Acarbose Impairs Gut Bacteroides Growth by Targeting Intracellular GH97 Enzymes.

Acarbose is a type-2 diabetes medicine that inhibits dietary starch breakdown into glucose by inhibiting host amylase and glucosidase enzymes. Numerous gut species in the Bacteroides genus enzymatically break down starch and change in relative abundance within the gut microbiome in acarbose-treated individuals. To mechanistically explain this observation, we used two model starch-degrading Bacteroides, Bacteroides ovatus (Bo) and Bacteroides thetaiotaomicron (Bt). Bt growth is severely impaired by acarbose whereas Bo growth is not. The Bacteroides use a starch utilization system (Sus) to grow on starch. We hypothesized that Bo and Bt Sus enzymes are differentially inhibited by acarbose. Instead, we discovered that although acarbose primarily targets the Sus periplasmic GH97 enzymes in both organisms, the drug affects starch processing at multiple other points. Acarbose competes for transport through the Sus beta-barrel proteins and binds to the Sus transcriptional regulators. Further, Bo expresses a non-Sus GH97 (BoGH97D) when grown in starch with acarbose. The Bt homolog, BtGH97H, is not expressed in the same conditions, nor can overexpression of BoGH97D complement the Bt growth inhibition in the presence of acarbose. This work informs us about unexpected complexities of Sus function and regulation in Bacteroides, including variation between related species. Further, this indicates that the gut microbiome may be a source of variable response to acarbose treatment for diabetes.

Host factors are associated with vaginal microbiome structure in pregnancy in the ECHO Cohort Consortium.

Using pooled vaginal microbiota data from pregnancy cohorts (N = 683 participants) in the Environmental influences on Child Health Outcomes (ECHO) Program, we analyzed 16S rRNA gene amplicon sequences to identify clinical and demographic host factors that associate with vaginal microbiota structure in pregnancy both within and across diverse cohorts. Using PERMANOVA models, we assessed factors associated with vaginal community structure in pregnancy, examined whether host factors were conserved across populations, and tested the independent and combined effects of host factors on vaginal community state types (CSTs) using multinomial logistic regression models. Demographic and social factors explained a larger amount of variation in the vaginal microbiome in pregnancy than clinical factors. After adjustment, lower education, rather than self-identified race, remained a robust predictor of L. iners dominant (CST III) and diverse (CST IV) (OR = 8.44, 95% CI = 4.06-17.6 and OR = 4.18, 95% CI = 1.88-9.26, respectively). In random forest models, we identified specific taxonomic features of host factors, particularly urogenital pathogens associated with pregnancy complications (Aerococcus christensenii and Gardnerella spp.) among other facultative anaerobes and key markers of community instability (L. iners). Sociodemographic factors were robustly associated with vaginal microbiota structure in pregnancy and should be considered as sources of variation in human microbiome studies.

Discovery of sparse, reliable omic biomarkers with Stabl.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research.

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth.

MaLiAmPi enables generalizable and taxonomy-independent microbiome features from technically diverse 16S-based microbiome studies.

For studies using microbiome data, the ability to robustly combine data from technically and biologically distinct microbiome studies is a crucial means of supporting more robust and clinically relevant inferences. Formidable technical challenges arise when attempting to combine data from technically diverse 16S rRNA gene variable region amplicon sequencing (16S) studies. Closed operational taxonomic units and taxonomy are criticized as being heavily dependent upon reference sets and with limited precision relative to the underlying biology. Phylogenetic placement has been demonstrated to be a promising taxonomy-free manner of harmonizing microbiome data, but it has lacked a validated count-based feature suitable for use in machine learning and association studies. Here we introduce a phylogenetic-placement-based, taxonomy-independent, compositional feature of microbiota: phylotypes. Phylotypes were predictive of clinical outcomes such as obesity or pre-term birth on technically diverse independent validation sets harmonized post hoc. Thus, phylotypes enable the rigorous cross-validation of 16S-based clinical prognostic models and associative microbiome studies.

Feasibility of a dietary intervention to modify gut microbial metabolism in patients with hematopoietic stem cell transplantation.

