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The cancer microbiome field tremendously accelerated following the release of our manuscript nearly three years ago1, including direct validation of our cancer type-specific conclusions in independent, international cohorts2,3and the tumor microbiome’s adoption into the hallmarks of cancer4. Disentangling contamination signals from biological signals is an important consideration for this research field. Therefore, despite numerous, high-impact, peer-reviewed research papers that either validated our conclusions or extended them using data we released2,5–13, we carefully considered criticism raised by Gihawiet al. about potential mishandling of contaminants, batch effects, and machine learning approaches—all of which were central topics in our manuscript. Nonetheless, a close examination of each concern alongside the original manuscript and re-analyses of our published data strongly demonstrates the robustness of the original findings. To remove all doubt, however, we have reproduced all key conclusions from the original manuscript using only overlapping bacterial genera identified in a highly decontaminated, multi-cancer, international cohort (Weizmann Institute of Science, WIS)2, with or without batch correction, and with multiclass machine learning analyses to mitigate class imbalances. Our published pan-cancer mycobiome manuscript3also affirms these findings using updated, state-of-the-art methods. We also note that every analysis shown here was possible using public data and code that we had already provided.

Background and Purpose Intestinal alkaline phosphatase (IAP) is an important apical brush border enzyme that dephosphorylates lipopolysaccharide released from bacteria. Considering its protective effect, we determined the effect of intragastric administration of IAP on the course of experimental colitis in mice exercising on a forced treadmill with diet-induced obesity. Experimental approach C57BL/6 mice with TNBS colitis fed a high-fat diet (HFD) were subjected to forced treadmill exercise with or without intragastric treatment with IAP. Grip muscle strength and disease activity index (DAI) were monitored, pro- and anti-inflammatory cytokines and markers of oxidative stress were measured by Luminex and ELISA, respectively. The mRNA expression of the barrier proteins ZO-1, MUC2 and claudins was assessed by RT-PCR, and the fecal composition of the intestinal microbiota was assessed by the NGS. Key results Significant increases in DAI, MDA+4-HNE in the colonic mucosa, and plasma leptin, MCP, TNF-α, IL-6, and IL-17a were observed in obese mice with colitis on a treadmill, and these effects have been significantly reduced by IAP treatment. IAP increased ZO-1, claudins and Muc2 mRNAs expression in the colonic mucosa. NGS revealed the relative proportion of Firmicutes in favor of higher Verrucomicbiota content and reducing the incidence of pathogenic Clostridia and Odoribacter. Conclusions and Implications IAP treatment ameliorates the worsening effect of forced exercise on murine colitis due to attenuation of oxidative stress, downregulation of pro-inflammatory biomarkers in the colonic mucosa, and beneficial changes in the gut microbiota. IAP deserves attention as promising candidate in future clinical trials of ulcerative colitis.

BackgroundThe sinus microbiome in patients with chronic rhinosinusitis (CRS) is considered homogenous across the sinonasal cavity. The middle nasal meatus is the recommended sampling site for 16S rRNA sequencing. However, individuals with unusually high between-site variability between the middle meatus and the sinuses were identified in previous studies. This study aimed to identify which factors determine increased microbial heterogeneity between sampling sites in the sinuses.MethodologyIn this cross-sectional study samples for 16S rRNA sequencing were obtained from the middle meatus, the maxillary and the frontal sinus in 50 patients with CRS. The microbiome diversity between sampling sites was analysed in relation to the size of the sinus ostia and clinical metadata.ResultsIn approximately 15% of study participants, the differences between sampling sites within one patient were greater than between the patient and other individuals. Contrary to a popular hypothesis, obstruction of the sinus ostium resulted in decreased dissimilarity between the sinus and the middle meatus. The dissimilarity between the sampling sites was patient-specific: greater between-sinus differences were associated with greater meatus-sinus differences, regardless of the drainage pathway patency. Decreased spatial variability was observed in patients with nasal polyps and extensive mucosal changes in the sinuses.ConclusionsSampling from the middle meatus is not universally representative of the sinus microbiome. The differences between sites cannot be predicted from the patency of communication pathways between them.

