Clostridioides difficile infection (CDI) remains a significant cause of morbidity and mortality in the health care setting and in the community (Guh et al., 2020). Antibiotic treatments, among other risk factors associated with weakened colonization resistance, increase susceptibility to CDI (Dubberke and Olsen, 2012; Eze et al., 2017). C. difficile residence in the human gastrointestinal (GI) tract may result in a spectrum of clinical manifestations, from asymptomatic colonization to severe CDI-related colitis and fatal toxic megacolon (Crobach et al., 2018). Diagnosis of CDI relies on detection of the protein toxin, most commonly by enzyme immunoassay (EIA), or the detection of the toxin-encoding genes tcdA and tcdB, by nucleic acid amplification test (NAAT). These diagnostic tools serve as rough benchmarks for assessing the severity of disease. Discrepancies between the results of these assays, as in the case of patients with clinically significant diarrhea (CSD) who are EIA negative (EIA-) but NAAT positive for toxigenic C. difficile (Cx+), highlight the complexity of states in which C. difficile can exist in the GI tract. Because CDI is a multi-factorial interaction between the host, pathogen, and microbiome, clarifying the differences in biological correlates between asymptomatic colonization (Cx+/EIA-) and CDI (Cx+/EIA+) is critical for identifying mechanisms of colonization resistance, and for defining novel probiotic or prebiotic avenues for treatment or prevention of CDI (Kondepudi et al., 2012; Rätsep et al., 2017).

C. difficile enters the GI tract as a spore, germinates in the presence of primary bile acids, and replicates through consumption of amino acids and other microbiota or host-derived nutrients (Hryckowian et al., 2017). Notably, many of these metabolic cues are characteristic of a perturbed microbiome (Nagao-Kitamoto et al., 2020; Battaglioli et al., 2018). The hallmark of C. difficile pathogenesis is the expression of the toxin locus encoded on the tcd operon; this locus is tightly regulated by nutrient levels (Martin-Verstraete et al., 2016). Correspondingly, it is hypothesized that an environment replete of nutrients induces toxinogenesis, allowing C. difficile to restructure the gut environment and acquire nutrients through inflammation (Fletcher et al., 2018; Fletcher et al., 2021). The instances of patients who are colonized but have no detectable C. difficile toxin in their stool suggests that these patients’ microbiomes may be less permissive towards CDI development. Identification of metabolic traits within the microbiome of asymptomatic, C. difficile-colonized patients could reveal a number of potential therapeutic pathways toward precise amelioration of symptomatic C. difficile disease.

A multitude of probiotic and prebiotic approaches have demonstrated efficacy to curb C. difficile proliferation in vivo (Rätsep et al., 2017; Chen et al., 2020; Pereira et al., 2020). While restoration of the microbiota through fecal microbiota transplantation can provide colonization resistance (Laffin et al., 2017), the molecular mechanisms of how this resistance is conferred remain unclear. Recent studies using a murine model of infection have indicated that the administration of carbohydrates (both complex and simple) in the diet can be used to curb or prevent CDI (Mefferd et al., 2020; Schnizlein et al., 2020; Hryckowian et al., 2018). Paradoxically, integrated metabolomics and transcriptomics data collected during murine C. difficile colonization indicates that simple carbohydrates are imperative for pathogen replication (Fletcher et al., 2018). It is critical to understand the mechanism by which catabolism of specific carbohydrates could inhibit C. difficile proliferation in the human GI tract.

Here, we perform a multi-level investigation of two relevant patient populations, those colonized with C. difficile but EIA negative (asymptomatically colonized) and those who are EIA positive (CDI) to understand the microbial and metabolic features that may underlie protection from CDI. First, we use microbiome analyses to identify a number of non-C. difficile, clostridial species that are negatively correlated with C. difficile in asymptomatically colonized individuals. Secondly, interrogation of a metabolomics dataset from the same patient population (Robinson et al., 2019) reveals increased abundance of a number of carbohydrate metabolites in asymptomatically colonized patients. Finally, we show that some metabolites enriched in asymptomatically colonized individuals are largely non-utilizable by C. difficile isolates. Together, these datasets reveal that asymptomatically colonized patients are defined by an interaction of clostridial species and carbohydrate metabolites that may serve as a last-line of resistance against CDI in colonized patients.

The clinical manifestation of C. difficile colonization in a host gastrointestinal tract is determined by a multi-factorial interaction between the host, their microbiome, and the pathogen. We hypothesized that, among these factors, natural variation in C. difficile strains infecting patients might differentiate asymptomatic from CDI patients (Dubberke et al., 2018). Through retrospective analysis of a human cohort of 124 patients (Supplementary file 1) with clinically significant diarrhea (CSD) and stool submitted for C. difficile toxin testing, we defined two cohorts: those diagnosed with CDI (Cx+/EIA+) or those asymptomatically colonized (Cx+/EIA-) (Robinson et al., 2019). EIA status (EIA+ or EIA-) was determined by the result of the clinical toxin EIA performed on the stool specimen, and a positive toxigenic culture (a C. difficile isolate with with tcdA and/or tcdB; Cx+) (Dubberke et al., 2018). In-depth analysis of C. difficile isolate factors related to EIA status was performed on the isolates corresponding to the 102 metagenomic samples analyzed (see Materials and Methods, Supplementary file 2). Multiplex PCR was used to identify isolates with cdtAB, the binary toxin locus (Cowardin et al., 2016). Notably, there was a significant enrichment of isolates with cdtAB in the stools of patients with CDI (Figure 1A; p = 0.0012, Fisher’s exact test). Additionally, there were differences in the distribution of C. difficile strains associated with the two patient cohorts; CDI patients were more likely to be infected by a C. difficile isolate of the ribotype 027 lineage (Figure 1A; p = 0.0058, Fisher’s exact test), a clade likely to contain virulent members (Merrigan et al., 2010). Interestingly, of the isolates positive for cdtAB (22 out of 102 isolates), 36% were considered a ribotype 027 strain. Given these genetic indicators of potential differences in virulence, we asked if strains from both groups were capable of producing toxin, using culture supernatants from in vitro broth culture. We found that 56% of isolates expressed detectable TcdA/B, with no significant different (p = 0.86) in the capacity of strains from Cx+/EIA- stools (24 out of 54 isolates) or Cx+/EIA+ (24 out of 48 isolates) to elaborate toxin (Figure 1—figure supplement 1A). Predictably, differences in genetic indicators of strain virulence (as indicated by prevalence of both a prominent ribotype and a second toxin locus) were significant correlates of EIA status.

