Filimonov, D. | bioRxiv Science

bioRxiv2020

Comprehensive analysis of HIV elite controllers and progressors' transcriptional profiles from CD8+ T lymphocytes demonstrates heterogeneity of pathways and master regulators, which may be essential for disease nonprogression

Cytotoxic and noncytotoxic CD8+ T lymphocyte responses are essential for the control of HIV infection. Understanding the mechanisms underlying HIV control in elite controllers (ECs), who do not progress to AIDS for many years without treatment, may facilitate the development of new effective therapeutic strategies. We performed a comprehensive analysis of the transcriptional profiles of CD8+ cells from ECs and treated and untreated progressors using an original pipeline. Cluster analysis enabled the identification of five distinct groups (EC groups 1-5) of ECs based on their transcription profiles. Profiles of EC groups 2-4 were associated with different numbers of differentially expressed genes, but the corresponding genes shared the same cellular processes. These three groups were associated with increased metabolism, survival, proliferation, and the absence of an "exhausted" phenotype of CD8+ lymphocytes, compared to untreated progressors. The transcriptional profiles of EC group 1 were opposite to those of EC groups 2-4 and similar to those of the treated progressors. This group may be associated with residual dysfunction of T lymphocytes. The EC group 5 was indistinguishable from normal. EC groups 1 and 5 can have mechanisms of nonprogression unrelated to CD8+ cells. The transcription changes in CD8+ lymphocytes from ECs may be attributable to the receptors that modulate the functional states of CD8+ cells. We identified 22 receptors, which are potential master regulators and may be responsible for the observed expression changes of CD8+ cells in ECs. These receptors can be considered potential targets of therapeutic intervention, which may decelerate disease progression. IMPORTANCEA small group of HIV-infected individuals, known as elite controllers (ECs), do not develop AIDS for many years, despite being untreated. Understanding the mechanisms governing HIV control in ECs may help develop new effective therapeutic strategies. Since CD8+ T lymphocytes seem to play a significant role in HIV control, we analyzed the large number of transcriptional profiles in total CD8+ cells from ECs, treated, and untreated progressors. We found distinct groups of ECs, which may have different mechanisms governing HIV nonprogression, and we identified related pathways and cellular processes that are dissimilar from those in progressors. We also identified master regulators, key proteins in the signaling network that can be responsible for observed transcriptional changes in CD8+ cells from ECs and may be essential for disease nonprogression. These proteins may represent potential targets for therapeutic interventions.

microbiology↗

bioRxiv2019

Assessment of the cardiovascular adverse effects of drug-drug interactions through a combined analysis of spontaneous reports and predicted drug-target interactions

Adverse drug effects (ADEs) are one of the leading causes of death in developed countries and are the main reason for drug recalls from the market, whereas the ADEs that are associated with action on the cardiovascular system are the most dangerous and widespread. The treatment of human diseases often requires the intake of several drugs, which can lead to undesirable drug-drug interactions (DDIs), thus causing an increase in the frequency and severity of ADEs. An evaluation of DDI-induced ADEs is a nontrivial task and requires numerous experimental and clinical studies. Therefore, we developed a computational approach to assess the cardiovascular ADEs of DDIs. This approach is based on the combined analysis of spontaneous reports (SRs) and predicted drug-target interactions to estimate the five cardiovascular ADEs that are induced by DDIs, namely, myocardial infarction, ischemic stroke, ventricular tachycardia, cardiac failure, and arterial hypertension. We applied a method based on least absolute shrinkage and selection operator (LASSO) logistic regression to SRs for the identification of interacting pairs of drugs causing corresponding ADEs, as well as noninteracting pairs of drugs. As a result, five datasets containing, on average, 3100 ADE-causing and non-ADE-causing drug pairs were created. The obtained data, along with information on the interaction of drugs with 1553 human targets predicted by PASS Targets software, were used to create five classification models using the Random Forest method. The average area under the ROC curve of the obtained models, sensitivity, specificity and balanced accuracy were 0.838, 0.764, 0.754 and 0.759, respectively. The predicted drug targets were also used to hypothesize the potential mechanisms of DDI-induced ventricular tachycardia for the top-scoring drug pairs. The created five classification models can be used for the identification of drug combinations that are potentially the most or least dangerous for the cardiovascular system. Author summaryAssessment of adverse drug effects as well as the influence of drug-drug interactions on their manifestation is a nontrivial task that requires numerous experimental and clinical studies. We developed a computational approach for the prediction of adverse effects that are induced by drug-drug interactions, which are based on a combined analysis of spontaneous reports and predicted drug-target interactions. Importantly, the approach requires only structural formulas to predict adverse effects, and, therefore, may be applied for new, insufficiently studied drugs. We applied the approach to predict five of the most important cardiovascular adverse effects, because they are the most dangerous and widespread. These effects are myocardial infarction, ischemic stroke, ventricular tachycardia, arterial hypertension and cardiac failure. The accuracies of predictive models were relatively high, in the range of 73-81%; therefore, we performed a prediction of the five cardiovascular adverse effects for the large number of drug pairs and revealed the combinations that are the most dangerous for the cardiovascular system. We consider that the developed approach can be used for the identification of pairwise drug combinations that are potentially the most or least dangerous for the cardiovascular system.

bioinformatics↗