Shotgun Mass Spectrometry Workflow Combining IEF and LC-MALDI-TOF/TOF

We present a high throughput shotgun mass spectrometry workflow using a bidimensional peptide fractionation procedure consisting of isoelectric focusing and RP-HPLC prior to mass spectrometric analysis, with the aim of optimizing peptide separation and protein identification. As part of the workflow we used the ‘Isotope-Coded Protein Labeling’ (ICPL) method for accurate relative quantitation of protein expression. Such workflow was successfully applied to a comparative proteome analysis of schizophrenia versus healthy control brain tissues and can be an alternative to proteome researches

Plasmodium falciparum CRK4 directs continuous rounds of DNA replication during schizogony.

: Plasmodium parasites, the causative agents of malaria, have evolved a unique cell division cycle in the clinically relevant asexual blood stage of infection(1). DNA replication commences approximately halfway through the intracellular development following invasion and parasite growth. The schizont stage is associated with multiple rounds of DNA replication and nuclear division without cytokinesis, resulting in a multinucleated cell. Nuclei divide asynchronously through schizogony, with only the final round of DNA replication and segregation being synchronous and coordinated with daughter cell assembly(2,3). However, the control mechanisms for this divergent mode of replication are unknown. Here, we show that the Plasmodium-specific kinase PfCRK4 is a key cell-cycle regulator that orchestrates multiple rounds of DNA replication throughout schizogony in Plasmodium falciparum. PfCRK4 depletion led to a complete block in nuclear division and profoundly inhibited DNA replication. Quantitative phosphoproteomic profiling identified a set of PfCRK4-regulated phosphoproteins with greatest functional similarity to CDK2 substrates, particularly proteins involved in the origin of replication firing. PfCRK4 was required for initial and subsequent rounds of DNA replication during schizogony and, in addition, was essential for development in the mosquito vector. Our results identified an essential S-phase promoting factor of the unconventional P. falciparum cell cycle. PfCRK4 is required for both a prolonged period of the intraerythrocytic stage of Plasmodium infection, as well as for transmission, revealing a broad window for PfCRK4-targeted chemotherapeutics.<br/

C-STrap Sample Preparation Method—In-Situ Cysteinyl Peptide Capture for Bottom-Up Proteomics Analysis in the STrap Format

Recently we introduced the concept of Suspension Trapping (STrap) for bottom-up proteomics sample processing that is based upon SDS-mediated protein extraction, swift detergent removal and rapid reactor-type protein digestion in a quartz depth filter trap. As the depth filter surface is made of silica, it is readily modifiable with various functional groups using the silane coupling chemistries. Thus, during the digest, peptides possessing specific features could be targeted for enrichment by the functionalized depth filter material while non-targeted peptides could be collected as an unbound distinct fraction after the digest. In the example presented here the quartz depth filter surface is functionalized with the pyridyldithiol group therefore enabling reversible in-situ capture of the cysteine-containing peptides generated during the STrap-based digest. The described C-STrap method retains all advantages of the original STrap methodology and provides robust foundation for the conception of the targeted in-situ peptide fractionation in the STrap format for bottom-up proteomics. The presented data support the method’s use in qualitative and semi-quantitative proteomics experiments

In vivo incorporation of fenfluramine and norfenfluramine into pigmented and nonpigmented hair of rats measured by HPLC-fluorescence detection

