The Presence of Genotoxic and/or Pro-inflammatory Bacterial Genes in Gut Metagenomic Databases and Their Possible Link With Inflammatory Bowel Diseases

Background: The human gut microbiota is a dynamic community of microorganisms that mediate important biochemical processes. Differences in the gut microbial composition have been associated with inflammatory bowel diseases (IBD) and other intestinal disorders. In this study, we quantified and compared the frequencies of eight genotoxic and/or pro-inflammatory bacterial genes found in metagenomic Whole Genome Sequences (mWGSs) of samples from individuals with IBD vs. a cohort of healthy human subjects.

Methods: The eight selected gene sequences were clbN, clbB, cif, cnf-1, usp, tcpC from Escherichia coli, gelE from Enterococcus faecalis and murB from Akkermansia muciniphila. We also included the sequences for the conserved murB genes from E. coli and E. faecalis as markers for the presence of Enterobacteriaceae or Enterococci in the samples. The gene sequences were chosen based on their previously reported ability to disrupt normal cellular processes to either promote inflammation or to cause DNA damage in cultured cells or animal models, which could be linked to a role in IBD. The selected sequences were searched in three different mWGS datasets accessed through the Human Microbiome Project (HMP): a healthy cohort (N = 251), a Crohn’s disease cohort (N = 60) and an ulcerative colitis cohort (N = 17).

Results: Firstly, the sequences for the murB housekeeping genes from Enterobacteriaceae and Enterococci were more frequently found in the IBD cohorts (32% E. coli in IBD vs. 12% in healthy; 13% E. faecalis in IBD vs. 3% in healthy) than in the healthy cohort, confirming earlier reports of a higher presence of both of these taxa in IBD. For some of the sequences in our study, especially usp and gelE, their frequency was even more sharply increased in the IBD cohorts than in the healthy cohort, suggesting an association with IBD that is not easily explained by the increased presence of E. coli or E. faecalis in those samples.

Conclusion: Our results suggest a significant association between the presence of some of these genotoxic or pro-inflammatory gene sequences and IBDs. In addition, these results illustrate the power and limitations of the HMP database in the detection of possible clinical correlations for individual bacterial genes.

Introduction

The human-associated microbiota is a diverse and dynamic community of microorganisms that carry out numerous biochemical processes, many of them associated with the maintenance of good health but others associated with diseases such as colorectal cancer and inflammatory diseases (Blaser, 2014; Bonnet et al., 2014; Marchesi et al., 2016). In recent years, there has been an increase in the number of studies aimed at delineating the involvement of the microbiota in the etiology, presentation and frequency of human diseases (Turnbaugh et al., 2006; Karlsson et al., 2013; Blaser, 2014; Sinha et al., 2016). The wide availability of parallel methods for DNA sequencing has made it possible to answer questions on whether the microbial community profile (the composition and distribution of species as measured by 16S rRNA gene) or the gene composition within the microbial community (by shotgun sequencing methods), are associated with disease.

With the goal of elucidating the role of the microbiome in disease, the Human Microbiome Project (HMP) was established in 2008 with the financial backing of the NIH Common Fund (Proctor, 2016). Through this interdisciplinary effort, the microbial communities and metagenomic whole genome sequence (mWGS) datasets from different body sites, were obtained from healthy individuals and from individuals diagnosed with a specific disease, such as Crohn’s disease or ulcerative colitis. The overarching goals of the HMP were to define a core microbiota for healthy individuals and to determine whether variations from this core microbiota could be associated with disease. In order to accomplish this, the HMP carried out the analysis of samples from 242 healthy adults from 15 or 18 body sites up to three times, which generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence (Human Microbiome Project Consortium, 2012a). Additionally, the HMP also sponsored 15 demonstration projects with the goal of testing hypothesized correlations between the microbiome and human disease. These investigator-initiated projects leveraged advances made by the HMP’s large scale sequencing efforts to examine the relationship between changes in the human microbiome and diseases of interest, using 16S rRNA, metagenomic shotgun sequencing or both¹.

Previous research from our group, rather than focusing on the presence of particular microbial taxa, emphasized on detecting specific gut microbial genes in the human population by PCR analysis of stool samples (Gomez-Moreno et al., 2014). Of particular interest to us were bacterial genes that encode genotoxic or pro-inflammatory factors that have been shown to cause, or correlate with, tissue inflammation either in cell-based assays, animal models or in epidemiological studies. Our hypothesis was that by interrogating a set of bacterial genes, it should be possible to assess the inflammatory status of the GI tract, an indicator of susceptibility to inflammatory bowel diseases or colorectal cancer (Terzic et al., 2010). This set of genes includes the cyclomodulins reported to be in some strains of E. coli, such as cif (cycle inhibiting factor), cnf-1 (cytotoxic necrotizing factor) and the pks island (which encodes the production of the natural product colibactin) (Nougayrède et al., 2005, 2006). Other genes present in some strains of E. coli, such as tcpC, which encodes an inhibitor of toll-like receptors and induces the formation of urinary tract abscesses, and usp that encodes the uropathogenic specific protein, which is a highly toxic nuclease that causes DNA damage (Yadav et al., 2010; Zaw et al., 2013). The preliminary frequencies for these genotoxic or pro-inflammatory bacterial genes in the general population was determined through a simple PCR analysis of stool samples obtained from a clinical laboratory in Puerto Rico (Gomez-Moreno et al., 2014). Although the sample size in that study was relatively small (N = 41), the pathogenic genes were found to be present in frequencies as high as 20%, a figure that is consistent with previous epidemiological analysis carried out on bacterial isolates (Johnson et al., 2008; Arthur et al., 2012).

