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.

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

cCMP and cUMP phosphodiesterases in viral infections.

In bacteria, cCMP and cUMP have a key role in defense against infection with bacterial viruses. Bacteriophages encode phosphodiesterases (PDEs; 'nucleases'; Apyc1), which cleave cCMP/cUMP, counteracting this defense. We propose that PDEs are of broader biological relevance, including cCMP/cUMP-cleaving PDEs of eukaryotic viruses, which may constitute new drug targets.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery.

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension.

Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.

Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites.

Metabolite levels measured in the human population are endophenotypes for biological processes. We combined sequencing data for 3,924 (whole-exome sequencing, WES, discovery) and 2,805 (whole-genome sequencing, WGS, replication) donors from a prospective cohort of blood donors in England. We used multiple approaches to select and aggregate rare genetic variants (minor allele frequency [MAF] < 0.1%) in protein-coding regions and tested their associations with 995 metabolites measured in plasma by using ultra-high-performance liquid chromatography-tandem mass spectrometry. We identified 40 novel associations implicating rare coding variants (27 genes and 38 metabolites), of which 28 (15 genes and 28 metabolites) were replicated. We developed algorithms to prioritize putative driver variants at each locus and used mediation and Mendelian randomization analyses to test directionality at associations of metabolite and protein levels at the ACY1 locus. Overall, 66% of reported associations implicate gene targets of approved drugs or bioactive drug-like compounds, contributing to drug targets' validating efforts.

Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction.

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

A CcdB toxin-derived peptide acts as a broad-spectrum antibacterial therapeutic in infected mice.

The bacterial toxin CcdB (Controller of Cell death or division B) targets DNA Gyrase, an essential bacterial topoisomerase, which is also the molecular target for fluoroquinolones. Here, we present a short cell-penetrating 24-mer peptide, CP1-WT, derived from the Gyrase-binding region of CcdB and examine its effect on growth of Escherichia coli, Salmonella Typhimurium, Staphylococcus aureus and a carbapenem- and tigecycline-resistant strain of Acinetobacter baumannii in both axenic cultures and mouse models of infection. The CP1-WT peptide shows significant improvement over ciprofloxacin in terms of its in vivo therapeutic efficacy in treating established infections of S. Typhimurium, S. aureus and A. baumannii. The molecular mechanism likely involves inhibition of Gyrase or Topoisomerase IV, depending on the strain used. The study validates the CcdB binding site on bacterial DNA Gyrase as a viable and alternative target to the fluoroquinolone binding site.

Are secondary effects of bisphosphonates on the vascular system of bone contributing to increased risk for atypical femoral fractures in osteoporosis?

Osteoporosis (OP) is a bone disease which affects a number of post-menopausal females and puts many at risk for fractures. A large number of patients are taking bisphosphonates (BPs) to treat their OP and a rare complication is the development of atypical femoral fractures (AFF). No real explanations for the mechanisms underlying the basis for development of where AFF develop while on BPs has emerged. The present hypothesis will discuss the possibility that part of the risk for an AFF is a secondary effect of BPs on a subset of vascular cells in a genetically at-risk population, leading to localized deregulation of the endothelial cell (EC)-bone cell-matrix units in nutrient channels/canals of the femur and increased risk for AFF. This concept of targeting ECs is consistent with location of AFF in the femur, the bilateral risk for occurrence of AFF, and the requirement for long term exposure to the drugs.

Functional Impact of Protein-RNA Variation in Clinical Cancer Analyses.

