PEPPI: a peptidomic database of human protein isoforms for proteomics experiments

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.

Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq

Background

Alternative splicing isoforms have been reported as a new and robust class of diagnostic biomarkers. Over 95% of human genes are estimated to be alternatively spliced as a powerful means of producing functionally diverse proteins from a single gene. The emergence of next-generation sequencing technologies, especially RNA-seq, provides novel insights into large-scale detection and analysis of alternative splicing at the transcriptional level. Advances in Proteomic Technologies such as liquid chromatography coupled tandem mass spectrometry (LC–MS/MS), have shown tremendous power for the parallel characterization of large amount of proteins in biological samples. Although poor correspondence has been generally found from previous qualitative comparative analysis between proteomics and microarray data, significantly higher degrees of correlation have been observed at the level of exon. Combining protein and RNA data by searching LC–MS/MS data against a customized protein database from RNA-Seq may produce a subset of alternatively spliced protein isoform candidates that have higher confidence.

Results

We developed a bioinformatics workflow to discover alternative splicing biomarkers from LC–MS/MS using RNA-Seq. First, we retrieved high confident, novel alternative splicing biomarkers from the breast cancer RNA-Seq database. Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customized alternative splicing database for MS searching. Lastly, we ran the Open Mass spectrometry Search Algorithm against the customized alternative splicing database with breast cancer plasma proteome. Twenty six alternative splicing biomarker peptides with one single intron event and one exon skipping event were identified. Further interpretation of biological pathways with our Integrated Pathway Analysis Database showed that these 26 peptides are associated with Cancer, Signaling, Metabolism, Regulation, Immune System and Hemostasis pathways, which are consistent with the 256 alternative splicing biomarkers from the RNA-Seq.

Conclusions

This paper presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biomarkers from the breast cancer proteome. As a complement to synthetic alternative splicing database technique for alternative splicing identification, this method combines the advantages of two platforms: mass spectrometry and next generation sequencing and can help identify potentially highly sample-specific alternative splicing isoform biomarkers at early-stage of cancer.

Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine

The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies.

Function, clinical application, and strategies of Pre-mRNA splicing in cancer

Pre-mRNA splicing is a fundamental process that plays a considerable role in generating protein diversity. Pre-mRNA splicing is also the key to the pathology of numerous diseases, especially cancers. In this review, we discuss how aberrant splicing isoforms precisely regulate three basic functional aspects in cancer: proliferation, metastasis and apoptosis. Importantly, clinical function of aberrant splicing isoforms is also discussed, in particular concerning drug resistance and radiosensitivity. Furthermore, this review discusses emerging strategies how to modulate pathologic aberrant splicing isoforms, which are attractive, novel therapeutic agents in cancer. Last we outline current and future directions of isoforms diagnostic methodologies reported so far in cancer. Thus, it is highlighting significance of aberrant splicing isoforms as markers for cancer and as targets for cancer therapy.

Event Analysis: Using Transcript Events To Improve Estimates of Abundance in RNA-seq Data

Alternative splicing leverages genomic content by allowing the synthesis of multiple transcripts and, by implication, protein isoforms, from a single gene. However, estimating the abundance of transcripts produced in a given tissue from short sequencing reads is difficult and can result in both the construction of transcripts that do not exist, and the failure to identify true transcripts. An alternative approach is to catalog the events that make up isoforms (splice junctions and exons). We present here the Event Analysis (EA) approach, where we project transcripts onto the genome and identify overlapping/unique regions and junctions. In addition, all possible logical junctions are assembled into a catalog. Transcripts are filtered before quantitation based on simple measures: the proportion of the events detected, and the coverage. We find that mapping to a junction catalog is more efficient at detecting novel junctions than mapping in a splice aware manner. We identify 99.8% of true transcripts while iReckon identifies 82% of the true transcripts and creates more transcripts not included in the simulation than were initially used in the simulation. Using PacBio Iso-seq data from a mouse neural progenitor cell model, EA detects 60% of the novel junctions that are combinations of existing exons while only 43% are detected by STAR. EA further detects ∼5,000 annotated junctions missed by STAR. Filtering transcripts based on the proportion of the transcript detected and the number of reads on average supporting that transcript captures 95% of the PacBio transcriptome. Filtering the reference transcriptome before quantitation, results in is a more stable estimate of isoform abundance, with improved correlation between replicates. This was particularly evident when EA is applied to an RNA-seq study of type 1 diabetes (T1D), where the coefficient of variation among subjects (n = 81) in the transcript abundance estimates was substantially reduced compared to the estimation using the full reference. EA focuses on individual transcriptional events. These events can be quantitate and analyzed directly or used to identify the probable set of expressed transcripts. Simple rules based on detected events and coverage used in filtering result in a dramatic improvement in isoform estimation without the use of ancillary data (e.g., ChIP, long reads) that may not be available for many studies.

