A cryptic plasmid is among the most numerous genetic elements in the human gut

SUMMARY

Plasmids are extrachromosomal genetic elements that often encode fitness enhancing features. However, many bacteria carry ‘cryptic’ plasmids that do not confer clear beneficial functions. We identified one such cryptic plasmid, pBI143, which is ubiquitous across industrialized gut microbiomes, and is 14 times as numerous as crAssphage, currently established as the most abundant genetic element in the human gut. The majority of mutations in pBI143 accumulate in specific positions across thousands of metagenomes, indicating strong purifying selection. pBI143 is monoclonal in most individuals, likely due to the priority effect of the version first acquired, often from one’s mother. pBI143 can transfer between Bacteroidales and although it does not appear to impact bacterial host fitness in vivo, can transiently acquire additional genetic content. We identified important practical applications of pBI143, including its use in identifying human fecal contamination and its potential as an inexpensive alternative for detecting human colonic inflammatory states.

In brief:

A widely distributed and highly conserved human gut plasmid is extremely numerous in industrialized human gut metagenomes and increases its copy number in response to stress.

Graphical Abstract

INTRODUCTION

The tremendous density of microorganisms in the human gut provides a playground for the contact-dependent transfer of mobile genetic elements ¹ including plasmids. Plasmids are typically defined as extrachromosomal elements that replicate autonomously from the host chromosome ^1–4. In addition to being a workhorse for molecular biology, plasmids have been extensively studied for their ability to expedite microbial evolution ⁵ and enhance host fitness by providing properties such as antibiotic resistance, heavy metal resistance, virulence factors, or metabolic functions ^6–11.

Plasmids have been a major focus of microbiology not only for their biotechnological applications to molecular biology ^12–15 but also for their role in the evolution and dissemination of genes for antibiotic resistance ^16,17, which is a growing global public health concern ¹⁸. However, outside the spotlight lie a group of plasmids that appear to lack genetic functions of interest and that do not contain genes encoding obvious beneficial functions for their hosts ^19,20. Such ‘cryptic plasmids’ are typically small and multi-copy ²¹, and are often difficult to study as they lack any measurable phenotypes or selectable markers ^22,23, despite their presence in a broad range of microbial taxa ^24–27 and their distribution across many different environments ^20,28. In the absence of a clear advantage to their hosts, and the presumably non-zero cost of their maintenance, these plasmids are often described as selfish elements ²⁹ or genetic parasites ³⁰. While they may provide unknown benefits to their hosts, a high transfer rate could also be a factor that enables cryptic plasmids to counteract the negative selection pressure of their maintenance ^30–32.

Analyses of cryptic plasmids are often performed on monocultured bacteria, limiting insights into their ecology in naturally occurring microbial habitats. However, recent advances in shotgun metagenomics ³³ and de novo plasmid prediction algorithms ^34–43 offer a powerful means to bridge this gap. For instance, in a recent study we characterized over 68,000 plasmids from the human gut ⁴³ and observed that one of the most prevalent reference plasmids across our dataset of geographically diverse human populations was a cryptic plasmid called pBI143 ⁴⁴. Here we conduct an in-depth characterization of this cryptic plasmid through ‘omics and experimental approaches to study its genetic diversity, host range, transmission routes, impact on the bacterial host, and associations with human health and disease states. Our findings reveal the astonishing success of pBI143 in the human gut, where we were able to detect it in up to 92% of individuals in industrialized countries with copy numbers at least 14 times higher on average than crAssphage, one of the most abundant phages in the human gut. We also demonstrate the potential of pBI143 as a cost-effective biomarker to assess the extent of stress that microbes experience in the human gut, and as a sensitive means to quantify the level of human fecal contamination in environmental samples.

RESULTS

pBI143 is extremely prevalent across industrialized human gut microbiomes

pBI143 (accession ID U30316.1) is a 2,747 bp circular plasmid first identified in 1985 in Bacteroides fragilis ^44,45, a member of the human gut microbiome that is frequently implicated in states of health ^46–48 and disease ^49,50. pBI143 encodes only two annotated genes: a mobilization protein (mobA) and a replication protein (repA) (Figure 1A). Due to the desirable features for cloning such as a high copy number and genetic stability, cryptic plasmids have often been primarily used as components of E. coli-Bacteroides shuttle vectors ^45,51. The absence of any ecological studies of pBI143 prompted us to characterize it further beginning with a characterization of its genetic diversity.

Figure 1. pBI143 prevalence and abundance in globally distributed human populations.

(A) Plasmid maps of the three distinct versions of pBI143, which differ primarily in the repA gene. IR = inverted repeat. The repA genes are colored according to Version 1 (blue), Version 2 (red) and Version 3 (green). (B) Read recruitment results from 4,516 metagenomes originating from 23 globally representative countries and mapped to pBI143. Top: The percentage of reads in each metagenome that mapped to pBI143 normalized by number of reads in the metagenome. Bottom: The proportion of individuals in a country that have pBI143 in their gut. Each red dot represents an individual metagenome. (C) Countries that are represented in our collection of 4,516 global adult gut metagenomes. Each country’s pie chart is colored based on the version(s) of pBI143 that is most prevalent in that country (Version 1 = blue, Version 2 = red, Version 3 = green). Each country is colored based on the proportion of Version 1, 2 or 3 present in the population, or gray if fewer than 20% of individuals carry pBI143. Pie charts show the proportions of pBI143 versions in all individuals that carry it within a country. See also Table S1, Figure S1.

To comprehensively sample the diversity of pBI143, we screened 2,137 individually assembled human gut metagenomes (Table S1) for pBI143-like sequences. By surveying all contigs using the known pBI143 sequence as reference, we found three distinct versions of pBI143 (Figure 1A), all of which had over 95% nucleotide sequence identity to one another throughout their entire length except at the repA gene, where the sequence identity was as low as 75% with a maximum of 81% between Version 1 and Version 2 (Table S1).

We then sought to quantify the prevalence of pBI143 across global human populations using a metagenomic read recruitment survey with an expanded set of 4,516 publicly available gut metagenomes from 23 countries ^{52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74} (Table S1). Recruiting metagenomic short reads from each gut metagenome using each pBI143 version independently (Figure 1, Table S1), we found that pBI143 was present in 3,295 metagenomes, or 73% of all samples at a detection threshold over 0.5 (Figure 1B, see Methods for details). However, the prevalence of pBI143 was not uniform across the globe (Figure 1B): pBI143 occurred predominantly in metagenomes of individuals who lived in relatively industrialized countries, such as Japan (92% of 636 individuals) and the United States (86% of 154 individuals). We rarely detected pBI143 in individuals who lived in relatively non-industrialized countries such as Madagascar (0.8% of 112 individuals) or Fiji (8.7% of 172 individuals). This difference is likely due to the non-dominant presence of taxa that harbor pBI143 in the gut microbiomes of individuals from relatively less industrialized countries, whose microbiomes typically differ from those who live in industrialized countries ⁷⁵. Within individuals who carried it, pBI143 was often highly abundant (Figure 1B), and despite its small size, it often recruited 0.1% to 3.5% of all metagenomic reads with a median coverage of over 7,000X (Figure S1, Table S1).

The distribution of pBI143 versions across industrialized human populations was also not uniform as different versions of pBI143 tended to be dominant in different geographic regions. pBI143 Version 1 (98% identical to the original reference sequence for pBI143 ⁴⁴) dominated individuals in North America and Europe, and occurred on average in 82.5% of all samples that carry pBI143 from Austria, Canada, Denmark, England, Finland, Italy, Netherlands, Spain, Sweden and the USA (Figure 1C, Table S1). In contrast, pBI143 Version 2 dominated countries in Asia and occurred in 63.6% of all samples that carry pBI143 in China, Japan, and Korea (Figure 1C, Table S1). pBI143 Version 3 was relatively rare, comprising only 7.4% of pBI143-positive samples, and mostly occurred in individuals from Japan, Korea, Australia, Sweden, and Israel (Figure 1C, Table S1).

The extremely high prevalence and coverage of pBI143 suggests that it is likely one of the most numerous genetic elements in the gut microbiota of individuals from industrialized countries. We compared the prevalence and relative abundance of pBI143 to crAssphage ^76,77, a 97 kbp bacterial virus that is widely recognized as the most abundant family of viruses in the human gut ⁷⁸. pBI143 was more prevalent (73% vs 27%) in our metagenomes than all 21 crAssphage genomes we analyzed, although individual samples differed widely with respect to the abundance of pBI143 and crAssphage (Table S1). The average percentage of metagenomic reads recruited by pBI143 and the most abundant crAssphage were 0.05% and 0.13%, respectively. However, taking into consideration that crAssphage is approximately 36 times larger than pBI143, and assuming that average coverage is an acceptable proxy to the abundance of genetic entities, these data suggest that on average pBI143 is at least 14 times more numerous than crAssphage in the human gut (Table S2).

Overall, these data demonstrate that pBI143 is one of the most widely distributed and numerous genetic elements in the gut microbiomes of industrialized human populations world-wide.

Multiple taxa within the order Bacteroidales carry pBI143

Interestingly, the detection patterns of pBI143 in metagenomes differed from the detection patterns we observed for its de facto host Bacteroides fragilis in the same samples; B. fragilis and pBI143 co-occurred in only 41% of the metagenomes. Sequencing depth did not explain this observation, as pBI143 was highly covered (i.e., >50X) in 25% of metagenomes where B. fragilis appeared to be absent (Table S2), suggesting that the host range of pBI143 extends beyond B. fragilis.

To investigate the host range of pBI143, we employed a collection of bacterial isolates from the human gut, which contained 717 genomes that represented 104 species in 54 genera (Table S1). We found pBI143 in a total of 82 isolates that resolved to 11 species across 3 genera: Bacteroides, Phocaeicola, and Parabacteroides. Many of the pBI143-carrying isolates of distinct species were from the same individuals, suggesting that pBI143 can be mobilized between species. To confirm this, we inserted a tetracycline resistance gene, tetQ, into pBI143 in the Phocaeicola vulgatus isolate MSK 17.67 (Figure S2, Table S1) and tested the ability of this engineered pBI143 to transfer to two strains of two different families of Bacteroidales, Bacteroides ovatus D2 and Parabacteroides johnsonii CL02T12C29. In these assays, we found that pBI143 was indeed transferred from the donor to the recipient strains at a frequency of 5 × 10⁻⁷ and 3 × 10⁻⁶ transconjugants per recipient, respectively (Figure S2).

pBI143 is primarily restricted to the human gut

Given the host range of pBI143, one interesting question is whether the ecological niche boundaries of pBI143 hosts exceed a single biome, since the members of Bacteroides, Phocaeicola, and Parabacteroides are not specific to the human gut and do occur in a range of other habitats including non-human primate guts ⁷⁹. For a comprehensive survey, we searched for pBI143 in over 100,000 metagenomes from 88 diverse environments including ocean, wastewater, soil, plants, hospital surfaces and animal guts (buffalo, cat, chicken, cow, deer, dog, elk, fish, goat, human, insect, macaques, mouse, panda, pig, rat, sheep, termite, vole, whale, boar, yak and zebu). The results of read recruitment from these metagenomes indicated pBI143 is largely specific to the human gut (Figure 2A, Figure S3). In an extreme example, pBI143 comprised an astonishing 20.1% of all reads (33 million) in an individual person in the United States, with a metagenomic read coverage of 377,600X (Table S1). But this was not a singular example: the average coverage of pBI143 exceeded 100,000X in over 40 individuals in the dataset, and was higher than 10,000X in over 1,500 humans (Table S1). In contrast, the detection of pBI143 in non-host associated environments was virtually zero, except in human-impacted habitats such as sewage and hospital surfaces, where pBI143 was systematically detected in very low abundances (Figure S3, Table S1). Across the gut metagenomes of 30 animal species, we consistently detected pBI143 only in rats housed in laboratories and pet cats (Figure S3). However, pBI143 represented only 0.003% and 0.001% (~50X and ~25X coverage) of all reads in cat and rat metagenomes (Figure S1) on average in contrast to 0.1% (~1600X coverage) of all reads in human gut metagenomes (Table S1). As pBI143 appeared to primarily flourish in humans, we also screened metagenomes from various human body sites, including the human skin, oral cavity, respiratory tract, nose and vagina ⁷⁴ and found that pBI143 was poorly detected on the human body outside of the gut environment (Figure 2A, Table S1).

