Bioinformatics in translational drug discovery

Bioinformatics-based discovery of biomarkers and immunoinflammatory targets in children with cerebral palsy: An observational study

Cerebral palsy (CP) is the most common disabling disease in children, and motor dysfunction is the core symptom of CP. Although relevant risk factors have been found to be closely associated with CP: congenital malformations, multiple gestation, prematurity, intrauterine inflammation and infection, birth asphyxia, thrombophilia, and perinatal stroke. Its important pathophysiological mechanism is amniotic fluid infection and intraamniotic inflammation leading to fetal developing brain damage, which may last for many years. However, the molecular mechanism of CP is still not well explained. This study aimed to use bioinformatics to identify key biomarker-related signaling pathways in CP. The expression profile of children with CP was selected from the Gene Expression Comprehensive Database, and the CP disease gene data set was obtained from GeneCards. A protein-protein interaction network was established and functional enrichment analysis was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. A total of 144 differential key intersection genes and 10 hub genes were identified through molecular biology. Gene Ontology functional enrichment analysis results show that differentially expressed genes are mainly concentrated in biological processes, such as immune response and neurogenesis. The cellular components involved mainly include axons, postsynaptic membranes, etc, and their molecular functions mainly involve proteoglycan binding, collagen binding, etc. Kyoto Encyclopedia of Genes and Genomes analysis shows that the intersection genes are mainly in signaling pathways related to the immune system, inflammatory response, and nervous system, such as Th17 cell differentiation, Toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, NF-κB signaling pathway, axon guidance, PI3K-Akt signaling pathway, HIF-1 signaling pathway, gap junction, etc. Jak-STAT signaling pathway, mTOR signaling pathway, and related hub genes regulate immune cells and inflammatory factors and play an important role in the development and progression of CP.

Skeletal Transformations of Terpenoid Forskolin Employing an Oxidative Rearrangement Strategy

The skeletal transformations of diterpenoid forskolin were achieved by employing an oxidative rearrangement strategy. A library of 36 forskolin analogues with structural diversity was effectively generated. Computational analysis shows that 12 CTD compounds with unique scaffolds and ring systems were produced during the course of this work.

Prognostic Model Construction of Disulfidptosis-Related Genes and Targeted Anticancer Drug Research in Pancreatic Cancer

Pancreatic cancer stands as one of the most lethal malignancies, characterized by delayed diagnosis, high mortality rates, limited treatment efficacy, and poor prognosis. Disulfidptosis, a recently unveiled modality of cell demise induced by disulfide stress, has emerged as a critical player intricately associated with the onset and progression of various cancer types. It has emerged as a promising candidate biomarker for cancer diagnosis, prognosis assessment, and treatment strategies. In this study, we have effectively established a prognostic risk model for pancreatic cancer by incorporating multiple differentially expressed long non-coding RNAs (DElncRNAs) closely linked to disulfide-driven cell death. Our investigation delved into the nuanced relationship between the DElncRNA-based predictive model for disulfide-driven cell death and the therapeutic responses to anticancer agents. Our findings illuminate that the high-risk subgroup exhibits heightened susceptibility to the small molecule compound AZD1208, positioning it as a prospective therapeutic agent for pancreatic cancer. Finally, we have elucidated the underlying mechanistic potential of AZD1208 in ameliorating pancreatic cancer through its targeted inhibition of the peroxisome proliferator-activated receptor-γ (PPARG) protein, employing an array of comprehensive analytical methods, including molecular docking and molecular dynamics (MD) simulations. This study explores disulfidptosis-related genes, paving the way for the development of targeted therapies for pancreatic cancer and emphasizing their significance in the field of oncology. Furthermore, through computational biology approaches, the drug AZD1208 was identified as a potential treatment targeting the PPARG protein for pancreatic cancer. This discovery opens new avenues for exploring targets and screening drugs for pancreatic cancer.

Tribuloside: Mechanisms and Efficacy in Treating Acute Lung Injury Revealed by Network Pharmacology and Experimental Validation

Background

Acute lung injury (ALI) is a serious illness that has few treatment options available. Tribuloside, a natural flavonoid extracted from the Tribulus Terrestris plant in China, is potent in addressing many health issues such as headaches, dizziness, itching, and vitiligo.

Objective

This study intends to explore the mechanisms of action of Tribuloside in treating ALI through a combination of network pharmacology and experimental validation.

Methods

We obtained the 2D structure and SMILES number of Tribuloside from the PubChem database. We used the SwissTargetPrediction database to identify pharmacological targets. We found 1215 targets linked to ALI by examining the GeneCards database. We used the String database and Cytoscape software to create the "drug or disease-target" network as well as the protein-protein interactions (PPI). Key targets were identified by evaluating associated biological processes and pathway enrichment. A Venny Diagram showed 49 intersection points between Tribuloside and ALI. Molecular docking with AutoDockTools found that Tribuloside had a high affinity for IL6, BCL2, TNF, STAT3, IL1B, and MAPK3, the top 6 targets in the PPI network by Degree values. To test Tribuloside's therapeutic efficacy in ALI, an acute lung damage model in mice was constructed using lipopolysaccharide. Tribuloside treatment reduced inflammatory cell infiltration, decreased fibrotic area, repaired damaged alveoli, and suppressed inflammatory factors IL-6, TNF-α, and IL-1β in the lungs through many pathways and targets.

Conclusion

This study reveals that Tribuloside has the potential to treat ALI by targeting various pathways and targets, according to network pharmacology predictions and experimental confirmation.

