Contribution de l'écologie du paysage à la diversification des agroécosystèmes à des fins de phytoprotection

Cet article de synthèse établit un lien entre la diversification des systèmes agricoles et le contrôle naturel des insectes ravageurs d'une part, et l'écologie du paysage d'autre part. L'analyse de la revue de littérature réalisée suggère que cette jeune science et le recours à la géomatique pourraient non seulement permettre de concevoir de nouvelles approches en recherche, mais aussi de participer à l'aménagement des agroécosystèmes à des fins de phytoprotection dans une perspective d'agriculture durable au Québec.In this review we establish a link between the diversification of agricultural systems and natural control of crop pests in one hand, and in the other hand the potential contribution of a young science, landscape ecology, which associated with geomatic, can elaborate new ways in research and take part in managing agroecosystems for crop protection in a sustainable manner in Québec

Urine sample preparation protocol standarization for selected reaction monitoring (SRM) bases assay

Comunicaciones a congreso

Chinese hamster ovary cells can produce galactose-α-1,3-galactose antigens on proteins

Chinese hamster ovary (CHO) cells are widely used for the manufacture of biotherapeutics, in part because of their ability to produce proteins with desirable properties, including 'human-like' glycosylation profiles. For biotherapeutics production, control of glycosylation is critical because it has a profound effect on protein function, including half-life and efficacy. Additionally, specific glycan structures may adversely affect their safety profile. For example, the terminal galactose-α-1,3-galactose (α-Gal) antigen can react with circulating anti α-Gal antibodies present in most individuals. It is now understood that murine cell lines, such as SP2 or NSO, typical manufacturing cell lines for biotherapeutics, contain the necessary biosynthetic machinery to produce proteins containing α-Gal epitopes. Furthermore, the majority of adverse clinical events associated with an induced IgE-mediated anaphylaxis response in patients treated with the commercial antibody Erbitux (cetuximab) manufactured in a murine myeloma cell line have been attributed to the presence of the α-Gal moiety. Even so, it is generally accepted that CHO cells lack the biosynthetic machinery to synthesize glycoproteins with α-Gal antigens. Contrary to this assumption, we report here the identification of the CHO ortholog of N-acetyllactosaminide 3-α-galactosyltransferase-1, which is responsible for the synthesis of the α-Gal epitope. We find that the enzyme product of this CHO gene is active and that glycosylated protein products produced in CHO contain the signature α-Gal antigen because of the action of this enzyme. Furthermore, characterizing the commercial therapeutic protein abatacept (Orencia) manufactured in CHO cell lines, we also identified the presence of α-Gal. Finally, we find that the presence of the α-Gal epitope likely arises during clonal selection because different subclonal populations from the same parental cell line differ in their expression of this gene. Although the specific levels of α-Gal required to trigger anaphylaxis reactions are not known and are likely product specific, the fact that humans contain high levels of circulating anti-α-Gal antibodies suggests that minimizing (or at least controlling) the levels of these epitopes during biotherapeutics development may be beneficial to patients. Furthermore, the approaches described here to monitor α-Gal levels may prove useful in industry for the surveillance and control of α-Gal levels during protein manufacture.National Center for Research Resources (U.S.) (Grant P41 RR018501-01

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

Interaction proteomics of synapse protein complexes

The brain integrates complex types of information, and executes a wide range of physiological and behavioral processes. Trillions of tiny organelles, the synapses, are central to neuronal communication and information processing in the brain. Synaptic transmission involves an intricate network of synaptic proteins that forms the molecular machinery underlying transmitter release, activation, and modulation of transmitter receptors and signal transduction cascades. These processes are dynamically regulated and underlie neuroplasticity, crucial to learning and memory formation. In recent years, interaction proteomics has increasingly been used to elucidate the constituents of synaptic protein complexes. Unlike classic hypothesis-based assays, interaction proteomics detects both known and novel interactors without bias. In this trend article, we focus on the technical aspects of recent proteomics to identify synapse protein complexes, and the complementary methods used to verify the protein–protein interaction. Moreover, we discuss the experimental feasibility of performing global analysis of the synapse protein interactome

Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration

<p>Abstract</p> <p>Background</p> <p>Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying "metrics" that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset.</p> <p>Results</p> <p>We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation.</p> <p>Conclusions</p> <p>Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages.</p

Calculation of partial isotope incorporation into peptides measured by mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Stable isotope probing (SIP) technique was developed to link function, structure and activity of microbial cultures metabolizing carbon and nitrogen containing substrates to synthesize their biomass. Currently, available methods are restricted solely to the estimation of fully saturated heavy stable isotope incorporation and convenient methods with sufficient accuracy are still missing. However in order to track carbon fluxes in microbial communities new methods are required that allow the calculation of partial incorporation into biomolecules.</p> <p>Results</p> <p>In this study, we use the characteristics of the so-called 'half decimal place rule' (HDPR) in order to accurately calculate the partial¹³C incorporation in peptides from enzymatic digested proteins. Due to the clade-crossing universality of proteins within bacteria, any available high-resolution mass spectrometry generated dataset consisting of tryptically-digested peptides can be used as reference.</p> <p>We used a freely available peptide mass dataset from <it>Mycobacterium tuberculosis </it>consisting of 315,579 entries. From this the error of estimated versus known heavy stable isotope incorporation from an increasing number of randomly drawn peptide sub-samples (100 times each; no repetition) was calculated. To acquire an estimated incorporation error of less than 5 atom %, about 100 peptide masses were needed. Finally, for testing the general applicability of our method, peptide masses of tryptically digested proteins from <it>Pseudomonas putida </it>ML2 grown on labeled substrate of various known concentrations were used and¹³C isotopic incorporation was successfully predicted. An easy-to-use script <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> was further developed to guide users through the calculation procedure for their own data series.</p> <p>Conclusion</p> <p>Our method is valuable for estimating¹³C incorporation into peptides/proteins accurately and with high sensitivity. Generally, our method holds promise for wider applications in qualitative and especially quantitative proteomics.</p