We now report the search for those genotoxic and/or pro-inflammatory bacterial gene sequences in a select group of HMP mWGS datasets: the HMP healthy cohort and two IBD cohorts from the HMP demonstration projects. We aimed to determine whether any of the genes in our set are found in higher (or lower) numbers in the stool samples from patients with IBDs than in the healthy cohort, as their increased presence would suggest an association with IBD. Our results show that the frequency for many of these genes, notably the pks island and tcpC, are lower in the HMP database than what is typically reported experimentally by PCR amplification. Also, some of these genes notably usp and gelE, are found more frequently in IBD cohorts than in the healthy population, indicating a possible role in promoting inflammation in the GI tract. Taken together these results illustrate the power of the HMP databases in the detection of the possible involvement of individual bacterial genes in diseases, while also highlighting the difficulties associated with monitoring gene sequences of low abundance in highly complex samples.

Materials and Methods

Gene Selections and Sequence Retrieval

A total of eight genotoxic or pro-inflammatory bacterial genes encoding cyclomodulins, and other gene products were selected for this analysis based on their reported ability to promote inflammation in vitro or in animal models. The first set of genes included clbN and clbB (genes within the pks island gene cluster from Escherichia, Klebsiella, Citrobacter, and Enterobacter species), along with cif, cnf-1 and usp. These genes were selected because their ability to interfere with the cell cycle (Nougayrède et al., 2005). The second set of genes were tcpC (from E. coli) and gelE (from E. faecalis), which have been shown to increase inflammation in infected tissues and in animal models (Cirl et al., 2008; Steck et al., 2011; Golinska et al., 2013). Finally, the murB gene, a specific marker for the presence of Akkermansia muciniphila, was also included in this study because of reports linking this species to colorectal cancer (Weir et al., 2013).

In addition to the genotoxic or pro-inflammatory gene sequences, we also included the sequences for two housekeeping genes: murB from E. coli and murB from E. faecalis. These were included in the analysis to account for the relative presence of these species (i.e., E. coli and E. faecalis) in the samples. We accessed GenBank database (Benson, 2005) from NCBI to acquire the DNA sequences in FASTA format for all the 10 genes (i.e., eight pathogenic pro-inflammatory genes and 2 housekeeping genes). Table 1 summarizes all the genes in the study.

TABLE 1

TABLE 1. Bacterial genes considered in this study.

Cohort Selections and Gene Hits

Three different mWGS cohorts were selected and accessed through the Human Microbiome Project – Data Analysis and Coordination Center (HMP-DACC) (Human Microbiome Project Consortium, 2012a,b). The first cohort was the HMP healthy cohort, which includes shotgun sequence data from stool samples of 251 healthy individuals directly accessed from HMP-DACC. The second and third cohorts contain sequences that were obtained through the HMP-DACC portal for the Demonstration Project Disease Cohorts (NCBI BioProject Accession PRJNA46305). The Crohn’s Disease cohort contains data extracted from the project “Metagenomic Analysis of the Structure and Function of the Human Gut Microbiota in Crohn’s Disease,” University of Maryland, Baltimore (dbGap Accession phs000257²). In this study, mWGS data was obtained from 60 stool samples from patients with Crohn’s Disease. The Ulcerative Colitis cohort included data from the project “The Role of the Gut Microbiota in Ulcerative Colitis” (dbGap Accession phs000262³; University of Michigan at Ann Arbor). In this case, mWGS data was obtained from 17 stool samples from Ulcerative Colitis patients. Information on the cohorts is summarized in Table 2 and all the biosamples used in this study are listed in Supplementary Tables S2–S4.

TABLE 2

TABLE 2. Information about the cohorts.

In order to obtain the mWGS data of stool samples from the 251 healthy individuals, the following steps were followed:

Step 1: Access the mWGS reads and extract each of their sample accession identifiers (i.e., Sequence Read Archive Sample Accessions – SRSIds);

Step 2: Retrieve the experimental identifiers (i.e., Sequence Read Experiment Accessions – SRXIds), using SRSIds;

Step 3: Use the SRXIds as BLAST parameters to query the corresponding pathogenic or housekeeping gene.

