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

Chemico-Biological Interactions 215 (2014) 7–16

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UPLC–MSE application in disease biomarker discovery: The discoveries in proteomics to metabolomics Ying-Yong Zhao a,⇑, Rui-Chao Lin b,⇑ a Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, The College of Life Sciences, Northwest University, No. 229 Taibai North Road, Xi’an, Shaanxi 710069, PR China b School of Chinese Materia Medica, Beijing University of Chinese Medicine, No. 11 North Third Ring Road, Beijing 100029, PR China

a r t i c l e

i n f o

Article history: Received 17 November 2013 Received in revised form 14 February 2014 Accepted 28 February 2014 Available online 12 March 2014 Keywords: Proteomics Metabolomics Ultra performance liquid chromatography Mass spectrometry MSE Disease biomarker

a b s t r a c t 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. Ó 2014 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2. 3. 4. 5.

6.

7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Ultra performance liquid chromatography–mass spectrometryElevated Energy (UPLC–MSE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 UPLC–MSE-based proteomics application in disease biomarker discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.1. UPLC–MSE-based proteomics in clinical research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5.2. LC–MSE-based proteomics in animal model or cell model research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Metabolomics application in disease biomarker discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.1. UPLC–MSE-based metabolomics in clinical research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 6.2. UPLC–MSE-based metabolomics in animal model research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Conclusion and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

⇑ Tel.: +86 29 88304569; fax: +86 29 88304368 (Y.-Y. Zhao). Tel.: +86 10 84738652; fax: +86 10 84738653 (R.-C. Lin). E-mail addresses: [email protected], [email protected] (Y.-Y. Zhao), [email protected] (R.-C. Lin). http://dx.doi.org/10.1016/j.cbi.2014.02.014 0009-2797/Ó 2014 Elsevier Ireland Ltd. All rights reserved.

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1. Introduction

3. Metabolomics

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.

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 system. It covers a broad range of small molecules such as cholesterol, lipids, peptides, amino acids, nucleic acids, carbohydrates, organic acids and vitamins tries to gain an overall understanding of metabolism and metabolic dynamics related to conditions of disease and drug exposure. As a basis of medical research, small molecule research is now reemerging from the limitations of molecular genetics, genomics, proteomics and other fields that bring with them technologies of immense power and insight. With the rapid development of metabonomic platforms, it is now possible to more completely visualize living organisms; the limited small molecules makes this an easier, more quantitative approach of analysis and answers pivotal problems that could not be fully addressed by the other ‘‘-omics’’ alone [2]. As a powerful analytical platform, the application of metabolomics has remarkably increased in disease diagnosis, drug discovery, drug safety assessment and epidemiology. From bacteria to humans, examples of this principle are accruing at a rapid pace that has been made possible by remarkable recent developments in analytical chemistry.

2. 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 multivariate assessment of protein expression in biological samples and the possible comprehension of complex pathological and physiological events using various techniques to identify and characterize proteins. There has been a growing interest in applying proteomics to research on clinical diagnostics and predictive medicine. Proteins types and concentrations can be followed at set time using proteomics in biomarker discovery and proteome correlation present in a disease state as compared to healthy state can be of high diagnostic value to understand the underlying disease mechanisms. The specific-proteins can aid medicine in earlier diagnosis and treatment disease. Because disease often will involve various protein expressions, a combination of several biomarkers is generally more effective than a single one.

4. 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 matrix a-cyano-4-hydroxycinnamic acid is mixed with an analyte in MALDI-MS. The analyte is desorbed from the matrix with a laser shot and is ionized [3]. Electrospray ionization technique was invented in 1989 [4]. The analyte is ionized from a liquid phase into the gas phase in electrospray ionization. Thus, liquid chromatography (LC) systems could be directly interfaced to mass spectrometers. Liquid chromatography–tandem mass spectrometry (LC–MS/MS) was applied to proteomics. Peptides were separated in LC and the MS/MS then records the intact peptides (full MS) before one precursor ion is selected and fragmented. Fragmentation is commonly produced

Fig. 1. Schematic representation of omics technologies. The flow of information starts from genes to metabolites running through transcripts and proteins.

Y.-Y. Zhao, R.-C. Lin / Chemico-Biological Interactions 215 (2014) 7–16

(A)

(B)

(C)

UPLC Speed

UPLC Separation

UPLC Intensity

9

HPLC Speed

HPLC Separation

HPLC Intensity

Fig. 2. A typical example was analyzed by UPLC and HPLC method to compared UPLC with HPLC on analytical speed (A), resolution (B) and sensitivity (C). The same six compounds were separated by UPLC and HPLC methods in 1 min and 4.5 min, respectively (A). For separation, UPLC could separate 4 compounds completely in 1.6 min with a higher resolution but HPLC could not separate the same compounds in 2.2 min (B). UPLC intensity of compound was 855 with a weak noise but HPLC intensity was 176 with a strong noise (C).

