Comprehensive identification of proteins in whole human saliva is critical for appreciating its full diagnostic potential. However, this is challenged by the large dynamic range of protein abundance within this fluid. To address this problem, we used an analysis platform that coupled hexapeptide libraries for dynamic range compression (DRC) with three-dimensional (3D) peptide fractionation. This approach identified 2340 proteins in whole saliva and represents the largest saliva proteomic dataset generated using a single analysis platform. Three dimensional peptide fractionation involving sequential steps of preparative IEF, strong cation exchange, and capillary reversed phase liquid chromatography was essential for maximizing gains from DRC. Compared to saliva not treated with hexapeptide libraries, DRC substantially increased identified proteins across physicochemical and functional categories. Approximately 20% of total salivary proteins are also seen in plasma, and proteins in both fluids show comparable functional diversity and disease-linkage. However, for a subset of diseases, saliva has higher apparent diagnostic potential. These results expand the potential for whole saliva in health monitoring/diagnostics and provide a general platform for improving proteomic coverage of complex biological samples.

Proteogenomics combines large-scale genomic and transcriptomic data with mass-spectrometry-based proteomic data to discover novel protein sequence variants and improve genome annotation. In contrast with conventional proteomic applications, proteogenomic analysis requires a number of additional data processing steps. Ideally, these required steps would be integrated and automated via a single software platform offering accessibility for wet-bench researchers as well as flexibility for user-specific customization and integration of new software tools as they emerge. Toward this end, we have extended the Galaxy bioinformatics framework to facilitate proteogenomic analysis. Using analysis of whole human saliva as an example, we demonstrate Galaxy’s flexibility through the creation of a modular workflow incorporating both established and customized software tools that improve depth and quality of proteogenomic results. Our customized Galaxy-based software includes automated, batch-mode BLASTP searching and a Peptide Sequence Match Evaluator tool, both useful for evaluating the veracity of putative novel peptide identifications. Our complex workflow (approximately 140 steps) can be easily shared using built-in Galaxy functions, enabling their use and customization by others. Our results provide a blueprint for the establishment of the Galaxy framework as an ideal solution for the emerging field of proteogenomics.

Whole human saliva possesses tremendous potential in clinical diagnostics, particularly for conditions within the oral cavity such as oral cancer. Although many have studied the soluble fraction of whole saliva, few have taken advantage of the diagnostic potential of the cells present in saliva, and none have taken advantage of proteomics capabilities for their study. We report on a novel proteomics method with which we characterized for the first time cells contained in whole saliva from patients diagnosed with oral squamous cell carcinoma. Our method uses three dimensions of peptide fractionation, combining the following steps: preparative IEF using free flow electrophoresis, strong cation exchange step gradient chromatography, and microcapillary reverse-phase liquid chromatography. We determined that the whole saliva samples contained enough cells, mostly exfoliated epithelial cells, providing adequate amounts of total protein for proteomics analysis. From a mixture of four oral cancer patient samples, the analysis resulted in a catalogue of over 1000 human proteins, each identified from at least two peptides, including numerous proteins with a role in oral squamous cell carcinoma signaling and tumorigenesis pathways. Additionally proteins from over 30 different bacteria were identified, some of which putatively contribute to cancer development. The combination of preparative IEF followed by strong cation exchange chromatography effectively fractionated the complex peptide mixtures despite the closely related physiochemical peptide properties of these separations (pI and solution phase charge, respectively). Furthermore compared with our two-step method combining preparative IEF and reversephase liquid chromatography, our three-step method identified significantly more cellular proteins while retaining higher confidence protein identification enabled by peptide pI information gained through IEF. Thus, for detecting salivary markers of oral cancer and possibly other conditions of the oral cavity, the results confirm both the potential of analyzing the cells in whole saliva and doing so with our proteomics method. Molecular & Cellular Proteomics 7:486 -498, 2008.Whole human saliva is easily collected in the clinic in a non-invasive, on-demand manner and in relatively large, easily stored quantities, making it an optimal bodily fluid for clinical diagnostics (1, 2). Diagnosis of oral cancer could benefit greatly from the development of whole saliva-based clinical tests given the physical proximity of the site of cancer development with the diagnostic fluid. In fact, oral cancer, usually diagnosed in the form of oral squamous cell carcinoma (OSCC), 1 has not seen a drop in its 50% mortality rate over the last 30 years (3), motivating the development of new and reliable, saliva-based clinical diagnostic tests for the early detection of OSCC. Such tests would lead to more informed treatment of patients and a reduction in the suffering and death caused by this cancer.To develop these tests, molecular markers that ar...

