Temporal Dynamics of Escherichia Coli and the Microbiome

TEMPORAL DYNAMICS OF ESCHERICHIA COLI AND THE MICROBIOME

Jonathan Nathan Vernon Martinson

A dissertation submitted in partial fulfillment of the requirements for the degree

Doctor of Philosophy

Microbiology

MONTANA STATE UNIVERSITY Bozeman, Montana

April 2020

©COPYRIGHT

Jonathan Nathan Vernon Martinson

2020

DEDICATION

I dedicate this work to my partner (Kristin) and to my family: my dad (Goodwin), my mom (Joanne), and my brothers Philip and Vince for their constant support and encouragement.

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ACKNOWLEDGEMENTS

Seth Walk is a tremendous scientist and mentor. The environment he created in the laboratory allowed me to grow as a scientist and as a human. I thank Seth for allowing me to develop and drive a new project within the laboratory and providing me with guidance along the way. I’m always surprised by Seth’s depth of knowledge and his ability to reframe problems as opportunities.

The members of the Walk lab have made research very enjoyable. Thank you to

Susan Broadaway for providing encouragement and a meticulously organized laboratory.

I thank Mike Coryell, Brittany Jenkins, Qian Wang, Mark McAlpine, Ben Deuling, Josh

Matts, Jinxing Li, and Genevieve Coe, for the conversation and comradery. Thank you to the host of undergraduates that assisted me in data collection – especially, Garrett Peters.

Thank you to Nick Pinkham for assisting me with bioinformatics and invaluable coffee breaks.

Next, I would like to thank my dissertation committee: Michael Franklin, Blake

Wiedenheft, and James Wilking for providing feedback and advice throughout my PhD.

Much thanks to the Kopriva and Murdock foundation for providing me with resources to pursue my research.

I would like to thank my friends and fellow graduate students within the

Microbiology and Immunology department (you know who you are).

Finally, I would like to thank the participants that provided their time (and stool) to my project. This work would not be possible without their generosity.

TABLE OF CONTENTS

1. ESCHERICHIA COLI RESIDENCY IN THE GUT OF HEALTHY HUMAN ADULTS ...... 1

Abstract ...... 3 Introduction ...... 4 Temporal E. coli Dynamics in the Pre-PCR Era ...... 6 Temporal E. coli Dynamics in the PCR Era ...... 13 Theoretical Considerations and Mechanisms for Clonal Turnover ...... 21 Nutrient Competition ...... 22 Competition for Physical Space ...... 25 Direct Damage and/or Killing of Competitors...... 29 Bacteriocins...... 30 Phages ...... 30 Response (or the Lack of) to Cellular Stress ...... 35 Conclusions and Future Directions ...... 36 Needed Areas of Research ...... 37 References Cited ...... 39

2. RETHINKING GUT MICROBIOME RESIDENCY AND THE ENTEROBACTERIACEAE IN HEALTHY HUMAN ADULTS ...... 49

Abstract ...... 52 Introduction ...... 52 Methods ...... 53 Sample Collection and Processing ...... 53 Sampling and Characterization of Resident Clones...... 53 16S rRNA Sequencing and Analysis ...... 53 Results ...... 54 Study Participants and Stool Samples...... 54 16S rRNA Sequencing Reveals Similar Microbiomes Within Participants Over Time ...... 54 OUT/ASV Residency Varies Significantly ...... 55 Culture-Based Evaluation of Enterobacteriaceae in the Healthy Human Adult Gut ...... 58 Enterobacteriaceae Residency ...... 58 16S rRNA Sequencing Is Not Diagnostic for Enterobacteriaceae or Escherichia/Shigella ...... 58 Discussion ...... 60 References Cited ...... 62 Supplemental Figures and Tables ...... 64

TABLE OF CONTENTS CONTINUED

3. PHENOTYPIC PREDICTORS OF ESCHERICHIA COLI RESIDENCY IN THE GUT OF HEALTHY HUMAN ADULTS ...... 81

Abstract ...... 83 Introduction ...... 84 Materials and Methods ...... 86 Direct Competition Assay ...... 87 Cellulose Production ...... 89 Catalase Activity ...... 89 Antibiotic Resistance ...... 90 Results ...... 90 Cellulose Production is Not Significantly Different Among Residents and Transients ...... 90 Antibiotic Resistance Among Residents and Transients Does Not Differ ...... 91 Direct Competition in the Gut Sometimes Predicts Turnover Events ...... 92 Direct Competition Predicts Residency Time...... 92 Engraftment of Probiotics and Pathogen Colonization Resistance ...... 94 Discussion ...... 94 References Cited ...... 97 Figures and Tables ...... 100 Legends ...... 100 Figures...... 101 Tables ...... 106

4. CONCLUSIONS AND FUTURE STUDIES ...... 107 Conclusions ...... 107 Future Directions ...... 109 References Cited ...... 110

CUMULATIVE REFERENCES CITED ...... 111

APPENDIX ...... 126

LIST OF TABLES

Table Page

2.1 Summary of samples, sampling days, number of OTUs and residence time according to operationally defined estimates of time ...... 55

S2.1 Summary of non-E. coli Enterobacteriaceae characterization and identification by API 20E biochemical profiling ...... 78

S2.2 Clonotyping results for representative isolates of resident clones belonging to phylogroups A and B2.3 ...... 79

S2.3 Estimated diagnostics for ASV- and OTU-based analyses compared to culture-based results for Escherichia and Enterobacteriaceae ...... 80

S3.1 Antibiotic concentrations used in study and the percentage of clones resistant to each antibiotic ...... 106

S3.2 The number of turnover events potentially mediated by direct competition observed in vivo ...... 106

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LIST OF FIGURES

Figure Page

2.1 16S rRNA sequencing-based OUT analysis ...... 56

2.2 Stacked bar charts of microbiome residency using the average estimate ...... 57

2.3 Presence of Enterobacteriaceae clones ...... 59

2.4 Enterobacteriaceae clone residency using the average estimate ...... 60

S2.1 Schematic of Clermont et al. multiplex PCR method for phylogrouping E. coli and cryptic Escherichia clade isolates...... 64

S2.2 Flow chart showing the algorithm used to identify resident and transient clones...... 65

S2.3 Timeline of stool sample collection for eight participants...... 66

S2.4 Silhouette plots of OTUs and ASVs ...... 67

S2.5 Bray-Curtis dissimilarities for all pairwise comparisons between samples within participants with respect to time...... 68

S2.6 16S rRNA sequencing-based ASV analysis ...... 69

S2.7 Procrustes analysis of OTU- and ASV-based NMDS for each participant...... 70

S2.8 Alpha diversity as Inverse Simpson’s estimates based on OTUs and ASVs ...... 71

S2.9 Beta diversity estimates based on OTUs and ASVs ...... 72

S2.10 Example of residency period estimation...... 73

S2.11 A core microbiome does not exist between all participants ...... 74

S2.12 Stacked bar charts of microbiome residency using the Min and Max estimates and for OTUs and ASVs ...... 75

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LIST OF FIGURES CONTINUED

Figure Page

S2.13 Relative abundance of E. coli phylogroups and non-E. coli Enterobacteriaceae within participants through time...... 76

S2.14 All Enterobacteriaceae clones plotted according to their log 10 transformed Max and Min residence time in days ...... 77

3.1 Cellulose production is unrelated to residency...... 101

3.2 Catalase capabilities are indistinguishable between residents and transients ...... 102

3.3 Resident and transient E. coli are not different in antibiotic resistance ...... 102

3.4 Direct competition may mediate residency...... 103

3.5 Resident clones can directly inhibit probiotic and pathogenic E. coli ...... 104

S3.1 Cellulose production increases likelihood of resistance to antibiotics ...... 104

S3.2 Resident killers inhibit clones more often than transient killers ...... 105

ABSTRACT

Over the past two decades, our understanding of the gut microbiome has increased dramatically. However, most studies involving healthy adults have relied almost exclusively on cross-sectional design, negating the changes occurring within an individual’s microbiome through time. With this, we performed a small longitudinal study over a period of ~2 years with a cohort of 8 healthy adults. By sequencing the DNA encoding the 16S ribosomal RNA gene, we assessed the community level change in this cohort through time. Similar to previous findings, we found that using these methods there was remarkable stability through time with nearly 50% of the microbiome remaining the same throughout the study period in the participants. However, analysis of 16S ribosomal RNA sequences limits taxonomic resolution. By cultivating members of the Enterobacteriaceae, we found that turnover at the clone-level (below the species level) was common. Within the Enterobacteriaceae, Escherichia coli was the most numerically dominant species and most often observed as a long-term member of the gut (i.e. resident). Longitudinal analysis of Escherichia coli revealed that some phylogenetic groups within the species are more often long-term residents than other phylogroups. We next assessed the means by which the resident E. coli were capable of establishing and maintaining themselves in the gut. We found that residents were much more likely to produce antagonism (inhibition of other clones) than the E. coli that did not reside in the gut long-term. 1

CHAPTER ONE

ESCHERICHIA COLI RESIDENCY IN THE GUT OF HEALTHY HUMAN ADULTS

Contribution of Authors and Co-Authors

Author: Jonathan N. V. Martinson

Contributions: wrote, edited, and prepared manuscript

Co-Author: Seth T. Walk

Contributions: wrote, edited, and prepared manuscript

Manuscript Information

Jonathan N.V. Martinson and Seth T. Walk

EcoSal Plus

Status of Manuscript: ____ Prepared for submission to a peer-reviewed journal __X_ Officially submitted to a peer-reviewed journal ____ Accepted by a peer-reviewed journal __ _ Published in a peer-reviewed journal

ASM Press Submitted 14 January 2020

ABSTRACT

Escherichia coli is perhaps the most studied organism on earth, but several significant knowledge gaps remain regarding its ecology and natural history. Specifically, the most important factors influencing its life as a member of the healthy human gut microbiome are either under-evaluated or currently unknown. Interesting E. coli population dynamics have been observed over the past century from a handful of temporal studies conducted in healthy human adults. Early studies using serology up to the most recent studies based on

DNA sequencing and genotyping have all identified long-lived residents and short-lived transients. This review summarizes these discoveries and others that focused on identifying the underlying mechanisms that lead to establishment and maintenance of E. coli residency in healthy human adults. Many fundamental knowledge gaps remain and are highlighted to hopefully promote future studies in this exciting research area.

INTRODUCTION

E. coli was first isolated and described by the Bavarian doctor, Theodor Escherich, in

1885 (1). As a pediatrician, Escherich was interested in the role of microbes in digestion and intestinal disease, primarily because diarrhea was a leading cause of infant mortality in Europe at the time (2). In an 1885 article, Escherich wrote, “… it would appear to be a pointless and doubtful exercise to examine and disentangle the apparently randomly appearing bacteria in normal feces and the intestinal tract, a situation that seems controlled by a thousand coincidences.” Despite this cynicism, Escherich cultured, isolated, and used microscopy to describe bacteria present in neonatal stool samples at birth and throughout the first few months of life. As a result of his “pointless and doubtful exercise”, Escherich used Christian Gram’s newly developed staining technique to describe a Gram-negative, rod-shaped bacterium that appeared to be common among neonates (1). This bacterium, which he called Bacterium coli commune, was capable of growing on simple nutrient sources to form “a massive luxurious deep growth” (1,2). Bacterium coli commune was renamed Escherichia coli in 1919 to honor Theodor Escherich (3), and somewhat remarkably, perhaps the first E. coli isolated by Escherich was recently rediscovered and sequenced (4). NCTC86, the original strain designation at the National Culture Type

Collection (London, UK), was free of known pathogenicity and virulence genes and belonged to one of the four main phylogenetic lineages described for E. coli (phylogroup

A). The strain also belonged to multilocus sequence type 10, which is still commonly observed in healthy children and adults (5–7). Given its relevance to health and its ability 5 to be easily cultivated and manipulated in the laboratory, E. coli has served as a model organism to understand biology at its deepest levels.

