Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies.

Adoptive transfer of genetically modified immune cells holds great promise for cancer immunotherapy. CRISPR knockin targeting can improve cell therapies, but more high-throughput methods are needed to test which knockin gene constructs most potently enhance primary cell functions in vivo. We developed a widely adaptable technology to barcode and track targeted integrations of large non-viral DNA templates and applied it to perform pooled knockin screens in primary human T cells. Pooled knockin of dozens of unique barcoded templates into the T cell receptor (TCR)-locus revealed gene constructs that enhanced fitness in vitro and in vivo. We further developed pooled knockin sequencing (PoKI-seq), combining single-cell transcriptome analysis and pooled knockin screening to measure cell abundance and cell state ex vivo and in vivo. This platform nominated a novel transforming growth factor ß (TGF-ß) R2-41BB chimeric receptor that improved solid tumor clearance. Pooled knockin screening enables parallelized re-writing of endogenous genetic sequences to accelerate discovery of knockin programs for cell therapies.

Induction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling.

Interleukin-23 receptor plays a critical role in inducing inflammation and autoimmunity. Here, we report that Th1-like cells differentiated in vitro with IL-12 + IL-21 showed similar IL-23R expression to that of pathogenic Th17 cells using eGFP reporter mice. Fate mapping established that these cells did not transition through a Th17 cell state prior to becoming Th1-like cells, and we observed their emergence in vivo in the T cell adoptive transfer colitis model. Using IL-23R-deficient Th1-like cells, we demonstrated that IL-23R was required for the development of a highly colitogenic phenotype. Single-cell RNA sequencing analysis of intestinal T cells identified IL-23R-dependent genes in Th1-like cells that differed from those expressed in Th17 cells. The perturbation of one of these regulators (CD160) in Th1-like cells inhibited the induction of colitis. We thus uncouple IL-23R as a purely Th17 cell-specific factor and implicate IL-23R signaling as a pathogenic driver in Th1-like cells inducing tissue inflammation.

Single-Cell Genomics Unveils Critical Regulators of Th17 Cell Pathogenicity.

Extensive cellular heterogeneity exists within specific immune-cell subtypes classified as a single lineage, but its molecular underpinnings are rarely characterized at a genomic scale. Here, we use single-cell RNA-seq to investigate the molecular mechanisms governing heterogeneity and pathogenicity of Th17 cells isolated from the central nervous system (CNS) and lymph nodes (LN) at the peak of autoimmune encephalomyelitis (EAE) or differentiated in vitro under either pathogenic or non-pathogenic polarization conditions. Computational analysis relates a spectrum of cellular states in vivo to in-vitro-differentiated Th17 cells and unveils genes governing pathogenicity and disease susceptibility. Using knockout mice, we validate four new genes: Gpr65, Plzp, Toso, and Cd5l (in a companion paper). Cellular heterogeneity thus informs Th17 function in autoimmunity and can identify targets for selective suppression of pathogenic Th17 cells while potentially sparing non-pathogenic tissue-protective ones.

CD5L/AIM Regulates Lipid Biosynthesis and Restrains Th17 Cell Pathogenicity.

Th17 cells play a critical role in host defense against extracellular pathogens and tissue homeostasis but can induce autoimmunity. The mechanisms implicated in balancing "pathogenic" and "non-pathogenic" Th17 cell states remain largely unknown. We used single-cell RNA-seq to identify CD5L/AIM as a regulator expressed in non-pathogenic, but not in pathogenic Th17 cells. Although CD5L does not affect Th17 differentiation, it is a functional switch that regulates the pathogenicity of Th17 cells. Loss of CD5L converts non-pathogenic Th17 cells into pathogenic cells that induce autoimmunity. CD5L mediates this effect by modulating the intracellular lipidome, altering fatty acid composition and restricting cholesterol biosynthesis and, thus, ligand availability for Rorγt, the master transcription factor of Th17 cells. Our study identifies CD5L as a critical regulator of the Th17 cell functional state and highlights the importance of lipid metabolism in balancing immune protection and disease induced by T cells.

Fas Promotes T Helper 17 Cell Differentiation and Inhibits T Helper 1 Cell Development by Binding and Sequestering Transcription Factor STAT1.

