Total neoadjuvant therapy (TNT) combined with immunotherapy (iTNT) has shown promising results for locally advanced rectal cancer (LARC) in microsatellite stable (MSS) patients. However, traditional clinical approaches often fail to accurately forecast clinical outcomes, and reliable methods to predict treatment response and recurrence risk are essential for optimizing treatment strategies and maximizing organ preservation. We analyzed clinical and genetic data from 63 patients from the prospectively registered TORCH clinical trial (NCT04518280) who received iTNT from May 1, 2021, to September 15, 2022. Baseline tissue mutations, magnetic resonance tumor regression grade (mrTRG), and circulating tumor DNA (ctDNA) positivity at different time points were collected and examined for their association with complete response (CR) and recurrence. Integrated models incorporating these features were developed to assess their accuracy in distinguishing CR from non-CR patients. Prognostic features were analyzed using Cox proportional hazards regression and Kaplan-Meier analyses. Baseline SMAD4 mutation was significantly associated with poor response to iTNT (P = 0.007), while post-treatment mrTRG (P < 0.0001), tumor mutational burden (TMB) (P = 0.03) and latest ctDNA status (P = 0.03) also correlated with CR rates. Integration of post-treatment mrTRG, SMAD4 mutation, TMB, and latest ctDNA status significantly improved CR discrimination accuracy compared with the mrTRG-only model (area under the curve (AUC) 0.92 vs. 0.75, P = 0.0098). Early integrated models incorporating mid-iTNT mrTRG and these genetic features showed better discriminative performance than the mrTRG-only model (AUC 0.89 vs. 0.76, P = 0.047). Furthermore, baseline SMAD4 mutations and ctDNA positivity at later time points correlated with an increased risk of disease progression, including local recurrence and distant metastasis. Our findings highlight the discriminative power of combining genetic features, particularly SMAD4 mutation and ctDNA, with clinical assessments like post-treatment mrTRG, for improving CR identification and early recurrence risk stratification over mrTRG alone in iTNT-treated LARC. These models could serve as complementary risk stratification tools to guide personalized treatment strategies, enabling early identification of patients requiring intensified therapy or nonoperative management. https://ClinicalTrials.gov identifier NCT04518280; Registration date August 15, 2020.
A variety of common and rare genetic factors have been implicated in the development of amyotrophic lateral sclerosis (ALS), and the evidence is that a genetic component is present in most affected individuals. However, our current understanding of ALS genetics causally explains only a small proportion of sporadic ALS, which accounts for over 90
Liver transplantation is the definitive treatment for end-stage liver disease, but acute allograft rejection affects 15–30
Despite advances in sequencing technologies and variant interpretation frameworks, many variants identified in genetic testing, particularly missense variants, remain classified as variants of uncertain significance (VUS), posing ongoing challenges in diagnosis and clinical management. This review explores the ongoing challenges of VUS interpretation in clinical genomics and its impact on patients, clinicians, and healthcare systems. We summarize established strategies that support VUS resolution, including large population reference databases, data-sharing initiatives, computational prediction tools, consensus-based guidelines, deep phenotyping, and functional validation assays. Building on these foundations, we highlight emerging approaches that leverage multi-omics analyses, high-throughput experimental platforms (e.g., saturation genome editing and cell-based morphological assays), and artificial intelligence-driven tools to improve scalability and interpretive accuracy. Together, these complementary approaches aim to reduce uncertainty, increase diagnostic yield, and enhance the clinical utility of genomic testing in both research and precision medicine.
