Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells.

CD8 T cells play essential roles in anti-tumor immune responses. Here, we performed genome-scale CRISPR screens in CD8 T cells directly under cancer immunotherapy settings and identified regulators of tumor infiltration and degranulation. The in vivo screen robustly re-identified canonical immunotherapy targets such as PD-1 and Tim-3, along with genes that have not been characterized in T cells. The infiltration and degranulation screens converged on an RNA helicase Dhx37. Dhx37 knockout enhanced the efficacy of antigen-specific CD8 T cells against triple-negative breast cancer in vivo. Immunological characterization in mouse and human CD8 T cells revealed that DHX37 suppresses effector functions, cytokine production, and T cell activation. Transcriptomic profiling and biochemical interrogation revealed a role for DHX37 in modulating NF-κB. These data demonstrate high-throughput in vivo genetic screens for immunotherapy target discovery and establishes DHX37 as a functional regulator of CD8 T cells.

Multiplexed activation of endogenous genes by CRISPRa elicits potent antitumor immunity.

Immunotherapy has transformed cancer treatment. However, current immunotherapy modalities face various limitations. In the present study, we developed multiplexed activation of endogenous genes as an immunotherapy (MAEGI), a new form of immunotherapy that elicits antitumor immunity through multiplexed activation of endogenous genes in tumors. We leveraged CRISPR activation (CRISPRa) to directly augment the in situ expression of endogenous genes, and thereby the presentation of tumor antigens, leading to dramatic antitumor immune responses. Deploying this as a cell-based vaccination strategy showed efficacy in both prophylactic and therapeutic settings. Intratumoral adeno-associated virus delivery of CRISPRa libraries elicited strong antitumor immunity across multiple cancer types. Precision targeting of mutated gene sets eradicated a large fraction of established tumors at both local and distant sites. This treatment modality led to alterations in the tumor microenvironment, marked by enhanced T cell infiltration and antitumor immune signatures. Multiplexed endogenous gene activation is a versatile and highly scalable strategy to elicit potent immune responses against cancer, distinct from all existing cancer therapies.

Nonstructural Protein 1 of SARS-CoV-2 Is a Potent Pathogenicity Factor Redirecting Host Protein Synthesis Machinery toward Viral RNA.

The causative virus of the COVID-19 pandemic, SARS-CoV-2, uses its nonstructural protein 1 (Nsp1) to suppress cellular, but not viral, protein synthesis through yet unknown mechanisms. We show here that among all viral proteins, Nsp1 has the largest impact on host viability in the cells of human lung origin. Differential expression analysis of mRNA-seq data revealed that Nsp1 broadly alters the cellular transcriptome. Our cryo-EM structure of the Nsp1-40S ribosome complex shows that Nsp1 inhibits translation by plugging the mRNA entry channel of the 40S. We also determined the structure of the 48S preinitiation complex formed by Nsp1, 40S, and the cricket paralysis virus internal ribosome entry site (IRES) RNA, which shows that it is nonfunctional because of the incorrect position of the mRNA 3' region. Our results elucidate the mechanism of host translation inhibition by SARS-CoV-2 and advance understanding of the impacts from a major pathogenicity factor of SARS-CoV-2.

Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes.

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

Genome Engineering for Next-Generation Cellular Immunotherapies.

Over the past decade, cellular immunotherapies such as CAR-T, TCR-T, and NK cell therapies have achieved tremendous success in cancer treatment. However, various challenges and obstacles remain, including antigen escape, immunosuppression in the tumor microenvironment, toxicities, and on-target off-tumor effects. Recent strategies for overcoming these roadblocks have included the use of genome engineering. Multiplexed CRISPR-Cas and synthetic biology approaches facilitate the development of cell therapies with higher potency and sophisticated modular control; they also offer a toolkit for allogeneic therapy development. Engineering approaches have targeted genetic modifications to enhance long-term persistence through cytokine modulation, knockout of genes mediating immunosuppressive signals, and genes such as the endogenous TCR and MHC-I that elicit adverse host-graft interactions in an allogeneic context. Genome engineering approaches for other immune cell types are also being explored, such as CAR macrophages and CAR-NK cells. Future therapeutic development of cellular immunotherapies may also be guided by novel target discovery through unbiased CRISPR genetic screening approaches.

One-step generation of modular CAR-T cells with AAV-Cpf1.

