Mapping Relative Differences in Human Salivary Gland Secretions by Dried Saliva Spot Sampling and nanoLC–MS/MS

Dried saliva spot sampling is a minimally invasive technique for the spatial mapping of salivary protein distribution in the oral cavity. In conjunction with untargeted nano‐flow liquid chromatography tandem mass spectrometry (nanoLC–MS/MS) analysis, DSS is used to compare the proteomes secreted by unstimulated parotid and submandibular/sublingual salivary glands. Two hundred and twenty proteins show a statistically significant association with parotid gland secretion, while 30 proteins are at least tenfold more abundant in the submandibular/sublingual glands. Protein identifications and label‐free quantifications are highly reproducible across the paired glands on three consecutive days, enabling to establish the core proteome of glandular secretions categorized into eight salivary protein groups according to their biological functions. The data suggest that the relative contributions of the salivary glands fine‐tune the biological activity of human saliva via medium‐abundant proteins. A number of biomarker candidates for Sjögren's syndrome are observed among the gland‐specifically expressed proteins, which indicates that glandular origin is an important factor to consider in salivary biomarker discovery.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Proteomics and its applications for biomarker discovery in human saliva

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.     

Saliva proteome research: current status and future outlook

Human saliva harbours proteins of clinical relevance and about 30% of blood proteins are also present in saliva. This highlights that saliva can be used for clinical applications just as urine or blood. However, the translation of salivary biomarker discoveries into clinical settings is hampered by the dynamics and complexity of the salivary proteome. This review focuses on the current status of technological developments and achievements relating to approaches for unravelling the human salivary proteome. We discuss the dynamics of the salivary proteome, as well as the importance of sample preparation and processing techniques and their influence on downstream protein applications; post-translational modifications of salivary proteome and protein: protein interactions. In addition, we describe possible enrichment strategies for discerning post-translational modifications of salivary proteins, the potential utility of selected-reaction-monitoring techniques for biomarker discovery and validation, limitations to proteomics and the biomarker challenge and future perspectives. In summary, we provide recommendations for practical saliva sampling, processing and storage conditions to increase the quality of future studies in an emerging field of saliva clinical proteomics. We propose that the advent of technologies allowing sensitive and high throughput proteome-wide analyses, coupled to well-controlled study design, will allow saliva to enter clinical practice as an alternative to blood-based methods due to its simplistic nature of sampling, non-invasiveness, easy of collection and multiple collections by untrained professionals and cost-effective advantages.     

Exploring the sialiome using titanium dioxide chromatography and mass spectrometry

Strategies for biomarker discovery increasingly focus on biofluid protein and peptide expression patterns. Post-translational modifications contribute significantly to the pattern complexity and thereby increase the likelihood of obtaining specific biomarkers for diagnostics and disease monitoring. Glycosylation is a common post-translational modification that plays a role e.g. in cell adhesion and in cell-cell and receptor-ligand interactions. Abnormal protein glycosylation has important disease associations, and the glycoproteome is therefore a target for biomarker discovery. Here we present a simple and highly selective strategy for purification of sialic acid-containing glycopeptides (the sialiome) from complex peptide mixtures. The approach utilizes a high and selective affinity of sialic acids for titanium dioxide under specific buffer conditions. In combination with mass spectrometry we used this strategy to characterize the human plasma and saliva sialiomes where 192 and 97 glycosylation sites, respectively, were identified. Furthermore we illustrate the potential of this method in biomarker discovery.     

Detection of Tumor Cell-Specific mRNA and Protein in Exosome-Like Microvesicles from Blood and Saliva

