Cell genomics最新文献

Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.

Allele-specific expression (ASE) is pivotal in understanding the genetic underpinnings of phenotypic variation within species, differences in disease susceptibility, and responses to environmental factors. We processed 11 different tissue types collected from 12 age-matched healthy olive baboons (Papio anubis) for genome-wide ASE analysis. By sequencing their genomes at a minimum depth of 30×, we identified over 16 million single-nucleotide variants (SNVs). We also generated long-read sequencing data, enabling the phasing of all variants present within the coding regions of 96.5% of assayable protein-coding genes as a single haplotype block. Given the extensive heterozygosity of baboons relative to humans, we could quantify ASE across 72% of the total annotated protein-coding gene set. We identified genes that exhibit ASE and affect specific tissues and genotypes. We discovered ASE SNVs that also exist in human populations with identical alleles and that are designated as pathogenic by both the PrimateAI-3D and AlphaMissense models.

Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8⁺ clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Signaling pathway components are well studied, but how they mediate cell-type-specific transcription responses is an unresolved problem. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show that the DNA binding of signaling effectors is driven by cell-type-specific sequence rules that can be learned genome wide by deep learning models. Through model interpretation and experimental validation, we show that motifs for the cell-type-specific transcription factor TFAP2C enhance TEAD4/YAP1 binding in a nucleosome-range and distance-dependent manner, driving synergistic enhancer activation. We also discovered that Tead double motifs are widespread, highly active canonical response elements. Molecular dynamics simulations suggest that TEAD4 binds them cooperatively through surprisingly labile protein-protein interactions that depend on the DNA template. These results show that the response to signaling pathways is encoded in the cis-regulatory sequences and that interpreting the rules reveals insights into the mechanisms by which signaling effectors influence cell-type-specific enhancer activity.

The attachment of the kinetochore to the centromere is essential for genome maintenance, yet the highly repetitive nature of satellite regional centromeres limits our understanding of their chromatin organization. We demonstrate that single-molecule chromatin fiber sequencing (Fiber-seq) can uniquely co-resolve kinetochore and surrounding chromatin architectures along point centromeres, revealing largely homogeneous single-molecule kinetochore occupancy. In contrast, the application of Fiber-seq to regional centromeres exposed marked per-molecule heterogeneity in their chromatin organization. Regional centromere cores uniquely contain a dichotomous chromatin organization (dichromatin) composed of compacted nucleosome arrays punctuated with highly accessible chromatin patches. CENP-B occupancy phases dichromatin to the underlying alpha-satellite repeat within centromere cores but is not necessary for dichromatin formation. Centromere core dichromatin is conserved between humans and primates, including along regional centromeres lacking satellite repeats. Overall, the chromatin organization of regional centromeres is defined by marked per-molecule heterogeneity, buffering kinetochore attachment against sequence and structural variability within regional centromeres.

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data.

Differential susceptibilities to various diseases and corresponding metabolite variations have been documented across diverse ethnic populations, but the genetic determinants of these disparities remain unclear. Here, we performed large-scale genome-wide association studies of 171 directly quantifiable metabolites from a nuclear magnetic resonance-based metabolomics platform in 10,792 Han Chinese individuals. We identified 15 variant-metabolite associations, eight of which were successfully replicated in an independent Chinese population (n = 4,480). By cross-ancestry meta-analysis integrating 213,397 European individuals from the UK Biobank, we identified 228 additional variant-metabolite associations and improved fine-mapping precision. Moreover, two-sample Mendelian randomization analyses revealed evidence that genetically predicted levels of triglycerides in high-density lipoprotein were associated with a higher risk of coronary artery disease and that of glycine with a lower risk of heart failure in both ancestries. These findings enhance our understanding of the shared and specific genetic architecture of metabolites as well as their roles in complex diseases across populations.

Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.

Multiplexed assays of variant effect (MAVEs) enable scalable functional assessment of human genetic variants. However, established MAVEs are limited by exogenous expression of variants or constraints of genome editing. Here, we introduce a pooled prime editing (PE) platform to scalably assay variants in their endogenous context. We first improve efficiency of PE in HAP1 cells, defining optimal prime editing guide RNA (pegRNA) designs and establishing enrichment of edited cells via co-selection. We next demonstrate negative selection screening by testing over 7,500 pegRNAs targeting SMARCB1 and observing depletion of efficiently installed loss-of-function (LoF) variants. We then screen for LoF variants in MLH1 via 6-thioguanine selection, testing 65.3% of all possible SNVs in a 200-bp region including exon 10 and 362 non-coding variants from ClinVar spanning a 60-kb region. The platform's overall accuracy for discriminating pathogenic variants indicates that it will be highly valuable for identifying new variants underlying diverse human phenotypes across large genomic regions.