Search

Advances in microbiome therapeutics have been motivated by a deeper understanding of the role that the gastrointestinal microbiome plays in human health and disease. The FDA approval of two stool-derived live biotherapeutic products (LBPs), REBYOTA® 150 mL enema (fecal microbiota, live-jslm; formerly RBX2660) and VOWST® oral capsules (fecal microbiota spores, live-brpk; formerly SER-109), for the prevention of recurrent CDI in adults following antibiotic treatment for recurrent CDI provides promise and insights for the development of LBPs for other diseases associated with microbiome dysfunction. Donor-derived products carry risk of disease transmission that must be mitigated through a robust donor screening program and downstream manufacturing controls. Most published recommendations for donor screening practices are prescriptive and do not include a systematic, risk-based approach for donor stool-derived products. A general framework for an end-to-end donor screening program is needed using risk management strategies for donor-derived microbiome therapeutic using a matrixed approach, combining the elements of donor screening with manufacturing controls that are designed to minimize risk to patients. A donor screening paradigm that incorporates medical history, physical examination, laboratory testing, and donor sample inspection are only the first steps in reducing risk of transmission of infectious agents. Manufacturing controls are the cornerstone of risk mitigation when screening unwittingly fails. Failure Mode and Effects Analysis (FMEA) can be used as a tool to assess for residual risk that requires further donor or manufacturing controls. Together, a well-reasoned donor program and manufacturing controls are complementary strategies that must be revisited and reexamined frequently with constant vigilance to mitigate risk to patients. In the spirit of full disclosure and informed consent, physicians should discuss any limitations in the donor screening and manufacturing processes with their patients prior to treatment with microbiome-based therapeutics.

ABSTRACT 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) pretreatment 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 challenge 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. We then 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 an FCT diluted 1:103 became colonized with C. difficile but later cleared the infection. 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 an FCT as long as it complements the missing populations. IMPORTANCE Antibiotic use, ubiquitous with the health care 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 that gut bacterial 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 that the effectiveness depended on the structure of the community following the perturbation. Thus, a reduced bacterial community may be able to recover colonization resistance in patients treated with antibiotics.

Diarrheal samples from patients taking laxatives are typically rejected for Clostridioides difficileC. difficile

ABSTRACT 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 uses 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 postinfection, variation emerged from days 3 to 7 postinfection 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 postinfection using community composition data at baseline, post-clindamycin treatment, and 1 day postinfection. With these models, we identified 4 bacterial taxa that were predictive of whether C. difficile cleared. They varied across sources (Bacteroides) or were altered by clindamycin (Porphyromonadaceae) or both (Enterobacteriaceae and Enterococcus). Allowing for microbiota variation across sources better emulates human interindividual variation and can help identify bacterial drivers of phenotypic variation in the context of CDIs. IMPORTANCE Clostridioides 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 interindividual 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 interindividual diversity of the starting communities, we were able to better model human diversity. This provided a more nuanced perspective of C. difficile pathogenesis.

ABSTRACT Disrupted interactions between host and intestinal bacteria are implicated in colorectal cancer (CRC) development. However, activities derived from these bacteria and their interplay with the host are unclear. Here, we examine this interplay by performing mouse and microbiota RNA sequencing on colon tissues and 16S and small RNA sequencing on stools from germfree (GF) and gnotobiotic ApcMinΔ850/+;Il10−/− mice associated with microbes from biofilm-positive human CRC tumor (BF+T) and biofilm-negative healthy (BF-bx) tissues. The bacteria in BF+T mice differentially expressed (DE) >2,900 genes, including genes related to bacterial secretion, virulence, and biofilms but affected only 62 host genes. Small RNA sequencing of stools from these cohorts revealed eight significant DE host microRNAs (miRNAs) based on biofilm status and several miRNAs that correlated with bacterial taxon abundances. Additionally, computational predictions suggest that some miRNAs preferentially target bacterial genes while others primarily target mouse genes. 16S rRNA sequencing of mice that were reassociated with mucosa-associated communities from the initial association revealed a set of 13 bacterial genera associated with cancer that were maintained regardless of whether the reassociation inoculums were initially obtained from murine proximal or distal colon tissues. Our findings suggest that complex interactions within bacterial communities affect host-derived miRNA, bacterial composition, and CRC development. IMPORTANCE Bacteria and bacterial biofilms have been implicated in colorectal cancer (CRC), but it is still unclear what genes these microbial communities express and how they influence the host. MicroRNAs regulate host gene expression and have been explored as potential biomarkers for CRC. An emerging area of research is the ability of microRNAs to impact growth and gene expression of members of the intestinal microbiota. This study examined the bacteria and bacterial transcriptome associated with microbes derived from biofilm-positive human cancers that promoted tumorigenesis in a murine model of CRC. The murine response to different microbial communities (derived from CRC patients or healthy people) was evaluated through RNA and microRNA sequencing. We identified a complex interplay between biofilm-associated bacteria and the host during CRC in mice. These findings may lead to the development of new biomarkers and therapeutics for identifying and treating biofilm-associated CRCs.

