Smita Sahay

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.

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.

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.

Spinocerebellar ataxia type 2 (SCA2) is an autosomal dominant neurodegenerative disorder marked by cerebellar dysfunction, ataxic gait, and progressive motor impairments. SCA2 is caused by the pathologic expansion of CAG repeats in the ataxin-2 (ATXN2) gene, leading to a toxic gain-of-function mutation of the ataxin-2 protein. Currently, SCA2 therapeutic efforts are expanding beyond symptomatic relief to include disease-modifying approaches such as antisense oligonucleotides (ASOs), high-throughput screening (HTS) for small molecule inhibitors, and gene therapy aimed at reducing ATXN2 expression. In the present study, data mining and machine learning techniques were employed to analyze HTS data and identify robust molecular properties of potential inhibitors of ATXN2. Three HTS datasets were selected for analysis: ATXN2 gene expression, CMV promoter expression, and biochemical control (luciferase) gene expression. Compounds displaying significant ATXN2 inhibition with minimal impact on control assays were deciphered based on effectiveness (E) values (n = 1321). Molecular descriptors associated with these compounds were calculated using MarvinSketch (n = 82). The molecular descriptor data (MD model) was analyzed separately from the experimentally determined screening data (S model) as well as together (MD-S model). Compounds were clustered based on structural similarity independently for the three models using the SimpleKMeans algorithm into the optimal number of clusters (n = 26). For each model, the maximum response assay values were analyzed, and E values and total rank values were applied. The S clusters were further subclustered, and the molecular properties of compounds in the top candidate subcluster were compared to those from the bottom candidate subcluster. Six compounds with high ATXN2 inhibiting potential and 16 molecular descriptors were identified as significantly unique to those compounds (p < 0.05). These results are consistent with a quantitative HTS study that identified and validated similar small-molecule compounds, like cardiac glycosides, that reduce endogenous ATXN2 in a dose-dependent manner. Overall, these findings demonstrate that the integration of HTS analysis with data mining and machine learning is a promising approach for discovering chemical properties of candidate drugs for SCA2.

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.

Substance use disorders (SUDs) are a critical public health challenge characterized by high relapse rates, with existing treatments often proving inadequate. The focus of this review is to provide an update on the current state of transcranial magnetic stimulation (TMS) as a therapeutic intervention for SUDs and discuss neuroimaging-guided TMS practices. This review explores the neurobiology underlying SUDs, emphasizing the roles of the prefrontal cortex, striatal circuits, and dopaminergic pathways, and examines the theory that TMS modulates neurocircuitry to impact addiction-related behaviors. We discuss TMS procedural aspects and provide a comparative analysis of TMS protocols, focusing on repetitive, deep, single-pulse, paired-pulse, and a more recent approach, theta burst stimulation. We review recent randomized clinical trials (RCTs) to demonstrate reductions in cravings and use across SUDs as well as highlight the need for standardized protocols. We emphasize the power of combining neuroimaging techniques to show functional connectivity changes in the brain and identify potential biomarkers predictive of SUD treatment response, an unexplored area of discussion. With these topics, this review highlights the potential of TMS as a versatile and effective therapeutic modality for SUDs, especially when combined with neuroimaging. Key findings emphasize the necessity for future research to address methodological challenges, such as standardizing protocols and optimizing stimulation parameters. The integration of neuroimaging provides insights into functional connectivity changes, enabling enhanced precision and individualized treatment strategies. By validating TMS approaches and incorporating multimodal techniques, this field can advance toward a more robust clinical utility in addressing the complex neurocircuitry of addiction-related behaviors underlying SUDs.