Integrated Bioinformatics Analysis Identified ASNS and DDIT3 as the Therapeutic Target in Castrate-Resistant Prostate Cancer

Many studies have demonstrated the mechanisms of progression to castration-resistant prostate cancer (CRPC) and novel strategies for its treatment. Despite these advances, the molecular mechanisms underlying the progression to CRPC remain unclear, and currently, no effective treatments for CRPC are available. Here, we characterized the key genes involved in CRPC progression to gain insight into potential therapeutic targets. Bicalutamide-resistant prostate cancer cells derived from LNCaP were generated and named Bical R. RNA sequencing was used to identify differentially expressed genes (DEGs) between LNCaP and Bical R. In total, 631 DEGs (302 upregulated genes and 329 downregulated genes) were identified. The Cytohubba plug-in in Cytoscape was used to identify seven hub genes (ASNS, AGT, ATF3, ATF4, DDIT3, EFNA5, and VEGFA) associated with CRPC progression. Among these hub genes, ASNS and DDIT3 were markedly upregulated in CRPC cell lines and CRPC patient samples. The patients with high expression of ASNS and DDIT3 showed worse disease-free survival in patients with The Cancer Genome Atlas (TCGA)-prostate adenocarcinoma (PRAD) datasets. Our study revealed a potential association between ASNS and DDIT3 and the progression to CRPC. These results may contribute to the development of potential therapeutic targets and mechanisms underlying CRPC progression, aiming to improve clinical efficacy in CRPC treatment.

Role of prognostic gene DKK1 in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common squamous cell carcinomas of the head and neck. The morbidity and mortality rates of OSCC have increased in recent years. However, the pathogenesis of this disease remains unknown. The present study aimed to identify predictive biomarkers and therapeutic targets for OSCC. Bioinformatics screening of differentially expressed genes in OSCC was performed based on data from The Cancer Genome Atlas and Gene Expression Omnibus databases. Dickkopf Wnt signaling pathway inhibitor 1 (DKK1) was identified to be associated with survival, tumor immunity and DNA repair in OSCC. Furthermore, the effects of DKK1 were evaluated by the knockdown of DKK1 in two OSCC cell lines. The proliferation, clonogenicity, migration and invasion of the cells were assessed in vitro using Cell Counting Kit-8, colony formation, wound healing and Transwell assays, respectively, and were found to be inhibited by DKK1 knockdown. The present study suggests that DKK1 may be used in the prognosis of patients with OSCC and that targeting DKK1 is a potential strategy for OSCC therapy.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

Prediction of resveratrol target proteins: a bioinformatics analysis

Resveratrol is a natural compound with a wide range of biological functions that generate health benefits under normal conditions and in multiple diseases. This has attracted the attention of the scientific community, which has revealed that this compound exerts these effects through its action on different proteins. Despite the great efforts made, due to the challenges involved, not all the proteins with which resveratrol interacts have yet been identified. In this work, using protein target prediction bioinformatics systems, RNA sequencing analysis and protein-protein interaction networks, 16 proteins were identified as potential targets of resveratrol. Due to its biological relevance, the interaction of resveratrol with the predicted target CDK5 was further investigated. A docking analysis found that resveratrol can interact with CDK5 and be positioned in its ATP-binding pocket. Resveratrol forms hydrogen bonds between its three hydroxyl groups (-OH) and CDK5 residues C83, D86, K89 and D144. Molecular dynamics analysis showed that these bonds allow resveratrol to remain in the pocket and suggest inhibition of CDK5 activity. All this allows us to better understand how resveratrol acts and to consider CDK5 inhibition within its biological actions, mainly in neurodegenerative diseases where this protein has been shown to be relevant.Communicated by Ramaswamy H. Sarma.

Identifying the natural products in the treatment of atherosclerosis by increasing HDL-C level based on bioinformatics analysis, molecular docking, and in vitro experiment

BACKGROUND

Previous studies have demonstrated that high-density lipoprotein cholesterol (HDL-C) plays an anti-atherosclerosis role through reverse cholesterol transport. Several studies have validated the efficacy and safety of natural products in treating atherosclerosis (AS). However, the study of raising HDL-C levels through natural products to treat AS still needs to be explored.

METHODS

The gene sets associated with AS were collected and identified by differential gene analysis and database query. By constructing a protein-protein interaction (PPI) network, the core submodules in the network are screened out. At the same time, by calculating node importance (Nim) in the PPI network of AS disease and combining it with Kyoto Encyclopedia of genes and genomes (KEGG) pathways enrichment analysis, the key target proteins of AS were obtained. Molecular docking is used to screen out small natural drug molecules with potential therapeutic effects. By constructing an in vitro foam cell model, the effects of small molecules on lipid metabolism and key target expression of foam cells were investigated.

RESULTS

By differential gene analysis, 451 differential genes were obtained, and a total of 313 disease genes were obtained from 6 kind of databases, then 758 AS-related genes were obtained. The enrichment analysis of the KEGG pathway showed that the enhancement of HDL-C level against AS was related to Lipid and atherosclerosis, Cholesterol metabolism, Fluid shear stress and atherosclerosis, PPAR signaling pathway, and other pathways. Then we intersected 31 genes in the core module of the PPI network, the top 30 genes in Nims, and 32 genes in the cholesterol metabolism pathway, and finally found 3 genes. After the above analysis and literature collection, we focused on the following three related gene targets: APOA1, LIPC, and CETP. Molecular docking showed that Genistein has a good binding affinity for APOA1, CETP, and LIPC. In vitro, experiments showed that Genistein can up-regulated APOA1, LIPC, and CETP levels.

CONCLUSIONS

Based on our research, Genistein may have the effects of regulating HDL-C and anti-atherosclerosis. Its mechanism of action may be related to the regulation of LIPC, CETP, and APOA1 to improve lipid metabolism.

Investigating trial design variability in trials of disease-modifying therapies in Parkinson's disease: a scoping review protocol

INTRODUCTION

Parkinson's disease (PD) is a debilitating neurological disorder for which the identification of disease-modifying interventions represents a major unmet need. Diverse trial designs have attempted to mitigate challenges of population heterogeneity, efficacious symptomatic therapy and lack of outcome measures that are objective and sensitive to change in a disease modification setting. It is not clear whether consensus is emerging regarding trial design choices. Here, we report the protocol of a scoping review that will provide a contemporary update on trial design variability for disease-modifying interventions in PD.

