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PubMed2025

A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito.

The female Aedes aegypti mosquito's remarkable ability to hunt humans and transmit pathogens relies on her unique biology. Here, we present the Aedes aegypti Mosquito Cell Atlas, a comprehensive single-nucleus RNA sequencing dataset of more than 367,000 nuclei from 19 dissected tissues of adult female and male Aedes aegypti, providing cellular-level resolution of mosquito biology. We identify novel cell types and expand our understanding of sensory neuron organization of chemoreceptors to all sensory tissues. Our analysis uncovers male-specific cells and sexually dimorphic gene expression in the antenna and brain. In female mosquitoes, we find that glial cells in the brain, rather than neurons, undergo the most extensive transcriptional changes following blood feeding. Our findings provide insights into the cellular basis of mosquito behavior and sexual dimorphism. The Aedes aegypti Mosquito Cell Atlas resource enables systematic investigation of cell type-specific expression across all mosquito tissues.

Aedes aegypti↗

PubMed2025

Epithelial competition determines gene therapy potential to suppress Fanconi Anemia oral cancer risk.

Fanconi Anemia (FA) is a heritable syndrome characterized by DNA damage repair deficits, frequent malformations and a significantly elevated risk of bone marrow failure, leukemia, and mucosal head and neck squamous cell carcinomas (HNSCC). Hematopoietic stem cell gene therapy can prevent marrow failure and lower leukemia risk, but mucosal gene therapy to lower HNSCC risk remains untested. Major knowledge gaps include an incomplete understanding of how rapidly gene-corrected cellular lineages could spread through the oral epithelium, and which delivery parameters are critical for ensuring efficient gene correction. To answer these questions, we extended an agent-based model of the oral epithelium to include the delivery of gene correction in situ to FA cells and the competitive dynamics between cellular lineages with and without gene correction. We found that only gene-corrected lineages with substantial proliferative advantages (probability of resisting displacement out of the basal layer ≥ 0.1) could spread on clinically relevant timelines, and that these lineages were initially at high risk of loss in the generations following correction. Delivering gene correction to many cells minimizes the risk of loss, while delivery to many distinct locations within a tissue maximizes the rate of spread. To determine the impact of mucosal gene therapy in preventing the clonal expansion of pre-cancerous mutations, we compared the expected burden of TP53 mutations in simulated tissue sections with and without gene correction. We found that when FA cells have elevated genome instability or a TP53-dependent proliferative advantage, gene correction can substantially reduce the accumulation of pro-tumorigenic mutations. This model illustrates the power of computational frameworks to identify critical determinants of therapeutic success to enable experimental optimization and support novel and effective gene therapy applications.

Journal Article↗

PubMed2025

Pathway-specific genomic alterations in pancreatic cancer across diverse cohorts.

BACKGROUND/OBJECTIVES: Pancreatic cancer (PC) is an aggressive malignancy with rising incidence and poor survival rates. While Hispanic/Latino (H/L) patients have a lower overall incidence compared to Non-Hispanic White (NHW) patients, they are diagnosed at younger ages, often present with more advanced disease, and experience worse survival outcomes. The molecular drivers underlying these disparities remain poorly understood. Key oncogenic pathways, including TP53, WNT, PI3K, TGF-Beta, and RTK/RAS, play crucial roles in tumor progression, therapy resistance, and response to targeted treatments. However, their ethnicity-specific alterations and prognostic implications in PC remain largely unexplored. This study aims to characterize pathway-specific mutations in PC among H/L and NHW patients, assess tumor mutation burden, and identify ethnicity-specific oncogenic drivers using publicly available datasets. The findings may provide critical insights to optimize precision medicine strategies and enhance targeted therapies for underrepresented populations. METHODS: A bioinformatics analysis was performed using publicly available PC datasets to evaluate mutation frequencies in genes associated with the TGF-Beta, RTK/RAS, WNT, PI3K, and TP53 pathways. The study included 4,248 patients, with 407 identified as H/L and 3,841 as NHW. Patients were stratified by ethnicity to assess differences in mutation prevalence. Chi-squared tests were conducted to compare mutation rates between groups, while Kaplan-Meier survival analysis was performed to evaluate overall survival differences based on pathway-specific alterations. RESULTS: Significant differences were observed in the TGF-Beta pathway between H/L and NHW patients. TGF-Beta mutations were less prevalent in H/L patients (18.4% vs. 24.4%, p = 8.6e-3). Additionally, genes related to the TGF-Beta pathway showed significant alterations, with SMAD2 (1.5% vs. 0.4%, p = 6.3e-3) and SMAD4 (15% vs. 19.9%, p = 0.02) exhibiting notable differences. Although RTK/RAS, WNT, PI3K, and TP53 pathway mutations were not statistically significant overall, borderline significance was observed in genes associated with these pathways, including ERBB4 (3.4% vs. 1.8%, p = 0.03), ALK (2.7% vs. 1.1%, p = 0.01), HRAS (1.2% vs. 0.1%, p = 1.3e-4), and RIT1 (0.7% vs. 0.1%, p = 0.03) in the RTK/RAS pathway, as well as CTNNB1 (2.9% vs. 1.3%, p = 0.01) in the WNT pathway. Survival analysis revealed no significant differences in overall survival among H/L patients. However, NHW patients with TP53 pathway alterations exhibited borderline significant differences in survival outcomes.

