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PubMed2026

Advancements in the Understanding of the Genetics of Obsessive-Compulsive Disorder (OCD).

PURPOSE OF REVIEW: This review summarizes recent advances in the genetics of Obsessive-Compulsive Disorder (OCD), their contribution to understanding disorder biology, and implication for clinical translation. RECENT FINDINGS: Recent GWAS identified 30 genome-wide significant loci and prioritized 25 putatively causal genes. Rare variant studies implicated specific genes, including CHD8, CELSR3, SLITRK5, and QRICH1. Evidence from common and rare variants support brain- and immune-related pathways. Genetic overlap with obsessive compulsive symptoms and other psychiatric disorders indicate shared underlying biology. Current evidence is largely based on individuals of European ancestry, although global efforts are underway to improve ancestral diversity in OCD genetics. Given the urgent need for improved treatment, genetically informed clinical translation approaches hold promise, including pharmacogenetics and drug repurposing. Recent advances in OCD genetics support a highly polygenic architecture, implicate specific neuro-biological and immune pathways, and provide new opportunities for clinical translation.

Humans↗

PubMed2026

Multi-omics integration and colocalization analyses prioritize candidate molecular loci associated with hypothermia.

BACKGROUND: Hypothermia is a life-threatening condition lacking specific pharmacological treatments. This study aimed to prioritize genetically supported molecular loci associated with hypothermia and to explore their pharmacological tractability using multi-omics data. METHODS: Initially, 2532 druggable genes were curated from the Drug-Gene Interaction Database and established literature. These were cross-referenced with cis-eQTL and cis-pQTL datasets, encompassing 870,655 and 114,281 SNPs for blood, respectively, alongside 2379 shared SNPs across adipose, skeletal muscle, and heart tissues. Matched instrumental variables were integrated with hypothermia GWAS summary statistics for two-sample Mendelian randomization (MR) and Bayesian colocalization. Transcriptomic differential expression analysis (DEA) was subsequently conducted as an exploratory analysis of cold-exposure-associated expression changes. Database-derived compound annotations were systematically re-evaluated according to target specificity, established pharmacological mechanism, and concordance with the direction of the MR estimates. RESULTS: Among 671 gene-level MR tests, 36 genes reached nominal significance, whereas only ABCC8 remained significant after FDR correction. Colocalization was evaluable for 8 of these 36 genes, and 4 loci (COL18A1, SLC1A7, ADIPOQ, and MERTK) met the prespecified PP.H4>0.90 threshold. The remaining 28 loci were not evaluable because sufficient overlapping regional variants were unavailable after harmonization. Transcriptomic analysis identified altered expression of SLC1A3 and SLCO4A1 under cold exposure, although these findings did not directly validate the colocalization-supported loci. Re-evaluation of database-derived compound annotations did not identify any direct, selective, and directionally concordant drug-repurposing candidate for hypothermia. CONCLUSIONS: COL18A1, SLC1A7, ADIPOQ, and MERTK showed colocalization support among the 8 evaluable nominal MR-associated loci. Because colocalization coverage was limited, these genes should be regarded as preliminary candidate loci rather than established therapeutic targets. The pharmacological annotations were indirect, non-selective, unsupported, or directionally inconsistent and should be interpreted solely as hypothesis-generating information.

Bayesian colocalization↗

PubMed2026

Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.