Emerging evidence has linked the gut microbiome changes to schizophrenia. However, there has been limited research into the functional pathways by which the gut microbiota contributes to the phenotype of persons with chronic schizophrenia. We characterized the composition and functional potential of the gut microbiota in 48 individuals with chronic schizophrenia and 48 matched (sequencing plate, age, sex, BMI, and antibiotic use) non-psychiatric comparison subjects (NCs) using 16S rRNA sequencing. Patients with schizophrenia demonstrated significant beta-diversity differences in microbial composition and predicted genetic functional potential compared to NCs. Alpha-diversity of taxa and functional pathways were not different between groups. Random forests analyses revealed that the microbiome predicts differentiation of patients with schizophrenia from NCs using taxa (75% accuracy) and functional profiles (67% accuracy for KEGG orthologs, 70% for MetaCyc pathways). We utilized a new compositionally-aware method incorporating reference frames to identify differentially abundant microbes and pathways, which revealed that Lachnospiraceae is associated with schizophrenia. Functional pathways related to trimethylamine-N-oxide reductase and Kdo2-lipid A biosynthesis were altered in schizophrenia. These metabolic pathways were associated with inflammatory cytokines and risk for coronary heart disease in schizophrenia. Findings suggest potential mechanisms by which the microbiota may impact the pathophysiology of the disease through modulation of functional pathways related to immune signaling/response and lipid and glucose regulation to be further investigated in future studies.

/ SummaryFor the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don’t rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict ∼200,000 structures for diverse protein sequences from 1,003 representative genomes1across the microbial tree of life, and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for shifting the focus from obtaining structures to putting them into context, to transform all branches of biology, including a shift from sequence-based to sequence-structure-function based meta-omics analyses.

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions1–10, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas11 (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia–IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration. Microbial nucleic acids are detected in samples of tissues and blood from more than 10,000 patients with cancer, and machine learning is used to show that these can be used to discriminate between and among different types of cancer, suggesting a new microbiome-based diagnostic approach.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high-throughput sequencing, with highly similar sequences binned together, we obtain Operational Taxonomic Units (OTU) profiles for each subject. Due to the high-dimensionality and non-normality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance-based Beta-diversity between individuals. Analyses of such between-subject attributes are not amenable to the predominant within-subject based statistical paradigm, such as t-tests and linear regression. In this paper, we propose a new approach to model Beta-diversity as a response within a regression setting by utilizing the functional response models (FRM), a class of semiparametric models for between- as well as within-subject attributes. The new approach not only addresses limitations of current methods for Beta-diversity with cross-sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data. This article is protected by copyright. All rights reserved.

Schizophrenia and bipolar disorder (BD) are associated with debilitating psychiatric and cognitive dysfunction, worse health outcomes, and shorter life expectancies. The pathophysiological understanding of and therapeutic resources for these neuropsychiatric disorders are still limited. Humans harbor over 1,000 unique bacterial species in our gut, which have been linked to both physical and mental/cognitive health. The gut microbiome is a novel and promising avenue to understand the attributes of psychiatric diseases and, potentially, to modify them. Building upon our previous work, this systematic review evaluates the most recent evidence of the gut microbiome in clinical populations with serious mental illness (SMI). Sixteen articles that met our selection criteria were reviewed, including cross-sectional cohort studies and longitudinal treatment trials. All studies reported alterations in the gut microbiome of patients with SMI compared to non-psychiatric comparison subjects (NCs), and beta-diversity was consistently reported to be different between schizophrenia and NCs. Ruminococcaceae and Faecalibacterium were relatively decreased in BD, and abundance of Ruminococcaceae was reported across several investigations of SMI to be associated with better clinical characteristics. Lactic acid bacteria were relatively more abundant in SMI and associated with worse clinical outcomes. There was very limited evidence for the efficacy of probiotic or prebiotic interventions in SMI. As microbiome research in psychiatry is still nascent, the extant literature has several limitations. We critically evaluate the current data, including experimental approaches. There is a need for more unified methodological standards in order to arrive at robust biological understanding of microbial contributions to SMI.

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/., The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, the authors introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures.