PI3K pathway↗

PubMed2025

Comparative genomic analysis of key oncogenic pathways in hepatocellular carcinoma among diverse populations.

BACKGROUND/OBJECTIVES: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with significant racial and ethnic disparities in incidence, tumor biology, and clinical outcomes. Hispanic/Latino (H/L) patients tend to be diagnosed at younger ages and more advanced stages than Non-Hispanic White (NHW) patients, yet the molecular mechanisms underlying these disparities remain poorly understood. Key oncogenic pathways, including RTK/RAS, TGF-Beta, WNT, PI3K, and TP53, play pivotal roles in tumor progression, treatment resistance, and response to targeted therapies. However, ethnicity-specific alterations within these pathways remain largely unexplored. This study aims to compare pathway-specific mutations in HCC between H/L and NHW patients, assess tumor mutation burden, and identify ethnicity-associated oncogenic drivers using publicly available datasets. Findings from this analysis may inform precision medicine strategies for improving early detection and targeted therapies in underrepresented populations. METHODS: We conducted a bioinformatics analysis using publicly available HCC datasets to assess mutation frequencies in RTK/RAS, TGF-Beta, WNT, PI3K, and TP53 pathway genes. The study included 547 patients, consisting of 69 H/L patients and 478 NHW patients. Patients were stratified by ethnicity (H/L vs. NHW) to evaluate differences in mutation prevalence. Chi-squared tests were used to compare mutation frequencies, while Kaplan-Meier survival analysis assessed overall survival differences associated with pathway-specific alterations in both populations. RESULTS: Significant differences were observed in the RTK/RAS pathway related genes, particularly in FGFR4 mutations, which were more prevalent in H/L patients compared to NHW patients (4.3% vs. 0.6%, p = 0.02). Additionally, IGF1R mutations exhibited borderline significance (7.2% vs. 2.9%, p = 0.07). In the PI3K pathway, INPP4B alterations were more frequent in H/L patients than in NHW patients (4.3% vs. 1%, p = 0.06), while in the TGF-Beta pathway, TGFBR2 mutations were more common in H/L patients (2.9% vs. 0.4%, p = 0.07), suggesting potential ethnicity-specific variations. Survival analysis revealed no significant differences in overall survival between H/L and NHW patients, indicating that molecular alterations alone may not fully explain survival disparities and suggesting a role for additional factors such as immune response, environmental exposures, or access to targeted therapies. CONCLUSIONS: This study provides one of the first ethnicity-focused analyses of key oncogenic pathway alterations in HCC, revealing distinct molecular differences between H/L and NHW patients. The findings suggest that RTK/RAS (FGFR4, IGF1R), PI3K (INPP4B), and TGF-Beta (TGFBR2) pathway alterations may play a distinct role in HCC among H/L patients, while their prognostic significance in NHW patients remains unclear. These insights emphasize the importance of incorporating ethnicity-specific molecular profiling into precision medicine approaches to improve early detection, targeted therapies, and clinical outcomes in HCC, particularly for underrepresented populations.

PI3K pathway↗

PubMed2025

Development and Validation of a Novel LC-MS/MS Based Proteomics Method for Quantitation of Retinol Binding Protein 4 (RBP4) and Transthyretin (TTR).

