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Supplementary Figures 1-4 from Regulation of Secondary Antigen-Specific CD8+ T-Cell Responses by Natural Killer T Cells

Supplementary Figure Legends 1-4 from Regulation of Secondary Antigen-Specific CD8+ T-Cell Responses by Natural Killer T Cells

Connected components is a fundamental kernel in graph applications. The fastest existing parallel multicore algorithms for connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering., This is an extended version of a paper in PVLDB Volume 14 (appeared at VLDB'21)

Innate-like T (iT) cells comprise a population of immunoregulatory T cells whose effector function is imposed during their development in the thymus to provide protective immunity prior to antigen encounter. The molecular mechanism that drives the generation of iT cells remains unclear. Here, we report that the cytokine receptor γc plays a previously unappreciated role for thymic iT cells by controlling their cellular abundance, lineage commitment, and subset differentiation. As such, γc overexpression on thymocytes dramatically altered iT cell generation in the thymus, as it skewed the subset composition of invariant NKT (iNKT) cells and promoted the generation of IFNγ-producing innate CD8 T cells. Mechanistically, we found that the γc-STAT6 axis drives the differentiation of IL-4-producing iNKT cells, which in turn induced the generation of innate CD8 T cells. Collectively, these results reveal a cytokine-driven circuity of thymic iT cell differentiation that is controlled by the abundance of γc proteins.

Aminoacyl-tRNA synthetases (ARSs) are emerging as important regulators in various immune diseases; however, their roles in immune cells remain unclear. In this study, using alanyl-tRNA synthetase (AARS) mutant (sti) mice with neurodegenerative disorder, we investigated the effect of translational fidelity in immune cells. Dysfunctional AARS caused disorders in immune cell responses and cellularity. The impairment was caused by dampened TCR signaling than cytokine signaling. Therefore, sti mutant inhibits TCR signaling, impeding T cell survival and responses. B cell numbers were decreased in sti mice. Despite low B cell cellularity, serum IgM, IgA, and IgE levels were higher in sti mice than in wild-type mice. Misacylation of ARS and the consequent translational infidelity induce disturbances in signaling pathways critical for immune cell survival and responses. Our findings provide a novel mechanism by which translational fidelity might play a critical role in cellular and humoral immune responses.

We live in a new Cambrian Explosion of hardware devices. The end of conventional processor scaling has driven research and industry practice to explore a new generation of approaches. The old DNA of architecture design, including vectors, threads, shared or private memories, coherence or message passing, dataflow or von Neumann execution, are hybridized together in new and exciting ways. Each new architecture exposes a unique hardware-level API. Performance and energy efficiency are critically dependent on how well programs can use these APIs. One approach is to implement custom libraries for each new hardware architecture and application domain. A more scalable approach is to utilize a portable compiler infrastructure tailored to the application domain that makes it easy to generate efficient code for a diverse set of architectures with minimal porting effort. We propose the Unified GraphIt Compiler framework (UGC), which does exactly this for graph applications. UGC achieves portability with reasonable effort by decoupling the architecture-independent algorithm from the architecture-specific schedules and backends. We introduce a new domain-specific intermediate representation, GraphIR, that is key to this decoupling. GraphIR encodes high-level algorithm and optimization information needed for hardware-specific code generation, making it easy to develop different backends (GraphVMs) for diverse architectures, including CPUs, GPUs, and next-generation hardware such as Swarm and the HammerBlade manycore. We also build scheduling language extensions that make it easy to expose optimization decisions like load balancing strategies, blocking for locality, and other data structure choices. We evaluate UGC on five algorithms and 10 input graphs on these 4 distinct architectures and show that UGC enables implementing optimizations that can provide up to 53X speedup over programmer-generated straightforward implementations.

