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/abstract02262nas a2200193   4500008004100000245016300041210006900204520151300273100001501786700002901801700001501830700001801845700001601863700002101879700002601900700002201926700001401948856010601962       2009                        eng d00aMembrane transporters and carbon metabolism implicated in chloride homeostasis differentiate salt stress responses in tolerant and sensitive Citrus rootstocks0 aMembrane transporters and carbon metabolism implicated in chlori3 a<p>Salinity tolerance in Citrus is strongly related to leaf chloride accumulation. Both chloride homeostasis and specific genetic responses to Cl(-) toxicity are issues scarcely investigated in plants. To discriminate the transcriptomic network related to Cl(-) toxicity and salinity tolerance, we have used two Cl(-) salt treatments (NaCl and KCl) to perform a comparative microarray approach on two Citrus genotypes, the salt-sensitive Carrizo citrange, a poor Cl(-) excluder, and the tolerant Cleopatra mandarin, an efficient Cl(-) excluder. The data indicated that Cl(-) toxicity, rather than Na(+) toxicity and/or the concomitant osmotic perturbation, is the primary factor involved in the molecular responses of citrus plant leaves to salinity. A number of uncharacterized membrane transporter genes, like NRT1-2, were differentially regulated in the tolerant and the sensitive genotypes, suggesting its potential implication in Cl(-) homeostasis. Analyses of enriched functional categories showed that the tolerant rootstock induced wider stress responses in gene expression while repressing central metabolic processes such as photosynthesis and carbon utilization. These features were in agreement with phenotypic changes in the patterns of photosynthesis, transpiration, and stomatal conductance and support the concept that regulation of transpiration and its associated metabolic adjustments configure an adaptive response to salinity that reduces Cl(-) accumulation in the tolerant genotype.</p>1 aBrumos, J.1 aColmenero-Flores, J., M.1 aConesa, A.1 aIzquierdo, P.1 aSanchez, G.1 aIglesias, D., J.1 aLopez-Climent, M., F.1 aGomez-Cadenas, A.1 aTalon, M.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1919094400556nas a2200169   4500008004100000022001800041245005100059210005100110260005300161300001200214653001500226100001500241700001600256700001400272700001700286856008300303       2007                        eng d  a0-4153-7853-200aMicroarray Technology in Agricultural Research0 aMicroarray Technology in Agricultural Research  bF. Falciani. Publisher: Taylor and Francis Group  a173-20910ababelomics1 aConesa, A.1 aForment, J.1 aGadea, J.1 avan Dijk, J.  uhttps://clinbioinfosspa.es/content/microarray-technology-agricultural-research01934nas a2200193   4500008004100000245012000041210006900161300001300230490000700243520110800250653010701358653001901465653008801484100001501572700001801587700001501605700001401620856010601634       2006                        eng d00amaSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments0 amaSigPro a method to identify significantly differential express  a1096-1020 v223 aMOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.10a*Algorithms Computer Simulation Gene Expression/*physiology Gene Expression Profiling/*methods *Models10aGenetic Models10aStatistical Oligonucleotide Array Sequence Analysis/*methods *Software Time Factors1 aConesa, A.1 aNueda, M., J.1 aFerrer, A.1 aTalon, M.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16481333