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms, and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this article we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium, which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Innovative pathological network-based multitarget approaches for Alzheimer's disease treatment.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and is a major health threat globally. Its prevalence is forecasted to exponentially increase during the next 30 years due to the global aging population. Currently, approved drugs are merely symptomatic, being ineffective in delaying or blocking the relentless disease advance. Intensive AD research describes this disease as a highly complex multifactorial disease. Disclosure of novel pathological pathways and their interconnections has had a major impact on medicinal chemistry drug development for AD over the last two decades. The complex network of pathological events involved in the onset of the disease has prompted the development of multitarget drugs. These chemical entities combine pharmacological activities toward two or more drug targets of interest. These multitarget-directed ligands are proposed to modify different nodes in the pathological network aiming to delay or even stop disease progression. Here, we review the multitarget drug development strategy for AD during the last decade.

Exerkines: Benign adaptation for exercise and benefits for non-alcoholic fatty liver disease.

Exercise has multiple beneficial effects on human metabolic health and is regarded as a "polypill" for various diseases. At present, the lack of physical activity usually causes an epidemic of chronic metabolic syndromes, including obesity, cardiovascular diseases, and non-alcoholic fatty liver disease (NAFLD). Remarkably, NAFLD is emerging as a serious public health issue and is associated with the development of cirrhosis and hepatocellular carcinoma. Unfortunately, specific drug therapies for NAFLD and its more severe form, non-alcoholic steatohepatitis (NASH), are currently unavailable. Lifestyle modification is the foundation of treatment recommendations for NAFLD and NASH, especially for exercise. There are under-appreciated organs that crosstalk to the liver during exercise such as muscle-liver crosstalk. Previous studies have reported that certain exerkines, such as FGF21, GDF15, irisin, and adiponectin, are beneficial for liver metabolism and have the potential to be targeted for NAFLD treatment. In addition, some of exerkines can be modified for the new proteins and get enhanced functions, like IL-6/IC7Fc. Another importance of exercise is the physiological adaptation that combats metabolic diseases. Thus, this review aims to summarize the known exerkines and utilize a multi-omics mining tool to identify more exerkines for the future research. Overall, understanding the mechanisms by which exercise-induced exerkines exert their beneficial effects on metabolic health holds promise for the development of novel therapeutic strategies for NAFLD and related diseases.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Dissecting the shared genetic landscape of anxiety, depression, and schizophrenia.

BACKGROUND: Numerous studies highlight the genetic underpinnings of mental disorders comorbidity, particularly in anxiety, depression, and schizophrenia. However, their shared genetic loci are not well understood. Our study employs Mendelian randomization (MR) and colocalization analyses, alongside multi-omics data, to uncover potential genetic targets for these conditions, thereby informing therapeutic and drug development strategies. METHODS: We utilized the Consortium for Linkage Disequilibrium Score Regression (LDSC) and Mendelian Randomization (MR) analysis to investigate genetic correlations among anxiety, depression, and schizophrenia. Utilizing GTEx V8 eQTL and deCODE Genetics pQTL data, we performed a three-step summary-data-based Mendelian randomization (SMR) and protein-protein interaction analysis. This helped assess causal and comorbid loci for these disorders and determine if identified loci share coincidental variations with psychiatric diseases. Additionally, phenome-wide association studies, drug prediction, and molecular docking validated potential drug targets. RESULTS: We found genetic correlations between anxiety, depression, and schizophrenia, and under a meta-analysis of MR from multiple databases, the causal relationships among these disorders are supported. Based on this, three-step SMR and colocalization analyses identified ITIH3 and CCS as being related to the risk of developing depression, while CTSS and DNPH1 are related to the onset of schizophrenia. BTN3A1, PSMB4, and TIMP4 were identified as comorbidity loci for both disorders. Molecules that could not be determined through colocalization analysis were also presented. Drug prediction and molecular docking showed that some drugs and proteins have good binding affinity and available structural data. CONCLUSIONS: Our study indicates genetic correlations and shared risk loci between anxiety, depression, and schizophrenia. These findings offer insights into the underlying mechanisms of their comorbidities and aid in drug development.

Integrated systems biology approach identifies gene targets for endothelial dysfunction.