Alzheimer's disease (AD) is a multifactorial neurodegenerative condition in which accumulating genetic and molecular evidence implicates dysregulation of peripheral immune processes in disease pathogenesis. Nevertheless, the contribution of distinct peripheral immune cell subsets and associated gene regulatory landscapes to AD risk remains incompletely defined. To address this gap, we integrated single-cell expression quantitative trait loci (sc‑eQTL) data from the OneK1K cohort with AD GWAS summary statistics. We systematically interrogated immune cell-specific genes for their contributions to AD risk by integrating genetic causal inference with Bayesian colocalization analyses, and identified 24 eGenes that passed both the MR significance threshold (P < 0.05) and the criterion for strong shared genetic signals (PP.H4 > 0.8). Notable candidates included GATS, HLA-DOB, HLA-DQA1, PM20D1, and others, with each gene demonstrating a cell-type-specific association restricted to its corresponding immune cell type, such as monocytes, CD8 + T cells, or B cells. Independent peripheral blood single-cell transcriptomic data further supported disease-associated shifts in cell-type-specific expression patterns in AD. Phenome-wide association studies (PheWAS) indicated limited associations with off-target traits, indicating a favorable safety profile for therapeutic intervention, with the exceptions of B4GALNT3, PM20D1, and CNN2. Integration of immune gene targets with pharmacological databases yielded three candidate compound, including NSC321521 (targeting HLA-DQA1), phenoxybenzamine (targeting GSTP1), and rimexolone (targeting BIN1). Among these compounds, Predicted blood-brain barrier permeability was observed only for phenoxybenzamine and rimexolone, with docking studies indicating stable interactions, such as those between NSC321521 and HLA-DQA1, phenoxybenzamine and GSTP1, and rimexolone and BIN1. This integrative approach highlights key immune‑cell‑specific genes involved in AD and proposes repurposable drugs with central nervous system potential, paving the way for more targeted immunomodulatory strategies in AD.

Humans↗

PubMed2025

Novel mutations associated with clofazimine resistance in Mycobacterium intracellulare.

BACKGROUND: Clofazimine is a promising repurposed drug for treating Mycobacterium avium-intracellulare complex pulmonary disease, but its resistance mechanisms in Mycobacterium intracellulare remain poorly understood. OBJECTIVE: This study aims to elucidate the resistance mechanisms of M. intracellulare to clofazimine. METHODS: We isolated 36 clofazimine-resistant M. intracellulare mutants in vitro and performed whole-genome sequencing to identify resistance-associated mutations. Gene complementation was used to validate the role of the identified mutations. RESULTS: We identified various mutations in the marR gene (WP_009952290.1) in 61% of clofazimine-resistant mutants by whole-genome sequencing. Mutations were identified in additional genes encoding ssuD (flavin-dependent oxidoreductase, C67A), lppI (membrane lipoprotein, C207 deletion), GMC oxidoreductase (glucose-methanol-choline oxidoreductase, G157 deletion), MASE1 domain-containing protein (C62G) and PPE family protein (222C deletion). Gene complementation experiments demonstrated that introducing the wild-type marR in clofazimine-resistant strain (L72) with marR mutations reduced clofazimine MIC from 1 mg/L to susceptible baseline (0.25 mg/L), confirming its critical role in clofazimine resistance. Notably, the M. intracellulare MarR lacks homology to Mycobacterium tuberculosis MarR family protein Rv0678 (MmpR) involved in clofazimine and bedaquiline resistance but is flanked by non-efflux pump genes (dhmA and doxX), and unlike M. tuberculosis, its mutation does not cause bedaquiline cross-resistance, indicating a different MarR and distinct regulatory mechanism for clofazimine resistance in M. intracellulare. CONCLUSIONS: This work highlights marR as a key determinant of clofazimine resistance in M. intracellulare and underscores the need for further mechanistic studies with implications for rapid molecular detection and effective treatment.

Clofazimine↗

PubMed2026

Post-Translational Modifications in Traumatic Brain Injury: Decoding the Proteomic Landscape and Molecular Mechanisms of Secondary Injury.

