ORCID Profile
0000-0001-8628-2069
Current Organisation
University of California, San Diego
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Publisher: American Society for Microbiology
Date: 26-04-2022
DOI: 10.1128/MSYSTEMS.00167-22
Abstract: Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene licon sequencing for decoding the composition and structure of microbial communities. Current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution.
Publisher: Cold Spring Harbor Laboratory
Date: 11-11-2021
DOI: 10.1101/2021.11.10.21266163
Abstract: To examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort. We collected fecal s les of 5 572 Finns (mean age 48.7 years, 54.1% women) in 2002 who were followed up for incident type 2 diabetes until Dec 31 st , 2017. The s les were sequenced using shotgun metagenomics. We examined associations between gut microbiome compositions and incident diabetes using multivariable-adjusted Cox regression models. We first used the Eastern Finland sub-population to obtain initial findings and validated these in the Western Finland sub-population. Altogether 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected 4 species and 2 clusters consistently associated with incident diabetes in the validation models. These 4 species were Clostridium citroniae (HR, 1.21 95% CI, 1.04-1.42), C. bolteae (HR, 1.20 95% CI, 1.04-1.39), Tyzzerella nexilis (HR, 1.17 95% CI, 1.01-1.36), and Ruminococcus gnavus (HR = 1.17 95% CI, 1.01-1.36). The positively associated clusters, cluster 1 (HR, 1.18 95% CI, 1.02-1.38) and cluster 5 (HR, 1.18 95% CI, 1.02-1.36), mostly consisted of these same species. We observed robust species-level taxonomic features predictive of incident type 2 diabetes over a long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome could potentially be used to improve risk prediction and to uncover novel therapeutic targets for diabetes.
Publisher: Cold Spring Harbor Laboratory
Date: 02-01-2020
DOI: 10.1101/2019.12.30.19015842
Abstract: The collection of fecal material and developments in sequencing technologies have enabled cost-efficient, standardized, and non-invasive gut microbiome profiling. As a result, microbiome composition data from several large cohorts have been cross-sectionally linked to various lifestyle factors and diseases. 1–5 In spite of these advances, prospective associations between microbiome composition and health have remained uncharacterized due to the lack of sufficiently large and representative population cohorts with comprehensive follow-up data. 6–8 Here, we analyse the long-term association between gut microbiome variation and mortality in a large, well-phenotyped, and representative population cohort ( n = 7211, FINRISK 2002 Finland). 9 We report specific taxonomic and functional signatures related to the Enterobacteriaceae family in the human gut microbiome that predict mortality during a 15-year follow-up. These associations can be observed both in the Eastern and Western Finns who have differing genetic backgrounds, lifestyles, and mortality rates. 10,11 Our results supplement previously reported cross-sectional associations, 1–4,12 and help to establish a methodological and conceptual basis for examining long-term associations between human gut microbiome composition, incident outcomes, and general health status. These findings could serve as a solid framework for microbiome profiling in clinical risk prediction, paving the way towards clinical applications of human microbiome sequencing aimed at prediction, prevention, and treatment of disease.
Publisher: Cold Spring Harbor Laboratory
Date: 13-09-2020
DOI: 10.1101/2020.09.12.20193045
Abstract: Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped in iduals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-s ling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance ( p .0×10 -8 ), which included notable strong associations with LCT ( p =5.02×10 -35 ), ABO ( p =1.1×10 -12 ), and MED13L ( p =1.84×10 -12 ). A combination of genetics and dietary habits was shown to strongly shape the abundances of certain key bacterial members of the gut microbiota, and explain their genetic association. Genetic effects from the LCT locus on Bifidobacterium and three other associated taxa significantly differed according to dairy intake. Variation in mucin-degrading Faecalicatena lactaris abundances were associated with ABO , highlighting a preferential utilization of secreted A/B/AB-antigens as energy source in the gut, irrespectively of fibre intake. Enterococcus faecalis levels showed a robust association with a variant in MED13L , with putative links to colorectal cancer. Finally, we identified putative causal relationships between gut microbes and complex diseases using MR, with a predicted effect of Morganella on major depressive disorder that was consistent with observational incident disease analysis. Overall, we present striking ex les of the intricate relationship between humans and their gut microbial communities, and highlight important health implications.
Publisher: Informa UK Limited
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 08-2020
DOI: 10.1101/2020.07.30.20164962
Abstract: Fatty liver disease is the most common liver disease in the world. It is characterized by a buildup of excess fat in the liver that can lead to cirrhosis and liver failure. The link between fatty liver disease and gut microbiome has been known for at least 80 years. However, this association remains mostly unstudied in the general population because of underdiagnosis and small s le sizes. To address this knowledge gap, we studied the link between the Fatty Liver Index (FLI), a well-established proxy for fatty liver disease, and gut microbiome composition in a representative, ethnically homogeneous population s le in Finland. We based our models on biometric covariates and gut microbiome compositions from shallow metagenome sequencing. Our classification models could discriminate between in iduals with a high FLI (≥ 60, indicates likely liver steatosis) and low FLI ( 60) in our validation set, consisting of 30% of the data not used in model training, with an average AUC of 0.75. In addition to age and sex, our models included differences in 11 microbial groups from class Clostridia , mostly belonging to orders Lachnospirales and Oscillospirales . Pathway analysis of representative genomes of the FLI-associated taxa in (NCBI) Clostridium subclusters IV and XIVa indicated the presence of e.g ., ethanol fermentation pathways. Through modeling the fatty liver index, our results provide with high resolution associations between gut microbiota composition and fatty liver in a large representative population cohort and support the role of endogenous ethanol producers in the development of fatty liver.
Location: United States of America
No related grants have been discovered for Mohit Jain.