Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Gene discovery and biological insights into anxiety disorders from a large-scale multi-ancestry genome-wide association study

We leveraged information from more than 1.2 million participants, including 97,383 cases, to investigate the genetics of anxiety disorders across five continental groups. Through ancestry-specific and cross-ancestry genome-wide association studies, we identified 51 anxiety-associated loci, 39 of which were novel. In addition, polygenic risk scores derived from individuals of European descent were associated with anxiety in African, admixed American and East Asian groups. The heritability of anxiety was enriched for genes expressed in the limbic system, cerebral cortex, cerebellum, metencephalon, entorhinal cortex and brain stem. Transcriptome-wide and proteome-wide analyses highlighted 115 genes associated with anxiety through brain-specific and cross-tissue regulation. Anxiety also showed global and local genetic correlations with depression, schizophrenia and bipolar disorder and widespread pleiotropy with several physical health domains. Overall, this study expands our knowledge regarding the genetic risk and pathogenesis of anxiety disorders, highlighting the importance of investigating diverse populations and integrating multi-omics information.

The genetic landscape of basal ganglia and implications for common brain disorders

The basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted univariate and multivariate genome-wide association analyses (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 Europeans with replication in 4,808 white and generalization in 5,220 non-white Europeans. Our multivariate GWAS identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 55.6% at P < 0.05 and 87.5% showed the same direction, revealing a distributed genetic architecture across basal ganglia structures. Of these, 50 loci were novel, including exonic regions of APOE, NBR1 and HLAA. We examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The strongest genetic overlap was between basal ganglia and Parkinson's disease, as supported by robust LD-score regression-based genetic correlations. Mendelian randomization indicated genetic liability to larger striatal volume as potentially causal for Parkinson's disease, in addition to a suggestive causal effect of greater genetic liability to Alzheimer's disease on smaller accumbens. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders.

Identification of ubiquitin markers for survival and prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most common malignancies and a leading cause of death among women worldwide. The ubiquitin pathway plays an important role in OC development. Using the single nucleotide polymorphism data obtained using the prevalence and dominance strategies, four ubiquitin marker genes were identified according to their expression levels: BARD1, BRCA2, FANCA, and BRCA1. Based on these four genes, a consensus clustering of OC expression data was performed. The significant differences in the survival analysis, ESTIMATE, and CIBERSORT results among the clusters indicated the pivotal role of these four genes in OC development. Of the ubiquitin-representative genes in each cluster, two ubiquitin genes, TOP2A and MYLIP, were identified in the survival risk model after univariate survival, Least Absolute Shrinkage and Selection Operator regression, and multivariate survival analyses. The reliability and robustness of both the training and validation data were confirmed by comparing the significant survival difference between high- and low-risk patients. We further explored the association between our risk model and clinical outcomes as well as predicted potentially interacting drugs. The co-expression network showed clear interactions among the four marker genes and two model genes and between high- and low-risk differentially expressed genes. Significantly enriched genes were found in pathways associated with ion channels, channel activity, and neuroactive ligand-receptor interactions. Therefore, suggesting the involvement of ubiquitin genes in the survival and development of OC through neurohumoral regulation. Our results will provide valuable reference or supplementary information for studies investigating OC diagnosis and therapies.

The prognostic and therapeutic potential of vimentin in colorectal cancer

Several cells and molecules in the tumor microenvironment have been introduced as effective factors in the prognosis and progression of colorectal cancer. As a key element of the intermediate filament family, vimentin is expressed by mesenchymal cells in a ubiquitous manner and contributes significantly to cellular integrity and stress resistance in colorectal cancer. Recent studies have shown that alterations in the expression patterns of intermediate filaments are significantly related to cancer progression, especially in phenotypes associated with cellular migration and invasion. In addition to its multiple biological roles, vimentin also has a substantial function in mediating the epithelial-mesenchymal transition. Therefore, evaluating vimentin as an effective factor involved in the prognosis of colorectal cancer and targeting it as a novel approach to cancer therapy have become one of the main goals of many researchers worldwide. In this article, we will review the various biological functions of vimentin, as well as its relationship with colorectal cancer with the aim of providing novel insights into its clinical importance in the prognosis and treatment of colorectal cancer.

Pinpointing Novel Plasma and Brain Proteins for Common Ocular Diseases: A Comprehensive Cross-Omics Integration Analysis

The pathogenesis of ocular diseases (ODs) remains unclear, although genome-wide association studies (GWAS) have identified numerous associated genetic risk loci. We integrated protein quantitative trait loci (pQTL) datasets and five large-scale GWAS summary statistics of ODs under a cutting-edge systematic analytic framework. Proteome-wide association studies (PWAS) identified plasma and brain proteins associated with ODs, and 11 plasma proteins were identified by Mendelian randomization (MR) and colocalization (COLOC) analyses as being potentially causally associated with ODs. Five of these proteins (protein-coding genes ECI1, LCT, and NPTXR for glaucoma, WARS1 for age-related macular degeneration (AMD), and SIGLEC14 for diabetic retinopathy (DR)) are newly reported. Twenty brain-protein-OD pairs were identified by COLOC analysis. Eight pairs (protein-coding genes TOM1L2, MXRA7, RHPN2, and HINT1 for senile cataract, WARS1 and TDRD7 for AMD, STAT6 for myopia, and TPPP3 for DR) are newly reported in this study. Phenotype-disease mapping analysis revealed 10 genes related to the eye/vision phenotype or ODs. Combined with a drug exploration analysis, we found that the drugs related to C3 and TXN have been used for the treatment of ODs, and another eight genes (GSTM3 for senile cataract, IGFBP7 and CFHR1 for AMD, PTPMT1 for glaucoma, EFEMP1 and ACP1 for myopia, SIRPG and CTSH for DR) are promising targets for pharmacological interventions. Our study highlights the role played by proteins in ODs, in which brain proteins were taken into account due to the deepening of eye-brain connection studies. The potential pathogenic proteins finally identified provide a more reliable reference range for subsequent medical studies.

Graph databases in systems biology: a systematic review

Graph databases are becoming increasingly popular across scientific disciplines, being highly suitable for storing and connecting complex heterogeneous data. In systems biology, they are used as a backend solution for biological data repositories, ontologies, networks, pathways, and knowledge graph databases. In this review, we analyse all publications using or mentioning graph databases retrieved from PubMed and PubMed Central full-text search, focusing on the top 16 available graph databases, Publications are categorized according to their domain and application, focusing on pathway and network biology and relevant ontologies and tools. We detail different approaches and highlight the advantages of outstanding resources, such as UniProtKB, Disease Ontology, and Reactome, which provide graph-based solutions. We discuss ongoing efforts of the systems biology community to standardize and harmonize knowledge graph creation and the maintenance of integrated resources. Outlining prospects, including the use of graph databases as a way of communication between biological data repositories, we conclude that efficient design, querying, and maintenance of graph databases will be key for knowledge generation in systems biology and other research fields with heterogeneous data.

