@article {1046, title = {Molecular interactions between sugar beet and Polymyxa betae during its life cycle}, journal = {Annals of Applied Biology}, volume = {164}, number = {2}, year = {2014}, pages = {244{\textendash}256}, abstract = {Polymyxa 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{\textendash}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{\textendash}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.}, doi = {10.1111/aab.12095}, url = {http://onlinelibrary.wiley.com/doi/10.1111/aab.12095/abstract}, author = {N. Desoignies and Carbonell, J. and J.-S. Moreau and A. Conesa and Dopazo, J. and A. Legr{\`e}ve} } @article {17873908, title = {Molecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information}, journal = {Oncogene}, volume = {27}, number = {11}, year = {2008}, note = {

Montero-Conde, C Martin-Campos, J M Lerma, E Gimenez, G Martinez-Guitarte, J L Combalia, N Montaner, D Matias-Guiu, X Dopazo, J de Leiva, A Robledo, M Mauricio, D Research Support, Non-U.S. Gov{\textquoteright}t England Oncogene Oncogene. 2008 Mar 6;27(11):1554-61. Epub 2007 Sep 17.

}, pages = {1554-61}, abstract = {

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 \>2-fold difference in absolute values and false discovery rate of \<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.

}, keywords = {Adenoma/genetics/metabolism/pathology Adolescent Adult Aged Carcinoma/genetics/metabolism/pathology Carcinoma, Biological/*genetics/metabolism, Neoplasm/genetics/metabolism Reverse Transcriptase Polymerase Chain Reaction Signal Transduction Thyroid Neoplasms/classification/*genetics/metabolism Tumor Markers, Neoplastic Humans Male Middle Aged *Oligonucleotide Array Sequence Analysis Prognosis RNA, Papillary/genetics/metabolism/pathology Cell Differentiation Female *Gene Expression Profiling *Gene Expression Regulation}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17873908}, author = {Montero-Conde, C. and Martin-Campos, J. M. and Lerma, E. and Gimenez, G. and Martinez-Guitarte, J. L. and Combalia, N. and Montaner, D. and Matias-Guiu, X. and Dopazo, J. and de Leiva, A. and M. Robledo and Mauricio, D.} } @inbook {494, title = {Microarray Data Processing And Analysis}, booktitle = {Microarray data analysis II}, year = {2002}, pages = {43-63}, publisher = {Kluwer Academic}, organization = {Kluwer Academic}, author = {Dopazo, J.} } @article {11251224, title = {Methods and approaches in the analysis of gene expression data}, journal = {J Immunol Methods}, volume = {250}, number = {1-2}, year = {2001}, note = {

Dopazo, J Zanders, E Dragoni, I Amphlett, G Falciani, F Comparative Study Review Netherlands Journal of immunological methods J Immunol Methods. 2001 Apr;250(1-2):93-112.

}, pages = {93-112}, abstract = {

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 {\textquoteright}cell type specific{\textquoteright} 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.

}, url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11251224}, author = {Dopazo, J. and Zanders, E. and Dragoni, I. and Amphlett, G. and Falciani, F.} }