The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of optimizations or one hardware platform works well across all settings. To achieve high performance, the programmer must carefully select which set of optimizations and hardware platforms to use. The GraphIt programming language makes it easy for the programmer to write the algorithm once and optimize it for different inputs using a scheduling language. However, GraphIt currently has no support for generating highperformance code for GPUs. Programmers must resort to re-implementing the entire algorithm from scratch in a low-level language with an entirely different set of abstractions and optimizations in order to achieve high performance on GPUs. We propose G2, an extension to the GraphIt compiler framework, that achieves high performance on both CPUs and GPUs using the same algorithm specification. G2 significantly expands the optimization space of GPU graph processing frameworks with a novel GPU scheduling language and compiler that enables combining load balancing, edge traversal direction, active vertexset creation, active vertexset processing ordering, and kernel fusion optimizations. G2 also introduces two performance optimizations, Edge-based Thread Warps CTAs load balancing (ETWC) and EdgeBlocking, to expand the optimization space for GPUs. ETWC improves load balancing by dynamically partitioning the edges of each vertex into blocks that are assigned to threads, warps, and CTAs for execution. EdgeBlocking improves the locality of the program by reordering the edges and restricting random memory accesses to fit within the L2 cache. We evaluate G2 on 5 algorithms and 9 input graphs on both Pascal and Volta generation NVIDIA GPUs, and show that it achieves up to 5.11× speedup over state-of-the-art GPU graph processing frameworks, and is the fastest on 66 out of the 90 experiments.

Novel engineered T cells containing chimeric antigen receptors (CAR-T cells) that combine the benefits of antigen recognition and T cell response have been developed, and their effect in the anti-tumor immunotherapy of patients with relapsed/refractory leukemia has been dramatic. Thus, CAR-T cell immunotherapy is rapidly emerging as a new therapy. However, it has limitations that prevent consistency in therapeutic effects in solid tumors, which accounts for over 90% of all cancer patients. Here, we review the literature regarding various obstacles to CAR-T cell immunotherapy for solid tumors, including those that cause CAR-T cell dysfunction in the immunosuppressive tumor microenvironment, such as reactive oxygen species, pH, O2, immunosuppressive cells, cytokines, and metabolites, as well as those that impair cell trafficking into the tumor microenvironment. Next-generation CAR-T cell therapy is currently undergoing clinical trials to overcome these challenges. Therefore, novel approaches to address the challenges faced by CAR-T cell immunotherapy in solid tumors are also discussed here.

Lymphopenia-induced homeostatic proliferation (LIP) is a critical mechanism for restoring T cell immunity upon lymphodepleting insults or infections. LIP is primarily driven by homeostatic cytokines, such as IL-7 and IL-15, but not all T cells respond with the same efficiency to homeostatic proliferative cues. Although CD8 T cells vigorously proliferate under lymphopenic conditions, naive CD4 T cells are substantially impaired in their response to homeostatic cytokines, and they fail to fully expand. In this study, we show that the availability of IL-2Rβ (CD122), which is a receptor subunit shared by IL-2 and IL-15, affects both the cytokine responsiveness and the LIP of naive CD4 T cells in the mouse. The enumeration of surface IL-2Rβ molecules on murine naive CD4 and naive CD8 T cells revealed a 5-fold difference in IL-2Rβ abundance. Notably, it was the limited availability of IL-2Rβ that impaired CD4 T cell responsiveness to IL-15 and suppressed their LIP. As such, forced IL-2Rβ expression on CD4 T cells by transgenesis bestowed IL-15 responsiveness onto naive CD4 T cells, which thus acquired the ability to undergo robust LIP. Collectively, these results identify IL-2Rβ availability as a new regulatory mechanism to control cytokine responsiveness and the homeostatic proliferation of murine CD4 T cells.

© 2020 Association for Computing Machinery. Connected components and spanning forest are fundamental graphalgorithms due to their use in many important applications, suchas graph clustering and image segmentation. GPUs are an idealplatform for graph algorithms due to their high peak performanceand memory bandwidth. While there exist several GPU connectivity algorithms in the literature, many design choices have notyet been explored. In this paper, we explore various design choicesin GPU connectivity algorithms, including sampling, linking, andtree compression, for both the static as well as the incrementalsetting. Our various design choices lead to over 300 new GPU implementations of connectivity, many of which outperform state-ofthe-art. We present an experimental evaluation, and show that weachieve an average speedup of 2.47x speedup over existing static algorithms. In the incremental setting, we achieve a throughput of upto 48.23 billion edges per second. Compared to state-of-the-art CPUimplementations on a 72-core machine, we achieve a speedup of8.26 14.51x for static connectivity and 1.85 13.36x for incrementalconnectivity using a Tesla V100 GPU.