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Raw absorbance value for in vitro toxin ELISA of 102 C.difficile isolates.

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As antibiotics are a well-known risk factor for CDI, we analyzed previous inpatient antibiotic orders (within one month prior to diagnosis) for patients in the Cx+/EIA- and Cx+/EIA+ cohort, as a proxy for antibiotic exposure (Table 1). Fitting antibiotic exposure to a logistic regression model (McFadden’s R₂ = 0.306) revealed that CDI was significantly associated with cephalosporin exposure. Analysis of potential antibiotic exposures in our patient cohort confirms the risk that antibiotics pose for CDI development (Mullish and Williams, 2018; Webb et al., 2020).

Antibiotics increase susceptibility to CDI through disruption of colonization resistance, mainly conferred to the host via the gut microbiome (Theriot et al., 2014). To determine the microbial correlates of disease state, we performed shotgun metagenomic sequencing on patient stool samples from the asymptomatic (n = 54) and CDI (n = 48) groups, and classified species using MetaPhlAn2. Given the strong association with antibiotic exposure in our CDI cohort, we hypothesized that our asymptomatically colonized patients would have increased microbiome-mediated colonization resistance relative to CDI patients. We examined community structure in stool metagenomes and found that there was no significant difference in Faith’s diversity (Faith and Baker, 2007), a measure of alpha-diversity that incorporates phylogenetic relationships, between patient groups (Figure 1—figure supplement 1B, Wilcoxon rank-sum test, p = 0.1602). There were no significant differences in beta-diversity, as measured by weighted Unifrac distance, between EIA status (p = 0.233, permutational analysis of variance test [PERMANOVA]) (Figure 1B). Although we hypothesized that increased virulence (through additional toxin allele or ribotype) associated with EIA+ could affect microbiome structure, we found no significant association between beta-diversity and cdtAB presence (Figure 1—figure supplement 1C; p = 0.799, PERMANOVA) or ribotype distribution (Figure 1—figure supplement 1D; p = 0.982, PERMANOVA). Previous comparative microbiome studies have revealed phylum-level differences in Bacteroides and Firmicutes in CDI cases versus controls not colonized with C. difficile (Kachrimanidou and Tsintarakis, 2020). In contrast, we found no significant differences in relative abundance of bacterial phyla between asymptomatically colonized patients and patients with CDI (Figure 1—figure supplement 1E). These data indicate that there were no gross differences in microbiome structure related to either EIA status or pathogen features.

Instead, we hypothesized that differences between these states may manifest at higher resolution. We used a multivariable regression model, as implemented by MaAslin2 (Mallick et al., 2021) to identify microbial taxa predictive of either group. Interestingly, species from class Clostridia were most enriched in taxa significantly altered by EIA status (Fisher’s exact test, p = 0.0022). C. difficile was the strongest predictor of CDI state, whereas non-C. difficile clostridial taxa were predictive of asymptomatic state (Figure 1C, FDR < 0.25). Correspondingly, we saw increased C. difficile relative abundance in CDI patients and increased levels of a number of non-C. difficile clostridial species, including Eubacterium spp., Dorea spp., and Lachnospiraceae spp. in asymptomatic patients (Figure 1—figure supplement 1F). Given our inability to detect C. difficile in all sequenced stools (70 out of 102 culture-positive stool samples), we utilized an alternative metagenomic species classifier Kraken (Wood and Salzberg, 2014), to validate our findings. Using Kraken, we detected C. difficile in nearly all stool metagenomes (101 out of 102). Using the identical linear mixed modeling approach (MaAsLin2), we recapitulated data indicating that C. difficile abundance was the strongest predictor of EIA status and increased in Cx+/EIA+ patients. Additionally, a number of commensal clostridial taxa from the Eubacterium genus and Anaerostipes genus were strongly associated with EIA- status, confirming prior MetaPhlAn2 predictions (Figure 1—figure supplement 2A,B).

Using our microbiome data, we examined the association between C. difficile levels and pathogen markers previously associated with EIA status. We found that C. difficile relative abundance was not significantly different when stratified by isolate CDT status (Figure 1—figure supplement 2C; p = 0.3, Wilcoxon rank sum) or isolate ribotype (p = 0.78, Kruskal-Wallis). Notably, there was a slight, yet insignificant increase in C. difficile abundance in microbiomes associated with a ribotype 027 isolate (Figure 1—figure supplement 2D) relative to microbiomes associated with C. difficile isolates of other ribotypes. We also interrogated taxonomic features that were predictive of antibiotic exposure. Expectedly, we found that taxonomic features predictive of CDI state were also associated with antibiotic exposure (Figure 1D). Our data indicate that patients with asymptomatic C. difficile colonization or CDI do not have grossly different gut microbiome community structures but instead have distinctive alterations in a subset of species from class Clostridia and class Bacilli in the microbiota.

C. difficile pathogenesis is heavily affected by carbohydrate, amino acid, and bile acid levels in the gastrointestinal tract, related to the metabolism of competitive commensals (Sorbara and Pamer, 2019). To identify metabolic pathways in other clostridia that might enable them to outcompete C. difficile, we defined metabolic potential in patient microbiomes using HUMAnN2 to quantify microbial pathway abundances. We found no significant differences in alpha- or beta-diversity between overall metabolic pathway composition in the two patient microbiome groups (Figure 2—figure supplement 1A,B; p = 0.2393, Wilcoxon rank sum and p = 0.054, PERMANOVA). Therefore, we trained an elastic net model to identify specific pathways associated with EIA status (Figure 2A). We found a number of carbohydrate degradation pathways and amino acid biosynthetic pathways associated with the asymptomatically-colonized (Cx+/EIA-) patients, including sucrose degradation III and fucose and rhamnose degradation. Investigation of the genera that encode such pathways revealed that the sucrose degradation III pathway was increased in asymptomatic patients, largely due to Blautia spp. and Faecalibacterium spp. of the class Clostridia (Figure 2B). Interestingly, the fucose and rhamnose degradation pathways were entirely defined by Escherichia spp., presumably E. coli. This suggests that metabolic functions such as fucose and rhamnose degradation may be confined to a smaller number of taxa than carbohydrate degradation pathways such as sucrose degradation. Using the HUMAnN2 (Franzosa et al., 2018) gene family information, we used linear mixed modeling to identify carbohydrate-active enzymes differentially associated with EIA status (Figure 2C). Supporting the pathway analysis, we found an increased abundance of a subset of glycoside hydrolase genes, specifically involved in sucrose and starch metabolism in the asymptomatically colonized patients. Our metabolic pathway analyses highlight differentially abundant carbohydrate degradation processes in clostridial taxa that could contribute to colonization resistance against C. difficile in patient microbiomes.

