The MITRE trial protocol: a study to evaluate the microbiome as a biomarker of efficacy and toxicity in cancer patients receiving immune checkpoint inhibitor therapy.

BACKGROUND: The gut microbiome is implicated as a marker of response to immune checkpoint inhibitors (ICI) based on preclinical mouse models and preliminary observations in limited patient series. Furthermore, early studies suggest faecal microbial transfer may have therapeutic potential, converting ICI non-responders into responders. So far, identification of specific responsible bacterial taxa has been inconsistent, which limits future application. The MITRE study will explore and validate a microbiome signature in a larger scale prospective study across several different cancer types. METHODS: Melanoma, renal cancer and non-small cell lung cancer patients who are planned to receive standard immune checkpoint inhibitors are being recruited to the MITRE study. Longitudinal stool samples are collected prior to treatment, then at 6 weeks, 3, 6 and 12 months during treatment, or at disease progression/recurrence (whichever is sooner), as well as after a severe (≥grade 3 CTCAE v5.0) immune-related adverse event. Additionally, whole blood, plasma, buffy coat, RNA and peripheral blood mononuclear cells (PBMCs) is collected at similar time points and will be used for exploratory analyses. Archival tumour tissue, tumour biopsies at progression/relapse, as well as any biopsies from body organs collected after a severe toxicity are collected. The primary outcome measure is the ability of the microbiome signature to predict 1 year progression-free survival (PFS) in patients with advanced disease. Secondary outcomes include microbiome correlations with toxicity and other efficacy end-points. Biosamples will be used to explore immunological and genomic correlates. A sub-study will evaluate both COVID-19 antigen and antibody associations with the microbiome. DISCUSSION: There is an urgent need to identify biomarkers that are predictive of treatment response, resistance and toxicity to immunotherapy. The data generated from this study will both help inform patient selection for these drugs and provide information that may allow therapeutic manipulation of the microbiome to improve future patient outcomes. TRIAL REGISTRATION: NCT04107168 , ClinicalTrials.gov, registered 09/27/2019. Protocol V3.2 (16/04/2021).

Two microbiota subtypes identified in irritable bowel syndrome with distinct responses to the low FODMAP diet.

OBJECTIVE: Reducing FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides and polyols) can be clinically beneficial in IBS but the mechanism is incompletely understood. We aimed to detect microbial signatures that might predict response to the low FODMAP diet and assess whether microbiota compositional and functional shifts could provide insights into its mode of action. DESIGN: We used metagenomics to determine high-resolution taxonomic and functional profiles of the stool microbiota from IBS cases and household controls (n=56 pairs) on their usual diet. Clinical response and microbiota changes were studied in 41 pairs after 4 weeks on a low FODMAP diet. RESULTS: Unsupervised analysis of baseline IBS cases pre-diet identified two distinct microbiota profiles, which we refer to as IBSP (pathogenic-like) and IBSH (health-like) subtypes. IBSP microbiomes were enriched in Firmicutes and genes for amino acid and carbohydrate metabolism, but depleted in Bacteroidetes species. IBSH microbiomes were similar to controls. On the low FODMAP diet, IBSH and control microbiota were unaffected, but the IBSP signature shifted towards a health-associated microbiome with an increase in Bacteroidetes (p=0.009), a decrease in Firmicutes species (p=0.004) and normalisation of primary metabolic genes. The clinical response to the low FODMAP diet was greater in IBSP subjects compared with IBSH (p=0.02). CONCLUSION: 50% of IBS cases manifested a 'pathogenic' gut microbial signature. This shifted towards the healthy profile on the low FODMAP diet; and IBSP cases showed an enhanced clinical responsiveness to the dietary therapy. The effectiveness of FODMAP reduction in IBSP may result from the alterations in gut microbiota and metabolites produced. Microbiota signatures could be useful as biomarkers to guide IBS treatment; and investigating IBSP species and metabolic pathways might yield insights regarding IBS pathogenic mechanisms.

The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping.

