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

Concordant Gene Expression and Alternative Splicing Regulation under Abiotic Stresses in Arabidopsis

The current investigation endeavors to identify differentially expressed alternatively spliced (DAS) genes that exhibit concordant expression with splicing factors (SFs) under diverse multifactorial abiotic stress combinations in Arabidopsis seedlings. SFs serve as the post-transcriptional mechanism governing the spatiotemporal dynamics of gene expression. The different stresses encompass variations in salt concentration, heat, intensive light, and their combinations. Clusters demonstrating consistent expression profiles were surveyed to pinpoint DAS/SF gene pairs exhibiting concordant expression. Through rigorous selection criteria, which incorporate alignment with documented gene functionalities and expression patterns observed in this study, four members of the serine/arginine-rich (SR) gene family were delineated as SFs concordantly expressed with six DAS genes. These regulated SF genes encompass cactin, SR1-like, SR30, and SC35-like. The identified concordantly expressed DAS genes encode diverse proteins such as the 26.5 kDa heat shock protein, chaperone protein DnaJ, potassium channel GORK, calcium-binding EF hand family protein, DEAD-box RNA helicase, and 1-aminocyclopropane-1-carboxylate synthase 6. Among the concordantly expressed DAS/SF gene pairs, SR30/DEAD-box RNA helicase, and SC35-like/1-aminocyclopropane-1-carboxylate synthase 6 emerge as promising candidates, necessitating further examinations to ascertain whether these SFs orchestrate splicing of the respective DAS genes. This study contributes to a deeper comprehension of the varied responses of the splicing machinery to abiotic stresses. Leveraging these DAS/SF associations shows promise for elucidating avenues for augmenting breeding programs aimed at fortifying cultivated plants against heat and intensive light stresses.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Structural basis of the selective activation of enzyme isoforms: Allosteric response to activators of β1- and β2-containing AMPK complexes

AMP-activated protein kinase (AMPK) is a key energy sensor regulating the cell metabolism in response to energy supply and demand. The evolutionary adaptation of AMPK to different tissues is accomplished through the expression of distinct isoforms that can form up to 12 complexes, which exhibit notable differences in the sensitivity to allosteric activators. To shed light into the molecular determinants of the allosteric regulation of this energy sensor, we have examined the structural and dynamical properties of β1- and β2-containing AMPK complexes formed with small molecule activators A-769662 and SC4, and dissected the mechanical response leading to active-like enzyme conformations through the analysis of interaction networks between structural domains. The results reveal the mechanical sensitivity of the α2β1 complex, in contrast with a larger resilience of the α2β2 species, especially regarding modulation by A-769662. Furthermore, binding of activators to α2β1 consistently promotes the pre-organization of the ATP-binding site, favoring the adoption of activated states of the enzyme. These findings are discussed in light of the changes in the residue content of β-subunit isoforms, particularly regarding the β1Asn111 → β2Asp111 substitution as a key factor in modulating the mechanical sensitivity of β1- and β2-containing AMPK complexes. Our studies pave the way for the design of activators tailored for improving the therapeutic treatment of tissue-specific metabolic disorders.

IsoResolve: predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation

MOTIVATION

High resolution annotation of gene functions is a central goal in functional genomics. A single gene may produce multiple isoforms with different functions through alternative splicing. Conventional approaches, however, consider a gene as a single entity without differentiating these functionally different isoforms. Towards understanding gene functions at higher resolution, recent efforts have focused on predicting the functions of isoforms. However, the performance of existing methods is far from satisfactory mainly because of the lack of isoform-level functional annotation.

RESULTS

We present IsoResolve, a novel approach for isoform function prediction, which leverages the information from gene function prediction models with domain adaptation (DA). IsoResolve treats gene-level and isoform-level features as source and target domains, respectively. It uses DA to project the two domains into a latent variable space in such a way that the latent variables from the two domains have similar distribution, which enables the gene domain information to be leveraged for isoform function prediction. We systematically evaluated the performance of IsoResolve in predicting functions. Compared with five state-of-the-art methods, IsoResolve achieved significantly better performance. IsoResolve was further validated by case studies of genes with isoform-level functional annotation.