Abstract Background Bevacizumab is widely used as an anti-angiogenic maintenance therapy in ovarian cancer; however, there are currently no validated clinical criteria to guide patient selection for its use. Methods To satisfy the urgent need for bevacizumab response biomarkers, we created a novel RNA-seq dataset ( n = 244) and applied unsupervised and supervised machine learning to identify expression signatures associated with benefit from adding bevacizumab to standard treatment and validated our findings using a previously published microarray dataset ( n = 377). Additionally, we validated the existence of the discovered signatures using RNA-seq data from the TCGA-OV cohort ( n = 426) and performed public expression data mining to provide a biological interpretation of the prioritized signature. Results Among expression signatures reproducibly detected in independent datasets, one was prioritized as a potential predictive biomarker for bevacizumab benefit. Further stratified analysis revealed that over-expression of this signature was associated with improved overall survival in patients who received bevacizumab in addition to standard chemotherapy in both novel (HR = 0.41, 95% CI: (0.23–0.74), adj. p -value = 0.008) and previously published cohorts (HR = 0.51, 95% CI: (0.34–0.75), adj. p -value = 0.003), while no significant survival benefit from bevacizumab was observed in patients negative for this signature. We hypothesize that this signature may be associated with stemness-like features, possibly driven by CTCFL . In addition, we identified several other signatures reproducible in independent datasets and not related to known molecular subtypes of ovarian cancer, which may also represent biomarker candidates and require further validation in additional RNA-seq data. Conclusions We identified a previously undescribed expression signature with potential predictive value for bevacizumab benefit, and revealed transcriptional heterogeneity of ovarian cancer that extends beyond current molecular classifications. Given the high heterogeneity of ovarian cancer and that the novel signature only partially explains variation in survival outcomes under bevacizumab treatment, larger RNA-seq datasets are required to further improve predictive models.
The gut microbiome has been linked to responses to immune checkpoint inhibitors (ICIs), yet longitudinal multi-omics studies remain limited. Here, we serially collected gut microbiome data and performed multi-omics analyses, including RNA sequencing (RNA-seq) and whole-exome sequencing (WES), on samples from 92 patients with advanced non-small cell lung cancer (NSCLC) treated with pembrolizumab, to identify factors contributing to treatment response. We compared gut microbiome data with those of a healthy cohort (n = 92) and examined changes in gut microbial signatures during immunotherapy. Gut microbiome data from publicly available ICI-treated cohorts (n = 728) were also used together with our own cohort to develop and validate a score for classifying response to ICIs. Responders (defined as complete response, partial response, or stable disease of at least 6 months) were enriched for Faecalibacterium, while non-responders were enriched for Lactobacillus and Escherichia, with these taxa showing specific associations with dietary fiber intake and antibiotic use. Dynamic shifts in dysbiotic signatures were observed throughout treatment and disease progression, with dissimilarity from the healthy cohort decreasing after treatment only in responders but increasing at progression in both responders and non-responders. We also developed a cross-population score based on the ratio of responder- to non-responder-associated microbial markers. This score showed a significant positive correlation with the index reflecting gut microbial health status and improved response stratification when combined with tumor mutational burden (TMB). These findings highlight the importance of characterizing dysbiosis using gut microbial signatures, which act along a distinct and complementary axis to explain responses to ICIs.
