Clonagem, expressão recombinante e validação por microarray de proteínas alimentares potencialmente alergênicas / Cloning, recombinant expression and validation by microarray of potentially allergenic food proteins

A maioria das respostas alérgicas a alimentos é mediada por IgE, que pode ser detectada para fins de diagnóstico da alergia alimentar. No entanto, para isso é necessário que alérgenos purificados estejam disponíveis para a elaboração dos diferentes formatos de ensaio, inclusive por microarray, que se constitui em uma ferramenta bastante útil para análise simultânea, e também para a identificação de reatividade cruzada. A esse respeito, é imprescindível ampliar a plataforma de alérgenos que possam ser empregados para a confecção de microarrays. Atualmente, alguns alimentos que constituem objeto de interesse na clínica em função do número de casos de alergia, e sobre os quais as informações a respeito dos alérgenos são escassas, são: abacaxi, mamão, mandioca e manga. Assim, o objetivo desse trabalho foi clonar, expressar e purificar proteínas potencialmente alergênicas de alimentos de importância regional. Após confirmadas por ensaios imunológicos, essas proteínas foram utilizadas na construção e validação de um microarray através de ensaios com os soros de pacientes alérgicos aos alimentos selecionados. Para atingir esse objetivo, foram selecionadas proteínas potencialmente alergênicas coincidentes, apontadas tanto pela similaridade com espécies taxonomicamente mais próximas, quanto pela técnica 2D Western Blotting acoplada à espectrometria de massas. Dezenove proteínas, sendo 4 de abacaxi, 5 de mamão, 6 de mandioca e 4 de manga, foram expressas em Pichia pastoris, purificadas e impressas em um microarray. Após incubar essas proteínas com os soros dos pacientes alérgicos aos alimentos estudados, 18 proteínas mostraram-se potencialmente alergênicas. Além disso, foi observada reatividade cruzada entre proteínas dos alimentos estudados e também em relação ao látex e outros frutos

The majority of allergic reactions to foods is IgE-mediated, which can be detected for the diagnosis of food allergy. However, purified allergens are necessary to produce different kinds of allergy tests, including microarray, which is a useful tool for simultaneous analysis, as well as for the identification of cross-reactivity. In this respect, it is essential to expand the platform of allergens to include them on microarrays. Nowadays, some foods that are object of interest in the clinical area in Brazil and it is necessary a further evaluation about their potential allergens, since there is a limited information about them, are: pineapple, papaya, cassava and mango. Therefore, the aim of this study was cloning, expressing and purifying potentially allergenic proteins of important Brazilian foods. After confirmed by immunological tests, these proteins were used in microarray production and validation by assays with sera from allergic patients to the selected foods. Achieving this goal, matching potentially allergenic proteins were selected, which were identified by comparison among taxonomically closer species (in silico) and 2D Western Blotting coupled with Mass Spectrometry. Nineteen proteins: 4 from pineapple, 5 from papaya, 6 from cassava and 4 from mango were expressed in Pichia pastoris, purified and printed on a microarray. After incubating those proteins with sera from allergic patients to the selected foods, 18 proteins were detected as potentially allergenic. In addition, cross-reactivity was observed among the proteins from the studied foods, and also regarding to the latex and other fruits

Medical Devices; Immunology and Microbiology Devices; Classification of the Device To Detect and Identify Microbial Pathogen Nucleic Acids in Cerebrospinal Fluid. Final order.

The Food and Drug Administration (FDA or we) is classifying the device to detect and identify microbial pathogen nucleic acids in cerebrospinal fluid into class II (special controls). The special controls that will apply to the device type are identified in this order and will be part of the codified language for the device to detect and identify microbial pathogen nucleic acids in cerebrospinal fluid's classification. We are taking this action because we have determined that classifying the device into class II (special controls) will provide a reasonable assurance of safety and effectiveness of the device. We believe this action will also enhance patients' access to beneficial innovative devices, in part by reducing regulatory burdens.

Recomendaciones para el uso clínico del microarray genómico en diagnóstico prenatal / Recommendations for the clinical use of genomic microarray in prenatal diagnosis

No disponible

An integrated approach for identifying wrongly labelled samples when performing classification in microarray data.

BACKGROUND: Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier detection algorithms proposed so far are either not suitable for microarray data, or only solve the outlier detection problem on their own. RESULTS: We tackle the outlier detection problem based on a previously proposed Multiple-Filter-Multiple-Wrapper (MFMW) model, which was demonstrated to yield promising results when compared to other hybrid approaches (Leung and Hung, 2010). To incorporate outlier detection and overcome limitations of the existing MFMW model, three new features are introduced in our proposed MFMW-outlier approach: 1) an unbiased external Leave-One-Out Cross-Validation framework is developed to replace internal cross-validation in the previous MFMW model; 2) wrongly labeled samples are identified within the MFMW-outlier model; and 3) a stable set of genes is selected using an L1-norm SVM that removes any redundant genes present. Six binary-class microarray datasets were tested. Comparing with outlier detection studies on the same datasets, MFMW-outlier could detect all the outliers found in the original paper (for which the data was provided for analysis), and the genes selected after outlier removal were proven to have biological relevance. We also compared MFMW-outlier with PRAPIV (Zhang et al., 2006) based on same synthetic datasets. MFMW-outlier gave better average precision and recall values on three different settings. Lastly, artificially flipped microarray datasets were created by removing our detected outliers and flipping some of the remaining samples' labels. Almost all the 'wrong' (artificially flipped) samples were detected, suggesting that MFMW-outlier was sufficiently powerful to detect outliers in high-dimensional microarray datasets.