Identification of CCL20 as a Prognostic Predictor for Severe Fever With Thrombocytopenia Syndrome Based on Plasma Proteomics

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS), a lethal tick-borne hemorrhagic fever, prompted our investigation into prognostic predictors and potential drug targets using plasma Olink Proteomics.

METHODS

Employing the Olink assay, we analyzed 184 plasma proteins in 30 survivors and 8 nonsurvivors of SFTS. Validation was performed in a cohort of 154 patients with SFTS via enzyme-linked immunosorbent assay. We utilized the Drug-Gene Interaction Database to identify protein-drug interactions.

RESULTS

Nonsurvivors exhibited 110 differentially expressed proteins as compared with survivors, with functional enrichment in the cell chemotaxis-related pathway. Thirteen differentially expressed proteins-including C-C motif chemokine 20 (CCL20), calcitonin gene-related peptide alpha, and pleiotrophin-were associated with multiple-organ dysfunction syndrome. CCL20 emerged as the top predictor of death, demonstrating an area under the curve of 1 (P = .0004) and 0.9033 (P < .0001) in the discovery and validation cohorts, respectively. Patients with CCL20 levels exceeding 45.74 pg/mL exhibited a fatality rate of 45.65%, while no deaths occurred in those with lower CCL20 levels. Furthermore, we identified 202 Food and Drug Administration-approved drugs targeting 37 death-related plasma proteins.

CONCLUSIONS

Distinct plasma proteomic profiles characterize SFTS cases with different outcomes, with CCL20 emerging as a novel, sensitive, accurate, and specific biomarker for predicting SFTS prognosis.

Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a life-threatening autoimmune disease that often results in kidney failure caused by crescentic glomerulonephritis (GN). To date, treatment of most patients with ANCA-GN relies on non-specific immunosuppressive agents, which may have serious adverse effects and be only partially effective. Here, using spatial and single-cell transcriptome analysis, we characterize inflammatory niches in kidney samples from 34 patients with ANCA-GN and identify proinflammatory, cytokine-producing CD4+ and CD8+ T cells as a pathogenic signature. We then utilize these transcriptomic profiles for digital pharmacology and identify ustekinumab, a monoclonal antibody targeting IL-12 and IL-23, as the strongest therapeutic drug to use. Moreover, four patients with relapsing ANCA-GN are treated with ustekinumab in combination with low-dose cyclophosphamide and steroids, with ustekinumab given subcutaneously (90 mg) at weeks 0, 4, 12, and 24. Patients are followed up for 26 weeks to find this treatment well-tolerated and inducing clinical responses, including improved kidney function and Birmingham Vasculitis Activity Score, in all ANCA-GN patients. Our findings thus suggest that targeting of pathogenic T cells in ANCA-GN patients with ustekinumab might represent a potential approach and warrants further investigation in clinical trials.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Revealing the association between East Asian oral microbiome and colorectal cancer through Mendelian randomization and multi-omics analysis

BACKGROUND

Colorectal cancer (CRC) poses a global health threat, with the oral microbiome increasingly implicated in its pathogenesis. This study leverages Mendelian Randomization (MR) to explore causal links between oral microbiota and CRC using data from the China National GeneBank and Biobank Japan. By integrating multi-omics approaches, we aim to uncover mechanisms by which the microbiome influences cellular metabolism and cancer development.

METHODS

We analyzed microbiome profiles from 2017 tongue and 1915 saliva samples, and GWAS data for 6692 CRC cases and 27178 controls. Significant bacterial taxa were identified via MR analysis. Single-cell RNA sequencing and enrichment analyses elucidated underlying pathways, and drug predictions identified potential therapeutics.

RESULTS

MR identified 19 bacterial taxa significantly associated with CRC. Protective effects were observed in taxa like RUG343 and Streptococcus_umgs_2425, while HOT-345_umgs_976 and W5053_sp000467935_mgs_712 increased CRC risk. Single-cell RNA sequencing revealed key pathways, including JAK-STAT signaling and tyrosine metabolism. Drug prediction highlighted potential therapeutics like Menadione Sodium Bisulfite and Raloxifene.

CONCLUSION

This study establishes the critical role of the oral microbiome in colorectal cancer development, identifying specific microbial taxa linked to CRC risk. Single-cell RNA sequencing and drug prediction analyses further elucidate key pathways and potential therapeutics, providing novel insights and personalized treatment strategies for CRC.

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis

Background

The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer.

Methods

Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets.

Results

Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer.

Conclusions

The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

Gene Therapy in the Light of Lifestyle Diseases: Budesonide, Acetaminophen and Simvastatin Modulates rAAV Transduction Efficiency

Recombinant AAV (rAAV) vectors are increasingly favored for gene therapy due to their useful features of vectorology, such as transfection of dividing and nondividing cells, the presence of tissue-specific serotypes, and biosafety considerations. This study investigates the impact of commonly used therapeutic drugs-acetaminophen, budesonide, and simvastatin-on rAAV transduction efficiency in HEK-293 cells. Cells were transduced with an AAV mosaic vector under the control of a cytomegalovirus (CMV) promoter encoding green fluorescent protein (GFP). Transduction efficiency was assessed by qPCR and fluorescent microscopy. Analysis of functional interactions between genes potentially involved in rAAV transduction in drug-exposed cells was also performed. This study showed a clear effect of drugs on rAAV transmission. Notably, acetaminophen enhanced transduction efficiency by 9-fold, while budesonide and simvastatin showed 2-fold and 3-fold increases, respectively. The gene analysis illustrates the possible involvement of genes related to cell membranes in the potentiation of rAAV transduction induced by the drugs under investigation. Attention should be paid to S100A8, which is a common drug-modified gene for drugs showing anti-inflammatory effects (budesonide and simvastatin), demonstrating an interaction with the gene encoding the receptor for AAV (HGFR). This study underscores the significance of assessing rAAV pharmacokinetics/pharmacodynamics (PKs/PDs) and drug-gene therapy interactions in optimizing gene therapy protocols.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Harmine inhibits pulmonary fibrosis through regulating DNA damage repair-related genes and activation of TP53-Gadd45α pathway

BACKGROUND

Harmine has many pharmacological activities and has been found to significantly inhibit the fibrosis of keloid fibroblasts. DNA damage repair (DDR) is essential to prevent fibrosis. This study aimed to investigate the effects of harmine on pulmonary fibrosis and its underlying mechanisms.

METHODS

Bleomycin and TGF-β1 were used to construct pulmonary fibrosis models in vivo and in vitro, then treated with harmine to explore harmine's effects in treating experimental pulmonary fibrosis and its related mechanisms. Then, RNA sequencing was applied to investigate further the crucial DDR-related genes and drug targets of harmine against pulmonary fibrosis. Finally, the expression levels of DDR-related genes were verified by real-time quantitative PCR (RT-qPCR) and western blot.

