Curr Cancer Drug Targets. 2024 Jul 9. doi: 10.2174/0115680096303479240614061136. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Globally, Gastric Cancer (GC) ranks as the fifth leading cause of cancer-related deaths. GC is a multifaceted malignancy with diverse etiologies; however, understanding the shared molecular mechanisms can aid in discovering novel targeted therapies for GC. This study has employed a drug repositioning approach to explore new drug candidates for treating GC.

METHODS: The human GC cell lines AGS, MKN-45, and KATO-III were treated with different concentrations of dopamine, cabergoline, thioridazine, and entacapone to determine effective doses and IC50 values. In vitro, cytotoxic activity on cancer cell lines was screened based on dose/time using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) was used to measure the mRNA expression of B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax), and Proliferating Cell Nuclear Antigen (PCNA) in each group. The percentage of apoptotic cells was evaluated using Annexin V/PI staining.

RESULTS: Dopamine, cabergoline, thioridazine, and entacapone elicited cytotoxic effects on AGS and KATO-III cells in a dose-dependent manner and elevated the percentage of Annexin V-positive cells, suggesting the occurrence of apoptosis. The expression of Bcl-2 and PCNA was significantly decreased, whereas the expression of Bax was considerably increased in the AGS and KATO-III cells compared to that in the blank group (p < 0.05); however, no similar effect was observed in MKN-45 cells.

CONCLUSION: Through in vitro experiments, this study provides evidence that the antipsychotic drugs cabergoline, dopamine, thioridazine, and entacapone can inhibit gastric cancer growth in AGS and KATO-III cells. These findings suggest that these drugs could be repurposed as novel therapeutic agents for the treatment of gastric cancer.

PMID:38984576 | DOI:10.2174/0115680096303479240614061136

Chin Clin Oncol. 2024 Jun;13(3):32. doi: 10.21037/cco-23-151.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths globally. To reduce HCC-related mortality, early diagnosis and therapeutic improvement are essential. Hub differentially expressed genes (HubGs) may serve as potential diagnostic and prognostic biomarkers, also offering therapeutic targets for precise therapies. Therefore, we aimed to identify top-ranked hub genes for the diagnosis, prognosis, and therapy of HCC.

METHODS: Through a systematic literature review, 202 HCC-related HubGs were derived from 59 studies, yet consistent detection across these was lacking. Then, we identified top-ranked HubGs (tHubGs) by integrated bioinformatics analysis, highlighting their functions, pathways, and regulators that might be more representative of the diagnosis, prognosis, and therapies of HCC.

RESULTS: In this study, eight HubGs (CDK1, AURKA, CDC20, CCNB2, TOP2A, PLK1, BUB1B, and BIRC5) were identified as the tHubGs through the protein-protein interaction (PPI) network and survival analysis. Their differential expression in different stages of HCC, validated using The Cancer Genome Atlas (TCGA) Program database, suggests their potential as early HCC markers. The enrichment analyses revealed some important roles in HCC-related biological processes (BPs), molecular functions (MFs), cellular components (CCs), and signaling pathways. Moreover, the gene regulatory network analysis highlighted key transcription factors (TFs) and microRNAs (miRNAs) that regulate these tHubGs at transcriptional and post-transcriptional. Finally, we selected three drugs (CD437, avrainvillamide, and LRRK2-IN-1) as candidate drugs for HCC treatment as they showed strong binding with all of our proposed and published protein receptors.

CONCLUSIONS: The findings of this study may provide valuable resources for early diagnosis, prognosis, and therapies for HCC.

PMID:38984486 | DOI:10.21037/cco-23-151

Pharmacol Res Perspect. 2024 Aug;12(4):e1244. doi: 10.1002/prp2.1244.

ABSTRACT

We hypothesize that a "Faustian bargain"-the trading of increased SARS-CoV2 viral infection with a concurrent potential for prevention of life-threatening lower lung infection explains the previous and future morbidity and mortality from COVID-19. Further, this trade-off is made feasible by fundamental principles of thermodynamics and receptor affinity.

PMID:38982716 | DOI:10.1002/prp2.1244

Med Oncol. 2024 Jul 9;41(8):198. doi: 10.1007/s12032-024-02419-0.

