{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T18:08:17Z","timestamp":1757614097396,"version":"3.44.0"},"reference-count":93,"publisher":"Bentham Science Publishers Ltd.","issue":"2","content-domain":{"domain":["eurekaselect.com"],"crossmark-restriction":true},"short-container-title":["MIRNA"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n<jats:title>Background:<\/jats:title>\n<jats:p>miRNAs are small non-coding conserved RNA molecules (18-24 nts) that act as crucial gene regulators via post-transcriptional\/translational modifications through in-teracting with the respective mRNAs during various pathophysiological conditions. Recent re-search has suggested that non-coding RNAs, particularly miRNAs, can be passed from one spe-cies to another to regulate gene expression. Since miRNA-mediated gene regulation has not yet been found in Plasmodia, it is hypothesized that erythrocytic miRNAs from Plasmodium falcipa-rum (P. falciparum) could potentially migrate from the cytoplasm to the parasitophorous vacuole developed intracellularly by the parasite to regulate its transcriptome.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Objective:<\/jats:title>\n<jats:p>The objective of this study is to investigate the role of trans-kingdom interactions in host-parasite dynamics and their implications for malaria infection.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods:<\/jats:title>\n<jats:p>Using the trans-kingdom target gene prediction tool, psRNA target server, a total of 15 human erythrocytic miRNAs from 12 distinct families were selected and obtained from miRBase to find potential P. falciparum candidate genes. This study utilized ShinyGO (version 0.80) for gene enrichment analysis with statistical analysis of the selected features. The PPI-network analysis was performed using the Maximal Clique Centrality (MCC) approach, along with the CytoHubba plugin for identifying hub nodes. The PPI network was visualized using Cytoscape version 3.7.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results:<\/jats:title>\n<jats:p>A total of 145 target genes of Pf3D7 were predicted, with the following genes repeatedly targeted: conserved Plasmodium proteins, conserved Plasmodium membrane proteins, PfEMP1, rifin, RAD54, E3 ubiquitin-protein ligase, and transcription factors related genes. Outputs of ShinyGO included enriched GO pathways of 62 uniquely identified Pf3D7 genes with detailed descriptions and visualized networks. For overlapping relationships, a hierarchical clustering tree of enriched gene sets was carried out, along with a genome plot for representing the chromosomal locations of these genes. According to their coding-noncoding distribution chart, most of these genes were found to be members of the coding gene family. Additionally, PPI-network analysis reported the top 10 hub nodes: PFE0400w, MAL13P1.380, MAL7P1.167, PFD0900w, PF11_0243, PFE0440w, PFE1120w, MAL13P1.315, PF08_0126, and MAL8P1.23. Three KEGG pathway diagrams of pfa 05144 for Malaria, pfa 03440 for homologous recombination, and pfa 00750 for vitamin B6 metabolism with identified Pf3D7 genes were drawn and high-lighted in red.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Conclusion:<\/jats:title>\n<jats:p>The important target genes of Plasmodium falciparum 3D7 were identified by car-rying out a trans-kingdom investigation, thus offering preliminary insights into the potential of erythrocytic miRNAs-mediated trans-kingdom regulation.<\/jats:p>\n<\/jats:sec>","DOI":"10.2174\/0122115366321119250123113447","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T02:20:31Z","timestamp":1739413231000},"page":"147-169","update-policy":"https:\/\/doi.org\/10.2174\/bsp_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Potential Regulatory Network of Selected Human Erythrocytic miRNAs with Plasmodium falciparum 3D7 mRNAs: A Computational Analysis"],"prefix":"10.2174","volume":"14","author":[{"given":"Urja","family":"Joshi","sequence":"first","affiliation":[{"name":"Department of Biochemistry, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harsha","family":"Motwani","sequence":"additional","affiliation":[{"name":"Department of Botany, Bioinformatics and CCIM, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhara","family":"Jani","sequence":"additional","affiliation":[{"name":"Department of Zoology, Biomedical Technology and Human Genetics, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linz-buoy","family":"George","sequence":"additional","affiliation":[{"name":"Department of Zoology, Biomedical Technology and Human Genetics, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyacinth","family":"Highland","sequence":"additional","affiliation":[{"name":"Department of Zoology, Biomedical Technology and Human Genetics, School of Sciences, Gujarat University, Ahmedabad, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"965","reference":[{"key":"ref=1","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.1016\/j.csbj.2024.04.015","volume":"23","author":"Bader S.","year":"2024","unstructured":"Bader S.; Tuller T.; Advanced computational predictive models of miRNA-mRNA interaction efficiency. 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