TET2-Driven Clonal Hematopoiesis and Response to Canakinumab: An Exploratory Analysis of the CANTOS Randomized Clinical Trial.

Importance: Clonal hematopoiesis of indeterminate potential (CHIP) is associated with increased risk of atherosclerotic cardiovascular disease, and mouse experiments suggest that CHIP related to Tet2 loss of function in myeloid cells accelerates atherosclerosis via augmented interleukin (IL) 1ß signaling. Objective: To assess whether individuals with CHIP have greater cardiovascular event reduction in response to IL-1ß neutralization in the Canankinumab Anti-inflammatory Thrombosis Outcomes Trial (CANTOS). Design, Setting, and Participants: This randomized clinical trial took place from April 2011 to June 2017 at more than 1000 clinical sites in 39 countries. Targeted deep sequencing of genes previously associated with CHIP in a subset of trial participants using genomic DNA prepared from baseline peripheral blood samples were analyzed. All participants had prior myocardial infarction and elevated high-sensitivity C-reactive protein level above 0.20 mg/dL. Analysis took place between June 2017 and December 2021. Interventions: Canakinumab, an anti-IL-1ß antibody, given at doses of 50, 150, and 300 mg once every 3 months. Main Outcomes and Measures: Major adverse cardiovascular events (MACE). Results: A total of 338 patients (8.6%) were identified in this subset with evidence for clonal hematopoiesis. As expected, the incidence of CHIP increased with age; the mean (SD) age of patients with CHIP was 66.3 (9.2) years and 61.5 (9.6) years in patients without CHIP. Unlike other populations that were not preselected for elevated C-reactive protein, in the CANTOS population variants in TET2 were more common than DNMT3A (119 variants in 103 patients vs 86 variants in 85 patients). Placebo-treated patients with CHIP showed a nonsignificant increase in the rate of MACE compared with patients without CHIP using a Cox proportional hazard model (hazard ratio, 1.32 [95% CI, 0.86-2.04]; P = .21). Exploratory analyses of placebo-treated patients with a somatic variant in either TET2 or DNMT3A (n = 58) showed an equivocal risk for MACE (hazard ratio, 1.65 [95% CI, 0.97-2.80]; P = .06). Patients with CHIP due to somatic variants in TET2 also had reduced risk for MACE while taking canakinumab (hazard ratio, 0.38 [95% CI, 0.15-0.96]) with equivocal difference compared with others (P for interaction = .14). Conclusions and Relevance: These results are consistent with observations of increased risk for cardiovascular events in patients with CHIP and raise the possibility that those with TET2 variants may respond better to canakinumab than those without CHIP. Future studies are required to further substantiate this hypothesis. Trial Registration: ClinicalTrials.gov Identifier: NCT01327846.

Release of stem cells from quiescence reveals gliogenic domains in the adult mouse brain.

Quiescent neural stem cells (NSCs) in the adult mouse ventricular-subventricular zone (V-SVZ) undergo activation to generate neurons and some glia. Here we show that platelet-derived growth factor receptor beta (PDGFRß) is expressed by adult V-SVZ NSCs that generate olfactory bulb interneurons and glia. Selective deletion of PDGFRß in adult V-SVZ NSCs leads to their release from quiescence, uncovering gliogenic domains for different glial cell types. These domains are also recruited upon injury. We identify an intraventricular oligodendrocyte progenitor derived from NSCs inside the brain ventricles that contacts supraependymal axons. Together, our findings reveal that the adult V-SVZ contains spatial domains for gliogenesis, in addition to those for neurogenesis. These gliogenic NSC domains tend to be quiescent under homeostasis and may contribute to brain plasticity.

Single residue in CD28-costimulated CAR-T cells limits long-term persistence and antitumor durability.

