Patient Stratification and Measuring Protein Heterogeneity using MS

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Patient Stratification and Measuring Protein Heterogeneity using MS 2016-11-10T19:25:41+00:00

Notable peer-reviewed articles–stratifying and classifying diseases in populations using biomarkers identified by proteomic fingerprinting

 

Articles of note

Biomarkers for the prediction of mortality and morbidity in patients with renal replacement therapy

The mortality of end-stage renal disease (ESRD) patients on dialysis remains high despite great improvement of dialysis technologies in the past decades. These patients die due to infectious diseases (mainly sepsis), cardiovascular diseases such as myocardial infarction, heart failure, stroke, and, in particular, sudden cardiac death. End stage renal disease is a complex condition, where the failure of kidney function is accompanied by numerous metabolic changes affecting almost all organ systems of the human body. Many of the biomarker characteristics of the individually affected organ systems have been associated with adverse outcomes. These biomarkers are different in patients with ESRD compared to the general population in the prediction of morbidity and mortality. Read more ›

Intrapersonal and populational heterogeneity of the chemokine RANTES (free full text)

BACKGROUND: Current immunoassays for the chemokine RANTES (regulated on activation, normal T-cell expressed and secreted) are not tailored for specific isoforms that exist endogenously, despite the fact that variants with modified activity are known to exist. This is surprising in view of this protein’s ubiquitous increased presence in many diseases and that the 2 established isoforms are truncated by enzymes also correlated to disease. An in-depth population survey of RANTES heterogeneity in the context of multiple diseases via a mass spectrometric immunoassay (MSIA) may resolve this issue.
METHODS: We developed an MSIA for RANTES and endogenous variants apparent in human plasma. Samples from multiple cohorts of individuals (type 2 diabetes, congestive heart failure, history of myocardial infarction, and cancer patients) were run in parallel with samples from healthy individuals (239 people total). We used 230 microL of plasma per individual and tabulated relative percent abundance (RPA) values for identified isoforms. Read more ›

Making sense of OMICS data in population-based environmental health studies

Although experience from the application of OMICS technologies in population-based environmental health studies is still relatively limited, the accumulated evidence shows that it can allow the identification of features (genes, proteins, and metabolites), or sets of such features, which are targeted by particular exposures or correlate with disease risk. Such features or profiles can therefore serve as biomarkers of exposure or disease risk. Blood-based OMIC profiles appear to reflect to some extent events occurring in target tissues and are associated with toxicity or disease and therefore have the potential to facilitate the elucidation of exposure-disease relationships. Read more ›

Serum proteomic profiling of lung cancer in high-risk groups and determination of clinical outcomes

HYPOTHESIS: Lung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.
METHODS: In the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples. Read more ›

Variation and genetic control of protein abundance in humans (free full text)

Gene expression differs among individuals and populations and is thought to be a major determinant of phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been analysed extensively in human populations, our knowledge is limited regarding the differences in human protein abundance and the genetic basis for this difference. Variation in messenger RNA expression is not a perfect surrogate for protein expression because the latter is influenced by an array of post-transcriptional regulatory mechanisms, and, empirically, the correlation between protein and mRNA levels is generally modest. Here we used isobaric tag-based quantitative mass spectrometry to determine relative protein levels of 5,953 genes in lymphoblastoid cell lines from 95 diverse individuals genotyped in the HapMap Project. Read more ›