Neurological disorders caused by infectious diseases, chemical exposures, and physical trauma pose significant public health challenges and are critical concerns in military medicine worldwide. Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as EEV in this manuscript), organophosphorus nerve agents (OPNA), and traumatic brain injuries (TBI) are particularly significant and interrelated threats to both civilian populations and military personnel. Despite their distinct etiologies, these conditions share common features in their neurological manifestations and potential for severe long-term consequences (Ronca et al., 2017; Mouzon et al., 2012; VanderGiessen et al., 2024) suggesting possible overlapping molecular mechanisms that could be leveraged for therapeutic development.

EEVs represent a significant threat to both human and animal health throughout the Americas. Venezuelan equine encephalitis virus (VEEV), eastern equine encephalitis virus (EEEV), and western equine encephalitis virus (WEEV) belong to the genus Alphavirus in the family Togaviridae (Aguilar et al., 2011; Luethy, 2023) and are transmitted primarily through mosquitos, but have also been weaponized for use as potential bioweapons by both the US and Soviet Union. These viruses can cause severe neurological diseases, with mortality rates ranging from 1% (VEEV) to 70% (EEEV) (Ronca et al., 2016; Guzman-Teran et al., 2020; Zacks and Paessler, 2010). Previous research has demonstrated that these viruses cause systemic infection which is either asymptomatic, or presents as a mild flu-like illness in the acute phase of infection (Cain et al., 2024). Neuroinvasion occurs around day 4 post infection through the olfactory epithelium, but can also increase blood–brain barrier (BBB) permeability to enter the brain via transcytosis, but this is less thoroughly understood for EEEV and WEEV (Salimi et al., 2020; Honnold et al., 2015). Recent studies using animal models have revealed that VEEV infection triggers a cascade of inflammatory responses, including the activation of pro-inflammatory cytokines such as IL-1β, TNF-α, and IFN-γ, which contribute to neuronal damage and subsequent neurological symptoms (Dahal et al., 2020). Despite significant advances in understanding their pathogenesis, current therapeutic options are limited to supportive care, with no specific antiviral treatments available.

Organophosphorus nerve agents (OPNAs) are highly toxic chemicals that interfere with the normal functioning of the nervous system. These compounds, including G-series agents (tabun, sarin, soman) and V-series nerve agents (VE, VG, VM, VR, VX), irreversibly inhibit acetylcholinesterase, leading to excessive accumulation of acetylcholine at synapses (Wang et al., 2022). Research over the past decades has revealed that OPNA toxicity extends beyond acute cholinergic crisis. Studies have shown that OPNA exposure initiates complex cellular cascades involving oxidative stress, neuroinflammation, and excitotoxicity (Aroniadou-Anderjaska et al., 2023). Long-term studies in animal models and human survivors have documented persistent neurological deficits, including cognitive impairment, anxiety, and depression (Francois et al., 2022; Vasanthi et al., 2023). Current treatment protocols rely primarily on a combination of anticholinergic drugs (such as atropine), oximes for enzyme reactivation, and anticonvulsants (Worek et al., 2020; Newmark, 2019; Saalbach, 2023; Reddy, 2024). However, these treatments must be administered rapidly after exposure and may not prevent long-term neurological consequences. Recent research has focused on understanding the molecular mechanisms of delayed neurotoxicity and developing more effective neuroprotective strategies.

Traumatic brain injury (TBI) is a major global health concern, affecting an estimated 69 million people worldwide each year (Dewan et al., 2019). The spectrum of TBI ranges from mild concussions to severe injuries with devastating consequences. The pathophysiology of TBI involves both primary injury mechanisms (direct mechanical damage) and secondary injury (inflammatory cascades, BBB breakdown, hemorrhage) that can persist for months or years after the initial trauma (Howlett et al., 2022). Extensive research has identified key molecular pathways involved in TBI pathogenesis, including neuroinflammation, oxidative stress, excitotoxicity, and disruption of the blood–brain barrier. Recent studies have revealed the complexity of TBI’s molecular signature, with altered expression of numerous genes involved in inflammation (e.g., IL-1β, TNF-α), cell death pathways (e.g., caspase-3, BAX), and neuroplasticity (e.g., BDNF, NGF) (Ng and Lee, 2019; Freire et al., 2023). Advanced neuroimaging techniques combined with molecular studies have demonstrated that TBI triggers both acute and chronic changes in brain structure and function (Hu et al., 2022). Despite this growing understanding, therapeutic options remain limited, with most treatments focusing on symptom management rather than addressing the underlying pathological mechanisms.

Research on the pathogenesis of these conditions faces numerous challenges. For EEVs, the requirement for high-containment facilities and the complexity of virus-host interactions have limited comprehensive studies (Hughes et al., 2021). OPNA research faces similar chemical safety concerns, along with ethical considerations that restrict human studies. While TBI research has progressed more rapidly due to greater accessibility and established animal models (Bellotti et al., 2024), many aspects of its molecular pathology remain poorly understood. However, recent advances in high-throughput genomic technologies and bioinformatics approaches have opened new avenues for investigating these conditions through comparative analysis of gene expression profiles (Siddiqui et al., 2020; Siddiqui et al., 2020; Siddiqui et al., 2017; Siddiqui and Islam, 2016; Siddiqui et al., 2019). This allows us to leverage the extensive body of research in one area to inform the understanding of related conditions, potentially identifying common pathways and novel therapeutic targets. By implementing machine learning techniques, we can now integrate and analyze complex transcriptomic datasets from various experimental platforms, identifying both shared and condition-specific molecular signatures (Long et al., 2024; Martinez et al., 2022). Such an integrated approach not only provides insights into the fundamental biology of these neurological disorders but also has the potential to guide the development of medical countermeasures that could be effective across multiple conditions. Understanding the commonalities and differences in gene expression patterns among these disorders may reveal new therapeutic targets, ultimately leading to more effective interventions for affected individuals.

