Cardiovascular translational medicine (III). Genomics and proteomics in heart failure research

Phosphoproteomic profiling of human myocardial tissues distinguishes ischemic from non-ischemic end stage heart failure

The molecular differences between ischemic (IF) and non-ischemic (NIF) heart failure are poorly defined. A better understanding of the molecular differences between these two heart failure etiologies may lead to the development of more effective heart failure therapeutics. In this study extensive proteomic and phosphoproteomic profiles of myocardial tissue from patients diagnosed with IF or NIF were assembled and compared. Proteins extracted from left ventricular sections were proteolyzed and phosphopeptides were enriched using titanium dioxide resin. Gel- and label-free nanoscale capillary liquid chromatography coupled to high resolution accuracy mass tandem mass spectrometry allowed for the quantification of 4,436 peptides (corresponding to 450 proteins) and 823 phosphopeptides (corresponding to 400 proteins) from the unenriched and phospho-enriched fractions, respectively. Protein abundance did not distinguish NIF from IF. In contrast, 37 peptides (corresponding to 26 proteins) exhibited a ≥ 2-fold alteration in phosphorylation state (p<0.05) when comparing IF and NIF. The degree of protein phosphorylation at these 37 sites was specifically dependent upon the heart failure etiology examined. Proteins exhibiting phosphorylation alterations were grouped into functional categories: transcriptional activation/RNA processing; cytoskeleton structure/function; molecular chaperones; cell adhesion/signaling; apoptosis; and energetic/metabolism. Phosphoproteomic analysis demonstrated profound post-translational differences in proteins that are involved in multiple cellular processes between different heart failure phenotypes. Understanding the roles these phosphorylation alterations play in the development of NIF and IF has the potential to generate etiology-specific heart failure therapeutics, which could be more effective than current therapeutics in addressing the growing concern of heart failure.

Peptidomic profiles of post myocardial infarction rats affinity depleted plasma using matrix-assisted laser desorption/ionization time of flight (MALDI-ToF) mass spectrometry

Background

Despite major advances in drug development, effective cardiovascular therapies and suitable cardiovascular biomarkers remain limited. The aim of this study was to leverage mass spectrometry (MS) based peptide profiling strategies to identify changes that occur in peptidomic profiles of rat plasma following coronary artery ligation generated myocardial infarction (MI).

Methods

One week after MI, rats were randomized to receive either an ACE inhibitor (ramipril, Ram-1 mg/kg/day), or vehicle (Veh) for 12 weeks. Echocardiography and hemodynamic measurements were made before sacrifice and plasma collection. High abundance proteins were depleted with affinity capture before MS profiling. Differentially expressed peptide ions were identified using proprietary software (ClinProtTools).

Results

MI increased heart/body weight (18%), lung/body weight (56%), and left ventricular (LV) end diastolic pressure (LVEDP, 247%); and significantly reduced percentage fractional shortening (FS, 75%) and rate of pressure rise in the LV (dP/dtmax, 20%). Ram treatment significantly attenuated the changes in LVEDP (61%) and FS (27%). Analysis of MALDI-ToF generated mass spectra demonstrated that peptide ions 1271, 1878, 1955, 2041 and 2254 m/z were consistently decreased by Ram treatment (p < 0.001) and thus may be associated with the agent’s therapeutic effects. Among peptides that were significantly changed, synapsin-2, adenomatous polyposis coli protein and transcription factor jun-D were identified as significantly reduced by Ram treatment.

Conclusions

This approach allows us to screen for potential biomarkers in a window of the blood proteome that previously has been difficult to access. The data obtained from such an approach may potentially useful in prognosis, diagnosis, and monitoring of treatment response.