Lipids, lipid-modified drug target genes, and the risk of male infertility: a Mendelian randomization study.

Background: Previous observational studies have reported a possible association between circulating lipids and lipid-lowering drugs and male infertility (MIF), as well as the mediating role of circulating vitamin D. Then, due to issues such as bias, reverse causality, and residual confounding, inferring causal relationships from these studies may be challenging. Therefore, this study aims to explore the effects of circulating lipids and lipid-lowering drugs on MIF through Mendelian randomization (MR) analysis and evaluate the mediating role of vitamin D. Method: Genetic variations related to lipid traits and the lipid-lowering effect of lipid modification targets are extracted from the Global Alliance for Lipid Genetics Genome-Wide Association Study. The summary statistics for MIF are from the FinnGen 9th edition. Using quantitative expression feature loci data from relevant organizations to obtain genetic variations related to gene expression level, further to explore the relationship between these target gene expression levels and MIF risk. Two-step MR analysis is used to explore the mediating role of vitamin D. Multiple sensitivity analysis methods (co-localization analysis, Egger intercept test, Cochrane's Q test, pleiotropy residuals and outliers (MR-PRESSO), and the leave-one-out method) are used to demonstrate the reliability of our results. Result: In our study, we observed that lipid modification of four lipid-lowering drug targets was associated with MIF risk, the LDLR activator (equivalent to a 1-SD decrease in LDL-C) (OR=1.94, 95% CI 1.14-3.28, FDR=0.040), LPL activator (equivalent to a 1-SD decrease in TG) (OR=1.86, 95% CI 1.25-2.76, FDR=0.022), and CETP inhibitor (equivalent to a 1-SD increase in HDL-C) (OR=1.28, 95% CI 1.07-1.53, FDR=0.035) were associated with a higher risk of MIF. The HMGCR inhibitor (equivalent to a 1-SD decrease in LDL-C) was associated with a lower risk of MIF (OR=0.38, 95% CI 0.17-0.83, FDR=0.39). Lipid-modifying effects of three targets were partially mediated by serum vitamin D levels. Mediation was 0.035 (LDLR activator), 0.012 (LPL activator), and 0.030 (CETP inhibitor), with mediation ratios of 5.34% (LDLR activator), 1.94% (LPL activator), and 12.2% (CETP inhibitor), respectively. In addition, there was no evidence that lipid properties and lipid modification effects of six other lipid-lowering drug targets were associated with MIF risk. Multiple sensitivity analysis methods revealed insignificant evidence of bias arising from pleiotropy or genetic confounding. Conclusion: This study did not support lipid traits (LDL-C, HDL-C, TG, Apo-A1, and Apo-B) as pathogenic risk factors for MIF. It emphasized that LPL, LDLR, CETP, and HMGCR were promising drug targets for improving male fertility.

Mutations in Ras homolog family member A in patients with peripheral T-cell lymphoma and implications for personalized medicine.

Genome sequencing has revealed frequent mutations in Ras homolog family member A (RHOA) among various cancers with unique aberrant profiles and pathogenic effects, especially in peripheral T-cell lymphoma (PTCL). The discrete positional distribution and types of RHOA amino acid substitutions vary according to the tumor type, thereby leading to different functional and biological properties, which provide new insight into the molecular pathogenesis and potential targeted therapies for various tumors. However, the similarities and discrepancies in characteristics of RHOA mutations among various histologic subtypes of PTCL have not been fully elucidated. Herein we highlight the inconsistencies and complexities of the type and location of RHOA mutations and demonstrate the contribution of RHOA variants to the pathogenesis of PTCL by combining epigenetic abnormalities and activating multiple downstream pathways. The promising potential of targeting RHOA as a therapeutic modality is also outlined. This review provides new insight in the field of personalized medicine to improve the clinical outcomes for patients.

Systematic Druggable Genome-Wide Mendelian Randomization Identifies Therapeutic Targets for Functional Outcome After Ischemic Stroke.

