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.

SUMMARY Dynamic range compression (DRC) by hexapeptide libraries increases MS/MS-based identification of lower-abundance proteins in complex mixtures. However, two unanswered questions impede fully realizing DRC’s potential in shotgun proteomics. First, does DRC enhance identification of post-translationally modified proteins? Second, can DRC be incorporated into a workflow enabling relative protein abundance profiling? We sought to answer both questions analyzing human whole saliva. Addressing question one, we coupled DRC with covalent glycopeptide enrichment and MS/MS. With DRC we identified ~2 times more N-linked glycoproteins and their glycosylation sites than without DRC, dramatically increasing the known salivary glycoprotein catalog. Addressing question two, we compared differentially stable isotope-labeled saliva samples pooled from healthy and metastatic breast cancer women using a multidimensional peptide fractionation-based workflow, analyzing in parallel one sample portion with DRC and one portion without. Our workflow categorizes proteins with higher absolute abundance, whose relative abundance ratios are altered by DRC, from proteins of lower absolute abundance detected only after DRC. Within each of these salivary protein categories we identified novel abundance changes putatively associated with breast cancer, demonstrating feasibility and benefits of DRC for relative abundance profiling. Collectively, our results bring us closer to realizing the full potential of DRC for proteomic studies.

SUMMARY Loss of minichromosome maintenance protein 10 (Mcm10) causes replication stress. We uncovered that S. cerevisiae mcm10-1 mutants rely on the E3 SUMO ligase Mms21 and the SUMO-targeted ubiquitin ligase complex Slx5/8 for survival. Using quantitative mass spectrometry, we identified changes in the SUMO proteome of mcm10-1 mutants and revealed candidates regulated by Slx5/8. Such candidates included subunits of the chromosome passenger complex (CPC), Bir1 and Sli15, known to facilitate spindle assembly checkpoint (SAC) activation. We show here that Slx5 counteracts SAC activation in mcm10-1 mutants under conditions of moderate replication stress. This coincides with the proteasomal degradation of sumoylated Bir1. Importantly, Slx5-dependent mitotic relief was not only triggered by Mcm10 deficiency but also by treatment with low doses of the alkylating drug methyl methanesulfonate. Based on these findings, we propose a model in which Slx5/8 allows for passage through mitosis when replication stress is tolerable.

Pulsed Q dissociation enables combining LTQ ion trap instruments with isobaric peptide tagging. Unfortunately, this combination lacks a technique which accurately reports protein abundance ratios and is implemented in a freely-available, flexible software pipeline. We developed and implemented a technique assigning collective reporter ion intensity-based weights to each peptide abundance ratio and calculating a protein's weighted average abundance ratio and P value. Using an iTRAQ-labeled standard mixture, we compared our technique's performance to the commercial software Mascot, finding that it performed better than Mascot's non-weighted averaging and median peptide ratio techniques, and equal to its weighted averaging technique. We also compared performance of the LTQ-Orbitrap plus our technique to 4800 MALDI TOF/TOF plus Protein Pilot, by analyzing an iTRAQ-labeled stem cell lysate. We found highly correlated protein abundance ratios, indicating that the LTQ-Orbitrap plus our technique yields results comparable to the current standard. We implemented our technique in a freely available, automated software pipeline, called LTQ-iQuant, which: is mzXML-compatible; supports iTRAQ 4-plex and 8-plex LTQ data; and can be modified for and have weights trained to a user's LTQ and other isobaric peptide tagging methods. LTQ-iQuant should make LTQ instruments and isobaric peptide tagging accessible to more proteomics researchers.

As the main catalytic and structural molecules within living systems, proteins are the most likely biomolecules to be affected by radiation exposure. Proteomics, the comprehensive characterization of proteins within complex biological samples, is therefore a research approach ideally suited to assess the effects of radiation exposure on cells and tissues. For comprehensive characterization of proteomes, an analytical platform capable of quantifying protein abundance, identifying post-translation modifications and revealing members of protein complexes on a system-wide level is necessary. Mass spectrometry (MS), coupled with technologies for sample fractionation and automated data analysis, provides such a versatile and powerful platform. In this chapter we offer a view on the current state of MS-proteomics, and focus on emerging technologies within three areas: (1) New instrumental methods; (2) New computational methods for peptide identification; and (3) Label-free quantification. These emerging technologies should be valuable for researchers seeking to better understand biological effects of radiation on living systems.