Human health and disease have increasingly been shown to be impacted by the gut microbiota, and mouse models are essential for investigating these effects. However, the compositions of human and mouse gut microbiotas are distinct, limiting translation of microbiota research between these hosts. To address this, we constructed the Mouse Gastrointestinal Bacteria Catalogue (MGBC), a repository of 26,640 high-quality mouse microbiota-derived bacterial genomes. This catalog enables species-level analyses for mapping functions of interest and identifying functionally equivalent taxa between the microbiotas of humans and mice. We have complemented this with a publicly deposited collection of 223 bacterial isolates, including 62 previously uncultured species, to facilitate experimental investigation of individual commensal bacteria functions in vitro and in vivo. Together, these resources provide the ability to identify and test functionally equivalent members of the host-specific gut microbiotas of humans and mice and support the informed use of mouse models in human microbiota research.

Host adaptation in gut Firmicutes is associated with sporulation loss and altered transmission cycle.

BACKGROUND: Human-to-human transmission of symbiotic, anaerobic bacteria is a fundamental evolutionary adaptation essential for membership of the human gut microbiota. However, despite its importance, the genomic and biological adaptations underpinning symbiont transmission remain poorly understood. The Firmicutes are a dominant phylum within the intestinal microbiota that are capable of producing resistant endospores that maintain viability within the environment and germinate within the intestine to facilitate transmission. However, the impact of host transmission on the evolutionary and adaptive processes within the intestinal microbiota remains unknown. RESULTS: We analyze 1358 genomes of Firmicutes bacteria derived from host and environment-associated habitats. Characterization of genomes as spore-forming based on the presence of sporulation-predictive genes reveals multiple losses of sporulation in many distinct lineages. Loss of sporulation in gut Firmicutes is associated with features of host-adaptation such as genome reduction and specialized metabolic capabilities. Consistent with these data, analysis of 9966 gut metagenomes from adults around the world demonstrates that bacteria now incapable of sporulation are more abundant within individuals but less prevalent in the human population compared to spore-forming bacteria. CONCLUSIONS: Our results suggest host adaptation in gut Firmicutes is an evolutionary trade-off between transmission range and colonization abundance. We reveal host transmission as an underappreciated process that shapes the evolution, assembly, and functions of gut Firmicutes.

Genetic and morphological estimates of androgen exposure predict social deficits in multiple neurodevelopmental disorder cohorts.

BACKGROUND: Neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) display a strong male bias. Androgen exposure is profoundly increased in typical male development, but it also varies within the sexes, and previous work has sought to connect morphological proxies of androgen exposure, including digit ratio and facial morphology, to neurodevelopmental outcomes. The results of these studies have been mixed, and the relationships between androgen exposure and behavior remain unclear. METHODS: Here, we measured both digit ratio masculinity (DRM) and facial landmark masculinity (FLM) in the same neurodevelopmental cohort (N = 763) and compared these proxies of androgen exposure to clinical and parent-reported features as well as polygenic risk scores. RESULTS: We found that FLM was significantly associated with NDD diagnosis (ASD, ADHD, ID; all [Formula: see text]), while DRM was not. When testing for association with parent-reported problems, we found that both FLM and DRM were positively associated with concerns about social behavior ([Formula: see text], [Formula: see text]; [Formula: see text], [Formula: see text], respectively). Furthermore, we found evidence via polygenic risk scores (PRS) that DRM indexes masculinity via testosterone levels ([Formula: see text], [Formula: see text]), while FLM indexes masculinity through a negative relationship with sex hormone binding globulin (SHBG) levels ([Formula: see text], [Formula: see text]). Finally, using the SPARK cohort (N = 9419) we replicated the observed relationship between polygenic estimates of testosterone, SHBG, and social functioning ([Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text] for testosterone and SHBG, respectively). Remarkably, when considered over the extremes of each variable, these quantitative sex effects on social functioning were comparable to the effect of binary sex itself (binary male: [Formula: see text]; testosterone: [Formula: see text] from 0.1%-ile to 99.9%-ile; SHBG: [Formula: see text] from 0.1%-ile to 99.9%-ile). LIMITATIONS: In the devGenes and SPARK cohorts, our analyses rely on indirect, rather than direct measurement of androgens and related molecules. CONCLUSIONS: These findings and their replication in the large SPARK cohort lend support to the hypothesis that increasing net androgen exposure diminishes capacity for social functioning in both males and females.

Identification of bacteria-derived HLA-bound peptides in melanoma.