AVAILABILITY AND IMPLEMENTATION

IsoResolve is freely available at https://github.com/genemine/IsoResolve.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Reflections on the HUPO Human Proteome Project, the Flagship Project of the Human Proteome Organization, at 10 Years

We celebrate the 10th anniversary of the launch of the HUPO Human Proteome Project (HPP) and its major milestone of confident detection of at least one protein from each of 90% of the predicted protein-coding genes, based on the output of the entire proteomics community. The Human Genome Project reached a similar decadal milestone 20 years ago. The HPP has engaged proteomics teams around the world, strongly influenced data-sharing, enhanced quality assurance, and issued stringent guidelines for claims of detecting previously "missing proteins." This invited perspective complements papers on "A High-Stringency Blueprint of the Human Proteome" and "The Human Proteome Reaches a Major Milestone" in special issues of Nature Communications and Journal of Proteome Research, respectively, released in conjunction with the October 2020 virtual HUPO Congress and its celebration of the 10th anniversary of the HUPO HPP.

Computational Methods for Predicting Functions at the mRNA Isoform Level

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.

A bioinformatics workflow for the evaluation of RT-qPCR primer specificity: Application for the assessment of gene expression data reliability in toxicological studies

The reliability of Reverse Transcription quantitative real-time PCR (RT-qPCR) gene expression data depends on proper primer design and RNA quality controls. Despite freely available genomic databases and bioinformatics tools, primer design deficiencies can be found across life science publications. In order to assess the prevalence of such deficiencies in the toxicological literature, 504 primer sets extracted from a random selection of 70 recent rat toxicological studies were evaluated. The specificity of each primer set was systematically analysed using a bioinformatics workflow developed from publicly available resources (NCBI Primer BLAST, in silico PCR in UCSC genome browser, Ensembl DNA database). Potential mismatches (9%), cross-matches (13.5%), co-amplification of multiple gene splice variants (9%) and sub-optimal amplicon sizes (25%) were identified for a significant proportion of the primer sets assessed in silico. Quality controls for gDNA contamination of RNA samples were infrequently reported in the surveyed manuscripts. Hence, the impacts of gDNA contamination on RT-qPCR data were further investigated, revealing that lowly expressed genes presented higher susceptibility to contaminating gDNA. In addition to the retrospective identification of potential primer design issues presented in this study, the described bioinformatics workflow can also be used prospectively to select candidate primer sets for experimental validation.

Comprehensive landscape of subtype-specific coding and non-coding RNA transcripts in breast cancer

Molecular classification of breast cancer into clinically relevant subtypes helps improve prognosis and adjuvant-treatment decisions. The aim of this study is to provide a better characterization of the molecular subtypes by providing a comprehensive landscape of subtype-specific isoforms including coding, long non-coding RNA and microRNA transcripts. Isoform-level expression of all coding and non-coding RNAs is estimated from RNA-sequence data of 1168 breast samples obtained from The Cancer Genome Atlas (TCGA) project. We then search the whole transcriptome systematically for subtype-specific isoforms using a novel algorithm based on a robust quasi-Poisson model. We discover 5451 isoforms specific to single subtypes. A total of 27% of the subtype-specific isoforms have better accuracy in classifying the intrinsic subtypes than that of their corresponding genes. We find three subtype-specific miRNA and 707 subtype-specific long non-coding RNAs. The isoforms from long non-coding RNAs also show high performance for separation between Luminal A and Luminal B subtypes with an AUC of 0.97 in the discovery set and 0.90 in the validation set. In addition, we discover 1500 isoforms preferentially co-expressed in two subtypes, including 369 isoforms co-expressed in both Normal-like and Basal subtypes, which are commonly considered to have distinct ER-receptor status. Finally, analyses at protein level reveal four subtype-specific proteins and two subtype co-expression proteins that successfully validate results from the isoform level.

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.

Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications

The HUPO Human Proteome Project (HPP) has two overall goals: (1) stepwise completion of the protein parts list-the draft human proteome including confidently identifying and characterizing at least one protein product from each protein-coding gene, with increasing emphasis on sequence variants, post-translational modifications (PTMs), and splice isoforms of those proteins; and (2) making proteomics an integrated counterpart to genomics throughout the biomedical and life sciences community. PeptideAtlas and GPMDB reanalyze all major human mass spectrometry data sets available through ProteomeXchange with standardized protocols and stringent quality filters; neXtProt curates and integrates mass spectrometry and other findings to present the most up to date authorative compendium of the human proteome. The HPP Guidelines for Mass Spectrometry Data Interpretation version 2.1 were applied to manuscripts submitted for this 2016 C-HPP-led special issue [ www.thehpp.org/guidelines ]. The Human Proteome presented as neXtProt version 2016-02 has 16,518 confident protein identifications (Protein Existence [PE] Level 1), up from 13,664 at 2012-12, 15,646 at 2013-09, and 16,491 at 2014-10. There are 485 proteins that would have been PE1 under the Guidelines v1.0 from 2012 but now have insufficient evidence due to the agreed-upon more stringent Guidelines v2.0 to reduce false positives. neXtProt and PeptideAtlas now both require two non-nested, uniquely mapping (proteotypic) peptides of at least 9 aa in length. There are 2,949 missing proteins (PE2+3+4) as the baseline for submissions for this fourth annual C-HPP special issue of Journal of Proteome Research. PeptideAtlas has 14,629 canonical (plus 1187 uncertain and 1755 redundant) entries. GPMDB has 16,190 EC4 entries, and the Human Protein Atlas has 10,475 entries with supportive evidence. neXtProt, PeptideAtlas, and GPMDB are rich resources of information about post-translational modifications (PTMs), single amino acid variants (SAAVSs), and splice isoforms. Meanwhile, the Biology- and Disease-driven (B/D)-HPP has created comprehensive SRM resources, generated popular protein lists to guide targeted proteomics assays for specific diseases, and launched an Early Career Researchers initiative.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

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.

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).

Computational Inferences of the Functions of Alternative/Noncanonical Splice Isoforms Specific to HER2+/ER-/PR- Breast Cancers, a Chromosome 17 C-HPP Study

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The main objective is to identify and evaluate functionality of a set of specific noncanonical isoforms expressed in HER2-neu positive, estrogen receptor negative (ER-), and progesterone receptor negative (PR-) breast cancers (HER2+/ER-/PR- BC), an aggressive subtype of breast cancers that cause significant morbidity and mortality. We identified 11 alternative splice isoforms that were differentially expressed in HER2+/ER-/PR- BC compared to normal mammary, triple negative breast cancer and triple positive breast cancer tissues (HER2+/ER+/PR+). We used a stringent criterion that differentially expressed noncanonical isoforms (adjusted p value < 0.05) and have to be expressed in all replicates of HER2+/ER-/PR- BC samples, and the trend in differential expression (up or down) is the same in all comparisons. Of the 11 protein isoforms, six were overexpressed in HER2+/ER-/PR- BC. We explored possible functional roles of these six proteins using several complementary computational tools. Biological processes including cell cycle events and glycolysis were linked to four of these proteins. For example, glycolysis was the top ranking functional process for DMXL2 isoform 3, with a fold change of 27 compared to just two for the canonical protein. No previous reports link DMXL2 with any metabolic processes; the canonical protein is known to participate in signaling pathways. Our results clearly indicate distinct functions for the six overexpressed alternative splice isoforms, and these functions could be specific to HER2+/ER-/PR- tumor progression. Further detailed analysis is warranted as these proteins could be explored as potential biomarkers and therapeutic targets for HER2+/ER-/PR- BC patients.