Figure 2. Presence or absence of pBI143 in non-human gut environments.

(A) Survey of pBI143 across more than 100,000 metagenomes from diverse environments. (B) Copy number of pBI143 compared to two established human fecal markers Bacteroides or Lachnospiraceae, as measured by qPCR. Zero, trace, moderate, high and sewage categories and sample order designations are determined based on pBI143 copy number relative to the established markers. See also Table S1, S2, Figure S2, S3.

Finally, to confirm the results of our metagenomic screen, we designed and tested a highly specific qPCR assay for pBI143 (Table S3). While there was a robust amplification of pBI143 from sewage samples (Figure 2B), pBI143 was virtually absent in fecal samples from dog, alligator, raccoon, horse, pig, deer, cow, chicken, goose, cat, rabbit, deer, or gull (Table S3). Our qPCR data did show low levels of amplification in three of the four cats tested, however at a copy number that was 73-fold less than human fecal content of sewage.

The near-absolute exclusivity of pBI143 to the human gut presents practical opportunities, such as the accurate detection of human fecal contamination outside the human gut. Using the same PCR primers, we also amplified pBI143 from water and sewage samples and compared its sensitivity to the gold standard markers currently used for detecting human fecal contamination in the environment (16S rRNA gene amplification of human Bacteroides and Lachnospiraceae) ^80,81. pBI143 had higher amplification in all 41 samples where Bacteroides and Lachnospiraceae were also detected (Figure 2). pBI143 was also amplified in 6 samples with no Bacteroides or Lachnospiraceae amplification, suggesting it is a highly sensitive marker for detecting the presence of human-specific fecal material.

Overall, these data show that pBI143 thrives specifically in the human gut environment, and can serve as a sensitive biomarker to detect human fecal contamination.

pBI143 is monoclonal within individuals, and its variants across individuals are maintained by strong purifying selection

So far our investigation of pBI143 has focused on its ecology. Next, we sought to understand the evolutionary forces that have conserved the pBI143 sequence by quantifying the sequence variation among the three distinct versions and examining the distribution of single nucleotide variants (SNVs) within and across globally distributed individuals. Across the three versions, both pBI143 genes had low dN/dS values (mobA = 0.11, repA = 0.04) (Table S4), suggesting the presence of strong forces of purifying selection acting on mobA and repA resulting in primarily synonymous substitutions. While the comparison of the three representative sequences provide some insights into the conserved nature of pBI143, it is unlikely they capture its entire genetic diversity across gut metagenomes.

To explore the pBI143 variation landscape, we analyzed metagenomic reads that matched the Version 1 of mobA to gain insights into the population genetics of pBI143 in naturally occurring habitats through single-nucleotide variants (SNVs). Since the mobA gene was more conserved across distinct versions of the plasmid compared to the repA gene, focusing on mobA enabled characterization of variation from all plasmid versions using a single read recruitment analysis. Surprisingly, the vast majority (83.2%) of mutation hotspots that varied in any metagenome matched a nucleotide position that differed between at least one pair of the three plasmid versions (Figure 3A, Table S4). In other words, pBI143 variation across metagenomes was predominantly localized to certain nucleotide positions that differed between the representative sequences of pBI143 for Version 1, 2 and 3, indicating that the three representative versions capture the majority of permissible pBI143 variation within our collection of gut metagenomes. Indeed, only 24.5% of metagenomes had more than three additional SNVs that were not present in at least one plasmid version, and 84.8% of metagenomes had a pBI143 population that was within 2-nucleotide distance (i.e., over 99.93% sequence identity) of one of the three versions. In addition to the primarily localized variation of pBI143, we also observed that the vast majority of metagenomes had zero non-consensus SNVs (Figure 3C, teal). In other words, pBI143 populations in most metagenomes either had no SNVs and were identical to one of three pBI143 versions, or any SNVs found in a given metagenome were fixed in the population (for more details see Methods). A similar analysis with the repA gene also showed similar patterns (Figure S4, Table S4). Overall, these data suggest that most humans carry a monoclonal population of pBI143 with little to no within-individual variation (Figure 3C, Table S4).

Figure 3. The mutational landscape of pBI143 in sewage and the human gut.

(A) The proportion of SNVs across 4,516 human gut metagenomes that are present in the same location (match) or different locations (do not match) as variation in one of the versions of pBI143 (turquoise). Each point is a single metagenome. (B) The proportion of SNVs across 68 sewage gut metagenomes that are present in the same location (match) or different locations (do not match) as variation in one of the versions of pBI143 (pink). (C) Non-consensus SNVs present in 4,516 human gut metagenomes and 68 sewage metagenomes. (D) AlphaFold 2 predicted structure of the catalytic domain of MobA with single amino acid variants from all 4,516 human gut metagenomes superimposed as ball- and-stick residues. oriT DNA (gray) and a Mn2+ ion marking the active site (purple) were modeled based on 4lvi.pdb ⁸⁵. The size of the ball-and-stick spheres indicate the proportion of samples carrying variation in that position (the larger the sphere, the more prevalent the variation at the residue) and the color is in CPK format. The color of the ribbon diagram indicates the pLDDT from AlphaFold 2 with red = very high (> 90 pLDDT) and orange = confident (80 pLDDT). See also Table S3, S4.

Next, we sought to investigate the functional context of non-synonymous environmental variants of MobA given its structure. For this, we employed single-amino acid variants ⁸² (SAAVs) we recovered from gut metagenomes and superimposed them on the AlphaFold 2 ^82,83 predicted structure of MobA using anvi’o structure ⁸⁴. The predicted catalytic domain of pBI143 MobA was structurally similar to MobM of the MobV-family (Protein Data Bank accession: 4LVI) encoded by plasmid pMV158 ⁸⁵. We used the structurally similar catalytic domain in MobA to model the binding of the oriT of pBI143 to MobA. We found that there were only 21 SAAVs throughout MobA that were present in greater than 5% of the gut metagenomes (Figure 3D, Table S4). Interestingly, highly prevalent SAAVs occurred exclusively near the DNA binding site (L56, E49, and A64), leading us to hypothesize that the non-synonymous variants we observe in the context of MobA may be involved in altering the DNA binding specificity for the oriT sequence ⁸⁵ demonstrating the coevolution of the oriT with the MobA protein between distinct pBI143 versions. Additionally, we find it likely that the cluster of high prevalence variation at residues V251, A246, V239, T238, I235, and L234 (Figure S4) could be driven by interactions with different host conjugation machinery for plasmid transfer. The functional implications of prevalent SAAVs given the structural context of the MobA gene suggest a likely role for adaptive processes on the evolution of pBI143 versions.

In contrast to the individual gut metagenomes, the pBI143 populations did not occur in a monoclonal fashion in sewage metagenomes (Table S4). Sewage metagenomes had, on average, 35 SNVs with a departure from consensus value of lower than 0.9, revealing the polyclonal nature of pBI143 in sewage (Figure 3C, Table S4). Similar to the individual gut metagenomes, most SNVs in sewage metagenomes (78.8%) occurred at a nucleotide position that was variable between at least one pair of the three pBI143 versions (Figure 3B, Table S4), suggesting that the majority of the variability in sewage is from the mixing of different versions of pBI143. However, the number of additional SNVs was much higher in sewage: 61.8% of sewage samples had greater than three SNVs that did not match a variable position in one of the three reference plasmids (Figure 3B). Given the marked increase in the number of additional SNVs in sewage, it is likely there are alternate but relatively rare versions of pBI143 in the human gut.

Overall, these results indicate that pBI143 has a highly restricted mutational landscape in natural habitats, frequently occurs as a monoclonal element in individual gut metagenomes, and the non-synonymous variants of MobA in the environment may be responsible for altering its DNA binding.

pBI143 is vertically transmitted, its variants are more specific to individuals than their host bacteria, and priority effects best explain its monoclonality in most individuals

The largely monoclonal nature of pBI143 presents an interesting ecological question: how do individuals acquire it, and what maintains its monoclonality? Multiple phenomena could explain the monoclonality of pBI143 in individual gut metagenomes, including (1) low frequency of exposure (i.e., most individuals are only ever exposed to one version), (2) bacterial host specificity (i.e., some plasmid versions replicate more effectively in certain bacterial hosts), or (3) priority effects (i.e., the first version of pBI143 establishes itself in the ecosystem and excludes others). The sheer prevalence and abundance of pBI143 across industrialized populations renders the ‘low frequency of exposure’ hypothesis an unlikely explanation. Yet the remaining two hypotheses warrant further investigation.

Bacterial host specificity is a plausible driver for the presence of a singular pBI143 version within an individual, given the interactions between plasmid replication genes and host replication machinery ^29,86. However our analysis of 82 bacterial cultures isolated from 10 donors shows that the plasmid is more specific to individuals than it is to certain bacterial hosts (Figure 4, Table S5). Indeed, identical pBI143 sequences often occurred in multiple distinct taxa isolated from the same individual, in agreement with the monoclonality of pBI143 in gut metagenomes and its ability to transfer within Bacteroidales. If pBI143 monoclonality is not driven by rare exposure or host specificity, it could be driven by priority effects ⁸⁷, where the initial pBI143 version somehow prevents other pBI143 versions from establishing in the same gut community.

Figure 4. Phylogeny of pBI143 in human donors versus the phylogeny of bacterial isolates recovered from the same individuals.

pBI143 (left) and bacterial host (right) genome phylogenies. The pBI143 phylogeny was constructed using the MobA and RepA genes; the bacterial phylogeny was constructed using 38 ribosomal proteins (see Methods). Blue alluvial plots are isolates with Version 1 pBI143 and red alluvial plots are isolates with Version 2 pBI143. No isolates had the rarer Version 3. See also Table S1, S4.

To examine if priority effects play a role in pBI143 monoclonality, we aimed to determine how pBI143 is acquired. The vertical transmission of microbes from mother to infant⁶⁴ is a well understood mechanism that transfers not only microbial populations ⁶⁴ but also their mobile genetic elements, such as phages and transposons ⁸⁸. We investigated evidence for the vertical transmission of pBI143 using our ability to track pBI143 SNVs between environments and followed the inheritance of identical pBI143 SNV patterns among 154 mother and infant gut metagenomes from four countries, Finland ⁶¹, Italy ⁶⁴, Sweden ⁷², and the USA ⁷³, where each study followed participants from birth to 3 to 12 months of age. It is reasonable to assume that in some cases infants may acquire pBI143 from other caregivers aside from the mother, but these data were not appropriate to test other transmission routes. We recruited reads from each metagenome to Version 1 pBI143 (Table S1) and identified the location of each SNV in mobA (Table S6). These data revealed a large number of cases where pBI143 had identical SNV patterns in mother-infant pairs (Figure 5A, Table S6). A network analysis of shared SNV positions across metagenomes appeared to cluster family members more closely, indicating mother-infant pairs had more SNVs in common than they had with unrelated individuals, which we could further confirm by quantifying the relative distance between each sample to others (Figure S5, Table S6, Methods).

Figure 5. Transfer and maintenance of pBI143.

(A) The network shows the degree of similarity between pBI143 SNVs across 154 mother and infant metagenomes from Finland, Italy, Sweden and the USA. Each node is an individual metagenome and nodes are colored based on family grouping. The surrounding coverage plots (colored) are visual representations of SNV patterns present in the indicated metagenomes. Nodes labeled with an “M” are mothers; nodes with no labels are infants. (B) Representative coverage plots showing different coverage patterns (maintained, two versions or wilt) observed in plasmids transferred from mothers to infants. See also Table S5, Figure S5.