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We hypothesized that differences in metabolic potential of fecal microbiome communities might be reflected in metabolomic profiles, and therefore sought to identify metabolites that are altered in CDI patients relative to those asymptomatically colonized with C. difficile (Robinson et al., 2019). Ordination of Euclidean distances between Cx+/EIA- and Cx+/EIA+ stool metabolomes revealed no significant differences in metabolome structure (Figure 2—figure supplement 1C, PERMANOVA = 0.426). We again used MaAslin2 to determine metabolites associated with each disease state. Consistent with previous analysis, a number of end-product Stickland fermentation metabolites (4-methypentanoic acid and 5-aminovalerate) were associated with CDI patients. While 4-hydroxyproline was the strongest predictor of asymptomaticallycolonized patients, many of the significant metabolites that were associated with asymptomatic patients were predicted to be carbohydrates (Figure 3A, FDR < 0.25; Supplementary file 3). Putative metabolite identities were initially annotated by matching metabolite spectra to the NIST14 GC-MS spectral library. The preponderance of carbohydrates in asymptomatically colonized patients and the substantial similarity of carbohydrate spectra prompted us to rigorously validate the identities of these metabolites by comparing EI spectra and GC retention times against authentic standards, where commercially available (Supplementary file 3, Figure 3—figure supplement 1). These data reveal a carbohydrate signature that is depleted in CDI patients. Notably, fructose and rhamnose are either substrates or products of the sucrose degradation III and fucose and rhamnose degradation pathways, which we found to be enriched in asymptomatically colonized patients. The co-occurrence of these microbial pathways and their corresponding metabolites in asymptomatically colonized patients suggests that a commensal carbohydrate catabolism may contribute to suppression of C. difficile pathogenesis.

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Growth curve data for C. difficile isolates.

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Our examination of taxa, metabolic pathways, and metabolites revealed a number of carbohydrates which we predict are undigestible by C. difficile or are end-products of a more complex commensal metabolism that is exclusionary to C. difficile. Using a set of clinical C. difficile isolates cultured from this patient cohort (8 isolates representing six different ribotypes), we examined growth of C. difficile on carbohydrates associated with asymptomatically colonized patients. Using a defined minimal media (CDMM)( Karasawa et al., 1995) to test nutrient utilization, we found that C. difficile isolates grew robustly on fructose as expected (median maximum A₆₀₀ of 0.90), but did not proliferate on rhamnose or lactulose (median maximum A₆₀₀ of 0.19 and 0.24, respectively). Notably, in the case of sorbitol, we found that a subset of strains, including the reference strain C. difficile 630 and C. difficile VPI10643, grew to a maximum A₆₀₀ of greater than 0.47 (Figure 3B). Given that we had found sucrose degradation as a metabolic pathway enriched in asymptomatically colonized patients, we hypothesized that C. difficile would be unable to use this carbohydrate. Indeed, when grown on sucrose as the sole carbon source, strains achieved a median maximum A₆₀₀ ~4.7-fold less than that of growth on fructose. C. difficile’s restricted carbohydrate metabolism, coupled with the presence of commensal Clostridia could hamper progression to CDI.

We hypothesized that the differential abundance of identified stool metabolites in these patient cohorts is related to the metabolism of specific microbes or host processes. We performed a sparse partial least-squares-discriminatory analysis (sPLS-DA) with the mixOmics package to define relationships between the most predictive features of patient metabolomes and microbiomes. We optimized the number of latent components (Figure 4—figure supplement 1A) and number of variables (Figure 4—figure supplement 1B). Our final model contained two latent components, with the first one composed of 15 metabolites and 25 microbial species. Of the largest metagenomic variable weights, four out of five species (C. difficile, a Lachnospiraceae spp., Anaerostipes hadrus, and Clostridium clostridioforme) were also significantly associated with an EIA state (Figure 1). Of the metabolomic variable weights (Figure 4A), the 10 highest-weighted metabolites were also discovered by previous analyses (Figure 2). The predictive value of each of the components per block was greater that an area under the curve (AUC) of 0.85, with the second metagenomic block component having the best performance (AUC = 0.94, Figure 4—figure supplement 1C). The strong performance of the latent components in classifying samples via EIA status validated our previous findings. Using the variables defining the first latent component, we performed correlational analyses (Figure 4B) and found a number of striking correlations. C. difficile abundance was positively correlated with a number of well-known Stickland metabolites (5-amino-valeric acid and 4-methylpentanoic acid, rho = 0.48 and 0.36, respectively)(Robinson et al., 2019), whereas C. difficile had negative correlations with fructose, rhamnose, and hydroxyproline (rho = –0.27, 0.36, and –0.34, respectively). Given our metagenomic data suggesting that Kraken metagenomic profiling yielded more sensitive estimates of C. difficile abundance, we performed an independent multi-omics analysis on the same dataset using the Kraken metagenomic data. Using a similar process of model building as above, the final model consisted of two latent components, with 15 metabolites and 15 microbes in the first component (Figure 4—figure supplement 1C). C. difficile was also most positively correlated to 5-amino-valeric acid and 4-methylpentanoic acid, with corresponding negative correlations to fructose, rhamnose, and hydroxyproline (Figure 4—figure supplement 1D). These microbe-metabolite relationships highlight the known pathophysiology of CDI, and identify novel C. difficile-carbohydrate relationships that define asymptomatic colonization.

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Given the anticorrelation between C. difficile and rhamnose, we sought to explain the enrichment of this carbohydrate in asymptomatically colonized patients. Though C. difficile cannot grow on rhamnose as the sole carbohydrate, in other organisms rhamnose has substantial transcriptional influence over carbon catabolite gene clusters (Egan and Schleif, 1993; Hirooka et al., 2015). We wanted to rule out the possibility that rhamnose may impact C. difficile through possibly cryptic transcriptional reprogramming, perhaps contributing to C. difficile repression in vivo. Accordingly, we performed whole transcriptome RNA sequencing on C. difficile cultures exposed to a metabolizable substrate, fructose, or a non-metabolizable substrate, rhamnose. In the presence of fructose, we found 555 genes significantly altered (adjusted p-value < 0.05 and |fold-change| > 2) (Supplementary file 4). Some of the most altered genes were indicative of carbon catabolite repression of sugar transport and upregulation of glycolytic processes to metabolize fructose. In contrast, we found only three genes significantly increased in the rhamnose condition. The lack of striking systems-level or targeted (toxin expression, sporulation) regulation by rhamnose, and C. difficile’s inability to utilize it, leads us to conclude that its association with asymptomatically colonized patients’ microbiomes is not through direct interaction or suppression of C. difficile. Instead, we speculate that rhamnose may be the byproduct of a complex commensal metabolism of other dietary polysaccharide substrates, which could exclude C. difficile from the GI tract.

Metagenomics reads were deposited under BioProject accession number PRJNA748262 and RNA sequencing reads were deposited under BioProject accession number PRJNA748261.

1. 1. Fishbein SRS

   (2021) NCBI BioProject

   ID PRJNA748262. Fecal metagenomes of C. difficile colonized patients.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA748262
2. 1. Fishbein SRS

   (2021) NCBI BioProject

   ID PRJNA748261. C. difficile carbohydrate transcriptomics.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA748261

1. 1. Adachi S

   (1958) Formation of lactulose and tagatose from lactose in strongly heated milk

   Nature 181:840–841.

   https://doi.org/10.1038/181840a0

   • PubMed
   • Google Scholar
2. 1. Agarwalla A
   2. Weber A
   3. Davey S
   4. Hamilton K
   5. Goldberg D
   6. Rhim AD
   7. Yang YX

   (2017) Lactulose Is Associated With Decreased Risk of Clostridium difficile Infection in Decompensated Cirrhosis

   Clinical Gastroenterology and Hepatology 15:953–954.

   https://doi.org/10.1016/j.cgh.2017.01.012

   • PubMed
   • Google Scholar
3. 1. Akerlund T
   2. Svenungsson B
   3. Lagergren A
   4. Burman LG

   (2006) Correlation of disease severity with fecal toxin levels in patients with Clostridium difficile-associated diarrhea and distribution of PCR ribotypes and toxin yields in vitro of corresponding isolates

   Journal of Clinical Microbiology 44:353–358.

   https://doi.org/10.1128/JCM.44.2.353-358.2006

   • PubMed
   • Google Scholar
4. 1. Battaglioli EJ
   2. Hale VL
   3. Chen J
   4. Jeraldo P
   5. Ruiz-Mojica C
   6. Schmidt BA
   7. Rekdal VM
   8. Till LM
   9. Huq L
   10. Smits SA
   11. Moor WJ
   12. Jones-Hall Y
   13. Smyrk T
   14. Khanna S
   15. Pardi DS
   16. Grover M
   17. Patel R
   18. Chia N
   19. Nelson H
   20. Sonnenburg JL
   21. Farrugia G
   22. Kashyap PC

   (2018) Clostridioides difficile uses amino acids associated with gut microbial dysbiosis in a subset of patients with diarrhea

   Science Translational Medicine 10:464.

   https://doi.org/10.1126/scitranslmed.aam7019

   • PubMed
   • Google Scholar
5. 1. Baym M
   2. Kryazhimskiy S
   3. Lieberman TD
   4. Chung H
   5. Desai MM
   6. Kishony R

   (2015) Inexpensive multiplexed library preparation for megabase-sized genomes

   PLOS ONE 10:e0128036.

   https://doi.org/10.1371/journal.pone.0128036

   • PubMed
   • Google Scholar
6. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for Illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • PubMed
   • Google Scholar
7. 1. Burnham C-AD
   2. Carroll KC

   (2013) Diagnosis of Clostridium difficile infection: an ongoing conundrum for clinicians and for clinical laboratories

   Clinical Microbiology Reviews 26:604–630.

   https://doi.org/10.1128/CMR.00016-13

   • PubMed
   • Google Scholar
8. 1. Chen K
   2. Zhu Y
   3. Zhang Y
   4. Hamza T
   5. Yu H
   6. Saint Fleur A
   7. Galen J
   8. Yang Z
   9. Feng H

   (2020) A probiotic yeast-based immunotherapy against Clostridioides difficile infection

   Science Translational Medicine 12:567.

   https://doi.org/10.1126/scitranslmed.aax4905

   • PubMed
   • Google Scholar
9. 1. Cowardin CA
   2. Buonomo EL
   3. Saleh MM
   4. Wilson MG
   5. Burgess SL
   6. Kuehne SA
   7. Schwan C
   8. Eichhoff AM
   9. Koch-Nolte F
   10. Lyras D
   11. Aktories K
   12. Minton NP
   13. Petri WA

   (2016) The binary toxin CDT enhances Clostridium difficile virulence by suppressing protective colonic eosinophilia

   Nature Microbiology 1:16108.

   https://doi.org/10.1038/nmicrobiol.2016.108

   • PubMed
   • Google Scholar
10. 1. Crobach MJT
    2. Vernon JJ
    3. Loo VG
    4. Kong LY
    5. Péchiné S
    6. Wilcox MH
    7. Kuijper EJ

    (2018) Understanding Clostridium difficile Colonization

    Clinical Microbiology Reviews 31:e00021-17.

    https://doi.org/10.1128/CMR.00021-17

    • PubMed
    • Google Scholar
11. 1. Desai MS
    2. Seekatz AM
    3. Koropatkin NM
    4. Kamada N
    5. Hickey CA
    6. Wolter M
    7. Pudlo NA
    8. Kitamoto S
    9. Terrapon N
    10. Muller A
    11. Young VB
    12. Henrissat B
    13. Wilmes P
    14. Stappenbeck TS
    15. Núñez G
    16. Martens EC

    (2016) A Dietary Fiber-Deprived Gut Microbiota Degrades the Colonic Mucus Barrier and Enhances Pathogen Susceptibility

    Cell 167:1339–1353.

    https://doi.org/10.1016/j.cell.2016.10.043

    • PubMed
    • Google Scholar
12. 1. Deshpande A
    2. Pasupuleti V
    3. Thota P
    4. Pant C
    5. Rolston DDK
    6. Sferra TJ
    7. Hernandez AV
    8. Donskey CJ

    (2013) Community-associated Clostridium difficile infection and antibiotics: a meta-analysis

    The Journal of Antimicrobial Chemotherapy 68:1951–1961.

