Prediction of tolerance in children with IgE mediated cow's milk allergy by microarray profiling and chemometric approach

The sera of a retrospective cohort (n = 41) composed of children with well characterized cow's milk allergy collected from multiple visits were analyzed using a protein microarray system measuring four classes of immunoglobulins. The frequency of the visits, age and gender distribution reflected real situation faced by the clinicians at a pediatric reference center for food allergy in 530 Paulo, Brazil. The profiling array results have shown that total IgG and IgA share similar specificity whilst IgM and in particular IgE are distantly related. The correlation of specificity of IgE and IgA is variable amongst the patients and this relationship cannot be used to predict atopy or the onset of tolerance to milk. The array profiling technique has corroborated the clinical selection criteria for this cohort albeit it clearly suggested that 4 out of the 41 patients might have allergies other than milk origin. There was also a good correlation between the array data and ImmunoCAP results, casein in particular. By using qualitative and quantitative multivariate analysis routines it was possible to produce validated statistical models to predict with reasonable accuracy the onset of tolerance to milk proteins. If expanded to larger study groups, the array profiling in combination with the multivariate techniques show potential to improve the prognostic of milk allergic patients. (C) 2012 Elsevier B.V. All rights reserved.

Global gene expression profiling of oral cavity cancers suggests molecular heterogeneity within anatomic subsites

Abstract Background Oral squamous cell carcinoma (OSCC) is a frequent neoplasm, which is usually aggressive and has unpredictable biological behavior and unfavorable prognosis. The comprehension of the molecular basis of this variability should lead to the development of targeted therapies as well as to improvements in specificity and sensitivity of diagnosis. Results Samples of primary OSCCs and their corresponding surgical margins were obtained from male patients during surgery and their gene expression profiles were screened using whole-genome microarray technology. Hierarchical clustering and Principal Components Analysis were used for data visualization and One-way Analysis of Variance was used to identify differentially expressed genes. Samples clustered mostly according to disease subsite, suggesting molecular heterogeneity within tumor stages. In order to corroborate our results, two publicly available datasets of microarray experiments were assessed. We found significant molecular differences between OSCC anatomic subsites concerning groups of genes presently or potentially important for drug development, including mRNA processing, cytoskeleton organization and biogenesis, metabolic process, cell cycle and apoptosis. Conclusion Our results corroborate literature data on molecular heterogeneity of OSCCs. Differences between disease subsites and among samples belonging to the same TNM class highlight the importance of gene expression-based classification and challenge the development of targeted therapies.

Transcriptional profiling of Saccharomyces cerevisiae exposed to propolis

Abstract Background Propolis is a natural product of plant resins collected by honeybees (Apis mellifera) from various plant sources. Our previous studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis. Here, we extended our understanding of propolis-mediated cell death in the yeast Saccharomyces cerevisiae by applying systems biology tools to analyze the transcriptional profiling of cells exposed to propolis. Methods We have used transcriptional profiling of S. cerevisiae exposed to propolis. We validated our findings by using real-time PCR of selected genes. Systems biology tools (physical protein-protein interaction [PPPI] network) were applied to analyse the propolis-induced transcriptional bevavior, aiming to identify which pathways are modulated by propolis in S. cerevisiae and potentially influencing cell death. Results We were able to observe 1,339 genes modulated in at least one time point when compared to the reference time (propolis untreated samples) (t-test, p-value 0.01). Enrichment analysis performed by Gene Ontology (GO) Term finder tool showed enrichment for several biological categories among the genes up-regulated in the microarray hybridization such as transport and transmembrane transport and response to stress. Real-time RT-PCR analysis of selected genes showed by our microarray hybridization approach was capable of providing information about S. cerevisiae gene expression modulation with a considerably high level of confidence. Finally, a physical protein-protein (PPPI) network design and global topological analysis stressed the importance of these pathways in response of S. cerevisiae to propolis and were correlated with the transcriptional data obtained thorough the microarray analysis. Conclusions In summary, our data indicate that propolis is largely affecting several pathways in the eukaryotic cell. However, the most prominent pathways are related to oxidative stress, mitochondrial electron transport chain, vacuolar acidification, regulation of macroautophagy associated with protein target to vacuole, cellular response to starvation, and negative regulation of transcription from RNA polymerase II promoter. Our work emphasizes again the importance of S. cerevisiae as a model system to understand at molecular level the mechanism whereby propolis causes cell death in this organism at the concentration herein tested. Our study is the first one that investigates systematically by using functional genomics how propolis influences and modulates the mRNA abundance of an organism and may stimulate further work on the propolis-mediated cell death mechanisms in fungi.

