Clustering algorithms and shape factor methods to discriminate among small GTPase phenotypes using DIC image analysis.

Naïvement perçu, le processus d’évolution est une succession d’événements de duplication et de mutations graduelles dans le génome qui mènent à des changements dans les fonctions et les interactions du protéome. La famille des hydrolases de guanosine triphosphate (GTPases) similaire à Ras constitue un bon modèle de travail afin de comprendre ce phénomène fondamental, car cette famille de protéines contient un nombre limité d’éléments qui diffèrent en fonctionnalité et en interactions. Globalement, nous désirons comprendre comment les mutations singulières au niveau des GTPases affectent la morphologie des cellules ainsi que leur degré d’impact sur les populations asynchrones. Mon travail de maîtrise vise à classifier de manière significative différents phénotypes de la levure Saccaromyces cerevisiae via l’analyse de plusieurs critères morphologiques de souches exprimant des GTPases mutées et natives. Notre approche à base de microscopie et d’analyses bioinformatique des images DIC (microscopie d’interférence différentielle de contraste) permet de distinguer les phénotypes propres aux cellules natives et aux mutants. L’emploi de cette méthode a permis une détection automatisée et une caractérisation des phénotypes mutants associés à la sur-expression de GTPases constitutivement actives. Les mutants de GTPases constitutivement actifs Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V ont été analysés avec succès. En effet, l’implémentation de différents algorithmes de partitionnement, permet d’analyser des données qui combinent les mesures morphologiques de population native et mutantes. Nos résultats démontrent que l’algorithme Fuzzy C-Means performe un partitionnement efficace des cellules natives ou mutantes, où les différents types de cellules sont classifiés en fonction de plusieurs facteurs de formes cellulaires obtenus à partir des images DIC. Cette analyse démontre que les mutations Cdc42 Q61L, Rho5 Q91H, Ras1 Q68L et Rsr1 G12V induisent respectivement des phénotypes amorphe, allongé, rond et large qui sont représentés par des vecteurs de facteurs de forme distincts. Ces distinctions sont observées avec différentes proportions (morphologie mutante / morphologie native) dans les populations de mutants. Le développement de nouvelles méthodes automatisées d’analyse morphologique des cellules natives et mutantes s’avère extrêmement utile pour l’étude de la famille des GTPases ainsi que des résidus spécifiques qui dictent leurs fonctions et réseau d’interaction. Nous pouvons maintenant envisager de produire des mutants de GTPases qui inversent leur fonction en ciblant des résidus divergents. La substitution fonctionnelle est ensuite détectée au niveau morphologique grâce à notre nouvelle stratégie quantitative. Ce type d’analyse peut également être transposé à d’autres familles de protéines et contribuer de manière significative au domaine de la biologie évolutive.

Recent advances in capillary electrophoresis methods for food analysis

This review article addresses recent advances in the analysis of foods and food components by capillary electrophoresis (CE). CE has found application to a number of important areas of food analysis, including quantitative chemical analysis of food additives, biochemical analysis of protein composition, and others. The speed, resolution and simplicity of CE, combined with low operating costs, make the technique an attractive option for the development of improved methods of food analysis for the new millennium.

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

Human regulatory protein Ki-1/57 has characteristics of an intrinsically unstructured protein

The human protein Ki-1/57 was first identified through the cross reactivity of the anti-CD30 monoclonal antibody Ki-1; in Hodgkin lymphoma cells. The expression of Ki-1/57 in diverse cancer cells and its phosphorylation in peripheral blood leukocytes after mitogenic activation suggested its possible role in cell signaling. Ki-1/57 interacts with several other regulatory proteins involved in cellular signaling, transcriptional regulation and RNA metabolism, suggesting it may have pleiotropic functions. In a previous spectroscopic analysis, we observed a low content of secondary structure for Ki-1/57 constructs. Here, Circular dichroism experiments, in vitro RNA binding analysis, and limited proteolysis assays of recombinant Ki-1/57(122-413) and proteolysis assays of endogenous full length protein from human HEK293 cells suggested that Ki-1/57 has characteristics of an intrinsically unstructured protein. Small-angle X-ray scattering (SAXS) experiments were performed with the C-terminal fragment Ki-1/57(122-413). These results indicated an elongated shape and a partially unstructured conformation of the molecule in solution, confirming the characteristics of an intrinsically unstructured protein. Experimental curves together with ab initio modeling approaches revealed an extended and flexible molecule in solution. An elongated shape was also observed by analytical gel filtration. Furthermore, sedimentation velocity analysis suggested that Ki-1/57 is a highly asymmetric protein. These findings may explain the functional plasticity of Ki-1/57, as suggested by the wide array of proteins with which it is capable of interacting in yeast two-hybrid interaction assays.

