Myocardial Posttranscriptional Landscape in Peripartum Cardiomyopathy
The present study is the first to characterize downstream proteomic and metabolomic changes present in the myocardial tissue of patients with peripartum cardiomyopathy derived from end-stage transplantation. This study identified several key factors including downregulation of SBSPON and TNS3 which distinguished peripartum cardiomyopathy from age- and sex-matched donor and AHSG heart failure control dilated cardiomyopathy samples. Altered contractile and calcium handling protein expression and energy metabolism profile are distinguishing features of peripartum cardiomyopathy hearts, which suggest it is not as simple as dilated cardiomyopathy with an environmental hit of pregnancy. 

Peripartum cardiomyopathy is a complex and potentially devastating form of heart failure with emerging evidence suggesting that it has both shared and distinct features with nonischemic dilated cardiomyopathy. It is therefore important to investigate the relationship between inherited predisposition and environmental factors in understanding the etiology of peripartum cardiomyopathy when a patient presents with heart failure in the late stages of pregnancy or in the postpartum period. 

Data Availability All data and materials have been made publicly available at the ProteomeXchange Consortium via the PRIDE partner repository with identifier PXD055986. Supporting data are available within the article and its Supplemental Material . Human Heart Tissue Left ventricular samples from 7 PPCM, 6 familial DCM, and 18 age-matched noncardiac death nonfailing controls (donors) were sourced from the Sydney Heart Bank (The University of Sydney, Sydney, Australia). All hearts were not procured postmortem. The PPCM hearts were from White patients, except for 1 patient who was Indigenous. PPCM was diagnosed after delivery at least 3 months (up to 1 year) postpartum. There was no history of preeclampsia. Donor hearts, with no known cardiac risk factors or comorbidities, were procured initially for, but ultimately not used for, cardiac transplantation (eg, due to transportation logistics, donor-recipient size mismatch, and immune incompatibility), while HF (DCM and PPCM) samples were from those undergoing cardiac transplantation. Standard HF treatments were used for both the DCM and PPCM groups, and both were end-stage HF requiring transplantation. All samples from donors and HF patients were collected, snap-frozen, and stored in liquid nitrogen (−196 °C) as previously described. , The study was approved by the Human Research Ethics Committee of the University of Sydney (No. 2021/122) in concordance with the Declaration of Helsinki. Informed and written consent was obtained to collect and use tissue specimens for research. Proteomics Proteomics on human heart samples from the Sydney Heart Bank was performed as previously described. , , Cryopreserved human myocardium was powdered in liquid nitrogen and weighed to ≈10 mg. The powder was homogenized in 4% sodium deoxycholate and 100 mM Tris-HCl, pH 7.5. Samples were heated to 95 °C before sonication using QSonica R2 (Q Sonica) at 70% amplitude. Samples were clarified at 18 000 g , and the pellet was discarded. Protein concentration was determined by bicinchoninic acid assay (Pierce). Notably, 20 µg of samples were digested with trypsin overnight at 37 °C. Peptides were resuspended in 5% formic acid for injection. Peptide samples were directly injected onto a 30-cm×70-µm C18 (Dr Maisch, Ammerbuch, Germany, 1.9 µm) fused-silica analytical column with a 10-µm pulled tip, coupled online to a nanospray electrospray ionization source. Peptides were resolved over a 120-min gradient with a flow rate of 300 nL/min. Peptides were ionized by electrospray ionization at 2.3 kV. Tandem mass spectrometry analysis was performed using a Q-Exactive Fusion Lumos mass spectrometer (Thermo Fisher) with 27% normalized higher-energy collisional dissociation for fragmentation. Spectra were attained in a data-independent acquisition using 20 variable isolation windows. RAW data files were analyzed using the integrated quantitative proteomics software DIA-NN (version 1.7). The database provided to the search engine for identification contained the UniProt human database downloaded on May 5, 2020. The false discovery rate was set to 1% of precursor ions. Both remove likely interferences and match between runs were enabled. Trypsin was set as the digestion enzyme with a maximum of 2 missed cleavages. Carbamidomethylation of Cys was set as a fixed modification, and oxidation of Met was set as variable modifications. Retention time-dependent profiling was used, and the quantification setting was set to any liquid chromatography (high accuracy). Protein inference was based on genes. The neutral network classifier was set to double-pass mode. The MaxLFQ algorithm was used for label-free quantitation, integrated into the DIA-NN environment. Metabolomics Metabolites from heart tissue were extracted as previously described. For liquid chromatography-tandem mass spectrometry analysis, a Shimadzu Nexera LC-30AD UHPLC (Shimadzu Corporation, Kyoto, Japan) system, coupled to a QTRAP6500 mass spectrometer (AB Sciex, Foster City, CA), was used to measure hydrophilic metabolites in positive and negative ionization modes. The separation of the polar metabolites was achieved using hydrophilic interaction liquid chromatography. Specifically, an Atlantis hydrophilic interaction liquid chromatography column (Waters) and an XBridgeTM Amide column (Waters), respectively, were used, as previously described. Sample analysis was conducted in a randomized sample order. All data were collected in a single batch on the same day to minimize variability. The Sciex OS 2.0 software was used for multiple reaction monitoring Q1/Q3 peak integration. Statistics All statistical analyses were performed in R (4.2.0). For proteomics data analysis, proteins that are missing in >80% samples were filtered out. Proteomics and metabolomics data were normalized by RUVIII. Differential expression (DE) analysis for proteins and differential abundance (DA) analysis for metabolites were performed using lmFit and eBayes functions in the limma R package. A linear model was fitted for each protein or metabolite to compare the protein expression levels or the metabolite abundance levels between condition groups while adjusting for log 2 -tranformed age. The Benjamini-Hochberg (BH) method was used to calculate adjusted P values to account for multiple comparisons in both DE and DA analyses. For pathway analysis, we used the kegga() function in the limma R package. The cutoff of BH-adjusted P ≤0.05 was used to determine significant DE proteins and DA metabolites. Pathway Analyses Pathways were annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Proteomic KEGG pathway analysis was performed using kegga from limma. For the metabolomics pathways, competitive gene set tests were performed using cameraPR from limma. All pathways with a P <0.05 were considered significant. Network Analyses Protein and metabolite coexpression networks were generated using the igraph R package. Top 200 DE proteins (circles) ranked by the BH-adjusted P value and all DA metabolites (n=29, squares) between PPCM and donors were used as nodes to construct the networks. For edge weights, the absolute value of pairwise Pearson correlation coefficients in intensities between nodes was calculated. Nodes with an absolute correlation coefficient value >0.75 had twice the weight of the rest of the edges in the network plots. The graph layout was generated by the Fruchterman-Reingold algorithm by the layout_with_fr function in igraph together with the minimal spanning tree and k-nearest neighbor algorithms. Distances between nodes were determined by the Manhattan distance. Protein and metabolite clusters were identified via short random walks by the cluster_walktrap function in igraph. Gene ontology molecular function (MF) terms (v7.5.1) were downloaded from MSigDB. Protein clusters were annotated by enriched ( P ≤0.05) gene ontology MF terms via competitive gene set tests, and metabolite clusters were annotated by internally curated metabolomic pathways. 

All data and materials have been made publicly available at the ProteomeXchange Consortium via the PRIDE partner repository with identifier PXD055986. Supporting data are available within the article and its Supplemental Material . 

Left ventricular samples from 7 PPCM, 6 familial DCM, and 18 age-matched noncardiac death nonfailing controls (donors) were sourced from the Sydney Heart Bank (The University of Sydney, Sydney, Australia). All hearts were not procured postmortem. The PPCM hearts were from White patients, except for 1 patient who was Indigenous. PPCM was diagnosed after delivery at least 3 months (up to 1 year) postpartum. There was no history of preeclampsia. Donor hearts, with no known cardiac risk factors or comorbidities, were procured initially for, but ultimately not used for, cardiac transplantation (eg, due to transportation logistics, donor-recipient size mismatch, and immune incompatibility), while HF (DCM and PPCM) samples were from those undergoing cardiac transplantation. Standard HF treatments were used for both the DCM and PPCM groups, and both were end-stage HF requiring transplantation. All samples from donors and HF patients were collected, snap-frozen, and stored in liquid nitrogen (−196 °C) as previously described. , The study was approved by the Human Research Ethics Committee of the University of Sydney (No. 2021/122) in concordance with the Declaration of Helsinki. Informed and written consent was obtained to collect and use tissue specimens for research. 

