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

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Prognostic value and potential function of splicing events in prostate adenocarcinoma

Prostate adenocarcinoma (PRAD) is one of the most common types of malignancy in males and at present, effective prognostic indicators are limited. The development of PRAD has been associated with abnormalities in alternative splicing (AS), a requisite biological process of gene expression in eukaryotic cells; however, the prognostic value of AS products and splicing events remains to be elucidated. In the present study, the data of splicing events and the clinical information of PRAD patients were obtained from The Cancer Genome Atlas (TCGA)SpliceSeq and TCGA databases, respectively. A prognostic index (PI) was generated from disease-free survival-associated splicing events (DFS-SEs), which were identified by univariate/multivariate Cox regression analysis. A total of 6,909 DFS-SEs were identified in PRAD. The corresponding genes for the DFS-SEs were significantly enriched in mitochondria and their associated pathways according to Gene Ontology annotation and in the pathways of fatty acid metabolism, oxidative phosphorylation and Huntington's disease according to a Kyoto Encyclopedia of Genes and Genomes pathway analysis. The PI for mutually exclusive exons had the greatest ability to predict the probability of five-year disease-free survival of patients with PRAD, with an area under the time-dependent receiver-operating characteristic curve of 0.7606. Patients with PRAD, when divided into a 'low' and a 'high' group based on their median PI for exon skip values, exhibited a marked difference in disease-free survival (low vs. high, 3,588.45±250.51 vs. 1,531.08±136.50 days; P=7.43×10⁻⁹). A correlation network between DFS-SEs of splicing factors and non-splicing factors was constructed to determine the potential mechanisms in PRAD, which included the potential regulatory interaction between the splicing event of splicing factor RNA binding motif protein 5-alternate terminator (AT)-64957 and the splicing event of non-splicing factor heterochromatin protein 1 binding protein 3-AT-939. In conclusion, the PIs derived from DFS-SEs are valuable prognostic factors for patients with PRAD, and the function of splicing events in PRAD deserves further exploration.

Nano-delivery system targeting to cancer stem cell cluster of differentiation biomarkers

Cancer stem cells (CSCs) are one of the most important origins of cancer progression and metastasis. CSCs have unique self-renewal properties and diverse cell membrane receptors that induced the resistance to the conventional chemotherapeutic agents. Therefore, the therapeutic removal of CSCs could result in the cancer cure with lack of recurrence and metastasis. In this regard, targeting CSCs in accordance to their specific biomarkers is a talented attitude in cancer therapy. Various CSCs surface biomarkers have been described, which some of them exhibited similarities on different cancer cell types, while the others are cancer specific and have just been reported on one or a few types of cancers. In this review, the importance of CSCs in cancer development and therapeutic response has been stated. Different CSCs cluster of differentiation (CD) biomarkers and their specific function and applications in the treatment of cancers have been discussed, Special attention has been made on targeted nano-delivery systems. In this regard, several examples have been illustrated concerning specific natural and artificial ligands against CSCs CD biomarkers that could be decorated on various nanoparticulated drug delivery systems to enhance therapeutic index of chemotherapeutic agents or anticancer gene therapy. The outlook of CSCs biomarkers discovery and therapeutic/diagnostic applications was discussed.

Identification of Alternative Splice Variants Using Unique Tryptic Peptide Sequences for Database Searches

Alternative splicing is a mechanism in eukaryotes by which different forms of mRNAs are generated from the same gene. Identification of alternative splice variants requires the identification of peptides specific for alternative splice forms. For this purpose, we generated a human database that contains only unique tryptic peptides specific for alternative splice forms from Swiss-Prot entries. Using this database allows an easy access to splice variant-specific peptide sequences that match to MS data. Furthermore, we combined this database without alternative splice variant-1-specific peptides with human Swiss-Prot. This combined database can be used as a general database for searching of LC-MS data. LC-MS data derived from in-solution digests of two different cell lines (LNCaP, HeLa) and phosphoproteomics studies were analyzed using these two databases. Several nonalternative splice variant-1-specific peptides were found in both cell lines, and some of them seemed to be cell-line-specific. Control and apoptotic phosphoproteomes from Jurkat T cells revealed several nonalternative splice variant-1-specific peptides, and some of them showed clear quantitative differences between the two states.

Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks

Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.