Evaluation of the impact of dietary intervention on gastrointestinal microbiota and metabolites after allogeneic hematopoietic stem cell transplantation (HCT) is lacking. We conducted a feasibility study as the first of a two-phase trial. Ten adults received resistant potato starch (RPS) daily from day -7 to day 100. The primary objective was to test the feasibility of RPS and its effect on intestinal microbiome and metabolites, including the short-chain fatty acid butyrate. Feasibility met the preset goal of 60% or more, adhering to 70% or more doses; fecal butyrate levels were significantly higher when participants were on RPS than when they were not (P < 0.0001). An exploratory objective was to evaluate plasma metabolites. We observed longitudinal changes in plasma metabolites compared to baseline, which were independent of RPS (P < 0.0001). However, in recipients of RPS, the dominant plasma metabolites were more stable compared to historical controls with significant difference at engraftment (P < 0.05). These results indicate that RPS in recipients of allogeneic HCT is feasible; in this study, it was associated with significant alterations in intestinal and plasma metabolites. A phase 2 trial examining the effect of RPS on graft-versus-host disease in recipients of allogeneic HCT is underway. ClinicalTrials.gov registration: NCT02763033 .

The Fecal Microbiome in Quiescent Crohn's Disease with Persistent Gastrointestinal Symptoms Show Enrichment of Oral Microbes But Depletion of Butyrate and Indole Producers.

Background and Aims: Even in the absence of inflammation, persistent symptoms in Crohn's disease (CD) are prevalent and negatively impact quality of life. We aimed to determine whether quiescent CD patients with persistent symptoms ( qCD+symptoms ) have changes in microbial structure and functional potential compared to those without symptoms ( qCD-symptoms ). Methods: We performed a prospective multi-center observational study nested within the SPARC IBD study. CD patients were included if they had evidence of quiescent disease as defined by fecal calprotectin level < 150 mcg/g. Persistent symptoms were defined by the CD-PRO2 questionnaire. Active CD ( aCD ), diarrhea-predominant irritable bowel syndrome ( IBS-D ), and healthy controls ( HC ) were included as controls. Stool samples underwent whole genome shotgun metagenomic sequencing. Results: A total of 424 patients were analyzed, including 39 qCD+symptoms, 274 qCD-symptoms, 21 aCD, 40 IBS-D, and 50 HC. Patients with qCD+symptoms had a less diverse microbiome, including significant reductions in Shannon diversity ( P <.001) and significant differences in microbial community structure ( P <.0001), compared with qCD-symptoms, IBS-D, and HC. Further, patients with qCD+symptoms showed significant enrichment of bacterial species that are normal inhabitants of the oral microbiome, including Klebsiella pneumoniae (q=.003) as well as depletion of important butyrate and indole producers, such as Eubacterium rectale (q=.001), Lachnospiraceae spp . (q<.0001), and Faecalibacterium prausnitzii (q<.0001), compared with qCD-symptoms. Finally, qCD+symptoms showed significant reductions in bacterial tnaA genes, which mediate tryptophan metabolism, as well as significant tnaA allelic variation, compared with qCD-symptoms. Conclusion: The microbiome in patients with qCD+symptoms show significant changes in diversity, community profile, and composition compared with qCD-symptoms. Future studies will focus on the functional significance of these changes. What You Need to Know: Background: Persistent symptoms in quiescent Crohn's disease (CD) are prevalent and lead to worse outcomes. While changes in the microbial community have been implicated, the mechanisms by which altered microbiota may lead to qCD+symptoms remain unclear.Findings: Quiescent CD patients with persistent symptoms demonstrated significant differences in microbial diversity and composition compared to those without persistent symptoms. Specifically, quiescent CD patients with persistent symptoms were enriched in bacterial species that are normal inhabitants of the oral microbiome but depleted in important butyrate and indole producers compared to those without persistent symptoms.Implications for Patient Care: Alterations in the gut microbiome may be a potential mediator of persistent symptoms in quiescent CD. Future studies will determine whether targeting these microbial changes may improve symptoms in quiescent CD.

Human Microbiomes and Disease for the Biomedical Data Scientist.

The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

VMAP: Vaginal Microbiome Atlas During Pregnancy.

