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Intracellular proteins are in a state of flux, continually being degraded into amino acids and resynthesized into new proteins. The rate of this biochemical recycling process varies across proteins and is emerging as an important consideration in drug discovery and development. Here, we developed a triple-stage quadrupole mass spectrometry assay based on product ion measurements at unit resolution and H(2)(18)O stable tracer incorporation to measure relative protein synthesis rates. As proof of concept, we selected to measure the relative in vivo synthesis rate of ApoB100, an apolipoprotein where elevated levels are associated with an increased risk of coronary heart disease, in plasma-isolated very low density lipoprotein (VLDL) and low density lipoprotein (LDL) in a mouse in vivo model. In addition, serial time points were acquired to measure the relative in vivo synthesis rate of mouse LDL ApoB100 in response to vehicle, microsomal triacylglycerol transfer protein (MTP) inhibitor, and site-1 protease inhibitor, two potential therapeutic targets to reduce plasma ApoB100 levels at 2 and 6 h post-tracer-injection. The combination of H(2)(18)O tracer with the triple quadrupole mass spectrometry platform creates an assay that is relatively quick and inexpensive to transfer across different biological model systems, serving as an ideal rapid screening tool for relative protein synthesis in response to treatment.

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF A beta 42 peptide, a known pharmacodynamic marker for gamma-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF.

Inhibitors of class 1 and class 2 histone deacetylase (HDAC) enzymes have shown antitumor activity in human clinical trials. More recently, there has been interest in developing subtype-selective HDAC inhibitors designed to retain anticancer activity while reducing potential side effects. Efforts have been initiated to selectively target HDAC1 given its role in tumor proliferation and survival. The development of HDAC1-specific inhibitors will require the identification of HDAC1-selective pharmacodynamic markers that correlate closely with HDAC1-inhibition in vitro and in vivo. Existing histone markers of HDAC target engagement were developed using pan-HDAC inhibitors and do not necessarily represent robust readouts for isoform-specific inhibitors. Therefore, we have initiated a proteomic approach to identify readouts for HDAC1 inhibition. This approach involves the use of differential mass spectrometry (dMS) to identify post-translational changes in histones by profiling histone-enriched cellular fractions treated with various HDAC inhibitors. In this study, we profiled histones isolated from the HCT116 human colon cancer cell line that have been treated with compounds from multiple chemical classes that are specific for HDAC1; HDAC1 and 3; and HDAC1, 3, and 6 enzymes. In two independent experiments, we identified 24 features that correlated with HDAC1-inhibition. Among the peptides modulated by HDAC1-selective inhibitors were Ac-H2B-K5 from histone H2B, and Ac-H3-K18 from histone H3. Commercially available antibodies to specific histone acetyl-lysine residues were used to confirm that these peptides also provide pharmacodynamic readouts for HDAC1-selective inhibitors in vivo and in vitro. These results show the utility of dMS in guiding the identification of specific readouts to aid in the development of HDAC-selective inhibitors.

Estrogens are a class of steroid hormones that interact with two related but distinct nuclear receptors, estrogen receptor (ER) alpha and beta. To identify potential ER biomarkers, we profiled the rat plasma glycoproteome after treatment with vehicle or 17beta-estradiol (E2) or an ERalpha-selective agonist PPT by differential mass spectrometry. Our comparative proteomic experiment identifies novel E2- and PPT-responsive proteins, such as serine protease inhibitor family members.

High-throughput genome sequencing continues to accelerate the rate at which complete genomes are available for biological research. Many of these new genome sequences have little or no genome annotation currently available and hence rely upon computational predictions of protein coding genes. Evidence of translation from proteomic techniques could facilitate experimental validation of protein coding genes, but the techniques for whole genome searching with MS/MS data have not been adequately developed to date. Here we describe GENQUEST, a novel method using peptide isoelectric focusing and accurate mass to greatly reduce the peptide search space, making fast, accurate, and sensitive whole human genome searching possible on common desktop computers. In an initial experiment, almost all exonic peptides identified in a protein database search were identified when searching genomic sequence. Many peptides identified exclusively in the genome searches were incorrectly identified or could not be experimentally validated, highlighting the importance of orthogonal validation. Experimentally validated peptides exclusive to the genomic searches can be used to reannotate protein coding genes. GENQUEST represents an experimental tool that can be used by the proteomics community at large for validating computational approaches to genome annotation.

Orthogonal analysis of amino acid substitutions as a result of SNPs in existing proteomic datasets provides a critical foundation for the emerging field of population-based proteomics. Large-scale proteomics datasets, derived from shotgun tandem MS analysis of complex cellular protein mixtures, contain many unassigned spectra that may correspond to alternate alleles coded by SNPs. The purpose of this work was to identify tandem MS spectra in LC-MS/MS shotgun proteomics datasets that may represent coding nonsynonymous SNPs (nsSNP). To this end, we generated a tryptic peptide database created from allelic information found in NCBI's dbSNP. We searched this database with tandem MS spectra of tryptic peptides from DU4475 breast tumor cells that had been fractioned by pI in the first-dimension and reverse-phase LC in the second dimension. In all we identified 629 nsSNPs, of which 36 were of alternate SNP alleles not found in the reference NCBI or IPI protein databases. Searches for SNP-peptides carry a high risk of false positives due both to mass shifts caused by modifications and because of multiple representations of the same peptide within the genome. In this work, false positives were filtered using a novel peptide pI prediction algorithm and characterized using a decoy database developed by random substitution of similarly sized reference peptides. Secondary validation by sequencing of corresponding genomic DNA confirmed the presence of the predicted SNP in 8 of 10 SNP-peptides. This work highlights that the usefulness of interpreting unassigned spectra as polymorphisms is highly reliant on the ability to detect and filter false positives.