Abstract: Objective: Pulmonary infectious diseases (PID) include viral pneumonia (VP) and pulmonary tuberculosis (PT). Mongolian medicine (MM) is an effective treatment option in China, however, the core group medicines (CGMs) in the treatment of PID and their underlying therapeutic mechanisms remain unclear. In this study, through the method of data mining, the CGMs of MM for the treatment of PID were excavated, and the possible mechanism of action of the CGMs in the treatment of PID was explored by using network pharmacology. Methods: First, 89 MM formulae for the treatment of pulmonary infectious diseases collected from Gan Lu Si Bu, Meng Yi Jin Kui, People's Republic of China Ministry of Health Drug Standards (Mongolian Medicine Volume), Standard of Mongolian Medicine Preparations in Inner Mongolia (2007 Edition), and Standard of Mongolian Medicine Preparations in Inner Mongolia (2014 Edition). The CGMs of MM for PID were excavated through association rule analysis and cluster analysis. Then, the active ingredients and potential targets of the CGMs were obtained from TCMSP, TCMIP, BATMAN-TCM databases. PID targets information was collected from OMIM, GeneCards, and DrugBank databases. The possible targets of CGMs treatment for PID were obtained by intersection. The PPI network was constructed through the STRING database, and the topology analysis of the network was performed. Through the enrichment analysis of the intersection targets by R language, the main action pathways and related target proteins of CGMs in the treatment of PID were screened out. The results were verified by molecular docking. Results: A total of 89 formulae were included, involving 164 MM herbs. The efficacy of the drugs was mainly cough-suppressing and panting-calming herbs, and heat-clearing herbs. The nature and flavor were mainly bitter and cold. The CGMs of MM to treatment of PID was excavated as the classic famous formula Sanzi Decoction (Toosendan Fructus-Chebulae Fructus-Gardeniae Fructus). A total of 28 candidate components and 237 predicted targets of CGMs were collected, and 61 common targets with PID were obtained, including key compounds such as quercetin, kaempferol, beta-sitosterol and stigmastero and key targets such as VEGFA, IL6, TP53, AKT1. KEGG enrichment analysis yielded AGE-RAGE signaling pathways, IL-17 signaling pathways, and TNF signaling pathways. Molecular docking results showed that the key targets were well matched with the potential active ingredients of CGMs. Conclusion: This study found that MM commonly used cough-suppressing and panting-calming herbs in combination with heat-clearing herbs to treat PID, and the CGMs for the treatment of PID is "Toosendan Fructus-Chebulae Fructus-Gardeniae Fructus". CGMs mainly play a role in the treatment of PID by acting on VEGFA, IL6, TP53, AKT1 and other targets, regulating AGE-RAGE signaling pathways, IL-17 signaling pathways, and TNF signaling pathways.

Abstract: The beta3 -adrenergic receptor (beta3 -AR) is found in several tissues such as adipose tissue and urinary bladder. It is a therapeutic target because it plays a role in thermogenesis, lipolysis, and bladder relaxation. Two beta3 -AR agonists are used clinically: mirabegron 1 and vibegron 2, which are indicated for overactive bladder syndrome. However, these drugs show adverse effects, including increased blood pressure in mirabegron patients. Hence, new beta3 -AR agonists are needed as starting points for drug development. Previous pharmacophore modeling studies of the beta3 -AR did not involve experimental in vitro validation. Therefore, this study aimed to conduct prospective virtual screening and confirm the biological activity of virtual hits. Ligand-based pharmacophore modeling was performed since no 3D structure of human beta3 -AR is yet available. A dataset consisting of beta3 -AR agonists was prepared to build and validate the pharmacophore models. The best model was employed for prospective virtual screening, followed by physicochemical property filtering and a docking evaluation. To confirm the activity of the virtual hits, an in vitro assay was conducted, measuring cAMP levels at the cloned beta3 -AR. Out of 35 tested compounds, 4 compounds were active in CHO-K1 cells expressing the human beta3 -AR, and 8 compounds were active in CHO-K1 cells expressing the mouse beta3 -AR.

