0115 966 7955 Today's Opening Times 10:00 - 20:00 (BST)

Application of Proteomics in Disease Diagnosis

Published: Last Edited:

Disclaimer: This essay has been submitted by a student. This is not an example of the work written by our professional essay writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

Abstract

Introduction

Chapter 1: Techniques used in proteomics

Chapter 2: Application of proteomics in the diagnosis of human disease

2.1 Application of proteomics in the diagnosis of blood-related disease

2.2 Application of proteomics in cancer diagnosis

2.3 Application of proteomics in the diagnosis of infectious disease

2.4 Other applications of proteins to human disease diagnosis

Chapter 3: Challenges for proteomics in the diagnosis of human disease

Chapter 4: Conclusions and recommendations for future research

References

Abstract

Although genomics has been the focus of considerable research in recent years, it is now widely acknowledged that proteins are responsible for the biochemical complexity which exists within the cells of the human body due to their dynamic nature and interactions with other proteins. Advancing techniques in proteomics offer a broad range of tools for the identification and characterisation of proteins from human cell and tissue samples. Proteomics plays a major role in the identification and validation of new diagnostic biomarkers for many types of blood-related and infectious diseases and various cancers. Current proteomics techniques and the application of proteomics in each of these disease areas will be examined.

Introduction

The term ‘proteome’ was first used in 1994 and refers to all of the proteins within a cell or tissue (Cristea et al. 2004). Proteomics aims to determine not only the level of expression of a given protein, but also its structure, modifications, localisation and interaction with other proteins (Rezaul et al. 2008). The ultimate goal of proteomics is to study all of the expressed proteins within a given biological system (de Hoog and Mann 1997; Bertucci et al. 2006). Genomics has been the focus of intense research in recent years; sequencing of the human genome is now complete and its size has been shown to be approximately 3,200 Mb. A total of 35,000 protein-coding genes have been identified, comprising just 2% of the total genomic DNA. By studying the genome to better understand the function of these genes, it became apparent that it is difficult to accurately predict gene products from the gene sequence alone, since genes are transcribed in messenger RNA (mRNA). Cells use a process known as alternative splicing to generate diversity. This yields a family of structurally related mRNAs each with a subset of exons that encodes one protein within a whole family of protein isoforms and there is no single relationship between gene sequence and mRNA transcript. Investigating gene expression at the protein level is therefore more informative - it can be argued that proteins are responsible for the true biochemical complexity within cells and tissues since a single gene can result in many different protein isoforms which can become active by post-translational medication or interaction with other proteins. In many human diseases, incorrect modification or conformation leads to the disease state (e.g. in Alzheimer’s disease and Parkinson’s disease, both caused by abnormal folding), and these modifications cannot be detected at the gene level.

In contrast to the human genome, a complete proteome has not yet been characterised for any organism. Further, while the genome is relatively static and unchanging, cellular proteins are highly dynamic and are constantly undergoing changes such as cell membrane binding, interactions with other proteins and/or gaining or losing various chemical groups. Proteins also play a vital role in intra- and inter-cellular communication networks to enable them to respond to the changing needs of the organism. Proteomics has the potential for broad applicability across many areas of medicine and biology and is becoming increasingly widely used in the study of human disease to identify novel biomarkers for use in screening, diagnosis and early detection, and the prediction of therapeutic response (Azad et al. 2006). Although a few clinically approved protein biomarkers are currently available, effective biomarkers are not yet available for many cancers and other diseases and further research is needed to identify suitable markers (Cho 2007). Other promising areas of proteomics research include the investigation of patterns of altered protein expression at the cellular, tissue and subcellular levels; and the identification of new therapeutic protein targets (Hanash 2003). The international Human Proteome Organisation (HUPO) was established in 2001 with the aim of creating a global network to encourage the development of proteomics technologies and sharing knowledge and expertise. A number of databases have also been established to store the sequences of the proteomes of both normal and disease human cells and tissues.

This paper examines techniques for proteome profiling in current practice and discusses present and future applications of proteomics in the diagnosis of human diseases including blood-related disease, various types of cancer, and infectious disease.

Chapter 1: Techniques used in proteomics

1.1 Brief history of the development of proteomics

The earliest technique for mass spectrometry was described by Thompson back in 1899; however, it was not until 1975 that two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) was developed by O’Farrell as a method for separating proteins from Escherichia coli. In 1988, matrix-assisted laser desorption ionisation (MALDI) was developed, a technique that overcame the technical difficulties associated with earlier ionisation methods used in mass spectrometry that allowed large biomolecules to be analysed. One year later, in 1989, the use of electrospray ionisation (ESI) was described offering an additional ionisation method for large biomolecules. In the same year, peptide mass fingerprinting was developed, but this method was widely used due to the need for specialised instruments and it not until the introduction of the first commercial instruments for MALDI mass spectrometry in 1992 that the use of peptide mass fingerprinting became widespread. The use of isotope affinity tags for the quantitative analysis of complex protein mixtures was developed in 1999, followed two years later by the development of the first highly sensitive techniques for protein detection in 2001. These included difference-gel 2D-electrophoresis (DIGE), surface enhanced laser desorption/ionisation time of flight (SELDI-TOF) mass spectrometry and protein arrays. In 2003, techniques for immunodepletion (i.e. the removal of high abundance proteins from protein mixtures to allow the identification and analysis of low abundance proteins) were introduced. Those techniques which have had the greatest impact on furthering our understanding of proteins involved in human disease are discussed below.

1.2 Two-dimensional protein electrophoresis

Two-dimensional (2D) protein electrophoresis was traditionally the method of choice for protein separation, allowing up to 10,000 proteins to be distinguished visually from complex protein mixtures from cells, tissues or body fluids (Andreoli et al. 2006). In the first dimension, proteins are separated on a thin gel layer according to their isoelectric charge (i.e. a pH value at which the overall charge on the protein is equal to zero). A pH gradient is created within the gel such that when an electric current is applied, proteins migrate towards either the anode or cathode, according to their overall charge, to the point where the pH of the gel is equal to the isoelectric point of the protein. In the second dimension, polyacrylamide gel electrophoresis (PAGE) is used to separate proteins according to their size. After both stages of the separation, proteins are visualised by either staining or radioactive labelling. Individual proteins can be excised and purified, for example by mass spectrometry (Kavallaris and Marshall 2005).

