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The clinical course of the 2019 coronavirus disease (COVID-19) is variable and to a substantial degree still unpredictable, especially in persons who have neither been vaccinated nor recovered from previous infection. We hypothesized that disease progression and inflammatory responses were associated with alterations in the microbiome and metabolome. To test this, we integrated metagenome, metabolome, cytokine, and transcriptome profiles of longitudinally collected samples from hospitalized COVID-19 patients at the beginning of the pandemic (before vaccines or variants of concern) and non-infected controls, and leveraged detailed clinical information and post-hoc confounder analysis to identify robust within- and cross-omics associations. Severe COVID-19 was directly associated with a depletion of potentially beneficial intestinal microbes mainly belonging to Clostridiales, whereas oropharyngeal microbiota disturbance appeared to be mainly driven by antibiotic use. COVID-19 severity was also associated with enhanced plasma concentrations of kynurenine, and reduced levels of various other tryptophan metabolites, lysophosphatidylcholines, and secondary bile acids. Decreased abundance of Clostridiales potentially mediated the observed reduction in 5-hydroxytryptophan levels. Moreover, altered plasma levels of various tryptophan metabolites and lower abundances of Clostridiales explained significant increases in the production of IL-6, IFNγ and/or TNFα. Collectively, our study identifies correlated microbiome and metabolome alterations as a potential contributor to inflammatory dysregulation in severe COVID-19.Graphical Abstract

ObjectivesSpondyloarthritis (SpA) is a group of immune-mediated diseases highly concomitant with non-musculoskeletal inflammatory disorders, such as acute anterior uveitis (AAU) and Crohn’s disease (CD). The gut microbiome represents a promising avenue to elucidate shared and distinct underlying pathophysiology.MethodWe performed 16S rRNA sequencing on stool samples of 277 patients (72 CD, 103 AAU, and 102 SpA) included in the German Spondyloarthritis Inception Cohort (GESPIC) and 62 back pain controls without any inflammatory disorder. Discriminatory statistical methods were used to disentangle microbial disease signals from one another and a wide range of potential confounders. Patients were naïve to or had not received treatment with biological disease-modifying anti-rheumatic drugs for at least three months before enrollment, providing a better approximation of a true baseline disease signal.ResultsWe identified a shared, immune-mediated disease signal represented by low abundances of Lachnospiraceae taxa relative to controls, most notablyFusicatenibacter, which partially mediated higher serum CRP levels and was most abundant in controls receiving NSAID monotherapy. Patients with SpA drove an enrichment ofCollinsella, while HLA-B27+ individuals displayed enrichedFaecalibacterium. CD patients had higher abundances of aRuminococcustaxon, and previous csDMARD therapy was associated with increasedAkkermansia.ConclusionOur work supports the existence of a common gut dysbiosis in SpA and related inflammatory pathologies. We reveal shared and disease-specific microbial associations and potential mediators of disease activity. Validation studies are needed to clarify the role ofFusicatenibacterin gut-joint inflammation, and metagenomic resolution is needed to understand the relationship betweenFaecalibacteriumcommensals and HLA-B27.

Testing for differential abundance is a crucial task in metagenome-wide association studies, complicated by technical or biological confounding and a lack of consensus regarding statistical methodology. Here, we developed a framework for benchmarking differential abundance testing methods based on implanting signals into real data. This strategy yields a ground truth for benchmarking while retaining the statistical characteristics of real metagenomic data, which we quantitatively validated in comparison to previous approaches. Our benchmark revealed dramatic issues with elevated false discovery rates or limited sensitivity for the majority of methods with the exception of limma, linear models and the Wilcoxon test. When additionally modeling confounders, we observed these issues to be exacerbated, but also that linear mixed-effect models or the blocked Wilcoxon test effectively address them. Exploratory analysis of cardiometabolic disease cohorts illustrates the confounding potential of medications and the need to consider confounders to prevent spurious associations in real-world applications.

Background & Aims After birth, the immune system matures via interactions with microbes in the gut. The S100 calcium binding proteins S100A8 and S100A9, and their extracellular complex form, S100A8–A9, are found in high amounts in human breast milk. We studied levels of S100A8–A9 in fecal samples (also called fecal calprotectin) from newborns and during infancy, and their effects on development of the intestinal microbiota and mucosal immune system. Methods We collected stool samples (n = 517) from full-term (n = 72) and preterm infants (n = 49) at different timepoints over the first year of life (days 1, 3, 10, 30, 90, 180, and 360). We measured levels of S100A8–A9 by enzyme-linked immunosorbent assay and analyzed fecal microbiomes by 16S sRNA gene sequencing. We also obtained small and large intestine biopsies from 8 adults and 10 newborn infants without inflammatory bowel diseases (controls) and 8 infants with necrotizing enterocolitis and measured levels of S100A8 by immunofluorescence microscopy. Children were followed for 2.5 years and anthropometric data and medical information on infections were collected. We performed studies with newborn C57BL/6J wild-type and S100a9–/– mice (which also lack S100A8). Some mice were fed or given intraperitoneal injections of S100A8 or subcutaneous injections of Staphylococcus aureus. Blood and intestine, mesenterial and celiac lymph nodes were collected; cells and cytokines were measured by flow cytometry and studied in cell culture assays. Colon contents from mice were analyzed by culture-based microbiology assays. Results Loss of S100A8 and S100A9 in mice altered the phenotypes of colonic lamina propria macrophages, compared with wild-type mice. Intestinal tissues from neonatal S100-knockout mice had reduced levels of CX3CR1 protein, and Il10 and Tgfb1 mRNAs, compared with wild-type mice, and fewer T-regulatory cells. S100-knockout mice weighed 21% more than wild-type mice at age 8 weeks and a higher proportion developed fatal sepsis during the neonatal period. S100-knockout mice had alterations in their fecal microbiomes, with higher abundance of Enterobacteriaceae. Feeding mice S100 at birth prevented the expansion of Enterobacteriaceae, increased numbers of T-regulatory cells and levels of CX3CR1 protein and Il10 mRNA in intestine tissues, and reduced body weight and death from neonatal sepsis. Fecal samples from term infants, but not preterm infants, had significantly higher levels of S100A8–A9 during the first 3 months of life than fecal samples from adults; levels decreased to adult levels after weaning. Fecal samples from infants born by cesarean delivery had lower levels of S100A8–A9 than from infants born by vaginal delivery. S100 proteins were expressed by lamina propria macrophages in intestinal tissues from infants, at higher levels than in intestinal tissues from adults. High fecal levels of S100 proteins, from 30 days to 1 year of age, were associated with higher abundance of Actinobacteria and Bifidobacteriaceae, and lower abundance of Gammaproteobacteria—particularly opportunistic Enterobacteriaceae. A low level of S100 proteins in infants' fecal samples associated with development of sepsis and obesity by age 2 years. Conclusion S100A8 and S100A9 regulate development of the intestinal microbiota and immune system in neonates. Nutritional supplementation with these proteins might aide in development of preterm infants and prevent microbiota-associated disorders in later years.

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 revealed some biomarkers to be disease-specific, others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de.