Proteomics on human heart samples from the Sydney Heart Bank was performed as previously described. , , Cryopreserved human myocardium was powdered in liquid nitrogen and weighed to ≈10 mg. The powder was homogenized in 4% sodium deoxycholate and 100 mM Tris-HCl, pH 7.5. Samples were heated to 95 °C before sonication using QSonica R2 (Q Sonica) at 70% amplitude. Samples were clarified at 18 000 g , and the pellet was discarded. Protein concentration was determined by bicinchoninic acid assay (Pierce). Notably, 20 µg of samples were digested with trypsin overnight at 37 °C. Peptides were resuspended in 5% formic acid for injection. Peptide samples were directly injected onto a 30-cm×70-µm C18 (Dr Maisch, Ammerbuch, Germany, 1.9 µm) fused-silica analytical column with a 10-µm pulled tip, coupled online to a nanospray electrospray ionization source. Peptides were resolved over a 120-min gradient with a flow rate of 300 nL/min. Peptides were ionized by electrospray ionization at 2.3 kV. Tandem mass spectrometry analysis was performed using a Q-Exactive Fusion Lumos mass spectrometer (Thermo Fisher) with 27% normalized higher-energy collisional dissociation for fragmentation. Spectra were attained in a data-independent acquisition using 20 variable isolation windows. RAW data files were analyzed using the integrated quantitative proteomics software DIA-NN (version 1.7). The database provided to the search engine for identification contained the UniProt human database downloaded on May 5, 2020. The false discovery rate was set to 1% of precursor ions. Both remove likely interferences and match between runs were enabled. Trypsin was set as the digestion enzyme with a maximum of 2 missed cleavages. Carbamidomethylation of Cys was set as a fixed modification, and oxidation of Met was set as variable modifications. Retention time-dependent profiling was used, and the quantification setting was set to any liquid chromatography (high accuracy). Protein inference was based on genes. The neutral network classifier was set to double-pass mode. The MaxLFQ algorithm was used for label-free quantitation, integrated into the DIA-NN environment. 

Metabolites from heart tissue were extracted as previously described. For liquid chromatography-tandem mass spectrometry analysis, a Shimadzu Nexera LC-30AD UHPLC (Shimadzu Corporation, Kyoto, Japan) system, coupled to a QTRAP6500 mass spectrometer (AB Sciex, Foster City, CA), was used to measure hydrophilic metabolites in positive and negative ionization modes. The separation of the polar metabolites was achieved using hydrophilic interaction liquid chromatography. Specifically, an Atlantis hydrophilic interaction liquid chromatography column (Waters) and an XBridgeTM Amide column (Waters), respectively, were used, as previously described. Sample analysis was conducted in a randomized sample order. All data were collected in a single batch on the same day to minimize variability. The Sciex OS 2.0 software was used for multiple reaction monitoring Q1/Q3 peak integration. 

All statistical analyses were performed in R (4.2.0). For proteomics data analysis, proteins that are missing in >80% samples were filtered out. Proteomics and metabolomics data were normalized by RUVIII. Differential expression (DE) analysis for proteins and differential abundance (DA) analysis for metabolites were performed using lmFit and eBayes functions in the limma R package. A linear model was fitted for each protein or metabolite to compare the protein expression levels or the metabolite abundance levels between condition groups while adjusting for log 2 -tranformed age. The Benjamini-Hochberg (BH) method was used to calculate adjusted P values to account for multiple comparisons in both DE and DA analyses. For pathway analysis, we used the kegga() function in the limma R package. The cutoff of BH-adjusted P ≤0.05 was used to determine significant DE proteins and DA metabolites. 

Pathways were annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG). Proteomic KEGG pathway analysis was performed using kegga from limma. For the metabolomics pathways, competitive gene set tests were performed using cameraPR from limma. All pathways with a P <0.05 were considered significant. 

Protein and metabolite coexpression networks were generated using the igraph R package. Top 200 DE proteins (circles) ranked by the BH-adjusted P value and all DA metabolites (n=29, squares) between PPCM and donors were used as nodes to construct the networks. For edge weights, the absolute value of pairwise Pearson correlation coefficients in intensities between nodes was calculated. Nodes with an absolute correlation coefficient value >0.75 had twice the weight of the rest of the edges in the network plots. The graph layout was generated by the Fruchterman-Reingold algorithm by the layout_with_fr function in igraph together with the minimal spanning tree and k-nearest neighbor algorithms. Distances between nodes were determined by the Manhattan distance. Protein and metabolite clusters were identified via short random walks by the cluster_walktrap function in igraph. Gene ontology molecular function (MF) terms (v7.5.1) were downloaded from MSigDB. Protein clusters were annotated by enriched ( P ≤0.05) gene ontology MF terms via competitive gene set tests, and metabolite clusters were annotated by internally curated metabolomic pathways. 