The death receptor Fas removes activated lymphocytes through apoptosis. Previous transcriptional profiling predicted that Fas positively regulates interleukin-17 (IL-17)-producing T helper 17 (Th17) cells. Here, we demonstrate that Fas promoted the generation and stability of Th17 cells and prevented their differentiation into Th1 cells. Mice with T-cell- and Th17-cell-specific deletion of Fas were protected from induced autoimmunity, and Th17 cell differentiation and stability were impaired. Fas-deficient Th17 cells instead developed a Th1-cell-like transcriptional profile, which a new algorithm predicted to depend on STAT1. Experimentally, Fas indeed bound and sequestered STAT1, and Fas deficiency enhanced IL-6-induced STAT1 activation and nuclear translocation, whereas deficiency of STAT1 reversed the transcriptional changes induced by Fas deficiency. Thus, our computational and experimental approach identified Fas as a regulator of the Th17-to-Th1 cell balance by controlling the availability of opposing STAT1 and STAT3 to have a direct impact on autoimmunity.

CRISPR screen in regulatory T cells reveals modulators of Foxp3.

Regulatory T (Treg) cells are required to control immune responses and maintain homeostasis, but are a significant barrier to antitumour immunity1. Conversely, Treg instability, characterized by loss of the master transcription factor Foxp3 and acquisition of proinflammatory properties2, can promote autoimmunity and/or facilitate more effective tumour immunity3,4. A comprehensive understanding of the pathways that regulate Foxp3 could lead to more effective Treg therapies for autoimmune disease and cancer. The availability of new functional genetic tools has enabled the possibility of systematic dissection of the gene regulatory programs that modulate Foxp3 expression. Here we developed a CRISPR-based pooled screening platform for phenotypes in primary mouse Treg cells and applied this technology to perform a targeted loss-of-function screen of around 500 nuclear factors to identify gene regulatory programs that promote or disrupt Foxp3 expression. We identified several modulators of Foxp3 expression, including ubiquitin-specific peptidase 22 (Usp22) and ring finger protein 20 (Rnf20). Usp22, a member of the deubiquitination module of the SAGA chromatin-modifying complex, was revealed to be a positive regulator that stabilized Foxp3 expression; whereas the screen suggested that Rnf20, an E3 ubiquitin ligase, can serve as a negative regulator of Foxp3. Treg-specific ablation of Usp22 in mice reduced Foxp3 protein levels and caused defects in their suppressive function that led to spontaneous autoimmunity but protected against tumour growth in multiple cancer models. Foxp3 destabilization in Usp22-deficient Treg cells could be rescued by ablation of Rnf20, revealing a reciprocal ubiquitin switch in Treg cells. These results reveal previously unknown modulators of Foxp3 and demonstrate a screening method that can be broadly applied to discover new targets for Treg immunotherapies for cancer and autoimmune disease.

Imaging Thermally Fluctuating Néel Vectors in van der Waals Antiferromagnet NiPS3.

Studying antiferromagnetic domains is essential for fundamental physics and potential spintronics applications. Despite their importance, few systematic studies have been performed on antiferromagnet (AFM) domains with high spatial resolution in van der Waals (vdW) materials, and direct probing of the Néel vectors remains challenging. In this work, we found multidomain states in the vdW AFM NiPS3, a material extensively investigated for its unique magnetic exciton. We employed photoemission electron microscopy combined with the X-ray magnetic linear dichroism (XMLD-PEEM) to image the NiPS3's magnetic structure. The nanometer-spatial resolution of XMLD-PEEM allows us to determine local Néel vector orientations and discover thermally fluctuating Néel vectors that are independent of the crystal symmetry even at 65 K, well below the TN of 155 K. We demonstrate that an in-plane orbital moment of the Ni ion is responsible for the weak magnetocrystalline anisotropy. The observed thermal fluctuations of the antiferromagnetic domains may explain the broadening of magnetic exciton peaks at higher temperatures.

Doubly robust nonparametric instrumental variable estimators for survival outcomes.

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this article, we propose nonparametric estimators for the local average treatment effect on survival probabilities under both covariate-dependent and outcome-dependent censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and double robustness of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

Induction and molecular signature of pathogenic TH17 cells.

