Rajasree Menon

Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics

BackgroundDefining the location of genes and the precise nature of gene products remains a fundamental challenge in genome annotation. Interrogating tandem mass spectrometry data using genomic sequence provides an unbiased method to identify novel translation products. A six-frame translation of the entire human genome was used as the query database to search for novel blood proteins in the data from the Human Proteome Organization Plasma Proteome Project. Because this target database is orders of magnitude larger than the databases traditionally employed in tandem mass spectra analysis, careful attention to significance testing is required. Confidence of identification is assessed using our previously described Poisson statistic, which estimates the significance of multi-peptide identifications incorporating the length of the matching sequence, number of spectra searched and size of the target sequence database.ResultsApplying a false discovery rate threshold of 0.05, we identified 282 significant open reading frames, each containing two or more peptide matches. There were 627 novel peptides associated with these open reading frames that mapped to a unique genomic coordinate placed within the start/stop points of previously annotated genes. These peptides matched 1,110 distinct tandem MS spectra. Peptides fell into four categories based upon where their genomic coordinates placed them relative to annotated exons within the parent gene.ConclusionThis work provides evidence for novel alternative splice variants in many previously annotated genes. These findings suggest that annotation of the genome is not yet complete and that proteomics has the potential to further add to our understanding of gene structures.

Identification of Novel Alternative Splice Isoforms of Circulating Proteins in a Mouse Model of Human Pancreatic Cancer

To assess the potential of tumor-associated, alternatively spliced gene products as a source of biomarkers in biological fluids, we have analyzed a large data set of mass spectra derived from the plasma proteome of a mouse model of human pancreatic ductal adenocarcinoma. MS/MS spectra were interrogated for novel splice isoforms using a nonredundant database containing an exhaustive three-frame translation of Ensembl transcripts and gene models from ECgene. This integrated analysis identified 420 distinct splice isoforms, of which 92 did not match any previously annotated mouse protein sequence. We chose seven of those novel variants for validation by reverse transcription-PCR. The results were concordant with the proteomic analysis. All seven novel peptides were successfully amplified in pancreas specimens from both wild-type and mutant mice. Isotopic labeling of cysteine-containing peptides from tumor-bearing mice and wild-type controls enabled relative quantification of the proteins. Differential expression between tumor-bearing and control mice was notable for peptides from novel variants of muscle pyruvate kinase, malate dehydrogenase 1, glyceraldehyde-3-phosphate dehydrogenase, proteoglycan 4, minichromosome maintenance, complex component 9, high mobility group box 2, and hepatocyte growth factor activator. Our results show that, in a mouse model for human pancreatic cancer, novel and differentially expressed alternative splice isoforms are detectable in plasma and may be a source of candidate biomarkers.

Proteomic Characterization of Novel Alternative Splice Variant Proteins in Human Epidermal Growth Factor Receptor 2/neu–Induced Breast Cancers

Multifaceted alternative splicing in cancer cells greatly diversifies protein structure independently of genome changes, but the characterization of cancer-associated splice variants is quite limited. In this study, we used mass spectrometric data to interrogate a custom-built database created with three-frame translations of mRNA sequences from Ensembl and ECgene to find alternative splice variant proteins. In mass spectrometric files from liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of normal mouse mammary glands or mammary tumors derived from conditional human epidermal growth factor receptor 2 (Her2)/neu transgenic mice, we identified a total of 608 alternative splice variants, of which peptides from 216 proteins were found only in the tumor sample. Among the 608 splice variants were 68 novel proteins that were not completely matched to any known protein sequence in mice, for which we found known functional motifs. Biological process enrichment analysis of the splice variants identified suggested the involvement of these proteins especially in cell motility and translation initiation. The cancer-associated differentially expressed splice variant proteins offer novel biomarker candidates that may function in breast cancer progression or metastasis.

The emerging era of genomic data integration for analyzing splice isoform function.

The vast majority of multi-exon genes in humans undergo alternative splicing, which greatly increases the functional diversity of protein species. Predicting functions at the isoform level is essential to further our understanding of developmental abnormalities and cancers, which frequently exhibit aberrant splicing and dysregulation of isoform expression. However, determination of isoform function is very difficult, and efforts to predict isoform function have been limited in the functional genomics field. Deep sequencing of RNA now provides an unprecedented amount of expression data at the transcript level. We describe here emerging computational approaches that integrate such large-scale whole-transcriptome sequencing (RNA-seq) data for predicting the functions of alternatively spliced isoforms, and we discuss their applications in developmental and cancer biology. We outline future directions for isoform function prediction, emphasizing the need for heterogeneous genomic data integration and tissue-specific, dynamic isoform-level network modeling, which will allow the field to realize its full potential.

