Candidate Drug Targets Research Articles

Illuminating the enigmatic pathogenesis of Kawasaki disease: Unveiling novel therapeutic avenues by targeting FCGR3B-S100A12 pathway

Kawasaki disease (KD) primarily affects the pediatric population and exhibits a notable incidence of drug resistance, resulting in coronary artery damage and thrombosis. This study aimed to identify innovative therapeutic targets for KD treatment. By harnessing single-cell data derived from peripheral blood mononuclear cells, we identified differentially expressed genes. Through the integration of eQTL data and Mendelian randomization analysis, we identified FCGR3B and S100A12 were causally linked to KD. The DrugBank database showed their potential as drug target candidates. GSEA further elucidated their roles on coronary artery damage and thrombosis. Furthermore, we have confirmed that the ligand-FCGR3B complex enhances the intracellular calcium concentration (Ca2+) within the cytoplasm, which in turn accelerates the secretion of S100A12, a pro-inflammatory cytokine that targets endothelial cells, from neutrophils. By integrating existing research, we proposed a synergistic effect that FCGR3B-S100A12 pathway positively modulates the development of coronary artery damage and thrombus formation, suggesting their perspectives in clinical treatment.

Pulrodemstat, a selective inhibitor of KDM1A, suppresses head and neck squamous cell carcinoma growth by triggering apoptosis

BackgroundChemotherapy is often ineffective as a first-line treatment for head and neck squamous cell carcinoma (HNSCC), and a more precise and effective therapeutic option is urgently needed.MethodsHigh-throughput screening of a histone demethylase inhibitor library was performed to identify potential drugs for treating HNSCC. The Cancer Genome Atlas (TCGA) and single-cell sequencing were used to evaluate the potential diagnostic value and expression distribution of candidate drug targets. Colony formation, transwell assays, and flow cytometry analyses were used to assess the antitumor function of the potential drugs. The CCK-8 assay was used to compare the antitumor activity of the candidate drug and the traditional chemotherapy drug. Bioinformatic analysis based on TCGA database was used for unveiling the upstream signaling.ResultsPulrodemstat, a selective KDM1A inhibitor that is ongoing clinical trial, stood out as the most effective candidate anti-HNSCC drug based on the high-throughput screening. IC50 analysis revealed that Pulrodemstat might possess stronger anti-tumor activity than 5-Fu. Additionally, Pulrodemstat dramatically suppressed HNSCC cell proliferation and migration without inducing toxicity in normal cells. TCGA analysis revealed that KDM1A is positively associated with tumor proliferation, DNA repair, and DNA replication in HNSCC. Consistent with these results, Pulrodemstat substantially induced apoptosis in the HNSCC cells. Furthermore, TCGA analysis revealed that KDM1A was aberrantly overexpressed in HNSCC, positively correlated with malignancy, and negatively associated with the clinical outcomes of HNSCC patients. Notably, single-cell analysis indicated that KDM1A was mainly distributed in the malignant cells of HNSCC samples, highlighting that Pulrodemstat may be a more precise therapeutic option for HNSCC. In addition, methylation occupancies in the KDM1A promoter were substantially low in HNCC tumors, and low methylation occupancies in the KDM1A promoter predicted poor clinical outcomes in HNSCC. These data are consistent with the KDM1A expression in HNSCC. Moreover, TET3, a DNA demethylase, was strongly and positively correlated with KDM1A expression.ConclusionsPulrodemstat is an effective therapeutic drug for HNSCC. Thus, the TET3/KDM1A axis may account for the malignant phenotype of HNSCC.

