Although chronic stress is an important risk factor for cardiovascular diseases (CVD) onset, the underlying mechanisms driving such pathophysiologic complications remain relatively unknown. Here, dysregulation of innate stress response systems and the effects of downstream mediators are strongly implicated, with the vascular endothelium emerging as a primary target of excessive glucocorticoid and catecholamine action. This review article therefore explores the development of stress-related endothelial dysfunction by focusing on the following: 1) assessing the phenomenon of stress and complexities surrounding this notion; 2) discussing mechanistic links between chronic stress and endothelial dysfunction; and 3) evaluating the utility of various preclinical models currently employed to study mechanisms underlying the onset of stress-mediated complications such as endothelial dysfunction. The data reveal that preclinical models play an important role in our efforts to gain an increased understanding of mechanisms underlying stress-mediated endothelial dysfunction. It is our understanding that this provides a good foundation going forward and we propose that further efforts should be made to a) more clearly define the concept of stress and b) standardize protocols of animal models with specific guidelines to better indicate the mental complications that are simulated.

Background: Global life expectancy has been increasing without a corresponding increase in health span and with greater risk for aging-associated diseases such as Alzheimer′s disease (AD). An urgent need to delay the onset of aging-associated diseases has arisen and a dramatic increase in the number of potential molecular targets has led to the challenge of prioritizing targets to promote successful aging. Here, we developed a pipeline to prioritize aging-related genes which integrates the plethora of publicly available genomic, transcriptomic, proteomic and morphological data of C. elegans by applying a supervised machine learning approach. Additionally, a unique biological post-processing analysis of the computational output was performed to better reveal the prioritized gene′s function within the context of pathways and processes involved in aging across the lifespan of C. elegans. Results: Four known aging-related genes, daf-2, involved in insulin signaling; let-363 and rsks-1, involved in mTOR signaling; age-1, involved in PI3 kinase signaling, were present in the top 10% of 4380 ranked genes related to different markers of cellular dysfunction, validating the computational output. Further, our ranked output showed that 91% of the top 438 ranked genes consisted of known genes on GenAge, while the remaining genes had thus far not yet been associated with aging-related processes. Conclusion: These ranked genes can be translated to known human orthologs potentially uncovering previously unknown information about the basic aging processes in humans. These genes (and their downstream pathways) could also serve as targets against aging-related diseases, such as AD.

Growing evidence indicates that the pathophysiological link between the brain and heart underlies cardiovascular diseases, specifically acute myocardial infarction (AMI). Astrocytes are the most abundant glial cells in the central nervous system and provide support/protection for neurons. Astrocytes and peripheral glial cells are emerging as key modulators of the brain-heart axis in AMI, by affecting sympathetic nervous system activity (centrally and peripherally). This review therefore aimed to gain an improved understanding of glial cell activity and AMI risk. This includes discussions on the potential role for contributing factors in AMI risk, i.e., autonomic nervous system dysfunction, glial-neurotrophic and ischemic risk markers (glial cell line-derived neurotrophic factor [GDNF], astrocytic S100 calcium-binding protein B [S100B], silent myocardial ischemia, and cardiac troponin T [cTnT]). Consideration of glial cell activity and related contributing factors in certain brain-heart disorders namely blood-brain barrier dysfunction, myocardial ischemia, and chronic psychological stress, may improve our understanding regarding the pathological role that glial dysfunction can play in the development/onset of AMI. Here, findings demonstrated perturbations in glial cell activity and contributing factors (especially sympathetic activity). Moreover, emerging AMI risk included sympatho-vagal imbalance, low GDNF levels reflecting prothrombic risk, hypertension, and increased ischemia due to perfusion deficits (indicated by S100B and cTnT levels). Such perturbations impacted blood-barrier function and perfusion which were exacerbated during psychological stress. Thus, greater insights and consideration regarding such biomarkers may help drive future studies investigating brain-heart axis pathologies to gain a deeper understanding of astrocytic glial cell contributions and unlock potential novel therapies for AMI.

