Identification Of Potential Drug Targets Research Articles

Identification of potential drug targets for insomnia by Mendelian randomization analysis based on plasma proteomics.

Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia. In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf-PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia. Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved.

Glucagon-like peptide (GLP)-1 regulation of lipid and lipoprotein metabolism

Abstract Metabolic health is highly dependent on intestinal and hepatic handling of dietary and endogenous lipids and lipoproteins. Disorders of lipid and lipoprotein metabolism are commonly observed in patients with insulin resistant states such as obesity, metabolic syndrome, and type 2 diabetes. Evidence from both animal models and human studies indicates that a major underlying factor in metabolic or diabetic dyslipidemia is the overproduction of hepatic and intestinal apolipoprotein (apo)B-containing lipoprotein particles. These particles are catabolized down into highly proatherogenic remnants, which can be taken up into the arterial intima and promote plaque development. Several gut-derived peptides have been identified as key regulators of energy metabolism; one such peptide is the incretin hormone glucagon-like peptide (GLP)-1. Our laboratory has previously demonstrated that GLP-1 can signal both centrally and peripherally to reduce postprandial and fasting lipoprotein secretion. Moreover, we have demonstrated that GLP-1 receptor (GLP-1R) agonists can ameliorate diet-induced dyslipidemia. Recently, we published evidence for a novel vagal neuroendocrine signalling pathway by which native GLP-1 may exert its anti-lipemic effects. Furthermore, we demonstrated a novel role for other gut-derived peptides in regulating intestinal lipoprotein production. Overall, ample evidence supports a key role for GLP-1R on the portal vein afferent neurons and nodose ganglion in modulating intestinal fat absorption and lipoprotein production and identifies other gut-derived peptides as novel regulators of postprandial lipemia. Insights from these data may support identification of potential drug targets and the development of new therapeutics targeting treatment of diabetic dyslipidemia.

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study.

Allergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one-fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases. We employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome-Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA etal. (nCase=180,129, nControl=180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein-protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases. At Bonferroni significance (p<3.25×10-5), the Mendelian randomization analysis revealed 11 significantly associated protein-allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76-0.88), 0.74 (0.66-0.82), 0.49 (0.45-0.55), and 0.81 (0.75-0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03-1.05), 1.25 (1.18-1.34), 1.48 (1.25-1.75), 1.14 (1.11-1.18), 1.09 (1.05-1.12), 1.96 (1.56-2.47), and 1.05 (1.03-1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf-PPH4=0.819) and TNFAIP3 (coloc.abf-PPH4=0.930) share the same variant associated with allergic diseases. Our study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.

Advancing Breast Cancer Drug Delivery: The Transformative Potential of Bioinformatics and Artificial Intelligence

Abstract: Breast cancer remains a significant global health challenge, necessitating innovative approaches to improve treatment efficacy while minimizing side effects. This review explores the promising advancements in breast cancer drug delivery driven by the transformative potential of bioinformatics and Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate genomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers. The integration of multi-omics data facilitates a comprehensive understanding of the disease, guiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in biomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment. AI, particularly machine learning and deep learning, has revolutionized breast cancer research. Machine learning models empower accurate diagnosis through image analysis, improve survival prediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural networks, enable precise tumor detection and classification from medical imaging data, notably mammograms and MRI scans. Additionally, natural language processing techniques facilitate the mining of vast scientific literature, uncovering hidden insights and identifying potential drug targets. Network-based approaches integrated with AI algorithms facilitate the identification of central proteins as promising drug targets within complex biological networks. This review also examines AIoptimized nanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded nanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery, offering promising solutions to overcome the challenges of conventional drug delivery.

Identification of gene variation feature for targeted therapy of non-small cell lung cancer through combined method of DNA and RNA sequencing