Traumatic brain injury (TBI) initiates a complex secondary injury cascade that significantly contributes to long-term neurological deficits, with post-translational modifications (PTMs) emerging as pivotal molecular regulators of this process. Unlike primary mechanical damage, secondary injury evolves over hours to years and involves intricate proteomic alterations that changes in gene expression alone cannot fully explain. PTMs-including phosphorylation, ubiquitination, acetylation, SUMOylation, glycosylation, and emerging modifications such as succinylation, lactylation, and nitrosylation-serve as dynamic molecular switches that fine-tune protein function, stability, localization, and interactions in response to TBI-induced stressors. These modifications play dual roles: they can either promote neuroprotection and recovery or drive pathological processes such as neuronal cell death (via apoptosis, necroptosis, and ferroptosis), neuroinflammation through glial activation and inflammasome signaling, blood-brain barrier disruption, mitochondrial dysfunction, and impaired synaptic plasticity. Critically, extensive crosstalk exists among different PTM pathways-such as the interplay between phosphorylation and ubiquitination in protein degradation or the competitive balance between acetylation and SUMOylation-that collectively shape cellular fate after injury. This nuanced regulatory network presents both challenges and opportunities for therapeutic intervention. Targeting PTM-related enzymes, including kinases, phosphatases, E3 ligases, and histone deacetylases, has shown promise in preclinical models, while novel strategies like Proteolysis-Targeting Chimeras (PROTACs) and repurposed drugs (e.g., metformin, resveratrol) offer innovative avenues for modulating the PTM landscape. Advances in high-throughput proteomics and mass spectrometry are enabling the mapping of TBI-specific PTM signatures across spatiotemporal phases, facilitating the identification of pro-survival versus pro-death modification thresholds. Despite hurdles in clinical translation-such as blood-brain barrier penetration and off-target effects-the growing understanding of PTM dynamics underscores their potential as both biomarkers and therapeutic targets. Future TBI management may thus rely on precision medicine approaches that integrate multi-PTM profiling to guide combination therapies aimed at tipping the balance toward neural repair and functional recovery.

Brain Injuries, Traumatic↗

PubMed2025

Genome-wide association study of adolescent-onset depression.

Adolescent depression is a heritable psychiatric condition with rising global prevalence and severe long-term outcomes, yet its biological underpinnings remain poorly understood. We conducted the first genome-wide association study of adolescent-onset depression, comprising 102,428 cases (diagnosis or clinical symptom thresholds) and 286,911 controls, including diverse ancestries. Cross-ancestry meta-analysis identified 52 independent variants across 17 loci; European-only analysis found 61 variants at 29 loci, with a SNP-based heritability of 9.8%. Comparative analyses revealed two genes unique to adolescent-onset versus lifetime depression, enriched in neuronal subtypes, and two genes as potential drug repurposing targets. Polygenic scores were associated with adolescent-onset depression across ancestries, persistent depression trajectories, more severe outcomes, as well as reduced cortical volume, surface area and white matter integrity. Genetic correlation and Mendelian randomisation analyses support shared genetic liability and causal links with early puberty and modifiable health and behavioural risk factors. These findings uncover novel genetic loci and refine biological pathways underlying adolescent-onset depression, revealing age-specific mechanisms and early intervention opportunities.

Journal Article↗

PubMed2026

Proteome-wide Mendelian randomisation of lung function to identify potential therapeutic targets for respiratory disease.

BACKGROUND: Despite multiple clinical trials, disease-modifying treatments for COPD are currently limited. Since many drugs target proteins, identifying causality between proteins and lung function informs understanding of COPD pathophysiology and may suggest novel targets. We used Mendelian randomisation (MR) to prioritise proteins as potentially causal for imparied lung function. For prioritised proteins, we explored their potential suitability as drug targets by predicting their effects on a range of clinical outcomes. METHODS: We used genome-wide association study (GWAS) data on 2923 proteins (n=48 195, UK Biobank) to identify single genetic variants (protein quantitative trait loci (cis-pQTLs)) associated with protein levels (p≤5×10-9, variant ≤100 kb of a transcription start site). We performed cis-pQTL-MR analyses of four spirometric traits (n=149 166, 36 independent cohorts). Sensitivity analyses included colocalisation and reverse direction MR. We report associations between cis-pQTLs for prioritised proteins and multiple clinical respiratory outcomes, and use phenome-wide analysis to explore potential adverse effects or drug repurposing opportunities. FINDINGS: 1841 proteins had a suitable cis-pQTL. We implicated 16 proteins as potentially causal for lung function (p<1.71×10-5): seven proteins have not been implicated by previous lung function GWAS or MR (CCND2, DTD1, PILRA, PTPRK, TDRKH, GRHPR, NUDT5), and we provide corroborative evidence for 10 proteins. We add to the literature identifying surfactant protein D (SFTPD) as a candidate, yet predict that integrin subunit alpha V (ITGAV) inhibition could impair some lung function measures, mimicking adverse results from a recent trial. INTERPRETATION: Our approach identifies proteins (some novel) that are potentially therapeutic targets for respiratory disease, and which warrant follow-up for utility and safety.