ABSTRACT

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In the present study, 150 FDA-approved drugs have been screened using the concept of drug repurposing against VEGFR-2 by employing the molecular docking, molecular dynamics, grouping data with Machine Learning algorithms, and density functional theory (DFT) approaches. The identified compounds such as Pazopanib, Atogepant, Drosperinone, Revefenacin and Zanubrutinib shown the binding energy - 7.0 to - 9.5 kcal/mol against VEGF receptor in the molecular docking studies and have been observed as stable in the molecular dynamic simulations performed for the period of 500 ns. The MM/GBSA analysis shows that the value ranging from - 44.816 to - 82.582 kcal/mol. Harnessing the machine learning approaches revealed that clustering with K = 10 exhibits the relevance through high binding energy and satisfactory logP values, setting them apart from compounds in distinct clusters. Therefore, the identified compounds are found to be potential to inhibit the VEGFR-2 and the present study will be a benchmark to validate the compounds experimentally.

PMID:38981988 | DOI:10.1007/s12032-024-02419-0

J Microbiol. 2024 Jul 9. doi: 10.1007/s12275-024-00154-9. Online ahead of print.

ABSTRACT

Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.

PMID:38980578 | DOI:10.1007/s12275-024-00154-9

Brief Bioinform. 2024 May 23;25(4):bbae328. doi: 10.1093/bib/bbae328.

ABSTRACT

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

PMID:38980370 | DOI:10.1093/bib/bbae328

J Med Microbiol. 2024 Jul;73(7). doi: 10.1099/jmm.0.001850.

ABSTRACT

Introduction. The development of new antifungal drugs has become a global priority, given the increasing cases of fungal diseases together with the rising resistance to available antifungal drugs. In this scenario, drug repositioning has emerged as an alternative for such development, with advantages such as reduced research time and costs.Gap statement. Propafenone is an antiarrhythmic drug whose antifungal activity is poorly described, being a good candidate for further study.Aim. This study aims to evaluate propafenone activity against different species of Candida spp. to evaluate its combination with standard antifungals, as well as its possible action mechanism.Methodology. To this end, we carried out tests against strains of Candida albicans, Candida auris, Candida parapsilosis, Candida tropicalis, Candida glabrata and Candida krusei based on the evaluation of the MIC, minimum fungicidal concentration and tolerance level, along with checkerboard and flow cytometry tests with clinical strains and cell structure analysis by scanning electron microscopy (SEM).Results. The results showed that propafenone has a 50% MIC ranging from 32 to 256 µg ml-1, with fungicidal activity and positive interactions with itraconazole in 83.3% of the strains evaluated. The effects of the treatments observed by SEM were extensive damage to the cell structure, while flow cytometry revealed the apoptotic potential of propafenone against Candida spp.Conclusion. Taken together, these results indicate that propafenone has the potential for repositioning as an antifungal drug.

PMID:38979984 | DOI:10.1099/jmm.0.001850

OMICS. 2024 Jul 9. doi: 10.1089/omi.2024.0121. Online ahead of print.

ABSTRACT

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

PMID:38979602 | DOI:10.1089/omi.2024.0121

Front Immunol. 2024 Jun 24;15:1390821. doi: 10.3389/fimmu.2024.1390821. eCollection 2024.

ABSTRACT

INTRODUCTION: IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear.

METHODS: We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning.

RESULTS: Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS.

CONCLUSION: Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

PMID:38979419 | PMC:PMC11229174 | DOI:10.3389/fimmu.2024.1390821

bioRxiv [Preprint]. 2024 Jun 28:2024.06.24.600340. doi: 10.1101/2024.06.24.600340.

