Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Role of JAK/STAT signaling in neuroepithelial stem cell maintenance and proliferation in the Drosophila optic lobe

Human urine contains a large number of proteins and peptides (the urinary proteome). Global analysis of the human urinary proteome is important for understanding urinary tract diseases. Bladder cancer is the most common urological cancer with higher incidence rates in endemic areas of Blackfoot disease (BFD) in southern Taiwan. The aim of this study was to use the proteomic approach to establish urinary protein biomarkers of bladder cancer. ADAM28, identified by proteomic approaches and confirmed by Western blotting, showed significant differences compared with normal individuals, so it may be a biomarker of bladder cancer.     

Clinical cancer proteomics: promises and pitfalls

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.     

Serum proteome analysis of vivax malaria: An insight into the disease pathogenesis and host immune response

Vivax malaria is the most widely distributed human malaria resulting in 80-300 million clinical cases every year. It causes severe infection and mortality but is generally regarded as a benign disease and has not been investigated in detail. The present study aimed to perform human serum proteome analysis in a malaria endemic area in India to identify potential serum biomarkers for vivax malaria and understand host response. The proteomic analysis was performed on 16 age and gender matched subjects (vivax patients and control) in duplicate. Protein extraction protocols were optimized for large coverage of the serum proteome and to obtain high-resolution data. Identification of 67 differentially expressed and statistically significant (Student's t-test; p<0.05) protein spots was established by MALDI-TOF/TOF mass spectrometry. Many of the identified proteins such as apolipoprotein A and E, serum amyloid A and P, haptoglobin, ceruloplasmin, and hemopexin are interesting from a diagnostic point of view and could further be studied as potential serum biomarkers. The differentially expressed serum proteins in vivax malaria identified in this study were subjected to functional pathway analysis using multiple software, including Ingenuity Pathway Analysis (IPA), Protein ANalysis THrough Evolutionary Relationships (PANTHER) and Database for Annotation, Visualization and Integrated Discovery (DAVID) functional annotation tool for better understanding of the biological context of the identified proteins, their involvement in various physiological pathways and association with disease pathogenesis. Functional pathway analysis of the differentially expressed proteins suggested the modulation of multiple vital physiological pathways, including acute phase response signaling, complement and coagulation cascades, hemostasis and vitamin D metabolism pathway due to this parasitic infection. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Proteomic Analysis of the Aqueous Humor in Age-related Macular Degeneration (AMD) Patients

Age-related macular degeneration (AMD) can lead to irreversible central vision loss in the elderly. Although large number of growth factor pathways, including the vascular endothelial growth factor (VEGF), has been implicated in the pathogenesis of AMD, no study has directly assessed the whole proteomic composition in the aqueous humor (AH) among AMD patients. The AH contains proteins secreted from the anterior segment tissue, and these proteins may play an important role in the pathogenesis of AMD. Thus, comparisons between the AH proteomic profiles of AMD patients and non-AMD controls may lead to the verification of novel pathogenic proteins useful as potential clinical biomarkers. In this study, we used discovery-based proteomics and Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) to analyze AH from AMD patients and AH from controls who underwent cataract surgery. A total of 154 proteins with at least two unique peptides were identified in the AH. Of these 154 proteins identified by discovery-based proteomics, 10 AH proteins were novel identifications. The protein composition in the AH was different between AMD patients and non-AMD controls. Subsequently, a systematic MRM-MS assay was performed in seven highly abundant differentially expressed proteins from these groups. Differential expression of three proteins was observed in the AH of AMD patients compared with that of cataract controls (p < 0.0312). Elucidation of the aqueous proteome will establish a foundation for protein function analysis and identify differentially expressed markers associated with AMD. This study demonstrates that integrated proteomic technologies can yield novel biomarkers to detect exudative AMD.     

Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons. We tested the hypothesis that proteomic analysis will identify protein biomarkers that provide insight into disease pathogenesis and are diagnostically useful. To identify ALS specific biomarkers, we compared the proteomic profile of cerebrospinal fluid (CSF) from ALS and control subjects using surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS). We identified 30 mass ion peaks with statistically significant (p < 0.01) differences between control and ALS subjects. Initial analysis with a rule-learning algorithm yielded biomarker panels with diagnostic predictive value as subsequently assessed using an independent set of coded test subjects. Three biomarkers were identified that are either decreased (transthyretin, cystatin C) or increased (carboxy-terminal fragment of neuroendocrine protein 7B2) in ALS CSF. We validated the SELDI-TOF-MS results for transthyretin and cystatin C by immunoblot and immunohistochemistry using commercially available antibodies. These findings identify a panel of CSF protein biomarkers for ALS.     