A study of performance on microarray data sets for a classifier based on information theoretic learning.

Gene-expression microarray is a novel technology that allows the examination of tens of thousands of genes at a time. For this reason, manual observation is not feasible and machine learning methods are progressing to face these new data. Specifically, since the number of genes is very high, feature selection methods have proven valuable to deal with these unbalanced-high dimensionality and low cardinality-data sets. In this work, the FVQIT (Frontier Vector Quantization using Information Theory) classifier is employed to classify twelve DNA gene-expression microarray data sets of different kinds of cancer. A comparative study with other well-known classifiers is performed. The proposed approach shows competitive results outperforming all other classifiers.

Expresión de p63 y citoqueratina 5/6 en los diferentes tipos moleculares del carcinoma de mama / p63 and citokeratin 5/6 expression in different molecular subtypes of breast carcinoma

Antecedentes. El cáncer de mama es un grupo heterogéneo de tumores. Los estudios de microarrays de ADN han llevado a la clasificación del carcinoma invasor de mama en diferentes clases moleculares. El objetivo de este estudio fue determinar la expresión de p63 y citoqueratina 5/6 en carcinomas ductales invasores y su relación con las diferentes clases moleculares, en especial con el subgrupo de tipo basal. Métodos. Se realizó estudio inmunohistoquímico con los anticuerpos p63 y CK5/6 en 200 muestras de carcinoma ductal invasor sin otra especificación. En cada caso se había determinado previamente el estado de los receptores de estrógeno y progesterona (RE, RP), y de HER2. De acuerdo a estos datos, los tumores se clasificaron como luminal A, luminal B, HER2+ y tipo basal (triple negativo). Resultados. Se observó expresión de p63 en 5 casos de HER2+ y 19 casos de tumores del tipo basal (23,2%), se demostró una fuerte relación entre la expresión de CK5/6 y los tumores de tipo basal (59,8%, p<0,0001), pero también se expresó en un caso luminal A, 3 luminal B y 8 HER2+. Conclusiones. No todos los casos triple negativo son de tipo basal. Es necesario estandarizar la clasificación molecular basada en inmunohistoquímica, así como el panel de anticuerpos a utilizar, en especial para la identificación del tipo basal(AU)

Background. Breast cancer is a heterogeneous group of tumors. DNA microarray profiling studies have led to the classification of invasive breast carcinoma called molecular classes. AIMS: To study the expression of p63 and cytokeratin (CK) 5/6 in invasive ductal carcinomas and their relationship to the different molecular classes, especially the basal like subgroup. Methods. Immunohistochemistry with the antibodies p63 and CK5/6 was performed in 200 samples of invasive ductal carcinomas with no other specification. Each case had previous results of estrogen and progesterone receptor (ER, PR), and HER2. According to these data they were classified as luminal A, luminal B, HER2+ and basal like (triple negative). Results. p63 was expressed in 5 cases of HER2+ and 19 cases of basal like tumours (19.5%). There was a strong relationship between CK5/6 expression and basal like tumours (68.9%, p<0.0001), but it was also expressed in one luminal A, three luminal B and eight HER2+ cases. Conclusions. Not every triple negative tumors express basal markers. It is necesary to standarize the molecular classification of breast cancer and the panel of markers to use in its caracterization, especially for the basal like(AU)

Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction.

BACKGROUND: In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data), since such analyses are particularly exposed to this kind of bias. METHODS: In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps) within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. RESULTS: We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case) and the bias resulting from the choice of the classification method are examined both separately and jointly. CONCLUSIONS: The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

Microarray data classification using automatic SVM kernel selection.

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.

A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies.

Over the last decade, gene expression microarrays have had a profound impact on biomedical research. The diversity of platforms and analytical methods available to researchers have made the comparison of data from multiple platforms challenging. In this study, we describe a framework for comparisons across platforms and laboratories. We have attempted to include nearly all the available commercial and 'in-house' platforms. Using probe sequences matched at the exon level improved consistency of measurements across the different microarray platforms compared to annotation-based matches. Generally, consistency was good for highly expressed genes, and variable for genes with lower expression values as confirmed by quantitative real-time (QRT)-PCR. Concordance of measurements was higher between laboratories on the same platform than across platforms. We demonstrate that, after stringent preprocessing, commercial arrays were more consistent than in-house arrays, and by most measures, one-dye platforms were more consistent than two-dye platforms.