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.

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.

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.

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 .

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.

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.

Double knockout CRISPR screen for cancer resistance to T cell cytotoxicity.

Immunotherapy has transformed cancer treatments; however, a large fraction of patients encounter resistance. Such resistance is mediated by complex factors, often involving interactions between multiple genes. Thus, it is crucially important to identify genetic interactions between genes that are significantly mutated in cancer patients and those involved in immune responses, ideally the ones that currently have chemical compounds for direct targeting. To systematically interrogate such genetic interactions that mediate cancer cells' response to T cell killing, we designed an asymmetric dual perturbation library targeting the matched combinations between significantly mutated tumor suppressors and immune resistance genes. We performed a combinatorial double knockout screen on 1159 gene pairs and identified those where joint loss-of-function renders altered cellular response to T cell cytotoxicity. We also performed comparative transcriptomics-based analyses on tumor and normal samples from TCGA and GTEx cohorts, mutational profiling analyses, and survival analyses to further characterize the significance of identified hits in clinical patients. Interactions between significantly mutated tumor suppressors and potentially druggable immune resistance genes may offer insights on potential new concepts of how to target clinically relevant cancer mutations with currently available agents. This study also provides a technology platform and an asymmetric double knockout library for interrogating genetic interactions between cancer mutations and immune resistance pathways under various settings.

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.

Multiplexed LNP-mRNA vaccination against pathogenic coronavirus species.

Although COVID-19 vaccines have been developed, multiple pathogenic coronavirus species exist, urging on development of multispecies coronavirus vaccines. Here we develop prototype lipid nanoparticle (LNP)-mRNA vaccine candidates against SARS-CoV-2 Delta, SARS-CoV, and MERS-CoV, and we test how multiplexing LNP-mRNAs can induce effective immune responses in animal models. Triplex and duplex LNP-mRNA vaccinations induce antigen-specific antibody responses against SARS-CoV-2, SARS-CoV, and MERS-CoV. Single-cell RNA sequencing profiles the global systemic immune repertoires and respective transcriptome signatures of vaccinated animals, revealing a systemic increase in activated B cells and differential gene expression across major adaptive immune cells. Sequential vaccination shows potent antibody responses against all three species, significantly stronger than simultaneous vaccination in mixture. These data demonstrate the feasibility, antibody responses, and single-cell immune profiles of multispecies coronavirus vaccination. The direct comparison between simultaneous and sequential vaccination offers insights into optimization of vaccination schedules to provide broad and potent antibody immunity against three major pathogenic coronavirus species.

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.

Variant-specific vaccination induces systems immune responses and potent in vivo protection against SARS-CoV-2.

Lipid nanoparticle (LNP)-mRNA vaccines offer protection against COVID-19; however, multiple variant lineages caused widespread breakthrough infections. Here, we generate LNP-mRNAs specifically encoding wild-type (WT), B.1.351, and B.1.617 SARS-CoV-2 spikes, and systematically study their immune responses. All three LNP-mRNAs induced potent antibody and T cell responses in animal models; however, differences in neutralization activity have been observed between variants. All three vaccines offer potent protection against in vivo challenges of authentic viruses of WA-1, Beta, and Delta variants. Single-cell transcriptomics of WT- and variant-specific LNP-mRNA-vaccinated animals reveal a systematic landscape of immune cell populations and global gene expression. Variant-specific vaccination induces a systemic increase of reactive CD8 T cells and altered gene expression programs in B and T lymphocytes. BCR-seq and TCR-seq unveil repertoire diversity and clonal expansions in vaccinated animals. These data provide assessment of efficacy and direct systems immune profiling of variant-specific LNP-mRNA vaccination in vivo.

A genome-scale gain-of-function CRISPR screen in CD8 T cells identifies proline metabolism as a means to enhance CAR-T therapy.

Chimeric antigen receptor (CAR)-T cell-based immunotherapy for cancer and immunological diseases has made great strides, but it still faces multiple hurdles. Finding the right molecular targets to engineer T cells toward a desired function has broad implications for the armamentarium of T cell-centered therapies. Here, we developed a dead-guide RNA (dgRNA)-based CRISPR activation screen in primary CD8+ T cells and identified gain-of-function (GOF) targets for CAR-T engineering. Targeted knockin or overexpression of a lead target, PRODH2, enhanced CAR-T-based killing and in vivo efficacy in multiple cancer models. Transcriptomics and metabolomics in CAR-T cells revealed that augmenting PRODH2 expression reshaped broad and distinct gene expression and metabolic programs. Mitochondrial, metabolic, and immunological analyses showed that PRODH2 engineering enhances the metabolic and immune functions of CAR-T cells against cancer. Together, these findings provide a system for identification of GOF immune boosters and demonstrate PRODH2 as a target to enhance CAR-T efficacy.

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.

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.