E. coli is a species of almost exclusively non-pathogenic bacteria. In cross-sectional studies of human adults, E. coli is present is a member of the intestinal microbiome of over

90% of individuals (7). However, E. coli ecology has been biased heavily towards pathogenic and antibiotic resistant strains, and remarkably little is known about biotic and abiotic factors influencing the abundance and distribution of non-pathogenic strains. E. coli is a pioneering species of the human gut, being one of the first bacteria to colonize neonate at birth (8). As a facultative anaerobe, E. coli may help deplete oxygen in the gut of newborns, creating a hospitable environment for strict anaerobes to colonize and become dominant (8). Given their role in pioneering the gut, their capacity to produce vitamin K

(9), and their ability to confer colonization resistance (protection against pathogens) (10), human associated E. coli should be considered mutualists even though much of the literature refers to them as ‘commensal’ (11). Over the past century, several research groups have begun to ask important ecological questions about E. coli, including: ‘How do populations change through time?’ and ‘What causes these changes?’ Remarkably little is known about such dynamics, due in part to the above mentioned emphasis on pathogens but also due to the use of cross-sectional rather than temporal study designs, the former of which captures only a snapshot of population-level diversity. More data and different approaches are needed to demystify E. coli dynamics.

Temporal E. coli Dynamics in the Pre-PCR Era

The study of natural dynamics in bacteria first requires the ability to identify the same individual in different samples and a variety of phenotypic and genetic techniques have been developed for E. coli with this purpose in mind. E. coli were originally identified using serotyping, which differentiates isolates together based on agglutination reactions between antisera (usually raised in rabbits) and bacterial antigens of surface-exposed oligosaccharides (O), capsule (K), and flagella (H) (12). As of 2016, 186 O-antigens and

53 H-antigens have been described to serotype E. coli but due to technical difficulties, K- antigens are rarely used (12). An E. coli naming convention based on unique combinations of O- and H-antigens was long used for clinical and epidemiological purposes. More recently, DNA sequencing revealed that serotyping was not as discriminant as originally believed and recombination in genes encoding for O- and H-antigens can masked phylogenetic relatedness in certain cases (e.g. distantly related E. coli may have the same

O-:H- designation) (13,14). Regardless, studies of temporal E. coli dynamics in humans that utilized serotyping first described trends still observed today with more advanced methodologies (e.g. genome sequencing).

The second requirement for studying natural bacterial dynamics is the ability to culture and isolate the individual(s) under investigation. DNA sequencing techniques can theoretically identify individuals without the necessity of culture. Such is often not the case because individual(s) of interest are either present below detection limits or become so at some point during the study period. Cultivation is still required in most situations to enrich or select for bacteria of interest, and especially when the point of the study is to identify 7 individuals as opposed to higher taxonomic groups (e.g. phylogenetic groups, species, genera, etc.). Thus, it is almost exclusively through the lens of cultivation that E. coli dynamics have been studied. Enteric (gut inhabiting) bacteria, like E. coli, are adapted to many conditions in the human gastrointestinal tract that act as barriers to other bacteria, such as stomach acidity (the baseline pH in healthy humans is approximately 1.5 (15)) and bile (a complex mixture of reactive metabolites and detergents produced by the liver and secreted by the gall bladder into the small intestine (16)). Alfred MacConkey took advantage of this physiological filter to create a selective medium containing bile and crystal violet to inhibit Gram-positive bacteria (17,18). Furthermore, MacConkey included a pH sensitive dye to determine whether bacteria growing on solidified (agar-containing) medium fermented lactose (4,18). This selective and differential medium, now known as

MacConkey agar, still serves as an important cultivation technique for E. coli and nearly all currently named members of the Enterobacteriaceae family.

The earliest study of temporal E. coli dynamics in human adults that we are aware of was published in 1902 by K. Totsuka, a Japanese scientist in residence at the Institute for

Infectious Disease in Berlin under the direction of Robert Koch. Totsuka self-collected stool samples on a weekly basis for four months and evaluated up to 32 isolates per sample.

Totsuka first tested for differences in motility, indole production, acid production, and the ability to coagulate milk between different isolates (14). Using an early form of serotyping, he then described gradual shifts from one antigenic type to another (19,20). The emergence of different serotypes over the study period that Totsuka observed was in contrast to the serotype stability/homogeneity observed in a temporal study in infants a few years earlier 8

(1899) (20,21). Little work was performed to follow up on the Totsuka study until 1943 when Wallick and Stuart described E. coli serotype dynamics in a single man over a 14- month period (20). They found at least 4 distinct serotypes that persisted for months at a time, some of which overlapped (i.e. simultaneously colonized). Of the 650 isolates evaluated, 85.3% were antigenically identical to one of 10 isolates used to produce antisera, suggesting that the bulk of population-level genetic diversity was the result of a handful of closely related isolates. Interestingly, they observed that some serotypes emerged, became dominant for several months, and then disappeared; thus providing the first evidence that closely-related E. coli stably colonize the human gut but are inevitably replaced. Wallick and Stuart also discovered that E. coli isolated from the study subject on the same day as

E. coli isolated from a family member were antigenically identical, suggesting that transmission between individuals in close contact with one another may explain at least some of the observed dynamics. Moreover, only one E. coli serotype observed in the study subject was identical to 100 isolates from unrelated individuals (1 isolate considered per person), which on face value may seem like this serotype was shared between distantly related individuals. However, the authors found multiple examples where isolates belonging to the same serotype were phenotypically distinct (i.e. utilization of 6 different carbon sources differed) and so it seems possible that this single example of a serotype match was not due to widespread sharing of an E. coli lineage. Since the single isolate from the non-relative was not evaluated using biochemical testing, it is impossible to know which of these scenarios was more likely. Regardless, this study provided fundamental information regarding E. coli in the adult human gut. Wallick and Stuart’s work was 9 conducted at Brown University (Providence, RI, USA) and was interrupted by World War

II when the study subject (and lead author) was drafted into the military. As an interesting and noteworthy historical coincidence, two German scientists, Kauffman and Perch, reported similar results the same year as Wallick and Stuart (1943) with data from two study participants, each followed for four months (22).

Sears et al. further explored E. coli serotype dynamics in human adults by studying five adult participants in two separate studies that were sampled for between three and 30 months (23,24). The observed dynamics were again similar to those reported previously, but importantly, the authors defined E. coli serotypes persisting for “relatively long periods of time” as residents and those persisting for short periods (days to weeks) as transients.

Consistent terminology has followed in nearly all subsequent studies. In addition to confirming previous findings, Sears et al. also generated novel information. They found that, in general, only 1-2 serotypes were present in an individual at any one point in time, meaning that most individuals host at least one resident E. coli. The authors also attempted to determine the cause of transitions from one resident to another and found that diarrhea, gastroenteritis, and usage of certain antibiotics did not explain transition events in their study. Some participants even volunteered to ingest cultured isolates of known serotypes multiple times at high doses (106-107 CFU) but interestingly, none of these became established and sometimes were not even transiently observed. One of the participants even ingested 108 CFU of a resident that they had previously hosted and lost. Again, the isolate did not become established. These studies clearly demonstrated that E. coli residence in the 10 adult human gut is not explained by simply being exposed and suggest that other important factors are involved.

Sears et al. also tested the hypothesis that residents produced antibiotic substances (now known as bacteriocins) that prevented invasion of other clones “by possessing a wide range of antibiotic activity against other E. coli strains.” The authors made no comment on whether any of the clones were lysogens or if they produced viral plaques against other isolates. After screening several hundred strains collected during the study with a test similar to the interference assay used by Halbert (25) – a method similar to the double- layer plaque assay in which an antibiotic producer is dropped onto a lawn of target bacteria.

Notably, the authors found that only two residents produced widespread antagonistic activity. They concluded that the residents, as a whole, did not produce antibiotic activity more often than transient strains. Moreover, the most antagonistic isolates observed were, in fact, two transients. These results call into question the extent to which bacteriocins contribute to resident turnover.

Harriette Robinet added significantly to the work of Sears et al. in 1962, when she followed E. coli serotype dynamics in six adults and collected serum samples from each participant at the same time as stool sample collection (26). Robinet wanted to test the hypothesis that host antibody production caused resident E. coli turnover. To do so, she used a hemagglutination test to quantify serum antibody levels in each participant that were active against resident isolates from the same individuals. Robinet concluded, “The study revealed that serotypes were lost and acquired in the bowel of hosts irrespective of high or low antibody levels specific for the antigenic types.” (antigenic types = serotypes). 11

Although not definitive proof, this study suggested that host antibody production does not explain resident E. coli dynamics. Subsequent to this study, Robinet’s group explored the relationship between resident turnover and bacteriocin production with the same isolate collection (27). They tested whether isolates collected during a 1-month period were active against E. coli collected the previous month. They also tested whether the isolates could inhibit a bacteriocin-sensitive control strain (E. coli phi). The group found that residents produced bacteriocins more often than transients and that serotype diversity within samples was lower when residents produced colicins and higher when residents did not produce colicins. Although the authors commented on the fact that some of the E. coli inhibited clones present in the previous month’s sample, they did not state the frequency at which this was observed.

Shooter et al. questioned previous results based solely on O-antigen typing by including

H-antigen typing to increase discriminatory power. In a three month study of nine middle- aged adults (14,28), the authors found that many residents with identical O-antigen carried different H-antigens, suggesting that earlier studies may have grouped together isolates that were actually different. Ultimately, this study illustrated the need for more discriminant techniques to identify E. coli “clones”, or cells that belong to the same asexually reproducing population, or lineage.

A technique called multilocus enzyme electrophoresis (MLEE) was applied to E. coli dynamics in 1981, when Caugant, Selander, and Levin provided the first glimpse of the population genetic structure of resident and transient E. coli from a single human adult over an 11-month period (29). For the first time, temporally collected E. coli were defined 12 through the lens of multilocus genotypes and not by serotyping. Overall, results confirmed previously observed resident and transient patterns, but more importantly, the study ended an ongoing debate at the time as to whether recombination or the acquisition and loss of individual clones explained the large amount of genetic diversity observed among human

E. coli (30–32). Caugant et al. demonstrated beautifully that clonal diversity was generated by the gain and loss of novel clones in the same host (29,33). Caugant et al. evaluated changes in plasmid and/or episomal DNA content using agarose gel electrophoresis within what they believed were identical clones isolated over time. Extensive gain/loss events were observed over time. Recent work in our lab has shown that residents belonging to the same multilocus genotype but not the same clonal lineage (i.e. do not belong to the same asexually reproducing population) can co-occur and/or replace one another in human adults

(5). So in retrospect, and consistent with Shooter et al., at least some results reported by

Caugant may have been due to the discriminatory power of their technique and not actual biology.

MLEE was also used to characterize E. coli dynamics in members of a British Antarctic

Survey residing at the same research station (34). Study participants lived in the highly controlled setting of an Antarctic base with a small number of individuals (n=16, all males) and no additional human contact over an extended period. E. coli were isolated from fecal swabs self-collected every two weeks over a 32-week period. In total, 1,447 isolates were collected, but only 592 grew after being transported to a laboratory in the United Kingdom, strongly suggesting isolation bias. Similarly, of the 592 isolates cultured, only 269 were identified as E. coli using biochemical testing (API-20E test kit), which again, is not 13 consistent with other studies using similar isolation methods (i.e. the majority of isolates from MacConkey agar plates used in this study should have been E. coli (5)). These isolates were characterized by MLEE, assigned genotypes, and profiled for plasmid content.

Resident clones (present in more than one sample) were observed in 14 of the 16 participants and half of the individuals were colonized for the majority of the study period.

None of the clones were observed for the entirety of the study, which again may have resulted from technical difficulties but is consistent with previously observed resident clonal turnovers (5,20,23,29). Regardless, there was extensive sharing of clones between participants in this study, suggesting that inter-individual transmission was common.

