Enhanced analysis of equine plasma for the presence of recombinant human erythropoietin - Implementation of an improved workflow.

An improved screening workflow and a robust capillary flow LC-MS confirmatory method for the detection of recombinant human erythropoietin (rHuEPO) has been implemented to increase the sensitivity of rHuEPO detection and to reduce the number of suspect samples committed to confirmatory testing. The influence of repeated dosing of epoetin-ß on the detection window of rHuEPO in equine plasma was assessed using the optimised method. Samples were initially assessed using an economical R&D Human EPO Duo-Set ELISA Development System. Samples indicating a result greater than the batch baseline were analysed using the complementary R&D Human EPO Quantikine IVD ELISA kit. All samples recording an abnormal screening result were subjected to confirmatory analysis. Confirmation of rHuEPO in plasma (≥2.5 ml) in the range of 4-13 mIU/ml (n = 6) was achieved using immunoaffinity enrichment, tryptic digestion, and capillary flow LC-MS/MS. Four horses were administered a single dose of epoetin-ß (10,000 IU) via the subcutaneous and intravenous routes, on two occasions, seven days apart. The excretion profile was rapid with epoetin-ß detection times of 48 to 72 h following each administration, with no appreciable difference observed between the two routes of administration. This workflow has been shown as an effective anti-doping strategy related to rHuEPO misuse and supports the use of out-of-competition testing of horses in the 2 to 3-day period prior to race-day.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessing their suitability for targeted proteomics applications.

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, urine and prognostic protein panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing MRM assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SIGNIFICANCE: Mass spectrometry-based proteomics platforms have accumulated substantial data on protein differential regulation in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessing GCs. We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation. Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of multiple reaction monitoring (MRM) assays in a SCIEX QTRAP instrument. With the moto of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data. Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics.

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

Typic: A Practical and Robust Tool to Rank Proteotypic Peptides for Targeted Proteomics.

The selection of a suitable proteotypic peptide remains a challenge for designing a targeted quantitative proteomics assay. Although the criteria are well-established in the literature, the selection of these peptides is often performed in a subjective and time-consuming manner. Here, we have developed a practical and semiautomated workflow implemented in an open-source program named Typic. Typic is designed to run in a command line and a graphical interface to help selecting a list of proteotypic peptides for targeted quantitation. The tool combines the input data and downloads additional data from public repositories to produce a file per protein as output. Each output file includes relevant information to the selection of proteotypic peptides organized in a table, a colored ranking of peptides according to their potential value as targets for quantitation and auxiliary plots to assist users in the task of proteotypic peptides selection. Taken together, Typic leads to a practical and straightforward data extraction from multiple data sets, allowing the identification of most suitable proteotypic peptides based on established criteria, in an unbiased and standardized manner, ultimately leading to a more robust targeted proteomics assay.

Identification of transient receptor potential melastatin 3 proteotypic peptides employing an efficient membrane protein extraction method for natural killer cells.

Introduction: Mutations and misfolding of membrane proteins are associated with various disorders, hence they make suitable targets in proteomic studies. However, extraction of membrane proteins is challenging due to their low abundance, stability, and susceptibility to protease degradation. Given the limitations in existing protocols for membrane protein extraction, the aim of this investigation was to develop a protocol for a high yield of membrane proteins for isolated Natural Killer (NK) cells. This will facilitate genetic analysis of membrane proteins known as transient receptor potential melastatin 3 (TRPM3) ion channels in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) research. Methods: Two protocols, internally identified as Protocol 1 and 2, were adapted and optimized for high yield protein extraction. Protocol 1 utilized ultrasonic and salt precipitation, while Protocol 2 implemented a detergent and chloroform/methanol approach. Protein concentrations were determined by the Pierce Bicinchoninic Acid (BCA) and the Bio-Rad DC (detergent compatible) protein assays according to manufacturer's recommendation. Using Protocol 2, protein samples were extracted from NK cells of n = 6 healthy controls (HC) and n = 4 ME/CFS patients. In silico tryptic digest and enhanced signature peptide (ESP) predictor were used to predict high-responding TRPM3 tryptic peptides. Trypsin in-gel digestion was performed on protein samples loaded on SDS-PAGE gels (excised at 150-200 kDa). A liquid chromatography-multiple reaction monitoring (LC-MRM) method was optimized and used to evaluate the detectability of TRPM3 n = 5 proteotypic peptides in extracted protein samples. Results: The detergent-based protocol protein yield was significantly higher (p < 0.05) compared with the ultrasonic-based protocol. The Pierce BCA protein assay showed more reproducibility and compatibility compared to the Bio-Rad DC protein assay. Two high-responding tryptic peptides (GANASAPDQLSLALAWNR and QAILFPNEEPSWK) for TRPM3 were detectable in n = 10 extracted protein samples from NK cells isolated from HC and ME/CFS patients. Conclusion: A method was optimized for high yield protein extraction from human NK cells and for the first time TRPM3 proteotypic peptides were detected using LC-MRM. This new method provides for future research to assess membrane protein structural and functional relationships, particularly to facilitate proteomic investigation of TRPM3 ion channel isoforms in NK cells in both health and disease states, such as ME/CFS.