While undoubtedly still a core technique for the analysis of complex protein mixtures in human samples, the value of 2D electrophoresis in modern proteomics research is the subject of debate. It is argued that this technique is time-consuming and still lacking adequate automation (Cristea et al. 2004); further, poor reproducibility; the possible loss of certain proteins within a sample during the extraction process; and lack of sensitivity, with certain types of proteins such as membrane proteins, those present in very small amounts, or those very small in size being difficult to visualise, also present issues for concern.

1.3 Mass spectrometry

Mass spectrometry has been increasingly used over the past decade for analyses involving complex samples of proteins. Many of the recent advances in proteomics are down to the increased sensitivity of mass spectrometry and the ability of this technique to detect and characterise low levels of proteins (Cristea et al. 2004). Mass spectrometry measures the mass-to-charge ratio (m/z) of a mixture of ions in the gas phase under vacuum, and the number of ions present at each m/z value. The mass spectrometer consists of a source of ionisation, a mass analyser and a detector (Aebersold and Mann 2003). Proteins are first digested with an enzyme (usually trypsin) which cleaves the protein at specific amino acid sequences. The ionisation source removes electrons from the protein fragments to produce positively charged particles. The charged peptide fragments are separated in the spectrometer according to their m/z, using a method that varies according to the type of spectrometer (e.g. time of flight [TOF] through a flight tube), while the detector measures the signal intensity of each fragment. The resulting mass spectrum consists of a series of peaks, each of which corresponds to a particular peptide fragment present within the sample. The height of the peak is related to the abundance of protein in the sample (Aebersold and Mann 2003).

A major drawback of the older mass spectrometry techniques was that proteins became too fragmented. The development of two newer low-energy ionisation techniques, matrix assisted laser detection of desorption/ionisation (MALDI) and electrospray ionisation (ESI) has been beneficial since they can produce ions in the gas phase from a variety of biomolecules including peptides, proteins, drug metabolites and oligonucleotides, without fragmenting them too much and destroying them (Cristea et al. 2004). A number of commercial mass spectrometers are now available that use either MALDI or ESI.

1.3.1 MALDI-TOF and SELDI-TOF mass spectrometry

In MALDI-TOF mass spectrometry, protein samples are mixed with a laser-reactive matrix solution. The resulting solution is then applied to the surface of a metal MALDI plate and the proteins that are bound to the substrate are ionised. SELDI-TOF mass spectrometry is a combination of chromatography and mass spectrometry that uses an affinity-based method of mass spectrometry comprising a protein chip modified with a chromatographic affinity surface. The protein sample is applied to the chip and captured proteins are ionised using matrix-assisted ionisation. A spectrum of ion m/z ratios is generated from the sample. While individual proteins within the sample cannot be identified from these spectra, the relative abundance of proteins with each m/z ratio can be determined.

1.4 Protein arrays

Protein arrays or protein chips first became available in 2001 and allow the detection and comparison of large number of different proteins. Early approaches attempted to reproduce existing biochemical and immunological assays, such as the enzyme-linked immunosorbent assay (ELISA) in a miniature form (Emili and Cagney 2000); Laurell and Marko-Varga 2002). Protein arrays comprise a library of proteins immobilised on a 2D grid contained on a biochip. These chips are able to extract and retain proteins from media

Various types of protein chips are now available including antibody arrays, antigen arrays and tissue arrays. The major advantage of this type of technology is that it allows large numbers of proteins on a single chip to be scanned simultaneously and data obtained on individual proteins. Limitations of existing protein array techniques include the method of attachment of proteins to the chip, which can result in proteins being fixed in random orientations that can cause damage and inactivation; and the need for prior purification of proteins which adds additional time and cost to this methodology.

1.5 Protein databases

Identification of proteins from the sequence obtained by mass spectrometry has been made possible by the development of a number of protein databases. The first major protein database, Swiss-Prot, was established in 1986 and is maintained by a number of institutions across Europe. Since then, a number of other databases including the Protein Information Resource (OIR), Protein Research Foundation (PRF) and the Protein Data Bank (PDB) have been set up in a variety of different countries.

Chapter 2: Application of proteomics in the diagnosis of human disease

2.1 Application of proteomics in the diagnosis of blood-related disease

According to a quote from Lion and Tissot (2008) in their recent editorial: ...proteomics is pervading all fields of blood-related disciplines...and is beginning to be used as a qualification tool for clinical practices in transfusion services”. Plasma is among the most widely studied of all body fluids. In 1977, a total of just 40 proteins had been identified, while the HUPO Plasma Proteome Project now holds data on a total of 3020 non-redundant proteins (Omenn et al. 2005; States et al. 2006). In contrast to the plasma proteome, the red blood cell and platelet proteomes are both much less well researched. By 2006, a total of 566 red blood cell membrane proteins had been identified (Pasini et al. 2006), with 641 platelet proteins identified in 2005 (Moebiust et al. 2005). Proteomics can be used to diagnose blood-related diseases, by comparison with reference cell lines or healthy control individuals, and study disease pathology. Two examples of the application of proteomics in the diagnosis of blood-related disease are discussed below.

2.1.1 Hodgkins lymphoma

Hodgkin’s lymphoma is a cancer originating in the lymphocytes that is characterised by the presence of large multinucleate cells known as Reed-Sternberg cells. An experiment was conducted to investigate various lymphoid neoplasms including Hodgkin’s lymphoma, Burkitt’s lymphoma, B-cell acute lymphoblastic leukemia, anaplastic large-cell lymphoma, adult T-cell leukemia and cutaneous T-cell lymphoma (Fujii et al. 2005; Fujii et al. 2006). Results showed that the protein profile of Hodgkin’s lymphoma cells was different from other lymphoid neoplasm cells and also from the control cells used in the experiment (i.e. reference B and T cells), and although Hodgkin’s lymphoma cells develop from B cells, they appear to develop their own distinct pattern of protein expression throughout the progression of the disease.

The profile of proteins of Hodgkin and Reed-Steenberg cells in culture have been analysed, in order to investigate their secretory factors that interact with inflammatory cells and which may act as biomarkers (Ma et al. 2008). A total of 1290 proteins were identified in the cell culture medium, of which 368 were believed to be secretory proteins; among these, 37 were classed inflammation or immune response proteins and 26 as cell communication proteins. Further assessment of the inflammation/immune response proteins revealed the presence of classical Hodgkin’s lymphoma-associated chemokines (e.g. TARC, which recruits Th2 and CD4+CD25+ regulatory T cells; IP-10; and RANTES, which attracts T cells, natural killer cells and eosinophils) and some new ones (e.g. fractalkine), together with a group of cytokine-related proteins (e.g. IL-IR2, MIF, CD44 and IL-25). The presence of these new secretory proteins in the supernatant of Hodgkin’s lymphoma cells allow hypotheses to be proposed on the mechanisms by which these cells evade the cytotoxic T-lymphocyte response and trigger the inflammation and proliferation of reactive cells. These proteins may also serve as biomarkers.