Neonatal extraintestinal pathogenic Escherichia coli (ExPEC), which can cause severe long-term sequelae by systemic infections, is gradually becoming the primary pathogen threatening neonatal health. The lack of large-scale genomic epidemiological investigation hinders further understanding of neonatal ExPEC. We conducted this nationwide multicenter study to support further strategies for improving neonatal ExPEC management. The neonatal ExPEC strains and clinical information, including antimicrobial resistance phenotype, were collected from nine centers within 7 provinces across China between 2018 and 2023. Whole-genome sequencing was performed. Sequence types (ST) and serotypes were acquired to characterize the strains. Phylogenetic analysis and pan-genomic analysis were conducted to identify the population structure. Bioinformatics analysis associated with virulence factors, antimicrobial resistance genes, and mobile genetic elements were conducted. To characterize the situation of horizontal gene transfer, we developed a computational tool for identifying horizontal evolutionary patterns from large-scale genomic draft assemblies. Co-occurrence and co-localization metrics were used to describe the synergistic effects and transmission mechanism of genes. A total of 411 neonatal ExPEC strains were included. ST1193 (18·0
Abstract Background Identifying individuals at risk for future weight gain is challenging, partly because associations with traditional clinical risk factors may be biased by confounding and reverse causation. Polygenic risk scores (PRS) provide a stable, lifelong measure of genetic predisposition to obesity. However, existing PRS have not been evaluated for their association with longitudinal weight change in adulthood and often lack generalizability across diverse genetic ancestry groups. Methods We conducted ancestry-specific genome-wide association study meta-analyses of body mass index (BMI) in populations of European, African or African American, Admixed American, East Asian, and South Asian ancestries and developed ancestry-specific PRS. A multi-ancestry polygenic risk score (MAPRS) was trained using ancestry-specific PRS in a model selection dataset ( N = 39,685) from the All of Us Research Program (AoU). We evaluated the MAPRS in an independent AoU model evaluation dataset ( N = 158,743) for BMI prediction and in a separate AoU test dataset ( N = 78,219) with repeated measurements over 1.5–2.5 years for weight change prediction. The outcomes included change in BMI and ≥ 10% or ≥ 5% total body weight (TBW) gain. We further examined the relationship between MAPRS and 12 clinical risk factors commonly comorbid with obesity in relation to weight change. Results The MAPRS captured 7.05% of the variance in measured BMI in the AoU model evaluation dataset and demonstrated improved generalizability across all non-European genetic ancestry groups. In the AoU test dataset, conditioned on baseline BMI at the second-to-last measurement, a one SD increase in MAPRS was associated with a 0.16 kg/m 2 increase in future BMI (standard error = 0.012 kg/m 2 ; p -value = 2.2 × 10 –39 ), 1.27-fold increased odds of experiencing ≥ 10% TBW gain (95% CI: 1.24–1.31; p -value = 1.4 × 10 –55 ), and 1.15-fold increased odds of experiencing ≥ 5% TBW gain (95% CI: 1.13–1.18; p -value = 2.8 × 10 –39 ). These associations were observed across all genetic ancestry groups and remained highly consistent after adjustment for any clinical risk factor. In contrast, most clinical risk factors demonstrated inconsistent or weaker associations with weight change outcomes. Conclusions We developed an MAPRS for BMI that represents a robust and generalizable risk factor for longitudinal weight gain in adulthood, providing a foundation for genetically informed risk stratification and earlier, more targeted obesity prevention strategies.
Liquid biopsy has emerged as a transformative development in oncology, enabling the minimally invasive detection and monitoring of cancer through the analysis of tumor-derived material in blood. Moving beyond single-variable analysis, multifeature sequencing-based liquid biopsy (MSLB) integrates diverse classes of data from a single blood sample to provide deep multifactorial insight into tumor biology. In this review, MSLB is defined as the extraction of multiple biological signals from a single sequencing dataset and is put in the context of other layers of multimodal diagnostics. We focus on how recent advances in patient-, and potentially microbe-derived, cell-free nucleic acid analysis expand the biological information that can be extracted from a single blood sample. MSLB enables this by allowing the concurrent assessment of, for example, DNA methylation, copy number, fragmentation, and, in exploratory workflows, microbe-associated signals. This provides a broader view of tumor, immune, and microenvironment states. When combined with emerging bioinformatic and machine-learning frameworks, these complementary signals may improve early detection, disease monitoring, and treatment selection. Addressing challenges in standardization, validation, and regulatory alignment will be essential to determine how MSLB can be integrated into routine oncologic practice.
