Integrated Transcriptomic and Network Analysis Identifies Candidate Kinase-Mediated Thyroid-Associated Targets of Withaferin A
- 1Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
- 2Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia
Abstract
Withaferin A (WA), a bioactive steroidal lactone derived from Withania somnifera, has been reported to exhibit diverse pharmacological activities, including anti-inflammatory and metabolic regulatory effects. Although previous studies suggest a potential link between WA and thyroid-related molecular pathways, the molecular mechanisms underlying these effects remain incompletely understood. Here, we employed a comprehensive computational approach to explore WA-responsive molecular changes potentially relevant to thyroid-associated processes. RNA-sequencing data from WA-treated MCF-10A human mammary epithelial cells (GSE158085) were analysed to identify differentially expressed genes (DEGs). Because thyroid-specific WA transcriptomic datasets are not currently available, the DEGs were intersected with curated hypothyroidism-associated genes to generate a cross-tissue, exploratory set of candidate genes. The genes that overlapped were used to construct protein–protein interaction networks, prioritize high-centrality candidate genes, and conduct enrichment analyses for transcription factors and kinases. Further analysis of selected upstream kinases was performed using molecular docking and molecular dynamics simulations with WA. A total of 1458 differentially expressed genes (DEGs) were identified, 10 of which overlapped with hypothyroidism-associated genes. Network analysis identified candidate interactions among the overlapping genes, while higher-confidence analysis retained four connected genes (PTPN22, STAT4, DUOX2, and DUOXA2). Transcription factor enrichment identified ERG, SUZ12, NANOG, TCF3, RUNX1, and TP63 as potential upstream regulators, while kinase enrichment identified candidate upstream kinases associated with the predicted regulatory network. Based on enrichment evidence, structural availability, and literature-supported biological relevance, MAPK14 (p38α), AKT1, and MAPK8 (JNK1) were selected for molecular docking and molecular dynamics simulations. Structural analyses suggested potential interactions between WA and the selected kinases, with target-specific dynamic behaviour across the simulated systems. Collectively, the findings highlight WA-responsive genes and predicted gene–TF–kinase relationships that may be relevant to thyroid-associated molecular processes. However, these associations are hypothesis-generating and provide a basis for further investigation in thyroid-derived experimental models.
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BibTeX
@article{abdullah2026integrated,
title = {{Integrated Transcriptomic and Network Analysis Identifies Candidate Kinase-Mediated Thyroid-Associated Targets of Withaferin A}},
author = {Ejlal Mohamed Abdullah and Alaa Alnoor Alameen and Nouf Omar AlAfaleq and Mona Shujaa Alharbi},
journal = {International journal of molecular sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27198926},
url = {https://europepmc.org/article/PMC/PMC13665287},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrated Transcriptomic and Network Analysis Identifies Candidate Kinase-Mediated Thyroid-Associated Targets of Withaferin A
AU - Ejlal Mohamed Abdullah
AU - Alaa Alnoor Alameen
AU - Nouf Omar AlAfaleq
AU - Mona Shujaa Alharbi
PY - 2026
DA - 2026/10/01
JO - International journal of molecular sciences
DO - 10.3390/ijms27198926
UR - https://europepmc.org/article/PMC/PMC13665287
AB - Withaferin A (WA), a bioactive steroidal lactone derived from Withania somnifera, has been reported to exhibit diverse pharmacological activities, including anti-inflammatory and metabolic regulatory effects. Although previous studies suggest a potential link between WA and thyroid-related molecular pathways, the molecular mechanisms underlying these effects remain incompletely understood. Here, we employed a comprehensive computational approach to explore WA-responsive molecular changes potentially relevant to thyroid-associated processes. RNA-sequencing data from WA-treated MCF-10A human mammary epithelial cells (GSE158085) were analysed to identify differentially expressed genes (DEGs). Because thyroid-specific WA transcriptomic datasets are not currently available, the DEGs were intersected with curated hypothyroidism-associated genes to generate a cross-tissue, exploratory set of candidate genes. The genes that overlapped were used to construct protein–protein interaction networks, prioritize high-centrality candidate genes, and conduct enrichment analyses for transcription factors and kinases. Further analysis of selected upstream kinases was performed using molecular docking and molecular dynamics simulations with WA. A total of 1458 differentially expressed genes (DEGs) were identified, 10 of which overlapped with hypothyroidism-associated genes. Network analysis identified candidate interactions among the overlapping genes, while higher-confidence analysis retained four connected genes (PTPN22, STAT4, DUOX2, and DUOXA2). Transcription factor enrichment identified ERG, SUZ12, NANOG, TCF3, RUNX1, and TP63 as potential upstream regulators, while kinase enrichment identified candidate upstream kinases associated with the predicted regulatory network. Based on enrichment evidence, structural availability, and literature-supported biological relevance, MAPK14 (p38α), AKT1, and MAPK8 (JNK1) were selected for molecular docking and molecular dynamics simulations. Structural analyses suggested potential interactions between WA and the selected kinases, with target-specific dynamic behaviour across the simulated systems. Collectively, the findings highlight WA-responsive genes and predicted gene–TF–kinase relationships that may be relevant to thyroid-associated molecular processes. However, these associations are hypothesis-generating and provide a basis for further investigation in thyroid-derived experimental models.
SN - 1422-0067
ER -