Biology subjects

Sakaue, S.

Publications and source records attributed to Sakaue, S..

14 recordsLinked to original sources

bioRxiv2025

A population-scale atlas of blood and tissue in lupus nephritis

One Sentence SummaryA single-cell atlas of paired blood and tissue samples from Lupus Nephritis patients and healthy controls identified stromal and immune populations within renal tissue, including the scar-associated macrophage populations, which correlate with and may drive renal disease activity. Lupus nephritis (LN), a severe manifestation of Systemic Lupus Erythematosus (SLE), is a heterogeneous disease driven by diverse immune and tissue cell types. We obtained 538K single-cell and 140K single-nuclear profiles from kidney biopsies of 155 LN patients and 30 pre-implantation transplant biopsy controls, along with 325K single-cell blood profiles overlapping many of these patients. We identified key tissue cell types and cell states, and immune cell states; we were able to determine cell states that were tissue specific, and those that were present in the blood. We observed that LN pathological features are significantly associated with cell states using differential gene expression and Covarying Neighborhood Analysis (CNA). These analyses revealed broad changes in cell states associated with irreversible chronic tissue damage. After controlling for the effects of ongoing tissue damage, we observed that expansion of key glomerular and Scar Associated Macrophages (SAMs) populations tracked with increasing inflammatory disease activity. SAMs appear to drive LN fibrosis and, in active disease, infiltrate the glomeruli more than other myeloid cells. These observations strongly support that therapeutic targeting of myeloid populations may offer an as-of-yet unproven strategy to prevent renal inflammation and ongoing kidney damage in LN.

molecular biology↗

bioRxiv2025

Early and late RNA eQTL are driven by different genetic mechanisms

Understanding the genetic regulation of RNA abundance is essential to defining disease mechanisms. However, conventional expression quantitative loci (eQTL) studies quantify RNA molecules across the transcript lifecycle. While most eQTL likely affect transcription by altering promoter or enhancer function within the nucleus, it is also possible that they modulate any processes after transcription, including chemical modifications and RNA stability in the cytosol. To elucidate distinct eQTL mechanisms of early versus late RNA, we compared eQTL from mature cellular RNA and nascent nuclear RNA in the brain and the kidney. Across tissues, we identified different causal variants for cellular and nuclear eQTL for the same eGene. Cellular eQTL were enriched in transcribed regions (P=3.3x10-126), suggesting the importance of post-transcriptional regulation. Conversely, nuclear eQTL were enriched in distal regulatory elements (P=7.0x10-32), highlighting the role of DNA transcriptional regulation. For example, we identified stop-gain eQTL variants likely acting through nonsense-mediated decay in cellular eQTL that had no effect in nuclear eQTL. Cellular eQTL were enriched for loci with multiple causal variants in linkage disequilibrium within the transcribed regions, where they may in concert affect RNA stability. We also identified examples of nuclear eQTL variants within enhancers that had no effect in cellular eQTL. We show that such eQTL (e.g., TUBGCP4) sometimes uniquely colocalize with disease alleles (schizophrenia). This study reveals key differences in the genetic mechanisms of cellular and nuclear eQTL.

genomics↗

bioRxiv2023

Identifying genetic variants that influence the abundance of cell states in single-cell data

Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1 GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96x10-11) associates with increased abundance of NK cells expressing TNF- response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