Journal Article↗

PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗

PubMed2026

PGM1 deficiency is linked to sarcomeric and mitochondrial dysfunction in patient-derived iPSC-cardiomyocytes.

BACKGROUND: PGM1-congenital disorder of glycosylation (PGM1-CDG) is frequently associated with cardiomyopathy. Although galactose therapy corrects glycosylation defects, cardiac dysfunction typically persists, suggesting a glycosylation-independent mechanism. Recent evidence of mitochondrial abnormalities in PGM1-deficient human and murine heart, together with the association of PGM1 with the Z-disk protein LDB3 (ZASP/Cypher), suggests a critical role for PGM1 in cardiomyocyte structural and energetic homeostasis. We hypothesized that PGM1-related cardiomyopathy arises from a glycosylation-independent disruption of Z-disk-mitochondrial coupling driven by loss of PGM1-LDB3 interactions, resulting in mitochondrial energy failure and impaired contractile function. METHODS: Induced pluripotent stem cell-derived cardiomyocytes (iCMs) were generated from PGM1-deficient patient fibroblasts. Multielectrode array (MEA) recordings, untargeted (glyco)proteomics, and pathway analysis were performed to assess functional and molecular changes. Key findings were validated using tracer metabolomics and mitochondrial respiration assays. RESULTS: PGM1-deficient iCMs exhibited reduced beating frequency, impaired contractility, and prolonged contraction kinetics. Proteomic analyses revealed depletion of Z-disk components, including LDB3. AlphaFold3 structural modeling predicted a direct interaction between PGM1 and LDB3, implicating PGM1 in Z-disk integrity, which was confirmed in vitro. In addition, mitochondrial proteins were severely depleted, prompting us to investigate mitochondrial function. Functional validation confirmed extensive metabolic rewiring, energy depletion, and severely impaired mitochondrial respiration. Finally, the in silico drug repurposing identified possible therapeutic options that could target PGM1-deficient cardiomyopathy. CONCLUSION: Our data suggests PGM1 is key regulator of cardiomyocyte function, linking sarcomeric Z-disk integrity with mitochondrial metabolism. These mechanistic insights offer a foundation for developing targeted therapies for PGM1-CDG and potentially other cardiomyopathies involving Z-disk dysfunction.

Humans↗

PubMed2026

Polygenic risk scores associate with asthma phenotypes and proteomic analyses implicate IL1R1 in two family-based studies.

Despite its high prevalence and the discovery of hundreds of genetic associations, the genetic determinants and heterogeneous manifestations of asthma remain incompletely understood. Incorporating polygenic risk scores (PRS) into asthma research offers a powerful approach to quantify inherited susceptibility, refine risk profiles, and advance mechanistic understanding of disease development. For this study, we leveraged whole-genome sequencing (WGS) data from two family-based cohorts of childhood asthma - the Genetics of Asthma in Costa Rica Study (GACRS) and the Childhood Asthma Management Program (CAMP) - to examine the transmission profiles of externally derived asthma PRS and their associations with clinical phenotypes in children with asthma. To further elucidate molecular mechanisms, we integrated large-scale external genome-wide association study (GWAS) summary statistics and genetic prediction models of protein abundance in a two-step proteome-wide association study (PWAS) of asthma. Our findings provide robust evidence supporting the validity of externally derived asthma PRS (asthma PRS association p-value p = 10-24 [GACRS and CAMP trios combined] for the Global Biobank Meta-analysis Initiative [GBMI]) and reveal consistent associations with spirometry measures and atopy markers across both studies, as 13 of 21 traits (62%) were significantly associated with the GBMI-PRS in the meta-analysis after multiple-testing correction. Moreover, the results of the integrative proteomic analysis implicate IL-1 signaling in the etiology of asthma, reinforcing the candidacy of IL1R1 antagonists for drug repurposing.

Journal Article↗

PubMed2026

Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple 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 associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings 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. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans↗

PubMed2026

No phenotypic resistance observed for most group-3 and -4 variants in Mycobacterium tuberculosis genes related to bedaquiline, clofazimine, delamanid, and pretomanid in a Central and West African context.