Sialic Acids and Cancer: Pathophysiological Association between Metastatic Progress and Treatment

Abstract: Neoplasm metastasis is a multi-step process with a high rate of cancer mortality (>60%). Several complex pathogenesis pathways and key therapeutic targets are unclear to us now. To change this scenario, effective drug targets and underlying mechanisms should be found, and high-quality metastasis treatment should be supported. Aberrant tumor sialylation was proposed as a putative drug target candidate to bridge the gaps between metastatic spread and drug responses (genetic, molecular, and animal models). More recently, several promising therapeutic mechanisms and benefits against neoplasm metastasis have been observed by potential association for the target of higher levels and diverse forms of sialic acids (sia) analogues, antigens, glycan, sialylation enzymes, and conjugates. Subsequently, sia-related pathophysiology in cancer diagnosis, prognosis, and therapeutic responses has been reviewed. New algorithms, computation, experimental evaluations, and modern technology might see breakthroughs in therapeutic targets, responses, and immune regulation via sialylation enzymes, associated genes, different glycol conjugates, and other hallmarks of cancer.

Tyrosine modifications of insulin-degrading enzyme enable favorable control of substrate specificity for both Alzheimer’s disease and type-2 diabetes mellitus

Insulin-degrading enzyme (IDE) cleaves amyloid beta (Aβ), insulin, and other bioactive peptides. Because Aβ and insulin are closely related to Alzheimer’s disease (AD) and type-2 diabetes mellitus (T2DM), respectively, IDE is a candidate drug target for treating both AD and T2DM. However, the activity of IDE has opposing effects, including decreasing AD risk by degrading Aβ and increasing T2DM risk by degrading insulin. The opposed substrate specificity is associated with the exo- and active sites containing Tyr314 and Tyr831 residues, the plausible modification targets for controlling substrate specificity. In this study, we used a tyrosine-specific modification regent, Cookson reagent (4-phenyl-1,2,4-triazoline-3,5-dione, PTAD), for IDE and examined the degradation activities on Aβ40 and insulin. Fifteen tyrosine residues, including Tyr314 and Tyr831, were modified by PTAD. After incubation with PTAD-modified IDE for 3 days, insulin remained intact, whereas Aβ40 was completely degraded. This favorable change of substrate specificity was also observed in the mixture of Aβ40 and insulin, suggesting that tyrosine modification of IDE might be a therapeutic strategy for AD and T2DM.

ERL-ProLiGraph: Enhanced representation learning on protein-ligand graph structured data for binding affinity prediction.

Predicting Protein-Ligand Binding Affinity (PLBA) is pivotal in drug development, as accurate estimations of PLBA expedite the identification of promising drug candidates for specific targets, thereby accelerating the drug discovery process. Despite substantial advancements in PLBA prediction, developing an efficient and more accurate method remains non-trivial. Unlike previous computer-aid PLBA studies which primarily using ligand SMILES and protein sequences represented as strings, this research introduces a Deep Learning-based method, the Enhanced Representation Learning on Protein-Ligand Graph Structured data for Binding Affinity Prediction (ERL-ProLiGraph). The unique aspect of this method is the use of graph representations for both proteins and ligands, intending to learn structural information continued from both to enhance the accuracy of PLBA predictions. In these graphs, nodes represent atomic structures, while edges depict chemical bonds and spatial relationship. The proposed model, leveraging deep-learning algorithms, effectively learns to correlate these graphical representations with binding affinities. This graph-based representations approach enhances the model's ability to capture the complex molecular interactions critical in PLBA. This work represents a promising advancement in computational techniques for protein-ligand binding prediction, offering a potential path toward more efficient and accurate predictions in drug development. Comparative analysis indicates that the proposed ERL-ProLiGraph outperforms previous models, showcasing notable efficacy and providing a more suitable approach for accurate PLBA predictions.

Glycolytic reprogramming in microglia: A potential therapeutic target for ischemic stroke

Ischemic stroke is currently the second leading cause of mortality worldwide, with limited treatment options available. As resident immune cells, microglia promptly respond to cerebral ischemic injury, influencing neuroinflammatory damage and neurorepair. Studies suggest that microglia undergo metabolic reprogramming from mitochondrial oxidative phosphorylation to glycolysis in response to ischemia, significantly impacting their function during ischemic stroke. Therefore, this study aims to investigate the roles and regulatory mechanisms involved in this process, aiming to identify a new therapeutic target or potential drug candidate.