QIIME 2 is a completely re‐engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open‐source web‐based platform, to re‐use available data for meta‐analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug‐ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta‐analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://qiime2.org. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://forum.qiime2.org., Current protocols in bioinformatics, 70 (1)

The human microbiota (the microscopic organisms that inhabit us) and microbiome (their genes) hold considerable potential for improving pharmacological practice. Recent advances in multi-"omics" techniques have dramatically improved our understanding of the constituents of the microbiome and their functions. The implications of this research for human health, including microbiome links to obesity, drug metabolism, neurological diseases, cancer, and many other health conditions, have sparked considerable interest in exploiting the microbiome for targeted therapeutics. Links between microbial pathways and disease states further highlight a rich potential for companion diagnostics and precision medicine approaches. For example, the success of fecal microbiota transplantation to treat Clostridium difficile infection has already started to redefine standard of care with a microbiome-directed therapy. In this review we briefly discuss the nature of human microbial ecosystems and with pathologies and biological processes linked to the microbiome. We then review emerging computational metagenomic, metabolomic, and wet lab techniques researchers are using today to learn about the roles host-microbial interactions have with respect to pharmacological purposes and vice versa. Finally, we describe how drugs affect the microbiome, how the microbiome can impact drug response in different people, and the potential of the microbiome itself as a source of new therapeutics.

Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer “core” genes, such as the ribosomal proteins. The robustness of the results was tested with respect to several variables, including taxon and site sampling, amino acid substitution heterogeneity and saturation, non-vertical evolution, and the impact of exclusion of candidate phyla radiation (CPR) taxa. Our results provide an updated view of domain-level relationships., The authors build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on 381 markers. The results indicate a remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that used fewer “core” genes, such as the ribosomal proteins.

Complex microbial communities shape the dynamics of various environments, ranging from the mammalian gastrointestinal tract to the soil. Advances in DNA sequencing technologies and data analysis have provided drastic improvements in microbiome analyses, for example, in taxonomic resolution, false discovery rate control and other properties, over earlier methods. In this Review, we discuss the best practices for performing a microbiome study, including experimental design, choice of molecular analysis technology, methods for data analysis and the integration of multiple omics data sets. We focus on recent findings that suggest that operational taxonomic unit-based analyses should be replaced with new methods that are based on exact sequence variants, methods for integrating metagenomic and metabolomic data, and issues surrounding compositional data analysis, where advances have been particularly rapid. We note that although some of these approaches are new, it is important to keep sight of the classic issues that arise during experimental design and relate to research reproducibility. We describe how keeping these issues in mind allows researchers to obtain more insight from their microbiome data sets.

Author(s): Nguyen, Tanya; Kosciolek, Tomasz; Daly, Rebecca; Vazquez-Baeza, Yoshiki; Swafford, Austin; Knight, Rob; Jeste, Dilip

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions

Background Use of skin personal care products on a regular basis is nearly ubiquitous, but their effects on molecular and microbial diversity of the skin are unknown. We evaluated the impact of four beauty products (a facial lotion, a moisturizer, a foot powder, and a deodorant) on 11 volunteers over 9 weeks. Results Mass spectrometry and 16S rRNA inventories of the skin revealed decreases in chemical as well as in bacterial and archaeal diversity on halting deodorant use. Specific compounds from beauty products used before the study remain detectable with half-lives of 0.5–1.9 weeks. The deodorant and foot powder increased molecular, bacterial, and archaeal diversity, while arm and face lotions had little effect on bacterial and archaeal but increased chemical diversity. Personal care product effects last for weeks and produce highly individualized responses, including alterations in steroid and pheromone levels and in bacterial and archaeal ecosystem structure and dynamics. Conclusions These findings may lead to next-generation precision beauty products and therapies for skin disorders. Electronic supplementary material The online version of this article (10.1186/s12915-019-0660-6) contains supplementary material, which is available to authorized users.

The human microbiota (the microscopic organisms that inhabit us) and microbiome (their genes) hold considerable potential for improving pharmacological practice. Recent advances in multi-“omics” techniques have dramatically improved our understanding of the constituents of the microbiome and their functions. The implications of this research for human health, including microbiome links to obesity, drug metabolism, neurological diseases, cancer, and many other health conditions, have sparked considerable interest in exploiting the microbiome for targeted therapeutics. Links between microbial pathways and disease states further highlight a rich potential for companion diagnostics and precision medicine approaches. For example, the success of fecal microbiota transplantation to treat Clostridium difficile infection has already started to redefine standard of care with a microbiome-directed therapy. In this review we briefly discuss the nature of human microbial ecosystems and with pathologies and biological processes linked to the microbiome. We then review emerging computational metagenomic, metabolomic, and wet lab techniques researchers are using today to learn about the roles host-microbial interactions have with respect to pharmacological purposes and vice versa. Finally, we describe how drugs affect the microbiome, how the microbiome can impact drug response in different people, and the potential of the microbiome itself as a source of new therapeutics.