Multiple Reaction Monitoring-Mass Spectrometry Enables Robust Quantitation of Plasma Proteins Regardless of Whole Blood Processing Delays That May Occur in the Clinic.

Plasma is an important biofluid for clinical research and diagnostics. In the clinic, unpredictable delays-from minutes to hours-between blood collection and plasma generation are often unavoidable. These delays can potentially lead to protein degradation and modification and might considerably affect intact protein measurement methods such as sandwich enzyme-linked immunosorbent assays that bind proteins on two epitopes to increase specificity, thus requiring largely intact protein structures. Here, we investigated, using multiple reaction monitoring mass spectrometry (MRM-MS), how delays in plasma processing affect peptide-centric "bottom-up" proteomics. We used validated assays for proteotypic peptide surrogates of 270 human proteins to analyze plasma generated after whole blood had been kept at room temperature from 0 to 40 h to mimic delays that occur in the clinic. Moreover, we evaluated the impact of different plasma-thawing conditions on MRM-based plasma protein quantitation. We demonstrate that >90% of protein concentration measurements were unaffected by the thawing procedure and by up to 40-h delayed plasma generation, reflected by relative standard deviations (RSDs) of <30%. Of the 159 MRM assays that yielded quantitative results in 60% of the measured time points, 139 enabled a stable protein quantitation (RSD <20%), 14 showed a slight variation (RSD 20-30%), and 6 appeared unstable/irreproducible (RSD > 30%). These results demonstrate the high robustness and thus the potential for MRM-based plasma-protein quantitation to be used in a clinical setting. In contrast to enzyme-linked immunosorbent assay, peptide-based MRM assays do not require intact three-dimensional protein structures for an accurate and precise quantitation of protein concentrations in the original sample.

Application and optimization of label-free shotgun approaches in the study of Quercus ilex.

Advances in proteomic equipment, algorithms and wet protocols are being increasingly reported. Each step in the experimental workflow must be adapted and optimized to the target experimental system and objectives. The influence of the amount of peptides loaded onto the column in shotgun platforms has rarely been considered to date even though it dictates the confidence with which proteins can be identified and quantified. An experiment using variable dilutions of protein equivalent mixtures of root, leaf and seed tissue extracts of Quercus ilex was performed by subjecting BSA protein equivalent amounts of 1-100 µg to SDS-PAGE, the resulting bands being trypsin digested and peptides (10-1000 ng protein equivalents) loaded onto an LC column. Mass spectra were used to identify proteins against the in-house Q. ilex transcriptome database. Determinations included SEQUEST quantification (average of the three most abundant distinct peptides for each protein) and proteotypic peptides. The number of proteins identified was found to depend on peptide load and to peak at 2054 with 600 ng. Smaller loads led to linearly decreasing identifications from 1859 with 400 ng to 495 with 10 ng. Both quantification strategies provided similar results. The linear dynamic range was from 100 to 600 ng.

Unraveling the Composition of Insecticidal Crystal Proteins in Bacillus thuringiensis: a Proteomics Approach.