Retinol binding protein 4 (RBP4), the circulating carrier of retinol, complexes with transthyretin (TTR) and is a potential biomarker of cardiometabolic disease. However, RBP4 quantitation relies on immunoassays and western blots without retinol and TTR measurement. A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous absolute quantitation of circulating RBP4 and TTR is critical to establishing their biomarker potential. Surrogate peptides with reproducible, linear LC-MS/MS response were selected. Purified proteins were used as quantitation standards and heavy-labelled peptides as internal standards. Matrix effects were evaluated. The validated method was applied to measure inter- and intra-individual variability in RBP4 and TTR concentrations in healthy individuals and patients with diabetic kidney disease. Quantitation was linear for the clinically relevant concentration ranges of RBP4 (0.5-6 μM) and TTR (5.8-69 μM). Assay inter-day variability was <12% and precision within 5%. The inter-individual variability for RBP4 and TTR concentrations was 18-26%, while intra-individual variability was similar to assay variability. RBP4 and TTR quantitation correlated with commercially available ELISA assays. The developed LC-MS/MS method enables simultaneous absolute quantitation of RBP4 and TTR in serum and plasma that can be applied to clinical biomarker studies and stoichiometric measurements of circulating RBP4, TTR, and retinol.

Retinol binding protein 4 (RBP4)↗

PubMed2025

Recurrent patterns of widespread neuronal genomic damage shared by major neurodegenerative disorders.

Amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD) are common neurodegenerative disorders for which the mechanisms driving neuronal death remain unclear. Single-cell whole-genome sequencing of 429 neurons from three C9ORF72 ALS, six C9ORF72 FTD, seven AD, and twenty-three neurotypical control brains revealed significantly increased burdens in somatic single nucleotide variant (sSNV) and insertion/deletion (sIndel) in all three disease conditions. Mutational signature analysis identified a disease-associated sSNV signature suggestive of oxidative damage and an sIndel process, affecting 28% of ALS, 79% of FTD, and 65% of AD neurons but only 5% of control neurons (diseased vs. control: OR=31.20, p = 2.35×10-10). Disease-associated sIndels were primarily two-basepair deletions resembling signature ID4, which was previously linked to topoisomerase 1 (TOP1)-mediated mutagenesis. Duplex sequencing confirmed the presence of sIndels and identified similar single-strand events as potential precursor lesions. TOP1-associated sIndel mutagenesis and resulting genome instability may thus represent a common mechanism of neurodegeneration.

Journal Article↗

PubMed2025

Utility of Plasma Cell-free Chromatin Immunoprecipitation to Detect Cardiac Allograft Rejection.

BACKGROUND: Antibody-mediated rejection (AMR) remains the major risk factor for allograft loss across all solid organ transplantation. Unfortunately, its diagnosis relies on biopsy, an invasive gold standard that often sample unaffected allograft tissue leading to missed diagnosis. Plasma donor-derived cell-free DNA (dd-cfDNA) is noninvasive biomarker that has high sensitivity but low specificity for AMR diagnosis. This proof-of-concept study assessed the utility of cell-free chromatin immunoprecipitation (cfChIP) as a surrogate for gene expression to detect cardiac AMR and the associated pathobiology. METHODS: The discovery GRAfT multicenter cohort of heart transplant patients (NCT02423070) identified AMR, acute cellular rejection (ACR), and stable controls based on biopsy and ddcfDNA results. Plasma cfChIP-sequencing was performed to identify peaks, associated genes and pathobiological pathways. Plasma from an external cohort (GTD, NCT01985412) was also analyzed to verify pathways identified. Digital droplet PCR (ddPCR) assays targeting differential regions were constructed to test the diagnostic performance of cfDNA to detect AMR/ACR from stable controls (rejection-specific assays) or AMR from ACR (AMR-specific assays). RESULTS: The cohort included 21 AMR, 28 ACR, and 45 stable controls from GRAfT and GTD, and 23 healthy controls. cfChIP detected expected active genes, including housekeeping genes and gene targets of transplant immunosuppressive drugs but not inactive genes. Unsupervised clustering of the discovery GRAfT cohort assigned 95% of samples correctly as AMR, ACR or stable control. Differential analysis identified pathobiological pathways of AMR such as neutrophil degranulation and complement activation. The pathways were consistent in GTD samples. Rejection-specific assays detected AMR/ACR from controls with AUC of 0.78 - 0.95. AMR-specific assays detected AMR from ACR with AUC of 0.71 - 0.85, sensitivities of 0.73 - 0.94 and specificities of 0.73 - 0.80. CONCLUSION: This study provides valuable preliminary data supporting the use of cfChIP to detect AMR and the associated pathobiological pathways.

Allograft rejection↗

PubMed2025

Demographics, Overlap, and Latency of Severe Cutaneous Adverse Reactions in an FDA Database.