Regular exercise confers numerous physical and mental health benefits, yet individual variability in exercise participation and outcomes is still poorly understood. Uncovering the neurobiological mechanisms governing exercise behavior is essential for promoting physical activity and developing targeted interventions for related disorders. While genetic studies have provided insights, they often cannot account for protein-level alterations, such as changes in kinase activity. Here, we employ protein kinase activity profiling to delineate brain protein kinase activity and signaling networks modulated by acute voluntary exercise in rats. Focusing on the dorsal striatum, which governs voluntary exercise, as well as the hippocampus, which is susceptible to modulation by physical activity, we aim to understand the molecular basis of exercise behavior. Utilizing high throughput kinome array profiling and advanced pathway analyses, we identified protein kinase signaling pathways implicated in regulating voluntary exercise. Pathway analysis using Gene Ontology (GO) revealed significant alterations in 155 GO terms in the dorsal striatum and 206 GO terms in the hippocampus. Changes in kinase activity were observed in the striatum and hippocampus between the exercise (voluntary wheel running, VWR) and sedentary control rats. In both regions, global serine-threonine kinase (STK) activity was decreased, while global phospho-tyrosine kinase (PTK) activity was increased in VWR rats compared to control rats. We also identified specific kinases altered in VWR rats, including the IKappaB Kinase (IKK) and protein kinase delta (PKD) families. C-terminal src Kinase (CSK), epidermal growth factor (EGFR), and vascular endothelial growth factor receptor (VEGFR) tyrosine kinase were also enriched. These findings suggest regional heterogeneity of kinase activity following voluntary exercise, emphasizing potential molecular mechanisms underlying exercise behavior. This exploratory study lays the groundwork for future investigations into the causality of variations in exercise outcomes among individuals and different sexes, as well as the development of targeted interventions to promote physical activity and combat associated chronic diseases.

Suicide is a major public health priority, and its molecular mechanisms appear to be related to imbalanced purine metabolism in the brain. This exploratory study investigates purinergic gene expression in the postmortem dorsolateral prefrontal cortex (DLPFC) tissue isolated from subjects with major depressive disorder (MDD) who died by suicide (MDD-S, n = 10), MDD subjects who did not die by suicide (MDD-NS, n = 6) and non-psychiatrically ill controls (CTL, n = 9–10). Purinergic system transcripts were assayed by quantitative polymerase chain reactions (qPCR) in superficial and deep gray matter as well as white matter DLPFC cortical layers using laser microdissection (LMD). Across all subjects, regardless of sex, P2RY12 (F_(2,23) = 5.40, p = 0.004) and P2RY13 (KW statistic = 11.82, p = 0.001) transcript levels were significantly greater in MDD-S compared to MDD-NS subjects. Several other perturbations were observed in the white matter tissue isolated from females: NT5E (F_(2,10) = 13.37, p = 0.001) and P2RY13 (F_(2,9) = 3.99, p = 0.011, controlled for age) transcript expression was significantly greater in MDD-S vs. MDD-NS female groups. ENTPD2 (F_(2,10) = 5.20, p = 0.03), ENTPD3 (F_(2,10) = 28.99, p < 0.0001), and NT5E (F_(2,10) = 13.37, p = 0.001) were among the transcripts whose expression was significantly elevated in MDD-S vs. CTL female groups. Transcripts that exhibited significantly altered expression in the superficial and deep gray matter included ENTPD2, NT5E, PANX1, and P2RY13 (p ≤ 0.05). Our medication analysis revealed that the expression of these transcripts was not significantly altered by antidepressants. This is the first study to holistically quantify the purinergic metabolic pathway transcripts in suicide and MDD utilizing human postmortem brain tissue. Our preliminary findings support evidence implicating changes in purinergic P2 receptors in the brain in suicide and provide support for broader purinergic system dysregulation in mood disorders.

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.

Schizophrenia is characterized by substantial alterations in brain function, and previous studies suggest insulin signaling pathways, particularly involving AKT, are implicated in the pathophysiology of the disorder. This study demonstrates elevated mRNA expression of AKT1-3 in neurons from schizophrenia subjects, contrary to unchanged or diminished total AKT protein expression reported in previous postmortem studies, suggesting a potential decoupling of transcript and protein levels. Sex-specific differential AKT activity was observed, indicating divergent roles in males and females with schizophrenia. Alongside AKT, upregulation of PDPK1, a critical component of the insulin signaling pathway, and several protein phosphatases known to regulate AKT were detected. Moreover, enhanced expression of the transcription factor FOXO1, a regulator of glucose metabolism, hints at possible compensatory mechanisms related to insulin signaling dysregulation. Findings were largely independent of antipsychotic medication use, suggesting inherent alterations in schizophrenia. These results highlight the significance of AKT and related signaling pathways in schizophrenia, proposing that these changes might represent a compensatory response to a primary defect of canonical insulin signaling pathways. This research underscores the need for a detailed understanding of these signaling pathways for the development of effective therapeutic strategies.