Vascular endothelial cells are subjected to hemodynamic forces such as mechanical stretch due to the pulsatile nature of blood flow. Mechanical stretch of different intensities is detected by mechanoreceptors on the cell surface which enables the conversion of external mechanical stimuli to biochemical signals in the cell, activating downstream signaling pathways. This activation may vary depending on whether the cell is exposed to physiological or pathological stretch intensities. Substantial stretch associated with normal physiological functioning is important in maintaining vascular homeostasis as it is involved in the regulation of cell structure, vascular angiogenesis, proliferation and control of vascular tone. However, the elevated pressure that occurs with hypertension exposes cells to excessive mechanical load, and this may lead to pathological consequences through the formation of reactive oxygen species, inflammation and/or apoptosis. These processes are activated by downstream signaling through various pathways that determine the fate of cells. Identification of the proteins involved in these processes may help elucidate novel mechanisms involved in vascular disease associated with pathological mechanical stretch and could provide new insight into therapeutic strategies aimed at countering the mechanisms’ negative effects.

The Human Proteome Project (HPP) aims to discover high-stringency data for all proteins encoded by the human genome. Currently, ∼18% of the proteins in the human proteome (the missing proteins) do not have high-stringency evidence (for example, mass spectrometry) confirming their existence, while much additional information is available about many of these missing proteins. Here, we present MissingProteinPedia as a community resource to accelerate the discovery and understanding of these missing proteins.

Background: One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current populationbased screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin). Methods: A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets. Results: Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC. Conclusion: MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

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Human plasma arguably represents the most comprehensive version of the human proteome. Despite its immense theoretical discovery potential, plasma has many high and medium abundance proteins that mask low abundance protein disease biomarkers of relevance, making the discovery of novel diagnostic markers particularly difficult. Some form of protein depletion and/or fractionation is essential in order to detect markers of low abundance. Here, we describe a "proof of concept" two-pronged approach to immunodeplete abundant proteins from human plasma. The method, called API (Abundant Protein Immunodepletion), involves the fractionation of plasma using dual ion exchange columns (protein repetitive orthogonal offline fractionation (PROOF)) to simplify the proteome, the production of polyclonal IgY against each fraction and finally using the purified antibodies in a immunodepletion column. We explored the use of this product for immunodepletion of human plasma and identified a total of 165 nonredundant proteins after depletion. Of these, 38 proteins that were not previously identified in nondepleted plasma were now detected. It is envisaged that further optimization of the method as well as its cyclic implementation (by reinjecting depleted plasma into chickens for second round of antibody production) can make this technology highly robust, extremely cost-effective, and ideal for high throughput biomarker discovery.

BackgroundCurrent methods widely deployed for colorectal cancers (CRC) screening lack the necessary sensitivity and specificity required for population-based early disease detection. Cancer-specific protein biomarkers are thought to be produced either by the tumor itself or other tissues in response to the presence of cancers or associated conditions. Equally, known examples of cancer protein biomarkers (e.g., PSA, CA125, CA19-9, CEA, AFP) are frequently found in plasma at very low concentration (pg/mL-ng/mL). New sensitive and specific assays are therefore urgently required to detect the disease at an early stage when prognosis is good following surgical resection. This study was designed to meet the longstanding unmet clinical need for earlier CRC detection by measuring plasma candidate biomarkers of cancer onset and progression in a clinical stage-specific manner. EDTA plasma samples (1 μL) obtained from 75 patients with Dukes’ staged CRC or unaffected controls (age and sex matched with stringent inclusion/exclusion criteria) were assayed for expression of 92 human proteins employing the Proseek® Multiplex Oncology I proximity extension assay. An identical set of plasma samples were analyzed utilizing the Bio-Plex Pro™ human cytokine 27-plex immunoassay.ResultsSimilar quantitative expression patterns for 13 plasma antigens common to both platforms endorsed the potential efficacy of Proseek as an immune-based multiplex assay for proteomic biomarker research. Proseek found that expression of Carcinoembryonic Antigen (CEA), IL-8 and prolactin are significantly correlated with CRC stage.ConclusionsCEA, IL-8 and prolactin expression were found to identify between control (unaffected), non-malignant (Dukes’ A + B) and malignant (Dukes’ C + D) stages.Electronic supplementary materialThe online version of this article (doi:10.1186/s12014-015-9081-x) contains supplementary material, which is available to authorized users.