Fig. 3. Three-dimensional plots of retention time, m/z, and intensity from control white male mouse urine using (A) HPLC–MS with a 2.1 cm  100 mm Waters Symmetry 3.5 lm C18 column, eluted with 0–95% linear gradient of water with 0.1% formic acid/acetonitrile with 0.1% formic acid over 10 min at a flow rate of 0.6 mL/min and (B) UPLC–MS with 2.1 cm  100 mm Waters ACQUITY 1.7 lm C18 column, eluted with the same solvents at a flow rate of 0.5 mL/min. UPLC has enabled dramatic increases in chromatographic performance to be obtained for complex mixture separation. This increase in performance was manifested in improved peak separation, increased speed and high sensitivity. UPLC offers significant advantages over conventional reversed-phase HPLC amounting to a more than doubling of peak capacity, an almost 10-fold increase in speed and a 3- to 5-fold increase in sensitivity compared to that generated with a conventional 3.5 lm stationary phase.

by argon or nitrogen collision. The fragments are recorded in a MS/ MS spectrum and the fragmentation pattern reveals a specific D mass for each amino acid in the peptide. With the development of LC, UPLC, as a novel analytical technology, was produced and applied to ‘‘omics’’ study. UPLC operates with sub-2 lm chromatographic particles and a fluid system capable of operating at pressures up to 15,000 psi, providing an increased LC resolution

compared with conventional HPLC using larger particles. The investigator compared UPLC–MS with conventional HPLC–MS under similar analytical conditions are showed in Fig. 2 and threedimensional chromatogram are showed in Fig. 3 [5]. In 2005, Wrona et al. introduced the MSE technique [6], in which two scanning functions are simultaneously used for collection data. In the first function, Q1 is scanned from m/z 50–1000,

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Table 1 UPLC–MSE-based proteomic applications for discovering disease biomarkers in clinical research, animal model and cell model research. Application

Metabolite association

Specimen types

Bipolar disorder Major depressive disorder Schizophrenia Psychiatric disorders

36 proteins 90 phosphopeptides 72 phosphoproteins 488 proteins

Periodontitis

L-plastin, Annexin-1

Spinal cord injury Cystic fibrosis

638 proteins 638 different proteins

Inner ear disorders

20 proteins in inner ear and 8 proteins in otic bone 1584 proteins 1272 proteins

Cells, human serum Human brain tissue Human serum Human prefrontal cortex tissue Human pituitary tissue Human colorectal tissue Human bone marrow plasma Human peripheral blood cells Human gingival crevicular fluid Human seminal plasma Human temporal bone tissue Human inner ear, otic bone Mouse liver ER Brain

Pituitary disorders Colorectal cancer Chronic myeloid leukemia

1007 proteins 56 proteins 54 responsive and 63 resistant proteins

Ankylosing spondylitis

Monocyte protein

480 proteins

Neuroblastoma cell

Respiratory syncytial virus CP CP

1352 proteins 93 extracellular proteins 11 exoproteins

HEp2 cell Strains 1002 and C231 Strains 1002 and C231

Gatifloxacin

12 proteins

Yeast

Botulinum neurotoxin type/G

BoNT, NTNH, HA70, HA17 proteins

Clostridium argentinense strain

Type 2 diabetes Haloperidol or olanzapine treatment for schizophrenia Cisplatin resistance in neuroblastoma

Analytical methods E

References

UPLC–MS UPLC–MSE IMAC, UPLC–MSE GE, UPLC–MSE

[19] [20] [22] [23]

UPLC–MSE UPLC–MSE UPLC–MSE

[24] [25] [27]

2D GE, UPLC–MSE

[28]

UPLC–MS

E

[29]

2DSDS–PAGE, UPLC–MSE 2DE, UPLC–MSE

[30] [31]

UPLC–MSE

[32]

E

UPLC–MS UPLC–MSE

[33] [34]

2DE, MALDI-TOF MS, UPLC– MSE 2DSDS–PAGE, UPLC–MSE UPLC–MSE 2DE, MALDI-TOF MS, UPLC– MSE 2DE, MALDI-TOF MS, AC, UPLC–MSE 1D SDS–PAGE, UPLC–MSE

[35,36] [37] [38] [39] [40] [43]

2DE: two-dimensional electrophoresis; 2DGE: two-dimensional gel electrophoresis; 2DSDS–PAGE: two-dimensional sodium dodecyl sulfate–polyacrylamide gel electrophoresis; AC: affinity chromatography; CP: Corynebacterium pseudotuberculosis; CRC: colorectal cancer; ER: endoplasmic reticulum; GE: gel electrophoresis; IMAC: immobilised metal ion affinity chromatography.