ABSTRACT

The landscape of sex differences in Colorectal Cancer (CRC) has not been well characterized with respect to the mechanisms of action for oncogenes such as KRAS. However, our recent study showed that tumors from male patients with KRAS mutations have decreased iron-dependent cell death called ferroptosis. Building on these findings, we further examined ferroptosis in CRC, considering both sex of the patient and KRAS mutations, using public databases and our in-house CRC tumor cohort. Through subsampling inference and variable importance analysis (VIMP), we identified significant differences in gene expression between KRAS mutant and wild type tumors from male patients. These genes suppress (e.g., SLC7A11 ) or drive (e.g., SLC1A5 ) ferroptosis, and these findings were further validated with Gaussian mixed models. Furthermore, we explored the prognostic value of ferroptosis regulating genes and discovered sex- and KRAS-specific differences at both the transcriptional and metabolic levels by random survival forest with backward elimination algorithm (RSF-BE). Of note, genes and metabolites involved in arginine synthesis and glutathione metabolism were uniquely associated with prognosis in tumors from males with KRAS mutations. Additionally, drug repurposing is becoming popular due to the high costs, attrition rates, and slow pace of new drug development, offering a way to treat common and rare diseases more efficiently. Furthermore, increasing evidence has shown that ferroptosis inhibition or induction can improve drug sensitivity or overcome chemotherapy drug resistance. Therefore, we investigated the correlation between gene expression, metabolite levels, and drug sensitivity across all CRC primary tumor cell lines using data from the Genomics of Drug Sensitivity in Cancer (GDSC) resource. We observed that ferroptosis suppressor genes such as DHODH , GCH1 , and AIFM2 in KRAS mutant CRC cell lines were resistant to cisplatin and paclitaxel, underscoring why these drugs are not effective for these patients. The comprehensive map generated here provides valuable biological insights for future investigations, and the findings are supported by rigorous analysis of large-scale publicly available data and our in-house cohort. The study also emphasizes the potential application of VIMP, Gaussian mixed models, and RSF-BE models in the multi-omics research community. In conclusion, this comprehensive approach opens doors for leveraging precision molecular feature analysis and drug repurposing possibilities in KRAS mutant CRC.

PMID:38979294 | PMC:PMC11230177 | DOI:10.1101/2024.06.24.600340

Front Cell Neurosci. 2024 Jun 24;18:1403734. doi: 10.3389/fncel.2024.1403734. eCollection 2024.

ABSTRACT

Mitochondrial diseases are a group of severe pathologies that cause complex neurodegenerative disorders for which, in most cases, no therapy or treatment is available. These organelles are critical regulators of both neurogenesis and homeostasis of the neurological system. Consequently, mitochondrial damage or dysfunction can occur as a cause or consequence of neurodevelopmental or neurodegenerative diseases. As genetic knowledge of neurodevelopmental disorders advances, associations have been identified between genes that encode mitochondrial proteins and neurological symptoms, such as neuropathy, encephalomyopathy, ataxia, seizures, and developmental delays, among others. Understanding how mitochondrial dysfunction can alter these processes is essential in researching rare diseases. Three-dimensional (3D) cell cultures, which self-assemble to form specialized structures composed of different cell types, represent an accessible manner to model organogenesis and neurodevelopmental disorders. In particular, brain organoids are revolutionizing the study of mitochondrial-based neurological diseases since they are organ-specific and model-generated from a patient's cell, thereby overcoming some of the limitations of traditional animal and cell models. In this review, we have collected which neurological structures and functions recapitulate in the different types of reported brain organoids, focusing on those generated as models of mitochondrial diseases. In addition to advancements in the generation of brain organoids, techniques, and approaches for studying neuronal structures and physiology, drug screening and drug repositioning studies performed in brain organoids with mitochondrial damage and neurodevelopmental disorders have also been reviewed. This scope review will summarize the evidence on limitations in studying the function and dynamics of mitochondria in brain organoids.

PMID:38978706 | PMC:PMC11228165 | DOI:10.3389/fncel.2024.1403734

Nat Commun. 2024 Jul 8;15(1):5703. doi: 10.1038/s41467-024-50024-6.

ABSTRACT

Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug's therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson's disease.

PMID:38977662 | DOI:10.1038/s41467-024-50024-6

J Mol Graph Model. 2024 Jul 3;131:108817. doi: 10.1016/j.jmgm.2024.108817. Online ahead of print.