2.1.2 Acute leukaemias

2.1.2.1 Acute myeloid leukaemia

Acute myeloid leukaemia (AML) is an aggressive blood cancer that is characterised by the accumulation of myeloid precursor cells in the bone marrow (Sjøholt et al. 2008). Five-year survival rates are typically poor (approximately 50%) with current therapies. Proteomic analysis using 2-DE and MALDI-TOF analysis identified a number of proteins that are differentially expressed between different subtypes of AML cells (López-Pedrera et al. 2006). These included suppressor gene proteins, metabolic enzymes, antioxidants, structural proteins and signal transduction mediators. Of these, seven proteins were found to be significantly altered in AML blast cells compared with normal mononuclear blood cells: alpha-enolase, RhoGD12, annexin A10, catalase, peroxiredoxin 2, tromomyosin 3, and lipocortin 1 (annexin 1). All of these are known to play major role in cellular functions such as glycolysis, tumour suppression, apoptosis, angiogenesis and metastasis and may servce as potential candidates for biomarkers for diagnosis of AML.

2.1.2.2 Childhood acute lymphoblastic leukaemia

In childhood acute lymphoblastic leukaemia (ALL), there is an accumulation of immature lymphocyte cells, known as blast cells blast cells, within the bone marrow. Response to initial glucocorticoid therapy is a reliable predictor of response to multiagent chemotherapy (Lauten et al. 2006). Patients who are resistant to glucocorticoids (i.e. prednisone poor responders, PPR) show poorer survival than glucocorticoid sensitive (i.e. prednisone good responders, PGR) patients. Experiments using 2-DE and ELDI-TOF mass spectrometry were performed to investigate differential protein expression in leukaemic blasts from PPR and PGR patients. Results showed that those proteins overexpressed in the PPR group included catalase, RING finger protein 22 alpha, valosin-containing protein (VCP) and a G-protein-coupled receptor. Further validation of VCP showed that high levels of protein expression are associated with poor prednisone response and this protein may provide a suitable biomarker candidate for diagnosis of glucocorticoid resistance in ALL.

2.2 Application of proteomics in cancer diagnosis

Over 11 million people worldwide are currently diagnosed with cancer every year, a figure estimated to rise to 16 million by the year 2020 (Cho 2007). Biomarkers offer enormous potential in oncology, both as diagnostic tools and in monitoring disease progression and the efficacy and safety of cancer therapies. The application of proteomics to this field provides the opportunity to develop a full understanding of disease pathology, and develop new biomarkers for detection and early diagnosis. The use of proteomics to identify and characterise potential new biomarkers for various blood-related cancers was discussed in the previous chapter. This chapter will focus on the use of proteomics in other types of cancer.

2.2.1 Ovarian cancer

Epithelial ovarian cancer is the leading cause of mortality among the gynaecological cancers due to its asymptomatic nature, lack of early detection markers and resistance to currently available chemotherapies (Jurisicova et al. 2008; Nossov et al. 2008). Ovarian cancer is typically diagnosed at an advanced stage with a survival rate after 5 years of just 20%. Currently available tests for ovarian cancer include CA-125, transvaginal ultrasound, or a combination of both of these but all lack adequate sensitivity and specificity for general screening. There is therefore an urgent need for new biomarkers for diagnosis. A number of proteomics-based research projects have identified several potential candidates. For example, glycoproteomic analyses were used to investigate glycosylated proteins present in media from ovarian cell lines, in sera from patients with ovarian cancer, and in sera from healthy control patients (Li et al. 2008). Analysis of sera from cancer patients showed the presence of 4 abundant glycoproteins. Of these, fibronectin and immunoglobulin A1 were shown to produce N-linked glycan fragments that differed from those in control serum samples and may have potential as disease biomarkers.

2.2.2 Prostate cancer

Prostate cancer is the most common non-cutaneous male cancer in Western Europe and the United States (Byrne et al. 2008; Shariat et al. 2008). While the discovery of prostate-specific antigen (PSA) significantly improved management of this disease during the 1990s, the lack of specificity of PSA and its inability to differentiate between prostate cancer and other benign conditions have led to over-detection and over-treatment in some patients (Byrne et al. 2008; Shariat et al. 2008). There is therefore a need for new prostate cancer biomarkers with improved sensitivity to improve the accuracy of diagnosis. One promising biomarker identified using 2-DE and mass spectrometry is annexin I, which experiments have shown is underexpressed in early stage prostate cancer (Cho 2007). In other research, proteomic analysis of serum samples from 12 patients with either of two grades of prostate cancer (Gleason score 5 or 7) was performed using immunoaffinity depletion and 2D-DIGE. Results showed differential expression of 63 proteins between the 2 groups, with 13 of these differences reaching statistical significance (p0.05) (Byrne et al. 2008). Two of these differentially expressed proteins, pigment epithelium-derived factor (PEDF) and zinc-alpha2-glycoprotein (ZAG) have undergone extensive validation and it has been found that PEDF is a more accurate predictor of early stage prostate cancer than ZAG.

Other blood-based biomarkers that have shown promise in early phase studies include human glandular kallikrein; early prostate cancer antigens; insulin-like growth factor-I (IGF-I) and its binding proteins, IGFBP-2 and IGFBP-3; urokinase plasminogen activation system, transforming growth factor beta1; interleukin-6; chromogranin A, prostate secretory protein; prostate-specific membrane antigen; PCa-specific autoantibodies; and alpha-methylacyl-CoA racemase (Bensalah et al. 2008).

2.2.3 Breast cancer

Breast cancer is a significant cause of morbidity and mortality worldwide. Early detection is key in reducing mortality, but the lack of efficient detection methods hinders diagnosis of this disease (Gast et al. 2008). Isotope-coded affinity tag tandem mass spectrometry has been used to show that alpha2 HS-glycoprotein is under-expressed in nipple aspirate fluid from tumour-containing breasts, while the proteins lipophilin B, beta-globin, hemopexin and vitamin D-binding protein precursor are all overexpressed (Pawlik et al. 2006). The use of other proteomics techniques including MALDI-TOF and SELDI-TOF mass spectrometry have yielded many protein peaks that may be candidate biomarkers, but few of these have been structurally identified (Garrisi et al. 2008). There is therefore considerable potential for new biomarkers for early diagnosis of breast cancer but further research is needed to fully elucidate those candidates identified to date.