and Q2 (collision cell) uses a normal low collision energy that provides for transmission of intact ions through cell collisions. These ions are then pushed into quadrupole time-of-flight (QTOF) analyzer and detected with high resolution and mass accuracy. The second scan function also scans Q1 over the same mass range; however, Q2 now has a high collision energy that fragments all of the ions transmitted through Q1. The resulting ions are again detected in the QTOF analyzer. In this way, two mass chromatograms are generated, one with information on the intact molecules from the Q1 function, and the other with the fragmented ion information from the Q2 function. A variety of data-processing algorithms can be used to extract metabolite information from these data [7]. The benefits of MSE have been shown to be: MSE can provide parallel alternating scans for acquisition at either low collision energy to obtain precursor ion information (MS) or high collision energy to obtain full-scan accurate mass fragment, precursor ion and neutral loss information (MSE), providing similar information to conventional MS2 (MS/MS) in one analytical run. This ability is of major importance, as it offers the structural information required for the identification of unknown biomarkers in the context of untargeted analyses [8]. Advanced LC–MS improves coverage, sensitivity and throughput and helps address many key needs for proteomics and metabolomics. Multiple LC techniques and their continuous improvements in separation components as well as their hyphened technologies are providing further advances and enabling increasingly effective large-scale proteomics and metabolomics. However, as requirements for analytical high throughput and sensitivity increase, the need for even more sensitive and faster LC techniques continues to discover disease biomarkers. Recently, the UPLC– MSE technique has been proven to be a powerful and reliable analytical approach for top-down proteomics study, especially this

method could substantially overcome ion suppression shortcoming for quantitative proteomics and disadvantage of bottom-up proteomics method [9–12]. UPLC–MSE is becoming increasingly popular in the analysis of biological fluids in the field of metabolomics because they provide high resolution, accurate mass measurement and structural information [13–18]. UPLC–MSE-based proteomics and metabolomics applications for discovering biomarkers of various diseases in clinical research, animal model and cell model research were summarized in Tables 1 and 2.

5. UPLC–MSE-based proteomics application in disease biomarker discovery 5.1. 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 remarkably complex disorder with a multitude of behavioral and biological perturbations. Little is known about the molecular mechanisms that are altered in remitting mental illness patients. Bipolar disorder patients usually experience alternating episodes of hypomania, mania or depression with symptom-free episodes. Proteome profiling was investigated for peripheral blood mononuclear cells and serum from bipolar disorder patients by UPLC–MSE. Cytoskeletal and

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Y.-Y. Zhao, R.-C. Lin / Chemico-Biological Interactions 215 (2014) 7–16 Table 2 UPLC–MSE-based metabolomic applications for discovering disease biomarkers in clinical research and animal model research. Application

Metabolite association

Specimen types

Analytical methods

References

Hepatocarcinoma Lung cancer

Glycocholic acid sn-1 lysoPC(16:0), sn-2 lysoPC(16:0), sn-1 lysoPC(18:0), sn-1 lysoPC(18:1), sn-1 lysoPC(18:2) Alanine, aspartate and glutamate-related metabolites

Human urine Human plasma Human urine

UPLC–HDMSE UPLC–MSE

[47] [48]

UPLC–HDMSE

[49]

Pentose, glucuronate, ascorbate, aldarate, cysteine, methionine, tyrosine, tryptophan, amino sugar, nucleotide sugar

Human urine

UPLC–HDMSE

[50]

284 lipids

Human plasma Serum

UPLC–HDMSE

[51]

UPLC–MS

E

[52,54,58,61]

Urine

UPLC–MSE

[52,55,59,62]

Faece

UPLC–MSE

[56,60]

Kidney tissue

UPLC–HDMSE

[52,57,63]

Urine

UPLC–HDMSE

[64]

Urine

E

UPLC–HDMS

[65]

Serotonin, melatonin, prostaglandin D2, 5-hydroxy-L-tryptophan, N-octadecanoyl tryptophan 5-Methyl tetrahydrofolate, androstenol, adenosinetriphosphate, serotonin, antepan, uridine triphosphate, prostaglandin D2, enkephalin, dopamine, melatonin, pantothenic acid Glycolysis or gluconeogenesis, unsaturated fatty acids, fatty acid and purine metabolism Starch and sucrose, pentose and glucuronate interconversions metabolism

Drosophila

UPLC–HDMSE

[66]

Drosophila

UPLC–HDMSE

[67]

Urine

UPLC–HDMSE

[68]

Urine

UPLC–HDMSE

[69]

Imidazoleacetate, PE(22:6/20:4), PI(22:5/18:0), indoleacrylic acid, tryptophan, 3methyluridine, deoxyadenosine, kynurenic acid, proline, proline betaine, Nacetylleucine, nicotinamide ribotide, xanthurenic acid Phospholipids, amino acids, L-acetylcarnitine, L-carnitine Phenylacetylglycine, creatinine, deoxycytidine, phenylacetaldehyde, tridecanoylglycine, kynurenic acid, xanthurenic acid, pantothenic acid Taurine, hypotaurine, ether lipid, glycerophospholipid, arachidonic acid, tryptophan Octadecanamide, oleamide, tryptophan, citric acid, ursodeoxycholic acid, creatinine, ascorbalamic acid, phenylalanine, 3-O-methyldopa, proline

