Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks

Quantitative omnigenic model discovers interpretable genome-wide associations

As their statistical power grows, genome-wide association studies (GWAS) have identified an increasing number of loci underlying quantitative traits of interest. These loci are scattered throughout the genome and are individually responsible only for small fractions of the total heritable trait variance. The recently proposed omnigenic model provides a conceptual framework to explain these observations by postulating that numerous distant loci contribute to each complex trait via effect propagation through intracellular regulatory networks. We formalize this conceptual framework by proposing the "quantitative omnigenic model" (QOM), a statistical model that combines prior knowledge of the regulatory network topology with genomic data. By applying our model to gene expression traits in yeast, we demonstrate that QOM achieves similar gene expression prediction performance to traditional GWAS with hundreds of times less parameters, while simultaneously extracting candidate causal and quantitative chains of effect propagation through the regulatory network for every individual gene. We estimate the fraction of heritable trait variance in cis- and in trans-, break the latter down by effect propagation order, assess the trans- variance not attributable to transcriptional regulation, and show that QOM correctly accounts for the low-dimensional structure of gene expression covariance. We furthermore demonstrate the relevance of QOM for systems biology, by employing it as a statistical test for the quality of regulatory network reconstructions, and linking it to the propagation of nontranscriptional (including environmental) effects.

Gene regulatory network structure informs the distribution of perturbation effects

Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.

A digital CRISPR-dCas9-based gene remodeling biocomputer programmed by dietary compounds in mammals

CRISPR-dCas9 (dead Cas9 protein) technology, combined with chemical molecules and light-triggered genetic switches, offers customizable control over gene perturbation. However, these simple ON/OFF switches cannot precisely determine the sophisticated perturbation process. Here, we developed a resveratrol and protocatechuic acid-programmed CRISPR-mediated gene remodeling biocomputer (REPACRISPR) for conditional endogenous transcriptional regulation of genes in vitro and in vivo. Two REPACRISPR variants, REPACRISPRi and REPACRISPRa, were designed for the logic control of gene inhibition and activation, respectively. We successfully demonstrated the digital computations of single or multiplexed endogenous gene transcription by using REPACRISPRa. We also established mathematical models to predict the dose-responsive transcriptional levels of a target endogenous gene controlled by REPACRISPRa. Moreover, high levels of endogenous gene activation in mice mediated by the AND logic gate demonstrated computational control of CRISPR-dCas9-based epigenome remodeling in mice. This CRISPR-based biocomputer expands the synthetic biology toolbox and can potentially advance gene-based precision medicine. A record of this paper's transparent peer review process is included in the supplemental information.

Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease

The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation has been challenging due to small effects, but experimental perturbations offer a complementary approach. Using CRISPR, we knocked out 84 genes in primary CD4+ T cells, targeting inborn error of immunity (IEI) disease transcription factors (TFs) and TFs without immune disease association. We developed a novel gene network inference method called linear latent causal Bayes (LLCB) to estimate the network from perturbation data and observed 211 regulatory connections between genes. We characterized programs affected by the TFs, which we associated with immune genome-wide association study (GWAS) genes, finding that JAK-STAT family members are regulated by KMT2A, an epigenetic regulator. These analyses reveal the trans-regulatory cascades linking GWAS genes to signaling pathways.

Dynamic chromatin architecture identifies new autoimmune-associated enhancers for IL2 and novel genes regulating CD4+ T cell activation

Genome-wide association studies (GWAS) have identified hundreds of genetic signals associated with autoimmune disease. The majority of these signals are located in non-coding regions and likely impact cis-regulatory elements (cRE). Because cRE function is dynamic across cell types and states, profiling the epigenetic status of cRE across physiological processes is necessary to characterize the molecular mechanisms by which autoimmune variants contribute to disease risk. We localized risk variants from 15 autoimmune GWAS to cRE active during TCR-CD28 co-stimulation of naïve human CD4+ T cells. To characterize how dynamic changes in gene expression correlate with cRE activity, we measured transcript levels, chromatin accessibility, and promoter-cRE contacts across three phases of naive CD4+ T cell activation using RNA-seq, ATAC-seq, and HiC. We identified ~1200 protein-coding genes physically connected to accessible disease-associated variants at 423 GWAS signals, at least one-third of which are dynamically regulated by activation. From these maps, we functionally validated a novel stretch of evolutionarily conserved intergenic enhancers whose activity is required for activation-induced IL2 gene expression in human and mouse, and is influenced by autoimmune-associated genetic variation. The set of genes implicated by this approach are enriched for genes controlling CD4+ T cell function and genes involved in human inborn errors of immunity, and we pharmacologically validated eight implicated genes as novel regulators of T cell activation. These studies directly show how autoimmune variants and the genes they regulate influence processes involved in CD4+ T cell proliferation and activation.