BACKGROUND: Stroke is a leading cause of death worldwide, with a lack of effective treatments for improving the prognosis. The aim of the present study was to identify novel therapeutic targets for functional outcome after ischemic stroke . METHODS AND RESULTS: Cis-expression quantitative trait loci data for druggable genes were used as instrumental variables. The primary outcome was the modified Rankin Scale score at 3 months after ischemic stroke, evaluated as a dichotomous variable (3-6 versus 0-2) and also as an ordinal variable. Drug target Mendelian randomization, Steiger filtering analysis, and colocalization analysis were performed. Additionally, phenome-wide Mendelian randomization analysis was performed to identify the safety of the drug target genes at the genetic level. Among >2600 druggable genes, genetically predicted expression of 16 genes (ABCC2, ATRAID, BLK, CD93, CHST13, NR1H3, NRBP1, PI3, RIPK4, SEMG1, SLC22A4, SLC22A5, SLCO3A1, TEK, TLR4, and WNT10B) demonstrated the causal associations with ordinal modified Rankin Scale (P<1.892×10-5) or poor functional outcome (modified Rankin Scale 3-6 versus 0-2, P<1.893×10-5). Steiger filtering analysis suggested potential directional stability (P<0.05). Colocalization analysis provided further support for the associations between genetically predicted expression of ABCC2, NRBP1, PI3, and SEMG1 with functional outcome after ischemic stroke. Furthermore, phenome-wide Mendelian randomization revealed additional beneficial indications and few potential safety concerns of therapeutics targeting ABCC2, NRBP1, PI3, and SEMG1, but the robustness of these results was limited by low power. CONCLUSIONS: The present study revealed 4 candidate therapeutic targets for improving functional outcome after ischemic stroke, while the underlying mechanisms need further investigation.

Key target genes related to anti-breast cancer activity of ATRA: A network pharmacology, molecular docking and experimental investigation.

All-trans retinoic acid (ATRA) has promising activity against breast cancer. However, the exact mechanisms of ATRA's anticancer effects remain complex and not fully understood. In this study, a network pharmacology and molecular docking approach was applied to identify key target genes related to ATRA's anti-breast cancer activity. Gene/disease enrichment analysis for predicted ATRA targets was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), the Comparative Toxicogenomics Database (CTD), and the Gene Set Cancer Analysis (GSCA) database. Protein-Protein Interaction Network (PPIN) generation and analysis was conducted via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and cytoscape, respectively. Cancer-associated genes were evaluated using MyGeneVenn from the CTD. Differential expression analysis was conducted using the Tumor, Normal, and Metastatic (TNM) Plot tool and the Human Protein Atlas (HPA). The Glide docking program was used to predict ligand-protein binding. Treatment response predication and clinical profile assessment were performed using Receiver Operating Characteristic (ROC) Plotter and OncoDB databases, respectively. Cytotoxicity and gene expression were measured using MTT/fluorescent assays and Real-Time PCR, respectively. Molecular functions of ATRA targets (n = 209) included eicosanoid receptor activity and transcription factor activity. Some enriched pathways included inclusion body myositis and nuclear receptors pathways. Network analysis revealed 35 hub genes contributing to 3 modules, with 16 of them were associated with breast cancer. These genes were involved in apoptosis, cell cycle, androgen receptor pathway, and ESR-mediated signaling, among others. CCND1, ESR1, MMP9, MDM2, NCOA3, and RARA were significantly overexpressed in tumor samples. ATRA showed a high affinity towards CCND1/CDK4 and MMP9. CCND1, ESR1, and MDM2 were associated with poor treatment response and were downregulated after treatment of the breast cancer cell line with ATRA. CCND1 and ESR1 exhibited differential expression across breast cancer stages. Therefore, some part of ATRA's anti-breast cancer activity may be exerted through the CCND1/CDK4 complex.

Causal relationship between complement C1QB and colorectal cancer: a drug target Mendelian randomization study.

