Integrative Proteogenomics Research Articles

Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model

Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.

An integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla.

Long noncoding RNAs (lncRNAs) are intracellular transcripts longer than 200 nucleotides and lack protein-coding information. A subclass of lncRNA known as long intergenic noncoding RNAs (lincRNAs) are transcribed from genomic regions that share no overlap with annotated protein-coding genes. Increasing evidence has shown that some annotated lincRNA transcripts do in fact contain open reading frames (ORFs) encoding functional short peptides in the cell. Few robust methods for lincRNA-encoded peptide identification have been reported, and the tissue-specific expression of these peptides has been largely unexplored. Here we propose an integrative workflow for lincRNA-encoded peptide discovery and test it on the mouse kidney inner medulla (IM). In brief, low molecular weight protein fractions were enriched from homogenate of IMs and trypsinized into shorter peptides, which were sequenced by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). To curate a hypothetical lincRNA-encoded peptide database for peptide-spectrum matching following LC-MS/MS, we performed RNA-Seq on IMs, computationally removed reads overlapping with annotated protein-coding genes, and remapped the remaining reads to a database of mouse noncoding transcripts to infer lincRNA expression. Expressed lincRNAs were searched for ORFs by an existing rule-based algorithm, and translated ORFs were used for peptide-spectrum matching. Peptides identified by LC-MS/MS were further evaluated by using several quality control criteria and bioinformatics methods. We discovered three novel lincRNA-encoded peptides, which are conserved in mouse, rat, and human. The workflow can be adapted for discovery of small protein-coding genes in any species or tissue where noncoding transcriptome information is available.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources.

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Aberrant ERBB4-SRC Signaling as a Hallmark of Group 4 Medulloblastoma Revealed by Integrative Phosphoproteomic Profiling

The current consensus recognizes four main medulloblastoma subgroups (wingless, Sonic hedgehog, group 3 and group 4). While medulloblastoma subgroups have been characterized extensively at the (epi-)genomic and transcriptomic levels, the proteome and phosphoproteome landscape remain to be comprehensively elucidated. Using quantitative (phospho)-proteomics in primary human medulloblastomas, we unravel distinct posttranscriptional regulation leading to highly divergent oncogenic signaling and kinase activity profiles in groups 3 and 4 medulloblastomas. Specifically, proteomic and phosphoproteomic analyses identify aberrant ERBB4-SRC signaling in group 4. Hence, enforced expression of an activated SRC combined with p53 inactivation induces murine tumors that resemble group 4 medulloblastoma. Therefore, our integrative proteogenomics approach unveils an oncogenic pathway and potential therapeutic vulnerability in the most common medulloblastoma subgroup.

Comparative proteomic analysis of mouse models of pathological and physiological cardiac hypertrophy, with selection of biomarkers of pathological hypertrophy by integrative proteogenomics

To determine fundamental characteristics of pathological cardiac hypertrophy, protein expression profiles in two widely accepted models of cardiac hypertrophy (swimming-trained mouse for physiological hypertrophy and pressure-overload-induced mouse for pathological hypertrophy) were compared using a label-free quantitative proteomics approach. Among 3955 proteins (19,235 peptides, false-discovery rate < 0.01) identified in these models, 486 were differentially expressed with a log2 fold difference ≥ 0.58, or were detected in only one hypertrophy model (each protein from 4 technical replicates, p < .05). Analysis of gene ontology biological processes and KEGG pathways identified cellular processes enriched in one or both hypertrophy models. Processes unique to pathological hypertrophy were compared with processes previously identified in cardiac-hypertrophy models. Individual proteins with differential expression in processes unique to pathological hypertrophy were further confirmed using the results of previous targeted functional analysis studies. Using a proteogenomic approach combining transcriptomic and proteomic analyses, similar patterns of differential expression were observed for 23 proteins and corresponding genes associated with pathological hypertrophy. A total of 11 proteins were selected as early-stage pathological-hypertrophy biomarker candidates, and the results of western blotting for five of these proteins in independent samples confirmed the patterns of differential expression in mouse models of pathological and physiological cardiac hypertrophy.

