<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">León, Carlos</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Llames, Sara</style></author><author><style face="normal" font="default" size="100%">García-Pérez, Eva</style></author><author><style face="normal" font="default" size="100%">Carretero, Marta</style></author><author><style face="normal" font="default" size="100%">Arriba, María Del Carmen</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Del Rio, Marcela</style></author><author><style face="normal" font="default" size="100%">Escamez, Maria José</style></author><author><style face="normal" font="default" size="100%">Martínez-Santamaría, Lucía</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptomic Analysis of a Diabetic Skin-Humanized Mouse Model Dissects Molecular Pathways Underlying the Delayed Wound Healing Response.</style></title><secondary-title><style face="normal" font="default" size="100%">Genes (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genes (Basel)</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Experimental</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Nude</style></keyword><keyword><style  face="normal" font="default" size="100%">Microarray Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Transplantation</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Ulcer</style></keyword><keyword><style  face="normal" font="default" size="100%">Streptozocin</style></keyword><keyword><style  face="normal" font="default" size="100%">Tissue Engineering</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Transplantation, Heterologous</style></keyword><keyword><style  face="normal" font="default" size="100%">Wound Healing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 31</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Defective healing leading to cutaneous ulcer formation is one of the most feared complications of diabetes due to its consequences on patients' quality of life and on the healthcare system. A more in-depth analysis of the underlying molecular pathophysiology is required to develop effective healing-promoting therapies for those patients. Major architectural and functional differences with human epidermis limit extrapolation of results coming from rodents and other small mammal-healing models. Therefore, the search for reliable humanized models has become mandatory. Previously, we developed a diabetes-induced delayed humanized wound healing model that faithfully recapitulated the major histological features of such skin repair-deficient condition. Herein, we present the results of a transcriptomic and functional enrichment analysis followed by a mechanistic analysis performed in such humanized wound healing model. The deregulation of genes implicated in functions such as angiogenesis, apoptosis, and inflammatory signaling processes were evidenced, confirming published data in diabetic patients that in fact might also underlie some of the histological features previously reported in the delayed skin-humanized healing model. Altogether, these molecular findings support the utility of such preclinical model as a valuable tool to gain insight into the molecular basis of the delayed diabetic healing with potential impact in the translational medicine field.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33396192?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gil-Ibañez, Pilar</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Bernal, Juan</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global Transcriptome Analysis of Primary Cerebrocortical Cells: Identification of Genes Regulated by Triiodothyronine in Specific Cell Types.</style></title><secondary-title><style face="normal" font="default" size="100%">Cereb Cortex</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cereb Cortex</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Astrocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Fluorescent Antibody Technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, 129 Strain</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Inbred BALB C</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Inbred C57BL</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Piperazines</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">Triiodothyronine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017 01 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">706-717</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Thyroid hormones, thyroxine, and triiodothyronine (T3) are crucial for cerebral cortex development acting through regulation of gene expression. To define the transcriptional program under T3 regulation, we have performed RNA-Seq of T3-treated and untreated primary mouse cerebrocortical cells. The expression of 1145 genes or 7.7% of expressed genes was changed upon T3 addition, of which 371 responded to T3 in the presence of cycloheximide indicating direct transcriptional regulation. The results were compared with available transcriptomic datasets of defined cellular types. In this way, we could identify targets of T3 within genes enriched in astrocytes and neurons, in specific layers including the subplate, and in specific neurons such as prepronociceptin, cholecystokinin, or cortistatin neurons. The subplate and the prepronociceptin neurons appear as potentially major targets of T3 action. T3 upregulates mostly genes related to cell membrane events, such as G-protein signaling, neurotransmission, and ion transport and downregulates genes involved in nuclear events associated with the M phase of cell cycle, such as chromosome organization and segregation. Remarkably, the transcriptomic changes induced by T3 sustain the transition from fetal to adult patterns of gene expression. The results allow defining in molecular terms the elusive role of thyroid hormones on neocortical development.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26534908?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Panadero, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrated gene set analysis for microRNA studies.