Chimeric antigen receptor-T (CAR-T) cell therapies can eliminate relapsed and refractory tumors, but the durability of antitumor activity requires in vivo persistence. Differential signaling through the CAR costimulatory domain can alter the T cell metabolism, memory differentiation, and influence long-term persistence. CAR-T cells costimulated with 4-1BB or ICOS persist in xenograft models but those constructed with CD28 exhibit rapid clearance. Here, we show that a single amino acid residue in CD28 drove T cell exhaustion and hindered the persistence of CD28-based CAR-T cells and changing this asparagine to phenylalanine (CD28-YMFM) promoted durable antitumor control. In addition, CD28-YMFM CAR-T cells exhibited reduced T cell differentiation and exhaustion as well as increased skewing toward Th17 cells. Reciprocal modification of ICOS-containing CAR-T cells abolished in vivo persistence and antitumor activity. This finding suggests modifications to the costimulatory domains of CAR-T cells can enable longer persistence and thereby improve antitumor response.

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells.

Transcriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The assay for transposase-accessible chromatin (ATAC)-seq, coupled with TF motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to improve gene expression modeling. We test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources. In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference, combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF knockouts, and ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs ("TF-TF modules") in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

Inhibition of ß-catenin-TCF1 interaction delays differentiation of mouse embryonic stem cells.

The ability of mouse embryonic stem cells (mESCs) to self-renew or differentiate into various cell lineages is regulated by signaling pathways and a core pluripotency transcriptional network (PTN) comprising Nanog, Oct4, and Sox2. The Wnt/ß-catenin pathway promotes pluripotency by alleviating T cell factor TCF3-mediated repression of the PTN. However, it has remained unclear how ß-catenin's function as a transcriptional activator with TCF1 influences mESC fate. Here, we show that TCF1-mediated transcription is up-regulated in differentiating mESCs and that chemical inhibition of ß-catenin/TCF1 interaction improves long-term self-renewal and enhances functional pluripotency. Genetic loss of TCF1 inhibited differentiation by delaying exit from pluripotency and conferred a transcriptional profile strikingly reminiscent of self-renewing mESCs with high Nanog expression. Together, our data suggest that ß-catenin's function in regulating mESCs is highly context specific and that its interaction with TCF1 promotes differentiation, further highlighting the need for understanding how its individual protein-protein interactions drive stem cell fate.

Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases.

Many complex human diseases are highly sexually dimorphic, suggesting a potential contribution of the X chromosome to disease risk. However, the X chromosome has been neglected or incorrectly analyzed in most genome-wide association studies (GWAS). We present tailored analytical methods and software that facilitate X-wide association studies (XWAS), which we further applied to reanalyze data from 16 GWAS of different autoimmune and related diseases (AID). We associated several X-linked genes with disease risk, among which (1) ARHGEF6 is associated with Crohn's disease and replicated in a study of ulcerative colitis, another inflammatory bowel disease (IBD). Indeed, ARHGEF6 interacts with a gastric bacterium that has been implicated in IBD. (2) CENPI is associated with three different AID, which is compelling in light of known associations with AID of autosomal genes encoding centromere proteins, as well as established autosomal evidence of pleiotropy between autoimmune diseases. (3) We replicated a previous association of FOXP3, a transcription factor that regulates T-cell development and function, with vitiligo; and (4) we discovered that C1GALT1C1 exhibits sex-specific effect on disease risk in both IBDs. These and other X-linked genes that we associated with AID tend to be highly expressed in tissues related to immune response, participate in major immune pathways, and display differential gene expression between males and females. Combined, the results demonstrate the importance of the X chromosome in autoimmunity, reveal the potential of extensive XWAS, even based on existing data, and provide the tools and incentive to properly include the X chromosome in future studies.

ICOS-based chimeric antigen receptors program bipolar TH17/TH1 cells.