The interpretation of genetic variants' association (or not) with phenotypic resistance to newly introduced and repurposed antituberculosis drugs remains challenging, as many mutations detected by whole-genome sequencing (WGS) are classified as of uncertain significance (group 3) or not associated with resistance-interim (group 4) by the World Health Organization (WHO) mutation catalog v2. We evaluated the phenotypic impact of such variants on minimum inhibitory concentrations (MICs) for bedaquiline (BDQ), clofazimine (CFZ), delamanid (DLM), and pretomanid (PA) in Mycobacterium tuberculosis complex isolates from the multi-country DIAMA cohort in sub-Saharan Africa (SSA), which recruited RR/RS-TB patients naïve to these drugs. Among 1,475 isolates with available WGS data, 163 variants met eligibility criteria; due to viable strain unavailability, 89 isolates carrying 29 unique BDQ/CFZ-related and 60 unique DLM/PA-related variants were tested for MIC determination using broth microdilution. Additional structural modeling was performed to explore potential effects of amino-acid substitutions on protein stability. Among BDQ/CFZ-related variants, MICs above the critical concentrations (CCs) were consistently associated with mmpR5 variants, whereas variants in atpE, pepQ, and Rv1979c were not. DLM/PA variants (ddn, fbiA-D, and fgd1) were frequently detected as non-fixed populations, yet rarely yielding MIC values above the CC. Predicted structural destabilization showed no consistent association with MIC values or variant fixation status. Under the conditions tested, phenotypic resistance was not detected for most group 3 and 4 variants detected by WGS. Our data provide evidence from SSA to support improved interpretation of resistance-associated mutations for new and repurposed antituberculosis drugs.IMPORTANCEWhole-genome sequencing increasingly detects Mycobacterium tuberculosis complex mutations classified by the World Health Organization (WHO) mutation catalog v2 as group 3 variants of uncertain significance or group 4 variants not associated with resistance-interim, limiting reliable prediction of resistance to new and repurposed antituberculosis drugs. By generating minimum inhibitory concentration (MIC) data for such variants identified in a multi-country sub-Saharan African cohort, this study provides phenotypic evidence to support future refinement and expansion of the WHO mutation catalog v2. Notably, mmpR5 variants associated with elevated bedaquiline/clofazimine MICs were identified in eight isolates, suggesting that some patients in this cohort may have harbored pre-existing resistance-associated variants yet remained potentially eligible for bedaquiline-containing regimens. These findings contribute to improving the interpretation of genomic resistance data and strengthening surveillance of resistance to bedaquiline, clofazimine, delamanid, and pretomanid.

Mycobacterium tuberculosis↗

PubMed2025

Identification and Validation of Novel Combinatorial Genetic Risk Factors for Endometriosis across Multiple UK and US Patient Cohorts.

BACKGROUND: Endometriosis affects about 10% of women usually of reproductive age. It often has severe negative impacts on patients' quality of life, but the average time to a definitive diagnosis remains 7-9 years, and there are few effective therapeutic options. Relatively little is known about the genetic drivers of the disease even though its heritability is fairly high. A recent large genome wide association study (GWAS) meta-analysis identified 42 genomic loci associated with risk of endometriosis, but together these explain only 5% of disease variance. METHODS: We used the PrecisionLife® combinatorial analytics platform to identify multi-SNP disease signatures significantly associated with endometriosis in a white European UK Biobank (UKB) cohort. We assessed the reproducibility of these multi-SNP disease signatures as well as 35 of the 42 meta-GWAS SNPs in a multi-ancestry American endometriosis cohort from All of Us (AoU) after controlling for population structure. RESULTS: We identified 1,709 disease signatures, comprising 2,957 unique SNPs in combinations of 2-5 SNPs, that were associated with increased prevalence of endometriosis in UKB. Pathways enriched in the disease signatures included cell adhesion, proliferation and migration, cytoskeleton remodeling, angiogenesis as well as biological processes involved in fibrosis and neuropathic pain.We observed a significant enrichment of these signatures (58-88%, p<0.04) that are also positively associated with endometriosis in the AoU cohort, including one 2-SNP signature that is individually significant. Reproducibility rates were greatest for higher frequency signatures, ranging from 80-88% for signatures with greater than 9% frequency (p<0.01) in AoU. Encouragingly, the disease signatures also show high reproducibility rates in non-white European AoU sub-cohorts (66-76%, p<0.04 for signatures with greater than 4% frequency).A total of 195 unique SNPs mapping to 98 genes were identified in the high frequency reproducing signatures (>9%). Of these, 7 genes were previously identified in the endometriosis meta-GWAS study and 16 genes have a previous association with endometriosis. 75 novel genes were identified in this study.We characterized 9 novel genes that occur at the highest frequency in reproducing signatures and that do not contain any SNPs linked to known GWAS genes, providing new evidence for links between endometriosis and autophagy and macrophage biology. Reproducibility rates, ranging between 73% to 85%. are especially strong for the signatures that contain these 9 genes independently of any SNPs mapping to the meta-GWAS genes. CONCLUSION: Although using much smaller, less well-characterized datasets than the previous whole genome meta-GWAS study, combinatorial analysis has provided important new insights into the genetics and biology of endometriosis including reproducible biologically relevant genes that are overlooked by GWAS approaches.The 75 novel gene associations provide new insights and routes for study of the disease and potential new therapies. Several of the novel genes identified are credible targets for drug discovery, repurposing and/or repositioning. Using the disease signatures identified as genetic biomarkers in trials of candidates drugs targeting specific mechanisms will enable precision medicine-based approaches. We hope this will encourage new targeted therapy discovery efforts.

