Maternal plasma proteome profiling of biomarkers and pathogenic mechanisms of early-onset and late-onset preeclampsia.

Preeclampsia is still the leading cause of morbidity and mortality in pregnancy without a cure. There are two phenotypes of preeclampsia, early-onset (EOPE) and late-onset (LOPE) with poorly defined pathogenic differences. This study aimed to facilitate better understanding of the mechanisms of pathophysiology of EOPE and LOPE, and identify specific biomarkers or therapeutic targets. In this study, we conducted an untargeted, label-free quantitative proteomic analyses of plasma samples from pregnant women with EOPE (n = 17) and LOPE (n = 11), and age, BMI-matched normotensive controls (n = 18). Targeted proteomics approach was also employed to validate a subset of proteins (n = 17). In total, there were 26 and 20 differentially abundant proteins between EOPE or LOPE, and normotensive controls, respectively. A series of angiogenic and inflammatory proteins, including insulin-like growth factor-binding protein 4 (IGFBP4; EOPE: FDR = 0.0030 and LOPE: FDR = 0.00396) and inter-alpha-trypsin inhibitor heavy chain H2-4 (ITIH2-4), were significantly altered in abundance in both phenotypes. Through validation we confirmed that ITIH2 was perturbed only in LOPE (p = 0.005) whereas ITIH3 and ITIH4 were perturbed in both phenotypes (p < 0.05). Overall, lipid metabolism/transport proteins associated with atherosclerosis were highly abundant in LOPE, however, ECM proteins had a more pronounced role in EOPE. The complement cascade and binding and uptake of ligands by scavenger receptors, pathways, were associated with both EOPE and LOPE.

Systemic Biomarkers and Unique Pathways in Different Phenotypes of Heart Failure with Preserved Ejection Fraction.

Heart failure with preserved ejection fraction (HFpEF) accounts for around 50% of all heart failure cases. It is a heterogeneous condition with poorly understood pathogenesis. Here, we aimed to identify unique pathogenic mechanisms in acute and chronic HFpEF and hypertrophic cardiomyopathy (HCM). We performed unbiased, comprehensive proteomic analyses of plasma samples from gender- and BMI-matched patients with acute HFpEF (n = 8), chronic HFpEF (n = 9) and HCM (n = 14) using liquid chromatography-mass spectrometry. Distinct molecular signatures were observed in different HFpEF forms. Clusters of biomarkers differentially abundant between HFpEF forms were predominantly associated with microvascular inflammation. New candidate protein markers were also identified, including leucine-rich alpha-2-glycoprotein 1 (LRG1), serum amyloid A1 (SAA1) and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Our study is the first to apply systematic, quantitative proteomic screening of plasma samples from patients with different subtypes of HFpEF and identify candidate biomarkers for improved management of acute and chronic HFpEF and HCM.

What is Normalization? The Strategies Employed in Top-Down and Bottom-Up Proteome Analysis Workflows.

The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.