ABSTRACT

The global antibiotic resistance problem necessitates fast and effective approaches to finding novel inhibitors to treat bacterial infections. In this study, we propose a computational workflow to identify plausible high-affinity compounds from FDA-approved, investigational, and experimental libraries for the decoding center on the small subunit 30S of the E. coli ribosome. The workflow basically consists of two molecular docking calculations on the intact 30S, followed by molecular dynamics (MD) simulations coupled with MM-GBSA calculations on a truncated ribosome structure. The parameters used in the molecular docking suits, Glide and AutoDock Vina, as well as in the MD simulations with Desmond were carefully adjusted to obtain expected interactions for the ligand-rRNA complexes. A filtering procedure was followed, considering a fingerprint based on aminoglycoside's binding site on the 30S to obtain seven hit compounds either with different clinical usages or aminoglycoside derivatives under investigation, suggested for in vitro studies. The detailed workflow developed in this study promises an effective and fast approach for the estimation of binding free energies of large protein-RNA and ligand complexes.

PMID:38976944 | DOI:10.1016/j.jmgm.2024.108817

iScience. 2024 May 18;27(7):110025. doi: 10.1016/j.isci.2024.110025. eCollection 2024 Jul 19.

ABSTRACT

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

PMID:38974972 | PMC:PMC11226970 | DOI:10.1016/j.isci.2024.110025

Toxicology. 2024 Jul 3:153879. doi: 10.1016/j.tox.2024.153879. Online ahead of print.

ABSTRACT

A bidirectional relationship exists between atrial fibrillation (AF) and kidney function. Uncontrolled AF may lead to kidney injury, whereas renal dysfunction may contribute to AF initiation and maintenance. This study aimed to investigate the protective effect of the sodium glucose cotransporter-2 inhibitor empagliflozin (EMPA) on acute kidney injury (AKI) associated with AF induced by acetylcholine and calcium chloride (ACh/CaCl2) in rats and elucidate the potential underlying mechanism. Rats were randomly divided as follows: control (CTRL) group: administered vehicles only; AF group: intravenously injected 1ml/kg of an ACh/CaCl2 mixture for seven days to induce AF; EMPA group: orally administered EMPA (30mg/kg) for seven days; AF+EMPA10 and AF+EMPA30 groups: co-administered the induction mixture and EMPA (10 and 30mg/kg, respectively) for seven days. Our results showed that EMPA (10 and 30mg/kg) effectively maintained kidney function and demonstrated a significant antioxidant potential. EMPA also suppressed AF-induced renal tubulointerstitial injury and fibrotic changes concurrently with reducing renal levels of the pro-inflammatory cytokines tumour necrosis factor-α (TNF-α) and interleukin-6, as well as the pro-fibrotic marker transforming growth factor beta-1 and collagen type I. Mechanistically, EMPA boosted nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) renal tissue expression while repressing nuclear factor kappa B (NF-κB) activation. In addition, these beneficial effects of EMPA on kidneys were concurrent with its ability to effectively inhibit AF-related electrocardiographic changes, reduce incidence and duration of AF episodes, and markedly suppress serum B-type natriuretic peptide and C-reactive protein levels. In conclusion, EMPA protected against AKI associated with AF induced by ACh/CaCl2 in rats through simultaneous modulation of the Nrf2/HO-1 and the NF-κB/TNF-α signaling pathways, exerting antioxidant, anti-inflammatory, and anti-fibrotic effects.

PMID:38971551 | DOI:10.1016/j.tox.2024.153879

Gynecol Oncol. 2024 Jul 5;188:162-168. doi: 10.1016/j.ygyno.2024.06.029. Online ahead of print.

ABSTRACT

OBJECTIVE: Dedifferentiated endometrial carcinoma (DDEC) characterized by SWItch/Sucrose Non-Fermentable (SWI/SNF) complex inactivation is a highly aggressive type of endometrial cancer without effective systemic therapy options. Its uncommon nature and aggressive disease trajectory pose significant challenges for therapeutic progress. To address this obstacle, we focused on developing preclinical models tailored to this tumor type and established patient tumor-derived three-dimensional (3D) spheroid models of DDEC.

METHODS: High-throughput drug repurposing screens were performed on in vitro 3D spheroid models of DDEC cell lines (SMARCA4-inactivated DDEC-1 and ARID1A/ARID1B co-inactivated DDEC-2). The dose-response relationships of the identified candidate drugs were evaluated in vitro, followed by in vivo evaluation using xenograft models of DDEC-1 and DDEC-2.