Chronic psychosocial stress is implicated in the onset and progression of noncommunicable diseases, and mechanisms underlying this relationship include alterations to the intracellular redox state. However, such changes are often investigated in isolation, with few studies adopting a system level approach. Here, male Wistar rats were exposed to 9.5 weeks of chronic unpredictable mild stress and redox status assays were subsequently performed on cardiac, hepatic, and brain tissues versus matched controls. The stressed rats displayed an anxious phenotype, with lowered plasma corticosterone levels (p = 0.04 vs. Controls) and higher plasma epinephrine concentrations (p = 0.03 vs. Controls). Our findings showed organ‐specific redox profiles, with stressed rats displaying increased myocardial lipid peroxidation (p = 0.04 vs. Controls) in the presence of elevated nonenzymatic antioxidant capacity (p = 0.04 vs. Controls). Conversely, hepatic tissues of stressed rats exhibited lowered nonenzymatic antioxidant capacity (p < 0.001 vs. Controls) together with increased superoxide dismutase (SOD) activity (p = 0.05 vs. Controls). The brain displayed region‐specific antioxidant perturbations, with increased SOD activity (p = 0.01 vs. Controls) in the prefrontal cortex of the stressed rats. These findings reveal distinct stress‐related organ‐specific vulnerability to redox perturbations and may provide novel insights into putative therapeutic targets.

Introduction The analysis of signaling pathways is a cornerstone in clarifying the biological mechanisms involved in complex genetic disorders. These pathways have intricate topologies, and the existing methods that are used for the interpretation of these pathways, remain limited. We have therefore developed the Adaptable Large-Scale Causal Analysis (ALaSCA) computational platform, which uses causal analysis and counterfactual simulation techniques. ALaSCA offers the ability to simulate the outcome of a number of different hypotheses to gain insight into the complex dynamics of biological mechanisms prior to, or even without, wet lab experimentation. ALaSCA is offered as a proprietary Python library for bioinformaticians and data scientists to use in their life sciences workflows. Here we demonstrate the ability of ALaSCA to untangle the pivots and redundancies within biological pathways of various drivers of a specific phenotypic process. This is achieved by studying a major disease of global relevance, namely Type 1 Diabetes (T1D), and quantifying causal relationships between antioxidant proteins and T1D progression. ALaSCA is also benchmarked against standard associative analysis methods. Methods We use our in silico simulation platform, ALaSCA, to apply both a number of machine learning (ML) and data imputation techniques, and perform causal inference and counterfactual simulation. ALaSCA uses standard ML and causal analysis libraries as well as custom code developed for data imputation and counterfactual simulation. Counterfactual simulation is a method for simulating potential or hypothetical model outcomes in the field of causal analysis (Glymour, Pearl and Jewell, 2016). We apply ALaSCA to T1D by using proteomic data from Liu et al. (2018), as the patients were selected based on the presence of T1D susceptible HLA (human leukocyte antigen)-DR/DQ alleles through genotyping at birth and followed prospectively. The genetic cause of T1D in this cohort is therefore known and the mechanism and proteins through which it causes T1D are well-characterized. This biological mechanism was converted into a directed acyclic graph (DAG) for the subsequent causal analyses. The dataset was used to benchmark the causal inference and counterfactual simulation capabilities of ALaSCA. Results and discussion After data imputation of the Liu et al. (2018) dataset, causal inference and counterfactual simulation were completed. The causal inference output of the HLA, antioxidant, and non-causal proteins showed that the HLA proteins had the overall strongest causal effects on T1D, with antioxidant proteins having the overall second largest causal effects on T1D. The non-causal proteins showed negligibly small effects on T1D in comparison with the HLA and antioxidant proteins. With counterfactual simulation we were able to replicate evidence for and gain understanding into the protective effect that antioxidant proteins, specifically Superoxide dismutase 1 (SOD1), have in T1D, a trend which is seen in literature. We were also able to replicate an unusual case from literature where antioxidant proteins, specifically Catalase, do not have a protective effect on T1D. Conclusion By analyzing the disease mechanism, with the inferred causal effects and counterfactual simulation, we identified the upstream HLA proteins, specifically the DR alpha chain and DR beta 4 chain proteins as causes of the protective effect of the antioxidant proteins on T1D. In contrast, through counterfactual simulation of the unusual case, in which the DR alpha chain and DR beta 4 chain proteins are not present in the model, we saw that the adverse effect which the antioxidant proteins have on T1D is due to the HLA protein, DQ beta 1 chain, and not the antioxidant proteins themselves. Future work would entail the application of the ALaSCA platform on various other diseases, and to integrate it into wet lab experimental design in a number of different biological study areas and topics.