Proteomics in cardiovascular disease: recent progress and clinical implication and implementation

Introduction: Although multiple efforts have been initiated to shed light into the molecular mechanisms underlying cardiovascular disease, it still remains one of the major causes of death worldwide. Proteomic approaches are unequivocally powerful tools that may provide deeper understanding into the molecular mechanisms associated with cardiovascular disease and improve its management. Areas covered: Cardiovascular proteomics is an emerging field and significant progress has been made during the past few years with the aim of defining novel candidate biomarkers and obtaining insight into molecular pathophysiology. To summarize the recent progress in the field, a literature search was conducted in PubMed and Web of Science. As a result, 704 studies from PubMed and 320 studies from Web of Science were retrieved. Findings from original research articles using proteomics technologies for the discovery of biomarkers for cardiovascular disease in human are summarized in this review. Expert commentary: Proteins associated with cardiovascular disease represent pathways in inflammation, wound healing and coagulation, proteolysis and extracellular matrix organization, handling of cholesterol and LDL. Future research in the field should target to increase proteome coverage as well as integrate proteomics with other omics data to facilitate both drug development as well as clinical implementation of findings

Serum proteomics to detect early changes in type 1 diabetes and carotid atherosclerosis

The detection of early markers is the key issue in predicting the outcome of inflammatory diseases such as type 1 diabetes and atherosclerosis. Whilst biochemical testing approaches have improved prediction of inflammatory diseases, validated biomarkers with better diagnostic specificities are still needed. Currently, majority of the disease-related proteomics studies have focused on their endpoints. The work presented in this thesis includes the first comprehensive proteomics analyses on serum samples collected from two unique Finnish longitudinal cohorts, namely The Diabetes Prediction and Prevention Project (DIPP) and The Cardiovascular Risk in Young Finns Study (YFS), to identify early markers associated with type 1 diabetes and carotid atherosclerosis. Using mass spectrometry (MS)-based quantitative serum proteomics, profiling was carried out to the study temporal variation in pre-diabetic samples and early markers of plaque formation with the T1D and YFS cohorts, respectively. The analyses revealed consistent differences in the abundance of a number of proteins in subjects having an ongoing asymptomatic changes, several of which are functionally relevant to the disease process. Taken together, the discovered markers are candidates for further validation studies in an independent cohorts and may be used to characterize an increased risk, progression and early onset of these diseases.Tyypin 1 diabeteksen ja ateroskleroosin kehittymiseen liittyvät varhaiset muutokset seerumiproteomissa Yksi keskeinen haaste tulehduksellisten sairauksien, kuten tyypin 1 diabeteksen ja ateroskleroosin, ennustamisessa on varhaisten tautimarkkerien löytäminen. Vaikka erilaiset biokemialliset testit ovat jo parantaneet tulehdusperäisten sairauksien ennustamista, uusia tarkempia biomarkkereita tarvitaan edelleen. Tästä huolimatta monissa näiden alojen proteomiikkatöissä on nykyisin keskitytty sairastumishetken tutkimiseen. Tämän väitöskirjatyön aikana olemme tehneet laajamittaiset proteomiikka-analyysit seeruminäytteille, jotka on kerätty osana kahta ainutlaatuista suomalaista seurantatutkimusta: DIPP-tutkimusta (tyypin 1 diabeteksen ennustaminen ja ennaltaehkäisy) ja YFS-tutkimusta (sydän- ja verisuonitautien riski nuorilla suomalaisilla). Näissä tutkimuksissa seerumiproteomiikkaa hyödynnettiin ensimmistä kertaa varhaisten tyypin 1 diabetes- ja ateroskleroosimarkkerien etsimiseen. Tutkimme tyypin 1 diabeteksen kehittymiseen ja ateroskleroottisten plakkien muodostumiseen liittyviä muutoksia seerumin proteomiprofiileissa massaspektrometriaan perustuvan kvantitatiivisen proteomiikan avulla. Nämä analyysit paljastivat johdonmukaisia eroja lukuisissa proteiineissa myöhemmin sairastuneiden oireettomien henkilöiden ja terveinä pysyneiden kontrollien välillä. Monet näistä proteiineista saattavat myös liittyä olennaisesti tautien kehittymiseen. Tutkimuksissamme löydetyt markkerit tarjoavat lähtökohdan tuleville validointitutkimuksille, ja niitä voitaisiin tulevaisuudessa käyttää yksilön kohonneen sairastumisriskin, taudin etenemisen sekä taudin varhaisen puhkeamisen kartoittamiseen