2.2.4 Colorectal cancer

Improvements in early detection of colorectal cancer are urgently needed (Liu et al. 2006; Ransohoff et al. 2008). SELDI-TOF mass spectrometry was used to analyse the differential expression of proteins in serum of patients with colorectal cancer and healthy control subjects. Results showed that the pattern of differentially expressed peaks within the spectra obtained could distinguish between patients with colorectal cancer and healthy subjects with 95% sensitivity and specificity (Liu et al. 2006). In other research, immunoblotting and microarray analysis were used to identify six proteins that are overexpressed in colorectal tumour tissues: ANXA3, BMP4, LCN2, SPARC, MMP7 and MMP11 (Madoz-Gurpide et al. 2006). To identify those proteins that are regulated during colorectal cancer, 2-DE was performed using samples of neoplastic colorectal tissue and normal samples for comparison (Xing et al. 2006). The protein secretagogin, which is expressed by the endocrine cells, was shown to be significantly down-regulated in neoplastic colorectal tissue and this protein may therefore be a potential disease biomarker for colorectal cancer.

2.2.5 Bladder cancer

A number of proteins have been shown using 2-DE and mass spectrometry to be associated with bladder cancer including fatty acid binding proteins, annexin V, heat shock protein (Hsp) 27 and lactate dehydrogenase (Sheng et al. 2006). The same researchers also identified a group of proteins which show altered patterns of expression in patients with bladder cancer, and this group includes annexin I, 15-hydroxyprostaglandin dehydrogenase, galactin-1, lysophospholipase and mitochondrial short-chain enoyl-conezyme A hydratase 1 precursor. Further validation of these proteins may identify diagnostic biomarker candidates.

2.2.6 Melanoma

Although melanomas constitute just 4% of all skin cancers, they are responsible for 80% of skin cancer-related deaths and current diagnostic methods are often inadequate (Rezaul et al. 2008). Accurate early diagnosis may reduce the need for adjuvant therapy and long-term follow-up, while the identification of patients at high risk of recurrence may allow for more cost-effective disease management. The use of proteomics to increase understanding of the changes that take place during disease progression and recurrence would therefore be highly beneficial in diagnosis and management.

2.2.7 Other cancers

Proteomics techniques have also been used to investigate differential protein expression and regulation in many other types of cancers in addition to those discussed here. These include oesophageal cancer, gastrointestinal stromal tumours, hepatocellular carcinomas, nasopharyngeal carcinoma, pancreatic cancer, renal cancer, and urothelial carcinoma (Cho 2007).

2.3 Application of proteomics in the diagnosis of infectious disease

Many infectious diseases have been investigated using proteomics including HIV/AIDS, tuberculosis, malaria, measles, hepatitis and severe acute respiratory syndrome (SARS) (List et al. 2008). This paper will discuss the application of proteomics in infectious disease in tuberculosis and SARS.

2.3.1 HIV/AIDS

The human immunodeficiency virus (HIV) infects several different types of cells during the progression of infection to full-blown acquired immune deficiency syndrome (AIDS), and a persistent infection is established in CD4+ T lymphocytes and macrophages (Berro et al. 2007). MALDI-TOF has been used to demonstrate the presence of 17 different proteins on the surface of T-cells infected with HIV virus-1 (HIV-1), compared with parental cells, and the differential expression of a number of these proteins including Bruton’s tyrosine kinase and X-linked inhibitor of apoptosis. These proteins may be of potential value as diagnostic biomarkers for HIV, but further validation and characterisation is needed.

Proteomics has also been used to study the interaction between HIV and mononuclear phagocytes (Ciborowski and Gendelman 2006). Mononuclear phagocytes include bone marrow monocyte-derived macrophages; histiocytes; alveolar macrophages; Kupffer cells; perivascular macrophages; and microglia) and function as reservoirs for HIV. Proteomics has been used to investigate the way in which the virus alters the immunoregulatory activities of mononuclear phagocytes and examine the relationship between the virus and the host cell.

2.3.2 Tuberculosis

Tuberculosis constitutes a considerable global burden. Increased migration has led to growing prevalence rates of TB in countries such as the UK, where this disease has previously been well-controlled through effective screening and immunisation programmes. Further, drug-resistant strains of the causative pathogen, Mycobacterium tuberculosis, are also a growing cause for concern (Kavallaris et al. 2005). A reliable screening test to detect pre-clinical infection is needed to allow early initiation of treatment which could potentially reduce transmission of the disease. The use of proteomics has identified two proteins that are secreted in vitro by common clinical isolates of M. tuberculosis, namely rRv3369 and rRv3874, which may have potential as serodiagnostic antigens (Bank et al. 2004). More recently, proteomic fingerprinting has been used together with mass spectrometry and pattern recognition methods to identify other diagnostic biomarkers for tuberculosis (Agranoff et al. 2006). A total of twenty peaks were found to be the most informative to discriminate between patients with active tuberculosis and control patients, with a diagnostic accuracy of 90%. From these, two particular peptides, serum amyloid A and transthyretin, were identified and quantitated and were shown to have diagnostic accuracies of up to 84% for tuberculosis. It was concluded that these potential biomarkers may be used for accurate diagnosis of tuberculosis and may therefore be valuable for use in new diagnostic tests.

2.3.3 Severe acute respiratory syndrome (SARS)

SARS is a relatively new infectious disease that has already has a significant global impact (Ren et al. 2004). For the effective management and control of SARS, a full understanding of the pathogenesis of this disease and reliable diagnostic methods are required. The use of proteomics to analyse sera from patients with SARS identified truncated forms of (1)-antitrypsin which were consistently found in higher concentrations compared with sera from healthy control patients (Ren et al. 2004). ProteinChip array profiling has also been used to identify serum biomarkers with the potential for use in monitoring disease progression. A total of 12 peaks corresponding to 12 different proteins were differentially expressed in patients with SARS caused by a new strain of coronavirus which was responsible for 774 deaths globally in 2002-2003 (Yip et al. 2002). One of these proteins was identified as serum amyloid A protein, and was of particular interest since changes in peak intensity of this protein were found to be directly correlated with the extent of SARS-coronavirus induced pneumonia (as defined by a serial chest X-ray opacity score).

