Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2's Main Protease (Mpro).

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer.

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Robust regression based genome-wide multi-trait QTL analysis.

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the ß-likelihood function induced from the ß-divergence with multivariate normal distribution. When ß = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering.

Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word's HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.

A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs.

Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum ß-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as ß-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The ß-weight function plays the significant role on the performance of the proposed method. The proposed method uses ß-weight function as a measure of outlier detection by setting ß = 0.2. We unify both classical and robust estimates using ß-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum ß-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein-protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer.

Identification of Prognostic Biomarker Signatures and Candidate Drugs in Colorectal Cancer: Insights from Systems Biology Analysis.

Colorectal cancer (CRC) is the second most common cause of cancer-related death in the world, but early diagnosis ameliorates the survival of CRC. This report aimed to identify molecular biomarker signatures in CRC. We analyzed two microarray datasets (GSE35279 and GSE21815) from the Gene Expression Omnibus (GEO) to identify mutual differentially expressed genes (DEGs). We integrated DEGs with protein⁻protein interaction and transcriptional/post-transcriptional regulatory networks to identify reporter signaling and regulatory molecules; utilized functional overrepresentation and pathway enrichment analyses to elucidate their roles in biological processes and molecular pathways; performed survival analyses to evaluate their prognostic performance; and applied drug repositioning analyses through Connectivity Map (CMap) and geneXpharma tools to hypothesize possible drug candidates targeting reporter molecules. A total of 727 upregulated and 99 downregulated DEGs were detected. The PI3K/Akt signaling, Wnt signaling, extracellular matrix (ECM) interaction, and cell cycle were identified as significantly enriched pathways. Ten hub proteins (ADNP, CCND1, CD44, CDK4, CEBPB, CENPA, CENPH, CENPN, MYC, and RFC2), 10 transcription factors (ETS1, ESR1, GATA1, GATA2, GATA3, AR, YBX1, FOXP3, E2F4, and PRDM14) and two microRNAs (miRNAs) (miR-193b-3p and miR-615-3p) were detected as reporter molecules. The survival analyses through Kaplan⁻Meier curves indicated remarkable performance of reporter molecules in the estimation of survival probability in CRC patients. In addition, several drug candidates including anti-neoplastic and immunomodulating agents were repositioned. This study presents biomarker signatures at protein and RNA levels with prognostic capability in CRC. We think that the molecular signatures and candidate drugs presented in this study might be useful in future studies indenting the development of accurate diagnostic and/or prognostic biomarker screens and efficient therapeutic strategies in CRC.

NTyroSite: Computational Identification of Protein Nitrotyrosine Sites Using Sequence Evolutionary Features.

Nitrotyrosine is a product of tyrosine nitration mediated by reactive nitrogen species. As an indicator of cell damage and inflammation, protein nitrotyrosine serves to reveal biological change associated with various diseases or oxidative stress. Accurate identification of nitrotyrosine site provides the important foundation for further elucidating the mechanism of protein nitrotyrosination. However, experimental identification of nitrotyrosine sites through traditional methods are laborious and expensive. In silico prediction of nitrotyrosine sites based on protein sequence information are thus highly desired. Here, we report a novel predictor, NTyroSite, for accurate prediction of nitrotyrosine sites using sequence evolutionary information. The generated features were optimized using a Wilcoxon-rank sum test. A random forest classifier was then trained using these features to build the predictor. The final NTyroSite predictor achieved an area under a receiver operating characteristics curve (AUC) score of 0.904 in a 10-fold cross-validation test. It also significantly outperformed other existing implementations in an independent test. Meanwhile, for a better understanding of our prediction model, the predominant rules and informative features were extracted from the NTyroSite model to explain the prediction results. We expect that the NTyroSite predictor may serve as a useful computational resource for high-throughput nitrotyrosine site prediction. The online interface of the software is publicly available at https://biocomputer.bio.cuhk.edu.hk/NTyroSite/.

Genome-wide identification and characterization of DTX family genes highlighting their locations, functions, and regulatory factors in banana (Musa acuminata).

