Gcn5 and Esa1 function as histone crotonyltransferases to regulate crotonylation-dependent transcription

Kollenstart, Leonie; Groot, Anton J.L. de; Janssen, George M.C.; Cheng, Xue; Vreeken, Kees; Martino, Fabrizio; Côté, Jacques; Veelen, Peter A. van; Attikum, Haico van

doi:10.1074/jbc.ra119.010302

Cited by 78 publications

(60 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In vitro crotonylation assays ( Kollenstart et al., 2019 ) were performed with 10 µg BspF, 0.5 µM octamers and 300 µM crotonyl-CoA, in reaction buffer (50 mM Tris-Cl, pH 7.5; 100 mM NaCl; 1 mM EDTA, 1 mM DTT) with a final volume of 50 µl. Reactions were performed for 1 h at 37°C, and inhibited by adding 5 × loading buffer and analyzed by immunoblotting.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Global Lysine Crotonylation Alterations of Host Cell Proteins Caused by Brucella Effector BspF

Zhu

Dong

et al. 2021

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

In Brucella spp., the type IV secretion system (T4SS) is essential for bacterial intracellular survival and inhibition of the host innate immune response. The Brucella T4SS secretes 15 different effectors to escape host immunity and promote intracellular replication. Among them, BspF has a GNAT-family acetyltransferase domain, implying its acetyltransferase activity. We confirmed that BspF has acetyltransferase activity (data not shown) and de-crotonyltransferase activity. However, BspF overexpressed in HEK-293T cells can also enhance octamer crotonylation in vitro. Then we enriched crotonylated proteins and conducted LC-MS to study the crotonylation changes of proteins in HEK-293T cells caused by BspF overexpression. A total of 5,559 crotonylation sites were identified on 1,525 different proteins, of which 331 sites on 265 proteins were significantly changed. We found that Rab9A and RAP1B in proteomics data have a great impact on Brucella survival, so we speculate that BspF may influence the function of host proteins by altering crotonylation, thereby promoting the intracellular propagation of Brucella.

show abstract

Section: Methodsmentioning

confidence: 99%

“…This was the first study to show that a large number of non-histone proteins are crotonylated ( Xu et al., 2017 ), but the function of non-histone crotonylation remains unclear. Therefore, in vitro studies on crotonyltransferases generally use histone octamers as substrates for experiments ( Kollenstart et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Global Lysine Crotonylation Alterations of Host Cell Proteins Caused by Brucella Effector BspF

Zhu

Dong

et al. 2021

Front. Cell. Infect. Microbiol.

View full text Add to dashboard Cite

show abstract

“…A routine task for proteomics laboratories and core facilities has long been the identification and characterization of the proteins from specific regions of a gel-based separation [27]. Despite the age of these gel-based methods, these types of analyses are still valuable for answering specific biological questions [28–31]. In these settings, a common goal is to detect the proteins—and potentially the post-translational modifications—that are present within an excised band from a 1-dimensional SDS-PAGE gel.…”