Interestingly, shared clones were almost always resident in one participant and transient in other participants where they were observed, highlighting the possibility that they were uniquely adapted to one individual’s gut but not to others. It should be emphasized that this study utilized methods identical to Caugant et al. and so the clonal resolution was similarly limited. Consistent with this potential limitation, the researchers showed that isolates belonging to one of the clones observed in several subjects had three different plasmid profiles. While it is possible that this clone diversified while spreading between hosts, it seems equally likely that what was believed to be a single, widespread clone was actually three separate ones.

Temporal E. coli Dynamics in the PCR Era

The polymerase chain reaction (PCR) significantly enhanced the discriminatory power of bacterial genotyping techniques and began replacing MLEE for E. coli in the 1990s. E coli phylogenetic groups, or phylogroups, originally defined by MLEE (32,35,36) were 14 later resolved in greater detail with Sanger sequencing (multilocus sequence typing,

MLST) (37,38) and most recently by whole genome sequence-enabled “phylogenomics”

(39,40). As a result of these phylogenetic analyses, one of the most often-used technique for differentiating E. coli was a multiplex PCR protocol that effectively bins isolates into one of seven well-supported phylogroups (41,42). Repetitive DNA analysis via PCR has also received considerable attention since the discovery of repeated DNA sequences interspersed between genes in bacteria (43,44). A 35 base pair palindrome (43,44) dubbed the REP sequence (repetitive extragenic palindromic) was shown to directly precede or flank certain protein coding genes. Because 1) the REP sequence is distributed hundreds of times throughout nearly all E. coli genomes but at variable locations and 2) short oligonucleotide primers successfully anneal to REP sequences (45), a single PCR produces dozens of amplicons that when sized (agarose gel electrophoresis) and compared, can

“fingerprint” isolates with discriminatory power approaching and often achieving clonal resolution. The REP sequence was the first of many intergenic DNA repeats detected in bacterial genomes (46) and other PCR protocols were developed (45,47). For example,

ERIC (Enterobacterial Repetitive Intergenic Consensus) sequences and simple trinucleotide repeats (GTG-5) have been used as fingerprinting PCRs (45,47,48). GTG repeats are the most common trinucleotide repeat found in E. coli genomes (49,50) and the corresponding PCR has been shown to be the most discriminant to differentiate E. coli

(47). All of the above PCR-based assays have been extensively used for tracking clones in temporal E. coli studies because of their relative ease of use and low-cost. 15

Although, fingerprinting PCRs provided a means to discriminate between clones, results are not easily comparable between institutions and do not correlate well with phylogenetic relationships (51). By Sanger-style sequencing several conserved housekeeping genes, called multilocus sequence typing (MLST), overcame these particular drawbacks. MLST was first applied to identify different phylogenetic lineages of Neisseria meningitidis (37) and soon after to E. coli (38). Isolates with identical sequences, called sequence types (STs), have been used extensively for epidemiologic purposes. MLST databases are now available based on different sets of genes and thereby different definitions of STs (52–55). Regardless, MLST provided a much sought after balance between discriminatory power and phylogenetic resolution.

Until the advent of (relatively) inexpensive and reliable full genome sequencing, pulsed-field gel electrophoresis (PFGE) was the gold standard for epidemiologic source tracking during infectious disease outbreaks because of its ability to discriminate clones, reproducibility, and portability. This method utilizes restriction enzymes to cleave an isolate’s chromosome in specific places which are then size resolved with gel electrophoresis. This method is noted to be time consuming and technically challenging

(56,57). Due to this, this method is rarely used in temporal studies of human-associated E. coli.

Johnson et al. and Damborg et al. characterized temporal E. coli dynamics from adults and children of co-habitating family members and their respective pet dogs (58–60).

Johnson et al. used MLST and PFGE to identify 14 E. coli clones from a family of five and their dog over a 3 year period. Clone dynamics within this study were difficult to discern 16 given the infrequent sampling regimen (2 to 103-week gaps). Still, Johnson et al. found 4 clones that occurred in consecutive samples suggesting that they were resident over a 4 to

45-week period. They also found that 5 clones were recurrent within an individual during the sampling period (i.e. the clones appeared in non-consecutive samples), suggesting that re-colonization was common or they were below the limit of detection. Johnson et al. also observed clones that were shared among family members. Notably, they detected a clone shared between the family’s dog and four of five human family members. This particular clone caused UTI in the dog and belonged to ST73 and the phylogroup B2, both of which are commonly associated with UTIs in humans. The mother in the same household experienced a UTI during the study period but the causative clone was another clone, ST95

(also phylogroup B2). This clone was also observed in samples from other family member, suggesting that it may have been transmitted.

Damborg et al. temporally collected fecal swabs from 5 households (18 humans, 13 dogs) to describe E. coli clone dynamics within and between individuals living together.

Ten fecal swabs were collected over a 6 month period with sampling intervals ranging from

3 days to 36 days. After streaking these swabs onto MacConkey agar, a single colony was randomly selected and differentiated using amplified fragment length polymorphism

(AFLP) to differentiate and group isolates and the Clermont multiplex PCR to identify phylogroups. AFLP is a technique that uses restriction enzymes to digest chromosomal

DNA which is then ligated to oligonucleotides that act as a template for PCR amplification.

These PCR products are then sized using gel-capillary electrophoresis. Damborg et al., identified 154 unique AFLP profiles (clones) from 322 isolates analyzed. Among humans, 17 they found that isolates belonging to phylogroup A was most often recovered (32%). 21 clones were found to be resident (defined as present for greater than 3 weeks) in the participants. Damborg et al., also observed clone sharing among family members and their respective pets. These shared clones belonged most often to phylogroup B2 and D, both of which are commonly associated with UTI. Both Johnson et al. and Damborg et al. provided strong evidence that clones maybe acquired from cohabitating family members/animals often asoociated with UTIs.

McBurney et al. assessed residency dynamics of Enterobacteriaceae from two adults

(a 25-year-old female and a 50-year-old male) over a 1-year period (61). In this study, stool samples were collected on a monthly basis and 10-15 colonies were picked randomly from

MacConkey agar. The authors observed a large range of Enterobacteriaceae abundance

(102 to 109 CFU per gram of stool) and used ribotyping to understand genotypic diversity.

Ribotyping is a general genotyping technique that takes advantage of conserved nucleotide sequences in rRNA encoding genes. Briefly, DNA was extracted, digested with restriction enzymes, and electrophoresed on an agarose gel. DNA was then transferred onto a nylon membrane and radiolabeled with oligonucleotide probes specific to 16S rRNA-encoding genes. The resulting banding patterns were visualized using autoradiography and grouped into ‘ribotypes’ based on pattern similarity. Previous work by the group using PFGE supported that the ribotyping protocol was sufficient for identifying clonal E. coli populations. Ribotyping of isolates revealed that the male subject hosted a single resident clone whereas the female subject hosted many different clones. Both individuals hosted resident and transient E. coli. Interestingly, the female subject underwent antibiotic 18 chemotherapy (amoxicillin for 7 days) to treat a respiratory infection, which appeared to significantly impact Enterobacteriaceae diversity. Prior to the antibiotic, representative E. coli clones were found to be sensitive to ampicillin, doxycycline, and trimethoprim. After the antibiotic treatment, the E. coli clones present in her stool had changed and were resistant to these drugs. The authors concluded that amoxicillin selected for resistant E. coli and while this is a possibility, amoxicillin did not cause the observed turnover of resident E. coli in this subject because all Enterobacteriaceae were below detectible levels for at least four weeks prior to chemotherapy. This study highlights the potentially important influence of antibiotics on temporal E. coli dynamics but was not designed to directly test for these effects. It should also be noted that ribotyping as described in this study has not been used in any other temporal E. coli study that we are aware of, so it is impossible to know how these results compare to those of other studies.

Following the work of McBurney, a Ph.D. student, Sashindran Anantham, published a dissertation describing the dynamics of antibiotic resistant E. coli in healthy adults (28).

We recognize that as a dissertation, this work has not been peer-reviewed to journal standards, but since it presents results that are relevant to temporal E. coli dynamics in human adults, we feel it is appropriate to highlight some of the more important findings.

Anantham collected fecal samples from 11 adults over a 4-year period with the primary goal of determining whether antibiotic resistant E. coli persisted in healthy humans even in the absence of antibiotic use. Most of the study participants provided fewer than 5 samples over the study period, but in contrast to McBurney et al., antibiotic resistant residents were observed in the absence of antibiotic treatment. The long-term residents 19 belonged primarily to the phylogroups A, B2, and D, and of the 73 clones identified, 34

(47%) were resistant to at least one antibiotic. Long-term residents also belonged to ST10 and ST95, which is similar to results from our lab discussed below (5,62). Anantham also quantified the carriage of genes expressing so-called “virulence factors”, or factors associated with E. coli pathogens. It is important to note that just because a factor is cited or referred to as a virulence factor, only a subset of these factors have been experimentally validated to influence virulence in humans and that most are defined simply due the fact that pathogens carry the gene(s) or that they influenced virulence in an animal model. In other words, these factors likely play important roles beyond (and independent of) virulence. Regardless, over 80% of the long-term residents identified by Anatham carried genes encoding for fimbriae, capsule biosynthesis, and a siderophore receptor previously associated with E. coli pathogens, suggesting that these particular factors may be better referred to as residency as opposed to virulence factors per se. Remarkably, one of the study participants traveled abroad for a year, was treated with the antibiotic, tetracycline, and yet did not lose the tetracycline-sensitive resident they originally hosted. Since both antibiotic treatment and travel were found to displace residents in previous studies (24,63), it is possible that the study subject was either recolonized by this particular resident or that travel/tetracycline have minor effects on clonal turnover.

Our lab recently evaluated temporal microbiome and Enterobacteriaceae dynamics in a cohort of eight healthy adults (5). Participants were sampled on a ~biweekly basis for six months to two years. Stool samples were plated onto MacConkey agar and 95 isolated colonies were analyzed per sample (both lactose-fermenting and non-fermenting colonies 20 were included). Our protocol was able to: 1) identify all resident clones in each of the eight subjects, 2) the species-level identity of all resident clones, and 3) the phylogroup that each resident E. coli belonged to. A few important results from this study are as follows. Of the

32,470 isolates evaluated, 87% were E. coli, which clearly demonstrated the dominance of this species among other Enterobacteriaceae in healthy human adults. Isolates belonging to phylogroups A, B2, and F were more likely to be residents, suggesting that these phylogroups are better adapted to the human gut. Surprisingly, residents often did not ferment lactose, which is noteworthy because they are often not recognized as E. coli in studies using MacConkey agar. Finally, and perhaps most significantly, the amount of clonal turnover varied markedly between study participants. For example, one participant simultaneously hosted three distinct residents for over 400 days, whereas another subject hosted primarily one resident at a time, sometimes with 30- 50-day gaps between any E. coli being observed, and none of the residents persisting longer than 54 days.

In summary, resident E. coli have been identified in temporal studies of human adults dating back well over 100 years and using a variety of microbiologic and molecular methodologies. It is becoming clear that E. coli are not equal with respect to their ability to reside in the adult human gut and that phylogroups A, B2, and F are better adapted to this environment. Residence time and residency dynamics differ markedly between individuals and potential explanatory factors, such as travel, antibiotic use, phage/bacteriocin production, and acute episodes of diarrhea/gastroenteritis do not always lead to clonal turnover. Clearly, more research is needed to understand this important aspect of microbial ecology. 21

Theoretical Considerations and Mechanisms for Clonal Turnover

Many factors seem to alter the genus and higher taxon composition of the adult human gut microbiome, including antibiotics, heavy metals, diet, travel, and disease (63–65).