Bacillus thuringiensis (Bt) is the most widely used active ingredient for biological insecticides. The composition of δ-endotoxins (Cry and Cyt proteins) in the parasporal crystal determines the toxicity profile of each Bt strain. However, a reliable method for their identification and quantification has not been available, due to the high sequence identity of the genes that encode the δ-endotoxins and the toxins themselves. Here, we have developed an accurate and reproducible mass spectrometry-based method (liquid chromatography-tandem mass spectrometry-multiple reaction monitoring [LC-MS/MS-MRM]) using isotopically labeled proteotypic peptides for each protein in a particular mixture to determine the relative proportion of each δ-endotoxin within the crystal. To validate the method, artificial mixtures containing Cry1Aa, Cry2Aa, and Cry6Aa were analyzed. Determination of the relative abundance of proteins (in molarity) with our method was in good agreement with the expected values. This method was then applied to the most common commercial Bt-based products, DiPel DF, XenTari GD, VectoBac 12S, and Novodor, in which between three and six δ-endotoxins were identified and quantified in each product. This novel approach is of great value for the characterization of Bt-based products, not only providing information on host range, but also for monitoring industrial crystal production and quality control and product registration for Bt-based insecticides.IMPORTANCEBacillus thuringiensis (Bt)-based biological insecticides are used extensively to control insect pests and vectors of human diseases. Bt-based products provide greater specificity and biosafety than broad-spectrum synthetic insecticides. The biological activity of this bacterium resides in spores and crystals comprising complex mixtures of toxic proteins. We developed and validated a fast, accurate, and reproducible method for quantitative determination of the crystal components of Bt-based products. This method will find clear applications in the improvement of various aspects of the industrial production process of Bt. An important aspect of the production of Bt-based insecticides is its quality control. By specifically quantifying the relative proportion of each of the toxins that make up the crystal, our method represents the most consistent and repeatable evaluation procedure in the quality control of different batches produced in successive fermentations. This method can also contribute to the design of specific culture media and fermentation conditions that optimize Bt crystal composition across a range of Bt strains that target different pestiferous insects. Quantitative information on crystal composition should also prove valuable to phytosanitary product registration authorities that oversee the safety and efficacy of crop protection products.

Identification of recombinant human insulin and biosynthetic insulin analogues by multiplexed targeted unlabeled mass spectrometry of proteotypic tryptic peptides.

Direct qualitative methods that allow the rapid screening and identification of insulin products during early stages of the drug development process and those already in the market can be of great utility for manufacturers and regulatory agencies and the recent scientific literature describes several methods. Herein, a qualitative proteomic method is presented for the identification of recombinant human insulin and all marketed biosynthetic analogues -insulin lispro, aspart, glulisine, glargine, detemir and degludec- via tryptic digestion and identification of proteotypic peptides for each insulin. Individual insulins were first denatured under reducing conditions and the cysteine residues blocked by iodoacetamide. The proteins were then digested with trypsin and the peptide products separated by reversed phase liquid chromatography on an Ascentis® Express ES-C18 column and detected by positive polarity ESI-MS/MS. The digestion peptides were characterized using a multiplexed MRM approach that monitors the fragmentation of the doubly charged unlabeled precursor ion of each peptide into a collection of signature y and b ions. The MRM transitions for the individual peptides were optimized to allow maximal ionization on a standard triple quadrupole mass spectrometer. All products of the digestion procedure for all insulins were detected with adequate signal intensity except for the C-terminal B30Thr whenever it was present and cleaved and the tryptic B1-3 tripeptide of insulin glulisine. The unique proteotypic peptides identified for each of the insulin analogues coupled with their signature y and b ions permitted the unambiguous verification of all sequence variations and chemical modifications. The elution of the A polypeptide chain for all insulins and the tryptic peptides of the B chain, with the exception of a very few, occurred around the same time point. This underscores the close similarity in the physicochemical properties between the digestion peptides and is consistent with the subtle variations in amino acid sequence among the various insulins. Therefore, the identification and distinction of the different types of insulin based solely on the chromatographic retention time of their respective proteolytic products can be deceptive without proper mass spectrometric analysis and may result in false positives.

Selecting Optimal Peptides for Targeted Proteomic Experiments in Human Plasma Using In Vitro Synthesized Proteins as Analytical Standards.

In targeted proteomics, the development of robust methodologies is dependent upon the selection of a set of optimal peptides for each protein-of-interest. Unfortunately, predicting which peptides and respective product ion transitions provide the greatest signal-to-noise ratio in a particular assay matrix is complicated. Using in vitro synthesized proteins as analytical standards, we report here an empirically driven method for the selection of said peptides in a human plasma assay matrix.

PPLine: An Automated Pipeline for SNP, SAP, and Splice Variant Detection in the Context of Proteogenomics.