RESULTS

Our in vivo experiments showed that harmine treatment could improve weight loss and lung function and reduce tissue fibrosis in mice with pulmonary fibrosis. The results confirmed that harmine could inhibit the viability and migration of TGF-β1-induced MRC-5 cells, induce their apoptosis, and suppress the F-actin expression, suggesting that harmine could suppress the phenotypic transition from lung fibroblasts to lung myoblasts. In addition, RNA sequencing identified 1692 differential expressed genes (DEGs), and 10 DDR-related genes were screened as critical DDR-related genes. RT-qPCR and western blotting showed that harmine could down-regulate the expression of CHEK1, ERCC1, ERCC4, POLD1, RAD51, RPA1, TOP1, and TP53, while up-regulate FEN1, H2AX and GADD45α expression.

CONCLUSIONS

Harmine may inhibit pulmonary fibrosis by regulating DDR-related genes and activating the TP53-Gadd45α pathway.

Combined cancer immunotherapy based on targeting adenosine pathway and PD-1/PDL-1 axis

INTRODUCTION

Cancer immunotherapy has revolutionized the field of oncology, offering new hope to patients with advanced malignancies. Tumor-induced immunosuppression limits the effectiveness of current immunotherapeutic strategies, such as PD-1/PDL-1 checkpoint inhibitors. Adenosine, a purine nucleoside molecule, is crucial to this immunosuppression because it stops T cells from activating and helps regulatory T cells grow. Targeting the adenosine pathway and blocking PD-1/PDL-1 is a potential way to boost the immune system's response to tumors.

AREAS COVERED

This review discusses the current understanding of the adenosine pathway in tumor immunology and the preclinical and clinical data supporting the combination of adenosine pathway inhibitors with PD-1/PDL-1 blockade. We also discuss the challenges and future directions for developing combination immunotherapy targeting the adenosine pathway and the PD-1/PDL-1 axis for cancer treatment.

EXPERT OPINION

The fact that the adenosine signaling pathway controls many immune system processes suggests that it has a wide range of therapeutic uses. Within the next five years, there will be tremendous progress in this area, and the standard of care for treating malignant tumors will have switched from point-to-point therapy to the integration of immunological networks comprised of multiple signaling pathways, like the adenosine axis.

Genetic insights into drug targets for sporadic Creutzfeldt-Jakob disease: Integrative multi-omics analysis

OBJECTIVE

Sporadic Creutzfeldt-Jakob disease (sCJD) is a fatal rapidly progressive neurodegenerative disorder with no effective therapeutic interventions. We aimed to identify potential genetically-supported drug targets for sCJD by integrating multi-omics data.

METHODS

Multi-omics-wide association studies, Mendelian randomization, and colocalization analyses were employed to explore potential therapeutic targets using expression, single-cell expression, DNA methylation, and protein quantitative trait locus data from blood and brain tissues. Outcome data was from a case-control genome-wide association study, which included 4110 sCJD patients and 13,569 controls. Further investigations encompassed druggability, potential side effects, and associated biological pathways of the identified targets.

RESULTS

Integrative multi-omics analysis identified 23 potential therapeutic targets for sCJD, with five targets (STX6, XYLT2, PDIA4, FUCA2, KIAA1614) having higher levels of evidence. One target (XYLT2) shows promise for repurposing, two targets (XYLT2, PDIA4) are druggable, and three (STX6, KIAA1614, and FUCA2) targets represent potential future breakthrough points. The expression level of STX6 and XYLT2 in neurons and oligodendrocytes was closely associated with an increased risk of sCJD. Brain regions with high expression of STX6 or causal links to sCJD were often those areas commonly affected by sCJD.

CONCLUSIONS

Our study identified five potential therapeutic targets for sCJD. Further investigations are warranted to elucidate the mechanisms underlying the new targets for developing disease therapies or initiate clinical trials.

Unlocking reproducible transcriptomic signatures for acute myeloid leukaemia: Integration, classification and drug repurposing

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, which lead to various findings in transcriptomic research. This study addresses these challenges by integrating 34 datasets, including 26 control groups, 6 prognostic datasets and 2 single-cell RNA sequencing (scRNA-seq) datasets to identify 10,000 AML-related genes (ARGs). We focused on genes with low variability and high consistency and successfully discovered 191 AML signatures (ASs). Leveraging machine learning techniques, specifically the XGBoost model and our custom framework, we classified AML subtypes with both scRNA-seq and bulk RNA-seq data, complementing the ELN2022 classification approach. Our research also identified promising treatments for AML through drug repurposing, with solasonine showing potential efficacy for high-risk AML patients, supported by molecular docking and transcriptomic analyses. To enhance reproducibility and customizability, we developed CSAMLdb, a user-friendly database platform. It facilitates the reuse and personalized analysis of nearly all results obtained in this research, including single-gene prognostics, multi-gene scoring, enrichment analysis, machine learning risk assessment, drug repositioning analysis and literature abstract named entity recognition. CSAMLdb is available at http://www.csamldb.com.

Identifying genetically-supported drug repurposing targets for non-small cell lung cancer through mendelian randomization of the druggable genome

Background

Lung cancer is responsible for most cancer-related deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Targeted therapy has made promising advancements in systemic treatment for NSCLC over the last two decades, but inadequate drug targets with clinically proven survival benefits limit its universal application in clinical practice compared to chemotherapy and immunotherapy. There is an urgent need to explore new drug targets to expand the beneficiary group. This study aims to identify druggable genes and to predict the efficacy and prognostic value of the corresponding targeted drugs in NSCLC.

Methods

Two-sample mendelian randomization (MR) of druggable genes was performed to predict the efficacy of their corresponding targeted therapy for NSCLC. Subsequent sensitivity analyses were performed to assess potential confounders. Accessible RNA sequencing data were incorporated for subsequent verifications, and Kaplan-Meier survival curves of different gene expressions were used to explore the prognostic value of candidate druggable genes.

Results

MR screening encompassing 4,863 expression quantitative trait loci (eQTL) and 1,072 protein quantitative trait loci (pQTL, with 453 proteins overlapping) were performed. Seven candidate druggable genes were identified, including CD33, ENG, ICOSLG and IL18R1 for lung adenocarcinoma, and VSIR, FSTL1 and TIMP2 for lung squamous cell carcinoma. The results were validated by further transcriptomic investigations.

Conclusions

Drugs targeting genetically supported genomes are considerably more likely to yield promising efficacy and succeed in clinical trials. We provide compelling genetic evidence to prioritize drug development for NSCLC.

In-silico identification and functional characterization of common genes associated with type 2 diabetes and hypertension

Type 2 diabetes (T2D) and hypertension are global public health concerns and major metabolic disorders in humans. Experimental evidence indicates considerable hereditary influences on the etiology of T2D and hypertension, but the molecular basis of these diseases is still limited. Thus, the current study analyzed 185 (132 T2D and 53 hypertension) GWAS catalog datasets and identified 83 common genes linked to T2D and hypertension pathogenesis. These genes were further examined using various bioinformatics approaches to elucidate their molecular mechanisms underlying the pathophysiology of T2D and hypertension. Gene ontology (GO) analysis revealed the biological, cellular, and molecular functions of these genes, which were also linked to different T2D and hypertension pathways. Specifically, seven genes were found to be crucial for T2D, and nine were directly associated with hypertension. Protein-protein interaction (PPI) analysis identified 28 candidate genes and seven hub genes through 11 topological methods. Among 231 miRNAs, seven were significant in interacting with the hub genes, and nine transcription factors (TFs) out of 36 were linked to these hub genes. Additionally, two of the seven hub genes were downregulated by 43 FDA-approved drugs. These findings elucidate the molecular processes underlying T2D and hypertension, suggesting that targeting these genes could lead to future drug development and therapeutic strategies to treat T2D and hypertension.