Study Population LV samples from 31 unique human hearts consisting of PPCM, DCM, and nonfailing donors underwent proteomic and metabolomic analysis (Figure A). For proteomics, 6 PPCM, 6 DCM, and 18 nonfailing donors were studied (Figure B, top). For metabolomics, 7 PPCM, 5 DCM, and 7 nonfailing donors were studied (Figure B, bottom). Samples utilized in this study overlapped across proteomics and metabolomic analysis (Figure C). PPCM and DCM exhibited comparable LV ejection fraction, fractional shortening, cardiac output, cardiac index, LV end-diastolic dimension, and LV end-systolic dimension dimensions (Table). Mass Spectrometry Proteomic Profiling Multidimensional scaling plots revealed distinct clustering of the nonfailing donor proteome visibly separated from the HF proteome (Figure A). PPCM and DCM clusters were inseparable. The expression values for each protein within each sample ( Table S1 ) are listed in Table S2 . Protein Expression Profile of PPCM Of the 4121 proteins analyzed in PPCM, 775 proteins were significantly upregulated and 170 were significantly downregulated compared with nonfailing donors (Figure B). Figure C shows MYH10 (myosin heavy chain 10; FC=2.3, P BH =3.1×10 –4 ), COL18A1 (collagen type XVIII alpha-1; FC=2.3, P BH =3.1×10 –5 ), SOD3 (superoxide dismutase 3; FC=2.9, P BH =3.1×10 –5 ), HABP2 (hyaluronic acid binding protein 2; FC=3.3, P BH =3.1×10 –5 ), SERPINA4 (kallistatin; FC=4.4, P BH =3.5×10 –5 ), LTBP2 (latent transforming growth factor beta-binding protein 2; FC=6.7, P BH =5.1×10 –5 ), C2 (complement component 2; FC=3.8, P BH =4.7×10 –5 ), SERPINC1 (antithrombin III; FC=3.1, P BH =4.7×10 –5 ), MXRA7 (matrix remodeling associated 7; FC=2.4, P BH =3.1×10 –5 ), VSNL1 (visinin like 1; FC=2.1, P BH =3.1×10 –5 ), and TNS3 (tensin 3; FC=−4.0, P BH =3.5×10 –5 ) as the most significant DE proteins. We also quantified changes in MYH6 (alpha myosin heavy chain; FC=−2.3, P BH =4.6×10 −2 ), PLN (phospholamban; FC=3.6, P BH =1.8×10 −3 ), and ATP2A2 (SERCA; FC=−1.3; P BH =3.1×10 −2 ) expression ( Table S3 ). The top 100 DE proteins in PPCM were represented in the heat map (Figure D). Hierarchical clustering revealed groupings of DE PPCM proteins corresponding to distinct cellular functions such as upregulation of cardiac hypertrophy (CA3 [carbonic anhydrase 3] and FMOD [fibromodulin]) and B-cell-mediated response (IGHG1 [immunoglobulin heavy constant gamma 1] and IGLV1-47 [immunoglobulin lambda variable 1-47]) and downregulation of mitochondrial metabolism (PYGM [glycogen phosphorylase, muscle associated] and MT-CO1 [mitochondrially encoded cytochrome C oxidase I]). KEGG analysis on DE PPCM proteins identified significantly 20 upregulated and 18 downregulated pathways (Figure E). Upregulated pathways include those involved with complement and coagulation cascades ( P BH =2.7×10 –18 ), regulation of actin cytoskeleton ( P BH =1.7×10 –17 ), protein processing in the endoplasmic reticulum (ER; P BH =2.4×10 –4 ), MAPK (mitogen-activated protein kinase; P BH =6.5×10 –4 ), and PI3K-Akt (phosphoinositide 3-kinase−protein kinase B; P BH =3.3×10 –3 ) signaling pathways. Downregulated pathways identified include those implicated in diabetic cardiomyopathy pathogenesis ( P BH =6.1×10 –9 ), reactive oxygen species production ( P BH =6.4×10 –7 ), cardiac muscle contraction ( P BH =1.9×10 −6 ), oxidative phosphorylation ( P BH =4.5×10 −6 ), cGMP-PKG (cGMP−protein kinase G) signaling ( P BH =1.8×10 −4 ), calcium signaling ( P BH =1.1×10 −3 ), and thyroid hormone signaling and metabolic ( P BH =1.1×10 −3 ) pathways. Protein Expression Profile of DCM Next, we examined the DE proteins in DCM compared with donor hearts. Of the 4147 DE proteins, 398 proteins were significantly upregulated, while 38 proteins were significantly downregulated in DCM compared with donors (Figure B). Intriguingly, this is half as many DE proteins as compared with PPCM. The DCM proteome had numerous upregulated proteins, including MFGE8 (lactadherin; FC=2.4, P BH =1.7×10 –4 ), COL6A2 (collagen type VI alpha 2; FC=1.9, P BH =2.0×10 –4 ), CLEC3B (tetranectin; FC=3.1, P BH =2.0×10 –4 ), PLXDC2 (plexin domain-containing protein 2; FC=2.5, P BH =2.0×10 –4 ), VCAN (versican; FC=3.3, P BH =2.7×10 –4 ), AHSG (fetuin-A; FC=2.9, P BH =3.1×10 –4 ), and NPPA (natriuretic peptide A; FC=15, P BH =3.4×10 –2 ; Figure A). We also identified DE of PLN (FC=−2.5, P BH =3.9×10 –2 ) but not in MYH6 or SERCA ( Table S4 ). KEGG analysis of DE proteins in DCM identified 2 downregulated pathways involved in carbohydrate metabolism and 42 upregulated pathways implicated in immunologic processes, cell signaling, and cell-cell communication (Figure C). Prominent Features of PPCM To tease out features of PPCM from end-stage HF, we compared the proteome of PPCM to DCM and found 357 DE proteins with overlapping expression where 340 were upregulated and 17 were downregulated (Figure B, bottom inset). The overlap in upregulated and downregulated DE proteins ( Table S5 ), respectively, accounted for 85% and 45% of the DE proteins in DCM and 44% and 10% of DE proteins in PPCM. This prominent overlap was further validated by the log fold change mapping of DE proteins (relative to donors) in PPCM and DCM (Figure E). These findings at the level of DE proteins were similarly reflected when KEGG pathways were compared ( Table S6 ). Among the 66 DE pathways, 14 pathways overlapped, leaving 28 upregulated pathways exclusive to DCM, whereas 22 were exclusive to PPCM (Figure D). Surprisingly, no overlap in the downregulated pathways was identified between PPCM and DCM. Finally, PPCM protein expression was examined relative to DCM, which revealed only 2 significantly downregulated DE proteins: SBSPON (somatomedin B and thrombospondin type 1 domain-containing protein precursor; FC=−9.4, P BH =3.6×10 –2 ) and TNS3 (FC=−5.8, P BH =1.0×10 –4 ; Figure F). No DE proteins were upregulated in PPCM. The relative expressions of SBSPON (FC=−4.6, P BH =3.2×10 −3 ) and TNS3 (FC=−4.0, P BH =3.5×10 −5 ) in PPCM (Figure G and H) were significantly downregulated in PPCM compared with donors (Figure C and D), which suggests that the directionality of protein expression is downregulated in PPCM rather than upregulated in DCM (Figure H). Metabolomics Metabolites in PPCM To investigate the involvement of energy metabolism and homeostasis pathways in HF, quantitative metabolomics was applied to examine metabolites across all heart samples. Multidimensional scaling analysis showed distinct separation of metabolites between HF and donor hearts along a single dimension (Figure A). Of the 206 metabolites analyzed, we identified a total of 29 metabolites that were differentially abundant in PPCM and donors (Figure B). Twelve metabolites were significantly enhanced in PPCM with the most abundant being deoxyribose-phosphate (FC=7.3, P BH =1.2×10 −3 ), cytidine (FC=5.0, P BH =8.9×10 −3 ), and cytosine (FC=4.2, P BH =9.8×10 −3 ), which are all involved in nucleotide metabolism (Figure C). Seventeen metabolites were significantly less abundant than donors, and these include rhamnose (FC=−86, P BH =2.6×10 −6 ), homocysteate (FC=−17, P BH =4.0×10 −4 ), and riboflavin 5’-monophosphate (FC=−10, P BH =1.5×10 −2 ; Figure C). A heat map was used to visualize the 29 significant metabolites, which demonstrated consistent abundances across samples within the same group (Figure D). Metabolite abundances for each sample ( Table S7 ) are listed in Table S8 . Pathway analysis using KEGG revealed 11 key pathways in PPCM compared with donors (Figure E). Pathways in arginine biosynthesis ( P BH =6.1×10 −3 ), arginine and proline metabolism (urea cycle; P BH =8.5×10 −3 ), and mTOR (mammalian target of rapamycin) signaling ( P BH =2.7×10 −2 ) are upregulated and may be implicated in nitric oxide, L-monomethyl-arginine, and asymmetrical dimethylarginine synthesis, in addition to cellular hypertrophy. In contrast, glucagon signaling ( P BH =9.6×10 −3 ), cysteine and methionine metabolism ( P BH =2.2×10 −2 ), and alanine, aspartate, and glutamate metabolism (citric acid cycle; P BH =4.9×10 −2 ) are downregulated, which suggests impaired intracellular stores of amino acids and reduced capacity to be utilized by the heart under stress. Metabolites in DCM Next, we examined the DA metabolites in DCM relative to donor samples (Figure ). We identified 40 DA metabolites, of which 16 were more abundant while 24 were less abundant in DCM (Figure B). The upregulated metabolites included cytidine (FC=7.2, P BH =9.3×10 −3 ), cytosine (FC=6.1, P BH =9.3×10 −3 ), and deoxyribose-phosphate (FC=5.9, P BH =9.3×10 −3 ; Figure A), which were implicated in the same significantly upregulated metabolites (Figure D) and pathways (Figure E) in PPCM (Figure B). Downregulated DCM metabolites such as isocitrate (FC=−16, P BH =8.4×10 −4 ), orotate (FC=−14, P BH =3.3×10 −3 ), and citrate (FC=−18, P BH =9.3×10 −3 ) contributed to unique KEGG pathways: citrate cycle (tricarboxylic acid [TCA] cycle; P BH =6.5×10 −3 ), oxidative phosphorylation ( P BH =9.8×10 −3 ), and glyoxylate and dicarboxylate metabolism ( P BH =2.8×10 −2 ; Figure C). These downregulated pathways suggest impaired energy synthesis from various substrates may be an important contributing factor in DCM. The overlap in metabolites and resulting KEGG metabolic pathways were clearly illustrated in Figure B and D, respectively. Metabolic Comparison of PPCM and DCM Furthermore, we examined the DA metabolites unique to PPCM, those unique to DCM, and those common to both conditions (Figure E). Four metabolites were identified as unique to PPCM with homogentisate (FC=3.8) and deoxycholate (FC=3.2) upregulated and lactate (FC=−1.4) and alanine (FC=−1.8) downregulated. However, no metabolites were significant ( P BH <0.05) when compared between PPCM and DCM (Figure F and G). Overall, these data suggest that there is common metabolic involvement contributing to HF, although the significant metabolically related pathology is more prominent in DCM. Network Analysis We performed correlated network analysis of all DE proteins and DA metabolites to identify coregulated nodes in PPCM (Figure ). Each of the 18 DE protein clusters represents a single MF or grouping of similar MFs. There are 3 core clusters presented in Figure A that account for most interactions: the calcium ion-binding cluster 5 connected by DYNC1L12 (dynein cytoplasmic 1 light intermediate chain 2), the cytoskeletal protein-binding cluster 1, and the MF regulator cluster 8 centered around ARHGEF19 (rho guanine nucleotide exchange factor 19). Cluster 5 has a wide ranging reach, being associated with clusters 11 (ligase activity) and 14 (protein and RNA binding), which also overlap with clusters 7 (oxidoreductase activity) and 9 (MF regulator). Cluster 1 associates with clusters 6 (adaptor activity) and 12 (heparin binding) with spatial overlap with clusters 4 and 17. Finally, cluster 8 is connected to cluster 10 (cell adhesion and structural molecule activity) via cluster 2 (cytoskeletal protein binding), which overlaps with the previously mentioned cluster 14. Based on the DA metabolites, we identified 7 clusters (Figure B). Notable clusters include respiratory complex 1 (cluster 1), which is closely associated with nicotinamide adenine dinucleotide and lipid metabolism (cluster 3); nitric oxide pathways (cluster 2) interconnected to valine synthesis (cluster 4); and TCA anaerobic metabolism (cluster 7). These visualized clusters provide context to metabolite expression involving modified oxidative and lipid metabolism and the inhibition of nitric oxide synthesis to promote cellular hypertrophy. 

