Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions.

Despite the association of prevalent health conditions with coronavirus disease 2019 (COVID-19) severity, the disease-modifying biomolecules and their pathogenetic mechanisms remain unclear. This study aimed to understand the influences of COVID-19 on different comorbidities and vice versa through network-based gene expression analyses. Using the shared dysregulated genes, we identified key genetic determinants and signaling pathways that may involve in their shared pathogenesis. The COVID-19 showed significant upregulation of 93 genes and downregulation of 15 genes. Interestingly, it shares 28, 17, 6 and 7 genes with diabetes mellitus (DM), lung cancer (LC), myocardial infarction and hypertension, respectively. Importantly, COVID-19 shared three upregulated genes (i.e. MX2, IRF7 and ADAM8) with DM and LC. Conversely, downregulation of two genes (i.e. PPARGC1A and METTL7A) was found in COVID-19 and LC. Besides, most of the shared pathways were related to inflammatory responses. Furthermore, we identified six potential biomarkers and several important regulatory factors, e.g. transcription factors and microRNAs, while notable drug candidates included captopril, rilonacept and canakinumab. Moreover, prognostic analysis suggests concomitant COVID-19 may result in poor outcome of LC patients. This study provides the molecular basis and routes of the COVID-19 progression due to comorbidities. We believe these findings might be useful to further understand the intricate association of these diseases as well as for the therapeutic development.

Developing phytocompound-based new drugs against multi-drug-resistant Staphylococcus aureus.

Staphylococcus aureus, a prevalent component of the human microbiota, is associated with skin infections to life-threatening diseases, presenting challenges in treatment options and necessitating the development of effective treatments. This study integrated computational and in vitro approaches to identify promising phytocompounds with therapeutic potential. Staphopain B emerged as a target protein for its role in immune evasion, exhibiting stability during molecular dynamic simulation (MDS) with a root mean square deviation value of 2.376 Å. Screening 115 phytocompounds with antibacterial properties from the PubChem database identified 12 with drug-like properties, nine of which showed superior binding affinity to Staphopain B compared to a commercial antibiotic, doxycycline (-7.8 kcal mol-1). Notably, epoxyazadiradione and nimbolide displayed higher estimated free energy of binding scores (-7.91 and -7.93 kcal mol-1, respectively), indicating strong protein-ligand interactions. The root mean square fluctuation values for epoxyazadiradione and nimbolide were 1.097 and 1.034 Å, respectively, which was confirmed through MDS. Crude ethanolic extracts (100% and 70%) of neem (Azadirachta indica) leaves demonstrated narrow inhibition against the bacteria in comparison to doxycycline in the disc-diffusion assay. This study underscores the potential of phytocompounds as therapeutic agents against S. aureus; however, further in vitro experiments and testing of the phytocompounds in vivo are required.

Targeting Shikimate Kinase Pathway of Acinetobacter baumannii: A Structure-Based Computational Approach to Identify Antibacterial Compounds.

Acinetobacter baumannii (A. baumannii) is an opportunistic bacterium that has developed multidrug resistance (MDR) to most of today's antibiotics, posing a significant risk to human health. Considering the fact that developing novel drugs is a time-consuming and expensive procedure, this research focuses on utilizing computational resources for repurposing antibacterial agents for A. baumannii. We targeted shikimate kinase, an essential enzyme in A. baumannii, that plays a significant role in the metabolic process. The basis for generating new therapeutic compounds is to inhibit the shikimate kinase and thereby targeting the shikimate pathway. Herein, 1941 drug-like compounds were investigated in different in silico techniques for assessing drug-likeness properties, ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling, binding affinity, and conformation analysis utilizing Autodock-vina and SwissDock. CHEMBL1237, CHEMBL1237119, CHEMBL2018096, and CHEMBL39167178 were determined as potential drug candidates for suppressing shikimate kinase protein. Molecular Dynamics Simulation (MDS) results for root mean square deviation, root mean square fluctuation, hydrogen bond, and gyration radius confirm the drug candidates' molecular stability with the target protein. According to this study, CHEMBL1237 (Lisinopril) could be the most suitable candidate for A. baumannii. Our investigation suggests that the inhibitors of shikimate kinase could represent promising treatment options for A. baumannii. However, further in vitro and in vivo studies are necessary to validate the therapeutic potential of the suggested drug candidates.