Next generation sequencing (NGS) is typically used to reveal tumor gene variation feature for targeted therapy of various types of human cancers, including non-small cell lung cancer (NSCLC). Here, we report the role and potential applicable value of combining DNA and RNA sequencing in gene variation detection in NSCLC. 386 NSCLC patients with stage II-IV were enrolled and detected using NGS sequencing of DNA and RNA panels that covered all well-documented target driver genes from the Chinese Society of Clinical Oncology (CSCO). The rate of epidermal growth factor receptor (EGFR) single nucleotide variation (SNV)/indel, mesenchymal-epithelial transition factor (MET) copy number variation (CNV) and anaplastic lymphoma kinase (ALK) fusion were 52.1%, 4.1% and 6.0% in the NSCLC cohort. The landscapes of SNV/indel, CNV and gene fusion in the cohort were depicted as well. Meanwhile, we assessed detection efficacy of DNA and RNA sequencing in gene fusion. Detected number and types of gene fusion using the RNA sequencing were better than those using the DNA sequencing. Gene fusion with intergenic region was only detected by DNA sequencing and MET exon 14 skipping (METΔex14) was more easily identified by RNA sequencing. Finally, we investigated clinical correlations of SNV/indel/CNV/fusion with clinicopathologic features in the NSCLC cohort. Taken together, RNA sequencing significantly complements deficiency of DNA sequencing for gene fusion, which cooperatively presents comprehensive and reliable gene variation features and facilitate the identification of potential drug targets for NSCLC patients.

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.

Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies. Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy. The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC. Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

Prolyl isomerase Pin1 promotes autophagy and cancer cell viability through activating FoxO3 signalling.

Pin1-directed prolyl isomerization is a central common oncogenic mechanism to drive tumorigenic processes. However, the role of Pin1 in cellular autophagy is still poorly understood. Here we report that pharmacological inhibition of Pin1 decreased the formation of autophagosome/autolysosomes upon nutrient starvation. Inhibition of Pin1 reduced, whereas forced expression of Pin1 increased, the level of LC3 and viability of U2OS and PANC-1 cells. Pin1 could augment the accumulation of LC3 upon chloroquine treatment, while chloroquine also disturbed its function on cell viability. RNA-Seq and qPCR identified altered autophagic pathway upon Pin1 silencing. Mechanistically, FoxO3 was identified critical for Pin1-mediated autophagy. Knockdown of FoxO3 could rescue the changes of LC3 level and cellular viability caused by Pin1 overexpression. In xenograft mouse model, Pin1 reduced the sensitivity of PANC-1 to chloroquine while FoxO3 silencing could inhibit Pin1's function. Moreover, Pin1 could bind FoxO3 via its pS284-P motif, reduce its phosphorylation at T32, facilitate its nuclear retention, and therefore increased its transcriptional activity. S284A mutation of FoxO3 interfered with its T32 phosphorylation, reduced its nuclear localization and disrupted its function to support cell viability upon nutrient starvation. Furthermore, the protein level of Pin1 positively correlated with FoxO3 nuclear localization and LC3 level in pancreatic adenocarcinoma and osteosarcoma samples. Together, this study highlights an important role for Pin1-FoxO3 axis in regulating autophagy and cancer cell viability. Intervening in the Pin1-FoxO3 interaction would serve as an effective therapeutic strategy and the pS284-P motif of FoxO3 provides a potential target for drug design.

TRIF-dependent signaling and its role in liver diseases.

TIR domain-containing adaptor inducing IFN-β (TRIF) is a crucial adaptor molecule downstream of toll-like receptors 3 (TLR3) and 4 (TLR4). TRIF directly binds to TLR3 through its TIR domain, while it associates with TLR4 indirectly through the bridge adaptor molecule TRIF-related adaptor molecule (TRAM). TRIF plays a pivotal role in regulating interferon beta 1 (IFN-β) response, nuclear factor kappa B (NF-κB) signaling, apoptosis, and necroptosis signaling mediated by TLR3 and TLR4. It accomplishes these by recruiting and activating various kinases or transcription factors via its distinct domains. In this review, we comprehensively summarize the TRIF-dependent signaling pathways mediated by TLR3 and TLR4, elucidating key target molecules and downstream pathways. Furthermore, we provide an overview of TRIF's impact on several liver disorders, including drug-induced liver injury, ischemia-reperfusion liver injury, autoimmune hepatitis, viral hepatitis, alcohol-associated liver disease (ALD), metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). We also explore its effects on liver steatosis, inflammation, fibrosis, and carcinogenesis. A comprehensive understanding of the TRIF-dependent signaling pathways, as well as the intricate relationship between TRIF and liver diseases, can facilitate the identification of potential drug targets and the development of novel and effective therapeutics against hepatic disorders.