The fundamental mission of the Chromosome-Centric Human Proteome Project (C-HPP) is the research of human proteome diversity, including rare variants. Liver tissues, HepG2 cells, and plasma were selected as one of the major objects for C-HPP studies. The proteogenomic approach, a recently introduced technique, is a powerful method for predicting and validating proteoforms coming from alternative splicing, mutations, and transcript editing. We developed PPLine, a Python-based proteogenomic pipeline providing automated single-amino-acid polymorphism (SAP), indel, and alternative-spliced-variants discovery based on raw transcriptome and exome sequence data, single-nucleotide polymorphism (SNP) annotation and filtration, and the prediction of proteotypic peptides (available at https://sourceforge.net/projects/ppline). In this work, we performed deep transcriptome sequencing of HepG2 cells and liver tissues using two platforms: Illumina HiSeq and Applied Biosystems SOLiD. Using PPLine, we revealed 7756 SAP and indels for HepG2 cells and liver (including 659 variants nonannotated in dbSNP). We found 17 indels in transcripts associated with the translation of alternate reading frames (ARF) longer than 300 bp. The ARF products of two genes, SLMO1 and TMEM8A, demonstrate signatures of caspase-binding domain and Gcn5-related N-acetyltransferase. Alternative splicing analysis predicted novel proteoforms encoded by 203 (liver) and 475 (HepG2) genes according to both Illumina and SOLiD data. The results of the present work represent a basis for subsequent proteomic studies by the C-HPP consortium.

Proteomic Validation of Transcript Isoforms, Including Those Assembled from RNA-Seq Data.

Human proteome analysis now requires an understanding of protein isoforms. We recently published the PG Nexus pipeline, which facilitates high confidence validation of exons and splice junctions by integrating genomics and proteomics data. Here we comprehensively explore how RNA-seq transcriptomics data, and proteomic analysis of the same sample, can identify protein isoforms. RNA-seq data from human mesenchymal (hMSC) stem cells were analyzed with our new TranscriptCoder tool to generate a database of protein isoform sequences. MS/MS data from matching hMSC samples were then matched against the TranscriptCoder-derived database, along with Ensembl and the neXtProt database. Querying the TranscriptCoder-derived or Ensembl database could unambiguously identify ∼450 protein isoforms, with isoform-specific proteotypic peptides, including candidate hMSC-specific isoforms for the genes DPYSL2 and FXR1. Where isoform-specific peptides did not exist, groups of nonisoform-specific proteotypic peptides could specifically identify many isoforms. In both the above cases, isoforms will be detectable with targeted MS/MS assays. Unfortunately, our analysis also revealed that some isoforms will be difficult to identify unambiguously as they do not have peptides that are sufficiently distinguishing. We covisualize mRNA isoforms and peptides in a genome browser to illustrate the above situations. Mass spectrometry data is available via ProteomeXchange (PXD001449).

Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry.

The simultaneous quantification of protein concentrations via proteotypic peptides in human blood by liquid chromatography coupled to quadrupole MS/MS is an important field of bioanalytical research with a high potential for routine diagnostic applications. This review summarizes currently available sample preparation procedures and trends for absolute protein quantification in blood using LC-MS/MS. It discusses approaches of transferring established qualitative protocols to a quantitative analysis regarding their reliability and reproducibility. Techniques used to enhance method sensitivity such as the depletion of high-abundant proteins or the immunoaffinity enrichment of proteins and peptides are described. Furthermore, workflows for (i) protein denaturation, (ii) disulfide bridge reduction and (iii) thiol alkylation as well as (iv) enzymatic digestion for absolute protein quantification are presented. The main focus is on the tryptic digestion as a bottleneck of protein quantification via proteotypic peptides. Conclusively, requirements for a high-throughput application are discussed.

Ovarian endometriosis signatures established through discovery and directed mass spectrometry analysis.

New molecular information on potential therapeutic targets or tools for noninvasive diagnosis for endometriosis are important for patient care and treatment. However, surprisingly few efforts have described endometriosis at the protein level. In this work we enumerate the proteins in patient endometrium and ovarian endometrioma by extensive and comprehensive analysis of minute amounts of cryosectioned tissues in a three-tiered mass spectrometric approach. Quantitative comparison of the tissues revealed 214 differentially expressed proteins in ovarian endometrioma and endometrium. These proteins are reported here as a resource of SRM (selected reaction monitoring) assays that are unique, standardized, and openly available. Pathway analysis of the proteome measurements revealed a potential role for Transforming growth factor ß-1 in ovarian endometriosis development. Subsequent mRNA microarray analysis further revealed clear ovarian endometrioma specificity for a subset of these proteins, which was also supported by further in silico studies. In this process two important proteins emerged, Calponin-1 and EMILIN-1, that were additionally confirmed in ovarian endometrioma tissues by immunohistochemistry and Western blotting. This study provides the most comprehensive molecular description of ovarian endometriosis to date and researchers with new molecular methods and tools for high throughput patient screening using the SRM assays.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. BIOLOGICAL SIGNIFICANCE: Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms.