METHODS AND ANALYSIS

The Population, Intervention, Comparator, Outcome and Study design (PICOS) framework will be used to structure the review, inform study selection and analysis. The databases MEDLINE, Web of Science, Cochrane and the trial registry ClinicalTrials.gov will be systematically searched to identify published studies and registry entries in English. Two independent reviewers will screen study titles, abstracts and full text for eligibility, with disagreements being resolved through discussion or by a third reviewer where necessary. Data on general study information, eligibility criteria, outcome measures, trial design, retention and statistically significant findings will be extracted into a standardised form. Extracted data will be presented in a descriptive analysis. We will report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review extension.

ETHICS AND DISSEMINATION

This work will provide an overview of variation and emerging trends in trial design choices for disease-modifying trials of PD. Due to the nature of this study, there are no ethical or safety considerations. We plan to publish our findings in a peer-reviewed journal.

Danggui Shaoyao San ameliorates the lipid metabolism via the PPAR signaling pathway in a Danio rerio (zebrafish) model of hyperlipidemia

The escalating prevalence of hyperlipidemia has a profound impact on individuals' daily physiological well-being. The traditional Chinese medicine (TCM) prescription Danggui Shaoyao San (DSS) has demonstrated significant clinical efficacy and promising prospects for clinical application. Leveraging network pharmacology and bioinformatics, we hypothesize that DSS can ameliorate lipid metabolic disorders in hyperlipidemia by modulating the PPAR signaling pathway. In this study, we employed a zebrafish model to investigate the impact of DSS on lipid metabolism in hyperlipidemia. Body weight alterations were monitored by pre- and postmodeling weight measurements. Behavioral assessments and quantification of liver biochemical markers were conducted using relevant assay kits. Pathways associated with lipid metabolism were identified through network pharmacology and GEO analysis, while PCR was utilized to assess genes linked to lipid metabolism. Western blotting was employed to analyze protein expression levels, and liver tissue underwent Oil Red O and immunofluorescence staining to evaluate liver lipid deposition. Our findings demonstrate that DSS effectively impedes weight gain and reduces liver lipid accumulation in zebrafish models with elevated lipid levels. The therapeutic effects of DSS on lipid metabolism are mediated through its modulation of the PPAR signaling pathway, resulting in a significant reduction in lipid accumulation within the body and alleviation of certain hyperlipidemia-associated symptoms.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

BACKGROUND

Membranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was to identify novel biomarkers and immune-related mechanisms in the progression of MN through an integrated bioinformatics approach.

METHODS

The microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between MN and normal samples were identified and analyzed by the Gene Ontology analysis, the Kyoto Encyclopedia of Genes and Genomes analysis and the Gene Set Enrichment Analysis (GSEA) enrichment. Hub The hub genes were screened and identified by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm. The receiver operating characteristic (ROC) curves evaluated the diagnostic value of hub genes. The single-sample GSEA analyzed the infiltration degree of several immune cells and their correlation with the hub genes.

RESULTS

We identified a total of 574 DEGs. The enrichment analysis showed that metabolic and immune-related functions and pathways were significantly enriched. Four co-expression modules were obtained using WGCNA. The candidate signature genes were intersected with DEGs and then subjected to the LASSO analysis, obtaining a total of 6 hub genes. The ROC curves indicated that the hub genes were associated with a high diagnostic value. The CD4+ T cells, CD8+ T cells and B cells significantly infiltrated in MN samples and correlated with the hub genes.

CONCLUSIONS

We identified six hub genes (ZYX, CD151, N4BP2L2-IT2, TAPBP, FRAS1 and SCARNA9) as novel biomarkers for MN, providing potential targets for the diagnosis and treatment.

Using CADD tools to inhibit the overexpressed genes FAP, FN1, and MMP1 by repurposing ginsenoside C and Rg1 as a treatment for oral cancer

Oral cancer is one of the most common cancer types. Many factors can express certain genes that cause the proliferation of oral tissues. Overexpressed genes were detected in oral cancer patients; three were highly impacted. FAP, FN1, and MMP1 were the targeted genes that showed inhibition results in silico by ginsenoside C and Rg1. Approved drugs were retrieved from the DrugBank database. The docking scores show an excellent interaction between the ligands and the targeted macromolecules. Further molecular dynamics simulations showed the binding stability of the proposed natural products. This work recommends repurposing ginsenoside C and Rg1 as potential binders for the selected targets and endorses future experimental validation for the treatment of oral cancer.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Integrative analysis of tertiary lymphoid structures and immune microenvironment in patients with esophageal carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common upper gastrointestinal malignancies worldwide. Tertiary lymphoid structures (TLS) are tumor-infiltrating immune cells aggregates coupled with stromal cells which are similar to secondary lymphoid organs. The objective of this study is to explore the predictive effects of two common genes associated with TLS models on prognosis and immunotherapy effects in ESCC patients.

METHODS

Clinical information for ESCC patients in the TCGA(The Cancer Genome Altas) cohort and GSE 53625 were collected. All of the samples were classified as either high score group or low score group based on two TLS signatures, and the association between TLS signatures and survival, clinical indicators, genomic burden, stemness indices analysis, tumor microenvironment and immunotherapy response were performed. Furthermore, the mature TLS was also assessed in ESCC tissue microarray.

RESULTS

In our study, we quantified the score of TLS_9 and TLS_12, respectively, reflecting the different statuses of TLS (TLS_9 = B and T cells in TLSs; TLS_12 = neogenesis of TLSs). Subsequently, we explored the effect of TLS score on ESCC tumor microenvironment quantified by multiple algorithms. We found that a correlation analysis indicated that TLS_9 and TLS_12 were all positively correlated with CD8+ T cell, NK cells, CD4+ T cells, M1 macrophages and so on. Meanwhile, some cells present a different correlation pattern of TLS_9 and TLS_12, including activated CD4+ memory T cells and Tgd cells. Immune-related analysis revealed that the TLS_12 and TLS_9 scores were all positively correlated with immune dysfunction, yet negatively correlated with immune exclusion. Following this, the biological roles of TLS_9 and TLS_12 scores were investigated. Also, we noticed that the TLS score could significantly affect the CAFs infiltration and be associated with the genomic burden and tumor stemness. In addition, we explored the prognostic value of mature TLS through tissue microarray (TMA). Our result displayed ESCC patients with the presence of mature TLS had a better prognosis than ESCC patients without it.