Exploring causal correlations between plasma proteins and peripheral neuropathy: a Mendelian randomization

Background

Peripheral neuropathy (PN) is a common neurological disorder, and circulating plasma proteins with causal genetic evidence are a major source of therapeutic targets. This study identifies several potential plasma proteins that are causally related to PN risk, providing new insights into protein-mediated pathogenesis of PN and potential targets for novel therapies.

Methods

To identify potential therapeutic targets for PN, we employed two-sample Mendelian randomization (MR) to identify plasma proteins associated with six common PN. First, we screened for proteins related to PN using genome-wide association studies (GWAS), obtaining genetic data on plasma proteomes from 35,559 Icelanders. Summary data for six common PN, including Carpal Tunnel Syndrome (CTS), Trigeminal Neuralgia (TN), Alcoholic Neuropathy (AIP), Drug-induced Neuropathy (DIP), Diabetic Neuropathy (DP), and Guillain-Barré syndrome (GBS), were obtained from the FinnGen database. Two-sample MR and colocalization analyses were then conducted to further identify protein-PN pairs with presumed causal relationships. Enrichment analysis of positive proteins revealed potential biological processes and pathways. Based on drug-gene interaction analysis, we ultimately identified causal proteins associated with PN that could serve as potential drug targets for treating PN.

Results

Through MR analysis, we identified eight proteins (UBC12, SEM4C, IL23R, Prothrombin, CBS, Microglobulin, MATN4, COLEC12) with causal relationships to PN. We found that UBC12 is a protective factor for DP and CTS, while the remaining proteins are risk factors. Further colocalization analysis showed a posterior probability of hypothesis 4 (PPH4) less than 0.75, indicating no positive colocalization results were found. From the pathway enrichment analysis, we discovered that the proteins were mainly concentrated in pathways related to defense response to bacterium, receptor signaling pathway via STAT, cell killing, negative regulation of cytokine production, and leukocyte mediated immunity. Finally, in Drug-Gene Interaction database (DGIdb), we identified three protein-coding genes (IL23R, F2, CBS) as potential drug targets for PN.

Conclusion

Mendelian randomization studies confirm the causal relationship between genetically predicted PN-related risk and genetically predicted plasma protein abundance. Plasma proteins, as biomarkers associated with PN, can provide potential drug targets for etiological intervention research in PN.

EWS-WT1 fusion isoforms establish oncogenic programs and therapeutic vulnerabilities in desmoplastic small round cell tumors

EWS fusion oncoproteins underlie several human malignancies including Desmoplastic Small Round Cell Tumor (DSRCT), an aggressive cancer driven by EWS-WT1 fusion proteins. Here we combine chromatin occupancy and 3D profiles to identify EWS-WT1-dependent gene regulation networks and target genes. We show that EWS-WT1 is a powerful chromatin activator controlling an oncogenic gene expression program that characterizes primary tumors. Similar to wild type WT1, EWS-WT1 has two isoforms that differ in their DNA binding domain and we find that they have distinct DNA binding profiles and are both required to generate viable tumors that resemble primary DSRCT. Finally, we identify candidate EWS-WT1 target genes with potential therapeutic implications, including CCND1, whose inhibition by the clinically-approved drug Palbociclib leads to marked tumor burden decrease in DSRCT PDXs in vivo. Taken together, our studies identify gene regulation programs and therapeutic vulnerabilities in DSRCT and provide a mechanistic understanding of the complex oncogenic activity of EWS-WT1.

Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach

BACKGROUND AND HYPOTHESIS

Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets.

STUDY DESIGN

To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined.

STUDY RESULTS

Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons.

CONCLUSIONS

These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.

Multi-Omics Integration Analysis Pinpoint Proteins Influencing Brain Structure and Function: Toward Drug Targets and Neuroimaging Biomarkers for Neuropsychiatric Disorders

Integrating protein quantitative trait loci (pQTL) data and summary statistics from genome-wide association studies (GWAS) of brain image-derived phenotypes (IDPs) can benefit in identifying IDP-related proteins. Here, we developed a systematic omics-integration analytic framework by sequentially using proteome-wide association study (PWAS), Mendelian randomization (MR), and colocalization (COLOC) analyses to identify the potentially causal brain and plasma proteins for IDPs, followed by pleiotropy analysis, mediation analysis, and drug exploration analysis to investigate potential mediation pathways of pleiotropic proteins to neuropsychiatric disorders (NDs) as well as candidate drug targets. A total of 201 plasma proteins and 398 brain proteins were significantly associated with IDPs from PWAS analysis. Subsequent MR and COLOC analyses further identified 313 potentially causal IDP-related proteins, which were significantly enriched in neural-related phenotypes, among which 91 were further identified as pleiotropic proteins associated with both IDPs and NDs, including EGFR, TMEM106B, GPT, and HLA-B. Drug prioritization analysis showed that 6.33% of unique pleiotropic proteins had drug targets or interactions with medications for NDs. Nine potential mediation pathways were identified to illustrate the mediating roles of the IDPs in the causal effect of the pleiotropic proteins on NDs, including the indirect effect of TMEM106B on Alzheimer's disease (AD) risk via radial diffusivity (RD) of the posterior limb of the internal capsule (PLIC), with the mediation proportion being 11.18%, and the indirect effect of EGFR on AD through RD of PLIC, RD of splenium of corpus callosum (SCC), and fractional anisotropy (FA) of SCC, with the mediation proportion being 18.99%, 22.79%, and 19.91%, respectively. These findings provide novel insights into pathogenesis, drug targets, and neuroimaging biomarkers of NDs.

Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

OBJECTIVES

This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients.

METHODS

GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study also examined the functional and immunological aspects of these genes, potential drug targets, and ceRNA networks. A mouse model of sepsis was created to test the reliability of these signature genes.

RESULTS

LDSC confirmed a positive genetic correlation between AKI and sepsis, although no significant shared loci were found. Bidirectional MR analysis indicated mutual increased risks of AKI and sepsis. Then, 311 key genes common to sepsis and AKI were identified, with 42 significantly linked to sepsis prognosis. Six genes, selected through LASSO, SVM-RFE, and RF algorithms, showed excellent predictive performance for sepsis, AKI, and SA-AKI. The models demonstrated near-perfect AUCs in both training and testing datasets, and a perfect AUC in a sepsis mouse model. Significant differences in immune cells, immune-related pathways, HLA, and checkpoint genes were found between high- and low-risk groups. The study identified 62 potential drug treatments for sepsis and AKI and constructed a ceRNA network.

CONCLUSIONS

The identified signature genes hold potential clinical applications, including prognostic evaluation and targeted therapeutic strategies for sepsis and AKI. However, further research is needed to confirm these findings.