Gliomas comprise a heterogeneous group of Central Nervous System (CNS) tumors in which gene fusions (GFs) are important oncogenic drivers with emerging diagnostic and therapeutic relevance. However, a portion of glioma samples remain negative on targeted clinical GF panels, leaving underlying molecular drivers unresolved. To investigate the fusion landscape of these diagnostically challenging samples, we analyzed 49 high- and low-grade gliomas previously classified as fusion-negative by the Children’s Hospital of Philadelphia Fusion Panel using whole-transcriptome long-read sequencing with Oxford Nanopore Technologies (ONT). We identified numerous candidate oncogenic GFs beyond panel constraints, including fusions involving COSMIC Cancer Gene Census genes and recurrent CNS fusion partners not captured by the clinical panel. Long-read sequencing further enabled direct resolution of full-length fusion transcripts and associated isoform structures. Integrating GF detection with isoform-level transcript analysis identified fusion-associated isoforms with alternative splicing patterns near reported GF breakpoints, including ZNF254::GNAS and PTPRK::NOX3, which have not been reported in literature or existing fusion databases. To assess functional relevance, 15 candidate GFs were evaluated using the Drosophila melanogaster model, with ventral nerve cord (VNC) morphology serving as a quantitative in vivo readout of fusion-induced disruption of glial regulation. Eight candidate GFs induced significant VNC abnormalities relative to wild-type controls, including glial overgrowth and tumor-like structural disruption in vivo. Notably, CLDND1::WRN and DUSP22::APOE produced the most pronounced VNC phenotypes. Together, these findings demonstrate that transcriptome-wide long-read sequencing can uncover previously undetected candidate GFs with potential functional and clinical relevance in panel-negative gliomas.
Duchenne muscular dystrophy (DMD) is a X-linked disease affecting skeletal and cardiac muscle and is caused by mutations in the dystrophin gene (DMD). Patient-derived induced pluripotent stem cells (iPSCs) serve as reliable in vitro disease models. Their genetic correction by CRISPR/Cas9 allows the generation of isogenic controls and holds promises for gene therapy. However, restoring full-length dystrophin, especially when deletions involve multiple exons, constitutes a technological challenge. This study aimed to fully repair the dystrophin gene from a DMD iPSC line carrying the deletion of exons 49–50 and to characterize the rescue of the cardiac phenotype. We developed an innovative CRISPR/Cas9-based approach involving the insertion of coding sequences of the deleted region, at the 3’ of exon 48, thereby generating a single continuous coding sequence encompassing exons 48-49-50. Subsequently, iPSCs were differentiated into cardiomyocytes and cardiac fibroblasts. Cardiac phenotypes were analysed by western blot, immunofluorescence, ELISA, FACS, Ionoptix, 3D engineered heart tissue (EHT) and single-nuclei RNA-seq. The correction of a two-exons DMD gene deletion in Duchenne iPSCs, using CRISPR/Cas9, enabled the re-expression of a stable and functional full-length dystrophin in cardiomyocytes resulting in the rescue of cardiac pathological phenotypes. Edited cardiomyocytes showed improved morphology, reduced release of the cardiomyocytes damage marker troponin I, and decreased ROS production. Moreover, dystrophin restoration enhanced contractility and ameliorated the Ca2+ kinetics. Notably, edited iPSC derived fibroblasts showed reduced pro-fibrotic stimuli response. In parallel, we also observed enhanced functioning of a 3D engineered heart tissue and profound change in the transcriptomic profile in both cardiomyocytes and fibroblasts after the re-expression of full-length dystrophin. We developed an innovative approach that enabled the re-expression of full-length dystrophin in a DMD iPSC line with consequent complete rescue of in vitro DMD cardiac phenotypes. On the long term, these results could lay a foundation for future applications of cell therapy or in vivo CRISPR/Cas9-based intervention to treat DMD.
