Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics.

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry.

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Differential Detergent Fractionation of Membrane Protein From Small Samples of Hepatocytes and Liver Tissue for Quantitative Proteomic Analysis of Drug Metabolizing Enzymes and Transporters.

The fractionation of enough membrane protein from limited samples is challenging for MS-based quantitative targeted absolute proteomics (QTAP) of drug metabolizing enzymes (DMEs) and transporters. This study evaluated differential detergent fractionation (DDF) of membrane protein from progressively smaller numbers of primary mouse hepatocytes (5 million down to 50,000 cells) and limited liver tissue (25-50 mg) in quantifying select DMEs and transporters by QTAP. Two non-ionic detergents, digitonin and Triton-X-100, were applied in sequence to permeabilize cells and extract membrane proteins. Comparison was made with a membrane protein extraction kit and with homogenization in hypotonic buffer and subsequent differential centrifugation (DC). DDF produced linear membrane protein yields with increasing hepatocyte numbers and better permeabilization evidenced by the higher ratio of cytosolic to membrane protein yields. DDF produced 5-times more membrane protein from liver tissue than DC. The concentration of DMEs and transporters remained consistent in the fractions prepared by DDF from progressively smaller numbers of hepatocytes, but declined in kit fractions. In liver tissue, the concentrations were comparatively higher in DDF versus kit and DC. In conclusion, sequential digitonin and Triton-X-100 fractionation of membrane protein from limited samples is efficient, reproducible and cost-effective for QTAP of DMEs and transporters.

Intracellular sorting and transcytosis of the rat transferrin receptor antibody OX26 across the blood-brain barrier in vitro is dependent on its binding affinity.

The blood-brain barrier (BBB) is a formidable obstacle to the delivery of therapeutics to the brain. Antibodies that bind transferrin receptor (TfR), which is enriched in brain endothelial cells, have been shown to cross the BBB and are being developed as fusion proteins to deliver therapeutic cargos to brain targets. Various antibodies have been developed for this purpose and their in vivo evaluation demonstrated that either low affinity or monovalent receptor binding re-directs their transcellular trafficking away from lysosomal degradation and toward improved exocytosis on the abluminal side of the BBB. However, these studies have been performed with antibodies that recognize different TfR epitopes and have different binding characteristics, preventing inter-study comparisons. In this study, the efficiency of transcytosis in vitro and intracellular trafficking in endosomal compartments were evaluated in an in vitro BBB model for affinity variants (Kd from 5 to174 nM) of the rat TfR-binding antibody, OX26. Distribution in subcellular fractions of the rat brain endothelial cells was determined using both targeted quantitative proteomics-selected reaction monitoring and fluorescent imaging with markers of early- and late endosomes. The OX26 variants with affinities of 76 and 108 nM showed improved trancytosis (Papp values) across the in vitro BBB model compared with a 5 nM OX26. Although ~40% of the 5 nM OX26 and ~35% of TfR co-localized with late-endosome/lysosome compartment, 76 and 108 nM affinity variants showed lower amounts in lysosomes and a predominant co-localization with early endosome markers. The study links bivalent TfR antibody affinity to mechanisms of sorting and trafficking away from late endosomes and lysosomes, resulting in improvement in their transcytosis efficiency. OPEN PRACTICES: Open Science: This manuscript was awarded with the Open Materials Badge. For more information see: https://cos.io/our-services/open-science-badges/ Cover Image for this issue: doi: 10.1111/jnc.14193.

Targeted unlabeled multiple reaction monitoring analysis of cell markers for the study of sample heterogeneity in isolated rat brain cortical microvessels.

Liquid chromatography coupled to tandem mass spectrometry-based targeted absolute protein quantification (in fmol of the analyte protein per µg of total protein) is employed for the molecular characterization of the blood-brain barrier using isolated brain microvessels. Nevertheless, the heterogeneity of the sample regarding the levels of different cells co-isolated within the microvessels and bovine serum albumin (BSA) contamination (from buffers) are not always evaluated. We developed an unlabeled targeted liquid chromatography coupled to tandem mass spectrometry method to survey the levels of endothelial cells (ECs), astrocytes, and pericytes, as well as BSA contaminant in rat cortical microvessels. Peptide peak identities were evaluated using a spectral library and chromatographic parameters. Sprague-Dawley rat microvessels obtained on three different days were analyzed with this method complemented by an absolute quantification multiple reaction monitoring method for transporter proteins P-gp, Bcrp, and Na+ /K+ ATPase pump using stable isotope labeled peptides as internal standard. Inter-day differences in the cell markers and BSA contamination were observed. Levels of cell markers correlated positively between each other. Then, the correlation between cell marker proteins and transporter proteins was evaluated to choose the best EC marker protein for protein quantification normalization. The membrane protein Pecam-1 showed a very high correlation with the EC-specific transporter P-gp (Pearson product-moment correlation coefficient (r) > 0.89) and moderate to high with Bcrp (r ≥ 0.77), that can be found also in pericytes and astrocytes. Therefore, Pecam-1 was selected as a marker for the normalization of the quantification of the proteins of endothelial cells.

LC-MS/MS-based targeted proteomics quantitatively detects the interaction between p53 and MDM2 in breast cancer.

