Prolyl hydroxylation regulates protein degradation, synthesis, and splicing in human induced pluripotent stem cell-derived cardiomyocytes

Abstract

Aims: Protein hydroxylases are oxygen- and α-ketoglutarate-dependent enzymes that catalyse hydroxylation of amino acids such as proline, thus linking oxygen and metabolism to enzymatic activity. Prolyl hydroxylation is a dynamic post-translational modification that regulates protein stability and protein-protein interactions; however, the extent of this modification is largely uncharacterized. The goals of this study are to investigate the biological consequences of prolyl hydroxylation and to identify new targets that undergo prolyl hydroxylation in human cardiomyocytes.

Methods and results: We used human induced pluripotent stem cell-derived cardiomyocytes in combination with pulse-chase amino acid labelling and proteomics to analyse the effects of prolyl hydroxylation on protein degradation and synthesis. We identified 167 proteins that exhibit differences in degradation with inhibition of prolyl hydroxylation by dimethyloxalylglycine (DMOG); 164 were stabilized. Proteins involved in RNA splicing such as serine/arginine-rich splicing factor 2 (SRSF2) and splicing factor and proline- and glutamine-rich (SFPQ) were stabilized with DMOG. DMOG also decreased protein translation of cytoskeletal and sarcomeric proteins such as α-cardiac actin. We searched the mass spectrometry data for proline hydroxylation and identified 134 high confidence peptides mapping to 78 unique proteins. We identified SRSF2, SFPQ, α-cardiac actin, and cardiac titin as prolyl hydroxylated. We identified 29 prolyl hydroxylated proteins that showed a significant difference in either protein degradation or synthesis. Additionally, we performed next-generation RNA sequencing and showed that the observed decrease in protein synthesis was not due to changes in mRNA levels. Because RNA splicing factors were prolyl hydroxylated, we investigated splicing ± inhibition of prolyl hydroxylation and detected 369 alternative splicing events, with a preponderance of exon skipping.

Conclusions: This study provides the first extensive characterization of the cardiac prolyl hydroxylome and demonstrates that inhibition of α-ketoglutarate hydroxylases alters protein stability, translation, and splicing.

Keywords: Hypoxia; Prolyl hydroxylation; Protein degradation; Proteomics; Splicing.

Figure 1

Analysis of pulse-chase labelled amino acids in human iPSC-derived cardiomyocytes by mass spectrometry. (A) Human iPSC-derived cardiomyocytes were analysed 23 days after initiation of differentiation. Cardiomyocytes were adapted to dialysed foetal bovine serum containing light medium for 3 days and then switched to heavy label (¹³C₆ l-lysine-2HCl, ¹³C₆¹⁵N₄ l-arginine-HCl). Cells were separated into two groups: vehicle and DMOG (1 mM). After 2, 6, and 18 h, groups were harvested and underwent tryptic digestion and mass spectrometry analysis. Representative ion chromatographs are shown for light peptides identified as significantly different (determined by QUOIL) between control and DMOG 18 h after switching to heavy media for the peptide HLAGLGLTEAIDKNKADLSR from Serpin H1 (B) and IGNAKGDDALEKR from Phosphoserine aminotransferase (C). Peaks are shown from three biological samples per group. (D) Summary of the proteomic analysis. See Supplementary material online, Methods for details.

Figure 2

Quantitative assessment of light proteins. (A) Volcano plot analysis shows the distribution of light proteins between control and DMOG. First-order kinetic slopes were calculated from three time points. Two-way ANOVA was used for calculating the P-values from the fit of first-order equations. Interesting targets are labelled and highlighted by arrows. Proteins stabilized by DMOG are on the left side and those destabilized are on the right of the origin. Y-axis shows the negative log₁₀ P-value and the X-axis shows the differences in rate (control − DMOG). (B) Representative fits for selected proteins. The dotted lines show the measure of error. Distribution of protein half-lives under control (C) or DMOG (D) obtained by the first-order equations to multi-point data (2, 6, and 18 h). Light peptide areas are shown normalized to the 2 h time point for both groups.

