Journal: Microbial Ecology

Article Title: Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

doi: 10.1007/s00248-024-02370-7

Figure Lengend Snippet: Summary of quantitative frameworks commonly employed to study microbial interactions with a brief discussion on its implementation, strengths and limitations

Article Snippet: , COBRA model with flux balance analysis: Novel use of constraint based microbial community modeling on an individual with episodic inflammation of GI tract , COBRA and Microbiome Modeling Toolbox—MATLAB (Mathworks) , Inclusion of flux balance analysis allowed for the adoption of the COBRA framework for understanding net metabolite production as a function of changes in gut microbiota composition , Does not account for inclusion of confounding factors such as antibiotic use. However, the model can integrate metagenomic and metabolite data efficiently to generate novel hypothesis for testing , [ ] .

Techniques: Ex Vivo, Construct, Sequencing, Transformation Assay, Gene Expression, Biomarker Discovery, In Silico, Combined Bisulfite Restriction Analysis Assay

Journal: Microbial Ecology

Article Title: Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

doi: 10.1007/s00248-024-02370-7

Figure Lengend Snippet: Summary of qualitative methods available to study microbial interactions with a description of the characterized microbial interactions/behavior

Article Snippet: , COBRA model with flux balance analysis: Novel use of constraint based microbial community modeling on an individual with episodic inflammation of GI tract , COBRA and Microbiome Modeling Toolbox—MATLAB (Mathworks) , Inclusion of flux balance analysis allowed for the adoption of the COBRA framework for understanding net metabolite production as a function of changes in gut microbiota composition , Does not account for inclusion of confounding factors such as antibiotic use. However, the model can integrate metagenomic and metabolite data efficiently to generate novel hypothesis for testing , [ ] .

Techniques: Fluorescence, Imaging, Electron Microscopy, Transmission Assay, Microscopy, Isolation, Concentration Assay, In Vitro, Cell Culture, Confocal Microscopy, Produced, Chromatography, Algae

Journal: Gut Microbes

Article Title: Fecal microbiota composition associates with the capacity of human peripheral blood monocytes to differentiate into immunogenic dendritic cells in vitro

doi: 10.1080/19490976.2021.1921927

Figure Lengend Snippet: Differences in fecal microbiota diversity are associated with the capacity of healthy donors’ monocytes to differentiate into imDCs and mDCs. Box plots represent the comparison of Shannon diversity indices (a) between donors arranged in two groups according to each of the 60 immune markers expression at ≤ median and at > median level by DCs. Distance groups comparison between the donors that express the immune marker at ≤ median level by DC (white column), between the donors that express immune marker at ≤ median level by DC and the donors that express immune marker at > median level by DC (light gray column), and between the donors that express immune marker at > median level by DC (dark gray column) (b) are based on Bray-Curtis distance matrix and significance determined by ANOSIM following 999 permutations. Principal coordinates’ analysis plot (c) of beta diversity based on Bray-Curtis distance matrix for two groups of donors. Only statistically significant comparisons after correction with Benjamini-Hochberg procedure for multiple testing FDR are presented

Article Snippet: For all the comparisons with the W coefficient higher than 1, the statistical significance was confirmed with Pairwise Wilcoxon rank-sum test with the Benjamini-Hochberg procedure for multiple testing FDR control and plotted in RStudio using several packages, qiime2R, phyloseq, microbiome, ggplot2, and stats (R Core Team).

Techniques: Comparison, Expressing, Marker

Journal: Gut Microbes

Article Title: Fecal microbiota composition associates with the capacity of human peripheral blood monocytes to differentiate into immunogenic dendritic cells in vitro

doi: 10.1080/19490976.2021.1921927

Figure Lengend Snippet: Different fecal microbiota composition is associated with the capacity of healthy donors’ monocytes to differentiate into imDCs and mDCs. Differential abundance of 6 phylum (a), 32 family (b), 58 genus (c), and 63 species (d) between two donors’ groups classified according to each of the 60 immune markers expression at ≤ median and at > median level by DCs. The analysis was performed by QIIME2 p -composition plugin for analysis of the composition of microbiomes (ANCOM) and all comparisons resulted in coefficient W higher than 1 statistical significance was confirmed with Pairwise Wilcoxon rank-sum test with the Benjamini-Hochberg procedure for multiple testing FDR control. To assess the relationship between the immune markers and bacterial taxa (same as A–D) linear regression analysis was performed (e) using R lm function, adjusting all models for age and gender. Beta coefficients and FDR adjusted p values were used for heat map generation in the Graph Pad software. For additional information see Materials and Methods. Only statistically significant comparisons after Benjamini-Hochberg procedure (FDR) are presented

Article Snippet: For all the comparisons with the W coefficient higher than 1, the statistical significance was confirmed with Pairwise Wilcoxon rank-sum test with the Benjamini-Hochberg procedure for multiple testing FDR control and plotted in RStudio using several packages, qiime2R, phyloseq, microbiome, ggplot2, and stats (R Core Team).

Techniques: Expressing, Control, Software