Biology subjects

Elyahu, Y.

Publications and source records attributed to Elyahu, Y..

3 recordsLinked to original sources

bioRxiv2024

TGFβ determines morphology and key cellular processes of activated CD4+ T cells

During an immune response, cells are simultaneously exposed to multiple cytokine signals that collectively determine their phenotype. Transforming growth factor {beta} (TGF{beta}) is a pleiotropic cytokine acting as a key regulator of T-cell differentiation with activating and suppressive effects on their immune function. Here, we systematically analyze the cellular responses of CD4+ T cells to TGF{beta} across diverse cytokine environments in the presence or absence of TGF{beta}. We found that TGF{beta} had a profound dominant effect independent of the presence of other cytokines, modulating the expression of more than 4,000 genes. In the presence of TGF{beta}, cells exhibit lower expression of translation-related and apoptosis-related genes, accompanied by increased survival of activated T cells. Notably, cells cultured in the presence of TGF{beta} were smaller in size while preserving their proliferative ability. Accordingly, we identified a dense network of transcription factors that were modulated by TGF{beta}, suggesting a core gene set connecting TGF{beta} signaling to the regulation of T-cell size. We found N-Myc to be at the center of this network, and we directly show that TGF{beta} regulates its gene expression level, protein level, and nuclear localization. Our work provides a system to study cell size control and demonstrate the profound effect of TGF{beta} in the modulation and regulation of T-cell properties, expanding its role beyond guiding their phenotype. Significance StatementTGF{beta} is a key determinant of CD4+ T-cell differentiation; however, understanding its effect on additional aspects of T-cell state is lacking. Here, we systematically studied the role of TGF{beta} in regulating T-cell physiology. Exposing cells to diverse combinations of cytokines enabled us to distill the core effect of TGF{beta}. We found TGF{beta} to have a profound effect on multiple cellular processes critical to T-cell function. Significantly, TGF{beta} induced smaller T-cells both in vitro and in vivo, suggesting that TGF{beta} could skew the population towards tissue infiltration and residency. Furthermore, TGF{beta} can be used to fine-tune T-cell size, providing a system for studying cell size control. Overall, our findings demonstrate the profound effect of TGF{beta} in the regulation of T-cell physiology.

immunology↗

bioRxiv2024

CD4 T Cells Acquire Cytotoxic Properties to Modulate Cellular Senescence and Aging

Aging is characterized by the progressive deterioration of tissue structure and function, leading to increased vulnerability to diseases and, eventually, death. One prominent process in aging is the accumulation of senescent cells. Although the immune system has been recognized as crucial for the elimination of senescent cells, the associated mechanisms remain incompletely understood. Here we show that CD4 T cells differentiate into cytotoxic T lymphocytes (CTLs) in a senescent cell-rich environment and that a reduction in the senescent cell load, achieved using chemical senolytic drugs, was sufficient to halt this differentiation. We further demonstrate that eliminating CD4 CTLs in the context of late aging by selectively deleting the Eomes transcription factor in CD4 T cells resulted in the increased accumulation of senescent cells, profound physical deterioration, and a decreased life span. In liver cirrhosis, a model of localized chronic inflammation, CD4 CTL elimination increased the senescent load and worsened the disease. Collectively, our findings demonstrate the fundamental role of CD4 CTLs in modulating tissue senescence and unveil a new aspect of age-related T-cell biology implicated in disease susceptibility and longevity.

immunology↗

bioRxiv2022

CausalCell: applying causal discovery to single-cell analyses

Correlation between objects does not answer many scientific questions because of the lack of causal but the excess of spurious information and is prone to happen by coincidence. Causal discovery infers causal relationships from data upon conditional independence test between objects without prior assumptions (e.g., variables have linear relationships and data follow the Gaussian distribution). Causal interactions within and between cells provide valuable information for investigating gene regulation, identifying diagnostic and therapeutic targets, and designing experimental and clinical studies. The rapid increase of single-cell data permits inferring causal interactions in many cell types. However, because no algorithms have been designed for handling abundant variables and few algorithms have been evaluated using real data, how to apply causal discovery to single-cell data remains a challenge. We report a pipeline and web server (http://www.gaemons.net/causalcell/causalDiscovery/) for accurately and conveniently performing causal discovery. The pipeline has been developed upon the benchmarking of 18 algorithms and the analyses of multiple datasets. Our applications indicate that only complicated algorithms can generate satisfactorily reliable results. Critical issues are discussed, and tips for best practices are provided.

bioinformatics↗