    https://doi.org/10.1093/jac/dkt129

    • PubMed
    • Google Scholar
13. 1. Dubberke ER
    2. Olsen MA

    (2012) Burden of Clostridium difficile on the healthcare system

    Clinical Infectious Diseases 55 Suppl 2:S88–S92.

    https://doi.org/10.1093/cid/cis335

    • PubMed
    • Google Scholar
14. 1. Dubberke ER
    2. Reske KA
    3. Hink T
    4. Kwon JH
    5. Cass C
    6. Bongu J
    7. Burnham CAD
    8. Henderson JP

    (2018) Clostridium difficile colonization among patients with clinically significant diarrhea and no identifiable cause of diarrhea

    Infection Control and Hospital Epidemiology 39:1330–1333.

    https://doi.org/10.1017/ice.2018.225

    • PubMed
    • Google Scholar
15. 1. Egan SM
    2. Schleif RF

    (1993) A regulatory cascade in the induction of rhaBAD

    Journal of Molecular Biology 234:87–98.

    https://doi.org/10.1006/jmbi.1993.1565

    • PubMed
    • Google Scholar
16. 1. Eze P
    2. Balsells E
    3. Kyaw MH
    4. Nair H

    (2017) Risk factors for Clostridium difficile infections - an overview of the evidence base and challenges in data synthesis

    Journal of Global Health 7:010417.

    https://doi.org/10.7189/jogh.07.010417

    • PubMed
    • Google Scholar
17. 1. Faith DP
    2. Baker AM

    (2007)

    Phylogenetic diversity (PD) and biodiversity conservation: some bioinformatics challenges

    Evolutionary Bioinformatics Online 2:121–128.

    • PubMed
    • Google Scholar
18. 1. Fishbein SRS
    2. Hink T
    3. Reske KA
    4. Cass C
    5. Struttmann E
    6. Iqbal ZH
    7. Seiler S
    8. Kwon JH
    9. Burnham CA
    10. Dantas G
    11. Dubberke ER

    (2021a) Randomized Controlled Trial of Oral Vancomycin Treatment in Clostridioides difficile-Colonized Patients

    MSphere 6:e00936-20.

    https://doi.org/10.1128/mSphere.00936-20

    • PubMed
    • Google Scholar
19. Software

    1. Fishbein SRS

    (2021b) 2021EIACdiff_multiomics, version swh:1:rev:0c2a33d873e43194afb5818733e46c6ff28d6947

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:bc7e2445e6c16ffcedd04e6c0566d7fd5e3cd192;origin=https://github.com/srsfishbein/2021EIACdiff_multiomics;visit=swh:1:snp:e3b72c68e69c68cee04167d5124e03f6d6413000;anchor=swh:1:rev:0c2a33d873e43194afb5818733e46c6ff28d6947
20. 1. Fletcher JR
    2. Erwin S
    3. Lanzas C
    4. Theriot CM

    (2018) Shifts in the Gut Metabolome and Clostridium difficile Transcriptome throughout Colonization and Infection in a Mouse Model

    MSphere 3:e00089-18.

    https://doi.org/10.1128/mSphere.00089-18

    • PubMed
    • Google Scholar
21. 1. Fletcher JR
    2. Pike CM
    3. Parsons RJ
    4. Rivera AJ
    5. Foley MH
    6. McLaren MR
    7. Montgomery SA
    8. Theriot CM

    (2021) Clostridioides difficile exploits toxin-mediated inflammation to alter the host nutritional landscape and exclude competitors from the gut microbiota

    Nature Communications 12:462.

    https://doi.org/10.1038/s41467-020-20746-4

    • PubMed
    • Google Scholar
22. 1. Franzosa EA
    2. McIver LJ
    3. Rahnavard G
    4. Thompson LR
    5. Schirmer M
    6. Weingart G
    7. Lipson KS
    8. Knight R
    9. Caporaso JG
    10. Segata N
    11. Huttenhower C

    (2018) Species-level functional profiling of metagenomes and metatranscriptomes

    Nature Methods 15:962–968.

    https://doi.org/10.1038/s41592-018-0176-y

    • PubMed
    • Google Scholar
23. 1. Ghimire S
    2. Roy C
    3. Wongkuna S
    4. Antony L
    5. Maji A
    6. Keena MC
    7. Foley A
    8. Scaria J

    (2020) Identification of Clostridioides difficile-Inhibiting Gut Commensals Using Culturomics

    Phenotyping, and Combinatorial Community Assembly. MSystems 5:e19.

    https://doi.org/10.1128/mSystems.00620-19

    • Google Scholar
24. 1. Guh A
    2. Mu Y
    3. Winston LG
    4. Johnston H
    5. Olson D
    6. Farley MM
    7. Wilson LE
    8. Holzbauer SM
    9. Phipps EC
    10. Dumyati GK
    11. Beldavs ZG
    12. Kainer MA
    13. Karlsson M
    14. Gerding DN

    (2020) Emerging Infections Program Clostridioides difficile Infection Working, Trends in U.S

    Burden of Clostridioides Difficile Infection and Outcomes. N Engl J Med 382:1320–1330.

    https://doi.org/10.1056/NEJMoa1910215

    • Google Scholar
25. 1. Hirooka K
    2. Kodoi Y
    3. Satomura T
    4. Fujita Y

    (2015) Regulation of the rhaEWRBMA Operon Involved in l-Rhamnose Catabolism through Two Transcriptional Factors, RhaR and CcpA, in Bacillus subtilis

    Journal of Bacteriology 198:830–845.

    https://doi.org/10.1128/JB.00856-15

    • PubMed
    • Google Scholar
26. 1. Hopkins MJ
    2. Macfarlane GT

    (2003) Nondigestible oligosaccharides enhance bacterial colonization resistance against Clostridium difficile in vitro

    Applied and Environmental Microbiology 69:1920–1927.

    https://doi.org/10.1128/AEM.69.4.1920-1927.2003

    • PubMed
    • Google Scholar
27. 1. Hryckowian AJ
    2. Pruss KM
    3. Sonnenburg JL

    (2017) The emerging metabolic view of Clostridium difficile pathogenesis

    Current Opinion in Microbiology 35:42–47.

    https://doi.org/10.1016/j.mib.2016.11.006

    • PubMed
    • Google Scholar
28. 1. Hryckowian AJ
    2. Van Treuren W
    3. Smits SA
    4. Davis NM
    5. Gardner JO
    6. Bouley DM
    7. Sonnenburg JL