Immune-cell engineering opens new capabilities for fundamental immunology research and immunotherapy. We developed a system for efficient generation of chimeric antigen receptor (CAR)-engineered T cells (CAR-T cells) with considerably enhanced features by streamlined genome engineering. By leveraging trans-activating CRISPR (clustered regularly interspaced short palindromic repeats) RNA (tracrRNA)-independent CRISPR-Cpf1 systems with adeno-associated virus (AAV), we were able to build a stable CAR-T cell with homology-directed-repair knock-in and immune-checkpoint knockout (KIKO CAR-T cell) at high efficiency in one step. The modularity of the AAV-Cpf1 KIKO system enables flexible and highly efficient generation of double knock-in of two different CARs in the same T cell. Compared with Cas9-based methods, the AAV-Cpf1 system generates double-knock-in CAR-T cells more efficiently. CD22-specific AAV-Cpf1 KIKO CAR-T cells have potency comparable to that of Cas9 CAR-T cells in cytokine production and cancer cell killing, while expressing lower levels of exhaustion markers. This versatile system opens new capabilities of T-cell engineering with simplicity and precision.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

Histamine stimulates hydrogen peroxide production by bronchial epithelial cells via histamine H1 receptor and dual oxidase.

Oxidative stress has been implicated in the pathogenesis of bronchial asthma. Besides granulocytes, the airway epithelium can produce large amounts of reactive oxygen species and can contribute to asthma-related oxidative stress. Histamine is a major inflammatory mediator present in large quantities in asthmatic airways. Whether histamine triggers epithelium-derived oxidative stress is unknown. We therefore aimed at characterizing human airway epithelial H2O2 production stimulated by histamine. We found that air-liquid interface cultures of primary human bronchial epithelial cells (BECs) and an immortalized BEC model (Cdk4/hTERT HBEC) produce H2O2 in response to histamine. The main source of airway epithelial H2O2 is an NADPH dual oxidase, Duox1. Out of the four histamine receptors (H1R-H4R), H1R has the highest expression in BECs and mediates the H2O2-producing effects of histamine. IL-4 induces Duox1 gene and protein expression levels and enhances histamine-induced H2O2 production by epithelial cells. Using HEK-293 cells expressing Duox1 or Duox2 and endogenous H1R, histamine triggers an immediate intracellular calcium signal and H2O2 release. Overexpression of H1R further increases the oxidative output of Duox-expressing HEK-293 cells. Our observations show that BECs respond to histamine with Duox-mediated H2O2 production. These findings reveal a mechanism that could be an important contributor to oxidative stress characteristic of asthmatic airways, suggesting novel therapeutic targets for treating asthmatic airway disease.

NLRP3 inflammasome activation and interleukin-1ß release in macrophages require calcium but are independent of calcium-activated NADPH oxidases.

OBJECTIVE AND DESIGN: We studied the involvement of calcium and calcium-activated NADPH oxidases in NLRP3 inflammasome activation and IL-1ß release to better understand inflammasome signaling in macrophages. MATERIAL OR SUBJECTS: Human volunteer blood donors were recruited to isolate monocytes to differentiate them into macrophages. Wild-type or DUOX1-deficient C57/B6 mice were used to prepare bone marrow-derived macrophages. TREATMENT: Murine or human macrophages were treated in vitro with NLRP3 inflammasome agonists (ATP, silica crystals) or calcium agonists (thapsigargin, ionomycin) in calcium-containing or calcium-free medium. METHODS: Intracellular calcium changes were followed by measuring FURA2-based fluorescence. Gene expression changes were measured by quantitative real-time PCR. Protein expression was assessed by western blotting. Enzymatic activity was measured by fluorescence caspase-1 activity assay. IL-1ß release was determined by ELISA. ELISA data were analyzed by ANOVA and Tukey's post hoc test. RESULTS: Our data show that calcium is essential for IL-1ß release in human macrophages. Increases in cytosolic calcium alone lead to IL-1ß secretion. Calcium removal blocks caspase-1 activation. Human macrophages express Duox1, a calcium-regulated NADPH oxidase that produces reactive oxygen species. However, Duox1-deficient murine macrophages show normal IL-1ß release. CONCLUSIONS: Human macrophage inflammasome activation and IL-1ß secretion requires calcium but does not involve NADPH oxidases.

In vivo AAV-SB-CRISPR screens of tumor-infiltrating primary NK cells identify genetic checkpoints of CAR-NK therapy.

