylogram

Figure 2. Ochrobactrum: MAFFT-ML Phylogram

Figure 3. Ochrobactrum: Kalign-MrBayes Phylogram

Figure 4. Chitinophaga: T-Coffee-ML Phylogram

Figure 5. Chitinophaga: MAFFT-ML Phylogram

Figure 6. Chitinophaga: Kalign-ML Phylogram

Figure 7. Pedobacter : T-Coffee-PhyML Phylogram

Figure 8. Pedobacter : MAFFT-PhyML Phylogram

Figure 9. Pedobacter : Kalign-PhyML Phylogram

Waterman-Eggert score: 7002; 1520.4 bits; E(1) < 0; 98.6% identity (98.6% similar)

Figure 10A. LALIGN Plot: Ochrobactrum sp. clone Acro231 compared against the consensus sequence of Ochrobactrum clones

Waterman-Eggert score: 6666; 1540.9 bits; E(1) < 0; 94.3% identity (94.3% similar)

Figure 10B . LALIGN Plot: Chitinophaga sp. clone Acro210 compared against the consensus sequence of Chitinophaga clones

Waterman-Eggert score: 6532; 1447.5 bits; E(1) < 0; 94.0% identity (94.0% similar)

Figure 10C. LALIGN Plot: of Pedobacter sp. clone Acro279 compared against the consensus sequence of Pedobacter clones

CONCLUDING COMMENTS

BACTERIAL COMMUNITY IDENTIFICATION: 16S rRNA GENE MARKER SURVEY VERSUS SHOTGUN METAGENOMICS

The following discussion will compare differences between 16S rRNA gene

marker surveys and shotgun metagenomics, including the advantages, pitfalls,

and considerations to be taken before deciding which method to utilize in a

bacterial community identification study. The primary difference between the two

methods is that the 16S rRNA gene survey focuses on a single gene to identify

members of a bacterial community, whereas metagenomics focuses on the

totality of all genomic information in a sample. The first step involved in any

bacterial community identification project is to strategically plan what questions

are to be answered and the goals to be accomplished, then to decide which of

these methods are most applicable given time considerations and financial

resources available.

16S rRNA Gene Marker Surveys

The 16S rRNA gene is about 1500 nucleotides long and has become the

marker of choice for profiling bacterial communities since the mid-1990s (18).

There are universal primers available that allow for near full-length amplification of the 16S rRNA gene (14). The advantage of having these universal primers for amplification is that it permits for simple gene library construction and

sequencing, allowing for rapid identification of unknown isolates. A query

sequence can be input into NCBI’s BLAST to yield fast approximate matching or

exact identification without the need for additional laboratory work (1).

Furthermore, separate forward and reverse sequencing reactions are possible

using each PCR primer as well as multiple robust forward or internal primers,

leading to more refined sequence data in terms of both accuracy and ability to

generate robust assemblies of the entire gene.

On the other hand, single gene approaches are hampered by various limitations. A common problem associated with creating clone libraries of PCR-

amplified 16S rRNA genes is that it may not give an accurate representation of

the microbial community. High species diversity, taxon-specific amplification

problems, and undersampling can lead to some members of the microbial

community not being represented in the clone library (2, 7, 27). Another

drawback is that copy number of the 16S rRNA gene varies between bacterial

species. Species with low abundance of the gene may not be as readily amplified

during PCR as those with a high copy number, creating an amplification bias (9,

24). Despite these minor drawbacks, 16S rRNA gene surveys continue to be

performed to determine the microbial community composition in a variety of

samples and substrates (4, 11, 21).

Shotgun Metagenomics

Shotgun metagenomics involves using high throughput sequencing or

massively parallel pyrosequencing to sequence DNA directly from environmental

samples in order to study community structure and microbial biodiversity (5). As

with single-gene methods, shotgun metagenomics allows for the discovery of

bacterial species that cannot be cultured in the lab and as an additional

advantage it can aid in elucidating the genomes of unculturable species provided

those are represented with high abundance in the sample (23). Common

problems associated with shotgun metagenomics are that it applies a general

DNA extraction method to a sample containing many microbial organisms and

that a sample with low biomass may yield insufficient quantities of DNA for proper

analysis, letting rare species go undetected (13).