    (2018) Microbiota-accessible carbohydrates suppress Clostridium difficile infection in a murine model

    Nature Microbiology 3:662–669.

    https://doi.org/10.1038/s41564-018-0150-6

    • PubMed
    • Google Scholar
29. 1. Hunt JJ
    2. Ballard JD

    (2013) Variations in virulence and molecular biology among emerging strains of Clostridium difficile

    Microbiology and Molecular Biology Reviews 77:567–581.

    https://doi.org/10.1128/MMBR.00017-13

    • PubMed
    • Google Scholar
30. 1. Jenior ML
    2. Leslie JL
    3. Young VB
    4. Schloss PD

    (2017) Clostridium difficile Colonizes Alternative Nutrient Niches during Infection across Distinct Murine Gut Microbiomes

    MSystems 2:e00063-17.

    https://doi.org/10.1128/mSystems.00063-17

    • PubMed
    • Google Scholar
31. 1. Kachrimanidou M
    2. Tsintarakis E

    (2020) Insights into the Role of Human Gut Microbiota in Clostridioides difficile Infection

    Microorganisms 8:E200.

    https://doi.org/10.3390/microorganisms8020200

    • PubMed
    • Google Scholar
32. 1. Karasawa T
    2. Ikoma S
    3. Yamakawa K
    4. Nakamura S

    (1995) A defined growth medium for Clostridium difficile

    Microbiology 141 (Pt 2):371–375.

    https://doi.org/10.1099/13500872-141-2-371

    • PubMed
    • Google Scholar
33. 1. Kondepudi KK
    2. Ambalam P
    3. Nilsson I
    4. Wadström T
    5. Ljungh A

    (2012) Prebiotic-non-digestible oligosaccharides preference of probiotic bifidobacteria and antimicrobial activity against Clostridium difficile

    Anaerobe 18:489–497.

    https://doi.org/10.1016/j.anaerobe.2012.08.005

    • PubMed
    • Google Scholar
34. 1. Kuhn M

    (2008) Building Predictive Models in R Using the caret Package

    Journal of Statistical Software 28:1–26.

    https://doi.org/10.18637/jss.v028.i05

    • Google Scholar
35. 1. Kumar N
    2. Browne HP
    3. Viciani E
    4. Forster SC
    5. Clare S
    6. Harcourt K
    7. Stares MD
    8. Dougan G
    9. Fairley DJ
    10. Roberts P
    11. Pirmohamed M
    12. Clokie MRJ
    13. Jensen MBF
    14. Hargreaves KR
    15. Ip M
    16. Wieler LH
    17. Seyboldt C
    18. Norén T
    19. Riley TV
    20. Kuijper EJ
    21. Wren BW
    22. Lawley TD

    (2019) Adaptation of host transmission cycle during Clostridium difficile speciation

    Nature Genetics 51:1315–1320.

    https://doi.org/10.1038/s41588-019-0478-8

    • PubMed
    • Google Scholar
36. 1. Laffin M
    2. Millan B
    3. Madsen KL

    (2017) Fecal microbial transplantation as a therapeutic option in patients colonized with antibiotic resistant organisms

    Gut Microbes 8:221–224.

    https://doi.org/10.1080/19490976.2016.1278105

    • PubMed
    • Google Scholar
37. 1. Lewis S
    2. Burmeister S
    3. Brazier J

    (2005) Effect of the prebiotic oligofructose on relapse of Clostridium difficile-associated diarrhea: a randomized, controlled study

    Clinical Gastroenterology and Hepatology 3:442–448.

    https://doi.org/10.1016/s1542-3565(04)00677-9

    • PubMed
    • Google Scholar
38. 1. Littmann ER
    2. Lee JJ
    3. Denny JE
    4. Alam Z
    5. Maslanka JR
    6. Zarin I
    7. Matsuda R
    8. Carter RA
    9. Susac B
    10. Saffern MS
    11. Fett B
    12. Mattei LM
    13. Bittinger K
    14. Abt MC

    (2021) Host immunity modulates the efficacy of microbiota transplantation for treatment of Clostridioides difficile infection

    Nature Communications 12:755.

    https://doi.org/10.1038/s41467-020-20793-x

    • PubMed
    • Google Scholar
39. 1. Mallick H
    2. Rahnavard A
    3. McIver LJ
    4. Ma S
    5. Zhang Y
    6. Nguyen LH
    7. Tickle TL
    8. Weingart G
    9. Ren B
    10. Schwager EH
    11. Chatterjee S
    12. Thompson KN
    13. Wilkinson JE
    14. Subramanian A
    15. Lu Y
    16. Waldron L
    17. Paulson JN
    18. Franzosa EA
    19. Bravo HC
    20. Huttenhower C

    (2021) Multivariable association discovery in population-scale meta-omics studies

    PLOS Computational Biology 17:e1009442.

    https://doi.org/10.1371/journal.pcbi.1009442

    • PubMed
    • Google Scholar
40. 1. Maltz C
    2. Miskovitz PF
    3. Hajifathalian K

    (2020) Lactulose may reduce Clostridium difficile-related diarrhea among patients receiving antibiotics

    JGH Open 4:1088–1090.

    https://doi.org/10.1002/jgh3.12390

    • PubMed
    • Google Scholar
41. 1. Martin-Verstraete I
    2. Peltier J
    3. Dupuy B

    (2016) The Regulatory Networks That Control Clostridium difficile Toxin Synthesis

    Toxins 8:E153.

    https://doi.org/10.3390/toxins8050153

    • PubMed
    • Google Scholar
42. 1. McGovern BH
    2. Ford CB
    3. Henn MR
    4. Pardi DS
    5. Khanna S
    6. Hohmann EL
    7. O’Brien EJ
    8. Desjardins CA
    9. Bernardo P
    10. Wortman JR
    11. Lombardo M-J
    12. Litcofsky KD
    13. Winkler JA
    14. McChalicher CWJ
    15. Li SS
    16. Tomlinson AD
    17. Nandakumar M
    18. Cook DN
    19. Pomerantz RJ
    20. Auninš JG
    21. Trucksis M

    (2021) SER-109, an Investigational Microbiome Drug to Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial

    Clinical Infectious Diseases 72:2132–2140.