The vaginal microbiome has been shown to be associated with pregnancy outcomes including preterm birth (PTB) risk. Here we present VMAP: Vaginal Microbiome Atlas during Pregnancy (http://vmapapp.org), an application to visualize features of 3,909 vaginal microbiome samples of 1,416 pregnant individuals from 11 studies, aggregated from raw public and newly generated sequences via an open-source tool, MaLiAmPi. Our visualization tool (http://vmapapp.org) includes microbial features such as various measures of diversity, VALENCIA community state types (CST), and composition (via phylotypes and taxonomy). This work serves as a resource for the research community to further analyze and visualize vaginal microbiome data in order to better understand both healthy term pregnancies and those associated with adverse outcomes.

A Randomized Trial of Mesenchymal Stromal Cells for Moderate to Severe Acute Respiratory Distress Syndrome from COVID-19.

Rationale: There are limited therapeutic options for patients with coronavirus disease (COVID-19)-related acute respiratory distress syndrome with inflammation-mediated lung injury. Mesenchymal stromal cells offer promise as immunomodulatory agents. Objectives: Evaluation of efficacy and safety of allogeneic mesenchymal cells in mechanically-ventilated patients with moderate or severe COVID-19-induced respiratory failure. Methods: Patients were randomized to two infusions of 2 million cells/kg or sham infusions, in addition to the standard of care. We hypothesized that cell therapy would be superior to sham control for the primary endpoint of 30-day mortality. The key secondary endpoint was ventilator-free survival within 60 days, accounting for deaths and withdrawals in a ranked analysis. Measurements and Main Results: At the third interim analysis, the data and safety monitoring board recommended that the trial halt enrollment as the prespecified mortality reduction from 40% to 23% was unlikely to be achieved (n = 222 out of planned 300). Thirty-day mortality was 37.5% (42/112) in cell recipients versus 42.7% (47/110) in control patients (relative risk [RR], 0.88; 95% confidence interval, 0.64-1.21; P = 0.43). There were no significant differences in days alive off ventilation within 60 days (median rank, 117.3 [interquartile range, 60.0-169.5] in cell patients and 102.0 [interquartile range, 54.0-162.5] in control subjects; higher is better). Resolution or improvement of acute respiratory distress syndrome at 30 days was observed in 51/104 (49.0%) cell recipients and 46/106 (43.4%) control patients (odds ratio, 1.36; 95% confidence interval, 0.57-3.21). There were no infusion-related toxicities and overall serious adverse events over 30 days were similar. Conclusions: Mesenchymal cells, while safe, did not improve 30-day survival or 60-day ventilator-free days in patients with moderate and/or severe COVID-19-related acute respiratory distress syndrome.

Exposure to antibiotics with anaerobic activity before respiratory viral infection is associated with respiratory disease progression after hematopoietic cell transplant.

We examined associations between specific antibiotic exposures and progression to lower respiratory tract disease (LRTD) following individual respiratory viral infections (RVIs) after hematopoietic cell transplantation (HCT). We analyzed allogeneic HCT recipients of all ages with their first RVI during the first 100 days post-HCT. For the 21 days before RVI onset, we recorded any receipt of specific groups of antibiotics, and the cumulative sum of the number of antibiotics received for each day (antibiotic-days). We used Cox proportional hazards models to assess the relationship between antibiotic exposure and progression to LRTD. Among 469 patients with RVI, 124 progressed to LRTD. Compared to no antibiotics, use of antibiotics with broad anaerobic activity in the prior 21 days was associated with progression to LRTD after adjusting for age, virus type, hypoalbuminemia, neutropenia, steroid use, and monocytopenia (HR 2.2, 95% CI 1.1-4.1). Greater use of those antibiotics (≥7 antibiotic days) was also associated with LRTD in adjusted models (HR 2.2, 95% CI 1.1-4.3). Results were similar after adjusting for lymphopenia instead of monocytopenia. Antibiotic use is associated with LRTD after RVI across different viruses in HCT recipients. Prospective studies using anaerobe-sparing antibiotics should be explored to assess impact on LRTD in patients undergoing HCT.

Mucolytic bacteria license pathobionts to acquire host-derived nutrients during dietary nutrient restriction.