In the case of Crohn’s disease and ulcerative colitis datasets, we had direct access to the SRXIds, available in each project and went directly to Step 3, using BLAST to query the pathogenic and housekeeping genes with SRXIds as BLAST parameters. The BLAST database selected for this analysis was Sequence Read Archive (SRA-BLAST) (Leinonen et al., 2011) and the algorithm was MegaBLAST (Morgulis et al., 2008). The default parameters selected for the MegaBLAST algorithm were: Expected Value = 10, Word size = 28, Reward match = 1, Penalty mismatch = -2, Gap Existence = 0, Gap extension = 2.5, Percent Identity = None, Filtering = Low Complexity and Matrix = Not Applicable for this Algorithm. Gene hits were considered as the BLAST alignment results of a gene in mWGS with a query coverage value greater than 0. The number of Samples with Hits for each gene was the number of samples with at least one hit for a particular gene. The Total Samples with Hits per Cohort was obtained by counting the total number of samples with at least one hit within a cohort. The Frequency for each gene was then computed as the percentage of Samples with Hits divided by the total number of samples in each cohort.

Statistical Analysis

There are numerous challenges in providing statistical analysis for SRA-BLAST/WGS reads, mainly because of many factors that could confound the final results (for example, differences in size of the datasets, differences in sequencing quality among datasets). We employed a simple framework which tends to produce more reliable results when several variables simultaneously influence a process (Nei and Kumar, 2001; Rogozin and Pavlov, 2003). As a first step, we compared the sizes of metagenomic datasets for healthy and disease cohorts (see Supplementary Table S1) using the two-tail Student t-test. Significant differences were found between them: metagenomic datasets for healthy individuals tend to have a substantially larger size (see Supplementary Table S1). Thus, we expected that metagenomic datasets for healthy individuals should be substantially enriched in SRA-BLAST hits. The null hypothesis is that the frequency of samples with SRA-BLAST hits is the same for healthy and affected individuals. In order to test this null hypothesis, we compared raw numbers of samples with SRA-BLAST hits for healthy and affected individuals using the one-tail Fisher exact test. In a different method, a Monte Carlo modification of the Pearson χ² test of spectra homogeneity (Adams and Skopek, 1987) was used to compare raw numbers of samples with SRA-BLAST hits in each gene for healthy and affected individuals. The calculations were done using the COLLAPSE program (Khromov-Borisov et al., 1999).

Results

Presence of Genotoxic or Pro-inflammatory Genes in the Healthy Population

The presence of the genotoxic or pro-inflammatory gene sequences in the HMP Healthy cohort is shown in the Table 3, Healthy Cohort. The most frequently encountered sequence was AMmurB (murB from A. muciniphila) with 10% frequency, meaning it was present in 10% of the samples from this population. AMmurB was closely followed by the usp and tcpC genes, with 5 and 3% frequencies, respectively. The other bacterial sequences have frequencies of less than 1. In total, genotoxic or pro-inflammatory sequences were found in 16% of individuals in the healthy cohort with 4% harboring more than one of these sequences. We will use this data as a baseline for statistical comparisons with samples from IBD cohorts, i.e., Crohn’s disease and ulcerative colitis cohorts.

TABLE 3

TABLE 3. Presence of genotoxic or pro-inflammatory genes of the three cohorts selected for this analysis, i.e., Healthy, Crohn’s disease and Ulcerative colitis.

Presence of Genotoxic or Pro-inflammatory Genes in Individuals With Crohn’s Disease

The genes gelE and usp were the most commonly found sequences in the mWGS samples from individuals with Crohn’s disease (15 and 10% frequency, respectively). They are followed by cnf-1 and AMmurB with 7% frequency each. Finally, clbN, clbB and tcpC genes are less common, with frequencies ranging between 2 and 3%. In this cohort, the cif gene was not present. More information about the genes, samples with hits, and frequencies can be seen in Table 3.

The total number of individual samples positive for genotoxic or pro-inflammatory gene sequences in the Crohn’s Disease cohort was significantly higher than in the healthy cohort. In total 19 samples were positive for at least one gene in the Crohn’s Disease cohort (31% total frequency) with 8 samples (13%) harboring more than one gene. The analysis reveals a statistically significant difference with P = 0.00004 according to the Fisher exact test vs. the healthy cohort (40 samples with hits vs. 211 samples without hits). This result is significant even if we take into account the conservative Bonferroni correction for multiple hits (the threshold p-value is 0.025 = 0.05/2). This result suggests that there is a substantial and significant excess of some genotoxic or pro-inflammatory bacterial genes in Crohn’s disease samples, particularly gelE and usp.

Presence of Genotoxic or Pro-inflammatory Genes in Individuals With Ulcerative Colitis

The ulcerative colitis cohort also reveals usp as the most common pro-inflammatory gene sequence with a frequency of 35%. It is followed by cif with 6% frequency. All the other six pathogenic gene sequences were not found in this small cohort of 16 samples.

