Cell genomics最新文献

We present a collection of 17 high-quality long-read inbred mouse strain genomes with complete annotation (contig N50s of 0.8-33.9 Mbp). This collection includes 12 widely used classical laboratory strains and 5 wild-derived strains. We have resolved previously incomplete genomic regions, including the major histocompatibility complex (MHC), defensin cluster, T cell receptor, and Ly49 complexes. Hundreds of non-reference genes from previous publications not found in GRCm39, such as Defa1, Raet1a, and Klra20 (Ly49T), were localized in the new reference genomes. We conducted a genome-wide scan of variable number tandem repeats (VNTRs) within the coding regions, identifying over 400 genes with VNTR polymorphisms with up to 600 repeat copies and repeat units reaching 990 nucleotides. Our strain-specific annotations enhance RNA sequencing (RNA-seq) analyses, as demonstrated in PWK/PhJ, where we observed a 5.1% improvement in read mapping and expression-level differences in 2.1% of coding genes compared to using GRCm39.

Genetic mutations that yield a defective cystic fibrosis (CF) transmembrane regulator (CFTR) protein cause CF, a life-limiting autosomal-recessive Mendelian disorder. A protective role of CFTR loss-of-function mutations in inflammatory bowel disease (IBD) has been suggested, but its evidence has been inconclusive and contradictory. Here, leveraging a large IBD exome sequencing dataset comprising 38,558 cases and 66,945 controls of European ancestry in the discovery stage and a combined total of 42,475 cases and 192,050 controls across diverse ancestry groups in the replication stage, we established a protective role of CF-risk variants against IBD based on the association test of CFTR deltaF508 (p = 8.96E-11) and the gene-based burden test of CF-risk variants (p = 3.9E-07). Furthermore, we assessed variant prioritization methods, including AlphaMissense, using clinically annotated CF-risk variants as the gold standard. Our findings highlight the critical and unmet need for effective variant prioritization in gene-based burden tests.

Polygenic scores (PGSs) that can predict response to interventions can facilitate precision medicine and are detectable in observational datasets as PGS-by-exposure (PGS×E) interactions. PGSs based on interactions (iPGSs) or variance effects (vPGSs) may be more powerful than standard PGSs for detecting PGS×E, but these have yet to be systematically compared. We describe a generalized pipeline for developing and comparing these PGS types and apply it to detect genetic modification of the relationship between adiposity (measured by BMI) and a broad set of cardiometabolic risk factors. Our applied analysis in the UK Biobank identified significant PGS×BMI for 16/20 risk factors, most consistently for the iPGS approach. Many interactions replicated in All of Us (AoU); for example, we observed a 72% larger BMI-alanine aminotransferase association in the top iPGS decile in AoU. Our study provides a framework for the comparison of PGS×E strategies and informs efforts toward clinically useful response-focused PGSs.

Complex RNA-protein networks play a pivotal role in the formation of many types of biomolecular condensates. How RNA features contribute to condensate formation, however, remains incompletely understood. Here, we integrate tailored transcriptomics assays to identify a distinct class of developmental condensation-prone RNAs termed "smOOPs" (semi-extractable, orthogonal-organic-phase-separation-enriched RNAs). These transcripts localize to larger intracellular foci, form denser RNA subnetworks than expected, and are heavily bound by RNA-binding proteins (RBPs). Using an explainable deep learning framework, we reveal that smOOPs harbor characteristic sequence composition, with lower sequence complexity, increased intramolecular folding, and specific RBP-binding patterns. Intriguingly, these RNAs encode proteins bearing extensive intrinsically disordered regions and are highly predicted to be involved in biomolecular condensates, indicating an interplay between RNA- and protein-based features in phase separation. This work advances our understanding of condensation-prone RNAs and provides a versatile resource to further investigate RNA-driven condensation principles.