However and although not investigated in nearly enough detail, resident E. coli clones often turn over without an obvious cause, leading to the question, “Is resident turnover a random process?”. Some studies suggest turnover is not random and that competition between different clones is an important factor (66,67). In the most general terms, the competitive exclusion principle, or Gause’s Law, proposes that two organisms cannot coexist on the same limiting resource (68) and for the purposes of an ecological framework for clonal interference, this law seems like reasonable place to begin. That said, this principle establishes both the type (negative) and outcome of the interaction (one excludes the other), but it does not identify the mechanism(s) involved. Both direct and indirect “competitive phenotypes” exist in bacteria (69), where direct competition includes damaging or killing competitors (e.g. via phage or bacteriocin production) and indirect competition include passive resource utilization (e.g. the more rapid use of a carbon source or occupancy of colonization sites). While there is little consensus as to which competition mechanism, if any, is the most important for resident turnover in the adult human gut, we present evidence for the competitive mechanisms that have been most commonly explored. We also discuss possible environmental selection unrelated to competition that may be relevant to clonal dynamics. For example, it is possible that resistance to small molecules/compounds 22 produced by non-competing members of the microbiome and/or resistance to abiotic factors are most important. These possibilities are also discussed.

Nutrient Competition

The nutrient niche hypothesis proposed by Rolf Freter suggests that competition for nutrients select for species that most efficiently use them, making these more likely to colonize and persist (66,70,71). This hypothesis was supported by experiments performed in continuous flow reactors seeded with a mouse cecal microbiome (70,71). The primary force controlling the abundance of E. coli within reactors was the concentration of a specific nutrient that members had specialized on (71). Inhibitory substances produced by other microbiome members were found to have an effect on E. coli growth but did not appear to be the primary driving force affecting overall abundance (71). Thus, in general terms, Freter believed the overall presence-absence of E. coli and other species (i.e. richness) of the gut microbiome was explained by interactions between inhibitory substances and nutrient availability.

E. coli do not readily colonize the gut of conventional lab mice most likely due to an innate function of a mature, climax community, termed “colonization resistance”.

Streptomycin pre-treatment ameliorates this resistance, allowing Enterobacteriaceae to colonize and in some cases to cause disease (72). Cohen and Conway used a streptomycin- treated mouse model to introduce E. coli strains into the gut and study their nutritional requirements. They demonstrated that carbohydrate metabolism predicted gut colonization success (73). Several studies published by this group showed that E. coli colonized the mucus layer of the murine epithelium and so much of their work focused on catabolism of 23 sugars available in mucus, such as arabinose, fucose, galactose, gluconate, hexuronates, lactose, mannose, N-acetylglucosamine, N-acetylgalactosamine, N-acetylneuraminate, ribose, and sucrose. Mutant strains lacking specific catabolism pathways were generated and competed in vivo against iso-genic wild-type counterparts (73). These experiments showed that only a subset of the total carbon sources an E. coli clone can possibly catabolize are actually utilized in the gut, suggesting that the realized niche is indeed more narrow that the fundamental niche of E. coli (74,75). Cohen and Conway also showed that a non-pathogenic strain could competitively exclude a pathogen only if the strains utilized the same carbon sources. Another important suggestion from this body of work was that E. coli’s metabolic niche is relatively stable because mucus is host derived and not subject to the compositional changes due to shifts in diet.

Although Cohen and Conway’s work furthered understanding of nutrient competition in the gut, it is possible that some of the methods employed added more than minimal bias.

For example, streptomycin treatment causes inflammation in the murine gut, which alters the expression of inducible nitric oxide synthase which, in turn, alters levels of secreted sugars in the epithelial mucus layer (76,77). Under normal conditions, the levels of oxidized sugars like galactarate and glucarate, are low compared to levels following streptomycin treatment. Furthermore, streptomycin-induced alteration of epithelial and immune cell function (akin to low-grade inflammation (77)), resulting in increased levels of the E. coli electron donor, lactate, and electron acceptors, oxygen (aerobic) and nitrate

(anaerobic). This shift in nutrients allows E. coli and other Enterobacteriaceae to outcompete strict anaerobes and increase in abundance. Such alteration to the metabolic 24 landscape raises legitimate questions as to whether the streptomycin mouse model is a reasonable model to study clonal dynamics in the context of homeostasis (i.e. non-disease conditions). Similarly, Cohen and Conway almost exclusively considered laboratory strains that have been passaged in vitro for many years (e.g. MG1655, K-12). It seems likely that mutations may have accumulated in such strains that mask their true (original) phenotypes. Competition for trace metals has also been demonstrated to be important in competitive exclusion in the gut. For instance, one of the mechanisms of action of the probiotic E. coli Nissle 1917 is suggested to be production of siderophores (78).

Siderophores are small molecules produced by polyketide synthases that capture free iron.

Since iron is an essential micronutrient for nearly all living cells, it is feasible that levels in the gut either are or transiently become low enough to become limiting, especially during periods of stress (79). Anantham and Wold found that the ability to produce siderophores was common among resident E. coli in humans (28,80). Thus, it appears that competition for iron may play an important role in resident turnover.

Finally, the nutrient niche hypothesis has been criticized because it neglects how variation in the concentration and composition of nutrients in the gut over time affects niche stability (66). Since nutrient composition of the gut changes dramatically throughout the day (between meals) as well as between days, it is difficult to understand how a single nutrient could be responsible for long-term residency. Little is known about the spatial heterogeneity (patchiness) of the gut within and between individuals, but Pereira and Berry suggested that successful residents must have the metabolic flexibility to utilize different nutrients as they become available (66). Taken together, whether and/or the degree to 25 which competition over limiting nutrients drives population-level diversity in the human gut remains controversial.

Competition for Physical Space

At the population level, Freter suggested that physical space was a key determinant of clonal diversity in the gut (70). Results from mouse experiments showed that single E. coli strains could not invade or persist when orally inoculated into conventionalized germ free mice (i.e. germ free mice colonized with a full microbiome by cohousing with other conventional mice), whereas the same strains readily colonized when inoculated into germ free mice two days prior to conventionalization (70). Since the E. coli strains being evaluated were able to colonize/establish, the colonization resistance observed in conventionalized mice cannot be easily explained simply by competition of limiting nutrients (i.e. it is assumed the same nutrients existed in all conventionalized mice). The evidence that bacterial “adhesion sites” were the determining factor came from a mathematical model that showed fitness for a limiting nutrient had little, if any, influence on colonization when physical space was unavailable. In fact, Freter’s model predicted that even less fit clones (i.e. clones with lower fitness compared to the clone already residing in the gut) would be able to invade and persist if adhesion sites were present. It should be noted that no in vitro or in vivo data were provided by Freter to support the role of adhesion sites.

Although the surface area of the human GI tract is roughly 32 square meters (the equivalent of over seven and a half ping pong tables) (81), space in the gut has long been argued to be a limiting resource for microbes. Similar to siderophores, attachment factors, 26 like adhesins and fimbriae, are enriched in human-associated E. coli (28,80). Remarkably, application of a high-affinity, small molecule inhibitor of mannose binding by the type 1 adhesion pilus, FimH, appears to be sufficient to dramatically reduce at least some E. coli in the murine gut (82). Adherence sites in the gut are likely more diverse than simply sites on epithelial cells. Similar to nearly all environments where microorganisms are found

(83), the human gut likely supports a substantial component of microbial diversity in an epithelial-associated biofilm, or a surface adhered structure composed of microbes embedded in a matrix. Biofilm dynamics are not the same as those of free-living, planktonic cells and it is well-known that biofilm-associated bacteria: 1) are more resistant to stressors (e.g. osmotic imbalance, antibiotics, predation), 2) stratify into subpopulations with markedly physiologic and metabolic states, 3) increase horizontal gene transfer, and

4) are protected from physical forces like shear. It is difficult to study intestinal biofilms in vivo and so the contribution of these structures to human health remains controversial. Still, shifts in polymicrobial biofilm dynamics seems to be associated with GI diseases like colorectal cancer and inflammatory bowel disease (84). Certain models of the gut also call into question the relevance of GI biofilms because the rate of gut transit, mucus production, and epithelial cell turnover may be too great to allow a stationary structure like a biofilm to replenish itself before being washed out (84). That said, complex biofilms have been observed in the appendix of healthy individuals and perhaps, due to the lack of flow through this anatomical site, the mature biofilm that forms there acts as a ‘seed bank’ to repopulate the gut after acute disruption (e.g. diarrhea, inflammation, antibiotic treatment) (84,85). 27

Biofilm-like structures have been reported in many mouse models, and Cohen and Conway suggested that life for non-pathogenic E. coli in the human gut is as a biofilm (73).

Goblet cells in the human epithelium produce and secrete a matrix, called mucus, that might promote establishment of a polymicrobial, mature biofilm (66,86). Interestingly, the number of goblet cells increases in the distal GI tract and so their abundance correlates well with overall bacterial abundance. In the colon, two distinct layers of mucus have been described, where the inner layer, directly overlying the epithelium, is thick (>200 µm in humans) and impenetrable to bacteria while the outer layer is more diffuse, compositionally different, and amenable to microbial colonization (87,88). Defects in mucus production resulting in decreased thickness in the colon leads to increased bacteria-epithelium contact and inflammatory diseases. Interestingly, there is evidence that the overall structure- function of the mucus layer depends on the taxonomic structure of the microbiome (89,90).

The taxa involved, molecular cross-talk, and this overall phenomenon have not been well studied in humans. Regardless, if it is true that the human colon is protected from bacterial colonization by a thick, impenetrable mucus layer, then attachment to epithelial cells would play a minor role in microbiome residency and attachment to sites on the epithelial cell surface (e.g. mannose) would not be conducive for survival. More research is needed to understand this fundamental aspect of the gut ecosystem.

Microorganisms also produce biofilm matrix components that may be important for a gut-biofilm lifestyle. Bokranz et al. screened for the production of biofilm components in a collection of 52 E. coli clones isolated from 16 adults and 5 children (91), including extracellular polymeric substances (cellulose) known to confer structural cohesion and 28 amyloid structural proteins (curli) known to mediate adhesion between cells and surfaces.

Bokranz et al. used LB agar lacking salt and supplemented with Congo Red (an amyloid protein stain) and calcofluor white (a fluorescent dye that binds cellulose) to identify E. coli with these phenotypes along with Western blotting and MALDI-TOF to confirm the presence of curli. This was a cross-sectional study and many of the participants were cohabitating family members that shared E. coli clones (identical PFGE types).

Representative isolates of some of the shared clones showed different curli and cellulose expression, suggesting that these traits may have changed during residence within and/or transmission between individuals. Because biofilm assays were performed in vitro, these results say little about in vivo relevance. However, this study did support a link between E. coli from healthy human adults and biofilm formation.

White et al. used a similar assay to screen for biofilm production in natural populations of E. coli (84). One objective of their study was to determine whether biofilm production may be a signature of host adaptation and E. coli from several sources were compared

(humans, clinical samples and healthy donors; animals, birds and non-human mammals; and contaminated water). Contrary to results from Bokranz et al., human E. coli isolates in

White et al. had a reduced capacity to produce biofilm components (curli/cellulose) compared to isolates from non-human sources. Interestingly, isolates belonging phylogroup B2 from humans produced biofilm components less often than B2 isolates from non-human sources. Such an observation that accounts for phylogenetic relatedness is powerful because of the large inter-isolate differences in phenotypic/genotypic composition of E. coli. Results from White et al. (i.e. human-adapted E. coli have 29 decreased biofilm forming capacity) suggest that perhaps mucus production by the human epithelium provides sufficient matrix material for the microbiome and that a beneficial adaptation may be to down-regulate or otherwise shut off genes in this pathway. More experiments are needed to test this intriguing hypothesis but if supported, mucus production may indeed turn out to be the most important determinant of the human gut- microbiome symbiosis.