DIGE enables the detection of a putative serum biomarker of fungal origin in a mouse model of invasive aspergillosis

Invasive aspergillosis (IA) is a major threat for immunocompromised patients. Diagnostic difficulties often delay specific treatment initiation, which increases mortality. Finding new biomarkers to improve and speed accurate diagnosis is thus vital. To investigate the ability of proteomic methods for discovering new biomarkers of IA, we used a DIGE approach to perform a proteomic analysis on both bronchoalveolar lavages (BAL) and sera at different time-points of infection in a mouse model of invasive pulmonary aspergillosis. Progression of the infection was monitored using a bioluminescent strain of Aspergillus fumigatus. Sera proteins were enriched using the ProteoMiner kit (Biorad). This method allowed us to identify a fungal protein, the A. fumigatus major allergen Asp f 2, in sera of mice one day after the infection. However, this fungal protein was not detected three days after the infection. Importantly, in BAL, this work provides evidence of an in vivo complement evasion mechanism through the cleavage of C3b into three fragments during aspergillosis. Finally, our results underlining the inflammatory host response to IA in both lung and blood compartments at different times of infection may provide new insights into the pathophysiology of this disease.     

蛋白质组学技术在感染研究中的应用

蛋白质组学的目标在于阐明特定生物体、组织、细胞或亚细胞结构中全部蛋白质的表达模式和功能模式,其技术平台由高通量的蛋白质分离技术、鉴定技术和生物信息学组成。在许多研究领域,蛋白质组学技术为阐明疾病过程和生命现象的分子机制提供了全面、网络和动态的蛋白质组信息。感染是重要的基本致病因素之一,蛋白质组学的研究策略和技术方法有利于快速分离鉴定病原体蛋白质组、宿主免疫细胞蛋白质组、感染相关蛋白、疫苗候：选抗原蛋白、生物标志物和药物靶标,从而明显加快病原体、宿主反应、感染发病机制以及感染预防、诊断和治疗等相关研究的进程。     

Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

The emerging scientific field of proteomics encompasses the identification, characterization, and quantification of the protein content or proteome of whole cells, tissues, or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside, and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (i.e., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets, and the limited understanding of the effects of growth, development, and age on the normal plasma proteome. Strategies to overcome these challenges are discussed.     

Stage-specific quantitative changes in renal and urinary proteome during the progression and development of streptozotocin-induced diabetic nephropathy in rats

Diabetic nephropathy (DN) is a microvascular complication associated with diabetes causing slow deterioration of kidneys leading to end-stage renal disease. Timely intervention and diagnosis are crucial in order to ameliorate and halt the progression of DN. Current diagnosis of DN consists of urine assays for detection of microalbuminuria, which have inadequate specificity and sensitivity. Hence, there arises a need to discover stage-specific biomarkers which can aid in the early detection of DN and also in identifying the mechanisms underlying pathogenesis of DN. Therefore the present study was undertaken to identify the differentially expressed proteins in the urine and to examine the pattern of proteomic changes occurring in the rat kidneys during the course of progression of streptozotocin-induced model of DN in rats. Two-dimensional gel electrophoresis coupled to MALDI-TOF mass spectrometry was employed to identify the differentially expressed proteins under diabetic conditions. Among the identified proteins Calgranulin A and Calgranulin B appeared in the urinary proteome at the fourth week of induction of diabetes while we recorded a time-dependent decrease in the expression of major urinary protein (alpha 2u globulin) in the urine as well as kidneys of diabetic rats. Parallel monitoring of targeted proteomic changes in the renal proteome revealed an increase in histone H2B phosphorylation at serine14 along with a gradual decrease in Bcl-2 and MMP-13 expression during the course of progression and development of streptozotocin-induced DN.     

