A Review of Radiation-Induced Alterations of Multi-Omic Profiles, Radiation Injury Biomarkers, and Countermeasures.

Increasing utilization of nuclear power enhances the risks associated with industrial accidents, occupational hazards, and the threat of nuclear terrorism. Exposure to ionizing radiation interferes with genomic stability and gene expression resulting in the disruption of normal metabolic processes in cells and organs by inducing complex biological responses. Exposure to high-dose radiation causes acute radiation syndrome, which leads to hematopoietic, gastrointestinal, cerebrovascular, and many other organ-specific injuries. Altered genomic variations, gene expression, metabolite concentrations, and microbiota profiles in blood plasma or tissue samples reflect the whole-body radiation injuries. Hence, multi-omic profiles obtained from high-resolution omics platforms offer a holistic approach for identifying reliable biomarkers to predict the radiation injury of organs and tissues resulting from radiation exposures. In this review, we performed a literature search to systematically catalog the radiation-induced alterations from multi-omic studies and radiation countermeasures. We covered radiation-induced changes in the genomic, transcriptomic, proteomic, metabolomic, lipidomic, and microbiome profiles. Furthermore, we have covered promising multi-omic biomarkers, FDA-approved countermeasure drugs, and other radiation countermeasures that include radioprotectors and radiomitigators. This review presents an overview of radiation-induced alterations of multi-omics profiles and biomarkers, and associated radiation countermeasures.

Bromelain Inhibits SARS-CoV-2 Infection in VeroE6 Cells.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The initial interaction between Transmembrane Serine Protease 2 (TMPRSS2) primed SARS-CoV-2 spike (S) protein and host cell receptor angiotensin-converting enzyme 2 (ACE-2) is a pre-requisite step for this novel coronavirus pathogenesis. Here, we expressed a GFP-tagged SARS-CoV-2 S-Ectodomain in Tni insect cells. That contained sialic acid-enriched N- and O-glycans. Surface resonance plasmon (SPR) and Luminex assay showed that the purified S-Ectodomain binding to human ACE-2 and immunoreactivity with COVID-19 positive samples. We demonstrate that bromelain (isolated from pineapple stem and used as a dietary supplement) treatment diminishes the expression of ACE-2 and TMPRSS2 in VeroE6 cells and dramatically lowers the expression of S-Ectodomain. Importantly, bromelain treatment reduced the interaction between S-Ectodomain and VeroE6 cells. Most importantly, bromelain treatment significantly diminished the SARS-CoV-2 infection in VeroE6 cells. Altogether, our results suggest that bromelain or bromelain rich pineapple stem may be used as an antiviral against COVID-19. HIGHLIGHTS: Bromelain inhibits / cleaves the expression of ACE-2 and TMPRSS2Bromelain cleaves / degrades SARS-CoV-2 spike proteinBromelain inhibits S-Ectodomain binding and SARS-CoV-2 infection.

Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism.

BACKGROUND: Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways. RESULTS: ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and vitamins. CONCLUSIONS: The ECemble method is able to hierarchically assign high quality enzyme annotations to genomic and metagenomic data. This study demonstrated the real application of ECemble to understand the indispensable role played by microbe-encoded enzymes in the healthy functioning of human metabolic systems.

Complete chloroplast genome sequences of Solanum bulbocastanum, Solanum lycopersicum and comparative analyses with other Solanaceae genomes.

Despite the agricultural importance of both potato and tomato, very little is known about their chloroplast genomes. Analysis of the complete sequences of tomato, potato, tobacco, and Atropa chloroplast genomes reveals significant insertions and deletions within certain coding regions or regulatory sequences (e.g., deletion of repeated sequences within 16S rRNA, ycf2 or ribosomal binding sites in ycf2). RNA, photosynthesis, and atp synthase genes are the least divergent and the most divergent genes are clpP, cemA, ccsA, and matK. Repeat analyses identified 33-45 direct and inverted repeats >or=30 bp with a sequence identity of at least 90%; all but five of the repeats shared by all four Solanaceae genomes are located in the same genes or intergenic regions, suggesting a functional role. A comprehensive genome-wide analysis of all coding sequences and intergenic spacer regions was done for the first time in chloroplast genomes. Only four spacer regions are fully conserved (100% sequence identity) among all genomes; deletions or insertions within some intergenic spacer regions result in less than 25% sequence identity, underscoring the importance of choosing appropriate intergenic spacers for plastid transformation and providing valuable new information for phylogenetic utility of the chloroplast intergenic spacer regions. Comparison of coding sequences with expressed sequence tags showed considerable amount of variation, resulting in amino acid changes; none of the C-to-U conversions observed in potato and tomato were conserved in tobacco and Atropa. It is possible that there has been a loss of conserved editing sites in potato and tomato.