</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Sep 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">2809-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.&lt;/p&gt;&lt;p&gt;&lt;b&gt;AVAILABILITY AND IMPLEMENTATION: &lt;/b&gt;The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONTACT: &lt;/b&gt;: david.montaner@gmail.com&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUPPLEMENTARY INFORMATION: &lt;/b&gt;Supplementary data are available at Bioinformatics online.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27324197?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moschen, Sebastián</style></author><author><style face="normal" font="default" size="100%">Bengoa Luoni, Sofía</style></author><author><style face="normal" font="default" size="100%">Di Rienzo, Julio A</style></author><author><style face="normal" font="default" size="100%">Caro, María Del Pilar</style></author><author><style face="normal" font="default" size="100%">Tohge, Takayuki</style></author><author><style face="normal" font="default" size="100%">Watanabe, Mutsumi</style></author><author><style face="normal" font="default" size="100%">Hollmann, Julien</style></author><author><style face="normal" font="default" size="100%">Gonzalez, Sergio</style></author><author><style face="normal" font="default" size="100%">Rivarola, Máximo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Hopp, Horacio Esteban</style></author><author><style face="normal" font="default" size="100%">Hoefgen, Rainer</style></author><author><style face="normal" font="default" size="100%">Fernie, Alisdair R</style></author><author><style face="normal" font="default" size="100%">Paniego, Norma</style></author><author><style face="normal" font="default" size="100%">Fernandez, Paula</style></author><author><style face="normal" font="default" size="100%">Heinz, Ruth A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Biotechnol J</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Biotechnol J</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Gas Chromatography-Mass Spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Helianthus</style></keyword><keyword><style  face="normal" font="default" size="100%">Ions</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Leaves</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Feb</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">719-34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Leaf senescence is a complex process, which has dramatic consequences on crop yield. In sunflower, gap between potential and actual yields reveals the economic impact of senescence. Indeed, sunflower plants are incapable of maintaining their green leaf area over sustained periods. This study characterizes the leaf senescence process in sunflower through a systems biology approach integrating transcriptomic and metabolomic analyses: plants being grown under both glasshouse and field conditions. Our results revealed a correspondence between profile changes detected at the molecular, biochemical and physiological level throughout the progression of leaf senescence measured at different plant developmental stages. Early metabolic changes were detected prior to anthesis and before the onset of the first senescence symptoms, with more pronounced changes observed when physiological and molecular variables were assessed under field conditions. During leaf development, photosynthetic activity and cell growth processes decreased, whereas sucrose, fatty acid, nucleotide and amino acid metabolisms increased. Pathways related to nutrient recycling processes were also up-regulated. Members of the NAC, AP2-EREBP, HB, bZIP and MYB transcription factor families showed high expression levels, and their expression level was highly correlated, suggesting their involvement in sunflower senescence. The results of this study thus contribute to the elucidation of the molecular mechanisms involved in the onset and progression of leaf senescence in sunflower leaves as well as to the identification of candidate genes involved in this process. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/26132509?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hillung, Julia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Cuevas, José M</style></author><author><style face="normal" font="default" size="100%">Elena, Santiago F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The transcriptomics of an experimentally evolved plant-virus interaction.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Host-Pathogen Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Potyvirus</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 04 26</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">24901</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Models of plant-virus interaction assume that the ability of a virus to infect a host genotype depends on the matching between virulence and resistance genes. Recently, we evolved tobacco etch potyvirus (TEV) lineages on different ecotypes of Arabidopsis thaliana, and found that some ecotypes selected for specialist viruses whereas others selected for generalists. Here we sought to evaluate the transcriptomic basis of such relationships. We have characterized the transcriptomic responses of five ecotypes infected with the ancestral and evolved viruses. Genes and functional categories differentially expressed by plants infected with local TEV isolates were identified, showing heterogeneous responses among ecotypes, although significant parallelism existed among lineages evolved in the same ecotype. Although genes involved in immune responses were altered upon infection, other functional groups were also pervasively over-represented, suggesting that plant resistance genes were not the only drivers of viral adaptation. Finally, the transcriptomic consequences of infection with the generalist and specialist lineages were compared. Whilst the generalist induced very similar perturbations in the transcriptomes of the different ecotypes, the perturbations induced by the specialist were divergent. Plant defense mechanisms were activated when the infecting virus was specialist but they were down-regulated when infecting with generalist.