Perinatal methylmercury exposure perturbs the expression of Plp1 and Cnp splice variants in cerebellum of rat pups

Early life exposure to environmental chemicals can interfere with myelin formation in the developing brain, leading to neurological disorders. The Proteolipid Protein 1 (Plp1), Myelin Basic Protein (Mbp) and 2',3'-Cyclic Nucleotide 3'Phosphodiesterase (Cnp) genes expressed in oligodendrocytes and involved in myelination processes can be useful biomarkers of potential developmental neurotoxicity. In an earlier study, we concluded that the reduction in the expression levels of Mbp splice variants in juvenile rat cerebellum following perinatal methylmercury (MeHg) exposure were compatible with an overall reduction of mature oligodendrocytes population. This observation prompted us to analyze the expression of Plp1 and Cnp in developing rat cerebellum to further confirm and investigate the toxic effects of MeHg on vulnerable oligodendrocytes. Splice variants of Plp1 in human and of Cnp in mouse are curated in NCBI RefSeq database, but not for rat. Lack of annotation of splice variants can pose significant challenge for the reliable quantification of gene expression levels in toxicological studies. Therefore, we applied a "comparative sequence analysis" approach, relying on annotated splice variants in human/mouse and on evolutionary conservation of intron-exon structures, to identify additional splice variants of Plp1 and Cnp in rat. Then, we confirmed their identity by nucleotide sequencing and characterized their temporal expression patterns during brain development by RT-PCR. The measurement of total transcripts and individual splice variants of Plp1 and Cnp in the cerebellum of MeHg-exposed rat pups revealed a relatively similar level of reduction in their expression levels. This study further confirms that perinatal exposure to MeHg can impact oligodendrocytes in pups. Based on these observations, we conclude that monitoring the expression of these oligodendrocyte-enriched genes can be useful to identify toxic chemicals affecting myelination.

Mapping Splicing Quantitative Trait Loci in RNA-Seq

BACKGROUND

One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins that drive the progression of cancer. A better understanding of the misregulation of alternative splicing will shed light on the development of novel targets for pharmacological interventions of cancer.

METHODS

In this study, we evaluated three statistical methods, random effects meta-regression, beta regression, and generalized linear mixed effects model, for the analysis of splicing quantitative trait loci (sQTL) using RNA-Seq data. All the three methods use exon-inclusion levels estimated by the PennSeq algorithm, a statistical method that utilizes paired-end reads and accounts for non-uniform sequencing coverage.

RESULTS

Using both simulated and real RNA-Seq datasets, we compared these three methods with GLiMMPS, a recently developed method for sQTL analysis. Our results indicate that the most reliable and powerful method was the random effects meta-regression approach, which identified sQTLs at low false discovery rates but higher power when compared to GLiMMPS.

CONCLUSIONS

We have evaluated three statistical methods for the analysis of sQTLs in RNA-Seq. Results from our study will be instructive for researchers in selecting the appropriate statistical methods for sQTL analysis.

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.

Mapping Splicing Quantitative Trait Loci in RNA-Seq

BACKGROUND

One of the major mechanisms of generating mRNA diversity is alternative splicing, a regulated process that allows for the flexibility of producing functionally different proteins from the same genomic sequences. This process is often altered in cancer cells to produce aberrant proteins that drive the progression of cancer. A better understanding of the misregulation of alternative splicing will shed light on the development of novel targets for pharmacological interventions of cancer.

METHODS

In this study, we evaluated three statistical methods, random effects meta-regression, beta regression, and generalized linear mixed effects model, for the analysis of splicing quantitative trait loci (sQTL) using RNA-Seq data. All the three methods use exon-inclusion levels estimated by the PennSeq algorithm, a statistical method that utilizes paired-end reads and accounts for non-uniform sequencing coverage.

RESULTS

Using both simulated and real RNA-Seq datasets, we compared these three methods with GLiMMPS, a recently developed method for sQTL analysis. Our results indicate that the most reliable and powerful method was the random effects meta-regression approach, which identified sQTLs at low false discovery rates but higher power when compared to GLiMMPS.

CONCLUSIONS

We have evaluated three statistical methods for the analysis of sQTLs in RNA-Seq. Results from our study will be instructive for researchers in selecting the appropriate statistical methods for sQTL analysis.

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.

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.