The total number of individual samples positive for genotoxic or pro-inflammatory gene sequences in the Ulcerative Colitis cohort was higher than in the healthy cohort. In this case, a total of 5 individual samples (out of 17 samples) contained at least one of the gene sequences in our (29% total frequency) versus 16% total frequency in the healthy cohort (Table 3). Also, this result is statistically significant with a P = 0.016 according to the Fisher exact test and is significant after the Bonferroni adjustment for the number of comparisons (2 in this case). The higher frequency of hits in the ulcerative colitis cohort vs. healthy, mirrors the findings in the Crohn’s disease samples, especially for the usp.

Presence of the Housekeeping Genes in the Three Different Cohorts

We initially intended to use the 16S rRNA gene sequence of E. coli as an indicator of the presence of this species in the population. However, very few samples were found to contain this specific sequence. Instead we chose to monitor the presence of sequences for MurB, an essential gene for the production of peptidoglycan in both species: E. coli and E. faecalis. However, ECMurB is approximately 80% similar to the MurB of other members of the Enterobacteriaceae family, such as Citrobacter, Enterobacter, Klebsiella etc. Thus, although the detection of this gene may be indicative of the presence of E. coli, it may also indicate the presence of related Enterobacteriaceae. Similarly, the EFMurB was also found to be around 80% similar to the MurB of other related Enterococcus species.

The presence of these bacterial housekeeping gene sequences in the mWGS cohorts are shown in Table 4. The most common bacterial housekeeping sequence that we found in all cohorts was ECmurB, consistent with the generalized prevalence of Enterobacteriaceae in the gut microbiota. The gene murB from E. faecalis is also highly present in the samples. Results show what appears to be an increased presence of Enterobacteriaceae and Enterococci in the IBD cohorts versus the healthy cohort (Table 4).

TABLE 4

TABLE 4. Presence of housekeeping genes in the metagenomic sequences for all cohorts.

This higher frequency of EFMurB and ECMurB in the IBD samples could reflect the fact that these two bacterial species (E. faecalis and E. coli) are more abundant in IBD samples than in control and thus, their genes (including the studied pathogenic genes) are also more abundant. In order to test whether the observed association between specific bacterial genes and IBD was only tracking the presence of certain species, we compared the frequency of samples with SRA-BLAST hits for the pathogenic genes and housekeeping genes.

We tested homogeneity (Khromov-Borisov et al., 1999) of tables that contain raw numbers of samples with SRA-BLAST hits in each gene for healthy and affected individuals (Supplementary Table S5). The comparison between ECmurB and the genotoxic or pro-inflammatory gene sequences revealed a statistically significant heterogeneity of the table (P = 0.0088), a similar result (P = 0.0086) was found for the comparison between EFmurB and the genotoxic or pro-inflammatory genes (Supplementary Table S6). Analysis of individual genes suggested that certain genes are over-represented in IBD. The gelE gene is over-represented in Crohn’s disease Cohort for comparison with EFmurB (P = 0.00028, the Fisher exact test). The usp gene was found with a marginally significantly higher frequency in the Ulcerative Colitis Cohort (P = 0.0426, the Fisher exact test) for comparison with ECmurB. These results suggest that the presence of genotoxic and/or pro-inflammatory genes in the IBD cohorts is higher than would be expected based on the expansion of their bacterial hosts in these samples.

Discussion

The advent of parallel sequencing methods and the development of computational tools designed to handle the troves of resulting data, have been instrumental in the growing field of microbiome research. At present, there are many explanations for how the microbiome contributes to human disease and this knowledge has required the implementation of diverse experimental and computational strategies, including systematic searches using the data that has already been generated and deposited in the HMP databases. In this work, we searched for specific bacterial genes in datasets that were generated under the auspices of the HMP. The fact that a common set of protocols was followed for sample collection, preparation and data acquisition, should ensure consistency in the quality and comparability of the data across cohorts. The data analyzed in this work was generated at multiple centers using two different sequencing platforms: the healthy cohort DNA was analyzed on an Illumina GAIIx platform, whereas the DNA from the IBD samples was sequenced on a 454 FLX Titanium platform. There are reported differences between these two platforms in terms of the read length and the total number of reads in a dataset (Luo et al., 2011). However, the HMP protocols were developed to ensure that the data generated using these two platforms are comparable (Human Microbiome Project Consortium, 2012b). Part of the HMP internal validation involved the use of a mock microbial community (HMP Accession No. PRJNA48475) that was analyzed and assembled using both Illumina and 454 sequencing and the ensuing comparisons between the centers and across platforms demonstrated high consistency of target sequencing depth and success rates (Human Microbiome Project Consortium, 2012a). More recently, it has been shown that shotgun metagenomic data obtained from 454 and Illumina platforms result in the identification of 90% of the same genes, suggesting that despite differences in read length and number or reads, the resulting data can be compared (Luo et al., 2011).