Pathobionts employ unique metabolic adaptation mechanisms to maximize their growth in disease conditions. Adherent-invasive Escherichia coli (AIEC), a pathobiont enriched in the gut mucosa of patients with inflammatory bowel disease (IBD), utilizes diet-derived L-serine to adapt to the inflamed gut. Therefore, the restriction of dietary L-serine starves AIEC and limits its fitness advantage. Here, we find that AIEC can overcome this nutrient limitation by switching the nutrient source from the diet to the host cells in the presence of mucolytic bacteria. During diet-derived L-serine restriction, the mucolytic symbiont Akkermansia muciniphila promotes the encroachment of AIEC to the epithelial niche by degrading the mucus layer. In the epithelial niche, AIEC acquires L-serine from the colonic epithelium and thus proliferates. Our work suggests that the indirect metabolic network between pathobionts and commensal symbionts enables pathobionts to overcome nutritional restriction and thrive in the gut.

Host-microbe interactions and outcomes in multiple myeloma and hematopoietic stem cell transplantation.

Microbiota are essential to normal immune development and there is growing recognition of its importance to human health and disease and deepening understanding of the complexity of host-microbe interactions in the human gut and other tissues. Commensal microbes not only can influence host immunity locally through impacts of bioactive microbial metabolites and direct interactions with epithelial cells and innate immune receptors but also can exert systemic immunomodulatory effects via impacts on host immune cells capable of trafficking beyond the gut. Emerging data suggest microbiota influence the development of multiple myeloma (MM), a malignancy of the immune system derived from immunoglobulin-producing bone marrow plasma cells, through the promotion of inflammation. Superior treatment outcomes for MM correlate with a higher abundance of commensal microbiota capable of influencing inflammatory responses through the production of butyrate. In patients with hematologic malignancies, higher levels of diversity of the gut microbiota correlate with superior outcomes after hematopoietic stem cell transplantation. Correlative data support the impact of commensal microbiota on survival, risk of infection, disease relapse, and graft-versus-host disease (GVHD) after transplant. In this review, we will discuss the current understanding of the role of host-microbe interactions and the inflammatory tumor microenvironment of multiple myeloma, discuss data describing the key role of microbiota in hematopoietic stem cell transplantation for treatment of hematologic malignancies, and highlight several possible concepts for interventions directed at the gut microbiota to influence treatment outcomes.

A potential pathogenic association between periodontal disease and Crohn's disease.

Oral conditions are relatively common in patients with inflammatory bowel disease (IBD). However, the contribution of oral maladies to gut inflammation remains unexplored. Here, we investigated the effect of periodontitis on disease phenotypes of patients with IBD. In all, 60 patients with IBD (42 with ulcerative colitis [UC] and 18 with Crohn's disease [CD]) and 45 healthy controls (HCs) without IBD were recruited for this clinical investigation. The effects of incipient periodontitis on the oral and gut microbiome as well as IBD characteristics were examined. In addition, patients were prospectively monitored for up to 12 months after enrollment. We found that, in both patients with UC and those with CD, the gut microbiome was significantly more similar to the oral microbiome than in HCs, suggesting that ectopic gut colonization by oral bacteria is increased in patients with IBD. Incipient periodontitis did not further enhance gut colonization by oral bacteria. The presence of incipient periodontitis did not significantly affect the clinical outcomes of patients with UC and CD. However, the short CD activity index increased in patients with CD with incipient periodontitis but declined or was unchanged during the study period in patients without periodontitis. Thus, early periodontitis may associate with worse clinically symptoms in some patients with CD.

Comparative Analysis of Public RNA-Sequencing Data from Human Intestinal Enteroid (HIEs) Infected with Enteric RNA Viruses Identifies Universal and Virus-Specific Epithelial Responses.