All‐in‐Human Target Discovery in Alzheimer’s Disease and Resilient Brains

AbstractBackgroundAlzheimer’s disease (AD) is characterized by neuropathological hallmarks including amyloid plaques and tau tangles and neurodegeneration in the frontal cortex. While there is a strong association of tau pathology with neurodegeneration and cognitive decline, around 30% of amyloid plaque‐carrying seniors remain cognitively healthy. We hypothesize that protective cellular mechanisms upstream of tau phosphorylation and neurodegeneration may underlie this resilience.MethodWe applied high resolution spatial transcriptomics and single nuclei RNA sequencing on postmortem frontal lobes from 10 demented and 10 non‐demented individuals with comparable degrees of amyloid pathology. We integrated the generated transcriptomics data with proteomics, genomics and drug action databases and interpret cellular transcriptome responses to pathology in the context of human specific protein‐protein interaction networks.ResultUsing spatial and single nuclei transcriptomics, we identify specific transcriptomic signatures and regulatory networks in cellular microenvironments around amyloid plaques and pTau pathology niches. Our results suggest that specific neuronal and glial subpopulations carry enriched vulnerability towards amyloid plaque and pTau depositions. We further find that certain cell death signaling pathways related to neurodegeneration are overrepresented in the context of pTau pathology. Finally, our multi‐omics database integration approach has led to the identification of potential drug targets for therapeutics addressing cellular mechanisms of neuroprotection.ConclusionSpatial transcriptomics from human brain tissue is a powerful approach to reveal cellular mechanisms of pathogenic lesions and enables us to identify neuroprotective mechanisms that may prevent the development of tauopathy and ultimately neuronal loss. The combination of single cell and spatial transcriptomics together with protein‐protein interaction networks provides a promising approach for the identification of novel therapeutic targets for AD.

Lipoprotein lipids and apolipoproteins in primary immune thrombocytopenia: Results from a clinical characteristics and causal relationship verification, potential drug target identification by Mendelian randomization analyses.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disease. Cellular and systemic lipid metabolism plays a significant role in the regulation of immune cell activities. However, the role of lipoprotein lipids and apolipoproteins in ITP remains elusive. The automatic biochemistry analyser was used to measure the levels of serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoA-I), apoB, apoE and lipoprotein a [LP(a)]. Genetic variants strongly associated with circulating lipoprotein lipids and apolipoproteins (LDL-C, apoB, TG, HDL-C and apoA-I) were extracted to perform Mendelian randomization (MR) analyses. Finally, drug-target MR and passive ITP mice model was used to investigate the potential druggable targets of ITP. Levels of HDL-C, apoA-I, decreased and LP(a) increased in ITP patients compared with healthy controls. Low HDL-C was causally associated with ITP susceptibility. Through drug-target MR and animal modelling, ABCA1 was identified as a potential target to design drugs for ITP. Our study found that lipid metabolism is related to ITP. The causative association between HDL-C and the risk of ITP was also established. The study provided new evidence of the aetiology of ITP. ABCA1 might be a potential drug target for ITP.

Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis.

Aging is a growing problem worldwide, and the prevalence and mortality of arterial and venous thromboembolism (VTE) are higher in the elderly than in the young population. To address this issue, various anticoagulants have been used. However, no evidence can confirm that antithrombotic agents are suitable for the elderly. Therefore, this study aims to investigate the platelet proteome of aged mice and identify antithrombotic drug targets specific to the elderly. Based on the proteome analysis of platelets from aged mice, 308 increased or decreased proteins were identified. Among these proteins, three targets were selected as potential antithrombotic drug targets. These targets are membrane proteins or related to platelet function and include beta-2-glycoprotein 1 (β2GP1, ApolipoproteinH (ApoH)), alpha-1-acid glycoprotein2 (AGP2, Orosomucoid-2 (Orm2)), and Ras-related protein (Rab11a).

Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study

IntroductionRheumatoid arthritis (RA) is a chronic inflammatory illness that mostly affects the joints of the hands and feet and can reduce life expectancy by an average of 3 to 10 years. Although tremendous progress has been achieved in the treatment of RA, a large minority of patients continue to respond poorly to existing medications, owing in part to a lack of appropriate therapeutic targets.MethodsTo find therapeutic targets for RA, a Mendelian randomization (MR) was performed. Cis-expression quantitative trait loci (cis-eQTL, exposure) data were obtained from the eQTLGen Consortium (sample size 31,684). Summary statistics for RA (outcome) were obtained from two largest independent cohorts: sample sizes of 97,173 (22,350 cases and 74,823 controls) and 269,377 (8279 cases and 261,098), respectively. Colocalisation analysis was used to test whether RA risk and gene expression were driven by common SNPs. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets.ResultsSeven drug targets were significant in both cohorts in MR analysis and supported by localization. PheWAS at the gene level showed only ATP2A1 associated with other traits. These genes are strongly associated with immune function in terms of biological significance. Molecular docking showed excellent binding for drugs and proteins with available structural data.ConclusionThis study identifies seven potential drug targets for RA. Drugs designed to target these genes have a higher chance of success in clinical trials and is expected to help prioritise RA drug development and save on drug development costs.