Deciphering regulation of FOXP3 expression in human conventional T cells

FOXP3 is a lineage-defining transcription factor that controls differentiation and maintenance of suppressive function of regulatory T cells (Tregs). Foxp3 is exclusively expressed in Tregs in mice. However, in humans, FOXP3 is not only constitutively expressed in Tregs; it is also transiently expressed in stimulated CD4+CD25- conventional T cells (Tconvs)1-3. Mechanisms governing the expression of FOXP3 in human Tconvs are not understood. Here, we performed CRISPR interference (CRISPRi) screens using a 15K-member gRNA library tiling 39 kb downstream of the FOXP3 transcriptional start site (TSS) to 85 kb upstream of the TSS in Treg and Tconvs. The FOXP3 promoter and conserved non-coding sequences (CNS0, CNS1, CNS2 and CNS3), characterized as enhancer elements in murine Tregs, were required for maintenance of FOXP3 in human Tregs. In contrast, FOXP3 in human Tconvs depended on regulation at CNS0 and a novel Tconv-specific noncoding sequence (TcNS+) located upstream of CNS0. Arrayed validations of these sites identified an additional repressive cis-element overlapping with the PPP1R3F promoter (TcNS-). Pooled CRISPR knockouts revealed multiple transcription factors required for proper expression of FOXP3 in Tconvs, including GATA3, STAT5, IRF4, ETS1 and DNA methylation-associated regulators DNMT1 and MBD2. Analysis of ChIP-seq and ATAC-seq paired with knock-out (KO) of GATA3, STAT5, IRF4, and ETS1 revealed regulation of CNS0 and TcNS+ accessibility. Collectively, this work identified Treg-shared and Tconv-specific cis-elements and the trans-factors that interact with them, building a network of regulators controlling FOXP3 expression in human Tconvs.

Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis

LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.

Transcriptome data are insufficient to control false discoveries in regulatory network inference

Inference of causal transcriptional regulatory networks (TRNs) from transcriptomic data suffers notoriously from false positives. Approaches to control the false discovery rate (FDR), for example, via permutation, bootstrapping, or multivariate Gaussian distributions, suffer from several complications: difficulty in distinguishing direct from indirect regulation, nonlinear effects, and causal structure inference requiring "causal sufficiency," meaning experiments that are free of any unmeasured, confounding variables. Here, we use a recently developed statistical framework, model-X knockoffs, to control the FDR while accounting for indirect effects, nonlinear dose-response, and user-provided covariates. We adjust the procedure to estimate the FDR correctly even when measured against incomplete gold standards. However, benchmarking against chromatin immunoprecipitation (ChIP) and other gold standards reveals higher observed than reported FDR. This indicates that unmeasured confounding is a major driver of FDR in TRN inference. A record of this paper's transparent peer review process is included in the supplemental information.

Scalable genetic screening for regulatory circuits using compressed Perturb-seq

Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

Trans-eQTL hotspots shape complex traits by modulating cellular states

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.

Stochastic Modeling of Biophysical Responses to Perturbation

Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the 'how' behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.

