Liebler, D.C.

AbstractBACKGROUNDFor many years, basic and clinical researchers have taken advantage of the analytical sensitivity and specificity afforded by mass spectrometry in the measurement of proteins. Clinical laboratories are now beginning to deploy these work flows as well. For assays that use proteolysis to generate peptides for protein quantification and characterization, synthetic stable isotope–labeled internal standard peptides are of central importance. No general recommendations are currently available surrounding the use of peptides in protein mass spectrometric assays.CONTENTThe Clinical Proteomic Tumor Analysis Consortium of the National Cancer Institute has collaborated with clinical laboratorians, peptide manufacturers, metrologists, representatives of the pharmaceutical industry, and other professionals to develop a consensus set of recommendations for peptide procurement, characterization, storage, and handling, as well as approaches to the interpretation of the data generated by mass spectrometric protein assays. Additionally, the importance of carefully characterized reference materials—in particular, peptide standards for the improved concordance of amino acid analysis methods across the industry—is highlighted. The alignment of practices around the use of peptides and the transparency of sample preparation protocols should allow for the harmonization of peptide and protein quantification in research and clinical care.

Aims: Determine the mechanism by which C-terminus of HSC70-interacting protein (CHIP) induction alters neuronal survival under conditions of mitochondrial stress induced by oxygen glucose deprivation. Results: We report that animals deficient in the E3 ubiquitin ligase, CHIP, have high baseline levels of central nervous system protein oxidation and lipid peroxidation, reduced antioxidant defenses, and decreased energetic status. Stress-associated molecules typically linked to Parkinson's disease such as the mitochondrial kinase, PTEN-inducible putative kinase 1 (PINK1), and another E3 ligase, Parkin, are upregulated in brains from CHIP knockout (KO) animals. Utilizing a novel biotin–avidin capture technique, we found that the oxidation status of Parkin and the mitochondrial fission protein, dynamin-related protein 1 (Drp1), are altered in a CHIP-dependent manner. We also found that following oxygen–glucose deprivation (OGD), the expression of CHIP, PINK1, and the autophagic marker, LC3, increase and there is activation of the redox-sensitive kinase p66 shc . Under conditions of OGD, CHIP relocalizes from the cytosol to mitochondria. Mitochondria from CHIP KO mice have profound impairments in stress response induced by calcium overload, resulting in accelerated permeability transition activity. While CHIP-deficient neurons are morphologically intact, they are more susceptible to OGD consistent with a previously unknown neuroprotective role for CHIP in maintaining mitochondrial homeostasis. Innovation: CHIP relocalization to the mitochondria is essential for the regulation of mitochondrial integrity and neuronal survival following OGD. Conclusions: CHIP is an essential regulator of neuronal bioenergetics and redox tone. Altering the expression of this protein has profound effects on neuronal survival when cells are exposed to OGD. Antioxid. Redox Signal . 23, 535–549.

Abstract In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.