Biology subjects

Schloss, P. D.

Publications and source records attributed to Schloss, P. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Rarefaction is better than robust Aitchison PCA and other compositional data analysis methods at controlling for uneven sequencing effort

Amplicon sequencing typically results in a wide distribution in the number of sequences obtained from each sample. How best to account for this variation has been a persistent problem in the microbial ecology literature. Historically, rarefaction has been used in ecology and this practice was adopted by microbial ecologists. But rarefaction has been strongly criticized leading to the development of compositional data analysis and normalization methods. Therefore, I reassessed the benchmarking data that was generated by the developers of one such method, robust Aitchison PCA. I found numerous problems in the Python code that led to the support of robust Aitchison PCA. These problems extended to the creation of simulated datasets, implementation of machine learning methods, and choice of analysis parameters. Furthermore, the analysis of the simulated and case study datasets was done in a manner that was foreign to standard microbiome analyses. I corrected the problems in the original code, added datasets with smaller effect sizes, and expanded the collection of methods that purport to correct for uneven sequencing effort. Contrary to the claims of the original analysis, robust Aitchison PCA was not insensitive to uneven sequencing effort and did not perform as well as rarefaction. In fact, even using the benchmarking framework from the original analysis, rarefaction outperformed robust Aitchison PCA, other compositional data analysis methods, and other normalization methods. Rarefaction remains the preferred method of controlling for uneven sampling effort in amplicon sequence studies. ImportanceEfforts to connect the structure of microbial communities with environmental processes and host health have captured widespread interest among scientists and the general public. The methods used to generate the sequencing data that are fundamental to these efforts result in wide variation in the number of sequences per sample. This variation and how to account for it can have detrimental effects on the ability to draw valid conclusions. Compositional data analysis methods including robust Aitchison PCA have grown in popularity for mitigating these effects and have been proposed as alternatives to rarefaction. In this study, I reviewed and fixed the code that was used to benchmark robust Aitchison PCA and expanded the analysis. With this improved and expanded analysis, I found that rarefaction was still superior to robust Aitchison PCA and other methods of controlling for uneven sequencing effort.

microbiology↗

bioRxiv2023

Waste not, want not: Revisiting the analysis that called into question the practice of rarefaction

In 2014, McMurdie and Holmes published the provocatively titled, "Waste not, want not: why rarefying microbiome data is inadmissible". The claims of their study have significantly altered how microbiome researchers control for the unavoidable uneven sequencing depths that are inherent in modern 16S rRNA gene sequencing. Confusion over the distinction between the definitions of rarefying and rarefaction continues to cloud the interpretation of their results. More importantly, the authors made a variety of choices in designing and analyzing their simulations that were potentially problematic. I identified 11 factors that could have compromised the results of the original study. I reproduced the original simulation results and assessed the impact of those 11 factors on the underlying conclusion that rarefying data is inadmissible. Throughout, the design of the original study made choices that caused rarefying and rarefaction to appear to perform worse than they truly did. Most important were the approaches used to assess ecological distances, the removal of samples with low sequencing depth, and not accounting for conditions where sequencing effort is confounded with treatment group. Although the original study criticized rarefying for the arbitrary removal of valid data, repeatedly rarefying data many times (i.e., rarefaction) incorporates all the data. In contrast, it is the removal of rare taxa that would appear to remove valid data. Overall, I show that rarefaction is the most robust approach to control for uneven sequencing effort when considered across a variety of alpha and beta diversity metrics. ImportanceOver the past 10 years, the best method for normalizing the sequencing depth of samples characterized by 16S rRNA gene sequencing has been contentious. An often cited paper by McMurdie and Holmes forcefully argued that rarefying the number of sequence counts was "inadmissible" and should not be employed. However, I identified a number of problems with the design of their simulations and analysis that could compromise their results. In fact, when I reproduced and expanded upon their analysis, it was clear that rarefaction was actually the most robust approach for controlling for uneven sequencing effort across samples. Rarefaction limits the rate of falsely detecting and rejecting differences between treatment groups. Far from being "inadmissible", rarefaction is a valuable tool for analyzing microbiome sequence data.

microbiology↗

bioRxiv2023

Rarefaction is currently the best approach to control for uneven sequencing effort in amplicon sequence analyses