The discovery of disease-specific biomarkers in oral fluids has revealed a new dimension in molecular diagnostics. Recent studies have reported the mechanistic involvement of tumor cells derived mediators, such as exosomes, in the development of saliva-based mRNA biomarkers. To further our understanding of the origins of disease-induced salivary biomarkers, we here evaluated the hypothesis that tumor-shed secretory lipidic vesicles called exosome-like microvesicles (ELMs) that serve as protective carriers of tissue-specific information, mRNAs, and proteins, throughout the vasculature and bodily fluids. RNA content was analyzed in cell free-saliva and ELM-enriched fractions of saliva. Our data confirmed that the majority of extracellular RNAs (exRNAs) in saliva were encapsulated within ELMs. Nude mice implanted with human lung cancer H460 cells expressing hCD63-GFP were used to follow the circulation of tumor cell specific protein and mRNA in the form of ELMs in vivo. We were able to identify human GAPDH mRNA in ELMs of blood and saliva of tumor bearing mice using nested RT-qPCR. ELMs positive for hCD63-GFP were detected in the saliva and blood of tumor bearing mice as well as using electric field-induced release and measurement (EFIRM). Altogether, our results demonstrate that ELMs carry tumor cell–specific mRNA and protein from blood to saliva in a xenografted mouse model of human lung cancer. These results therefore strengthen the link between distal tumor progression and the biomarker discovery of saliva through the ELMs.     

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.     

Enrichment and identification of glycoproteins in human saliva using lectin magnetic bead arrays

Aberrant glycosylation of proteins is a hallmark of tumorigenesis and could provide diagnostic value in cancer detection. Human saliva is an ideal source of glycoproteins due to the relatively high proportion of glycosylated proteins in the salivary proteome. Moreover, saliva collection is noninvasive and technically straightforward, and the sample collection and storage is relatively easy. Although differential glycosylation of proteins can be indicative of disease states, identification of differential glycosylation from clinical samples is not trivial. To facilitate salivary glycoprotein biomarker discovery, we optimized a method for differential glycoprotein enrichment from human saliva based on lectin magnetic bead arrays (saLeMBA). Selected lectins from distinct reactivity groups were used in the saLeMBA platform to enrich salivary glycoproteins from healthy volunteer saliva. The technical reproducibility of saLeMBA was analyzed with liquid chromatography–tandem mass spectrometry (LC–MS/MS) to identify the glycosylated proteins enriched by each lectin. Our saLeMBA platform enabled robust glycoprotein enrichment in a glycoprotein- and lectin-specific manner consistent with known protein-specific glycan profiles. We demonstrated that saLeMBA is a reliable method to enrich and detect glycoproteins present in human saliva.     

Metabolic profiling of human saliva before and after induced physiological stress by ultra-high performance liquid chromatography–ion mobility–mass spectrometry

A method has been developed for metabolite profiling of the salivary metabolome based on protein precipitation and ultra-high performance liquid chromatography coupled with ion mobility-mass spectrometry (UHPLC–IM–MS). The developed method requires 0.5 mL of human saliva, which is easily obtainable by passive drool. Standard protocols have been established for the collection, storage and pre-treatment of saliva. The use of UHPLC allows rapid global metabolic profiling for biomarker discovery with a cycle time of 15 min. Mass spectrometry imparts the ability to analyse a diverse number of species reproducibly over a wide dynamic range, which is essential for profiling of biofluids. The combination of UHPLC with IM–MS provides an added dimension enabling complex metabolic samples to be separated on the basis of retention time, ion mobility and mass-to-charge ratio in a single chromatographic run. The developed method has been applied to targeted metabolite identification and untargeted metabolite profiling of saliva samples collected before and after exercise-induced physiological stress. δ-Valerolactam has been identified as a potential biomarker on the basis of retention time, MS/MS spectrum and ion mobility drift time.     

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.     

Human body fluid proteome analysis

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.     

Analysis of age and gender associated N-glycoproteome in human whole saliva

Background

Glycoproteins comprise a large portion of the salivary proteome and have great potential for biomarker discovery and disease diagnosis. However, the rate of production and the concentration of whole saliva change with age, gender and physiological states of the human body. Therefore, a thorough understanding of the salivary glycoproteome of healthy individuals of different ages and genders is a prerequisite for saliva to have clinical utility.

Methods

Formerly N-linked glycopeptides were isolated from the pooled whole saliva of six age and gender groups by hydrazide chemistry and hydrophilic affinity methods followed by mass spectrometry identification. Selected physiochemical characteristics of salivary glycoproteins were analyzed, and the salivary glycoproteomes of different age and gender groups were compared based on their glycoprotein components and gene ontology.