IMPORTANCE: Severe cutaneous adverse reactions (SCARs), including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS-TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), and generalized bullous fixed drug eruption (GBFDE), are rare but life-threatening drug hypersensitivity syndromes. Due to their low incidence and diagnostic complexity, large-scale characterization of SCAR is challenging. OBJECTIVE: To characterize the demographics, causative agents, trends, latency, and phenotypic overlap of SCAR using a large-scale, sanitized pharmacovigilance dataset from FAERS (FDA Adverse Event Reporting System). DESIGN: Cross-sectional study of spontaneous adverse event reports. Cases were drawn from the U.S. Food and Drug Administration Adverse Event Reporting System (FDA FAERS) from January 2004 to December 2023 and subjected to sanitization and deduplication. Disproportionality analysis was used to characterize causative agents. Machine learning (random forest classifiers) was used to analyze predictors of drug latency and mortality. SETTING: Global pharmacovigilance reports submitted to FAERS. PARTICIPANTS: A total of 56,683 deduplicated SCAR reports were identified, representing 0.33% of reports during the study period. EXPOSURES: Suspected causative drugs, including both small molecules and biologics. MAIN OUTCOMES AND MEASURES: Main outcomes included the frequency and distribution of SCAR syndromes, reporting trends over time, latency from drug start to reaction onset, drug-specific disproportionality (PRR, ROR, IC), and co-reporting between SCAR types and related conditions. RESULTS: A total of 56,683 unique SCAR reports were identified, including SJS-TEN (28,871), DRESS (22,444), AGEP (6,183), and GBFDE (150). We identified 237 drugs with significant disproportionality for SCAR overall. Co-reporting between SCARs was significantly enriched (p < 1e-200), suggesting overlapping phenotypes. Latency varied by drug and syndrome (median: GBFDE 3 days, AGEP 4 days, SJS-TEN 12 days, DRESS 20 days). CONCLUSIONS AND RELEVANCE: SCAR syndromes display distinct but overlapping phenotypes, with variable latency and diverse causative agents. These findings, based on the largest SCAR dataset to date, highlight the need for improved classification frameworks and molecular validation. Large-scale pharmacovigilance, integrated with genomic and histopathologic data, will be critical to improving diagnosis, mechanistic understanding, and clinical management of SCAR.

Acute Generalized Exanthematous Pustulosis↗

PubMed2025

Efficient detection and characterization of targets of natural selection using transfer learning.

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

Journal Article↗

PubMed2025

The TONSL-MMS22L complex and FANCM form an interdependent complex on chromatin to counter replication stress.

FANCM is branchpoint DNA translocase essential for cellular response to replication stress. Here, we show that replication stress stimulates FANCM and the TONSL-MMS22L heterodimer bound to histones H3-H4 to form an interdependent complex on chromatin. TONSL-MMS22L recruits FANCM and Fanconi anemia (FA) core complex to stalled and collapsed forks, maintains FANCM on replication-stressed chromatin, promotes FANCD2 monoubiquitination, facilitates both repair and replication traverse of DNA interstrand crosslinks (ICLs), and suppresses sister chromatid exchanges, through its interactions with FANCM and H3-H4. Reciprocally, both DNA translocase activity and phosphorylation of FANCM facilitate recruitment of TONSL-MMS22L and RAD51 to perturbed forks. Moreover, TONSL-MMS22L and FANCM function together to promote activation of the FA pathway, ICL repair, homologous recombination and replication traverse. Cancer patients with tumors with wildtype FANCM and low expression of TONSL-MMS22L have a more favorable prognosis than those with high expression. Thus, FANCM-TONSL-MMS22L acts coordinately as a complex on chromatin that resolves replication stress, and this complex may present a therapeutic target for wildtype FANCM-linked cancer.

FANCM↗

PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗

PubMed2025

Charting Postnatal Heart Development Using In Vivo Single-Cell Functional Genomics.