Novel therapeutic targets for primary open-angle glaucoma identified through multicenter proteome-wide mendelian randomization

Background

This study aimed to identify novel therapeutic targets for primary open-angle glaucoma (POAG).

Methods

The summary-data-based Mendelian randomization (SMR) method was used to evaluate the genetic association between plasma proteins and POAG. Two sets of plasma protein quantitative trait loci (pQTLs) data considered exposures were obtained from the Icelandic Decoding Genetics Study and UK Biobank Pharma Proteomics Project. The summary-level genome-wide association studies data for POAG were extracted from the latest Round 10 release of the FinnGen consortium (8,530 cases and 391,275 controls) and the UK Biobank (4,737 cases and 458,196 controls). Colocalization analysis was used to screen out pQTLs that share the same variant with POAG as drug targets identified. The two-sample Mendelian randomization, reverse causality testing and phenotype scanning were performed to further validate the main findings. Protein-protein interaction, pathway enrichment analysis and druggability assessment were conducted to determine whether the identified plasma proteins have potential as drug targets.

Results

After systematic analysis, this study identified eight circulating proteins as potential therapeutic targets for POAG. Three causal proteins with strong evidence of colocalization, ROBO1 (OR = 1.38, p = 1.48 × 10-4, PPH4 = 0.865), FOXO3 (OR = 0.35, p = 4.34 × 10-3, PPH4 = 0.796), ITIH3 (OR = 0.89, p = 2.76 × 10-4, PPH4 = 0.767), were considered tier one targets. Five proteins with medium support evidence of colocalization, NCR1 (OR = 1.25, p = 4.18 × 10-4, PPH4 = 0.682), NID1 (OR = 1.38, p = 1.54 × 10-3, PPH4 = 0.664), TIMP3 (OR = 0.91, p = 4.01 × 10-5, PPH4 = 0.659), SERPINF1 (OR = 0.81, p = 2.77 × 10-4, PPH4 = 0.59), OXT (OR = 1.17, p = 9.51 × 10-4, PPH4 = 0.526), were classified as tier two targets. Additional sensitivity analyses further validated the robustness and directionality of these findings. According to druggability assessment, Pimagedine, Resveratrol, Syringaresinol and Clozapine may potentially be important in the development of new anti-glaucoma agents.

Conclusion

Our integrated study identified eight potential associated proteins for POAG. These proteins play important roles in neuroprotection, extracellular matrix regulation and oxidative stress. Therefore, they have promising potential as therapeutic targets to combat POAG.

Pan-cancer proteogenomics expands the landscape of therapeutic targets

Fewer than 200 proteins are targeted by cancer drugs approved by the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 cancer types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable proteins reveals a wide abundance range and identifies biological factors that affect mRNA-protein correlation. Integration of proteomic data from tumors and genetic screen data from cell lines identifies protein overexpression- or hyperactivation-driven druggable dependencies, enabling accurate predictions of effective drug targets. Proteogenomic identification of synthetic lethality provides a strategy to target tumor suppressor gene loss. Combining proteogenomic analysis and MHC binding prediction prioritizes mutant KRAS peptides as promising public neoantigens. Computational identification of shared tumor-associated antigens followed by experimental confirmation nominates peptides as immunotherapy targets. These analyses, summarized at https://targets.linkedomics.org, form a comprehensive landscape of protein and peptide targets for companion diagnostics, drug repurposing, and therapy development.

The mechanism of geniposide in patients with COVID-19 and atherosclerosis: A pharmacological and bioinformatics analysis

In patients with severe acute respiratory syndrome coronavirus 2 (which causes coronavirus disease 2019 [COVID-19]), oxidative stress (OS) is associated with disease severity and death. OS is also involved in the pathogenesis of atherosclerosis (AS). Previous studies have shown that geniposide has anti-inflammatory and anti-viral properties, and can protect cells against OS. However, the potential target(s) of geniposide in patients with COVID-19 and AS, as well as the mechanism it uses, are unclear. We combined pharmacology and bioinformatics analysis to obtain geniposide against COVID-19/AS targets, and build protein-protein interaction network to filter hub genes. The hub genes were performed an enrichment analysis by ClueGO, including Gene Ontology and KEGG. The Enrichr database and the target microRNAs (miRNAs) of hub genes were predicted through the MiRTarBase via Enrichr. The common miRNAs were used to construct the miRNAs-mRNAs regulated network, and the miRNAs' function was evaluated by mirPath v3.0 software. Two hundred forty-seven targets of geniposide were identified in patients with COVID-19/AS comorbidity by observing the overlap between the genes modulated by geniposide, COVID-19, and AS. A protein-protein interaction network of geniposide in patients with COVID-19/AS was constructed, and 27 hub genes were identified. The results of enrichment analysis suggested that geniposide may be involved in regulating the OS via the FoxO signaling pathway. MiRNA-mRNA network revealed that hsa-miR-34a-5p may play an important role in the therapeutic mechanism of geniposide in COVID-19/AS patients. Our study found that geniposide represents a promising therapy for patients with COVID-19 and AS comorbidity. Furthermore, the target genes and miRNAs that we identified may aid the development of new treatment strategies against COVID-19/AS.

Nominating novel proteins for anxiety via integrating human brain proteomes and genome-wide association study

BACKGROUND

The underlying pathogenesis of anxiety remain elusive, making the pinpointing of potential therapeutic and diagnostic biomarkers for anxiety paramount to its efficient treatment.

METHODS

We undertook a proteome-wide association study (PWAS), fusing human brain proteomes from both discovery (ROS/MAP; N = 376) and validation cohorts (Banner; N = 152) with anxiety genome-wide association study (GWAS) summary statistics. Complementing this, we executed transcriptome-wide association studies (TWAS) leveraging human brain transcriptomic data from the Common Mind Consortium (CMC) to discern the confluence of genetic influences spanning both proteomic and transcriptomic levels. We further scrutinized significant genes through a suite of methodologies.

RESULTS

We discerned 14 genes instrumental in the genesis of anxiety through their specific cis-regulated brain protein abundance. Out of these, 6 were corroborated in the confirmatory PWAS, with 4 also showing associations with anxiety via their cis-regulated brain mRNA levels. A heightened confidence level was attributed to 5 genes (RAB27B, CCDC92, BTN2A1, TMEM106B, and DOC2A), taking into account corroborative evidence from both the confirmatory PWAS and TWAS, coupled with insights from mendelian randomization analysis and colocalization evaluations. A majority of the identified genes manifest in brain regions intricately linked to anxiety and predominantly partake in lysosomal metabolic processes.

LIMITATIONS

The limited scope of the brain proteome reference datasets, stemming from a relatively modest sample size, potentially curtails our grasp on the entire gamut of genetic effects.

CONCLUSION

The genes pinpointed in our research present a promising groundwork for crafting therapeutic interventions and diagnostic tools for anxiety.

Identification of potential therapeutic targets for skin cutaneous melanoma on the basic of transcriptomics

BACKGROUND

Advanced skin cutaneous melanoma (SKCM) is responsible for the majority of skin cancer-related deaths. Apart from the rare BRAF V600F mutation, which can be targeted with specific drugs, there are currently no other novel effective therapeutic targets.

