Biology subjects

Trynka, G.

Publications and source records attributed to Trynka, G..

16 recordsLinked to original sources

bioRxiv2026

Single-cell multi-ancestry regulatory map of systemic lupus erythematosus

Integrating genomic and single-cell transcriptomic profiles from patient cohorts can uncover regulatory effects underlying genetic associations with disease. Here we present SLEmap, a multi-ancestry single-cell expression quantitative trait locus (sc-eQTL) map generated from peripheral blood mononuclear cells from 281 patients with systemic lupus erythematosus (SLE). We identified 18,608 independent eQTLs in 5,656 genes, of which 149 signals in 66 genes colocalized with SLE GWAS loci. Nearly half of these colocalizations were detected exclusively through cell type-level analyses, highlighting the importance of cellular context for interpreting disease-associated genetic variation. Multi-ancestry data identified regulatory signals robust across diverse ancestral backgrounds and improved signal resolution. Most colocalizations were undetectable in sc-eQTLs from a healthy cohort (OneK1K), with novel SLE colocalizations highlighting disease-relevant regulatory mechanisms across distinct cell types, including the NF-{kappa}B pathway. These findings demonstrate the value of disease-specific, multi-ancestry single-cell regulatory maps for resolving the genes and cellular mechanisms underlying disease-associated genetic variation.

genetics↗

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

bioRxiv2026

Genome-scale mapping of variant, enhancer and gene function in primary human CD4+ T cells

CD4+ T cells harbor a disproportionate enrichment of immune disease risk loci and represent the primary cellular context for immune disease biology, yet the genes and regulatory programs these variants affect remain largely unknown. We combined targeted Perturb-seq of 1,032 cis-regulatory elements (CREs) overlapping 4,724 variants across 14 immune diseases with genome-wide Perturb-seq of all expressed genes in primary human CD4+ T cells, spanning 4.1 million cells. We identified 626 CRE-gene pairs, and connected CRE targets to downstream regulatory cascades. At the TYK2 and DEXI/CLEC16A loci, we resolved target genes and linked noncoding variants to inflammatory and metabolic programs. Across diseases, we revealed that dispersed variants converged on shared and disease-specific programs. Our work provides a framework for tracing variant-to-CRE-to-gene-to-network in disease-relevant primary cells.

genetics↗

bioRxiv2026

Predicting how perturbations reshape cellular trajectories with PerturbGen

A major challenge in biology is predicting how cells transition between states over time and how perturbations disrupt these transitions. Understanding such dynamics is critical for identifying interventions that reverse pathological programs or reprogram cells toward desired states. Although recent computational approaches can predict single-cell perturbation responses in silico, they cannot predict responses across dynamic cell trajectories, for example how early perturbations reconfigure later cell states. To address this gap, we introduce PerturbGen, a generative foundation model trained on over 100 million single-cell transcriptomes that predicts perturbation responses along cellular trajectories. PerturbGen predicts how genetic perturbation at source state shapes downstream states, alters gene programs and trajectories across time, for example in differentiation or disease progression. We apply PerturbGen to three newly generated multi-condition human single-cell datasets spanning immune responses, hematopoiesis and skin development. In an in vivo immune challenge, PerturbGen predicts that knocking out an IL1B signal in myeloid cells attenuates later cytokine-interferon programs, with downstream changes consistent with a reversal of IL-1{beta} stimulation signature. In hematopoiesis, anchoring perturbation-induced programs to human genetics enables simulation of monogenic blood disorders, recapitulates established disease-associated biology whilst systematically revealing lineage-specific programs, including in lineages where this was not previously possible. In skin organoids, PerturbGen predicted that Wnt activation enhances stromal differentiation recapitulating the trajectory observed in human prenatal skin, findings that were functionally validated by experimentally activating Wnt signaling. Together, PerturbGen extends modeling of gene perturbations from static to dynamic cellular systems. We envision PerturbGen enabling the creation of in silico, trajectory-aware perturbation atlases and virtual cells across diverse biological scenarios, supporting optimization of disease models and prioritization of candidate molecular interventions for therapeutic discovery.

bioinformatics↗

bioRxiv2026

Division state reveals hidden genetic regulation during T cell activation and identifies immune disease-linked gene programmes