The transition at birth, marked by increased circulatory demands and rapid growth, necessitates extensive remodeling of the heart's structure, function, and metabolism. This transformation requires precise spatial and temporal coordination among diverse cardiac cell types; central to this process is cardiomyocyte maturation, yet the regulatory mechanisms driving these changes remain poorly understood. Here, we present a temporal and spatial atlas of postnatal hearts by integrating single-nucleus transcriptomics with image-based spatial transcriptomics, which uncovers the dynamic regulatory networks of cardiomyocyte maturation. To functionally interrogate candidate regulators in vivo , we developed Probe-based Indel-detectable Perturb-seq (PIP-seq), a high-throughput platform that uses probe-based chemistry to directly capture sgRNA expression, perturbation status, and transcriptomic profiles at single-nucleus resolution. Applying PIP-seq to postnatal cardiac development identified 21 novel regulators of cardiomyocyte maturation, highlighting critical nodal points in this process. Our study establishes a high-resolution framework for dissecting postnatal heart development, underscoring the integrative and highly ordered roles of microenvironment and intercellular communication in cardiomyocyte maturation. Importantly, PIP-seq enables systematic, high-throughput exploration of gene function and networks underlying complex biological processes in their native in vivo context.

Journal Article↗

PubMed2025

Associations of Genetic Liability to Six Psychiatric Disorders With Cardiometabolic Diseases.

IMPORTANCE: Individuals with psychiatric disorders have increased risk of cardiometabolic diseases (CMDs). Evaluating how psychiatric genetic liability relates to CMD may clarify mechanisms. OBJECTIVE: Identify genetic overlap between psychiatric disorders and CMDs independent of cross-disorder pleiotropy, BMI, and smoking. DESIGN SETTING AND PARTICIPANTS: Three Northern European cohorts (the Swedish Twin Registry, the Estonian Biobank, and the Norwegian Mother, Father and Child Cohort Study [MoBa]) totaling 355,159 individuals. Associations with CMDs were estimated as adjusted odds ratios (AORs) from logistic models mutually adjusted for all psychiatric PRSs and in models additionally adjusting for body mass index (BMI) and smoking. Cohort-specific AORs were pooled by inverse-variance weighting. MAIN OUTCOMES AND MEASURES: Exposures were PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), anxiety disorder, posttraumatic stress disorder (PTSD), bipolar disorder, and schizophrenia. Outcomes were diagnoses of CMDs (hyperlipidemia, obesity, type 2 diabetes, hypertensive diseases, arteriosclerosis, ischemic heart disease, heart failure, thromboembolic disease, cerebrovascular disease, and arrhythmias), ascertained from electronic health records. RESULTS: The MDD PRS was associated with increased risk of all CMDs across analyses (AORs ranged from 1.13 [95% CI, 1.10-1.15] for heart failure to 1.02 [95% CI, 1.00-1.05] for arrhythmias). The ADHD PRS was associated with increased risk of all CMDs (AOR ranged from 1.11 [95% CI, 1.09-1.12] for obesity to 1.02 [95% CI, 1.01-1.03] for hyperlipidemia), however associations where attenuated when adjusting for BMI and smoking (lifestyle adjusted AOR for obesity: 1.03 [95% CI, 1.02-1.05]). When not mutually adjusting for all psychiatric PRSs, anxiety disorder and PTSD PRSs were associated with all CMDs; these associations diminished after adjustment. The bipolar and schizophrenia PRSs were inversely associated with most CMDs (AOR for schizophrenia PRS and obesity, 0.93 [95% CI, 0.92-0.94]). CONCLUSIONS AND RELEVANCE: Associations between psychiatric PRSs and CMDs diverged: ADHD, MDD, anxiety disorder, and PTSD PRSs were positively associated with CMDs, whereas bipolar and schizophrenia PRSs were inversely associated. Genetic liability to MDD showed robust associations with CMDs independent of cross-disorder pleiotropy, BMI, and smoking status, whereas associations between the ADHD PRS and CMDs were largely attenuated after adjustment for BMI and smoking.

Journal Article↗

PubMed2025

Multi-step genomics on single cells and live cultures in sub-nanoliter capsules.

Single-cell sequencing methods uncover natural and induced variation between cells. Many functional genomic methods, however, require multiple steps that cannot yet be scaled to high throughput, including assays on living cells. Here we develop capsules with amphiphilic gel envelopes (CAGEs), which selectively retain cells and large analytes while being freely accessible to media, enzymes and reagents. Capsules enable high-throughput multi-step assays combining live-cell culture with genome-wide readouts. We establish methods for barcoding CAGE DNA libraries, and apply them to measure persistence of gene expression programs in cells by capturing the transcriptomes of tens of thousands of expanding clones in CAGEs. The compatibility of CAGEs with diverse enzymatic reactions will facilitate the expansion of the current repertoire of single-cell, high-throughput measurements and extension to live-cell assays.

Journal Article↗

PubMed2025

Genomic Language Model for Predicting Enhancers and Their Allele-Specific Activity in the Human Genome.