Direct Damage and/or Killing of Competitors

Negative microbe-microbe interactions in the human gut microbiome are diverse and lead to variable outcomes. Unlike the indirect or passive competition mechanisms discussed above, bacteria produce factors that directly inhibit other bacteria, having both narrow and broad spectrums of activity. As such, at least some of these factors in at least some contexts have little to do with true competition because cells producing them, and cells being affected by them do not interact and thus do not compete. Some factors seemingly made for the explicit purpose of inhibiting competitors include: diffusible toxins

(bacteriocins), contact-dependent toxin delivery systems (type VI secretion systems—

T6SS), and phages (69,93–95). Remarkably little is known about the overall influence and outcome of these factors in E. coli populations over short and long time scales and specifically in the context of resident turnover. Here, we focus on the potential influence of bacteriocins and prophage as these two factors have been at least superficially explored with respect to human-associated E. coli. Not surprisingly, and as briefly mentioned above, reported results are not in agreement et al et al(23,27) and more studies are needed to 30 understand the true impact of direct inhibition in structuring microbiome populations and communities.

Bacteriocins Bacteriocins are antimicrobial peptides that target and inhibit other members of the same species or members of other species of bacteria. E. coli bacteriocins can be divided into colicins and microcins (94,96), but it is important to note that nomenclature is inconsistent and, at times, contradictory. Size is one property used to differentiate colicins (30-80 kDa) and microcins (< 10 kDa) (97), but due to their genetic/functional diversity, these rules do not always apply (94,97).

In 1925, Gratia discovered that a heat-labile product of E. coli V could kill E. coli φ

(98,99). Later, Gratia and Fredericq showed these factors were composed of polypeptides and called them colicins (99,100). Colicins are released by cell lysis or active secretion and due to associated fitness costs (96), only a subpopulation produce them at any point in time

(i.e. expression is tightly regulated). Stress such as increased temperature and DNA damage can induce colicin production in vitro and in vivo (96,99,101). Once released, a colicin will bind to a receptor (typically a nutrient transporter) located on the target cell surface such that resistance due to receptor modification often results in reduced fitness (96). After binding, colicins either depolarize their targeted cell by pore formation or inhibition of cell wall synthesis or are transported into the cytoplasm where they degrade nucleic acids (99).

With colicin E1, a single cell induced and then lysed is estimated to release 100,000 colicin peptides and other colicins release far more (96). Once these peptide find target cells, their capacity to kill is great as their activity follows single hit (i.e. one colicin-one target cell) kinetics (96,99). 31

Some microcins are induced under low iron conditions and released by active transport such that host cell lysis is not required for dissemination. Under such conditions, host cells upregulate expression of siderophores and siderophore receptors to capture free iron (97).

Microcins selectively bind to siderophore receptors of competitors and kill them by depolarization or DNA/RNA degradation (97). Microcin production has been reported to be essential for the probiotic activity of E. coli Nissle 1917 and competition in an inflamed gut environment (79,102). Sassone-Corsi et al., found that in mice with DSS-induced colitis Nissle-produced microcins were capable of killing non-pathogenic/mouse-adapted

E. coli, adherent-invasive E. coli, and Salmonella enterica (102).

Estimates of bacteriocin production among E. coli range from 10% to >70%, depending on the isolation source (96). E. coli in low cell density environments like freshwater appear to have a lower frequency of bacteriocin production than E. coli in high cell density environments like the gut (96). It is possible that competition is increased in high versus low density populations, but low cell density environments may also be nutrient poor and thus making competition more fierce. Alternatively, diffusible toxins like bacteriocins are more likely to contact target cells when population density is high and so this overall pattern may simply be due to minimizing the costs and maximizing the benefits of production.

Gordon et al. screened for bacteriocin production in a collection of 266 E. coli isolated from 266 Australian children and adults (101). Only 30% of participants (n=77) were asymptomatic university students and the remaining (n=189) were hospitalized patients with symptoms of gastrointestinal disease. Thus, results were heavily biased toward 32 pathogenic E. coli and/or non-pathogenic E. coli in patients with active gastroenteritis.

With this in mind, bacteriocin production in mitomycin C-treated cells (i.e. under DNA damaging stress) was quantified using two susceptible K-12 derivative strains and further differentiated from phage production using at least four different comparative approaches

(trypsin treatment, freeze-thaw, size filtration, and dilution). Finally, bacteriocins were phenotyped and genotyped to evaluate the distribution of individual types. Of the 266 isolates evaluated, 38% (n=102) produced at least one bacteriocin. Interestingly, of the isolates that produced bacteriocins, less than half (42%) produced only a single type, suggesting that most bacteriocin-producing E. coli produce bacteriocin “cocktails”. If individual bacteriocins in cocktails overlap in their specificity for target cells but use different receptors, this would theoretically decrease the likelihood that competitors evolve resistance. However, E. coli capable of producing a bacteriocin are also protected from its activity, so it is also possible that bacteriocin cocktail-production is a result from this selective pressure (96). More experiments are needed to clarify this interesting pattern of activity. Finally, E. coli belonging to the phylogroup B2 were more likely to carry multiple bacteriocins compared to other phylogroups and since more human residents belong to this group, it seems reasonable that stability in the gut is associated with bacteriocin production.

As above, this hypothesis requires additional testing.

In vitro experiments and theoretical modeling suggest that bacteriocin production is an effective means to invade structured environments like biofilms more so than unstructured environments like a shaking culture (103). In these studies, if the concentration of bacteriocin was not high enough it was ineffective against its competitors (104). Perhaps, 33 resident E. coli of the human gut living in biofilms kill off competitors by producing bacteriocin cocktails at sufficiently high concentrations, whereas bacteriocin-producing would-be biofilm invaders fail to do so because their killing activity remains too dilute.

Controlled gnotobiotic mouse modeling with engineered/bacteriocin-defined E. coli may help clarify these possibilities.

Phages Bacterial viruses (bacteriophages or phages) were independently discovered by

Twort and d’Hérelle in 1915 and 1917, respectively (105–107). Although Twort was the first to observe hallmark phage plaquing phenomena, d’Hérelle identified the parasitic, self-replicating nature of phages. During World War I, while investigating a particularly severe outbreak of dysentery (108), d’Hérelle mixed cultures of the dysentery-causing bacterium and filtrate from the stool of symptomatic patients. After plating, he observed glassy, bacteria-free zones (plaques) that he could propagate with naïve, susceptible bacteria that further replicated the phages.

Although perhaps an oversimplification, phages can be separated into two groups: lytic and lysogenic (105). Lytic phages undergo a cycle of infecting host cells, replicating, lysing host cells, and releasing to find new host cells. Lysogenic (temperate) phages also infect, replicate in, and lyse host cells, but can also integrate their DNA into the host’s chromosome or otherwise be maintained inside host cells as extra-chromosomal (episomal)

DNA. In this state, phages lay dormant until induced to replicate and lyse once more. As with bacteriocins, stress (e.g. UV irradiation, oxidative stress, nutrient deprivation, etc.) is an often cited inducing event for lysogenic phages and host cells carrying the same or similar lysogenic phage (lysogens) are immune to re-infection. 34

Furuse et al. (109) showed that the prevalence and type of E. coli phages differed markedly in stool samples from healthy adults and patients with leukemia. Using the phage-sensitive indicator strain, E. coli C, they found that 14% (15/109) of the leukemia patients shed >105 phages (quantified as plaque forming units, PFU) per gram of stool, compared to only 1.6% (6/371) of healthy adults. They also found that phages in stool samples with low phage density (PFU/g stool) were mostly lysogenic phages, while phages from high phage density stool were mostly lytic. More recent work supported this observation (i.e. patients tend to have higher phage titers in their feces) (95). Furuse et al. also collected stool samples from 19 healthy adults every two weeks for ~4.5 months and found that phages isolated from filtrate were primarily lysogenic. They found that phages were antigenically identical within individuals and remained at a relatively consistent density. They also isolated individual E. coli from some samples, UV irradiated them to induce lysogenic phages, and compared induced phages to those recovered from filtrate.

They found that induced phages were antigenically similar to those recovered from the stool, suggesting that healthy adults normally shed a low level of lysogenic E. coli phages.

Lysogenic phages may act as “self-replicating weapons” to kill competitors (67,93) and like bacteriocins, they may be effective killing agents of would-be invaders (103).

Somewhat unlike bacteriocins, experimental and theoretical work indicates that lysogenic phage production is an effective means to invade complex communities even when the lysogen is at a low concentrations (103). Thus, current evidence seems to support that phages are more likely to play a role in clonal turnover in the gut, while both phages and bacteriocins likely have a role in restricting invasion by incoming transients. 35

Response (or the Lack of) to Cellular Stress

Biofilm (Curli and cellulose), bacteriocin, and phage production as well scavenging for nutrients (e.g. iron), carbon source utilization, and response to low pH are stress-associated phenotypes that in E. coli and the Enterobacteriaceae are regulated by the alternate sigma factor, RpoS. Contrary to the hypothesis that human resident E. coli have an increased ability to respond to stress, interestingly, White et al. found that less than half of E. coli isolated from humans (n=51 of 115) displayed a fully active RpoS phenotype (negative for glycogen production and catalase activity) compared to 90% (n=151 of 169) of isolates from non-human sources (cows, birds, other non-human mammals, and water) (84). While this observation (fewer E. coli with active RpoS phenotypes from humans versus non- human sources) held across all phylogroups, isolates belonging to phylogroup B2 isolates were the most disparate (33% in human isolates versus 91% in non-human isolates) and statistically significant. These results, as White et al discussed, suggest that an RpoS-driven stress response is deleterious for long-term survival in the human gut. With respect to biofilm production, if bacteria rely more on host mucus as the primary biofilm matrix, there may be a distinct advantage to mitigating rpoS expression and shutting off production of bacterial matrix in the gut. In addition, some E. coli with wild-type rpoS alleles have reduced metabolic capabilities (i.e. number of carbon sources utilized) compared to their isogenic rpoS-null counterparts (110), so shutting off rpoS expression may broaden their nutrient niche. All things being equal, the human gut is more stable than environments outside the gut (e.g. temperature), making global stress responses more costly with respect 36 to overall fitness. White et al did not consider temporally collected E. coli, which will be an important consideration for future studies.

Conclusions and Future Directions

In the 135 years since E. coli was first identified, only a few studies have attempted to describe the temporal dynamics of E. coli populations in healthy human adults. As this research area continues to emerge for E. coli and other microbial inhabitants of the human gut, it is important to organize and compare observations and experimentally identify/validate mechanisms governing the gut ecosystem. To this end, and from our perspective, we list fundamental hypotheses according to the amount/quality of evidence supporting them with the hope that hypotheses under each category are added, pruned, and/or revised with future research. Finally, we summarize a few important areas where experiments are needed to help clarify important aspects of E. coli population dynamics in adult human gut.

Well-Supported Hypotheses • E. coli are either resident and able to establish in the gut or transient and lost at the rate of gut transit. • E. coli residency is dynamic and resident clones turnover at different rates in different individuals. • Resident E. coli clones are shared among cohabitating humans and their pets but are not necessarily resident in all individuals. • Resident E. coli clones are rarely shared among non-cohabitating individuals.

Somewhat Supported Hypotheses • Residency in the human gut is not a random process and is determined by definable factors. • Long-lived resident E. coli are adapted to the gut of adult humans and belong to phylogroups A, B2, and F. 37

Still Controversial Hypotheses • Competition for nutrients is the most important factor determining E. coli residency in the adult human gut. • Competition for space is the most important factor determining E. coli residency in the adult human gut. • The ability to directly damage and/or kill competitors is the most important factor determining E. coli residency in the adult human gut. • Adaptation to environmental factors is the most important factor determining E. coli residency in the adult human gut.

Needed Areas of Research

Conceptually speaking, an organism’s decision to migrate or reside is a function of environmental condition-dependent fitness costs and benefits (111,112). Specialization to conditions in the gut may limit a resident’s ability to colonize new hosts due to the loss of fitness in environments and conditions between them. Moreover, the population dynamics of E. coli appear to be very different from other microbiome species like Bacteroides fragilis that is highly uniform with little or no turnover (113). Additional research is needed to clarify whether and how often prominent members of the human gut reside or migrate to other environments.