A variety of species of bacteria are known to colonize human tumours1-11, proliferate within them and modulate immune function, which ultimately affects the survival of patients with cancer and their responses to treatment12-14. However, it is not known whether antigens derived from intracellular bacteria are presented by the human leukocyte antigen class I and II (HLA-I and HLA-II, respectively) molecules of tumour cells, or whether such antigens elicit a tumour-infiltrating T cell immune response. Here we used 16S rRNA gene sequencing and HLA peptidomics to identify a peptide repertoire derived from intracellular bacteria that was presented on HLA-I and HLA-II molecules in melanoma tumours. Our analysis of 17 melanoma metastases (derived from 9 patients) revealed 248 and 35 unique HLA-I and HLA-II peptides, respectively, that were derived from 41 species of bacteria. We identified recurrent bacterial peptides in tumours from different patients, as well as in different tumours from the same patient. Our study reveals that peptides derived from intracellular bacteria can be presented by tumour cells and elicit immune reactivity, and thus provides insight into a mechanism by which bacteria influence activation of the immune system and responses to therapy.

Functional Microbiomics Reveals Alterations of the Gut Microbiome and Host Co-Metabolism in Patients With Alcoholic Hepatitis.

Alcohol-related liver disease is a major public health burden, and the gut microbiota is an important contributor to disease pathogenesis. The aim of the present study is to characterize functional alterations of the gut microbiota and test their performance for short-term mortality prediction in patients with alcoholic hepatitis. We integrated shotgun metagenomics with untargeted metabolomics to investigate functional alterations of the gut microbiota and host co-metabolism in a multicenter cohort of patients with alcoholic hepatitis. Profound changes were found in the gut microbial composition, functional metagenome, serum, and fecal metabolomes in patients with alcoholic hepatitis compared with nonalcoholic controls. We demonstrate that in comparison with single omics alone, the performance to predict 30-day mortality was improved when combining microbial pathways with respective serum metabolites in patients with alcoholic hepatitis. The area under the receiver operating curve was higher than 0.85 for the tryptophan, isoleucine, and methionine pathways as predictors for 30-day mortality, but achieved 0.989 for using the urea cycle pathway in combination with serum urea, with a bias-corrected prediction error of 0.083 when using leave-one-out cross validation. Conclusion: Our study reveals changes in key microbial metabolic pathways associated with disease severity that predict short-term mortality in our cohort of patients with alcoholic hepatitis.

Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth.

Immediately after birth, newborn babies experience rapid colonization by microorganisms from their mothers and the surrounding environment1. Diseases in childhood and later in life are potentially mediated by the perturbation of the colonization of the infant gut microbiota2. However, the effects of delivery via caesarean section on the earliest stages of the acquisition and development of the gut microbiota, during the neonatal period (≤1 month), remain controversial3,4. Here we report the disrupted transmission of maternal Bacteroides strains, and high-level colonization by opportunistic pathogens associated with the hospital environment (including Enterococcus, Enterobacter and Klebsiella species), in babies delivered by caesarean section. These effects were also seen, to a lesser extent, in vaginally delivered babies whose mothers underwent antibiotic prophylaxis and in babies who were not breastfed during the neonatal period. We applied longitudinal sampling and whole-genome shotgun metagenomic analysis to 1,679 gut microbiota samples (taken at several time points during the neonatal period, and in infancy) from 596 full-term babies born in UK hospitals; for a subset of these babies, we collected additional matched samples from mothers (175 mothers paired with 178 babies). This analysis demonstrates that the mode of delivery is a significant factor that affects the composition of the gut microbiota throughout the neonatal period, and into infancy. Matched large-scale culturing and whole-genome sequencing of over 800 bacterial strains from these babies identified virulence factors and clinically relevant antimicrobial resistance in opportunistic pathogens that may predispose individuals to opportunistic infections. Our findings highlight the critical role of the local environment in establishing the gut microbiota in very early life, and identify colonization with antimicrobial-resistance-containing opportunistic pathogens as a previously underappreciated risk factor in hospital births.

Whole-genome sequencing in a family with twin boys with autism and intellectual disability suggests multimodal polygenic risk.

