Loganathan, T. | bioRxiv Science

bioRxiv2023

Alcohol Responsive Genes in the Neutrophils of Acute-On-Chronic Liver Failure Reveal Modulation of Intracellular Calcium, ROS, and Phagocytosis

Acute-on-chronic liver failure (ACLF) is characterized by rapid depletion of hepatic reserves, innate immunity-driven pathogenesis and high short-term mortality. Elevation of neutrophil counts is associated with mortality in alcoholic-ACLF. In India, one of the most common etiologies in ACLF for both chronic and acute injury is alcohol. We used a combination of clinical transcriptomics and a novel in vitro laboratory model to look at gene signatures unique in alcoholic ACLF patients and study phenotypic changes upon alcohol treatment. CCL20 and IL1{beta} were two of the top overexpressed genes in alcoholic-ACLF which were validated in the alcohol-neutrophil model, using neutrophils isolated from CLD patients (acute-on-chronic model), and healthy individual (acute model). In the alcoholic-neutrophil models, 300 mg/dL dose of ethanol for 24 hours, caused reduced ROS generation, reduced phagocytic ability and dampening of intracellular calcium reserves. We conclude that the effects of alcohol at least partially explain immune dysfunction in ACLF such as dampening of ROS and phagocytosis in neutrophils while retaining inflammatory roles such as expression of CCL20 and IL1{beta}. Systematic curation of previously published alcohol related liver tissue transcriptomic data also confirmed overlapping genes with alcoholic-ACLF circulating neutrophils. Docking of ethanol with CCL20 and IL1{beta} showed spontaneous binding.

cell biology↗

bioRxiv2021

Predicting cross-tissue hormone-gene relations usingbalanced word embeddings

MotivationLarge volumes of biomedical literature present an opportunity to build whole-body human models comprising both within-tissue and across-tissue interactions among genes. Current studies have mostly focused on identifying within-tissue or tissue-agnostic associations, with a heavy emphasis on associations among disease, genes and drugs. Literature mining studies that extract relations pertaining to inter-tissue communication, such as between genes and hormones, are solely missing. ResultsWe present here a first study to identify from literature the genes involved in inter-tissue signaling via a hormone in the human body. Our models BioEmbedS and BioEmbedS-TS respectively predict if a hormone-gene pair is associated or not, and whether an associated gene is involved in the hormones production or response. Our models are classifiers trained on word embeddings that we had carefully balanced across different strata of the training data such as across production vs. response genes of a hormone (or) well-studied vs. poorly-represented hormones in the literature. Model training and evaluation are enabled by a unified dataset called HGv1 of ground-truth associations between genes and known endocrine hormones that we had compiled. Our models not only recapitulate known gene mediators of tissue-tissue signaling (e.g., at average 70.4% accuracy for BioEmbedS), but also predicts novel genes involved in inter-tissue communication in humans. Furthermore, the species-agnostic nature of our ground-truth HGv1 data and our predictive modeling approach, demonstrated concretely using human data and generalized to mouse, hold much promise for future work on elucidating inter-tissue signaling in other multi-cellular organisms. AvailabilityProposed HGv1 dataset along with our models predictions, and the associated code to reproduce this work are available respectively at https://cross-tissue-signaling.herokuapp.com/, and https://github.com/BIRDSgroup/BioEmbedS. Contactnmanik@cse.iitm.ac.in

bioinformatics↗