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/27113435?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torre, Iratxe</style></author><author><style face="normal" font="default" size="100%">González-Tendero, Anna</style></author><author><style face="normal" font="default" size="100%">García-Cañadilla, Patricia</style></author><author><style face="normal" font="default" size="100%">Crispi, Fátima</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Bijnens, Bart</style></author><author><style face="normal" font="default" size="100%">Iruretagoyena, Igor</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Amat-Roldán, Ivan</style></author><author><style face="normal" font="default" size="100%">Gratacós, Eduard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Permanent cardiac sarcomere changes in a rabbit model of intrauterine growth restriction.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Blood Pressure</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Weight</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Models, Animal</style></keyword><keyword><style  face="normal" font="default" size="100%">Echocardiography</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetal Growth Retardation</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetal Heart</style></keyword><keyword><style  face="normal" font="default" size="100%">Fetus</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Organ Size</style></keyword><keyword><style  face="normal" font="default" size="100%">Placenta</style></keyword><keyword><style  face="normal" font="default" size="100%">Pregnancy</style></keyword><keyword><style  face="normal" font="default" size="100%">Rabbits</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcomeres</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">e113067</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Intrauterine growth restriction (IUGR) induces fetal cardiac remodelling and dysfunction, which persists postnatally and may explain the link between low birth weight and increased cardiovascular mortality in adulthood. However, the cellular and molecular bases for these changes are still not well understood. We tested the hypothesis that IUGR is associated with structural and functional gene expression changes in the fetal sarcomere cytoarchitecture, which remain present in adulthood.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;IUGR was induced in New Zealand pregnant rabbits by selective ligation of the utero-placental vessels. Fetal echocardiography demonstrated more globular hearts and signs of cardiac dysfunction in IUGR. Second harmonic generation microscopy (SHGM) showed shorter sarcomere length and shorter A-band and thick-thin filament interaction lengths, that were already present in utero and persisted at 70 postnatal days (adulthood). Sarcomeric M-band (GO: 0031430) functional term was over-represented in IUGR fetal hearts.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;The results suggest that IUGR induces cardiac dysfunction and permanent changes on the sarcomere.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/25402351?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Iglesias, Juan</style></author><author><style face="normal" font="default" size="100%">Beloqui, Izaskun</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Leis, Olatz</style></author><author><style face="normal" font="default" size="100%">Vazquez-Martin, Alejandro</style></author><author><style face="normal" font="default" size="100%">Eguiara, Arrate</style></author><author><style face="normal" font="default" size="100%">Cufi, Silvia</style></author><author><style face="normal" font="default" size="100%">Pavon, Andres</style></author><author><style face="normal" font="default" size="100%">Menendez, Javier A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Martin, Angel G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mammosphere formation in breast carcinoma cell lines depends upon expression of E-cadherin.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS One</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS One</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cadherins</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Proliferation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MCF-7 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplastic Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Spheroids, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Tumor Cells, Cultured</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e77281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tumors are heterogeneous at the cellular level where the ability to maintain tumor growth resides in discrete cell populations. Floating sphere-forming assays are broadly used to test stem cell activity in tissues, tumors and cell lines. Spheroids are originated from a small population of cells with stem cell features able to grow in suspension culture and behaving as tumorigenic in mice. We tested the ability of eleven common breast cancer cell lines representing the major breast cancer subtypes to grow as mammospheres, measuring the ability to maintain cell viability upon serial non-adherent passage. Only MCF7, T47D, BT474, MDA-MB-436 and JIMT1 were successfully propagated as long-term mammosphere cultures, measured as the increase in the number of viable cells upon serial non-adherent passages. Other cell lines tested (SKBR3, MDA-MB-231, MDA-MB-468 and MDA-MB-435) formed cell clumps that can be disaggregated mechanically, but cell viability drops dramatically on their second passage. HCC1937 and HCC1569 cells formed typical mammospheres, although they could not be propagated as long-term mammosphere cultures. All the sphere forming lines but MDA-MB-436 express E-cadherin on their surface. Knock down of E-cadherin expression in MCF-7 cells abrogated its ability to grow as mammospheres, while re-expression of E-cadherin in SKBR3 cells allow them to form mammospheres. Therefore, the mammosphere assay is suitable to reveal stem like features in breast cancer cell lines that express E-cadherin.