Increase of Hspa1a and Hspa1b genes in the resting B cells of Sirt1 knockout mice

Han, Younghwan; Kang, Yujin; Yu, Jaemin; Yu, Seong‐Lan; Park, Hwan‐Woo; Shin, Jongdae; Park, Seok‐Rae; Kang, Jaeku

doi:10.1007/s11033-019-04876-7

Cited by 9 publications

(4 citation statements)

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“…Here, we describe that SIRT1 deficiency leads to the induction of the chaperones Hsp70 and Hsp27, which are potentially able to reduce the toxicity of an acute ER stress. A recent study described that B cells isolated from tamoxifen-induced SIRT1 deficiency in mice expressed high levels of Hspa1a and Hspa1b transcripts, in agreement with our findings [ 42 ]. In contrast, another study [ 43 ] showed that SIRT1+/+ MEFs constitutively expressed Hsp70 and SIRT1−/− MEFs displayed a reduced expression.…”

Section: Discussionsupporting

confidence: 92%

Lack of the Histone Deacetylase SIRT1 Leads to Protection against Endoplasmic Reticulum Stress through the Upregulation of Heat Shock Proteins

Latorre,

de Vera,

Santalucía

et al. 2024

IJMS

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Histone deacetylase SIRT1 represses gene expression through the deacetylation of histones and transcription factors and is involved in the protective cell response to stress and aging. However, upon endoplasmic reticulum (ER) stress, SIRT1 impairs the IRE1α branch of the unfolded protein response (UPR) through the inhibition of the transcriptional activity of XBP-1 and SIRT1 deficiency is beneficial under these conditions. We hypothesized that SIRT1 deficiency may unlock the blockade of transcription factors unrelated to the UPR promoting the synthesis of chaperones and improving the stability of immature proteins or triggering the clearance of unfolded proteins. SIRT1+/+ and SIRT1−/− fibroblasts were exposed to the ER stress inducer tunicamycin and cell survival and expression of heat shock proteins were analyzed 24 h after the treatment. We observed that SIRT1 loss significantly reduced cell sensitivity to ER stress and showed that SIRT1−/− but not SIRT1+/+ cells constitutively expressed high levels of phospho-STAT3 and heat shock proteins. Hsp70 silencing in SIRT1−/− cells abolished the resistance to ER stress. Furthermore, accumulation of ubiquitinated proteins was lower in SIRT1−/− than in SIRT1+/+ cells. Our data showed that SIRT1 deficiency enabled chaperones upregulation and boosted the proteasome activity, two processes that are beneficial for coping with ER stress.

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Section: Discussionsupporting

confidence: 92%

Lack of the Histone Deacetylase SIRT1 Leads to Protection against Endoplasmic Reticulum Stress through the Upregulation of Heat Shock Proteins

Latorre,

de Vera,

Santalucía

et al. 2024

IJMS

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“…Although SIRT1/STAT3 axis has been extensively studied in cancer cells ( 11 , 12 ), there are few reports on its role on B-cells. However, it was recently demonstrated that B-cell specific SIRT1 deficient cells displayed an increase of IL10 expression in vitro following LPS stimulation ( 13 ). Moreover, SIRT1 is highly expressed in resting naïve mouse B cells but dramatically reduced following cell activation and immunization.…”

Section: Molecular Control Of the Production Of Tgfβ And Gzmb In B Cellsmentioning

confidence: 99%

Molecular Mechanisms Driving IL-10- Producing B Cells Functions: STAT3 and c-MAF as Underestimated Central Key Regulators?

et al. 2022

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Regulatory B cells (Bregs) have been highlighted in very different pathology settings including autoimmune diseases, allergy, graft rejection, and cancer. Improving tools for the characterization of Bregs has become the main objective especially in humans. Transitional, mature B cells and plasma cells can differentiate into IL-10 producing Bregs in both mice and humans, suggesting that Bregs are not derived from unique precursors but may arise from different competent progenitors at unrestricted development stages. Moreover, in addition to IL-10 production, regulatory B cells used a broad range of suppressing mechanisms to modulate the immune response. Although Bregs have been consistently described in the literature, only a few reports described the molecular aspects that control the acquisition of the regulatory function. In this manuscript, we detailed the latest reports describing the control of IL-10, TGFβ, and GZMB production in different Breg subsets at the molecular level. We focused on the understanding of the role of the transcription factors STAT3 and c-MAF in controlling IL-10 production in murine and human B cells and how these factors may represent an important crossroad of several key drivers of the Breg response. Finally, we provided original data supporting the evidence that MAF is expressed in human IL-10- producing plasmablast and could be induced in vitro following different stimulation cocktails. At steady state, we reported that MAF is expressed in specific human B-cell tonsillar subsets including the IgD+ CD27+ unswitched population, germinal center cells and plasmablast.

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“…Compound and protein features are extracted by graph neural network, recurrent neural network or convolutional neural network (CNN), and then concatenated for the regression of the target value, such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${k}_{cat}$\end{document} or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${K}_m$\end{document} [ 17 ]. For better performance, attention layers are added to capture the interaction between compound and protein features [ 18 , 19 ]. DLKcat [ 16 ], the first CPI deep learning model for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${k}_{cat}$\end{document} prediction, can predict \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathrm{lo}{\mathrm{g}}_{10}\left({k}_{cat}\right)$\end{document} with the root mean squared error (RMSE) score below 1 and Pearson’s r = 0.71 for the test data set.…”

Section: Introductionmentioning

confidence: 99%

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

Qiu,

Zhao,

Yang

2023

Briefings in Bioinformatics

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The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

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Increase of Hspa1a and Hspa1b genes in the resting B cells of Sirt1 knockout mice

Cited by 9 publications

References 50 publications

Lack of the Histone Deacetylase SIRT1 Leads to Protection against Endoplasmic Reticulum Stress through the Upregulation of Heat Shock Proteins

Lack of the Histone Deacetylase SIRT1 Leads to Protection against Endoplasmic Reticulum Stress through the Upregulation of Heat Shock Proteins

Molecular Mechanisms Driving IL-10- Producing B Cells Functions: STAT3 and c-MAF as Underestimated Central Key Regulators?

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates

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