03640nas a2200565 4500008004100000022001400041245014000055210006900195260001300264300001200277490000700289520189600296653004202192653003102234653001502265653001902280653002002299653002402319653001902343653003002362653003202392653001502424653001902439653002802458653001002486653001102496653001602507653004402523653001402567653003602581653002702617100002102644700003102665700001502696700001402711700001502725700002202740700001602762700001202778700002302790700001402813700001302827700001902840700002402859700001502883700001402898700001902912700001502931856012802946 2020 eng d a1469-069100aAssociation of a single nucleotide polymorphism in the ubxn6 gene with long-term non-progression phenotype in HIV-positive individuals.0 aAssociation of a single nucleotide polymorphism in the ubxn6 gen c2020 Jan a107-1140 v263 a
OBJECTIVES: The long-term non-progressors (LTNPs) are a heterogeneous group of HIV-positive individuals characterized by their ability to maintain high CD4 T-cell counts and partially control viral replication for years in the absence of antiretroviral therapy. The present study aims to identify host single nucleotide polymorphisms (SNPs) associated with non-progression in a cohort of 352 individuals.
METHODS: DNA microarrays and exome sequencing were used for genotyping about 240 000 functional polymorphisms throughout more than 20 000 human genes. The allele frequencies of 85 LTNPs were compared with a control population. SNPs associated with LTNPs were confirmed in a population of typical progressors. Functional analyses in the affected gene were carried out through knockdown experiments in HeLa-P4, macrophages and dendritic cells.
RESULTS: Several SNPs located within the major histocompatibility complex region previously related to LTNPs were confirmed in this new cohort. The SNP rs1127888 (UBXN6) surpassed the statistical significance of these markers after Bonferroni correction (q = 2.11 × 10). An uncommon allelic frequency of rs1127888 among LTNPs was confirmed by comparison with typical progressors and other publicly available populations. UBXN6 knockdown experiments caused an increase in CAV1 expression and its accumulation in the plasma membrane. In vitro infection of different cell types with HIV-1 replication-competent recombinant viruses caused a reduction of the viral replication capacity compared with their corresponding wild-type cells expressing UBXN6.
CONCLUSIONS: A higher prevalence of Ala31Thr in UBXN6 was found among LTNPs within its N-terminal region, which is crucial for UBXN6/VCP protein complex formation. UBXN6 knockdown affected CAV1 turnover and HIV-1 replication capacity.
10aAdaptor Proteins, Vesicular Transport10aAutophagy-Related Proteins10aCaveolin 110aCohort Studies10aDendritic Cells10aDisease Progression10aGene Frequency10aGene Knockdown Techniques10aGenetic Association Studies10aHeLa Cells10aHIV Infections10aHIV Long-Term Survivors10aHIV-110aHumans10aMacrophages10aOligonucleotide Array Sequence Analysis10aPhenotype10aPolymorphism, Single Nucleotide10awhole exome sequencing1 aDíez-Fuertes, F1 aDe La Torre-Tarazona, H, E1 aCalonge, E1 aPernas, M1 aBermejo, M1 aGarcía-Pérez, J1 aÁlvarez, A1 aCapa, L1 aGarcía-García, F1 aSaumoy, M1 aRiera, M1 aBoland-Auge, A1 aLópez-Galíndez, C1 aLathrop, M1 aDopazo, J1 aSakuntabhai, A1 aAlcamí, J uhttp://clinbioinfosspa.es/content/association-single-nucleotide-polymorphism-ubxn6-gene-long-term-non-progression-phenotype03249nas a2200493 4500008004100000022001400041245010800055210006900163260001300232300001100245490000700256520165600263653004101919653003001960653003801990653001802028653001702046653001502063653000902078653001702087653004402104653001702148653003302165653001902198653002602217100002402243700002602267700002402293700002802317700002002345700002202365700002102387700002102408700002202429700002902451700002002480700002702500700002002527700002402547700001902571700002102590700001902611856012502630 2016 eng d a1467-765200aIntegrating transcriptomic and metabolomic analysis to understand natural leaf senescence in sunflower.0 aIntegrating transcriptomic and metabolomic analysis to understan c2016 Feb a719-340 v143 aLeaf 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.
