In vivo versus in vitro protein abundance analysis of Shigella dysenteriae type 1 reveals changes in the expression of proteins involved in virulence, stress and energy metabolism

<p>Abstract</p> <p>Background</p> <p><it>Shigella dysenteriae </it>serotype 1 (SD1) causes the most severe form of epidemic bacillary dysentery. Quantitative proteome profiling of <it>Shigella dysenteriae </it>serotype 1 (SD1) <it>in vitro </it>(derived from LB cell cultures) and <it>in vivo </it>(derived from gnotobiotic piglets) was performed by 2D-LC-MS/MS and APEX, a label-free computationally modified spectral counting methodology.</p> <p>Results</p> <p>Overall, 1761 proteins were quantitated at a 5% FDR (false discovery rate), including 1480 and 1505 from <it>in vitro </it>and <it>in vivo </it>samples, respectively. Identification of 350 cytoplasmic membrane and outer membrane (OM) proteins (38% of <it>in silico </it>predicted SD1 membrane proteome) contributed to the most extensive survey of the <it>Shigella </it>membrane proteome reported so far. Differential protein abundance analysis using statistical tests revealed that SD1 cells switched to an anaerobic energy metabolism under <it>in vivo </it>conditions, resulting in an increase in fermentative, propanoate, butanoate and nitrate metabolism. Abundance increases of transcription activators FNR and Nar supported the notion of a switch from aerobic to anaerobic respiration in the host gut environment. High <it>in vivo </it>abundances of proteins involved in acid resistance (GadB, AdiA) and mixed acid fermentation (PflA/PflB) indicated bacterial survival responses to acid stress, while increased abundance of oxidative stress proteins (YfiD/YfiF/SodB) implied that defense mechanisms against oxygen radicals were mobilized. Proteins involved in peptidoglycan turnover (MurB) were increased, while β-barrel OM proteins (OmpA), OM lipoproteins (NlpD), chaperones involved in OM protein folding pathways (YraP, NlpB) and lipopolysaccharide biosynthesis (Imp) were decreased, suggesting unexpected modulations of the outer membrane/peptidoglycan layers <it>in vivo</it>. Several virulence proteins of the Mxi-Spa type III secretion system and invasion plasmid antigens (Ipa proteins) required for invasion of colonic epithelial cells, and release of bacteria into the host cell cytosol were increased <it>in vivo</it>.</p> <p>Conclusions</p> <p>Global proteomic profiling of SD1 comparing <it>in vivo vs. in vitro </it>proteomes revealed differential expression of proteins geared towards survival of the pathogen in the host gut environment, including increased abundance of proteins involved in anaerobic energy respiration, acid resistance and virulence. The immunogenic OspC2, OspC3 and IpgA virulence proteins were detected solely under <it>in vivo </it>conditions, lending credence to their candidacy as potential vaccine targets.</p

Comparison of two label-free global quantitation methods, APEX and 2D gel electrophoresis, applied to the Shigella dysenteriae proteome

The in vitro stationary phase proteome of the human pathogen Shigella dysenteriae serotype 1 (SD1) was quantitatively analyzed in Coomassie Blue G250 (CBB)-stained 2D gels. More than four hundred and fifty proteins, of which 271 were associated with distinct gel spots, were identified. In parallel, we employed 2D-LC-MS/MS followed by the label-free computationally modified spectral counting method APEX for absolute protein expression measurements. Of the 4502 genome-predicted SD1 proteins, 1148 proteins were identified with a false positive discovery rate of 5% and quantitated using 2D-LC-MS/MS and APEX. The dynamic range of the APEX method was approximately one order of magnitude higher than that of CBB-stained spot intensity quantitation. A squared Pearson correlation analysis revealed a reasonably good correlation (R2 = 0.67) for protein quantities surveyed by both methods. The correlation was decreased for protein subsets with specific physicochemical properties, such as low Mr values and high hydropathy scores. Stoichiometric ratios of subunits of protein complexes characterized in E. coli were compared with APEX quantitative ratios of orthologous SD1 protein complexes. A high correlation was observed for subunits of soluble cellular protein complexes in several cases, demonstrating versatile applications of the APEX method in quantitative proteomics

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O(i) value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O(i)). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances. Results: The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis. Conclusion: The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.National Institute of Allergy and Infectious Diseases (NIAID) N01-AI15447National Institutes of HealthNational Science Foundation, the Welsh and Packard FoundationsInternational Human Frontier Science ProgramCenter for Systems and Synthetic Biolog

The expression and activity of β-catenin in the thalamus and its projections to the cerebral cortex in the mouse embryo