QIIME 2 development was primarily funded by NSF Awards 1565100 to J.G.C. and 1565057 to R.K. Partial support was also provided by the following: grants NIH U54CA143925 (J.G.C. and T.P.) and U54MD012388 (J.G.C. and T.P.); grants from the Alfred P. Sloan Foundation (J.G.C. and R.K.); ERCSTG project MetaPG (N.S.); the Strategic Priority Research Program of the Chinese Academy of Sciences QYZDB-SSW-SMC021 (Y.B.); the Australian National Health and Medical Research Council APP1085372 (G.A.H., J.G.C., Von Bing Yap and R.K.); the Natural Sciences and Engineering Research Council (NSERC) to D.L.G.; and the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University. All NCI coauthors were supported by the Intramural Research Program of the National Cancer Institute. S.M.G. and C. Diener were supported by the Washington Research Foundation Distinguished Investigator Award.

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Dupilumab is a fully human antibody to interleukin-4 receptor alpha that improves the signs and symptoms of moderate to severe atopic dermatitis (AD). To determine the effects of dupilumab on Staphylococcus aureus colonization and microbial diversity on the skin, bacterial DNA was analyzed from swabs collected from lesional and nonlesional skin in a double-blind, placebo-controlled study of 54 patients with moderate to severe AD randomized (1:1) and treated with either dupilumab (200 mg weekly) or placebo for 16 weeks. Microbial diversity and relative abundance of Staphylococcus were assessed by DNA sequencing of 16S ribosomal RNA, and absolute S. aureus abundance was measured by quantitative PCR. Before treatment, lesional skin had lower microbial diversity and higher overall abundance of S. aureus than nonlesional skin. During dupilumab treatment, microbial diversity increased and the abundance of S. aureus decreased. Pronounced changes were seen in nonlesional and lesional skin. Decreased S. aureus abundance during dupilumab treatment correlated with clinical improvement of AD and biomarkers of type 2 immunity. We conclude that clinical improvement of AD that is mediated by interleukin-4 receptor alpha inhibition and the subsequent suppression of type 2 inflammation is correlated with increased microbial diversity and reduced abundance of S. aureus.

Intestinal microbiome and gut-brain axis have been receiving increasing attention for their role in the regulation of brain/behavior and possible biological basis of psychiatric disorders. Several recent clinical studies have linked the microbiome with neuropsychiatric conditions, although the literature on schizophrenia is quite limited. This study investigated gut microbiome composition in 50 individuals, including 25 persons with chronic schizophrenia and 25 demographically-matched non-psychiatric comparison subjects (NCs). Stool samples were collected and assayed using 16S rRNA sequencing of the V4 region. Examination of unweighted UniFrac and Bray-Curtis dissimilarities revealed significant community-level separation in microbiome composition between the two subject groups. At the phylum level, Proteobacteria were found to be relatively decreased in schizophrenia subjects compared to NCs. At the genus level, Anaerococcus was relatively increased in schizophrenia while Haemophilus, Sutterella, and Clostridium were decreased. Within individuals with schizophrenia, abundance of Ruminococcaceae was correlated with lower severity of negative symptoms; Bacteroides were associated with worse depressive symptoms; and Coprococcus was related to greater risk for developing coronary heart disease. Our findings provide evidence of altered gut microbial composition in persons with chronic schizophrenia and suggest a need for larger and longitudinal studies of microbiome in schizophrenia.

People's lived experiences provide intuitions about health. Can they transform these personal intuitions into testable hypotheses that could inform both science and their lives? This paper introduces an online learning architecture and provides system principles for people to brainstorm causal scientific theories. We describe the Learn-Train-Ask workflow that guides participants through learning domain-specific content, process training to frame their intuitions as hypotheses, and collaborating with anonymous peers to brainstorm related questions. 344 voluntary online participants from 27 countries created 399 personally-relevant questions about the human microbiome over 4 months, 75 (19%) of which microbiome experts found potentially scientifically novel. Participants with access to process training generated hypotheses of better quality. Access to learning materials improved the questions' microbiome-specific knowledge. These results highlight the promise of performing personally-meaningful scientific work using massive online learning systems.