Establishing that pBI143 is often vertically transferred, we next examined the impact of priority effects on pBI143 maintenance over time. We assumed that if priority effects are driving persistence of a single version of pBI143, the first version that enters the infant gut environment should be maintained over time. Indeed, many phage populations are influenced by priority effects where the presence of one phage provides a competitive advantage to the bacterial host ⁸⁹ or bacterial host immunity to infection with similar phages ^90–92. In our data, we found no instances where pBI143 acquired from the mother was fully replaced in the infant during and up to the first year of life (Table S6). While 69% of infants maintained the version received from the mother (Figure 5B), we also observed other, less common genotypes. These less common cases included a ‘two versions’ scenario where the mother possessed two versions of pBI143, both of which were passed to the infant (21%), and a ‘wilt’ case, where the transferred pBI143 was neither replaced nor persisted until the end of sampling (7%) (Figure 5B). Of the five total wilt cases where pBI143 was lost, only one did not show a corresponding drop in Bacteroides/Phocaeicola abundance (Figure S5). Although these less prevalent phenotypes are not necessarily explained by priority effects, 69% maintenance of the initial version of pBI143 suggests that priority effects have an important role in the maintenance of pBI143 in the gut, despite many incoming populations colonizing the infant and likely carrying other pBI143 versions.

Overall, by tracking SNV patterns between environments we established that pBI143 is vertically transferred from mothers to infants and that priority effects likely play a role in maintaining the predominantly monoclonal populations of pBI143.

pBI143 is a highly efficient parasitic plasmid

An intuitive interpretation of the surprising levels of prevalence and abundance of pBI143 across the human population, in addition to its limited variation maintained by strong evolutionary forces, is that it provides some benefit to the bacterial host. However, the two annotated genes in pBI143 appear to serve only the purpose of ensuring its own replication and transfer, contradicting this premise. The coverage of pBI143 and its Bacteroides, Phocaeicola and Parabacteroides hosts in gut metagenomes indeed show a significant positive correlation (R²: 0.5, p-value < 0.001) (Figure 6A, Table S2), however, these data are not suitable to distinguish whether pBI143 provides a benefit to the bacterial host fitness, or acts as a genetic hitchhiker.

Figure 6. The relationship between pBI143 and its bacterial hosts.

(A) The average coverage of pBI143 and the corresponding coverage of predicted host genomes (Bacteroides, Parabacteroides and Phocaeicola) in 4,516 metagenomes. (B) Competition experiments in gnotobiotic mice between B. fragilis with and without pBI143. The proportion of pBI143-carrying cells in male (M) and female (F) mice in the initial inoculum, at Day 14, Day 28 and Day 40. (C) Four examples of pBI143 assembled from metagenomes that carry additional cargo genes. Gray genes are the canonical repA and mobA genes of naive pBI143; lilac genes are additional cargo. See also Table S2, S6, Figure S6.

To experimentally investigate if pBI143 is advantageous or parasitic, we constructed isogenic pairs of B. fragilis 638R and B. fragilis 9343 with and without the native Version 1 sequence of pBI143. To ensure pBI143 is maintained in these new Bacteroides hosts, we passaged them in culture for 7 days and found that the plasmid was still present in all colonies in both strains (Table S2) showing that it is faithfully replicated. Next, we competed the B. fragilis 638R (with and without pBI143) in gnotobiotic mice for 40 days to determine if pBI143 affects the fitness of this bacterial host. In contrast to the stable maintenance of pBI143 we observed in human populations (Figure 6A, Figure S1), here we observed a gradual decline in the ratio of pBI143-containing cells to plasmid-free cells over time (Figure 6B, Table S2). The slow decrease in pBI143-containing cells suggests that pBI143 has a small but negative impact on B. fragilis 638R fitness in this in vivo model. However, it is worth noting that pBI143 may have different fitness effects on different bacterial hosts, as has been shown for other plasmids ⁹³.

If pBI143 exerts a cost to its hosts, how then is it maintained in Bacteroidales populations in the human gut? This falls under the umbrella of a more general question: Why are plasmids maintained in cells at all? The ‘plasmid paradox’ states that, in theory, plasmids should not even exist given that their conjugal transfer rate is too low to allow them to persist in populations by infectious transmission, and that over time the cost of their maintenance outweighs any benefits they may confer as those beneficial traits are eventually captured by the chromosome ^94,95. However, more recent work has suggested that plasmid conjugation rates are high enough to allow for infectious transfer and maintenance ^30,96,97. As a mobilizable yet non-conjugative plasmid, pBI143 relies on the conjugation machinery of other elements in the bacterial genome to transfer between cells, and most likely uses infectious transfer to maintain itself in Bacteroidales populations. In the absence of the opportunity for infectious transfer, it is conceivable to expect a decline of pBI143 in a population as our experiments demonstrated. Overall, it is likely that in the ‘wild’ environment of the human gut, pBI143 at least in part relies on transferring between organisms (Figure S2) to overcome the costs it exerts on its bacterial host.

Another strategy for pBI143 to maintain itself in the population and provide a benefit to its bacterial hosts is to act as a natural shuttle vector by transiently acquiring additional genetic material and transferring it between cells in a community. In fact, in our survey of assembled gut metagenomes we observed a few cases that may support such a role for pBI143. In most individuals, we assembled pBI143 in its native form with two genes. However, there were 10 instances where the assembled pBI143 sequence from a given metagenome contained additional genes (Figure 6C, Figure S6, Table S1). Many of the additional genes had no predicted function, but other cargo include predicted toxin-antitoxin genes conferring plasmid stability, as well as those that may confer beneficial functions to the bacterial host, such as galacturonosidase, pentapeptide transferase, phosphatase, and histidine kinase genes. A further examination of these larger plasmids suggested that the additional genetic material was likely acquired from both other extrachromosomal mobile elements and chromosomal DNA (Table S1) in Bacteroides and Eubacterium species (Table S3). There did not appear to be site specificity, as additional material recombined into both of pBI143’s intergenic regions (Figure S6). These occasional larger versions of pBI143 share a common backbone of repA and mobA and thus form a “plasmid system” ⁴³, a common plasmid evolutionary pattern suggesting the possibility that pBI143 may dynamically acquire different genes in different environments.

Overall, it appears that the native pBI143 can be mildly detrimental to cells under controlled environments, maintains itself in natural populations through infectious transfer, and is also capable of acquiring additional genes into its backbone which may provide benefits to the host cells.

pBI143 responds to oxidative stress in vitro, and its copy number is significantly higher in metagenomes from individuals who are diagnosed with IBD

Mobile genetic elements rely on their hosts for replication machinery, but many have developed mechanisms to increase their rates of replication and transfer during stressful conditions to increase the likelihood of their survival if the host cell dies ^98–101. To investigate whether the copy number of pBI143 changes as a function of stress, we first conducted an experiment with B. fragilis isolates that naturally carry pBI143.

Given that oxygen exposure upregulates oxidative stress response pathways in the anaerobic B. fragilis 102, we exposed two different B. fragilis cultures, B. fragilis RI6 (which was isolated from a healthy individual) and B. fragilis 214 (which was isolated from a pouchitis patient ¹⁰³) to 21% oxygen for increasing periods of time (Figure 7A, Table S7). To calculate the copy number of pBI143 in culture, we quantified the ratio between the total number of plasmids and the total number of cells in culture using a qPCR with primers targeting pBI143 and a B. fragilis-specific gene we identified through pangenomics. As the length of oxygen exposure increased, the copy number of pBI143 per cell also increased. Notably, the copy number was quickly reduced to control levels once the cultures were returned to anaerobic conditions, indicating that copy number fluctuation is a rapid and transient process that is dependent on host stress.

Figure 7. pBI143 copy number increases in stressful environments.

(A) Copy number of pBI143 in B. fragilis cultures with increasing exposure to oxygen. Top: B. fragilis 214. Bottom: B. fragilis RI6. Arrows indicate the time point at which the culture was returned to the anaerobic chamber. The control cultures (gray) were never exposed to oxygen. Opaque lines are the mean of 5 replicates (translucent lines). (B) Host-specific approximate copy number ratio (ACNR) of pBI143 in healthy individuals (gray) versus those with IBD (purple). See also Table S2, S7.

Oxidative stress is also a signature characteristic of inflammatory bowel disease (IBD), a group of intestinal disorders that cause inflammation of the gastrointestinal tract ¹⁰⁴. The dysregulation of the immune system during IBD typically leads to high levels of oxidative stress in the gut environment ¹⁰⁵. We thus hypothesized that, if oxidative stress is among the factors that drive the increased copy number of pBI143 in culture, one should expect a higher copy number of pBI143 in metagenomes from IBD patients compared to healthy controls.

To analyze the copy number of pBI143 in a given metagenome, we calculated the ratio of metagenomic read coverage between pBI143 and its bacterial host in metagenomes where pBI143 could confidently be assigned to a single host. With these considerations, we developed an approach to calculate an ‘approximate copy number ratio’ (ACNR) for pBI143 and its unambiguous bacterial host in a given metagenome using bacterial single-copy core genes (see Methods). We calculated the ACNR of pBI143 in 3,070 healthy and 1,350 IBD gut metagenomes (Table S1). Our analyses showed that the geometric mean of the ACNR for pBI143 and its host was 3.72 times larger (robust-Wald 95% CI: 2.66x - 5.20x, p-value < 10⁻¹³) in IBD compared to healthy metagenomes, indicating that the pBI143 ACNR was significantly higher in individuals with IBD compared to those who were healthy (Figure 7B, Table S7).

The copy number ratio of pBI143 to its B. fragilis host in culture calculated with qPCR primers was much lower (~5X on average) compared to its approximate copy number ratio in healthy metagenomes (~120X on average). Multiple factors can explain this difference, including biases associated with sequencing steps or the calculation of the coverage, or that the conditions naturally occuring communities experience vastly differ than those conditions encountered in culture media, even in the presence of oxygen. Nevertheless the marked increase of the relative coverages of pBI143 and its host in IBD metagenomes suggest the potential utility of this cryptic plasmid for unbiased measurements of stress. Overall, these results show that both in metagenomes and experimental conditions, an increased copy number of pBI143 is a consistent feature in the presence of host stress.

DISCUSSION

Our work shed lights on a mysterious corner of life in the human gut. Even though pBI143 is found in greater than 90% of all individuals in some countries, the prevalence of this cryptic plasmid has gone unnoticed for almost four decades since its discovery by Smith, Rollins, and Parker ⁴⁴. The remarkable ecology, evolution, and potential practical applications of pBI143 that we characterized here through ‘omics analyses as well as in vitro and in vivo experiments offer a glimpse of the world of understudied cryptic plasmids in the human gut, and elsewhere.

The application of population genetics principles to pBI143 through the recovery of single-nucleotide variants (SNVs) and single-amino acid variants (SAAVs) from gut metagenomes reveals not only the strong forces of purifying selection on the evolution of its sequence, but also hints the presence of adaptive processes at localized amino acid positions that are variable in the critical parts of the DNA-interacting residues of the catalytic domain of its mobilization protein. With our current measurements of fitness, the presence of pBI143 appears to be slightly detrimental to bacterial host fitness in vivo, which makes this cryptic plasmid seem a mundane parasite using host machinery for replication without providing a benefit, and somewhat contradicting the strict evolutionary pressures that maintain its environmental sequence variants.

That said, our observations from naturally occurring gut environments include cases where pBI143 carries additional genes, likely acting as a natural shuttle vector. Although traditionally mobile genetic elements are classified as mutualistic or parasitic with respect to the bacterial host, the fluidity of pBI143 to fluctuate between the cryptic 2-gene state and the larger 3 or more gene state with potentially beneficial functions, suggests that the boundaries between parasitism and mutualism for pBI143 are not clear cut. Instead, pBI143 may act as a ‘discretionary parasite’, where it has a cryptic form for the majority of its existence in which it could be best described as a parasite, while occasionally being found with additional functions that may be beneficial to its host as a function of environmental pressures. Testing this hypothesis with future experimentation, and if true, investigating to what extent discretionary parasitism applies to other cryptic plasmids, may lead to a deeper understanding of the role of this enigmatic group of mobile genetic elements in microbial fitness under changing environmental conditions.

