Numerical analyses of intestinal microbiota by data mining

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Analysis of the colonic mucosa associated microbiota (MAM) using brushing samples during colonic endoscopic procedures

The mucosa-associated microbiota is an important component in human microbiota. The aim was to investigate mucosa-associated microbiota using brush samples during endoscopic procedures and compare with fecal microbiota. Seven patients who were planning to undergo a routine colonoscopy were recruited. Mucosal brushing samples were taken from 3 sites (terminal ileum, ascending and sigmoid colon), and a fecal sample was taken on the morning of colonoscopy. The samples were immediately placed in microcentrifuge tubes containing DNA stabilization reagent and analyzed using the next generation sequencer. The individual differences in microbiota were more evident than the differences of the sampling sites. Actinobacteria was more abundant and Bacteroidetes was less in the brush samples than those in the fecal samples. Taxonomic composition at the genus level and the proportion of genes responsible for some functions in the brushing samples tended to be different from those in the fecal samples. Bulleidia and Oribacteriumi were significantly more abundant and the proportions of genes responsible for transcription factors and phosphotransferase system were higher in ileal mucous than those in fecal samples. Brushing during colonoscopic procedure instead of using feces samples might be useful to analyze mucosa-associated microbiota.

Bidirectional transfer of Anelloviridae lineages between graft and host during lung transplantation

Solid organ transplantation disrupts virus-host relationships, potentially resulting in viral transfer from donor to recipient, reactivation of latent viruses, and new viral infections. Viral transfer, colonization, and reactivation are typically monitored using assays for specific viruses, leaving the behavior of full viral populations (the "virome") understudied. Here we sought to investigate the temporal behavior of viruses from donor lungs and transplant recipients comprehensively. We interrogated the bronchoalveolar lavage and blood viromes during the peritransplant period and 6-16 months posttransplant in 13 donor-recipient pairs using shotgun metagenomic sequencing. Anelloviridae, ubiquitous human commensal viruses, were the most abundant human viruses identified. Herpesviruses, parvoviruses, polyomaviruses, and bacteriophages were also detected. Anelloviridae populations were complex, with some donor organs and hosts harboring multiple contemporaneous lineages. We identified transfer of Anelloviridae lineages from donor organ to recipient serum in 4 of 7 cases that could be queried, and immigration of lineages from recipient serum into the allograft in 6 of 10 such cases. Thus, metagenomic analyses revealed that viral populations move between graft and host in both directions, showing that organ transplantation involves implantation of both the allograft and commensal viral communities.