Endothelial dysfunction (ED) is critical in the development and progression of cardiovascular (CV) disorders, yet effective therapeutic targets for ED remain elusive due to limited understanding of its underlying molecular mechanisms. To address this gap, we employed a systems biology approach to identify potential targets for ED. Our study combined multi omics data integration, with siRNA screening, high content imaging and network analysis to prioritise key ED genes and identify a pro- and anti-ED network. We found 26 genes that, upon silencing, exacerbated the ED phenotypes tested, and network propagation identified a pro-ED network enriched in functions associated with inflammatory responses. Conversely, 31 genes ameliorated ED phenotypes, pointing to potential ED targets, and the respective anti-ED network was enriched in hypoxia, angiogenesis and cancer-related processes. An independent screen with 17 drugs found general agreement with the trends from our siRNA screen and further highlighted DUSP1, IL6 and CCL2 as potential candidates for targeting ED. Overall, our results demonstrate the potential of integrated system biology approaches in discovering disease-specific candidate drug targets for endothelial dysfunction.

Roles of alcohol dehydrogenase 1 in the biological activities of Candida albicans.

Candida albicans stands as the foremost prevalent human commensal pathogen and a significant contributor to nosocomial fungal infections. In the metabolism of C. albicans, alcohol dehydrogenase 1 (Adh1) is one of the important enzymes that converts acetaldehyde produced by pyruvate decarboxylation into ethanol at the end of glycolysis. Leveraging the foundational processes of alcoholic fermentation, Adh1 plays an active role in multiple biological phenomena, including biofilm formation, interactions between different species, the development of drug resistance, and the potential initiation of gastrointestinal cancer. Additionally, Adh1 within C. albicans has demonstrated associations with regulating the cell cycle, stress responses, and various intracellular states. Furthermore, Adh1 is extracellularly localized on the cell wall surface, where it plays roles in processes such as tissue invasion and host immune responses. Drawing from an analysis of ADH1 gene structure, expression patterns, and fundamental functions, this review elucidates the intricate connections between Adh1 and various biological processes within C. albicans, underscoring its potential implications for the prevention, diagnosis, and treatment of candidiasis.

Comparative interactome mapping of Tau-protein in classical and rapidly progressive Alzheimer's disease identifies subtype-specific pathways.

AIMS: Tau is a key player in Alzheimer's disease (AD) and other Tauopathies. Tau pathology in the brain directly correlates with neurodegeneration in AD. The recent identification of a rapid variant of AD demands an urgent need to uncover underlying mechanisms leading to differential progression in AD. Accordingly, we aimed to dissect the underlying differential mechanisms of toxicity associated with the Tau protein in AD subtypes and to find out subtype-dependent biomarkers and therapeutic targets. METHODS: To identify and characterise subtype-specific Tau-associated mechanisms of pathology, we performed comparative interactome mapping of Tau protein in classical AD (cAD) and rapidly progressive AD (rpAD) cases using co-immunoprecipitation coupled with quantitative mass spectrometry. The mass spectrometry data were extensively analysed using several bioinformatics approaches. RESULTS: The comparative interactome mapping of Tau protein revealed distinct and unique interactors (DPYSL4, ARHGEF2, TUBA4A and UQCRC2) in subtypes of AD. Interestingly, an analysis of the Tau-interacting proteins indicated enrichment of mitochondrial organisation processes, including negative regulation of mitochondrion organisation, mitochondrial outer membrane permeabilisation involved in programmed cell death, regulation of autophagy of mitochondrion and necroptotic processes, specifically in the rpAD interactome. While, in cAD, the top enriched processes were related to oxidation-reduction process, transport and monocarboxylic acid metabolism. CONCLUSIONS: Overall, our results provide a comprehensive map of Tau-interacting protein networks in a subtype-dependent manner and shed light on differential functions/pathways in AD subtypes. This comprehensive map of the Tau-interactome has provided subsets of disease-related proteins that can serve as novel biomarkers/biomarker panels and new drug targets.