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

Abstract

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).

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

“…It is feasible to predict functional differences between and among splice variants from the same gene using I-TASSER and other protein folding and ligand-binding algorithms. 31 It is also feasible to predict isoform-level functional networks for the different splice isoforms of a given gene such as Her2/neu (ERBB2) using Hisonet 32,33 and IsoFunc 34 from http://guanlab.ccmb.med.umich.edu/.…”

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

“…6. The HCI, but not the NCI, was reported to be expressed at the protein level in normal human retina [60], further supporting the “canonical” characteristics of HCIs.…”

“…In addition to isoform functions, Li et al built a genome-wide function relationship network at the isoform level for the human [60], an effort from the chromosome 17 Human Proteome Project [61]. The pipeline is described below:

Collect four types of isoform-level feature data, including RNA-Seq expression, pseudo-amino acid composition, protein-docking score, and conserved domains.

Construct a “gold standard” of functionally related gene pairs using GO biological processes and KEGG pathways.

Build a Bayesian network classifier using the MIL algorithm, and use the model to make genome-wide predictions of function relationships between isoforms.

Build a web server to store the human isoform network (http://guanlab.ccmb.med.umich.edu/hisonet).

…”

“…For example, the predictions of human isoform functions [57] and networks [60] are based on RefSeq (version 37.2) and Ensembl (version 74) gene annotations, respectively, so preliminary interpretation of comparative results should be viewed with caution. RefSeq annotation is of high quality but is much less complete compared to Ensembl, which contains many more (predicted) genes and isoforms.…”

Tissue-specific mouse mRNA isoform networks

“…Therefore, these cannot be directly applied to mRNA isoform function prediction because they ignore the distinct functions of alternatively spliced mRNA isoforms. However, important advancements have been made by recent studies towards mRNA isoform level understanding of gene functions [18,19,[21][22][23][24][25] such as the prediction of more immune related gene ontology terms for the mRNA isoform ADAM15B than isoform ADAM15A of ADAM15 gene, which is involved in B-cell-mediated immune mechanisms.…”