Abstract: Background: Iran has recently included integrase (INT) inhibitors (INTIs) in the first-line treatment regimen in human immunodeficiency virus (HIV)-infected patients. However, there is no bioinformatics data to elaborate the impact of resistance-associated mutations (RAMs) and naturally occurring polymorphisms (NOPs) on INTIs treatment outcome in Iranian patients. Method: In this cross-sectional survey, 850 HIV-1-infected patients enrolled; of them, 78 samples had successful sequencing results for INT gene. Several analyses were performed including docking screening, genotypic resistance, secondary/tertiary structures, post-translational modification (PTM), immune epitopes, etc. Result: The average docking energy (E value) of different samples with elvitegravir (EVG) and raltegravir (RAL) was more than other INTIs. Phylogenetic tree analysis and Stanford HIV Subtyping program revealed HIV-1 CRF35-AD was the predominant subtype (94.9%) in our cases; in any event, online subtyping tools confirmed A1 as the most frequent subtype. For the first time, CRF-01B and BF were identified as new subtypes in Iran. Decreased CD4 count was associated with several factors: poor or unstable adherence, naive treatment, and drug user status. Conclusion: As the first bioinformatic report on HIV-integrase from Iran, this study indicates that EVG and RAL are the optimal INTIs in first-line antiretroviral therapy (ART) in Iranian patients. Some conserved motifs and specific amino acids in INT-protein binding sites have characterized that mutation(s) in them may disrupt INT-drugs interaction and cause a significant loss in susceptibility to INTIs. Good adherence, treatment of naive patients, and monitoring injection drug users are fundamental factors to control HIV infection in Iran effectively.

Abstract: OBJECTIVE: Based on bioinformatics and network pharmacology, the treatment of Saussurea involucrata (SAIN) on novel coronavirus (COVID-19) was evaluated by the GEO clinical sample gene difference analysis, compound-target molecular docking, and molecular dynamics simulation. role in the discovery of new targets for the prevention or treatment of COVID-19, to better serve the discovery and clinical application of new drugs. MATERIALS AND METHODS: Taking the Traditional Chinese Medicine System Pharmacology Database (TCMSP) as the starting point for the preliminary selection of compounds and targets, we used tools such as Cytoscape 3.8.0, TBtools 1.098, AutoDock vina, R 4.0.2, PyMol, and GROMACS to analyze the compounds of SAIN and targets were initially screened. To further screen the active ingredients and targets, we carried out genetic difference analysis (n = 72) through clinical samples of COVID-19 derived from GEO and carried out biological process (BP) analysis on these screened targets (P = 0.05)., gene = 9), KEGG pathway analysis (FDR=0.05, gene = 9), protein interaction network (PPI) analysis (gene = 9), and compounds-target-pathway network analysis (gene = 9), to obtain the target Point-regulated biological processes, disease pathways, and compounds-target-pathway relationships. Through the precise molecular docking between the compounds and the targets, we further screened SAIN's active ingredients (Affinity = -7.2 kcal/mol) targets and visualized the data. After that, we performed molecular dynamics simulations and consulted a large number of related Validation of the results in the literature. RESULTS: Through the screening, analysis, and verification of the data, it was finally confirmed that there are five main active ingredients in SAIN, which are Quercitrin, Rutin, Caffeic acid, Jaceosidin, and Beta-sitosterol, and mainly act on five targets. These targets mainly regulate Tuberculosis, TNF signaling pathway, Alzheimer's disease, Pertussis, Toll-like receptor signaling pathway, Influenza A, Non-alcoholic fatty liver disease (NAFLD), Neuroactive ligand-receptor interaction, Complement and coagulation cascades, Fructose and mannose metabolism, and Metabolic pathways, play a role in preventing or treating COVID-19. Molecular dynamics simulation results show that the four active ingredients of SAIN, Quercitrin, Rutin, Caffeic acid, and Jaceosidin, act on the four target proteins of COVID-19, AKR1B1, C5AR1, GSK3B, and IL1B to form complexes that can be very stable in the human environment. Tertiary structure exists. CONCLUSION: Our study successfully explained the effective mechanism of SAIN in improving COVID-19, and at the same time predicted the potential targets of SAIN in the treatment of COVID-19, AKR1B1, IL1B, and GSK3B. It provides a new basis and provides great support for subsequent research on COVID-19.