The use of selected reaction monitoring in quantitative proteomics

Selected reaction monitoring (SRM) has a long history of use in the area of quantitative MS. In recent years, the approach has seen increased application to quantitative proteomics, facilitating multiplexed relative and absolute quantification studies in a variety of organisms. This article discusses SRM, after introducing the context of quantitative proteomics (specifically primarily absolute quantification) where it finds most application, and considers topics such as the theory and advantages of SRM, the selection of peptide surrogates for protein quantification, the design of optimal SRM co-ordinates and the handling of SRM data. A number of published studies are also discussed to demonstrate the impact that SRM has had on the field of quantitative proteomics. </jats:p

Characterization of the Extracellular Proteome of a Natural Microbial Community with an Integrated Mass Spectrometric / Bioinformatic Approach

Proteomics comprises the identification and characterization of the complete suite of expressed proteins in a given cell, organism or community. The coupling of high performance liquid chromatography (LC) with high throughput mass spectrometry (MS) has provided the foundation for current proteomic progression. The transition from proteomic analysis of a single cultivated microbe to that of natural microbial assemblages has required significant advancement in technology and has provided greater biological understanding of microbial community diversity and function. To enhance the capabilities of a mass spectrometric based proteomic analysis, an integrated approach combining bioinformatics with analytical preparations and experimental data collection was developed and applied. This has resulted in a deep characterization of the extracellular fraction of a community of microbes thriving in an acid mine drainage system. Among the notable features of this relatively low complexity community, they exist in a solution that is highly acidic (pH \u3c 1) and hot (temperature \u3e 40°C), with molar concentrations of metals. The extracellular fraction is of particular interest due to the potential to identify and characterize novel proteins that are critical for survival and interactions with the harsh environment. The following analyses have resulted in the specific identification and characterization of novel extracellular proteins. In order to more accurately identify which proteins are present in the extracellular space, a combined computational prediction and experimental identification of the extracellular fraction was performed. Among the hundreds of proteins identified, a highly abundant novel cytochrome was targeted and ultimately characterized through high performance MS. In order to achieve deep proteomic coverage of the extracellular fraction, a metal affinity based protein enrichment utilizing seven different metals was developed and employed resulting in novel protein identifications. A combined top down and bottom up analysis resulted in the characterization of the intact molecular forms of extracellular proteins, including the identification of post-translational modifications. Finally, in order to determine the effectiveness of current MS methodologies, a software package was designed to characterize the \u3e 100,000 mass spectra collected during an MS experiment, revealing that specific optimizations in the LC, MS and protein sequence database have a significant impact on proteomic depth

DART-ID increases single-cell proteome coverage.

Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net

Unbiased protein association study on the public human proteome reveals biological connections between co-occurring protein pairs

Mass-spectrometry-based, high-throughput proteomics experiments produce large amounts of data. While typically acquired to answer specific biological questions, these data can also be reused in orthogonal wayS to reveal new biological knowledge. We here present a novel method for such orthogonal data reuse of public proteomics data. Our method elucidates biological relationships between proteins based on the co-occurrence of these proteins across human experiments in the PRIDE database. The majority of the significantly co-occurring protein pairs that were detected by our method have been successfully mapped to existing biological knowledge. The validity of our novel method is substantiated by the extremely few pairs that can be mapped to existing knowledge based on random associations between the same set of proteins. Moreover, using literature searches and the STRING database, we were able to derive meaningful biological associations for unannotated protein pairs that were detected using our method, further illustrating that as-yet unknown associations present highly interesting targets for follow-up analysis

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p&#60;0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p&#60;0.0001 and ρ = −0.8400; p&#60;0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data