Our findings show that pBI143 has important potential practical applications beyond molecular biology. The first and most straightforward of these applications relies on the prevalence and human specificity of pBI143 to more sensitively detect human fecal contamination in water samples. Human fecal pollution is a global public health problem, and accurate and sensitive indicators of human fecal pollution are essential to identify and remediate contamination sources and to protect public health ¹⁰⁶. While culture assays for E. coli or enterococci have historically been used to detect human fecal contamination in environmental samples, the common occurrence of these organisms in many different mammalian guts and the poor sensitivity of such assays motivated researchers in the past two decades to utilize PCR amplification of 16S rRNA genes, specifically those from human-specific Bacteroides and Lachnospiraceae populations, to detect human-specific fecal contamination with minimal cross-reactivity with animal feces ^80,81. Our benchmarking of pBI143 with qPCR revealed that pBI143 is an extremely sensitive and specific marker of human fecal contamination that typically occurs in human fecal samples and sewage in numbers that are several-fold higher than the state-of-the-art markers, which enabled the quantification of fecal contamination in samples where it had previously gone undetected. Another practical application of pBI143 takes advantage of its natural shuttle vector capabilities to incorporate additional genetic material into its backbone. Our demonstration that pBI143 (1) replicates in many abundant gut microbes, (2) can be stably introduced to new hosts, and (3) naturally acquires genetic material makes this cryptic plasmid an ideal natural payload delivery system for future therapeutics targeting the human gut microbiome. Indeed, our observations of pBI143 with cargo genes in metagenomes indicates that this likely happens in nature. Yet another practical implication of pBI143 is its potential to measure the level of stress in the human gut. Surveying thousands of samples from individuals who are healthy or diagnosed with IBD, our results show that across all bacterial hosts, the approximate copy number of pBI143 increases in individuals with IBD.

From a more philosophical point of view, the prevalence and high conservancy of pBI143 across globally distributed human populations questions the traditional definition of the ‘core’ microbiome ¹⁰⁷. In its aim to define a core microbiome, the field of microbial ecology has primarily focused on bacteria, although sometimes including prevalent archaea or fungi ^108–111. However, our results indicate that there are mobile genetic elements that fit the standard criteria of prevalence to be defined as core. Broadening the definition of a core microbiome beyond microbial taxa may enable the recognition of other mobile genetic elements (eg. plasmids, phages, transposons) that are prevalent across human populations and fill critical gaps in our understanding of gut microbial ecology and evolution.

Limitations of the study

A precise understanding of the drivers of the ecology, evolution, and function of this cryptic plasmid in the human gut demands additional research. Given the complexity of generating bacterial strains with plasmids containing no antibiotic markers, we were limited in our ability to test the fitness of different pBI143 versions, or its impact on the fitness of different taxa. These experiments would more definitively show if different versions have competitive advantages over others, or if pBI143 differentially impacts fitness in different hosts. Our fitness experiments relied on growth measurements as the sole indicator of pBI143 impact on the host. Future experiments could explore if there are transcriptional changes that occur as a result of pBI143 carriage.

In our structure-informed interpretations of the genetic variants of the mobA gene, we point to residues that are involved in different MobA versions binding to the oriT sequence. While a deeper investigation into the functional role of observed variants was beyond the scope of this work, in vivo or in vitro experiments that explicitly test single-amino acid variants of MobA are necessary to establish robust insights into the DNA binding properties of this gene.

Our observation that pBI143 occasionally occurs in human gut metagenomes with a larger number of genes show the likelihood of cryptic plasmids to uptake additional DNA into their backbones. Our study was limited to computational characterizations of these larger, cargo-containing plasmids. Establishing precise insights into the functional impact of the additional genes in pBI143 and whether they serve as fitness determinants for host bacterial populations as a function of environmental requirements represent an exciting future direction to investigate additional roles of cryptic plasmids in microbial adaptation.

STAR Methods

Resource Availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, A. Murat Eren (meren@hifmb.de).

Materials Availability

Bacterial cultures for host range investigations, which are listed in Table S1, are courtesy of The Duchossois Family Institute (https://dfi.uchicago.edu/). Primer sequences can be found in Table S3. B. fragilis strains with pBI143 are available upon request from the Comstock Lab collection (https://comstocklab.uchicago.edu/).

Data and code availability

• All molecular data used in this study are publicly available via the NCBI Sequence Read Archive, and Table S1 reports the accession numbers for all genomes and metagenomes.

• Original code accompanying this paper, anvi’o data products that describe metagenomic read recruitment results, and sequences for pBI143 versions and bioinformatics workflows is available at https://merenlab.org/data/pBI143.

• Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Experimental Models and Study Participant Details

Husbandry and housing conditions of experimental animals.

Maintenance and handling of all mice were carried out in the Gnotobiotic Mouse Facility at the University of Chicago. All experimentation was approved by the Institutional Animal Care and Use Committee at the University of Chicago in compliance with federal regulations. Mice were housed in individually vented sterile cages at 22°C and 30–70% humidity and provided water and food ad libitum. The mouse chow pellets were LabDiet^® JL Rat and Mouse/Auto 6F 5K67 complete life-cycle diet that was autoclaved to sterilize. Three male and three female 10–15 week old germ-free C57BL/6J mice were gavaged with a 1:1 inoculum of B. fragilis 638R¹¹³ : B. fragilis 638R pBI143. Males and females were housed separately in isocages and remained gnotobiotic for the duration of the experiment.

Bacterial cell culture.

All experiments involving Bacteroidales organisms were performed in a Coy Anaerobic Chamber unless otherwise specified. The chamber atmosphere is H₂(7.5%), CO₂(10%), N₂(82.5%). We streaked the strain of interest onto Brain-Heart Infusion agar from Fisher Scientific supplemented with yeast extract, hemin and Vitamin K (BHIS media). We incubated the plates for 48 hours at 37°C. Appropriate antibiotics were added to plates or media as described in Method Details. Unless otherwise specified, we inoculated BHIS broth with colonies from the agar plates, and grew cultures for up to 24 hours at 37°C before performing downstream experiments. The Escherichia coli strains we used in this study were grown aerobically. We streaked the strain of interest onto Luria-Bertani (LB) agar plates with appropriate antibiotics and incubated at 37°C for 24 hours. When needed, we inoculated LB broth with strain of interest and grew at 37°C for 12–15 hours before performing downstream experiments.

Method Details

Genomes and metagenomes.

We acquired the original pBI143 genome from the National Center for Biotechnological Information (GenBank: U30316.1). We manually assembled the three reference versions of pBI143 (Version 1, 2 and 3) from metagenomes samples USA0006, CHI0054 and ISR0084. We acquired 717 human gut isolate genomes from the Duchossois Family Institute collection (Table S1). For our in-depth metagenomic analyses, we downloaded (1) 4,516 healthy human adult gut metagenomes from the National Center for Biotechnology Information (NCBI) from (Australia (Accession ID: PRJEB6092), Austria ⁵², Bangladesh ⁵³, Canada ⁵⁴, China ^55,56, Denmark ⁵⁷, England ⁵⁸, Ethiopia ⁵⁹, Fiji ⁶⁰, Finland ⁶¹, India ⁶², Israel ⁶³, Italy ^64,65, Japan ⁶⁶, Korea ⁶⁷, Madagascar ⁵⁹, Mongolia ^59,68, Netherlands ⁶⁹, Peru ⁷⁰, Spain ⁷¹, Sweden ⁷², Tanzania ⁶⁵, and the USA ^70,73,74) (Table S1), (2) 1,096 gut metagenomes from infantmother pairs from Italy, Finland, Sweden and the USA from NCBI (Table S1), and (3) 77 globally distributed sewage metagenomes (Table S1). We also conducted a large-scale survey of pBI143 in 100,029 metagenomes found on public databases (Table S1, we discarded 85 metagenomes which we could not reliably assign to a biome).

Metagenomic assembly, read recruitment, coverage and detection statistics.

Unless otherwise specified, we performed all metagenomic analyses throughout the manuscript within the open-source anvi’o v7 software ecosystem (https://anvio.org) ¹¹⁴. We automated assembly and read recruitment steps using the anvi’o metagenomics workflow ¹¹⁵ which used snakemake v5.10 ¹¹⁶. To quality-filter genomic and metagenomic raw paired-end reads (with the exception of the dataset of 100,029 metagenomes) we used illumina-utils v1.4.4 ¹¹⁷ program ‘iu-filter-quality-minoche’ with default parameters, and IDBA_UD v1.1.2 with the flag ‘--min_contig 1000’ to assemble the metagenomes ¹¹⁸. The 100,029 metagenomes were quality screened to remove adapters and spike-in control sequences, as well reads that matched to a reference human genome and those that were low quality based on the quality scores assigned by the sequencer ¹¹⁹. We used Bowtie2 v2.4 ¹²⁰ to recruit reads from the metagenomes to reference sequences and samtools v1.9 ¹²¹ to convert resulting SAM files into sorted and indexed BAM files. We generated anvi’o contigs databases (https://anvio.org/m/contigs-db) using the program ‘anvi-gen-contigs-database’, during which Prodigal v2.6.3 ¹²² identified open reading frames. We created anvi’o profile databases (https://anvio.org/m/profile-db) from the mapping results for each metagenome using ‘anvi-profile’, which stores coverage and detection statistics, and ‘anvi-merge’ to combine all profiles together. To recover coverage and detection statistics for a given merged profile database, we used ‘anvi-summarize’ with ‘--init-gene-coverages’ flag. To profile the distribution pattern of pBI143 across the larger set of 100K metagenomes we used the ‘anvi-profile-blitz’, which rapidly profiles read recruitment results to only report essential statistics (https://anvio.org/m/bam-stats-txt).

Detection of pBI143 and crAssphage in metagenomes.

Using mean coverage to assess the occurrence of a given sequence in a given sample based on metagenomic read recruitment can yield misleading insights due to non-specific read recruitment (i.e., recruitment of reads from metagenomes to a reference sequence from non-target populations). Thus, we relied upon the detection statistic reported by anvi’o, which is a measure of the proportion of the nucleotides in a given sequence that are covered by at least one short read. We considered pBI143 was present in a metagenome only if its detection value was 0.5 or above. Values of detection in metagenomic read recruitment results often follow a bimodal distribution for populations that are present and absent (see Supplementary Figure 2 in ref. ¹²³). Thus, 0.5 is a conservative cutoff to minimize a false-positive signal to assume presence.

Presence of distinct pBI143 versions in a genome or metagenome.

We used the results of individual read recruitments to each known version of pBI143 to measure the coverage of each gene in pBI143 in samples that had a detection of greater than 0.9 and compared the ratio of the coverage of each gene. The pBI143 version where the genes have the most even coverage ratio was considered the predominant version in that genome or metagenome.

Addition of tetQ to pIB143.

To study transfer of pBI143 from Phocaeicola vulgatus MSK 17.67 to other Bacteroidales species, we added tetQ to pBI143. We PCR amplified tetQ from Bacteroides caccae CL03T12C61 and inserted it at the site shown in Figure S2 (all primers are listed in Table S3). We PCR amplified the DNA regions flanking each side of this insertion site and the three PCR products were cloned into BamHI-digested pLGB13 ¹²⁴. We conjugally transferred this plasmid into Phocaeicola vulgatus MSK 17.67 and selected cointegrates on gentamycin 200 μg/ml and erythromycin 10 μg/ml. We passaged the cointegrate in non-selective media and selected the resolvents by plating on anhydrotetracycline (75 ng/ml). We confirmed pIB143 contained tetQ by WGS of the strain at the DFI Microbiome Metagenomics Facility.

Transfer assays.