LV samples from 31 unique human hearts consisting of PPCM, DCM, and nonfailing donors underwent proteomic and metabolomic analysis (Figure A). For proteomics, 6 PPCM, 6 DCM, and 18 nonfailing donors were studied (Figure B, top). For metabolomics, 7 PPCM, 5 DCM, and 7 nonfailing donors were studied (Figure B, bottom). Samples utilized in this study overlapped across proteomics and metabolomic analysis (Figure C). PPCM and DCM exhibited comparable LV ejection fraction, fractional shortening, cardiac output, cardiac index, LV end-diastolic dimension, and LV end-systolic dimension dimensions (Table). 

Multidimensional scaling plots revealed distinct clustering of the nonfailing donor proteome visibly separated from the HF proteome (Figure A). PPCM and DCM clusters were inseparable. The expression values for each protein within each sample ( Table S1 ) are listed in Table S2 . Protein Expression Profile of PPCM Of the 4121 proteins analyzed in PPCM, 775 proteins were significantly upregulated and 170 were significantly downregulated compared with nonfailing donors (Figure B). Figure C shows MYH10 (myosin heavy chain 10; FC=2.3, P BH =3.1×10 –4 ), COL18A1 (collagen type XVIII alpha-1; FC=2.3, P BH =3.1×10 –5 ), SOD3 (superoxide dismutase 3; FC=2.9, P BH =3.1×10 –5 ), HABP2 (hyaluronic acid binding protein 2; FC=3.3, P BH =3.1×10 –5 ), SERPINA4 (kallistatin; FC=4.4, P BH =3.5×10 –5 ), LTBP2 (latent transforming growth factor beta-binding protein 2; FC=6.7, P BH =5.1×10 –5 ), C2 (complement component 2; FC=3.8, P BH =4.7×10 –5 ), SERPINC1 (antithrombin III; FC=3.1, P BH =4.7×10 –5 ), MXRA7 (matrix remodeling associated 7; FC=2.4, P BH =3.1×10 –5 ), VSNL1 (visinin like 1; FC=2.1, P BH =3.1×10 –5 ), and TNS3 (tensin 3; FC=−4.0, P BH =3.5×10 –5 ) as the most significant DE proteins. We also quantified changes in MYH6 (alpha myosin heavy chain; FC=−2.3, P BH =4.6×10 −2 ), PLN (phospholamban; FC=3.6, P BH =1.8×10 −3 ), and ATP2A2 (SERCA; FC=−1.3; P BH =3.1×10 −2 ) expression ( Table S3 ). The top 100 DE proteins in PPCM were represented in the heat map (Figure D). Hierarchical clustering revealed groupings of DE PPCM proteins corresponding to distinct cellular functions such as upregulation of cardiac hypertrophy (CA3 [carbonic anhydrase 3] and FMOD [fibromodulin]) and B-cell-mediated response (IGHG1 [immunoglobulin heavy constant gamma 1] and IGLV1-47 [immunoglobulin lambda variable 1-47]) and downregulation of mitochondrial metabolism (PYGM [glycogen phosphorylase, muscle associated] and MT-CO1 [mitochondrially encoded cytochrome C oxidase I]). KEGG analysis on DE PPCM proteins identified significantly 20 upregulated and 18 downregulated pathways (Figure E). Upregulated pathways include those involved with complement and coagulation cascades ( P BH =2.7×10 –18 ), regulation of actin cytoskeleton ( P BH =1.7×10 –17 ), protein processing in the endoplasmic reticulum (ER; P BH =2.4×10 –4 ), MAPK (mitogen-activated protein kinase; P BH =6.5×10 –4 ), and PI3K-Akt (phosphoinositide 3-kinase−protein kinase B; P BH =3.3×10 –3 ) signaling pathways. Downregulated pathways identified include those implicated in diabetic cardiomyopathy pathogenesis ( P BH =6.1×10 –9 ), reactive oxygen species production ( P BH =6.4×10 –7 ), cardiac muscle contraction ( P BH =1.9×10 −6 ), oxidative phosphorylation ( P BH =4.5×10 −6 ), cGMP-PKG (cGMP−protein kinase G) signaling ( P BH =1.8×10 −4 ), calcium signaling ( P BH =1.1×10 −3 ), and thyroid hormone signaling and metabolic ( P BH =1.1×10 −3 ) pathways. Protein Expression Profile of DCM Next, we examined the DE proteins in DCM compared with donor hearts. Of the 4147 DE proteins, 398 proteins were significantly upregulated, while 38 proteins were significantly downregulated in DCM compared with donors (Figure B). Intriguingly, this is half as many DE proteins as compared with PPCM. The DCM proteome had numerous upregulated proteins, including MFGE8 (lactadherin; FC=2.4, P BH =1.7×10 –4 ), COL6A2 (collagen type VI alpha 2; FC=1.9, P BH =2.0×10 –4 ), CLEC3B (tetranectin; FC=3.1, P BH =2.0×10 –4 ), PLXDC2 (plexin domain-containing protein 2; FC=2.5, P BH =2.0×10 –4 ), VCAN (versican; FC=3.3, P BH =2.7×10 –4 ), AHSG (fetuin-A; FC=2.9, P BH =3.1×10 –4 ), and NPPA (natriuretic peptide A; FC=15, P BH =3.4×10 –2 ; Figure A). We also identified DE of PLN (FC=−2.5, P BH =3.9×10 –2 ) but not in MYH6 or SERCA ( Table S4 ). KEGG analysis of DE proteins in DCM identified 2 downregulated pathways involved in carbohydrate metabolism and 42 upregulated pathways implicated in immunologic processes, cell signaling, and cell-cell communication (Figure C). Prominent Features of PPCM To tease out features of PPCM from end-stage HF, we compared the proteome of PPCM to DCM and found 357 DE proteins with overlapping expression where 340 were upregulated and 17 were downregulated (Figure B, bottom inset). The overlap in upregulated and downregulated DE proteins ( Table S5 ), respectively, accounted for 85% and 45% of the DE proteins in DCM and 44% and 10% of DE proteins in PPCM. This prominent overlap was further validated by the log fold change mapping of DE proteins (relative to donors) in PPCM and DCM (Figure E). These findings at the level of DE proteins were similarly reflected when KEGG pathways were compared ( Table S6 ). Among the 66 DE pathways, 14 pathways overlapped, leaving 28 upregulated pathways exclusive to DCM, whereas 22 were exclusive to PPCM (Figure D). Surprisingly, no overlap in the downregulated pathways was identified between PPCM and DCM. Finally, PPCM protein expression was examined relative to DCM, which revealed only 2 significantly downregulated DE proteins: SBSPON (somatomedin B and thrombospondin type 1 domain-containing protein precursor; FC=−9.4, P BH =3.6×10 –2 ) and TNS3 (FC=−5.8, P BH =1.0×10 –4 ; Figure F). No DE proteins were upregulated in PPCM. The relative expressions of SBSPON (FC=−4.6, P BH =3.2×10 −3 ) and TNS3 (FC=−4.0, P BH =3.5×10 −5 ) in PPCM (Figure G and H) were significantly downregulated in PPCM compared with donors (Figure C and D), which suggests that the directionality of protein expression is downregulated in PPCM rather than upregulated in DCM (Figure H). 