IL-7 receptor signaling is essential for the generation and maintenance of conventional T cells. Immunosuppressive Foxp3+ Treg cells, however, express uniquely low amounts of the IL-7-proprietary IL-7Rα so that they are impaired in IL-7 signaling. Because Treg cells depend on IL-2, the loss of IL-7Rα has been considered irrelevant for Treg cells. In contrast, here, we report that IL-7Rα downregulation is necessary to maximize IL-2R signaling. While IL-7Rα overexpression promoted IL-7 signaling, unexpectedly, IL-2 signaling was suppressed in the same cells. Mechanistically, we found that γc, which is a receptor subunit shared by IL-7R and IL-2R, directly binds and pre-associates with IL-7Rα, thus limiting its availability for IL-2R binding. Consequently, overexpression of signaling-deficient, tailless IL-7Rα proteins inhibited IL-2R signaling, demonstrating that IL-7Rα sequesters γc and suppresses IL-2R signaling by extracellular interactions. Collectively, these results reveal a previously unappreciated regulatory mechanism of IL-2 receptor signaling that is governed by IL-7Rα abundance.

Understanding the homeostatic mechanism of invariant natural killer T (ιNKT) cells is a critical issue in ιNKT cell biology. Because IL-15 is required for the thymic generation of ιNKT cells, IL-15 has also been considered necessary for the homeostasis of peripheral ιNKT cells. Here, we delineated the in vivo cytokine requirement for ιNKT cells, and we came to the surprising conclusion that IL-7, not IL-15, is the homeostatic cytokine for ιNKT cells. Employing a series of experimental mouse models in which the availability of IL-7 or IL-15 were manipulated in peripheral tissues, either by genetic tools or by adult thymectomy and cytokine pump installation, we demonstrate that the abundance of IL-7, and not IL-15, limits the size of the peripheral ιNKT cell pool. These results establish a new cytokine requirement for ιNKT cells and reveal a previously unappreciated competition for IL-7 between ιNKT cells and conventional αβ T cells.

Understanding the homeostatic mechanism of invariant natural killer T (iNKT) cells is a critical issue in iNKT cell biology. Because interleukin (IL)-15 is required for the thymic generation of iNKT cells, IL-15 has also been considered necessary for the homeostasis of peripheral iNKT cells. Here, we delineated the in vivo cytokine requirement for iNKT cells, and we came to the surprising conclusion that IL-7, not IL-15, is the homeostatic cytokine for iNKT cells. Employing a series of experimental mouse models where the availability of IL-7 or IL-15 was manipulated in peripheral tissues, either by genetic tools or by adult thymectomy and cytokine pump installation, we demonstrate that the abundance of IL-7, and not IL-15, limits the size of the peripheral iNKT cell pool. These results redefine the cytokine requirement for iNKT cells and indicate competition for IL-7 between iNKT and conventional αβ T cells.

Previous studies have shown that soluble common γ-chain (sγc) modulates CD4+ T cell immunity with antagonistic functions in γc cytokine signaling. However, the role of sγc in functional properties of effector CD8+ T cells has not been fully defined. In this study, we report a new mechanism by which the anti-tumor activity of mouse CD8+ T cells is suppressed in sγc of their own producing. While sγc significantly inhibits cytotoxicity of CD8+ T cells, blocking sγc production by genetic modification leads to potentiated effector function of CD8+ T cells, establishing persistent CD8+ T cells. This is due to the modulation of IL-2 and IL-15 signaling, which is required for expansion and survival of CD8+ T cells as well as for optimal cytotoxic activity. More efficient management of tumor growth was achieved by an adoptive transfer of sγc-deficient CD8+ T cells than that of wild-type or sγc-overexpressing CD8+ T cells. Blocking of IL-2 and IL-15 signaling by sγc attenuates the capacity of CD8+ T cells to mount an optimal response to the tumor, with both quantitative and qualitative effects on antigen-specific CD8+ T cells. These results could have a critical implication for the generation and survival of optimal effector T cells for adoptive immunotherapy of cancer.