METHODS

We used SMR analysis with cis-expressed quantitative trait locus (cis-eQTL) as the exposure variable and SKCM as the outcome variable to identify potential therapeutic targets for SKCM. Colocalization assays and HEIDI tests are used to test whether SKCM risk and gene expression are driven by common SNPs. Replication analysis further validated the findings, and we also constructed protein-protein interaction networks to explore the relationship between the identified genes and known SKCM targets. Drug prediction and molecular docking further validated the medicinal value of drug targets. Transcriptome differential analysis further validated that there were differences between normal tissues and SKCM for the selected targets.

RESULTS

We identified 13 genes significantly associated with the risk of SKCM, including five protective genes and eight harmful genes. The HEIDI test and co-localization analysis further indicates a causal association between genes (SOX4, MAFF) and SKCM, categorized as Class 1 evidence targets. The remaining 11 genes, except for HELZ2 show a moderately causal association with SKCM, categorized as Class 2 evidence targets. Target druggability predictions from DGIdb suggest that SOX4, MAFF, ACSF3, CDK10, SPG7, and TCF25 are likely to be future drug targets.

CONCLUSION

The study provides genetic evidence for targeting available drug genes for the treatment of SKCM.

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets

BACKGROUND

Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe.

METHODS

We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes.

RESULTS

Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR.

CONCLUSION

By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Exploring and clinical validation of prognostic significance and therapeutic implications of copper homeostasis-related gene dysregulation in acute myeloid leukemia

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML.

Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for sarcopenia

BACKGROUND

There are no effective pharmacological treatments for sarcopenia. We aim to identify potential therapeutic targets for sarcopenia by integrating various publicly available datasets.

METHODS

We integrated druggable genome data, cis-eQTL/cis-pQTL from human blood and skeletal muscle tissue, and GWAS summary data of sarcopenia-related traits to analyse the potential causal relationships between drug target genes and sarcopenia using the Mendelian Randomization (MR) method. Sensitivity analyses and Bayesian colocalization were employed to validate the causal relationships. We also assessed the side effects or additional indications of the identified drug targets using a phenome-wide MR (Phe-MR) approach and investigated actionable drugs for target genes using available databases.

RESULTS

MR analysis identified 17 druggable genes with potential causation to sarcopenia in human blood or skeletal muscle tissue. Six of them (HP, HLA-DRA, MAP 3K3, MFGE8, COL15A1, and AURKA) were further confirmed by Bayesian colocalization (PPH4 > 90%). The up-regulation of HP [higher ALM (beta: 0.012, 95% CI: 0.007-0.018, P = 1.2*10-5) and higher grip strength (OR: 0.96, 95% CI: 0.94-0.98, P = 4.2*10-5)], MAP 3K3 [higher ALM (beta: 0.24, 95% CI: 0.21-0.26, P = 1.8*10-94), higher grip strength (OR: 0.82, 95% CI: 0.75-0.90, P = 2.1*10-5), and faster walking pace (beta: 0.03, 95% CI: 0.02-0.05, P = 8.5*10-6)], and MFGE8 [higher ALM (muscle eQTL, beta: 0.09, 95% CI: 0.06-0.11, P = 6.1*10-13; blood pQTL, beta: 0.05, 95% CI: 0.03-0.07, P = 3.8*10-09)], as well as the down-regulation of HLA-DRA [lower ALM (beta: -0.09, 95% CI: -0.11 to -0.08, P = 5.4*10-36) and lower grip strength (OR: 1.13, 95% CI: 1.07-1.20, P = 1.8*10-5)] and COL15A1 [higher ALM (muscle eQTL, beta: -0.07, 95% CI: -0.10 to -0.04, P = 3.4*10-07; blood pQTL, beta: -0.05, 95% CI: -0.06 to -0.03, P = 1.6*10-07)], decreased the risk of sarcopenia. AURKA in blood (beta: -0.16, 95% CI: -0.22 to -0.09, P = 2.1*10-06) and skeletal muscle (beta: 0.03, 95% CI: 0.02 to 0.05, P = 5.3*10-05) tissues showed an inverse relationship with sarcopenia risk. The Phe-MR indicated that the six potential therapeutic targets for sarcopenia had no significant adverse effects. Drug repurposing analysis supported zinc supplementation and collagenase clostridium histolyticum might be potential therapeutics for sarcopenia by activating HP and inhibiting COL15A1, respectively.

CONCLUSIONS

Our research indicated MAP 3K3, MFGE8, COL15A1, HP, and HLA-DRA may serve as promising targets for sarcopenia, while the effectiveness of zinc supplementation and collagenase clostridium histolyticum for sarcopenia requires further validation.

Capture of circulating metastatic cancer cell clusters from lung cancer patients can reveal unique genomic profiles and potential anti-metastatic molecular targets: A proof-of-concept study

Metastasis remains the leading cause of cancer deaths worldwide and lung cancer, known for its highly metastatic progression, remains among the most lethal of malignancies. Lung cancer metastasis can selectively spread to multiple different organs, however the genetic and molecular drivers for this process are still poorly understood. Understanding the heterogeneous genomic profile of lung cancer metastases is considered key in identifying therapeutic targets that prevent its spread. Research has identified the key source for metastasis being clusters of cells rather than individual cancer cells. These clusters, known as metastatic cancer cell clusters (MCCCs) have been shown to be 100-fold more tumorigenic than individual cancer cells. Unfortunately, access to these primary drivers of metastases remains difficult and has limited our understanding of their molecular and genomic profiles. Strong evidence in the literature suggests that differentially regulated biological pathways in MCCCs can provide new therapeutic drug targets to help combat cancer metastases. In order to expand research into MCCCs and their role in metastasis, we demonstrate a novel, proof of principle technology, to capture MCCCs directly from patients' whole blood. Our platform can be readily tuned for different solid tumor types by combining a biomimicry-based margination effect coupled with immunoaffinity to isolate MCCCs. Adopting a selective capture approach based on overexpressed CD44 in MCCCs provides a methodology that preferentially isolates them from whole blood. Furthermore, we demonstrate a high capture efficiency of more than 90% when spiking MCCC-like model cell clusters into whole blood. Characterization of the captured MCCCs from lung cancer patients by immunofluorescence staining and genomic analyses, suggests highly differential morphologies and genomic profiles. This study lays the foundation to identify potential drug targets thus unlocking a new area of anti-metastatic therapeutics.

Bioinformatics reveals the potential mechanisms and biomarkers of necroptosis in neuroblastoma

Background

Neuroblastoma (NB) is a malignant tumor primarily found in children, presenting significant challenges in its development and prognosis. The role of necroptosis in the pathogenesis of NB has been acknowledged as crucial for treatment. This study aimed to investigate the key genes and functional pathways associated with necroptosis, as well as immune infiltration analysis, in NB. Furthermore, we aimed to evaluate the diagnostic significance of these genes for prognostic assessment and explore their potential immunological characteristics.