10aGas Chromatography-Mass Spectrometry10aGene Expression Profiling10aGene Expression Regulation, Plant10aGene ontology10aGenes, Plant10aHelianthus10aIons10ametabolomics10aOligonucleotide Array Sequence Analysis10aPlant Leaves10aPrincipal Component Analysis10aRNA, Messenger10aTranscription Factors1 aMoschen, Sebastián1 aLuoni, Sofía, Bengoa1 aDi Rienzo, Julio, A1 aCaro, María, Del Pilar1 aTohge, Takayuki1 aWatanabe, Mutsumi1 aHollmann, Julien1 aGonzalez, Sergio1 aRivarola, Máximo1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aHopp, Horacio, Esteban1 aHoefgen, Rainer1 aFernie, Alisdair, R1 aPaniego, Norma1 aFernandez, Paula1 aHeinz, Ruth, A uhttp://clinbioinfosspa.es/content/integrating-transcriptomic-and-metabolomic-analysis-understand-natural-leaf-senescence03277nas a2200445 4500008004100000022001400041245015900055210006900214260001600283300000900299490000800308520183200316653001002148653000902158653001102167653004102178653002302219653003002242653004602272653001802318653003402336653001702370653001102387653001602398653002002414653001302434653004402447653001202491653003402503653002402537100002302561700002502584700001602609700002302625700001302648700001402661700001602675700001302691856012702704 2015 eng d a1879-003800aDeregulation of key signaling pathways involved in oocyte maturation in FMR1 premutation carriers with Fragile X-associated primary ovarian insufficiency.0 aDeregulation of key signaling pathways involved in oocyte matura c2015 Oct 15 a52-70 v5713 aFMR1 premutation female carriers are at risk for Fragile X-associated primary ovarian insufficiency (FXPOI). Insights from knock-in mouse model have recently demonstrated that FXPOI is due to an increased rate of follicle depletion or an impaired development of the growing follicles. Molecular mechanisms responsible for this reduced viability are still unknown. In an attempt to provide new data on the mechanisms that lead to FXPOI, we report the first investigation involving transcription profiling of total blood from FMR1 premutation female carriers with and without FXPOI. A total of 16 unrelated female individuals (6 FMR1 premutated females with FXPOI; 6 FMR1 premutated females without FXPOI; and 4 no-FXPOI females) were studied by whole human genome oligonucleotide microarray (Agilent Technologies). Fold change analysis did not show any genes with significant differential gene expression. However, functional profiling by gene set analysis showed large number of statistically significant deregulated GO annotations as well as numerous KEGG pathways in FXPOI females. These results suggest that the impairment of fertility in these females might be due to a generalized deregulation of key signaling pathways involved in oocyte maturation. In particular, the vasoendotelial growth factor signaling, the inositol phosphate metabolism, the cell cycle, and the MAPK signaling pathways were found to be down-regulated in FXPOI females. Furthermore, a high statistical enrichment of biological processes involved in cell death and survival were found deregulated among FXPOI females. Our results provide new strategic approaches to further investigate the molecular mechanisms and potential therapeutic targets for FXPOI not focused in a single gene but rather in the set of genes involved in these pathways.
10aAdult10aAged10aFemale10aFragile X Mental Retardation Protein10aFragile X Syndrome10aGene Expression Profiling10aGene Expression Regulation, Developmental10aGene ontology10aGenome-Wide Association Study10aHeterozygote10aHumans10aMiddle Aged10aModels, Genetic10amutation10aOligonucleotide Array Sequence Analysis10aOocytes10aPrimary Ovarian Insufficiency10aSignal Transduction1 aAlvarez-Mora, M, I1 aRodriguez-Revenga, L1 aMadrigal, I1 aGarcía-García, F1 aDuran, M1 aDopazo, J1 aEstivill, X1 aMilà, M uhttp://clinbioinfosspa.es/content/deregulation-key-signaling-pathways-involved-oocyte-maturation-fmr1-premutation-carriers02952nas a2200481 4500008004100000022001400041245013400055210006900189260001600258300001100274490000800285520141700293653002801710653002501738653001001763653001501773653001601788653002001804653002001824653003001844653001101874653000901885653001601894653004401910653001901954653001801973100002401991700002402015700002602039700002502065700002402090700002202114700001802136700002002154700002902174700002902203700001802232700002402250700002402274700002502298700002102323856012602344 2013 eng d a1873-349200aNovel genes detected by transcriptional profiling from whole-blood cells in patients with early onset of acute coronary syndrome.0 aNovel genes detected by transcriptional profiling from wholebloo c2013 Jun 05 a184-900 v4213 aBACKGROUND: 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.
