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.

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.

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.

Genetic Variation in Toll-Like Receptor 5 and Colonization with Flagellated Bacterial Vaginosis-Associated Bacteria.

Bacterial vaginosis (BV) is a vaginal dysbiotic condition linked to negative gynecological and reproductive sequelae. Flagellated bacteria have been identified in women with BV, including Mobiluncus spp. and BV-associated bacterium-1 (BVAB1), an uncultivated, putatively flagellated species. The host response to flagellin mediated through Toll-like receptor 5 (TLR5) has not been explored in BV. Using independent discovery and validation cohorts, we examined the hypothesis that TLR5 deficiency-defined by a dominant negative stop codon polymorphism, rs5744168-is associated with an increased risk for BV and increased colonization with flagellated bacteria associated with BV (BVAB1, Mobiluncus curtisii, and Mobiluncus mulieris). TLR5 deficiency was not associated with BV status, and TLR5-deficient women had decreased colonization with BVAB1 in both cohorts. We stimulated HEK-hTLR5-overexpressing NF-κB reporter cells with whole, heat-killed M. mulieris or M. curtisii and with partially purified flagellin from these species; as BVAB1 is uncultivated, we used cervicovaginal lavage (CVL) fluid supernatant from women colonized with BVAB1 for stimulation. While heat-killed M. mulieris and CVL fluid from women colonized with BVAB1 stimulate a TLR5-mediated response, heat-killed M. curtisii did not. In contrast, partially purified flagellin from both Mobiluncus species stimulated a TLR5-mediated response in vitro We observed no correlation between vaginal interleukin 8 (IL-8) and flagellated BVAB concentrations among TLR5-sufficient women. Interspecies variation in accessibility of flagellin recognition domains may be responsible for these observations, as reflected in the potentially novel flagellin products encoded by Mobiluncus species versus those encoded by BVAB1.

Tocilizumab for Treatment of Mechanically Ventilated Patients With COVID-19.

BACKGROUND: Severe coronavirus disease 2019 (COVID-19) can manifest in rapid decompensation and respiratory failure with elevated inflammatory markers, consistent with cytokine release syndrome for which IL-6 blockade is an approved treatment. METHODS: We assessed effectiveness and safety of IL-6 blockade with tocilizumab in a single-center cohort of patients with COVID-19 requiring mechanical ventilation. The primary endpoint was survival probability postintubation; secondary analyses included an ordinal illness severity scale integrating superinfections. Outcomes in patients who received tocilizumab compared with tocilizumab-untreated controls were evaluated using multivariable Cox regression with propensity score inverse probability of treatment weighting (IPTW). RESULTS: 154 patients were included, of whom 78 received tocilizumab and 76 did not. Median follow-up was 47 days (range, 28-67). Baseline characteristics were similar between groups, although tocilizumab-treated patients were younger (mean: 55 vs 60 years), less likely to have chronic pulmonary disease (10% vs 28%), and had lower D-dimer values at time of intubation (median: 2.4 vs 6.5 mg/dL). In IPTW-adjusted models, tocilizumab was associated with a 45% reduction in hazard of death (HR, .55; 95% CI, .33-.90) and improved status on the ordinal outcome scale [OR per 1-level increase, .58; .36-.94). Although tocilizumab was associated with an increased proportion of patients with superinfections (54% vs 26%; P < .001), there was no difference in 28-day case fatality rate among tocilizumab-treated patients with versus without superinfection (22% vs 15%; P = .42). Staphylococcus aureus accounted for ~50% of bacterial pneumonia. CONCLUSIONS: In this cohort of mechanically ventilated COVID-19 patients, tocilizumab was associated with lower mortality despite higher superinfection occurrence.

In silico benchmarking of metagenomic tools for coding sequence detection reveals the limits of sensitivity and precision.