Urine

UPLC–HDMSE

[70,71]

Serum Urine

UPLC–MSE UPLC–MSE

[72] [73]

Urine Urine

UPLC–HDMSE UPLC–HDMSE

[74] [75]

Jaundice syndrome with liver disease Human liver-stagnation and spleen-deficiency syndrome Osteoarthritis Chronic renal failure

Chronic renal failure

Chronic renal failure Chronic renal failure Kidney yin deficiency Hepatoprotective effect of scoparone Insomnia and treatment of Suanzaoren Jujuboside A treatment for insomnia MIS and treatment of WenXin-Formula Tianqijiangtang-capsule for type 2 diabetes Yeast-induced pyrexia

Pinelliae Rhizoma toxicity Pinelliae Rhizoma toxicity Hepatitis C virus Hyperlipidemia

PC(16:0/18:2), lysoPC(18:1), creatinine, lysoPC(17:0), lysoPC (16:0), dihydrosphingosine, tryptophan, ceramide(18:0/16:0), L-acetylcarnitine, ceramide(18:0/14:0), phytosphingosine Phytosphingosine, adrenosterone, tryptophan, 2,8-dihydroxyadenine, kynurenic acid, creatinine, dihydrosphingosine, dopamine, phenylalanine, ethyl-N2-acetyl-Largininate, N-acetylleucine, 3-O-methyldopa Chenodeoxychrolic acid, palmitic acid, phytosphingosine, lysoPE(18:2/0:0), MG(24:1/ 0:0/0:0), 12-hydroxy-3-oxocholadienic acid, lysoPE(16:0/0:0), 7-ketolithocholic acid Polyunsaturated fatty acids, indoxyl sulfate, p-cresyl sulfate, allantoin, phenylacetylglycine, xanthine Uric acid, aminoadipic acid, 3-methyldioxyindole, glucosamine, cytidine, indoxyl sulfate, cis-aconitic acid, creatinine, estrone, 3-methoxytyrosine, 3-methylguanine Glycocholate, 2-Hydoxybutanoic acid

stress response-related proteins are associated with cell death/survival pathways in mononuclear cells, while inflammatory response was related to serum samples. These results suggested that bipolar disorder patients carry a peripheral fingerprint that has harmful effects on cell function and that could be used to distinguish bipolar disorder patients from healthy subjects despite being in a remission phase. It is hoped that additional study of bipolar disorder patients in the manic and depressed stages could lead to the identification of a molecular fingerprint that could predict episodic switching and for guiding treatment strategy [19]. Major depressive disorder is characterized by feelings of self-esteem and low mood and by loss of interest in activity. Tissue extracts were analyzed UPLC–MSE and 5195 phosphopeptides were identified. 90 proteins showed differential phosphorylation in patient tissues. The majority of these phosphorylated proteins were related to synaptic transmission and cellular architecture not only discovering potential biomarker candidates but mainly highlighting the major depressive disorder pathobiology [20]. Schizophrenia is characterized by complex and dynamically interacting perturbations in multiple neurochemical systems. A lot of theories have been proposed over the years that aim to conceptualize the pathological processes inherent to schizophrenia, including altered neurotransmission and signal transduction, autoimmune dysfunction, neuropeptides.

[21]. The report showed both hyper-serotonemia and hypo-serotonemia were related to the longitudinal course of schizophrenia, suggesting a disturbance of 5-hydroxytryptamine function. However, evidence of these alterations has been collected piecemeal, limiting our understanding of the interactions among relevant biological systems. Therefore, new biomarkers with higher sensitivity and specificity are waiting to emerge. Jaros et al. used immobilized metal ion affinity chromatography for enrichment of phosphoproteins combined with label-free UPLC–MSE for identification and measurement of protein and phosphoprotein levels. The analysis showed 35 proteins from immobilized metal ion affinity chromatography fractions and 72 phosphoproteins from enriched fraction were altered in schizophrenia patients. This study showed that schizophrenia patients feature serum abnormalities in phosphorylation of proteins involved in acute phase response and coagulation pathways [22]. In addition, dorsolateral prefrontal cortex proteome from 12 postmortem brain patients was also analyzed using gel electrophoresis (GE) and label-free UPLC–MSE. 488 proteins were identified and involved predominantly in cytoskeletal architecture, metabolism, transcription/translation and synaptic function [23]. Pituitary proteins were analyzed to provide new insight into pituitary-related disorders mechanism in the above-mentioned research group. Identified 1007 proteins consisted predominantly