Predicting and deciphering the regulatory logic of enhancers is a challenging problem, due to the intricate sequence features and lack of consistent genetic or epigenetic signatures that can accurately discriminate enhancers from other genomic regions. Recent machine-learning based methods have spotlighted the importance of extracting nucleotide composition of enhancers but failed to learn the sequence context and perform suboptimally. Motivated by advances in genomic language models, we developed DNABERT-Enhancer, a novel enhancer prediction method, by applying DNABERT pre-trained language model on the human genome. We trained two different models, using large collection of enhancers curated from the ENCODE registry of candidate cis-Regulatory Elements. The best fine-tuned model achieved 88.05% accuracy with Matthews correlation coefficient of 76% on independent set aside data. Further, we present the analysis of the predicted enhancers for all chromosomes of the human genome by comparing with the enhancer regions reported in publicly available databases. Finally, we applied DNABERT-Enhancer along with other DNABERT based regulatory genomic region prediction models to predict candidate SNPs with allele-specific enhancer and transcription factor binding activity. The genome-wide enhancer annotations and candidate loss-of-function genetic variants predicted by DNABERT-Enhancer provide valuable resources for genome interpretation in functional and clinical genomics studies.

Journal Article↗

PubMed2025

Novel insights into the genetic architecture and mechanisms of host/microbiome interactions from a multi-cohort analysis of outbred laboratory rats.

The intestinal microbiome influences health and disease. Its composition is affected by host genetics and environmental exposures. Understanding host genetic effects is critical but challenging in humans, due to the difficulty of detecting, mapping and interpreting them. To address this, we analysed host genetic effects in four cohorts of outbred laboratory rats exposed to distinct but controlled environments. We found that polygenic host genetic effects were consistent across environments. We identified three replicated microbiome-associated loci, one of which involved a sialyltransferase gene and Paraprevotella. We found a similar association in a human cohort, between ST6GAL1 and Paraprevotella, both of which have been linked with immune and infectious diseases. Moreover, we found evidence of indirect genetic effects on microbiome phenotypes, which substantially increased their total genetic variance. Finally, we identified a novel mechanism whereby indirect genetic effects can contribute to "missing heritability".

Journal Article↗

PubMed2025

OpenSpliceAI: An efficient, modular implementation of SpliceAI enabling easy retraining on non-human species.

The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data. Here we introduce OpenSpliceAI, a trainable, open-source version of SpliceAI implemented in PyTorch to address these challenges. OpenSpliceAI supports both training from scratch and transfer learning, enabling seamless retraining on species-specific datasets and mitigating human-centric biases. Our experiments show that it achieves faster processing speeds and lower memory usage than the original SpliceAI code, allowing large-scale analyses of extensive genomic regions on a single GPU. Additionally, OpenSpliceAI's flexible architecture makes for easier integration with established machine learning ecosystems, simplifying the development of custom splicing models for different species and applications. We demonstrate that OpenSpliceAI's output is highly concordant with SpliceAI. In silico mutagenesis (ISM) analyses confirm that both models rely on similar sequence features, and calibration experiments demonstrate similar score probability estimates.

Journal Article↗

PubMed2025

U2AF1 mutations rescue deleterious exon skipping induced by KRAS mutations.

The mechanisms by which somatic mutations of splicing factors, such as U2AF1S34F in lung adenocarcinoma, contribute to cancer pathogenesis are not well understood. Here, we used prime editing to modify the endogenous U2AF1 gene in lung adenocarcinoma cells and assessed the resulting impact on alternative splicing. These analyses identified KRAS as a key target modulated by U2AF1S34F. One specific KRAS mutation, G12S, generates a cryptic U2AF1 binding site that leads to skipping of KRAS exon 2 and generation of a non-functional KRAS transcript. Expression of the U2AF1S34F mutant reverts this exon skipping and restores KRAS function. Analysis of cancer genomes reveals that U2AF1S34F mutations are enriched in KRASG12S-mutant lung adenocarcinomas. A comprehensive analysis of splicing factor/oncogene mutation co-occurrence in cancer genomes also revealed significant co-enrichment of KRASQ61R and U2AF1I24T mutations. Experimentally, KRASQ61R mutation leads to KRAS exon 3 skipping, which in turn can be rescued by the expression of U2AF1I24T. Our findings provide evidence that splicing factor mutations can rescue splicing defects caused by oncogenic mutations. More broadly, they demonstrate a dynamic process of cascading selection where mutational events are positively selected in cancer genomes as a consequence of earlier mutations.

Journal Article↗