METHODS AND RESULTS: 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.
CONCLUSIONS: 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.
10aAcute Coronary Syndrome10aAcute-Phase Proteins10aAdult10abiomarkers10aBlood Cells10aEarly Diagnosis10agene expression10aGene Expression Profiling10aHumans10aMale10aMiddle Aged10aOligonucleotide Array Sequence Analysis10aRNA, Messenger10aTranscriptome1 aSilbiger, Vivian, N1 aLuchessi, André, D1 aHirata, Rosário, D C1 aLima-Neto, Lídio, G1 aCavichioli, Débora1 aCarracedo, Ángel1 aBrión, Maria1 aDopazo, Joaquin1 aGarcia-Garcia, Francisco1 aSantos, Elizabete, S Dos1 aRamos, Rui, F1 aSampaio, Marcelo, F1 aArmaganijan, Dikran1 aSousa, Amanda, G M R1 aHirata, Mario, H uhttp://clinbioinfosspa.es/content/novel-genes-detected-transcriptional-profiling-whole-blood-cells-patients-early-onset-002970nas a2200313 4500008004100000022001400041245008300055210006900138260000900207300001100216490000600227520199700233653001202230653003302242653001102275653001702286653000902303653001802312653004402330653002502374653001902399653002702418100001902445700001902464700002002483700002002503700002002523856011302543 2012 eng d a1932-620300aExtensive translatome remodeling during ER stress response in mammalian cells.0 aExtensive translatome remodeling during ER stress response in ma c2012 ae359150 v73 aIn this work we have described the translatome of two mammalian cell lines, NIH3T3 and Jurkat, by scoring the relative polysome association of ∼10,000 mRNA under normal and ER stress conditions. We have found that translation efficiencies of mRNA correlated poorly with transcript abundance, although a general tendency was observed so that the highest translation efficiencies were found in abundant mRNA. Despite the differences found between mouse (NIH3T3) and human (Jurkat) cells, both cell types share a common translatome composed by ∼800-900 mRNA that encode proteins involved in basic cellular functions. Upon stress, an extensive remodeling in translatomes was observed so that translation of ∼50% of mRNA was inhibited in both cell types, this effect being more dramatic for those mRNA that accounted for most of the cell translation. Interestingly, we found two subsets comprising 1000-1500 mRNA whose translation resisted or was induced by stress. Translation arrest resistant class includes many mRNA encoding aminoacyl tRNA synthetases, ATPases and enzymes involved in DNA replication and stress response such as BiP. This class of mRNA is characterized by high translation rates in both control and stress conditions. Translation inducible class includes mRNA whose translation was relieved after stress, showing a high enrichment in early response transcription factors of bZIP and zinc finger C2H2 classes. Unlike yeast, a general coordination between changes in translation and transcription upon stress (potentiation) was not observed in mammalian cells. Among the different features of mRNA analyzed, we found a relevant association of translation efficiency with the presence of upstream ATG in the 5'UTR and with the length of coding sequence of mRNA, and a looser association with other parameters such as the length and the G+C content of 5'UTR. A model for translatome remodeling during the acute phase of stress response in mammalian cells is proposed.