A Network of Splice Isoforms for the Mouse

The laboratory mouse is the primary mammalian species used for studying alternative splicing events. Recent studies have generated computational models to predict functions for splice isoforms in the mouse. However, the functional relationship network, describing the probability of splice isoforms participating in the same biological process or pathway, has not yet been studied in the mouse. Here we describe a rich genome-wide resource of mouse networks at the isoform level, which was generated using a unique framework that was originally developed to infer isoform functions. This network was built through integrating heterogeneous genomic and protein data, including RNA-seq, exon array, protein docking and pseudo-amino acid composition. Through simulation and cross-validation studies, we demonstrated the accuracy of the algorithm in predicting isoform-level functional relationships. We showed that this network enables the users to reveal functional differences of the isoforms of the same gene, as illustrated by literature evidence with Anxa6 (annexin a6) as an example. We expect this work will become a useful resource for the mouse genetics community to understand gene functions. The network is publicly available at: http://guanlab.ccmb.med.umich.edu/isoformnetwork.

A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling

The products of multi-exon genes are a mixture of alternatively spliced isoforms, from which the translated proteins can have similar, different or even opposing functions. It is therefore essential to differentiate and annotate functions for individual isoforms. Computational approaches provide an efficient complement to expensive and time-consuming experimental studies. The input data of these methods range from DNA sequence, to RNA selection pressure, to expressed sequence tags, to full-length complementary DNA, to exon array, to RNA-seq expression, to proteomic data. Notably, RNA-seq technology generates quantitative profiling of transcript expression at the genome scale, with an unprecedented amount of expression data available for developing isoform function prediction methods. Integrative analysis of these data at different molecular levels enables a proteogenomic approach to systematically interrogate isoform functions. Here, we briefly review the state-of-the-art methods according to their input data sources, discuss their advantages and limitations and point out potential ways to improve prediction accuracies.

Rescuing discarded spectra: Full comprehensive analysis of a minimal proteome

A common problem encountered when performing large-scale MS proteome analysis is the loss of information due to the high percentage of unassigned spectra. To determine the causes behind this loss we have analyzed the proteome of one of the smallest living bacteria that can be grown axenically, Mycoplasma pneumoniae (729 ORFs). The proteome of M. pneumoniae cells, grown in defined media, was analyzed by MS. An initial search with both Mascot and a species-specific NCBInr database with common contaminants (NCBImpn), resulted in around 79% of the acquired spectra not having an assignment. The percentage of non-assigned spectra was reduced to 27% after re-analysis of the data with the PEAKS software, thereby increasing the proteome coverage of M. pneumoniae from the initial 60% to over 76%. Nonetheless, 33,413 spectra with assigned amino acid sequences could not be mapped to any NCBInr database protein sequence. Approximately, 1% of these unassigned peptides corresponded to PTMs and 4% to M. pneumoniae protein variants (deamidation and translation inaccuracies). The most abundant peptide sequence variants (Phe-Tyr and Ala-Ser) could be explained by alterations in the editing capacity of the corresponding tRNA synthases. About another 1% of the peptides not associated to any protein had repetitions of the same aromatic/hydrophobic amino acid at the N-terminus, or had Arg/Lys at the C-terminus. Thus, in a model system, we have maximized the number of assigned spectra to 73% (51,453 out of the 70,040 initial acquired spectra). All MS data have been deposited in the ProteomeXchange with identifier PXD002779 (http://proteomecentral.proteomexchange.org/dataset/PXD002779).

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project

Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as "dominant", "principal", or "major" isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

Research progress in applying proteomics technology to explore early diagnosis biomarkers of breast cancer, lung cancer and ovarian cancer

According to the China tumor registry 2013 annual report , breast cancer, lung cancer, and ovarian cancer are three common cancers in China nowadays, with high mortality due to the absence of early diagnosis technology. However, proteomics has been widespreadly implanted into every field of life science and medicine as an important part of post-genomics era research. The development of theory and technology in proteomics has provided new ideas and research fields for cancer research. Proteomics can be used not only for elucidating the mechanisms of carcinogenesis focussing on whole proteins of the tissue or cell, but also seeking the biomarkers for diagnosis and therapy of cancer. In this review, we introduce proteomics principles, covering current technology used in exploring early diagnosis biomarkers of breast cancer, lung cancer and ovarian cancer.

Highlights of B/D-HPP and HPP Resource Pillar Workshops at 12th Annual HUPO World Congress of Proteomics: September 14-18, 2013, Yokohama, Japan

At the 12th Annual HUPO World Congress of Proteomics in Japan, the Human Proteome Project (HPP) presented 16 scientific workshop sessions. Here we summarize highlights of ten workshops from the Biology and Disease-driven HPP (B/D-HPP) teams and three from the HPP Resource Pillars. Highlights of the three Chromosome-centric HPP sessions appeared in the many articles of the 2014 C-HPP special issue of the Journal of Proteome Research .