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/24124614?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silbiger, Vivian N</style></author><author><style face="normal" font="default" size="100%">Luchessi, André D</style></author><author><style face="normal" font="default" size="100%">Hirata, Rosário D C</style></author><author><style face="normal" font="default" size="100%">Lima-Neto, Lídio G</style></author><author><style face="normal" font="default" size="100%">Cavichioli, Débora</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Brión, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dos Santos, Elizabete S</style></author><author><style face="normal" font="default" size="100%">Ramos, Rui F</style></author><author><style face="normal" font="default" size="100%">Sampaio, Marcelo F</style></author><author><style face="normal" font="default" size="100%">Armaganijan, Dikran</style></author><author><style face="normal" font="default" size="100%">Sousa, Amanda G M R</style></author><author><style face="normal" font="default" size="100%">Hirata, Mario H</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Chim Acta</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Chim Acta</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acute Coronary Syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">Acute-Phase Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Blood Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Early Diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">gene expression</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Jun 05</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">421</style></volume><pages><style face="normal" font="default" size="100%">184-90</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Genome-wide expression analysis using microarrays has been used as a research strategy to discovery new biomarkers and candidate genes for a number of diseases. We aim to find new biomarkers for the prediction of acute coronary syndrome (ACS) with a differentially expressed mRNA profiling approach using whole genomic expression analysis in a peripheral blood cell model from patients with early ACS.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS AND RESULTS: &lt;/b&gt;This study was carried out in two phases. On phase 1 a restricted clinical criteria (ACS-Ph1, n=9 and CG-Ph1, n=6) was used in order to select potential mRNA biomarkers candidates. A subsequent phase 2 study was performed using selected phase 1 markers analyzed by RT-qPCR using a larger and independent casuistic (ACS-Ph2, n=74 and CG-Ph2, n=41). A total of 549 genes were found to be differentially expressed in the first 48 h after the ACS-Ph1. Technical and biological validation further confirmed that ALOX15, AREG, BCL2A1, BCL2L1, CA1, COX7B, ECHDC3, IL18R1, IRS2, KCNE1, MMP9, MYL4 and TREML4, are differentially expressed in both phases of this study.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Transcriptomic analysis by microarray technology demonstrated differential expression during a 48 h time course suggesting a potential use of some of these genes as biomarkers for very early stages of ACS, as well as for monitoring early cardiac ischemic recovery.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23535507?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Puig-Butille, Joan Anton</style></author><author><style face="normal" font="default" size="100%">Malvehy, Josep</style></author><author><style face="normal" font="default" size="100%">Potrony, Miriam</style></author><author><style face="normal" font="default" size="100%">Trullas, Carles</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Puig, Susana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of CPI-17 in restoring skin homoeostasis in cutaneous field of cancerization: effects of topical application of a film-forming medical device containing photolyase and UV filters.</style></title><secondary-title><style face="normal" font="default" size="100%">Exp Dermatol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Exp Dermatol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Administration, Topical</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Biopsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Deoxyribodipyrimidine Photo-Lyase</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Enzymologic</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Homeostasis</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation</style></keyword><keyword><style  face="normal" font="default" size="100%">Intracellular Signaling Peptides and Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Liposomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Muscle Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Phosphoprotein Phosphatases</style></keyword><keyword><style  face="normal" font="default" size="100%">Reactive Oxygen Species</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin</style></keyword><keyword><style  face="normal" font="default" size="100%">Skin Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Ultraviolet Rays</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">494-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Cutaneous field of cancerization (CFC) is caused in part by the carcinogenic effect of the cyclobutane pyrimidine dimers CPD and 6-4 photoproducts (6-4PPs). Photoreactivation is carried out by photolyases which specifically recognize and repair both photoproducts. The study evaluates the molecular effects of topical application of a film-forming medical device containing photolyase and UV filters on the precancerous field in AK from seven patients. Skin improvement after treatment was confirmed in all patients by histopathological and molecular assessment. A gene set analysis showed that skin recovery was associated with biological processes involved in tissue homoeostasis and cell maintenance. The CFC response was associated with over-expression of the CPI-17 gene, and a dependence on the initial expression level was observed (P = 0.001). Low CPI-17 levels were directly associated with pro-inflammatory genes such as TNF (P = 0.012) and IL-1B (P = 0.07). Our results suggest a role for CPI-17 in restoring skin homoeostasis in CFC lesions. &lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/23800065?