01989nas a2200181   4500008004100000245008700041210006900128300001400197490000800211520142000219100001901639700001801658700001801676700001501694700001501709700001701724856006601741       2014                        eng d00aMolecular interactions between sugar beet and Polymyxa betae during its life cycle0 aMolecular interactions between sugar beet and Polymyxa betae dur  a244–2560 v1643 aPolymyxa betae is a biotrophic obligate sugar beet parasite that belongs to plasmodiophorids. The infection of sugar beet roots by this parasite is asymptomatic, except when it transmits Beet necrotic yellow vein virus (BNYVV), the causal agent of rhizomania. To date, there has been little work on P. betae–sugar beet molecular interactions, mainly because of the obligate nature of the parasite and also because research on rhizomania has tended to focus on the virus. In this study, we investigated these interactions through differential transcript analysis, using suppressive subtractive hybridization. The analysis included 76 P. betae and 120 sugar beet expressed sequence tags (ESTs). The expression of selected ESTs from both organisms was monitored during the protist life cycle, revealing a potential role of two P. betae proteins, profilin and a Von Willebrand factor domain-containing protein, in the early phase of infection. This study also revealed an over-expression of some sugar beet genes involved in defence, such as those encoding PR proteins, stress resistance proteins or lectins, especially during the plasmodial stage of the P. betae life cycle. In addition to providing new information on the molecular aspects of P. betae–sugar beet interactions, this study also enabled previously unknown ESTs of P. betae to be sequenced, thus enhancing our knowledge of the genome of this protist.1 aDesoignies, N.1 aCarbonell, J.1 aMoreau, J.-S.1 aConesa, A.1 aDopazo, J.1 aLegrève, A.  uhttp://onlinelibrary.wiley.com/doi/10.1111/aab.12095/abstract03141nas a2200313   4500008004100000245013000041210006900171300001200240490000700252520169500259653011401954653003602068653016902104653009402273653012702367100002202494700002602516700001402542700001602556700003002572700001702602700001702619700002002636700001502656700001702671700001602688700001702704856010602721       2008                        eng d00aMolecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information0 aMolecular profiling related to poor prognosis in thyroid carcino  a1554-610 v273 a<p>Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with &gt;2-fold difference in absolute values and false discovery rate of &lt;0.15. According to known molecular interaction and reaction networks, the products of these genes were mainly clustered in the MAPkinase signaling pathway, the TGF-beta signaling pathway, focal adhesion and cell motility, activation of actin polymerization and cell cycle. An exhaustive search in several databases allowed us to identify various members of the matrix metalloproteinase, melanoma antigen A and collagen gene families within the upregulated gene set. We also identified a prognosis classifier comprising just 30 transcripts with an overall accuracy of 95%. These findings may clarify the molecular mechanisms involved in thyroid tumor dedifferentiation and provide a potential prognosis predictor as well as targets for new therapies.</p>10aAdenoma/genetics/metabolism/pathology Adolescent Adult Aged Carcinoma/genetics/metabolism/pathology Carcinoma10aBiological/*genetics/metabolism10aNeoplasm/genetics/metabolism Reverse Transcriptase Polymerase Chain Reaction Signal Transduction Thyroid Neoplasms/classification/*genetics/metabolism Tumor Markers10aNeoplastic Humans Male Middle Aged *Oligonucleotide Array Sequence Analysis Prognosis RNA10aPapillary/genetics/metabolism/pathology Cell Differentiation Female *Gene Expression Profiling *Gene Expression Regulation1 aMontero-Conde, C.1 aMartin-Campos, J., M.1 aLerma, E.1 aGimenez, G.1 aMartinez-Guitarte, J., L.1 aCombalia, N.1 aMontaner, D.1 aMatias-Guiu, X.1 aDopazo, J.1 ade Leiva, A.1 aRobledo, M.1 aMauricio, D.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1787390800363nas a2200109   4500008004100000245004400041210004400085260002000129300001000149100001500159856007900174       2002                        eng d00aMicroarray Data Processing And Analysis0 aMicroarray Data Processing And Analysis  bKluwer Academic  a43-631 aDopazo, J.  uhttps://clinbioinfosspa.es/content/microarray-data-processing-and-analysis01764nas a2200169   4500008004100000245006700041210006700108300001100175490000800186520121300194100001501407700001601422700001601438700001701454700001701471856010601488       2001                        eng d00aMethods and approaches in the analysis of gene expression data0 aMethods and approaches in the analysis of gene expression data  a93-1120 v2503 a<p>The application of high-density DNA array technology to monitor gene transcription has been responsible for a real paradigm shift in biology. The majority of research groups now have the ability to measure the expression of a significant proportion of the human genome in a single experiment, resulting in an unprecedented volume of data being made available to the scientific community. As a consequence of this, the storage, analysis and interpretation of this information present a major challenge. In the field of immunology the analysis of gene expression profiles has opened new areas of investigation. The study of cellular responses has revealed that cells respond to an activation signal with waves of co-ordinated gene expression profiles and that the components of these responses are the key to understanding the specific mechanisms which lead to phenotypic differentiation. The discovery of ’cell type specific’ gene expression signatures have also helped the interpretation of the mechanisms leading to disease progression. Here we review the principles behind the most commonly used data analysis methods and discuss the approaches that have been employed in immunological research.</p>1 aDopazo, J.1 aZanders, E.1 aDragoni, I.1 aAmphlett, G.1 aFalciani, F.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11251224