Schizophrenia and bipolar disorder are among the leading causes of disability, morbidity, and mortality worldwide. In addition to being serious mental illnesses, these disorders are associated with considerable systemic physiological dysfunction, including chronic inflammation and elevated oxidative stress. The advent of sophisticated sequencing techniques has led to a growing interest in the potential role of gut microbiota in human health and disease. Advances in this area have transformed our understanding of a number of medical conditions and have generated a new perspective suggesting that gut microbiota might be involved in the development and maintenance of brain/mental health. Animal models have demonstrated strong though indirect evidence for a contributory role of intestinal microbiota in psychiatric symptomatology and have linked the microbiome with neuropsychiatric conditions. We present a systematic review of clinical studies of microbiome in schizophrenia and bipolar disorder. The published literature has a number of limitations; however, the investigations suggest that these disorders are associated with reduced microbial diversity and show global community differences compared to non-psychiatric comparison samples. In some reports, specific microbial taxa were associated with clinical disease characteristics, including physical health, depressive and psychotic symptoms, and sleep, but little information on the functional potential of those community changes. Studies also suggest increased intestinal inflammation and permeability, which may be among the principal mechanisms by which microbial dysbiosis impacts systemic physiological functioning. We highlight gaps in the current literature and implications for diagnosis and therapeutic interventions, and outline future directions for microbiome research in psychiatry.

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.ImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

Background:The microbiota-gut-brain axis and membrane dysfunction in the brain has attracted increasing attention in the field of psychiatric research. However, the possible interactive role of gut microbiota and brain function in the prodromal stage of schizophrenia has not been studied yet.Methods:To explore this, we collected fecal samples and performed Magnetic Resonance Spectroscopy (MRS) scans in 81 high risk (HR) subjects, 19 ultra-high risk (UHR) subjects and 69 health controls (HC). Then we analyzed the differences in gut microbiota and choline concentrations in the anterior cingulate cortex (ACC).Results:Presences of the orders Clostridiales, Lactobacillales and Bacteroidales were observed at increase levels in fecal samples of UHR subjects compared to the other two groups. The composition changes of gut microbiota indicate the increased production of Short Chain Fatty Acids (SCFAs), which could activate microglia and then disrupt membrane metabolism. Furthermore, this was confirmed by an increase of choline levels, a brain imaging marker of membrane dysfunction, which is also significantly elevated in UHR subjects compared to the HR and HC groups.Conclusion:Both gut microbiome and imaging studies of UHR subjects suggest the membrane dysfunction in the brain and hence might support the membrane hypothesis of schizophrenia.

The prospect of identifying contacts in protein structures purely from aligned protein sequences has lured researchers for a long time, but progress has been modest until recently. Here, we reviewed the most successful methods for identifying structural contacts from sequence and how these methods differ and made an initial assessment of the overlap of predicted contacts by alternative approaches. We then discussed the limitations of these methods and possibilities for future development and highlighted the recent applications of contacts in tertiary structure prediction, identifying the residues at the interfaces of protein-protein interactions, and the use of these methods in disentangling alternative conformational states. Finally, we identified the current challenges in the field of contact prediction, concentrating on the limitations imposed by available data, dependencies on the sequence alignments, and possible future developments.

Motivation: Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Results: Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts—around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. Availability and implementation: MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Learners worldwide collectively spend millions of hours per week testing their skills on assignments with known answers. Might some of this time fruitfully be spent posing and exploring novel questions? This paper investigates an approach for learners to contribute scientific ideas. The Gut Instinct system embodies this approach, hosting online learning materials and invites learners to collaboratively brainstorm potential influences on people's microbiome. A between-subjects experiment compared the performance of participants who engaged in just learning, just contributing, or a combination. Participants in the learning condition scored highest on a summative test. Participants in both the contribution and combined conditions generated novel, useful questions; there was not a significant difference between the two. Though participants in the combined condition both learned and contributed, this setting did not exhibit an additive benefit, such as better learning in the combined condition. These results highlight the promise and difficulty of double-bottom-line learning experiences.

In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.