10aAnimals10aEndoplasmic Reticulum Stress10aHumans10aJurkat Cells10aMice10aNIH 3T3 Cells10aOligonucleotide Array Sequence Analysis10aProtein Biosynthesis10aRNA, Messenger10aTranscription, Genetic1 aVentoso, Iván1 aKochetov, Alex1 aMontaner, David1 aDopazo, Joaquin1 aSantoyo, Javier uhttp://clinbioinfosspa.es/content/extensive-translatome-remodeling-during-er-stress-response-mammalian-cells03355nas a2200529 4500008004100000022001400041245009200055210007000147260001300217300001100230490000600241520169800247653001801945653001801963653002301981653001502004653002602019653001302045653001702058653003002075653003002105653001702135653001102152653002102163653002702184653005002211653002202261653001202283653001502295653002702310653001502337653004402352653002102396653002402417100002002441700002402461700001802485700002002503700002902523700002002552700002202572700002302594700002302617700003002640700002202670856013302692 2012 eng d a2629-327700aIL1β induces mesenchymal stem cells migration and leucocyte chemotaxis through NF-κB.0 aIL1β induces mesenchymal stem cells migration and leucocyte chem c2012 Sep a905-160 v83 aMesenchymal 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.
10aCell Adhesion10aCell Movement10aCell Proliferation10aChemokines10aChemotaxis, Leukocyte10aCollagen10aFibronectins10aGene Expression Profiling10aGene Knockdown Techniques10aHEK293 Cells10aHumans10aI-kappa B Kinase10aInflammation Mediators10aIntercellular Signaling Peptides and Proteins10aInterleukin-1beta10aLaminin10aLeukocytes10aMesenchymal Stem Cells10aNF-kappa B10aOligonucleotide Array Sequence Analysis10aRNA Interference10aSignal Transduction1 aCarrero, Rubén1 aCerrada, Inmaculada1 aLledó, Elisa1 aDopazo, Joaquin1 aGarcia-Garcia, Francisco1 aRubio, Mari-Paz1 aTrigueros, César1 aDorronsoro, Akaitz1 aRuiz-Sauri, Amparo1 aMontero, José, Anastasio1 aSepúlveda, Pilar uhttp://clinbioinfosspa.es/content/il1%CE%B2-induces-mesenchymal-stem-cells-migration-and-leucocyte-chemotaxis-through-nf-%CE%BAb02499nas a2200277 4500008004100000022001400041245005900055210005600114260001300170300001200183490000700195520165100202653001501853653002801868653003001896653003101926653001101957653002001968653004401988100002002032700003002052700002002082700002002102700001602122856008302138 2011 eng d a1549-546900aDifferential expression in RNA-seq: a matter of depth.0 aDifferential expression in RNAseq a matter of depth c2011 Dec a2213-230 v213 aNext-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.
10aAlgorithms10aExpressed Sequence Tags10aGene Expression Profiling10aGene Expression Regulation10aHumans10aModels, Genetic10aOligonucleotide Array Sequence Analysis1 aTarazona, Sonia1 aGarcía-Alcalde, Fernando1 aDopazo, Joaquin1 aFerrer, Alberto1 aConesa, Ana uhttp://clinbioinfosspa.es/content/differential-expression-rna-seq-matter-depth03501nas a2200469 4500008004100000022001400041245011200055210006900167260001300236300001200249490000800261520192600269653001602195653002202211653002802233653002002261653001702281653002102298653003002319653003802349653002202387653001002409653001302419653004402432653003502476653002802511653003102539653005202570653001902622653002402641653002602665653002702691100002902718700002202747700002902769700001802798700002002816700001902836700002302855700002202878856013102900 2011 eng d a1532-254800aEarly transcriptional defense responses in Arabidopsis cell suspension culture under high-light conditions.0 aEarly transcriptional defense responses in Arabidopsis cell susp c2011 Jul a1439-560 v1563 aThe 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.