The Equine PeptideAtlas: a resource for developing proteomics-based veterinary research.

Progress in MS-based methods for veterinary research and diagnostics is lagging behind compared to the human research, and proteome data of domestic animals is still not well represented in open source data repositories. This is particularly true for the equine species. Here we present a first Equine PeptideAtlas encompassing high-resolution tandem MS analyses of 51 samples representing a selection of equine tissues and body fluids from healthy and diseased animals. The raw data were processed through the Trans-Proteomic Pipeline to yield high quality identification of proteins and peptides. The current release comprises 24 131 distinct peptides representing 2636 canonical proteins observed at false discovery rates of 0.2% at the peptide level and 1.4% at the protein level. Data from the Equine PeptideAtlas are available for experimental planning, validation of new datasets, and as a proteomic data mining resource. The advantages of the Equine PeptideAtlas are demonstrated by examples of mining the contents for information on potential and well-known equine acute phase proteins, which have extensive general interest in the veterinary clinic. The extracted information will support further analyses, and emphasizes the value of the Equine PeptideAtlas as a resource for the design of targeted quantitative proteomic studies.

A Candida albicans PeptideAtlas.

Candida albicans public proteomic datasets, though growing steadily in the last few years, still have a very limited presence in online repositories. We report here the creation of a C. albicans PeptideAtlas comprising near 22,000 distinct peptides at a 0.24% False Discovery Rate (FDR) that account for over 2500 canonical proteins at a 1.2% FDR. Based on data from 16 experiments, we attained coverage of 41% of the C. albicans open reading frame sequences (ORFs) in the database used for the searches. This PeptideAtlas provides several useful features, including comprehensive protein and peptide-centered search capabilities and visualization tools that establish a solid basis for the study of basic biological mechanisms key to virulence and pathogenesis such as dimorphism, adherence, and apoptosis. Further, it is a valuable resource for the selection of candidate proteotypic peptides for targeted proteomic experiments via Selected Reaction Monitoring (SRM) or SWATH-MS. BIOLOGICAL SIGNIFICANCE: This C. albicans PeptideAtlas resolves the previous absence of fungal pathogens in the PeptideAtlas project. It represents the most extensive characterization of the proteome of this fungus that exists up to the current date, including evidence for uncharacterized ORFs. Through its web interface, PeptideAtlas supports the study of interesting proteins related to basic biological mechanisms key to virulence such as apoptosis, dimorphism and adherence. It also provides a valuable resource to select candidate proteotypic peptides for future (SRM) targeted proteomic experiments. This article is part of a Special Issue entitled: Trends in Microbial Proteomics.

Zinc finger nuclease knock-out of NADPH:cytochrome P450 oxidoreductase (POR) in human tumor cell lines demonstrates that hypoxia-activated prodrugs differ in POR dependence.

Hypoxia, a ubiquitous feature of tumors, can be exploited by hypoxia-activated prodrugs (HAP) that are substrates for one-electron reduction in the absence of oxygen. NADPH:cytochrome P450 oxidoreductase (POR) is considered one of the major enzymes responsible, based on studies using purified enzyme or forced overexpression in cell lines. To examine the role of POR in HAP activation at endogenous levels of expression, POR knock-outs were generated in HCT116 and SiHa cells by targeted mutation of exon 8 using zinc finger nucleases. Absolute quantitation by proteotypic peptide mass spectrometry of DNA sequence-confirmed multiallelic mutants demonstrated expression of proteins with residual one-electron reductase activity in some clones and identified two (Hko2 from HCT116 and S2ko1 from SiHa) that were functionally null by multiple criteria. Sensitivities of the clones to 11 HAP (six nitroaromatics, three benzotriazine N-oxides, and two quinones) were compared with wild-type and POR-overexpressing cells. All except the quinones were potentiated by POR overexpression. Knocking out POR had a marked effect on antiproliferative activity of the 5-nitroquinoline SN24349 in both genetic backgrounds after anoxic exposure but little or no effect on activity of most other HAP, including the clinical stage 2-nitroimidazole mustard TH-302, dinitrobenzamide mustard PR-104A, and benzotriazine N-oxide SN30000. Clonogenic cell killing and reductive metabolism of PR-104A and SN30000 under anoxia also showed little change in the POR knock-outs. Thus, although POR expression is a potential biomarker of sensitivity to some HAP, identification of other one-electron reductases responsible for HAP activation is needed for their rational clinical development.