Identification of Novel Target DCTPP1 for Colorectal Cancer Therapy with the Natural Small‐Molecule Inhibitors Regulating Metabolic Reprogramming

AbstractColorectal cancer (CRC) is one of the most common malignant tumors. Identification of new effective drug targets for CRC and exploration of bioactive small‐molecules are clinically urgent. The human dCTP pyrophosphatase 1 (DCTPP1) is a newly identified pyrophosphatase regulating the cellular nucleotide pool but remains unexplored as potential target for CRC treatment. Here, twelve unprecedented chemical architectures terpene‐nonadride heterodimers (1–12) and their monomers (13–20) were isolated from endophyte Bipolaris victoriae S27. Compounds 1–12 represented the first example of terpene‐nonadride heterodimers, in which nonadride monomers of 1 and 2 were also first example of 5/6 bicyclic nonadrides. A series of assays showed that 2 could repress proliferation and induce cell cycle arrest, apoptotic and autophagic CRC cell death in vitro and in vivo. Clinical cancer samples data revealed that DCTPP1 was a novel target associated with poor survival in CRC. DCTPP1 was also identified as a new target protein of 2. Mechanically, compound 2 bound to DCTPP1, inhibited its enzymatic activity, intervened with amino acid metabolic reprogramming, and exerted anti‐CRC activity. Our study demonstrates that DCTPP1 was a novel potential biomarker and therapeutic target for CRC, and 2 was the first natural anti‐CRC drug candidate targeting DCTPP1.

Identification of Novel Target DCTPP1 for Colorectal Cancer Therapy with the Natural Small-Molecule Inhibitors Regulating Metabolic Reprogramming.

Colorectal cancer (CRC) is one of the most common malignant tumors. Identification of new effective drug targets for CRC and exploration of bioactive small-molecules are clinically urgent. The human dCTP pyrophosphatase 1 (DCTPP1) is a newly identified pyrophosphatase regulating the cellular nucleotide pool but remains unexplored as potential target for CRC treatment. Here, twelve unprecedented chemical architectures terpene-nonadride heterodimers (1-12) and their monomers (13-20) were isolated from endophyte Bipolaris victoriae S27. Compounds 1-12 represented the first example of terpene-nonadride heterodimers, in which nonadride monomers of 1 and 2 were also first example of 5/6 bicyclic nonadrides. A series of assays showed that 2 could repress proliferation and induce cell cycle arrest, apoptotic and autophagic CRC cell death in vitro and in vivo. Clinical cancer samples data revealed that DCTPP1 was a novel target associated with poor survival in CRC. DCTPP1 was also identified as a new target protein of 2. Mechanically, compound 2 bound to DCTPP1, inhibited its enzymatic activity, intervened with amino acid metabolic reprogramming, and exerted anti-CRC activity. Our study demonstrates that DCTPP1 was a novel potential biomarker and therapeutic target for CRC, and 2 was the first natural anti-CRC drug candidate targeting DCTPP1.

GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model.

Deciphering universal gene regulatory mechanisms in diverse organisms holds great potential for advancing our knowledge of fundamental life processes and facilitating clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms and does not integrate various cell types across species. Recent breakthroughs in single-cell sequencing and deep learning techniques present an unprecedented opportunity to address this challenge. In this study, we built an extensive dataset of over 120 million human and mouse single-cell transcriptomes. After data preprocessing, we obtained 101,768,420 single-cell transcriptomes and developed a knowledge-informed cross-species foundation model, named GeneCompass. During pre-training, GeneCompass effectively integrated four types of prior biological knowledge to enhance our understanding of gene regulatory mechanisms in a self-supervised manner. By fine-tuning for multiple downstream tasks, GeneCompass outperformed state-of-the-art models in diverse applications for a single species and unlocked new realms of cross-species biological investigations. We also employed GeneCompass to search for key factors associated with cell fate transition and showed that the predicted candidate genes could successfully induce the differentiation of human embryonic stem cells into the gonadal fate. Overall, GeneCompass demonstrates the advantages of using artificial intelligence technology to decipher universal gene regulatory mechanisms and shows tremendous potential for accelerating the discovery of critical cell fate regulators and candidate drug targets.