In this work, we searched these HMP datasets and demonstration projects for the presence of a subset of bacterial gene sequences that had previously been found to be associated with genotoxicity or with the promotion of inflammation (Gomez-Moreno et al., 2014). We chose bacterial gene sequences purposefully to match the subset of genes for which experimental data was already available (Sifri et al., 2002; Cuevas-Ramos et al., 2010; Nipic et al., 2013; Snyder et al., 2013).

In the healthy cohort, the most abundant sequence was that of AMmurB from Akkermansia muciniphila, a bacterium previously associated with colorectal cancer (Weir et al., 2013). However, no statistical association with IBD was found for AMmurB (p-value = 0.3026). For all other genes in this healthy cohort of 251 samples, we found the frequency for all genotoxic or pro-inflammatory bacterial sequences in the databases to be lower than the gene frequency obtained through PCR detection as reported previously (Johnson et al., 2008; Arthur et al., 2012; Gomez-Moreno et al., 2014). For instance, previous studies had shown pks island genes to be present in 20% of E. coli strains isolated from the mucosa samples of healthy human donors (Arthur et al., 2012). In a separate study, the pks island genes were found in 32% of E. coli strains isolated from stool samples of hospitalized veterans (Johnson et al., 2008). Also, the same pks island genes were present in 20% of stool samples obtained from a local clinical laboratory in Puerto Rico (Gomez-Moreno et al., 2014). Others have documented the prevalence of pks island genes in France, Japan, and United Kingdom to be between 15 and 40% (Shimpoh et al., 2017). By contrast, the present study reveals the pks island sequences to be present in only one sample out of 251 from healthy individuals in the HMP database (0.4%). The same can be said for the tcpC gene, which had been previously found in 17% of stool samples by PCR, yet its sequence was present in just 3% of the samples in the HMP database (Gomez-Moreno et al., 2014). Similarly, gelE, a known virulence factor from E. faecalis, had been found in higher numbers by PCR analysis of stool samples (7%), than in the HMP database (0.8%). This underrepresentation of bacterial gene sequences of possible clinical relevance in the databases, could be indicative of the challenges associated with the detection of genes that are naturally in low abundance such as virulence factors or enzymes involved in secondary metabolism, both of which have been shown to be underrepresented in this type of dataset (Sansonetti, 2010). By contrast, there is a reported over-representation of high-abundance genes such as those involved in primary metabolism (Sansonetti, 2010). Thus, it is possible that the specific detection and study of low-abundance genes will require the development of new sample preparation or sequencing protocols specific for this purpose.

Despite the very low frequency of detection for some of the genotoxic or pro-inflammatory bacterial gene sequences, there were statistically significant increases in their detection frequency in the IBD cohorts, suggesting a possible link with IBD. The gene gelE was found in higher numbers in the Crohn’s disease cohort (15% vs. 0.8% in healthy). Similarly, usp was also found more commonly in both Crohn’s and ulcerative colitis cohorts (35% vs. 5% in healthy). The correlation between the presence of gelE and IBD has been suggested previously (Steck et al., 2011; Golinska et al., 2013). Studies with mice lacking both copies of the IL10 gene registered higher levels of intestinal inflammation when infected with strain of E. faecalis harboring the gelE gene than those infected with a strain lacking gelE (Steck et al., 2011). Also, the gelE gene was found in higher frequency in isolates of E. faecalis obtained from the feces of children with IBD vs. children without IBD (Golinska et al., 2013). It is possible that the presence of gelE protease in the colon could damage the mucosal lining in a way that promotes inflammation by impairing the epithelial barrier, thus exposing cells to the microbial community (Steck et al., 2011). Our current study confirms this association, thus providing some means of validation for the approach of searching for specific bacterial genes in metagenomic databases.

The correlation of usp with IBD was more unexpected. The gene usp was originally characterized in isolates of uropathogenic E. coli (Nakano et al., 2001). Its activity was confirmed to be part of a nuclease containing and H-N-H motif, a structural element important for its function as a cytotoxic bacteriocin (Zaw et al., 2013). However, the role of usp in intestinal strains of E. coli remains unknown. In this study, we report a possible correlation between the presence of usp in the gut and IBD, possibly through the same cell damage mechanisms that cause it to affect the urinary tract. Further investigations will be required before this correlation can be fully confirmed experimentally in a clinical study, and its mechanism can be delineated.