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carrero, Rubén</style></author><author><style face="normal" font="default" size="100%">Cerrada, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Lledó, Elisa</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Rubio, Mari-Paz</style></author><author><style face="normal" font="default" size="100%">Trigueros, César</style></author><author><style face="normal" font="default" size="100%">Dorronsoro, Akaitz</style></author><author><style face="normal" font="default" size="100%">Ruiz-Sauri, Amparo</style></author><author><style face="normal" font="default" size="100%">Montero, José Anastasio</style></author><author><style face="normal" font="default" size="100%">Sepúlveda, Pilar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IL1β induces mesenchymal stem cells migration and leucocyte chemotaxis through NF-κB.</style></title><secondary-title><style face="normal" font="default" size="100%">Stem Cell Rev Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stem Cell Rev Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Adhesion</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Movement</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Proliferation</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Chemotaxis, Leukocyte</style></keyword><keyword><style  face="normal" font="default" size="100%">Collagen</style></keyword><keyword><style  face="normal" font="default" size="100%">Fibronectins</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Knockdown Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">HEK293 Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">I-kappa B Kinase</style></keyword><keyword><style  face="normal" font="default" size="100%">Inflammation Mediators</style></keyword><keyword><style  face="normal" font="default" size="100%">Intercellular Signaling Peptides and Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Interleukin-1beta</style></keyword><keyword><style  face="normal" font="default" size="100%">Laminin</style></keyword><keyword><style  face="normal" font="default" size="100%">Leukocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Mesenchymal Stem Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">NF-kappa B</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Interference</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">905-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Mesenchymal stem cells are often transplanted into inflammatory environments where they are able to survive and modulate host immune responses through a poorly understood mechanism. In this paper we analyzed the responses of MSC to IL-1β: a representative inflammatory mediator. Microarray analysis of MSC treated with IL-1β revealed that this cytokine activateds a set of genes related to biological processes such as cell survival, cell migration, cell adhesion, chemokine production, induction of angiogenesis and modulation of the immune response. Further more detailed analysis by real-time PCR and functional assays revealed that IL-1β mainly increaseds the production of chemokines such as CCL5, CCL20, CXCL1, CXCL3, CXCL5, CXCL6, CXCL10, CXCL11 and CX(3)CL1, interleukins IL-6, IL-8, IL23A, IL32, Toll-like receptors TLR2, TLR4, CLDN1, metalloproteins MMP1 and MMP3, growth factors CSF2 and TNF-α, together with adhesion molecules ICAM1 and ICAM4. Functional analysis of MSC proliferation, migration and adhesion to extracellular matrix components revealed that IL-1β did not affect proliferation but also served to induce the secretion of trophic factors and adhesion to ECM components such as collagen and laminin. IL-1β treatment enhanced the ability of MSC to recruit monocytes and granulocytes in vitro. Blockade of NF-κβ transcription factor activation with IκB kinase beta (IKKβ) shRNA impaired MSC migration, adhesion and leucocyte recruitment, induced by IL-1β demonstrating that NF-κB pathway is an important downstream regulator of these responses. These findings are relevant to understanding the biological responses of MSC to inflammatory environments.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/22467443?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">González-Pérez, Sergio</style></author><author><style face="normal" font="default" size="100%">Gutiérrez, Jorge</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Osuna, Daniel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Lorenzo, Oscar</style></author><author><style face="normal" font="default" size="100%">Revuelta, José L</style></author><author><style face="normal" font="default" size="100%">Arellano, Juan B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Early transcriptional defense responses in Arabidopsis cell suspension culture under high-light conditions.