Transcription profiling of Prss16 (Tssp) can be used to find additional peptidase genes that are candidates for self-peptide generation in the thymus

Positive selection (PS) in the thymus involves the presentation of self-peptides that are bound to MHC class II on the surface of cortical thymus epithelial cells (cTECs). Prss16 gene corresponds to one important element regulating the PS of CD4(+) T lymphocytes, which encodes Thymus-specific serine protease (Tssp), a cTEC serine-type peptidase involved in the proteolytic generation of self-peptides. Nevertheless, additional peptidase genes participating in the generation of self-peptides need to be found. Because of its role in the mechanism of PS and its expression in cTECs, the Prss16 gene might be used as a transcriptional marker to identify new genes that share the same expression profile and that encode peptidases in the thymus. To test this hypothesis, we compared the differential thymic expression of 4,500 mRNAs of wild-type (WT) C57BL/6 mice with their respective Prss16-knockout (KO) mutants by using microarrays. From these, 223 genes were differentially expressed, of which 115 had known molecular/biological functions. Four endopeptidase genes (Casp1, Casp2, Psmb3 and Tpp2) share the same expression profile as the Prss16 gene; i.e., induced in WT and repressed in KO while one endopeptidase gene, Capns1, features opposite expression profile. The Tpp2 gene is highlighted because it encodes a serine-type endopeptidase functionally similar to the Tssp enzyme. Profiling of the KO mice featured down-regulation of Prss16, as expected, along with the genes mentioned above. Considering that the Prss16-KO mice featured impaired PS, the shared regulation of the four endopeptidase genes suggested their participation in the mechanism of self-peptide generation and PS.

Venomics Profiling of Thamnodynastes strigatus Unveils Matrix Metalloproteinases and Other Novel Proteins Recruited to the Toxin Arsenal of Rear-Fanged Snakes

Rear-fanged and aglyphous snakes are usually considered not dangerous to humans because of their limited capacity of injecting venom. Therefore, only a few studies have been dedicated to characterizing the venom of the largest parcel of snake fauna. Here, we investigated the venom proteome of the rear-fanged snake Thamnodynastes strigatus, in combination with a transcriptomic evaluation of the venom gland. About 60% of all transcripts code for putative venom components. A striking finding is that the most abundant type of transcript (similar to 47%) and also the major protein type in the venom correspond to a new kind of matrix metalloproteinase (MMP) that is unrelated to the classical snake venom metalloproteinases found in all snake families. These enzymes were recently suggested as possible venom components, and we show here that they are proteolytically active and probably recruited to venom from a MMP-9 ancestor. Other unusual proteins were suggested to be venom components: a protein related to lactadherin and an EGF repeat-containing transcript. Despite these unusual molecules, seven toxin classes commonly found in typical venomous snakes are also present in the venom. These results support the evidence that the arsenals of these snakes are very diverse and harbor new types of biologically important molecules.

Solid-phase microextraction combined with comprehensive two-dimensional gas chromatography for fatty acid profiling of cell wall phospholipids

The combination of solid-phase microextraction (SPME) with comprehensive two-dimensional gas chromatography is evaluated here for fatty acid (FA) profiling of the glycerophospholipid fraction from human buccal mucosal cells. A base-catalyzed derivatization reaction selective for polar lipids such as glycerophospholipid was adopted. SPME is compared to a miniaturized liquidliquid extraction procedure for the isolation of FA methyl esters produced in the derivatization step. The limits of detection and limits of quantitation were calculated for each sample preparation method. Because of its lower values of limits of detection and quantitation, SPME was adopted. The extracted analytes were separated, detected, and quantified by comprehensive two-dimensional gas chromatography with flame ionization detection (FID). The combination of SPME and comprehensive two-dimensional gas chromatography with FID, using a selective derivatization reaction in the preliminary steps, proved to be a simple and fast procedure for FA profiling, and was successfully applied to the analysis of adult human buccal mucosal cells.