The detoxification efflux carriers (DTX) are a significant group of multidrug efflux transporter family members that play diverse functions in all kingdoms of living organisms. However, genome-wide identification and characterization of DTX family transporters have not yet been performed in banana, despite its importance as an economic fruit plant. Therefore, a detailed genome-wide analysis of DTX family transporters in banana (Musa acuminata) was conducted using integrated bioinformatics and systems biology approaches. In this study, a total of 37 DTX transporters were identified in the banana genome and divided into four groups (I, II, III, and IV) based on phylogenetic analysis. The gene structures, as well as their proteins' domains and motifs, were found to be significantly conserved. Gene ontology (GO) annotation revealed that the predicted DTX genes might play a vital role in protecting cells and membrane-bound organelles through detoxification mechanisms and the removal of drug molecules from banana cells. Gene regulatory analyses identified key transcription factors (TFs), cis-acting elements, and post-transcriptional regulators (miRNAs) of DTX genes, suggesting their potential roles in banana. Furthermore, the changes in gene expression levels due to pathogenic infections and non-living factor indicate that banana DTX genes play a role in responses to both biotic and abiotic stresses. The results of this study could serve as valuable tools to improve banana quality by protecting them from a range of environmental stresses.

Discovery of Bacterial Key Genes from 16S rRNA-Seq Profiles That Are Associated with the Complications of SARS-CoV-2 Infections and Provide Therapeutic Indications.

SARS-CoV-2 infections, commonly referred to as COVID-19, remain a critical risk to both human life and global economies. Particularly, COVID-19 patients with weak immunity may suffer from different complications due to the bacterial co-infections/super-infections/secondary infections. Therefore, different variants of alternative antibacterial therapeutic agents are required to inhibit those infection-causing drug-resistant pathogenic bacteria. This study attempted to explore these bacterial pathogens and their inhibitors by using integrated statistical and bioinformatics approaches. By analyzing bacterial 16S rRNA sequence profiles, at first, we detected five bacterial genera and taxa (Bacteroides, Parabacteroides, Prevotella Clostridium, Atopobium, and Peptostreptococcus) based on differentially abundant bacteria between SARS-CoV-2 infection and control samples that are significantly enriched in 23 metabolic pathways. A total of 183 bacterial genes were found in the enriched pathways. Then, the top-ranked 10 bacterial genes (accB, ftsB, glyQ, hldD, lpxC, lptD, mlaA, ppsA, ppc, and tamB) were selected as the pathogenic bacterial key genes (bKGs) by their protein-protein interaction (PPI) network analysis. Then, we detected bKG-guided top-ranked eight drug molecules (Bemcentinib, Ledipasvir, Velpatasvir, Tirilazad, Acetyldigitoxin, Entreatinib, Digitoxin, and Elbasvir) by molecular docking. Finally, the binding stability of the top-ranked three drug molecules (Bemcentinib, Ledipasvir, and Velpatasvir) against three receptors (hldD, mlaA, and lptD) was investigated by computing their binding free energies with molecular dynamic (MD) simulation-based MM-PBSA techniques, respectively, and was found to be stable. Therefore, the findings of this study could be useful resources for developing a proper treatment plan against bacterial co-/super-/secondary-infection in SARS-CoV-2 infections.

A comprehensive meta-analysis comprising 149 case-control studies to investigate the association between IL-6 gene rs1800795 polymorphism and multiple disease risk.

BACKGROUND: Individual genome-wide association studies (GWAS) or single case-specific meta-analyses may not be sufficient evidence to take action against a specific gene function. Thus, we tried to determine a consensus association between the IL-6 gene rs1800795 polymorphism and multiple disease risks through an updated statistical meta-analysis. METHOD: After systematically searching online databases, we found 149 case-control relevant datasets with a sample size of 96,153 (cases: 38,291 and controls: 57862) and conducted the meta-analysis using updated statistical models. RESULTS: The analyses of this comprehensive meta-analysis revealed a significant association between IL-6 -174G/C polymorphism and overall disorder risk under all genetic models (C vs G: OR = 1.11, 95% CI = 1.08-1.13; p-value = 4.8E-17; CC vs GG: OR = 1.19, 95% CI = 1.13-1.26; p-value = 9.4E-12; CG vs GG: OR = 1.10, 95% CI = 1.06-1.14; p-value = 1.1E-07; CC + CG vs GG: OR = 1.13, 95% CI = 1.10-1.17; p-value = 1.1E-13; CC vs CG + GG: OR = 1.18, 95% CI = 1.06-1.31; p-value = 0.0019) and (OR > 1) with Asian ethnicity. The subgroup analyses based on the diseases revealed that the polymorphism was highly significantly increasing the risk of coronary artery disease (CAD) under all genetic models. Likewise, a significant association was observed with increased risk under three genetic models of inflammatory diseases (C vs G; CC vs GG; and CC vs CG + GG), and rheumatoid arthritis (C vs G; CG vs GG; and CC + CG vs GG). Conversely, the -174G/C SNP significantly decreased the risk of ischemic stroke under the two genetic models (C vs G; and CG vs GG). However, the other diseases included in this study showed no significant association with IL-6 (-174G/C) polymorphism. CONCLUSION: This meta-analysis provided strong evidence for the association between IL-6 gene rs1800795 polymorphism and multiple disease risks. The IL-6 gene could be a useful prognostic biomarker for CAD, inflammatory disease, ischemic stroke, and rheumatoid arthritis.