Several temporal studies have documented sharing of E. coli clones among cohabitants, including pets (23,34,59,60). Even though there is little evidence that sharing leads to resident turnover, cohabitants represent a potentially important source of new residents that need to be further investigated (114). It is also possible that new residents are acquired from other sources (e.g. water, food, or contact with pets/other humans) but studies comparing E. coli inside and outside the gut consistently report marked differences

(genotypic and phenotypic) in the populations found there, which decreases the likelihood 38 of environmental acquisition. Although difficult to assess, more studies are needed to understand whether human resident E. coli are acquired from other humans, other mammals, or from other sources altogether.

Genome sequencing will play an important role in future temporal studies. Only two studies (115,116) to our knowledge have sequenced E. coli clones isolated temporally from healthy human adults. Both studies found that the mutation rate was relatively low

(2.26 x 10-7 to 6.90 x 10-7 mutations per base pair per year) and reported little to no evidence of selection, suggesting that random drift may explain the bulk of accumulated population genetic diversity in the gut environment. This suggestion is in stark contrast to recent comparative genomic results reported for B. fragilis (113), where polymorphisms were interpreted to be adaptative. Future comparative genomics studies promise to shed much needed light on the impact of selection on residency and overall population genetic structure.

Many studies reviewed above, including our own, found that resident E. coli in human adults belong to the phylogroup B2. Our group also found E. coli of phylogroups A and F to be long-lived residents in human adults. Additional studies are needed to understand whether shared versus clone-specific traits result in the statistically significant overabundance of these phylogroups in the human gut. For example, there is evidence from studies of infants and adolescent children that virulence factors in pathogenic E. coli are important “residency factors” in non-pathogenic E. coli (80,117,118). Gene association studies are warranted in this context to better define residency-associated traits.

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CHAPTER TWO

RETHINKING GUT MICROBIOME RESIDENCY AND THE

ENTEROBACTERIACEAE IN HEALTHY HUMAN ADULTS

Contribution of Authors and Co-Authors

Author: Jonathan N.V. Martinson

Contributions: project design, experimentation, data collection/analysis, figure generation and manuscript writing

Co-Author: Nicholas V. Pinkham

Contributions: data analysis, figure generation, and manuscript writing

Co-Author: Garrett W. Peters

Contributions: data collection

Co-Author: Hanybul Cho

Contributions: data collection

Co-Author: Jeremy Heng

Contributions: data collection

Co-Author: Mychiel Rauch

Contributions: data collection

Co-Author: Susan C. Broadaway

Contributions: data collection and project logistics

Co-Author: Seth T. Walk

Contributions: project oversight, project design, data collection, data analysis, figure generation, and manuscript writing

Manuscript Information

Jonathan N.V. Martinson, Nicholas V. Pinkham, Garrett W. Peters, Hanybul Cho, Jeremy Heng, Mychiel Rauch, Susan C. Broadaway, Seth T. Walk

The ISME Journal

Status of Manuscript:

_ _ Prepared for submission to a peer-reviewed journal ____ Officially submitted to a peer-reviewed journal _ __ Accepted by a peer-reviewed journal _X Published in a peer-reviewed journal

Nature Publishing Group Submitted 16 January 2019 Accepted 27 April 2019 Published 14 May 2019 Volume 13 Issue 9 https://doi.org/10.1038/s41396-019-0435-7 Terms of Use: This article is licensed under a Creative Commons Attribution 4.0 International License. 52

Supplementary Figure 3. Timeline of stool sample collection for eight participants. An average of 49 samples were collected per participant. Hashes denote individual samples plated onto MacConkey agar (population-level dynamics).

Red dots were used for 16S rRNA sequence analysis (community-level dynamics).

T i m e l i n e o f s t o o l s a m p l e c o l l e c t i o n s

P a r t i c i p a n t 1 ( n = 9 7 )

P a r t i c i p a n t 2 ( n = 7 6 )

P a r t i c i p a n t 3 ( n = 2 5 )

P a r t i c i p a n t 4 ( n = 3 2 )

P a r t i c i p a n t 5 ( n = 4 4 )

P a r t i c i p a n t 6 ( n = 5 0 )

P a r t i c i p a n t 7 ( n = 4 2 )

P a r t i c i p a n t 8 ( n = 2 6 )

3 / 2 0 1 6 8 / 2 0 1 6 3 / 2 0 1 7 8 / 2 0 1 7 3 / 2 0 1 8 67

CHAPTER THREE

PHENOTYPIC PREDICTORS OF ESCHERICHIA COLI RESIDENCY IN THE GUT

OF HEALTHY HUMAN ADULTS

Contribution of Authors and Co-Authors

Author: Jonathan N. V. Martinson

Contributions: experimental design, performed experiments, data analysis, figure preparation, and wrote manuscript

Co-Author: Nicholas V. Pinkham

Contributions: data analysis and figure preparation

Co-Author: Seth T. Walk

Contributions: experimental design, data analysis, and wrote manuscript

Manuscript Information

Jonathan N.V. Martinson, Nicholas V. Pinkham, and Seth T. Walk

Applied and Environmental Microbiology

Status of Manuscript: _X__ Prepared for submission to a peer-reviewed journal __ _ Officially submitted to a peer-reviewed journal ____ Accepted by a peer-reviewed journal __ _ Published in a peer-reviewed journal

ASM Press

Abstract

The factors allowing microbial populations to reside in the dense, competitive ecosystem of the gut remain mysterious. With a collection of E. coli longitudinally collected over 2 years, we attempted to determine what factors predict residency for long periods of time. The production of the biofilm matrix component, cellulose, appeared to be unrelated in residency. Interestingly, resident clones were more likely to directly compete (i.e. inhibit) other clones than transients. We present statistical evidence that resident E. coli populations directly kill competitors more often than transients. Furthermore, we found that many of these resident clones directly inhibit the probiotic E. coli Nissle 1917 as well as pathogenic strains of E. coli. This work suggests that interference plays a major role in residency dynamics in the healthy human gut.

Introduction

Residency (the amount of time an organism occupies an environment) varies dramatically in the gut microbiome (1–3). Bacteroides fragilis has been shown to be maintained within an individual’s gut for years and predicted to occupy a host for decades. On the other hand, over 100 years of research has shown that clonal turnover occurs regularly in Escherichia coli carried by healthy human adults (2,4–6). Recently, our research group captured ~bi-weekly stool samples from 8 healthy adults over a 6- month to 2-year period and cataloged the population dynamics of E. coli. Resident

(present > 2 weeks) E. coli were found in all 8 participants. Furthermore, all volunteers carried transient populations of E. coli. Several research groups have identified the phylogenetic group (phylogroup) B2 as a common resident lineage within humans. In addition to the phylogroup B2, we also identified that the phylogroups A and F were more likely to be resident within humans than the other E. coli phylogroups. In this current work, our goal was to identify potential traits that allow resident E. coli populations to inhabit the gut for long periods of time. To assess this, we tested whether the synthesis of biofilm matrix components (cellulose) and interference

(bacteriocin/phage production) predicted residency time in the human gut. Furthermore, we investigated resistance to antibiotics among resident and transient E. coli.

Metabolism, another likely indicator of residency, will be assessed in an upcoming work.

Biofilms are structures composed of surface adhered microbes embedded in a matrix (7). Bacteria in almost every environment are found to exist as a biofilm (7,8).

Pathogenic, non-pathogenic, and environmental clones of E. coli have been documented 85 to produce biofilms (9,10). Although complex in composition, cellulose is a major component forming the matrix in E. coli biofilms (9). However, unlike environmental E. coli (i.e. derived from water, soil, etc.) that generally always produce biofilm components, there appears to be significant variation in biofilm production in E. coli derived from healthy human stool (10,11). Interestingly, White et al., also found that human-derived E. coli were significantly more likely to have limited stress responses

(e.g. RpoS response) compared to their environmental brethren. Biofilm production is partially regulated by RpoS, so White et al. work suggested that human-associated E. coli may have ablated stress-responses causing reduced cellulose production. Although, RpoS null mutants have reduced abilities to respond to stress, they are generally more metabolic active (12). Given the variation in biofilm production and stress response, we assessed the cellulose synthesis capabilities and catalase activity in a collection of 98 E. coli clones with known residence time to determine if these factors predicted the length of tenure in the gut.

Direct competition between microbes occurs with the production factors that damage or kill competitors (13). Two means of direct competition is the production of bacteriocins and the induction of lysogenic phage (14). Bacteriocins are small secreted proteinaceous toxins that that bind to receptors on target cells and inflict a lethal blow

(15). Temperate phage are bacterial viruses that have become integrated into a host bacterium’s chromosome; under stressful conditions they are induced and released by cell lysis (16). Direct competition with these factors has also been considered a potential means for non-pathogenic human-associated E. coli to invade a microbiome to establish 86 residency and subsequently maintain their position as residents (14). A cross-sectional study of 266 E. coli isolates collected from asymptomatic college students and hospitalized patients with gastroenteritis found that 38 % produced bacteriocins (17).In a group of healthy adults, Furuse et al., tracked E. coli phage composition from stool filtrates collected temporally over a 4-month period and found that the participants shed low (but consistent) levels of phage particles that were serologically similar to temperate phage (18). Furthermore, this study suggested that resident E. coli were responsible for the stable production of phage. Even still, little is known about the ecological role of temperate phage, bacteriocins, and direct competition in general, with respect to E. coli’s residency in the gut environment. With this in mind, we attempted to elucidate the role of direct competition in residency of E. coli.

Materials and Methods

E. coli isolation E. coli were recovered from temporally collected stool samples as described previously (2). Briefly, the institutional review board of Montana State University approved this study and 8 healthy adults (2 females, 6 males) were enrolled in this study with informed consent. Except for age and sex, no additional information was recorded.

Once every 2 weeks stool samples were requested every 2 weeks from March 2016 to

January 2018. In an anaerobe chamber (Coy Laboratory Products, Inc., Grass Lake, MI,

USA), stool samples were mixed with reduced sterile phosphate buffered saline (PBS), sterile glycerol (15% final concentration), and frozen at -80°C. Later, stool slurries were thawed, serially diluted in sterile PBS, plated onto MacConkey agar, and incubated 87 overnight at 37°C. 95 colonies were picked using sterile toothpicks, transferred to

Lysogeny broth (LB broth) in a 96 deep-well plate, and incubated overnight at 37°C.

Isolates were preserved as freezer stocks in 15% glycerol and frozen at -80°C. Cultures were diluted 1:10 for use in PCR based genotyping. The Clermont method (19) was used to identify E. coli and the phylogroup in which they belonged. Resident clones were identified using the GTG5 rep-PCR (20). Using these methods, we identified resident and transient 117 E. coli populations from a collection of over 30,000 isolates. However further biochemical testing revealed that 17 clones were not E. coli strictu and 2 clones were not recovered from freezing. With the remaining 98 strains, we selected a representative clone from each lineage (a clone from the first sample that the lineage appeared in the temporal study) for the tests assayed in this study. All tests unless otherwise noted were performed using the “Average” definition of residency as described previously. The average definition identifies residency periods between the sample date in which a clone is first observed and the day it is lost.

Direct competition assay To determine whether clones could produce inhibition factors or were susceptible to interference, we adapted the top agar assay as previously described (21,22). Square petri dishes petri dishes (Greiner Bio-One North America, Inc. Monroe, NC, USA) were filled with 60 mL of LB agar and allowed to solidify overnight at room temperature. 98

E. coli clones were arrayed in two 96-well PCR plates and frozen with 15% glycerol in

LB broth (100 µL total) (Fisher Bioreagents, Fair Lawn, NJ, USA). Freezer stocks were inoculated into 225 µL of LB broth into two 96-well deep well plates using a sterilized hedgehog inoculator (Boekel Scientific, Feasterville, PA, USA). These clones were 88 grown overnight at 37°C under static conditions. On the same day, a single colony of this clone was inoculated into 5 mL of LB broth in a 15 mL snap cap tube (Falcon) and shaken overnight (12-16 hours) at 37°C at 200 rotations per minute.

