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1. Histone Acid Extraction and High Throughput Mass Spectrometry to Profile Histone Modifications in Arabidopsis thaliana

   https://doi.org/10.1002/cpz1.527  

   Scheid, Ray ; Dowell, James A. ; Sanders, Dean ; Jiang, Jianjun ; Denu, John M. ; Zhong, Xuehua ( August 2022 , Current Protocols)

   Histone post‐translational modifications (PTMs) play important roles in many biological processes, including gene regulation and chromatin dynamics, and are thus of high interest across many fields of biological research. Chromatin immunoprecipitation coupled with sequencing (ChIP‐seq) is a powerful tool to profile histone PTMsin vivo. This method, however, is largely dependent on the specificity and availability of suitable commercial antibodies. While mass spectrometry (MS)–based proteomic approaches to quantitatively measure histone PTMs have been developed in mammals and several other model organisms, such methods are currently not readily available in plants. One major challenge for the implementation of such methods in plants has been the difficulty in isolating sufficient amounts of pure, high‐quality histones, a step rendered difficult by the presence of the cell wall. Here, we developed a high‐yielding histone extraction and purification method optimized forArabidopsis thalianathat can be used to obtain high‐quality histones for MS. In contrast to other methods used in plants, this approach is relatively simple, and does not require membranes or additional specialized steps, such as gel excision or chromatography, to extract highly purified histones. We also describe methods for producing MS‐ready histone peptides through chemical labeling and digestion. Finally, we describe an optimized method to quantify and analyze the resulting histone PTM data using a modified version of EpiProfile 2.0 for Arabidopsis. In all, the workflow described here can be used to measure changes to histone PTMs resulting from various treatments, stresses, and time courses, as well as in different mutant lines. © 2022 Wiley Periodicals LLC.

   Basic Protocol 1: Nuclear isolation and histone acid extraction

   Basic Protocol 2: Peptide labeling, digestion, and desalting

   Basic Protocol 3: Histone HPLC‐MS/MS and data analysis

    

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2. Prediction of histone post-translational modifications using deep learning

   https://doi.org/10.1093/bioinformatics/btaa1075  

   Baisya, Dipankar Ranjan ; Lonardi, Stefano ( December 2020 , Bioinformatics)

   Cowen, Lenore (Ed.)

   Abstract Motivation Histone post-translational modifications (PTMs) are involved in a variety of essential regulatory processes in the cell, including transcription control. Recent studies have shown that histone PTMs can be accurately predicted from the knowledge of transcription factor binding or DNase hypersensitivity data. Similarly, it has been shown that one can predict PTMs from the underlying DNA primary sequence. Results In this study, we introduce a deep learning architecture called DeepPTM for predicting histone PTMs from transcription factor binding data and the primary DNA sequence. Extensive experimental results show that our deep learning model outperforms the prediction accuracy of the model proposed in Benveniste et al. (PNAS 2014) and DeepHistone (BMC Genomics 2019). The competitive advantage of our framework lies in the synergistic use of deep learning combined with an effective pre-processing step. Our classification framework has also enabled the discovery that the knowledge of a small subset of transcription factors (which are histone-PTM and cell-type-specific) can provide almost the same prediction accuracy that can be obtained using all the transcription factors data. Availabilityand implementation https://github.com/dDipankar/DeepPTM. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

   Thapa N, Chaudari M ( June 2021 , Scientific report)

   null (Ed.)

   Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga. 

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4. A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

   https://doi.org/10.1038/s41598-021-91840-w  

   Thapa, Niraj ; Chaudhari, Meenal ; Iannetta, Anthony A. ; White, Clarence ; Roy, Kaushik ; Newman, Robert H. ; Hicks, Leslie M. ; KC, Dukka B. ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Protein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii , a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga. 

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5. PTMViz: a tool for analyzing and visualizing histone post translational modification data

   https://doi.org/10.1186/s12859-021-04166-9  

   Chappell, Kevin ; Graw, Stefan ; Washam, Charity L. ; Storey, Aaron J. ; Bolden, Chris ; Peterson, Eric C. ; Byrum, Stephanie D. ( May 2021 , BMC Bioinformatics)

   Histone post-translational modifications (PTMs) play an important role in our system by regulating the structure of chromatin and therefore contribute to the regulation of gene and protein expression. Irregularities in histone PTMs can lead to a variety of different diseases including various forms of cancer. Histone modifications are analyzed using high resolution mass spectrometry, which generate large amounts of data that requires sophisticated bioinformatics tools for analysis and visualization. PTMViz is designed for downstream differential abundance analysis and visualization of both protein and/or histone modifications.

   PTMViz provides users with data tables and visualization plots of significantly differentiated proteins and histone PTMs between two sample groups. All the data is packaged into interactive data tables and graphs using the Shiny platform to help the user explore the results in a fast and efficient manner to assess if changes in the system are due to protein abundance changes or epigenetic changes. In the example data provided, we identified several proteins differentially regulated in the dopaminergic pathway between mice treated with methamphetamine compared to a saline control. We also identified histone post-translational modifications including histone H3K9me, H3K27me3, H4K16ac, and that were regulated due to drug exposure.

   Histone modifications play an integral role in the regulation of gene expression. PTMViz provides an interactive platform for analyzing proteins and histone post-translational modifications from mass spectrometry data in order to quickly identify differentially expressed proteins and PTMs.

    

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