Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF

Plasma proteomic approach in patients with heart failure:insights into pathogenesis of disease progression and potential novel treatment targets

Aims To provide insights into pathogenesis of disease progression and potential novel treatment targets for patients with heart failure by investigation of the plasma proteome using network analysis. Methods and results The plasma proteome of 50 patients with heart failure who died or were rehospitalised were compared with 50 patients with heart failure, matched for age and sex, who did not have an event. Peptides were analysed on two‐dimensional liquid chromatography coupled to tandem mass spectrometry (2D LC ESI‐MS/MS) in high definition mode (HDMSE). We identified and quantified 3001 proteins, of which 51 were significantly up‐regulated and 46 down‐regulated with more than two‐fold expression changes in those who experienced death or rehospitalisation. Gene ontology enrichment analysis and protein–protein interaction networks of significant differentially expressed proteins discovered the central role of metabolic processes in clinical outcomes of patients with heart failure. The findings revealed that a cluster of proteins related to glutathione metabolism, arginine and proline metabolism, and pyruvate metabolism in the pathogenesis of poor outcome in patients with heart failure who died or were rehospitalised. Conclusions Our findings show that in patients with heart failure who died or were rehospitalised, the glutathione, arginine and proline, and pyruvate pathways were activated. These pathways might be potential targets for therapies to improve poor outcomes in patients with heart failure

The capture of proteins in complex samples using molecular imprinting biopolymer chemistry in the field of proteomics

Cardiovascular disease is the number one cause of death globally, leading to high proportion of premature deaths. Since there is an aging population, the economic burden on health care systems increasingly requires the discovery of new biomarkers. Plasma proteomics is an approach to identify novel biomarkers using plasma. However, there are limitations with plasma due to the complexity and dynamic range between high abundant and low abundant proteins.Approaches of overcoming complexity and dynamic range include Molecular imprinting which is a promising tool in the field of proteomics. This thesis has created an imprint using polystyrene as substrate, which can be used to enrich albumin specifically with polymerised dopamine offering huge advantages such as polymerisation in the presence of urea. Dopamine does not require complex chemicals, scalable, cheap and biodegradable. Mouse heart organ was also used to create imprints to heart proteins onto polystyrene coated with polydopamine.Mass spectrometry was used to analyse proteins enriched from human plasma. A Nano-LC system was coupled to an Orbitrap (Q-Exactive) providing both quantitative and qualitative data. Both shotgun and targeted proteomics were employed. The Q-Exactive was operated using parallel reaction monitoring which utilises the high resolution capability of the Q-Exactive to quantitatively target peptides of proteins. Shotgun proteomics using data dependent acquisition was also employed to assess all proteins involved in the imprinting and enrichment. Extracts from molecular imprints (MIPs) contacted with plasma and their respective non-imprint controls (NIPs) were analysed using mass spectrometry. Results from mouse organ MIP contacted with plasma from five myocardial infarction patients, showed the presence of: Troponin, Fatty acid binding protein 3, Creatine Kinase, Lactate Dehydrogenase, Cardiac myosin binding protein C.The imprint created from tissue and organ shows the potential to analyse other tissue and organ MIPs to specifically study organ damage in other diseases, for example kidney, liver and lung injury related to COVID-19 and the effects of COVID-19 on organs.</div

Examination of human osteoarchaeological remains as a feasible source of polar and apolar metabolites to study past conditions

Abstract Metabolomics is a modern tool that aids in our understanding of the molecular changes in organisms. Archaeological science is a branch of archaeology that explores different archaeological materials using modern analytical tools. Human osteoarchaeological material are a frequent finding in archaeological contexts and have the potential to offer information about previous human populations, which can be illuminating about our current condition. Using a set of samples comprising different skeletal elements and bone structures, here we explore for the first time the possibility of extracting metabolites from osteoarchaeological material. Here, a protocol for extraction and measurement of extracted polar and less-polar/apolar metabolites by ultra-high performance liquid chromatography hyphenated to high resolution mass spectrometry is presented to measure the molecules separated after a reversed phase and hydrophilic interaction liquid chromatography column. Molecular information was obtained, showing that osteoarchaeological material is a viable source of molecular information for metabolomic studies

Proteomic Characterization of Circulating Molecular Perturbations Associated With Pancreatic Adenocarcinoma Following Intravenous omega-3 Fatty Acid and Gemcitabine Administration: A Pilot Study