A simple boiling-based DNA extraction for RAPD profiling of landfarm soil to provide representative metagenomic content

Landfarm soils are employed in industrial and petrochemical residue bioremediation. This process induces selective pressure directed towards microorganisms capable of degrading toxic compounds. Detailed description of taxa in these environments is difficult due to a lack of knowledge of culture conditions required for unknown microorganisms. A metagenomic approach permits identification of organisms without the need for culture. However, a DNA extraction step is first required, which can bias taxonomic representativeness and interfere with cloning steps by extracting interference substances. We developed a simplified DNA extraction procedure coupled with metagenomic DNA amplification in an effort to overcome these limitations. The amplified sequences were used to generate a metagenomic data set and the taxonomic and functional representativeness were evaluated in comparison with a data set built with DNA extracted by conventional methods. The simplified and optimized method of RAPD to access metagenomic information provides better representativeness of the taxonomical and metabolic aspects of the environmental samples.

Aerosol profiling with lidar in the Amazon Basin during the wet and dry season

For the first time, multiwavelength polarization Raman lidar observations of optical and microphysical particle properties over the Amazon Basin are presented. The fully automated advanced Raman lidar was deployed 60 km north of Manaus, Brazil (2.5 degrees S, 60 degrees W) in the Amazon rain forest from January to November 2008. The measurements thus cover both the wet season (Dec-June) and the dry or burning season (July-Nov). Two cases studies of young and aged smoke plumes are discussed in terms of spectrally resolved optical properties (355, 532, and 1064 nm) and further lidar products such as particle effective radius and single-scattering albedo. These measurement examples confirm that biomass burning aerosols show a broad spectrum of optical, microphysical, and chemical properties. The statistical analysis of the entire measurement period revealed strong differences between the pristine wet and the polluted dry season. African smoke and dust advection frequently interrupt the pristine phases during the wet season. Compared to pristine wet season conditions, the particle scattering coefficients in the lowermost 2 km of the atmosphere were found to be enhanced, on average, by a factor of 4 during periods of African aerosol intrusion and by a factor of 6 during the dry (burning) season. Under pristine conditions, the particle extinction coefficients and optical depth for 532 nm wavelength were frequently as low as 10-30 Mm(-1) and <0.05, respectively. During the dry season, biomass burning smoke plumes reached to 3-5 km height and caused a mean optical depth at 532 nm of 0.26. On average during that season, particle extinction coefficients (532 nm) were of the order of 100 Mm(-1) in the main pollution layer (up to 2 km height). Angstrom exponents were mainly between 1.0 and 1.5, and the majority of the observed lidar ratios were between 50-80 sr.

N-glycome profiling of Bothrops jararaca newborn and adult venoms

Glycosylation is an important post-translational modification of snake venom proteins and contributes to venom proteome complexity. Many snake venom components are known to be glycosylated, however, very little is known about the carbohydrate structures present in venom glycoproteins. Previous studies showed that the ontogenetic shift in diet, from ectothermic prey in early life to endothermic prey in adulthood, and shift in animal size are associated with changes in the venom proteome of the snake Bothrops jararaca. In this study we explored the composition of the N-glycome released from newborn and adult B. jararaca venom proteins. We used an ion trap mass spectrometer (IT-MS) to disassemble glycan structures based on the use of several pathways of MS (MSn) and demonstrate the presence of some structural isomers in both newborn and adult venom B. jararaca N-glycans. The main N-glycans identified in both venoms are of the hybrid/complex type however some mannose-rich type structures were also detected. The N-glycan composition of newborn and adult venoms did not vary indicating that differences in the utilization of the N-glycosylation motif could be the explanation for the differences in the glycosylation levels indicated by the differential electrophoretic profiles previously reported for B. jararaca newborn and adult venoms. (C) 2011 Elsevier B.V. All rights reserved.

Proteomic profiling of the rat hypothalamus

Background: The hypothalamus plays a pivotal role in numerous mechanisms highly relevant to the maintenance of body homeostasis, such as the control of food intake and energy expenditure. Impairment of these mechanisms has been associated with the metabolic disturbances involved in the pathogenesis of obesity. Since rodent species constitute important models for metabolism studies and the rat hypothalamus is poorly characterized by proteomic strategies, we performed experiments aimed at constructing a two-dimensional gel electrophoresis (2-DE) profile of rat hypothalamus proteins. Results: As a first step, we established the best conditions for tissue collection and protein extraction, quantification and separation. The extraction buffer composition selected for proteome characterization of rat hypothalamus was urea 7 M, thiourea 2 M, CHAPS 4%, Triton X-100 0.5%, followed by a precipitation step with chloroform/methanol. Two-dimensional (2-D) gels of hypothalamic extracts from four-month-old rats were analyzed; the protein spots were digested and identified by using tandem mass spectrometry and database query using the protein search engine MASCOT. Eighty-six hypothalamic proteins were identified, the majority of which were classified as participating in metabolic processes, consistent with the finding of a large number of proteins with catalytic activity. Genes encoding proteins identified in this study have been related to obesity development. Conclusion: The present results indicate that the 2-DE technique will be useful for nutritional studies focusing on hypothalamic proteins. The data presented herein will serve as a reference database for studies testing the effects of dietary manipulations on hypothalamic proteome. We trust that these experiments will lead to important knowledge on protein targets of nutritional variables potentially able to affect the complex central nervous system control of energy homeostasis.

