Diarrheal disease and gut microbiome.

Diarrheal disease remains a great public health problem in many countries. Enteric infections caused by several viral, bacterial and parasitic species not only affect the host, but also alter the gut microbiome. The host physiology dictates the intestinal milieu and decides the composition and richness of gut microbiota, which forms a homeostatic ecosystem with numerous functions and provide protection against invading pathogens. During diarrheal infection, patients are affected by gut microbial dysbiosis, which benefits the pathogenic and pro-inflammatory bacteria by enhancing their colonization and proliferation. Gut microbes are associated with several pathophysiological mechanisms, including distorted motility, intestinal barrier dysfunction, malabsorption, immunity disorder, systemic inflammation and changes in the gut-organ axis. Several abiotic factors and childhood malnutrition have negative influences on the gut microbiota, including antibiotics that lead to antibiotic-associated diarrhea and persistent infection. DNA sequencing and bioinformatic analyses enhanced our perception of the gut microbiota, network of metabolic interdependence and their role in health and disease. However, the precise functions of microbiota in gut homeostasis are not well defined. In this chapter, we recapitulate the impact of gut microbiota on diarrheal pathogens, their importance in the immune system and how reshaping the gut microbiota can help during the recovery phase. Additionally, we discuss about impediments and influences beyond diarrhea, particularly on the nutritional status of children.

In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worldwide spread of coronavirus disease 19 (COVID-19), and till now, it has caused death to more than 6.2 million people. Although various vaccines and drug candidates are being tested globally with limited to moderate success, a comprehensive therapeutic cure is yet to be achieved. In this study, we applied computational drug repurposing methods complemented with the analyses of the already existing gene expression data to find better therapeutics in treatment and recovery. Primarily, we identified the most crucial proteins of SARS-CoV-2 and host human cells responsible for viral infection and host response. An in-silico screening of the existing drugs was performed against the crucial proteins for SARS-CoV-2 infection, and a few existing drugs were shortlisted. Further, we analyzed the gene expression data of SARS-CoV-2 in human lung epithelial cells and investigated the molecules that can reverse the cellular mRNA expression profiles in the diseased state. LINCS L1000 and Comparative Toxicogenomics Database (CTD) were utilized to obtain two sets of compounds that can be used to counter SARS-CoV-2 infection from the gene expression perspective. Indomethacin, a nonsteroidal anti-inflammatory drug (NSAID), and Vitamin-A were found in two sets of compounds, and in the in-silico screening of existing drugs to treat SARS-CoV-2. Our in-silico findings on Indomethacin were further successfully validated by in-vitro testing in Vero CCL-81 cells with an IC50 of 12 µM. Along with these findings, we briefly discuss the possible roles of Indomethacin and Vitamin-A to counter the SARS-CoV-2 infection in humans.

Genome Scale-Differential Flux Analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection.

The COVID-19 pandemic is posing an unprecedented threat to the whole world. In this regard, it is absolutely imperative to understand the mechanism of metabolic reprogramming of host human cells by SARS-CoV-2. A better understanding of the metabolic alterations would aid in design of better therapeutics to deal with COVID-19 pandemic. We developed an integrated genome-scale metabolic model of normal human bronchial epithelial cells (NHBE) infected with SARS-CoV-2 using gene-expression and macromolecular make-up of the virus. The reconstructed model predicts growth rates of the virus in high agreement with the experimental measured values. Furthermore, we report a method for conducting genome-scale differential flux analysis (GS-DFA) in context-specific metabolic models. We apply the method to the context-specific model and identify severely affected metabolic modules predominantly comprising of lipid metabolism. We conduct an integrated analysis of the flux-altered reactions, host-virus protein-protein interaction network and phospho-proteomics data to understand the mechanism of flux alteration in host cells. We show that several enzymes driving the altered reactions inferred by our method to be directly interacting with viral proteins and also undergoing differential phosphorylation under diseased state. In case of SARS-CoV-2 infection, lipid metabolism particularly fatty acid oxidation, cholesterol biosynthesis and beta-oxidation cycle along with arachidonic acid metabolism are predicted to be most affected which confirms with clinical metabolomics studies. GS-DFA can be applied to existing repertoire of high-throughput proteomic or transcriptomic data in diseased condition to understand metabolic deregulation at the level of flux.