Time course decomposition of cell heterogeneity in TFEB signaling states reveals homeostatic mechanisms restricting the magnitude and duration of TFEB responses to mTOR activity modulation

BMC Cancer

Research Article

Giovanni Dalmasso¹  Carsten Jörn Beese¹  Paula Andrea Marin Zapata¹  Anja Jünger²  Anne Hamacher-Brady³  Nathan Ryan Brady⁴ 

[1] Lysosomal Systems Biology, German Cancer Research Center (DKFZ) and BioQuant, University of Heidelberg, Heidelberg, Germany;Lysosomal Systems Biology, German Cancer Research Center (DKFZ) and BioQuant, University of Heidelberg, Heidelberg, Germany;Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ) and BioQuant, University of Heidelberg, Heidelberg, Germany;Lysosomal Systems Biology, German Cancer Research Center (DKFZ) and BioQuant, University of Heidelberg, Heidelberg, Germany;W. Harry Feinstone Department of Molecular Microbiology & Immunology, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St., 21205, Baltimore, MD, USA;Systems Biology of Cell Death Mechanisms, German Cancer Research Center (DKFZ) and BioQuant, University of Heidelberg, Heidelberg, Germany;Department of Surgery, Heidelberg University Hospital, Heidelberg, Germany;W. Harry Feinstone Department of Molecular Microbiology & Immunology, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St., 21205, Baltimore, MD, USA;

关键词: Transcription Factor EB (TFEB);    Mammalian target of rapamycin (mTOR);    Autophagy;    Lysosomes;    Proteasome;    Systems biology;    Subpopulation dynamics;    Single cell;    Multispectral imaging cytometry;   

DOI  :  10.1186/s12885-016-2388-9

 received in 2015-12-17, accepted in 2016-05-26,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundTFEB (transcription factor EB) regulates metabolic homeostasis through its activation of lysosomal biogenesis following its nuclear translocation. TFEB activity is inhibited by mTOR phosphorylation, which signals its cytoplasmic retention. To date, the temporal relationship between alterations to mTOR activity states and changes in TFEB subcellular localization and concentration has not been sufficiently addressed.MethodsmTOR was activated by renewed addition of fully-supplemented medium, or inhibited by Torin1 or nutrient deprivation. Single-cell TFEB protein levels and subcellular localization in HeLa and MCF7 cells were measured over a time course of 15 hours by multispectral imaging cytometry. To extract single-cell level information on heterogeneous TFEB activity phenotypes, we developed a framework for identification of TFEB activity subpopulations. Through unsupervised clustering, cells were classified according to their TFEB nuclear concentration, which corresponded with downstream lysosomal responses.ResultsBulk population results revealed that mTOR negatively regulates TFEB protein levels, concomitantly to the regulation of TFEB localization. Subpopulation analysis revealed maximal sensitivity of HeLa cells to mTOR activity stimulation, leading to inactivation of 100 % of the cell population within 0.5 hours, which contrasted with a lower sensitivity in MCF7 cells. Conversely, mTOR inhibition increased the fully active subpopulation only fractionally, and full activation of 100 % of the population required co-inhibition of mTOR and the proteasome. Importantly, mTOR inhibition activated TFEB for a limited duration of 1.5 hours, and thereafter the cell population was progressively re-inactivated, with distinct kinetics for Torin1 and nutrient deprivation treatments.ConclusionTFEB protein levels and subcellular localization are under control of a short-term rheostat, which is highly responsive to negative regulation by mTOR, but under conditions of mTOR inhibition, restricts TFEB activation in a manner dependent on the proteasome. We further identify a long-term, mTOR-independent homeostatic control negatively regulating TFEB upon prolonged mTOR inhibition. These findings are of relevance for developing strategies to target TFEB activity in disease treatment. Moreover, our quantitative approach to decipher phenotype heterogeneity in imaging datasets is of general interest, as shifts between subpopulations provide a quantitative description of single cell behaviour, indicating novel regulatory behaviors and revealing differences between cell types.

【 授权许可】

CC BY   
© The Author(s). 2016