Endometriosis↗

PubMed2026

The AI Revolution: Shaping the Present and Future of Pharmaceutical Research and Development.

The transformative role of artificial intelligence (AI) in the pharmaceutical industry is examined, with a focus on its significant contributions to drug discovery, development, and clinical trial processes. It highlights the inefficiencies and high costs associated with traditional drug development and explores how AI and machine learning (ML) can enhance these processes by analyzing extensive biological datasets. The historical context of AI in pharmaceutical development is examined, noting how advances in computational power and data accessibility have facilitated innovative methodologies, such as predictive analytics and natural language processing. Contemporary trends reveal the integration of AI technologies in drug design, repurposing, and patient response forecasting. This study also addresses the challenges of participant recruitment for clinical trials and proposes AI-driven solutions to optimize patient selection and data management. Furthermore, it discusses AI's role in tailored medicine, emphasizing its potential for advancing precision therapy through targeted drug development and personalized treatment strategies. The importance of digital tools, genomic data analysis, and AI-driven imaging technologies for customizing therapeutic approaches is underscored, along with the regulatory and ethical challenges posed by AI deployment in healthcare. This study illustrates the complexities of AI applications in the pharmaceutical sector, offering insights into both successful and unsuccessful initiatives. The findings suggest that the digitalization of the pharmaceutical industry and enhanced AI integration hold promise for developing safer and more effective therapeutic strategies, while also identifying obstacles to their widespread adoption and optimal functionality.

Artificial intelligence↗

PubMed2026

Genome-scale CRISPR screening uncovers SRSF6 as a target to sensitize hepatocellular carcinoma to radiotherapy.