Of the 4121 proteins analyzed in PPCM, 775 proteins were significantly upregulated and 170 were significantly downregulated compared with nonfailing donors (Figure B). Figure C shows MYH10 (myosin heavy chain 10; FC=2.3, P BH =3.1×10 –4 ), COL18A1 (collagen type XVIII alpha-1; FC=2.3, P BH =3.1×10 –5 ), SOD3 (superoxide dismutase 3; FC=2.9, P BH =3.1×10 –5 ), HABP2 (hyaluronic acid binding protein 2; FC=3.3, P BH =3.1×10 –5 ), SERPINA4 (kallistatin; FC=4.4, P BH =3.5×10 –5 ), LTBP2 (latent transforming growth factor beta-binding protein 2; FC=6.7, P BH =5.1×10 –5 ), C2 (complement component 2; FC=3.8, P BH =4.7×10 –5 ), SERPINC1 (antithrombin III; FC=3.1, P BH =4.7×10 –5 ), MXRA7 (matrix remodeling associated 7; FC=2.4, P BH =3.1×10 –5 ), VSNL1 (visinin like 1; FC=2.1, P BH =3.1×10 –5 ), and TNS3 (tensin 3; FC=−4.0, P BH =3.5×10 –5 ) as the most significant DE proteins. We also quantified changes in MYH6 (alpha myosin heavy chain; FC=−2.3, P BH =4.6×10 −2 ), PLN (phospholamban; FC=3.6, P BH =1.8×10 −3 ), and ATP2A2 (SERCA; FC=−1.3; P BH =3.1×10 −2 ) expression ( Table S3 ). The top 100 DE proteins in PPCM were represented in the heat map (Figure D). Hierarchical clustering revealed groupings of DE PPCM proteins corresponding to distinct cellular functions such as upregulation of cardiac hypertrophy (CA3 [carbonic anhydrase 3] and FMOD [fibromodulin]) and B-cell-mediated response (IGHG1 [immunoglobulin heavy constant gamma 1] and IGLV1-47 [immunoglobulin lambda variable 1-47]) and downregulation of mitochondrial metabolism (PYGM [glycogen phosphorylase, muscle associated] and MT-CO1 [mitochondrially encoded cytochrome C oxidase I]). KEGG analysis on DE PPCM proteins identified significantly 20 upregulated and 18 downregulated pathways (Figure E). Upregulated pathways include those involved with complement and coagulation cascades ( P BH =2.7×10 –18 ), regulation of actin cytoskeleton ( P BH =1.7×10 –17 ), protein processing in the endoplasmic reticulum (ER; P BH =2.4×10 –4 ), MAPK (mitogen-activated protein kinase; P BH =6.5×10 –4 ), and PI3K-Akt (phosphoinositide 3-kinase−protein kinase B; P BH =3.3×10 –3 ) signaling pathways. Downregulated pathways identified include those implicated in diabetic cardiomyopathy pathogenesis ( P BH =6.1×10 –9 ), reactive oxygen species production ( P BH =6.4×10 –7 ), cardiac muscle contraction ( P BH =1.9×10 −6 ), oxidative phosphorylation ( P BH =4.5×10 −6 ), cGMP-PKG (cGMP−protein kinase G) signaling ( P BH =1.8×10 −4 ), calcium signaling ( P BH =1.1×10 −3 ), and thyroid hormone signaling and metabolic ( P BH =1.1×10 −3 ) pathways. 

Next, we examined the DE proteins in DCM compared with donor hearts. Of the 4147 DE proteins, 398 proteins were significantly upregulated, while 38 proteins were significantly downregulated in DCM compared with donors (Figure B). Intriguingly, this is half as many DE proteins as compared with PPCM. The DCM proteome had numerous upregulated proteins, including MFGE8 (lactadherin; FC=2.4, P BH =1.7×10 –4 ), COL6A2 (collagen type VI alpha 2; FC=1.9, P BH =2.0×10 –4 ), CLEC3B (tetranectin; FC=3.1, P BH =2.0×10 –4 ), PLXDC2 (plexin domain-containing protein 2; FC=2.5, P BH =2.0×10 –4 ), VCAN (versican; FC=3.3, P BH =2.7×10 –4 ), AHSG (fetuin-A; FC=2.9, P BH =3.1×10 –4 ), and NPPA (natriuretic peptide A; FC=15, P BH =3.4×10 –2 ; Figure A). We also identified DE of PLN (FC=−2.5, P BH =3.9×10 –2 ) but not in MYH6 or SERCA ( Table S4 ). KEGG analysis of DE proteins in DCM identified 2 downregulated pathways involved in carbohydrate metabolism and 42 upregulated pathways implicated in immunologic processes, cell signaling, and cell-cell communication (Figure C). 

To tease out features of PPCM from end-stage HF, we compared the proteome of PPCM to DCM and found 357 DE proteins with overlapping expression where 340 were upregulated and 17 were downregulated (Figure B, bottom inset). The overlap in upregulated and downregulated DE proteins ( Table S5 ), respectively, accounted for 85% and 45% of the DE proteins in DCM and 44% and 10% of DE proteins in PPCM. This prominent overlap was further validated by the log fold change mapping of DE proteins (relative to donors) in PPCM and DCM (Figure E). These findings at the level of DE proteins were similarly reflected when KEGG pathways were compared ( Table S6 ). Among the 66 DE pathways, 14 pathways overlapped, leaving 28 upregulated pathways exclusive to DCM, whereas 22 were exclusive to PPCM (Figure D). Surprisingly, no overlap in the downregulated pathways was identified between PPCM and DCM. Finally, PPCM protein expression was examined relative to DCM, which revealed only 2 significantly downregulated DE proteins: SBSPON (somatomedin B and thrombospondin type 1 domain-containing protein precursor; FC=−9.4, P BH =3.6×10 –2 ) and TNS3 (FC=−5.8, P BH =1.0×10 –4 ; Figure F). No DE proteins were upregulated in PPCM. The relative expressions of SBSPON (FC=−4.6, P BH =3.2×10 −3 ) and TNS3 (FC=−4.0, P BH =3.5×10 −5 ) in PPCM (Figure G and H) were significantly downregulated in PPCM compared with donors (Figure C and D), which suggests that the directionality of protein expression is downregulated in PPCM rather than upregulated in DCM (Figure H). 

Metabolites in PPCM To investigate the involvement of energy metabolism and homeostasis pathways in HF, quantitative metabolomics was applied to examine metabolites across all heart samples. Multidimensional scaling analysis showed distinct separation of metabolites between HF and donor hearts along a single dimension (Figure A). Of the 206 metabolites analyzed, we identified a total of 29 metabolites that were differentially abundant in PPCM and donors (Figure B). Twelve metabolites were significantly enhanced in PPCM with the most abundant being deoxyribose-phosphate (FC=7.3, P BH =1.2×10 −3 ), cytidine (FC=5.0, P BH =8.9×10 −3 ), and cytosine (FC=4.2, P BH =9.8×10 −3 ), which are all involved in nucleotide metabolism (Figure C). Seventeen metabolites were significantly less abundant than donors, and these include rhamnose (FC=−86, P BH =2.6×10 −6 ), homocysteate (FC=−17, P BH =4.0×10 −4 ), and riboflavin 5’-monophosphate (FC=−10, P BH =1.5×10 −2 ; Figure C). A heat map was used to visualize the 29 significant metabolites, which demonstrated consistent abundances across samples within the same group (Figure D). Metabolite abundances for each sample ( Table S7 ) are listed in Table S8 . Pathway analysis using KEGG revealed 11 key pathways in PPCM compared with donors (Figure E). Pathways in arginine biosynthesis ( P BH =6.1×10 −3 ), arginine and proline metabolism (urea cycle; P BH =8.5×10 −3 ), and mTOR (mammalian target of rapamycin) signaling ( P BH =2.7×10 −2 ) are upregulated and may be implicated in nitric oxide, L-monomethyl-arginine, and asymmetrical dimethylarginine synthesis, in addition to cellular hypertrophy. In contrast, glucagon signaling ( P BH =9.6×10 −3 ), cysteine and methionine metabolism ( P BH =2.2×10 −2 ), and alanine, aspartate, and glutamate metabolism (citric acid cycle; P BH =4.9×10 −2 ) are downregulated, which suggests impaired intracellular stores of amino acids and reduced capacity to be utilized by the heart under stress. Metabolites in DCM Next, we examined the DA metabolites in DCM relative to donor samples (Figure ). We identified 40 DA metabolites, of which 16 were more abundant while 24 were less abundant in DCM (Figure B). The upregulated metabolites included cytidine (FC=7.2, P BH =9.3×10 −3 ), cytosine (FC=6.1, P BH =9.3×10 −3 ), and deoxyribose-phosphate (FC=5.9, P BH =9.3×10 −3 ; Figure A), which were implicated in the same significantly upregulated metabolites (Figure D) and pathways (Figure E) in PPCM (Figure B). Downregulated DCM metabolites such as isocitrate (FC=−16, P BH =8.4×10 −4 ), orotate (FC=−14, P BH =3.3×10 −3 ), and citrate (FC=−18, P BH =9.3×10 −3 ) contributed to unique KEGG pathways: citrate cycle (tricarboxylic acid [TCA] cycle; P BH =6.5×10 −3 ), oxidative phosphorylation ( P BH =9.8×10 −3 ), and glyoxylate and dicarboxylate metabolism ( P BH =2.8×10 −2 ; Figure C). These downregulated pathways suggest impaired energy synthesis from various substrates may be an important contributing factor in DCM. The overlap in metabolites and resulting KEGG metabolic pathways were clearly illustrated in Figure B and D, respectively. Metabolic Comparison of PPCM and DCM Furthermore, we examined the DA metabolites unique to PPCM, those unique to DCM, and those common to both conditions (Figure E). Four metabolites were identified as unique to PPCM with homogentisate (FC=3.8) and deoxycholate (FC=3.2) upregulated and lactate (FC=−1.4) and alanine (FC=−1.8) downregulated. However, no metabolites were significant ( P BH <0.05) when compared between PPCM and DCM (Figure F and G). Overall, these data suggest that there is common metabolic involvement contributing to HF, although the significant metabolically related pathology is more prominent in DCM. 