Our findings of a higher frequency of E. coli genes or E. faecalis genes in IBD samples vs. healthy controls, could reflect the fact that these two bacterial species are more abundant in IBD samples than in control and thus, their genes are also more abundant. To test this, we assumed that the frequency of the housekeeping genes ECmurB and EFmurB for both species approximately corresponds to the abundance of E. coli and E. faecalis bacteria in the different cohorts, although the genes could also be detecting other related species. Clearly there is a higher presence of these species in the samples from IBD cohorts than the healthy cohort, as evidenced by the increased presence of the murB housekeeping genes. The association between the presence of E. coli and IBD is well established (Martinez-Medina and Garcia-Gil, 2014). Also, E. faecalis strains have been shown to modulate inflammation through a MAPK signaling pathway, and this effect is consistent with a possible involvement in IBDs (Wang et al., 2014). However, the results of the statistical analysis indicate that the increase in the presence of genotoxic and/or pro-inflammatory genes in the IBD cohorts (vs. healthy) is significantly higher than the increase in the E. coli and E faecalis murB genes in the same IBD samples, suggesting that the increased presence of usp and gelE is not just due to an increased presence of their bacterial hosts.

Overall, we present a strategy for querying the HMP databases in a focused and hypothesis-based manner in search for bacterial genes that may be of clinical interest. Our hypothesis is that preliminary associations with disease may emerge from a search that emphasizes on specific bacterial genes for which the mechanisms of toxicity or tissue damage are known. This search for candidate genes would be greatly facilitated if the vast amounts of data in existence were to be available through a common repository that is both searchable and user-friendly. Efforts such as the United States National Microbiome Initiative constitute a step in the right direction especially if it allows for the establishment of a searchable global microbiome database, a necessary generator of testable hypotheses on the role of the gut microbiota in intestinal diseases (Bouchie, 2016).

Author Contributions

AR-L designed and led the bioinformatic analysis of the data. AB-O led the team to test the hypothesis and achieve the goals. AR-L and AB-O mostly contributed to the drafting of this manuscript. KC-C, DV-M, RG-M, and JC executed and processed the analysis, from different perspectives. KC-C also participated in the writing of the manuscript. IR performed and wrote the statistical analysis. All the authors reviewed and approved the manuscript.

Funding

This research was supported by Center for Collaborative Research in Health Disparities Award Number U54 MD007600 from the National Institute on Minority Health and Health Disparities, National Institute of Diabetes and Digestive and Kidney Diseases to Dr. Marcia Cruz-Correa (1R21CA198963-01), NIH Grant R25GM061838 (NIGMS-RISE Program) to RG-M, and by the Intramural Research Program of the National Library of Medicine at the National Institutes of Health. Oligonucleotides were purchased from an institutional facility supported by NIH Grant G12RR03051 (RCMI Program).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer CH and handling Editor declared their shared affiliation.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2018.00116/full#supplementary-material

Footnotes

1. ^ https://www.hmpdacc.org/health/projectdemos.php
2. ^ The data/analyses presented in the current publication are based on the use of study data downloaded from the dbGaP web site, under phs000257.v2.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000257.v2.p1).
3. ^ The data/analyses presented in the current publication are based on the use of study data downloaded from the dbGaP web site, under phs000262.v2.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000262.v2.p1)

References

Adams, W. T., and Skopek, T. R. (1987). Statistical test for the comparison of samples from mutational spectra. J. Mol. Biol. 194, 391–396.

PubMed Abstract | Google Scholar

Arthur, J. C., Perez-Chanona, E., Mühlbauer, M., Tomkovich, S., Uronis, J. M., Fan, T., et al. (2012). Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338, 120–123. doi: 10.1126/science.1224820

PubMed Abstract | CrossRef Full Text | Google Scholar

Benson, D. A. (2005). GenBank. Nucleic Acids Res. 33(Database issue), D34–D38. doi: 10.1093/nar/gki063

PubMed Abstract | CrossRef Full Text | Google Scholar

Blaser, M. J. (2014). The microbiome revolution. J. Clin. Invest. 124, 4162–4165. doi: 10.1172/JCI78366

PubMed Abstract | CrossRef Full Text | Google Scholar

Bonnet, M., Buc, E., Sauvanet, P., Darcha, C., Dubois, D., Pereira, B., et al. (2014). Colonization of the human gut by E. coli and colorectal cancer risk. Clin. Cancer Res. 20, 859–867. doi: 10.1158/1078-0432.CCR-13-1343

PubMed Abstract | CrossRef Full Text | Google Scholar

Bouchie, A. (2016). White house unveils national microbiome initiative. Nat. Biotechnol. 34:580. doi: 10.1371/journal.pone.0097523

PubMed Abstract | CrossRef Full Text | Google Scholar

Cirl, C., Wieser, A., Yadav, M., Duerr, S., Schubert, S., Fischer, H., et al. (2008). Subversion of Toll-like receptor signaling by a unique family of bacterial Toll/interleukin-1 receptor domain-containing proteins. Nat. Med. 14, 399–406. doi: 10.1038/nm1734