Considering it is common to find as much as 100-fold variation in the number of 16S rRNA gene sequences found across samples in a study, researchers need to control for the effect of uneven sequencing effort. How to do this has become a contentious question. Some have argued that rarefying or rarefaction is "inadmissible" because it omits valid data. A number of alternative approaches have been developed to normalize and rescale the data that purport to be invariant to the number of observations. I generated community distributions based on 12 published datasets where I was able to assess the ability of multiple methods to control for uneven sequencing effort. Rarefaction was the only method that could control for variation in uneven sequencing effort when measuring commonly used alpha and beta diversity metrics. Next, I compared the false detection rate and power to detect true differences between simulated communities with a known effect size using various alpha and beta diversity metrics. Although all methods of controlling for uneven sequencing effort had an acceptable false detection rate when samples were randomly assigned to two treatment groups, rarefaction was consistently able to control for differences in sequencing effort when sequencing depth was confounded with treatment group. Finally, the statistical power to detect differences in alpha and beta diversity metrics was consistently the highest when using rarefaction. These simulations underscore the importance of using rarefaction to normalize the number of sequences across samples in amplicon sequencing analyses. ImportanceSequencing 16S rRNA gene fragments has become a fundamental tool for understanding the diversity of microbial communities and the factors that affect their diversity. Due to technical challenges, it is common to observe wide variation in the number of sequences that are collected from different samples within the same study. However, the diversity metrics used by microbial ecologists are sensitive to differences in sequencing effort. Therefore, tools are needed to control for the uneven levels of sequencing. This simulation-based analysis shows that despite a longstanding controversy, rarefaction is the most robust approach to control for uneven sequencing effort. The controversy started because of confusion over the definition of rarefaction and violation of assumptions that are made by methods that have been borrowed from other fields. Microbial ecologists should use rarefaction.

microbiology↗

bioRxiv2022

Streamlined implementation of a machine learning model to classify screen relevant neoplasia using reference-based OTU clustering

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity diagnose patients based on the composition of their microbiome. In some applications the taxonomic resolution that provides the best models may require the use of de novo OTUs whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences.

microbiology↗

bioRxiv2022

Simplified fecal community transplant restores Clostridioides difficile colonization resistance to antibiotic perturbed murine communities

Fecal communities transplanted into individuals can eliminate recurrent Clostridioides difficile infection (CDI) with high efficacy. However, this treatment is only used once CDI becomes resistant to antibiotics or has recurred multiple times. We sought to investigate whether a fecal community transplant (FCT) pre-treatment could be used to prevent CDI altogether. We treated male C57BL/6 mice with either clindamycin, cefoperazone, or streptomycin, and then inoculated them with the microbial community from untreated mice before challenging with C. difficile. We measured colonization and sequenced the V4 region of the 16S rRNA gene to understand the dynamics of the murine fecal community in response to the FCT and C. difficile challenge. Clindamycin-treated mice became colonized with C. difficile but cleared it naturally and did not benefit from the FCT. Cefoperazone-treated mice became colonized by C. difficile, but the FCT enabled clearance of C. difficile. In streptomycin-treated mice, the FCT was able to prevent C. difficile from colonizing. Then we diluted the FCT and repeated the experiments. Cefoperazone-treated mice no longer cleared C. difficile. However, streptomycin-treated mice colonized with 1:102 dilutions resisted C. difficile colonization. Streptomycin-treated mice that received a FCT diluted 1:103, C. difficile colonized but later was cleared. In streptomycin-treated mice, inhibition of C. difficile was associated with increased relative abundance of a group of bacteria related to Porphyromonadaceae and Lachnospiraceae. These data demonstrate that C. difficile colonization resistance can be restored to a susceptible community with a FCT as long as it complements the missing populations. ImportanceAntibiotic use, ubiquitous with the healthcare environment, is a major risk factor for Clostridioides difficile infection (CDI), the most common nosocomial infection. When C. difficile becomes resistant to antibiotics, a fecal microbiota transplant from a healthy individual can effectively restore the gut bacterial community and eliminate the infection. While this relationship between the gut bacteria and CDI is well established, there are no therapies to treat a perturbed gut community to prevent CDI. This study explored the potential of restoring colonization resistance to antibiotic-induced susceptible gut communities. We described the effect gut bacteria community variation has on the effectiveness of a fecal community transplant for inhibiting CDI. These data demonstrated that communities susceptible to CDI can be supplemented with fecal communities but the effectiveness depended on the structure of the community following the perturbation. Thus, a simplified bacterial community may be able to recover colonization resistance to patients treated with antibiotics.