The following day, 125 mL of LB top agar (0.7% agar) supplemented with divalent cations (0.2 M CaCl2 and 0.2 M MgSO4) was melted in a microwave and placed into a preheated 55°C water bath. The molten agar was allowed to cool in the water bath for 20 minutes. 0.4 mL of the shaking overnight culture was diluted into 4mL of LB broth. 8 mL of the preheated top agar was then pipetted into the LB broth/overnight culture, swirled, and promptly poured into the square LB agar petri dish. The square petri dishes with the overlay were allowed to dry under a laminar flow hood for 10 minutes.

Cultures in the deep well plates were mixed with a p200 multichannel pipette. Using a p10 multichannel pipette, 3 µL of the static overnight cultures were spotted onto the Top agar overlay. As soon as all 98 drops dried on the top agar overlay, the plates were inverted and incubated overnight at 37°C. The following day, images of the plates were taken using a Samsung S7 phone camera using a custom-built dark box. Clearing or haziness in the top agar surrounding a colony were defined as interference events.

In addition to all of the pairwise interactions between all 98 clones, we also assessed the ability of the 98 clones to inhibit probiotic E. coli (Nissle 1917),

Enterotoxigenic E. coli (ETEC—TW04903), Enteropathogenic E. coli (EPEC—

TW06375), Enteroaggregative E. coli (EAEC—TW04393), and Enterohemorrhagic E. coli (EHEC—TW08264) using identical methods.

Cellulose production Cellulose production was assessed using calcofluor white supplemented LB agar without salt as described, previously (23). 0.2 µm filter-sterilized Calcofluor White

(Brightener-28) (MP Biomedicals, LLC, Solon, OH, USA) added at a final concentration of 50 µg/mL to molten, autoclaved LB agar without NaCl (2 g agar, 2 g tryptone, 1 g yeast extract, 3.2 g agar, 200 mL H2O). 60 mL of LB Agar with calcofluor white was pipetted into a square petri dish and allowed to cool overnight at room temperature in the dark and then stored at 4°C. As described above, 1.5µL of the clone collection was dispensed and allowed to dry under the laminar flow hood. Calcofluor white produces fluorescence when bound to cellulose. After 24 hours of incubation (27°C and 37°C) the colonies were placed on a UV transilluminator (VWR Cat. No. 89131-480) set at 365nm and imaged with a Samsung S7 smartphone using a custom-built dark box and the following settings: white balance 10000K, shutter speed 1/350, an ISO 50. Colony fluorescence was analyzed using ImageJ (24). Images were imported into ImageJ and split based on color. All analyses were performed using the blue color channel. After cropping the image, the background was removed without smoothing and a rolling ball radius of 500 pixels. The mean gray value (a proxy for fluorescence) of each colony was measured. All experimental conditions were repeated in triplicate. Cellulose producing clones had a fluorescence intensity of over 20 units – this threshold was determined, subjectively.

Catalase activity We adapted the catalase activity assay as described by (10). Freezer stocks of 98

E. coli clones were inoculated into LB (with salt) broth as described above and incubated 90 at 37°C for 14 hours. 1.5µL of the overnight culture was pipetted onto LB agar with salt.

This plate was incubated overnight at 37°C. The following day, 1.5µL of 30% hydrogen peroxide (Fisher Bioreagents, Fair Lawn, NJ, USA) was pipetted onto each colony.

Colonies were scored as catalase positive if they formed gas bubbles quickly and vigorously. Colonies were designated catalase deficient if they did not form bubbles or if there was a delay in bubble formation/less bubbles formed.

Antibiotic resistance Antibiotic resistance of the E. coli clones was assessed by supplementing LB agar with antibiotics at the resistance threshold concentrations defined by the Center for

Disease Control. Sterilized antibiotics (Supplemental table 1) were added to molten LB agar and 60 mL was pipetted into a square petri dish. As above, the 98 clones of the E. coli collection were incubated overnight at 37°C in LB under static conditions. 3µL of the overnight culture was spotted onto the antibiotic containing agar, allowed to dry under the hood and incubated overnight. The following day, pictures were taken and the resulting images were analyzed. Clones with full growth or partial growth on the antibiotic agar were considered antibiotic resistant. Clones that did not grow on the antibiotic agar were considered antibiotic sensitive.

Results

Cellulose production is not significantly different among residents and transients To assess the potential role of the biofilm matrix component, cellulose, on residence time, we cultured the 98 representative clones overnight in LB broth. The following day, 1.5µL of culture was spotted on to LB agar without salt supplemented 91 with calcofluor white. After drying, plates were incubated at 27°C and 37°C for 24 hours.

Calcofluor white binds to cellulose and produces fluorescence under UV. At 27°C and

37°C, no difference was observed in cellulose production (Fig. 1A) using a discrete variable (cellulose production) and continuous variable (fluorescence intensity).

Similarly, at 37°C cellulose production was not significantly different (Fig. 1B).

Furthermore, we found no correlation between cellulose production and residence time at

27°C or 37°C (Fig. 1C).

Given the relationship between biofilm production and stress response (e.g. RpoS) outlined by White et al. (10), we tested the catalase capability of E. coli collection by spotting 30% hydrogen peroxide onto colonies grown overnight at 37°C on LB agar with salt. Catalase negative (RpoS-null mutants) clones mostly belonged to resident populations (12/17 were resident) but were not significantly different than transient populations (Figure 2; Fisher’s exact test; p = 0.1804).

Antibiotic resistance among residents and transients does not differ Several temporal studies of E. coli from healthy individuals have found variable levels of antibiotic resistance among the collected clones (25,26). Given the global issue of antibiotic resistance we wanted to understand the extent to which resident and transient clones were resistant to antibiotics. It should be noted that since we did not collect medical information from the participants enrolled in this study, we are unable to infer if antibiotics were used during the course of the study. We assessed antibiotic resistance with 6 antibiotics (ampicillin, cefoxitin, ciprofloxacin, kanamycin, streptomycin, and tetracycline) at the CDC resistance breakpoint concentrations (27). We found no 92 difference in antibiotic resistance in residents and transients (Fig. 3—Fisher’s exact test, p > 0.9999). On average, residents and transients were resistant to 1.25 and 1.35 antibiotics, respectively. As somewhat of an aside, we found that cellulose producers were more likely to be antibiotic resistant (Supplemental Fig. 1—Fisher’s exact test; p =

0.0181). This finding supports other work suggesting biofilms increase tolerance to antibiotics and other stresses (7).

Direct competition in the gut sometimes predicts turnover events To determine how often turnover events could be explained by direct competition, we used the top agar assay to determine if clones could inhibit one another. Clones that overlapped during the study were considered to possibly interact. Clones that appeared in the same stool sample clones were considered to have the potential to interact. In addition, situations in which a clone was lost in one sample, but in the subsequent sample a novel clone was gained, were also considered to be a period of overlap between clones.

Out of 152 potential interactions, interference between clones in overlapping samples occurred 16 times. 10 of these interference events explained the dynamic seen in the gut

(i.e. the clone producing the inhibition replaced or persisted after the susceptible clone appeared). However, on 6 occasions, the clone producing inhibitory factors was replaced subsequent to the appearance of the susceptible clone (Supplemental Table 2).

Direct competition predicts residency time Even though only 7% of turnover events could potentially be explained by direct competition between clones, antagonism between microbes may still play an important mechanism for residency. Some factors like bacteriocin production require high 93 concentrations in order to be effective (14,15). Because resident E. coli are generally the most numerically dominant clone within a stool sample (unpublished data), it seemed reasonable to assume that they are capable of producing direct competition factors at levels high enough to inhibit invading populations. During these situations, it would be unlikely to recover failed invaders as they would be lysed and/or at levels below detection. To test the hypothesis that resident clones were capable of direct competition more often than transients, we performed 98 x 98 pairwise top agar assays (9,604 combinations).

Clones capable of inhibiting at least one other clone were deemed ‘killers’, while clones incapable of this were called ‘non-killers’. Among the 98 clones, 39 were killers and 59 were non-killers. The number of strains killers could inhibit ranged from 1 to 76 with the mean number of strains inhibited being 10.95 (SD = 19.28). Although there were not significantly more resident ‘killers’ than transient ‘killers’ (Fig. 4A—Fisher’s exact test; p = 0.0986), residents killed more often than transients (Fig. 4B—Mann-Whitney test; p = 0.0340). Interestingly, the number of times a clone killed correlated positively with time present in the gut (Fig 4C—Spearman correlation; r = 0.2487; 95% confidence interval 0.04693 to 0.4309; p = 0.0135), suggesting that residency is associated with direct competition. When comparing all 9,604 interactions between all the clones, resident killers inhibited clones far more often than transient killers (Supplemental Fig.

2A—Fisher’s exact test; p < 0.0001). Furthermore, we compared killing susceptibility of residents and transients and found that residents killed more often than transients 94 regardless of who they killed (residents or transients) (Supplementary Figure 2B—Chi- square test; df = 281.4, 3; p < 0.0001).

Engraftment of probiotics and pathogen colonization resistance Engraftment of probiotics is inconsistent and unreliable (28). To assess if direct competition could play a role in the exclusion of probiotics from the gut, we tested the 98

E. coli clones’ ability to kill the probiotic E. coli, Nissle 1917. 14 of the clones were capable of inhibiting Nissle 1917 in the top agar assay and these clones were disproportionately resident (Fig. 5A—Fisher’s exact test; p = 0.0033).

Colonization resistance is a commonly reported function of the healthy microbiome. To assess the potential of human-associated E. coli clones to prevent colonization by pathogenic E. coli we screened 98 clones’ ability to inhibit four pathogenic strains representing ETEC, EPEC, EAEC, and EHEC. 9 of the 98 human- associated clones could inhibit at least one of the pathogenic clones. Residents were significantly more likely to inhibit pathogenic strains than transients (Fig 5B—Fisher’s exact test; p = 0.0137).

Discussion

Little is known about the mechanisms of residency in the gut microbiome. Even with over 100 years of research on the temporal dynamics of E. coli in healthy human adults, many questions remain. In this study we attempted to identify factors involved in

E. coli residency. Production of biofilm components (cellulose) was not different between residents and transients, suggesting that these populations may be under similar selective pressures for this trait. It must be stated that we investigated only a single 95 component of biofilm, attachment proteins like curli or other matrix components (like colonic acid) may play a role in establishing and persisting in the gut environment

(9).Antibiotic resistance was common among residents and transients. We found that direct competition may play a major role in residency. Interestingly, residents were capable of interfering with probiotic and pathogenic E. coli supporting the suggestion that resident bacterial populations provide colonization resistance. Clones from 7/8 participants inhibited the probiotic, Nissle 1917. Furthermore, the mean residence time for these clones was 147 days (range 9 – 689 days), suggesting that direct competition may be a reason why probiotic organisms are quickly lost from the gut microbiome after ingestion (28,29). This work is not without precedent. Early work in the 1950s and ‘60s attempted to determine the role of anatagonistic activity of resident and transient populations (30,31). Sears et al. found insufficient evidence for bacterial antagonism playing a role in residency. On the contrary, Branche et al. found some evidence that residents carried bacteriocins more often than transients. However, to our knowledge, this current study is the first to address the question of the role of direct competition in human-adult resident E. coli in over 50 years. One limitation of this study is that the mechanism of inhibition was not investigated. Future work must establish what means of interference (e.g. bacteriocin, induced prophage, etc.) is responsible for observed inhibition.