2,4-Dipropylphloroglucinol inhibits the growth of human lung and colorectal cancer cells through induction of apoptosis.

Scientists are finding the most effective chemotherapeutic agents for the treatment of cancer. In the present study, we evaluated the anticancer mechanism of DPPG, a derivative of DAPG (2,4-diacetylphloroglucinol), for the first time. DPPG and DAPG inhibited 83 and 59% of human colorectal cancer HCT116 cell growth at 40.0 µg/ml, and 74 and 57% of human lung cancer A549 cell growth at 10.0 µg/ml concentrations respectively. Furthermore, DPPG and DAPG inhibited 97 and 73% colony formation of the HCT116 cells at 20.0 µg/ml concentration. DPPG and DAPG induced apoptosis in the HCT116 and A549 cells that was confirmed by Hoechst 33342 and FITC-annexin V staining. This result also revealed that ROS generated in both the HCT116 and A549 cells after treatment with DPPG. However, no ROS production was observed in HCT116 and A549 cells after treatment with DAPG. Both DAPG and DPPG significantly increased the CASP3 protein expression that was detected by staining the cells with the super-view 488-CASP3 substrate. Expression of WNT1 gene was eliminated in DPPG and DAPG treated HCT116. Expression of MAPK1 gene was entirely abolished in DPPG treated cells, whereas a significant decrease was observed for DAPG. An intense band of CASP8 gene product was observed agarose gel for DPPG treated HCT116 cells than DAPG. Molecular docking simulation showed the high binding affinities (≥ 6.5 kcal/mol) of DPPG and DAPG with target proteins WNT1, MAPK1, CASP8, and CASP3 in HCT116 cells. This manuscript demonstrated that DAPG and DPPG inhibited lung and colorectal cancer cells by inducing apoptosis. DAPG and DPPG inhibited A549 and HCT116 cells growth by inducing apoptosis.

Identification of Drug Targets and Agents Associated with Hepatocellular Carcinoma through Integrated Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death globally. The mechanisms underlying the development of HCC are mostly unknown till now. OBJECTIVE: The main goal of this study was to identify potential drug target proteins and agents for the treatment of HCC. METHODS: The publicly available three independent mRNA expression profile datasets were downloaded from the NCBI-GEO database to explore common differentially expressed genes (cDEGs) between HCC and control samples using the Statistical LIMMA approach. Hub-cDEGs as drug targets highlighting their functions, pathways, and regulators were identified by using integrated bioinformatics tools and databases. Finally, Hub-cDEGs-guided top-ranked drug agents were identified by molecular docking study for HCC. RESULTS: We identified 160 common DEGs (cDEGs) from three independent mRNA expression datasets in which ten cDEGs (CDKN3, TK1, NCAPG, CDCA5, RACGAP1, AURKA, PRC1, UBE2T, MELK, and ASPM) were selected as Hub-cDEGs. The GO functional and KEGG pathway enrichment analysis of Hub-cDEGs revealed some crucial cancer-stimulating biological processes, molecular functions, cellular components, and signaling pathways. The interaction network analysis identified three TF proteins and five miRNAs as the key transcriptional and post-transcriptional regulators of HubcDEGs. Then, we detected the proposed Hub-cDEGs guided top-ranked three anti-HCC drug molecules (Dactinomycin, Vincristine, Sirolimus) that were also highly supported by the already published top-ranked HCC-causing Hub-DEGs mediated receptors. CONCLUSION: The findings of this study would be useful resources for diagnosis, prognosis, and therapies of HCC.

Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.

BACKGROUND: Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches. METHODS: To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins. RESULTS: We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC. CONCLUSION: The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections.