PubMed Abstract | CrossRef Full Text | Google Scholar

Cuevas-Ramos, G., Petit, C. R., Marcq, I., Boury, M., Oswald, E., and Nougayrède, J. (2010). Escherichia coli induces DNA damage in vivo and triggers genomic instability in mammalian cells. Proc. Natl. Acad. Sci. U.S.A. 107, 11537–11542. doi: 10.1073/pnas.1001261107

PubMed Abstract | CrossRef Full Text | Google Scholar

Golinska, E., Tomusiak, A., Gosiewski, T., Wiecek, G., Machul, A., Mikolajczyk, D., et al. (2013). Virulence factors of Enterococcus strains isolated from patients with inflammatory bowel disease. World J. Gastroenterol. 19, 3562–3572. doi: 10.3748/wjg.v19.i23.3562

PubMed Abstract | CrossRef Full Text | Google Scholar

Gomez-Moreno, R., Robledo, I. E., and Baerga-Ortiz, A. (2014). Direct detection and quantification of bacterial genes associated with inflammation in DNA isolated from stool. Adv. Microbiol. 4, 1065–1075. doi: 10.4236/aim.2014.415117

PubMed Abstract | CrossRef Full Text | Google Scholar

Human Microbiome Project Consortium (2012a). A framework for human microbiome research. Nature 486, 215–221. doi: 10.1038/nature11209

PubMed Abstract | CrossRef Full Text | Google Scholar

Human Microbiome Project Consortium (2012b). Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214. doi: 10.1038/nature11234

PubMed Abstract | CrossRef Full Text | Google Scholar

Johnson, J. R., Johnston, B., Kuskowski, M. A., Nougayrede, J., and Oswald, E. (2008). Molecular epidemiology and phylogenetic distribution of the Escherichia coli pks genomic island. J. Clin. Microbiol. 46, 3906–3911. doi: 10.1128/JCM.00949-08

PubMed Abstract | CrossRef Full Text | Google Scholar

Karlsson, F., Tremaroli, V., Nielsen, J., and Bäckhed, F. (2013). Assessing the human gut microbiota in metabolic diseases. Diabetes 62, 3341–3349. doi: 10.2337/db13-0844

PubMed Abstract | CrossRef Full Text | Google Scholar

Khromov-Borisov, N. N., Rogozin, I. B., Pegas Henriques, J. A., and de Serres, F. J. (1999). Similarity pattern analysis in mutational distributions. Mutat. Res. 430, 55–74.

PubMed Abstract | Google Scholar

Leinonen, R., Sugawara, H., and Shumway, M. (2011). The sequence read archive. Nucleic Acids Res. 39, D19–D21. doi: 10.1093/nar/gkq1019

PubMed Abstract | CrossRef Full Text | Google Scholar

Luo, C., Tsementzi, D., Kyrpides, N., Read, T., and Konstantinidis, K. T. (2011). Direct comparisons of Illumina vs. Roche 454 sequencing technologies on the same microbial community DNA sample. PLoS One 7:e30087. doi: 10.1371/journal.pone.0030087

PubMed Abstract | CrossRef Full Text | Google Scholar

Marchesi, J. R., Adams, D. H., Fava, F., Hermes, G. D. A., Hirschfield, G. M., Hold, G., et al. (2016). The gut microbiota and host health: a new clinical frontier. Gut 65, 330–339. doi: 10.1136/gutjnl-2015-309990

PubMed Abstract | CrossRef Full Text | Google Scholar

Martinez-Medina, M., and Garcia-Gil, L. (2014). Escherichia coli in chronic inflammatory bowel diseases: an update on adherent invasive Escherichia coli pathogenicity. World J. Gastrointest. Pathophysiol. 5, 213–227. doi: 10.4291/wjgp.v5.i3.213

PubMed Abstract | CrossRef Full Text | Google Scholar

Morgulis, A., Coulouris, G., Raytselis, Y., Madden, T. L., Agarwala, R., and Schäffer, A. A. (2008). Database indexing for production MegaBLAST searches. Bioinformatics 15, 1757–1764. doi: 10.1093/bioinformatics/btn322

PubMed Abstract | CrossRef Full Text | Google Scholar

Nakano, M., Yamamoto, S., Terai, A., Ogawa, O., Makino, S., Hayashi, H., et al. (2001). Structural and sequence diversity of the pathogenicity island of uropathogenic Escherichia coli which encodes the USP protein. FEMS Microbiol. Lett. 205, 71–76. doi: 10.1111/j.1574-6968.2001.tb10927.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Nei, M., and Kumar, S. (2001). Molecular Evolution and Phylogenetics. Oxford: Oxford University.