Interleukin 17 (IL-17)-producing helper T cells (T(H)17 cells) are often present at the sites of tissue inflammation in autoimmune diseases, which has led to the conclusion that T(H)17 cells are main drivers of autoimmune tissue injury. However, not all T(H)17 cells are pathogenic; in fact, T(H)17 cells generated with transforming growth factor-ß1 (TGF-ß1) and IL-6 produce IL-17 but do not readily induce autoimmune disease without further exposure to IL-23. Here we found that the production of TGF-ß3 by developing T(H)17 cells was dependent on IL-23, which together with IL-6 induced very pathogenic T(H)17 cells. Moreover, TGF-ß3-induced T(H)17 cells were functionally and molecularly distinct from TGF-ß1-induced T(H)17 cells and had a molecular signature that defined pathogenic effector T(H)17 cells in autoimmune disease.

Prognostic score-based methods for estimating center effects based on survival probability: Application to post-kidney transplant survival.

In evaluating the performance of different facilities or centers on survival outcomes, the standardized mortality ratio (SMR), which compares the observed to expected mortality has been widely used, particularly in the evaluation of kidney transplant centers. Despite its utility, the SMR may exaggerate center effects in settings where survival probability is relatively high. An example is one-year graft survival among U.S. kidney transplant recipients. We propose a novel approach to estimate center effects in terms of differences in survival probability (ie, each center versus a reference population). An essential component of the method is a prognostic score weighting technique, which permits accurately evaluating centers without necessarily specifying a correct survival model. Advantages of our approach over existing facility-profiling methods include a metric based on survival probability (greater clinical relevance than ratios of counts/rates); direct standardization (valid to compare between centers, unlike indirect standardization based methods, such as the SMR); and less reliance on correct model specification (since the assumed model is used to generate risk classes as opposed to fitted-value based 'expected' counts). We establish the asymptotic properties of the proposed weighted estimator and evaluate its finite-sample performance under a diverse set of simulation settings. The method is then applied to evaluate U.S. kidney transplant centers with respect to graft survival probability.

Author Correction: Discovery of stimulation-responsive immune enhancers with CRISPR activation.

In this Letter, analysis of steady-state regulatory T (Treg) cell percentages from Il2ra enhancer deletion (EDEL) and wild-type (WT) mice revealed no differences between them (Extended Data Fig. 9d). This analysis included two mice whose genotypes were incorrectly assigned. Even after correction of the genotypes, no significant differences in Treg cell percentages were seen when data across experimental cohorts were averaged (as was done in Extended Data Fig. 9d). However, if we normalize the corrected data to account for variation among experimental cohorts, a subtle decrease in EDEL Treg cell percentages is revealed and, using the corrected and normalized data, we have redrawn Extended Data Fig. 9d in Supplementary Fig. 1. The Supplementary Information to this Amendment contains the corrected and reanalysed Extended Data Fig. 9d. The sentence "This enhancer deletion (EDEL) strain also had no obvious T cell phenotypes at steady state (Extended Data Fig. 9)." should read: "This enhancer deletion (EDEL) strain had a small decrease in the percentage of Treg cells (Extended Data Fig. 9).". This error does not affect any of the main figures in the Letter or the data from mice with the human autoimmune-associated single nucleotide polymorphism (SNP) knocked in or with a 12-base-pair deletion at the site (12DEL). In addition, we stated in the Methods that we observed consistent immunophenotypes of EDEL mice across three founders, but in fact, we observed consistent phenotypes in mice from two founders. This does not change any of our conclusions and the original Letter has not been corrected.

Finding influential subjects in a network using a causal framework.

Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.

Distinguishing nontuberculous mycobacterial lung disease and Mycobacterium tuberculosis lung disease on X-ray images using deep transfer learning.

