Dataset Information

Computational study of zebrafish immune-targeted microarray data for prediction of preventive drug candidates.

ABSTRACT: Viral hemorrhagic septicemia virus (VHSV) is a rhabdovirus reported to cause economic loss in fish farms. Because of the lack of adequate preventative treatments, the identification of multipath genes involved in VHS infection might be an alternative to explore the possibility of using drugs for the seasonal prevention of this fish disease. We propose labeling a category of drug molecules by further classification and interpretation of the Drug Gene Interaction Database using gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment scores. The study investigated disease networks of up-and down-regulated genes to find those with high interaction as substantial genes in pathways among the different disease networks. We prioritized these genes based on their relationship to those associated with VHS infection in the context of human protein-protein interaction networks and disease pathways. Among the 29 genes as potential drug targets, nine were selected as promising druggable genes (ERBB2, FGFR3, ITGA2B, MAP2K1, NGF, NTRK1, PDGFRA, SCN2B, and SERPINC1). PDGFRA is the most important druggable up-and down-regulated gene and is considered an important gene in the IMATINIB pathway. This study findings indicate a promising approach for drug target prediction for VHS treatment, which might be useful for disease therapeutics.

SUBMITTER: Ebrahimpour Gorji A

PROVIDER: S-EPMC8094140 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Spring viremia carp virus (SVCV) is a rhabdovirus seasonally affecting warm-water cyprinid fish farming causing high impacts in worldwide economy. Because of the lack of effective preventive treatments, the identification of multipath genes involved in SVCV infection might be an alternative to explore the possibilities of using drugs for seasonal prevention of this fish disease. Because the zebrafish (Danio rerio) is a cyprinid susceptible to SVCV and their genetics and genome sequence are well advanced, it has been chosen as a model for SVCV infections. We have used newly designed pathway-targeted microarrays 3-4-fold enriched for immune/infection functional-relevant probes by using zebrafish orthologous to human genes from selected pathways of the Kyoto Encyclopedia of Genes and Genomes (KEGG). The comparative analysis of differential expression of genes through 20 pathways in 2-day exposed or 30-day survivors of SVCV infection allowed the identification of 16 multipath genes common to more than 6 pathways. In addition, receptors (Toll-like, B-cell, T-cell, RIG1-like) as well as viral RNA infection pathways were identified as the most important human-like pathways targeted by SVCV infection. Furthermore, by using bioinformatic tools to compare the promoter sequences corresponding to up and downregulated multipath gene groups, we identified putative common transcription factors which might be controlling such responses in a coordinated manner. Possible drug candidates to be tested in fish, can be identified now through search of data bases among those associated with the human orthologous to the zebrafish multipath genes. With the use of pathway-targeted microarrays, we identified some of the most important genes and transcription factors which might be implicated in viral shutoff and/or host survival responses after SVCV infection. These results could contribute to develop novel drug-based prevention methods and consolidate the zebrafish/SVCV as a model for vertebrate viral diseases.

Project description:BackgroundMicroarrays are widely used to investigate the blood stage of Plasmodium falciparum infection. Starting with synchronized cells, gene expression levels are continually measured over the 48-hour intra-erythrocytic cycle (IDC). However, the cell population gradually loses synchrony during the experiment. As a result, the microarray measurements are blurred. In this paper, we propose a generalized deconvolution approach to reconstruct the intrinsic expression pattern, and apply it to P. falciparum IDC microarray data.MethodsWe develop a statistical model for the decay of synchrony among cells, and reconstruct the expression pattern through statistical inference. The proposed method can handle microarray measurements with noise and missing data. The original gene expression patterns become more apparent in the reconstructed profiles, making it easier to analyze and interpret the data. We hypothesize that reconstructed gene expression patterns represent better temporally resolved expression profiles that can be probabilistically modeled to match changes in expression level to IDC transitions. In particular, we identify transcriptionally regulated protein kinases putatively involved in regulating the P. falciparum IDC.ResultsBy analyzing publicly available microarray data sets for the P. falciparum IDC, protein kinases are ranked in terms of their likelihood to be involved in regulating transitions between the ring, trophozoite and schizont developmental stages of the P. falciparum IDC. In our theoretical framework, a few protein kinases have high probability rankings, and could potentially be involved in regulating these developmental transitions.ConclusionsThis study proposes a new methodology for extracting intrinsic expression patterns from microarray data. By applying this method to P. falciparum microarray data, several protein kinases are predicted to play a significant role in the P. falciparum IDC. Earlier experiments have indeed confirmed that several of these kinases are involved in this process. Overall, these results indicate that further functional analysis of these additional putative protein kinases may reveal new insights into how the P. falciparum IDC is regulated.

Project description:Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between -6.5 and -7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Dataset Information

Computational study of zebrafish immune-targeted microarray data for prediction of preventive drug candidates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets