Proteomic Biomarker Discovery Research Articles

Predictive proteomic biomarkers for inflammatory bowel disease-associated cancer: Where are we now in the era of the next generation proteomics?

Recent advances in genomic medicine have opened up the possibility of tailored medicine that may eventually replace traditional "one-size-fits all" approaches to the treatment of inflammatory bowel disease (IBD). In addition to exploring the interactions between hosts and microbes, referred to as the microbiome, a variety of strategies that can be tailored to an individual in the coming era of personalized medicine in the treatment of IBD are being investigated. These include prompt genomic screening of patients at risk of developing IBD, the utility of molecular discrimination of IBD subtypes among patients diagnosed with IBD, and the discovery of proteome biomarkers to diagnose or predict cancer risks. Host genetic factors influence the etiology of IBD, as do microbial ecosystems in the human bowel, which are not uniform, but instead represent many different microhabitats that can be influenced by diet and might affect processes essential to bowel metabolism. Further advances in basic research regarding intestinal inflammation may reveal new insights into the role of inflammatory mediators, referred to as the inflammasome, and the macromolecular complex of metabolites formed by intestinal bacteria. Collectively, knowledge of the inflammasome and metagenomics will lead to the development of biomarkers for IBD that target specific pathogenic mechanisms involved in the spontaneous progress of IBD. In this review article, our recent results regarding the discovery of potential proteomic biomarkers using a label-free quantification technique are introduced and on-going projects contributing to either the discrimination of IBD subtypes or to the prediction of cancer risks are accompanied by updated information from IBD biomarker research.

Proteomic Approach to Gastrointestinal Stromal Tumor Identified Prognostic Biomarkers

Biomarker development is a major research theme in cancer proteomics. Cancer is a genetically and clinically diverse disease, and biomarkers for risk stratification therapy are urgently required. A considerable number of biomarker candidates have been discovered by proteomics, and over the last decade proteomics modalities have been developed to identify promising candidates. Validation studies involving hundreds of samples in independent cohorts is the next challenge to prove the clinical utility of any discovered biomarker candidates. Here, we review our efforts directed toward tissue biomarker development using a proteomics approach. With the aim of developing a prognostic biomarker for gastrointestinal stromal tumor (GIST), we examined the protein expression profiles of primary tumors from 17 GIST patients with different risks of recurrence and prognosis after surgery. Through a comparative study using two-dimensional difference gel electrophoresis and mass spectrometry, we found that overexpression of pfetin was specific to GIST patients with a low risk of metastasis and a favorable prognosis after surgery. Using immunohistochemistry, we examined pfetin expression in 422 additional cases of GIST at four hospitals, and confirmed that GIST patients with pfetin-positive primary tumors had a significantly favorable prognosis in all four cohorts. Moreover, the other research group independently validated the prognostic significance of pfetin in 62 cases of GIST at two hospitals. Pfetin was found to be an independent prognostic factor with significant prognostic utility in all risk classification groups, which are based on tumor size and mitosis status. In addition to pfetin, we also identified DDX39 as a biomarker of unfavorable prognosis using a proteomics approach, and KCTD10 as a marker of favorable prognosis using a knowledge-based approach. Our experience demonstrates the utility of proteomics for biomarker discovery, and the possible clinical application of pfetin for risk stratification therapy in GIST.

Mass Spectrometry in Clinical Diagnosis: A Preliminary Application in Tumor Cellular Proteomics for Biomarker Discovery

Cancer is a group of diseases characterized by uncontrolled growth and spread of abnormal cells, which cause high mortality rates. The purpose of this study is to characterize the proteins secreted from the HepG2, MCF-7 and HT-29 cells, which may relate to cell differentiation and tumor metastasis. In the proteomic analysis, the secretome proteins were identified by reverse phase nano-high performance liquid chromatography/electrospray ionization tandem mass spectrometry (RP-nano-UPLC-ESI-MS/MS) followed by peptide fragmentation pattern analysis. Moreover, identifications of tumor cell protein expressions by proteomic approaches were indicated that several proteins may activate or enhance the PI3K/AKT/mTOR pathway. The PI3K/AKT/mTOR pathway has been shown to be related to the cancer cell metastasis and proliferation, and the ubiquitin C (UBC) may serve as potential protein diagnostic biomarker to be examined in further investigations.

The potential of biomarkers in psychiatry: focus on proteomics

The etiology and pathogenesis of many psychiatric disorders are unclear with many signaling pathways and complex interactions still unknown. Primary information provided from gene expression or brain activity imaging experiments is useful, but can have limitations. There is a current effort focusing on the discovery of diagnostic and prognostic proteomic potential biomarkers for psychiatric disorders. Despite this work, there is still no biological diagnostic test available for any mental disorder. Biomarkers may advance the care of psychiatric illnesses and have great potential to knowledge of psychiatric disorders but several drawbacks must be considered. Here, we describe the potential of proteomic biomarkers for better understanding and diagnosis of psychiatric disorders and current putative biomarkers for schizophrenia, depression, autism spectrum disorder and attention deficit/hyperactivity disorder.

Two-Dimensional Electrophoresis-Based Selection of Aptamers Against an Unidentified Protein in a Tissue Sample

A fully automated two-dimensional electrophoresis (2DE) system was employed for DNA aptamer selection against an unidentified protein in a mouse liver tissue extract as a model target. A 2DE-based systematic evolution of ligands by exponential enrichment (2DE-SELEX) was demonstrated for aptamer selection against a single protein spot that was separated on a nitrocellulose membrane. After four iterative 2DE-SELEX cycles, the oligonucleotide pool was sequenced and aptamer sequences were identified. A blotting assay showed that an identified aptamer with a stable stem–loop structure had specific binding activity against the target protein. The 2DE-SELEX was shown to be promising for the development of aptamers against unidentified proteins in complex samples for proteomic analysis and biomarker discovery. Supplemental materials are available for this article. Go to the publisher's online edition of Analytical Letters to view the supplemental file.

Biomarkers for Fatal Immune Response to Stem Cell Treatment Could Reduce Mortality

To improve the efficacy of allogeneic stem cell transplantation, a method is needed for identify- ing stem cell transplant recipients who are at high risk for graft-versus-host disease (GVHD), a potentially fatal condition in which transplanted immune cells recognize and attack the recipient's tissues as foreign. Despite advances that have reduced the impact of GVHD, it remains a lead- ing cause of death among patients who receive stem cells from another person, which is the rea- son such transplantations are often used as a last resort in treating blood and bone marrow cancers including leukemia and multiple myeloma. In this article, the discovery of proteomic GVHD biomarkers, their validation in large retrospective datasets and relevant clinical applications within the context of stem cell transplantation will be

Application of fluorescence two-dimensional difference in-gel electrophoresis as a proteomic biomarker discovery tool in muscular dystrophy research.

In this article, we illustrate the application of difference in-gel electrophoresis for the proteomic analysis of dystrophic skeletal muscle. The mdx diaphragm was used as a tissue model of dystrophinopathy. Two-dimensional gel electrophoresis is a widely employed protein separation method in proteomic investigations. Although two-dimensional gels usually underestimate the cellular presence of very high molecular mass proteins, integral membrane proteins and low copy number proteins, this method is extremely powerful in the comprehensive analysis of contractile proteins, metabolic enzymes, structural proteins and molecular chaperones. This gives rise to two-dimensional gel electrophoretic separation as the method of choice for studying contractile tissues in health and disease. For comparative studies, fluorescence difference in-gel electrophoresis has been shown to provide an excellent biomarker discovery tool. Since aged diaphragm fibres from the mdx mouse model of Duchenne muscular dystrophy closely resemble the human pathology, we have carried out a mass spectrometry-based comparison of the naturally aged diaphragm versus the senescent dystrophic diaphragm. The proteomic comparison of wild type versus mdx diaphragm resulted in the identification of 84 altered protein species. Novel molecular insights into dystrophic changes suggest increased cellular stress, impaired calcium buffering, cytostructural alterations and disturbances of mitochondrial metabolism in dystrophin-deficient muscle tissue.

Statistical Design for Biospecimen Cohort Size in Proteomics-based Biomarker Discovery and Verification Studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Identifying differentially expressed proteins in two-dimensional electrophoresis experiments: inputs from transcriptomics statistical tools

Two-dimensional electrophoresis is a crucial method in proteomics that allows the characterization of proteins' function and expression. This usually implies the identification of proteins that are differentially expressed between two contrasting conditions, for example, healthy versus diseased in human proteomics biomarker discovery and stressful conditions versus control in animal experimentation. The statistical procedures that lead to such identifications are critical steps in the 2-DE analysis workflow. They include a normalization step and a test and probability correction for multiple testing. Statistical issues caused by the high dimensionality of the data and large-scale multiple testing have been a more active topic in transcriptomics than proteomics, especially in microarray analysis. We thus propose to adapt innovative statistical tools developed for microarray analysis and incorporate them in the 2-DE analysis pipeline. In this article, we evaluate the performance of different normalization procedures, different statistical tests and false discovery rate calculation methods with both real and simulated datasets. We demonstrate that the use of statistical procedures adapted from microarrays lead to notable increase in power as well as a minimization of false-positive discovery rate. More specifically, we obtained the best results in terms of reliability and sensibility when using the 'moderate t-test' from Smyth in association with classic false discovery rate from Benjamini and Hochberg. The methods discussed are freely available in the 'prot2D' open source R-package from Bioconductor (http://www.bioconductor.org//) under the terms of the GNU General Public License (version 2 or later). sebastien.artigaud@univ-brest.fr or sebastien.artigaud@gmx.com.

Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: Assessment of technical and inter-individual variation

Background Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.

Seminal Plasma as a Source of Prostate Cancer Peptide Biomarker Candidates for Detection of Indolent and Advanced Disease

BackgroundExtensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease.Methodology/Principal FindingsIn an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.Conclusions/SignificanceSeminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.

An effect size filter improves the reproducibility in spectral counting-based comparative proteomics

The microarray community has shown that the low reproducibility observed in gene expression-based biomarker discovery studies is partially due to relying solely on p-values to get the lists of differentially expressed genes. Their conclusions recommended complementing the p-value cutoff with the use of effect-size criteria. The aim of this work was to evaluate the influence of such an effect-size filter on spectral counting-based comparative proteomic analysis. The results proved that the filter increased the number of true positives and decreased the number of false positives and the false discovery rate of the dataset. These results were confirmed by simulation experiments where the effect size filter was used to evaluate systematically variable fractions of differentially expressed proteins. Our results suggest that relaxing the p-value cut-off followed by a post-test filter based on effect size and signal level thresholds can increase the reproducibility of statistical results obtained in comparative proteomic analysis. Based on our work, we recommend using a filter consisting of a minimum absolute log2 fold change of 0.8 and a minimum signal of 2-4 SpC on the most abundant condition for the general practice of comparative proteomics. The implementation of feature filtering approaches could improve proteomic biomarker discovery initiatives by increasing the reproducibility of the results obtained among independent laboratories and MS platforms. Quality control analysis of microarray-based gene expression studies pointed out that the low reproducibility observed in the lists of differentially expressed genes could be partially attributed to the fact that these lists are generated relying solely on p-values. Our study has established that the implementation of an effect size post-test filter improves the statistical results of spectral count-based quantitative proteomics. The results proved that the filter increased the number of true positives whereas decreased the false positives and the false discovery rate of the datasets. The results presented here prove that a post-test filter applying a reasonable effect size and signal level thresholds helps to increase the reproducibility of statistical results in comparative proteomic analysis. Furthermore, the implementation of feature filtering approaches could improve proteomic biomarker discovery initiatives by increasing the reproducibility of results obtained among independent laboratories and MS platforms. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

Quantitative Label-Free Proteomics for Discovery of Biomarkers in Cerebrospinal Fluid: Assessment of Technical and Inter-Individual Variation

BackgroundBiomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.Methods/ResultsFourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i) incomplete MAF and keratins; ii) globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii) oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5%) whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and individual samples.ConclusionsQuantitative label-free LC-MS/MS can measure scores of CSF proteins with low technical variability and can segregate samples according to desired criteria. Thus, this technique shows potential for biomarker discovery for neurological diseases.

Current Status and Advances in Quantitative Proteomic Mass Spectrometry

The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.

Molecular biomarkers in glaucoma.

The seventh annual ARVO/Pfizer Ophthalmic Research Institute conference was held Friday and Saturday, April 29 and 30, 2011, at the Fort Lauderdale Hyatt Regency Pier 66, Fort Lauderdale, Florida. The conference, funded by The ARVO Foundation for Eye Research through a grant from Pfizer Ophthalmics, provided an opportunity to gather experts from within and outside ophthalmology to determine the state of knowledge pertaining to molecular biomarkers associated with glaucoma, as well as the methods to identify and validate them to predict (a) those who would be susceptible to development of glaucoma; (b) markers that will enable prediction of glaucoma progression; and (c) markers that will predict efficacy of treatment of glaucoma. Identification of such biomarkers will aid in prevention of glaucoma-related vision loss and blindness. The conference focused on an evaluation of glaucoma molecular biomarkers and progress needed for future validation of glaucoma biomarkers. A working group of 21 glaucoma researchers, 7 scientists focused on diseases other than glaucoma and with expertise in areas such as proteomic biomarkers or molecular mechanisms for neurodegeneration, and 60 observers from ARVO, Pfizer, and clinical and basic ophthalmic research convened to evaluate current understanding of the molecular biomarkers of glaucoma. The meeting format emphasized discussion and concentrated on questions within areas of glaucoma molecular biomarker research: Session I: How to define a biomarker in medicine? Current knowledge about biomarkers in human health and in glaucoma Session II: Genetic biomarkers in glaucoma Session III: Proteomic biomarkers in glaucoma Session IV: Pre-immune and immune events: Immunoproteomics and its possible applications in glaucoma Session V: From bench to bedside: How can a translational approach be successful? Each session began with a 10-minute overview by a glaucoma researcher followed by a 30-minute presentation by an outside expert, with parallels between their fields of expertise and the eye included. Invited outside experts covered several areas of research, including proteomic biomarker discovery in cancer (Emanuel Petricoin, PhD, George Mason University, Maryland; and Akhilesh Pandey, MD, PhD, Johns Hopkins University, Maryland) and astroglial cells in neurodegeneration (Stephen D. Miller, PhD, Northwestern University, Illinois).

Quantitative mass spectrometry for colorectal cancer proteomics

This review documents the uses of quantitative MS applied to colorectal cancer (CRC) proteomics for biomarker discovery and molecular pathway profiling. Investigators are adopting various labeling and label-free MS approaches to quantitate differential protein levels in cells, tumors, and plasma/serum. We comprehensively review recent uses of this technology to examine mouse models of CRC, CRC cell lines, their secretomes and subcellular fractions, CRC tumors, CRC patient plasma/serum, and stool samples. For biomarker discovery these approaches are uncovering proteins with potential diagnostic and prognostic utility, while in vitro cell culture experiments are characterizing proteomic and phosphoproteomic responses to disrupted signaling pathways due to mutations or to inhibition of drugable enzymes.

Integration of Proteomics, Bioinformatics, and Systems Biology in Traumatic Brain Injury Biomarker Discovery

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.

Breast cancer biomarkers: proteomic discovery and translation to clinically relevant assays

Although the molecular classification and prognostic assessment of breast tumors based on gene expression profiling is well established, a number of proteomic studies that propose potential breast cancer biomarkers has not yet led to any new diagnostic, prognostic or predictive test in wide clinical use. This review examines the current status of breast cancer biomarkers, discusses sample types (including plasma, tumor tissue, nipple aspirate and ductal lavage, as well as cell culture models) and different electrophoretic and mass spectrometry methods that have been widely used for the discovery of proteomic biomarkers in breast cancer, and also considers several approaches to biomarker validation. The pathway leading from the initial proteomic discovery and validation process to translation into a clinically useful test is also discussed.

PROTEOMIC ANALYSIS OF VITREOUS BIOPSY TECHNIQUES

To compare vitreous biopsy methods using analysis platforms used in proteomics biomarker discovery. Vitreous biopsies from 10 eyes were collected sequentially using a 23-gauge needle and a 23-gauge vitreous cutter instrument. Paired specimens were evaluated by UV absorbance spectroscopy, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and liquid chromatography tandem mass spectrometry (LC-MS/MS). The total protein concentration obtained with a needle and vitrectomy instrument biopsy averaged 1.10 mg/mL (standard error of the mean = 0.35) and 1.13 mg/mL (standard error of the mean = 0.25), respectively. In eight eyes with low or medium viscidity, there was a very high correlation (R = 0.934) between the biopsy methods. When data from 2 eyes with high viscidity vitreous were included, the correlation was reduced (R = 0.704). The molecular weight protein sodium dodecyl sulfate-polyacrylamide gel electrophoresis profiles of paired needle and vitreous cutter samples were similar, except for a minority of pairs with single band intensity variance. Using LC-MS/MS, equivalent peptides were identified with similar frequencies (R ≥ 0.90) in paired samples. Proteins and peptides collected from vitreous needle biopsies are nearly equivalent to those obtained from a vitreous cutter instrument. This study suggests both techniques may be used for most proteomic and biomarker discovery studies of vitreoretinal diseases, although a minority of proteins and peptides may differ in concentration.

Analysis of the extreme diversity of salivary alpha-amylase isoforms generated by physiological proteolysis using liquid chromatography–tandem mass spectrometry

Saliva is a crucial biofluid for oral health and is also of increasing importance as a non-invasive source of disease biomarkers. Salivary alpha-amylase is an abundant protein in saliva, and changes in amylase expression have been previously associated with a variety of diseases and conditions. Salivary alpha-amylase is subject to a high diversity of post-translational modifications, including physiological proteolysis in the oral cavity. Here we developed methodology for rapid sample preparation and non-targeted LC–ESI-MS/MS analysis of saliva from healthy subjects and observed an extreme diversity of alpha-amylase proteolytic isoforms. Our results emphasize the importance of consideration of post-translational events such as proteolysis in proteomic studies, biomarker discovery and validation, particularly in saliva.