Cell genomics最新文献

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.

Generative genomic models hold immense potential for creating intricate biological systems, yet designing precise functional sequences remains challenging. Merchant et al. in Nature present semantic design, which employs the Evo genomic language model to generate novel functional genes based solely on genomic context. Moreover, SynGenome database houses over 120 billion sequences generated through semantic design, spanning a diverse range of functions.

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.

Recent algorithmic advances have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from large genomic cohorts, providing detailed models of genealogical relatedness along the genome. These inferred ARGs can complement genotype imputation by capturing the effects of unobserved variants, but their use in large-scale linear mixed-model analyses has been computationally prohibitive. Here, we develop methods that leverage the ARG to perform genotype-matrix multiplications in sublinear time and implement scalable randomized algorithms for mixed-model analyses. We introduce ARG-RHE, a randomized Haseman-Elston approach for estimating narrow-sense heritability and performing region-based association testing using ARGs, enabling parallel analysis of multiple quantitative traits. Through extensive simulations, we demonstrate the computational efficiency and statistical power of this approach. Applied to 21,159 genes and 52 blood traits in 337,464 UK Biobank participants, ARG-RHE identifies 8% more gene-trait associations than imputation alone, demonstrating that genome-wide genealogies may be leveraged to complement genotype imputation in complex trait analyses.

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.

Cellular senescence, a stress-induced program causing stable cell-cycle arrest, is a hallmark of liver aging, fibrosis, and cancer. However, the cell-type-specific mechanisms, spatial organization, and cancer-associated alterations in the liver remain unclear. We profiled 43 normal human livers spanning ages and fibrosis stages using a single-cell multiome, Xenium spatial transcriptomics, and CODEX, complemented by fibrotic mouse models and 24 colorectal cancer liver metastases. We found CDKN1A+ senescent hepatocytes, fibroblasts, cholangiocytes, and endothelial cells associated with age, liver disease, or cancer. Senescence differed between aged and fibrotic livers, with similar patterns in mice. Spatially, CDKN1A+ hepatocytes localized periportally, while SERPINE1+ aging-associated hepatocytes formed spatial clusters, potentially mediated by Claudins and THBS1. Fibrotic regions contained CXCL12+ senescent fibroblasts interacting with CXCR4+ immune cells. Chemotherapy intensified senescence in hepatocytes by 5-fold relative to aging and led to unique CDKN2A+ populations. Across conditions, senescent cells shared AP-1 activation, pro-inflammatory cytokines, and apoptosis resistance, suggesting therapeutic opportunities.

Phenotypic variation arises from interactions between genetic and environmental factors, but disentangling these effects for complex traits remains challenging in observational cohorts like human biobanks. Model organisms with controlled genetic and environmental variation complement human studies in analyzing higher-order effects such as gene-by-environment (G×E) interactions, dominance, and epistasis. We utilized 76 medaka strains from the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel to compare heart rate plasticity across temperatures. An F2 segregation analysis identified 16 quantitative trait loci (QTLs), many exhibiting dominance, G×E, G×G, and G×G×E interactions. We experimentally validated four candidate genes, revealing temperature-sensitive heart rate effects. Finally, we simulated how genome-wide association study (GWAS) discovery power depends on statistical model choice. Our results suggest that the limited detection of non-additive effects in human GWASs stems from current study designs and sample sizes. This work demonstrates the value of controlled model organism studies for dissecting complex trait genetics and informing association study design.

Cell-free DNA (cfDNA) end motifs serve as fragmentomics biomarkers for cancer. Prior studies primarily focused on 5' ends, whereas 3' ends were overlooked due to artifactual modification in existing sequencing protocols. We utilized single-stranded library preparation ("2-end sequencing") to assess the native 5' and 3' end motifs (EM5 and EM3, respectively). Additionally, we demonstrated diagnostic power from the nucleotide motifs located immediately upstream and downstream of 5' and 3' ends, named pre-end motifs (PREMs) and post-end motifs (POEMs). These fragmentomics markers collectively achieved an area under the curve (AUC) of 0.95 for hepatocellular carcinoma (HCC) detection. Fragmentomics-based methylation analysis of 3' ends (3' FRAGMA) improved detection of HCC (AUC: 0.97). We further developed "4-end sequencing" to interrogate both ends of both strands of a double-stranded cfDNA molecule, enhancing fragmentomics-based cancer detection. Holistic end profiling adds to the armamentarium of liquid biopsy and sheds light on the biology of cfDNA fragmentation.

The nematode Caenorhabditis elegans is a favorable model for studying cell-surface protein interactomes, given its well-defined and stereotyped intercellular contacts. Here, we report an extracellular interactome dataset for C. elegans. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover interactions for all four major axon guidance pathways, including ectodomain interactions between three of them. We demonstrate that a protein family, previously known for maintaining axon positioning, functions as secreted binders for insulins and that their overexpression in vivo extends lifespan, consistent with inhibition of insulin signaling. We reveal interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth factors to nematodes. Finally, our dataset constitutes a resource for uncovering the logic of neuronal connectivity, intercellular communication and adhesion, and signaling pathways involved in aging and disease.

The study of early evolutionary history provides an account of how the foundational features of life as we know it first emerged. Phylogenetic analysis is a powerful method in the study of early evolution because it uses molecular evidence that has been inherited from the ancient organisms themselves. Here, we describe an important yet understudied type of protein family, universal paralogs, that retain phylogenetic signals from evolutionary events predating the last universal common ancestor of life, offering a unique window into early evolution. We survey recent advances in the study of universal paralogs and discuss how emerging computational tools enhance our ability to use these protein families to describe the very earliest stages of evolution with increasing detail and accuracy. Such research will greatly improve our understanding of how life emerged and subsequently evolved on the ancient Earth.