The strains tested as recipients in the pBI143-tetQ transfer assays were Parabacteroides johnsonii CL02T12C29 and Bacteroides ovatus D2, both erythromycin resistant and tetracycline sensitive. We grew the donor strain Phocaeicola vulgatus MSK 17.67 pBI143-tetQ and recipient strains to an OD600 of ~ 0.7 and mixed them at a 10:1 ratio (v:v) donor to recipient, and spotted 10 μl onto BHIS plates and grew them anaerobically for 20 h. We resuspended the co-culture spot in 1 mL basal media and cultured 10-fold serial dilutions on plates with erythromycin (to calculate number of recipients) or erythromycin and tetracycline (4.5 μg/ml) (to select for transconjugants). We performed multiplex PCR ^125,126 to confirm that the tetracycline and erythromycin resistant colonies were the recipient strain containing pBI143-tetQ (Figure S2).

Purifying selection and single nucleotide variant characterization.

We calculated dN/dS ratios in anvi’o ⁸⁴ (see https://merenlab.org/data/anvio-structure/chapter-IV/#calculating-dndstextgene-for-1-gene for details). To determine the mutational landscape of pBI143 across metagenomes, we first identified all variable positions present in the reference pBI143 sequences. We used ‘anvi-script-gen-short-reads’ to generate artificial short reads from the version 2 and version 3 pBI143 sequences and recruited these reads to the pBI143 version 1 sequence to generate data similar to the read recruitment from metagenomes. Then, we combined these data with the read recruitment results from the global human gut metagenomes and sewage metagenomes against the same pBI143 version. We used ‘anvi-gen-variability-profile’ to recover the location of each single-nucleotide variant (SNV) from both artificial read recruitment results and the global human gut and sewage metagenomes in which the Q2Q3 coverage of pBI143 exceeded 10X Q2Q3. We then compared the SNV positions in each gut or sewage metagenome to those positions that varied between the three versions of pBI143 and for each metagenome we calculated the number of SNVs that occurred in a location that was also variable in pBI143 versions, and the number of SNVs in metagenomes that did not match to a known variant between pBI143 versions. To calculate the number of ‘non-consensus SNVs’ in a metagenome, we ran ‘anvi-gen-profile-database’ on the same metagenomes, with the flags ‘--gene-caller-ids 0’ (i.e., the gene call in the anvi’o contigs-db that matched to the mobA gene in pBI143), ‘--min-departure-from-consensus 0.1’ (to minimize noise), ‘--include-contig-names’ (for a verbose output) and ‘--quince-mode’ (to include coverage information for SNV positions even from metagenomes in which they do not vary). The resulting file described the variation in every single SNV position across metagenomes, and gave access to the ‘departure from consensus’ statistic to identify positions that are variable in the environment.

pBI143 structural and polymorphism analysis.

To explore the impact of single-amino acid variants (SAAVs) on the protein structure of pBI143 MobA, we de novo predicted the monomer and dimer structures using AlphaFold 2 (AF) in ColabFold with default settings ⁸³. AlphaFold 2 confidently predicted the structure of the catalytic domain but had low pLDDT scores for the coil domains and the dimer interactions. However, we explored variants across the whole dimer complex. Next, we integrated the pBI143 MobA AF structure into anvi’o structure by running ‘anvi-gen-structure-database’ ⁸². After that, we summarized SNV data as SAAVs from the metagenomic read recruitment data using ‘anvi-gen-variability-profile’ with the ‘--engine AA’ flag to recover a variability profile (https://anvio.org/m/variability-profile). Subsequently, we superimposed the SAAV data variability profile on the structure with ‘anvi-display-structure’ which filtered for variants that had at least 0.05 departure from consensus (reducing our metagenomic samples size from 2221 to 1706). Finally, we analyzed SAAVs that were prevalent in at least 5% of remaining samples. This left us with 21 SAAVs to analyze on the monomer. Next, we explored the relationship between SAAVs, relative solvent accessibility (RSA), and ligand binding residues in pBI143 MobA. To do this, we identified the homologous structure PDB 4LVI (MobM) by searching the high pLDDT pBI143 AF domain against the structure database PDB100 2201222 using Foldseek (https://search.foldseek.com/search). We next structurally aligned the pBI143 MobA AF structure to PDB 4LVI (MobM) ¹²⁷ using PyMol ¹²⁸. We chose the MobM structure 4LVI rather than a MobA because it had more structural and sequence homology to the pBI143 MobA catalytic domain AF structure than any PDB MobA structures. Additionally, we leveraged residue conservation values from the pre-calculated 4LVI ConSurf analysis to further explore ligand binding residues ^129,130.

Phylogenetic tree construction.

To construct the pBI143 phylogeny, we identified pBI143 contigs from the assemblies of bacterial isolates (Table S1) using BLAST ¹³¹. We ran ‘anvi-gen-contigs-database’ on each pBI143 contig followed by ‘anvi-export-gene-calls’ with the flag ‘--gene-caller prodigal’ and concatenated the resulting amino acid sequences. For the bacterial host phylogeny, we ran ‘anvi-gen-contigs-database’ on each assembled genome, then extracted ribosomal genes (Ribosomal_L1, Ribosomal_L13, Ribosomal_L14, Ribosomal_L16, Ribosomal_L17, Ribosomal_L18p, Ribosomal_L19, Ribosomal_L2, Ribosomal_L20, Ribosomal_L21p, Ribosomal_L22, Ribosomal_L23, Ribosomal_L27, Ribosomal_L27A, Ribosomal_L28, Ribosomal_L29, Ribosomal_L3, Ribosomal_L32p, Ribosomal_L35p, Ribosomal_L4, Ribosomal_L5, Ribosomal_L6, Ribosomal_L9_C, Ribosomal_S10, Ribosomal_S11, Ribosomal_S13, Ribosomal_S15, Ribosomal_S16, Ribosomal_S17, Ribosomal_S19, Ribosomal_S2, Ribosomal_S20p, Ribosomal_S3_C, Ribosomal_S6, Ribosomal_S7, Ribosomal_S8, Ribosomal_S9, ribosomal_L24) using the command ‘anvi-get-sequences-for-hmm-hits’ with the flags ‘--return-best-hit’, ‘--get-aa-sequences’, ‘--concatenate’ and ‘--min-num-bins-gene-occurs 82’ and ‘--hmm-source Bacteria_71’. For both phylogenies, we aligned the genes with MUSCLE v3.8.1551 ¹³², trimmed the alignments with trimAl ¹³³ using the flag ‘-gt 0.5’, and computed the phylogeny with IQ-TREE 2.2.0-beta using the flags ‘-m MFP’ and ‘-bb 1000’ ¹³⁴. We visualized the trees with ‘anvi-interactive’ in ‘--manual-mode’, and used the metadata provided by the Duchossois Family Institute to label the isolates to their corresponding donors. We used the ‘geom_alluvium’ function in ggplot2 to make the alluvial plots.

Mother-infant single nucleotide variant network.

To investigate whether single-nucleotide variants (SNVs) suggest a vertical transmission of pBI143, we used metagenomic read recruitment results from four independent study that generated metagenomic sequencing of fecal samples collected from mothers and their infants in Finland ⁶¹, Italy ⁶⁴, Sweden ⁷², and the USA ⁷³, against the pBI143 Version 1 reference sequence. The primary input for this investigation was the anvi’o variability data, which is calculated by the anvi’o program ‘anvi-profile’, and reported by ‘anvi-gen-variability-profile’ (with the flag ‘--engine NT’). The program ‘anvi-gen-variability-profile’ (https://anvio.org/m/anvi-gen-variability-profile) offers a comprehensive description of the single-nucleotide variants in metagenomes for downstream analyses. Since the mobA gene was conserved enough to represent all three versions of pBI143, for downstream analyses we limited the context to study variants to the mobA gene. The total number of samples in the entire dataset with at least one variable nucleotide position was 309, which represented a total of 102 families (Sweden: 52, USA: 24, Finland: 14, Italy: 11). We removed any sample that did not belong to a minimal complete family (i.e., at least one sample for the mother, and at least one sample of her infant), which reduced the number of families in which both members are represented to 57 families (Sweden: 36, USA: 16, Finland: 3, Italy: 2). We further removed families if the coverage of the mobA gene was not 50X or more in at least one mother and one infant sample in the family, which reduced the number of families with both members represented and with a reliable coverage of mobA to 49 families (Sweden: 33, USA: 13, Finland: 2, Italy: 1), and from a given family, we only used the samples that had at least 50X for downstream analyses. We subsampled the variability data in R to only include the variable nucleotide position data for the final list of samples. We then used the list of single-nucleotide variants reported in this file to generate a network description of these data using the program ‘anvi-gen-variability-network’, which reports an ‘edge’ between any sample pairs that share a SNV with the same competing nucleotides. We then used Gephi ¹³⁵, an open-source network visualization program, with the ForceAtlas2 algorithm ¹³⁶ to visualize the network. To quantify the extent of similarity between family members based on single-nucleotide patterns in the data, we generated a distance matrix from the same dataset using the ‘pdist’ function in Python’s standard library with ‘cosine’ distances. We calculated the average distance of each sample to all other samples in its familial group (‘within distance’), as well as the average distance from each sample to all other samples not present in their familial group (‘between distance’). We subtracted the within distance from the between distance to get the ‘subtracted distance’.

Metagenomic taxonomy estimation.

We used Kraken 2.0.8-beta with the flags ‘--output’, ‘--report’, ‘--use-mpa-style’, ‘--quick’, ‘--use-names’, ‘--paired’ and ‘--classified-out’ to estimate taxonomic composition of each metagenome ¹³⁷. For the genus-level taxonomic data, we filtered for metagenomes where the total number of reads recruited to a Bacteroides, Parabacteroides or Phocaeicola genome was >1000 and the mean coverage of pBI143 was >20X. For the species-level taxonomic data, we used a cutoff of >0.1% percent of reads recruited to designate presence or absence of B. fragilis and >0.0001% for pBI143 based on the sizes of the genomes respectively (the B. fragilis genome is 3 orders of magnitude larger than pBI143).

Isogenic strain construction.

We constructed the plasmid vector pEF108 (Figure S6A) by PCR amplifying the desired sections with primers vec_108F, vec_108R, frag1_108F, frag1_108R, frag2_108R and frag2_108R (Table S3) from existing plasmids. We assembled the three fragments via Gibson assembly using standard conditions described for NEB Gibson assembly mastermix (https://www.neb.com/protocols/2012/12/11/gibson-assembly-protocol-e5510). See pEF108 plasmid map below. We transformed the construct into E. coli S17 λpir via electroporation with a BioRad micropulser using 0.1cm cuvettes and selected on LB-carbenicillin 100ug/mL agar plates. We conjugally transferred pEF108 from E. coli S17 λpir into B. fragilis 638R or 9343 ¹³⁸. Briefly, we grew the donor and recipient strains in LB-carbenicillin 100ug/mL broth and vitamin K supplemented brain-heart infusion media (BHIS) broth respectively for 12–15 hours. We spun down the cultures and resuspended in BHIS and combined at a 1:5 ratio of donor to recipient. We spotted the donor and recipient mixture onto BHIS plates and incubated for 12 hours aerobically. We scraped the cells off the plate, resuspended in BHIS, then plated on BHIS + erythromycin 25ug/mL. We restreaked the colonies and validated the presence of the construct via PCR and sanger sequencing. Next, we wanted to select for cells where a recombination event had removed the vector containing pheS, ampicillin and erythromycin resistance and left pBI143 in its native form. Colonies with the full sized pEF108 construct were grown in Bacteroides minimal media (BMM) with 10mM p-chlorophenylalanine (PCPA) broth for 24 hours, and plated onto BMM + 10mM PCPA. PCPA prevents the growth of cells that are expressing the pheS gene. We used PCR and WGS to screen for pBI143-positive, pheS-negative colonies that grew on BMM + 10mM PCPA and used these isolates for downstream experiments.

Mouse competitive colonization assays.