Strategies for Genomic and Proteomic Profiling of Cancers

Omics-based technology platforms have made new kinds of cancer profiling tests feasible. There are several valuable examples in clinical practice, and many more under development. A concerted, transparent process of discovery with lock-down of candidate assays and classifiers and clear specification of intended clinical use is essential. The Institute of Medicine has now proposed a three-stage scheme of confirming and validating analytical findings, validating performance on clinical specimens, and demonstrating explicit clinical utility for an approvable test (Micheel et al., Evolution of translational omics: lessons learned and path forward, 2012).

Resolving chromosome-centric human proteome with translating mRNA analysis: a strategic demonstration

Chromosome-centric human proteome project (C-HPP) aims at differentiating chromosome-based and tissue-specific protein compositions in terms of protein expression, quantification, and modification. We previously found that the analysis of translating mRNA (mRNA attached to ribosome-nascent chain complex, RNC-mRNA) can explain over 94% of mRNA-protein abundance. Therefore, we propose here to use full-length RNC-mRNA information to illustrate protein expression both qualitatively and quantitatively. We performed RNA-seq on RNC-mRNA (RNC-seq) and detected 12,758 and 14,113 translating genes in human normal bronchial epithelial (HBE) cells and human colorectal adenocarcinoma Caco-2 cells, respectively. We found that most of these genes were mapped with >80% of coding sequence coverage. In Caco-2 cells, we provided translating evidence on 4180 significant single-nucleotide variations. While using RNC-mRNA data as a standard for proteomic data integration, both translating and protein evidence of 7876 genes can be acquired from four interlaboratory data sets with different MS platforms. In addition, we detected 1397 noncoding mRNAs that were attached to ribosomes, suggesting a potential source of new protein explorations. By comparing the two cell lines, a total of 677 differentially translated genes were found to be nonevenly distributed across chromosomes. In addition, 2105 genes in Caco-2 and 750 genes in HBE cells are expressed in a cell-specific manner. These genes are significantly and specifically clustered on multiple chromosomes, such as chromosome 19. We conclude that HPP/C-HPP investigations can be considerably improved by integrating RNC-mRNA analysis with MS, bioinformatics, and antibody-based verifications.

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.

Distinct splice variants and pathway enrichment in the cell-line models of aggressive human breast cancer subtypes

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The United States team of C-HPP is focused on characterizing the protein-coding genes in chromosome 17. Despite its small size, chromosome 17 is rich in protein-coding genes; it contains many cancer-associated genes, including BRCA1, ERBB2, (Her2/neu), and TP53. The goal of this study was to examine the splice variants expressed in three ERBB2 expressed breast cancer cell-line models of hormone-receptor-negative breast cancers by integrating RNA-Seq and proteomic mass spectrometry data. The cell lines represent distinct phenotypic variations subtype: SKBR3 (ERBB2+ (overexpression)/ER-/PR-; adenocarcinoma), SUM190 (ERBB2+ (overexpression)/ER-/PR-; inflammatory breast cancer), and SUM149 (ERBB2 (low expression) ER-/PR-; inflammatory breast cancer). We identified more than one splice variant for 1167 genes expressed in at least one of the three cancer cell lines. We found multiple variants of genes that are in the signaling pathways downstream of ERBB2 along with variants specific to one cancer cell line compared with the other two cancer cell lines and with normal mammary cells. The overall transcript profiles based on read counts indicated more similarities between SKBR3 and SUM190. The top-ranking Gene Ontology and BioCarta pathways for the cell-line specific variants pointed to distinct key mechanisms including: amino sugar metabolism, caspase activity, and endocytosis in SKBR3; different aspects of metabolism, especially of lipids in SUM190; cell-to-cell adhesion, integrin, and ERK1/ERK2 signaling; and translational control in SUM149. The analyses indicated an enrichment in the electron transport chain processes in the ERBB2 overexpressed cell line models and an association of nucleotide binding, RNA splicing, and translation processes with the IBC models, SUM190 and SUM149. Detailed experimental studies on the distinct variants identified from each of these three breast cancer cell line models that may open opportunities for drug target discovery and help unveil their specific roles in cancer progression and metastasis.