A major challenge in human genetics is to connect disease-associated variants to the cellular functions they perturb. Variants associated with immune-mediated disease have implicated CD4 T-cell activation as a disease-causal process; however, it remains unresolved whether these variants act on pathways initiating cell division, driving effector differentiation, or maintaining proliferative capacity. Activated T cells comprise mixtures of cells that have undergone different numbers of divisions, potentially obscuring genetic effects that act at specific stages of this process. Here, we study CD4 T-cell activation through cell division using division-resolved single-cell transcriptomics of naive, memory and regulatory T cells. We show that division state constitutes an important axis of transcriptional organisation, with approximately one third of genes exhibiting division-dependent regulation across shared and subset-specific programmes. We develop CellDivider, a machine-learning model that accurately infers proliferative state directly from single-cell transcriptomes, enabling a scalable approach for cohort level mapping of variant effects on cell proliferation phenotypes. Incorporating inferred division state into single-cell eQTL models reveals regulatory effects that are attenuated or missed when cells are analysed by activation time alone. Finally, we resolve gene expression programmes that govern distinct stages of T cell activation and proliferation. We show that polygenic immune disease risk preferentially converges on early activation and differentiation modules, rather than on proliferative capacity itself. Consistent with this model, division-resolved analysis of the pleiotropic immune risk gene RPS26 localises its genetic effects to early division states, providing mechanistic insight into its broad disease associations. Together, these findings illustrate how resolving dynamic cellular consequences of activation sharpens variant-to-function inference in immune disease genetics.

genetics↗

bioRxiv2026

Multiplexed high-content imaging uncovers morphological diversity of lymphocyte activation and dysfunction

Single-cell transcriptomic and proteomic technologies enable molecular profiling of immune cells at scale but provide limited access to cellular phenotypes shaped by spatial organisation, organelle architecture and cytoskeletal remodelling. Here we present TGlow, a scalable high-content imaging platform optimized for systematic single-cell phenotyping of primary human lymphocytes. TGlow integrates cyclic immunofluorescence, deep z-stack confocal imaging, and open-source data processing pipelines, including both classical and self-supervised vision transformer-based feature extraction, to jointly quantify cellular morphology, organelle organization, and immune activation states. Applied across over 400,000 primary human T cells spanning CD4+ activation time courses, drug perturbations, CRISPR knockouts and CD8+ T-cell exhaustion, TGlow resolves distinct and reproducible phenotypic states. We uncover dose-dependent and mechanism-specific drug phenotypes, such as defective endoplasmic reticulum polarisation under mycophenolic acid and tofacitinib. We show that mitochondrial clustering reveals activation- and cell-cycle-linked remodelling programs, CRISPR perturbations map gene-specific phenotypes that reposition cells along activation trajectories, and we identify a previously unrecognised collapse of cytoskeletal architecture in exhausted CD8+ T cells. TGlow provides a scalable framework for high-dimensional phenotyping of lymphocyte states advancing functional genomics, perturbation screening and population-level immune profiling by resolving the morphological and functional heterogeneity of lymphocytes and enabling systematic linkage of genetic and pharmacological perturbations to cellular function.

immunology↗

bioRxiv2025

Gene expression profiling of dendritic cell tolerance dysfunction in women with Systemic lupus erythematosus

Dendritic cells (DCs) are central regulators of immune tolerance, and disturbances in their phenotype and function contribute to the breakdown of self-tolerance in systemic lupus erythematosus (SLE). Tolerogenic DCs (tolDCs), which suppress autoreactive responses and promote peripheral tolerance, are a promising therapeutic focus in autoimmune diseases. Here, we analyzed the transcriptional profiles of in vitro generated DCs derived from monocytes of individuals with SLE and healthy controls to identify disease-specific disruptions in tolerance associated pathways. Interferon stimulated genes (ISGs) emerged as dominant markers across all cellular contexts, with monocytes exhibiting the most substantial enrichment; key ISGs (IFI27, IFI44L, USP18, IFI6) acted as central hubs in regulatory networks, underscoring their diagnostic and pathogenic significance. In tolDCs from SLE donors, lipid metabolism pathways were selectively altered, suggesting impaired synthesis of pro-resolving lipid mediators. Additionally, diminished IL10RA expression and dysregulated IRF4 activity in SLE moDCs indicated intrinsic defects in IL-10 mediated tolerogenic differentiation. Together, these findings suggest that interferon driven transcriptional rewiring, impaired IL-10 signaling, and aberrant lipid metabolic programming converge to compromise DCs tolerogenic capacity in SLE. This highlights key mechanistic pathways that could be targeted to restore immune tolerance and reduce chronic inflammation.