Proteogenomic analysis prioritises functional single nucleotide variants in cancer samples.

Massively parallel DNA sequencing enables the detection of thousands of germline and somatic single nucleotide variants (SNVs) in cancer samples. The functional analysis of these mutations is often carried out through in silico predictions, with further downstream experimental validation rarely performed. Here, we examine the potential of using mass spectrometry-based proteomics data to further annotate the function of SNVs in cancer samples. RNA-seq and whole genome sequencing (WGS) data from Jurkat cells were used to construct a custom database of single amino acid variant (SAAV) containing peptides and identified over 1,000 such peptides in two Jurkat proteomics datasets. The analysis enabled the detection of a truncated form of splicing regulator YTHDC1 at the protein level. To extend the functional annotation further, a Jurkat phosphoproteomics dataset was analysed, identifying 463 SAAV containing phosphopeptides. Of these phosphopeptides, 24 SAAVs were found to directly impact the phosphorylation event through the creation of either a phosphorylation site or a kinase recognition motif. We identified a novel phosphorylation site created by a SAAV in splicing factor SF3B1, a protein that is frequently mutated in leukaemia. To our knowledge, this is the first study to use phosphoproteomics data to directly identify novel phosphorylation events arising from the creation of phosphorylation sites by SAAVs. Our study reveals multiple functional mutations impacting the splicing pathway in Jurkat cells and demonstrates potential benefits of an integrative proteogenomics analysis for high-throughput functional annotation of SNVs in cancer.

Abstract 1565: OnPLS-based integrative proteogenomics analysis of lung squamous cell cancer

Abstract Introduction: Multivariate projection methods such as PCA and PLS has been widely applied for analysis of biological and chemical data. OnPLS is a recent extension to these methods suitable for integrative analysis of omics data. With OnPLS it is possible to compare multiple omics datasets to identify joint variation and variation locally unique for each of the studied datasets. OnPLS is a new approach for truly integrative analysis of omics data to be contrasted to commonly applied approaches limiting analysis to 1) comparing findings from individually analyzed blocks of data 2) pairwise comparison of individual probes. Experimental: A Java based implementation of OnPLS was used for the statistical modeling. 116 lung squamous cell cancer samples were characterized using gene expression profiling and global proteomics. The OnPLS model was applied to jointly model variation between mRNA and protein expression. Enrichment analysis of factor loadings was performed using the Enrichr tools to identify biological mechanisms explained by the different joint and unique components of the OnPLS model. Results: Using a cross-validation procedure the model with the highest predictive ability was calculated having two joint components and one locally unique component for each of the proteomics and gene expression datasets. The model explained 21.9% of the variation in the expression data and 26.1% of the variation in the proteomics data. The first joint component captures the highest degree of common variation between mRNA and protein activity. From the mRNA data, this component is related to immune infiltrates, especially monocytes and B-cells, whereas this component is related to extracellular matrix activity from the protein data. This suggests covariance of mRNA immune-related gene expression and extracellular matrix-related protein expression. As expected, local variation specific to the protein measurements involved regulation of protein activation and processing. mRNA-specific variation is related to keratinization, a key process in squamous cell cancer. Conclusion: OnPLS offers an interesting approach for integrative analysis of omics data. Applying this approach to proteo-genomics data of lung squamous cell cancers suggest similar patterns of activity is represented in protein and gene expression data, however the biological processes associated with this activity may be distinct. Citation Format: Fredrik Pettersson, Paul A. Stewart, Robbert J. Slebos, Eric A. Welsh, Ling Cen, Yonghong Zhang, Zhihua Chen, Chia-Ho Cheng, Guolin Zhang, Bin Fang, Victoria Izumi, Sean Yoder, Katherine Fellows, Y Ann Chen, Jamie K. Teer, Steven Eschrich, John M. Koomen, Anders Berglund, Eric B. Haura. OnPLS-based integrative proteogenomics analysis of lung squamous cell cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1565. doi:10.1158/1538-7445.AM2017-1565

Abstract 205: Underlying mechanisms of genome-proteome discordance in squamous cell lung cancer