BACKGROUND: Nontuberculous mycobacterial lung disease (NTM-LD) and Mycobacterium tuberculosis lung disease (MTB-LD) have similar clinical characteristics. Therefore, NTM-LD is sometimes incorrectly diagnosed with MTB-LD and treated incorrectly. To solve these difficulties, we aimed to distinguish the two diseases in chest X-ray images using deep learning technology, which has been used in various fields recently. METHODS: We retrospectively collected chest X-ray images from 3314 patients infected with Mycobacterium tuberculosis (MTB) or nontuberculosis mycobacterium (NTM). After selecting the data according to the diagnostic criteria, various experiments were conducted to create the optimal deep learning model. A performance comparison was performed with the radiologist. Additionally, the model performance was verified using newly collected MTB-LD and NTM-LD patient data. RESULTS: Among the implemented deep learning models, the ensemble model combining EfficientNet B4 and ResNet 50 performed the best in the test data. Also, the ensemble model outperformed the radiologist on all evaluation metrics. In addition, the accuracy of the ensemble model was 0.85 for MTB-LD and 0.78 for NTM-LD on an additional validation dataset consisting of newly collected patients. CONCLUSIONS: In previous studies, it was known that it was difficult to distinguish between MTB-LD and NTM-LD in chest X-ray images, but we have successfully distinguished the two diseases using deep learning methods. This study has the potential to aid clinical decisions if the two diseases need to be differentiated.

Early lysosome defects precede neurodegeneration with amyloid-ß and tau aggregation in NHE6-null rat brain.

Loss-of-function mutations in the X-linked endosomal Na+/H+ exchanger 6 (NHE6) cause Christianson syndrome in males. Christianson syndrome involves endosome dysfunction leading to early cerebellar degeneration, as well as later-onset cortical and subcortical neurodegeneration, potentially including tau deposition as reported in post-mortem studies. In addition, there is reported evidence of modulation of amyloid-ß levels in experimental models wherein NHE6 expression was targeted. We have recently shown that loss of NHE6 causes defects in endosome maturation and trafficking underlying lysosome deficiency in primary mouse neurons in vitro. For in vivo studies, rat models may have an advantage over mouse models for the study of neurodegeneration, as rat brain can demonstrate robust deposition of endogenously-expressed amyloid-ß and tau in certain pathological states. Mouse models generally do not show the accumulation of insoluble, endogenously-expressed (non-transgenic) tau or amyloid-ß. Therefore, to study neurodegeneration in Christianson syndrome and the possibility of amyloid-ß and tau pathology, we generated an NHE6-null rat model of Christianson syndrome using CRISPR-Cas9 genome-editing. Here, we present the sequence of pathogenic events in neurodegenerating NHE6-null male rat brains across the lifespan. NHE6-null rats demonstrated an early and rapid loss of Purkinje cells in the cerebellum, as well as a more protracted neurodegenerative course in the cerebrum. In both the cerebellum and cerebrum, lysosome deficiency is an early pathogenic event, preceding autophagic dysfunction. Microglial and astrocyte activation also occur early. In the hippocampus and cortex, lysosome defects precede loss of pyramidal cells. Importantly, we subsequently observed biochemical and in situ evidence of both amyloid-ß and tau aggregation in the aged NHE6-null hippocampus and cortex (but not in the cerebellum). Tau deposition is widely distributed, including cortical and subcortical distributions. Interestingly, we observed tau deposition in both neurons and glia, as has been reported in Christianson syndrome post-mortem studies previously. In summary, this experimental model is among very few examples of a genetically modified animal that exhibits neurodegeneration with deposition of endogenously-expressed amyloid-ß and tau. This NHE6-null rat will serve as a new robust model for Christianson syndrome. Furthermore, these studies provide evidence for linkages between endolysosome dysfunction and neurodegeneration involving protein aggregations, including amyloid-ß and tau. Therefore these studies may provide insight into mechanisms of more common neurodegenerative disorders, including Alzheimer's disease and related dementias.

Discovery of stimulation-responsive immune enhancers with CRISPR activation.

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa) to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (TH17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs.

Ultraefficient Electrocatalytic Hydrogen Evolution from Strain-Engineered, Multilayer MoS2.

