A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships

Kim, Su Yeon; Thapa, Ishwor; Zhang, Ling; Ali, Hesham

doi:10.1186/s12864-019-6288-7

Cited by 10 publications

(6 citation statements)

References 44 publications

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“…The Genus Bacteroides and Eubacterium have multiple subgroups, and the Pectinophilus group was selected for Bacteroides, and the nodatum group was selected as a stand-in for Eubacterium, and both passed our filtering procedures. In the last example, the authors had used a linear discriminant analysis effect size approach to determine three important taxa, two from the Family-level and one from the Order level, all of which are present in our features (Kim et al, 2019).…”

Section: Evaluation Of Machine Learning Resultsmentioning

confidence: 99%

Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction

Song

Wright

Zhou

2020

Front. Mol. Biosci.

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Microbiome composition profiles generated from 16S rRNA sequencing have been extensively studied for their usefulness in phenotype trait prediction, including for complex diseases such as diabetes and obesity. These microbiome compositions have typically been quantified in the form of Operational Taxonomic Unit (OTU) count matrices. However, alternate approaches such as Amplicon Sequence Variants (ASV) have been used, as well as the direct use of k-mer sequence counts. The overall effect of these different types of predictors when used in concert with various machine learning methods has been difficult to assess, due to varied combinations described in the literature. Here we provide an in-depth investigation of more than 1,000 combinations of these three clustering/counting methods, in combination with varied choices for normalization and filtering, grouping at various taxonomic levels, and the use of more than ten commonly used machine learning methods for phenotype prediction. The use of short k-mers, which have computational advantages and conceptual simplicity, is shown to be effective as a source for microbiome-based prediction. Among machine-learning approaches, tree-based methods show consistent, though modest, advantages in prediction accuracy. We describe the various advantages and disadvantages of combinations in analysis approaches, and provide general observations to serve as a useful guide for future trait-prediction explorations using microbiome data.

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Section: Evaluation Of Machine Learning Resultsmentioning

confidence: 99%

Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction

Song

Wright

Zhou

2020

Front. Mol. Biosci.

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“…The authors of the Kim et al 60 study analyzed data originally collected by Eun et al 67 but only the former study was retrieved by our systematic search strategy; thus, this dataset is referred to as Kim et al…”

Section: Methodsmentioning

confidence: 99%

Searching for a Consensus Among Inflammatory Bowel Disease Studies: A Systematic Meta-Analysis

Abdel-Rahman

Morgan

2022

Inflammatory Bowel Diseases

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Background Numerous studies have examined the gut microbial ecology of patients with Crohn’s disease (CD) and ulcerative colitis, but inflammatory bowel disease–associated taxa and ecological effect sizes are not consistent between studies. Methods We systematically searched PubMed and Google Scholar and performed a meta-analysis of 13 studies to analyze how variables such as sample type (stool, biopsy, and lavage) affect results in inflammatory bowel disease gut microbiome studies, using uniform bioinformatic methods for all primary data. Results Reduced alpha diversity was a consistent feature of both CD and ulcerative colitis but was more pronounced in CD. Disease contributed significantly variation in beta diversity in most studies, but effect size varied, and the effect of sample type was greater than the effect of disease. Fusobacterium was the genus most consistently associated with CD, but disease-associated genera were mostly inconsistent between studies. Stool studies had lower heterogeneity than biopsy studies, especially for CD. Conclusions Our results indicate that sample type variation is an important contributor to study variability that should be carefully considered during study design, and stool is likely superior to biopsy for CD studies due to its lower heterogeneity.

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“…we found that, in Liaoning, Henan, and Shanghai, sodium consumption was positively associated with pathogenic bacteria including Staphylococcus, which causes a wide variety of severe infections (38), and Moraxellaceae, a biomarker for Crohn disease (47), indicating that high sodium intake may increase the susceptibility to gut infection and inflammation. Indeed, sodium exposure has been shown to enhance proinflammatory cytokine production in human intestinal mononuclear cells and a highsodium diet exacerbated colitis in mice (48).…”

Section: Discussionmentioning

confidence: 79%

Associations of sodium and potassium consumption with the gut microbiota and host metabolites in a population-based study in Chinese adults

Wang

Howard

et al. 2020

The American Journal of Clinical Nutrition

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Background There is increasing evidence that sodium consumption alters the gut microbiota and host metabolome in murine models and small studies in humans. However, there is a lack of population-based studies that capture large variations in sodium consumption as well as potassium consumption. Objective We examined the associations of energy-adjusted dietary sodium (milligrams/kilocalorie), potassium, and sodium-to-potassium (Na/K) ratio with the microbiota and plasma metabolome in a well-characterized Chinese cohort with habitual excessive sodium and deficient potassium consumption. Methods We estimated dietary intakes from 3 consecutive validated 24-h recalls and household inventories. In 2833 adults (18–80 y old, 51.2% females), we analyzed microbial (genus-level 16S ribosomal RNA) between-person diversity, using distance-based redundancy analysis (dbRDA), and within-person diversity and taxa abundance using linear regression, accounting for geographic variation in both. In a subsample (n = 392), we analyzed the overall metabolome (dbRDA) and individual metabolites (linear regression). P values for specific taxa and metabolites were false discovery rate adjusted (q-value). Results Sodium, potassium, and Na/K ratio were associated with microbial between-person diversity (dbRDA P < 0.01) and several specific taxa with large geographic variation, including pathogenic Staphylococcus and Moraxellaceae, and SCFA-producing Phascolarctobacterium and Lachnospiraceae (q-value < 0.05). For example, sodium and Na/K ratio were positively associated with Staphylococcus and Moraxellaceae in Liaoning, whereas potassium was positively associated with 2 genera from Lachnospiraceae in Shanghai. Additionally, sodium, potassium, and Na/K ratio were associated with the overall metabolome (dbRDA P ≤ 0.01) and several individual metabolites, including butyrate/isobutyrate and gut-derived phenolics such as 1,2,3-benzenetriol sulfate, which was negatively associated with sodium in Guizhou (q-value < 0.05). Conclusions Our findings suggest that sodium and potassium consumption is associated with taxa and metabolites that have been implicated in cardiometabolic health, providing insights into the potential roles of gut microbiota and host metabolites in the pathogenesis of sodium- and potassium-associated diseases. More studies are needed to confirm our results.

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A novel graph theoretical approach for modeling microbiomes and inferring microbial ecological relationships

Cited by 10 publications

References 44 publications

Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction

Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based Disease Prediction

Searching for a Consensus Among Inflammatory Bowel Disease Studies: A Systematic Meta-Analysis

Associations of sodium and potassium consumption with the gut microbiota and host metabolites in a population-based study in Chinese adults

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