2.4 Other applications of proteomics in human disease diagnosis

2.4.1 Cardiovascular disease

Cardiovascular disease is a leading cause of morbidity and mortality worldwide (Stanley et al. 2004). The World Health Organization (WHO) has estimated that 20 million people will die annually from this disease over the next decade (Edwards et al. 2008), placing a growing strain on healthcare resources (Anderson 2005). The term cardiovascular disease refers to a range of conditions from atherosclerosis, which can progress over many years, to myocardial infarction, which is a rapid event that can quickly prove to be fatal. The changes involved can also vary enormously, from minor molecular changes such as the activation of coagulation enzymes at an arterial artery rupture site, to large blood clots which can block major arteries. Genetic, environmental and lifestyle factors also play a key role in the development of cardiovascular disease (Anderson 2005). Given this broad diversity, numerous opportunities exist for the development of diagnostic tests to identify all forms of cardiovascular disease. Of particular interest are tests to identify those individuals with existing hypertension or atherosclerosis who may be at future risk of acute events such as myocardial infarction or stroke.

2.4.1.1 Cardiovascular disease biomarkers

Cardiovascular disease is the most likely of all the human diseases to provide serum protein biomarkers (Anderson 2005). Cardiovascular disease pathology typically involves plasma proteins (e.g. the coagulation cascade; lipid transport proteins; or proteins that interact with blood vessel walls and/or platelets). As might be expected, a large number of diagnostic biomarkers have been identified to date. A review published in 2005 described 177 such proteins in three groups (i.e. coagulation pathway, lipid transport or acute phase reactant proteins), all with proven association with cardiovascular disease or stroke and which might be suitable candidates for use in diagnostic tests (Anderson 2005). Of note among these is troponin I (TnI, a muscle contractile protein) which is a marker for myocardial infarction (Jaffe 2001) and is often used in combination with the cardiac isozyme of creatine kinase (CK-MB) and myoglobin. Elevated levels of TnI (i.e. 1 ng/ml) indicates myocardial infarction, thereby allowing treatment to be initiated.

There is currently a lag time of approximately 10 years between the discovery of a cardiovascular biomarker and its commercial use (e.g. in a diagnostic testing kit). For example, cardiac troponin was first identified in the plasma of patients with myocardial infarction in 1987 (Cummins et al. 1987), yet a test incorporating this marker was not introduced until 1995. A number of other markers, including myoglobin, cardiac fatty acid-binding protein and cardiac myosin light chain I have been the subject of research for many years without yet being exploited commercially (Rosano et al. 1977; Kleine et al. 1992; Uchino et al. 1994). In additional to single protein biomarkers, a number of biomarkers have also been identified that comprise protein complexes (e.g. plasmin-alpha(2)-antiplasmin complex [PAP]); modified proteins (e.g. glycated haemoglobin or lipoprotein); antibodies (e.g. to cardiac actin and myosin); and smaller molecules (e.g. homocysteine) (Sakkinen et al. 1999; Schillinger et al. 2003; Ebeling et al. 2003; Abbate et al. 2003).

Despite the finding that so many plasma proteins show changes in patients with cardiovascular disease compared with healthy individuals, in many cases these changes are not specific enough to provide the basis of diagnostic or other clinical tests. For example, the change may be too small, or a similar change may also occur in the pathology of other diseases making accurate diagnosis impossible (Anderson 2005).

2.4.2 Alzheimers disease

Initiation of early treatment of Alzheimer’s disease with acetylcholinesterase inhibitors can improve the progression of this disease; therefore, early diagnosis is essential (Biennow 2005). Over the past twenty years, research has focused on identifying cerebrospinal fluid (CSF) biomarkers for Alzheimer’s disease, which currently include total tau, phosphorylated tau and a 42-amino acid form of the beta- amyloid protein.

2.4.3 Inflammatory bowel disease

The identification of serum biomarkers for inflammatory bowel disease (IBD) has been the subject of intense research in recent years (Li et al. 2008). Among the new biomarkers that have so far been discovered are five antiglycan antibodies: anti-chitobioside IgA (ACCA), anti-laminaribioside IgG (ALCA), anti-manobioside IgG (AMCA) and two antibodies against the chemically synthesised epitopes, Man alpha-1,3 Man alpha-1,2 Man (SigmaMan 3) and Man alpha-1,3 Man alpha-1,2 Man alpha-1,2 Man (SigmaMan 4). Research has also revealed that the prevalence of the ALCA and AMCA antibodies were associated with single nucleotide polymorphisms of IBD-susceptible genes including NOD2/CARD15, NOD1/CARD4, toll-like receptors 2 and 4, and beta-defensin-1. These biomarkers have proved invaluable in the differential diagnosis of Crohn’s disease, ulcerative colitis and non-IBD gut diseases. Other newly identified serum/plasma IBD biomarkers include ubiqutination factor E4A (UBE4A), the chemokine CXCL16, resistin, and apolipoprotein A-IV (Li et al. 2008). Importantly, some of these new serological markers for IBD have been shown to be associated with an earlier age of disease onset and complicated patterns of disease behaviour.

Chapter 3: Challenges for proteomics in the diagnosis of human disease

Although the field of proteomics has advanced rapidly in recent years, a number of technical challenges still remain to be overcome. Samples of human tissue and body fluids are often highly complex and the proteins they contain may have a number of isoforms and other variants. Some proteins may only be expressed at very low levels in certain cells (e.g. low abundance proteins such as transcription factors) and these may be difficult to detect as their presence is masked by those high abundance proteins present within the sample (e.g. cytoskeletal proteins and immunoglobulins). There is a need for the use of protein enrichment techniques to concentrate those proteins present in small amounts, together with detection instruments with increased sensitivity. The Proximity Ligation Polymerase Chain reaction (PCR) analysis is a PCR-based method that uses antibodies that can bind a target protein and DNA strands that are joined by ligation when pairs of antibodies bind the same target protein molecule. Through this process, proteins may be amplified and identified with high sensitivity.