To investigate the involvement of energy metabolism and homeostasis pathways in HF, quantitative metabolomics was applied to examine metabolites across all heart samples. Multidimensional scaling analysis showed distinct separation of metabolites between HF and donor hearts along a single dimension (Figure A). Of the 206 metabolites analyzed, we identified a total of 29 metabolites that were differentially abundant in PPCM and donors (Figure B). Twelve metabolites were significantly enhanced in PPCM with the most abundant being deoxyribose-phosphate (FC=7.3, P BH =1.2×10 −3 ), cytidine (FC=5.0, P BH =8.9×10 −3 ), and cytosine (FC=4.2, P BH =9.8×10 −3 ), which are all involved in nucleotide metabolism (Figure C). Seventeen metabolites were significantly less abundant than donors, and these include rhamnose (FC=−86, P BH =2.6×10 −6 ), homocysteate (FC=−17, P BH =4.0×10 −4 ), and riboflavin 5’-monophosphate (FC=−10, P BH =1.5×10 −2 ; Figure C). A heat map was used to visualize the 29 significant metabolites, which demonstrated consistent abundances across samples within the same group (Figure D). Metabolite abundances for each sample ( Table S7 ) are listed in Table S8 . Pathway analysis using KEGG revealed 11 key pathways in PPCM compared with donors (Figure E). Pathways in arginine biosynthesis ( P BH =6.1×10 −3 ), arginine and proline metabolism (urea cycle; P BH =8.5×10 −3 ), and mTOR (mammalian target of rapamycin) signaling ( P BH =2.7×10 −2 ) are upregulated and may be implicated in nitric oxide, L-monomethyl-arginine, and asymmetrical dimethylarginine synthesis, in addition to cellular hypertrophy. In contrast, glucagon signaling ( P BH =9.6×10 −3 ), cysteine and methionine metabolism ( P BH =2.2×10 −2 ), and alanine, aspartate, and glutamate metabolism (citric acid cycle; P BH =4.9×10 −2 ) are downregulated, which suggests impaired intracellular stores of amino acids and reduced capacity to be utilized by the heart under stress. 

Next, we examined the DA metabolites in DCM relative to donor samples (Figure ). We identified 40 DA metabolites, of which 16 were more abundant while 24 were less abundant in DCM (Figure B). The upregulated metabolites included cytidine (FC=7.2, P BH =9.3×10 −3 ), cytosine (FC=6.1, P BH =9.3×10 −3 ), and deoxyribose-phosphate (FC=5.9, P BH =9.3×10 −3 ; Figure A), which were implicated in the same significantly upregulated metabolites (Figure D) and pathways (Figure E) in PPCM (Figure B). Downregulated DCM metabolites such as isocitrate (FC=−16, P BH =8.4×10 −4 ), orotate (FC=−14, P BH =3.3×10 −3 ), and citrate (FC=−18, P BH =9.3×10 −3 ) contributed to unique KEGG pathways: citrate cycle (tricarboxylic acid [TCA] cycle; P BH =6.5×10 −3 ), oxidative phosphorylation ( P BH =9.8×10 −3 ), and glyoxylate and dicarboxylate metabolism ( P BH =2.8×10 −2 ; Figure C). These downregulated pathways suggest impaired energy synthesis from various substrates may be an important contributing factor in DCM. The overlap in metabolites and resulting KEGG metabolic pathways were clearly illustrated in Figure B and D, respectively. 

Furthermore, we examined the DA metabolites unique to PPCM, those unique to DCM, and those common to both conditions (Figure E). Four metabolites were identified as unique to PPCM with homogentisate (FC=3.8) and deoxycholate (FC=3.2) upregulated and lactate (FC=−1.4) and alanine (FC=−1.8) downregulated. However, no metabolites were significant ( P BH <0.05) when compared between PPCM and DCM (Figure F and G). Overall, these data suggest that there is common metabolic involvement contributing to HF, although the significant metabolically related pathology is more prominent in DCM. 

We performed correlated network analysis of all DE proteins and DA metabolites to identify coregulated nodes in PPCM (Figure ). Each of the 18 DE protein clusters represents a single MF or grouping of similar MFs. There are 3 core clusters presented in Figure A that account for most interactions: the calcium ion-binding cluster 5 connected by DYNC1L12 (dynein cytoplasmic 1 light intermediate chain 2), the cytoskeletal protein-binding cluster 1, and the MF regulator cluster 8 centered around ARHGEF19 (rho guanine nucleotide exchange factor 19). Cluster 5 has a wide ranging reach, being associated with clusters 11 (ligase activity) and 14 (protein and RNA binding), which also overlap with clusters 7 (oxidoreductase activity) and 9 (MF regulator). Cluster 1 associates with clusters 6 (adaptor activity) and 12 (heparin binding) with spatial overlap with clusters 4 and 17. Finally, cluster 8 is connected to cluster 10 (cell adhesion and structural molecule activity) via cluster 2 (cytoskeletal protein binding), which overlaps with the previously mentioned cluster 14. Based on the DA metabolites, we identified 7 clusters (Figure B). Notable clusters include respiratory complex 1 (cluster 1), which is closely associated with nicotinamide adenine dinucleotide and lipid metabolism (cluster 3); nitric oxide pathways (cluster 2) interconnected to valine synthesis (cluster 4); and TCA anaerobic metabolism (cluster 7). These visualized clusters provide context to metabolite expression involving modified oxidative and lipid metabolism and the inhibition of nitric oxide synthesis to promote cellular hypertrophy. 