In Silico Identification and Analysis of Potentially Bioactive Antiviral Phytochemicals against SARS-CoV-2: A Molecular Docking and Dynamics Simulation Approach.

SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3 (Nonstructural protein 3) and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2.

Correction: Oxa-376 and Oxa-530 variants of ß-lactamase: computational study uncovers potential therapeutic targets of Acinetobacter baumannii.

[This corrects the article DOI: 10.1039/D2RA02939A.].

Oxa-376 and Oxa-530 variants of ß-lactamase: computational study uncovers potential therapeutic targets of Acinetobacter baumannii.

Antimicrobial resistance is a major global health crisis, resulting in thousands of deaths each year. Antibiotics' effectiveness against microorganisms deteriorates over time as multidrug resistance (MDR) develops, which is exacerbated by irregular antibiotic use, poor disease management, and the evasive nature of bacteria. The World Health Organization has recognized multidrug resistance as a critical public health concern, and Acinetobacter baumannii has been at the center of attention due to its ability to develop multidrug resistance (MDR). It generally produces carbapenem-hydrolyzing oxacillinase, which has been identified as the primary source of beta-lactam resistance in MDR bacteria. Recently, point mutations in A. baumannii have been identified as a key factor of multidrug resistance, making them a prime concern for researchers. The goal of the current work was to establish a unique way of finding multidrug-resistant variants and identify the most damaging mutations in the existing databases. We characterized the deleterious variants of oxacillinases using several computational tools. Following a thorough analysis, Oxa-376 and Oxa-530 were found to be more damaging when compared with the wild-type Oxa-51. The mutants' 3D structures were then prepared and refined with RaptorX, GalaxyRefine, and SAVES servers. Our research incorporates seven antimicrobial agents to illustrate the resistance capability of the variants of oxacillinase by evaluating binding affinity in Autodock-vina and Schrodinger software. RMSD, RMSF, Radius of gyration analysis, the solvent-accessible surface area (SASA), hydrogen bonding analysis and MM-GBSA from Molecular Dynamics Simulation revealed the dynamic nature and stability of wild-type and Oxa-376 and Oxa-530 variants. Our findings will benefit researchers looking for the deleterious mutations of Acinetobacter baumannii and new therapeutics to combat those variants. However, further studies are necessary to evaluate the mechanism of hydrolyzing activity and antibiotic resistance of these variants.

Identification of the most damaging nsSNPs in the human CFL1 gene and their functional and structural impacts on cofilin-1 protein.

The cofilin-1 protein, encoded by CFL1, is an actin-binding protein that regulates F-actin depolymerization and nucleation activity through phosphorylation and dephosphorylation. CFL1 has been implicated in the development of neurodegenerative diseases (Alzheimer's disease and Huntington's disease), neuronal migration disorders (lissencephaly, epilepsy, and schizophrenia), and neural tube closure defects. Mutations in CFL1 have been associated with impaired neural crest cell migration and neural tube closure defects. In our study, various computational approaches were utilized to explore single-nucleotide polymorphisms (SNPs) in CFL1. The Variation Viewer and gnomAD databases were used to retrieve CFL1 SNPs, including 46 nonsynonymous SNPs (nsSNPs). The functional and structural annotation of SNPs was performed using 12 sequence-based web applications, which identified 20 nsSNPs as being the most likely to be deleterious or disease-causing. The conservation of cofilin-1 protein structures was illustrated using the ConSurf and PROSITE web servers, which projected the 12 most deleterious nsSNPs onto conserved domains, with the potential to disrupt the protein's functionality. These 12 nsSNPs were selected for protein structure construction, and the DynaMut/DUET servers predicted that the protein variants V7G, L84P, and L99A were the most likely to be damaging to the cofilin-1 protein structure or function. The evaluation of molecular docking studies demonstrated that the L99A and L84P cofilin-1 variants reduce the binding affinity for actin compared with the native cofilin-1 structure, and molecular dynamic simulation studies confirmed that these variants might destabilize the protein structure. The consequences of putative mutations on protein-protein interactions and post-translational modification sites in the cofilin-1 protein structure were analyzed. This study represents the first complete approach to understanding the effects of nsSNPs within the actin-depolymerizing factor/cofilin family, which suggested that SNPs resulting in L84P (rs199716082) and L99A (rs267603119) variants represent significant CFL1 mutations associated with disease development.