CONCLUSIONS

Our study indicated that ESCC patients with the presence of TLS had better outcomes and an inflamed immune microenvironment. In addition, both TLS-9 and TLS-12 gene signatures could be used as potential biomarkers for the immunotherapy of ESCC patients.

OTTM: an automated classification tool for translational drug discovery from omics data

Omics data from clinical samples are the predominant source of target discovery and drug development. Typically, hundreds or thousands of differentially expressed genes or proteins can be identified from omics data. This scale of possibilities is overwhelming for target discovery and validation using biochemical or cellular experiments. Most of these proteins and genes have no corresponding drugs or even active compounds. Moreover, a proportion of them may have been previously reported as being relevant to the disease of interest. To facilitate translational drug discovery from omics data, we have developed a new classification tool named Omics and Text driven Translational Medicine (OTTM). This tool can markedly narrow the range of proteins or genes that merit further validation via drug availability assessment and literature mining. For the 4489 candidate proteins identified in our previous proteomics study, OTTM recommended 40 FDA-approved or clinical trial drugs. Of these, 15 are available commercially and were tested on hepatocellular carcinoma Hep-G2 cells. Two drugs-tafenoquine succinate (an FDA-approved antimalarial drug targeting CYC1) and branaplam (a Phase 3 clinical drug targeting SMN1 for the treatment of spinal muscular atrophy)-showed potent inhibitory activity against Hep-G2 cell viability, suggesting that CYC1 and SMN1 may be potential therapeutic target proteins for hepatocellular carcinoma. In summary, OTTM is an efficient classification tool that can accelerate the discovery of effective drugs and targets using thousands of candidate proteins identified from omics data. The online and local versions of OTTM are available at http://otter-simm.com/ottm.html.

Identification of key genes in spontaneous cerebral hemorrhage and prevention of disease damage: LASSO and SVM regression

Prevention is more important than treatment, and the incidence of intracerebral hemorrhage can be effectively reduced by intervening on the risk factors of intracerebral hemorrhage. By studying the risk factors of spontaneous intracerebral hemorrhage, we can identify the risk factors to achieve the target of treatment and prevention. Through the use of the Least Absolute Shrinkage and Selection Operator (LASSO) and the Support Vector Machine (SVM), the two essential SICH-related genes, NUAK1 and ERO1L, were eliminated from consideration. A Venn analysis was performed, and based on the two important modules, it found that SICH was related with four critical genes: VCM1, CRNDE, COL6A2, and HSPB6. One gene (NUAK1) was dramatically downregulated in the illness group compared to the control group, whereas three essential genes (ERO1L, VCAM1, and COL6A2) were significantly upregulated in the disease group. In the end, the genes ERO1L, VCAM1, COL6A2, and NUAK1 were shown to be the most important ones for SICH. It is anticipated that these genes will become novel biomarkers as well as targets for the development of new pharmacotherapies for SICH.

Identification of Hub Genes in the Pathogenesis of Bronchiolitis Obliterans via Bioinformatic Analysis and Experimental Verification

Background

Bronchiolitis obliterans (BO) is a chronic disease that can arise as a complication of severe childhood pneumonia and can also impact the long-term survival of patients after lung transplantation. However, the precise molecular mechanism underlying BO remains unclear. We aimed to identify BO-associated hub genes and their molecular mechanisms.

Methods

BO-associated transcriptome datasets (GSE52761, GSE137169, and GSE94557) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Additional bioinformatics analyses, such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analyses, were performed to determine functional roles and DEG-associated regulatory networks. Prediction of hub genes using the 12 algorithms available in the Cytohubba plugin of Cytoscape software was also performed. Verification was performed using the BO mouse model.

Results

Our results revealed 57 DEGs associated with BO, of which 18 were down-regulated and 39 were up-regulated. The Cytohubba plugin data further narrowed down the 57 DEGs into 9 prominent hub genes (CCR2, CD1D, GM2A, TFEC, MPEG1, CTSS, GPNMB, BIRC2, and CTSZ). Genes such as CCR2, TFEC, MPEG1, CTSS, and CTSZ were dysregulated in 2,3-butanedione-induced BO mice, whereas TFEC, CTSS, and CTSZ were dysregulated in nitric acid-induced BO mouse models.

Conclusion

Our study identified and validated four novel BO biomarkers, which may allow further investigation into the development of distinct BO diagnostic markers and novel therapeutic avenues.

A review of SARS-CoV-2 drug repurposing: databases and machine learning models

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Introducing JMIR Bioinformatics and Biotechnology: A Platform for Interdisciplinary Collaboration and Cutting-Edge Research

JMIR Bioinformatics and Biotechnology supports interdisciplinary research and welcomes contributions that push the boundaries of bioinformatics, genomics, artificial intelligence, and pathology informatics.

Identification of Potential Biomarkers and Small Molecule Drugs for Bisphosphonate-Related Osteonecrosis of the Jaw (BRONJ): An Integrated Bioinformatics Study Using Big Data

This study aimed to identify potential molecular mechanisms and therapeutic targets for bisphosphonate-related osteonecrosis of the jaw (BRONJ), a rare but serious side effect of bisphosphonate therapy. This study analyzed a microarray dataset (GSE7116) of multiple myeloma patients with BRONJ (n = 11) and controls (n = 10), and performed gene ontology, a pathway enrichment analysis, and a protein-protein interaction network analysis. A total of 1481 differentially expressed genes were identified, including 381 upregulated and 1100 downregulated genes, with enriched functions and pathways related to apoptosis, RNA splicing, signaling pathways, and lipid metabolism. Seven hub genes (FN1, TNF, JUN, STAT3, ACTB, GAPDH, and PTPRC) were also identified using the cytoHubba plugin in Cytoscape. This study further screened small-molecule drugs using CMap and verified the results using molecular docking methods. This study identified 3-(5-(4-(Cyclopentyloxy)-2-hydroxybenzoyl)-2-((3-hydroxybenzo[d]isoxazol-6-yl) methoxy) phenyl) propanoic acid as a potential drug treatment and prognostic marker for BRONJ. The findings of this study provide reliable molecular insight for biomarker validation and potential drug development for the screening, diagnosis, and treatment of BRONJ. Further research is needed to validate these findings and develop an effective biomarker for BRONJ.

