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Background One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions. Results We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the “all-pairs log-ratio model”, the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data). Conclusions coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN (https://cran.r-project.org/web/packages/coda4microbiome/) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/

Gastrointestinal (GI) symptoms have been described in a conspicuous percentage of coronavirus disease 2019 (COVID-19) patients. This clinical evidence is supported by the detection of viral RNA in stool, which also supports the hypothesis of a possible fecal-oral transmission route. The involvement of GI tract in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is corroborated by the theoretical assumption that angiotensin converting enzyme 2, which is a SARS-CoV-2 target receptor, is present along the GI tract. Studies have pointed out that gut dysbiosis may occur in COVID-19 patients, with a possible correlation with disease severity and with complications such as multisystem inflammatory syndrome in children. However, the question to be addressed is whether dysbiosis is a consequence or a contributing cause of SARS-CoV-2 infection. In such a scenario, pharmacological therapies aimed at decreasing GI permeability may be beneficial for COVID-19 patients. Considering the possibility of a fecal-oral transmission route, water and environmental sanitation play a crucial role for COVID-19 containment, especially in developing countries.

Background The gut microbiota plays a central role in host physiology and in several pathological mechanisms in humans. Antibiotics compromise the composition and functions of the gut microbiota inducing long-lasting detrimental effects on the host. Recent studies suggest that the efficacy of different clinical therapies depends on the action of the gut microbiota. Here, we investigated how different antibiotic treatments affect the ability of the gut microbiota to control intestinal inflammation upon fecal microbiota transplantation in an experimental colitis model and in ex vivo experiments with human intestinal biopsies. Results Murine fecal donors were pre-treated with different antibiotics, i.e., vancomycin, streptomycin, and metronidazole before FMT administration to colitic animals. The analysis of the gut microbiome, fecal metabolome, and the immunophenotyping of colonic lamina propria immune cells revealed that antibiotic pre-treatment significantly influences the capability of the microbiota to control intestinal inflammation. Streptomycin and vancomycin-treated microbiota failed to control intestinal inflammation and were characterized by the blooming of pathobionts previously associated with IBD as well as with metabolites related to the presence of oxidative stress and metabolism of simple sugars. On the contrary, the metronidazole-treated microbiota retained its ability to control inflammation co-occurring with the enrichment of Lactobacillus and of innate immune responses involving iNKT cells. Furthermore, ex vivo cultures of human intestinal lamina propria mononuclear cells and iNKT cell clones from IBD patients with vancomycin pre-treated sterile fecal water showed a Th1/Th17 skewing in CD4+ T-cell populations; metronidazole, on the other hand, induced the polarization of iNKT cells toward the production of IL10. Conclusions Diverse antibiotic regimens affect the ability of the gut microbiota to control intestinal inflammation in experimental colitis by altering the microbial community structure and microbiota-derived metabolites.

Significance The incidence of chronic inflammatory autoimmune conditions, such as celiac disease (CD), is increasing at an alarming rate. CD is the only autoimmune condition for which the trigger, gluten, is known. However, its etiology and pathogenesis remain incompletely defined as recent studies suggest other environmental stimuli may play a key role in CD pathogenesis. Here, we prospectively examine the trajectory of the gut microbiota starting 18 mo before CD onset in 10 infants who developed CD and 10 infants who did not. We identified alterations in the gut microbiota, functional pathways, and metabolome before CD onset, suggesting our approach may be used for disease prediction with the ultimate goal of identifying early preventive interventions to reestablish tolerance and prevent autoimmunity., Other than exposure to gluten and genetic compatibility, the gut microbiome has been suggested to be involved in celiac disease (CD) pathogenesis by mediating interactions between gluten/environmental factors and the host immune system. However, to establish disease progression markers, it is essential to assess alterations in the gut microbiota before disease onset. Here, a prospective metagenomic analysis of the gut microbiota of infants at risk of CD was done to track shifts in the microbiota before CD development. We performed cross-sectional and longitudinal analyses of gut microbiota, functional pathways, and metabolites, starting from 18 mo before CD onset, in 10 infants who developed CD and 10 matched nonaffected infants. Cross-sectional analysis at CD onset identified altered abundance of six microbial strains and several metabolites between cases and controls but no change in microbial species or pathway abundance. Conversely, results of longitudinal analysis revealed several microbial species/strains/pathways/metabolites occurring in increased abundance and detected before CD onset. These had previously been linked to autoimmune and inflammatory conditions (e.g., Dialister invisus, Parabacteroides sp., Lachnospiraceae, tryptophan metabolism, and metabolites serine and threonine). Others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects (e.g., Streptococcus thermophilus, Faecalibacterium prausnitzii, and Clostridium clostridioforme). Additionally, we uncovered previously unreported microbes/pathways/metabolites (e.g., Porphyromonas sp., high mannose–type N-glycan biosynthesis, and serine) that point to CD-specific biomarkers. Our study establishes a road map for prospective longitudinal study designs to better understand the role of gut microbiota in disease pathogenesis and therapeutic targets to reestablish tolerance and/or prevent autoimmunity.

The intestinal microbiome may trigger celiac disease (CD) in individuals with a genetic disposition when exposed to dietary gluten. Research demonstrates that nutrition during infancy is crucial to the intestinal microbiome engraftment. Very few studies to date have focused on the breast milk composition of subjects with a history of CD on a gluten-free diet. Here, we utilize a multi-omics approach with shotgun metagenomics to analyze the breast milk microbiome integrated with metabolome profiling of 36 subjects, 20 with CD on a gluten-free diet and 16 healthy controls. These analyses identified significant differences in bacterial and viral species/strains and functional pathways but no difference in metabolite abundance. Specifically, three bacterial strains with increased abundance were identified in subjects with CD on a gluten-free diet of which one (Rothia mucilaginosa) has been previously linked to autoimmune conditions. We also identified five pathways with increased abundance in subjects with CD on a gluten-free diet. We additionally found four bacterial and two viral species/strains with increased abundance in healthy controls. Overall, the differences observed in bacterial and viral species/strains and in functional pathways observed in our analysis may influence microbiome engraftment in neonates, which may impact their future clinical outcomes.

Additional file 1: Figure S1. Gut Microbiota composition and metabolomics analysis of DSS colitic animals after FMT with antibiotics pre-conditioned microbiota analysed according to their inflammation status. a) Partial Least Square discriminant analysis showing clustered samples according to the inflammation status. b) Metabolites that differentiate samples according to the inflammation status with a Variable Important in Projection (VIP) score > 2. c) Beta-diversity analysis on Bray-Curtis dissimilarity (p=0.459, PERMANOVA) d) Most discriminant bacterial taxa identified by LEfSe analysis. Positive and negative LDA scores indicate taxa enriched or depleted in the gut microbiota of samples grouped based on their inflammation status. Only taxa having a p|2.0| are shown.

Additional file 2: Table S1. FACS antibodies and dyes.

Additional file 3: Table S2. PERMANOVA of beta-diversity analysis as measured by Bray-Curtis dissimilarity.

Additional file 3: Taxonomic composition at the genus and family level for metagenomes.

Additional file 9: Boxplots for significant features in the cross-sectional and longitudinal analysis