The anti-tumor function of engineered T cells expressing chimeric antigen receptors (CARs) is dependent on signals transduced through intracellular signaling domains (ICDs). Different ICDs are known to drive distinct phenotypes, but systematic investigations into how ICD architectures direct T cell function-particularly at the molecular level-are lacking. Here, we use single-cell sequencing to map diverse signaling inputs to transcriptional outputs, focusing on a defined library of clinically relevant ICD architectures. Informed by these observations, we functionally characterize transcriptionally distinct ICD variants across various contexts to build comprehensive maps from ICD composition to phenotypic output. We identify a unique tonic signaling signature associated with a subset of ICD architectures that drives durable persistence and efficacy in liquid, but not solid, tumors. Our findings work toward decoding CAR signaling design principles, with implications for the rational design of next-generation ICD architectures optimized for function.
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http://dx.doi.org/10.1101/2024.04.29.591541 | DOI Listing |
Comput Biol Med
January 2025
INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal; FCTUC - Faculty of Sciences and Technology of the University of Coimbra, Coimbra, Portugal. Electronic address:
Traumatic Brain Injury (TBI) is a form of brain injury caused by external forces, resulting in temporary or permanent impairment of brain function. Despite advancements in healthcare, TBI mortality rates can reach 30%-40% in severe cases. This study aims to assist clinical decision-making and enhance patient care for TBI-related complications by employing Artificial Intelligence (AI) methods and data-driven approaches to predict decompensation.
View Article and Find Full Text PDFNat Commun
November 2024
Institute for Computational Design and Construction (ICD), University of Stuttgart, Stuttgart, Germany.
Cell Oncol (Dordr)
December 2024
Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Immunotherapy resistance poses a significant challenge in oncology, necessitating novel strategies to enhance the therapeutic efficacy. Immunogenic cell death (ICD), including necroptosis, pyroptosis and ferroptosis, triggers the release of tumor-associated antigens and numerous bioactive molecules. This release can potentiate a host immune response, thereby overcoming resistance to immunotherapy.
View Article and Find Full Text PDFTrends Endocrinol Metab
October 2024
Department of Immunology, School of Basic Medicine, Tongji Medical College and State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Huazhong University of Science and Technology, Wuhan, China; Key Laboratory of Organ Transplantation, Ministry of Education, NHC Key Laboratory of Organ Transplantation, Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China; Cell Architecture Research Institute, Huazhong University of Science and Technology, Wuhan, China. Electronic address:
Five acyl-CoA synthetase long-chain family members (ACSLs) are responsible for catalyzing diverse long-chain fatty acids (LCFAs) into LCFA-acyl-coenzyme A (CoA) for their subsequent metabolism, including fatty acid oxidation (FAO), lipid synthesis, and protein acylation. In this review, we focus on ACSLs and their LCFA substrates and introduce their involvement in regulation of cancer proliferation, metastasis, and therapeutic resistance. Along with the recognition of the decisive role of ACSL4 in ferroptosis - an immunogenic cell death (ICD) initiated by lipid peroxidation - we review the functions of ACSLs on regulating ferroptosis sensitivity.
View Article and Find Full Text PDFPLoS One
September 2024
Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
Background: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.
Methods: We developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV.
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