All animal experimentation was approved by the Institutional Animal Care and Use Committee at the University of Chicago. We gavaged three male and three female 10–15 week old germ-free C57BL/6J mice with a 1:1 inoculum of B. fragilis 638R:B. fragilis 638R pBI143. Males and females were housed separately in isocages and remained gnotobiotic for the duration of the experiment. We collected fecal pellets after 14, 28, and 40 days, diluted and plated on BHIS plates. One mouse was lost during fecal collection. We performed PCR on 48 colonies per mouse using a mixture of four primers (Table S3), one set that amplifies a 1248-bp region of the 638R chromosome and a second set that amplifies a 662-bp segment of pBI143. All colonies produced PCR amplicons for the 1248-bp region of the 638R chromosome and a subset also contained the amplicon for pBI143, allowing calculation of the ratio over time. The exact starting ratio for gavage was also calculated using this same PCR.

PlasX prediction of pBI143 additional gene origin.

To determine if the additional genes acquired by pBI143 are of plasmid origin, we ran the plasmid prediction software PlasX ⁴³ (https://github.com/michaelkyu/PlasX). To identify genes and annotate COGs and Pfams we used the anvi’o programs ‘anvi-gen-contigs-database’, ‘anvi-export-gene-calls’, ‘anvi-run-ncbi-cogs’, ‘anvi-run-pfams, and ‘anvi-export-functions’. To annotate de novo gene families, we ran ‘plasx search_de_novo_families’. With the outputs from anvi’o and the de novo gene families file, we used PlasX to classify the sequences as plasmid or non-plasmid sequences using the command ‘plasx predict’.

‘Approximate copy number ratio’ calculation.

The first challenge to use metagenomic coverage values to study pBI143 copy number trends in human gut metagenomes is the unambiguous identification of gut metagenomes that appear to have a single possible pBI143 bacterial host beyond reasonable doubt. To establish insights into the taxonomic make up of the gut metagenomes we previously assembled, we first ran the program ‘anvi-estimate-scg-taxonomy’ (https://anvio.org/m/anvi-estimate-scg-taxonomy) with the flags ‘--metagenome-mode’ (to profile every single single-copy core gene (SCG) independently) and ‘--compute-scg-coverages’ (to compute coverages of each SCG from the read recruitment results). We also used the flag ‘--scg-name-for-metagenome-mode’ to limit the search space for a single ribosomal protein. We used the following list of ribosomal proteins for this step as they are included among the SCGs anvi’o assigns taxonomy using GTDB, and we merged resulting output files: Ribosomal_S2, Ribosomal_S3_C, Ribosomal_S6, Ribosomal_S7, Ribosomal_S8, Ribosomal_S9, Ribosomal_S11, Ribosomal_S20p, Ribosomal_L1, Ribosomal_L2, Ribosomal_L3, Ribosomal_L4, Ribosomal_L6, Ribosomal_L9_C, Ribosomal_L13, Ribosomal_L16, Ribosomal_L17, Ribosomal_L20, Ribosomal_L21p, Ribosomal_L22,ribosomal_L24, and Ribosomal_L27A. For our downstream analyses that relied upon the merged SCG taxonomy and coverage output reported by anvi’o, we considered Bacteroides, Parabacteroides and Phocaeicola as the genera for candidate pBI143 host ‘species’, and only considered metagenomes in which a single species from these genera was present. Our determination of whether or not a single species of these genera was present in a given metagenome relied on the coverage of species-specific single-copy core genes (SCGs), where the taxonomic assignment to a given SCG resolved all the way down to the level of species unambiguously. We excluded any metagenome from further consideration if three or more candidate host species had positive coverage in any SCG in a metagenome. Due to highly conserved nature of ribosomal proteins and bioinformatics artifacts, it is possible that even when a single species is present in a metagenome, one of its ribosomal proteins may match to a different species in the same genus given the limited representation of genomes in public databases compared to the diversity of environmental populations. So, to minimize the removal of metagenomes from our analysis, we took extra caution with metagenomes before discarding them if only two candidate host species had positive coverage in any SCG. We kept such a metagenome in our downstream analyses only if one species was detected with only a single SCG, and the other one was detected by at least 8. In this case we assumed the large representation of one species (with 8 or more ribosomal genes) suggests the presence of this organism in this habitat confidently, and assumed the single hit to another species within the same genus was likely due to bioinformatics artifacts. It is the most unambiguous case if only a single candidate host species was detected in a given metagenome, but we still removed a given metagenome from further consideration if that single species had 3 or fewer SCGs in the metagenome. These criteria deemed 584 of 2580 metagenomes to have an unambiguous pBI143 host that resolved to 21 distinct species names. We further removed from our modeling the metagenomes where the candidate host species did not occur in any other metagenome, which removed 5 of these candidate host species from further consideration. Finally, we further removed any metagenome in which the pBI143 coverage was less than 5X. Our final dataset to calculate the “approximate copy number ratio” (ACNR) of pBI143 in metagenomes through coverage ratios contained 579 metagenomes with one of 16 unambiguous pBI143 hosts. We calculated the ACNR by dividing the observed coverage of pBI143 by the empirical mean coverage of the host by averaging the coverage of all host SCGs found in the metagenome. To estimate the multiplicative difference in the geometric mean ACNR, we fit a linear model for the expected value of the logarithm of the ACRN, with disease status and bacterial host as predictors using rigr to construct the interval and estimate ¹³⁹.

Oxidative stress experiments.

We grew B. fragilis in 5 mL BHIS for 15 hours in an anaerobic chamber. We inoculated 750 μL of this culture into 30 mL BHIS in quintuplicate, and grew them for 3 hours. We divided the 30 mL into a further 5 culture flasks of 5 mL BHIS, and exposed each to oxygen with constant shaking for the appropriate time before returning the flask to the anaerobic chamber. At each time point, we took an aliquot of culture to determine the copy number of pBI143 in that sample. We extracted DNA from the cultures using a Thermal NaOH preparation ¹⁴⁰ to prepare them for qPCR. Copy number calculated can be found in Table S7.

pBI143 copy number qPCR.

To evaluate plasmid copy number (CN), we developed a real-time TaqMan probe multiplex PCR assay to amplify both pBI143 and a single-copy B. fragilis-specific genomic reference gene (referred to as hsp [heat shock protein]) in the same reaction.

Primer design for hsp and pBI143.

We aligned the canonical pBI143 plasmid DNA sequence from GenBank, whole genome assemblies and metagenome-assembled genomes (MAGs) as outlined in Table S3. The two known pBI143 genes, rep and mob, are common plasmid features across the bacterial kingdom (DelSolar et al., 1998; Wawrzyniak et al, 2017) and use of either gene alone had high potential for cross-amplification from other mobile genetic elements. To ensure pBI143 specificity, we designed our primer set so that the forward primer was located within the 3’ region of the rep gene (Table S3) while the reverse primer was located in the intergenic region (Table S3). This required that two conditions would have to be met for amplification to occur: (1) presence of the gene of interest and (2) homology to the pBI143 plasmid backbone. Despite the existence of plasmid variants differing across the rep gene, the 3’ region used in the forward primer design is conserved across the source sequences. The 38-yr old canonical pBI143 sequence (U30316.1) demonstrated greater sequence variation in intergenic regions than more contemporary sequences as determined by existing publicly-available metagenomic data. In designing the reverse primer, we strategically excluded U30316.1 in favor of using the more recent pBI143 sequences. The FAM-labeled hydrolysis probe was designed within a conserved plasmid feature, the 56-bp inverted repeat (IR) region and in concert with the designed primers, amplified/detected a 145-bp product (Table S3). The choice to use rep, over mob, as our target was based on (1) its conservancy in the 3’ gene region and (2) technical difficulties in optimizing a mob-based assay. To perform relative quantification experiments and to normalize bacterial cell numbers between samples for the purposes of determining the copy number of pBI143 per genome equivalent required identifying a suitable genomic reference gene. A key prerequisite was identifying a single copy gene present in the genus Bacteroides, but absent in other common gastrointestinal (GI) tract organisms. We employed a pangenomic analysis of 12 Bacteroides and 15 other human commensal gut microbe genomes to determine potential candidates and used the program ‘anvi-run-workflow’ with ‘--workflow pangenomics’. Anvi’o pangenomics workflow is detailed elsewhere (Delmont and Eren, 2018). Briefly, the pangenomic analysis used the NCBI’s BLAST (Altschul et al., 1990) to quantify similarity between each pair of genes, and the Markov Cluster algorithm (MCL) (Enright et al., 2002) (with inflation parameter of 2) to resolve clusters of homologous genes. The program ‘anvi-summarize’ created summary tables for pangenomes and ‘anvi-display-pan’ provided interactive visualizations of pangenomes. Using the criteria of (1) maximum functional homogeneity of 0.99 and (2) maximum geometric homogeneity of 0.99, we identified 35 gene clusters for further interrogation. The corresponding DNA sequences were gathered using the program ‘anvi-get-sequences-for-gene-cluster’ and aligned using Kalign ¹⁴¹ (https://www.ebi.ac.uk/Tools/msa/kalign) for multiple sequences.

Despite our initial desire to identify a target that could serve as a reference gene across all Bacteroides spp., we found that within these 35 gene clusters, the percent sequence identity dropped from >98% in B. fragilis to ~50–85% in non-B. fragilis sequences. Therefore, we focused on finding a B. fragilis-specific target by further requiring 100% coverage and 99.8% - 100% percent identity across all B. fragilis genomes. Five gene clusters qualified; a single gene cluster demonstrated 100% identity across all seven B. fragilis genomes used in the pangenome. Using this gene clusters’ 177-bp nucleotide sequence, we performed BLASTN ¹⁴² on the NCBI Reference Sequence (RefSeq) Database (release 99, 3/2/2020), using Megablast (optimize for highly similar sequences) to conduct a systematic and thorough in-silico assessment of B. fragilis specificity. A list of the 17,785 complete genomes was downloaded from RefSeq (https://www.ncbi.nlm.nih.gov/genome/browse#!/prokaryotes/, accessed 3/31/2020) and found to contain 39 Bacteroides genomes; of which, 15 were cataloged as B. fragilis. 6 organisms labeled as B. fragilis in this collection appeared to be a different species, given their overall ANI to B. fragilis genomes was only 84–85% and we disregarded these organisms. The only significant BLAST alignments of the hsp gene were to the nine true B. fragilis genomes. These genomes annotated the gene cluster protein product as hypothetical or conserved hypothetical protein (n=2); DUF4250 domain-containing protein (n=5); or heat shock protein (n=2) (Table S3).

Based on its specificity to B. fragilis only, the previous use of heat shock proteins to discriminate amongst anaerobes ¹⁴³, and the documented conservancy of these molecules ¹⁴⁴, this gene cluster was chosen as our candidate reference gene and is hereafter referred to as hsp. The primers and Cy5-labeled hydrolysis probe were designed to amplify/detect a 101-bp product (Table S3).

qPCR analytical specificity.

We assessed the in vitro analytical specificity of the hsp qPCR assay using DNA templates extracted from a collection of 41 bacterial isolates (13 aerobes, 28 anaerobes; representing 16 commonly encountered commensal gastrointestinal tract genera). hsp was not detected in any aerobic or anaerobic microorganisms, except for the collections’ four B. fragilis isolates. The lack of amplification in other Bacteroides spp., including B. ovatus (n=3), B. thetaiotamicron (n=2), B. uniformis (n=1) and B. vulgatus (n=3) corroborated the previous in silico results. qPCR experimental conditions. We performed real-time PCR amplification on a LightCycler 480 II system (Roche Diagnostics), using 10 microliter reactions consisting of 2X PrimeTime Gene Expression master mix (Integrated DNA Technologies, Coralville, IA), 0.8 μM pBI143_R, and 0.4 μM of pBI143_F, B.fragilis_hsp_F, and B.fragilis_hsp_R primers. We used optimized probe concentrations of 0.2 μM HSP and 0.4 μM pBI143 probe. Probe and primer sequences are outlined in Table S3. We assessed triplicate PCR reactions using genomic DNA templates (2-l volume per reaction) and the optimal cycling conditions of an initial denaturation step of 95°C for 3-min, followed by 40 cycles of 95°C for 15-s (denaturation) and 60°C for 60-s (annealing and extension).

qPCR assay performance characteristics.