A novel system for large-scale gene expression analysis: bacterial colonies array

In the present work, we report the use of bacterial colonies to optimize macroarray technique. The devised system is significantly cheaper than other methods available to detect large-scale differential gene expression. Recombinant Escherichia coli clones containing plasmid-encoded copies of 4,608 individual expressed sequence tag (ESTs) were robotically spotted onto nylon membranes that were incubated for 6 and 12 h to allow the bacteria to grow and, consequently, amplify the cloned ESTs. The membranes were then hybridized with a beta-lactamase gene specific probe from the recombinant plasmid and, subsequently, phosphorimaged to quantify the microbial cells. Variance analysis demonstrated that the spot hybridization signal intensity was similar for 3,954 ESTs (85.8%) after 6 h of bacterial growth. Membranes spotted with bacteria colonies grown for 12 h had 4,017 ESTs (87.2%) with comparable signal intensity but the signal to noise ratio was fivefold higher. Taken together, the results of this study indicate that it is possible to investigate large-scale gene expression using macroarrays based on bacterial colonies grown for 6 h onto membranes.

Crystallization and preliminary X-ray diffraction analysis of XAC1151, a small heat-shock protein from Xanthomonas axonopodis pv. citri belonging to the alpha-crystallin family

The hspA gene (XAC1151) from Xanthomonas axonopodis pv. citri encodes a protein of 158 amino acids that belongs to the small heat-shock protein ( sHSP) family of proteins. These proteins function as molecular chaperones by preventing protein aggregation. The protein was crystallized using the sitting-drop vapour-diffusion method in the presence of ammonium phosphate. X-ray diffraction data were collected to 1.65 angstrom resolution using a synchrotron-radiation source. The crystal belongs to the rhombohedral space group R3, with unit-cell parameters a = b = 128.7, c = 55.3 angstrom. The crystal structure was solved by molecular-replacement methods. Structure refinement is in progress.

A comparison of mycolic acid analysis for nontuberculous mycobacteria identification by thin-layer chromatography and molecular methods

The development of fast, inexpensive, and reliable tests to identify nontuberculous mycobacteria (NTM) is needed. Studies have indicated that the conventional identification procedures, including biochemical assays, are imprecise. This study evaluated a proposed alternative identification method in which 83 NTM isolates, previously identified by conventional biochemical testing and in-house M. avium IS1245-PCR amplification, were submitted to the following tests: thin-layer chromatography (TLC) of mycolic acids and PCR-restriction enzyme analysis of hsp65 (PRA). High-performance liquid chromatography (HPLC) analysis of mycolic acids and Southern blot analysis for M. avium IS1245 were performed on the strains that evidenced discrepancies on either of the above tests. Sixty-eight out of 83 (82%) isolates were concordantly identified by the presence of IS1245 and PRA and by TLC mycolic acid analysis. Discrepant results were found between the phenotypic and molecular tests in 12/83 (14.4%) isolates. Most of these strains were isolated from non-sterile body sites and were most probably colonizing in the host tissue. While TLC patterns suggested the presence of polymycobacterial infection in 3/83 (3.6%) cultures, this was the case in only one HPLC-tested culture and in none of those tested by PRA. The results of this study indicated that, as a phenotypic identification procedure, TLC mycolic acid determination could be considered a relatively simple and cost-effective method for routine screening of NTM isolates in mycobacteriology laboratory practice with a potential for use in developing countries. Further positive evidence was that this method demonstrated general agreement on MAC and M. simiae identification, including in the mixed cultures that predominated in the isolates of the disseminated infections in the AIDS patients under study. In view of the fact that the same treatment regimen is recommended for infections caused by these two species, TLC mycolic acid analysis may be a useful identification tool wherever molecular methods are unaffordable.