Profiling proteoforms: promising follow-up of proteomics for biomarker discovery

Today, proteomics usually compares clinical samples by use of bottom-up profiling with high resolution mass spectrometry, where all protein products of a single gene are considered as an integral whole. At the same time, proteomics of proteoforms, which considers the variety of protein species, offers the potential to discover valuable biomarkers. Proteoforms are protein species that arise as a consequence of genetic polymorphisms, alternative splicing, post-translational modifications and other less-explored molecular events. The comprehensive observation of proteoforms has been an exclusive privilege of top-down proteomics. Here, we review the possibilities of a bottom-up approach to address the microheterogeneity of the human proteome. Special focus is given to shotgun proteomics and structure-based bioinformatics as a source of hypothetical proteoforms, which can potentially be verified by targeted mass spectrometry to determine the relevance of proteoforms to diseases.

Expression profile of ZFX isoform3/variant 5 in gastric cancer tissues and its association with tumor size

OBJECTIVES

Previous studies demonstrate that changes in pre-mRNA splicing play a significant role in human disease development. Furthermore, many cancer-associated genes are regulated by alternative splicing. There are mounting evidences that splice variants which express predominantly in tumors, have clear diagnostic value and may provide potential drug targets. Located on the X chromosome, ZFX gene functions as a transcription regulator for self-renewal of stem cells. This gene has 5 splice variants that encode 3 isoforms. In the present study, we evaluated the clinicopathological relevance of the expression of ZFX isoform 3/variant 5 gene in gastric carcinoma.

MATERIALS AND METHODS

A total of 60 tumoral and non-tumoral gastric specimens were evaluated for ZFX isoform 3/variant 5 gene expression using quantitative real-time PCR.

RESULTS

Our results showed that the expression of ZFX isoform 3/variant 5 transcript was heterogeneous in gastric specimens. We further showed that there was a positive correlation between the variant expression and tumor size, but not with other clinicopathological features of gastric tumors.

CONCLUSION

This report shows that the expression of ZFX isoform 3/variant 5 transcript was heterogeneous in gastric specimens. Furthermore, there was no significant association between ZFX isoform 3/variant 5 expression and most of clinicopathological features of gastric tumors except for a positive correlation with tumor size. The elucidation of the precise molecular mechanisms governed by the ZFX isoforms/variants needs further investigation.

Colorectal cancer biomarker discovery and validation using LC-MS/MS-based proteomics in blood: truth or dare?

Globally, colorectal cancer (CRC) is the third most common malignant neoplasm. However, highly sensitive, specific, noninvasive tests that allow CRC diagnosis at an early stage are still needed. As circulatory blood reflects the physiological status of an individual and/or the disease status for several disorders, efforts have been undertaken to identify candidate diagnostic CRC markers in plasma and serum. In this review, the challenges, bottlenecks and promising properties of mass spectrometry (MS)-based proteomics in blood are discussed. More specifically, important aspects in clinical design, sample retrieval, sample preparation, and MS analysis are presented. The recent developments in targeted MS approaches in plasma or serum are highlighted as well.

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.

Deep sequencing reveals new aspects of progesterone receptor signaling in breast cancer cells

Despite the pleiotropic effects of the progesterone receptor in breast cancer, the molecular mechanisms in play remain largely unknown. To gain a global view of the PR-orchestrated networks, we used next-generation sequencing to determine the progestin-regulated transcriptome in T47D breast cancer cells. We identify a large number of PR target genes involved in critical cellular programs, such as regulation of transcription, apoptosis, cell motion and angiogenesis. Integration of the transcriptomic data with the PR-binding profiling of hormonally treated cells identifies numerous components of the small-GTPases signaling pathways as direct PR targets. Progestin-induced deregulation of the small GTPases may contribute to the PR's role in mammary tumorigenesis. Transcript expression analysis reveals significant expression changes of specific transcript variants in response to the extracellular hormonal stimulus. Using the NET1 gene as an example, we show that the PR can dictate alternative promoter usage leading to the upregulation of an isoform that may play a role in metastatic breast cancer. Future studies should aim to characterize these selectively regulated variants and evaluate their clinical utility in prognosis and targeted therapy of hormonally responsive breast tumors.

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.