The incorporation profiles for fenfluramine (Fen) and its metabolite norfenfluramine (Norf) into black hair and white hair of Zucker rats and into white hair of Wistar rats after intraperitoneal (i.p.) administration of Fen or N-nitrosofenfluramine (N-Fen) were studied in great detail. The target compounds were determined by high-performance liquid chromatography with fluorescence detection using 4-(4,5-diphenyl-1 H-imidazol-2-yl)benzoyl chloride as a derivatization reagent. After repeated i.p. administration of Fen (5 mg/kg) for 4 days to Zucker rats, shaft and root samples of black and white hair were obtained 1 week after the first administration. It was surprising that Fen and Norf levels in root samples of white hair were much higher than those in shaft or root samples of black hair, strongly suggesting that unknown mechanisms other than the action of melanin take place in the white hair root. Time course profiles for Fen and Norf after administration of a single i.p. dose of Fen or N-Fen were constructed for Zucker and Wistar rats. The percent level of Fen or Norf in white hair was 15-50% of that in black hair at any interval within 600 min after a single administration of Fen in Zucker rats. Even with Wistar rats having only white hair, we could demonstrate the time courses for incorporation of Fen and Norf into white hair. Finally, time course profiles for Fen and Norf were also followed after a single i.p. administration of N-Fen; this experiment showed that the levels of Norf were much higher than those of Fen for both black and white hair samples of Zucker rats at any interval tested

A Temporal Proteomic Map of Epstein-Barr Virus Lytic Replication in B Cells

Epstein-Barr virus (EBV) replication contributes to multiple human diseases, including infectious mononucleosis, nasopharyngeal carcinoma, B cell lymphomas, and oral hairy leukoplakia. We performed systematic quantitative analyses of temporal changes in host and EBV proteins during lytic replication to gain insights into virus-host interactions, using conditional Burkitt lymphoma models of type I and II EBV infection. We quantified profiles of >8,000 cellular and 69 EBV proteins, including >500 plasma membrane proteins, providing temporal views of the lytic B cell proteome and EBV virome. Our approach revealed EBV-induced remodeling of cell cycle, innate and adaptive immune pathways, including upregulation of the complement cascade and proteasomal degradation of the B cell receptor complex, conserved between EBV types I and II. Cross-comparison with proteomic analyses of human cytomegalovirus infection and of a Kaposi-sarcoma-associated herpesvirus immunoevasin identified host factors targeted by multiple herpesviruses. Our results provide an important resource for studies of EBV replication

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

Quantitative temporal in vivo proteomics (QTiPs) deciphers the transition of virus-driven myeloid cells into M2 macrophages

Myeloid cells play a central role in the context of viral eradication, yet precisely how these cells differentiate throughout the course of acute infections is poorly understood. In this study, we have developed a novel quantitative temporal in vivo proteomics (QTiPs) platform to capture proteomic signatures of temporally transitioning virus-driven myeloid cells directly in situ, thus taking into consideration host–virus interactions throughout the course of an infection. QTiPs, in combination with phenotypic, functional, and metabolic analyses, elucidated a pivotal role for inflammatory CD11b⁺, Ly6G‾, Ly6C^high-low cells in antiviral immune response and viral clearance. Most importantly, the time-resolved QTiPs data set showed the transition of CD11b⁺, Ly6G‾, Ly6C^high-low cells into M2-like macrophages, which displayed increased antigen-presentation capacities and bioenergetic demands late in infection. We elucidated the pivotal role of myeloid cells in virus clearance and show how these cells phenotypically, functionally, and metabolically undergo a timely transition from inflammatory to M2-like macrophages in vivo. With respect to the growing appreciation for in vivo examination of viral–host interactions and for the role of myeloid cells, this study elucidates the use of quantitative proteomics to reveal the role and response of distinct immune cell populations throughout the course of virus infection.This work was supported by grants from the Canadian Institutes of Health Research (CIHR) and Terry Fox Research Institute (TFRI) to S.G. and P.W.L. Authors D.R.C., Y.K., and T.S. are supported by the CIHR. J.P.M. and B.E.K. are supported through the Cancer Research Training Program (CRTP) of BHCRI. D.R.C. was supported previously by CRTP from BHCRI and the Nova Scotia Health Research Foundation (NSHRF). Nova Scotia Graduate Scholarships fund both N.H. and P.K. Work by J.A.P. was funded in part by NIH/NIDDK grant K01 DK098285. M.P.W. was supported by a Wellcome Trust Senior Fellowship (108070/Z/15/Z). We acknowledge Devanand Pinto and Ken Chisholm (National Research Council) as well as Alejandro Cohen at the Dalhousie Proteomics Core Facility and Derek Rowter and Renee Raudonis at Dalhousie Flow cytometry suites

Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes

Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases

Assembling proteomics data as a prerequisite for the analysis of large scale experiments

<p>Abstract</p> <p>Background</p> <p>Despite the complete determination of the genome sequence of a huge number of bacteria, their proteomes remain relatively poorly defined. Beside new methods to increase the number of identified proteins new database applications are necessary to store and present results of large- scale proteomics experiments.</p> <p>Results</p> <p>In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of <it>Mycobacterium tuberculosis</it>, <it>Helicobacter pylori</it>, <it>Salmonella typhimurium </it>and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML.</p> <p>Conclusion</p> <p>The Oracle based data repository system SQL-LIMS played the central role in the proteomics workflow concept. Technical operations of our proteomics labs were used as standards for SQL-LIMS templates. Using a Java based parser, post-processed data of different approaches such as LC/ESI-MS, MALDI-MS and 1-DE and 2-DE were stored in SQL-LIMS. Thus, unique data formats of different instruments were unified and stored in SQL-LIMS tables. Moreover, a unique submission identifier allowed fast access to all experimental data. This was the main advantage compared to multi software solutions, especially if personnel fluctuations are high. Moreover, large scale and high-throughput experiments must be managed in a comprehensive repository system such as SQL-LIMS, to query results in a systematic manner. On the other hand, these database systems are expensive and require at least one full time administrator and specialized lab manager. Moreover, the high technical dynamics in proteomics may cause problems to adjust new data formats. To summarize, SQL-LIMS met the requirements of proteomics data handling especially in skilled processes such as gel-electrophoresis or mass spectrometry and fulfilled the PSI standardization criteria. The data transfer into a public domain via DTT facilitated validation of proteomics data. Additionally, evaluation of mass spectra by post-processing using MS-Screener improved the reliability of mass analysis and prevented storage of data junk.</p

Plasma proteome changes in cardiovascular disease patients: novel isoforms of apolipoprotein A1

<p>Abstract</p> <p>Background</p> <p>The aim of this proteomic study was to look for changes taking place in plasma proteomes of patients with acute myocardial infarction (AMI), unstable angina pectoris (UAP), and stable angina pectoris (SAP).</p> <p>Methods</p> <p>Depleted plasma proteins were separated by 2D SDS-PAGE (pI 4-7), and proteomes were compared using Progenesis SameSpots statistical software. Proteins were identified by nanoLC-MS/MS. Proteins were quantified using commercial kits. Apolipoprotein A1 was studied using 1D and 2D SDS-PAGE, together with western blotting.</p> <p>Results</p> <p>Reciprocal comparison revealed 46 unique, significantly different spots; proteins in 34 spots were successfully identified and corresponded to 38 different proteins. Discrete comparisons of patient groups showed 45, 41, and 8 significantly different spots when AMI, UAP, and SAP were compared with the control group. On the basis of our proteomic data, plasma levels of two of them, alpha-1 microglobulin and vitamin D-binding protein, were determined. The data, however, failed to prove the proteins to be suitable markers or risk factors in the studied groups. The plasma level and isoform representation of apolipoprotein A1 were also estimated. Using 1D and 2D SDS-PAGE, together with western blotting, we observed extra high-molecular weight apolipoprotein A1 fractions presented only in the patient groups, indicating that the novel high-molecular weight isoforms of apolipoprotein A1 may be potential new markers or possible risk factors of cardiovascular disease.</p> <p>Conclusion</p> <p>The reported data show plasma proteome changes in patients with AMI, UAP, and SAP. We propose some apolipoprotein A1 fractions as a possible new disease-associated marker of cardiovascular disorders.</p