Abstract Background The premature aging disorder Hutchinson-Gilford Progeria Syndrome (HGPS) is caused by de novo LMNA mutations producing the aberrant Lamin A isoform progerin. HGPS patients die from cardiovascular disease, with their arteries showing extensive cellular and structural remodeling, but the mechanisms driving vascular dysfunction are not fully understood. Methods To define molecular processes underlying progressive vascular degeneration in HGPS, we performed single-cell RNA-sequencing (scRNA-seq) of aortic arch cells from Lmna G609G/G609G mice without atheroprone stimuli. These mice carry the murine equivalent of the most common HGPS-causing mutation and faithfully recapitulate the vascular phenotype. Sequencing was performed at multiple ages to capture disease-related and time-dependent transcriptional changes. We used Smart-seq2 for sequencing, due to its high sensitivity and full-length transcript coverage. Histology, immunostaining and in situ hybridization were used for arterial characterization. Results The aortic arch of Lmna G609G/G609G mice exhibited a gradual age-dependent vascular smooth muscle cell (VSMC) loss, accompanied by a transient proliferation surge, and ultimately by increased apoptosis. scRNA-seq identified transcriptionally distinct cell populations with unique features that evolved during disease progression. Disease-enriched VSMCs at early stages were characterized by elevated endoplasmic reticulum (ER) stress. With disease development, these VSMCs further underwent phenotypic switching toward a fibroblast-like state, which was predicted to expand through non-cell-autonomous mechanisms. At later stages, disease-enriched VSMCs upregulated apoptotic gene expression, partially coinciding with sustained ER stress. Furthermore, progeria VSMCs showed an increase in both DNA damage and somatic SNVs, with the increased number of SNVs correlating with high expression of ER stress, ROS and p53-related genes. In contrast, progeria-enriched fibroblasts either became activated or increased their cartilage production and showed a delayed accumulation of somatic SNVs compared to VSMCs, highlighting both a cell-type-specific progerin response and differences in somatic mutation susceptibility. Conclusions Our study shows that progerin leads to somatic mutation accumulation particularly in VSMCs, highlighting the need for early, cell-type-specific therapeutic intervention in HGPS to prevent permanent vascular tissue damage. In addition, the cell-type-specific molecular dynamics of the aortic arch VSMCs and fibroblasts during HGPS disease progression are provided in a user-friendly searchable scRNA-seq database available for preclinical research targeting vascular aging.
Minigene RT-PCR assays are widely used to assess variant impact on splicing, with increasing reports of massively parallel splicing assays (MPSAs) demonstrating potential to upscale diagnostic use of construct-based data. This study conducted a comprehensive evaluation of > 41,000 variants from construct-based splicing assays, to build evidence-based recommendations to support the consistent application of such assays in clinical variant interpretation. Seven MPSAs were reviewed for design limitations, and their discriminatory performance evaluated by assessing: assay score distribution; consistency of splice-impact thresholds with expectations based on SpliceAI predictions. A traditional minigene RT-PCR dataset comprising 673 variants from 14 studies was analysed to: assess potential for SpliceAI score to predict level of aberration; demonstrate performance of SpliceAI-10k calculator (SAI-10k-calc) to accurately predict variant-induced splicing events; calibrate evidence strength towards or against spliceogenicity based on SpliceAI score. Traditional minigene results were compared to patient-derived RNA results, and calibrated for evidence strength towards or against pathogenicity using assertions from ClinVar. MPSA datasets lacked specific information on variant-induced transcripts and had design limitations preventing detection of some aberrant splice events. Assay scores generated by five of seven MPSAs were unable to differentiate aberrant from natural splicing events. Traditional minigene results showed high predictive agreement between predicted and observed variant-induced events: SpliceAI score of 0.285 showed 90