Global and targeted quantitative proteomics for biomarker discovery

The extraordinary developments made in proteomic technologies in the past decade have enabled investigators to consider designing studies to search for diagnostic and therapeutic biomarkers by scanning complex proteome samples using unbiased methods. The major technology driving these studies is mass spectrometry (MS). The basic premises of most biomarker discovery studies is to use the high data-gathering capabilities of MS to compare biological samples obtained from healthy and disease-afflicted patients and identify proteins that are differentially abundant between the two specimen. To meet the need to compare the abundance of proteins in different samples, a number of quantitative approaches have been developed. In this article, many of these will be described with an emphasis on their advantageous and disadvantageous for the discovery of clinically useful biomarkers.     

Quantitative Proteome Analysis of Brain Subregions and Spinal Cord from Experimental Autoimmune Encephalomyelitis Mice by TMT‐Based Mass Spectrometry

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS); its cause is unknown. To understand the pathogenesis of MS, researchers often use the experimental autoimmune encephalomyelitis (EAE) mouse model. Here, the aim is to build a proteome map of the biological changes that occur during MS at the major onset sites—the brain and the spinal cord. Quantitative proteome profiling is performed in five specific brain regions and the spinal cord of EAE and healthy mice with high‐resolution mass spectrometry based on tandem mass tags. On average, 7400 proteins per region are quantified, with the most differentially expressed proteins in the spinal cord (1691), hippocampus (104), frontal cortex (83), cerebellum (63), brainstem (50), and caudate nucleus (41). Moreover, region‐specific and commonly expressed proteins in each region are identified and bioinformatics analysis is performed. Pathway analysis reveals that protein clusters resemble their functions in disease pathogenesis (i.e., by inducing inflammatory responses, immune activation, and cell–cell adhesion). In conclusion, the study provides an understanding of the pathogenesis of MS in the EAE animal model. It is expected that the comprehensive proteome map of the brain and spinal cord can be used to identify biomarkers for the pathogenesis of MS.     

Top-down label-free LC-MALDI analysis of the peptidome during neural progenitor cell differentiation reveals complexity in cytoskeletal protein dynamics and identifies progenitor cell markers

In the field of stem cell research, there is a strong requirement for the discovery of new biomarkers that more accurately define stem and progenitor cell populations, as well as their differentiated derivatives. The very-low-molecular-weight (<5?kDa) proteome/peptidome remains a poorly investigated but potentially rich source of cellular biomarkers. Here we describe a label-free LC-MALDI-TOF/TOF quantification approach to screen the very-low-molecular-weight proteome, i.e. the peptidome, of neural progenitor cells and derivative populations to identify potential neural stem/progenitor cell biomarkers. Twelve different proteins were identified on the basis of MS/MS analysis of peptides, which displayed differential abundance between undifferentiated and differentiated cultures. These proteins included major cytoskeletal components such as nestin, vimentin, and glial fibrillary acidic protein, which are all associated with neural development. Other cytoskeletal proteins identified were dihydropyrimidinase-related protein 2, prothymosin (thymosin α-1), and thymosin β-10. These findings highlight novel stem cell/progenitor cell marker candidates and demonstrate proteomic complexity, which underlies the limitations of major intermediate filament proteins long established as neural markers.     

蛋白质组学技术及其在生物医学上的应用

蛋白质组学部分承用了创立于二十多年前的二维电泳技术。基于其高分辩能力 ,二维电泳主要用于分离和检测复杂混合物中的蛋白质。虽然没有获得更多的改进 ,但是二维电泳结合了通过质谱测定蛋白质的最新进展而成为蛋白质组学中的一项重要技术。随着人类基因组计划项目的完成及由此而产生的大量基因数据库和使用这些数据的生物信息技术 ,科学家们的下一个目标是解析生物体的完整蛋白质组 ,把蛋白质组学数据与基因组学数据关联起来并有机地结合而成为一项有力的工具以阐明病理学中的蛋白质功能、衰老的过程及发现新药目标蛋白质和疾病标识物等。文章综述了蛋白质组学技术的最新知识及其在生物医学研究中的潜在应用     

The proteomics of prostate cancer exosomes

Exosomes and other microvesicles are emerging as rich reservoirs of tumor-specific proteins and biomarkers for cancer detection and progression. For prostate cancer, exosomes secreted by the prostate can be isolated from prostatic secretions, seminal fluid, tissue, urine or blood for further proteomic analysis. Structurally, prostate-derived exosomes are distinct in size, membrane composition and specific prostate protein content, potentially providing a novel and easily isolatable source of biomarkers from clinical biofluids. The key to these isolation strategies will be the targeting of specific prostatic proteins expressed in these exosomes, thus requiring detailed proteomic characterizations. A summary of ongoing efforts to characterize the proteome of these unique prostate cancer-associated exosomes and their potential applications for use in biomarker assays is presented.     