This paper reports an approach to repurpose low-cost, bulk multilayer MoS2 for development of ultraefficient hydrogen evolution reaction (HER) catalysts over large areas (>cm2). We create working electrodes for use in HER by dry transfer of MoS2 nano- and microflakes to gold thin films deposited on prestrained thermoplastic substrates. By relieving the prestrain at a macroscopic scale, a tunable level of tensile strain is developed in the MoS2 and consequently results in a local phase transition as a result of spontaneously formed surface wrinkles. Using electrochemical impedance spectroscopy, we verified that electrochemical activation of the strained MoS2 lowered the charge transfer resistance within the materials system, achieving HER activity comparable to platinum (Pt). Raman and X-ray photoelectron spectroscopy show that desulfurization in the multilayer MoS2 was promoted by the phase transition; the combined effect of desulfurization and the lower charge resistance induced superior HER performance.

Multimodal Capturing of Polysulfides by Phosphorus-Doped Carbon Composites for Flexible High-Energy-Density Lithium-Sulfur Batteries.

The widespread adoption of Li-ion batteries is currently limited by their unstable electrochemical performance and high flammability under mechanical deformation conditions and a relatively low energy density. Herein, high-energy-density lithium-sulfur (Li-S) batteries are developed for applications in next-generation flexible electronics and electric vehicles with long cruising distances. Freestanding high-S-loading carbon nanotubes cathodes are assembled with a phosphorus (P)-doped carbon interlayer coated on commercial separators. Strategies for the active materials and structural design of both the electrodes and separators are highly efficient for immobilizing the lithium polysulfides via multimodal capturing effects; they significantly improve the electrochemical performance in terms of the redox kinetics and cycling stability. The foldable Li-S cells show stable specific capacities of 850 mAh g-1 over 100 cycles, achieving high gravimetric and volumetric energy densities of 387 Wh kgcell -1 and 395 Wh Lcell -1 , respectively. The Li-S cells show highly durable mechanical flexibilities under severe deformation conditions without short circuit or failure. Finally, the Li-S battery is explored as a light-weight and flexible energy storage device aboard airplane drones to ensure at least fivefold longer flight times than traditional Li-ion batteries. Nanocarbon-based S cathodes and P-doped carbon interlayers offer a promising solution for commercializing rechargeable Li-S batteries.

Broadband high-performance terahertz polarizer based on a dense array of 5†nm gap slit antennas.

Critical factors for terahertz polarizers include broadband operation, high transmittance, and a good extinction ratio. In this paper, using a 5 nm-wide metallic slit array with a 200 nm periodicity as a wire grid polarizer, we achieved over 95% transmittance with an average extinction ratio of 40 dB, over the entire spectrum as defined by the terahertz time-domain spectroscopy (0.4 ∼ 2 THz). Theoretical calculations revealed that the slit array can show 100% transmission up to 5 THz, and wider bandwidths with a higher cutoff frequency can be achieved by reducing the slit periodicity. These results provide a novel approach for achieving a broadband THz polarizer and open a new path for seamless integration of the polarizers with nanophotonic applications.

Two robust tools for inference about causal effects with invalid instruments.

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well-known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals outperform traditional instrumental variable confidence intervals when invalid instruments are present and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low-density lipoprotein on globulin levels.

Flexible propensity score estimation strategies for clustered data in observational studies.

Existing studies have suggested superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, it is unclear whether the advantages of nonparametric propensity score modeling are carried to settings where there is clustering of individuals, especially when there is unmeasured cluster-level confounding. In this work we examined the performance of logistic regression (all main effects), Bayesian additive regression trees and generalized boosted modeling for propensity score weighting in clustered settings, with the clustering being accounted for by including either cluster indicators or random intercepts. We simulated data for three hypothetical observational studies of varying sample and cluster sizes. Confounders were generated at both levels, including a cluster-level confounder that is unobserved in the analyses. A binary treatment and a continuous outcome were generated based on seven scenarios with varying relationships between the treatment and confounders (linear and additive, nonlinear/nonadditive, nonadditive with the unobserved cluster-level confounder). Results suggest that when the sample and cluster sizes are large, nonparametric propensity score estimation may provide better covariate balance, bias reduction, and 95% confidence interval coverage, regardless of the degree of nonlinearity or nonadditivity in the true propensity score model. When the sample or cluster sizes are small, however, nonparametric approaches may become more vulnerable to unmeasured cluster-level confounding and thus may not be a better alternative to multilevel logistic regression. We applied the methods to the National Longitudinal Study of Adolescent to Adult Health data, estimating the effect of team sports participation during adolescence on adulthood depressive symptoms.