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

Understanding PTM Cross Talk Through a Visualization Tool, PTMViz

The advancement of sequencing technologies has expanded our understanding of biological complexity through mechanisms such as allelic variations, alternative splicing of RNA, degradation of RNA by microRNAs, and posttranslational modifications (PTMs). In this chapter, we describe a method, PTMViz, for analyzing proteoforms identified by mass spectrometry. This interactive platform provides differential abundance analysis and visualization of protein and posttranslational modifications. We describe the detailed steps to prepare mass spectrometry database search results into the necessary format for PTMViz, how to set up the experimental conditions for differential abundance analysis, and the visualization of the results. The application is freely available at https://github.com/ByrumLab/PTMViz .

A Strategy to Characterize the Global Landscape of Histone Post-Translational Modifications Within Tissues of Nonmodel Organisms

Histone post-translational modifications (PTMs) are epigenetic marks that play a critical role in the expression and maintenance of DNA, but they remain largely uninvestigated in nonmodel organisms due to technical challenges. To begin alleviating this issue, we developed a workflow for histone PTM analysis in Mozambique tilapia (Oreochromis mossambicus), being a widespread and environmentally hardy fish, using mass spectrometry methods. By incorporating multiple protein digestion methods into the preparation of each sample, we reliably quantified 214 biologically relevant histone PTMs. All of these histone PTMs, collectively referred to as the global histone PTM landscape, were characterized in the gills, kidney, and testes of this fish. By comparing the global histone PTM landscape between the three tissues, we found that 91.59% of histone PTMs were tissue-dependent. The workflow and tools for histone PTM analysis described in this study are now publicly available and enable comprehensive investigation into the influence of environmental stress on histone PTMs in nonmodel organisms. Given the functionality and flexibility of histone PTMs, we anticipate that the study of histone PTMs in ecologically relevant contexts will provide ground-breaking insights into comparative physiology and evolution.

MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)-based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.