The Human Eye Proteome Project: perspectives on an emerging proteome

There are an estimated 285 million people with visual impairment worldwide, of whom 39 million are blind. The pathogenesis of many eye diseases remains poorly understood. The human eye is currently an emerging proteome that may provide key insight into the biological pathways of disease. We review proteomic investigations of the human eye and present a catalogue of 4842 nonredundant proteins identified in human eye tissues and biofluids to date. We highlight the need to identify new biomarkers for eye diseases using proteomics. Recent advances in proteomics do now allow the identification of hundreds to thousands of proteins in tissues and fluids, characterization of various PTMs and simultaneous quantification of multiple proteins. To facilitate proteomic studies of the eye, the Human Eye Proteome Project (HEPP) was organized in September 2012. The HEPP is one of the most recent components of the Biology/Disease-driven Human Proteome Project (B/D-HPP) whose overarching goal is to support the broad application of state-of-the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. The large repertoire of investigative proteomic tools has great potential to transform vision science and enhance understanding of physiology and disease processes that affect sight.

High-throughput identification of putative receptors for cancer-binding peptides using biopanning and microarray analysis

Phage-display peptide biopanning has been successfully used to identify cancer-targeting peptides in multiple models. For cancer-binding peptides, identification of the peptide receptor is necessary to demonstrate the mechanism of action and to further optimize specificity and target binding. The process of receptor identification can be slow and some peptides may turn out to bind ubiquitous proteins not suitable for further drug development. In this report, we describe a high-throughput method for screening a large number of peptides in parallel to identify peptide receptors, which we have termed "reverse biopanning." Peptides can then be selected for further development based on their receptor. To demonstrate this method, we screened a library of 39 peptides previously identified in our laboratory to bind specifically to cancers after irradiation. The reverse biopanning process identified 2 peptides, RKFLMTTRYSRV and KTAKKNVFFCSV, as candidate ligands for the protein tax interacting protein 1 (TIP-1), a protein previously identified in our laboratory to be expressed in tumors and upregulated after exposure to ionizing radiation. We used computational modeling as the initial method for rapid validation of peptide-TIP-1 binding. Pseudo-binding energies were calculated to be -360.645 kcal mol(-1), -487.239 kcal mol(-1), and -595.328 kcal mol(-1) for HVGGSSV, TTRYSRV, and NVFFCSV respectively, suggesting that the peptides would have at least similar, if not stronger, binding to TIP-1 compared to the known TIP-1 binding peptide HVGGSSV. We validated peptide binding in vitro using electrophoretic mobility shift assay, which showed strong binding of RKFLMTTRYSRV and the truncated form TTRYSRV. This method allows for the identification of many peptide receptors and subsequent selection of peptides for further drug development based on the peptide receptor.

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.

New and improved proteomics technologies for understanding complex biological systems: addressing a grand challenge in the life sciences

This White Paper sets out a Life Sciences Grand Challenge for Proteomics Technologies to enhance our understanding of complex biological systems, link genomes with phenotypes, and bring broad benefits to the biosciences and the US economy. The paper is based on a workshop hosted by the National Institute of Standards and Technology (NIST) in Gaithersburg, MD, 14-15 February 2011, with participants from many federal R&D agencies and research communities, under the aegis of the US National Science and Technology Council (NSTC). Opportunities are identified for a coordinated R&D effort to achieve major technology-based goals and address societal challenges in health, agriculture, nutrition, energy, environment, national security, and economic development.