With the notable exception of B-cell malignancies, the efficacy of chimeric antigen receptor (CAR) T cells has been limited, and CAR T cells have not been shown to expand and persist in patients with nonlymphoid tumors. Here we demonstrate that redirection of primary human T cells with a CAR containing the inducible costimulator (ICOS) intracellular domain generates tumor-specific IL-17-producing effector cells that show enhanced persistence. Compared with CARs containing the CD3ζ chain alone, or in tandem with the CD28 or the 4-1BB intracellular domains, ICOS signaling increased IL-17A, IL-17F, and IL-22 following antigen recognition. In addition, T cells redirected with an ICOS-based CAR maintained a core molecular signature characteristic of TH17 cells and expressed higher levels of RORC, CD161, IL1R-1, and NCS1. Of note, ICOS signaling also induced the expression of IFN-γ and T-bet, consistent with a TH17/TH1 bipolarization. When transferred into mice with established tumors, TH17 cells that were redirected with ICOS-based CARs mediated efficient antitumor responses and showed enhanced persistence compared with CD28- or 4-1BB-based CAR T cells. Thus, redirection of TH17 cells with a CAR encoding the ICOS intracellular domain is a promising approach to augment the function and persistence of CAR T cells in hematologic malignancies.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks.

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

Inhibition of androgen receptor and ß-catenin activity in prostate cancer.

Androgen receptor (AR) is the major therapeutic target in aggressive prostate cancer. However, targeting AR alone can result in drug resistance and disease recurrence. Therefore, simultaneous targeting of multiple pathways could in principle be an effective approach to treating prostate cancer. Here we provide proof-of-concept that a small-molecule inhibitor of nuclear ß-catenin activity (called C3) can inhibit both the AR and ß-catenin-signaling pathways that are often misregulated in prostate cancer. Treatment with C3 ablated prostate cancer cell growth by disruption of both ß-catenin/T-cell factor and ß-catenin/AR protein interaction, reflecting the fact that T-cell factor and AR have overlapping binding sites on ß-catenin. Given that AR interacts with, and is transcriptionally regulated by ß-catenin, C3 treatment also resulted in decreased occupancy of ß-catenin on the AR promoter and diminished AR and AR/ß-catenin target gene expression. Interestingly, C3 treatment resulted in decreased AR binding to target genes accompanied by decreased recruitment of an AR and ß-catenin cofactor, coactivator-associated arginine methyltransferase 1 (CARM1), providing insight into the unrecognized function of ß-catenin in prostate cancer. Importantly, C3 inhibited tumor growth in an in vivo xenograft model and blocked renewal of bicalutamide-resistant sphere-forming cells, indicating the therapeutic potential of this approach.

A validated regulatory network for Th17 cell specification.

Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively bound BATF and IRF4 contribute to initial chromatin accessibility and, with STAT3, initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple data sets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease.

DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical models.

BACKGROUND: Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge. METHODOLOGY: We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations. CONCLUSION/SIGNIFICANCE: Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.

BACKGROUND: Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions. METHODOLOGY: We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model. CONCLUSIONS/SIGNIFICANCE: Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. "who regulates who" (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.

The Inferelator 2.0: a scalable framework for reconstruction of dynamic regulatory network models.

Current methods for reconstructing biological networks often learn either the topology of large networks or the kinetic parameters of smaller networks with a well-characterized topology. We have recently described a network reconstruction algorithm, the Inferelator 1.0, that given a set of genome-wide measurements as input, simultaneously learns both topology and kinetic-parameters. Specifically, it learns a system of ordinary differential equations (ODEs) that describe the rate of change in transcription of each gene or gene-cluster, as a function of environmental and transcription factors. In order to scale to large networks, in Inferelator 1.0 we have approximated the system of ODEs to be uncoupled, and have solved each ODE using a one-step finite difference approximation. Naturally, these approximations become crude as the simulated time-interval increases. Here we present, implement, and test a new Markov-Chain-Monte-Carlo (MCMC) dynamical modeling method, Inferelator 2.0, that works in tandem with Inferelator 1.0 and is designed to relax these approximations. We show results for the prokaryote Halobacterium that demonstrate a marked improvement in our predictive performance in modeling the regulatory dynamics of the system over longer time-scales.

Learning global models of transcriptional regulatory networks from data.

Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

A predictive model for transcriptional control of physiology in a free living cell.

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among approximately 80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organism's interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.