The Role of Deep Learning in Pharma: Revolutionizing Drug Discovery and Development.

- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs.&#x0D; Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.

COVID Variants, Villain and Victory: A Bioinformatics Perspective.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.

Otargen: GraphQL-based R Package for Tidy Data Accessing and Processing From Open Targets Genetics.

Open Target Genetics is a comprehensive resource portal that offers variant-centric statistical evidence, enabling the prioritization of causal variants and the identification of potential drug targets. The portal uses GraphQL technology for efficient data query and provides endpoints for programmatic access for R and Python users. However, leveraging GraphQL for data retrieval can be challenging, time-consuming, and repetitive, requiring familiarity with the GraphQL query language and processing outputs in nested JSON(JavaScript Object Notation) format into tidy data tables. Therefore, developing open-source tools are required to simplify data retrieval processes to integrate valuable genetic information into data-driven target discovery pipelines seamlessly. otargen is an open-source R package designed to make data retrieval and analysis from the Open Target Genetics portal as simple as possible for R users. The package offers a suite of functions covering all query types, allowing streamlined data access in a tidy table format. By executing only a single line of code, theotargen users avoid the repetitive scripting of complex GraphQL queries, including the post-processing steps. In addition, otargen contains convenient plotting functions to visualize and gain insights from complex data tables returned by several key functions. otargen is available at https://amirfeizi.github.io/otargen/.

Matrix stiffness-induced α-tubulin acetylation is required for skin fibrosis formation through activation of Yes-associated protein.

Skin fibrosis, a pathological process featured by fibroblast activation and extracellular matrix (ECM) deposition, makes a significant contribution to morbidity. Studies have identified biomechanics as the central element in the complex network of fibrogenesis that drives the profibrotic feedback loop. In this study, we found that the acetylation of α-tubulin at lysine 40 (K40) was augmented in fibrotic skin tissues. Further analysis showed that α-tubulin acetylation is required for fibroblast activation, including contraction, migration, and ECM deposition. More importantly, we revealed that biomechanics-induced upregulation of K40 acetylation promotes fibrosis by mediating mechanosensitive Yes-associated protein S127 dephosphorylation and its cytoplasm nucleus shuttle. Furthermore, we demonstrated that the knockdown of α-tubulin acetyltransferase 1 could rescue the K40 acetylation upregulation caused by increased matrix rigidity and ameliorate skin fibrosis both in vivo and in vitro. Herein, we highlight the critical role of α-tubulin acetylation in matrix stiffness-induced skin fibrosis and clarify a possible molecular mechanism. Our research suggests α-tubulin acetylation as a potential target for drug design and therapeutic intervention.

Venlafaxine's effect on resilience to stress is associated with a shift in the balance between glucose and fatty acid utilization.

Brain metabolism is a fundamental process involved in the proper development of the central nervous system and in the maintenance of the main higher functions in humans. As consequence, energy metabolism imbalance has been commonly associated to several mental disorders, including depression. Here, by employing a metabolomic approach, we aimed to establish if differences in energy metabolite concentration may underlie the vulnerability and resilience in an animal model of mood disorder named chronic mild stress (CMS) paradigm. In addition, we have investigated the possibility that modulation of metabolite concentration may represent a pharmacological target for depression by testing whether repeated treatment with the antidepressant venlafaxine may normalize the pathological phenotype by acting at metabolic level. The analyses were conducted in the ventral hippocampus (vHip) for its key role in the modulation of anhedonia, a core symptom of patients affected by depression. Interestingly, we showed that a shift from glycolysis to beta oxidation seems to be responsible for the vulnerability to chronic stress and that vHip metabolism contributes to the ability of the antidepressant venlafaxine to normalize the pathological phenotype, as shown by the reversal of the changes observed in specific metabolites. These findings may provide novel perspectives on metabolic changes that could serve as diagnostic markers and preventive strategies for the early detection and treatment of depression as well as for the identification of potential drug targets.

Identification of potential drug targets for varicose veins: a Mendelian randomization analysis.