Differential expression and cellular localization of novel isoforms of the tendon biomarker tenomodulin

Tenomodulin (Tnmd, also called Tendin) is classified as a type II transmembrane glycoprotein and is highly expressed in developing as well as in mature tendons. Along with scleraxis (scx), Tnmd is a candidate marker gene for tenocytes. Its function is unknown, but it has been reported to have anti-angiogenic properties. Results in a knockout mouse model did not substantiate that claim. It has homology to chondromodulin-I. Single nucleotide polymorphisms of TNMD have been associated with obesity, macular degeneration, and Alzheimer's disease in patients. In the present study, three Tnmd isoforms with deduced molecular weights of 20.3 (isoform II), 25.4 (isoform III), and 37.1 (isoform I) kDa were proposed and verified by Western blot from cells with green fluorescent protein-linked, overexpressed constructs, tissue, and by qPCR of isoforms from human tissues and cultured cells. Overexpression of each Tnmd isoform followed by immunofluorescence imaging showed that isoforms I and II had perinuclear localization while isoform III was cytoplasmic. Results of qPCR demonstrated differential expression of each Tnmd isoform in patient's specimens taken from flexor carpi radialis, biceps brachii, and flexor digitorum profundus tendons. Knockdown of Tnmd increased the expression of both scleraxis (scx) and myostatin, indicating a potential negative feedback loop between Tnmd and its regulators. Knockdown of all Tnmd isoforms simultaneously also reduced tenocyte proliferation. I-TASSER protein three-dimensional conformation modeling predictions indicated each Tnmd isoform had different structures and potential functions: isoform 1, modeled as a cytosine methyltransferase; isoform 2, a SUMO-1-like SENP-1 protease; and isoform 3, an α-syntrophin, plextrin homology domain scaffolding protein. Further functional studies with each Tnmd isoform may help us to better understand regulation of tenocyte proliferation, tendon development, response to injury and strain, as well as mechanisms in tendinoses. These results may indicate novel therapeutic targets in specific tenomodulin isoforms as well as treatments for tendon diseases.

Selective constraint on human pre-mRNA splicing by protein structural properties

Alternative splicing (AS) is a major mechanism of increasing proteome diversity in complex organisms. Different AS transcript isoforms may be translated into peptide sequences of significantly different lengths and amino acid compositions. One important question, then, is how AS is constrained by protein structural requirements while peptide sequences may be significantly changed in AS events. Here, we address this issue by examining whether the intactness of three-dimensional protein structural units (compact units in protein structures, namely protein units [PUs]) tends to be preserved in AS events in human. We show that PUs tend to occur in constitutively spliced exons and to overlap constitutive exon boundaries. Furthermore, when PUs are located at the boundaries between two alternatively spliced exons (ASEs), these neighboring ASEs tend to co-occur in different transcript isoforms. In addition, such PU-spanned ASE pairs tend to have a higher frequency of being included in transcript isoforms. ASE regions that overlap with PUs also have lower nonsynonymous-to-synonymous substitution rate ratios than those that do not overlap with PUs, indicating stronger negative selection pressure in PU-overlapped ASE regions. Of note, we show that PUs have protein domain- and structural orderness-independent effects on messenger RNA (mRNA) splicing. Overall, our results suggest that fine-scale protein structural requirements have significant influences on the splicing patterns of human mRNAs.

The human proteome - a scientific opportunity for transforming diagnostics, therapeutics, and healthcare

A National Institutes of Health (NIH) workshop was convened in Bethesda, MD on September 26–27, 2011, with representative scientific leaders in the field of proteomics and its applications to clinical settings. The main purpose of this workshop was to articulate ways in which the biomedical research community can capitalize on recent technology advances and synergize with ongoing efforts to advance the field of human proteomics. This executive summary and the following full report describe the main discussions and outcomes of the workshop.

Evolutionary molecular medicine

Evolution has long provided a foundation for population genetics, but some major advances in evolutionary biology from the twentieth century that provide foundations for evolutionary medicine are only now being applied in molecular medicine. They include the need for both proximate and evolutionary explanations, kin selection, evolutionary models for cooperation, competition between alleles, co-evolution, and new strategies for tracing phylogenies and identifying signals of selection. Recent advances in genomics are transforming evolutionary biology in ways that create even more opportunities for progress at its interfaces with genetics, medicine, and public health. This article reviews 15 evolutionary principles and their applications in molecular medicine in hopes that readers will use them and related principles to speed the development of evolutionary molecular medicine.