Biology subjects

Bidkhori, G.

Publications and source records attributed to Bidkhori, G..

5 recordsLinked to original sources

bioRxiv2023

MIGRENE: The toolbox for microbial and individualized GEMs, reactobiome and community network modelling

MotivationUnderstanding microbial metabolism is crucial for evaluating shifts in human host-microbiome interactions during health and disease. However, the primary hurdle in the realm of constraint-based modeling and genome-scale metabolic models (GEMs) pertaining to host-microbiome interactions lies in the efficient utilization of metagenomic data for constructing GEMs that encompass unexplored and uncharacterized genomes. Challenges persist in effectively employing metagenomic data to address individualized microbial metabolism to investigate host-microbiome interactions. ResultTo tackle this issue, we have created a computational framework designed for personalized microbiome metabolism. This framework takes into account factors such as microbiome composition, metagenomic species profiles and microbial gene catalogues. Subsequently, it generates GEMs at microbial level and individualized microbiome metabolism, including reaction richness, reaction abundance, reactobiome, individualized reaction set enrichment (iRSE), and community models. Availability and implementationThe MIGRENE toolbox is freely available at our GitHub repository https://github.com/sysbiomelab/MIGRENE. Comprehensive tutorials on the GitHub were provided for each step of the workflow.

systems biology↗

bioRxiv2022

Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment

The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how the dietary guidelines may improve drug efficiency through the gut microbiota.

systems biology↗

bioRxiv2021

The Reactobiome Unravels a New Paradigm in Human Gut Microbiome Metabolism

Changes in microbial metabolism have been used as the main approach to assess function and elucidate environmental and host-microbiome interactions. This can be hampered by uncharacterised metagenome species and lack of metabolic annotation. To address this, we present a comprehensive computational platform for population stratification based on microbiome composition, the underlying metabolic potential and generation of metagenome species and community level metabolic models. We revisit the concepts of enterotype and microbiome richness introducing the reactobiome as a stratification method to unravel the metabolic features of the human gut microbiome. The reactobiome encapsulates resilience and microbiome dysbiosis at a functional level. We describe five reactotypes in healthy populations from 16 countries, with specific amino acid, carbohydrate and xenobiotic metabolic features. The validity of the approach was tested to unravel host-microbiome and environmental interactions by applying the reactobiome analysis on a one-year Swedish longitudinal cohort, integrating gut metagenomics, plasma metabolomics and clinical data.

systems biology↗

bioRxiv2019

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

systems biology↗

bioRxiv2019

RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

Relative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

bioinformatics↗