A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies.

BACKGROUND: In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations. RESULTS: Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications. CONCLUSIONS: Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Gut microbiota dysbiosis is associated with altered tryptophan metabolism and dysregulated inflammatory response in COVID-19.

The clinical course of COVID-19 is variable and often unpredictable. To test the hypothesis that disease progression and inflammatory responses associate with alterations in the microbiome and metabolome, we analyzed metagenome, metabolome, cytokine, and transcriptome profiles of repeated samples from hospitalized COVID-19 patients and uninfected controls, and leveraged clinical information and post-hoc confounder analysis. Severe COVID-19 was associated with a depletion of beneficial intestinal microbes, whereas oropharyngeal microbiota disturbance was mainly linked to antibiotic use. COVID-19 severity was also associated with enhanced plasma concentrations of kynurenine and reduced levels of several other tryptophan metabolites, lysophosphatidylcholines, and secondary bile acids. Moreover, reduced concentrations of various tryptophan metabolites were associated with depletion of Faecalibacterium, and tryptophan decrease and kynurenine increase were linked to enhanced production of inflammatory cytokines. Collectively, our study identifies correlated microbiome and metabolome alterations as a potential contributor to inflammatory dysregulation in severe COVID-19.

Shared and Distinct Gut Microbiota in Spondyloarthritis, Acute Anterior Uveitis, and Crohn's Disease.

OBJECTIVE: Spondyloarthritis (SpA) is a group of immune-mediated diseases highly concomitant with nonmusculoskeletal 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. METHODS: We performed 16S ribosomal RNA sequencing on stool samples of 277 patients (72 CD, 103 AAU, and 102 SpA) included in the German Spondyloarthritis Inception Cohort 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 naive to or had not received treatment with biological disease-modifying antirheumatic drugs (DMARDs) for >3 months before enrollment, providing a better approximation of a true baseline disease signal. RESULTS: We identified a shared, immune-mediated disease signal represented by low abundances of Lachnospiraceae taxa relative to controls, most notably Fusicatenibacter, which was most abundant in controls receiving nonsteroidal antiinflammatory drug monotherapy and implied to partially mediate higher serum C-reactive protein. Patients with SpA showed an enrichment of Collinsella, whereas human leukocyte antigen (HLA)-B27+ individuals displayed enriched Faecalibacterium. CD patients had higher abundances of a Ruminococcus taxon, and previous conventional/synthetic DMARD therapy was associated with increased Akkermansia. CONCLUSION: Our work supports the existence of a common gut dysbiosis in SpA and related inflammatory pathologies. We reveal shared and disease-specific microbial associations and suggest potential mediators of disease activity. Validation studies are needed to clarify the role of Fusicatenibacter in gut-joint inflammation, and metagenomic resolution is needed to understand the relationship between Faecalibacterium commensals and HLA-B27.

Dynamic Changes in Microbiome Composition Following Mare's Milk Intake for Prevention of Collateral Antibiotic Effect.

Introduction: Probiotics and prebiotics are widely used for recovery of the human gut microbiome after antibiotic treatment. High antibiotic usage is especially common in children with developing microbiome. We hypothesized that dry Mare's milk, which is rich in biologically active substances without containing live bacteria, could be used as a prebiotic in promoting microbial diversity following antibiotic treatment in children. The present pilot study aims to determine the impacts of dry Mare's milk on the diversity of gut bacterial communities when administered during antibiotic treatment and throughout the subsequent recovery phase. Methods: Six children aged 4 to 5 years and diagnosed with bilateral bronchopneumonia were prescribed cephalosporin antibiotics. During the 60 days of the study, three children consumed dry Mare's milk whereas the other three did not. Fecal samples were collected daily during antibiotic therapy and every 5 days after antibiotic therapy. Total DNA was isolated and taxonomic composition of gut microbiota was analyzed by 16S rRNA amplicon sequencing. To assess the immune status of the gut, stool samples were analyzed by bead-based multiplex assays. Results: Mare's milk treatment seems to prevent the bloom of Mollicutes, while preventing the loss of Coriobacteriales. Immunological analysis of the stool reveals an effect of Mare's milk on local immune parameters under the present conditions.

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

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 .

S100A8 and S100A9 Are Important for Postnatal Development of Gut Microbiota and Immune System in Mice and Infants.

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.

Salt bridge as a gatekeeper against partial unfolding.

Salt bridges are frequently observed in protein structures. Because the energetic contribution of salt bridges is strongly dependent on the environmental context, salt bridges are believed to contribute to the structural specificity rather than the stability. To test the role of salt bridges in enhancing structural specificity, we investigated the contribution of a salt bridge to the energetics of native-state partial unfolding in a cysteine-free version of Escherichia coli ribonuclease H (RNase H*). Thermolysin cleaves a protruding loop of RNase H(*) through transient partial unfolding under native conditions. Lys86 and Asp108 in RNase H(*) form a partially buried salt bridge that tethers the protruding loop. Investigation of the global stability of K86Q/D108N RNase H(*) showed that the salt bridge does not significantly contribute to the global stability. However, K86Q/D108N RNase H(*) is greatly more susceptible to proteolysis by thermolysin than wild-type RNase H(*) is. The free energy for partial unfolding determined by native-state proteolysis indicates that the salt bridge significantly increases the energy for partial unfolding by destabilizing the partially unfolded form. Double mutant cycles with single and double mutations of the salt bridge suggest that the partially unfolded form is destabilized due to a significant decrease in the interaction energy between Lys86 and Asp108 upon partial unfolding. This study demonstrates that, even in the case that a salt bridge does not contribute to the global stability, the salt bridge may function as a gatekeeper against partial unfolding that disturbs the optimal geometry of the salt bridge.

Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates.

PURPOSE: Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates. METHODS: We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site. RESULTS: We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively. CONCLUSIONS: We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.