Background: Colorectal cancer is influenced by several factors such as unhealthy habits and genetic factors. C1QB has been linked to a number of malignancies. However, uncertainty surrounds the connection between C1QB and CRC. Therefore, this study aimed to explore a bidirectional causal relationship of C1QB as a drug target in CRC through Mendelian randomization (MR) analysis. Methods: The GWASs for C1QB and CRC were obtained from the Integrative Epidemiology Unit Open GWAS database. There were five strategies to investigate MR. Sensitivity analysis was carried out via tests for heterogeneity, horizontal pleiotropy and leave-one-out effects to evaluate the dependability of the MR analysis results. Furthermore, colocalization analysis of C1QB and CRC, protein-protein interaction network and drug prediction according to exposure factors as well as phenotype scanning were performed. Results: The results of forward MR analysis demonstrated that C1QB was a risk factor for CRC (OR = 1.104, p = 0.033). However, we did not find a causal relationship between CRC and C1QB (reverse MR). Rs294180 and rs291985 corresponded to the same linkage interval and had the potential to influence C1QB and CRC, respectively. The PPI results demonstrated that C1QB interacted with 10 genes (C1QA, C1QC, C1R, C1S, C2, C4A, C4B, CALR, SERPING1, and VSIG4). Additionally, 21 medications were predicted to match C1QB. Molecular docking data, including for benzo(a)pyrene, 1-naphthylisothiocyanate, calcitriol and medroxyprogesterone acetate, revealed excellent binding for drugs and proteins. Moreover, we identified 29 diseases that were associated with C1QB and related medicines via disease prediction and intersection methods. As a therapeutic target for CRC, phenotypic scanning revealed that C1QB does not significantly affect weight loss, liver cirrhosis, or nonalcoholic fatty liver disease, but might have protective impacts on ovarian cancer and melanoma. Conclusion: The results highlight a causal relationship between C1QB and CRC and imply an oncogenic role for C1QB in CRC, as potential drug targets. Drugs designed to target C1QB have a greater chance of success in clinical trials and are expected to help prioritize CRC drug development and reduce drug development costs. That provided a theoretical foundation and reference for research on CRC and C1QB in MR.

Comprehensive analysis of single-cell transcriptomics and genetic factors reveals the mechanisms and preventive strategies for the progression from pulmonary fibrosis to lung cancer.

BACKGROUND: Pulmonary fibrosis (PF) leads to excessive deposition of fibrous connective tissue in the lungs, increasing the risk of lung cancer due to the enhanced activity of fibroblasts (FBs). Fibroblast-mediated collagen fiber deposition creates a tumor-like microenvironment, laying the foundation for tumorigenesis. Clinically, numerous cases of lung cancer induced by pulmonary fibrosis have been observed. In recent years, the study of nucleotide point mutations, which provide more detailed insights than gene expression, has made significant advancements, offering new perspectives for clinical research. METHODS: We initially employed Mendelian randomization to ascertain that the initial stage of lung cancer induced by PF belongs to small cell lung cancer (SCLC). Subsequently, pulmonary neuroendocrine cells (PNECs) were identified by using pseudo-time series analysis as cell clusters with carcinogenic potential. We categorized FBs into four groups according to their cellular metabolism, and then analyzed the cellular communication between FBs and PNECs, as well as changes in intracellular pathways of PNECs. Additionally, we examined the characteristic genome of FBs which is significantly associated with PF and investigated the impact of FBs on immune cells in the PF microenvironment. Finally, we explored strategies for preventing the progression from PF to lung cancer. RESULTS: The genetic features of cells with carcinogenic potential in PF tissues were revealed, characterized by upregulation of Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1), Homeobox B2 (HOXB2), Teashirt Zinc Finger Homeobox 2 (TSHZ2), Insulinoma-associated 1 (INSM1), and reduced activity of RE1 Silencing Transcription Factor (REST). FBs characterized by high glycolysis and low tricarboxylic acid (TCA) cycling played a key role in the progression of PF. The microenvironment of PF resembles the tumor microenvironment, providing a conducive immunosuppressive environment for the occurrence of cancer cells. In dendritic cells, rs9265808 is a susceptibility locus for progression from pulmonary fibrosis to lung cancer, mutations at this locus increase the expression of Complement Factor B (CFB), and excessive activation of the complement pathway is a crucial factor leading to lung cancer development in patients with pulmonary fibrosis. Ensuring adequate nutritional supply and physical function is one of the effective measures to prevent progression from pulmonary fibrosis to lung cancer. CONCLUSION: CFB promotes lung cancer occurrence by inducing the accumulation and polarization of a large number of monocytes/macrophages in the lungs, driving disease progression by reducing the physical fitness of patients with pulmonary fibrosis.

