Author Correction: Targeting MondoA-TXNIP restores antitumour immunity in lactic-acid-induced immunosuppressive microenvironment.
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Published Erratum↗
Biomedical subjects
Wenyi Zhao
Publications and source records attributed to Wenyi Zhao.
Targeting MondoA-TXNIP restores antitumour immunity in lactic-acid-induced immunosuppressive microenvironment.
In the tumour microenvironment, accumulated lactic acid (LA) promotes tumour immune evasion by facilitating regulatory T cell (Treg) immunosuppressive function and restraining CD8+ T cell cytotoxicity, but the underlying mechanism remains elusive. Here we report that transcriptional factor MondoA-induced thioredoxin interacting protein (TXNIP) transcription is a common feature of both Treg and CD8+ T cells in response to lactic acid. In contrast to reduction in immunosuppressive capacity in MondoA-deficient Treg cells, loss of MondoA enhanced CD8+ T cell cytotoxic function in the lactic-acid-induced immunosuppressive microenvironment, by restoring glucose uptake and glycolysis. Mechanistically, lactic acid relied on sentrin/SUMO-specific protease 1 (SENP1) to stimulate the MondoA-TXNIP axis, which impaired TCR/CD28-signal-induced CD8+ T cell activation. Importantly, targeting the MondoA-TXNIP axis potentiated antitumour immunity in multiple cancer types and synergized with anti-PD-1 therapy to promote effective T cell responses in colorectal cancer. Our results demonstrate that the MondoA-TXNIP axis is a promising therapeutic target for improving cancer immunotherapy.
Tumor Microenvironment↗
TRAIT: A Comprehensive Database for T-cell Receptor-antigen Interactions.
Comprehensive and integrated resources on interactions between T-cell receptors (TCRs) and antigens are still lacking for adoptive T-cell-based immunotherapies, highlighting a significant gap that must be addressed to fully understand the mechanisms of antigen recognition by T cells. In this study, we present the T-cell receptor-antigen interaction database (TRAIT), a comprehensive database that profiles the interactions between TCRs and antigens. TRAIT stands out due to its comprehensive description of TCR-antigen interactions by integrating sequences, structures, and affinities. It provides millions of experimentally validated TCR-antigen pairs, resulting in an exhaustive landscape of antigen-specific TCRs. Notably, TRAIT emphasizes single-cell omics as a major reliable data source for TCR-antigen interactions and includes millions of reliable non-interactive TCRs. Additionally, it thoroughly demonstrates the interactions between mutations of TCRs and antigens, thereby benefiting affinity optimization of engineered TCRs as well as vaccine design. TCRs on clinical trials are innovatively provided. With the significant efforts made toward elucidating the complex interactions between TCRs and antigens, TRAIT is expected to ultimately contribute superior algorithms and substantial advancements in the field of T-cell-based immunotherapies. TRAIT is freely accessible at https://pgx.zju.edu.cn/traitdb.
Receptors, Antigen, T-Cell↗
A practical mild, one-pot, regiospecific synthesis of 2,3-disubstituted indoles via consecutive Sonogashira and Cacchi reactions.
[Structure: see text] A practical one-pot, regiospecific three-component process for the synthesis of 2,3-disubstituted indoles was developed via consecutive Pd-catalyzed Sonogashira coupling, amidopalladation, and reductive elimination.
Catalysis↗
Alignment of continuous video onto 3D point clouds.
We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are difficult to model; for example, trees. To compute 3D point clouds from video, motion stereo is used along with a state-of-the-art algorithm for camera pose estimation. Our experiments with real data demonstrate the advantages of the proposed registration algorithm for texturing models in large-scale semiurban environments. The capability to align video before a 3D model is built from the 3D sensor data offers new practical opportunities for 3D modeling. We introduce a novel modeling-through-registration approach that fuses 3D information from both the 3D sensor and the video. Initial experiments with real data illustrate the potential of the proposed approach.
Algorithms↗