Despite the wealth of recent data on blood proteins, there has been relatively limited gain to date for clinical haematological practice, largely due to difficulties in translating these data into clinically relevant information (Lion and Tissot 2008). Limitations in current methodologies have contributed to these difficulties. The way in which samples are processed can have a significant influence on the resulting profile of proteins and peptides observed. For example, the use of protease inhibitors can dramatically alter the profiles observed using 2D-gel electrophoresis (Olivieri et al. 2001). A relationship has also been demonstrated between the time of sample collection and plasma preparation, with delays of 2 hours influencing the concentration of 99 soluble factors including interleukins, chemokines, and matrix metalloproteinases (Ayache et al. 2006).

More recently research has shown that delays in the isolation of plasma from serum in blood samples results in the appearance of in vitro degradation fragments, mainly consisting of fibrinogen, which arise from exposure of plasma proteins to the clotting system (Davis et al. 2007). The presence of circulating microparticles (i.e small vesicles 1 m in size released by red blood cells and platelets) also presents problems for blood proteomics. Microparticles are not eliminated from serum by low speed centrifugation, unlike red blood cells and platelets, and remain in isolated plasma (Lion and Tissot 2008). The role of these microparticles is unknown, but it is thought that they may function in cell-cell communication, haemostasis or protein degradation and clearance. Analysis of microparticles released from the proteome of a T-lymphocyte cell line demonstrated these consisted of 390 proteins, of which a total of 34% were plasma membrane proteins (Miguet et al. 2006). These, and other in vitro artifacts not discussed here, make developing and implementing standard procedures for blood sample collection and processing difficult and costly (Rai et al. 2005; Tammen 2007). The type and quality of samples can also affect the profile of other proteins in addition to blood proteins and this will require further investigation. Different types of samples may have specific storage requirements in order to preserve the integrity of the proteins within the sample (Kavallaris et al. 2005).

Chapter 4: Conclusions and recommendations for future research

Although genomics has increased our understanding of many human diseases, it is acknowledged that the ‘one gene one protein’ theory no longer holds true. Due to the dynamic nature and considerable diversity of proteins, the investigation of protein expression may therefore provide a greater level of information on disease pathology, leading to improved diagnosis and a greater understanding of disease progression.

Advances in proteomics techniques have addressed some of the earlier limitations of the more traditional methods; for example, automation has allowed for high-throughput of samples and a reduction in manpower, although some remaining challenges must still be overcome. The establishment of proteome databases has been hugely beneficial in the sharing of information among research groups worldwide, and offers considerable benefits in speeding up protein identification and characterisation.

The identification and validation of new serum biomarkers for various human diseases offers considerable potential which has yet to be fully exploited. Although research is well underway to identify potential markers in many diseases, as discussed in this paper, there is much work to be done on the elucidation of suitable candidates, and in incorporating these into commercially available diagnostics tests which will be of clinical benefit.

References

Abbate, R., Sofi, F., Brogi, D., Marcucci, R. 2003, ‘Emerging risk factors for ischemic stroke’, Neurological Sciences, vol. 24, Suppl. 1, pp. S11-2.

Aebersold, R. & Mann, M. 2003, ‘Mass spectrometry-based proteomics’, Nature, vol. 422, pp. 198-207.

Agranoff, D., Fernandez-Reyes, D., Papadopoulos, M. C., Rojas, S. A., Herbster, M., Loosemore, A., Tarelli, E., Sheldon, J., Schwenk, A., Pollok, R., Rayner, C. F., Krishna, S. 2006, ‘Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum’, Lancet, vol. 368, no. 9540, pp. 1012-21.

Anderson, L. 2005, ‘Candidate-based proteomics in the search for biomarkers of cardiovascular disease’, Journal of Physiology, vol. 563, no. 1, pp. 23-60.

Andreoli, T., Ritz, E., Rosivall, L. 2006, ‘Nephrology, hypertension, dialysis, transplantation. In: Dihazi, H., Mueller, G. A. eds, Proteomics in Renal Diseases, State Printing Company, Budapest, pp. 163-82.

Ayache, S., Panelli, M., Marincola, F. M., Stroncek, D. F. 2006, ‘Effects of storage time and exogenous protease inhibitors on plasma protein levels’, American Journal of Clinical Pathology, vol. 126, no. 2, pp. 174-84.

Azad, N. S., Rasool, N., Annunziata, C. M., Minasian, L., Whiteley, G., Kohn, E. C. 2006, ‘Proteomics in clinical trials and practice: present uses and future promise’, Molecular and Cellular Proteomics, vol. 5, no. 10, pp. 1819-29.

Bahk, Y. Y., Kim, S. A., Kim, J. S., Euh, H-J., Bai, G-H., Cho, S-N., Kim, Y. S. 2004, ‘Antigens secreted from Mycobacterium tuberculosis: identification by proteomics approach and test for diagnostic marker’, Proteomics, vol. 4, pp. 3229-307.

Bensalah, K., Lotan, Y., Karam, J. A., Shariat, S. F. 2008, ‘New circulating biomarkers for prostate cancer’, Prostate Cancer and Prostatic Diseases, vol. 11, no. 2, pp. 112-20.

Berro, R., de la Fuente, C., Klase, Z., Kehn, K., Parvin, L., Pumfrey, A., Agbottah, E., Vertes, A., Nekhai, S., Kashanchi, F. 2006, ‘Identifying the membrane proteome of HIV-1 latently infected cells’, Journal of Biological Chemistry, vol. 282, no. 11, pp. 8207-18.

Bertucci, F., Finetti, P., Cervera, N., Maraninchi, D., Viens, P., Birnbaum, D. 2006, ‘Gene expression profiling and clinical outcome in breast cancer’, Omics, vol. 10, no. 4, pp. 429-43.

Biennow, K. 2005, ‘CSF biomarkers for Alzheimer’s disease: use in early diagnosis and evaluation of drug treatment’, Expert Review of Molecular Diagnostics, vol. 5, no. 5, pp. 661-72.

Byrne, J. C., Downes, M. R., O’Donoghue, N., O’Keane, C., O’Neill, A., Fan, Y., Fitzpatrick, J. M., Dunn, M. J., Watson, R. W. 2008, ‘2D-DIGE as a strategy to identify serum markers for the progression of prostate cancer’, Journal of Proteome Research [Epub ahead of print, Dec 18 2008].

Cho, W. C. 2007, ‘Contribution of oncoproteomics to cancer biomarker discovery’, Mol. Cancer, vol. 6, pp. 25.

Ciborowski, P. & Gendelamn, H. E. 2006, ‘Human immunodeficiency virus-mononuclear phagocyte interactions: emerging avenues of biomarker discovery’, Current HIV Research, vol. 4, no. 3, pp. 279-91.