Exploring the role of differentially expressed metabolic genes and their mechanisms in bone metastatic prostate cancer

Background

Approximately 10-20% of patients diagnosed with prostate cancer (PCa) evolve into castration-resistant prostate cancer (CRPC), while nearly 90% of patients with metastatic CRPC (mCRPC) exhibit osseous metastases (BM). These BM are intimately correlated with the stability of the tumour microenvironment.

Purpose

This study aspires to uncover the metabolism-related genes and the underlying mechanisms responsible for bone metastatic prostate cancer (BMPCa).

Methods

Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets of PCa and BM were analyzed through R Studio software to identify differentially expressed genes (DEGs). The DEGs underwent functional enrichment via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), with key factors screened by a random forest utilized to establish a prognostic model for PCa. The study explored the relationship between DEGs and the stability of the immune microenvironment. The action and specificity of CRISP3 in PCa was validated through western blot analysis, CCK-8 assay, scratch assay, and cellular assay.

Results

The screening of GEO and TCGA datasets resulted in the identification of 199 co-differential genes. Three DEGs, including DES, HBB, and SLPI, were selected by random forest classification model and cox regression model. Immuno-infiltration analysis disclosed that a higher infiltration of naïve B cells and resting CD4 memory T cells occurred in the high-expression group of DES, whereas infiltration of resting M1 macrophages and NK cells was greater in the low-expression group of DES. A significant infiltration of neutrophils was observed in the high-expression group of HBB, while greater infiltration of gamma delta T cells and M1 macrophages was noted in the low-expression group of HBB. Resting dendritic cells, CD8 T cells, and resting T regulatory cells (Tregs) infiltrated significantly in the high-expression group of SLPI, while only resting mast cells infiltrated significantly in the low-expression group of SLPI. CRISP3 was established as a critical gene in BMPCa linked to DES expression. Targeting CRISP3, d-glucopyranose may impact tumour prognosis. During the mechanistic experiments, it was established that CRISP3 can advance the proliferation and metastatic potential of PCa by advancing epithelial-to-mesenchymal transition (EMT).

Conclusion

By modulating lipid metabolism and maintaining immunological and microenvironmental balance, DES, HBB, and SLPI suppress prostate cancer cell growth. The presence of DES-associated CRISP3 is a harbinger of unfavorable outcomes in prostate cancer and may escalate tumor proliferation and metastatic capabilities by inducing epithelial-mesenchymal transition.

Elucidation of the Key Therapeutic Targets and Potential Mechanisms of Marmesine against Knee Osteoarthritis via Network Pharmacological Analysis and Molecular Docking

Background

Marmesine, a major active ingredient isolated from Radix Angelicae biseratae (Duhuo), has been reported to have multiple pharmacological activities. However, its therapeutic effects against knee osteoarthritis (OA) remain poorly investigated. The present study is aimed at uncovering the core targets and signaling pathways of marmesine against osteoarthritis using a combined method of bioinformatics and network pharmacology.

Methods

We utilized SwissTargetPrediction and PharmMapper to collect the potential targets of marmesine. OA-related differentially expressed genes (DEGs) were identified from GSE98918 dataset. Then, the intersection genes between DEGs and candidate genes of marmesine were subjected to protein-protein interaction (PPI) network construction and functional enrichment analysis. The core targets were verified using the molecular docking technology.

Results

A total of 320 marmesine-related genes and 5649 DEGs and 60 ingredient-disease targets between them were identified. The results of functional enrichment analyses revealed that response to oxygen levels, neuroinflammatory response, PI3K-Akt signaling pathway, MAPK signaling pathway, FoxO signaling pathway, and osteoclast differentiation was identified as the potential mechanisms of marmesine against OA. EGFR, CASP3, MMP9, PPARG, and MAPK1 served as hub genes regulated by marmesine in the treatment of OA, and the molecular docking further verified the results.

Conclusion

Marmesine exerts the therapeutic effects against OA through multitarget and multipathways, in which EGFR, CASP3, MMP9, PPARG, and MAPK1 might be hub genes. Our research indicated that the combination of bioinformatics and network pharmacology could serve as an effective approach for investigating the potential mechanisms of natural product.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Development and expansion of the CRISPR/Cas9 toolboxes for powerful genome engineering in yeast

Yeasts represent a group of the microorganisms most frequently seen in biotechnology. Recently, the class 2 type II CRISPR system (CRISPR/Cas9) has become the principal toolbox for genome editing. By efficiently implementing genetic manipulations such as gene integration/knockout, base editor, and transcription regulation, the development of biotechnological applications in yeasts has been extensively promoted. The genome-level tools based on CRISPR/Cas9, used for screening and identifying functional genes/gene clusters, are also advancing. In general, CRISPR/Cas9-assisted editing tools have gradually become standardized and function as host-orthogonal genetic systems, which results in time-saving for strain engineering and biotechnological application processes. In this review, we summarize the key points of the basic elements in the CRISPR/Cas9 system, including Cas9 variants, guide RNA, donors, and effectors. With a focus on yeast, we have also introduced the development of various CRISPR/Cas9 systems and discussed their future possibilities.