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of enzymes, transporters, transcription/translation factors, cell structure and secreted proteins [24]. Colorectal cancer (CRC) is one of the most frequently occurring malignancies in the world, and its prognosis at early stages is poor. 56 proteins from insoluble fractions were differentially expressed between the tumor and adjacent normal tissue. The connections between these proteins are involved in reciprocal networks associated with tumorigenesis, cancer incidence based on genetic disorder and skeletal and muscular disorders. Further validation of a panel of proteins (JUP, KRT5, COL6A1 and TUBB) confirmed the differential expression. These proteins gave specific network information for CRC, and yielded a panel of novel biomarkers and potential targets for treatment [25]. Yang et al. compared the patterns of cysteine oxidation in the membrane fractions between the tumor and non-tumor CRC patient tissues. 31 proteins including 37 oxidationsensitive cysteines were identified by UPLC–MSE. These proteins were observed with IAM-binding cysteines in non-tumoral region more than tumoral region from CRC patients. The protein networks showed redox status is altered by oxidative stress, perhaps leading to changes in cellular functionality that promotes tumorigenesis [26]. Chronic myeloid leukemia is a hematopoietic disorder that is currently considered incurable. The tyrosine kinase product of the Philadelphia chromosome (P210 BCR-ABL) provided a pathogenetic explanation for the initiation of the chronic myeloid leukemia chronic phase and was the molecular therapeutic target for the disease. Protein expression was identified from chronic myeloid leukemia patients using UPLC–MSE proteomic approach. Oxidative lipid metabolism and regulation of the switch from canonical to noncanonical WNT signaling may contribute to chronic myeloid leukemia resistance in the bone marrow compartment [27]. Ankylosing spondylitis (AS) is an inflammatory rheumatic disease. Clinical hallmarks of ankylosing spondylitis include enthesitis, uveitis, sacroiliitis and persistent spinal inflammation. AS remains difficult to diagnose and the pathogenic mechanisms of disease causation and perpetuation are poorly understood. A serum proteomic platform including 2D GE and UPLC–MSE analysis has been used to distinguish AS patients, rheumatoid arthritis patients and healthy subjects by peripheral blood monocytes. The beta subunit of proteasome activator 28 was increased in AS monocytes. The vascular endothelial growth factor, leucocyte extravasation, integrin and Toll-like receptor signaling pathways were associated with rheumatoid arthritis monocytes and AS patients. Increased endoplasmic reticulum stress response pathway was not found in either rheumatoid arthritis monocytes or AS patients. Finally, the proteasome activator 28 complex was shown to increase the generation of human leucocyte antigen B27 antigenic epitopes by proteasome. The results demonstrated monocytes play an important role in the pathogenesis of rheumatoid arthritis and AS patients [28]. In addition, UPLC–MSE proteomic approach was applied to periodontal disease, spinal cord injury, cystic fibrosis and inner ear disorders [29–32]. 5.2. LC–MSE-based proteomics in animal model or cell model research The endoplasmic reticulum (ER) plays a crucial role in the regulation of the cellular response to insulin. ER stress has been known to decrease the insulin sensitivity of the liver and lead to type 2 diabetes. Park et al. performed proteomics of mouse liver ER by UPLC–MSE. 1584 proteins were identified in control and type 2 diabetic mice livers. Comparison of the rough ER and smooth ER proteomes from normal mice showed that proteins included protein synthesis and metabolic processes were enriched in the rough ER, while those related to transport and cellular homeostasis were localized to the smooth ER. In addition, proteins included protein folding and ER stresses were found only in the rough ER. Rough