microbiology↗

bioRxiv2022

Intestinal inflammation reversibly alters the microbiota to drive susceptibility to Clostridioides difficile colonization in a mouse model of colitis

Susceptibility to Clostridioides difficile infection (CDI) typically follows the administration of antibiotics. Patients with inflammatory bowel disease (IBD) have increased incidence of CDI, even in the absence of antibiotic treatment. However, the mechanisms underlying this susceptibility are not well understood. To explore the intersection between CDI and IBD, we recently described a mouse model where colitis triggered by the murine gut bacterium, Helicobacter hepaticus, in IL-10-/- mice led to susceptibility to C. difficile colonization without antibiotic administration. The current work disentangles the relative contributions of inflammation and gut microbiota in colonization resistance to C. difficile in this model. We show that inflammation drives changes in microbiota composition, which leads to CDI susceptibility. Decreasing inflammation with an anti-p40 monoclonal antibody promotes a shift of the microbiota back toward a colonization-resistant state. Transferring microbiota from susceptible and resistant mice to germ-free animals transfers the susceptibility phenotype, supporting the primacy of the microbiota in colonization resistance. These findings shine light on the complex interactions between the host, microbiota, and C. difficile in the context of intestinal inflammation, and may form a basis for the development of strategies to prevent or treat CDI in IBD patients. ImportancePatients with inflammatory bowel disease (IBD) have an increased risk of developing C. difficile infection (CDI), even in the absence of antibiotic treatment. Yet, mechanisms regulating C. difficile colonization in IBD patients remain unclear. Here, we use an antibiotic-independent mouse model to demonstrate that intestinal inflammation alters microbiota composition to permit C. difficile colonization in mice with colitis. Notably, treating inflammation with an anti-p40 monoclonal antibody, a clinically relevant IBD therapeutic, restores microbiota-mediated colonization resistance to the pathogen. Through microbiota transfer experiments in germ-free mice, we confirm that the microbiota shaped in the setting of IBD is the primary driver of susceptibility to C. diffiicile colonization. Collectively, our findings provide insight into CDI pathogenesis in the context of intestinal inflammation, which may inform methods to manage infection in IBD patients. More broadly, this work advances our understanding of mechanisms by which the host-microbiota interface modulates colonization resistance to C. difficile.

microbiology↗

bioRxiv2022

The gut bacterial community potentiates Clostridioides difficile infection severity

The severity of Clostridioides difficile infections (CDI) has increased over the last few decades. Patient age, white blood cell count, creatinine levels as well as C. difficile ribotype and toxin genes have been associated with disease severity. However, it is unclear whether there is an association between members of the gut microbiota and disease severity. The gut microbiota is known to interact with C. difficile during infection. Perturbations to the gut microbiota are necessary for C. difficile to colonize the gut. The gut microbiota can inhibit C. difficile colonization through bile acid metabolism, nutrient consumption and bacteriocin production. Here we sought to demonstrate that members of the gut bacterial communities can also contribute to disease severity. We derived diverse gut communities by colonizing germ-free mice with different human fecal communities. The mice were then infected with a single C. difficile ribotype 027 clinical isolate which resulted in moribundity and histopathologic differences. The variation in severity was associated with the human fecal community that the mice received. Generally, bacterial populations with pathogenic potential, such as Escherichia, Helicobacter, and Klebsiella, were associated with more severe outcomes. Bacterial groups associated with fiber degradation, bile acid metabolism and lantibiotic production, such as Anaerostipes and Coprobacillus, were associated with less severe outcomes. These data indicate that, in addition to the host and C. difficile, populations of gut bacteria can influence CDI disease severity. ImportanceClostridioides difficile colonization can be asymptomatic or develop into an infection, ranging in severity from mild diarrhea to toxic megacolon, sepsis, and death. Models that predict severity and guide treatment decisions are based on clinical factors and C. difficile characteristics. Although the gut microbiome plays a role in protecting against CDI, its effect on CDI disease severity is unclear and has not been incorporated into disease severity models. We demonstrated that variation in the microbiome of mice colonized with human feces yielded a range of disease outcomes. These results revealed groups of bacteria associated with both severe and mild C. difficile infection outcomes. Gut bacterial community data from patients with CDI could improve our ability to identify patients at risk of developing more severe disease and improve interventions which target C. difficile and the gut bacteria to reduce host damage.

