Proteogenomics reveals sex-biased aging genes and coordinated splicing in cardiac aging.

The risks of heart diseases are significantly modulated by age and sex, but how these factors influence baseline cardiac gene expression remains incompletely understood. Here, we used RNA sequencing and mass spectrometry to compare gene expression in female and male young adult (4 mo) and early aging (20 mo) mouse hearts, identifying thousands of age- and sex-dependent gene expression signatures. Sexually dimorphic cardiac genes are broadly distributed, functioning in mitochondrial metabolism, translation, and other processes. In parallel, we found over 800 genes with differential aging response between male and female, including genes in cAMP and PKA signaling. Analysis of the sex-adjusted aging cardiac transcriptome revealed a widespread remodeling of exon usage patterns that is largely independent from differential gene expression, concomitant with upstream changes in RNA-binding protein and splice factor transcripts. To evaluate the impact of the splicing events on cardiac proteoform composition, we applied an RNA-guided proteomics computational pipeline to analyze the mass spectrometry data and detected hundreds of putative splice variant proteins that have the potential to rewire the cardiac proteome. Taken together, the results here suggest that cardiac aging is associated with 1) widespread sex-biased aging genes and 2) a rewiring of RNA splicing programs, including sex- and age-dependent changes in exon usages and splice patterns that have the potential to influence cardiac protein structure and function. These changes contribute to the emerging evidence for considerable sexual dimorphism in the cardiac aging process that should be considered in the search for disease mechanisms.NEW & NOTEWORTHY Han et al. used proteogenomics to compare male and female mouse hearts at 4 and 20 mo. Sex-biased cardiac genes function in mitochondrial metabolism, translation, autophagy, and other processes. Hundreds of cardiac genes show sex-by-age interactions, that is, sex-biased aging genes. Cardiac aging is accompanied with a remodeling of exon usage in functionally coordinated genes, concomitant with differential expression of RNA-binding proteins and splice factors. These features represent an underinvestigated aspect of cardiac aging that may be relevant to the search for disease mechanisms.

Computation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart.

Alternative splicing is prevalent in the heart and implicated in many cardiovascular diseases, but not every alternative transcript is translated and detecting non-canonical isoforms at the protein level remains challenging. Here we show the use of a computation-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart. We build on a recent strategy to integrate deep RNA-seq and large-scale mass spectrometry data to identify candidate translated isoform peptides. A machine learning approach is then applied to predict their fragmentation patterns and design protein isoform-specific parallel reaction monitoring detection (PRM) assays. As proof-of-principle, we built PRM assays for 29 non-canonical isoform peptides and detected 22 peptides in a human heart lysate. The predictions-aided PRM assays closely mirrored synthetic peptide standards for non-canonical sequences. This approach may be useful for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.

Determining Alternative Protein Isoform Expression Using RNA Sequencing and Mass Spectrometry.

Alternative splicing greatly expands the coding capacity of the human genome, but how many alternative transcripts are translated as proteins or carry functional importance remains unknown and awaits experimental verification. Here, we describe a protocol that combines transcriptomics (RNA-seq) and proteomics (mass spectrometry [MS]) analyses to identify alternative isoforms in proteomes. This workflow is applicable to custom-generated RNA-seq and MS data from matching samples, as well as the reanalysis of existing transcriptomics and proteomics datasets in public repositories. For complete details on the use and execution of this protocol, please refer to Lau et al. (2019).