Methods

The NB dataset (GSE19274, GSE73517, and GSE85047) was obtained from the Gene Expression Omnibus (GEO) database, and genes associated with necroptosis were collected from GeneCards and previous literature. First, we conducted differential expression analysis and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We employed gene set enrichment analysis (GSEA) to identify overlapping enriched functional pathways from the NB dataset. In addition, we constructed a protein-protein interaction (PPI) network, predicting relevant microRNAs (miRNAs) and transcription factors (TFs), as well as their corresponding drug predictions. Furthermore, the diagnostic value was assessed using receiver operating characteristic (ROC) curves. Finally, an immune infiltration analysis was performed.

Results

We identified six necroptosis-related differentially expressed genes (NRDEGs) closely associated with necroptosis in NB. They were enriched in Tuberculosis, Apoptosis-multiple species, Salmonella infection, legionellosis, and platinum drug resistance. GSEA and PPI network analyses, along with mRNA-drug interaction network, revealed 38 potential drugs corresponding to BIRC2, CAMK2G, CASP3, and IL8. ROC curve analysis showed that in GSE19274, FLOT2 with area under the ROC curve (AUC) of 0.850 and DAPK1 with AUC of 0.789.

Conclusions

Our study elucidates the key genes and functional pathways associated with necroptosis in NB, offering valuable insights to enhance our comprehension of the pathogenesis of NB, and improve prognosis assessment.

Transcriptomics explores potential mechanisms for the development of Primary Sjogren's syndrome to diffuse large B-cell lymphoma in B cells

PURPOSE

Primary Sjogren's syndrome (pSS) is a prevalent autoimmune disease. The immune dysregulation it causes often leads to the development of diffuse large B-cell lymphoma (DLBCL) in clinical practice. However, how it contributes to these two disorders at the molecular level is not yet known. This study explored the potential molecular mechanisms associated with the differences between DLBCL and pSS.

PATIENTS AND METHODS

Gene expression matrices from discovery cohort 1, discovery cohort 2, and the validation cohort were downloaded from the GEO and TCGA databases. Weighted gene coexpression network analysis (WGCNA) was performed to identify the coexpression modules of DLBCL and pSS in discovery cohort 1 and obtain shared genes. GO and KEGG enrichment analyses and PPI network analysis were performed on the shared genes. Immune-related genes (IRGs) were intersected with shared genes to obtain common genes. Afterward, common genes were identified via machine learning methods. The immune infiltration analysis, miRNA-TF-hub gene regulatory chart, gene interactions of the hub genes, and gene‒drug target analysis were performed. Finally, STAT1 was identified as a possible essential gene by the above analysis, and immune infiltration and GSEA pathway analyses were performed in the high- and low-expression groups in discovery cohort 2. The diagnostic efficacy of the hub genes was assessed in the validation cohort, and clinical samples were collected for validation.

RESULTS

By WGCNA, one modular gene in each group was considered highly associated with the disease, and we obtained 28 shared genes. Enrichment analysis revealed shared genes involved in the viral response and regulation. We obtained four hub genes (ISG20, STAT1, TLR7, and RSAD2) via the algorithm. Hub genes and similar genes are primarily involved in regulating type I IFNs. The construction of a miRNA-TF-hub gene regulatory chart revealed that hsa-mir-155-5p, hsa-mir-146b-5p, hsa-mir-21-3p, and hsa-mir-126-3p play essential roles in both diseases. Hub genes were differentially expressed in B-cell memory according to immune infiltration analysis. Hub genes had a strong diagnostic effect on both diseases. STAT1 plays an essential role in immune cells in both diseases.

CONCLUSION

We identified hub susceptibility genes for DLBCL and pSS and identified hub genes and potential therapeutic targets that may act as biomarkers.

Personalized differential expression analysis in triple-negative breast cancer

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Integrative Bioinformatics Analysis for Targeting Hub Genes in Hepatocellular Carcinoma Treatment

Background

The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

Objective

This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

Methodology

Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

Results

The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

Conclusion

The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment's heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

Genome-wide meta-analyses of cross substance use disorders in European, African, and Latino ancestry populations

Genetic risks for substance use disorders (SUDs) are due to both SUD-specific and SUD-shared genes. We performed the largest multivariate analyses to date to search for SUD-shared genes using samples of European (EA), African (AA), and Latino (LA) ancestries. By focusing on variants having cross-SUD and cross-ancestry concordant effects, we identified 45 loci. Through gene-based analyses, gene mapping, and gene prioritization, we identified 250 SUD-shared genes. These genes are highly expressed in amygdala, cortex, hippocampus, hypothalamus, and thalamus, primarily in neuronal cells. Cross-SUD concordant variants explained ~ 50% of the heritability of each SUD in EA. The top 5% individuals having the highest polygenic scores were approximately twice as likely to have SUDs as others in EA and LA. Polygenic scores had higher predictability in females than in males in EA. Using real-world data, we identified five drugs targeting identified SUD-shared genes that may be repurposed to treat SUDs.

Integrating plasma proteomics with genome-wide association data to identify novel drug targets for inflammatory bowel disease

Inflammatory bowel disease (IBD) is a chronic disease that includes Crohn's disease (CD) and ulcerative colitis (UC). Although genome-wide association studies (GWASs) have identified many relevant genetic risk loci, the impact of these loci on protein abundance and their potential utility as clinical therapeutic targets remain uncertain. Therefore, this study aimed to investigate the pathogenesis of IBD and identify effective therapeutic targets through a comprehensive and integrated analysis. We systematically integrated GWAS data related to IBD, UC and CD (N = 25,305) by the study of de Lange KM with the human blood proteome (N = 7213) by the Atherosclerosis Risk in Communities (ARIC) study. Proteome-wide association study (PWAS), mendelian randomisation (MR) and Bayesian colocalisation analysis were used to identify proteins contributing to the risk of IBD. Integrative analysis revealed that genetic variations in IBD, UC and CD affected the abundance of five (ERAP2, RIPK2, TALDO1, CADM2 and RHOC), three (VSIR, HGFAC and CADM2) and two (MST1 and FLRT3) cis-regulated plasma proteins, respectively (P < 0.05). Among the proteins identified via Bayesian colocalisation analysis, CADM2 was found to be an important common protein between IBD and UC. A drug and five druggable target genes were identified from DGIdb after Bayesian colocalisation analysis. Our study's findings from genetic and proteomic approaches have identified compelling proteins that may serve as important leads for future functional studies and potential drug targets for IBD (UC and CD).

Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning

There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.

Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling

The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder, and phobias) are highly prevalent, often onset early, persist throughout life, and cause substantial global disability. Although distinct in their clinical presentations, they likely represent differential expressions of a dysregulated threat-response system. Here we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant ANX risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 of the 58 associated variants were replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism, and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism underlying ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.

Identifying novel potential drug targets for endometriosis via plasma proteome screening

Background

Endometriosis (EM) is a chronic painful condition that predominantly affects women of reproductive age. Currently, surgery or medication can only provide limited symptom relief. This study used a comprehensive genetic analytical approach to explore potential drug targets for EM in the plasma proteome.