BACKGROUND: High-throughput sequencing can establish the functional capacity of a microbial community by cataloging the protein-coding sequences (CDS) present in the metagenome of the community. The relative performance of different computational methods for identifying CDS from whole-genome shotgun sequencing is not fully established. RESULTS: Here we present an automated benchmarking workflow, using synthetic shotgun sequencing reads for which we know the true CDS content of the underlying communities, to determine the relative performance (sensitivity, positive predictive value or PPV, and computational efficiency) of different metagenome analysis tools for extracting the CDS content of a microbial community. Assembly-based methods are limited by coverage depth, with poor sensitivity for CDS at < 5X depth of sequencing, but have excellent PPV. Mapping-based techniques are more sensitive at low coverage depths, but can struggle with PPV. We additionally describe an expectation maximization based iterative algorithmic approach which we show to successfully improve the PPV of a mapping based technique while retaining improved sensitivity and computational efficiency. CONCLUSION: Our benchmarking approach reveals the trade-offs of assembly versus alignment-based approaches and the relative performance of specific implementations when one wishes to extract the protein coding capacity of microbial communities.

Graft-Derived Reconstitution of Mucosal-Associated Invariant T Cells after Allogeneic Hematopoietic Cell Transplantation.

Mucosal-associated invariant T (MAIT) cells express a semi-invariant Vα7.2+ T cell receptor (TCR) that recognizes ligands from distinct bacterial and fungal species. In neonates, MAIT cells proliferate coincident with gastrointestinal (GI) bacterial colonization. In contrast, under noninflammatory conditions adult MAIT cells remain quiescent because of acquired regulation of TCR signaling. Effects of inflammation and the altered GI microbiota after allogeneic hematopoietic cell transplantation (HCT) on MAIT cell reconstitution have not been described. We conducted an observational study of MAIT cell reconstitution in myeloablative (n = 41) and nonmyeloablative (n = 66) allogeneic HCT recipients and found that despite a rapid and early increase to a plateau at day 30 after HCT, MAIT cell numbers failed to normalize for at least 1 year. Cord blood transplant recipients and those who received post-HCT cyclophosphamide for graft versus host disease (GVHD) prophylaxis had profoundly impaired MAIT cell reconstitution. Sharing of TCRß gene sequences between MAIT cells isolated from HCT grafts and blood of recipients after HCT showed early MAIT cell reconstitution was due at least in part to proliferation of MAIT cells transferred in the HCT graft. Inflammatory cytokines were required for TCR-dependent MAIT cell proliferation, suggesting that bacterial Vα7.2+ TCR ligands might promote MAIT cell reconstitution after HCT. Robust MAIT cell reconstitution was associated with an increased GI abundance of Blautia spp. MAIT cells suppressed proliferation of conventional T cells consistent with a possible regulatory role. Our data identify modifiable factors impacting MAIT cell reconstitution that could influence the risk of GVHD after HCT.

Antibiotic Exposure Prior to Respiratory Viral Infection Is Associated with Progression to Lower Respiratory Tract Disease in Allogeneic Hematopoietic Cell Transplant Recipients.

Recent publications note an association between antibiotic exposure and respiratory viral infections (RVIs). Antibiotics affect microbiota and impair immune response against RVIs in mice, and low microbiome diversity is associated with pulmonary complications including viral lower respiratory tract disease (LRTD) in hematopoietic cell transplantation (HCT) recipients. In this study, we examined whether antibiotic exposure was associated with increased risk of disease progression in RVIs post-transplantation. We analyzed patients who underwent allogeneic HCT (June 2008 to February 2016) and had their first RVI due to parainfluenza virus (PIV), respiratory syncytial virus (RSV), or human metapneumovirus (MPV) during the initial 100 days post-transplantation. Antibiotic exposure in the 3 weeks before RVI onset was defined as (1) use of specific antibiotics versus none of these antibiotics and (2) number of antibiotic-days. Cox proportional hazards models were used to examine associations between antibiotic exposures and risk of viral disease progression to proven/probable/possible LRTD. Ninety HCT recipients (84 adults, 6 children) fulfilled study criteria; 33 progressed to LRTD. The number of antibiotic-days was associated with progression to LRTD after adjusting for neutropenia, steroid use, and either lymphopenia (hazard ratio, 1.41 [95% confidence interval, 1.04 to 1.92], P = .027) or monocytopenia (hazard ratio, 1.46 [95% confidence interval, 1.11 to 1.91], P = .006). Specific antibiotic classes was not associated with the outcome. Cumulative antibiotic exposure immediately before RVI onset is a risk factor for disease progression following PIV, RSV, and MPV infections post-transplantation. Larger cohort studies are needed to determine the impact of specific antibiotics or antibiotic classes on disease severity.

Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities.

BACKGROUND: Microbiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD. RESULTS: In-silico we generated 100 synthetic bacterial communities approximating human stool microbiomes to be used as a gold-standard for evaluating the colligative performance of microbiome analysis software. Our synthetic data closely matched the composition and complexity of actual healthy human stool microbiomes. Genus-level taxonomic classification was correctly done for only 50.4-74.8% of the source organisms. Miscall rates varied from 11.9 to 23.5%. Species-level classification was less successful, (6.9-18.9% correct); miscall rates were comparable to those of genus-level targets (12.5-26.2%). The degree of miscall varied by clade of organism, pipeline and specific settings used. OTU generation accuracy varied by strategy (closed, de novo or subsampling), reference database, algorithm and software implementation. Shannon diversity estimation accuracy correlated generally with OTU-generation accuracy. Beta-diversity estimates with Double Principle Coordinate Analysis (DPCoA) were more robust against errors introduced in processing than Weighted UniFrac. The settings suggested in the tutorials were among the worst performing in all outcomes tested. CONCLUSIONS: Even when using the same classification pipeline, the specific OTU-generation strategy, reference database and downstream analysis methods selection can have a dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation. Even minor changes in settings adversely affected the accuracy of the results, bringing them far from the best-observed result. Thus, specific details of how a pipeline is used (including OTU generation strategy, reference sets, clustering algorithm and specific software implementation) should be specified in the methods section of all microbiome studies. Researchers should evaluate their chosen pipeline and settings to confirm it can adequately answer the research question rather than assuming the tutorial or standard-operating-procedure settings will be adequate or optimal.

Stool Microbiota at Neutrophil Recovery Is Predictive for Severe Acute Graft vs Host Disease After Hematopoietic Cell Transplantation.

BACKGROUND: Graft-versus-host disease (GVHD) is common after allogeneic hematopoietic cell transplantation (HCT). Risk for death from GVHD has been associated with low bacterial diversity in the stool microbiota early after transplant; however, the specific species associated with GVHD risk remain poorly defined. METHODS: We prospectively collected serial weekly stool samples from 66 patients who underwent HCT, starting pre-transplantation and continuing weekly until 100 days post-transplant, a total of 694 observations in HCT recipients. We used 16S rRNA gene polymerase chain reaction with degenerate primers, followed by high-throughput sequencing to assess the relative abundance of sequence reads from bacterial taxa in stool samples over time. RESULTS: The gut microbiota was highly dynamic in HCT recipients, with loss and appearance of taxa common on short time scales. As in prior studies, GVHD was associated with lower alpha diversity of the stool microbiota. At neutrophil recovery post-HCT, the presence of oral Actinobacteria and oral Firmicutes in stool was positively correlated with subsequent GVHD; Lachnospiraceae were negatively correlated. A gradient of bacterial species (difference of the sum of the relative abundance of positive correlates minus the sum of the relative abundance of negative correlates) was most predictive (receiver operator characteristic area under the curve of 0.83) of subsequent severe acute GVHD. CONCLUSIONS: The stool microbiota around the time of neutrophil recovery post-HCT is predictive of subsequent development of severe acute GVHD in this study.

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.

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.

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.

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.

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 .

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.

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.