Abstract Introduction: Genomic analyses have yielded a tremendous amount of data on the genetic changes in lung cancers, but translating these experiments into actionable information benefitting lung squamous cell carcinoma (SQLC) patients has proven more difficult. Studies by the NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC), our group, and others have demonstrated that gene and protein expression show only moderate correlation, demonstrating limitations in explaining phenotypic changes from genomics alone. These findings indicate a clear need for integrative proteogenomics to better understand tumor biology, especially in a complex disease like SQLC. Experimental: We have assembled a comprehensive proteogenomic dataset including DNA copy number (Affymetrix CytoScan HD Assay), targeted exome sequencing (Agilent Comprehensive Cancer Panel), RNA-sequencing (Illumina NextSeq), and shotgun proteomics (Q Exactive LC-MS/MS) on 116 surgically resected SQLC tumor samples with extensive clinical and follow up data. Results: We have identified 6584 high confidence proteins from preliminary proteomic analysis. After quality control filtering, we utilized 5562 gene-protein pairs for further analysis. Clustering of patient RNA expression in this patient cohort has been unable to fully reproduce the molecular classification previously published for SQLC. Furthermore, proteomic results indicate yet another potential classification strategy selecting patient subgroups that differ at protein level. We observed a 0.29 median Spearman’s correlation of 5562 gene-protein pairs. There were 2781 highly correlated gene-protein pairs (greater than median) and 2781 poorly correlated gene-protein pairs (less than median) including 773 anti-correlated gene-protein pairs (less than 0). We hypothesized that poorly correlated gene-protein pairs could be functionally related in a pathway-dependent manner. Enrichment analysis of poorly correlated proteins identified pathways related to mRNA processing, growth factor signaling (EGFR, FGFR), and nonsense-mediated decay (NMD). Interestingly, there were 9 frequently mutated SQLC genes in the low correlation gene-protein pairs but only 3 in the highly correlated pairs. We found three distinct patient subgroups by clustering poorly correlated proteins. Analysis of these subgroups showed differentially expressed pathways related to mRNA processing, ubiquitination, and NMD. Conclusion: Differential modulation of the proteome outside of genomic regulation may suggest important regulatory mechanisms in cancer and give new insights into treating SQLC. Analysis of poorly correlated gene-protein pairs suggests certain pathways are dysregulated in cancer, and ongoing DNA analysis and future analyses involving miRNAs, RNA-binding proteins, and the ubiquitin proteome system will help elucidate our preliminary findings. Citation Format: Paul A. Stewart, Robbert J. Slebos, Eric A. Welsh, Ling Cen, Yonghong Zhang, Zhihua Chen, Chia-Ho Cheng, Fredrik Pettersson, Anders Berglund, Guolin Zhang, Bin Fang, Victoria Izumi, Sean Yoder, Katherine Fellows, Ann Chen, Jamie K. Teer, Steven A. Eschrich, John M. Koomen, Eric B. Haura. Underlying mechanisms of genome-proteome discordance in squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 205. doi:10.1158/1538-7445.AM2017-205

JUMPg: An Integrative Proteogenomics Pipeline Identifying Unannotated Proteins in Human Brain and Cancer Cells.

Proteogenomics is an emerging approach to improve gene annotation and interpretation of proteomics data. Here we present JUMPg, an integrative proteogenomics pipeline including customized database construction, tag-based database search, peptide-spectrum match filtering, and data visualization. JUMPg creates multiple databases of DNA polymorphisms, mutations, splice junctions, partially trypticity, as well as protein fragments translated from the whole transcriptome in all six frames upon RNA-seq de novo assembly. We use a multistage strategy to search these databases sequentially, in which the performance is optimized by re-searching only unmatched high-quality spectra and reusing amino acid tags generated by the JUMP search engine. The identified peptides/proteins are displayed with gene loci using the UCSC genome browser. Then, the JUMPg program is applied to process a label-free mass spectrometry data set of Alzheimer's disease postmortem brain, uncovering 496 new peptides of amino acid substitutions, alternative splicing, frame shift, and "non-coding gene" translation. The novel protein PNMA6BL specifically expressed in the brain is highlighted. We also tested JUMPg to analyze a stable-isotope labeled data set of multiple myeloma cells, revealing 991 sample-specific peptides that include protein sequences in the immunoglobulin light chain variable region. Thus, the JUMPg program is an effective proteogenomics tool for multiomics data integration.

