Biology subjects

Lesniak, N. A.

Publications and source records attributed to Lesniak, N. A..

8 recordsLinked to original sources

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

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

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↗

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↗

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↗

bioRxiv2019

Fecal short-chain fatty acids are not predictive of colonic tumor status and cannot be predicted based on bacterial community structure

Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria which produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with normal colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power.\n\nImportanceConsidering colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anti-carcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentration of a cohort of individuals with varying colonic tumor burden who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.

microbiology↗