Diagnostic utility of immunohistochemistry in distinguishing trichoepithelioma and basal cell carcinoma: evaluation using tissue microarray samples

Trichoepithelioma is a benign neoplasm that shares both clinical and histological features with basal cell carcinoma. It is important to distinguish these neoplasms because they require different clinical behavior and therapeutic planning. Many studies have addressed the use of immunohistochemistry to improve the differential diagnosis of these tumors. These studies present conflicting results when addressing the same markers, probably owing to the small number of basaloid tumors that comprised their studies, which generally did not exceed 50 cases. We built a tissue microarray with 162 trichoepithelioma and 328 basal cell carcinoma biopsies and tested a panel of immune markers composed of CD34, CD10, epithelial membrane antigen, Bcl-2, cytokeratins 15 and 20 and D2-40. The results were analyzed using multiple linear and logistic regression models. This analysis revealed a model that could differentiate trichoepithelioma from basal cell carcinoma in 36% of the cases. The panel of immunohistochemical markers required to differentiate between these tumors was composed of CD10, cytokeratin 15, cytokeratin 20 and D2-40. The results obtained in this work were generated from a large number of biopsies and resulted in the confirmation of overlapping epithelial and stromal immunohistochemical profiles from these basaloid tumors. The results also corroborate the point of view that trichoepithelioma and basal cell carcinoma tumors represent two different points in the differentiation of a single cell type. Despite the use of panels of immune markers, histopathological criteria associated with clinical data certainly remain the best guideline for the differential diagnosis of trichoepithelioma and basal cell carcinoma. Modern Pathology (2012) 25, 1345-1353; doi: 10.1038/modpathol.2012.96; published online 8 June 2012

Profiling Three-Dimensional Nuclear Telomeric Architecture of Myelodysplastic Syndromes and Acute Myeloid Leukemia Defines Patient Subgroups

Purpose: Myelodysplastic syndromes (MDS) are a group of disorders characterized by cytopenias, with a propensity for evolution into acute myeloid leukemias (AML). This transformation is driven by genomic instability, but mechanisms remain unknown. Telomere dysfunction might generate genomic instability leading to cytopenias and disease progression. Experimental Design: We undertook a pilot study of 94 patients with MDS (56 patients) and AML (38 patients). The MDS cohort consisted of refractory cytopenia with multilineage dysplasia (32 cases), refractory anemia (12 cases), refractory anemia with excess of blasts (RAEB) 1 (8 cases), RAEB2 (1 case), refractory anemia with ring sideroblasts (2 cases), and MDS with isolated del(5q) (1 case). The AML cohort was composed of AML-M4 (12 cases), AML-M2 (10 cases), AML-M5 (5 cases), AML-M0 (5 cases), AML-M1 (2 cases), AML-M4eo (1 case), and AML with multidysplasia-related changes (1 case). Three-dimensional quantitative FISH of telomeres was carried out on nuclei from bone marrow samples and analyzed using TeloView. Results: We defined three-dimensional nuclear telomeric profiles on the basis of telomere numbers, telomeric aggregates, telomere signal intensities, nuclear volumes, and nuclear telomere distribution. Using these parameters, we blindly subdivided the MDS patients into nine subgroups and the AML patients into six subgroups. Each of the parameters showed significant differences between MDS and AML. Combining all parameters revealed significant differences between all subgroups. Three-dimensional telomeric profiles are linked to the evolution of telomere dysfunction, defining a model of progression from MDS to AML. Conclusions: Our results show distinct three-dimensional telomeric profiles specific to patients with MDS and AML that help subgroup patients based on the severity of telomere dysfunction highlighted in the profiles. Clin Cancer Res; 18(12); 3293-304. (C) 2012 AACR.

Evaluating different methods of microarray data normalization

Abstract Background With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. Results Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. Conclusion In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

A feature selection approach for identification of signature genes from SAGE data

Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.