Inhibitory Effects of Reynoutria japonica Houtt. on Pain and Cartilage Breakdown in Osteoarthritis Based on Its Multifaceted Anti-Inflammatory Activity: An In Vivo and In Vitro Approach.

In the past 30 years, the number of years lived with disability due to osteoarthritis (OA) has doubled, making it an increasing global health burden. To address this issue, interventions that inhibit the progressive pathology driven by age-related low-grade inflammation, the primary mechanism of OA, are being actively pursued. Recent investigations have focused on modulating the age-related low-grade inflammatory pathology of this disease as a therapeutic target. However, no agent has successfully halted the disease's progression or reversed its irreversible course. Reynoutria japonica Houtt. (RJ), a promising East Asian herbal medicine, has been utilized for several diseases due to its potent anti-inflammatory activity. This study aims to determine RJ's capacity to inhibit OA symptoms and associated inflammation, exploring its potential for further development. In vivo and in vitro experiments demonstrated RJ's anti-OA activity and modulation of multifaceted inflammatory targets. RJ significantly inhibited pain, gait deterioration, and cartilage destruction in a monosodium iodoacetate-induced OA rat model, with its analgesic effect further confirmed in an acetic acid-induced writhing model. RJ exhibited consistent anti-inflammatory activity against multiple targets in serum and cartilage of the OA rat model and lipopolysaccharide-induced RAW 264.7 cells. The inhibition of inflammatory cytokines, including interleukin-1β, interleukin-6, matrix metalloproteinase-13, tumor necrosis factor-α, and nitric oxide synthase 2, suggests that RJ's alleviation of OA manifestations relates to its multifaceted anti-inflammatory activity. These results indicate that RJ merits further investigation as a disease-modifying drug candidate targeting OA's inflammatory pathology. To further characterize the pharmacological properties of RJ, future studies with expanded designs are warranted.

Genetically Proxied Antidiabetic Drug Target and Primary Open-Angle Glaucoma: A Mendelian Randomization Study.

Observational studies suggest that antidiabetic drugs may lower POAG risk; while the causal relationship remains unclear. Naturally occurring variation in genes encoding antidiabetics drug targets can be used as proxies to investigate long-term therapeutic effect of these drugs on POAG risk. We performed a two-sample Mendelian randomization study to evaluate the potential effect of antidiabetic drug targets on POAG in Europeans and East Asians. To proxy antidiabetic drugs (ABCC8, PPARG, GLP1R, SLC5A2), we leveraged genetic variants located near or within drug target genes that were associated with HbA1c. The validity of our ancestry-specific genetic instrument was checked with multipul positive control outcomes. Genetic summary statistics of POAG from the International Glaucoma Genetics Consortium, Global Biobank Meta-analysis Initiative, and FinnGen consortia were analyzed for Europeans (38,164 cases and 1,576,179 controls) and East Asians (16,650 cases and 288,833 controls) separately. Inverse-variance weighted random-effects models were used as primary method. MR results provided consistent evidence of a protective effect of ABCC8 inhibition on POAG using data sets from IGG, GBMI, and FinnGen. Genetically predicted one-standard deviation reduction in HbA1c from ABCC8 inhibition were significant associated with lower risk of POAG in Europeans (OR = 0.211, 95% CI: 0.133-0.333; p < 0.001) and East Asians (OR = 0.070, 95% CI: 0.011-0.459; p = 0.0056). The association between genetically predicted ABCC8 inhibition and risk of POAG was mainly mediated through intraocular pressure. No association was found for PPARG, SLC5A2, or GLP1R. Sensitivity analyses supported this observation. We found a protective effect of genetically proxied ABCC8 inhibition on POAG risk in both Europeans and East Asians, highlighting ABCC8 as a promising candidate drug target for POAG, and mechanisms underlying the protective effect should also be investigated.

