Analysis of gene expression profile for identification of novel gene signatures during dengue infection.

Background: Dengue is a major arthropod-borne viral disease spreading rapidly across the globe. The absence of vaccines and inadequate vector control measures leads to further expansion of dengue in many regions globally. Hence, the identification of genes involved in the pathogenesis of dengue will help to understand the molecular basis of the disease and the genes responsible for the disease progression. Methods: In the present study, a meta-analysis was carried out using dengue gene expression data obtained from Gene Expression Omnibus repository. The differentially expressed genes such as CCNB1 and CCNB2 (G2/mitotic-specific cyclin-B2 and B1) were upregulated in dengue fever to control (DF-CO) and severe dengue (dengue hemorrhagic fever [DHF]) to control (DHF-CO) were identified as key genes for controlling the major pathways (cell cycle, oocyte meiosis, p53 signaling pathway, cellular senescence and progesterone-mediated oocyte maturation). Similarly, interferon alpha-inducible (IFI27) genes, type-I and type-III interferon (IFN) signaling genes (STAT1 and STAT2), B cell activation and survival genes (TNFSF13B, TNFRSF17) and toll like receptor (TLR7) genes were differentially up activated during DF-CO and DHF-CO. Followed by, Cytoscape was used to identify the immune system process and topological analysis. Results: The results showed that the top differentially expressed genes under the statistical significance p <0.001, which is majorly involved in Kyoto Encyclopedia of Genes and Genomes orthology K05868 and K21770 with gene names CCNB1 and CCNB2. In addition to this, the immune system profile showed up-regulation of IL12A, CXCR3, TNFSF13B, IFI27, TNFRSF17, STAT, STAT2, and TLR7 genes in DF-CO and DHF-CO act as immunological signatures for inducing the immune response towards dengue infection. Conclusions: The current study could aid in understanding of molecular pathogenesis, genes and corresponding pathway upon dengue infection, and could facilitate for identification of novel drug targets and prognostic markers.

Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy.

Hypertensive disorders of pregnancy (HDP) result in major maternal and fetal complications. Our study aimed to find a panel of protein markers to identify HDP by applying machine-learning models. The study was conducted on a total of 133 samples, divided into four groups, healthy pregnancy (HP, n = 42), gestational hypertension (GH, n = 67), preeclampsia (PE, n = 9), and ante-partum eclampsia (APE, n = 15). Thirty circulatory protein markers were measured using Luminex multiplex immunoassay and ELISA. Significant markers were screened for potential predictive markers by both statistical and machine-learning approaches. Statistical analysis found seven markers such as sFlt-1, PlGF, endothelin-1(ET-1), basic-FGF, IL-4, eotaxin and RANTES to be altered significantly in disease groups compared to healthy pregnant. Support vector machine (SVM) learning model classified GH and HP with 11 markers (eotaxin, GM-CSF, IL-4, IL-6, IL-13, MCP-1, MIP-1α, MIP-1ß, RANTES, ET-1, sFlt-1) and HDP with 13 markers (eotaxin, G-CSF, GM-CSF, IFN-gamma, IL-4, IL-5, IL-6, IL-13, MCP-1, MIP-1ß, RANTES, ET-1, sFlt-1). While logistic regression (LR) model classified PE with 13 markers (basic FGF, IL-1ß, IL-1ra, IL-7, IL-9, MIP-1ß, RANTES, TNF-alpha, nitric oxide, superoxide dismutase, ET-1, PlGF, sFlt-1) and APE by 12 markers (eotaxin, basic-FGF, G-CSF, GM-CSF, IL-1ß, IL-5, IL-8, IL-13, IL-17, PDGF-BB, RANTES, PlGF). These markers may be used to diagnose the progression of healthy pregnant to a hypertensive state. Future longitudinal studies with large number of samples are needed to validate these findings.

Environmental phenol exposure associates with urine metabolome alteration in young Northeast Indian females.

Urinary biomonitoring delivers the most accurate environmental phenols exposure assessment. However, environmental phenol exposure-related biomarkers are required to improve risk assessment to understand the internal processes perturbed, which may link exposure to specific health outcomes. This study aimed to investigate the association between environmental phenols exposure and the metabolome of young adult females from India. Urinary metabolomics was performed using liquid chromatography-mass spectrometry. Environmental phenols-related metabolic biomarkers were investigated by comparing the low and high exposure of environmental phenols. Seven potential biomarkers, namely histidine, cysteine-s-sulfate, 12-KETE, malonic acid, p-hydroxybenzoic acid, PE (36:2), and PS (36:0), were identified, revealing that environmental phenol exposure altered the metabolic pathways such as histidine metabolism, beta-Alanine metabolism, glycerophospholipid metabolism, and other pathways. This study also conceived an innovative strategy for the early prediction of diseases by combining urinary metabolomics with machine learning (ML) algorithms. The differential metabolites predictive accuracy by ML models was >80%. This is the first mass spectrometry-based metabolomics study on young adult females from India with environmental phenols exposure. The study is valuable in demonstrating multiple urine metabolic changes linked to environmental phenol exposure and a better understanding of the mechanisms behind environmental phenol-induced effects in young female adults.

In silico structural characterization of Cytochrome c oxidase Subunit 1: A transmembrane protein from Aedes aegypti.

BACKGROUND & OBJECTIVES: The present study proposed a series of computational techniques such as homology modelling, molecular simulation, and molecular docking to be performed to explore the structural features and binding mechanism of Cytochrome c oxidase subunit I (COX1) protein with known inhibitors. METHODS: Elucidation of the three-dimensional structure of COX1 protein was carried out by using MODELLER software. The modelled protein was validated using GROMACS, structural qualitative tools and web servers. Finally the model was docked with carbon monoxide (CO) and nitric oxide (NO) using Auto Dock Tools. RESULTS: The three-dimensional structure of mitochondrial transmembrane protein COX1 was built using homology modelling based on high-resolution crystal structures of Bos taurus. Followed by inserting the lipid bilayer, molecular dynamics simulation was performed on the modelled protein structure. The modelled protein was validated using qualitative structural indices. Known inhibitors such as carbon monoxide (CO) and nitric oxide (NO) inhibit their active binding sites of mitochondrial COX1 and the inhibitors were docked into the active site of attained model. A structure-based virtual screening was performed on the basis of the active site inhibition with best scoring hits. The COX1 model was submitted and can be accessible from the Model Archive site through the following link https://www.modelarchive.org/doi/10.5452/ma-at44v. INTERPRETATION & CONCLUSION: Structural characterization and active site identification can be further used as target for the planning of potent mosquitocidal compounds, thereby assisting the information in the field of research.