Exploring the causal effect between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a respiratory disorder of obscure etiology and limited treatment options, possibly linked to dysregulation in lipid metabolism. While several observational studies suggest that lipid-lowering agents may decrease the risk of IPF, the evidence is inconsistent. The present Mendelian randomization (MR) study aims to determine the association between circulating lipid traits and IPF and to assess the potential influence of lipid-modifying medications for IPF. METHODS: Summary statistics of 5 lipid traits (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, apolipoprotein A, and apolipoprotein B) and IPF were sourced from the UK Biobank and FinnGen Project Round 10. The study's focus on lipid-regulatory genes encompassed PCSK9, NPC1L1, ABCG5, ABCG8, HMGCR, APOB, LDLR, CETP, ANGPTL3, APOC3, LPL, and PPARA. The primary effect estimates were determined using the inverse-variance-weighted method, with additional analyses employing the contamination mixture method, robust adjusted profile score, the weighted median, weighted mode methods, and MR-Egger. Summary-data-based Mendelian randomization (SMR) was used to confirm significant lipid-modifying drug targets, leveraging data on expressed quantitative trait loci in relevant tissues. Sensitivity analyses included assessments of heterogeneity, horizontal pleiotropy, and leave-one-out methods. RESULTS: There was no significant effect of blood lipid traits on IPF risk (all P＞0.05). Drug-target MR analysis indicated that genetic mimicry for inhibitor of NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 were associated with increased IPF risks, with odds ratios (ORs) and 95% confidence intervals (CIs) as follows: 2.74 (1.05-7.12, P = 0.039), 1.36 (1.02-1.82, P = 0.037), 1.66 (1.12-2.45, P = 0.011), 1.68 (1.14-2.48, P = 0.009), and 1.42 (1.20-1.67, P = 3.17×10-5), respectively. The SMR method identified a significant association between PCSK9 gene expression in whole blood and reduced IPF risk (OR = 0.71, 95% CI: 0.50-0.99, P = 0.043). Sensitivity analyses showed no evidence of bias. CONCLUSIONS: Serum lipid traits did not significantly affect the risk of idiopathic pulmonary fibrosis. Drug targets MR studies examining 12 lipid-modifying drugs indicated that PCSK9 inhibitors could dramatically increase IPF risk, a mechanism that may differ from their lipid-lowering actions and thus warrants further investigation.

Genetic proxies for therapy of insulin drug targets and risk of osteoarthritis: a drug-target Mendelian randomization analysis.