ER and smooth ER were severely disrupted, including the capacity to resolve ER stress in mouse livers [33]. Haloperidol and olanzapine are widely used antipsychotic drugs for schizophrenia and other psychotic disorders. UPLC–MSE proteomics was employed to identify differentially proteins in rat frontal cortex following subchronic treatment with haloperidol or olanzapine. The profiling identified 531 and 741 annotated proteins in fractions I (cytoplasmic-) and II (membrane enriched-) in two drug treatments. 59 proteins were altered significantly by haloperidol treatment, 74 by olanzapine and 21 were common to both treatments. Pathway analysis revealed that both drugs altered similar classes of proteins associated with cellular assembly/organization, nervous system development/function and neurological disorders. The haloperidol results showed a stronger association with Huntington’s disease signaling, while olanzapine showed stronger effects on glycolysis/gluconeogenesis. This could either relate to a difference in clinical efficacy or side effect profile of the two drugs [34]. Neuroblastoma is one of the most aggressive solid tumors in childhood. Therapy resistance to anticancer drugs represents the major limitation to the effectiveness of clinical treatment. Human neuroblastoma cell SH-SY5Y and its cisplatin resistant counterpart were studied by 2-DE electrophoresis-MS and label-free UPLC–MSE proteomic approach. The results demonstrated nuclear factor-erythroid 2-related factor 2 (Nrf2) pathway play a protective role in normal cells and may represent a potential novel target to control cisplatin resistance in neuroblastoma [35]. Confocal microscopy experiments, enzyme assay, and Western blotting of proteins regulated by Nrf2 provided evidences that Nrf2 pathway in cisplatinresistant neuroblastoma cell line. Western blotting showed the increment of Nrf2 in a resistant cell line was clearly evident. Confocal microscopy experiment showed the Nrf2 signal was mostly distributed in the cytoplasm both in the sensitive cell line and in the resistant cell line. This result was in agreement with Nrf2 activation and translocation into nuclei in the resistant cell line. In addition, proteins that are known to be under the control of Nrf2, antioxidant genes peroxiredoxins were found modulated by current proteomic analysis, and in the case of peroxiredoxin 1, the expression change level was validated by Western blotting [35]. Other evidence of the activation of Nrf2 down stream enzymes came from the measurements of glutathione peroxidase 1 activity. Results revealed an increased activity in resistant cell line compared to the sensitive cell line. Another UPLC–MSE proteomics was applied to study the cellular response to curcumin in a SH-SY5Y cell line sensitive to cisplatin [36]. The results showed that sixty-six proteins were different expressed in response to curcumin treatment in sensitive cells, whereas thirty-two proteins were significantly modulated in treated resistant cells. Functional analysis demonstrated that proteins involved in cellular assembly and organization, biosynthesis and glycolysis were down-regulated by curcumin treatment. Proteome changes were associated to cell cycle arrest in the G2/M phase and accumulation of polyubiquitinated proteins. Human respiratory syncytial virus is a pathogen of the family of Paramyxoviridae, causing severe infection of the lower respiratory tract predominantly in young children and the elderly. A quantitative study was carried out to compare the proteome of respiratory syncytial virus infected versus uninfected cells to determine new pathways regulated during viral infection. The purified peptides were characterized by UPLC–MSE. 1352 proteins were identified and their abundance compared between infected and non-infected cells. Synthesis of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) and 50 -30 -exoribonuclease 2 (XRN2) mRNAs were found to be highly induced upon respiratory syncytial virus infection in a time dependent manner. Accordingly, IFIT3 protein accumulated during the time course of infection. In contrast, little

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variation was observed in XRN2 protein, but different forms were present in infected versus non-infected cells [37]. Bacterial exported proteins represent key components of the host-pathogen interplay. UPLC–MSE was employed for protein identification and quantification form Corynebacterium pseudotuberculosis (CP). 93 proteins were identified with high confidence and 44 proteins were commonly identified in two different strains. Comparative analyses of the exoproteomes of two CP strains were helpful to gain novel insights into the contribution of the exported proteins in the virulence of this bacterium [38]. This research group identified 45 extracellular proteins. The comparative analysis between the strains 1002 and C231 identified 13 and 3 strain-specific proteins, respectively, 11 of which are novel [39]. In addition, UPLC–MSE proteomic approaches were applied to study gatifloxacin, biotherapeutic proteins, influenza vaccination, botulinum neurotoxin type/G, bacterial endosymbiont, Marek’s disease virus and cysteine oxidation [40–46]. 6. Metabolomics application in disease biomarker discovery 6.1. 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 clinical diagnostic indicator that uncovered pathophysiological changes. Dong et al. have discriminated between different types of lysoPCs from lung cancer patients and healthy subjects. 14 pairs of plasma lysoPCs regioisomers were identified. All lung cancer patients had the same 5 lysoPCs metabolic abnormalities, specifically in sn-1 lysoPC(18:1), sn-1 lysoPC(18:2), sn-1 lysoPC(18:0), sn-1 lysoPC(16:0) and sn-2 lysoPC(16:0). Thus, the function of isomers may be related to lung cancer [48]. Understanding syndromes is a core study to develop more efficient therapeutic strategies, classification, and diagnostic criteria for patients. Pharmacological study and clinical practice have shown that patients with liver disease are complicated by jaundice syndrome. Wang’s study established UPLC–HDMSE metabolomics for study the metabolic profiling of jaundice syndrome patients with liver disease. 44 metabolites were identified from jaundice syndrome. Aspartate, glutamate and alanine metabolism and synthesis and ketone body degradation were found to be perturbed in jaundice syndrome patients [49]. Metabolite profiling of human spleen-deficiency and liver-stagnation syndrome was performed by UPLC–HDMSE. 12 urinary metabolites were identified involving metabolic pathways of pentose and glucuronate interconversions, aldarate, ascorbate, tryptophan, methionine, cysteine, tyrosine, nucleotide sugar and amino sugar metabolism. L-homocystine, prolylhydroxyproline, a-N-phenylacetyl-L-glutamine and 2-octenoylcarnitine were effective for the diagnosis of human spleen-deficiency and liver-stagnation syndrome, with an 93.0% sensitivity [50]. Other investigators developed an UPLC–MSE approach to analyze lipid profiling from osteoarthritis patients. Lipid metabolism associated with osteoarthritis and the release of arachidonic acid from phospholipids [51]. 6.2. UPLC–MSE-based metabolomics in animal model research UPLC–based metabolomics has been used to study kidney diseases in the last several years [52]. A series of experimental studies