genetics↗

bioRxiv2023

The influence of HLA genetic variation on plasma protein expression

Polymorphism in the human leukocyte antigen class I (HLA-I) and class II (HLA-II) genes is strongly implicated in susceptibility to immune-mediated diseases. However, the molecular effects of HLA genetic variation, including and beyond antigen presentation, remain unclear. Here we examined the effect of HLA genetic variation on the expression of 2940 plasma proteins using imputed HLA variants in 45,330 Europeans in the UK Biobank. We detected 504 proteins (17.1% of all proteins tested) affected by HLA genetic variation (HLA-pQTL), including widespread trans regulation of protein expression by autoimmune disease risk alleles. HLA-pQTL were enriched in gene families related to antigen presentation (e.g. B2M), T cell fate (CD8A; CD4), chemokines (CCL19; CCL21), and NK and macrophage receptors (KIR; LILRA/B), suggesting that HLA polymorphism affects both adaptive and innate immunity. HLA-pQTL also affected expression of diverse proteins with unclear roles in the immune response (e.g. SFTPD, LRPAP1, ENPP6, NPTX1), as well as drug targets for immune-mediated diseases, suggesting complex regulatory roles of the HLA loci. Among trans HLA-pQTL, HLA variants explained 0.1-42.9% of the protein expression variance. Fine-mapping revealed that most HLA-pQTL implicated amino acid positions within the peptide binding groove, suggesting that trans regulation of plasma protein expression by the HLA loci is primarily a consequence of antigen presentation. We also show that HLA-I and II uniquely affect different proteins and biological mechanisms. Altogether, our data reveal the effects of HLA genetic variation on protein expression and aid the interpretation of associations between HLA alleles and immune-mediated diseases.

genetics↗

bioRxiv2023

The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis

Synovial tissue inflammation is the hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. We measured genome-wide open chromatin at single cell resolution from 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identified 24 chromatin classes and predicted their associated transcription factors, including a CD8+ GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating an RA tissue transcriptional atlas, we found that the chromatin classes represented superstates corresponding to multiple transcriptional cell states. Finally, we demonstrated the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.

immunology↗

bioRxiv2023

Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium

Rheumatoid arthritis (RA) is an autoimmune disease initiated by antigen-specific T cells and B cells, which promote synovial inflammation through a complex set of interactions with innate immune and stromal cells. To better understand the phenotypes and clonal relationships of synovial T and B cells, we performed single-cell RNA and repertoire sequencing on paired synovial tissue and peripheral blood samples from 12 donors with seropositive RA ranging from early to chronic disease. Paired transcriptomic-repertoire analyses highlighted 3 clonally distinct CD4 T cells populations that were enriched in RA synovium: T peripheral helper (Tph) and T follicular helper (Tfh) cells, CCL5+ T cells, and T regulatory cells (Tregs). Among these cells, Tph cells showed a unique transcriptomic signature of recent T cell receptor (TCR) activation, and clonally expanded Tph cells expressed an elevated transcriptomic effector signature compared to non-expanded Tph cells. CD8 T cells showed higher oligoclonality than CD4 T cells, and the largest CD8 T cell clones in synovium were highly enriched in GZMK+ cells. TCR analyses revealed CD8 T cells with likely viral-reactive TCRs distributed across transcriptomic clusters and definitively identified MAIT cells in synovium, which showed transcriptomic features of TCR activation. Among B cells, non-naive B cells including age-associated B cells (ABC), NR4A1+ activated B cells, and plasma cells, were enriched in synovium and had higher somatic hypermutation rates compared to blood B cells. Synovial B cells demonstrated substantial clonal expansion, with ABC, memory, and activated B cells clonally linked to synovial plasma cells. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate RA synovium.

immunology↗

bioRxiv2022

A statistical genetics guide to identifying HLA alleles driving complex disease

The human leukocyte antigen (HLA) locus is associated with more human complex diseases than any other locus. In many diseases it explains more heritability than all other known loci combined. Investigators have now demonstrated the accuracy of in silico HLA imputation methods. These approaches enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and infectious diseases, such as HIV infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine-mapping. Here, we present comprehensive statistical genetics guide to impute HLA alleles from genotype data. We provide detailed protocols, including standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids including a web-based Michigan Imputation Server. We updated the HLA imputation reference panel representing global populations (African, East Asian, European and Latino) available at the Michigan Imputation Server (n = 20,349) and achived high imputation accuracy (mean dosage correlation r = 0.981). We finally offer best practice recommendations to conduct association tests in order to define the alleles, amino acids, and haplotypes affecting human traits. This protocol will be broadly applicable to the large-scale genotyping data and contribute to defining the role of HLA in human diseases across global populations.

genetics↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

A Saturated Map of Common Genetic Variants Associated with Human Height from 5.4 Million Individuals of Diverse Ancestries

Common SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

genetics↗

bioRxiv2021

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

genomics↗

bioRxiv2021

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution without the need for annotation of individual cells to cell types; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =346K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

bioinformatics↗

bioRxiv2021

Genetically independent phenotype analysis identifies LPA and VCAM1 as drug targets for human ageing

