Terms of use This work is brought to you by the University of Southern Denmark through the SDU Research Portal. Unless otherwise specified it has been shared according to the terms for self-archiving. If no other license is stated, these terms apply: • You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access version Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer Authors

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The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.

The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.

Antibiotics are used for fighting pathogens, but also target our commensal bacteria as a side effect, disturbing the gut microbiota composition and causing dysbiosis and disease [1][2][3] . Despite this well-known collateral damage, the activity spectrum of the different antibiotic classes on gut bacteria remains poorly characterized. Having monitored the activities of >1,000 marketed drugs on 38 representative species of the healthy human gut microbiome 4 , we here characterize further the 144 antibiotics therein, representing all major classes. We determined >800 Minimal Inhibitory Concentrations (MICs) and extended the antibiotic profiling to 10 additional species to validate these results and link to available data on antibiotic breakpoints for gut microbes. Antibiotic classes exhibited distinct inhibition spectra, including generation-dependent effects by quinolones and phylogeny-independence by βlactams. Macrolides and tetracyclines, two prototypic classes of bacteriostatic protein synthesis inhibitors, inhibited almost all commensals tested. We established that both kill different subsets of prevalent commensal bacteria, and cause cell lysis in specific cases. This species-specific activity challenges the long-standing divide of antibiotics into bactericidal and bacteriostatic, and provides a possible explanation for the strong impact of macrolides on the gut microbiota composition in animals 5-8 and humans [9][10][11] . To mitigate the collateral damage of macrolides and tetracyclines on gut commensals, we exploited the fact that drug combinations have species-specific outcomes in bacteria 12 and sought marketed drugs, which could antagonize the activity of these antibiotics in abundant gut commensal species. By screening >1,000 drugs, we identified several such antidotes capable of protecting gut species from these antibiotics without compromising their activity against relevant pathogens. Altogether, this study broadens our understanding of antibiotic action on gut commensals, uncovers a previously unappreciated and broad bactericidal effect of prototypical bacteriostatic antibiotics on gut bacteria, and opens avenues for preventing the collateral damage caused by antibiotics on human gut commensals..

BackgroundRecent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.ObjectiveTo explore the faecal and salivary microbiota as potential diagnostic biomarkers.MethodsWe applied shotgun metagenomic and 16S rRNA amplicon sequencing to samples from a Spanish case–control study (n=136), including 57 cases, 50 controls, and 29 patients with chronic pancreatitis in the discovery phase, and from a German case–control study (n=76), in the validation phase.ResultsFaecal metagenomic classifiers performed much better than saliva-based classifiers and identified patients with PDAC with an accuracy of up to 0.84 area under the receiver operating characteristic curve (AUROC) based on a set of 27 microbial species, with consistent accuracy across early and late disease stages. Performance further improved to up to 0.94 AUROC when we combined our microbiome-based predictions with serum levels of carbohydrate antigen (CA) 19–9, the only current non-invasive, Food and Drug Administration approved, low specificity PDAC diagnostic biomarker. Furthermore, a microbiota-based classification model confined to PDAC-enriched species was highly disease-specific when validated against 25 publicly available metagenomic study populations for various health conditions (n=5792). Both microbiome-based models had a high prediction accuracy on a German validation population (n=76). Several faecal PDAC marker species were detectable in pancreatic tumour and non-tumour tissue using 16S rRNA sequencing and fluorescence in situ hybridisation.ConclusionTaken together, our results indicate that non-invasive, robust and specific faecal microbiota-based screening for the early detection of PDAC is feasible.

Factor analysis is a widely used method for dimensionality reduction in genome biology, with applications from personalized health to single-cell biology. Existing factor analysis models assume independence of the observed samples, an assumption that fails in spatio-temporal profiling studies. Here we present MEFISTO, a flexible and versatile toolbox for modeling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multimodal data, but enables the performance of spatio-temporally informed dimensionality reduction, interpolation, and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. To illustrate MEFISTO, we apply the model to different datasets with spatial or temporal resolution, including an evolutionary atlas of organ development, a longitudinal microbiome study, a single-cell multi-omics atlas of mouse gastrulation and spatially resolved transcriptomics.