In breast cancer, p53 could be functionally compromised by interaction with several proteins. Among those proteins, MDM2 serves as a pivotal negative regulator and counteracts p53 activation. Thus, the ability to quantitatively and accurately monitor the changes in level of p53-MDM2 interaction with disease state can enable an improved understanding of this protein-protein interaction (PPI), provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI including p53-MDM2 interaction so far. In this study, a LC-MS/MS-based targeted proteomics assay was developed and coupled with co-immunoprecipitation (Co-IP) for the quantification of p53-MDM2 complex. A p53 antibody with the epitope residing at 156-214 residues achieved the greatest IP efficiency. 321KPLDGEYFTLQIR333 (p53) and 327ENWLPEDK334 (MDM2) were selected as surrogate peptides in the targeted analysis. Stable isotope-labeled synthetic peptides were used as internal standards. An LOQ (limit of quantification) of 2ng/mL was obtained. Then, the assay was applied to quantitatively detect total p53, total MDM2 and p53-MDM2 in breast cells and tissue samples. Western blotting was performed for a comparison. Finally, a quantitative time-course analysis in MCF-7 cells with the treatment of nutlin-3 as a PPI inhibitor was also monitored. BIOLOGICAL SIGNIFICANCE: Proteins do not function as single entities but rather as a team player that has to communicate. Protein-protein interaction (PPI), normally by means of non-covalent contact among binary or large protein complex, is essential for many cellular processes including cancer progression. Thus, the ability to quantitatively and accurately monitor the changes in level of PPI with disease state can enable an improved understanding of PPI, provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI so far. The major issue of current available approaches is the trade-off between sensitivity and specificity. Thus, techniques with the ability to quantify PPIs with both high sensitivity (low false-negative rate) and high specificity (low false-positive rate) are eagerly desired. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics has shown its potential to study biomolecules because of its high sensitivity, high selectivity and wide dynamic range. In this study, we made an effort to develop a LC-MS/MS-based targeted proteomics assay for the quantitative detection of p53-MDM2 interaction in breast cells and tissue samples.

MetAP1 and MetAP2 drive cell selectivity for a potent anti-cancer agent in synergy, by controlling glutathione redox state.

Fumagillin and its derivatives are therapeutically useful because they can decrease cancer progression. The specific molecular target of fumagillin is methionine aminopeptidase 2 (MetAP2), one of the two MetAPs present in the cytosol. MetAPs catalyze N-terminal methionine excision (NME), an essential pathway of cotranslational protein maturation. To date, it remains unclear the respective contribution of MetAP1 and MetAP2 to the NME process in vivo and why MetAP2 inhibition causes cell cycle arrest only in a subset of cells. Here, we performed a global characterization of the N-terminal methionine excision pathway and the inhibition of MetAP2 by fumagillin in a number of lines, including cancer cell lines. Large-scale N-terminus profiling in cells responsive and unresponsive to fumagillin treatment revealed that both MetAPs were required in vivo for M[VT]X-targets and, possibly, for lower-level M[G]X-targets. Interestingly, we found that the responsiveness of the cell lines to fumagillin was correlated with the ability of the cells to modulate their glutathione homeostasis. Indeed, alterations to glutathione status were observed in fumagillin-sensitive cells but not in cells unresponsive to this agent. Proteo-transcriptomic analyses revealed that both MetAP1 and MetAP2 accumulated in a cell-specific manner and that cell sensitivity to fumagillin was related to the levels of these MetAPs, particularly MetAP1. We suggest that MetAP1 levels could be routinely checked in several types of tumor and used as a prognostic marker for predicting the response to treatments inhibiting MetAP2.

Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers.

Protein biomarker discovery and validation in current omics era are vital for healthcare professionals to improve diagnosis, detect cancers at an early stage, identify the likelihood of cancer recurrence, stratify stages with differential survival outcomes, and monitor therapeutic responses. The success of such biomarkers would have a huge impact on how we improve the diagnosis and treatment of patients and alleviate the financial burden of healthcare systems. In the past, the genomics community (mostly through large-scale, deep genomic sequencing technologies) has been steadily improving our understanding of the molecular basis of disease, with a number of biomarker panels already authorized by the U.S. Food and Drug Administration (FDA) for clinical use (e.g., MammaPrint, two recently cleared devices using next-generation sequencing platforms to detect DNA changes in the cystic fibrosis transmembrane conductance regulator (CFTR) gene). Clinical proteomics, on the other hand, albeit its ability to delineate the functional units of a cell, more likely driving the phenotypic differences of a disease (i.e., proteins and protein-protein interaction networks and signaling pathways underlying the disease), "staggers" to make a significant impact with only an average ∼ 1.5 protein biomarkers per year approved by the FDA over the past 15-20 years. This statistic itself raises the concern that major roadblocks have been impeding an efficient transition of protein marker candidates in biomarker development despite major technological advances in proteomics in recent years.

Quantitative targeted proteomics for understanding the blood-brain barrier: towards pharmacoproteomics.

The blood-brain barrier (BBB) is formed by brain capillary endothelial cells linked together via complex tight junctions, and serves to prevent entry of drugs into the brain. Multiple transporters are expressed at the BBB, where they control exchange of materials between the circulating blood and brain interstitial fluid, thereby supporting and protecting the CNS. An understanding of the BBB is necessary for efficient development of CNS-acting drugs and to identify potential drug targets for treatment of CNS diseases. Quantitative targeted proteomics can provide detailed information on protein expression levels at the BBB. The present review highlights the latest applications of quantitative targeted proteomics in BBB research, specifically to evaluate species and in vivo-in vitro differences, and to reconstruct in vivo transport activity. Such a BBB quantitative proteomics approach can be considered as pharmacoproteomics.