Natural killer (NK) cells have clinical potential against cancer; however, multiple limitations hinder the success of NK cell therapy. Here, we performed unbiased functional mapping of tumor-infiltrating NK (TINK) cells using in vivo adeno-associated virus (AAV)-SB (Sleeping Beauty)-CRISPR (clustered regularly interspaced short palindromic repeats) screens in four solid tumor mouse models. In parallel, we characterized single-cell transcriptomic landscapes of TINK cells, which identified previously unexplored subpopulations of NK cells and differentially expressed TINK genes. As a convergent hit, CALHM2-knockout (KO) NK cells showed enhanced cytotoxicity and tumor infiltration in mouse primary NK cells and human chimeric antigen receptor (CAR)-NK cells. CALHM2 mRNA reversed the CALHM2-KO phenotype. CALHM2 KO in human primary NK cells enhanced their cytotoxicity, degranulation and cytokine production. Transcriptomics profiling revealed CALHM2-KO-altered genes and pathways in both baseline and stimulated conditions. In a solid tumor model resistant to unmodified CAR-NK cells, CALHM2-KO CAR-NK cells showed potent in vivo antitumor efficacy. These data identify endogenous genetic checkpoints that naturally limit NK cell function and demonstrate the use of CALHM2 KO for engineering enhanced NK cell-based immunotherapies.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

Human gene age dating reveals an early and rapid evolutionary construction of the adaptive immune system.

T cells are a type of white blood cell that play a critical role in the immune response against foreign pathogens through a process called T Cell Adaptive Immunity (TCAI). However, the evolution of the genes and nucleotide sequences involved in TCAI is not well understood. To investigate this, we performed comparative studies of gene annotations and genome assemblies of 28 vertebrate species and identified sets of human genes that are involved in TCAI, carcinogenesis, and ageing. We found that these gene sets share interaction pathways which may have contributed to the evolution of longevity in the vertebrate lineage leading to humans. Our human gene age dating analyses revealed that there was rapid origination of genes with TCAI-related functions prior to the Cretaceous eutherian radiation and these new genes mainly encode negative regulators. We identified no new TCAI-related genes after the divergence of placental mammals, but we did detect an extensive number of amino acid substitutions under strong positive selection in recently evolved human immunity genes suggesting they are co-evolving with adaptive immunity. More specifically, we observed that antigen processing and presentation and checkpoint genes are significantly enriched among new genes evolving under positive selection. These observations reveal an evolutionary process of T Cell Adaptive Immunity that were associated with rapid gene duplication in the early stages of vertebrates and subsequent sequence changes in TCAI-related genes. These processes together suggest an early genetic construction of the vertebrate immune system and subsequent molecular adaptation to diverse antigens.

Massively parallel knock-in engineering of human T cells.

The efficiency of targeted knock-in for cell therapeutic applications is generally low, and the scale is limited. In this study, we developed CLASH, a system that enables high-efficiency, high-throughput knock-in engineering. In CLASH, Cas12a/Cpf1 mRNA combined with pooled adeno-associated viruses mediate simultaneous gene editing and precise transgene knock-in using massively parallel homology-directed repair, thereby producing a pool of stably integrated mutant variants each with targeted gene editing. We applied this technology in primary human T cells and performed time-coursed CLASH experiments in blood cancer and solid tumor models using CD3, CD8 and CD4 T cells, enabling pooled generation and unbiased selection of favorable CAR-T variants. Emerging from CLASH experiments, a unique CRISPR RNA (crRNA) generates an exon3 skip mutant of PRDM1 in CAR-Ts, which leads to increased proliferation, stem-like properties, central memory and longevity in these cells, resulting in higher efficacy in vivo across multiple cancer models, including a solid tumor model. The versatility of CLASH makes it broadly applicable to diverse cellular and therapeutic engineering applications.

Immunogenetic Metabolomics Reveals Key Enzymes That Modulate CAR T-cell Metabolism and Function.

Immune evasion is a critical step of cancer progression that remains a major obstacle for current T cell-based immunotherapies. Hence, we investigated whether it is possible to genetically reprogram T cells to exploit a common tumor-intrinsic evasion mechanism whereby cancer cells suppress T-cell function by generating a metabolically unfavorable tumor microenvironment (TME). In an in silico screen, we identified ADA and PDK1 as metabolic regulators. We then showed that overexpression (OE) of these genes enhanced the cytolysis of CD19-specific chimeric antigen receptor (CAR) T cells against cognate leukemia cells, and conversely, ADA or PDK1 deficiency dampened this effect. ADA-OE in CAR T cells improved cancer cytolysis under high concentrations of adenosine, the ADA substrate, and an immunosuppressive metabolite in the TME. High-throughput transcriptomics and metabolomics analysis of these CAR T cells revealed alterations of global gene expression and metabolic signatures in both ADA- and PDK1-engineered CAR T cells. Functional and immunologic analyses demonstrated that ADA-OE increased proliferation and decreased exhaustion in CD19-specific and HER2-specific CAR T cells. ADA-OE improved tumor infiltration and clearance by HER2-specific CAR T cells in an in vivo colorectal cancer model. Collectively, these data unveil systematic knowledge of metabolic reprogramming directly in CAR T cells and reveal potential targets for improving CAR T-cell therapy.