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have been performed on chronic kidney disease (CKD) animal models to investigate the metabolic profiling of serum, urine, feces and kidney tissues, and these results have led to new insights into the development of CKD [52]. The adenine-induced CKD model has the advantage of being more similar to the development of human chronic renal injury in comparison to genetic models, and these models mirror the progression of renal injury after a prolonged period of development [53]. Increased serum lysoPC (18:1), PC(16:0/18:2), creatinine, lysoPC (16:0) and lysoPC(17:0) and decreased serum tryptophan, dihydrosphingosine, ceramides (18:0/ 16:0), L-acetylcarnitine, ceramides (18:0/14:0) and phytosphingosine were observed in adenine-induced CRF rats. Furthermore, CRF could be predicted according to various metabolites including lysoPC(18:1), lysoPC(17:0), ceramides (18:0/16:0), lysoPC (16:0), creatinine, ceramides (18:0/14:0) and tryptophan [54]. The urine of CRF rats were characterized by increased adrenosterone, phytosphingosine, tryptophan, creatinine, 2,8-dihydroxyadenine and dihydrosphingosine, together with decreased 3-O-methyldopa, N-acetylleucine, dopamine, ethyl-N2-acetyl-L-argininate, kynurenic acid and phenylalanine [55]. The altered metabolites demonstrated perturbations of phospholipids, amino acids and creatinine metabolism in the CRF rats. These results provided evidence for the complex perturbation of phospholipids, amino acids and creatinine metabolism in CKD. It was reported that changes in the fecal metabolite profile, such as palmitic acid, MG(24:1/0:0/ 0:0), chenodeoxychrolic acid, 12-hydroxy-3-oxocholadienic acid, phytosphingosine, lysoPE(16:0/0:0), lysoPE(18:2/0:0) and 7-ketolithocholic acid, could be used as early biomarkers for adenine-induced CRF rats [56]. Kidney metabolomics based on the UPLC–HDMSE was calculated to explore the excretion pattern in the adenine-induced CRF rats. The results showed that the most important CRF-related metabolites were polyunsaturated fatty acids, p-cresyl sulfate and indoxyl sulfate. p-cresyl sulfate and indoxyl sulfate were significantly increased in CRF rats [57]. Further, the above-mentioned UPLC-based metabolomics method was applied to therapeutic effect of ergone. The results showed that some biomarkers were completely reversed by ergone [58–60]. In addition, the results also showed that several biomarkers were reversed completely by the surface layer of Poria cocos [61–63]. UPLC–HDMSE metabonomics was also undertaken to explore thyroxine and reserpine-induced kidney yin deficiency and the therapeutic effect of Liu Wei Di Huang Wan [64]. Natural medicines, widely used for the treatment of a variety of diseases, have recently attracted the interest of the modern scientific community as alternative therapy. Scoparone is an important constituent of Artemisia annua L., and displayed bright prospects in the prevention and therapy of liver injury. The effects and possible mechanisms of scoparone against CCl4-induced liver injury were studied by UPLC–HDMSE metabolomic approach. The identified metabolites were associated with pyrimidine metabolism and primary bile acid biosynthesis. Scoparone has a potential pharmacological effect through regulating multiple disturbed pathways to the normal level [65]. Suanzaoren decoction was used to treat insomnia, and its mechanism remains unclear. UPLC–HDMSE metabonomics was study to explore globally metabolomic characters of the insomnia and the therapeutic effects of suanzaoren decoction. The identified metabolites were associated with perturbations in amino acid and fatty acid metabolism, in response to insomnia through immune and nervous system. The result showed suanzaoren decoction increases sleep activity and exhibits binding affinity for serotonin receptors. The therapeutic effects of suanzaoren decoction may mediate through serotonergic activation [66]. Other study showed metabolic profiling was restored to their baseline values after Jujuboside A treatment for insomnia [67]. UPLC–MSE was designed to explore metabolomic characters of the myocardial ischemia syndrome (MIS) and the therapeutic

