Mini-reviewUPLC–MSE application in disease biomarker discovery: The discoveries in proteomics to metabolomics
Graphical abstract
Introduction
Diseases are often discovered in an advanced stage because of the lack of high sensitivity and specificity biomarkers. An early diagnosis is therefore of vital importance in order to increase the survival rate, so novel biomarkers are urgently needed to stratify patients and personalize treatments. Specific biomarker discovery can be used to improve the accuracy of the clinical diagnosis. The process of biomarker discovery involves analysis of biomarkers in clinical patients or animal models. Systems biology including genomics, transcriptomics, proteomics and metabolomics offers enormous potential to understand the complexity of diseases. Genomics, transcriptomics, proteomics and metabolomics are related to the genome (DNA), the transcriptome (RNA), proteome (proteins) and metabolome (metabolites), respectively (Fig. 1). Currently, biomarker assessment is based on the quantification of a few proteins or metabolites [1]. Proteomics and metabolomics have an important effect on disease studies because of their unique strengths and because of the potential central pathogenic contribution of pathological proteins or metabolites to diseases. High throughput platforms such as proteomics and metabolomics can offer simultaneous readouts of hundreds of proteins and metabolites. In this review, we summarize the UPLC–MSE-based proteomics and metabolomics platforms that are currently applied in disease research and that may lead to the identification of novel biomarkers with clinical utility.
Section snippets
Proteomics
The Human Proteome Organization emerged from the Human Genome Project as a means of understanding gene and protein functions that may lead to the understanding of diseases and to the identification of diagnostic/prognostic biomarkers. Since proteins are responsible for all biological processes, changes in the concentration and structures are likely to reflect disease change, thereby making proteins attractive candidates in biomarker discovery. Proteomics is an emerging discipline for the
Metabolomics
Metabolomics is defined as the “quantitative measurement of the dynamic multi-parametric metabolic responses of living systems to pathophysiological stimuli or genetic modifications” [2]. Metabolomics is used to characterize the biochemical patterns of the endogenous metabolic compounds of serum, plasma, urine and tissue. In contrast to traditional biochemical approach that often focuses on a single metabolite, metabolomics is the analysis of collection small molecules that are found within a
Ultra performance liquid chromatography–mass spectrometryElevated Energy (UPLC–MSE)
The first proteomic techniques were developed in the 1970s. Initially, Edman sequencing was used but the major hurdle was the identification of proteins. This technique has been replaced by the biological mass spectrometry (MS). The first Nobel Prize for MS was awarded in 1920. The MS allowed separation of different isotopes. More recently, two inventions made it possible to analyze DNA, peptides and proteins by MS. Matrix-assisted laser desorption/ionization (MALDI) was invented in 1987. A
UPLC–MSE-based proteomics in clinical research
Clinical proteomics aims to comprehensively identify and quantify proteins in patient samples to gain insight into the cellular signaling pathways underlying disease and to discover novel biomarkers for screening, diagnosis and prognoses to specific treatments. Table 1 displays UPLC–MSE-based proteomics applications for discovering biomarkers of various diseases in clinical chemistry.
Neuropsychiatric diseases including bipolar disorder, major depressive disorder and schizophrenia are a
UPLC–MSE-based metabolomics in clinical research
Cancer biomarker discovery is important for early diagnosis, disease mechanism elucidation, and targeted therapy for the disease. Ultra-performance liquid-chromatography coupled with high-definition MSE (UPLC–HDMSE) metabolomics was used to identify and measure the metabolite profile of glycocholic acid from hepatocarcinoma patients. Urinary glycocholic acid expression was increased and primary and secondary bile acid biosynthesis and bile secretion were disturbed [47]. LysoPCs can be a
Conclusion and perspectives
The development and application of proteomics and metabolomics has increased tremendously over the decade. LC techniques coupled with MS improve coverage, sensitivity, and throughput and help address many key needs for proteomics and metabolomics research. the disease biomarker discovery need to analyze vast samples to obtain statistically relevant results demands high-throughput analytical platforms where LC separations must achieve maximal peak capacity in a short time period. With the
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgements
This study was supported by Program for New Century Excellent Talents in University, China (No. NCET-13-0954) and Changjiang Scholars and Innovative Research Team in University, China (No. IRT1174) National Natural Science Foundation of China, China (Nos. J1210063, 81001622, 81073029), As a Major New Drug to Create a Major National Science and Technology Special, China (Nos. 2011ZX09401-308-034, 2014ZX09304-307-02), China Postdoctoral Science Foundation, China (No. 2012M521831), Key Program for
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