We constructed a single plasmid, by standard recombinant DNA methods, containing both the entire pBI143 plasmid and the reference gene (hsp) DNA and then transformed the plasmid into E. coli EC100D. The DNA concentration of the recombinant plasmid was converted to the number of template copies using the mass of the plasmid molecule ¹⁴⁵. Using a 10-fold serial dilution series of the plasmid DNA standard (ranging from 3×10⁰ to 3×10⁶ copies/reaction), we constructed standard curves for both chromosomal reference gene and the target plasmid.

Each targets’ lower limit of detection (LOD) was determined to be 30 copies per reaction, as defined by the first dilution that detects 95% of positive samples ¹⁴⁶. We validated a linear dynamic range of six orders of magnitude for each target, and this range was then used in further assay performance metric calculations.

The primer amplification efficiencies were determined by standard procedure (Bustin et al., 2009) that includes (1) making a log₁₀ dilution series of target DNA, (2) calculating a linear regression based on the targets’ mean Cq data points and (3) inferring the efficiency from the slope of the line. Over 11 experiments, mean Cq values were derived and PCR efficiencies were calculated as 97.8% and 98.98% for pBI143 and hsp, respectively. We demonstrate less than a <=5.2% difference when comparing same run target and reference gene efficiency, demonstrating the two genes amplify similarly.

qPCR analysis of animal, untreated sewage and water samples.

Samples were tested with the pBI143 assay and two established assays for human fecal markers that included HF183 and Lachno3 ¹⁴⁷. Standard curves were generated based on a minimum of 16 runs (in triplicate) and consisted of linearized plasmids containing the HF183, Lachno3, and pBI143 target sequences. The plasmids used for the standard curves were purified using a Qiagen mini plasmid prep kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions. Standard curves were run with DNA serially diluted from 1.5 × 106 to 1.5 × 101 copies/reaction with resulting linear equations and efficiencies as follows:

HF183: Slope: −3.37, Y-intercept 39.363, R2 0.998, Eff% 98.18

Lachno3: Slope: −3.42, Y-intercept 38.13, R2 0.999, Eff% 95.92

pBI143: Slope: −3.43, Y-intercept 39.363, R2 0.999, Eff% 95.90

For each run, two of the standard concentrations (as quality assurance for the standard curve, sterile water (as negative control) and each sample was run in duplicate in a final volume of 25 μL with a final concentration of 1μM for each primer, 80 nM for the probe, 5 μL of sample DNA, and 12.5 μL of 2X Taqman^® Gene Expression Master Mix Kit (Applied Biosystems; Foster City, CA). DNA template was added as undiluted sample for surface water and animal samples, and 1:100 dilutions of sewage samples. Amplification conditions consisted of the following cycles: 1 cycle at 50° C for 2 minutes to activate the uracil-N-glycosylase (UNG); 1 cycle at 95° C for 10 minutes to inactivate the UNG and activate the Taq polymerase; 40 cycles of 95° C for 15 seconds; and 1 minute at 60o C for HF183 or 1 minute at 64o for Lachno3 using a StepOne Plus^™ instrument (Applied Biosystems; Foster City, CA).

Water samples that amplify after 35 cycles were considered below the standard curve limit of 15 CN/reaction and were therefore considered below limit of quantification. For water samples where 400 ml was filter for extraction, this value is 113 CN/100 ml. All no template controls (water) showed no amplification.

For screening of animal samples to assess the presence of this plasmid in non-human gut microbiomes, archived DNA from a previous study ¹⁴⁸ was analyzed and included 14 different animals encompassing 81 individual fecal samples. For assessment of fecal contamination of surface waters, archived DNA from 40 samples of river water ^149–151 and freshwater beaches ¹⁵² were analyzed. These water samples were chosen from these previous studies that represented a range of contamination based on HF183 and Lachno3 levels. A total of 20 archived untreated sewage samples as reported in Olds et al. ¹⁴⁷ were also analyzed for comparison. Since we were using archived samples from previous studies, we retested all the samples for the two human markers to account for any degradation.

Visualizations.

We used ggplot2 ¹⁵³ to generate all box and scatter plots. We generated coverage plots using anvi’o, with the program ‘anvi-script-visualize-split-coverages’. We finalized the figures for publication using Inkscape, an open-source vector graphics editor (available from http://inkscape.org/).

Quantification and Statistical Analysis

Details for the regression model for ACNR are provided under “Approximate copy number ratio calculation in metagenomes” in Materials and Methods. The function ‘regress’ from the R package ‘rigr’ was used with ‘fnctl = mean’ and ‘robustSE=TRUE’ to estimate effect sizes and calculate model-robust confidence intervals. A robust Wald test was used to test the null hypothesis of no change in the true log-mean ACNR (Figure 7B, upper right). n = 579 metagenome-host pairs were used to fit the regression model, which had 17 parameters (16 host and 1 disease coefficients). The use of model-robust testing and confidence intervals, as well as the large sample size, alleviated any need to investigate heteroskedasticity and normality for valid error rate control.

Supplementary Material

1

Figure S1. Representative coverage plots of metagenomes mapped to pBI143. Related to Figure 1. Each coverage plot shows the read recruitment results for an individual metagenome to pBI143. Vertical bars show single nucleotide variants (red bar = variant in first or second codon position, green bar = variant in third codon position, gray bar = intergenic variant). The x-axis is the pBI143 reference sequence. (A) Global human gut metagenomes. Version 1 (blue), Version 2 (red) and Version 3 (green). 3 coverage plots for each reference version of pBI143 are shown, the remaining 13,539 can be generated from the anvi’o databases at https://merenlab.org/data/pBI143. (B) pBI143 coverage plots from individuals sampled across time. Each coverage plot shows the coverage of pBI143 first and last sample collected for each subject possessing pBI143 this dataset¹¹². (C) The same pBI143 population is found within individual cat and rat cohorts from 3 separate studies. See also Table S1.

2

Figure S2. pBI143 transfers to other Bacteroidales species. Related to Figure 2. (A) Construct made to select for plasmid transfer. (B) Number of recipients (erythromycin) and number of transconjugants (erythromycin and tetracycline) for transfer of pBI143-tetQ to Bacteroides ovatus D2 and Parabacteroides johnsonii CL02T12C129. (C) PCR to confirm presence of pBI143-tetQ in recipient strain. See also Table S1, S2.

3

Figure S3. Presence or absence of pBI143 across 100,000 metagenomes, separated by environment. Related to Figure 2. See also Table S1.

4

Figure S4. pBI143 SNV and SAAV distribution across globally distributed genomes and metagenomes. Related to Figure 3. (A) Non-consensus SNVs present in 4,516 human gut metagenomes and 68 sewage metagenomes. (B),(C) Different angles of the MobA AlphaFold 2 dimer prediction with single amino acid variants from all 4,516 human gut metagenomes superimposed as ball-and-stick residues. The size of the ball-and-stick spheres indicate the proportion of samples carrying variation in that position (the larger the sphere, the more prevalent the variation at the residue) and the color is in CPK format. The color of the ribbon diagram indicates the pLDDT from AlphaFold 2 (red > 90 pLDDT) and blue < 50 pLDDT). The purple sphere is the Mn++ ion that marks the protein active site (oriT DNA and Mn2+ from 4lvi.pdb; 10.1073/pnas.1702971114). (D) Catalytic domain with high pLDDT with single amino acid variants from all 4,516 human gut metagenomes superimposed as ball-and-stick residues. Size and coloring is the same as in A,B. (E) The catalytic domain of the AlphaFold 2 predicted MobA (residues 1–177) shown shaded from blue to red active site residues are shown as sticks. (F) MobM from pMV158 bound to oriT DNA (gray) and a catalytic Mn2+ ion (purple) (PDB id 4lvi ⁸⁵) shown shaded from blue to red and active site residues are shown as sticks. (G) AlphaFold 2 pLDDT score representing structural prediction accuracy of MobA. (H) AlphaFold 2 predicted aligned error plot (PAE) for MobA dimer prediction. See also Table S3.

5

Figure S5. Mother-infant network quantification and kraken ‘wilt’ data. Related to Figure 5. (A) Quantification of distances between samples in the network, where distance is calculated by converting the network file to a distance matrix using the python ‘pdist’ function with cosine distances. The “subtracted difference” shows the mean within-family distances subtracted from mean between-family distances for each sample in the mother infant pair network. See Methods for more details. (B-F) Genus-level taxonomy of mother-infant gut metagenomes for pairs with a pBI143 ‘wilt’ phenotype. Line plots show the abundance of individual genera as estimated by kraken2. Bacteroides is highlighted in pink. Yellow coverage plots below show the coverage of pBI143 at corresponding timepoints. See also Table S5.

6

Figure S6. Additional genetic material in pBI143. Related to Figure 6. (A) Plasmid map of pEF108 construct used to create isogenic strain set of cells +/− pBI143 for mouse competition experiments (for details on construct recombination to form naive pBI143 see Method Details in STAR Methods). (B) Coverage plots of naturally-occurring pBI143 containing additional genetic material assembled from metagenomes and confirmed to be present in isolate genomes. Data comes from the metagenomes as labeled. pBI143 is dark gray, additional genes are in pink as in Figure 6. (B) Regions of additional gene acquisition on pBI143. See also Table S2, S6.

7

Table S1: Summary of genomes, metagenomes and pBI143 sequences. (1) global adult gut metagenomes: gut metagenomes used to determine pBI143 prevalence as seen in Figure 1. (2) sewage: metadata for 77 globally distributed sewage metagenomes. (3) IBD: metadata for all IBD gut metagenomes. (4) mother-infant metagenomes: metadata for all mother-infant metagenomes. (5) crassphage comparison: read recruitment data for the most prevalent crassphage genome (6) crassphage_summary: summary data of prevalence of 20 additional crassphage genomes (7) alt_environments_human_specific: metadata and read recruitment results for environmentally diverse metagenomes (8) top1000_PLSDB: the 1000 most prevalent plasmids in PLSDB when measured according to the gut metagenomes in tab 1 of this file. (9) pBI143_sequences: the nucleotide sequence and ANI information for the 3 reference versions of pBI143 assembled from metagenomes (10) Larger_pBI143: the nucleotide sequences for pBI143 assembled from metagenomes with additional genetic material, along with the nucleotide sequence of the DNA pBI143 potentially recombines with to form ENG0116_pBI143, ISR0195_pBI143 and MON0072_pBI143. (11) Isolate data: The metadata for the Duchossois Family Institute bacterial isolate genomes used in this study. Related to Figure 1, 2, 4, S1, S2, S3.

8

Table S2: pBI143 PCR/qPCR data. (1) Seq DataSource: contains the Gen-Bank accession numbers and other data sources used in primer and probe development. (2) Hsp BLAST result: contains BLASTN results of the hsp nucleotide sequence against the 15 Bacteroides fragilis RefSeq complete genomes. (3) animal_copy_number: contains the data showing sample and copy number of pBI143 in animal fecal samples. (4) environmental_copy_number: contains the data showing sample and copy number of pBI143 in water samples. (5) sewage_copy_number: contains the data showing sample and copy number of pBI143 in sewage samples. (6) Primer sequences: The names and sequences of all primers and probes used in this study. Related to Figure 2, 6, 7, S2, S6.