microbiology↗

bioRxiv2021

OptiFit: an improved method for fitting amplicon sequences to existing OTUs

Assigning amplicon sequences to operational taxonomic units (OTUs) is often an important step in characterizing the composition of microbial communities across large datasets. OptiClust, a de novo OTU clustering method, has been shown to produce higher quality OTU assignments than other methods and at comparable or faster speeds. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added to a dataset. However, in some cases one may wish to incorporate new samples into a previously clustered dataset without performing clustering again on all sequences, such as when comparing across datasets or deploying machine learning models where OTUs are features. Existing reference-based clustering methods produce consistent OTUs, but they only consider the similarity of each query sequence to a single reference sequence in an OTU, thus resulting in OTU assignments that are significantly worse than those generated by de novo methods. To provide an efficient and robust method to fit amplicon sequence data to existing OTUs, we developed the OptiFit algorithm. Inspired by OptiClust, OptiFit considers the similarity of all pairs of reference and query sequences in an OTU to produce OTUs of the best possible quality. We tested OptiFit using four microbiome datasets with two different strategies: by clustering to an external reference database or by splitting the dataset into a reference and query set and clustering the query sequences to the reference set after clustering it using OptiClust. The result is an improved implementation of closed and open-reference clustering. OptiFit produces OTUs of similar quality as OptiClust and at faster speeds when using the split dataset strategy, although the OTU quality and processing speed depends on the database chosen when using the external database strategy. OptiFit provides a suitable option for users who require consistent OTU assignments at the same quality afforded by de novo clustering methods. ImportanceAdvancements in DNA sequencing technology have allowed researchers to affordably generate millions of sequence reads from microorganisms in diverse environments. Efficient and robust software tools are needed to assign microbial sequences into taxonomic groups for characterization and comparison of communities. The OptiClust algorithm produces high quality groups by comparing sequences to each other, but the assignments can change when new sequences are added to a dataset, making it difficult to compare different studies. Other approaches assign sequences to groups by comparing them to sequences in a reference database to produce consistent assignments, but the quality of the groups produced is reduced compared to OptiClust. We developed OptiFit, a new reference-based algorithm that produces consistent yet high quality assignments like OptiClust. OptiFit allows researchers to compare microbial communities across different studies or add new data to existing studies without sacrificing the quality of the group assignments.

microbiology↗

bioRxiv2021

A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for non-invasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (p > 0.05) among family (mean AUC: 0.689), genus (mean AUC: 0.690), and OTU (mean AUC: 0.693) levels before decreasing at the ASV level (p < 0.05, mean AUC: 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g. phylum) is not distinct enough but fine resolution (e.g. ASV) is to individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears, mid-range resolution (i.e. family, genus, and OTU) is just right for optimal prediction of colorectal cancer from microbiome data. ImportanceDespite being highly preventable, colorectal cancer remains a leading cause of cancer related death in the United States. Low-cost, non-invasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

microbiology↗

bioRxiv2021

An osmotic laxative renders mice susceptible to prolonged Clostridioides difficile colonization and hinders clearance