Method development of efficient protein extraction in bone tissue for proteome analysis

Exploring bone proteome is an important and challenging task for understanding the mechanisms of physiological/pathological process of bone tissue. However, classical methods of protein extraction for soft tissues and cells are not applicable for bone tissue. Therefore, method development of efficient protein extraction is critical for bone proteome analysis. We found in this study that the protein extraction efficiency was improved significantly when bone tissue was demineralized by hydrochloric acid (HCl). A sequential protein extraction method was developed for large-scale proteome analysis of bone tissue. The bone tissue was first demineralized by HCl solution and then extracted using three different lysis buffers. As large amounts of acid soluble proteins also presented in the HCl solution, besides collection of proteins in the extracted lysis buffers, the proteins in the demineralized HCl solution were also collected for proteome analysis. Automated 2D-LC-MS/MS analysis of the collected protein fractions resulted in the identification of 6202 unique peptides which matched 2479 unique proteins. The identified proteins revealed a broad diversity in the protein identity and function. More than 40 bone-specific proteins and 15 potential protein biomarkers previously reported were observed in this study. It was demonstrated that the developed extraction method of proteins in bone tissue, which was also the first large-scale proteomic study of bone, was very efficient for comprehensive analysis of bone proteome and might be helpful for clarifying the mechanisms of bone diseases.     

BiomarkerDigger: A versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers

We have developed a proteome database (DB), BiomarkerDigger ( http://biomarkerdigger.org ) that automates data analysis, searching, and metadata‐gathering function. The metadata‐gathering function searches proteome DBs for protein–protein interaction, Gene Ontology, protein domain, Online Mendelian Inheritance in Man, and tissue expression profile information and integrates it into protein data sets that are accessed through a search function in BiomarkerDigger. This DB also facilitates cross‐proteome comparisons by classifying proteins based on their annotation. BiomarkerDigger highlights relationships between a given protein in a proteomic data set and any known biomarkers or biomarker candidates. The newly developed BiomarkerDigger system is useful for multi‐level synthesis, comparison, and analyses of data sets obtained from currently available web sources. We demonstrate the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.     

Urinary proteomics: a tool to discover biomarkers of kidney diseases

There is intense interest in applying proteomics to urine analysis in order to promote a better understanding of kidney disease processes, develop new biomarkers for diagnosis and detect early factors that contribute to end-stage renal diseases. This interest creates numerous opportunities as well as challenges. To fulfill this task, proteomics requires, in its different stages of realization, various technological platforms with high sensitivity, high throughput and large automation ability. In this review, we will give an overview of promising proteomic methods that can be used for analyzing urinary proteome and detecting biomarkers for different kidney diseases. Furthermore, we will focus on the current status and future directions in investigating kidney diseases using urinary proteomics.     

Proteomic analysis of gingival crevicular fluid for discovery of novel periodontal disease markers

The protein composition of gingival crevicular fluid (GCF) may reflect the pathophysiology of periodontal diseases. A standard GCF proteomic pattern of healthy individuals would serve as a reference to identify biomarkers of periodontal diseases by proteome analyses. However, protein profiles of GCF obtained from apparently healthy individuals have not been well explored. As a step toward detection of proteomic biomarkers for periodontal diseases, we applied both gel-based and gel-free methods to analyze GCF obtained from healthy subjects as compared with supragingival saliva. To ensure optimized protein extraction from GCF, a novel protocol was developed. The proteins in GCF were extracted with high yield by urea buffer combined with ultrafiltration and the intensity of spots with supragingival saliva and GCF was compared using agarose two-dimensional electrophoresis. Eight protein spots were found to be significantly more intense in GCF. They included superoxide dismutase 1 (SOD1), apolipoprotein A-I (ApoA-I), and dermcidin (DCD). Moreover, GCF proteins from healthy subjects were broken down into small peptide fragments and then analyzed directly by LC-MS/MS analysis. A total of 327 proteins including ApoA-I, SOD1, and DCD were identified in GCF. These results may serve as reference for future proteomic studies searching for GCF biomarkers of periodontal diseases.