immunology↗

bioRxiv2025

Integrated QTL mapping and CRISPR screening in pooled iPSC-derived microglia reveals genetic drivers of neurodegenerative risk

Mounting evidence implicates microglia in neurodegeneration, but linking disease-associated genetic variants to target genes and cellular phenotypes is hindered by the inaccessibility of these cells. We differentiated 261 human iPSC lines into microglia-like cells (iMGL) in pools with phenotypic (differentiation, phagocytosis and migration) and single-cell transcriptomic readouts. Burden analysis of deleterious variants detected 36 genes influencing microglial phenotypes. Expression quantitative trait locus (eQTL) analysis found 7,121 eGenes, and 79 colocalizations across four neurodegenerative disease GWAS, half of which had limited prior evidence of causality. Integration of eQTL and phenotypic associations highlighted the role of disease-relevant variants including LRRK2 and TREM2 acting via microglial phagocytosis. A coupled CRISPR screen identified a role of TREM2 in phagocytosis and highlighted the importance of cellular state in directionality of phenotype. By contextualizing variant effects within disease-relevant microglial states, we provide a comprehensive framework for interpreting the function of risk loci in neurodegenerative disorders.

genomics↗

bioRxiv2024

ONE-STEP tagging: a versatile method for rapid site-specificintegration by simultaneous reagent delivery

We present a novel, versatile genome editing method termed ONE-STEP tagging, which combines CRISPR-Cas9-mediated targeting with Bxb1 integrase-based site-specific integration for efficient, precise, and scalable protein tagging. Applied in hiPSCs, cancer cells, and primary T cells, this system enables rapid generation of endogenously tagged cell lines. By enhancing the nuclear localization signal (NLS) of the catalytically superior eeBxb1 integrase and co-delivering a DNA-PK inhibitor (AZD-7648), we achieved up to [~]90% integration efficiency at the ACTR10 locus. ONE-STEP tagging is robust across diverse loci and cell types and supports large DNA cargo integration, with efficiencies reaching 16.6% for a 14.4 kb construct. The method also enables multiplexed tagging of multiple proteins within the same cell and allows simultaneous CRISPR-based editing at secondary loci, such as gene knockouts or homology-directed repair (HDR) insertions. Importantly, we demonstrate successful application in primary T cells by targeting the T cell receptor (TCR) locus while simultaneously knocking out B2M, a key step toward generating immune-evasive, off-the-shelf CAR-T cells. Additionally, we introduce a dual-cassette version of the method compatible with universal donor plasmids, allowing use of entirely off-the-shelf reagents. Together, these advances establish ONE-STEP tagging as a powerful tool for both basic and therapeutic genome engineering.

molecular biology↗

bioRxiv2024

An atlas of protein-protein associations of human tissues prioritizes candidate disease genes

Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein-protein interactions we lack knowledge of how they differ between tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7873 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas with tissue-specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while signaling and metabolic interactions show larger variation. Further, we find that less than 18% of differences between tissues are estimated to be due to differences in gene expression while cell-type specific cellular structures, such as synaptic components, represent a significant driver of differences between tissues. We further supported the brain protein association network through co-fractionation experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models. Together these results illustrate how this brain specific protein interaction network can functionally prioritize candidate genes within loci linked to brain disorders.

systems biology↗

bioRxiv2021

Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation

During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.

genomics↗

bioRxiv2019

Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines

Naive CD4+ T cells coordinate the immune response by acquiring an effector phenotype in response to cytokines. However, the cytokine responses in memory T cells remain largely understudied. We used quantitative proteomics, bulk RNA-seq and single-cell RNA-seq of over 40,000 human naive and memory CD4+ T cells to generate a detailed map of cytokine-regulated gene expression programs. We demonstrated that cytokine response differs substantially between naive and memory T cells and showed that memory cells are unable to differentiate into the Th2 phenotype. Moreover, memory T cells acquire a Th17-like phenotype in response to iTreg polarization. At the single-cell level, we demonstrated that T cells form a continuum which progresses from naive to effector memory T cells. This continuum is accompanied by a gradual increase in the expression levels of chemokines and cytokines and thus represents an effectorness gradient. Finally, we found that T cell cytokine responses are determined by where the cells lie in the effectorness gradient and identified genes whose expression is controlled by cytokines in an effectorness-dependent manner. Our results shed light on the heterogeneity of T cells and their responses to cytokines, provide insight into immune disease inflammation and could inform drug development.