BackgroundCurrent practice in mass spectrometry (MS)-based proteomics is to identify peptides by comparison of experimental mass spectra with theoretical mass spectra derived from a reference protein database; however, this strategy necessarily fails to detect peptide and protein sequences that are absent from the database. We and others have recently shown that customized proteomic databases derived from RNA-Seq data can be employed for MS-searching to both improve MS analysis and identify novel peptides. While this general strategy constitutes a significant advance for the discovery of novel protein variations, it has not been readily transferable to other laboratories due to the need for many specialized software tools. To address this problem, we have implemented readily accessible, modifiable, and extensible workflows within Galaxy-P, short for Galaxy for Proteomics, a web-based bioinformatic extension of the Galaxy framework for the analysis of multi-omics (e.g. genomics, transcriptomics, proteomics) data.ResultsWe present three bioinformatic workflows that allow the user to upload raw RNA sequencing reads and convert the data into high-quality customized proteomic databases suitable for MS searching. We show the utility of these workflows on human and mouse samples, identifying 544 peptides containing single amino acid polymorphisms (SAPs) and 187 peptides corresponding to unannotated splice junction peptides, correlating protein and transcript expression levels, and providing the option to incorporate transcript abundance measures within the MS database search process (reduced databases, incorporation of transcript abundance for protein identification score calculations, etc.).ConclusionsUsing RNA-Seq data to enhance MS analysis is a promising strategy to discover novel peptides specific to a sample and, more generally, to improve proteomics results. The main bottleneck for widespread adoption of this strategy has been the lack of easily used and modifiable computational tools. We provide a solution to this problem by introducing a set of workflows within the Galaxy-P framework that converts raw RNA-Seq data into customized proteomic databases.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-703) contains supplementary material, which is available to authorized users.

BackgroundOral cancer survival rates increase significantly when it is detected and treated early. Unfortunately, clinicians now lack tests which easily and reliably distinguish pre-malignant oral lesions from those already transitioned to malignancy. A test for proteins, ones found in non-invasively-collected whole saliva and whose abundances distinguish these lesion types, would meet this critical need.Methodology/Principal FindingsTo discover such proteins, in a first-of-its-kind study we used advanced mass spectrometry-based quantitative proteomics analysis of the pooled soluble fraction of whole saliva from four subjects with pre-malignant lesions and four with malignant lesions. We prioritized candidate biomarkers via bioinformatics and validated selected proteins by western blotting. Bioinformatic analysis of differentially abundant proteins and initial western blotting revealed increased abundance of myosin and actin in patients with malignant lesions. We validated those results by additional western blotting of individual whole saliva samples from twelve other subjects with pre-malignant oral lesions and twelve with malignant oral lesions. Sensitivity/specificity values for distinguishing between different lesion types were 100%/75% (p = 0.002) for actin, and 67%/83% (p<0.00001) for myosin in soluble saliva. Exfoliated epithelial cells from subjects' saliva also showed increased myosin and actin abundance in those with malignant lesions, linking our observations in soluble saliva to abundance differences between pre-malignant and malignant cells.Conclusions/SignificanceSalivary actin and myosin abundances distinguish oral lesion types with sensitivity and specificity rivaling other non-invasive oral cancer tests. Our findings provide a promising starting point for the development of non-invasive and inexpensive salivary tests to reliably detect oral cancer early.