BACKGROUND & AIMS: Radiotherapy confers clinical benefits to patients with hepatocellular carcinoma (HCC) across all stages, yet its clinical efficacy is limited by radioresistance. This study aimed to identify key regulators of HCC radiosensitivity through genome-wide functional screening. METHODS: A genome-wide CRISPR-Cas9 screen in Huh7 cells identified radiosensitivity regulators, with SRSF6 validated by siRNA knockdown and γ-H2AX assessment. Stable shRNA-mediated SRSF6 knockdown was established in Huh7 and HepG2 cells, followed by clonogenic, EdU incorporation, apoptosis, micronucleus, and comet assays. Mechanistically, RNA-seq, Western blotting, mRNA stability assays, RIP-qPCR, and RAD51 overexpression rescue assays were performed. The therapeutic potential of the SRSF6 inhibitor indacaterol was evaluated using MTS assays, HCC xenograft mouse models (BALB/c-nu/nu, n = 28), and HCC patient-derived organoids (PDOs) (n = 3). In addition, SRSF6 expression and its correlation with patient survival were analyzed using data from The Cancer Genome Atlas and a tissue microarray (n = 14 HCC and 14 paired adjacent non-tumorous liver samples). RESULTS: We identified the RNA-binding protein SRSF6 as a driver of HCC radioresistance. SRSF6 depletion enhanced the radiosensitivity of HCC cells (p <0.05-0.0001) by post-transcriptionally destabilizing the mRNAs of critical DNA repair genes (p <0.05-0.0001), thereby impairing radiation-induced DNA damage repair. The radiosensitizing effect of SRSF6 depletion was partially abrogated by ectopic overexpression of the core DNA repair protein RAD51 (p <0.05-0.001). Indacaterol exhibited cytotoxic effects on HCC cells (p <0.05-0.0001) and enhanced the antitumor efficacy of radiation in vivo (p <0.05-0.0001), as further validated across multiple HCC patient-derived organoids (p <0.05-0.0001). CONCLUSIONS: SRSF6 is a key regulator of HCC radioresistance through its post-transcriptional control of DNA repair capacity, and represents a novel therapeutic target to sensitize HCC to radiotherapy. IMPACT AND IMPLICATIONS: In this study, we performed a genome-wide CRISPR-Cas9 knockout library screen to dissect the molecular determinants governing HCC radiosensitivity, and identified RNA-binding protein SRSF6 as a driver of HCC radioresistance. We demonstrate that SRSF6 depletion disrupts the post-transcriptional stability of key DNA repair gene mRNAs and enhances HCC radiosensitivity. These findings are important for radiation oncologists and translational researchers, as they identify SRSF6-dependent RNA regulation as a critical determinant of radiotherapy response in HCC. Practically, we show that the clinically approved bronchodilator indacaterol suppresses SRSF6 function and enhances the antitumor efficacy of radiotherapy, offering a readily repurposable pharmacological strategy to overcome radioresistance. These implications are based on preclinical evidence across multiple models; however, future clinical trials are needed to validate the safety and efficacy of indacaterol-based radiosensitization in patients with HCC.

DNA repair↗

PubMed2005

Paradoxical strategy for treating chronic diseases where the therapeutic effect is derived from compensatory response rather than drug effect.

Reversing chronic conditions remains an elusive goal of medicine. The modern medical paradigm based on blocking overactive pathways or augmenting deficient pathways offers symptomatic benefit, but tolerance to therapy can develop and treatment cessation can produce rebound symptoms due to compensatory mechanisms. We propose a paradoxical strategy for treating chronic conditions based on harnessing compensatory mechanisms for therapeutic benefit. Many current drugs may be repurposed for a paradoxical indication where the therapeutic effect is derived from compensatory response, rather than drug effect. For example, although exercise is associated with acute adrenergia, paradoxical downregulation of baseline sympathovagal ratio occurs as a remodeling response. For conditions that manifest chronic sympathetic bias such as cardiovascular diseases, judicious administration of adrenergic agonists may induce compensatory downregulation of baseline sympathovagal ratio. The concept may generalize to many other diseases, especially those involving pathways which exhibit strong homeostatic tendencies such as the neurologic, immune, and endocrine systems. Careful consideration of chronobiologic features is necessary to optimize dosing strategies for modulating compensatory responses, and eccentric dosing schedules, shorter-acting formulations, or pulsatile delivery may be desirable in some cases. To what extent the effect of desensitization to current therapy is mistaken for disease progression in conditions such as diabetes, myopia, depression, and hypertension warrants investigation. The merits of combining behavioral and drug therapies such as diet-insulin therapy for diabetes and exercise-beta-blockade for cardiovascular disease should be revisited since there is a risk for exacerbating the underlying dysfunction. The reduced dynamic range of various environmental experiences and the tendency to revert to the mean through medical intervention, thermoregulation, and other modern lifestyle changes may play under-recognized roles in human diseases. Perhaps alternating agonists and antagonist may exercise the entire dynamic range of pathways and improve health.

Adrenergic Agonists↗

PubMed2026

Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.