Abstract: BACKGROUND: Prostate cancer and multiple neurodegenerative diseases (NDD) share an age-associated pattern of onset. Therapy of prostate cancer is known to impact cognitive function. The objective of this study was to determine the impact of multiple classes of androgen-targeting therapeutics (ATT) on the risk of NDD. METHODS: A retrospective cohort study of men aged 45 and older with prostate within the US-based Mariner claims data set between January 1 and 27, 2021. A propensity score approach was used to minimize measured and unmeasured selection bias. Disease risk was determined using Kaplan-Meier survival analyses. RESULTS: Of the 1,798,648 men with prostate cancer, 209,722 met inclusion criteria. Mean (SD) follow-up was 6.4 (1.8) years. In the propensity score-matched population, exposure to ATT was associated with a minimal increase in NDD incidence (relative risk [RR], 1.07; 95% CI, 1.05-1.10; p < 0.001). However, GnRH agonists alone were associated with significantly increased NDD risk (RR, 1.47; 95% CI, 1.30-1.66; p <0.001). Abiraterone, commonly administered with GnRH agonists and low-dose prednisone, was associated with a significantly decreased risk (RR, 0.77; 95% CI, 0.68-0.87; p < 0.001) of any NDD. CONCLUSIONS: Among patients with prostate cancer, GnRH agonist exposure was associated with an increased NDD risk. Abiraterone acetate reduced the risks of Alzheimer's disease and Parkinson's disease conferred by GnRH agonists, whereas the risk for ALS was reduced by androgen receptor inhibitors. Outcomes of these analyses contribute to addressing controversies in the field and indicate that GnRH agonism may be a predictable instigator of risk for NDD with opportunities for risk mitigation in combination with another ATT.

Abstract: Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Abstract: Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.

Abstract: INTRODUCTION: Prominently accountable for the upsurge of COVID-19 cases as the world attempts to recover from the previous two waves, Omicron has further threatened the conventional therapeutic approaches. The lack of extensive research regarding Omicron has raised the need to establish correlations to understand this variant by structural comparisons. Here, we evaluate, correlate, and compare its genomic sequences through an immunoinformatic approach to understand its epidemiological characteristics and responses to existing drugs. METHODS: We reconstructed the phylogenetic tree and compared the mutational spectrum. We analyzed the mutations that occurred in the Omicron variant and correlated how these mutations affect infectivity and pathogenicity. Then, we studied how mutations in the receptor-binding domain affect its interaction with host factors through molecular docking. Finally, we evaluated the drug efficacy against the main protease of the Omicron through molecular docking and validated the docking results with molecular dynamics simulation. RESULTS: Phylogenetic and mutational analysis revealed the Omicron variant is similar to the highly infectious B.1.620 variant, while mutations within the prominent proteins are hypothesized to alter its pathogenicity. Moreover, docking evaluations revealed significant differences in binding affinity with human receptors, angiotensin-converting enzyme 2 and NRP1. Surprisingly, most of the tested drugs were proven to be effective. Nirmatrelvir, 13b, and Lopinavir displayed increased effectiveness against Omicron. CONCLUSION: Omicron variant may be originated from the highly infectious B.1.620 variant, while it was less pathogenic due to the mutations in the prominent proteins. Nirmatrelvir, 13b, and Lopinavir would be the most effective, compared to other promising drugs that were proven effective.

Abstract: Objective: To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement. Materials and Methods: Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified. Results: NLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications. Discussion: Challenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD. Conclusions: This review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.

Abstract: BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and the human papillomavirus (HPV(+))-driven subtype is the fastest rising cancer in North America. Although most cases of HPV(+) HNSCC respond favorably to the treatment via surgery followed by radiochemotherapy, up to 20% recur with a poor prognosis. The molecular and cellular mechanisms of recurrence are not fully understood. METHODS: To gain insights into the mechanisms of recurrence and to inform patient stratification and personalized treatment, we compared the proteome and phosphoproteome of recurrent and non-recurrent tumors by quantitative mass spectrometry. RESULTS: We observe significant differences between the recurrent and non-recurrent tumors in cellular composition, function, and signaling. The recurrent tumors are characterized by a pro-fibrotic and immunosuppressive tumor microenvironment (TME) featuring markedly more abundant cancer-associated fibroblasts, extracellular matrix (ECM), neutrophils, and suppressive myeloid cells. Defective T cell function and increased epithelial-mesenchymal transition potential are also associated with recurrence. These cellular changes in the TME are accompanied by reprogramming of the kinome and the signaling networks that regulate the ECM, cytoskeletal reorganization, cell adhesion, neutrophil function, and coagulation. CONCLUSIONS: In addition to providing systems-level insights into the molecular basis of recurrence, our work identifies numerous mechanism-based, candidate biomarkers and therapeutic targets that may aid future endeavors to develop prognostic biomarkers and precision-targeted treatment for recurrent HPV(+) HNSCC.