Cristea, I. M., Gaskell, S. J., Whetton, A. D. 2004, ‘Proteomics techniques and their application to hematology’, Blood, vol. 103, pp. 3624-34.

Davis, M. T., Auger, P., Spahr, C., Patterson, S. D. 2007, ‘Cancer biomarker discovery via low molecular weight serum proteome profiling – where is the tumour?’, Proteomics Clinical Applications, vol. 1, no. 12, pp. 1545-8.

de Hoog, C. L. & Mann, M. 1997, ‘Proteomics’, Annual Review of Genetics, vol. 5, pp. 267-93.

Ebeling, F., Petaja, J., Alanko, S., Hirvadniemi, A., Holm, T., Lahde, M., Nuutila, A.,. Pesonen, H., Vahtera, E., Rasi, V. 2003, ‘Infant stroke and beta-2-glycoprotein I antibodies: six cases’, European Journal of Paediatrics, vol. 162, pp. 678-81.

Edwards, A. V., White, M. Y., Cordwell, S. J. 2008, ‘The role of proteomics in clinical cardiovascular biomarker discovery’, Molecular and Cellular Proteomics, vol. 7, no. 10, pp. 1824-37.

Emili, A. Q. & Cagney, G. 2000, ‘Large-scale functional analysis using peptide or protein arrays’, Nature Biotechnology, vol. 18, pp. 393-7.

Fujii, K., Kondo, T., Yokoo, H., Yamada, T., Matsuno, Y., Iwatsuki, K., Hirohashi, S. 2005, ‘Protein expression pattern distinguishes different lymphoid neoplasms’, Proteomics, vol. 5, no. 16, pp. 4274-86.

Fujii, K., Kondo, T., Yamada, M., Iwatsuki, K., Hirohashi, S. 2006, ‘Toward a comprehensive quantitative proteome database: protein expression map of lymphoid neoplasms by 2-D DIGE and MS’, Proteomics, vol. 6, no. 17, pp. 4856-76.

Garrisi, V. M., Abbate, I., Quaranta, M., Mangia, A., Tommasi, S., Paradiso, A. 2008, ‘SELDI-TOF serum proteomics and breast cancer: which perspective?’, Expert Reviews in Proteomics, vol. 5, no. 6, pp. 779-85.

Gast, M. C., Schellens, J. H., Beijnen, J. H. 208, ‘Clinical proteomics in breast cancer: a review’, Breast Cancer Research and Treatment [Epub ahead of print, 11 Dec 2008].

Jaffe, A. S. 2001, ‘New standard for the diagnosis of acute myocardial infarction’, Cardiology Reviews, vol. 9, pp. 318-22.

Jurisicova, A., Jurisica, I., Kislinger, T. 2008, ‘Advances in ovarian cancer proteomics: the quest for biomarkers and improved therapeutic interventions’, Expert Reviews in Proteomics, vol. 5, no. 4, pp. 551-60.

Kavallaris, M. & Marshall, G. M. 2005, ‘Proteomics and disease: opportunities and challenges’, Medical Journal of Australia, vol. 182, no. 11, pp. 575-9.

Kleine, A. H., Glatz, J. F., van Nieuwenhoven, F. A., van der Vusse, G. J. 1992, ‘Release of heart fatty acid-binding protein into plasma after acute myocardial infarction in man’, Molecular and Cellular Biochemistry, vol. 116, pp. 155-62.

Laurell, T. & Marko-Varga, G. 2002, ‘Miniaturisation is mandatory unravelling the human proteome’, Proteomics, vol. 2, pp. 345-51.

Lauten, M., Schrauder, C., Kardinal, C., Harbott, J., Welte, K., Schelgelberger, B., Schrappe, N., von Neuhoff, N. 2006, ‘Unsupervised proteome analysis of human leukaemia cells identifies the valosin-containing protein as a putative marker for glucocorticoid resistance’, Leukemia, vol. 20, no. 5, pp. 820-6.

Li, B., An, H. J., Kirmiz, C., Lebrilla, C. B., Lam, K. S., Miyamoto, S. 2008, ‘Glycoproteomic analyses of ovarian cancer cell lines and sera from ovarian cancer patients show distinct glycosylation changes in individual proteins’, Journal of Proteome Research, vol. 7, no. 9, pp. 3776-88.

Li, X., Conklin, L., Alex, P. 2008, ‘New serological biomarkers of inflammatory bowel disease’, World Journal of Gastroenterology, vol. 14, no. 33, pp. 5115-24.

Lion, N. & Tissot, J-D. 2008, ‘Application of proteomics to hematology: the revolution is starting’, Expert Reviews in Proteomics, vol. 5, no. 3, pp. 375-9.

List, E. O., Berryman, D. E., Bower, B., Sackmannn-Sala, L., Gosney, E., Ding, J., Okada, S., Kopchick, J. J. 2008, ‘The use of proteomics to study infectious diseases’, Infectious Disorders Drug Targets, vol. 8, no. 1, pp. 31-45.

Liu, X. P., Shen, J., Li, Z. F., Gu, J. 2006, ‘A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry’, Cancer Invest, vol. 24, no. 8, pp. 747-53.

López-Pedrera, C., Villalba, J. M., Siendones, E., Barbarroja, N., Gómez-Diaz, C., Rodriguez-Ariza, A., Buendia, P., Torres, A., Velasco, F. 2006, ‘Proteomic analysis of acute myeloid leukemia: identification of potential early biomarkers and therapeutic targets’, Proteomics, vol. 6, Suppl. 1, pp. S293-9.

Ma, Y., Visser, L., Roelofsen, H. et al. 2008, ‘Proteomic analysis of Hodgkin lymphoma: identification of new players involved in the cross-talk between HRS cells and infiltrating lymphocytes’, Blood, vol. 111, no. 4, pp. 2339-46.

Madoz-Gurpide, J., Lopez-Serra, P., Martinez-Torrecuadrada, J. L., Sanchez, L., Lombardia, L., Casal, J. L. 2006, ‘Proteomics-based validation of genomic data: applications in colorectal cancer diagnosis’, Molecular and Cellular Proteomics, vol. 5, pp. 1471-83.

Miguet, L., Pacaud, K., Felden, C., Hugel, B., Martinez , M. C., Freyssinet , J-M., Herbrecht , R., Potier, N., van Dorsselaer , A., Mauvieux, L. 2006, ‘Proteomic analysis of malignant lymphocyte membrane microparticles using double ionization coverage optimization’, Proteomics, vol. 6, no. 1, pp. 153-71.