The length and quality of life is important to us all, yet identification of promising drug targets for human ageing using genetics has had limited success. Here, we combine six large European-ancestry genome-wide association studies (GWAS) of human ageing traits--healthspan, father and mother lifespan, exceptional longevity, frailty index, and self-rated health--in a principal component framework that maximises their shared genetic architecture. The first principal component (GIP1) is more heritable than the original studies and shows strong genetic correlations with length of life as well as multiple indices of mental and physical wellbeing. We identify 27 genomic regions associated with GIP1, and provide additional, independent evidence for an effect on human ageing for loci near HTT and MAML3 using a study of Finnish and Japanese subject survival. Across the genome, GIP1 associations are enriched in genes involved in haem metabolism and pathways related to transcription, neurogenesis, homeostasis, proteolysis, intracellular signalling, immunity, and the muscle system. Finally, using proteome-wide two-sample Mendelian randomisation and colocalisation, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) (LPA) and vascular cell adhesion molecule 1 (VCAM1) on GIP1. Together, our results demonstrate that combining multiple ageing traits using genetic principal components enhances power to detect biological targets for human ageing.

genomics↗

bioRxiv2019

Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan

Human genetics seeks a way to improve human health on a global scale. Expectations are running high for polygenic risk scores (PRSs) to be translated into clinical practice to predict an inborn susceptibility to health risks. While risk stratification based on PRS is one way to promote population health, a strategy to utilize genetics to prioritize modifiable risk factors and biomarkers driving heath outcome is also warranted. To this end, here we utilized PRSs to comprehensively investigate the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks (ntotal = 675,898). First, we conducted genome-wide association studies for 45 quantitative clinical phenotypes, constructed the individual PRSs, and associated them with the age at death of 179,066 participants in BioBank Japan. The PRSs revealed that the genetic susceptibility of high systolic blood pressure (sBP) was strongly associated with a shorter lifespan (hazard ratio [HR] = 1.03, P = 1.4x10-7). Next, we sought to replicate these associations in individuals of European ancestry in UK Biobank (n = 361,194) and FinnGen (n = 135,638). Among the investigated traits, the individuals with higher blood pressure-related PRSs were trans-ethnically associated with a shorter lifespan (HR = 1.03, Pmeta = 3.9x10-13 for sBP) and parental lifespan (HR = 1.06, PUKBB = 2.0x10-86 for sBP). Further, our trans-biobank study identified additional complex traits associated with lifespan (e.g., obesity, height, serum lipids, and platelet counts). Of them, obesity-related traits showed strikingly heterogeneous effects on lifespan between Japanese and European populations (Pheterogeneity = 9.5x10-8 for body mass index). Through trans-ethnic biobank collaboration, we elucidated the novel value of the PRS study in genetics-driven prioritization of risk factors and biomarkers which can be medically intervened to improve population health.

genetics↗

bioRxiv2019

Large scale genome-wide association study in a Japanese population identified 45 novel susceptibility loci for 22 diseases

INTRODUCTORY PARAGRAPHThe overwhelming majority of participants in current genetic studies are of European ancestry1-3, limiting our genetic understanding of complex disease in non-European populations. To address this, we aimed to elucidate polygenic disease biology in the East Asian population by conducting a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 383 independent signals in 331 loci for 30 diseases, among which 45 loci were novel (P < 5 x 10-8). Compared with known variants, novel variants have lower frequency in European populations but comparable frequency in East Asian populations, suggesting the advantage of this study in discovering these novel variants. Three novel signals were in linkage disequilibrium (r2 > 0.6) with missense variants which are monomorphic in European populations (1000 Genomes Project) including rs11235604(p.R220W of ATG16L2, a autophagy-related gene) associated with coronary artery disease. We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, andidentified 378 significant enrichments across nine diseases (FDR < 0.05) (e.g. NF-{kappa}B for immune-related diseases). This large-scale GWAS in a Japanese population provides insights into the etiology of common complex diseases and highlights the importance of performing GWAS in non-European populations.

genetics↗