Antibiotics are a major risk factor for Clostridioides difficile infections (CDIs) because of their impact on the microbiota. However, non-antibiotic medications such as the ubiquitous osmotic laxative polyethylene glycol (PEG) 3350 also alter the microbiota. Clinicians also hypothesize that PEG helps clear C. difficile. But whether PEG impacts CDI susceptibility and clearance is unclear. To examine how PEG impacts susceptibility, we treated C57Bl/6 mice with 5-day and 1-day doses of 15% PEG in the drinking water and then challenged the mice with C. difficile 630. We used clindamycin-treated mice as a control because they consistently clear C. difficile within 10 days post-challenge. PEG treatment alone was sufficient to render mice susceptible and 5-day PEG-treated mice remained colonized for up to 30 days post-challenge. In contrast, 1-day PEG treated mice were transiently colonized, clearing C. difficile within 7 days post-challenge. To examine how PEG treatment impacts clearance, we administered a 1-day PEG treatment to clindamycin-treated, C. difficile-challenged mice. Administering PEG to mice after C. difficile challenge prolonged colonization up to 30 days post-challenge. When we trained a random forest model with community data from 5 days post-challenge, we were able to predict which mice would exhibit prolonged colonization (AUROC = 0.90). Examining the dynamics of these bacterial populations during the post-challenge period revealed patterns in the relative abundances of Bacteroides, Enterobacteriaceae, Porphyromonadaceae, Lachnospiraceae, and Akkermansia that were associated with prolonged C. difficile colonization in PEG-treated mice. Thus, the osmotic laxative, PEG, rendered mice susceptible to C. difficile colonization and hindered clearance. ImportanceDiarrheal samples from patients taking laxatives are typically rejected for Clostridiodes difficile testing. However, there are similarities between the bacterial communities from people with diarrhea or C. difficile infections (CDI) including lower diversity compared to communities from healthy patients. This observation led us to hypothesize that diarrhea may be an indicator of C. difficile susceptibility. We explored how osmotic laxatives disrupt the microbiotas colonization resistance to C. difficile by administering a laxative to mice either before or after C. difficile challenge. Our findings suggest that osmotic laxatives disrupt colonization resistance to C. difficile, and prevent clearance among mice already colonized with C. difficile. Considering that most hospitals recommend not performing C. difficile testing on patients taking laxatives and laxatives are prescribed prior to administering fecal microbiota transplants via colonoscopy to patients with recurrent CDIs, further studies are needed to evaluate if laxatives impact microbiota colonization resistance in humans.

microbiology↗

bioRxiv2021

Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research

Inspired by well-established material and pedagogy provided by The Carpentries (Wilson 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al. 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.

scientific communication and education↗

bioRxiv2021

Amplicon sequence variants artificially split bacterial genomes into separate clusters

Amplicon sequencing variants (ASVs) have been proposed as an alternative to operational taxonomic units (OTUs) for analyzing microbial communities. ASVs have grown in popularity, in part, because of a desire to reflect a more refined level of taxonomy since they do not cluster sequences based on a distance-based threshold. However, ASVs and the use of overly narrow thresholds to identify OTUs increase the risk of splitting a single genome into separate clusters. To assess this risk, I analyzed the intragenomic variation of 16S rRNA genes from the bacterial genomes represented in a rrn copy number database, which contained 20,427 genomes from 5,972 species. As the number of copies of the 16S rRNA gene increased in a genome, the number of ASVs also increased. There was an average of 0.58 ASVs per copy of the 16S rRNA gene for full length 16S rRNA genes. It was necessary to use a distance threshold of 5.25% to cluster full length ASVs from the same genome into a single OTU with 95% confidence for genomes with 7 copies of the 16S rRNA, such as E. coli. This research highlights the risk of splitting a single bacterial genome into separate clusters when ASVs are used to analyze 16S rRNA gene sequence data. Although there is also a risk of clustering ASVs from different species into the same OTU when using broad distance thresholds, those risks are of less concern than artificially splitting a genome into separate ASVs and OTUs.

microbiology↗

bioRxiv2021

Protection from lethal Clostridioides difficile infection via intraspecies competition for co-germinant

Clostridioides difficile, a Gram-positive, spore-forming bacterium, is the primary cause of infectious nosocomial diarrhea. Antibiotics are a major risk factor for C. difficile infection (CDI) as they disrupt the gut microbial community, enabling increased germination of spores and growth of vegetative C. difficile. To date the only single-species bacterial preparation that has demonstrated efficacy in reducing recurrent CDI in humans is non-toxigenic C. difficile. Using multiple infection models we determined that pre-colonization with a less virulent strain is sufficient to protect from challenge with a lethal strain of C. difficile, surprisingly even in the absence of adaptive immunity. Additionally, we showed that protection is dependent on high levels of colonization by the less virulent strain and that it is mediated by exclusion of the invading strain. Our results suggest that reduction of amino acids, specifically glycine following colonization by the first strain of C. difficile is sufficient to decrease germination of the second strain thereby limiting colonization by the lethal strain. ImportanceAntibiotic-associated colitis is often caused by infection with the bacterium Clostridioides difficile. In this study we found that reduction of the amino acid glycine by pre-colonization with a less virulent strain of C. difficile is sufficient to decrease germination of a second strain. This finding demonstrates that the axis of competition for nutrients can include multiple life stages. This work is important, as it is the first to identify a possible mechanism through which pre-colonization with C. difficile, a current clinical therapy, provides protection from reinfection. Furthermore, our work suggests that targeting nutrients utilized by all life stages could be an improved strategy for bacterial therapeutics that aim to restore colonization resistance in the gut.

