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Elevated postprandial plasma glucose is a risk factor for development of type 2 diabetes and cardiovascular disease. We hypothesized that the inter-individual postprandial plasma glucose response varies partly depending on the intestinal microbiome composition and function. We analyzed data from Danish adults (n = 106), who were self-reported healthy and attended the baseline visit of two previously reported randomized controlled cross-over trials within the Gut, Grain and Greens project. Plasma glucose concentrations at five time points were measured before and during three hours after a standardized breakfast. Based on these data, we devised machine learning algorithms integrating bio-clinical, as well as shotgun-sequencing-derived taxa and functional potentials of the intestinal microbiome to predict individual postprandial glucose excursions. In this post hoc study, we found microbial and clinical features, which predicted up to 48% of the inter-individual variance of postprandial plasma glucose responses (Pearson correlation coefficient of measured vs. predicted values, R = 0.69, 95% CI: 0.45 to 0.84, p<0.001). The features were age, fasting serum triglycerides, systolic blood pressure, BMI, fasting total serum cholesterol, abundance of Bifidobacterium genus, richness of metagenomics species and abundance of a metagenomic species annotated to Clostridiales at order level. A model based only on microbial features predicted up to 14% of the variance in postprandial plasma glucose excursions (R = 0.37, 95% CI: 0.02 to 0.64, p = 0.04). Adding fasting glycaemic measures to the model including microbial and bio-clinical features increased the predictive power to R = 0.78 (95% CI: 0.59 to 0.89, p<0.001), explaining more than 60% of the inter-individual variance of postprandial plasma glucose concentrations. The outcome of the study points to a potential role of the taxa and functional potentials of the intestinal microbiome. If validated in larger studies our findings may be included in future algorithms attempting to develop personalized nutrition, especially for prediction of individual blood glucose excursions in dys-glycaemic individuals. [ABSTRACT FROM AUTHOR]

To identify the molecular mechanisms and novel therapeutic targets of late-onset Alzheimer's Disease (LOAD), we performed an integrative network analysis of multi-omics profiling of four cortical areas across 364 donors with varying cognitive and neuropathological phenotypes. Our analyses revealed thousands of molecular changes and uncovered neuronal gene subnetworks as the most dysregulated in LOAD. ATP6V1A was identified as a key regulator of a top-ranked neuronal subnetwork, and its role in disease-related processes was evaluated through CRISPR-based manipulation in human induced pluripotent stem cell-derived neurons and RNAi-based knockdown in Drosophila models. Neuronal impairment and neurodegeneration caused by ATP6V1A deficit were improved by a repositioned compound, NCH-51. This study provides not only a global landscape but also detailed signaling circuits of complex molecular interactions in key brain regions affected by LOAD, and the resulting network models will serve as a blueprint for developing next-generation therapeutic agents against LOAD. • Development of gene network models of four cortical areas affected by LOAD • Identification of region-specific molecular changes and gene subnetworks in LOAD • ATP6V1A is a top key regulator of a neuronal subnetwork most disrupted in LOAD • NCH-51 normalizes neuronal impairment and neurodegeneration caused by ATP6V1A deficit Employing an integrative network biology approach, Wang et al. identify critical gene subnetworks associated with late-onset Alzheimer's disease (LOAD) and predict ATP6V1A as a key regulator of a neuron-specific subnetwork most affected by LOAD. ATP6V1A deficit causes neuronal impairment and neurodegeneration, which are normalized by a predicted compound, NCH-51. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: To identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role., Methods: One hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool., Results: Measures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals., Conclusions: Individuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome., Competing Interests: PR reports personal fees from Bayer during the conduct of the study. He has received research support and personal fees from AstraZeneca and Novo Nordisk, and personal fees from Astellas Pharma, Boehringer Ingelheim, Eli Lilly, Gilead Sciences, Mundipharma, Sanofi, and Vifor Pharma. All fees are given to Steno Diabetes Center Copenhagen. Author MC-G was employed by company LEITAT Technological Center. Author JKV was employed by Clinical Microbiomics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Clos-Garcia, Ahluwalia, Winther, Henriksen, Ali, Fan, Stankevic, Lyu, Vogt, Hansen, Legido-Quigley, Rossing and Pedersen.)

The antiobesity effect of celastrol has been reported in numerous studies, but the underlying mechanism remains unclear. It is widely accepted that gut dysbiosis is closely related to obesity. The potential effect of celastrol on microbiota is worth exploring. In this study, the celastrol-induced weight loss was validated in high-fat diet (HFD)-induced obese mice, with the detection of reported phenotypes including a reduction in food intake, augments in dyslipidemia and glucose metabolism, and adipose thermogenesis. The anti-inflammatory effect of celastrol was also proved based on the alterations in serum cytokines. Antibiotic interference showed that gut microbiota contributes to celastrol-induced weight loss. Several key bacteria were identified using shotgun metagenomic sequencing to display the alterations of the intestinal microbiome in obese mice treated with celastrol. Meanwhile, the fecal and serum metabolic profiles were generated by pseudotargeted metabolomics, and changes in some critical metabolites related to appetite and metabolism were detected. Importantly, we applied in silico bidirectional mediation analysis to identify the precise connections among the alterations in gut microbes, serum metabolome, and host phenotypes induced by celastrol treatment for the first time. Therefore, we concluded that the celastrol-induced microbial changes partially contribute to the antiobesity effect via the serum metabolome. The mass spectrometry data are deposited on MetaboLights (ID: MTBLS3278).