9

Table S3: Data to quantify number and type of SNV in gut and sewage metagenomes and visualize SAAVs on the pBI143 AlphaFold structure. Variability profiles are generated by anvi’o to describe the variation found across all contigs of interest; for more information see https://merenlab.org/2015/07/20/analyzing-variability/. (1) artificial_reads_var_profile: The variability profile generated following artificial short read generation and read recruitment of pBI143 Version 2 and 3 to Version 1 (see Methods). (2) global_mg_var_profiles: The variability profile generated following read recruitment of all global gut metagenomes to pBI143 Version 1. (3) sewage_mg_var_profiles: The variability profile generated following read recruitment of all global sewage metagenomes to pBI143. (4) matching_SNVs_gut: The number of SNVs that do or do not match one of the reference versions of pBI143 in global gut metagenomes. (5) matching_SNVs_sewage: The number of SNVs that do or do not match one of the reference versions of pBI143 in global sewage metagenomes. (6) gut_var_profile_quince_mode: This file does not fit in excel. Link to online data to regenerate single nucleotide variant data at every position of pBI143 mobA gene across all global gut metagenomes (for more details on ‘quince-mode’ see https://merenlab.org/2015/07/20/analyzing-variability/#parameters-to-refine-the-output). (7) sewage_var_profile_quince_mode: single nucleotide variant data at every position of pBI143 mobA gene across all global sewage metagenomes. (8) gut_var_profile_quince_mode_rep: This file does not fit in excel. Link to online data to regenerate single nucleotide variant data at every position of pBI143 repA gene across all global gut metagenomes (for more details on ‘quince-mode’ see https://merenlab.org/2015/07/20/analyzing-variability/#parameters-to-refine-the-output). (9) sewage_var_profile_quince_mode_rep: single nucleotide variant data at every position of pBI143 repA gene across all global sewage metagenomes. (10) gut_non-consensus_SNVs: Data about the plasmid version and number of non-consensus SNVs in each global gut metagenome. (11) sewage_non-consensus_SNVs: Data about the plasmid version and number of non-consensus SNVs in each global sewage metagenome. (12) merged_variability: contains all SNV variability data calculated with ‘anvi-gen-variability-profile --engine AA’ which summarized metagenomic read recruitment results to pBI143; (13) merged_variability_filtered: filtered version of merged_variability that reflects the SAAV data visualized on the pBI143 structure in Fig. 3D. (14) most_prevelent_SAAVs: this tab contains a list of all SAAVs and their residue positions that are prevalent in at least 5% of samples. Related to Figure 3, S4.

10

Table S4: The necessary data to generate pBI143 and isolate genome phylogenies. (1) amino_acid_repA_mobA_concat: the concatenated MobA and RepA sequences from all 82 isolate genomes. Concatenated genes are separated by ‘XXX’. (2) repA_mobA_treefile: the treefile generated from the concatenated mobA and repA sequences. (3) amino_acid_SCG_concat: the concatenated ribosomal protein sequences from all 82 isolate genomes. Concatenated genes are separated by ‘XXX’. (4) SCG_treefile: the treefile generated from the concatenated ribosomal protein sequences. (5) species_donor_information: the associated isolate data that matches the donor, species and pBI143 version. Related to Figure 4.

11

Table S5: The data necessary for generating and quantifying the mother-infant network based on single nucleotide variants. (1) Finland_variability_profile: data for all single nucleotide variants (SNVs) present in pBI143 in Finnish mother and infant metagenomes. (2) Sweden_variability_profile: data for all single nucleotide variants present in pBI143 in Swedish mother and infant metagenomes. (3) Italy_variability_profile: data for all single nucleotide variants present in pBI143 in Italian mother and infant metagenomes. (4) USA_variability_profile: data for all single nucleotide variants present in pBI143 in American mother and infant metagenomes. (5) network_data: data used to generate the network. (6) distance_matrix_cosine: distance matrix calculated from network data used for quantification of distances between samples. (7) subtracted_distance_df: quantified distance between mother and infant samples based on cosine distance matrix. (8) maintenance_data: summary of pBI143 version maintenance in infants over the sampling period. Related to Figure 5, S5.

12

Table S6: Kraken and competition experiment data showing relationship between pBI143 and bacterial hosts. (1) Bfragilis_kraken_pBI143: Kraken data for the number of reads recruited to a B. fragilis compared to the coverage of pBI143 in the same metagenomes. (2) bacteroides_kraken_data: Kraken data for all Bacteroides (this includes Phocaeicola with old Bacteroides genus names) and Parabacteroides taxa in global gut metagenomes and the corresponding pBI143 coverage in each of these metagenomes. (3) mouse_competition_data: The data for the mouse competition experiments. (4) pBI143_maintenance_exp: The pBI143 maintenance in culture data. Related to Figure 6, S6.

13

Table S7: pBI143 copy number data. Data from each timepoint and condition of the Bacteroides fragilis stress experiments in culture as measured via qPCR, along with calculated ‘approximate copy number ratio’ and necessary data for these calculations (see Fig. 7). (1) 214_oxidative_stress_qPCR_data: contains data on the copy number for each test condition for the Bacteroides fragilis 214 strain. (2) R16_oxidative_stress_qPCR_data: contains data on the copy number for each test condition for the Bacteroides fragilis R16 strain. (3) Coverage_ratio_data: the final ACNR for all predicted single hosts of pBI143 in metagenomes. (4) pBI143_healthy: the coverage of pBI143 in healthy gut metagenomes. (5) pBI143_IBD: the coverage of pBI143 in IBD gut metagenomes. (6) SCG_taxonomy: Link to files containing SCG coverage data. Related to Figure 7.

KEY RESOURCES TABLE.

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Bacterial and virus strains
Bacteroides fragilis 214	Vineis, J.H., Ringus, D.L., Morrison, H.G., Delmont, T.O., Dalal, S., Raffals, L.H., Antonopoulos, D.A., Rubin, D.T., Eren, A.M., Chang, E.B., et al. (2016). Patient-Specific Bacteroides Genome Variants in Pouchitis. MBio 7. 10.1128/mBio.01713-16.	Bacteroides fragilis 214
Bacteroides fragilis RI6	This study.	Bacteroides fragilis RI6
Bacteroides fragilis 638R	Privitera, G., Dublanchet, A., and Sebald, M. (1979). Transfer of multiple antibiotic resistance between subspecies of Bacteroides fragilis. J. Infect. Dis. 139, 97–101.	Bacteroides fragilis 638R (also available from the Comstock Lab).
Bacteroides fragilis 9343	https://www.atcc.org/products/25285	Bacteroides fragilis (Veillon and Zuber) Castellani and Chalmers 25285
EC100	https://www.fishersci.com/shop/products/transformax-ec100-elec-ecoli/NC9768848	Catalog No.NC9768848
E. coli S17 Apir	https://www.fishersci.com/shop/products/e-coli-s17-1pir/NC1526716	Catalog No.NC1526716
Biological samples
Chemicals, peptides, and recombinant proteins
Critical commercial assays
Gibson Assembly® Master Mix	New England Biosciences	E2611S
Qiagen miniprep kit	Qiagen	Cat. No. 27104
Deposited data
Duchossois Family Institute bacterial genomes	Duchossois Family Institute at the University of Chicago	https://dfi.uchicago.edu/node/566
Raw sequencing data for 100,029 metagenomes	See Table S1 for accession information.	N/A
pB1143	GenBank	GenBank: U30316.1
Genome Taxonomy Database	GTDB	https://gtdb.ecogenomic.org/
Experimental models: Cell lines
Experimental models: Organisms/strains
Germ-free C57BL/6J mice	The Jackson Laboratory	https://www.jax.org/strain/000664
Oligonucleotides
See Table S2 for primer sequences	This study	N/A
Recombinant DNA
Software and algorithms
anvi’o v7	Eren, A.M., Kiefl, E., Shaiber, A., Veseli, I., Miller, S.E., Schechter, M.S., Fink, I., Pan, J.N., Yousef, M., et al. (2020). Community-led, integrated, reproducible multi-omics with anvi’o. Nature Microbiology 6, 3–6. 10.1038/s41564-020-00834-3.	https://anvio.org/
snakemake v5.10	Köster, J., and Rahmann, S. (2012). Snakemake--a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522.	https://snakemake.readthedocs.io/en/stable/
illumina-utils v1.4.4	Eren, A.M., Vineis, J.H., Morrison, H.G., and Sogin, M.L. (2013). A filtering method to generate high quality short reads using illumina paired-end technology. PLoS One 8, e66643.	https://github.com/merenlab/illumina-utils
IDBA_UD v1.1.2	Peng, Y., Leung, H.C.M., Yiu, S.M., and Chin, F.Y.L. (2012). IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428. 10.1093/bioinformatics/bts174.	https://github.com/loneknightpy/idba
Bowtie2 v2.4	Langmead, B., and Salzberg, S.L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359.	https://bowtie-bio.sourceforge.net/bowtie2/index.shtml
samtools v1.9	Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., and 1000 Genome Project Data Processing Subgroup (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079.	https://www.htslib.org/
Prodigal v2.6.3	Hyatt, D., Chen, G.-L., Locascio, P.F., Land, M.L., Larimer, F.W., and Hauser, L.J. (2010). Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119.	https://github.com/hyattpd/Prodigal
AlphaFold 2	Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., and Steinegger, M. (2022). ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682.	https://deepmind.google/technologies/alphafold/
PyMol	Delano, W.L. (2002). The PyMOL molecular graphics system. http://www.pymol.org/.	https://pymol.org/2/
ConSurf	Ben Chorin, A., Masrati, G., Kessel, A., Narunsky, A., Sprinzak, J., Lahav, S., Ashkenazy, H., and Ben-Tal, N. (2020). ConSurf-DB: An accessible repository for the evolutionary conservation patterns of the majority of PDB proteins. Protein Sci. 29, 258–267.	https://consurf.tau.ac.il/
BLAST	Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. (1990). Basic local alignment search tool. J. Mol. Biol. 215. 10.1016/S0022-2836(05)80360-2.	https://blast.ncbi.nlm.nih.gov/Blast.cgi
MUSCLE v3.8.1551	Edgar, R.C. (2004). MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113.	https://www.ebi.ac.uk/Tools/msa/muscle/
trimAl	Capella-Gutiérrez, S., Silla-Martínez, J.M., and Gabaldón, T. (2009). trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973.	http://trimal.cgenomics.org/trimal
IQ-TREE 2.2.0-beta	Nguyen, L.-T., Schmidt, H.A., von Haeseler, A., and Minh, B.Q. (2015). IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274.	http://www.iqtree.org/
Gephi	Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. ICWSM 3, 361–362.	https://gephi.org/
ForceAtlas2	Jacomy, M., Venturini, T., Heymann, S., and Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One 9, e98679.	https://github.com/gephi/gephi/wiki/Force-Atlas-2
Kraken 2.0.8-beta	Wood, D.E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257.	https://bio.tools/kraken2
PlasX	Yu, M.K., Fogarty, E.C., and Murat Eren, A. (2023). The genetic and ecological landscape of plasmids in the human gut. bioRxiv, 2020.11.01.361691. 10.1101/2020.11.01.361691.	https://github.com/michaelkyu/PlasX
rigr	Chen, Y.T., Williamson, B.D., Okonek, T., Wolock, C.J., Spieker, A.J., Hee Wai, T.Y., Hughes, J.P., Emerson, S.S., and Willis, A.D. (2022). rigr: Regression, Inference, and General Data Analysis Tools in R. Journal of Open Source Software 7, 4847. 10.21105/joss.04847.	https://github.com/statdivlab/rigr
Kalign	Lassmann, T. (2019). Kalign 3: multiple sequence alignment of large datasets. Bioinformatics 36, 1928–1929.	https://www.ebi.ac.uk/Tools/msa/kalign
ggplot2	Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (Springer).	https://cran.r-project.org/web/packages/ggplot2/index.html
Inkscape	N/A	http://inkscape.org/
Other

Highlights:

• pBI143 is a cryptic plasmid that is prevalent in industrialized human populations.

• pBI143 is only ~2.7Kb, yet it regularly makes up over 0.1% of gut metagenomic reads.

• The naive 2-gene form appears parasitic, but pBI143 can carry additional genes.

• The relative copy number of pBI143 increases during stress, including in IBD.