microbiology↗

bioRxiv2020

Removal of rare amplicon sequence variants from 16S rRNA gene sequence surveys biases the interpretation of community structure data

Methods for remediating PCR and sequencing artifacts in 16S rRNA gene sequence collections are in continuous development and have significant ramifications on the inferences that can be drawn. A common approach is to remove rare amplcon sequence variants (ASVs) from datasets. But, the definition of rarity is generally selected without regard for the number of sequences in the samples or the variation in sequencing depth across samples within a study. I analyzed the impact of removing rare ASVs on metrics of alpha and beta diversity using samples collected across 12 published datasets. Removal of rare ASVs significantly decreased the number of ASVs and operational taxonomic units as well as their diversity. Furthermore, their removal increased the variation in community structure between samples. When simulating a known effect size, removal of rare ASVs reduced the power to detect the effect relative to not removing rare ASVs. Removal of rare ASVs did not affect the false detection rate when samples were randomized to simulate a null model. However, the false detection rate increased when rare ASVs were removed using a null distribution and assignment of samples to simulated treatment groups according to their sequencing depth. The false detection rate did not vary when rare ASVs were retained. This analysis demonstrates the problems inherent in removing rare ASVs. Researchers are encouraged to retain rare ASVs, to select approaches that minimize PCR and sequencing artifacts, and to use rarefaction to control for uneven sequencing effort. ImportanceRemoving rare amplicon sequence variants (ASVs) from 16S rRNA gene sequence collections is an approach that has grown in popularity for limiting PCR and sequencing artifacts. Yet, it is unclear what impact an abundance-based filter has on downstream analyses. To investigate the effects of removing rare ASVs, I analyzed the community distributions found in the samples of 12 published datasets. Analysis of these data and simulations based on them showed that removal of rare ASVs distorts the representation of microbial communities. This has the effect of artificially making it more difficult to detect differences between treatment groups. Also of concern was the observation that if sequencing depth is confounded with the treatment, then the probability of falsely detecting a difference between the treatment groups increased with the removal of rare ASVs. The practice of removing rare ASVs should stop, lest researcher adversely affect the interpretation of their data.

microbiology↗

bioRxiv2020

Clearance of Clostridioides difficile colonization is associated with antibiotic-specific bacterial changes

The gut bacterial community prevents many pathogens from colonizing the intestine. Previous studies have associated specific bacteria with clearing Clostridioides difficile colonization across different community perturbations. However, those bacteria alone have been unable to clear C. difficile colonization. To elucidate the changes necessary to clear colonization, we compared differences in bacterial abundance between communities able and unable to clear C. difficile colonization. We treated mice with titrated doses of antibiotics prior to C. difficile challenge which resulted in no colonization, colonization and clearance, or persistent colonization. Previously, we observed that clindamycin-treated mice were susceptible to colonization but spontaneously cleared C. difficile. Therefore, we investigated whether other antibiotics would show the same result. We found reduced doses of cefoperazone and streptomycin permitted colonization and clearance of C. difficile. Mice that cleared colonization had antibiotic-specific community changes and predicted interactions with C. difficile. Clindamycin treatment led to a bloom in populations related to Enterobacteriaceae. Clearance of C. difficile was concurrent with the reduction of those blooming populations and the restoration of community members related to the Porphyromonadaceae and Bacteroides. Cefoperazone created a susceptible community characterized by a drastic reduction in the community diversity, interactions, and a sustained increase in abundance of many facultative anaerobes. Lastly, clearance in streptomycin-treated mice was associated with the recovery of multiple members of the Porphyromonadaceae, with little overlap in the specific Porphyromonadaceae observed in the clindamycin treatment. Further elucidation of how C. difficile colonization is cleared from different gut bacterial communities will improve C. difficile infection treatments. ImportanceThe community of microorganisms, known as the microbiota, in our intestines prevents pathogens, such as C. difficile, from establishing themselves and causing infection. This is known as colonization resistance. However, when a person takes antibiotics, their gut microbiota is disturbed. This disruption allows C. difficile to colonize. C. difficile infections (CDI) are primarily treated with antibiotics, which frequently leads to recurrent infections because the microbiota have not yet returned to a resistant state. The infection cycle often ends when the fecal microbiota from a presumed resistant person are transplanted into the susceptible person. Although this treatment is highly effective, we do not understand the mechanism of resistance. We hope to improve the treatment of CDI through elucidating how the bacterial community eliminates C. difficile colonization. We found C. difficile was able to colonize susceptible mice but was spontaneously eliminated in an antibiotic-treatment specific manner. These data indicate each community had different requirements for clearing colonization. Understanding how different communities clear colonization will reveal targets to improve CDI treatments.