In this study, we used advanced computational methods to systematically analyze and compare the transcriptomic profiles associated with TBI, EEV infection, and OPNA exposure. Our analytical framework is able to (1) integrate gene expression data from diverse experimental platforms and conditions, overcoming the limitations posed by data variability among these brain disorders, and (2) identify shared and condition-specific molecular signatures that are associated with the three neuropathologies. Our findings in this study not only highlight potential common therapeutic targets but also reveal unique pathways that could guide the development of targeted interventions, offering new insights into the treatment of these complex neurological disorders.

In this study, we developed and implemented a comprehensive framework for analyzing and comparing transcriptomic profiles across three distinct neurological conditions: TBI, EEV, and OPNA. By leveraging machine learning approaches, particularly using artificial neural networks, we identified both shared and condition-specific gene signatures that provide valuable insights into the underlying molecular mechanisms of these neurological disorders. Our comparative analysis revealed several key findings. First, the integration of diverse transcriptomic datasets demonstrated the feasibility of cross-platform data normalization and analysis. Second, the machine learning models enabled the identification of critical genes associated with each condition, suggesting potential therapeutic targets for medical countermeasures. Third, the pathway and gene ontology analyses highlighted specific biological processes and molecular functions that may play crucial roles in the pathogenesis of these neurological conditions.

Several limitations exist in this study. First, OPNA and EEV studies are highly limited, which limits our ability to have consistent tissue types in the data selected. Future studies would benefit from incorporating additional neurological conditions with more comprehensive data from the same species, tissue type, and background. Next, our analysis relies on microarray data, which provides only tissue-level expression profiles, and may overlook cell type-specific responses that are crucial for understanding the complex pathophysiology of neurological disorders. Additionally, microarray technology has inherent limitations in detecting novel transcripts and may identify fewer transcripts compared to more recent sequencing approaches. Future studies could address these limitations by incorporating single-cell RNA sequencing (scRNA-seq) data to identify cell type-specific responses to TBI, EEV infection and OPNA exposure and reveal cellular heterogeneity within affected tissues.

Regarding the specific findings of this study, the overwhelming similarities between EEVs, TBIs, and OPNAs are associated with the upregulation of the immune response. While this is an expected finding, it is challenging to utilize broad immune-related markers for biomarker analysis or therapeutics. Binary analysis of neurological disease phenotypes could be broadened to include other neuropathologies to identify signatures of gene expression that are indicators of damage in cases where the injury is unknown. Especially in the context of the warfighter, early markers of inflammation could be beneficial in distinguishing a healthy phenotype where immune-associated genes are lowly expressed from a recent injury or exposure to chemical or biological agents where these genes are upregulated. Further research is required to assess whether these biomarkers of disease in the brain also correlate with differences in the blood to make sample collection and testing realistic in the field. For the ternary analysis (EEV vs. TBI vs. OPNA), these results could be utilized to characterize types of injury. For example, in this analysis the gene Morc3 and Lgals9 appear to be upregulated in OPNA exposure and downregulated in EEVs. Therefore, these genes could be a feasible biomarker in clinical setting to distinguish whether an individual posing general malaise-like symptoms could have been exposed to a viral or chemical threat. This method of biomarker analysis has previously been used in the clinical context to assess whether patient inflammation is associated with infection where supportive antimicrobial therapeutics are necessary, or an underlying disease state such as cancer, ischemia, or pulmonary embolism where administration of antimicrobial agents could worsen disease and in some cases be fatal (Alexandra Binnie and Dos Santos, 2019). As there are few comparable transcriptomic studies for EEVs and OPNA, further validations and incorporation of additional datasets are crucial for assessing the feasibility of transcriptional biomarker identification.

Our findings provide a prospect for future investigations into these neurological conditions and demonstrate the value of machine learning in understanding complex disease mechanisms. Looking forward, we expect to expand our research in the following two directions. First, we would like to incorporate single-cell RNA sequencing data to explore cellular heterogeneity and identify specific cell-type contributions to the pathogenesis of TBI, EEV infection, and OPNA exposure. This expansion will provide a more granular understanding of the molecular and cellular mechanisms underlying these disorders. Second, we will focus more on the transparency, accountability, and fairness of the machine learning models used in this study. Advanced explainable AI methods, such as LIME, SHAP, and Saliency Maps and Attention Mechanisms, will be compared and incorporated to enhance the interpretability and precision of our machine learning framework. These advancements will enable us to uncover deeper insights into disease-specific pathways and refine the identification of potential therapeutic targets. Together, these efforts will contribute to the development of more effective medical countermeasures and advance our understanding of these complex neurological conditions.