The Development of a Universal In Silico Predictor of Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.

In Silico Analysis and Immunohistochemical Characterization of NaPi2b Protein Expression in Ovarian Carcinoma With Monoclonal Antibody Mx35

Introduction: Ovarian adenocarcinoma is frequently detected at the late stage, when therapy efficacy is limited and death occurs in up to 50% of the cases. A potential novel treatment for this disease is a monoclonal antibody that recognizes phosphate transporter sodium-dependent phosphate transporter protein 2b (NaPi2b). Materials and Methods: To better understand the expression of this protein in different histologic types of ovarian carcinomas, we immunostained 50 tumor samples with anti-NaPi2b monoclonal antibody MX35 and, in parallel, we assessed the expression of the gene encoding NaPi2b (SCL34A2) by in silico analysis of microarray data. Results: Both approaches detected higher expression of NaPi2b (SCL34A2) in ovarian carcinoma than in normal tissue. Moreover, a comprehensive analysis indicates that SCL34A2 is the only gene of the several phosphate transporters genes whose expression differentiates normal from carcinoma samples, suggesting it might exert a major role in ovarian carcinomas. Immunohistochemical and mRNA expression data have also shown that 2 histologic subtypes of ovarian carcinoma express particularly high levels of NaPi2b: serous and clear cell adenocarcinomas. Serous adenocarcinomas are the most frequent, contrasting with clear cell carcinomas, rare, and with worse prognosis. Conclusion: This identification of subgroups of patients expressing NaPi2b may be important in selecting cohorts who most likely should be included in future clinical trials, as a recently generated humanized version of MX35 has been developed.

Identification of protein expression signatures in gastric carcinomas using clustering analysis

Background and Aim: The identification of gastric carcinomas (GC) has traditionally been based on histomorphology. Recently, DNA microarrays have successfully been used to identify tumors through clustering of the expression profiles. Random forest clustering is widely used for tissue microarrays and other immunohistochemical data, because it handles highly-skewed tumor marker expressions well, and weighs the contribution of each marker according to its relatedness with other tumor markers. In the present study, we e identified biologically- and clinically-meaningful groups of GC by hierarchical clustering analysis of immunohistochemical protein expression. Methods: We selected 28 proteins (p16, p27, p21, cyclin D1, cyclin A, cyclin B1, pRb, p53, c-met, c-erbB-2, vascular endothelial growth factor, transforming growth factor [TGF]-beta I, TGF-beta II, MutS homolog-2, bcl-2, bax, bak, bcl-x, adenomatous polyposis coli, clathrin, E-cadherin, beta-catenin, mucin (MUC) 1, MUC2, MUC5AC, MUC6, matrix metalloproteinase [ MMP]-2, and MMP-9) to be investigated by immunohistochemistry in 482 GC. The analyses of the data were done using a random forest-clustering method. Results: Proteins related to cell cycle, growth factor, cell motility, cell adhesion, apoptosis, and matrix remodeling were highly expressed in GC. We identified protein expressions associated with poor survival in diffuse-type GC. Conclusions: Based on the expression analysis of 28 proteins, we identified two groups of GC that could not be explained by any clinicopathological variables, and a subgroup of long-surviving diffuse-type GC patients with a distinct molecular profile. These results provide not only a new molecular basis for understanding the biological properties of GC, but also better prediction of survival than the classic pathological grouping.