Human proteins with target sites of multiple post-translational modification types are more prone to be involved in disease

Many proteins can be modified by multiple types of post-translational modifications (Mtp-proteins). Although some post-translational modifications (PTMs) have recently been found to be associated with life-threatening diseases like cancers and neurodegenerative disorders, the underlying mechanisms remain enigmatic to date. In this study, we examined the relationship of human Mtp-proteins and disease and systematically characterized features of these proteins. Our results indicated that Mtp-proteins are significantly more inclined to participate in disease than proteins carrying no known PTM sites. Mtp-proteins were found significantly enriched in protein complexes, having more protein partners and preferred to act as hubs/superhubs in protein-protein interaction (PPI) networks. They possess a distinct functional focus, such as chromatin assembly or disassembly, and reside in biased, multiple subcellular localizations. Moreover, most Mtp-proteins harbor more intrinsically disordered regions than the others. Mtp-proteins carrying PTM types biased toward locating in the ordered regions were mainly related to protein-DNA complex assembly. Examination of the energetic effects of PTMs on the stability of PPI revealed that only a small fraction of single PTM events influence the binding energy of >2 kcal/mol, whereas the binding energy can change dramatically by combinations of multiple PTM types. Our work not only expands the understanding of Mtp-proteins but also discloses the potential ability of Mtp-proteins to act as key elements in disease development.

Plasma proteomics, the Human Proteome Project, and cancer-associated alternative splice variant proteins

This article addresses three inter-related subjects: the development of the Human Plasma Proteome Peptide Atlas, the launch of the Human Proteome Project, and the emergence of alternative splice variant transcripts and proteins as important features of evolution and pathogenesis. The current Plasma Peptide Atlas provides evidence on which peptides have been detected for every protein confidently identified in plasma; there are links to their spectra and their estimated abundance, facilitating the planning of targeted proteomics for biomarker studies. The Human Proteome Project (HPP) combines a chromosome-centric C-HPP with a biology and disease-driven B/D-HPP, upon a foundation of mass spectrometry, antibody, and knowledgebase resource pillars. The HPP aims to identify the approximately 7000 "missing proteins" and to characterize all proteins and their many isoforms. Success will enable the larger research community to utilize newly-available peptides, spectra, informative MS transitions, and databases for targeted analyses of priority proteins for each organ and disease. Among the isoforms of proteins, splice variants have the special feature of greatly enlarging protein diversity without enlarging the genome; evidence is accumulating of striking differential expression of splice variants in cancers. In this era of RNA-sequencing and advanced mass spectrometry, it is no longer sufficient to speak simply of increased or decreased expression of genes or proteins without carefully examining the splice variants in the protein mixture produced from each multi-exon gene. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

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.

In mammalian genomes, a single gene can be alternatively spliced into multiple isoforms which greatly increase the functional diversity of the genome. In the human, more than 95% of multi-exon genes undergo alternative splicing. It is hard to computationally differentiate the functions for the splice isoforms of the same gene, because they are almost always annotated with the same functions and share similar sequences. In this paper, we developed a generic framework to identify the ‘responsible’ isoform(s) for each function that the gene carries out, and therefore predict functional assignment on the isoform level instead of on the gene level. Within this generic framework, we implemented and evaluated several related algorithms for isoform function prediction. We tested these algorithms through both computational evaluation and experimental validation of the predicted ‘responsible’ isoform(s) and the predicted disparate functions of the isoforms of Cdkn2a and of Anxa6. Our algorithm represents the first effort to predict and differentiate isoforms through large-scale genomic data integration.

The strategy, organization, and progress of the HUPO Human Proteome Project

The Human Proteome Project is a major, comprehensive initiative of the Human Proteome Organization. This global collaborative effort aims to identify and characterize at least one protein product and many PTM, SAP, and splice variant isoforms from the 20,300 human protein-coding genes. The deliverables are an extensive parts list and an array of technology platforms, reagents, spectral libraries, and linked knowledge bases that advance the field and facilitate the use of proteomics by a much wider community of life scientists. Such enablement will help address the Grand Challenge of using proteomics to bridge major gaps between evidence of genomic variation and diverse phenotypes.

BIOLOGICAL SIGNIFICANCE

The HUPO Human Proteome Project (HPP) has made an outstanding launch, including a special issue of the Journal of Proteome Research on the Chromosome-centric HPP with a total of 48 articles. This article is part of a Special Issue: Can Proteomics Fill the Gap Between Genomics and Phenotypes?