Section: Resultsmentioning

confidence: 99%

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Fondrie

Noble

2019

Preprint

View full text Add to dashboard Cite

1Machine learning methods have proven invaluable for increasing the sensitivity of peptide de-2 tection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, 3 use semi-supervised algorithms to learn models directly from the datasets that they analyze. 4 Although these methods are effective for many proteomics experiments, we suspected that they 5 may be suboptimal for experiments of smaller scale. In this work, we found that the power and 6 consistency of Percolator results was reduced as the size of the experiment was decreased. As 7 an alternative, we propose a different operating mode for Percolator: learn a model with Per-8 colator from a large dataset and use the learned model to evaluate the small-scale experiment. 9 We call this a "static modeling" approach, in contrast to Percolator's usual "dynamic model" 10 that is trained anew for each dataset. We applied this static modeling approach to two settings: 11 small, gel-based experiments and single-cell proteomics. In both cases, static models increased 12 the yield of detected peptides and eliminated the model-induced variability of the standard dy-13 namic approach. These results suggest that static models are a powerful tool for bringing the 14 full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. 15 1 Introduction 16The assignment of peptide sequences to tandem mass spectra is a fundamental task in any pro-17 teomics experiment [1]. This task is most often performed by a database search algorithm, which 18 was first introduced with the SEQUEST search engine in 1994 [2]. Database search algorithms score 19 the theoretical mass spectra of peptides from a selected sequence database against the acquired 20 mass spectra from the experiment, yielding a set of peptide-spectrum matches (PSMs). Although 21 the score functions of individual search engines may differ greatly, the scores reported by each are 22 intended to reflect the quality of PSMs. 23 2 Machine learning strategies to re-score PSMs were first introduced due to their ability to inte-24 grate multiple, orthogonal scores and features from database search engines, thereby improving the 25 sensitivity of peptide detection. Two such methods were proposed simultaneously: one based on 26 linear discriminant analysis [3] and another that used support vector machine (SVM) models to a 27 similar end [4]. Critically, both methods were examples of supervised learning: they relied on fully 28 labeled datasets-where the correct and incorrect PSMs could be determined a priori -to train 29 their respective models. These trained models were then used to predict a new score for the PSMs 30 of a new dataset. We refer to the models used by these methods as static, meaning their parameters 31 do not change when the dataset is changed. Critically, the success of these static models depended 32 on the quality of the labeled dataset that was used for training and how well it reflected the datasets 33 that were subsequently analyzed. The origina...

show abstract

“…Of note, beyond acetylation, several KATs/KDACs have activity of other acylation modifications including propionyl, butyryl, 2-hydroxyisobutyryl, crotonyl, malonyl, succinyl, or glutaryl modification. For example, p300 has crotonyltransferase activity [ 40 ], while KAT2A/GCN5 has both crotonyltransferase and uccinyltransferase activity [ 41 , 42 ], whereas HDAC1/2/3/8 and SIRT1-3 possess decrotonylating activity [ 43 , 44 , 45 ]. Whether these changes in KATs or KDACs in PD patients or models also cause variation of other acylation modifications, and the roles of these acylation variations in PD pathology, deserve further research.…”

Section: Lysine Acetylation and Its Regulatory Mechanism And Functmentioning

confidence: 99%

Imbalance of Lysine Acetylation Contributes to the Pathogenesis of Parkinson’s Disease

Wang

Sun

Wang

et al. 2020

IJMS

View full text Add to dashboard Cite

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders. The neuropathological features of PD are selective and progressive loss of dopaminergic neurons in the substantia nigra pars compacta, deficiencies in striatal dopamine levels, and the presence of intracellular Lewy bodies. Interactions among aging and genetic and environmental factors are considered to underlie the common etiology of PD, which involves multiple changes in cellular processes. Recent studies suggest that changes in lysine acetylation and deacetylation of many proteins, including histones and nonhistone proteins, might be tightly associated with PD pathogenesis. Here, we summarize the changes in lysine acetylation of both histones and nonhistone proteins, as well as the related lysine acetyltransferases (KATs) and lysine deacetylases (KDACs), in PD patients and various PD models. We discuss the potential roles and underlying mechanisms of these changes in PD and highlight that restoring the balance of lysine acetylation/deacetylation of histones and nonhistone proteins is critical for PD treatment. Finally, we discuss the advantages and disadvantages of different KAT/KDAC inhibitors or activators in the treatment of PD models and emphasize that SIRT1 and SIRT3 activators and SIRT2 inhibitors are the most promising effective therapeutics for PD.

show abstract

Gcn5 and Esa1 function as histone crotonyltransferases to regulate crotonylation-dependent transcription

Cited by 78 publications

References 56 publications

Global Lysine Crotonylation Alterations of Host Cell Proteins Caused by Brucella Effector BspF

Global Lysine Crotonylation Alterations of Host Cell Proteins Caused by Brucella Effector BspF

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments

Imbalance of Lysine Acetylation Contributes to the Pathogenesis of Parkinson’s Disease

Contact Info

Product

Resources

About