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effects of Wen-Xin-Formula. 17 biomarkers were identified and metabolic pathway analysis suggested that the biosynthesis of unsaturated fatty acids metabolism, glycolysis or gluconeogenesis metabolism, purine metabolism and fatty acid biosynthesis networks were acutely disturbed by MIS. Wen-Xin-Formula has potential pharmacological effect through regulating multiple metabolic pathways to normal state [68]. The efficacy and mechanism of Tianqijiangtang-capsule for type 2 diabetes was evaluated by UPLC–MSE metabolomic approach. The changes metabolites suggested that the disorders of pentose and glucuronate interconversions and starch and sucrose metabolism are related to type 2 diabetes and the potential effect of Tianqijiangtang-capsule on all of these metabolic pathways. This work will provide better understanding of the mechanism of Tianqijiangtang-capsule in clinical use [69]. Fever is a prominent feature of many diseases. Fever can be easily judged by body temperature in clinic; however, the pathogenesis of fever is still not well clear. A febrile response is a systemic pathological process that can cause metabolic disorders. UPLC–MSE metabolomics was employed to investigate the urinary biochemical characteristics of yeast-induced pyrexia rats. 16 metabolites were identified as potential biomarkers. The thermoregulatory circuitry of ‘‘endogenous pyrogen"-hypothalamus Na+/ Ca2+-cAMP"’’ was confirmed. The disturbance of tryptophan metabolism might be one of the important mechanisms in explaining the biochemical basis of the febrile response [70]. Further, this method was applied to therapeutic effect of Qingkailing injection. The results demonstrated that the antipyretic effect of Qingkailing injection was performed by repairing the perturbation of amino acids metabolism [71]. The toxicity of natural medicines was also evaluated by UPLC– MSE metabonomic approach. Pinelliae Rhizoma (PR) is used to treat cough, vomiting, infection and inflammation. The toxicity of PR in rats was evaluated by UPLC–MSE. The serum amino acids, phospholipids, L-acetylcarnitine and L-carnitine showed significant differences after oral administration, which indicated the perturbations of phospholipid metabolism, amino acid metabolism and carnitine metabolism in PR-induced rats [72]. Further, urinary metabonomics was used to elucidate metabolome of rats induced by PR. 10 identified biomarkers indicated the perturbations of phenylacetylglycine tryptophan and pantothenic acid metabolism in PR-induced rats [73]. In addition, hepatitis C virus and hyperlipidemia rats were studied by UPLC–HDMSE metabolomics [74,75].

7. 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 emergence of more effective UPLC–MSE technologies and the variety of fractionation approaches, the proteomics and metabolomics have been greatly expanded. It offers significant potential for the discovery of novel candidate biomarkers from disease samples. Currently there is no single platform that represents the perfect technology for proteomics and metabolomics applications, and integration of multiple technologies is often required for detection and quantitation of low abundance proteins and metabolites. The improvement of dynamic range, high throughput, reproducibility and quantitation will further demand to promote technology development and improvement efforts. The new separation and analysis technologies with sensitivity and accuracy including UPLC

separations, ultra performance convergence chromatography (UPC2), and high efficiency QTOF/MS/MSE presently appear promising for future discovery platforms and applications. With improvements in quantitation accuracy, throughput, and robustness, the UPLC–MSE-based proteomics and metabolomics platform may eventually become a powerful tool for biomarkers discovery, disease diagnosis and targeted therapeutics that provides simultaneous measurements of many disease relevant analytes. The informatics and statistical analysis is an important part of proteomics and metabolomics platform. The effective software will be necessary for processing enormous datasets, which may involve run-to-run feature alignment, peak detection, intensity normalization, feature matching to the database, and statistical analysis to generate a list of high confidence potential biomarkers. Based on the complexity and challenge of proteomics and metabolomics study, especially in large scale clinical application, cooperation from different laboratories may be required for standard and better validation of the discovery results and reducing potential biases. The common standards are needed so that platform performance in different laboratories may be easily compared and proteomics results can be effectively shared and used. For proteomics and metabolomics, further developments of LC would likely have significant benefits for broad areas of application. Gene, protein and metabolite interact to execute molecular processes in biological systems. These complex interactions must not be exclusively addressed by a simplified method. The combined use of proteomics and metabolomics permits a more holistic view of biological systems and their alterations in disease. 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 the International S&T Cooperation Projects of Shaanxi Province, China (No. 2013KW31-01), Natural Science Foundation of Shaanxi Provincial Education Department, China (No. 2013JK0811) and Administration of Traditional Chinese Medicine of Shaanxi, China (No. 13-ZY006). References [1] C.X. Kim, K.R. Bailey, G.G. Klee, A.A. Ellington, G. Liu, T.H. Mosley, H. Rehman, I.J. Kullo, Sex and ethnic differences in 47 candidate proteomic markers of cardiovascular disease: the Mayo Clinic proteomic markers of arteriosclerosis study, PLoS One 5 (2010) e9065. [2] J.K. Nicholson, J. Connelly, J.C. Lindon, E. Holmes, Metabonomics: a platform for studying drug toxicity and gene function, Nat. Rev. Drug Discov. 1 (2002) 153– 161. [3] M. Karas, F. Hillenkamp, Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons, Anal. Chem. 60 (1988) 2299–2301. [4] J.B. Fenn, M. Mann, C.K. Meng, S.F. Wong, C.M. Whitehouse, Electrospray ionization for mass spectrometry of large biomolecules, Science 246 (1989) 64–71. [5] I.D. Wilson, J.K. Nicholson, J. Castro-Perez, J.H. Granger, K.A. Johnson, B.W. Smith, R.S. Plumb, 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. 4 (2005) 591–598. [6] M. Wrona, T. Mauriala, K.P. Bateman, R.J. Mortishire-Smith, D. O’Connor, ‘Allin-One’ analysis for metabolite identification using liquid chromatography/

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