Cancer remains one of the leading global health burdens, with increasing complexity in genomic, imaging, and clinical datasets presenting significant challenges for effective management. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges by enabling pattern recognition, knowledge integration, and data-driven decision-making. This review highlights recent advances in the application of AI across cancer research, diagnosis, and therapy. In research, AI accelerates drug discovery and repurposing, enhances genomic data interpretation, and facilitates biomarker identification through multi-omics integration. In diagnosis, AI has demonstrated high technical performance in radiology for lesion detection and image segmentation, in pathology for tumour grading and molecular prediction, and in liquid biopsy for non-invasive biomarker analysis. In therapy, AI supports precision medicine by predicting treatment responses, monitoring disease progression, and optimizing clinical trial design. Despite these advances, barriers such as data heterogeneity, algorithmic bias, interpretability, and regulatory challenges remain. Future directions, including explainable AI, federated learning, multimodal modelling, and digital twins, hold promise for translating AI-driven innovations into routine oncology practice. Significance Statement This review provides a timely synthesis of recent (2020-2025) advances in artificial intelligence across cancer research, diagnosis, and therapy, highlighting applications in drug discovery, genomics, multi-omics biomarker identification, and clinical decision-making. By integrating technological progress with translational and clinical relevance, this work serves as a valuable resource for bridging AI innovation with precision oncology practice. As a narrative review, the literature was identified through targeted PubMed, Scopus, and Google Scholar searches, combining terms for artificial intelligence, machine learning, and deep learning with cancer-related keywords, with priority given to peer-reviewed studies published between 2020 and 2025, seminal earlier works, and official regulatory or guideline documents. Within each domain, representative studies were selected to illustrate methodological diversity, clinical context, and current translational readiness rather than to provide exhaustive coverage of an extremely rapidly evolving field.

Artificial intelligence↗

PubMed2025

Fingolimod as a potent anti-Staphylococcus aureus: pH-dependent cell envelope damage and eradication of biofilms/persisters.

BACKGROUND: The urgent need for new antibacterial drugs has driven interest in repurposing therapies to combat Gram-positive biofilms and persisters. Fingolimod, an Food and Drug Administration (FDA)-approved drug for multiple sclerosis, shows bactericidal activity, particularly against Methicillin-resistant Staphylococcus aureus (MRSA) and biofilm-related infections. With a well-documented safety profile and strong translational potential, it aligns with World Health Organization's goals for antimicrobial repurposing. However, the action mode and mechanism of Fingolimod against gram-positive bacteria remain elusive. METHODS: This study utilized clinical Staphylococcus aureus (S. aureus), Enterococcus faecalis (E. faecalis), Streptococcus agalactiae (S. agalactiae). And their susceptibility to Fingolimod and other antibiotics was tested via Minimum Inhibitory Concentration (MIC) assays. Biofilm inhibition and hemolytic activity were evaluated using crystal violet staining, Confocal Laser Scanning Microscopy (CLSM), and hemolysis assays, respectively, while the effect of phospholipids on Fingolimod efficacy was assessed with checkerboard assays. Membrane permeability and integrity were measured using SYTOX green staining and transmission electron microscopy. Whole-genome sequencing was performed on Fingolimod-resistant S. aureus isolates to identify Single Nucleotide Polymorphisms (SNPs) linked to resistance. RESULTS: Our data indicated that Fingolimod exerted bactericidal activity against a wide spectrum of gram-positive bacteria, including S. aureus, E. faecalis, S. agalactiae. Moreover, Fingolimod could significantly eliminate the persisters, inhibit biofilm formation and eradicate in-vitro mature biofilms of S. aureus. The mechanism by which Fingolimod rapidly eradicated S. aureus involved a pH-dependent disruption of bacterial cell permeability and envelope integrity. Concomitantly, exogenous supplementation of phospholipids in the culture medium resulted in a dose-dependent increase in the MIC of Fingolimod. Specifically, the addition of 64 μg/mL of cardiolipin (CL) and phosphatidylethanolamine (PE) completely nullified the bactericidal activity of Fingolimod at a concentration of 4 times the MIC. After four months of Fingolimod exposure, the MIC values of S. aureus showed a slight increase, indicating that it is not prone to developing drug resistance. CONCLUSION: Fingolimod exhibits bactericidal activity against diverse gram-positive bacteria, with remarkable effects on S. aureus (including MRSA), disrupting bacterial cell structural integrity in a pH-dependent way and eradicating biofilms and persisters of S. aureus.

Biofilms↗