A model for accurate quantification of CRISPR effects in pooled FACS screens

CRISPR screens are powerful tools to identify key genes that underlie biological processes. One important type of screen uses fluorescence activated cell sorting (FACS) to sort perturbed cells into bins based on the expression level of marker genes, followed by guide RNA (gRNA) sequencing. Analysis of these data presents several statistical challenges due to multiple factors including the discrete nature of the bins and typically small numbers of replicate experiments. To address these challenges, we developed a robust and powerful Bayesian random effects model and software package called Waterbear. Furthermore, we used Waterbear to explore how various experimental design parameters affect statistical power to establish principled guidelines for future screens. Finally, we experimentally validated our experimental design model findings that, when using Waterbear for analysis, high power is maintained even at low cell coverage and a high multiplicity of infection. We anticipate that Waterbear will be of broad utility for analyzing FACS-based CRISPR screens.

Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

The Combination of Anti-CD47 Antibody with CTLA4 Blockade Enhances Anti-Tumor Immunity in Non-Small Cell Lung Cancer via Normalization of Tumor Vasculature and Reprogramming of the Immune Microenvironment

In solid tumors, the formidable anti-tumor impact resulting from blocking the "don't eat me" signal, arising from CD47-SIRPα interaction, is constrained, especially compared to its efficacy in hematopoietic malignancies. Activating macrophage anti-tumor activity not only necessitates the inhibition of the "don't eat me" signal, but also the activation of the "eat me" (pre-phagocyte) signal. Intriguingly, the cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibody (Ab) has been identified to stimulate Fc receptor-mediated active phagocytes in the tumor microenvironment, thereby generating "eat me" signals. This study postulates that concurrently targeting CD47 and CTLA4 could intensify the anti-tumor effects by simultaneously blocking the "don't eat me" signal while triggering the "eat me" signal. The experimental data from this investigation confirm that the combined targeting of CD47 and CTLA4 enhances immunity against solid tumors in LLC cell-transplanted tumor-bearing mice. This effect is achieved by reducing myeloid-derived suppressor cell infiltration while increasing the presence of effector memory CD8+ T cells, NK1.1+ CD8+ T cells, and activated natural killer T cells. Meanwhile, combination therapy also alleviated anemia. Mechanistically, the anti-CD47 Ab is shown to upregulate CTLA4 levels in NSCLC cells by regulating Foxp1. Furthermore, targeting CD47 is demonstrated to promote tumor vascular normalization through the heightened infiltration of CD4+ T cells. These findings suggest that the dual targeting of CD47 and CTLA4 exerts anti-tumor effects by orchestrating the "eat me" and "don't eat me" signals, reshaping the immune microenvironment, and fostering tumor vascular normalization. This combined therapeutic approach emerges as a potent strategy for effectively treating solid tumors.

Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types

Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.

Base-editing mutagenesis maps alleles to tune human T cell functions

CRISPR-enabled screening is a powerful tool for the discovery of genes that control T cell function and has nominated candidate targets for immunotherapies1-6. However, new approaches are required to probe specific nucleotide sequences within key genes. Systematic mutagenesis in primary human T cells could reveal alleles that tune specific phenotypes. DNA base editors are powerful tools for introducing targeted mutations with high efficiency7,8. Here we develop a large-scale base-editing mutagenesis platform with the goal of pinpointing nucleotides that encode amino acid residues that tune primary human T cell activation responses. We generated a library of around 117,000 single guide RNA molecules targeting base editors to protein-coding sites across 385 genes implicated in T cell function and systematically identified protein domains and specific amino acid residues that regulate T cell activation and cytokine production. We found a broad spectrum of alleles with variants encoding critical residues in proteins including PIK3CD, VAV1, LCP2, PLCG1 and DGKZ, including both gain-of-function and loss-of-function mutations. We validated the functional effects of many alleles and further demonstrated that base-editing hits could positively and negatively tune T cell cytotoxic function. Finally, higher-resolution screening using a base editor with relaxed protospacer-adjacent motif requirements9 (NG versus NGG) revealed specific structural domains and protein-protein interaction sites that can be targeted to tune T cell functions. Base-editing screens in primary immune cells thus provide biochemical insights with the potential to accelerate immunotherapy design.