Miniaturized Modular Click Chemistry-enabled Rapid Discovery of Unique SARS-CoV-2 Mpro Inhibitors With Robust Potency and Drug-like Profile.

The COVID-19 pandemic has required an expeditious advancement of innovative antiviral drugs. In this study, focused compound libraries are synthesized in 96- well plates utilizing modular click chemistry to rapidly discover potent inhibitors targeting the main protease (Mpro) of SARS-CoV-2. Subsequent direct biological screening identifies novel 1,2,3-triazole derivatives as robust Mpro inhibitors with high anti-SARS-CoV-2 activity. Notably, C5N17B demonstrates sub-micromolar Mpro inhibitory potency (IC50 = 0.12 µM) and excellent antiviral activity in Calu-3 cells determined in an immunofluorescence-based antiviral assay (EC50 = 0.078 µM, no cytotoxicity: CC50 > 100 µM). C5N17B shows superior potency to nirmatrelvir (EC50 = 1.95 µM) and similar efficacy to ensitrelvir (EC50 = 0.11 µM). Importantly, this compound displays high antiviral activities against several SARS-CoV-2 variants (Gamma, Delta, and Omicron, EC50 = 0.13 - 0.26 µM) and HCoV-OC43, indicating its broad-spectrum antiviral activity. It is worthy that C5N17B retains antiviral activity against nirmatrelvir-resistant strains with T21I/E166V and L50F/E166V mutations in Mpro (EC50 = 0.26 and 0.15 µM, respectively). Furthermore, C5N17B displays favorable pharmacokinetic properties. Crystallography studies reveal a unique, non-covalent multi-site binding mode. In conclusion, these findings substantiate the potential of C5N17B as an up-and-coming drug candidate targeting SARS-CoV-2 Mpro for clinical therapy.

Leveraging the Genetics of Psychiatric Disorders to Prioritize Potential Drug Targets and Compounds.

Genetics has the potential to inform biologically relevant drug treatment and repurposing which may ultimately improve patient care. In this study, we combine methods which leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the largest available genome-wide association studies, in European ancestry, of four psychiatric disorders [i.e., attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] along with genes encoding drug targets. With this data, we conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We then conducted a series of molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets shared across multiple disorders as well as disorder-specific targets. Drug prioritization indicated genetic support for several currently used psychotropic medications including the antipsychotic paliperidone as the top ranked drug for schizophrenia. We also observed genetic support for other commonly used psychotropics (e.g., clozapine, risperidone, duloxetine, lithium, and valproic acid). Opportunities for drug repurposing were revealed such as cholinergic drugs for ADHD, estrogens for depression, and gabapentin enacarbil for schizophrenia. Our findings also indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Here we present a series of complimentary and comprehensive analyses that highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders. Our findings present novel opportunities for refining psychiatric treatment.

Inflammasome components as new therapeutic targets in inflammatory disease.

Inflammation drives pathology in many human diseases for which there are no disease-modifying drugs. Inflammasomes are signalling platforms that can induce pathological inflammation and tissue damage, having potential as an exciting new class of drug targets. Small-molecule inhibitors of the NLRP3 inflammasome that are now in clinical trials have demonstrated proof of concept that inflammasomes are druggable, and so drug development programmes are now focusing on other key inflammasome molecules. In this Review, we describe the potential of inflammasome components as candidate drug targets and the novel inflammasome inhibitors that are being developed. We discuss how the signalling biology of inflammasomes offers mechanistic insights for therapeutic targeting. We also discuss the major scientific and technical challenges associated with drugging these molecules during preclinical development and clinical trials.

Capturing biomarkers associated with Alzheimer disease subtypes using data distribution characteristics.

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.

Melatonin downregulates angiogenesis and lymphangiogenesis by regulating tumor-associated macrophages via NLRP3 inflammasomes in lung adenocarcinoma.