<p>Abstract</p> <p>Background</p> <p>The mammalian thalamus relays sensory information from the periphery to the cerebral cortex for cognitive processing via the thalamocortical tract. The thalamocortical tract forms during embryonic development controlled by mechanisms that are not fully understood. β-catenin is a nuclear and cytosolic protein that transduces signals from secreted signaling molecules to regulate both cell motility via the cytoskeleton and gene expression in the nucleus. In this study we tested whether β-catenin is likely to play a role in thalamocortical connectivity by examining its expression and activity in developing thalamic neurons and their axons.</p> <p>Results</p> <p>At embryonic day (E)15.5, the time when thalamocortical axonal projections are forming, we found that the thalamus is a site of particularly high β-catenin mRNA and protein expression. As well as being expressed at high levels in thalamic cell bodies, β-catenin protein is enriched in the axons and growth cones of thalamic axons and its growth cone concentration is sensitive to Netrin-1. Using mice carrying the β-catenin reporter <it>BAT-gal </it>we find high levels of reporter activity in the thalamus. Further, Netrin-1 induces <it>BAT-gal </it>reporter expression and upregulates levels of endogenous transcripts encoding β-actin and L1 proteins in cultured thalamic cells. We found that β-catenin mRNA is enriched in thalamic axons and its 3'UTR is phylogenetically conserved and is able to direct heterologous mRNAs along the thalamic axon, where they can be translated.</p> <p>Conclusion</p> <p>We provide evidence that β-catenin protein is likely to be an important player in thalamocortcial development. It is abundant both in the nucleus and in the growth cones of post-mitotic thalamic cells during the development of thalamocortical connectivity and β-catenin mRNA is targeted to thalamic axons and growth cones where it could potentially be translated. β-catenin is involved in transducing the Netrin-1 signal to thalamic cells suggesting a mechanism by which Netrin-1 guides thalamocortical development.</p

TRABAJOS DE MÉTODO ARQUEOLÓGICO [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Small molecules, big targets: drug discovery faces the protein-protein interaction challenge.

Protein-protein interactions (PPIs) are of pivotal importance in the regulation of biological systems and are consequently implicated in the development of disease states. Recent work has begun to show that, with the right tools, certain classes of PPI can yield to the efforts of medicinal chemists to develop inhibitors, and the first PPI inhibitors have reached clinical development. In this Review, we describe the research leading to these breakthroughs and highlight the existence of groups of structurally related PPIs within the PPI target class. For each of these groups, we use examples of successful discovery efforts to illustrate the research strategies that have proved most useful.JS, DES and ARB thank the Wellcome Trust for funding.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nrd.2016.2

Método híbrido para categorización de texto basado en aprendizaje y reglas

En este artículo se presenta un nuevo método híbrido de categorización automática de texto, que combina un algoritmo de aprendizaje computacional, que permite construir un modelo base de clasificación sin mucho esfuerzo a partir de un corpus etiquetado, con un sistema basado en reglas en cascada que se emplea para filtrar y reordenar los resultados de dicho modelo base. El modelo puede afinarse añadiendo reglas específicas para aquellas categorías difíciles que no se han entrenado de forma satisfactoria. Se describe una implementación realizada mediante el algoritmo kNN y un lenguaje básico de reglas basado en listas de términos que aparecen en el texto a clasificar. El sistema se ha evaluado en diferentes escenarios incluyendo el corpus de noticias Reuters-21578 para comparación con otros enfoques, y los modelos IPTC y EUROVOC. Los resultados demuestran que el sistema obtiene una precisión y cobertura comparables con las de los mejores métodos del estado del arte

nEASE: a method for gene ontology subclassification of high-throughput gene expression data

Summary: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline

Proteomic analysis of laser-captured paraffin-embedded tissues: A molecular portrait of head and neck cancer progression

Purpose: Squamous cell carcinoma of the head and neck (HNSCC), the sixth most prevalent cancer among men worldwide, is associated with poor prognosis, which has improved only marginally over the past three decades. A proteomic analysis of HNSCC lesions may help identify novel molecular targets for the early detection, prevention, and treatment of HNSCC. Experimental Design: Laser capture microdissection was combined with recently developed techniques for protein extraction from formalin-fixed paraffin-embedded (FFPE) tissues and a novel proteomics platform. Approximately 20,000 cells procured from FFPE tissue sections of normal oral epithelium and well, moderately, and poorly differentiated HNSCC were processed for mass spectrometry and bioinformatic analysis. Results: A large number of proteins expressed in normal oral epithelium and HNSCC, including cytokeratins, intermediate filaments, differentiation markers, and proteins involved in stem cell maintenance, signal transduction, migration, cell cycle regulation, growth and angiogenesis, matrix degradation, and proteins with tumor suppressive and oncogenic potential, were readily detected. Of interest, the relative expression of many of these molecules followed a distinct pattern in normal squamous epithelia and well, moderately, and poorly differentiated HNSCC tumor tissues. Representative proteins were further validated using immunohistochemical studies in HNSCC tissue sections and tissue microarrays. Conclusions: The ability to combine laser capture microdissection and in-depth proteomic analysis of FFPE tissues provided a wealth of information regarding the nature of the proteins expressed in normal squamous epithelium and during HNSCC progression, which may allow the development of novel biomarkers of diagnostic and prognostic value and the identification of novel targets for therapeutic intervention in HNSCC. © 2008 American Association for Cancer Research