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Discrimination of cell-intrinsic and environment-dependent effects of natural genetic variation on Kupffer cell epigenomes and transcriptomes

Noncoding genetic variation drives phenotypic diversity, but underlying mechanisms and affected cell types are incompletely understood. Here, investigation of effects of natural genetic variation on the epigenomes and transcriptomes of Kupffer cells derived from inbred mouse strains identified strain-specific environmental factors influencing Kupffer cell phenotypes, including leptin signaling in Kupffer cells from a steatohepatitis-resistant strain. Cell-autonomous and non-cell-autonomous effects of genetic variation were resolved by analysis of F1 hybrid mice and cells engrafted into an immunodeficient host. During homeostasis, non-cell-autonomous trans effects of genetic variation dominated control of Kupffer cells, while strain-specific responses to acute lipopolysaccharide injection were dominated by actions of cis-acting effects modifying response elements for lineage-determining and signal-dependent transcription factors. These findings demonstrate that epigenetic landscapes report on trans effects of genetic variation and serve as a resource for deeper analyses into genetic control of transcription in Kupffer cells and macrophages in vitro.

An essential role for miR-15/16 in Treg suppression and restriction of proliferation

The miR-15/16 family targets a large network of genes in T cells to restrict their cell cycle, memory formation, and survival. Upon T cell activation, miR-15/16 are downregulated, allowing rapid expansion of differentiated effector T cells to mediate a sustained response. Here, we used conditional deletion of miR-15/16 in regulatory T cells (Tregs) to identify immune functions of the miR-15/16 family in T cells. miR-15/16 are indispensable to maintain peripheral tolerance by securing efficient suppression by a limited number of Tregs. miR-15/16 deficiency alters expression of critical Treg proteins and results in accumulation of functionally impaired FOXP3loCD25loCD127hi Tregs. Excessive proliferation in the absence of miR-15/16 shifts Treg fate and produces an effector Treg phenotype. These Tregs fail to control immune activation, leading to spontaneous multi-organ inflammation and increased allergic inflammation in a mouse model of asthma. Together, our results demonstrate that miR-15/16 expression in Tregs is essential to maintain immune tolerance.

Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease

The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation, including eQTL mapping, has been challenging due to small effects. Experimental perturbation approaches offer a complementary and powerful approach to mapping trans-regulators. We used CRISPR knockouts of 84 genes in primary CD4+ T cells to perturb an immune cell gene network, targeting both inborn error of immunity (IEI) disease transcription factors (TFs) and background TFs matched in constraint and expression level, but without a known immune disease association. We developed a novel Bayesian structure learning method called Linear Latent Causal Bayes (LLCB) to estimate the gene regulatory network from perturbation data and observed 211 directed edges among the genes which could not be detected in existing CD4+ trans-eQTL data. We used LLCB to characterize the differences between the IEI and background TFs, finding that the gene groups were highly interconnected, but that IEI TFs were much more likely to regulate immune cell specific pathways and immune GWAS genes. We further characterized nine coherent gene programs based on downstream effects of the TFs and linked these modules to regulation of GWAS genes, finding that canonical JAK-STAT family members are regulated by KMT2A, a global epigenetic regulator. These analyses reveal the trans-regulatory cascade from upstream epigenetic regulator to intermediate TFs to downstream effector cytokines and elucidate the logic linking immune GWAS genes to key signaling pathways.

Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AE-COPDs) are associated with a significant disease burden. Blood immune phenotyping may improve our understanding of a COPD endotype at increased risk of exacerbations. Objective: To determine the relationship between the transcriptome of circulating leukocytes and COPD exacerbations. Methods: Blood RNA sequencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed. Blood microarray data (n = 646) from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study were used for validation. We tested the association between blood gene expression and AE-COPDs. We imputed the abundance of leukocyte subtypes and tested their association with prospective AE-COPDs. Flow cytometry was performed on blood in SPIROMICS (Subpopulations and Intermediate Outcomes in COPD Study) (n = 127), and activation markers for T cells were tested for association with prospective AE-COPDs. Measurements and Main Results: Exacerbations were reported 4,030 and 2,368 times during follow-up in COPDGene (5.3 ± 1.7 yr) and ECLIPSE (3 yr), respectively. We identified 890, 675, and 3,217 genes associated with a history of AE-COPDs, persistent exacerbations (at least one exacerbation per year), and prospective exacerbation rate, respectively. In COPDGene, the number of prospective exacerbations in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage ⩾2) was negatively associated with circulating CD8+ T cells, CD4+ T cells, and resting natural killer cells. The negative association with naive CD4+ T cells was replicated in ECLIPSE. In the flow-cytometry study, an increase in CTLA4 on CD4+ T cells was positively associated with AE-COPDs. Conclusions: Individuals with COPD with lower circulating lymphocyte counts, particularly decreased CD4+ T cells, are more susceptible to AE-COPDs, including persistent exacerbations.