Over the past decade, a focus on de novo mutations has rapidly accelerated gene discovery in autism spectrum disorder (ASD), intellectual disability (ID), and other neurodevelopmental disorders (NDDs). However, recent studies suggest that only a minority of cases are attributable to de novo mutations, and instead these disorders often result from an accumulation of various forms of genetic risk. Consequently, we adopted an inclusive approach to investigate the genetic risk contributing to a case of male monozygotic twins with ASD and ID. At the time of the study, the probands were 7 yr old and largely nonverbal. Medical records indicated a history of motor delays, sleep difficulties, and significant cognitive deficits. Through whole-genome sequencing of the probands and their parents, we uncovered elevated common polygenic risk, a coding de novo point mutation in CENPE, an ultra-rare homozygous regulatory variant in ANK3, inherited rare variants in NRXN3, and a maternally inherited X-linked deletion situated in a noncoding regulatory region between ZNF81 and ZNF182 Although each of these genes has been directly or indirectly associated with NDDs, evidence suggests that no single variant adequately explains the probands' phenotype. Instead, we propose that the probands' condition is due to the confluence of multiple rare variants in the context of a high-risk genetic background. This case emphasizes the multifactorial nature of genetic risk underlying most instances of NDDs and aligns with the "female protective model" of ASD.

MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification.

Metagenomics is the study of microbial community diversity, especially the uncultured microorganisms by shotgun sequencing environmental samples. As the sequencers throughput and the data volume increase, it becomes challenging to develop scalable bioinformatics tools that reconstruct microbiome structure by binning sequencing reads to reference genomes. Standard alignment-based methods, such as BWA-MEM, provide state-of-the-art performance, but we demonstrate in Vervier et al. (2016) that compositional approaches using nucleotides motifs have faster analysis time, for comparable accuracy. In this work, we describe how to use MetaVW, a scalable machine learning implementation for short sequencing reads binning, based on their k-mers profile. We provide a step-by-step guideline on how we trained the classification models and how it can easily generalize to user-defined reference genomes and specific applications. We also give additional details on what effect parameters in the algorithm have on performances.

TiSAn: estimating tissue-specific effects of coding and non-coding variants.

Motivation: Model-based estimates of general deleteriousness, like CADD, DANN or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these approaches say little about the tissues in which the effects of deleterious variants will be most meaningful. Tissue-specific annotations have been recently inferred for dozens of tissues/cell types from large collections of cross-tissue epigenomic data, and have demonstrated sensitivity in predicting affected tissues in complex traits. It remains unclear, however, whether including additional genome-scale data specific to the tissue of interest would appreciably improve functional annotations. Results: Herein, we introduce TiSAn, a tool that integrates multiple genome-scale data sources, defined by expert knowledge. TiSAn uses machine learning to discriminate variants relevant to a tissue from those with no bearing on the function of that tissue. Predictions are made genome-wide, and can be used to contextualize and filter variants of interest in whole genome sequencing or genome-wide association studies. We demonstrate the accuracy and flexibility of TiSAn by producing predictive models for human heart and brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find the multiomics TiSAn model is better able to prioritize genetic variants according to their tissue-specific action than the current state-of-the-art method, GenoSkyLine. Availability and implementation: Software and vignettes are available at http://github.com/kevinVervier/TiSAn. Supplementary information: Supplementary data are available at Bioinformatics online.

SLINGER: large-scale learning for predicting gene expression.

Recent studies have established that single nucleotide polymorphisms are sufficient to build accurate predictive models of gene expression. Gamazon, et al., found that gene expression values predicted from cis neighborhood SNPs show statistical association with disease status. In this work, we remove the cis neighborhood constraint during the learning process, and propose a novel predictive approach called SLINGER. We demonstrate that models drawing from a genome-wide set of SNPs are able to predict expression for more genes than the ones built on cis neighborhood only. Results indicate that these new models significantly improve accuracy for a large number of genes. Thanks to a penalized linear model, we also show that the number of features used in our models remains comparable to the cis-only models. Finally, SLINGER application on seven Wellcome Trust Case-Control Consortium genome-wide association studies demonstrate that compared to a cis-only approach, our models lead to associations with greater fidelity to actual gene expression values.

Large-scale machine learning for metagenomics sequence classification.

MOTIVATION: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions. RESULTS: We propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise. AVAILABILITY AND IMPLEMENTATION: Data and codes are available at http://cbio.ensmp.fr/largescalemetagenomics CONTACT: pierre.mahe@biomerieux.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.