Mass spectrometry analysis of the variants of histone H3 and H4 of soybean and their post-translational modifications

Abstract Background Histone modifications and histone variants are of importance in many biological processes. To understand the biological functions of the global dynamics of histone modifications and histone variants in higher plants, we elucidated the variants and post-translational modifications of histones in soybean, a legume plant with a much bigger genome than that of Arabidopsis thaliana. Results In soybean leaves, mono-, di- and tri-methylation at Lysine 4, Lysine 27 and Lysine 36, and acetylation at Lysine 14, 18 and 23 were detected in HISTONE H3. Lysine 27 was prone to being mono-methylated, while tri-methylation was predominant at Lysine 36. We also observed that Lysine 27 methylation and Lysine 36 methylation usually excluded each other in HISTONE H3. Although methylation at HISTONE H3 Lysine 79 was not reported in A. thaliana, mono- and di-methylated HISTONE H3 Lysine 79 were detected in soybean. Besides, acetylation at Lysine 8 and 12 of HISTONE H4 in soybean were identified. Using a combination of mass spectrometry and nano-liquid chromatography, two variants of HISTONE H3 were detected and their modifications were determined. They were different at positions of A31F41S87S90 (HISTONE variant H3.1) and T31Y41H87L90 (HISTONE variant H3.2), respectively. The methylation patterns in these two HISTONE H3 variants also exhibited differences. Lysine 4 and Lysine 36 methylation were only detected in HISTONE H3.2, suggesting that HISTONE variant H3.2 might be associated with actively transcribing genes. In addition, two variants of histone H4 (H4.1 and H4.2) were also detected, which were missing in other organisms. In the histone variant H4.1 and H4.2, the amino acid 60 was isoleucine and valine, respectively. Conclusion This work revealed several distinct variants of soybean histone and their modifications that were different from A. thaliana, thus providing important biological information toward further understanding of the histone modifications and their functional significance in higher plants.</p

GmPHD5 acts as an important regulator for crosstalk between histone H3K4 di-methylation and H3K14 acetylation in response to salinity stress in soybean

<p>Abstract</p> <p>Background</p> <p>Accumulated evidence suggest that specific patterns of histone posttranslational modifications (PTMs) and their crosstalks may determine transcriptional outcomes. However, the regulatory mechanisms of these "histone codes" in plants remain largely unknown.</p> <p>Results</p> <p>In this study, we demonstrate for the first time that a salinity stress inducible PHD (plant homeodomain) finger domain containing protein GmPHD5 can read the "histone code" underlying the methylated H3K4. GmPHD5 interacts with other DNA binding proteins, including GmGNAT1 (an acetyl transferase), GmElongin A (a transcription elongation factor) and GmISWI (a chromatin remodeling protein). Our results suggest that GmPHD5 can recognize specific histone methylated H3K4, with preference to di-methylated H3K4. Here, we illustrate that the interaction between GmPHD5 and GmGNAT1 is regulated by the self-acetylation of GmGNAT1, which can also acetylate histone H3. GmGNAT1 exhibits a preference toward acetylated histone H3K14. These results suggest a histone crosstalk between methylated H3K4 and acetylated H3K14. Consistent to its putative roles in gene regulation under salinity stress, we showed that GmPHD5 can bind to the promoters of some confirmed salinity inducible genes in soybean.</p> <p>Conclusion</p> <p>Here, we propose a model suggesting that the nuclear protein GmPHD5 is capable of regulating the crosstalk between histone methylation and histone acetylation of different lysine residues. Nevertheless, GmPHD5 could also recruit chromatin remodeling factors and transcription factors of salt stress inducible genes to regulate their expression in response to salinity stress.</p

SET8 recognizes the sequence RHRK²⁰VLRDN within the N terminus of histone H4 and mono-methylates lysine 20

