Molina-Mora, J. A. | Search bioRxiv

bioRxiv2020

Common Marmoset Gut Microbiome Profiles in Health and Intestinal Disease

Chronic gastrointestinal (GI) diseases are the most common diseases in captive marmosets. The gut microbiome of healthy (n=91), inflammatory bowel disease (IBD) (n=59), and duodenal ulcer/stricture (n=23) captive marmosets was characterized. Healthy marmosets exhibited a "humanized," Bacteroidetes-dominant microbiome. Despite standardized conditions, cohorts subdivided into Prevotella- and Bacteroides-dominant groups based on marmoset source. IBD was highest in a Prevotella-dominant cohort while strictures were highest in a Bacteroides-dominant cohort. Stricture-associated dysbiosis was characterized by Anaerobiospirillum loss and Clostridium perfringens increases. Stricture tissue presented upregulation of lipid metabolism genes and increased abundance of C. perfringens, a causative agent of GI diseases and intestinal strictures in humans. IBD was associated with a lower Bacteroides:P. copri ratio within each source. Consistent with Prevotella-linked diseases, pro-inflammatory genes were upregulated. This report highlights the humanization of the captive marmoset microbiome and its potential as a "humanized" animal model of C. perfringens-induced enteritis/strictures and P. copri-associated IBD.

microbiology↗

bioRxiv2020

A first Pseudomonas aeruginosa perturbome: Identification of core genes related to multiple perturbations by a machine learning approach

Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response to different types of disturbances (termed as perturbome), leading to the idea of central control at the molecular level. We implemented a robust machine learning approach to identify and describe genes associated with multiple perturbations or perturbome in a Pseudomonas aeruginosa PAO1 model. Using microarray datasets from the Gene Expression Omnibus (GEO), we evaluated six approaches to rank and select genes: using two methodologies, data single partition (SP method) or multiple partitions (MP method) for training and testing datasets, we evaluated three classification algorithms (SVM Support Vector Machine, KNN K-Nearest neighbor and RF Random Forest). Gene expression patterns and topological features at the systems level were included to describe the perturbome elements. We were able to select and describe 46 core response genes associated with multiple perturbations in P. aeruginosa PAO1 and it can be considered a first report of the P. aeruginosa perturbome. Molecular annotations, patterns in expression levels, and topological features in molecular networks revealed biological functions of biosynthesis, binding, and metabolism, many of them related to DNA damage repair and aerobic respiration in the context of tolerance to stress. We also discuss different issues related to implemented and assessed algorithms, including data partitioning, classification approaches, and metrics. Altogether, this work offers a different and robust framework to select genes using a machine learning approach.

bioinformatics↗