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Proteomic analysis of donor and cardiomyopathy pre-mortem human hearts

ABSTRACT: Our understanding of heart failure (HF) has been provided by indirect surrogates, such as post-mortem histology, cardiovascular imaging, and molecular characterisation in vivo and in vitro, rather than directly in pre-mortem human cardiac tissue. Using our heart bank of pre-mortem hearts procured according to the most stringent protocols, we examined ischemic (ICM) and dilated cardiomyopathy (DCM) -- the most common causes of HF and leading causes of cardiac transplantation1. We performed unbiased, comprehensive, paired proteomic and metabolomic analysis of 51 left ventricular (LV) samples from 44 cryopreserved pre-mortem human ICM and DCM hearts, including age-matched, healthy, histopathologically-normal donor controls of both genders for comparison. Data integration via pathway and correlation network analysis revealed overlapping and divergent disease pathways in ICM and DCM, and, strikingly, precise sex-specific differences within each disease that unveil the interaction of gender with HF. Identified core functional nodes in each disease may serve as novel therapeutic targets, and we provide all proteomic and metabolomic results via an interactive online repository (https://mengboli.shinyapps.io/heartomics/) as a publicly available resource.

INSTRUMENT(S): Q Exactive HF-X

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Heart

DISEASE(S): Cardiomyopathy,Myocardial Ischemia

SUBMITTER: Benjamin Parker

LAB HEAD: Benjamin Parker

PROVIDER: PXD014826 | Pride | 2023-05-18

REPOSITORIES: Pride

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			Action	DRS
	20181116_QEHFX3_BP_RSLCcap_hHeart_DDA_1.raw	Raw
	20181116_QEHFX3_BP_RSLCcap_hHeart_DDA_10.raw	Raw
	20181116_QEHFX3_BP_RSLCcap_hHeart_DDA_11.raw	Raw
	20181116_QEHFX3_BP_RSLCcap_hHeart_DDA_12.raw	Raw
	20181116_QEHFX3_BP_RSLCcap_hHeart_DDA_2.raw	Raw

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Project description:There is an emerging hypothesis that dilated cardiomyopathy (DCM) is a manifestation of end-stage heart failure (ESHF) resulting from âfinal common pathwayâ despite heterogeneous primary etiologies. We performed genome-wide expression profiling by means of high-density oligonucleotide microarrays using cardiac muscles from patients with DCM or specific cardiomyopathy as well as non-disease control hearts. Differentially expressed genes between ESHF and non-disease samples should include both genes reactive to heart failure (HF) and those responsible for ESHF. With the aid of samples with acute HF without DCM and those with DCM without HF (corrected with left ventricular assist device), we successfully distinguished ESHF genes from HF genes. Our findings implicate that transcriptional signature of cardiac muscle can be potentially applied as a diagnostic or prognostic tool for severe HF. Experiment Overall Design: The expression profiles of approximately 20,227 genes were analyzed using a microarray, Human 1A ver.2 (Agilent Technologies). A total of 30 cardiac RNA samples (21 clinical samples and 9 purchased samples) were used in the hybridizations. 200ng of total RNA was used for T7 RNA polymerase-based cRNA labeling. The microarray experiments were then carried out using competitive hybridization experiments with Cy5-labeled heart RNAs as a test RNA and with Cy3-labeled pooled heart RNA (Sample N) as a template control for normalization. The glass slides were scanned using an Agilent G2565BA microarray scanner. Scanned images were then analyzed using Feature Extraction software. The average signal intensities were corrected for median background intensity and transferred with GenBank descriptors to a Microsoft Excel data spreadsheet (Microsoft, Redmond, WA). Experiment Overall Design: Data analysis was performed using Genespring software version 6.1 (Silicon Genetics, Redwood City, CA). To avoid âfalse positiveâ signals, we excluded certain genes from the analysis for which the average reference signal level constraints were under 70. After intensity dependent normalization (Lowess), the expression levels relative to the control were calculated as a ratio, and the expression profiles were then compared between each disease or normal sample. Statistical analysis was done using non-parametric tests. To order the samples according to the correlation coefficient, we applied âFind Similar Samplesâ algorithm using Spearman Correlation.

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Proteomic analysis of donor and cardiomyopathy pre-mortem human hearts

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