Dahale, S. | Search bioRxiv

bioRxiv2021

Cap analysis of gene expression (CAGE) sequencing reveals alternative promoter usage in complex disease

The role of alternative promoter usage in tissue specific gene expression has been well established, however, its role in complex diseases is poorly understood. We performed cap analysis of gene expression (CAGE) tag sequencing from the left ventricle (LV) of a rat model of hypertension, the spontaneously hypertensive rat (SHR), and a normotensive strain, the Brown Norway (BN) to understand role of alternative promoter usage in complex disease. We identified 26,560 CAGE-defined transcription start sites (TSS) in the rat LV, including 1,970 novel cardiac TSS resulting in new transcripts. We identified 27 genes with alternative promoter usage between SHR and BN which could lead to protein isoforms differing at the amino terminus between two strains. Additionally, we identified 475 promoter switching events where a shift in TSS usage was within 100bp between SHR and BN, altering length of the 5 UTR. Genomic variants located in the shifting promoter regions showed significant allelic imbalance in F1 crosses, confirming promoter shift. We found that the insulin receptor gene (Insr) showed a switch in promoter usage between SHR and BN in heart and liver. The Insr promoter shift was significantly associated with insulin levels and blood pressure within a panel of BXH/HXB recombinant inbred (RI) rat strains. This suggests that the hyperinsulinemia due to insulin resistance might lead to hypertension in SHR. Our study provides a preliminary evidence of alternative promoter usage in complex diseases.

bioinformatics↗

bioRxiv2019

GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogens obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.\n\nAuthor SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

microbiology↗