Methods

In this study, 2,923 plasma proteins were selected as exposure and EM as outcome for two-sample Mendelian randomization (MR) analyses. The plasma proteomic data were derived from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), while the EM dataset from the FinnGen consortium R10 release data. Several sensitivity analyses were performed, including summary-data-based MR (SMR) analyses, heterogeneity in dependent instruments (HEIDI) test, reverse MR analyses, steiger detection test, and bayesian co-localization analyses. Furthermore, proteome-wide association study (PWAS) and single-cell transcriptomic analyses were also conducted to validate the findings.

Results

Six significant (p < 3.06 × 10-5) plasma protein-EM pairs were identified by MR analyses. These included EPHB4 (OR = 1.40, 95% CI: 1.20 - 1.63), FSHB (OR = 3.91, 95% CI: 3.13 - 4.87), RSPO3 (OR = 1.60, 95% CI: 1.38 - 1.86), SEZ6L2 (OR = 1.44, 95% CI: 1.23 - 1.68) and WASHC3 (OR = 2.00, 95% CI: 1.54 - 2.59) were identified as risk factors, whereas KDR (OR = 0.80, 95% CI: 0.75 - 0.90) was found to be a protective factor. All six plasma proteins passed the SMR test (P < 8.33 × 10-3), but only four plasma proteins passed the HEIDI heterogeneity test (PHEIDI > 0.05), namely FSHB, RSPO3, SEZ6L2 and EPHB4. These four proteins showed strong evidence of co-localization (PPH4 > 0.7). In particular, RSPO3 and EPHB4 were replicated in the validated PWAS. Single-cell analyses revealed high expression of SEZ6L2 and EPHB4 in stromal and epithelial cells within EM lesions, while RSPO3 exhibited elevated expression in stromal cells and fibroblasts.

Conclusion

Our study identified FSHB, RSPO3, SEZ6L2, and EPHB4 as potential drug targets for EM and highlighted the critical role of stromal and epithelial cells in disease development. These findings provide new insights into the diagnosis and treatment of EM.

Integrating molecular pathway with genome-wide association data for causality identification in breast cancer

OBJECTIVE

The study purpose was to explore the causal association between pyruvate metabolism and breast cancer (BC), as well as the molecular role of key metabolic genes, by using bioinformatics and Mendelian randomization (MR) analysis.

METHODS

We retrieved and examined diverse datasets from the GEO database to ascertain differentially acting genes (DAGs) in BC via differential expression analysis. Following this, we performed functional and pathway enrichment analyses to ascertain noteworthy molecular functions and metabolic pathways in BC. Employing MR analysis, we established a causal association between pyruvate metabolism and the susceptibility to BC. Additionally, utilizing the DGIdb database, we identified potential targeted medications that act on genes implicated in the pyruvate metabolic pathway and formulated a competing endogenous RNA (ceRNA) regulatory network in BC.

RESULTS

We collected the datasets GSE54002, GSE70947, and GSE22820, and identified a total of 1127 DEGs between the BC and NC groups. GO and KEGG enrichment analysis showed that the molecular functions of these DEGs mainly included mitotic nuclear division, extracellular matrix, signaling receptor activator activity, etc. Metabolic pathways were mainly concentrated in PI3K-Akt signaling pathway, Cytokine-cytokine receptor binding and Pyruvate, Tyrosine, Propanoate and Phenylalanine metabolism, etc. In addition, MR analysis demonstrated a causal relationship between pyruvate metabolism and BC risk. Finally, we constructed a regulatory network between pathway genes (ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C) and targeted drugs, as well as a ceRNA (lncRNA-miRNA-mRNA) regulatory network for BC, further revealing their interactions.

CONCLUSIONS

Our research revealed a causal association between pyruvate metabolism and BC risk, found that ADH1B, ACSS2, ACACB, ADH1A, ALDH2, and ADH1C takes place an important part in the development of BC in the molecular mechanisms related to pyruvate metabolism, and identified some potential targeted small molecule drugs.

Proteomics couples electrical remodelling to inflammation in a murine model of heart failure with sinus node dysfunction

AIMS

In patients with heart failure (HF), concomitant sinus node dysfunction (SND) is an important predictor of mortality, yet its molecular underpinnings are poorly understood. Using proteomics, this study aimed to dissect the protein and phosphorylation remodelling within the sinus node in an animal model of HF with concurrent SND.

METHODS AND RESULTS

We acquired deep sinus node proteomes and phosphoproteomes in mice with heart failure and SND and report extensive remodelling. Intersecting the measured (phospho)proteome changes with human genomics pharmacovigilance data, highlighted downregulated proteins involved in electrical activity such as the pacemaker ion channel, Hcn4. We confirmed the importance of ion channel downregulation for sinus node physiology using computer modelling. Guided by the proteomics data, we hypothesized that an inflammatory response may drive the electrophysiological remodeling underlying SND in heart failure. In support of this, experimentally induced inflammation downregulated Hcn4 and slowed pacemaking in the isolated sinus node. From the proteomics data we identified proinflammatory cytokine-like protein galectin-3 as a potential target to mitigate the effect. Indeed, in vivo suppression of galectin-3 in the animal model of heart failure prevented SND.

CONCLUSION

Collectively, we outline the protein and phosphorylation remodeling of SND in heart failure, we highlight a role for inflammation in electrophysiological remodelling of the sinus node, and we present galectin-3 signalling as a target to ameliorate SND in heart failure.

Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data

Genome-wide association studies (GWASs) have identified risk loci for suicide attempt (SA), but deciphering how they confer risk for SA remains largely unknown. This study aims to identify the key proteins and gain insights into SA pathogenesis. We integrated data from the brain proteome (N = 376) and blood proteome (N = 35,559) and combined it with the largest SA GWAS summary statistics to date (N = 518,612). A comprehensive set of methods was employed, including Mendelian randomization (MR), Steiger filtering, Bayesian colocalization, proteome‑wide association studies (PWAS), transcript-levels, cell-type specificity, correlation, and protein-protein interaction (PPI) network analysis. Validation was performed using other protein datasets and the SA dataset from FinnGen study. We identified ten proteins (GLRX5, GMPPB, B3GALTL, FUCA2, TTLL12, ADCK1, MMAA, HIBADH, ACP1, DOC2A) associated with SA in brain proteomics. GLRX5, GMPPB, and FUCA2 showed strong colocalization evidence and were supported by PWAS and transcript-level analysis, and were predominantly expressed in glutamatergic neuronal cells. In blood proteomics, one significant protein (PEAR1) and three near-significant proteins (NDE1, EVA1C, B4GALT2) were identified, but lacked colocalization evidence. Moreover, despite the limited correlation between the same protein in brain and blood, the PPI network analysis provided new insights into the interaction between brain and blood in SA. Furthermore, GLRX5 was associated with the GSTP1, the target of Clozapine. The comprehensive analysis provides strong evidence supporting a causal association between three genetically determined brain proteins (GLRX5, GMPPB, and FUCA2) with SA. These findings offer valuable insights into SA's underlying mechanisms and potential therapeutic approaches.