Impact of Alternative Splicing on the Human Proteome

Alternative splicing is a critical determinant of genome complexity and, by implication, is assumed to engender proteomic diversity. This notion has not been experimentally tested in a targeted, quantitative manner. Here, we have developed an integrative approach to ask whether perturbations in mRNA splicing patterns alter the composition of the proteome. We integrate RNA sequencing (RNA-seq) (to comprehensively report intron retention, differential transcript usage, and gene expression) with a data-independent acquisition (DIA) method, SWATH-MS (sequential window acquisition of all theoretical spectra-mass spectrometry), to capture an unbiased, quantitative snapshot of the impact of constitutive and alternative splicing events on the proteome. Whereas intron retention is accompanied by decreased protein abundance, alterations in differential transcript usage and gene expression alter protein abundance proportionate to transcript levels. Our findings illustrate how RNA splicing links isoform expression in the human transcriptome with proteomic diversity and provides a foundation for studying perturbations associated with human diseases.

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

BACKGROUND

Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine.

RESULTS

The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI .

CONCLUSIONS

While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.

A method for identifying discriminative isoform-specific peptides for clinical proteomics application

Background

Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer.

However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids.

Results

Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis.

Conclusions

Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2907-8) contains supplementary material, which is available to authorized users.

Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation

Mass spectrometry-based proteomics has emerged as the leading method for detection, quantification, and characterization of proteins. Nearly all proteomic workflows rely on proteomic databases to identify peptides and proteins, but these databases typically contain a generic set of proteins that lack variations unique to a given sample, precluding their detection. Fortunately, proteogenomics enables the detection of such proteomic variations and can be defined, broadly, as the use of nucleotide sequences to generate candidate protein sequences for mass spectrometry database searching. Proteogenomics is experiencing heightened significance due to two developments: (a) advances in DNA sequencing technologies that have made complete sequencing of human genomes and transcriptomes routine, and (b) the unveiling of the tremendous complexity of the human proteome as expressed at the levels of genes, cells, tissues, individuals, and populations. We review here the field of human proteogenomics, with an emphasis on its history, current implementations, the types of proteomic variations it reveals, and several important applications.

Proteomics of the Synapse--A Quantitative Approach to Neuronal Plasticity

The advances in mass spectrometry based proteomics in the past 15 years have contributed to a deeper appreciation of protein networks and the composition of functional synaptic protein complexes. However, research on protein dynamics underlying core mechanisms of synaptic plasticity in brain lag far behind. In this review, we provide a synopsis on proteomic research addressing various aspects of synaptic function. We discuss the major topics in the study of protein dynamics of the chemical synapse and the limitations of current methodology. We highlight recent developments and the future importance of multidimensional proteomics and metabolic labeling. Finally, emphasis is given on the conceptual framework of modern proteomics and its current shortcomings in the quest to gain a deeper understanding of synaptic plasticity.

Novel alternative splicing isoform biomarkers identification from high-throughput plasma proteomics profiling of breast cancer

BACKGROUND

In the biopharmaceutical industry, biomarkers define molecular taxonomies of patients and diseases and serve as surrogate endpoints in early-phase drug trials. Molecular biomarkers can be much more sensitive than traditional lab tests. Discriminating disease biomarkers by traditional method such as DNA microarray has proved challenging. Alternative splicing isoform represents a new class of diagnostic biomarkers. Recent scientific evidence is demonstrating that the differentiation and quantification of individual alternative splicing isoforms could improve insights into disease diagnosis and management. Identifying and characterizing alternative splicing isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods used for alternative splicing isoform determination such as transcriptome-level, low level of coverage and poor focus on alternative splicing.

RESULTS

Therefore, we presented a peptidomics approach to searching novel alternative splicing isoforms in clinical proteomics. Our results showed that the approach has significant potential in enabling discovery of new types of high-quality alternative splicing isoform biomarkers.

