Mini-review
UPLC–MSE application in disease biomarker discovery: The discoveries in proteomics to metabolomics

https://doi.org/10.1016/j.cbi.2014.02.014Get rights and content

Highlights

  • Proteomics and metabolomics have been applied to disease biomarker discovery.

  • Proteomics and metabolomics have provided insight on the molecular events for disease.

  • UPLC–MSE is one of the most versatile techniques in proteomics and metabolomics.

  • UPLC–MSE application was highlighted to assess protein and metabolite in diseases.

Abstract

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of different diseases. Proteomics and metabolomics aims to comprehensively identify proteins and metabolites to gain insight into the cellular signaling pathways underlying disease and to discover novel biomarkers for screening, early detection and diagnosis, as well as for determining prognoses and predicting responses to specific treatments. For comprehensive analysis of cellular proteins and metabolites, analytical methods of wider dynamic range higher resolution and good sensitivity are required. Ultra performance liquid chromatography–mass spectrometryElevated Energy (UPLC–MSE) is currently one of the most versatile techniques. UPLC–MSE is an established technology in proteomics studies and is now expanding into metabolite research. MSE was used for simultaneous acquisition of precursor ion information and fragment ion data at low and high collision energy in one analytical run, providing similar information to conventional MS2. In this review, UPLC–MSE application in proteomics and metabolomics was highlighted to assess protein and metabolite changes in different diseases, including cancer, neuropsychiatric pharmacology studies from clinical trials and animal models. In addition, the future prospects for complete proteomics and metabolomics are discussed.

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

References (75)

  • Y.Y. Zhao et al.

    Effect of ergosta-4,6,8(14),22-tetraen-3-one (ergone) on adenine-induced chronic renal failure rat: a serum metabonomics study based on ultra performance liquid chromatography/high-sensitivity mass spectrometry coupled with MassLynx i-FIT algorithm

    Clin. Chim. Acta

    (2012)
  • Y.Y. Zhao et al.

    Urinary metabonomics study on the protective effects of ergosta-4,6,8(14),22-tetraen-3-one on chronic renal failure in rats using UPLC Q-TOF/MS and a novel MSE data collection technique

    Process Biochem.

    (2012)
  • Y.Y. Zhao et al.

    UPLC-Q-TOF/HSMS/MSE-based metabonomics for adenine-induced changes in metabolic profiles of rat faeces and intervention effects of ergosta-4,6,8(14),22-tetraen-3-one

    Chem.-Biol. Interact.

    (2013)
  • Y.Y. Zhao et al.

    Urinary metabonomic study of the surface layer of poria cocos as an effective treatment for chronic renal injury in rats

    J. Ethnopharmacol.

    (2013)
  • Y.Y. Zhao et al.

    Renal metabolic profiling of early renal injury and renoprotective effects of poria cocos epidermis using UPLC Q-TOF/HSMS/MSE

    J. Pharm. Biomed. Anal.

    (2013)
  • P. Wang et al.

    Thyroxine and reserpine-induced changes in metabolic profiles of rat urine and the therapeutic effect of Liu Wei Di Huang Wan detected by UPLC–HDMS

    J. Pharm. Biomed. Anal.

    (2010)
  • B. Yang et al.

    Metabolomic study of insomnia and intervention effects of Suanzaoren decoction using ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry

    J. Pharm. Biomed. Anal.

    (2012)
  • X. Wang et al.

    Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach

    J. Proteomics

    (2012)
  • X. Wang et al.

    Metabolomics study of intervention effects of Wen-Xin-Formula using ultra high-performance liquid chromatography/mass spectrometry coupled with pattern recognition approach

    J. Pharm. Biomed. Anal.

    (2013)
  • X. Gao et al.

    A urinary metabonomics study on biochemical changes in yeast-induced pyrexia rats: a new approach to elucidating the biochemical basis of the febrile response

    Chem. Biol. Interact.

    (2013)
  • Z.H. Zhang et al.

    General toxicity of Pinellia ternata (Thunb.) Berit. in rat: a metabonomic method for profiling of serum metabolic changes

    J. Ethnopharmacol.

    (2013)
  • Z.H. Zhang et al.

    Metabonomic study of biochemical changes in the rat urine induced by Pinellia ternata (Thunb.) Berit

    J. Pharm. Biomed. Anal.

    (2013)
  • H. Sun et al.

    Metabolomic analysis of key regulatory metabolites in hepatitis C virus-infected tree shrews

    Mol. Cell. Proteomics

    (2013)
  • C.X. Kim et al.

    Sex and ethnic differences in 47 candidate proteomic markers of cardiovascular disease: the Mayo Clinic proteomic markers of arteriosclerosis study

    PLoS One

    (2010)
  • J.K. Nicholson et al.

    Metabonomics: a platform for studying drug toxicity and gene function

    Nat. Rev. Drug Discov.

    (2002)
  • M. Karas et al.

    Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons

    Anal. Chem.

    (1988)
  • J.B. Fenn et al.

    Electrospray ionization for mass spectrometry of large biomolecules

    Science

    (1989)
  • I.D. Wilson et al.

    High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies

    J. Proteome Res.

    (2005)
  • M. Wrona et al.

    ‘All-in-One’ analysis for metabolite identification using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry with collision energy switching

    Rapid Commun. Mass Spectrom.

    (2005)
  • K.P. Bateman et al.

    MSE with mass defect filtering for in vitro and in vivo metabolite identification

    Rapid Commun. Mass Spectrom.

    (2007)
  • N.E. Madala et al.

    Collision energy alteration during mass spectrometric acquisition is essential to ensure unbiased metabolomic analysis

    Anal. Bioanal. Chem.

    (2012)
  • H.M. Michiel et al.

    Novel liquid-chromatography columns for proteomics research

    Trends Anal. Chem.

    (2011)
  • T.A. Koutroukides et al.

    Characterization of the human serum depletome by label-free shotgun proteomics

    J. Sep. Sci.

    (2011)
  • A.M. Murad et al.

    Detection and expression analysis of recombinant proteins in plant-derived complex mixtures using nanoUPLC–MSE

    J. Sep. Sci.

    (2011)
  • F. Mbeunkui et al.

    Investigation of solubilization and digestion methods for microsomal membrane proteome analysis using data-independent LC–MSE

    Proteomics

    (2011)
  • X. Wang et al.

    Ultra-performance liquid chromatography coupled to mass spectrometry as a sensitive and powerful technology for metabolomic studies

    J. Sep. Sci.

    (2011)
  • X. Wang et al.

    Metabolomics study on the toxicity of aconite root and its processed products using ultraperformance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition approach and ingenuity pathways analysis

    J. Proteome Res.

    (2012)
  • Cited by (0)

    View full text