Proteogenomic Methods to Improve Genome Annotation

Phosphotyrosine Profiling Using SILAC

Tyrosine phosphorylation on proteins is an important posttranslational modification that regulates various processes in cells. Mass spectrometry-based phosphotyrosine profiling can reveal tyrosine kinase signaling activity in cells. Using quantitative proteomics strategies such as stable isotope labeling with amino acids in cell culture (SILAC) allows comparison of tyrosine kinase signaling activity across two to -three different conditions. In this book chapter, we discuss the reagents required and a step-by-step protocol to carry out phosphotyrosine profiling using SILAC.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

Integrated Transcriptomic and Proteomic Analysis of Primary Human Umbilical Vein Endothelial Cells

Understanding the molecular profile of every human cell type is essential for understanding its role in normal physiology and disease. Technological advancements in DNA sequencing, mass spectrometry, and computational methods allow us to carry out multiomics analyses although such approaches are not routine yet. Human umbilical vein endothelial cells (HUVECs) are a widely used model system to study pathological and physiological processes associated with the cardiovascular system. In this study, next-generation sequencing and high-resolution mass spectrometry to profile the transcriptome and proteome of primary HUVECs is employed. Analysis of 145 million paired-end reads from next-generation sequencing confirmed expression of 12 186 protein-coding genes (FPKM ≥0.1), 439 novel long non-coding RNAs, and revealed 6089 novel isoforms that were not annotated in GENCODE. Proteomics analysis identifies 6477 proteins including confirmation of N-termini for 1091 proteins, isoforms for 149 proteins, and 1034 phosphosites. A database search to specifically identify other post-translational modifications provide evidence for a number of modification sites on 117 proteins which include ubiquitylation, lysine acetylation, and mono-, di- and tri-methylation events. Evidence for 11 "missing proteins," which are proteins for which there was insufficient or no protein level evidence, is provided. Peptides supporting missing protein and novel events are validated by comparison of MS/MS fragmentation patterns with synthetic peptides. Finally, 245 variant peptides derived from 207 expressed proteins in addition to alternate translational start sites for seven proteins and evidence for novel proteoforms for five proteins resulting from alternative splicing are identified. Overall, it is believed that the integrated approach employed in this study is widely applicable to study any primary cell type for deeper molecular characterization.

Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes

Background

Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes.

Results

Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data.

Conclusions

Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu.

Data are available via ProteomeXchange under identifier PXD010618.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5431-9) contains supplementary material, which is available to authorized users.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.