ABSTRACT Disrupted interactions between host and intestinal bacteria are implicated in colorectal cancer (CRC) development. However, activities derived from these bacteria and their interplay with the host are unclear. Here, we examine this interplay by performing mouse and microbiota RNA sequencing on colon tissues and 16S and small RNA sequencing on stools from germfree (GF) and gnotobiotic ApcMinΔ850/+;Il10−/− mice associated with microbes from biofilm-positive human CRC tumor (BF+T) and biofilm-negative healthy (BF-bx) tissues. The bacteria in BF+T mice differentially expressed (DE) >2,900 genes, including genes related to bacterial secretion, virulence, and biofilms but affected only 62 host genes. Small RNA sequencing of stools from these cohorts revealed eight significant DE host microRNAs (miRNAs) based on biofilm status and several miRNAs that correlated with bacterial taxon abundances. Additionally, computational predictions suggest that some miRNAs preferentially target bacterial genes while others primarily target mouse genes. 16S rRNA sequencing of mice that were reassociated with mucosa-associated communities from the initial association revealed a set of 13 bacterial genera associated with cancer that were maintained regardless of whether the reassociation inoculums were initially obtained from murine proximal or distal colon tissues. Our findings suggest that complex interactions within bacterial communities affect host-derived miRNA, bacterial composition, and CRC development. IMPORTANCE Bacteria and bacterial biofilms have been implicated in colorectal cancer (CRC), but it is still unclear what genes these microbial communities express and how they influence the host. MicroRNAs regulate host gene expression and have been explored as potential biomarkers for CRC. An emerging area of research is the ability of microRNAs to impact growth and gene expression of members of the intestinal microbiota. This study examined the bacteria and bacterial transcriptome associated with microbes derived from biofilm-positive human cancers that promoted tumorigenesis in a murine model of CRC. The murine response to different microbial communities (derived from CRC patients or healthy people) was evaluated through RNA and microRNA sequencing. We identified a complex interplay between biofilm-associated bacteria and the host during CRC in mice. These findings may lead to the development of new biomarkers and therapeutics for identifying and treating biofilm-associated CRCs.

ABSTRACT 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 of omeprazole per kg of body weight 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 that 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. IMPORTANCE Antibiotics are the primary risk factor for Clostridioides difficile infections (CDIs), but other factors may also increase a person’s risk. In epidemiological studies, proton pump inhibitor (PPI) use has been associated with CDI incidence and recurrence. PPIs have also been associated with alterations in the human intestinal microbiota in observational and interventional studies. We evaluated the effects of the PPI omeprazole on the structure of the murine intestinal microbiota and its ability to disrupt colonization resistance to C. difficile. We found omeprazole treatment had minimal impact on the murine fecal microbiota and did not promote C. difficile colonization. Further studies are needed to determine whether other factors contribute to the association between PPIs and CDIs seen in humans or whether aspects of murine physiology may limit its utility to test these types of hypotheses.

ABSTRACT IMPACT: Our goal is to identify bacterial biomarkers of adverse Clostridioides difficile infection outcomes OBJECTIVES/GOALS: We characterized microbiota features of Clostridioides difficile infections (CDIs) and will investigate the association between bacterial taxa and adverse outcomes, which includes severe and recurrent CDIs. METHODS/STUDY POPULATION: 1,517 stool samples were collected from patients diagnosed with a CDI at the University of Michigan along with 1,516 unformed and 910 formed stool control samples. We characterized the microbiota of the 3,943 stool samples by sequencing the V4 region of the 16S rRNA gene and used the Dirichlet Multinomial Mixtures method to cluster samples into community types. Severe CDI cases were defined using the Infectious Diseases Society of America criteria and recurrent CDIs were defined as CDIs that occurred within 2-12 weeks of the primary CDI. We will use machine learning to examine whether specific bacterial taxa can predict severe or recurrent CDIs. We will test 5 machine learning models with 80% training and 20% testing data split. RESULTS/ANTICIPATED RESULTS: Similar to findings from a previous study with 338 samples, we found there was no difference in diversity between CDI cases and unformed controls (Inverse Simpson index, p > 0.5) and samples from the 3 groups (CDIs, unformed controls, and formed controls) clustered into 12 community types. To investigate the bacterial taxa that are important for predicting adverse CDI outcomes, we will select the best machine learning model based on performance and training time and examine how much each feature contributes to performance. We anticipate the large number of CDI cases in our cohort and robust machine learning approaches will enable us to identify more bacteria associated with adverse outcomes compared to other studies that have attempted to predict CDI recurrence with fewer CDI cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: Adverse CDI outcomes are a significant source of the morbidities, mortalities, and healthcare costs associated with CDIs. Identifying bacterial biomarkers of severe and recurrent CDIs could enhance our ability to stratify patients into risk groups and may lead to the development of more targeted therapeutics.