Acute gastroenteritis (AGE) has a significant disease burden on society. Noroviruses, rotaviruses, and astroviruses are important viral causes of AGE but are relatively understudied enteric pathogens. Recent developments in novel biomimetic human models of enteric disease are opening new possibilities for studying human-specific host-microbe interactions. Human intestinal enteroids (HIE), which are epithelium-only intestinal organoids derived from stem cells isolated from human intestinal biopsy tissues, have been successfully used to culture representative norovirus, rotavirus, and astrovirus strains. Previous studies investigated host-virus interactions at the intestinal epithelial interface by individually profiling the epithelial transcriptional response to a member of each virus family by RNA sequencing (RNA-seq). Despite differences in the tissue origin, enteric virus used, and hours post infection at which RNA was collected in each data set, the uniform analysis of publicly available datasets identified a conserved epithelial response to virus infection focused around "type I interferon production" and interferon-stimulated genes. Additionally, transcriptional changes specific to only one or two of the enteric viruses were also identified. This study can guide future explorations into common and unique aspects of the host response to virus infections in the human intestinal epithelium and demonstrates the promise of comparative RNA-seq analysis, even if performed under different experimental conditions, to discover universal and virus-specific genes and pathways responsible for antiviral host defense.

geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response.

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe geneshot, a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data. By applying geneshot to two independent previously published cohorts, we identify microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth.

Signal Versus Noise: How to Analyze the Microbiome and Make Progress on Antimicrobial Resistance.

Antimicrobial resistance has become a worldwide medical challenge [1], so impactful that vancomycin-resistant Enterococcus (VRE) and methicillin-resistant Staphylococcus aureus (MRSA) have entered the common vernacular. We have attempted to reduce the selective pressure through antimicrobial stewardship, curtail the spread by identifying and isolating carriers and individuals with symptomatic infection, and treat antibiotic-resistant organisms (AROs) by developing novel antimicrobials. Despite these extraordinary measures, the challenge of AROs continues to grow. The gut microbiome, the ecosystem of microbes (ie, the microbiota) and metabolites present upon and within all humans, is an emerging target for both the risk for colonization and defense against infection with AROs. Here, informed from experiences and successes with understanding the role of the microbiome in mediating risk of Clostridioides difficile infection (CDI), we (1) review our understanding of the risk from ARO acquisition; (2) review our current understanding of the gut microbiome's ability to resist colonization with AROs; (3) describe how experimental model systems can test these initial, global insights to arrive at more granular, mechanistic ones; and (4) suggest a path forward to make further progress in the field.

SARS-CoV-2 vaccines: a triumph of science and collaboration.

Roughly 1 year after the first case of COVID-19 was identified and less than 1 year after the sequencing of SARS-CoV-2, multiple SARS-CoV-2 vaccines with demonstrated safety and efficacy in phase III clinical trials are available. The most promising vaccines have targeted the surface glycoprotein (S-protein) of SARS-CoV-2 and achieved an approximate 85%-95% reduction in the risk of symptomatic COVID-19, while retaining excellent safety profiles and modest side effects in the phase III clinical trials. The mRNA, replication-incompetent viral vector, and protein subunit vaccine technologies have all been successfully employed. Some novel SARS-CoV-2 variants evade but do not appear to fully overcome the potent immunity induced by these vaccines. Emerging real-world effectiveness data add evidence for protection from severe COVID-19. This is an impressive first demonstration of the effectiveness of the mRNA vaccine and vector vaccine platforms. The success of SARS-CoV-2 vaccine development should be credited to open science, industry partnerships, harmonization of clinical trials, and the altruism of study participants. The manufacturing and distribution of the emergency use-authorized SARS-CoV-2 vaccines are ongoing challenges. What remains now is to ensure broad and equitable global vaccination against COVID-19.

Targeting the Gut Microbiome to Mitigate Immunotherapy-Induced Colitis in Cancer.

Immune checkpoint inhibitors (ICIs) have been a transformational advance in cancer therapy in the past decade. However, ICIs can produce immune-related adverse effects (irAEs), which can lead to both morbidity and premature termination of therapy. Recent studies suggest that the gut microbiota and its metabolites affect ICI efficacy and toxicity. Herein, we review such evidence in the context of ICI-induced colitis. In particular, the short-chain fatty acid butyrate, a microbial metabolite, has known protective effects on the intestine. We discuss how the use of dietary prebiotics, which can be metabolized by bacteria to produce butyrate, can be an intriguing new investigational approach to prevent ICI-associated colitis and lead to improved patient outcomes.