Tumor-associated macrophages (TAMs), present within the tumor microenvironment (TME), strictly modulate tumor angiogenesis and lymphangiogenesis. Nevertheless, the associated signaling networks and candidate drug targets for these events remains to be elucidated. Given its antioxidative activities, we speculated that melatonin may reduce pyroptosis, and thereby modulate both angiogenesis and lymphangiogenesis. We revealed that a co-culture of A549 cells and THP-1 macrophages strongly enhanced expressions of the NLRP3 inflammasome axis members, and augmented angiogenesis and lymphangiogenesis. Next, we overexpressed NLRP3 in the A549 cells, and demonstrated that excess NLRP3 expression substantially upregulated VEGF and CXCL cytokine expressions, and enhanced lymphatic endothelial cells (LECs) tube formation. In contrast, NLRP3 inhibition produced the opposite effect. In addition, relative to controls, melatonin administration strongly inhibited the NLRP3 inflammasome axis, as well as angiogenesis and lymphangiogenesis in the co-culture system. Subsequent animal experiments using a Lewis Lung Carcinoma (LLC) subcutaneous tumor model in mice corroborate these findings. Melatonin treatment and NLRP3 knockdown significantly inhibit tumor growth and downregulate NLRP3 and IL-1β expression in tumor tissues. Furthermore, melatonin downregulates the expression of angiogenic and lymphangiogenic markers in tumor tissues. Taken together, the evidence suggested that a THP-1 macrophage and A549 cell co-culture stimulates angiogenesis and lymphangiogenesis via the NLRP3 axis. Melatonin protected against the TAMs- and NLRP3 axis-associated promotion of the aforementioned events in vitro and in vivo. Hence, melatonin is a promising candidate for managing for tumor-related angiogenesis and lymphangiogenesis in lung adenocarcinoma.

Leishmania major telomerase RNA knockout: From altered cell proliferation to decreased parasite infectivity

This study focuses on the biological impacts of deleting the telomerase RNA from Leishmania major (LeishTER), a parasite responsible for causing leishmaniases, for which no effective treatment or prevention is available. TER is a critical player in the telomerase ribonucleoprotein complex, containing the template sequence copied by the reverse transcriptase component during telomere elongation. The success of knocking out both LeishTER alleles was confirmed, and no off-targets were detected. LmTER−/− cells share similar characteristics with other TER-depleted eukaryotes, such as altered growth patterns and partial G0/G1 cell cycle arrest in early passages, telomere shortening, and elevated TERRA expression. They also exhibit increased γH2A phosphorylation, suggesting that the loss of LeishTER induces DNA damage signaling. Moreover, pro-survival autophagic signals and mitochondrion alterations were shown without any detectable plasma membrane modifications. LmTER−/− retained the ability to transform into metacyclics, but their infectivity capacity was compromised. Furthermore, the overexpression of LeishTER was also deleterious, inducing a dominant negative effect that led to telomere shortening and growth impairments. These findings highlight TER's vital role in parasite homeostasis, opening discussions about its potential as a drug target candidate against Leishmania.

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.

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

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

Quantitative Hydrogen-Deuterium Exchange Mass Spectrometry for Simultaneous Structural Characterization and Affinity Indexing of Single Target Drug Candidate Libraries.

Hydrogen-deuterium eXchange mass spectrometry (HDX-MS) is increasingly used in drug development to locate binding sites and to identify allosteric effects in drug/target interactions. However, the potential of this technique to quantitatively analyze drug candidate libraries remains largely unexplored. Here, a collection of 13 WDR5-targeting small molecules with surface plasmon resonance (SPR) dissociation coefficients (KD) ranging from 20 nM to ∼116 μM were characterized using differential HDX-MS (ΔHDX-MS). Conventional qualitative analysis of the ΔHDX-MS data set revealed the binding interfaces for all compounds and allosteric effects where present. We then demonstrated that ΔHDX-MS signal-to-noise (S/N) not only can rank library-relative affinity but also can accurately predict KD from a calibration curve constructed from high-quality SPR data. Three methods for S/N calculation are explored, each suitable for libraries with different characteristics. Our results demonstrate the potential for ΔHDX-MS use in drug candidate library affinity validation and/or determination while simultaneously characterizing structure.