Our study of proteins and metabolites in the heart tissue of end-stage PPCM patients provided new insights into the disease. By comparing these molecular signatures with those from DCM patients, we identified key features of PPCM. We found 2 proteins, SBSPON and TNS3, that were uniquely expressed in PPCM and were less abundant than in DCM and healthy hearts. Metabolomics analysis showed 4 unique metabolites: homogentisate and deoxycholate were higher, while lactate and alanine were lower, indicating problems with certain metabolic pathways in PPCM. Additionally, we confirmed that both PPCM and DCM share pathways related to inflammation, immune responses, and signal transduction. PPCM Protein Expression Our proteomics analysis supports findings from large-scale genetic studies comparing PPCM and DCM, which identified mutations in sarcomeres, cellular junctions, and membrane proteins. , , We found that several previously identified genes ( MYH6 , TTN , DSP , BAG3 , MHY7 , MYBPC3 , and TNNI3 ) had reduced protein expression in both PPCM and DCM, but only MYH6 was significantly reduced in PPCM. MYH6 encodes MyHC-α (alpha cardiac myosin heavy chain) predominately found in human atrial tissue. The 2-fold reduction of MyHC-α was previously reported in ventricular tissue at the mRNA level of DCM patients, but the protein was near undetectable using gel electrophoresis. Although the precise function of MyHC-α expression in diseased ventricles remains unknown, it was thought to reduce ATP utilization and preserve contractile energetics. In this study, we confirmed altered protein expression of genes previously implicated in DCM disease pathogenesis and identified the possible novel contribution of MYH6 to PPCM. We also identified 2 proteins, SBSPON and TNS3, that were uniquely downregulated in PPCM. SBSPON is a predicted extracellular matrix structural protein expressed in mesothelial cells of the fetal epicardium and ventricular muscle cells during late outflow tract development. Its role remains to be resolved, but the gene was similarly significantly downregulated in the placenta of patients with preeclampsia and the mammary glands in preclinical models. Preeclampsia in PPCM leads to severe LV remodeling, carries an adverse long-term prognosis, and shares similar genetic etiologies. TNS3 is a focal adhesion protein involved in actin remodeling, cell signaling, and cell proliferation. This protein plays an integral role in maintaining normal, healthy tissue through continuous remodeling of the extracellular matrix. Reduced expression of TNS3 has been linked to cancer metastisis and thyroid tumors, human septic cardiomyopathy, and population-wide early onset coronary artery disease. – The specific role and mechanism of TNS3 in the heart are unknown. Lowered levels of both SBSPON and TNS3 in PPCM may disrupt normal tissue remodeling during a stress event, such as proinflammatory response or pregnancy-related complications, , that lead to enhanced fibrosis. Altered Contractile Homeostasis in PPCM In both diseased hearts, common biological themes in immune and inflammatory responses (to infections) along with cell signaling (eg, MAPK, PI3K-Akt, and cGMP-PKG) and cellular remodeling (eg, actin cytoskeleton and focal adhesion) were present. These findings are consistent with the expected maladaptive responses that take place during chronic HF, which we and others have previously demonstrated to be associated with both innate and adaptive immune cell subsets at the tissue RNA and serum proteome levels. , Additionally, we found thyroid hormone signaling was enhanced in DCM but reduced in PPCM. Thyroid hormone plays an important role in transcriptional regulation of genes containing the thyroid response element in the promoter region, and these genes include PLN, SERCA, and myosin heavy chains, which impact heart rate, contractility, and vascular resistance. Our data set identified increased PLN in DCM and PPCM and reduced MYH6 and SERCA expression found in PPCM only, which modulates contractility and calcium handling characteristic of DCM – and PPCM. Notably, our PPCM samples were previously functionally characterized in Bollen et al, showing enhanced passive force and blunted length-dependent activation in PPCM. Increased passive force was associated with expression of the more compliant larger titin isoform in PPCM but not fibrosis, as similar levels were quantified in DCM and PPCM. Hypophosphorylation of cardiac troponin I in both PPCM and DCM partially accounted for the reduced length-dependent activation. However, our study further identified a 3.6-fold increase in PLN and a 1.3-fold reduction in SERCA, which in turn reduces the available calcium ions in the cytosol to account for length-dependent activation changes in PPCM and not DCM. Intriguingly, thyroid hormone can also drive the titin isoform transition toward the shorter, stiffer N2B isoform, and hypothyroidism is known to lead to increased N2BA isoform with increased collagen and collagen-based stiffness, similar to previous findings in PPCM. The combined effects of reduced expression of contractile modulating proteins and downregulated signaling pathways corroborate previous published works of PPCM exhibiting impaired contractile function distinct from DCM. Another Avenue to Change Energy Utilization in PPCM Protein processing in ER is upregulated in PPCM. The ER stores intracellular calcium and ensures the proper folding of newly synthesized proteins. Increased protein processing can produce misfolded proteins that trigger ER stress. Under normal conditions, misfolded proteins are rapidly degraded by the ubiquitin protease system, but if accumulation is prolonged, then this triggers cardiomyocyte autophagy and apoptosis, a prelude to HF. Several diseases are associated with ER stress, including DCM, diabetic CM, , metabolic CM, ischemic heart disease, and neurodegenerative diseases. These latter diseases in particular have decreased calcium concentrations released from the lumen of ER, which similarly triggers apoptosis. ER stress also disrupts energy metabolism by compromising mitochondrial calcium uptake and altering oxidative phosphorylation, resulting in a shift in energy substrate utilization from fatty acid to glycolysis. The exact ER mechanism responsible for aberrant switching of energy substrates such as that previously identified in PPCM cardiomyocytes is an area for further investigation. Similarly, endocytosis pathways may also be involved as they regulate cell surface receptors that determine the homeostasis of macromolecules (eg, cholesterol, glucose, and lipids) within a cell. Together, PPCM may reflect diabetes and neurodegenerative diseases , in more ways than previously considered, such as the accumulation of lipids and impaired lipid metabolism that causes deleterious effects through ER stress on other organ systems. This is surprisingly consistent with calcium signaling, diabetic CM, nonalcoholic fatty liver disease, and neurodegenerative disease pathways that were downregulated in PPCM but not DCM. Metabolic Signature of PPCM and DCM Altered metabolite levels of homogentisate, deoxycholate, lactate, and alanine were identified in PPCM; however, these levels were not different in DCM. These metabolites were respectively overrepresented in specific clusters, including complex 1 (NADH dehydrogenase), nicotinamide adenine dinucleotide and lipid metabolism, TCA cycle, and nitric oxide synthesis inhibition, respectively. Accumulation of homogentisate, an intermediate product of defective tyrosine metabolism, is linked to a genetic condition involving the connective tissue that appears as valvular defects. , Since valvular defects have not been reported in PPCM, elevated homogentisate levels may instead reflect valvular insufficiency developed during late-stage disease. Similarly, elevated levels of deoxycholate, a bile acid derivative, impair glucose metabolism, promote vasorelaxation, and are cardiotoxic. Deoxycholate reduces mitochondrial respiration, membrane potential, and enhanced calcium-sensitive mitochondrial permeability transition, all indicators of oxidative stress and cell death characteristic of end-stage disease. In heart disease, the switch from fatty acid oxidation to glucose metabolism is typical. However, in PPCM, both anaerobic glycolysis and glucose metabolism are reduced, leading to less lactate production and consumption for the TCA cycle. The availability of alanine, an amino acid crucial for glucose production, is also reduced in PPCM. Alanine is usually converted to glucose in the liver that is used by the heart for energy. Reduced alanine levels impair glucose availability. The reduction of lactate and alanine in PPCM hearts suggests an impairment in the efficient use of glucose for energy. Furthermore, a recent study of plasma biomarkers in PPCM showed an upregulation of the cholesterol metabolism-related anti-inflammatory pathway that may have important implications in cholesterol homeostasis in PPCM and hence acetyl-coenzyme A utilization in the TCA cycle. Our study is limited by several factors. First, the sample size is small. Second, control heart samples were obtained from nonpregnant, nonfailing donors. As such, we were unable to determine the influence of pregnancy, which we indicated has an extensive impact on systemic and cardiovascular physiology. However, nondiseased, non-postmortem human heart controls are rare, as are PPCM human heart samples. The disease control group (non-PPCM DCM hearts) was older in age compared with the PPCM samples, but there have been no previously documented changes in proteomics or metabolomics in age within human DCM hearts. , There was also a male in the DCM cohort, but this sample, when removed, does not affect the overall proteomic ( Figure S1 ) and metabolomic ( Figure S2 ) signatures. Also, we were unable to determine whether the inhibition or supplementation of proteins or metabolites could rescue the PPCM phenotype or prevent progression to end-stage HF. Conclusions Our study provides a comprehensive downstream molecular characterization of the human end-stage PPCM myocardium, revealing distinct differences from DCM (disease controls) and nonfailing donor hearts. The uniquely reduced expression of proteins SBSPON and TNS3 in PPCM impede modulation of extracellular matrix-mediated ventricular remodeling. Reduced alanine levels point to impaired glucose metabolism, which has knock-on effects such as lowering lactate concentrations crucial for energy production in PPCM hearts. The downregulation of thyroid hormone signaling in PPCM, contrasted with its enhancement in DCM, suggests differing mechanisms in contractile function and calcium handling. Elevated ER stress and altered endocytosis pathways in PPCM further differentiate its metabolic profile from DCM. Additionally, shared pathways related to inflammation, immune responses, and cell signaling underline common maladaptive responses in chronic HF. These findings enhance our understanding of PPCM’s molecular underpinnings that are distinct from the classical DCM phenotype, suggesting PPCM is not just a subtype of DCM with a simple environmental hit of pregnancy. 

Our proteomics analysis supports findings from large-scale genetic studies comparing PPCM and DCM, which identified mutations in sarcomeres, cellular junctions, and membrane proteins. , , We found that several previously identified genes ( MYH6 , TTN , DSP , BAG3 , MHY7 , MYBPC3 , and TNNI3 ) had reduced protein expression in both PPCM and DCM, but only MYH6 was significantly reduced in PPCM. MYH6 encodes MyHC-α (alpha cardiac myosin heavy chain) predominately found in human atrial tissue. The 2-fold reduction of MyHC-α was previously reported in ventricular tissue at the mRNA level of DCM patients, but the protein was near undetectable using gel electrophoresis. Although the precise function of MyHC-α expression in diseased ventricles remains unknown, it was thought to reduce ATP utilization and preserve contractile energetics. In this study, we confirmed altered protein expression of genes previously implicated in DCM disease pathogenesis and identified the possible novel contribution of MYH6 to PPCM. We also identified 2 proteins, SBSPON and TNS3, that were uniquely downregulated in PPCM. SBSPON is a predicted extracellular matrix structural protein expressed in mesothelial cells of the fetal epicardium and ventricular muscle cells during late outflow tract development. Its role remains to be resolved, but the gene was similarly significantly downregulated in the placenta of patients with preeclampsia and the mammary glands in preclinical models. Preeclampsia in PPCM leads to severe LV remodeling, carries an adverse long-term prognosis, and shares similar genetic etiologies. TNS3 is a focal adhesion protein involved in actin remodeling, cell signaling, and cell proliferation. This protein plays an integral role in maintaining normal, healthy tissue through continuous remodeling of the extracellular matrix. Reduced expression of TNS3 has been linked to cancer metastisis and thyroid tumors, human septic cardiomyopathy, and population-wide early onset coronary artery disease. – The specific role and mechanism of TNS3 in the heart are unknown. Lowered levels of both SBSPON and TNS3 in PPCM may disrupt normal tissue remodeling during a stress event, such as proinflammatory response or pregnancy-related complications, , that lead to enhanced fibrosis. 