BACKGROUND: The potential effects of insulin therapy on osteoarthritis (OA) risk are poorly understood. This study aimed to explore the causal relationship between insulin therapy and OA. METHODS: Mendelian randomization (MR) analysis was performed to examine the association between genetically proxied inhibition of insulin targets and the risk of overall, hip (HOA) and knee OA (KOA). We then performed univariable MR using summary statistics regarding insulin target genes derived from the DrugBank database. Data related to blood glucose reduction levels were used as a proxy for insulin levels. Two phenotypes, type 2 diabetes, and glycosylated hemoglobin levels, were selected as positive controls to confirm the direction and validity of the proxies. The OA datasets were derived from the UK Biobank cohort. Multivariable MR was adjusted for body mass index, sedentary behavior, cigarette smoking, frequency of alcohol intake, age, and genetic sex. RESULTS: Genetically proxied insulin therapy was associated with an increased risk of overall OA [odds ratio (OR):1.2595; 95% confidence interval (CI):1.0810-1.4675] and HOA (OR:1.4218; 95%CI:1.1240-1.7985), which remained consistent across multiple MR methods. After adjusting for confounders, we found evidence supporting a significant causal link with a higher risk of overall OA and HOA. A further two-step MR analysis revealed no significant mediation effects from the six mediators in the associations. CONCLUSION: There was a causal association between genetically proxied insulin therapy and a higher risk of OA, especially HOA.

Identification of potential drug targets for amyotrophic lateral sclerosis by Mendelian randomization analysis based on brain and plasma proteomics.

Amyotrophic lateral sclerosis as a fatal neurodegenerative disease currently lacks effective therapeutic agents. Thus, finding new therapeutic targets to drive disease treatment is necessary. In this study, we utilized brain and plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis to identify potential drug targets for amyotrophic lateral sclerosis. Additionally, we validated our results externally using other datasets. We also used Bayesian co-localization analysis and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Mendelian randomization analysis indicated that elevated levels of ANO5 (OR = 1.30; 95 % CI, 1.14-1.49; P = 1.52E-04), SCFD1 (OR = 3.82; 95 % CI, 2.39-6.10; P = 2.19E-08), and SIGLEC9 (OR = 1.05; 95% CI, 1.03-1.07; P = 4.71E-05) are associated with an increased risk of amyotrophic lateral sclerosis, with external validation supporting these findings. Co-localization analysis confirmed that ANO5, SCFD1, and SIGLEC9 (coloc.abf-PPH4 = 0.848, 0.984, and 0.945, respectively) shared the same variant with amyotrophic lateral sclerosis, further substantiating potential role of these proteins as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of amyotrophic lateral sclerosis. Our findings suggested that elevated levels of ANO5, SCFD1, and SIGLEC9 are connected with an increased risk of amyotrophic lateral sclerosis and might be promising therapeutic targets. However, further exploration is necessary to fully understand the underlying mechanisms involved.

Pantothenate kinase: A promising therapeutic target against pathogenic Clostridium species.

Current treatment of clostridial infections includes broad-spectrum antibiotics and antitoxins, yet antitoxins are ineffective against all Clostridiumspecies. Moreover, rising antimicrobial resistance (AMR) threatens treatment effectiveness and public health. This study therefore aimed to discover a common drug target for four pathogenic clostridial species, Clostridium botulinum, C. difficile, C. tetani, and C. perfringens through an in-silico core genomic approach. Using four reference genomes of C. botulinum, C. difficile, C. tetani, and C. perfringens, we identified 1484 core genomic proteins (371/genome) and screened them for potential drug targets. Through a subtractive approach, four core proteins were finally identified as drug targets, represented by type III pantothenate kinase (CoaX) and, selected for further analyses. Interestingly, the CoaX is involved in the phosphorylation of pantothenate (vitamin B5), which is a critical precursor for coenzyme A (CoA) biosynthesis. Investigation of druggability analysis on the identified drug target reinforces CoaX as a promising novel drug target for the selected Clostridium species. During the molecular screening of 1201 compounds, a known agonist drug compound (Vibegron) showed strong inhibitory activity against targeted clostridial CoaX. Additionally, we identified tazobactam, a beta-lactamase inhibitor, as effective against the newly proposed target, CoaX. Therefore, identifying CoaX as a single drug target effective against all four clostridial pathogens presents a valuable opportunity to develop a cost-effective treatment for multispecies clostridial infections.

Comprehensive applications of the artificial intelligence technology in new drug research and development.

Purpose: Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods: Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results: In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion: Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

A review of deep learning methods for ligand based drug virtual screening.

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.