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Physiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Physiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Blotting, Western</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Culture Techniques</style></keyword><keyword><style  face="normal" font="default" size="100%">Cells, Cultured</style></keyword><keyword><style  face="normal" font="default" size="100%">Chloroplasts</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Hydrogen Peroxide</style></keyword><keyword><style  face="normal" font="default" size="100%">Light</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Photosystem II Protein Complex</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Growth Regulators</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Reverse Transcriptase Polymerase Chain Reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Stress, Physiological</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">156</style></volume><pages><style face="normal" font="default" size="100%">1439-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The early transcriptional defense responses and reactive oxygen species (ROS) production in Arabidopsis (Arabidopsis thaliana) cell suspension culture (ACSC), containing functional chloroplasts, were examined at high light (HL). The transcriptional analysis revealed that most of the ROS markers identified among the 449 transcripts with significant differential expression were transcripts specifically up-regulated by singlet oxygen ((1)O(2)). On the contrary, minimal correlation was established with transcripts specifically up-regulated by superoxide radical or hydrogen peroxide. The transcriptional analysis was supported by fluorescence microscopy experiments. The incubation of ACSC with the (1)O(2) sensor green reagent and 2',7'-dichlorofluorescein diacetate showed that the 30-min-HL-treated cultures emitted fluorescence that corresponded with the production of (1)O(2) but not of hydrogen peroxide. Furthermore, the in vivo photodamage of the D1 protein of photosystem II indicated that the photogeneration of (1)O(2) took place within the photosystem II reaction center. Functional enrichment analyses identified transcripts that are key components of the ROS signaling transduction pathway in plants as well as others encoding transcription factors that regulate both ROS scavenging and water deficit stress. A meta-analysis examining the transcriptional profiles of mutants and hormone treatments in Arabidopsis showed a high correlation between ACSC at HL and the fluorescent mutant family of Arabidopsis, a producer of (1)O(2) in plastids. Intriguingly, a high correlation was also observed with ABA deficient1 and more axillary growth4, two mutants with defects in the biosynthesis pathways of two key (apo)carotenoid-derived plant hormones (i.e. abscisic acid and strigolactones, respectively). ACSC has proven to be a valuable system for studying early transcriptional responses to HL stress.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21531897?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nueda, Maria José</style></author><author><style face="normal" font="default" size="100%">Sebastián, Patricia</style></author><author><style face="normal" font="default" size="100%">Tarazona, Sonia</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Ferrer, Alberto</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Functional assessment of time course microarray data.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Jun 16</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10 Suppl 6</style></volume><pages><style face="normal" font="default" size="100%">S9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;MOTIVATION: &lt;/b&gt;Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/19534758?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Bro, Rasmus</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Prats, José Manuel</style></author><author><style face="normal" font="default" size="100%">Götz, Stefan</style></author><author><style face="normal" font="default" size="100%">Kjeldahl, Karin</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct functional assessment of the composite phenotype through multivariate projection strategies.</style></title><secondary-title><style face="normal" font="default" size="100%">Genomics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genomics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Mathematical Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Dec</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">373-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a novel approach for the analysis of transcriptomics data that integrates functional annotation of gene sets with expression values in a multivariate fashion, and directly assesses the relation of functional features to a multivariate space of response phenotypical variables. Multivariate projection methods are used to obtain new correlated variables for a set of genes that share a given function. These new functional variables are then related to the response variables of interest. The analysis of the principal directions of the multivariate regression allows for the identification of gene function features correlated with the phenotype. Two different transcriptomics studies are used to illustrate the statistical and interpretative aspects of the methodology. We demonstrate the superiority of the proposed method over equivalent approaches.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18652888?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tárraga, Joaquín</style></author><author><style face="normal" font="default" size="100%">Medina, Ignacio</style></author><author><style face="normal" font="default" size="100%">Carbonell, José</style></author><author><style face="normal" font="default" size="100%">Huerta-Cepas, Jaime</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Alloza, Eva</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">Vegas-Azcárate, Susana</style></author><author><style face="normal" font="default" size="100%">Goetz, Stefan</style></author><author><style face="normal" font="default" size="100%">Escobar, Pablo</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">Conesa, Ana</style></author><author><style face="normal" font="default" size="100%">Montaner, David</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GEPAS, a web-based tool for microarray data analysis and interpretation.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">Dose-Response Relationship, Drug</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Kinetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Jul 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">W308-14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Web Server issue</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/18508806?dopt=Abstract</style></custom1></record></records></xml>