An extra dimension in protein tagging by quantifying universal proteotypic peptides using targeted proteomics

The use of protein tagging to facilitate detailed characterization of target proteins has not only revolutionized cell biology, but also enabled biochemical analysis through efficient recovery of the protein complexes wherein the tagged proteins reside. The endogenous use of these tags for detailed protein characterization is widespread in lower organisms that allow for efficient homologous recombination. With the recent advances in genome engineering, tagging of endogenous proteins is now within reach for most experimental systems, including mammalian cell lines cultures. In this work, we describe the selection of peptides with ideal mass spectrometry characteristics for use in quantification of tagged proteins using targeted proteomics. We mined the proteome of the hyperthermophile Pyrococcus furiosus to obtain two peptides that are unique in the proteomes of all known model organisms (proteotypic) and allow sensitive quantification of target proteins in a complex background. By combining these 'Proteotypic peptides for Quantification by SRM' (PQS peptides) with epitope tags, we demonstrate their use in co-immunoprecipitation experiments upon transfection of protein pairs, or after introduction of these tags in the endogenous proteins through genome engineering. Endogenous protein tagging for absolute quantification provides a powerful extra dimension to protein analysis, allowing the detailed characterization of endogenous proteins

in silico verification and parallel reaction monitoring prevalidation of potential prostate cancer biomarkers

Purpose: Targeted proteomics of potential biomarkers is often challenging. Hence, we developed an intermediate workflow to streamline potential urinary biomarkers of prostate cancer (PCa). Materials & methods: Using previously discovered potential PCa biomarkers; we selected proteotypic peptides for targeted validation. Preliminary in silico immunohistochemical and single reaction monitoring (SRM) verification was performed. Successful PTPs were then prevalidated using parallel reaction monitoring (PRM) and reconfirmed in 15 publicly available databases. Results: Stringency-based targetable potential biomarkers were shortlisted following in silico screening. PRM reveals top 12 potential biomarkers including the top ranking seven in silico verification-based biomarkers. Database reconfirmation showed differential expression between PCa and benign/normal prostatic urine samples. Conclusion: The pragmatic penultimate screening step, described herein, would immensely improve targeted proteomics validation of ..

ATAQS: A computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Since its inception, proteomics has essentially operated in a discovery mode with the goal of identifying and quantifying the maximal number of proteins in a sample. Increasingly, proteomic measurements are also supporting hypothesis-driven studies, in which a predetermined set of proteins is consistently detected and quantified in multiple samples. Selected reaction monitoring (SRM) is a targeted mass spectrometric technique that supports the detection and quantification of specific proteins in complex samples at high sensitivity and reproducibility. Here, we describe ATAQS, an integrated software platform that supports all stages of targeted, SRM-based proteomics experiments including target selection, transition optimization and post acquisition data analysis. This software will significantly facilitate the use of targeted proteomic techniques and contribute to the generation of highly sensitive, reproducible and complete datasets that are particularly critical for the discovery and validation of targets in hypothesis-driven studies in systems biology.</p> <p>Result</p> <p>We introduce a new open source software pipeline, ATAQS (Automated and Targeted Analysis with Quantitative SRM), which consists of a number of modules that collectively support the SRM assay development workflow for targeted proteomic experiments (project management and generation of protein, peptide and transitions and the validation of peptide detection by SRM). ATAQS provides a flexible pipeline for end-users by allowing the workflow to start or end at any point of the pipeline, and for computational biologists, by enabling the easy extension of java algorithm classes for their own algorithm plug-in or connection via an external web site.</p> <p>This integrated system supports all steps in a SRM-based experiment and provides a user-friendly GUI that can be run by any operating system that allows the installation of the Mozilla Firefox web browser.</p> <p>Conclusions</p> <p>Targeted proteomics via SRM is a powerful new technique that enables the reproducible and accurate identification and quantification of sets of proteins of interest. ATAQS is the first open-source software that supports all steps of the targeted proteomics workflow. ATAQS also provides software API (Application Program Interface) documentation that enables the addition of new algorithms to each of the workflow steps. The software, installation guide and sample dataset can be found in <url>http://tools.proteomecenter.org/ATAQS/ATAQS.html</url></p

System-based proteomic and metabonomic analysis of the Df(16)A+/- mouse identifies potential miR-185 targets and molecular pathway alterations