Moebiust, J., Zahedi, R. P., Lewandrowski, U., Berger, C., Walter, U., Sickmann, A. 2005, ‘The human platelet membrane proteome reveals several new potential membrane proteins’, Molecular and Cellular Proteomics, vol. 4, no. 11, pp. 1754-61.

Nossov, V., Amneus, M., Su, F., Janco, J. M., Reddy, S. T., Farias-Eisner, R. 2008, ‘The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125?’, American Journal of Obstetrics and Gynecology, vol. 199, no. 3, pp. 215-23.

Olivieri, E., Herbert, B., Righetti, P. G. 2001, ‘The effect of protease inhibitors on the two-dimensional electrophoresis pattern of red blood cell membranes’, Electrophoresis, vol. 22, no. 3, pp. 560-5.

Omenn, G. S., States, D. J., Adamski, M. et al. 2005, ‘Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available data base’, Proteomics, vol. 5, no. 13, pp. 3226-45.

Pasini, E. M., Kirkgaard, M., Mortensen, P., Lutz, H. U., Thomas, A. W., Mann, M. 2006, ‘In-depth analysis of the membrane and cystolic proteome of red blood cells’, Blood, vol. 108, no. 3, pp. 791-801.

Pawlik, T. M., Hawke, D. H., Liu, Y., Krishnamurthy, S., Fritsche, H., Hunt, K. K., Kuerer, H. M. 2006, ‘Proteomic analysis of nipple aspirate fluid from women with early-stage breast cancer using isotope-coded affinity tags and tandem mass spectrometry reveals differential expression of vitamin D binding protein’, BMC Cancer, vol. 6, pp. 68.

Rai, A. J., Gelfand, C. A., Haywood, B. C. et al. 2005, ‘HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples’, Proteomics, vol. 5, no. 13, pp. 3262-77.

Ransohoff, D. F., Martin, C., Wiggins, W. S., Hitt, B. A., Keku, T. O., Galamko, J. A., Sandiler, R. S. 2008, ‘Assessment of serum proteomics to detect large colon adenomas’, Cancer Epidemiology Biomarkers and Prevention, vol. 17, no. 8, pp. 2188-93.

Ren, Y., He, Q. Y., Fan, J., Jones, B., Zhou, Y., Xie, Y., Cheung, C. Y., Wu, A., Chiu, J., Peiris, J. S., Tam, P. K. 2004, ‘The use of proteomics in the discovery of serum biomarkers from patients with severe acute respiratory syndrome’, Proteomics, vol. 4, pp. 3477-84.

Rezaul, K., Wilson, L. L., Han, D. K. 2008, ‘Direct tissue proteomics in human diseases: potential applications to melanoma research’, Expert Reviews in Proteomics, vol. 5, no. 3, pp. 405-12.

Rosano, T. G., Sanders, L. A., Johnson, E. S., Kenny, M. A., Clayson, K. J., Strandjord, P. E. 1977, ‘Myoglobin concentrations and muscle-enzyme activities in serum after myocardial infarction and cardiac arrhythmia’, Clinical Chemistry, vol. 23, pp. 868-70.

Sakkinen, P. A., Cushman, M., Psaty, B. M., Rodriguez, B., Boineau, R., Kuller, L. H., Tracy, R. P. 1999, ‘Relationship of plasmin generation to cardiovascular disease risk factors in elderly men and women’, Arteriosclerosis Thromb Vasc Biol, vol. 19, pp. 499-504.

Schillinger, M., Exner, M., Amighi, J., Mlekusch, W., Sabeti, S., Rumpold, H., Wagner, O., Minar, E. 2003, ‘Joint effects of C-reactive protein and glycated haemoglobin in predicting future cardiovascular events of patients with advanced atherosclerosis’, Circulation, vol. 108, pp. 2323-28.

Shariat, S. F., Karam, J. A., Roehrborn, C. G. 2008, ‘Blood biomarkers for prostate cancer detection and prognosis’, Future Oncology, vol. 3, no. 4, pp. 449-61.

Sheng, K. H., Yao, Y. C., Chuang, S. S., Wu, H., Wu, T. F. 2006, ‘Search for the tumor-related proteins of transition cell carcinoma in Taiwan by proteomic analysis’, Proteomics, vol. 6, pp. 1058-65.

Sjøholt, G., Bedringaas, S. L., Doskeland, A. P., Gjertsen, B. T. 2006, ‘Proteomic strategies for individualising therapy of acute myeloid leukemia (AML)’, Current Pharmaceutical Biotechnology, vol. 7, no. 3, pp. 159-70.

Stanley, B. A., Gundry, R. L., Cotter, R. J., van Eyk, J. E. 2004, ‘Heart disease, clinical proteomics and mass spectrometry’, Disease Markers, vol. 20, no. 3, pp. 167-78.

States, D. J., Omenn, G. S., Blackwell, T. W. et al. 2006, ‘Challenges in driving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study’, Nature Biotechnology, vol. 24, no. 3, pp. 333-8.

Tammen, H. 2007, ‘Specimen collection and handling: standardization of blood sample collection’, Methods in Molecular Biology, vol. 428, pp. 35-42.

Uchino, I., Belboul, A., El-Gatit, A., Roberts, D., Berglin, E., William-Olsson, G. 1994, ‘Assessment of myocardial damage by circulating cardiac myosin light chain I after heart transplantation’, Journal of Heart and Lung Transplantation, vol. 13, pp. 418-23.

Yip, T. T., Cho, W. C., Cheng, W. W., Chan, J. W., Ma, V. W., Yip, T. T., Lau Yip, C. N., Ngan, R. K., Law, S. C. 2007, ‘Application of ProteinChip array profiling in serum biomarker discovery for patients suffering from severe acute respiratory syndrome’, Methods in Molecular Biology, vol. 382, pp. 313-31.

Xing, X., Lai, M., Gartner, W., Xu, E., Huang, Q., Li, H., Chen, G. 2006, ‘Identification of differentially expressed proteins in colorectal cancer by proteomics: down-regulation of secretagogin’, Proteomics, vol. 6, pp. 2916-23.


To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Request Removal

If you are the original writer of this essay and no longer wish to have the essay published on the UK Essays website then please click on the link below to request removal:


More from UK Essays

We can help with your essay
Find out more
Build Time: 0.0057 Seconds