Shedding Light on the Drug-Target Prediction of the Anti-Inflammatory Peptide TnP with Bioinformatics Tools

Peptide–protein interactions are involved in various fundamental cellular functions, and their identification is crucial for designing efficacious peptide therapeutics. Drug–target interactions can be inferred by in silico prediction using bioinformatics and computational tools. We patented the TnP family of synthetic cyclic peptides, which is in the preclinical stage of developmental studies for chronic inflammatory diseases such as multiple sclerosis. In an experimental autoimmune enceph-alomyelitis model, we found that TnP controls neuroinflammation and prevents demyelination due to its capacity to cross the blood–brain barrier and to act in the central nervous system blocking the migration of inflammatory cells responsible for neuronal degeneration. Therefore, the identification of potential targets for TnP is the objective of this research. In this study, we used bioinformatics and computational approaches, as well as bioactivity databases, to evaluate TnP–target prediction for proteins that were not experimentally tested, specifically predicting the 3D structure of TnP and its biochemical characteristics, TnP–target protein binding and docking properties, and dynamics of TnP competition for the protein/receptor complex interaction, construction of a network of con-nectivity and interactions between molecules as a result of TnP blockade, and analysis of similarities with bioactive molecules. Based on our results, integrins were identified as important key proteins and considered responsible to regulate TnP-governed pharmacological effects. This comprehensive in silico study will help to understand how TnP induces its anti-inflammatory effects and will also facilitate the identification of possible side effects, as it shows its link with multiple biologically important targets in humans.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Bioinformatics and System Biology Approach to Reveal the Interaction Network and the Therapeutic Implications for Non-Small Cell Lung Cancer Patients With COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the leading cause of coronavirus disease-2019 (COVID-19), is an emerging global health crisis. Lung cancer patients are at a higher risk of COVID-19 infection. With the increasing number of non-small-cell lung cancer (NSCLC) patients with COVID-19, there is an urgent need of efficacious drugs for the treatment of COVID-19/NSCLC.

Methods: Based on a comprehensive bioinformatic and systemic biological analysis, this study investigated COVID-19/NSCLC interactional hub genes, detected common pathways and molecular biomarkers, and predicted potential agents for COVID-19 and NSCLC.

Results: A total of 122 COVID-19/NSCLC interactional genes and 21 interactional hub genes were identified. The enrichment analysis indicated that COVID-19 and NSCLC shared common signaling pathways, including cell cycle, viral carcinogenesis, and p53 signaling pathway. In total, 10 important transcription factors (TFs) and 44 microRNAs (miRNAs) participated in regulations of 21 interactional hub genes. In addition, 23 potential candidates were predicted for the treatment of COVID-19 and NSCLC.

Conclusion: This study increased our understanding of pathophysiology and screened potential drugs for COVID-19 and NSCLC.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

The Brain Research Hotspot Database (BRHD): A Panoramic Database of the Latest Hotspots in Brain Research

Brain science, an emerging, dynamic, multidisciplinary basic research field, is generating numerous valuable data. However, there are still several obstacles for the utilization of these data, such as data fragmentation, heterogeneity, availability, and annotation divergence. Thus, to overcome these obstacles and construct an online community, we developed a panoramic database named Brain Research Hotspot Database (BRHD). As of 30 January 2022, the database had been integrated with standardized vocabularies from various resources, including 423,681 papers, 46,344 patents, 9585 transcriptomic datasets, 261 cell markers, as well as with information regarding brain initiatives that were officially launched and well-known scholars in brain research. Based on the keywords entered by users and the search options they set, data can be accessed and retrieved through exact and fuzzy search scenarios. In addition, for brain diseases, we developed three featured functions based on deep data mining: (1) a brain disease–genome network, which collects the associations between common brain diseases, genes, and mutations reported in the literature; (2) brain and gut microbiome associations, based on the literature related to this topic, with added annotations for reference; (3) 3D brain structure, containing a high-precision brain anatomy model with visual links to quickly connect to an organ-on-a-chip database. In short, the BRHD integrates data from a variety of brain science resources to provide a friendly user interface and freely accessible viewing and downloading environment. Furthermore, the original functions developed based on these data provide references and insights for brain research.

Anticolon Cancer Targets and Molecular Mechanisms of Tao-He-Cheng-Qi Formula

Background

Tao-He-Cheng-Qi Formula (THCQF) is a traditional Chinese medicine that has been proven to have antitumor effects. The aim of this study was to elucidate the molecular targets and mechanisms of THCQF against colon cancer and construct a prognostic model based on network pharmacology, bioinformatics analysis, and in vitro experiments.

Methods

Potential THCQF compounds and targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine databases. Differentially expressed genes for colon cancer were screened in The Cancer Genome Atlas and Gene Expression Omnibus databases. The anticolon cancer mechanisms of THCQF were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking simulations and molecular dynamics analysis were used to evaluate the binding between target proteins and active compounds. Finally, the identified compounds were used to treat colon cancer cells from the HCT116 cell line, and expression of mRNA and protein after relevant posttreatment were tested using real-time polymerase chain reaction and western blotting.

Results

A total of 27 anticolon cancer targets of THCQF were selected, among which four genes (CCNB1, CCNA2, IL1A, and MMP3) were shown to effectively predict patient outcomes in a prognostic colon cancer model. GO and KEGG enrichment analyses indicated that the activity against colon cancer of THCQF was associated with the interleukin (IL)-4 and IL-3 signaling pathways. Two compounds in THCQF, aloe emodin (AE) and quercetin (QR), were shown to efficiently bind to cyclin B1, the protein encoded by CCNB1. Finally, incubation of HCT116 cells with AE and QR significantly decreased CCNB1 mRNA expression and cyclin B1 levels.

Conclusions

Taken together, the results indicate that AE and QR are the pivotal active compounds of THCQF, and CCNB1 is the main molecular target through which THCQF exerts its anticolon cancer effects. The study findings provide insight for studies investigating the anticancer effects of other traditional Chinese medicines.

A Pharmacoinformatics Analysis of Artemisinin Targets and de novo Design of Hits for Treating Ulcerative Colitis

Ulcerative colitis (UC), as an intractably treated disease, seriously affects the quality of life of patients and has an increase in terms of incidence and prevalence annually. However, due to the lack of a direct etiology and drug-induced side effects, the medical treatment of UC falls into a bottleneck. There are many natural phytochemicals with the potential to regulate immune function in nature. Herein, a potential mechanism of artemisinin in the treatment of UC and potential druggability compounds with an artemisinin peroxide bond were discussed and predicted based on computer-aided drug design (CADD) technology by using the methods of network pharmacology, molecular docking, de novo drug structure design and molecular dynamics through the integration of artemisinin related targets from TCMSP, ChEMBL and HERB databases. The networks were constructed based on 50 artemisinin-disease intersection targets related to inflammation, cytokines, proliferation and apoptosis, showing the importance of GALNT2, BMP7 and TGFBR2 in the treatment of disease, which may be due to the occupation of the ricin B-type lectin domain of GALNT2 by artemisinin compounds or de novo designed candidates. This result could guide the direction of experiments and actual case studies in the future. This study provides a new route for the application of artemisinin and the development of drugs.