Deletions on chromosome 22q11.2 are a strong genetic risk factor for development of schizophrenia and cognitive dysfunction. We employed shotgun liquid chromatography-mass spectrometry (LC-MS) proteomic and metabonomic profiling approaches on prefrontal cortex (PFC) and hippocampal (HPC) tissue from Df(16)A +/- mice, a model of the 22q11.2 deletion syndrome. Proteomic results were compared with previous transcriptomic profiling studies of the same brain regions. The aim was to investigate how the combined effect of the 22q11.2 deletion and the corresponding miRNA dysregulation affects the cell biology at the systems level. The proteomic brain profiling analysis revealed PFC and HPC changes in various molecular pathways associated with chromatin remodelling and RNA transcription, indicative of an epigenetic component of the 22q11.2DS. Further, alterations in glycolysis/gluconeogenesis, mitochondrial function and lipid biosynthesis were identified. Metabonomic profiling substantiated the proteomic findings by identifying changes in 22q11.2 deletion syndrome (22q11.2DS)-related pathways, such as changes in ceramide phosphoethanolamines, sphingomyelin, carnitines, tyrosine derivates and panthothenic acid. The proteomic findings were confirmed using selected reaction monitoring mass spectrometry, validating decreased levels of several proteins encoded on 22q11.2, increased levels of the computationally predicted putative miR-185 targets UDP-N-acetylglucosamine-peptide N-acetylglucosaminyltransferase 110 kDa subunit (OGT1) and kinesin heavy chain isoform 5A and alterations in the non-miR-185 targets serine/threonine-protein phosphatase 2B catalytic subunit gamma isoform, neurofilament light chain and vesicular glutamate transporter 1. Furthermore, alterations in the proteins associated with mammalian target of rapamycin signalling were detected in the PFC and with glutamatergic signalling in the hippocampus. Based on the proteomic and metabonomic findings, we were able to develop a schematic model summarizing the most prominent molecular network findings in the Df(16)A +/- mouse. Interestingly, the implicated pathways can be linked to one of the most consistent and strongest proteomic candidates, (OGT1), which is a predicted miR-185 target. Our results provide novel insights into system-biological mechanisms associated with the 22q11DS, which may be linked to cognitive dysfunction and an increased risk to develop schizophrenia. Further investigation of these pathways could help to identify novel drug targets for the treatment of schizophrenia

Multiple marker abundance profiling:combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples

Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live)

MS Analysis of a Dilution Series of Bacteria: Phytoplankton to Improve Detection of Low Abundance Bacterial Peptides

Assigning links between microbial activity and biogeochemical cycles in the ocean is a primary objective for ecologists and oceanographers. Bacteria represent a small ecosystem component by mass, but act as the nexus for both nutrient transformation and organic matter recycling. There are limited methods to explore the full suite of active bacterial proteins largely responsible for degradation. Mass spectrometry (MS)-based proteomics now has the potential to document bacterial physiology within these complex systems. Global proteome profiling using MS, known as data dependent acquisition (DDA), is limited by the stochastic nature of ion selection, decreasing the detection of low abundance peptides. The suitability of MS-based proteomics methods in revealing bacterial signatures outnumbered by phytoplankton proteins was explored using a dilution series of pure bacteria (Ruegeria pomeroyi) and diatoms (Thalassiosira pseudonana). Two common acquisition strategies were utilized: DDA and selected reaction monitoring (SRM). SRM improved detection of bacterial peptides at low bacterial cellular abundance that were undetectable with DDA from a wide range of physiological processes (e.g. amino acid synthesis, lipid metabolism, and transport). We demonstrate the benefits and drawbacks of two different proteomic approaches for investigating species-specific physiological processes across relative abundances of bacteria that vary by orders of magnitude

Comprehending meningioma signaling cascades using multipronged proteomics approaches & targeted validation of potential markers

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications

Novel urinary and serological markers of prostate cancer using proteomics techniques: an important tool for early cancer diagnosis and treatment monitoring

In Africa, Prostate cancer (PCa) is the most frequently diagnosed solid organ tumour in males and use of prostate specific antigen (PSA) is presently fraught with diagnostic inaccuracies. Not least, in a multi-ethnic society like South Africa, proteome differences between African, Caucasian and Mixed-Ancestry PCa patients are largely unknown. Hence, discovery and validation of affordable, non-invasive and reliable diagnostic biomarkers of PCa would expand the frontiers of PCa management. We have employed two high-throughput proteomics technologies to identify novel urine- and blood-based biomarkers for early diagnosis and treatment monitoring of prostate cancer in a South African cohort as well as elucidate proteome differences in patients from our heterogeneous cohort. We compared the urinary proteomes of PCa, Benign Prostatic Hyperplasia (BPH), disease controls comprising patients with other uropathies (DC) and normal healthy controls (NC) both by pooling and individual discovery shotgun proteomic assessment on a nano-Liquid chromatography (nLC) coupled Hybrid Quadrupole-Orbitrap Mass Spectrometer platform. In-silico verification of identified biomarkers was performed using the Human Protein Atlas (HPA) as well as SRMAtlas; and verified potential biomarkers were experimentally prevalidated using a targeted parallel reaction monitoring (PRM) proteomics approach. Further, we employed the CT100+ antigen microarray platform to assess the differential humoral antibody response of PCa, DC and BPH patients in our cohort to a panel of 123 tumour-associated cancer antigens. Candidate antigen biomarkers were analyzed for ethnic group variation in our cohort and potential cancer diagnostic and immunotherapeutic inferences were drawn. Using these approaches, we identified 5595 and 9991 non-redundant peptides from the pooled and individual experiments respectively. While nine proteins demonstrated ethnic trend, 37 and 73 proteins were differentially expressed by pooled and individual analysis respectively. All 32 verified biomarkers were prevalidated with parallel reaction monitoring. Good PRM signals for 12 top ranking biomarker was observed, including PSA and prostatic acid phosphatase. We also identified 41 potential diagnostic and immunotherapeutic antigen biomarkers. Proteogenomic functional pathway analyses of differentially expressed antigens showed similar enrichments of biologic processes. We identified herein novel urinary and blood-based potential diagnostic biomarkers and immunotherapeutic targets of PCa in a South African PCa Cohort using multiple proteomics approaches