Varicose veins are a common chronic disease that creates a significant economic burden on the healthcare system. Current treatment options, including pharmacological treatments, are not always effective, and there is a need for more targeted therapies. A Mendelian randomization (MR) method uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, and it has been successful in identifying therapeutic targets in other diseases. However, few studies have used MR to explore potential protein drug targets for varicose veins. To identify potential drug targets for varicose veins of lower extremities, we undertook a comprehensive screen of plasma protein with a two-sample MR method. We used recently reported cis-variants as genetic instruments of 2,004 plasma proteins, then applied MR to a recent meta-analysis of genome-wide association study on varicose veins (22,037 cases and 437,665 controls). Furthermore, pleiotropy detection, reverse causality testing, colocalization analysis, and external replication were utilized to strengthen the causal effects of prioritized proteins. Phenome-wide MR (PheW-MR) of the prioritized proteins for the risk of 525 diseases was conducted to screen potential side effects. We identified eight plasma proteins that are significantly associated with the risk of varicose veins after Bonferroni correction (P < 2.495 × 10-5), with five being protective (LUM, POSTN, RPN1, RSPO3, and VAT1) and three harmful (COLEC11, IRF3, and SARS2). Most identified proteins showed no pleiotropic effects except for COLLEC11. Bidirectional MR and MR Steiger testing excluded reverse causal relationship between varicose veins and prioritized proteins. The colocalization analysis indicated that COLEC11, IRF3, LUM, POSTN, RSPO3, and SARS2 shared the same causal variant with varicose veins. Finally, seven identified proteins replicated with alternative instruments except for VAT1. Furthermore, PheW-MR revealed that only IRF3 had potential harmful adverse side effects. We identified eight potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that IRF3, LUM, POSTN, RSPO3, and SARS2 might be potential drug targets for varicose veins.

Drug Discovery in the 21st Century

The 21st century has witnessed remarkable advancements in drug discovery that have revolutionized the pharmaceutical industry and transformed healthcare outcomes worldwide. This abstract provides an overview of the key trends and technological innovations that have shaped drug discovery in this era. In the past two decades, drug discovery has transitioned from a traditional, time-consuming process to a more efficient and targeted approach. The integration of cutting-edge technologies such as high-throughput screening, computational modeling, and artificial intelligence has accelerated the identification of potential drug candidates. These technologies allow for the rapid screening of large chemical libraries and the prediction of molecular interactions, significantly reducing the time and cost required to discover new drugs. Furthermore, the advent of genomics and personalized medicine has ushered in an era of precision drug discovery. With the mapping of the human genome and the development of advanced sequencing techniques, researchers can now identify specific genetic targets and biomarkers associated with various diseases. This knowledge enables the design of tailored therapies that have higher efficacy and fewer adverse effects because they are precisely targeted to individual patients or subgroups. Collaboration and data-sharing initiatives have also played a pivotal role in modern drug discovery. Pharmaceutical companies, academic institutions, and government agencies now engage in extensive partnerships, allowing for the pooling of resources, expertise, and data. This collaborative approach fosters a more holistic understanding of disease mechanisms and expedites the identification of potential drug targets. Ethical considerations and regulatory changes have influenced drug discovery practices in the 21st century. With a growing emphasis on patient safety and ethics, drug development processes have become more transparent, accountable, and patient-centric. Additionally, regulatory agencies have adopted flexible approval pathways for breakthrough therapies, facilitating faster access to life-saving drugs. Despite these advancements, drug discovery still faces challenges, such as the increasing complexity of diseases and drug resistance. However, the integration of multidisciplinary approaches, including nanotechnology, gene editing, and immunotherapy, shows great promise for overcoming these hurdles and shaping the future of drug discovery.

Factor-specific generative pattern from large-scale drug-induced gene expression profile

Drug discovery is a complex and interdisciplinary field that requires the identification of potential drug targets for specific diseases. In this study, we present FacPat, a novel approach that identifies the optimal factor-specific pattern explaining the drug-induced gene expression profile. FacPat uses a genetic algorithm based on pattern distance to mine the optimal factor-specific pattern for each gene in the LINCS L1000 dataset. We applied Benjamini–Hochberg correction to control the false discovery rate and identified significant and interpretable factor-specific patterns consisting of 480 genes, 7 chemical compounds, and 38 human cell lines. Using our approach, we identified genes that show context-specific effects related to chemical compounds and/or human cell lines. Furthermore, we performed functional enrichment analysis to characterize biological features. We demonstrate that FacPat can be used to reveal novel relationships among drugs, diseases, and genes.