Background: Administration of intravenous ω-3 fatty acid (ω-3FA) in advanced pancreatic adenocarcinoma patients receiving gemcitabine chemotherapy shows disease stabilization and improved progression-free survival. Using high-definition plasma proteomics, the underlying biological mechanisms responsible for these clinical effects are investigated. Methods and Results: A pilot study involving plasma that was collected at baseline from 13 patients with histologically confirmed, unresectable pancreatic adenocarcinoma (baseline group) after 1-month treatment with intravenous gemcitabine and ω-3FA (treatment group) and intravenous gemcitabine only (control group) and was prepared for proteomic analysis. A 2-arm study comparing baseline vs treatment and treatment vs control was performed. Proteins were isolated from plasma with extensive immunodepletion, then digested and labeled with isobaric tandem mass tag peptide tags. Samples were then combined, fractionated, and injected into a QExactive-Orbitrap Mass-Spectrometer and analyzed on Proteome Discoverer and Scaffold with ensuing bioinformatics analysis. Selective reaction monitoring analysis was performed for verification. In total, 3476 proteins were identified. Anti-inflammatory markers (C-reactive protein, haptoglobin, and serum amyloid-A1) were reduced in the treatment group. Enrichment analysis showed angiogenesis downregulation, complement immune systems upregulation, and epigenetic modifications on histones. Pathway analysis identified direct action via the Pi3K-AKT pathway. Serum amyloid-A1 significantly reduced (P <.001) as a potential biomarker of efficacy for ω-3FA. Conclusions: This pilot study demonstrates administration of ω-3FA has potential anti-inflammatory, antiangiogenic, and proapoptotic effects via direct interaction with cancer-signaling pathways in patients with advanced pancreatic adenocarcinoma. Further studies in a larger sample size is required to validate the clinical correlation found in this preliminary study

Growth hormone for risk stratification and effects of therapy in acute myocardial infarction

CONTEXT: Excess growth hormone (GH) is associated with early mortality. OBJECTIVES: We assessed the association of GH with prognosis after acute myocardial infarction (AMI), and the effects of secondary prevention therapies. METHODS: GH was measured using a high-sensitivity assay in 953 AMI patients (687 males, mean age 66.1 ± 12.8 years). RESULTS: During 2 years follow-up, there were 281 major adverse cardiac events (MACE). Patients with MACE had higher GH levels (median [range], 0.91 [0.04-26.28] μg/L) compared to event-free survivors (0.59 [0.02-21.6], p < 0.0005). In multivariate Cox survival analysis, GH was a significant predictor of MACE (hazard ratios 1.43, p = 0.026 and 1.49, p = 0.01, respectively) with significant interactions with beta blocker therapy (p = 0.047) and angiotensin converting enzyme inhibitor or angiotensin receptor blocker (ACE/ARB) therapy (p = 0.016). CONCLUSIONS: GH levels post-AMI are prognostic for MACE and may indicate those patients who benefit from beta blocker and ACE/ARB therapy

Pro-substance p for evaluation of risk in acute myocardial infarction.

Background Pro-substance P (ProSP) is a stable surrogate marker for labile substance P, which has pro-inflammatory effects, increases platelet aggregation and clot strength, and reduces fibrinolysis. Objectives This study assessed whether ProSP was associated with poor prognosis after acute myocardial infarction (AMI) to identify novel pathophysiological mechanisms. Methods ProSP was measured in 1,148 AMI patients (825 men, mean age 66.2 ± 12.8 years). Endpoints were major adverse cardiac events (composite of death, reinfarction, and heart failure [HF] hospitalization), death/reinfarction, and death/HF. GRACE (Global Registry of Acute Coronary Events) scores were compared with ProSP for death and/or reinfarction at 6 months. Results During 2-year follow-up, there were 140 deaths, 112 HF hospitalizations, and 149 re-AMI. ProSP levels were highest on the first 2 days after admission and related to estimated glomerular filtration rate, age, history of diabetes, ischemic heart disease or hypertension, Killip class, left ventricular wall motion index, and sex. Multivariate Cox regression models showed ProSP level was a predictor of major adverse events (hazard ratio [HR]: 1.30; 95% confidence interval [CI]: 1.10 to 1.54; p < 0.002), death and/or AMI (HR: 1.42; 95% CI: 1.20 to 1.68; p < 0.0005), death and/or HF (HR: 1.38; 95% CI: 1.14 to 1.67; p < 0.001). ProSP levels with GRACE scores were independent predictors of 6-month death and/or reinfarction (p < 0.0005 for both). ProSP-adjusted GRACE scores reclassified patients significantly (overall category-free net reclassification improvement of 31.6 (95% CI: 14.3 to 49.0; p < 0.0005) mainly by down-classifying those without endpoints. Conclusions ProSP levels post-AMI are prognostic for death, recurrent AMI, or HF, and they improve risk prediction of GRACE scores, predominantly by down-classifying risk in those without events