Alternative splice variants, a new class of protein cancer biomarker candidates: Findings in pancreatic cancer and breast cancer with systems biology implications

Alternative splicing plays an important role in protein diversity without increasing genome size. Earlier thought to be uncommon, splicing appears to affect the majority of genes. Alternative splice variants have been detected at the mRNA level in many diseases. We have designed and demonstrated a discovery pipeline for alternative splice variant (ASV) proteins from tandem MS/MS datasets. We created a modified ECgene database with entries from exhaustive three-frame translation of Ensembl transcripts and gene models from ECgene, with periodic updates. The human database has 14 million entries; the mouse database, 10 million entries. We match MS/MS findings against these potential translation products to identify and quantify known and novel ASVs. In this review, we summarize findings and systems biology implications of biomarker candidates from a mouse model of human pancreatic ductal adenocarcinoma [28] and a mouse model of human Her2/neu-induced breast cancer [27]. The same approach is being applied to human tumors, plasma, and cell line studies of other cancers.

Functional implications of structural predictions for alternative splice proteins expressed in Her2/neu-induced breast cancers.

Alternative splicing allows a single gene to generate multiple mRNA transcripts, which can be translated into functionally diverse proteins. However, experimentally determined structures of protein splice isoforms are rare, and homology modeling methods are poor at predicting atomic-level structural differences because of high sequence identity. Here we exploit the state-of-the-art structure prediction method I-TASSER to analyze the structural and functional consequences of alternative splicing of proteins differentially expressed in a breast cancer model. We first successfully benchmarked the I-TASSER pipeline for structure modeling of all seven pairs of protein splice isoforms, which are known to have experimentally solved structures. We then modeled three cancer-related variant pairs reported to have opposite functions. In each pair, we observed structural differences in regions where the presence or absence of a motif can directly influence the distinctive functions of the variants. Finally, we applied the method to five splice variants overexpressed in mouse Her2/neu mammary tumor: anxa6, calu, cdc42, ptbp1, and tax1bp3. Despite >75% sequence identity between the variants, structural differences were observed in biologically important regions of these protein pairs. These results demonstrate the feasibility of integrating proteomic analysis with structure-based conformational predictions of differentially expressed alternative splice variants in cancers and other conditions.

Systematically Differentiating Functions for Alternatively Spliced Isoforms through Integrating RNA-seq Data

Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires ‘ground-truth’ functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the ‘responsible’ isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the ‘responsible’ isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.

A mouse plasma peptide atlas as a resource for disease proteomics

We present an in-depth analysis of mouse plasma leading to the development of a publicly available repository composed of 568 liquid chromatography-tandem mass spectrometry runs. A total of 13,779 distinct peptides have been identified with high confidence. The corresponding approximately 3,000 proteins are estimated to span a 7 logarithmic range of abundance in plasma. A major finding from this study is the identification of novel isoforms and transcript variants not previously predicted from genome analysis.

A new class of protein cancer biomarker candidates: Differentially expressed splice variants of ERBB2 (HER2/neu) and ERBB1 (EGFR) in breast cancer cell lines ☆

Combined RNA-Seq and proteomics analyses reveal striking differential expression of splice isoforms of key proteins in important cancer pathways and networks. Even between primary tumor cell lines from histologically similar inflammatory breast cancers, we find striking differences in hormone receptor-negative cell lines that are ERBB2 (Her2/neu)-amplified versus ERBB1 (EGFR) over-expressed with low ERBB2 activity. We have related these findings to protein-protein interaction networks, signaling and metabolic pathways, and methods for predicting functional variants among multiple alternative isoforms. Understanding the upstream ligands and regulators and the downstream pathways and interaction networks for ERBB receptors is certain to be important for explanation and prediction of the variable levels of expression and therapeutic responses of ERBB+tumors in the breast and in other organ sites. Alternative splicing is a remarkable evolutionary development that increases protein diversity from multi-exonic genes without requiring expansion of the genome. It is no longer sufficient to report the up- or down-expression of genes and proteins without dissecting the complexity due to alternative splicing. This article is part of a Special Issue entitled: 20Years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini , Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Innovations in proteomic profiling of cancers: alternative splice variants as a new class of cancer biomarker candidates and bridging of proteomics with structural biology.

Alternative splicing allows a single gene to generate multiple RNA transcripts which can be translated into functionally diverse protein isoforms. Current knowledge of splicing is derived mainly from RNA transcripts, with very little known about the expression level, 3D structures, and functional differences of the proteins. Splicing is a remarkable phenomenon of molecular and biological evolution. Studies which simply report up-regulation or down-regulation of protein or mRNA expression are confounded by the effects of mixtures of these isoforms. Besides understanding the net biological effects of the mixtures, we may be able to develop biomarker tests based on the observable differential expression of particular splice variants or combinations of splice variants in specific disease states. Here we review our work on differential expression of splice variant proteins in cancers and the feasibility of integrating proteomic analysis with structure-based conformational predictions of the differences between such isoforms.