A limitation of shotgun metagenomics is that it is still very challenging and

cost-prohibitive to sequence microbial communities that are closely associated

with eukayotes, due to large eukaryote genome size and low microbial gene

coding density (13). During post-sequencing it is often difficult to separate

eukaryotic DNA from DNA belonging to the microbial community. This is one of

the reasons why we opted to perform a 16S rRNA survey of the bacterial

community associated with A. maximus rather than take the shotgun metagenomics approach. In addition, while the wet lab chemistry steps can be performed more efficiently and much faster relative to total species richness, shotgun metagenomics presents many downstream bioinformatics challenges in

90 processing and robust assembling of short sequencing reads into longer contigs

(19). Chimeric sequences caused by PCR amplification, intracellular sequence polymorphisms, pseudogenes, sequence contamination, and sequencing errors continue to pose particular challenges to assembly algorithms and procedures for assigning contigs to operational taxonomic units (20).

PCR-amplified 16S rRNA and Pyrosequencing

Recently pyrosequencing techniques have been applied to PCR-amplified

16S rRNA, bypassing the need to create traditional clone libraries (13). This low- cost high-throughput technology is allowing for a rapid increase in the amounts of

16S rRNA being generated. Traditional PCR cloning methods of the past looked at only a few dozen 16S rRNA sequences, whereas it is now possible to survey thousands of 16S rRNA sequences at a time (26).

Pyrosequencing technology, such as the 454 GS FLX Titanium XL+

System developed by Roche, allows for generation of about 1,000,000 shotgun reads per run, produces average read lengths up to 1,000 base pairs, and yields about 700 Mbs per run. Past problems with pyrosequencing technologies are that they produce short sequence read lengths ranging from 250 to 500 base pairs; reads of less than 500 base pairs are insufficient for phylogenetic analysis as they cannot be used to discern taxnonomic information down to the genus or species level (24). Other drawbacks of pyrosequencing include the high cost of reagents and high error rates associated with homopolymer repeats (15). Despite

91 these drawbacks, the long read lengths and deep sequencing capabilities of the

454 GS FLX Titanium XL+ have made it one of the most widely utilized sequencing technologies (10). Other popular second generation technologies, like the Illumina HiSeq 2500 are not ideal for 16S rRNA microbial community analysis. While the Illumina HiSeq 2500 can generate more than ten times the number of reads per run of the 454 GS FLX Titanium XL+, it suffers from short read lengths of about 100 nucleotides (3).

Sequencing Technologies and Future Considerations

The field of metagenomics is still in its infancy, however sequencing technologies and bioinformatics tools are developing at a rapid rate to accommodate the challenges of obtaining and analyzing large volumes of data in meaningful ways (8).

As third generation sequencing technologies develop and emerge, second generation technologies continue to improve in throughput and reliability, while becoming more cost-efficient. Second generation sequencing technologies developed by Roche, Illumina, Applied Biosystems, and IonTorrent make use of many individual cloned DNA fragments which are massively sequenced in parallel using enzymatic replication, whereas third generation sequencing technologies being developed by Helicos and Pacific Biosciences utilize single molecule sequencing methods (16). The Pacific Biosciences single molecule real time (SMRT) sequencing platform is the first sequencing technology to offer read

92 lengths averaging between 2000 to 3000 base pairs, however it suffers from error rates much higher than those seen in the second generation technologies, making it as of this writing not ideal for 16S rRNA microbial community surveys

(6).

If the third generation single molecule sequencing platforms overcome their high error rate hurdles, they might be able to supersede the second generation pyrosequencing technologies for microbial community studies. In addition to the much longer read lengths and bypassing the biases associated with an enzymatic amplification step, other advantages single molecule sequencing has over massively parallel sequencing is the ability to sequence the same DNA molecule multiple times for improved accuracy and the ability to easily sequence molecules with high GC content, homopolymer regions, and complex secondary structure (22). Detection of base modifications, methylation, and single nucleotide polymorphisms can be achieved much more readily with third generation single molecule sequencing platforms than with second generation sequencing technologies; recently the methylomes of six different bacteria was elucidated using SMRT sequencing (17). Ideally, single molecule sequencing could develop to the point where complete microbial genomes can be reconstructed from a single fragment library, greatly reducing the time and cost associated with post-sequencing assembly (12).

Another equally likely possibility is that as second generation pyrosequencing technologies continue to decrease in cost and increase in

93 throughput, they will make the development of these third generation sequencing platforms practically moot. Researchers of tomorrow have many considerations to take into account: the importance of sequencing longer reads, the potential for sequencing whole DNA strands, and cost-benefit analysis of chasing third and fourth generation sequencing technologies versus using new improvements associated with second generation technologies.

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