ArticleBMC genomics2019
A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Searching for a Consensus Among Inflammatory Bowel Disease Studies: A Systematic Meta-Analysis.Inflammatory bowel diseases · 2023Pooled it
- Comparative metagenomic analysis of bacterial communities associated with two mealybug species, Phenacoccus saccharifolii and Dysmicoccus carens infesting sugarcane in Tamil Nadu, India.World journal of microbiology & biotechnology · 2025Article
- The human microbiome in clinical translation: from bench to bedside.Frontiers in microbiology · 2025Review
- SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms.Scientific reports · 2024Article
- Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood.The Science of the total environment · 2024Article
- Strategies for tailoring functional microbial synthetic communities.The ISME journal · 2024Review
- Gut Microbial Shifts Indicate Melanoma Presence and Bacterial Interactions in a Murine Model.Diagnostics (Basel, Switzerland) · 2022Article
- The gut microbiota is associated with the small intestinal paracellular permeability and the development of the immune system in healthy children during the first two years of life.Journal of translational medicine · 2021Article
- Associations of sodium and potassium consumption with the gut microbiota and host metabolites in a population-based study in Chinese adults.The American journal of clinical nutrition · 2020Article
- Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.Metabolites · 2020Review
- Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction.Frontiers in molecular biosciences · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrobiomes play vital roles in shaping environments and stabilize them based on their compositions and inter-species relationships among its species. Variations in microbial properties have been reported to have significant impact on their host environment. For example, variants in gut microbiomes have been reported to be associated with several chronic conditions, such as inflammatory disease and irritable bowel syndrome. However, how microbial bacteria contribute to pathogenesis still remains unclear and major research questions in this domain remain unanswered.
methodsWe propose a split graph model to represent the composition and interactions of a given microbiome. We used metagenomes from Korean populations in this study. The dataset consists of three different types of samples, viz. mucosal tissue and stool from Crohn's disease patients and stool from healthy individuals. We use the split graph model to analyze the impact of microbial compositions on various host phenotypes. Utilizing the graph model, we have developed a pipeline that integrates genomic information and pathway analysis to characterize both critical informative components of inter-bacterial correlations and associations between bacterial taxa and various metabolic pathways.
resultsThe obtained results highlight the importance of the microbial communities and their inter-relationships and show how these microbial structures are correlated with Crohn's disease. We show that there are significant positive associations between detected taxonomic biomarkers as well as multiple functional modules in the split graph of mucosal tissue samples from CD patients. Bacteria Moraxellaceae and Pseudomonadaceae were detected as taxonomic biomarkers in CD groups. Higher abundance of these bacteria have been reported in previous study and several metabolic pathways associated with these bacteria were characterized in CD samples.
conclusionsThe proposed pipeline provides a new way to approach the analysis of complex microbiomes. The results obtained from this study show great potential in unraveling mechansims in complex biological systems to understand how various components in such complex environments are associated with critical biological functions.
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