Mutual modulation of gut microbiota and the immune system in type 1 diabetes models

The transgenic 116C-NOD mouse strain exhibits a prevalent Th17 phenotype, and reduced type 1 diabetes (T1D) compared to non-obese diabetic (NOD) mice. A cohousing experiment between both models revealed lower T1D incidence in NOD mice cohoused with 116C-NOD, associated with gut microbiota changes, reduced intestinal permeability, shifts in T and B cell subsets, and a transition from Th1 to Th17 responses. Distinct gut bacterial signatures were linked to T1D in each group. Using a RAG-2-/- genetic background, we found that T cell alterations promoted segmented filamentous bacteria proliferation in young NOD and 116C-NOD, as well as in immunodeficient NOD.RAG-2-/- and 116C-NOD.RAG-2-/- mice across all ages. Bifidobacterium colonization depended on lymphocytes and thrived in a non-diabetogenic environment. Additionally, 116C-NOD B cells in 116C-NOD.RAG-2-/- mice enriched the gut microbiota in Adlercreutzia and reduced intestinal permeability. Collectively, these results indicate reciprocal modulation between gut microbiota and the immune system in rodent T1D models.

Dysbiosis: An Indicator of COVID-19 Severity in Critically Ill Patients

Here, we examined the dynamics of the gut and respiratory microbiomes in severe COVID-19 patients in need of mechanical ventilation in the intensive care unit (ICU). We recruited 85 critically ill patients (53 with COVID-19 and 32 without COVID-19) and 17 healthy controls (HCs) and monitored them for up to 4 weeks. We analyzed the bacterial and fungal taxonomic profiles and loads of 232 gut and respiratory samples and we measured the blood levels of Interleukin 6, IgG, and IgM in COVID-19 patients. Upon ICU admission, the bacterial composition and load in the gut and respiratory samples were altered in critically ill patients compared with HCs. During their ICU stay, the patients experienced increased bacterial and fungal loads, drastic decreased bacterial richness, and progressive changes in bacterial and fungal taxonomic profiles. In the gut samples, six bacterial taxa could discriminate ICU-COV(+) from ICU-COV(-) cases upon ICU admission and the bacterial taxa were associated according to age, PaO2/FiO2, and CRP levels. In the respiratory samples of the ICU-COV(+) patients, bacterial signatures including Pseudomonas and Streptococcus were found to be correlated with the length of ICU stay. Our findings demonstrated that the gut and respiratory microbiome dysbiosis and bacterial signatures associated with critical illness emerged as biomarkers of COVID-19 severity and could be a potential predictor of ICU length of stay. We propose using a high-throughput sequencing approach as an alternative to traditional isolation techniques to monitor ICU patient infection.

Human gut metatranscriptome changes induced by a fermented milk product are associated with improved tolerance to a flatulogenic diet

Healthy plant-based diets rich in fermentable residues may induce gas-related symptoms, possibly mediated by the gut microbiota. We previously showed that consumption of a fermented milk product (FMP) containing Bifidobacterium animalis subsp. lactis CNCM I-2494 and lactic acid bacteria improved gastrointestinal (GI) comfort in response to a flatulogenic dietary challenge in healthy individuals. To study the effects of the FMP on gut microbiota activity from those participants, we conducted a metatranscriptomic analysis of fecal samples (n = 262), which were collected during the ingestion of a habitual diet and two series of a 3-day high-residue challenge diet, before and following 28-days of FMP consumption.

Most of the FMP species were detected or found enriched upon consumption of the product. FMP mitigated the effect of a flatulogenic diet on gas-related symptoms in several ways. First, FMP consumption was associated with the depletion of gas-producing bacteria and increased hydrogen to methane conversion. It also led to the upregulation of activities such as replication and downregulation of functions related to motility and chemotaxis. Furthermore, upon FMP intake, metabolic activities such as carbohydrate metabolism, attributed to B. animalis and S. thermophilus, were enriched; these activities were coincidentally found to be negatively associated with several GI symptoms. Finally, a more connected microbial ecosystem or mutualistic relationship among microbes was found in responders to the FMP intervention.

Taken together, these findings suggest that consumption of the FMP improved the tolerance of a flatulogenic diet through active interactions with the resident gut microbiota.

Dysbiosis and relapse-related microbiome in inflammatory bowel disease: A shotgun metagenomic approach

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CD more dysbiotic than UC both at the microbial taxonomic and functional level.

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E. coli enriched in CD and almost undetected in UC.

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Production of propionate driven by E. coli in CD and by Anaerostipes hadrus in UC and healthy controls.

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Microbial signatures and random forest enabled discrimination of IBD vs. non-IBD and prediction of flare.

Crohn’s disease (CD) and ulcerative colitis (UC), the two main forms of inflammatory bowel disease (IBD), affect several million people worldwide. CD and UC are characterized by periods of clinical remission and relapse. Although IBD patients present chronic alterations of the gut microbiome, called dysbiosis, little attention has been devoted to the relapse-related microbiome. To address this gap, we generated shotgun metagenomic data from the stools of two European cohorts—134 Spanish (followed up for one year) and 49 Belgian (followed up for 6 months) subjects—to characterize the microbial taxonomic and metabolic profiles present. To assess the predictive value of microbiome data, we added the taxonomic profiles generated from a previous study of 130 Americans. Our results revealed that CD was more dysbiotic than UC compared to healthy controls (HC) and that strategies for energy extraction and propionate production were different in CD compared to UC and HC. Remarkably, CD and UC relapses were not associated with alpha- or beta-diversity, or with a dysbiotic score. However, CD relapse was linked to alterations at the species and metabolic pathway levels, including those involved in propionate production. The random forest method using taxonomic profiles allowed the prediction of CD vs. non-CD with an AUC = 0.938, UC vs. HC with an AUC = 0.646, and CD relapse vs. remission with an AUC = 0.769. Our study validates previous taxonomic findings, points to different relapse-related growth and defence mechanisms in CD compared to UC and HC and provides biomarkers to discriminate IBD subtypes and predict disease activity.

Integrating Dietary Data into Microbiome Studies: A Step Forward for Nutri-Metaomics

Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies (n = 84: a first pilot study (n = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs); a second study (n = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman's correlation, Wilcoxon test, and Bland-Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies.