Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study.

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils' accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

Distinct microbial community along the chronic oil pollution continuum of the Persian Gulf converge with oil spill accidents.

The Persian Gulf, hosting ca. 48% of the world's oil reserves, has been chronically exposed to natural oil seepage. Oil spill studies show a shift in microbial community composition in response to oil pollution; however, the influence of chronic oil exposure on the microbial community remains unknown. We performed genome-resolved comparative analyses of the water and sediment samples along Persian Gulf's pollution continuum (Strait of Hormuz, Asalouyeh, and Khark Island). Continuous exposure to trace amounts of pollution primed the intrinsic and rare marine oil-degrading microbes such as Oceanospirillales, Flavobacteriales, Alteromonadales, and Rhodobacterales to bloom in response to oil pollution in Asalouyeh and Khark samples. Comparative analysis of the Persian Gulf samples with 106 oil-polluted marine samples reveals that the hydrocarbon type, exposure time, and sediment depth are the main determinants of microbial response to pollution. High aliphatic content of the pollution enriched for Oceanospirillales, Alteromonadales, and Pseudomonadales whereas, Alteromonadales, Cellvibrionales, Flavobacteriales, and Rhodobacterales dominate polyaromatic polluted samples. In chronic exposure and oil spill events, the community composition converges towards higher dominance of oil-degrading constituents while promoting the division of labor for successful bioremediation.

Co-abundance analysis reveals hidden players associated with high methane yield phenotype in sheep rumen microbiome.

Rumen microbial environment hosts a variety of microorganisms that interact with each other to carry out the feed digestion and generation of several by-products especially methane, which plays an essential role in global warming as a greenhouse gas. However, due to its multi-factorial nature, the exact cause of methane production in the rumen has not yet been fully determined. The current study is an attempt to use system modeling to analyze the relationship between interacting components of rumen microbiome and its role in methane production. Metagenomic data of sheep rumen, with equal numbers of high methane yield (HMY) and low methane yield (LMY) samples, were used. As a well-known approach for the systematic comparative study of complex traits, the co-abundance networks were constructed in both operational taxonomic unit (OTU) and gene levels. A gene-catalog of 1,444 different rumen microbial strains was developed as a reference to measure gene abundances. The results from both types of co-abundance networks showed that methanogens, which are the main ruminal source for methanogenesis, need other microbial species to accomplish the task of methane production through producing the main precursor molecules like H2 and acetate for methanogenesis pathway as their byproducts. KEGG Orthology(KO) analysis of the current study shows that the metabolism and growth rate of methanogens will be increased due to the higher rate of the metabolism and carbohydrate/fiber digestion pathways in the hidden elements. This finding proposes that any ruminant methane yield alteration strategy should consider complex interactions of rumen microbiome components as one tightly integrated unit rather than several separate parts.