Polyendocrine metabolic ovarian syndrome (PMOS) is a leading cause of anovulatory infertility. Although follicular developmental arrest is a defining feature of PMOS, the cell-type-specific mechanisms linking genetic susceptibility, endocrine imbalance and follicular microenvironmental dysfunction remain incompletely understood, particularly across clinical subtypes. We performed single-cell RNA sequencing of preovulatory follicular-fluid cells from healthy controls and women with uncomplicated PMOS, hyperandrogenic PMOS and metformin-treated insulin-resistant PMOS. We integrated these data with GWAS-derived disease relevance scores and ovarian chromatin-accessibility annotations to map genetic risk to follicular cell populations and regulatory programmes. Ligand–receptor analyses were used to infer intercellular communication networks. Transcriptomic metabolic findings were further assessed by follicular-fluid metabolic and steroid-hormone measurements, DHT-treated granulosa-cell functional assays, and reanalysis of a DHEA-induced mouse PMOS-like ovarian dataset. We generated a single-cell atlas of the PMOS follicular microenvironment comprising 220,983 high-quality cells. Granulosa cells showed the strongest enrichment of PMOS-related genetic risk, with regulatory-genetic support from ovarian snATAC-seq integration. Among granulosa cell states, a GC2_CYP19A1⁺ population was preferentially associated with hyperandrogenic PMOS and displayed a transcriptional programme characterized by steroidogenic remodelling coupled to reduced energy-metabolic activity. Biochemical profiling supported this endocrine–metabolic imbalance, showing increased follicular-fluid androstenedione (ASD) and estrone, reduced estradiol and E2/ASD ratio, decreased ATP, citrate and fumarate hydratase activity, and increased lactate and β-hydroxybutyrate. Prolonged DHT exposure impaired granulosa-cell mitochondrial respiration, ATP production and mitochondrial membrane potential. In parallel, PMOS subtypes showed immune remodelling and altered ligand–receptor signalling, with hyperandrogenic PMOS associated with increased inflammatory signalling and reduced homeostatic or tissue-repair pathways. Reanalysis of a DHEA-induced mouse ovarian dataset provided supportive evidence for androgen-associated steroidogenic pathway perturbation. This study provides a cell-resolved framework linking PMOS genetic susceptibility to endocrine–metabolic and immune remodelling in the preovulatory follicular microenvironment. The identification of a hyperandrogen-associated GC2_CYP19A1⁺ state highlights a candidate cellular programme in which steroidogenic imbalance is coupled to impaired granulosa-cell bioenergetics. These findings provide a resource for future validation and may inform microenvironment-guided stratification of PMOS and the development of strategies aimed at restoring follicular function.
Acute myeloid leukemia (AML) remains a therapeutically challenging hematological malignancy with high relapse rates, especially in patients harboring mutations in the FMS-like tyrosine kinase 3 (FLT3) gene. FLT3, a receptor tyrosine kinase involved in hematopoietic cell proliferation and survival, is frequently mutated or overexpressed in AML, contributing to leukemic proliferation and poor prognosis. Although FLT3 inhibitors have demonstrated clinical benefit, resistance often develops, underscoring the need for alternative treatment strategies. In this study, we developed and evaluated allogeneic anti-FLT3 CAR T cells engineered by CRISPR/Cas9-mediated targeted integration into the T cell receptor α constant (TRAC) locus (TRAC-CAR T). This approach enables CAR expression under the endogenous TRAC promoter while simultaneously disrupting native TCR expression, thereby allowing the use of allogeneic T cells with reduced risk of graft-versus-host disease. TRAC-CAR T cells demonstrated efficient disruption of TCRαβ expression, robust surface CAR expression, and superior integration efficiency compared to safe harbor AAVS1-targeted and lentivirally transduced CAR T cells (LV-CAR T). Functionally, TRAC-CAR T cells exhibited potent and selective cytotoxicity against FLT3-positive AML cell lines in vitro, achieving comparable killing relative to LV-CAR T cells. In a xenograft AML mouse model, TRAC-CAR T cells effectively reduced leukemia burden and prolonged mouse survival, with efficacy matching that of LV-CAR T cells. Notably, TRAC-CAR T cells exhibited reduced exhaustion marker expression. Together, these results highlight TRAC-integrated anti-FLT3 CAR T cells as a promising off-the-shelf immunotherapy candidate, combining precise gene editing, efficient manufacturing, and potent anti-leukemic activity.