In both diseased hearts, common biological themes in immune and inflammatory responses (to infections) along with cell signaling (eg, MAPK, PI3K-Akt, and cGMP-PKG) and cellular remodeling (eg, actin cytoskeleton and focal adhesion) were present. These findings are consistent with the expected maladaptive responses that take place during chronic HF, which we and others have previously demonstrated to be associated with both innate and adaptive immune cell subsets at the tissue RNA and serum proteome levels. , Additionally, we found thyroid hormone signaling was enhanced in DCM but reduced in PPCM. Thyroid hormone plays an important role in transcriptional regulation of genes containing the thyroid response element in the promoter region, and these genes include PLN, SERCA, and myosin heavy chains, which impact heart rate, contractility, and vascular resistance. Our data set identified increased PLN in DCM and PPCM and reduced MYH6 and SERCA expression found in PPCM only, which modulates contractility and calcium handling characteristic of DCM – and PPCM. Notably, our PPCM samples were previously functionally characterized in Bollen et al, showing enhanced passive force and blunted length-dependent activation in PPCM. Increased passive force was associated with expression of the more compliant larger titin isoform in PPCM but not fibrosis, as similar levels were quantified in DCM and PPCM. Hypophosphorylation of cardiac troponin I in both PPCM and DCM partially accounted for the reduced length-dependent activation. However, our study further identified a 3.6-fold increase in PLN and a 1.3-fold reduction in SERCA, which in turn reduces the available calcium ions in the cytosol to account for length-dependent activation changes in PPCM and not DCM. Intriguingly, thyroid hormone can also drive the titin isoform transition toward the shorter, stiffer N2B isoform, and hypothyroidism is known to lead to increased N2BA isoform with increased collagen and collagen-based stiffness, similar to previous findings in PPCM. The combined effects of reduced expression of contractile modulating proteins and downregulated signaling pathways corroborate previous published works of PPCM exhibiting impaired contractile function distinct from DCM. 

Protein processing in ER is upregulated in PPCM. The ER stores intracellular calcium and ensures the proper folding of newly synthesized proteins. Increased protein processing can produce misfolded proteins that trigger ER stress. Under normal conditions, misfolded proteins are rapidly degraded by the ubiquitin protease system, but if accumulation is prolonged, then this triggers cardiomyocyte autophagy and apoptosis, a prelude to HF. Several diseases are associated with ER stress, including DCM, diabetic CM, , metabolic CM, ischemic heart disease, and neurodegenerative diseases. These latter diseases in particular have decreased calcium concentrations released from the lumen of ER, which similarly triggers apoptosis. ER stress also disrupts energy metabolism by compromising mitochondrial calcium uptake and altering oxidative phosphorylation, resulting in a shift in energy substrate utilization from fatty acid to glycolysis. The exact ER mechanism responsible for aberrant switching of energy substrates such as that previously identified in PPCM cardiomyocytes is an area for further investigation. Similarly, endocytosis pathways may also be involved as they regulate cell surface receptors that determine the homeostasis of macromolecules (eg, cholesterol, glucose, and lipids) within a cell. Together, PPCM may reflect diabetes and neurodegenerative diseases , in more ways than previously considered, such as the accumulation of lipids and impaired lipid metabolism that causes deleterious effects through ER stress on other organ systems. This is surprisingly consistent with calcium signaling, diabetic CM, nonalcoholic fatty liver disease, and neurodegenerative disease pathways that were downregulated in PPCM but not DCM. 

Altered metabolite levels of homogentisate, deoxycholate, lactate, and alanine were identified in PPCM; however, these levels were not different in DCM. These metabolites were respectively overrepresented in specific clusters, including complex 1 (NADH dehydrogenase), nicotinamide adenine dinucleotide and lipid metabolism, TCA cycle, and nitric oxide synthesis inhibition, respectively. Accumulation of homogentisate, an intermediate product of defective tyrosine metabolism, is linked to a genetic condition involving the connective tissue that appears as valvular defects. , Since valvular defects have not been reported in PPCM, elevated homogentisate levels may instead reflect valvular insufficiency developed during late-stage disease. Similarly, elevated levels of deoxycholate, a bile acid derivative, impair glucose metabolism, promote vasorelaxation, and are cardiotoxic. Deoxycholate reduces mitochondrial respiration, membrane potential, and enhanced calcium-sensitive mitochondrial permeability transition, all indicators of oxidative stress and cell death characteristic of end-stage disease. In heart disease, the switch from fatty acid oxidation to glucose metabolism is typical. However, in PPCM, both anaerobic glycolysis and glucose metabolism are reduced, leading to less lactate production and consumption for the TCA cycle. The availability of alanine, an amino acid crucial for glucose production, is also reduced in PPCM. Alanine is usually converted to glucose in the liver that is used by the heart for energy. Reduced alanine levels impair glucose availability. The reduction of lactate and alanine in PPCM hearts suggests an impairment in the efficient use of glucose for energy. Furthermore, a recent study of plasma biomarkers in PPCM showed an upregulation of the cholesterol metabolism-related anti-inflammatory pathway that may have important implications in cholesterol homeostasis in PPCM and hence acetyl-coenzyme A utilization in the TCA cycle. Our study is limited by several factors. First, the sample size is small. Second, control heart samples were obtained from nonpregnant, nonfailing donors. As such, we were unable to determine the influence of pregnancy, which we indicated has an extensive impact on systemic and cardiovascular physiology. However, nondiseased, non-postmortem human heart controls are rare, as are PPCM human heart samples. The disease control group (non-PPCM DCM hearts) was older in age compared with the PPCM samples, but there have been no previously documented changes in proteomics or metabolomics in age within human DCM hearts. , There was also a male in the DCM cohort, but this sample, when removed, does not affect the overall proteomic ( Figure S1 ) and metabolomic ( Figure S2 ) signatures. Also, we were unable to determine whether the inhibition or supplementation of proteins or metabolites could rescue the PPCM phenotype or prevent progression to end-stage HF. 

Our study provides a comprehensive downstream molecular characterization of the human end-stage PPCM myocardium, revealing distinct differences from DCM (disease controls) and nonfailing donor hearts. The uniquely reduced expression of proteins SBSPON and TNS3 in PPCM impede modulation of extracellular matrix-mediated ventricular remodeling. Reduced alanine levels point to impaired glucose metabolism, which has knock-on effects such as lowering lactate concentrations crucial for energy production in PPCM hearts. The downregulation of thyroid hormone signaling in PPCM, contrasted with its enhancement in DCM, suggests differing mechanisms in contractile function and calcium handling. Elevated ER stress and altered endocytosis pathways in PPCM further differentiate its metabolic profile from DCM. Additionally, shared pathways related to inflammation, immune responses, and cell signaling underline common maladaptive responses in chronic HF. These findings enhance our understanding of PPCM’s molecular underpinnings that are distinct from the classical DCM phenotype, suggesting PPCM is not just a subtype of DCM with a simple environmental hit of pregnancy. 

Acknowledgments The authors thank the patients and staff of St Vincent’s Hospital (Sydney), and the Australian Red Cross Blood Service. Sources of Funding This study was supported by the National Health and Medical Research Council (NHMRC) of Australia and the National Heart Foundation (NHF) of Australia. Dr O’Sullivan is supported by an New South Wales Health and Medical Research Early-Mid Career Researcher Grant (G212785), an NHF Vanguard Grant (105595), a NHF Future Leader Fellowship (104853), and an NHMRC-Medical Research Futures Fund Cardiovascular Award (2024161). Dr Koay is supported by an New South Wales Health and Medical Research Collaborative Grant . The contents of the published material are solely the responsibility of the individual authors and do not reflect the view of NHMRC or the NHF. This study was also supported by philanthropic donations to the University of Sydney from The Baird Institute and by a grant from the R.T. Hall Trust . Disclosures None. Supplemental Material Tables S1–S8 Figures S1 and S2 

The authors thank the patients and staff of St Vincent’s Hospital (Sydney), and the Australian Red Cross Blood Service. 

This study was supported by the National Health and Medical Research Council (NHMRC) of Australia and the National Heart Foundation (NHF) of Australia. Dr O’Sullivan is supported by an New South Wales Health and Medical Research Early-Mid Career Researcher Grant (G212785), an NHF Vanguard Grant (105595), a NHF Future Leader Fellowship (104853), and an NHMRC-Medical Research Futures Fund Cardiovascular Award (2024161). Dr Koay is supported by an New South Wales Health and Medical Research Collaborative Grant . The contents of the published material are solely the responsibility of the individual authors and do not reflect the view of NHMRC or the NHF. This study was also supported by philanthropic donations to the University of Sydney from The Baird Institute and by a grant from the R.T. Hall Trust . 

None. 

Tables S1–S8 Figures S1 and S2 

 