Analysis of the mRNA expression of the S100 beta protein in adipocytes of patients with diabetes mellitus, type 2

Objective: This study aims to explore the possible relationship between the expression level of S100 beta protein mRNA with diabetes mellitus type 2 in adipocytes from patients with this disease in comparison with normoglycemic individuals. Materials and methods: Samples of adipose tissue of eight patients from the coronary section of the Institute Dante Pazzanese of Cardiology (IDPC), four in Group Diabetes and four of Normoglycemic group, were evaluated by RT-PCR real time. Results: An increase around 15 times values, between the threshold cycle (Delta Ct), of mRNA expression of S100 beta protein in adipocytes of the diabetes group was observed in comparison to the control group (p = 0.015). Conclusion: Our results indicate, for the first time, that there is coexistence of increased expression of the S100 beta and the type 2 diabetes mellitus gene. Arq Bras Endocrinol Metab. 2012;56(7):435-40

DNA repair gene excision repair cross complementing-group 1 (ERCC1) in head and neck squamous cell carcinoma: analysis of methylation and polymorphism (G19007A), protein expression and association with epidemiological and clinicopathological factors

Aims: To evaluate the associations of excision repair cross complementing-group 1 (ERCC1) (DNA repair protein) (G19007A) polymorphism, methylation and immunohistochemical expression with epidemiological and clinicopathological factors and with overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Methods and results: The study group comprised 84 patients with HNSCC who underwent surgery and adjuvant radiotherapy without chemotherapy. Bivariate and multivariate analyses were used. The allele A genotype variant was observed in 79.8% of the samples, GG in 20.2%, GA in 28.6% and AA in 51.2%. Individuals aged more than 45 years had a higher prevalence of the allelic A variant and a high (83.3%) immunohistochemical expression of ERCC1 protein [odds ratio (OR) = 4.86, 95% confidence interval (CI): 1.2-19.7, P = 0.027], which was also high in patients with advanced stage (OR= 5.04, 95% CI: 1.07-23.7, P = 0.041). Methylated status was found in 51.2% of the samples, and was higher in patients who did not present distant metastasis (OR = 6.67, 95% CI: 1.40-33.33, P = 0.019) and in patients with advanced stage (OR = 5.04, 95% CI: 1.07-23.7, P = 0.041). At 2 and 5 years, overall survival was 55% and 36%, respectively (median = 30 months). Conclusion: Our findings may reflect a high rate of DNA repair due to frequent tissue injury during the lifetime of these individuals, and also more advanced disease presentation in this population with worse prognosis.

Theoretical studies of Membrane Proteins : Properties, Prediction Methods and Genome-wide analysis

Membrane proteins are a large and important class of proteins. They are responsible for several of the key functions in a living cell, e.g. transport of nutrients and ions, cell-cell signaling, and cell-cell adhesion. Despite their importance it has not been possible to study their structure and organization in much detail because of the difficulty to obtain 3D structures. In this thesis theoretical studies of membrane protein sequences and structures have been carried out by analyzing existing experimental data. The data comes from several sources including sequence databases, genome sequencing projects, and 3D structures. Prediction of the membrane spanning regions by hydrophobicity analysis is a key technique used in several of the studies. A novel method for this is also presented and compared to other methods. The primary questions addressed in the thesis are: What properties are common to all membrane proteins? What is the overall architecture of a membrane protein? What properties govern the integration into the membrane? How many membrane proteins are there and how are they distributed in different organisms? Several of the findings have now been backed up by experiments. An analysis of the large family of G-protein coupled receptors pinpoints differences in length and amino acid composition of loops between proteins with and without a signal peptide and also differences between extra- and intracellular loops. Known 3D structures of membrane proteins have been studied in terms of hydrophobicity, distribution of secondary structure and amino acid types, position specific residue variability, and differences between loops and membrane spanning regions. An analysis of several fully and partially sequenced genomes from eukaryotes, prokaryotes, and archaea has been carried out. Several differences in the membrane protein content between organisms were found, the most important being the total number of membrane proteins and the distribution of membrane proteins with a given number of transmembrane segments. Of the properties that were found to be similar in all organisms, the most obvious is the bias in the distribution of positive charges between the extra- and intracellular loops. Finally, an analysis of homologues to membrane proteins with known topology uncovered two related, multi-spanning proteins with opposite predicted orientations. The predicted topologies were verified experimentally, providing a first example of "divergent topology evolution".

Multivariate spectroscopic methods for the analysis of solutions

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.