    https://doi.org/10.1093/cid/ciaa387

    • PubMed
    • Google Scholar
43. 1. Mefferd CC
    2. Bhute SS
    3. Phan JR
    4. Villarama JV
    5. Do DM
    6. Alarcia S
    7. Abel-Santos E
    8. Hedlund BP

    (2020) A High-Fat/High-Protein, Atkins-Type Diet Exacerbates Clostridioides (Clostridium)

    Difficile Infection in Mice, Whereas a High-Carbohydrate Diet Protects. MSystems 5:e19.

    https://doi.org/10.1128/mSystems.00765-19

    • Google Scholar
44. 1. Merrigan M
    2. Venugopal A
    3. Mallozzi M
    4. Roxas B
    5. Viswanathan VK
    6. Johnson S
    7. Gerding DN
    8. Vedantam G

    (2010) Human hypervirulent Clostridium difficile strains exhibit increased sporulation as well as robust toxin production

    Journal of Bacteriology 192:4904–4911.

    https://doi.org/10.1128/JB.00445-10

    • PubMed
    • Google Scholar
45. 1. Mills JP
    2. Rao K
    3. Young VB

    (2018) Probiotics for prevention of Clostridium difficile infection

    Current Opinion in Gastroenterology 34:3–10.

    https://doi.org/10.1097/MOG.0000000000000410

    • PubMed
    • Google Scholar
46. 1. Mistou M-Y
    2. Sutcliffe IC
    3. van Sorge NM

    (2016) Bacterial glycobiology: rhamnose-containing cell wall polysaccharides in Gram-positive bacteria

    FEMS Microbiology Reviews 40:464–479.

    https://doi.org/10.1093/femsre/fuw006

    • PubMed
    • Google Scholar
47. 1. Mullish BH
    2. Williams HR

    (2018) Clostridium difficile infection and antibiotic-associated diarrhoea

    Clinical Medicine 18:237–241.

    https://doi.org/10.7861/clinmedicine.18-3-237

    • PubMed
    • Google Scholar
48. 1. Mullish BH
    2. Allegretti JR

    (2021) The contribution of bile acid metabolism to the pathogenesis of Clostridioides difficile infection

    Therapeutic Advances in Gastroenterology 14:17562848211017725.

    https://doi.org/10.1177/17562848211017725

    • PubMed
    • Google Scholar
49. 1. Nagao-Kitamoto H
    2. Leslie JL
    3. Kitamoto S
    4. Jin C
    5. Thomsson KA
    6. Gillilland MG
    7. Kuffa P
    8. Goto Y
    9. Jenq RR
    10. Ishii C
    11. Hirayama A
    12. Seekatz AM
    13. Martens EC
    14. Eaton KA
    15. Kao JY
    16. Fukuda S
    17. Higgins PDR
    18. Karlsson NG
    19. Young VB
    20. Kamada N

    (2020) Interleukin-22-mediated host glycosylation prevents Clostridioides difficile infection by modulating the metabolic activity of the gut microbiota

    Nature Medicine 26:608–617.

    https://doi.org/10.1038/s41591-020-0764-0

    • PubMed
    • Google Scholar
50. 1. Palleja A
    2. Mikkelsen KH
    3. Forslund SK
    4. Kashani A
    5. Allin KH
    6. Nielsen T
    7. Hansen TH
    8. Liang S
    9. Feng Q
    10. Zhang C
    11. Pyl PT
    12. Coelho LP
    13. Yang H
    14. Wang J
    15. Typas A
    16. Nielsen MF
    17. Nielsen HB
    18. Bork P
    19. Wang J
    20. Vilsbøll T
    21. Hansen T
    22. Knop FK
    23. Arumugam M
    24. Pedersen O

    (2018) Recovery of gut microbiota of healthy adults following antibiotic exposure

    Nature Microbiology 3:1255–1265.

    https://doi.org/10.1038/s41564-018-0257-9

    • PubMed
    • Google Scholar
51. 1. Pereira FC
    2. Wasmund K
    3. Cobankovic I
    4. Jehmlich N
    5. Herbold CW
    6. Lee KS
    7. Sziranyi B
    8. Vesely C
    9. Decker T
    10. Stocker R
    11. Warth B
    12. von Bergen M
    13. Wagner M
    14. Berry D

    (2020) Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridiodes difficile colonization

    Nature Communications 11:5104.

    https://doi.org/10.1038/s41467-020-18928-1

    • PubMed
    • Google Scholar
52. 1. Porter NT
    2. Martens EC

    (2017) The Critical Roles of Polysaccharides in Gut Microbial Ecology and Physiology

    Annual Review of Microbiology 71:349–369.

    https://doi.org/10.1146/annurev-micro-102215-095316

    • PubMed
    • Google Scholar
53. 1. Pruss KM
    2. Sonnenburg JL

    (2021) C. difficile exploits a host metabolite produced during toxin-mediated disease

    Nature 593:261–265.

    https://doi.org/10.1038/s41586-021-03502-6

    • PubMed
    • Google Scholar
54. 1. Rashid MU
    2. Zaura E
    3. Buijs MJ
    4. Keijser BJF
    5. Crielaard W
    6. Nord CE
    7. Weintraub A

    (2015) Determining the Long-term Effect of Antibiotic Administration on the Human Normal Intestinal Microbiota Using Culture and Pyrosequencing Methods

    Clinical Infectious Diseases 60 Suppl 2:S77–S84.

    https://doi.org/10.1093/cid/civ137

    • PubMed
    • Google Scholar
55. 1. Rätsep M
    2. Kõljalg S
    3. Sepp E
    4. Smidt I
    5. Truusalu K
    6. Songisepp E
    7. Stsepetova J
    8. Naaber P
    9. Mikelsaar RH
    10. Mikelsaar M

    (2017) A combination of the probiotic and prebiotic product can prevent the germination of Clostridium difficile spores and infection

    Anaerobe 47:94–103.

    https://doi.org/10.1016/j.anaerobe.2017.03.019

    • PubMed
    • Google Scholar
56. 1. Robinson JI
    2. Weir WH
    3. Crowley JR
    4. Hink T
    5. Reske KA
    6. Kwon JH
    7. Burnham C-AD
    8. Dubberke ER
    9. Mucha PJ
    10. Henderson JP

    (2019) Metabolomic networks connect host-microbiome processes to human Clostridioides difficile infections

    The Journal of Clinical Investigation 129:3792–3806.

    https://doi.org/10.1172/JCI126905

    • PubMed
    • Google Scholar
57. 1. Rohart F
    2. Gautier B
    3. Singh A
    4. Lê Cao K-A