Single-cell genomics meets human genetics

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

A variegated model of transcription factor function in the immune system

Specific combinations of transcription factors (TFs) control the gene expression programs that underlie specialized immune responses. Previous models of TF function in immunocytes had restricted each TF to a single functional categorization [e.g., lineage-defining (LDTFs) vs. signal-dependent TFs (SDTFs)] within one cell type. Synthesizing recent results, we instead propose a variegated model of immunological TF function, whereby many TFs have flexible and different roles across distinct cell states, contributing to cell phenotypic diversity. We discuss evidence in support of this variegated model, describe contextual inputs that enable TF diversification, and look to the future to imagine warranted experimental and computational tools to build quantitative and predictive models of immunocyte gene regulatory networks.

GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks

Enhancers play a vital role in gene regulation and are critical in mediating the impact of noncoding genetic variants associated with complex traits. Enhancer activity is a cell-type-specific process regulated by transcription factors (TFs), epigenetic mechanisms and genetic variants. Despite the strong mechanistic link between TFs and enhancers, we currently lack a framework for jointly analysing them in cell-type-specific gene regulatory networks (GRN). Equally important, we lack an unbiased way of assessing the biological significance of inferred GRNs since no complete ground truth exists. To address these gaps, we present GRaNIE (Gene Regulatory Network Inference including Enhancers) and GRaNPA (Gene Regulatory Network Performance Analysis). GRaNIE (https://git.embl.de/grp-zaugg/GRaNIE) builds enhancer-mediated GRNs based on covariation of chromatin accessibility and RNA-seq across samples (e.g. individuals), while GRaNPA (https://git.embl.de/grp-zaugg/GRaNPA) assesses the performance of GRNs for predicting cell-type-specific differential expression. We demonstrate their power by investigating gene regulatory mechanisms underlying the response of macrophages to infection, cancer and common genetic traits including autoimmune diseases. Finally, our methods identify the TF PURA as a putative regulator of pro-inflammatory macrophage polarisation.

Massively parallel base editing to map variant effects in human hematopoiesis

Systematic evaluation of the impact of genetic variants is critical for the study and treatment of human physiology and disease. While specific mutations can be introduced by genome engineering, we still lack scalable approaches that are applicable to the important setting of primary cells, such as blood and immune cells. Here, we describe the development of massively parallel base-editing screens in human hematopoietic stem and progenitor cells. Such approaches enable functional screens for variant effects across any hematopoietic differentiation state. Moreover, they allow for rich phenotyping through single-cell RNA sequencing readouts and separately for characterization of editing outcomes through pooled single-cell genotyping. We efficiently design improved leukemia immunotherapy approaches, comprehensively identify non-coding variants modulating fetal hemoglobin expression, define mechanisms regulating hematopoietic differentiation, and probe the pathogenicity of uncharacterized disease-associated variants. These strategies will advance effective and high-throughput variant-to-function mapping in human hematopoiesis to identify the causes of diverse diseases.

Cross-disorder genetic analysis of immune diseases reveals distinct gene associations that converge on common pathways

Genome-wide association studies (GWAS) have mapped thousands of susceptibility loci associated with immune-mediated diseases. To assess the extent of the genetic sharing across nine immune-mediated diseases we apply genomic structural equation modelling to GWAS data from European populations. We identify three disease groups: gastrointestinal tract diseases, rheumatic and systemic diseases, and allergic diseases. Although loci associated with the disease groups are highly specific, they converge on perturbing the same pathways. Finally, we test for colocalization between loci and single-cell eQTLs derived from peripheral blood mononuclear cells. We identify the causal route by which 46 loci predispose to three disease groups and find evidence for eight genes being candidates for drug repurposing. Taken together, here we show that different constellations of diseases have distinct patterns of genetic associations, but that associated loci converge on perturbing different nodes in T cell activation and signalling pathways.

Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.

Precise modulation of transcription factor levels identifies features underlying dosage sensitivity

Transcriptional regulation exhibits extensive robustness, but human genetics indicates sensitivity to transcription factor (TF) dosage. Reconciling such observations requires quantitative studies of TF dosage effects at trait-relevant ranges, largely lacking so far. TFs play central roles in both normal-range and disease-associated variation in craniofacial morphology; we therefore developed an approach to precisely modulate TF levels in human facial progenitor cells and applied it to SOX9, a TF associated with craniofacial variation and disease (Pierre Robin sequence (PRS)). Most SOX9-dependent regulatory elements (REs) are buffered against small decreases in SOX9 dosage, but REs directly and primarily regulated by SOX9 show heightened sensitivity to SOX9 dosage; these RE responses partially predict gene expression responses. Sensitive REs and genes preferentially affect functional chondrogenesis and PRS-like craniofacial shape variation. We propose that such REs and genes underlie the sensitivity of specific phenotypes to TF dosage, while buffering of other genes leads to robust, nonlinear dosage-to-phenotype relationships.

Compressed Perturb-seq: highly efficient screens for regulatory circuits using random composite perturbations

Pooled CRISPR screens with single-cell RNA-seq readout (Perturb-seq) have emerged as a key technique in functional genomics, but are limited in scale by cost and combinatorial complexity. Here, we reimagine Perturb-seq's design through the lens of algorithms applied to random, low-dimensional observations. We present compressed Perturb-seq, which measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq at 4 to 20-fold reduced cost, with greater power to learn genetic interactions. We identify known and novel regulators of immune responses and uncover evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing GWAS or trans-eQTL studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.

An evolved AAV variant enables efficient genetic engineering of murine T cells

Precise targeting of large transgenes to T cells using homology-directed repair has been transformative for adoptive cell therapies and T cell biology. Delivery of DNA templates via adeno-associated virus (AAV) has greatly improved knockin efficiencies, but the tropism of current AAV serotypes restricts their use to human T cells employed in immunodeficient mouse models. To enable targeted knockins in murine T cells, we evolved Ark313, a synthetic AAV that exhibits high transduction efficiency in murine T cells. We performed a genome-wide knockout screen and identified QA2 as an essential factor for Ark313 infection. We demonstrate that Ark313 can be used for nucleofection-free DNA delivery, CRISPR-Cas9-mediated knockouts, and targeted integration of large transgenes. Ark313 enables preclinical modeling of Trac-targeted CAR-T and transgenic TCR-T cells in immunocompetent models. Efficient gene targeting in murine T cells holds great potential for improved cell therapies and opens avenues in experimental T cell immunology.

Impact of the Human Cell Atlas on medicine

Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand the mechanisms of disease at the cellular and tissue levels and can use this understanding to develop powerful disease diagnostics; identify promising new drug targets; predict their efficacy, toxicity and resistance mechanisms; and empower new kinds of therapies, from cancer therapies to regenerative medicine. Here, we lay out a vision for the potential of cell atlases to impact the future of medicine, and describe how advances over the past decade have begun to realize this potential in common complex diseases, infectious diseases (including COVID-19), rare diseases and cancer.

Erythrocyte-Plasmodium interactions: genetic manipulation of the erythroid lineage

Targeting critical host factors is an emerging concept in the treatment of infectious diseases. As obligate pathogens of erythrocytes, the Plasmodium spp. parasites that cause malaria must exploit erythroid host factors for their survival. However, our understanding of this important aspect of the malaria lifecycle is limited, in part because erythrocytes are enucleated cells that lack a nucleus and DNA, rendering them genetically intractable. Recent advances in genetic analysis of the erythroid lineage using small-hairpin RNAs and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated nuclease 9 (Cas9) in red-blood cells derived from stem cells have generated new insights into the functions of several candidate host factors for Plasmodium parasites. Along with efforts in other hematopoietic cells, these advances have also laid a strong foundation for genetic screens to identify novel erythrocyte host factors for malaria.