In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large-scale omics data, translating complex gene sets into actionable experimental insights. However, issues inherent to pathway databases and misinterpretations of pathway relevance often result in “pathway fails,” where findings, though statistically significant, lack biological applicability. For example, the Tumor Necrosis Factor (TNF) pathway was originally annotated based on its association with observed tumor necrosis, while it is multifunctional across diverse physiological processes in the body. This review broadly evaluates pathway analysis interpretation, including embedding-based, semantic similarity-based, and network-based approaches to clarify their ideal use-case scenarios. Each method for interpretation is assessed for its strengths, such as high-quality visualizations and ease of use, as well as its limitations, including data redundancy and database compatibility challenges. Despite advancements in the field, the principle of “garbage in, garbage out” (GIGO) shows that input quality and method choice are critical for reliable and biologically meaningful results. Methodological standardization, scalability improvements, and integration with diverse data sources remain areas for further development. By providing critical guidance with contextual examples such as TNF, we aim to help researchers align their objectives with the appropriate method. Advancing pathway analysis interpretation will further enhance the utility of pathway analysis, ultimately propelling progress in systems biology and personalized medicine.

In bioinformatics, pathway analyses are used to interpret biological data by mapping measured molecules with known pathways to discover their functional processes and relationships. Pathway analysis has become an essential tool for interpreting large-scale omics data, translating complex gene sets into actionable experimental insights. However, issues inherent to pathway databases and misinterpretations of pathway relevance often result in ‘pathway fails,’ where findings, though statistically significant, lack biological applicability. For example, the Tumor Necrosis Factor (TNF) pathway was originally annotated based on its association with observed tumor necrosis, while it is actually a multifunctional biological pathway across diverse physiological processes in the body. This review broadly evaluates pathway analysis interpretation, including embedding-based, semantic similarity-based, and network-based approaches. Each method for interpretation is assessed for its strengths, such as high-quality visualizations and ease of use, as well as its limitations, including data redundancy and database compatibility challenges. Despite advancements in the field, the principle of "garbage in, garbage out" (GIGO) shows that the reliability of any analysis ultimately hinges on high- quality input data. Methodological standardization, scalability improvements, and integration with diverse data sources remain areas for further development. By providing critical guidance with contextual examples such as the TNF pathway, we aim to help researchers align their objectives with the appropriate method. Advancing pathway analysis interpretation will further enhance the utility of pathway analysis, ultimately propelling progress in systems biology and personalized medicine.

Translation: The University of Toledo Journal of Medical Sciences is the online journal launched by the University of Toledo. Manuscripts will be considered on the understanding that they report original work and are not under consideration for publication by any other journal. The journal publishes original articles reporting experimental results of basic or clinical research, case reports, and reviews. The journal uses a single blind peer review system and each manuscript, based on the results presented in its original submission, will be evaluated by two student reviewers and one faculty reviewer. This process will provide an opportunity for medical students, graduate students, residents, fellows and faculty to publish research observation in a timely manner.

The enterovirus Coxsackievirus B3 causes a range of serious health problems, including aseptic meningitis, myocarditis, and pancreatitis. Currently, Coxsackievirus B3 has no targeted antiviral treatments or vaccines, leaving supportive care as the primary management option. Understanding how Coxsackievirus B3 interacts with and alters the blood–brain barrier may help identify new therapies to combat this often-devastating infection. We reanalyzed a previously published RNA sequencing dataset for Coxsackievirus B3-infected human-induced pluripotent stem-cell-derived brain endothelial cells (iBECs) to examine how Coxsackievirus B3 altered mRNA expression. By integrating GSEA, EnrichR, and iLINCs-based perturbagen analysis, we present a novel, systems-level approach to uncover potential drug repurposing candidates for CVB3 infection. We found dynamic changes in host transcriptomic response to Coxsackievirus B3 infection at 2- and 5-day infection time points. Downregulated pathways included ribosomal biogenesis and protein synthesis, while upregulated pathways included a defense response to viruses, and interferon production. Using iLINCs transcriptomic analysis, MEK, PDGFR, and VEGF inhibitors were identified as possible novel antiviral therapeutics. Our findings further elucidate Coxsackievirus B3-associated pathways in (iBECs) and highlight potential drug repurposing candidates, including pelitinib and neratinib, which may disrupt Coxsackievirus B3 pathology at the blood–brain barrier (BBB).

The ketogenic diet is emerging as an effective therapeutic option for patients with neurological disorders. The diet results in metabolism of fatty acids to ketone bodies like ?-hydroxybutyrates (BHBs), which serve as an alternative fuel source for brain cells. However, the molecular effect of BHB on neurons is not well understood. We hypothesized that BHB administration will induce upregulation of energy metabolism-related processes in neurons. To assess the effect of BHB administration on the neuronal transcriptome, we reanalyzed a publicly available RNAseq dataset (GSE252513) using a bioinformatic “3-pod” approach. We conducted pathway analysis and identified leading edge genes using Gene Set Enrichment Analysis (GSEA) and conducted chemical perturbagen analysis using the iLINCS repository to identify drugs that are concordant and discordant with BHB administration. We identified significantly altered (p<0.05) pathways associated with inflammation and immunity such as “regulation of proinflammatory cytokine production” and “modulation of inflammatory responses.” We did not identify significant modulation of energy metabolism-related pathways in response to BHB administration. Our results suggest that under normal conditions, BHBs primary actions include modulation of cellular neuronal immune responses.

Translation: The University of Toledo Journal of Medical Sciences is the online journal launched by the University of Toledo. Manuscripts will be considered on the understanding that they report original work and are not under consideration for publication by any other journal. The journal publishes original articles reporting experimental results of basic or clinical research, case reports, and reviews. The journal uses a single blind peer review system and each manuscript, based on the results presented in its original submission, will be evaluated by two student reviewers and one faculty reviewer. This process will provide an opportunity for medical students, graduate students, residents, fellows and faculty to publish research observation in a timely manner.