Fibroblast plasticity underlies a wide spectrum of pulmonary diseases, yet the molecular programs governing fibroblast state transitions in non-malignant contexts remain poorly understood. We integrated 1,470,730 single-cell transcriptomes across three non-cancer lung diseases to construct a comprehensive fibroblast atlas. Integrated clustering and regulon analyses were used to delineate fibroblast subtypes, disease-associated transcriptional programs, and state transitions. ANTXR1, identified as a representative regulator, was validated by tissue immunohistochemistry and siRNA knockdown in primary lung fibroblasts under TGF-β stimulation. Compared with malignancy, fibroblasts in non-cancer lungs exhibited relatively modest transcriptional alterations but maintained distinct disease-associated phenotypes. We further identified a set of fibroblast-specific genes unique to non-malignant contexts, defining molecular features that distinguish fibrotic remodeling from tumor-associated stromal activation. Analysis of fibroblast subtype- and disease-specific regulons revealed 76 gene signatures that were significantly inversely correlated with lung function and distinguished chronic from acute fibrotic fibroblast states. Functional validation demonstrated that ANTXR1 knockdown markedly reduced COL1A1 expression, underscoring its role in fibroblast activation. This work establishes a comprehensive single-cell fibroblast atlas of non-cancer lung diseases, uncovering disease- and subtype-specific regulatory programs and highlighting potential therapeutic targets for fibrotic remodeling.
Chronic pain is a major global health burden with a substantial but incompletely understood genetic basis. Although genome-wide association studies have identified multiple common-variant loci for pain-related traits, the contribution of rare coding variants to chronic pain susceptibility, biological mechanisms, and shared architecture across pain phenotypes remains unclear. We performed an exome-wide association study across ten chronic pain phenotypes using whole-exome sequencing data from 327,642 European participants in the UK Biobank. Variant-level and gene-level association analyses were conducted to identify common and rare coding signals. Downstream analyses included conditional analysis, statistical fine-mapping, validation in non-European populations, replication in independent cohorts, cross-trait meta-analysis, colocalization, pathway and tissue enrichment, single-cell enrichment, summary-data-based Mendelian randomization, and estimation of heritability and genetic correlation from common and rare variants. We identified 286 significant variant-level associations arising from 235 unique variants mapped to 117 genes, including 13 novel lead variants and 44 independent lead signals. Gene-level analyses identified ten significant genes driven predominantly by rare functional variants: ADAMTSL5, ANKRD12, ARID5A, DPP7, GMCL1, HCK, KIF20B, SLC13A1, UBR2, and ZNF558. Functional analyses implicated pathways related to nervous system development, axonogenesis, and synaptic organization, with enrichment in brain tissues and hTRPM8-expressing dorsal root ganglion neurons. Mendelian randomization analyses highlighted convergent regulatory effects across neural and immune-related tissues. Heritability analyses showed that rare coding variants explained a modest proportion of variance relative to common variants but exhibited larger average effects in the most damaging functional classes, particularly high-confidence loss-of-function variants. Genetic correlation analyses revealed shared genetic components among chronic pain conditions and positive correlations with selected psychiatric and nervous system disorders. These findings provide a comprehensive map of coding variation underlying chronic pain and show that both common and rare coding variants contribute to its genetic architecture. Rare coding variants explain only a limited fraction of heritability but offer strong mechanistic insight by implicating discrete genes and pathways related to neuronal development, neuroimmune signaling, and tissue homeostasis. This work advances understanding of chronic pain biology and provides a foundation for future multi-ancestry studies and functional validation.