Figure 3

Analysis for light and heavy proteins. (A) Using the online tool PANTHER classification system, protein class analysis was performed for light proteins that were significantly stabilized in the presence of DMOG as analysed by the multiple-point method or the single-point method (18 h). Percentage of total number of identified protein classes are shown. (B) Further characterization shows that the nucleic acid binding proteins were enriched for RNA-binding targets. (C) A volcano plot depicts the heavy proteins measured with the single-point method after 18 h. The horizontal line represents P = 0.05, the vertical lines represent the threshold for the log₂ fold change (log₂FC). (D) Using PANTHER, protein class analysis was performed for heavy protein targets that showed a significant reduction in protein synthesis with DMOG (18 h, single-point method). Synthesis of cytoskeletal proteins was strongly affected by DMOG.

Figure 4

Identification of prolyl hydroxylated peptides using mass spectrometry analysis of the pulse-chase labelled amino acids in human iPSC-CM. (A) The mass spectrometric data from the Orbitrap Fusion system was searched for proline oxidation in control (n = 3) and DMOG (n = 3) samples. Proteome Discoverer software (version 1.4) was used to filter the data for high peptide confidence (FDR of <1%) to identify prolyl oxidated peptides and for annotation to the respective proteins. Prolyl hydroxylated peptides were identified in at least two out of six samples. (B) Genomatrix Genome Analyser software was used to identify enriched gene ontology (GO) terms for the prolyl hydroxylated proteins. Listed here are the top 10. (C) Protein class analysis was performed for prolyl hydroxylated proteins identified in mass spectrometric analysis with PANTHER. Protein classes are shown as percentage of total number of identified protein classes. (D) Schematic illustration for overlap between prolyl hydroxylated proteins and proteins with a change in degradation/synthesis. (E) Effect of DMOG on the number of light vs. heavy prolyl hydroxylated peptides. Data are shown ± SEM. **P < 0.01 vs. control (Student's t-test).

Figure 5

Comparison of the cardiac transcriptome and protein translation. iPSC-derived cardiomyocytes were analysed 18 h after vehicle (control) or DMOG. (A) Principle component analysis of control (n = 5) and DMOG (n = 5) mRNA samples from RNA-seq. Blue label indicates the control group and orange label indicates DMOG. Clustering analysis showed sample grouping within the respective treatment. (B) Heatmap gene clustering analysis of control and DMOG samples (after 18 h). The expression for each gene is shown in rows, and samples are clustered in columns. The expression levels of each gene across the samples are shown as log₂CPM (counts). The scaled expression values are colour-coded according to the legend. The dendrogram depicting hierarchical clustering is based on the expression of significantly different genes. (C) Volcano plot of mRNA-sequencing data. The horizontal line represents the false-positive control (FDR) at 1%, the vertical lines represent the threshold for the log₂ fold change (log₂FC). The X-axis depicts the log₂ difference in estimated relative expression values. Vertical lines represent the threshold for the log₂ fold change. The DMOG transcripts that are up-regulated (right) or down-regulated (left) greater than four-fold are marked as *. Previously described HIF-1α target genes are highlighted in blue. (D) Correlation between differences in proteins (heavy) and mRNA synthesis ± DMOG.

Figure 6

RNA-seq data. (A) Pathway analysis for splice variants by the Ingenuity Pathway Analysis (IPA^®) showed differences in exon skipping between control and DMOG (n = 5 per group). The P-value was calculated by IPA^® based on Fisher's exact test. The percentage characterizes the number of identified genes associated with that pathway. (B) Sashimi plot and frequency analysis for two targets: BCAT2, involved in the branched-chain-amino-acid degradation pathway, and NT5C2, in the NAD salvage pathway. (C) The width of the interval corresponds to the confidence in the estimate of the Ψ value. (D) Exon skipping changes with DMOG for BCAT2 and NT5C2 were verified using qRT-PCR and visualized on an agarose gel. 18s RNA was used as a control. (E) HOMER analysis identified enriched binding motifs within splicing region sequences comparing to the shuffled background sequences. Purine-rich 7-mer ‘GAAGAAG’ was identified in the splicing-exon regions. Data are shown ± SEM. ***P < 0.001 vs. control (Student's t-test).