microbiology↗

bioRxiv2020

The initial gut microbiota and response to antibiotic perturbation influence Clostridioides difficile colonization in mice

The gut microbiota has a key role in determining susceptibility to Clostridioides difficile infections (CDIs). However, much of the mechanistic work examining CDIs in mouse models use animals obtained from a single source. We treated mice from 6 sources (2 University of Michigan colonies and 4 commercial vendors) with clindamycin, followed by a C. difficile challenge and then measured C. difficile colonization levels throughout the infection. The microbiota were profiled via 16S rRNA gene sequencing to examine the variation across sources and alterations due to clindamycin treatment and C. difficile challenge. While all mice were colonized 1-day post-infection, variation emerged from days 3-7 post-infection with animals from some sources colonized with C. difficile for longer and at higher levels. We identified bacteria that varied in relative abundance across sources and throughout the experiment. Some bacteria were consistently impacted by clindamycin treatment in all sources of mice including Lachnospiraceae, Ruminococcaceae, and Enterobacteriaceae. To identify bacteria that were most important to colonization regardless of the source, we created logistic regression models that successfully classified mice based on whether they cleared C. difficile by 7 days post-infection using community composition data at baseline, post-clindamycin, and 1-day post-infection. With these models, we identified 4 bacteria that were predictive of whether C. difficile cleared. They varied across sources (Bacteroides), were altered by clindamycin (Porphyromonadaceae), or both (Enterobacteriaceae and Enterococcus). Allowing for microbiota variation across sources better emulates human inter-individual variation and can help identify bacterial drivers of phenotypic variation in the context of CDIs. ImportanceClostridioides difficile is a leading nosocomial infection. Although perturbation to the gut microbiota is an established risk, there is variation in who becomes asymptomatically colonized, develops an infection, or has adverse infection outcomes. Mouse models of C. difficile infection (CDI) are widely used to answer a variety of C. difficile pathogenesis questions. However, the inter-individual variation between mice from the same breeding facility is less than what is observed in humans. Therefore, we challenged mice from 6 different breeding colonies with C. difficile. We found that the starting microbial community structures and C. difficile persistence varied by the source of mice. Interestingly, a subset of the bacteria that varied across sources were associated with how long C. difficile was able to colonize. By increasing the inter-individual diversity of the starting communities, we were able to better model human diversity. This provided a more nuanced perspective of C. difficile pathogenesis.

microbiology↗

bioRxiv2019

Effective application of machine learning to microbiome-based classification problems

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made towards developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs; n=490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1 and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, decision trees, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an AUROC of 0.695 [IQR 0.651-0.739] but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 [IQR 0.625-0.735], trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. ImportanceDiagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely over-optimistic. Moreover, there is a trend towards using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step towards developing more reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

microbiology↗

bioRxiv2019

The proton pump inhibitor omeprazole does not promote Clostridium difficile colonization in a murine model

Proton pump inhibitor (PPI) use has been associated with microbiota alterations and susceptibility to Clostridioides difficile infections (CDIs) in humans. We assessed how PPI treatment alters the fecal microbiota and whether treatment promotes CDIs in a mouse model. Mice receiving a PPI treatment were gavaged with 40 mg/kg of omeprazole during a 7-day pretreatment phase, the day of C. difficile challenge, and the following 9 days. We found that mice treated with omeprazole were not colonized by C. difficile. When omeprazole treatment was combined with a single clindamycin treatment, one cage of mice remained resistant to C. difficile colonization, while the other cage was colonized. Treating mice with only clindamycin followed by challenge resulted in C. difficile colonization. 16S rRNA gene sequencing analysis revealed that omeprazole had minimal impact on the structure of the murine microbiota throughout the 16 days of omeprazole exposure. These results suggest omeprazole treatment alone is not sufficient to disrupt microbiota resistance to C. difficile infection in mice that are normally resistant in the absence of antibiotic treatment.

microbiology↗