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Modulating the activity of human soluble guanylate cyclase (hsGC) through allosteric regulation of the beta H-NOX domain has been considered as an immediate treatment for cardiovascular disorder (CVDs). Currently available beta H-NOX domain-specific agonists including cinaciguat are unable to deal with the conundrum raised due to oxidative stress in the case of CVDs and their associated comorbidities. Therefore, the idea of investigating novel compounds for allosteric regulation of hsGC activation has been rekindled to circumvent CVDs. Current study aims to identify novel beta H-NOX domain-specific compounds that can selectively turn on sGC functions by modulating the conformational dynamics of the target protein. Through a comprehensive computational drug-discovery approach, we first executed a target-based performance assessment of multiple docking (PLANTS, QVina, LeDock, Vinardo, Smina) scoring functions based on multiple performance metrices. QVina showed the highest capability of selecting true-positive ligands over false positives thus, used to screen 4.8 million ZINC15 compounds against beta H-NOX domain. The docked ligands were further probed in terms of contact footprint and pose reassessment through clustering analysis and PLANTS docking, respectively. Subsequently, energy-based AMBER rescoring of top 100 low-energy complexes, per-residue energy decomposition analysis, and ADME-Tox analysis yielded the top three compounds i.e. ZINC000098973660, ZINC001354120371, and ZINC000096022607. The impact of three selected ligands on the internal structural dynamics of the beta H-NOX domain was also investigated through molecular dynamics simulations. The study revealed potential electrostatic interactions for better conformational dialogue between beta H-NOX domain and allosteric ligands that are critical for the activation of hsGC as compared to the reference compound.

Formation of atherosclerotic plaques, called atherogenesis, is a complex process affected by genetic and environmental factors. It was proposed that endoplasmic reticulum (ER) stress is an important factor in the pathogenesis of atherosclerosis and that vitamin E affects atherosclerotic plaque formation via its antioxidant properties. Here, we investigated ER stress-related molecular mechanisms in high-cholesterol diet (HCD, 2%)-induced atherosclerosis model and the role of vitamin E supplementation in it, beyond its antioxidant properties. The consequences of HCD and vitamin E supplementation were examined by determining protein levels of ER stress markers in aortic tissues. As vitamin E supplementation acts on several unfolded protein response (UPR) factors, it decreased ER stress induced by HCD. To elucidate the associated pathways, gene expression profiling was performed, revealing differentially expressed genes enriched in ER stress-related pathways such as the proteasome and the apoptosis pathways. We further assessed the proteasomal activity impaired by HCD in the aorta and showed that vitamin E reversed it to that of control animals. Overall, the study characterized the effects of HCD and vitamin E on ER stress-related gene expression, revealing the role of proteolytic systems during atherogenesis.

Next-generation sequencing (NGS) techniques allow high-throughput detection of a vast amount of variations in a cost-efficient manner. However, there still are inconsistencies and debates about how to process and analyse this ‘big data’. To accurately extract clinically relevant information from genomics data, choosing appropriate tools, knowing how to best utilize them and interpreting the results correctly is crucial. This chapter reviews state-of-the-art bioinformatics approaches in clinically relevant genomic variant detection. Best practices of reads-to-variant discovery workflows for germline and somatic short genomic variants are presented along with the most commonly utilized tools for each step. Additionally, methods for detecting structural variations are overviewed. Finally, approaches and current guidelines for clinical interpretation of genomic variants are discussed. As emphasized in this chapter, data processing and variant discovery steps are relatively well-understood. The differences in prioritization algorithms on the other hand can be perplexing, thus creating a bottleneck during interpretation. This review aims to shed light on the pros and cons of these differences to help experts give more informed decisions.

In recent years, next-generation sequencing (NGS) platforms that facilitate generation of a vast amount of genomic variation data have become widely used for diagnostic purposes in medicine. However, identifying the potential effects of the variations and their association with a particular disease phenotype is the main challenge in this field. Several strategies are used to discover the causative mutations among hundreds of variants of uncertain significance. Incorporating information from healthy population databases, other organisms’ databases, and computational prediction tools are evolution-based strategies that give valuable insight to interpret the variant pathogenicity. In this chapter, we first provide an overview of NGS analysis workflow. Then, we review how evolutionary principles can be integrated into the prioritization schemes of analyzed variants. Finally, we present an example of a real-life case where the use of evolutionary genetics information facilitated the discovery of disease-causing variants in medical genomics.