The study of drug-target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug-target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.

Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets.

Classical Hodgkin lymphoma (cHL) is a common B-cell cancer and a significant health concern, especially in Western and Asian countries. Despite the effectiveness of chemotherapy, many relapse cases are being reported, highlighting the need for improved treatments. This study aimed to address this issue by discovering biomarkers through the analysis of gene expression data specific to cHL. Additionally, potential anticancer inhibitors were explored to target the discovered biomarkers. This study proceeded by retrieving microarray gene expression data from cHL patients, which was then analyzed to identify significant differentially expressed genes (DEGs). Functional and network annotation of the upregulated genes revealed the active involvement of matrix metallopeptidase 12 (MMP12) and C-C motif metallopeptidase ligand 22 (CCL22) genes in the progression of cHL. Additionally, the mentioned genes were found to be actively involved in cancer-related pathways, i.e., oxidative phosphorylation, complement pathway, myc_targets_v1 pathway, TNFA signaling via NFKB, etc., and showed strong associations with other genes known to promote cancer progression. MMP12, topping the list with a logFC value of +6.6378, was selected for inhibition using docking and simulation strategies. The known anticancer compounds were docked into the active site of the MMP12 molecular structure, revealing significant binding scores of -7.7 kcal/mol and -7.6 kcal/mol for BDC_24037121 and BDC_27854277, respectively. Simulation studies of the docked complexes further supported the effective binding of the ligands, yielding MMGBSA and MMPBSA scores of -78.08 kcal/mol and -82.05 kcal/mol for MMP12-BDC_24037121 and -48.79 kcal/mol and -49.67 kcal/mol for MMP12-BDC_27854277, respectively. Our findings highlight the active role of MMP12 in the progression of cHL, with known compounds effectively inhibiting its function and potentially halting the advancement of cHL. Further exploration of downregulated genes is warranted, as associated genes may play a role in cHL. Additionally, CCL22 should be considered for further investigation due to its significant role in the progression of cHL.

Exploring novel drug targets for atopic dermatitis through plasma proteome with genome.

Atopic dermatitis (AD) is a chronic inflammatory disease with a complex and heterogeneous clinical presentation, leading to treatment limitations. Therefore, there is an urgent demand for new therapeutic drug targets. This study utilized Summary-data-based Mendelian randomization (SMR) to identify potential drug targets for AD. Summary statistics for 2,940 human plasma proteins were obtained from the UK Biobank, while AD statistics came from the Early Genetics and Epidemiology of Life Processes consortium and the FinnGen consortium. Furthermore, subsequent colocalization analyses confirmed the causal roles of candidate proteins. Moreover, Phenome-Wide Association Studies (PheWAS), protein-protein interaction (PPI), enrichment analysis, and single cell-type expression analysis provided additional insights. Additionally, drug prediction, druggability prediction, and molecular docking informed the discovery of novel drug targets. SMR analysis showed that eight plasma proteins were causally associated with AD: PVALB and TST were associated with a reduced risk of AD, while CA14, ECM1, IL22, IL6R, IL18R1, and MMP12 were associated with an increased risk of AD. Colocalization analysis confirmed significant associations for TST, IL22, and CA14. PheWAS further revealed that candidate drug targets were mainly linked to other allergic diseases. The corresponding protein-coding genes are predominantly expressed in melanocytes, T cells, and macrophages in skin tissue. Importantly, these proteins were identified to be involved in cytokine-cytokine receptor interaction, Th17 cell differentiation, and the JAK-STAT signaling pathway. All of these proteins are druggable, and six of them show great potential as drug targets. In conclusion, this study identified eight plasma proteins causally associated with AD and provided new insights into the etiology and potential drug targets for AD.

A novel role of ADAMTS16 in renal fibrosis through activating TGF-ß/Smad signaling.