Other factors likely play a role in residency. Nutrient availability (32,33) and attachment (34,35) have been previously suggested as key determining factors in success 96 in the gut. Future work must integrate multiple facets of biochemical and ecological to understand residency in the human gut.

References Cited

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13. Ghoul M, Mitri S. The Ecology and Evolution of Microbial Competition. Trends Microbiol. 2016 Oct 1;24(10):833–45. 14. Granato ET, Meiller-Legrand TA, Foster KR. The Evolution and Ecology of Bacterial Warfare. Curr Biol. 2019 Jun 3;29(11):R521–37. 15. Gordon DM. The Natural History of Bacteriocins. Bacteriocins Curr Knowl Future Prospects Dorit RL Roy SM Riley MA Eds. 2016;1–10. 16. Kortright KE, Chan BK, Koff JL, Turner PE. Phage Therapy: A Renewed Approach to Combat Antibiotic-Resistant Bacteria. Cell Host Microbe. 2019 Feb 13;25(2):219–32. 17. Gordon DM, O’Brien CL. Bacteriocin diversity and the frequency of multiple bacteriocin production in Escherichia coli. Microbiology,. 2006;152(11):3239–44. 18. Furuse K, Osawa S, Kawashiro J, Tanaka R, Ozawa A, Sawamura S, et al. Bacteriophage Distribution in Human Faeces: Continuous Survey of Healthy Subjects and Patients with Internal and Leukaemic Diseases. J Gen Virol. 1983;64(9):2039–43. 19. Clermont O, Christenson JK, Denamur E, Gordon DM. The Clermont Escherichia coli phylo-typing method revisited: improvement of specificity and detection of new phylo-groups. Environ Microbiol Rep. 2013 Feb 1;5(1):58–65. 20. Mohapatra BR, Mazumder A. Comparative efficacy of five different rep-PCR methods to discriminate Escherichia coli populations in aquatic environments. Water Sci Technol J Int Assoc Water Pollut Res. 2008;58(3):537–47. 21. Adams M. Bacteriophages. Interscience Publishers: New York, NY, USA; 1959. 22. Bonilla N, Rojas MI, Netto Flores Cruz G, Hung S-H, Rohwer F, Barr JJ. Phage on tap–a quick and efficient protocol for the preparation of bacteriophage laboratory stocks. PeerJ [Internet]. 2016 Jul 26;4. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975003/ 23. Cimdins A, Simm R. Semiquantitative Analysis of the Red, Dry, and Rough Colony Morphology of Salmonella enterica Serovar Typhimurium and Escherichia coli Using Congo Red. In: Sauer K, editor. c-di-GMP Signaling: Methods and Protocols [Internet]. New York, NY: Springer New York; 2017. p. 225–41. (Methods in Molecular Biology). Available from: https://doi.org/10.1007/978-1-4939-7240- 1_18 24. Abramoff MD, Magalhães PJ, Ram SJ. Image processing with ImageJ [Internet]. Biophotonics international. 2004 [cited 2019 Dec 16]. Available from: http://dspace.library.uu.nl/handle/1874/204900 99

25. Anantham S. Analysis of persistent and antibiotic resistant commensal Escherichia coli from healthy adults. 2013 [cited 2017 May 14]; Available from: http://ses.library.usyd.edu.au/bitstream/2123/10399/1/Anantham_SA_Thesis.pdf 26. McBurney WT. Perturbation of the enterobacterial microflora detected by molecular analysis. Microb Ecol Health Dis. 1999;11(3):175–179. 27. Antibiotics Tested by NARMS | NARMS | CDC [Internet]. 2019 [cited 2020 Jan 7]. Available from: https://www.cdc.gov/narms/antibiotics-tested.html 28. Prilassnig M, Wenisch C, Daxboeck F, Feierl G. Are probiotics detectable in human feces after oral uptake by healthy volunteers? Wien Klin Wochenschr. 2007;119(15–16):456–462. 29. Zmora N, Zilberman-Schapira G, Suez J, Mor U, Dori-Bachash M, Bashiardes S, et al. Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features. Cell. 2018 Sep 6;174(6):1388–1405.e21. 30. Sears HJ, Brownlee I, Uchiyama JK. Persistence of individual strains of Escherichia coli in the intestinal tract of man. J Bacteriol. 1950;59(2):293. 31. Branche Jr WC, Young VM, Robinet HG, Massey ED. Effect of colicine production on Escherichia coli in the normal human intestine. Proc Soc Exp Biol Med. 1963;114(1):198–201. 32. Pereira FC, Berry D. Microbial nutrient niches in the gut. Environ Microbiol. 2017 Feb 1;19:1366–78. 33. Freter R, Brickner H, Botney M, Cleven D, Aranki A. Mechanisms That Control Bacterial Populations in Continuous-Flow Culture Models of Mouse Large Intestinal Flora. Infect Immun. 1983 Feb 1;39(2):676–85. 34. Nowrouzian FL, Wold AE, Adlerberth I. Escherichia coli Strains Belonging to Phylogenetic Group B2 Have Superior Capacity to Persist in the Intestinal Microflora of Infants. J Infect Dis. 2005 Apr 1;191(7):1078–83. 35. Freter R, Brickner H, Fekete J, Vickerman MM, Carey KE. Survival and Implantation of Escherichia coli in the Intestinal Tract. Infect Immun. 1983 Feb 1;39(2):686–703.

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Figures and Legends

Legends Figure 1: Cellulose production is unrelated to residency. a) Cellulose production in resident and transient E. coli is not significantly different at 27°C (Fisher’s exact test; p = 0.6861) or 37°C (Fisher’s exact test; p = 0.1078). b) The amount of cellulose produced is not significantly different at 27°C or 37°C (Mann-Whitney test; p = 0.1998 and 0.1899, respectively). c) Cellulose production does not correlate with residence time (Spearman correlation; p = 0.1986).

Figure 2: Catalase capabilities are indistinguishable between residents and transients (Fisher’s exact test; p = 0.1804).

Figure 3: Resident and transient E. coli are not different in antibiotic resistance (Fisher’s exact test; p > 0.999).

Figure 4: Direct competition may mediate residency. a) The frequency of resident killers and transient killers is not significantly different (Fisher’s exact test: p = 0.0986). b) Resident killers inhibit competitors more often than transient inhibitors (Mann-Whitney test; p = 0.0340). The number of clones a killer inhibits correlates with residence time (Spearman correlation; r = 0.2487; 95% confidence interval 0.04693 to 0.4309; p = 0.0135).

Figure 5: Resident clones can directly inhibit probiotic and pathogenic E. coli. a) Residents disproportionately inhibit Nissle 1917 compared to transients (Fisher’s exact test; p = 0.0033). b) Residents kill pathogenic E. coli more frequently than transients (Fisher’s exact test; p = 0.0137).

Supplemental Figure 1: Cellulose production increases likelihood of resistance to antibiotics (Fisher’s exact test; p = 0.0181).

Supplemental Figure 2: a) Resident killers inhibit clones more often than transient killers (Fisher’s exact test; p < 0.0001). b) Residents kill competitors more often regardless of residency or transiency designation (Chi-square test; df = 281.4, 3; p < 0.0001).

Supplemental Table 1: Antibiotic concentrations used in study and the percentage of clones resistant to each antibiotic.

Supplemental Table 2: The number of turnover events potentially mediated by direct competition observed in vivo.

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Figures

Figure 1

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Tables

Supplemental Table 1 Final # of resistant % of clones Antibiotic Concentration clones resistant Ampicillin 32µg/mL 79 73% Cefoxitin 32µg/mL 30 28% Ciprofloxacin 4µg/mL 2 2% Kanamycin 64µg/mL 2 2% Streptomycin 32µg/mL 23 21% Tetracycline 16µg/mL 7 6%

Supplemental Table 2

Participant # Possible Interactions # of inhibition events Inhibition event explained turnover event Inhibition event failed to explain turnover event 1 31 4 1 3 2 7 1 0 1 3 15 0 0 0 4 25 2 0 2 5 10 1 1 0 6 23 0 0 0 7 30 5 5 0 8 11 3 3 0

TOTAL 121 12 9 3 % of possible interactions where interferenc occurred % of interference events that explain turnover % of interference events that fail to explain turnover 10% 75% 25%

% of total interactions explained 7%

107

CHAPTER FOUR

CONCLUSIONS AND FUTURE STUDIES

Conclusions

The temporal dynamics of Escherichia coli remains an understudied phenomenon even though it has been examined for over 100 years (1,2). Furthermore, even less is known about the factors that cause population turnovers in healthy individuals. In Chapter 1, we reviewed the literature regarding E. coli in healthy human adults and the potential factors that allow E. coli to survive and compete in the gut environment. All studies reviewed found that population change was common in human E. coli populations through time.

Observation of resident (long-lived) and transient (short-lived) populations of E. coli were documented in the earliest studies to the most recent (1–4). Several studies found that the phylogroup B2 is commonly resident in healthy humans (5–7). Yet little is known about why some populations live for months to years, while others are lost soon after they enter the gut environment. In Chapter 1, we also reviewed potential mechanisms of residency and clonal turnover. Although changes in diet, health, and other factors may explain some turnover dynamics, host-independent factors may also play a role. Competition between E. coli clones for nutrients and space in the gut environment may determine how populations survive and change in the gut environment. Related to this, clonal populations may compete directly for nutrients and space or produce inhibitory factors (bacteriocins, phage, etc.) to kill competitors. 108

In chapter 2, we described the temporal dynamics at the bacterial community level and the population level in 8 healthy human adults over a 6-month to 2-year period (4). We investigated the community dynamics using ribosomal RNA sequencing and, like others, found that stability was common at higher taxonomic levels (i.e. family/genus level).

However, when we analyzed the Enterobacteriaceae with culture-dependent techniques, we found that change and turnover was common. These results indicated that stability within the gut may be masked by 16S rRNA sequencing. Furthermore, some phylogenetic groups within E. coli appear to be adapted to the human gut environment as residents, while other groups are more often observed as transients (and likely ill adapted to this environment).

In chapter 3, we hoped to understand the signatures of adaptation to the human adult gut by studying the phenotypic traits of resident and transient E. coli. We analyzed the clones’ abilities to produce biofilm matrix components. We found that residents and transients produce similar levels of cellulose. Although not statistically significant, we also observed that residents often had ablated or reduced stress response, suggesting that the gut environment, which is relatively stable compared to a secondary environment (e.g. freshwater, sand, etc.) may select for clones that have reduced need for responding to shifting conditions. Antibiotic resistance was similar between residents and transients.

Importantly, we found that E. coli populations commonly produce inhibitory factors against other clones found in the gut. We observed that 7% of the potential turnover events may be explained by direct inhibition. Finally, we found that the number of strains a clone 109 can kill correlates with residence time, suggesting that the production of antagonistic systems is important for E. coli residency in the human gut.

Future Directions

In the short term, there are many traits that may also be important for residency such as attachment and metabolism (8,9). We plan to investigate these factors soon.

To understand the temporal dynamics of the healthy human gut, we must learn not only how populations change at the population level but must also understand how they change at the genetic level. Temporal sequencing of resident populations may give insights on what traits are selected for in the gut environment, as well as the impact of horizontal gene transfer.

Furthermore, mouse models of microbiome dynamics must be leveraged to understand how changes occur in the microbiome and to recapitulate dynamics observed in people.

E. coli is a single species among hundreds present in the gut microbiome. Some work suggests that populations of Bacteroides fragilis are more stable (years to decades) than E. coli (10). Like any ecosystem, residence time probably varies among species.

Cultivation-based studies and deep sequencing studies must be performed to better understand the natural variation of many more species.

Finally, larger temporal microbiome studies (n > 100) must be performed to increase statistical power and take into account the humanity’s variation of genetics, diet, lifestyle, and culture. Additionally, there is much to be learned about the ‘sociology’ of microbe transmission among households (11). 110

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CUMULATIVE REFERENCES CITED

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