Saliva is a body fluid with important functions in oral and general health. A consortium of three research groups catalogued the proteins in human saliva collected as the ductal secretions: 1166 identifications-914 in parotid and 917 in submandibular/sublingual saliva-were made. The results showed that a high proportion of proteins that are found in plasma and/or tears are also present in saliva along with unique components. The proteins identified are involved in numerous molecular processes ranging from structural functions to enzymatic/catalytic activities. As expected, the majority mapped to the extracellular and secretory compartments. An immunoblot approach was used to validate the presence in saliva of a subset of the proteins identified by mass spectrometric approaches. These experiments focused on novel constituents and proteins for which the peptide evidence was relatively weak. Ultimately, information derived from the work reported here and related published studies can be used to translate blood-based clinical laboratory tests into a format that utilizes saliva. Additionally, a catalogue of the salivary proteome of healthy individuals allows future analyses of salivary samples from individuals with oral and systemic diseases, with the goal of identifying biomarkers with diagnostic and/or prognostic value for these conditions; another possibility is the discovery of therapeutic targets.

We describe Census, a quantitative software tool compatible with many labeling strategies as well as with label-free analyses, single-stage mass spectrometry (MS1) and tandem mass spectrometry (MS/MS) scans, and high-and low-resolution mass spectrometry data. Census uses robust algorithms to address poor-quality measurements and improve quantitative efficiency, and it can support several input file formats. We tested Census with stable-isotope labeling analyses as well as label-free analyses. Keywords mass spectrometry; quantification; label free; metabolic labelingIn recent years, global quantification using mass spectrometry has garnered a significant level of interest due to the emergence of fields that rely on large scale profiling of peptides/ proteins (proteomics) and small molecules (metabolomics). In the field of proteomics, the identification of large numbers of peptides has become commonplace with the advent of new instrumentation(1-7) and informatics tools(8-11), however, progress with regards to the quantification process has been hampered by the extreme analytical challenges.In general, peptide/protein quantification by mass spectrometry is achieved via either stable isotope labeling or a label free approach. Stable isotope labeling has become the core technology for high throughput peptide quantification efforts employing mass spectrometry. Quantification is typically achieved by comparison of an unlabeled or "light" peptide (i.e., comprised of naturally abundant stable isotopes) to an internal standard that is chemically identical with the exception of atoms that are enriched with a "heavy" stable isotope. While the stable isotope labeling approach has been the most commonly employed over the past several years, label free approaches have been gaining momentum recently due to the inherent simplicity, increased throughput, and low cost. Several strategies for label free differential expression analysis have emerged and can generally be divided into two groups; those that are fundamentally based on identification of peptides prior to quantification and those that rely on first stage MS data alone. (Fig. 1). Census is based on a program previously written in our lab called RelEx(12), but has been re-written with many new features that significantly improve the accuracy and precision of resulting measurements and drastically improves computational performance (Supplementary Information online and Table 1). Census is capable of quantification from either MS or MS/MS scans and is thus able to process data generated from data-independent acquisition(13), SRM, or MRM analyses. Other features incorporated into Census include the ability to use high resolution and high mass accuracy MS data for improved quantification, as well as the ability to perform quantitative analyses based on both spectral counting and an LC-MS peak area approach utilizing chromatogram alignment. To minimize false positive measurements and improve protein/peptide ratio accuracy Census incorporates multiple algorithms such as...

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster.

DAF-2, an insulin receptor-like protein, regulates metabolism, development, and aging in Caenorhabditis elegans. In a quantitative proteomic study, we identified 86 proteins that were more or less abundant in long-lived daf-2 mutant worms than in wild-type worms. Genetic studies on a subset of these proteins indicated that they act in one or more processes regulated by DAF-2, including entry into the dauer developmental stage and aging. In particular, we discovered a compensatory mechanism activated in response to reduced DAF-2 signaling, which involves the protein phosphatase calcineurin.

To combat the functional decline of the proteome, cells use the process of protein turnover to replace potentially impaired polypeptides with new functional copies. Here we found that extremely long-lived proteins (ELLPs) did not turn over in post-mitotic cells of the rat central nervous system. These ELLPs were associated with chromatin and the nuclear pore complex, the central transport channels that mediate all molecular trafficking in and out of the nucleus. The longevity of these proteins would be expected to expose them to potentially harmful metabolites putting them at risk of accumulating damage over extended periods of time. Thus, it is possible that failure to maintain proper levels and functional integrity of ELLPs in non-proliferative cells might contribute to age-related deterioration in cell and tissue function.

Our findings reveal that Nampt protects against ischemic stroke through rescuing neurons from death via the SIRT1-dependent AMPK pathway and indicate that Nampt is a new therapeutic target for stroke.

Visfatin stimulates VSMC proliferation via NMN-mediated ERK1/2 and p38 signalling. The present study provides a molecular link of visfatin to the paracrine action of PVAT, demonstrates a novel function of visfatin in promoting VSMC proliferation, and reveals NMN as a novel signalling molecule that triggers the proliferative process.

Metabolism has been shown to integrate with epigenetics and transcription to modulate cell fate and function. Beyond meeting the bioenergetic and biosynthetic demands of T-cell differentiation, whether metabolism might control T-cell fate by an epigenetic mechanism is unclear. Here, through the discovery and mechanistic characterization of a small molecule, (aminooxy)acetic acid, that reprograms the differentiation of T helper 17 (T17) cells towards induced regulatory T (iT) cells, we show that increased transamination, mainly catalysed by GOT1, leads to increased levels of 2-hydroxyglutarate in differentiating T17 cells. The accumulation of 2-hydroxyglutarate resulted in hypermethylation of the Foxp3 gene locus and inhibited Foxp3 transcription, which is essential for fate determination towards T17 cells. Inhibition of the conversion of glutamate to α-ketoglutaric acid prevented the production of 2-hydroxyglutarate, reduced methylation of the Foxp3 gene locus, and increased Foxp3 expression. This consequently blocked the differentiation of T17 cells by antagonizing the function of transcription factor RORγt and promoted polarization into iT cells. Selective inhibition of GOT1 with (aminooxy)acetic acid ameliorated experimental autoimmune encephalomyelitis in a therapeutic mouse model by regulating the balance between T17 and iT cells. Targeting a glutamate-dependent metabolic pathway thus represents a new strategy for developing therapeutic agents against T17-mediated autoimmune diseases.