    (2017) mixOmics: An R package for ’omics feature selection and multiple data integration

    PLOS Computational Biology 13:e1005752.

    https://doi.org/10.1371/journal.pcbi.1005752

    • PubMed
    • Google Scholar
58. 1. Scaria J
    2. Chen JW
    3. Useh N
    4. He H
    5. McDonough SP
    6. Mao C
    7. Sobral B
    8. Chang YF

    (2014) Comparative nutritional and chemical phenome of Clostridium difficile isolates determined using phenotype microarrays

    International Journal of Infectious Diseases 27:20–25.

    https://doi.org/10.1016/j.ijid.2014.06.018

    • PubMed
    • Google Scholar
59. 1. Schmieder R
    2. Edwards R

    (2011) Fast identification and removal of sequence contamination from genomic and metagenomic datasets

    PLOS ONE 6:e17288.

    https://doi.org/10.1371/journal.pone.0017288

    • PubMed
    • Google Scholar
60. 1. Schnizlein MK
    2. Vendrov KC
    3. Edwards SJ
    4. Martens EC
    5. Young VB

    (2020) Dietary Xanthan Gum Alters Antibiotic Efficacy against the Murine Gut Microbiota and Attenuates Clostridioides difficile Colonization

    MSphere 5:e00708-19.

    https://doi.org/10.1128/mSphere.00708-19

    • PubMed
    • Google Scholar
61. 1. Silva J
    2. Ferraz R
    3. Dupree P
    4. Showalter AM
    5. Coimbra S

    (2020) Three Decades of Advances in Arabinogalactan-Protein Biosynthesis

    Frontiers in Plant Science 11:610377.

    https://doi.org/10.3389/fpls.2020.610377

    • PubMed
    • Google Scholar
62. 1. Sorbara MT
    2. Pamer EG

    (2019) Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them

    Mucosal Immunology 12:1–9.

    https://doi.org/10.1038/s41385-018-0053-0

    • PubMed
    • Google Scholar
63. 1. Stevens V
    2. Dumyati G
    3. Fine LS
    4. Fisher SG
    5. van Wijngaarden E

    (2011) Cumulative antibiotic exposures over time and the risk of Clostridium difficile infection

    Clinical Infectious Diseases 53:42–48.

    https://doi.org/10.1093/cid/cir301

    • PubMed
    • Google Scholar
64. 1. Teng C
    2. Reveles KR
    3. Obodozie-Ofoegbu OO
    4. Frei CR

    (2019) Clostridium difficile Infection Risk with Important Antibiotic Classes: An Analysis of the FDA Adverse Event Reporting System

    International Journal of Medical Sciences 16:630–635.

    https://doi.org/10.7150/ijms.30739

    • PubMed
    • Google Scholar
65. 1. Theriot CM
    2. Koenigsknecht MJ
    3. Carlson PE
    4. Hatton GE
    5. Nelson AM
    6. Li B
    7. Huffnagle GB
    8. Z Li J
    9. Young VB

    (2014) Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection

    Nature Communications 5:3114.

    https://doi.org/10.1038/ncomms4114

    • PubMed
    • Google Scholar
66. 1. Truong DT
    2. Franzosa EA
    3. Tickle TL
    4. Scholz M
    5. Weingart G
    6. Pasolli E
    7. Tett A
    8. Huttenhower C
    9. Segata N

    (2015) MetaPhlAn2 for enhanced metagenomic taxonomic profiling

    Nature Methods 12:902–903.

    https://doi.org/10.1038/nmeth.3589

    • PubMed
    • Google Scholar
67. 1. Webb BJ
    2. Subramanian A
    3. Lopansri B
    4. Goodman B
    5. Jones PB
    6. Ferraro J
    7. Stenehjem E
    8. Brown SM

    (2020) Antibiotic Exposure and Risk for Hospital-Associated Clostridioides difficile Infection

    Antimicrobial Agents and Chemotherapy 64:e02169-19.

    https://doi.org/10.1128/AAC.02169-19

    • PubMed
    • Google Scholar
68. 1. Westblade LF
    2. Chamberland RR
    3. MacCannell D
    4. Collins R
    5. Dubberke ER
    6. Dunne WM
    7. Burnham C-AD

    (2013) Development and evaluation of a novel, semiautomated Clostridium difficile typing platform

    Journal of Clinical Microbiology 51:621–624.

    https://doi.org/10.1128/JCM.02627-12

    • PubMed
    • Google Scholar
69. 1. Wirbel J
    2. Zych K
    3. Essex M
    4. Karcher N
    5. Kartal E
    6. Salazar G
    7. Bork P
    8. Sunagawa S
    9. Zeller G

    (2021) Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

    Genome Biology 22:93.

    https://doi.org/10.1186/s13059-021-02306-1

    • PubMed
    • Google Scholar
70. 1. Wood DE
    2. Salzberg SL

    (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments

    Genome Biology 15:R46.

    https://doi.org/10.1186/gb-2014-15-3-r46

    • PubMed
    • Google Scholar

Author details

1. Skye RS Fishbein

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9554-1170

2. John I Robinson

   Center for Women’s Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, United States

   Contribution

   Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3107-0047

3. Tiffany Hink

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

4. Kimberly A Reske

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

5. Erin P Newcomer

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Carey-Ann D Burnham

   1. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
   2. Department of Molecular Microbiology, Washington University School of Medicine, St Louis, United States
   3. Department of Pediatrics, Washington University School of Medicine, St. Louis, United States

   Contribution

   Data curation, Investigation, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

7. Jeffrey P Henderson

   Center for Women’s Infectious Disease Research, Division of Infectious Diseases, Department of Internal Medicine, Washington University School of Medicine, St Louis, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

8. Erik R Dubberke

   Division of Infectious Diseases, Washington University School of Medicine, St. Louis, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Resources, Supervision, Writing – review and editing

   For correspondence

   edubberk@wustl.edu

   Competing interests

   E.R.D. has received research support from Synthetic Biologics, Pfizer and Ferring, and has been a consultant for Summit, Merck, Ferring, Pfizer and Seres Therapeutics, all unrelated to this study

9. Gautam Dantas

   1. The Edison Family Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St Louis, United States
   2. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
   3. Department of Molecular Microbiology, Washington University School of Medicine, St Louis, United States
   4. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – review and editing

   For correspondence

   dantas@wustl.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0455-8370

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