Results and discussion

Among 85 N-glycoproteins identified in healthy human saliva, the majority were acidic proteins with low molecular weight. The numbers of salivary N-glycoproteins increased with age. Fifteen salivary glycoproteins were identified as potential age- or gender-associated glycoproteins, and many of them have functions related to innate immunity against microorganisms and oral cavity protection. Moreover, many salivary glycoproteins have been previously reported as disease related glycoproteins. This study reveals the important role of salivary glycoproteins in the maintenance of oral health and homeostasis and the great potential of saliva for biomarker discovery and disease diagnosis.     

Inactivation of Human Salivary Glutathione Transferase P1-1 by Hypothiocyanite: A Post-Translational Control System in Search of a Role

Glutathione transferases (GSTs) are a superfamily of detoxifying enzymes over-expressed in tumor tissues and tentatively proposed as biomarkers for localizing and monitoring injury of specific tissues. Only scarce and contradictory reports exist about the presence and the level of these enzymes in human saliva. This study shows that GSTP1-1 is the most abundant salivary GST isoenzyme, mainly coming from salivary glands. Surprisingly, its activity is completely obscured by the presence of a strong oxidizing agent in saliva that causes a fast and complete, but reversible, inactivation. Although salivary α-defensins are also able to inhibit the enzyme causing a peculiar half-site inactivation, a number of approaches (mass spectrometry, site directed mutagenesis, chromatographic and spectrophotometric data) indicated that hypothiocyanite is the main salivary inhibitor of GSTP1-1. Cys47 and Cys101, the most reactive sulfhydryls of GSTP1-1, are mainly involved in a redox interaction which leads to the formation of an intra-chain disulfide bridge. A reactivation procedure has been optimized and used to quantify GSTP1-1 in saliva of 30 healthy subjects with results of 42±4 mU/mg-protein. The present study represents a first indication that salivary GSTP1-1 may have a different and hitherto unknown function. In addition it fulfills the basis for future investigations finalized to check the salivary GSTP1-1 as a diagnostic biomarker for diseases.     

Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations)

Proteomic biomarker discovery has led to the identification of numerous potential candidates for disease diagnosis, prognosis, and prediction of response to therapy. However, very few of these identified candidate biomarkers reach clinical validation and go on to be routinely used in clinical practice. One particular issue with biomarker discovery is the identification of significantly changing proteins in the initial discovery experiment that do not validate when subsequently tested on separate patient sample cohorts. Here, we seek to highlight some of the statistical challenges surrounding the analysis of LC‐MS proteomic data for biomarker candidate discovery. We show that common statistical algorithms run on data with low sample sizes can overfit and yield misleading misclassification rates and AUC values. A common solution to this problem is to prefilter variables (via, e.g. ANOVA and or use of correction methods such as Bonferonni or false discovery rate) to give a smaller dataset and reduce the size of the apparent statistical challenge. However, we show that this exacerbates the problem yielding even higher performance metrics while reducing the predictive accuracy of the biomarker panel. To illustrate some of these limitations, we have run simulation analyses with known biomarkers. For our chosen algorithm (random forests), we show that the above problems are substantially reduced if a sufficient number of samples are analyzed and the data are not prefiltered. Our view is that LC‐MS proteomic biomarker discovery data should be analyzed without prefiltering and that increasing the sample size in biomarker discovery experiments should be a very high priority.     

Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base

Background  

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO).     

Improved candidate biomarker detection based on mass spectrometry data using the Hilbert-Huang transform

Mass spectrometry biomarker discovery may assist patient's diagnosis in time and realize the characteristics of new diseases. Our previous work built a preprocess method called HHTmass which is capable of removing noise, but HHTmass only a proof of principle to be peak detectable and did not tested for peak reappearance rate and used on medical data. We developed a modified version of biomarker discovery method called Enhance HHTMass (E-HHTMass) for MALDI-TOF and SELDI-TOF mass spectrometry data which improved old HHTMass method by removing the interpolation and the biomarker discovery process. E-HHTMass integrates the preprocessing and classification functions to identify significant peaks. The results show that most known biomarker can be found and high peak appearance rate achieved comparing to MSCAP and old HHTMass2. E-HHTMass is able to adapt to spectra with a small increasing interval. In addition, new peaks are detected which can be potential biomarker after further validation.     

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。