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

Genome-Wide Identification and Characterization of Major RNAi Genes Highlighting Their Associated Factors in Cowpea (Vigna unguiculata (L.) Walp.).

In different regions of the world, cowpea (Vigna unguiculata (L.) Walp.) is an important vegetable and an excellent source of protein. It lessens the malnutrition of the underprivileged in developing nations and has some positive effects on health, such as a reduction in the prevalence of cancer and cardiovascular disease. However, occasionally, certain biotic and abiotic stresses caused a sharp fall in cowpea yield. Major RNA interference (RNAi) genes like Dicer-like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) are essential for the synthesis of their associated factors like domain, small RNAs (sRNAs), transcription factors, micro-RNAs, and cis-acting factors that shield plants from biotic and abiotic stresses. In this study, applying BLASTP search and phylogenetic tree analysis with reference to the Arabidopsis RNAi (AtRNAi) genes, we discovered 28 VuRNAi genes, including 7 VuDCL, 14 VuAGO, and 7 VuRDR genes in cowpea. We looked at the domains, motifs, gene structures, chromosomal locations, subcellular locations, gene ontology (GO) terms, and regulatory factors (transcription factors, micro-RNAs, and cis-acting elements (CAEs)) to characterize the VuRNAi genes and proteins in cowpea in response to stresses. Predicted VuDCL1, VuDCL2(a, b), VuAGO7, VuAGO10, and VuRDR6 genes might have an impact on cowpea growth, development of the vegetative and flowering stages, and antiviral defense. The VuRNAi gene regulatory features miR395 and miR396 might contribute to grain quality improvement, immunity boosting, and pathogen infection resistance under salinity and drought conditions. Predicted CAEs from the VuRNAi genes might play a role in plant growth and development, improving grain quality and production and protecting plants from biotic and abiotic stresses. Therefore, our study provides crucial information about the functional roles of VuRNAi genes and their associated components, which would aid in the development of future cowpeas that are more resilient to biotic and abiotic stress. The manuscript is available as a preprint at this link: doi:10.1101/2023.02.15.528631v1.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer.

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing.

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Association of hypoxia inducible factor 1-Alpha gene polymorphisms with multiple disease risks: A comprehensive meta-analysis.

HIF1A gene polymorphisms have been confirmed the association with cancer risk through the statistical meta-analysis based on single genetic association (SGA) studies. A good number SGA studies also investigated the association of HIF1A gene with several other diseases, but no researcher yet performed statistical meta-analysis to confirm this association more accurately. Therefore, in this paper, we performed a statistical meta-analysis to draw a consensus decision about the association of HIF1A gene polymorphisms with several diseases except cancers giving the weight on large sample size. This meta-analysis was performed based on 41 SGA study's findings, where the polymorphisms rs11549465 (1772 C/T) and rs11549467 (1790 G/A) of HIF1A gene were analyzed based on 11544 and 7426 cases and 11494 and 7063 control samples, respectively. Our results showed that the 1772 C/T polymorphism is not significantly associated with overall disease risks. The 1790 G/A polymorphism was significantly associated with overall diseases under recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases though it is recessive. The subgroup analysis based on ethnicity showed the significant association of 1772 C/T polymorphism with overall disease for Caucasian population under the all genetic models, which indicates that the C allele controls overall diseases. The ethnicity subgroup showed the significant association of 1790 G/A polymorphism with overall disease for Asian population under the recessive model (AA vs. AG + GG), which indicates that the A allele is responsible for overall diseases. The subgroup analysis based on disease types showed that 1772 C/T is significantly associated with chronic obstructive pulmonary disease (COPD) under two genetic models (C vs. T and CC vs. CT + TT), skin disease under two genetic models (CC vs. TT and CC + CT vs. TT), and diabetic complications under three genetic models (C vs. T, CT vs. TT and CC + CT vs. TT), where C allele is high risk factor for skin disease and diabetic complications (since, ORs > 1), but low risk factor for COPD (since, ORs < 1). Also the 1790 G/A variant significantly associated with the subgroup of cardiovascular disease (CVD) under homozygote model, diabetic complications under allelic and homozygote models, and other disease under four genetic models, where the A is high risk factor for diabetic complications and low risk factor for CVD. Thus, this study provided more evidence that the HIF1A gene is significantly associated with COPD, CVD, skin disease and diabetic complications. These might be the severe comorbidities and risk factors for multiple cancers due to the effect of HIF1A gene and need further investigations accumulating large number of studies.