CONCLUSIONS

We developed a peptidomics approach for the proteomics community to analyze, identify, and characterize alternative splicing isoforms from MS-based proteomics experiments with more coverage and exclusive focus on alternative splicing. The approach can help generate novel hypotheses on molecular risk factors and molecular mechanisms of cancer in early stage, leading to identification of potentially highly specific alternative splicing isoform biomarkers for early detection of cancer.

SASD: the Synthetic Alternative Splicing Database for identifying novel isoform from proteomics

BACKGROUND

Alternative splicing is an important and widespread mechanism for generating protein diversity and regulating protein expression. High-throughput identification and analysis of alternative splicing in the protein level has more advantages than in the mRNA level. The combination of alternative splicing database and tandem mass spectrometry provides a powerful technique for identification, analysis and characterization of potential novel alternative splicing protein isoforms from proteomics.

RESULTS

We used a three-step pipeline to create a synthetic alternative splicing database (SASD) to identify novel alternative splicing isoforms and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing. First, we extracted information on gene structures of all genes in the Ensembl Genes 71 database and incorporated the Integrated Pathway Analysis Database. Then, we compiled artificial splicing transcripts. Lastly, we translated the artificial transcripts into alternative splicing peptides.

CONCLUSIONS

The SASD provides the scientific community with an efficient means to identify, analyze, and characterize novel Exon Skipping and Intron Retention protein isoforms from mass spectrometry and interpret them at the context of pathway, disease, drug and organ specificity or custom gene set with maximum coverage and exclusive focus on alternative splicing.

MScDB: a mass spectrometry-centric protein sequence database for proteomics

Protein sequence databases are indispensable tools for life science research including mass spectrometry (MS)-based proteomics. In current database construction processes, sequence similarity clustering is used to reduce redundancies in the source data. Albeit powerful, it ignores the peptide-centric nature of proteomic data and the fact that MS is able to distinguish similar sequences. Therefore, we introduce an approach that structures the protein sequence space at the peptide level using theoretical and empirical information from large-scale proteomic data to generate a mass spectrometry-centric protein sequence database (MScDB). The core modules of MScDB are an in-silico proteolytic digest and a peptide-centric clustering algorithm that groups protein sequences that are indistinguishable by mass spectrometry. Analysis of various MScDB uses cases against five complex human proteomes, resulting in 69 peptide identifications not present in UniProtKB as well as 79 putative single amino acid polymorphisms. MScDB retains ~99% of the identifications in comparison to common databases despite a 3-48% increase in the theoretical peptide search space (but comparable protein sequence space). In addition, MScDB enables cross-species applications such as human/mouse graft models, and our results suggest that the uncertainty in protein assignments to one species can be smaller than 20%.

Discovery and mass spectrometric analysis of novel splice-junction peptides using RNA-Seq

Human proteomic databases required for MS peptide identification are frequently updated and carefully curated, yet are still incomplete because it has been challenging to acquire every protein sequence from the diverse assemblage of proteoforms expressed in every tissue and cell type. In particular, alternative splicing has been shown to be a major source of this cell-specific proteomic variation. Many new alternative splice forms have been detected at the transcript level using next generation sequencing methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of next generation sequencing methods, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splice-junction peptides. Eighty million paired-end Illumina reads and ∼500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching. Based on the RefSeq gene models, we detected 136,123 annotated and 144,818 unannotated transcript junctions. Of those, 24,834 unannotated junctions passed various quality filters (e.g. minimum read depth) and these entries were translated into 33,589 polypeptide sequences and used for database searching. We discovered 57 splice junction peptides not present in the Uniprot-Trembl proteomic database comprising an array of different splicing events, including skipped exons, alternative donors and acceptors, and noncanonical transcriptional start sites. To our knowledge this is the first example of using sample-specific RNA-Seq data to create a splice-junction database and discover new peptides resulting from alternative splicing.

Potential blood biomarkers for stroke

Stroke is one of the most common causes of death worldwide and a major cause of acquired disability in adults. Despite advances in research during the last decade, prevention and treatment strategies still suffer from significant limitations, and therefore new theoretical and technical approaches are required. Technological advances in the proteomic and metabolomic areas, during recent years, have permitted a more effective search for novel biomarkers and therapeutic targets that may allow for effective risk stratification and early diagnosis with subsequent rapid treatment. This review provides a comprehensive overview of the latest candidate proteins and metabolites proposed as new potential biomarkers in stroke.