Methylation of lysine 20 in histone H4 has been proven to play important roles in chromatin structure and gene regulation. SET8 is one of the methyltransferases identified to be specific for this modification. In this study, the minimal active SET domain of SET8 has been mapped to the region of amino acids 195–352. This region completely retains the same methylation activity and substrate specificity as the full-length SET8. The SET domain recognizes a stretch of specific amino acid sequence around lysine 20 of H4 for its methylation activity. Methylation assays with N terminus mutants of H4 that contain deletions and single alanine or glutamine substitutions of charged residues revealed that SET8 requires the sequence RHRK²⁰VLRDN for methylation at lysine 20. The individual mutation of any charged residue in this sequence to alanine or glutamine abolished or greatly decreased levels of methylation of lysine 20 of H4 by SET8. Interestingly, mutation of lysine 16 to alanine, arginine, glutamine, ormethionine did not affect methylation of lysine 20 by the SET domain. Mass spectrometric analysis of synthesized H4 N-terminal peptides modified by SET8 showed that SET8 selectively mono-methylates lysine 20 of H4. Taken together, our results suggested that the coordination between the amino acid sequence RHRK²⁰VLRDN and the SET domain of SET8 determines the substrate specificity and multiplicity of methylation of lysine 20 of H4

Rapid peptide-based screening on the substrate specificity of severe acute respiratory syndrome (SARS) coronavirus 3C-like protease by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Severe acute respiratory syndrome coronavirus (SARS-CoV) 3C-like protease (3CLpro) mediates extensive proteolytic processing of replicase polyproteins, and is considered a promising target for anti-SARS drug development. Here we present a rapid and high-throughput screening method to study the substrate specificity of SARS-CoV 3CLpro. Six target amino acid positions flanking the SARS-CoV 3CLpro cleavage site were investigated. Each batch of mixed peptide substrates with defined amino acid substitutions at the target amino acid position was synthesized via the “cartridge replacement” approach and was subjected to enzymatic cleavage by recombinant SARS-CoV 3CLpro. Susceptibility of each peptide substrate to SARS-CoV 3CLpro cleavage was monitored simultaneously by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The hydrophobic pocket in the P2 position at the protease cleavage site is crucial to SARS-CoV 3CLpro-specific binding, which is limited to substitution by hydrophobic residue. The binding interface of SARS-CoV 3CLpro that is facing the P1′ position is suggested to be occupied by acidic amino acids, thus the P1′ position is intolerant to acidic residue substitution, owing to electrostatic repulsion. Steric hindrance caused by some bulky or β-branching amino acids in P3 and P2′ positions may also hinder the binding of SARS-CoV 3CLpro. This study generates a comprehensive overview of SARS-CoV 3CLpro substrate specificity, which serves as the design basis of synthetic peptide-based SARS-CoV 3CLpro inhibitors. Our experimental approach is believed to be widely applicable for investigating the substrate specificity of other proteases in a rapid and high-throughput manner that is compatible for future automated analysis

Comparison among Naïve Bayes classifier, simple SVM classifier and BPB-PPMS classifier in the 5-fold cross-validation experiment on the same training datasets.

<p>Comparison among Naïve Bayes classifier, simple SVM classifier and BPB-PPMS classifier in the 5-fold cross-validation experiment on the same training datasets.</p

Performance of BPB-PPMS and MeMo on independent test datasets in terms of BPB-PPMS.

*<p>Prediction threshold value is not avalable in MeMo.</p

ROC curves to assess the prediction performance of lysine prediction model.

<p>Red, blue, and green curve denotes 5-fold cross-validation prediction performance of Bi-profile Bayes SVM classifier, Simple SVM classifier, Naïve Bayes classifier, respectively. (The corresponding average AUC is 0.8383, 0.7498 and 0.7581, respectively.)</p

The optimal parameters and performance of BPB-PPMS.

<p>The optimal parameter combination was determined in a grid-based manner introduced in LIBSVM packages<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004920#pone.0004920-Chang1" target="_blank">[11]</a>.</p>(a)<p>Here, input window size for SVMs is two times sliding window size.</p>(b)<p>RBF, Radial Basis Function .</p>(c)<p>, the penalty parameter of the error term in objective function.</p>(d)<p>, the parameter in Radial Basis Function.</p>(e)<p>AUC, the area under ROC.</p>(f)<p>MCC, Matthews Correlation Coefficient.</p

ROC curves to assess the prediction performance of three arginine prediction models.

<p>Red, blue, and green curve denotes 5-fold cross-validation prediction performance of Bi-profile Bayes SVM classifier, Simple SVM classifier and Naïve Bayes classifier, respectively. (The corresponding average AUC is 0.9254, 0.8958 and 0.8909, respectively.)</p

Potential methylation sites predicted on Tat protein (P04610) through BPB-PPMS, Simple SVMs, and Naïve Bayes classifiers.

<p>The numbers in bracket denote the predictive probability of methylation at corresponding sites.</p