MSProGene: integrative proteogenomics beyond six-frames and single nucleotide polymorphisms.

Summary: Ongoing advances in high-throughput technologies have facilitated accurate proteomic measurements and provide a wealth of information on genomic and transcript level. In proteogenomics, this multi-omics data is combined to analyze unannotated organisms and to allow more accurate sample-specific predictions. Existing analysis methods still mainly depend on six-frame translations or reference protein databases that are extended by transcriptomic information or known single nucleotide polymorphisms (SNPs). However, six-frames introduce an artificial sixfold increase of the target database and SNP integration requires a suitable database summarizing results from previous experiments. We overcome these limitations by introducing MSProGene, a new method for integrative proteogenomic analysis based on customized RNA-Seq driven transcript databases. MSProGene is independent from existing reference databases or annotated SNPs and avoids large six-frame translated databases by constructing sample-specific transcripts. In addition, it creates a network combining RNA-Seq and peptide information that is optimized by a maximum-flow algorithm. It thereby also allows resolving the ambiguity of shared peptides for protein inference. We applied MSProGene on three datasets and show that it facilitates a database-independent reliable yet accurate prediction on gene and protein level and additionally identifies novel genes.Availability and implementation: MSProGene is written in Java and Python. It is open source and available at http://sourceforge.net/projects/msprogene/.Contact: renardb@rki.de

Proteogenomic analysis reveals exosomes are more oncogenic than ectosomes.

Extracellular vesicles (EVs) include the exosomes (30-100 nm) that are produced through the endocytic pathway via the multivesicular bodies and the ectosomes (100-1000 nm) that are released through the budding of the plasma membrane. Despite the differences in the mode of biogenesis and size, reliable markers that can distinguish between exosomes and ectosomes are non-existent. Moreover, the precise functional differences between exosomes and ectosomes remains poorly characterised. Here, using label-free quantitative proteomics, we highlight proteins that could be exploited as markers to discriminate between exosomes and ectosomes. For the first time, a global proteogenomics analysis unveiled the secretion of mutant proteins that are implicated in cancer progression through tumor-derived EVs. Follow up integrated bioinformatics analysis highlighted the enrichment of oncogenic cargo in exosomes and ectosomes. Interestingly, exosomes induced significant cell proliferation and migration in recipient cells compared to ectosomes confirming the oncogenic nature of exosomes. These findings ascertain that cancer cells facilitate oncogenesis by the secretion of mutant and oncoproteins into the tumor microenvironment via exosomes and ectosomes. The integrative proteogenomics approach utilized in this study has the potential to identify disease biomarker candidates which can be later assayed in liquid biopsies obtained from cancer patients.

Revisiting the identification of canonical splice isoforms through integration of functional genomics and proteomics evidence.

Canonical isoforms in different databases have been defined as the most prevalent, most conserved, most expressed, longest, or the one with the clearest description of domains or posttranslational modifications. In this article, we revisit these definitions of canonical isoforms based on functional genomics and proteomics evidence, focusing on mouse data. We report a novel functional relationship network-based approach for identifying the highest connected isoforms (HCIs). We show that 46% of these HCIs are not the longest transcripts. In addition, this approach revealed many genes that have more than one highly connected isoforms. Averaged across 175 RNA-seq datasets covering diverse tissues and conditions, 65% of the HCIs show higher expression levels than nonhighest connected isoforms at the transcript level. At the protein level, these HCIs highly overlap with the expressed splice variants, based on proteomic data from eight different normal tissues. These results suggest that a more confident definition of canonical isoforms can be made through integration of multiple lines of evidence, including HCIs defined by biological processes and pathways, expression prevalence at the transcript level, and relative or absolute abundance at the protein level. This integrative proteogenomics approach can successfully identify principal isoforms that are responsible for the canonical functions of genes.