immunology↗

bioRxiv2019

Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets

Identifying cellular functions dysregulated by disease associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. Variants associated with immune diseases point towards the role of CD4+ regulatory T cells (Tregs), cell type critical for immune homeostasis. To translate the effects of immune disease alleles on Treg function we mapped genetic regulation (QTL) of gene expression and chromatin activity in Tregs. We identified 123 loci where Treg QTLs colocalized with immune disease variants. These colocalizations indicated that dysregulation of key Treg pathways, including cell activation via CD28 and IL-2 signalling mediated by STAT5A contribute to the shared pathobiology of immune diseases. Finally, using disease GWAS signals colocalizing with Treg QTLs, we identified 34 known drug targets and 270 targets with drug tractability evidence. Our study is the first in-depth characterization of immune disease variant effects on Treg gene expression modulation and dysregulation of Treg function.

genetics↗

bioRxiv2019

Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases

Complex immune disease variants are enriched in active chromatin regions of T cells and macrophages. However, whether these variants function in specific cell states or stages of cell activation is unknown. We stimulated T cells and macrophages in the presence of thirteen different cytokine cocktails linked to immune diseases and profiled active enhancers and promoters together with regions of open chromatin. We observed that T cell activation induced major chromatin remodelling, while additional exposure to cytokines fine-tuned the magnitude of these changes. Therefore, we developed a new statistical method that accounts for subtle changes in chromatin landscape to identify SNP enrichment across cell states. Our results point towards the role of immune disease variants in early rather than late activation of memory CD4+ T cells, and with limited differences across polarizing cytokines. Furthermore, we demonstrate that inflammatory bowel disease variants are enriched in chromatin regions active in Th1 cells, while asthma variants overlap regions active in Th2 cells. We also show that Alzheimers disease variants are enriched in different macrophage cell states. Our results represent the first in-depth analysis of immune disease variants across a comprehensive panel of activation states of T cells and macrophages.

genetics↗

bioRxiv2018

Genomic profiling of T cell activation reveals dependency of memory T cells on CD28 costimulation

T cell activation is a critical driver of immune response and if uncontrolled, it can result in failure to respond to infection or in excessive inflammation and autoimmunity. CD28 costimulatory pathway is an essential regulator of CD4 T cell responses. To deconvolute how T cell receptor (TCR) and CD28 orchestrate activation of human CD4 T cells we stimulated cells using varying intensities of TCR and CD28 signals followed by gene expression profiling. We demonstrate that T-helper differentiation and cytokine expression are controlled by CD28. Strikingly, cell cycle and cell division are sensitive to CD28 in memory cells, but under TCR control in naive cells, in contrast to the paradigm that memory cells are CD28-independent. Using a combination of chromatin accessibility and enhancer profiling, we observe that IRFs and Blimp-1 (PRDM1) motifs are enriched in naive and memory T cells in response to TCR. In contrast, memory cells initiate AP1 transcriptional regulation only when both TCR and CD28 are engaged, implicating CD28 as an amplifier of transcriptional programmes in memory cells. Lastly, we show that CD28-sensitive genes are enriched in autoimmune disease loci, pointing towards the role of memory cells and the regulation of T cell activation through CD28 in autoimmune disease development. This study provides important insights into the differential role of CD28 in naive and memory T cell responses and offers a new platform for design and interpretation of costimulatory based therapies.\n\nOne-sentence summaryGenomic profiling of CD4 T cell activation reveals a sensitivity switch from TCR in naive to CD28 in memory cells.

genomics↗

bioRxiv2017

A Balance Between Regulatory Constraints And Pathogen Pressure Shapes The Evolution Of Innate Immunity

As the first line of defence against pathogens, cells mount an innate immune response, which is highly variable from cell to cell. The response must be potent yet carefully controlled to avoid self-damage. How these constraints have shaped the evolution of innate immunity remains poorly understood. Here, we characterise this programmes transcriptional divergence between species and expression variability across cells. Using bulk and single-cell transcriptomics in primate and rodent fibroblasts challenged with an immune stimulus, we reveal a striking architecture of the innate immune response. Rapidly diverging genes, including cytokines and chemokines, also vary across cells and have distinct promoter structures. Conversely, genes involved in response regulation, such as transcription factors and kinases, are conserved between species and display low cell-to-cell variability. We suggest that this unique expression pattern, observed across species and conditions, has evolved as a mechanism for fine-tuned regulation, to achieve an effective but balanced response.

evolutionary biology↗