Perturbomics of tumor-infiltrating NK cells.

Natural killer (NK) cells are an innate immune cell type that serves at the first level of defense against pathogens and cancer. NK cells have clinical potential, however, multiple current limitations exist that naturally hinder the successful implementation of NK cell therapy against cancer, including their effector function, persistence, and tumor infiltration. To unbiasedly reveal the functional genetic landscape underlying critical NK cell characteristics against cancer, we perform perturbomics mapping of tumor infiltrating NK cells by joint in vivo AAV-CRISPR screens and single cell sequencing. We establish a strategy with AAV-SleepingBeauty(SB)- CRISPR screening leveraging a custom high-density sgRNA library targeting cell surface genes, and perform four independent in vivo tumor infiltration screens in mouse models of melanoma, breast cancer, pancreatic cancer, and glioblastoma. In parallel, we characterize single-cell transcriptomic landscapes of tumor-infiltrating NK cells, which identifies previously unexplored sub-populations of NK cells with distinct expression profiles, a shift from immature to mature NK (mNK) cells in the tumor microenvironment (TME), and decreased expression of mature marker genes in mNK cells. CALHM2, a calcium homeostasis modulator that emerges from both screen and single cell analyses, shows both in vitro and in vivo efficacy enhancement when perturbed in chimeric antigen receptor (CAR)-NK cells. Differential gene expression analysis reveals that CALHM2 knockout reshapes cytokine production, cell adhesion, and signaling pathways in CAR- NKs. These data directly and systematically map out endogenous factors that naturally limit NK cell function in the TME to offer a broad range of cellular genetic checkpoints as candidates for future engineering to enhance NK cell-based immunotherapies.

Immunogenetic metabolomics revealed key enzymes that modulate CAR-T metabolism and function.

Immune evasion is a critical step of cancer progression that remains a major obstacle for current T cell-based immunotherapies. Hence, we seek to genetically reprogram T cells to exploit a common tumor-intrinsic evasion mechanism, whereby cancer cells suppress T cell function by generating a metabolically unfavorable tumor microenvironment (TME). Specifically, we use an in silico screen to identify ADA and PDK1 as metabolic regulators, in which gene overexpression (OE) enhances the cytolysis of CD19-specific CD8 CAR-T cells against cognate leukemia cells, and conversely, ADA or PDK1 deficiency dampens such effect. ADA -OE in CAR-T cells improves cancer cytolysis under high concentrations of adenosine, the ADA substrate and an immunosuppressive metabolite in the TME. High-throughput transcriptomics and metabolomics in these CAR-Ts reveal alterations of global gene expression and metabolic signatures in both ADA- and PDK1- engineered CAR-T cells. Functional and immunological analyses demonstrate that ADA -OE increases proliferation and decreases exhaustion in α-CD19 and α-HER2 CAR-T cells. ADA-OE improves tumor infiltration and clearance by α-HER2 CAR-T cells in an in vivo colorectal cancer model. Collectively, these data unveil systematic knowledge of metabolic reprogramming directly in CAR-T cells, and reveal potential targets for improving CAR-T based cell therapy. Synopsis: The authors identify the adenosine deaminase gene (ADA) as a regulatory gene that reprograms T cell metabolism. ADA-overexpression (OE) in α-CD19 and α-HER2 CAR-T cells increases proliferation, cytotoxicity, memory, and decreases exhaustion, and ADA-OE α-HER2 CAR-T cells have enhanced clearance of HT29 human colorectal cancer tumors in vivo .

Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV, and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome, including previously unappreciated features in spike, RdRp, and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural, and immunologic features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

Assessing Cutaneous Mosaicism at the Molecular Level.

Mosaicism results from postzygotic alterations during embryogenesis leading to genetically distinct populations of cells within individuals and has been historically recognized by phenotypes with visible, often patterned manifestations. Before the advent of molecular profiling assays and high-throughput sequencing, it was challenging to study mosaicism in human disease; however, the study of mosaic disorders has recently revealed unexpected and novel pathways for disease pathogenesis. In this paper, we will review the techniques for discovery of disease-causing alleles using Proteus syndrome; phakomatosis pigmentokeratotica; linear porokeratosis; and vacuoles, E1 enzyme, X-linked, autoinflammatory somatic syndrome as models. These tools represent powerful approaches for dissecting the genetic basis for human disorders.