Red blood cells (RBCs) transport oxygen but accumulate oxidative damage over time, reducing function in vivo and during storage, critical for transfusions. To explore the genetics of RBC resilience, we profiled proteins, metabolites, and lipids from fresh and stored RBCs from 350 genetically diverse mice. Our analysis identified over 6,000 quantitative trait loci (QTLs). Compared to other tissues, the prevalence of trans genetic effects over cis ones reflects the absence of de novo protein synthesis in anucleated RBCs. QTL hotspots at Hbb, Hba, Mon1a, and (storage-specific) Steap3 linked ferroptosis to hemolysis. Proteasome QTLs clustered at multiple loci, underscoring the importance of degrading oxidized proteins. Post-translational modification (PTM) QTLs mapped predominantly to hemoglobins, including cysteine residues. The loss of reactive C93 in humanized mice (hemoglobulin beta [HBB] C93A) disrupted redox balance, glutathione pools, glutathionylation, and redox PTMs. These findings highlight genetic regulation of RBC oxidation, with implications for transfusion biology and oxidative-stress-dependent hemolytic disorders.

Protein language models (PLMs) improve variant effect predictions, but their role in gene discovery for complex traits remains unclear. We introduce an allelic series-based regression test that uses PLM-derived variant effect predictions as proxies for effect sizes, identifying ∼46% more associations than standard burden tests. Extending this to isoform-level analysis, we find 26 gene-trait pairs with stronger associations in non-canonical versus canonical transcripts, highlighting isoform-specific effects. Finally, we identify evolutionary plausible variants (EPVs), missense variants assigned higher likelihoods than the wild-type alleles by PLMs, representing 0.45% of missense variants. EPVs show higher allele frequencies than synonymous variants, consistent with differential selection pressures, and are linked to nine traits, including protective associations with low-density lipoprotein (LDL) and bone mineral density. Together, our results demonstrate how PLMs can enhance rare-variant interpretation and gene-trait association discovery in exome data.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Cells store information by means of chromatin modifications that persist through cell divisions and can hold gene expression silenced over generations. However, how these modifications may maintain other gene expression states has remained unclear. This study shows that chromatin modifications can maintain a wide range of gene expression levels over time, thus uncovering analog epigenetic memory. By engineering a genomic reporter and epigenetic effectors, we tracked the gene expression dynamics following targeted perturbations to the chromatin state. We found that distinct grades of DNA methylation led to corresponding, persistent gene expression levels. Altering the DNA methylation grade, in turn, resulted in permanent loss of gene expression memory. Consistent with experiments, our chromatin modification model indicates that analog memory arises when the positive feedback between DNA methylation and repressive histone modifications is lacking. This discovery will lead to a deeper understanding of epigenetic memory and to new tools for synthetic biology.

Unmodified class II bacteriocins promise precision antimicrobials that spare bystander microbes. Zhang and colleagues introduce IIBacFinder, a genomics-guided pipeline that detects precursor and context genes with a curated pHMM library, infers leader-peptide cleavage, and triages candidates by meta-omics signals. The authors apply it across bacterial genomes, including an atlas of ∼280,000 human-gut genomes, and recover a vast reservoir of narrow-spectrum peptides and prioritize gut-resident candidates for synthesis. Of the 26 synthesized, 16 display activity in vitro, largely via membrane perturbation and with additive effects alongside vancomycin, while ex vivo assays show minimal compositional disruption of fecal communities compared with antibiotic controls. These results position unmodified class II bacteriocins as tractable, microbiome-sparing agents and illustrate how genome-scale mining coupled to meta-omics can bridge sequence to function in complex ecosystems.

Missense variants can have pleiotropic effects on protein function, and predicting these effects can be difficult. We performed near-saturation deep mutational scanning of P2RY8, a G protein-coupled receptor that promotes germinal center B cell confinement. We assayed the effect of each variant on surface expression, migration, and proliferation. We delineated variants that affected both expression and function, affected function independently of expression, and discrepantly affected migration and proliferation. We also used cryo-electron microscopy to determine the structure of activated, ligand-bound P2RY8, providing structural insights into the effects of variants on ligand binding and signal transmission. We applied the deep mutational scanning results to both improve computational variant effect predictions and to characterize the phenotype of germline variants and lymphoma-associated variants. Together, our results demonstrate the power of integrating deep mutational scanning, structure determination, and in silico prediction to advance the understanding of a receptor important in human health.