Google Scholar

Nipic, D., Podlesek, Z., Budic, M., Crnigoj, M., and Zgur-Bertok, D. (2013). Escherichia coli uropathogenic-specific protein, Usp, is a bacteriocin-like genotoxin. J. Infect. Dis. 208, 1545–1552. doi: 10.1093/infdis/jit480

PubMed Abstract | CrossRef Full Text | Google Scholar

Nougayrède, J., Taieb, F., Rycke, J. D., and Oswald, E. (2005). Cyclomodulins: bacterial effectors that modulate the eukaryotic cell cycle. Trends Microbiol. 3 13, 103–110. doi: 10.1016/j.tim.2005.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Nougayrède, J. P., Homburg, S., Taieb, F., Boury, M., Brzuszkiewicz, E., Gottschalk, G., et al. (2006). Escherichia coli induces DNA double-strand breaks in eukaryotic cells. Science 313, 848–851. doi: 10.1126/science.1127059

PubMed Abstract | CrossRef Full Text | Google Scholar

Proctor, L. M. (2016). The national institutes of health human microbiome project. Semin. Fetal Neonatal Med. 21, 368–372. doi: 10.1016/j.siny.2016.05.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Rogozin, I. B., and Pavlov, Y. I. (2003). Theoretical analysis of mutation hotspots and their DNA sequence context specificity. Mutat. Res. 544, 65–85. doi: 10.1016/S1383-5742(03)00032-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Sansonetti, P. (2010). Bacterial Virulence: Basic Principles, Models and Global Approaches. Weinheim: Wiley-VCH.

Google Scholar

Shimpoh, T., Hirata, Y., Ihara, S., Suzuki, N., Kinoshita, H., Hayakawa, Y., et al. (2017). Prevalence of pks-positive Escherichia coli in Japanese patients with or without colorectal cancer. Gut Pathog. 9:35. doi: 10.1186/s13099-017-0185-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Sifri, C. D., Mylonakis, E., Singh, K. V., Qin, X., Garsin, D. A., Murray, B. E., et al. (2002). Virulence effect of Enterococcus faecalis protease genes and the quorum-sensing locus fsr in Caenorhabditis elegans and mice. Infect. Immun. 70, 5647–5650.

PubMed Abstract | Google Scholar

Sinha, R., Ahn, J., Sampson, J. N., Shi, J., Yu, G., Xiong, X., et al. (2016). Fecal microbiota, fecal metabolome, and colorectal cancer interrelations. PLoS One 11:e0152126. doi: 10.1371/journal.pone.0152126

PubMed Abstract | CrossRef Full Text | Google Scholar

Snyder, G. A., Cirl, C., Jiang, J., Chen, K., Waldhuber, A., Smith, P., et al. (2013). Molecular mechanisms for the subversion of MyD88 signaling by TcpC from virulent uropathogenic Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 110, 6985–6990. doi: 10.1073/pnas.1215770110

PubMed Abstract | CrossRef Full Text | Google Scholar

Steck, N., Hoffmann, M., Sava, I. G., Kim, S. C., Hahne, H., Tonkonogy, S. L., et al. (2011). Enterococcus faecalis metalloprotease compromises epithelial barrier and contributes to intestinal inflammation. Gastroenterology 141, 959–971. doi: 10.1053/j.gastro.2011.05.035

PubMed Abstract | CrossRef Full Text | Google Scholar

Terzic, J., Grivennikov, S., Karin, E., and Karin, M. (2010). Inflammation and colon cancer. Gastroenterology 138, 2101–2114.e5.

Google Scholar

Turnbaugh, P. J., Ley, R. E., Mahowald, M. A., Magrini, V., Mardis, E. R., and Gordon, J. I. (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031. doi: 10.1038/nature05414

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, S., Hibberd, M. L., Pettersson, S., and Lee, Y. K. (2014). Enterococcus faecalis from healthy infants modulates inflammation through MAPK signaling pathways. PLoS One 9:e97523. doi: 10.1371/journal.pone.0097523

PubMed Abstract | CrossRef Full Text | Google Scholar

Weir, T. L., Manter, D. K., Sheflin, A. M., Barnett, B. A., Heuberger, A. L., and Ryan, E. P. (2013). Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PLoS One 8:e70803. doi: 10.1371/journal.pone.0070803

PubMed Abstract | CrossRef Full Text | Google Scholar

Yadav, M., Zhang, J., Fischer, H., Huang, W., Lutay, N., Cirl, C., et al. (2010). Inhibition of TIR domain signaling by TcpC: MyD88-dependent and independent effects on Escherichia coli virulence. PLoS Pathog. 6:e1001120. doi: 10.1371/journal.ppat.1001120

PubMed Abstract | CrossRef Full Text | Google Scholar

Zaw, M. T., Yamasaki, E., Yamamoto, S., Nair, G. B., Kawamoto, K., and Kurazono, H. (2013). Uropathogenic specific protein gene, highly distributed in extraintestinal uropathogenic Escherichia coli, encodes a new member of H-N-H nuclease superfamily. Gut Pathog. 5:13. doi: 10.1186/1757-4749-5-13

PubMed Abstract | CrossRef Full Text | Google Scholar