Chronic Kidney Disease (CKD) has emerged as a global public health concern, with its primary pathological basis being Renal Fibrosis (RF), crucial to halt its progression to End-Stage Renal Disease (ESRD). However, effective treatment options are currently lacking. Therefore, exploring the mechanisms of RF, identifying drug targets and diagnostic biomarkers are important. In this study, we identified ADAMTS16 as a newly expressed regulatory factor highly expressed in renal fibrosis tissue. ADAMTS16 interacts with latency-associated peptide (LAP)-transforming growth factor (TGF)-ß, leading to the activation of TGF-ß. Loss of ADAMTS16 expression effectively reduces TGF-ß-dependent transcription activity. Furthermore, the use of RRFR tetrapeptide derived from ADAMTS16 can activate the TGF-ß/Smad signaling axis, promoting RF. In summary, ADAMTS16 is induced in the progression of CKD, interacting with LAP-TGF-ß and potentially activating SMAD2/3. Therefore, targeting ADAMTS16 may serve as a crucial new strategy to alleviate RF and treat CKD patients.

Discovery of petroselinic acid with in vitro and in vivo antifungal activity by targeting fructose-1,6-bisphosphate aldolase.

BACKGROUND: The incidence of invasive fungal diseases (IFDs), represented by Candida albicans infection, is increasing year by year. However, clinically available antifungal drugs are very limited and encounter challenges such as limited efficacy, drug resistance, high toxicity, and exorbitant cost. Therefore, there is an urgent need for new antifungal drugs. PURPOSE: This study aims to find new antifungal compounds from plants, preferably those with good activity and low toxicity, and reveal their antifungal targets. METHODS: In vitro antifungal activities of compounds were investigated using broth microdilution method, spot assay, hyphal growth assay and biofilm formation assay. Synergistic effects were assessed using broth microdilution checkerboard technique. In vivo antifungal activities were evaluated using Galleria mellonella and murine candidiasis models. Cytotoxicity of compounds was investigated using Cell Counting Kit-8 (CCK-8). Discovery and validation of antifungal targets of compounds were conducted by using monoallelic knockout library of C. albicans, haploinsufficiency profiling (HIP), thermal shift assay (TSA), enzyme inhibitory effect assay, molecular docking, and in vitro and in vivo antifungal studies. RESULTS: 814 plant products were screened, among which petroselinic acid (PeAc) was found as an antifungal molecule. As a rare fatty acid isolated from coriander (Coriandrum sativum), carrot (Daucus carota) and other plants of the Apiaceae family, PeAc had not previously been found to have antifungal effects. In this study, PeAc was revealed to inhibit the growth of various pathogenic fungi, exhibited synergistic effects with fluconazole (FLC), inhibited the formation of C. albicans hyphae and biofilms, and showed antifungal effects in vivo. PeAc was less toxic to mammalian cells. Fructose-1,6-bisphosphate aldolase (Fba1p) was identified as a target of PeAc by using HIP, TSA, enzyme inhibitory effect assay and molecular docking methods. PeAc exerted antifungal effects more effectively on fba1Δ/FBA1 than wild-type (WT) strain both in vitro and in vivo. CONCLUSIONS: PeAc is an effective and low toxic antifungal compound. The target of PeAc is Fba1p. Fba1p is a promising target for antifungal drug development.

ERT-GFAN: A multimodal drug-target interaction prediction model based on molecular biology and knowledge-enhanced attention mechanism.