RESULTS: Drug screen in 3D models identified multiple cardiac glycosides including digoxin and digitoxin as candidate drugs in both DDEC-1 and DDEC-2. Subsequent in vitro dose-response analyses confirmed the inhibitory activity of digoxin and digitoxin with both drugs showing lower IC50 in DDEC cells compared to non-DDEC endometrial cancer cells. In in vivo xenograft models, digoxin significantly suppressed the growth of DDEC tumors at clinically relevant serum concentrations.

CONCLUSION: Using biologically precise preclinical models of DDEC derived from patient tumor samples, our study identified digoxin as an effective drug in suppressing DDEC tumor growth. These findings provide compelling preclinical evidence for the use of digoxin as systemic therapy for SWI/SNF-inactivated DDEC, which may also be applicable to other SWI/SNF-inactivated tumor types.

PMID:38970843 | DOI:10.1016/j.ygyno.2024.06.029

Hum Genet. 2024 Jul 6. doi: 10.1007/s00439-024-02684-z. Online ahead of print.

ABSTRACT

An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.

PMID:38969939 | DOI:10.1007/s00439-024-02684-z

Psychiatry Res. 2024 Jul 4;339:116033. doi: 10.1016/j.psychres.2024.116033. Online ahead of print.

ABSTRACT

Major Depressive Disorder (MDD) is a pleomorphic disease with substantial patterns of symptoms and severity with mensurable deficits in several associated domains. The broad spectrum of phenotypes observed in patients diagnosed with depressive disorders is the reflection of a very complex disease where clusters of biological and external factors (e.g., response/processing of life events, intrapsychic factors) converge and mediate pathogenesis, clinical presentation/phenotypes and trajectory. Patient-derived induced pluripotent stem cells (iPSCs) enable their differentiation into specialised cell types in the central nervous system to explore the pathophysiological substrates of MDD. These models may complement animal models to advance drug discovery and identify therapeutic approaches, such as cell therapy, drug repurposing, and elucidation of drug metabolism, toxicity, and mechanisms of action at the molecular/cellular level, to pave the way for precision psychiatry. Despite the remarkable scientific and clinical progress made over the last few decades, the disease is still poorly understood, the incidence and prevalence continue to increase, and more research is needed to meet clinical demands. This review aims to summarise and provide a critical overview of the research conducted thus far using patient-derived iPSCs for the modelling of psychiatric disorders, with a particular emphasis on MDD.

PMID:38968917 | DOI:10.1016/j.psychres.2024.116033

RSC Adv. 2024 Jul 4;14(29):21203-21212. doi: 10.1039/d4ra02593h. eCollection 2024 Jun 27.

ABSTRACT

The coronavirus disease 2019 (COVID-19) has spread worldwide with severe health, social, and economic repercussions. Although vaccines have significantly reduced the severity of symptoms and deaths, alternative medications derived from natural products (NPs) are vital to further decrease fatalities, especially in regions with low vaccine uptake. When paired with the latest computational developments, NPs, which have been used to cure illnesses and infections for thousands of years, constitute a renewed resource for drug discovery. In the present report, a combination of computational and in vitro methods reveals the repositioning of NPs and identifies salvinorin A and deacetylgedunin (DCG) as having potential anti-SARS-CoV-2 activities. Salvinorin A was found both in silico and in vitro to inhibit both SARS-CoV-2 spike/host ACE2 protein interactions, consistent with blocking viral cell entry, and well as live virus replication. Plant extracts from Azadirachta indica and Cedrela odorata, which contain high levels of DCG, inhibited viral cell replication by targeting the main protease (Mpro) and/or inhibited viral cell entry by blocking the interaction between spike RBD-ACE2 protein at concentrations lower than salvinorin A. Our findings suggest that salvinorin A represent promising chemical starting points where further optimization may result in effective natural product-derived and potent anti-SARS-CoV-2 inhibitors to supplement vaccine efforts.

PMID:38966817 | PMC:PMC11223729 | DOI:10.1039/d4ra02593h

Front Aging Neurosci. 2024 Jun 19;16:1383163. doi: 10.3389/fnagi.2024.1383163. eCollection 2024.

ABSTRACT

The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

PMID:38966801 | PMC:PMC11223503 | DOI:10.3389/fnagi.2024.1383163