Mineto Ota

Insight into population-level gene-environment (GxE) interactions is a major goal in understanding polygenic diseases, such as breast cancer. We integrated massively parallel reporter assays (MPRA), chromatin profiling, and network analysis to identify estrogen-responsive breast cancer variants. Screening 1,604 GWAS-identified breast cancer variants identified 73 estrogen-modulated SNVs (emSNVs). Chromatin accessibility modeling validated allele-specific effects, identifying variants that disrupt pioneer factor binding to chromatin and others that modulate transcription factor (TF) recruitment to pre-accessible enhancers. emSNV targets converged on pathways including mitochondrial metabolism, NF-κB signaling, and chromatin regulation. Aggregating emSNVs into a polygenic risk score (PRSE2) revealed interactions with reproductive risk factors in 13,026 post-menopausal BRCA cases and 108,265 controls, including age at first birth (p=0.0052); a control PRS lacking estrogen-responsive variants showed no interactions. This framework bridges molecular and epidemiological GxE studies to uncover variants whose disease associations depend on environmental context, with implications for understanding polygenic disease risk.

Objectives: Rheumatoid arthritis (RA) synovitis frequently persists despite cytokine-targeted therapies, suggesting the existence of pathogenic stromal programs that sustain chronic inflammation independently of canonical immune pathways. Although synovial fibroblasts (SF) are increasingly implicated in treatment resistance, the pathogenic fibroblast states driving refractory disease and their therapeutic vulnerabilities remain poorly defined. Methods: We integrated multimodal single-cell and spatial profiling of synovial tissue from 54 patients with RA with prospective treatment-response data and functional studies in human fibroblasts and experimental arthritis models. Results: We identified a C-X-C motif chemokine 12 (CXCL12) ^hi Apolipoprotein C1 (APOC1) ⁺ fibroblast population selectively enriched in treatment-refractory synovitis. Spatial analyses demonstrated that these fibroblasts establish CXCL12-dependent plasmablast niches within inflamed synovium, resembling inflammatory cancer-associated fibroblasts (iCAF) that orchestrate immune cell recruitment in the tumor microenvironment. CXCL12 ^hi APOC1 ⁺ fibroblasts exhibited a senescence-associated iCAF-like transcriptional program characterized by STAT3-C/EBP activation and APOC1 expression, and were associated with poor response to TNF and IL-6 pathway inhibition. Mechanistically, APOC1 knockdown in RA-SF attenuated invasive mesenchymal behavior and disrupted senescence-associated inflammatory programs, identifying APOC1 as a central regulator of pathogenic fibroblast reprogramming. Importantly, genetic or pharmacological elimination of senescent cells ameliorated experimental arthritis and enhanced the efficacy of TNF blockade. Conclusions: These findings identify iCAF-like fibroblasts with senescent properties as a mechanistic driver of refractory RA synovitis and nominate stromal senescence programs as actionable therapeutic targets beyond cytokine inhibition.

Gene regulatory networks encode the fundamental logic of cellular functions, but systematic network mapping remains challenging, especially in cell states relevant to human biology and disease. Here, we perturbed all expressed genes across 22 million primary human CD4+ T cells from four donors and developed a probe-based perturb-seq platform to measure the transcriptome effects in cells at rest and after stimulation. These data allow us to map genes that regulate known and novel pathways, including novel regulators of cytokine production. Importantly, active regulators and the gene programs they control change dramatically across stimulation conditions. Perturbation signatures enabled us to model T cell states observed in population-scale transcriptomic atlases, nominating regulators of Th1 and Th2 polarization and of age-related T cell phenotypes. Finally, we leveraged perturb-seq to implicate context-specific gene regulatory pathways in autoimmune disease risk. Our data provide a foundational resource to decode human immune function and genetic variation and for new approaches to study gene regulatory networks.

Primary human myeloid cells are promising candidates for immunotherapy, yet efficient and scalable technologies for genetic engineering and screening in these cells are limited. Here we present a virus-like particle (VLP)-based toolkit that delivers diverse CRISPR genome editing modalities to human monocytes, macrophages, and dendritic cells with high efficiency while preserving viability and innate immune responsiveness. VLP-mediated delivery of ribonucleoprotein payloads supports gene knockout, base editing and epigenetic silencing, and enables site-specific integration of large DNA sequences when combined with AAV donors for homology-directed repair. Leveraging sgRNA delivery via VPX-lentivirus combined with Cas9 protein delivery via engineered virus-like particle (eVLP) treatment ('SLICeVLP'), we performed the first pooled loss-of-function screens in human macrophages. We uncovered regulators of TNF production and CD80 expression in human macrophages, converging on TNFAIP3 as a central regulator of inflammatory polarization. TNFAIP3 ablation promoted a pro-inflammatory cell state that is resistant to suppressive polarization, and augmented cytotoxicity of engineered HER2CAR-macrophages. Taken together, this technology platform enables unbiased discovery and characterization of functional gene targets in primary human myeloid cells.

Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene–trait relationships from loss-of-function burden tests¹ with gene-regulatory connections inferred from Perturb-seq experiments² in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait³. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes ¹ . Although these methods are conceptually similar, by analysing association studies of 209 quantitative traits in the UK Biobank ^2–4 , we show that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: (1) trait importance — how much a gene quantitatively affects a trait; and (2) trait specificity — the importance of a gene for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, whereas burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, whereas burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

: Genetic association studies provide a unique tool for identifying causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose new approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait. As a proof-of-concept, we constructed a causal graph of the gene regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetics and biology.

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance — how much a gene quantitatively affects a trait; and 2) trait specificity — a gene’s importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants , while burden tests prioritize trait-specific genes . Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.