In drug discovery, precisely identifying drug-target interactions is crucial for finding new drugs and understanding drug mechanisms. Evolving drug/target heterogeneous data presents challenges in obtaining multimodal representation in drug-target prediction(DTI). To deal with this, we propose 'ERT-GFAN', a multimodal drug-target interaction prediction model inspired by molecular biology. Firstly, it integrates bio-inspired principles to obtain structure feature of drugs and targets using Extended Connectivity Fingerprints(ECFP). Simultaneously, the knowledge graph embedding model RotatE is employed to discover the interaction feature of drug-target pairs. Subsequently, Transformer is utilized to refine the contextual neighborhood features from the obtained structure feature and interaction features, and multi-modal high-dimensional fusion features of the three-modal information constructed. Finally, the final DTI prediction results are outputted by integrating the multimodal fusion features into a graphical high-dimensional fusion feature attention network (GFAN) using our innovative multimodal high-dimensional fusion feature attention. This multimodal approach offers a comprehensive understanding of drug-target interactions, addressing challenges in complex knowledge graphs. By combining structure feature, interaction feature, and contextual neighborhood features, 'ERT-GFAN' excels in predicting DTI. Empirical evaluations on three datasets demonstrate our method's superior performance, with AUC of 0.9739, 0.9862, and 0.9667, AUPR of 0.9598, 0.9789, and 0.9750, and Mean Reciprocal Rank(MRR) of 0.7386, 0.7035, and 0.7133. Ablation studies show over a 5% improvement in predictive performance compared to baseline unimodal and bimodal models. These results, along with detailed case studies, highlight the efficacy and robustness of our approach.

Identification of hub modules and therapeutic targets associated with CD8+T-cells in HF and their pan-cancer analysis.

Heart failure (HF) is a terminal condition of multiple cardiovascular disorders. Cancer is a deadly disease worldwide. The relationship between HF and cancer remains poorly understood. The Gene Expression Omnibus database was used to download the RNA sequencing data of 356 patients with hypertrophic cardiomyopathy-induced HF and non-HF. A co-expression network was established through the weighted correlation network analysis (WGCNA) to identify hub genes of HF and cancer. Cox risk analysis was performed to predict the prognostic risks of HF hub genes in pan-cancer. HF was linked to immune response pathway by the analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A positive correlation was observed between the expression levels of 4 hub genes and the infiltration of CD8+T-cells in pan-cancer. 4 hub genes were identified as beneficial prognostic factors in several cancers. Western blotting and real-time polymerase chain reaction validated the high expression of GZMM, NKG7, and ZAP70 in both mice and patients with HF compared to control groups. Our study highlights the shared immune pathogenesis of HF and cancer and provides valuable insights for developing novel therapeutic strategies, offering new opportunities for improving the management and treatment outcomes of both HF and cancer.

Identifying the molecular mechanisms of action of wound healing properties of Mathan tailam and Mahamegarajanga tailam in Wistar albino rats: Evidence through IL-10/VEGF/TNF-α signalling pathways.

The Siddha system of medicine (SSM) is the oldest medical science practised in the ancient period of the southern part of India and Sri Lanka. Many formulations were described for wound healing in the SSM, with specific diagnostic differentiation in the Siddha literature. Most preparations for wound healing were available in the form of oil-based formulations, especially for external usage. Mathan tailam (MT) and Mahamegarajanga tailam (MMRT) have been used by Siddha physicians and traditional practitioners to treat wounds. Mathan tailam is a popular regimen for skin lacerations, burns, skin infections, diabetic wounds, and dermatitis. Mahamegarajanga tailam has long been used by traditional vaidyars to treat cuts and burns. Both MT and MMRT are clinically well-appreciated drugs for wound healing and need to be studied for their mechanisms of action for scientific documentation. In an in vivo study on albino rats -excisional wound model, the histopathological changes, histo-immune response, biomarker analysis, and mRNA expression were studied and analysed. Wounds treated with MT and MMRT healed faster (p < 0.05) than the untreated group (CNT). Histological investigation showed rapid re-epithelialization, dense collagen deposition, increased enzymatic antioxidant activities and decreased lipid peroxidation in the MT and MMRT groups. mRNA expression reveals MT and MMRT-treated tissues able to induce convergent cell motility in wound space. Our study for the first time provides strong in vivo experimental evidence that Mathan tailam and Mahamegarajanga tailam play a crucial role in promoting skin tissue wound healing through IL-6/VEGF/TNF-α mediated mechanisms. Traditional practices continue to teach us valuable lessons, as seen by their continuous use in their locality for years.