Tandem repeat expansions have been implicated in various neurological conditions. Here, we present a novel hypermethylated CCG repeat expansion on Xp22 in the 5’UTR of BCLAF3 in males with neurodevelopmental disorders. We used patient-derived fibroblasts and neuronal models from a family with BCLAF3 repeat expansions to generate multiomic data and investigate downstream molecular consequences of the repeat expansion. To identify additional affected individuals with BCLAF3 repeat expansions, we screened methylation arrays (n = 12,375) and short-read genomes (n = 15,963) from probands with neurodevelopmental presentations. We also characterized BCLAF3 repeat expansions in the general population using long-read sequencing data (n = 793) and population-level short-read sequencing data (n = 410,076). Long-read sequencing validated hypermethylation of expanded repeats. Patient-derived cells showed repressed BCLAF3 RNA and protein expression. We show that the BCLAF3 CCG repeat expansion constitutes a previously uncharacterized fragile site (FRAXG) that shifts the surrounding chromatin compartment from open euchromatin to closed heterochromatin. Using our multiomic screening approaches, we identified three additional unrelated males and one related male cousin with long-read sequencing validated (n = 2) or short-read sequencing predicted (n = 2) repeat expansions. In one family, the BCLAF3 repeats segregate with more severe phenotypes than expected for the primary diagnoses. Long-read sequencing in three carrier mothers showed skewed X-inactivation against the repeat expansion, highlighting the potential deleterious effect of an allele with an expansion. Expansions were absent in long-read sequencing data from control populations. Assessment of the BCLAF3 repeat expansion in the UK Biobank indicates that it may be 20X rarer than FMR1 repeat expansions. CCG repeat expansions in the 5’UTR of BCLAF3 likely constitute a novel genetic etiology associated with X-linked neurodevelopmental phenotypes in males. Future work will be essential to delineate the phenotypic spectrum and determine a disease pathomechanism.
Predicting cognitive decline from brain MRI is a central question in neuroscience. Hippocampal volume (HV) is a key cognitive biomarker, and normative models can be augmented with multimodal information. Here we augment normative models with genetic information and show improvements in cognitive decline prediction across multiple experimental setups. We improve normative models for HV by integrating multi-threshold polygenic scores (PGS) with demographic and imaging data using Gaussian Process Regression (GPR). Models were trained on 23,997 participants from UK Biobank (UKBB) and validated on 3,000 out-of-sample participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European Prevention of Alzheimer's Disease (EPAD) cohorts. Our genetically-informed models significantly strengthened associations across six experimental designs and 13 key neurocognitive measures, including Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Alzheimer’s Disease Assessment Scale (ADAS), while enhancing prediction of future cognitive decline. Together, these findings underscore the promise of integrating multi-threshold PGS with neuroimaging-based predictive models to improve prognostication and early intervention strategies for neurodegenerative diseases.
The human gut microbiota interacts with host biology as exemplified by the effects of the Ruminococcus torques (RT) ATCC 27756 strain—a commensal human intestinal bacterial strain that synthesizes the polypeptides RORDEP1 and RORDEP2 which in rodents improve metabolism. Motivated by the metabolic effects of the RT strain in the rodent host, we explored in a short-term double blind and placebo-controlled randomized cross-over trial the feasibility and potential physiological responses of the same bacterial strain in humans. The trial undertaken at Herlev and Gentofte Hospital, Denmark included 32 healthy overweight adults. Decided by block randomization with blocks of six, we infused either 3.1 × 1011 colony forming units of the live RT ATCC 27756 strain or placebo into the duodenum during an observation period of six hours including a two-hour oral glucose tolerance test. Insulin sensitivity measured as Matsuda Insulin Sensitivity Index was the primary endpoint. The infusion of the bacterium was safe and well-tolerated. We found no effects on the primary endpoint of the trial. Compared to placebo, short-term RT infusion induced a relative rise in plasma concentrations of glucagon-like-peptide-1 (GLP-1) and peptide YY (PYY) in parallel with a relative decline in gastric inhibitory polypeptide (GIP). These intestinal hormone responses mirrored those previously reported from studies in rats. The abundance of secondary bile acids in plasma as well as a plasma marker of bone remodeling increased after infusion of RT compared to placebo. Measures of glucose tolerance, energy expenditure, cutaneous thermography, and main markers of systemic low-grade inflammation remained unchanged. Duodenal infusion during six hours of the RT ATCC 27756 strain in healthy overweight humans shows that the bacterial strain is well tolerated, and the short-term effects on intestinal hormone release align with those previously reported in rodents. The trial is registered prospectively in ClinicalTrials.gov on 2022–06-28 with ID NCT05448274 (https://clinicaltrials.gov/study/NCT05448274?intr=ruminococcus