IPAD: the Integrated Pathway Analysis Database for Systematic Enrichment Analysis

Background

Next-Generation Sequencing (NGS) technologies and Genome-Wide Association Studies (GWAS) generate millions of reads and hundreds of datasets, and there is an urgent need for a better way to accurately interpret and distill such large amounts of data. Extensive pathway and network analysis allow for the discovery of highly significant pathways from a set of disease vs. healthy samples in the NGS and GWAS. Knowledge of activation of these processes will lead to elucidation of the complex biological pathways affected by drug treatment, to patient stratification studies of new and existing drug treatments, and to understanding the underlying anti-cancer drug effects. There are approximately 141 biological human pathway resources as of Jan 2012 according to the Pathguide database. However, most currently available resources do not contain disease, drug or organ specificity information such as disease-pathway, drug-pathway, and organ-pathway associations. Systematically integrating pathway, disease, drug and organ specificity together becomes increasingly crucial for understanding the interrelationships between signaling, metabolic and regulatory pathway, drug action, disease susceptibility, and organ specificity from high-throughput omics data (genomics, transcriptomics, proteomics and metabolomics).

Results

We designed the Integrated Pathway Analysis Database for Systematic Enrichment Analysis (IPAD, http://bioinfo.hsc.unt.edu/ipad), defining inter-association between pathway, disease, drug and organ specificity, based on six criteria: 1) comprehensive pathway coverage; 2) gene/protein to pathway/disease/drug/organ association; 3) inter-association between pathway, disease, drug, and organ; 4) multiple and quantitative measurement of enrichment and inter-association; 5) assessment of enrichment and inter-association analysis with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources; and 6) cross-linking of multiple available data sources.

IPAD is a comprehensive database covering about 22,498 genes, 25,469 proteins, 1956 pathways, 6704 diseases, 5615 drugs, and 52 organs integrated from databases including the BioCarta, KEGG, NCI-Nature curated, Reactome, CTD, PharmGKB, DrugBank, PharmGKB, and HOMER. The database has a web-based user interface that allows users to perform enrichment analysis from genes/proteins/molecules and inter-association analysis from a pathway, disease, drug, and organ.

Moreover, the quality of the database was validated with the context of the existing biological knowledge and a "gold standard" constructed from reputable and reliable sources. Two case studies were also presented to demonstrate: 1) self-validation of enrichment analysis and inter-association analysis on brain-specific markers, and 2) identification of previously undiscovered components by the enrichment analysis from a prostate cancer study.

Conclusions

IPAD is a new resource for analyzing, identifying, and validating pathway, disease, drug, organ specificity and their inter-associations. The statistical method we developed for enrichment and similarity measurement and the two criteria we described for setting the threshold parameters can be extended to other enrichment applications. Enriched pathways, diseases, drugs, organs and their inter-associations can be searched, displayed, and downloaded from our online user interface. The current IPAD database can help users address a wide range of biological pathway related, disease susceptibility related, drug target related and organ specificity related questions in human disease studies.

A combined proteomic and genetic analysis of the highly variable platelet proteome: from plasmatic proteins and SNPs

High biological variation in protein expression represents a major challenge in clinical proteomics. In a study based on 2D-DIGE, we found that the standardised abundance of only a few proteins varied by more than 50%. While some of the highest variable proteins in platelets of 52 healthy elderly were of plasmatic origin, such as albumin or haptoglobin, absence of several other high-abundant plasma proteins strongly suggests that plasma-derived proteins represent an integral part of the platelet proteome. Amongst the highly variable platelet-derived proteins, two spots were both identified as GSTO1 and assigned to either the wild-type or mutant isoform of SNP A140D. Remarkably, when the spots were considered within the respective genotype groups, their CV decreased to about the median variation. Albeit 2D-DIGE allowed correct genotyping, two individuals seemed to be GSTO1*A140 deficient. Probing 2D-Western blots with novel mAb, however, detected A140 protein as additional spot at pH 8.1, caused by the SNPs E155del and E208K. In contrast to previous studies, we show that GSTO1 protein is expressed in vivo, despite the deletion E155. Our data indicate that incorporation of exogenous proteins and genetic polymorphisms of endogenous proteins represent the main source of extreme biological variation in the platelet proteome.