Inhibiting effect of miR-29 on proliferation and migration of uterine leiomyoma via the STAT3 signaling pathway

Aim: Uterine leiomyoma is the most common benign tumor of female genitalia, and the incidence is rising gradually. This study explores the mechanism of miR-29 and STAT3 signaling pathways on uterine leiomyoma.

Methods: GSE64763 and GSE5244 datasets were downloaded. Enrichment analyses were performed in GSE64763. PPI network was constructed, and the significant module was identified. Uterine leiomyoma cell lines were divided into NC, miR-29 mimic, anti-NC, and miR-29 inhibitor groups. Plate clone formation and Transwell assays detected the proliferation, invasion, and migration of cells. The expression levels of STAT3, proliferation, EMT, invasion-associated proteins were determined by Western blotting.

Results: Differently expressed genes were mainly enriched in positive regulation of cell migration and gene expression, cell proliferation. Through GSEA, JAK-STAT is a significantly correlated enrichment pathway. A Venn diagram was drawn to identify the common miRNA (miR-29-3p). miR-29 inhibitors promoted protein expression of STAT-3, Cyclin D1, and c-Myc compared with the anti-NC control (P < 0.01), and miR-29 inhibitors promoted cell proliferation in uterine leiomyoma cells (P < 0.05). Furthermore, miR-29 inhibitors promoted the protein expression of MMP-2 and MMP-9 (P < 0.01), and EMT promoting proteins N-cadherin, snail, vimentin, and Transwell assay showed that miR-29 inhibitors promoted cell migration in uterine leiomyoma (P < 0.01).

Conclusions: High expression of miR-29 could inhibit cell proliferation, invasion, and metastasis in uterine leiomyoma, which might be related to the inhibition of the STAT3 signaling pathway, and could provide a novel target for the treatment of uterine leiomyoma.

Construction of ceRNA network to identify the lncRNA and mRNA related to non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) harms human health, but its pathogenesis remains unclear. We wish to provide more molecular therapeutic targets for NSCLC.

METHODS

The NSCLC tissue and normal tissue samples were screened for genetic comparison in the TCGA database. The predicted lncRNA and mRNA in BEAS2B and A549 cells were detected.

RESULTS

Volcano plot displayed differentially expressed lncRNAs and mRNAs in adjacent tissues and NSCLC tissues. The survival curve showed that the lncRNA and mRNA had a significant impact on the patient's survival. The results of GO term enrichment analysis indicated that mRNA functions were enriched in cell cycle-related pathways. In the ceRNA interaction network, 13 lncRNAs and 20 miRNAs were found to have an interactive relationship. Finally, 3 significantly different lncRNAs (LINC00968, lnc-FAM92A-9 and lnc-PTGFR-1) and 6 mRNAs (CTCFL, KRT5, LY6D, TMEM, GBP6, and TMEM179) with potential therapeutic significance were screened out. And the cell experiment verified our results.

CONCLUSION

We screened out clinically significant 3 lncRNAs and 6 mRNAs involved in the ceRNA network, which were the key to our future research on the treatment of NSCLC.

Prioritization of Diagnostic and Prognostic Biomarkers for Lupus Nephritis Based on Integrated Bioinformatics Analyses

Lupus nephritis (LN) is an important driver of end-stage renal disease (ESRD). However, few biomarkers are available for evaluating the diagnosis and prognosis of LN. For this study, we downloaded microarray data of multiple LN expression profiles from the GEO database. We used the WGCNA and R limma packages to identify LN hub genes and differentially-expressed genes (DEGs). We identified nine co-DEGs in the intersection with LN-related genes from the Genecards database. We found DEGs that are primarily associated with immune-related functions and pathways (including with the complement pathway, primary immunodeficiency markers, and MHC-like protein complexes) through our comprehensive GSEA, GO, and KEGG enrichment analyses. We used other LN and SLE validation datasets and discovered six explicitly expressed co-DEGs: HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DRA, IL10RA, and IRF8 in the LN set; ROC and Precision-Recall curve analyses revealed that these six genes have a good diagnostic efficacy. The correlation analysis with prognostic data from the Nephroseq database indicates that the differential expression of these co-DEGs is associated with a low glomerular filtration rate in that cohort. Additionally, we used a single-cell LN database of immune cells (for the first time) and discovered these co-DEGs to be predominantly distributed in different types of macrophages and B cells. In conclusion, by integrating multiple approaches for DEGs discovery, we identified six valuable biomarkers that are strongly correlated with the diagnosis and prognosis of LN. These markers can help clarify the pathogenesis and improve the clinical management of LN.

Identification of the similarly expressed genes in patients with polycystic ovary syndrome and transsexuals

ABSTRACT

Polycystic ovary syndrome (PCOS) is a common female infertility, which may be caused by excessive androgen, but its mechanism remains unknown. Transsexuals are women who take androgen drugs for a long time, and gradually have male signs. Their ovaries may have received high concentrations of androgen, which leads to the failure of ovarian reproductive function. Therefore, we searched the relevant data of PCOS and transsexuals in gene expression omnibus database, used limma package to identify the most similarly genes, and then analyzed the possible mechanism of PCOS through gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Then, the protein-protein interaction network was constructed by searching the String database, and the top 5 hub genes were identified by the cytohubba plug-in of Cytoscape. Finally, ubiquitin conjugating enzyme E2 E1 (UBE2E1), ubiquitin C (UBC), transcription elongation factor B subunit 1 (TCEB1), ubiquitin conjugating enzyme E2 N (UBE2N), and ring finger protein 7 (RNF7) genes were identified as the most similarly expressed genes between PCOS and Transsexuals. They may cause the ubiquitination of androgen receptor and eventually lead to sinus follicular growth arrest. In conclusion, 5 Central genes were identified in PCOS and transsexuals. These genes can be used as targets for early diagnosis or treatment of PCOS.

