Cell genomics最新文献

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are fatal neurodegenerative diseases sharing clinical and pathological features. Both involve complex neuron-glia interactions, but cell-type-specific alterations remain poorly defined. We performed single-nucleus RNA sequencing of the frontal cortex from C9orf72-related ALS (with and without FTLD) and sporadic ALS (sALS). Neurons showed prominent changes in mitochondrial function, protein homeostasis, and chromatin remodeling. Comparison with independent datasets from other cortical regions revealed consistent pathway alterations, including upregulation of STMN2 and NEFL across brain regions and subtypes. We further examined dysregulation of alternative polyadenylation (APA), an understudied post-transcriptional mechanism, uncovering cell-type-specific APA patterns. To investigate its regulation, we developed the alternative polyadenylation network (APA-Net), a multi-modal deep learning model integrating transcript sequences and RNA-binding protein (RBP) expression profiles to predict APA. This atlas advances our understanding of ALS/FTLD molecular pathology and provides a valuable resource for future mechanistic studies.

Arterial segments show differing disease propensities, yet mechanisms remain unknown. We compiled a transcriptomic and spatial atlas of healthy human arterial cells across multiple segments to understand these differences. Arteries demonstrated a stereotyped pattern of cell-specific, segmental heterogeneity not captured by common marker genes. Arterial identities are encoded in fibroblast and smooth muscle cell (SMC) transcriptomes. Differentially expressed genes enrich for disease loci. Fibroblast gene expression enriches for a disproportionate number of disease loci, highlighting an underrecognized role for fibroblasts in disease risk. Cells of different segments cluster more by embryonic origin than anatomy. Global analysis of disease regulons in fibroblasts and SMCs identified developmental transcription factors that persist into adulthood, suggesting a functional role of these factors in disease. Lastly, the heterogeneity of non-coding transcriptomes rivals that of protein-coding transcriptomes. Differentially expressed lncRNAs enrich for genetic signals for vascular diseases, suggesting a role for lncRNAs in vascular disease.

Biobank-scale whole-genome sequencing (WGS) studies are increasingly pivotal in unraveling the genetic bases of diverse health outcomes. However, managing and analyzing these datasets' sheer volume and complexity presents significant challenges. We highlight the annotated genomic data structure (aGDS) format, substantially reducing the WGS data file size while enabling seamless integration of genomic and functional information for comprehensive WGS analyses. The aGDS format yielded 23 chromosome-specific files for the UK Biobank 500k WGS dataset, occupying only 1.10 tebibytes of storage. We develop the vcf2agds toolkit that streamlines the conversion of WGS data from VCF to aGDS format. Additionally, the STAARpipeline equipped with the aGDS files enabled scalable, comprehensive, and functionally informed WGS analysis, facilitating the detection of common and rare coding and noncoding phenotype-genotype associations. Overall, the vcf2agds toolkit and STAARpipeline provide a streamlined solution that facilitates efficient data management and analysis of biobank-scale WGS data across hundreds of thousands of samples.

An emerging hallmark of many human diseases is transcription of typically silenced repetitive DNA containing pathogen-associated molecular patterns (PAMPs). These PAMPs engage the innate immune system via pattern recognition receptors (PRRs)-a phenomenon known as viral mimicry. We propose a statistical physics framework to quantify viral mimicry by measuring "selective forces" that enrich PAMPs compared to a genome-wide reference distribution. We validate our predictions by identifying repeats that bind different PRRs and show potential viral mimics in different repeat families across eukaryotic genomes, suggesting shared mechanisms drive emergence and retention. We propose two non-exclusive evolutionary hypotheses. The first "repeat-centric" hypothesis posits PAMPs are integral to the repeat life cycle and are therefore enriched as they mediate repeat expansion. The second "organism-centric" hypothesis proposes viral mimicry functions as a cell-intrinsic feedback mechanism for sensing and reacting to transcriptional dysregulation, which provides a selective pressure to maintain PAMPs in genomes.

To support the high demand for ribosomes, ribosomal RNA (rRNA) is produced by hundreds of highly expressed gene copies. In this issue of Cell Genomics, Potapova et al. reveal chromosome-specific organization and heritable transcriptional activity of rRNA clusters in humans and other hominids.

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.

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.

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.