Biology subjects

Guttula, P. K.

Publications and source records attributed to Guttula, P. K..

3 recordsLinked to original sources

bioRxiv2022

Protein-protein interaction map in pre-eclampsia through the interaction of hub genes, transcription factors and microRNAs

Pre-eclampsia causes complications in pregnancy and characterized by uremia, proteinuria and hypertension in unattended cases. Blood biomarkers for pre-eclampsia are lacking. In this study, microarray gene expression data from peripheral blood of pre-eclampsia women was analyzed. In our study we developed a combined network approach for hub node prediction regulated by transcription factors and microRNAs corresponding to pre-eclampsia. Differentially expressed genes (DEGs) interaction map was constructed using STRING database. JUN, RPL35, NDUFB2, ATP5I, UQCRQ, COX7C, and FN1 were predicted as potential novel hub genes. Pathway analysis showed metabolic pathways, cytokine signaling in the immune system, Wnt, and MAPK signaling pathways involvement in pre-eclampsia. Regulatory network analysis showed that transcription factors JUN and STAT1 were connected with hub nodes, and microRNAs (miRNAs) like hsa-miR-26b-5p and hsa-miR-155-5p. In conclusion, the expression pattern of hub genes, analyzed deciphers a molecular signature for understanding the pathophysiology of pre-eclampsia and prediction of biomarkers for diagnosis.

bioinformatics↗

bioRxiv2022

Network analysis of Differentially Expressed Genes (DEGs) identified in zebrafish after infection with Spring viremia of carp virus (SVCV) - an in silico approach

Spring viremia of carp virus (SVCV) is a virus that belongs to family of spring viremia of carp (SVC) and frequently causes hemorrhagic symptoms in several types of cyprinids and causes severe economic and environmental losses. Therefore, the mechanism of the infection is not clearly understood. In this study, zebrafish was employed as the infection model to explore the pathogenesis of SVCV. 4 groups of zebrafish tissues were set and RNA sequencing (RNA-Seq) technology was employed to analyze the differentially expressed genes (DEGs) after SVCV-infection. A total of 360,971,498 clean reads were obtained from samples, 382 DEGs in the brain and 926 DEGs in the spleen were identified. These DEGs were annotated into three ontologies after gene ontology (GO) enrichment analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that these DEGs were primarily related to Influenza. A pathway and Herpes simplex infection pathway in brain and Tuberculosis and Toxoplasmosis pathways in spleen, and all of these pathways may be involved in response to pathogen invasion. The transcriptome analysis results demonstrated changes and tissue-specific influences caused by SVCV in vivo, which provided us with more information to understand the complex relationships between SVCV and its host.

bioinformatics↗

bioRxiv2021

Prediction of molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p-value and robust rank aggregation

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. E.coli and S.aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E.coli is an opportunistic environmental pathogen, and S.aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E.coli or S.aureus infected mammary epithelial cells (MECs) using p-value combination and robust rank aggregation (RRA) methods. High throughput transcriptome of bovine (MECs, infected with E.coli or S.aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p-value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-Gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF- Kappa B signalling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1 and MAP3K8 having significant expression levels in both E.coli and S.aureus dataset and on evaluating miRNA-Gene network 7 pairs were common to both sets identifying them as potential molecular markers.

molecular biology↗