Bioinformatics prediction of differential miRNAs in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. The drug resistance of NSCLC has clinically increased. This study aimed to screen miRNAs associated with NSCLC using bioinformatics analysis. We hope that the screened miRNA can provide a research direction for the subsequent treatment of NSCLC.

Methods

We screened out the common miRNAs after compared the NSCLC-related genes in the TCGA database and GEO database. Selected miRNA was performed ROC analysis, survival analysis, and enrichment analysis (GO term and KEGG pathway).

Results

A total of 21 miRNAs were screened in the two databases. And they were all highly expressed in normal and low in cancerous tissues. Hsa-mir-30a was selected by ROC analysis and survival analysis. Enrichment analysis showed that the function of hsa-mir-30a is mainly related to cell cycle regulation and drug metabolism.

Conclusion

Our study found that hsa-mir-30a was differentially expressed in NSCLC, and it mainly affected NSCLC by regulating the cell cycle and drug metabolism.

Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy

Acupuncture is an important part of Chinese medicine that has been widely used in the treatment of inflammatory diseases. During the coronavirus disease 2019 (COVID-19) epidemic, acupuncture has been used as a complementary treatment for COVID-19 in China. However, the underlying mechanism of acupuncture treatment of COVID-19 remains unclear. Based on bioinformatics/topology, this paper systematically revealed the multi-target mechanisms of acupuncture therapy for COVID-19 through text mining, bioinformatics, network topology, etc. Two active compounds produced after acupuncture and 180 protein targets were identified. A total of 522 Gene Ontology terms related to acupuncture for COVID-19 were identified, and 61 pathways were screened based on the Kyoto Encyclopedia of Genes and Genomes. Our findings suggested that acupuncture treatment of COVID-19 was associated with suppression of inflammatory stress, improving immunity and regulating nervous system function, including activation of neuroactive ligand–receptor interaction, calcium signaling pathway, cancer pathway, viral carcinogenesis, Staphylococcus aureus infection, etc. The study also found that acupuncture may have additional benefits for COVID-19 patients with cancer, cardiovascular disease and obesity. Our study revealed for the first time the multiple synergistic mechanisms of acupuncture on COVID-19. Acupuncture may play an active role in the treatment of COVID-19 and deserves further promotion and application. These results may help to solve this pressing problem currently facing the world.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Metformin coordinates osteoblast/osteoclast differentiation associated with ischemic osteonecrosis

In this study, we aimed to identify a candidate drug that can activate endogenous Angiopoietin 1 (Ang1) expression via drug repositioning as a pharmacological treatment for avascular osteonecrosis. After incubation with 821 drugs from the Food and Drug Administration (FDA)-approved drug library, Ang1 expression in U2OS cell culture media was examined by ELISA. Metformin, the first-line medication for treatment of type 2 diabetes, was selected as a candidate for in vitro and in vivo experimental evaluation. Ang1 was induced, and alkaline phosphatase activity was increased by metformin treatment in U2OS and MG63 cells. Wound healing and migration assay showed increased osteoblastic cell mobility by metformin treatment in U2OS and MG63 cells. Metformin upregulated expression of protein markers for osteoblastic differentiation in U2OS and MG63 cells but inhibited osteoclastic differentiation in Raw264.7 cells. Metformin (25 mg/kg) protected against ischemic necrosis in the epiphysis of the rat femoral head by maintaining osteoblast/osteocyte function and vascular density but inhibiting osteoclast activity in the necrotic femoral head. These findings provide novel insight into the specific biomarkers that are targeted and regulated by metformin in osteoblast differentiation and contribute to understanding the effects of these FDA-approved small-molecule drugs as novel therapeutics for ischemic osteonecrosis.

TDR Targets 6: driving drug discovery for human pathogens through intensive chemogenomic data integration

The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.

Is It Possible to Conduct a Multi-Arm Multi-Stage Platform Trial in Parkinson's Disease: Lessons Learned from Other Neurodegenerative Disorders and Cancer

Many potential disease modifying therapies have been identified as suitable for clinical evaluation in Parkinson's disease (PD). Currently, the evaluation of compounds in phase II and phase III clinical trials in PD are set up in isolation, a process that is lengthy, costly and lacks efficiency. This review will introduce the concept of a multi-arm, multi-stage (MAMS) trial platform which allows for the assessment of several potential therapies at once, transitioning seamlessly from a phase II safety and efficacy study to a phase III trial by means of an interim analysis. At the interim checkpoint, ineffective arms are dropped and replaced by new treatment arms, thereby allowing for the continuous evaluation of interventions. MAMS trial platforms already exist for prostate, renal and oropharyngeal cancer and are currently being developed for progressive multiple sclerosis (PMS) and motor neuron disease (MND) within the UK. As a MAMS trial will evaluate many potential treatments it is of critical importance that a widely endorsed core protocol is developed which will investigate outcomes and objectives meaningful to patients. This review will discuss the challenges of drug selection, trial design, stratification and outcome measures and will share strategies implemented in the planned MAMS trials for MND and PMS that may be of interest to the PD field.

Computational Drug Repurposing: Current Trends

:

Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Biomolecular Data Resources: Bioinformatics Infrastructure for Biomedical Data Science

Technological advances have continuously driven the generation of bio-molecular data and the development of bioinformatics infrastructure, which enables data reuse for scientific discovery. Several types of data management resources have arisen, such as data deposition databases, added-value databases or knowledgebases, and biology-driven portals. In this review, we provide a unique overview of the gradual evolution of these resources and discuss the goals and features that must be considered in their development. With the increasing application of genomics in the health care context and with 60 to 500 million whole genomes estimated to be sequenced by 2022, biomedical research infrastructure is transforming, too. Systems for federated access, portable tools, provision of reference data, and interpretation tools will enable researchers to derive maximal benefits from these data. Collaboration, coordination, and sustainability of data resources are key to ensure that biomedical knowledge management can scale with technology shifts and growing data volumes.