10aArabidopsis10aBlotting, Western10aCell Culture Techniques10aCells, Cultured10aChloroplasts10aCluster Analysis10aGene Expression Profiling10aGene Expression Regulation, Plant10aHydrogen Peroxide10aLight10amutation10aOligonucleotide Array Sequence Analysis10aPhotosystem II Protein Complex10aPlant Growth Regulators10aReproducibility of Results10aReverse Transcriptase Polymerase Chain Reaction10aRNA, Messenger10aSignal Transduction10aStress, Physiological10aTranscription, Genetic1 aGonzález-Pérez, Sergio1 aGutiérrez, Jorge1 aGarcia-Garcia, Francisco1 aOsuna, Daniel1 aDopazo, Joaquin1 aLorenzo, Oscar1 aRevuelta, José, L1 aArellano, Juan, B uhttp://clinbioinfosspa.es/content/early-transcriptional-defense-responses-arabidopsis-cell-suspension-culture-under-high-light02919nas a2200433 4500008004100000022001400041245013300055210006900188260000900257300001100266490000600277520154700283653001201830653002801842653001001870653002201880653001101902653002301913653001101936653001201947653001301959653001301972653002301985653004402008653003002052653003102082653002502113653001802138100003302156700001902189700002202208700002002230700002002250700002002270700002002290700002002310700002502330856013002355 2011 eng d a1932-620300aMutation screening of multiple genes in Spanish patients with autosomal recessive retinitis pigmentosa by targeted resequencing.0 aMutation screening of multiple genes in Spanish patients with au c2011 ae278940 v63 aRetinitis Pigmentosa (RP) is a heterogeneous group of inherited retinal dystrophies characterised ultimately by the loss of photoreceptor cells. RP is the leading cause of visual loss in individuals younger than 60 years, with a prevalence of about 1 in 4000. The molecular genetic diagnosis of autosomal recessive RP (arRP) is challenging due to the large genetic and clinical heterogeneity. Traditional methods for sequencing arRP genes are often laborious and not easily available and a screening technique that enables the rapid detection of the genetic cause would be very helpful in the clinical practice. The goal of this study was to develop and apply microarray-based resequencing technology capable of detecting both known and novel mutations on a single high-throughput platform. Hence, the coding regions and exon/intron boundaries of 16 arRP genes were resequenced using microarrays in 102 Spanish patients with clinical diagnosis of arRP. All the detected variations were confirmed by direct sequencing and potential pathogenicity was assessed by functional predictions and frequency in controls. For validation purposes 4 positive controls for variants consisting of previously identified changes were hybridized on the array. As a result of the screening, we detected 44 variants, of which 15 are very likely pathogenic detected in 14 arRP families (14%). Finally, the design of this array can easily be transformed in an equivalent diagnostic system based on targeted enrichment followed by next generation sequencing.
10aAlleles10aDNA Mutational Analysis10aExons10aGenetic Variation10aGenome10aHispanic or Latino10aHumans10aIntrons10aLanguage10amutation10aMutation, Missense10aOligonucleotide Array Sequence Analysis10aPolymerase Chain Reaction10aReproducibility of Results10aRetinitis pigmentosa10aUnited States1 adel Pozo, María, González-1 aBorrego, Salud1 aBarragán, Isabel1 aPieras, Juan, I1 aSantoyo, Javier1 aMatamala, Nerea1 aNaranjo, Belén1 aDopazo, Joaquin1 aAntiňolo, Guillermo uhttp://clinbioinfosspa.es/content/mutation-screening-multiple-genes-spanish-patients-autosomal-recessive-retinitis-pigmentosa03406nas a2200529 4500008004100000022001400041245007000055210006900125260000900194300000800203490000700211520186600218653000902084653002102093653001602114653002002130653002402150653001102174653003002185653001502215653001302230653001102243653001802254653001602272653001302288653002102301653004402322653002102366653003302387100002302420700002502443700001902468700001902487700002002506700002202526700002002548700002602568700002002594700002002614700002202634700002602656700001902682700002002701700002802721700002702749856010002776 2010 eng d a1465-542X00aDNA methylation epigenotypes in breast cancer molecular subtypes.0 aDNA methylation epigenotypes in breast cancer molecular subtypes c2010 aR770 v123 aINTRODUCTION: Identification of gene expression based breast cancer subtypes is considered as a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene expression changes occurring in breast cancer. So far, these epigenetic contributions to sporadic breast cancer subtypes have not been well characterized, and there is only a limited understanding of the epigenetic mechanisms affected in those particular breast cancer subtypes. The present study was undertaken to dissect the breast cancer methylome and deliver specific epigenotypes associated with particular breast cancer subtypes.
METHODS: Using a microarray approach we analyzed DNA methylation in regulatory regions of 806 cancer related genes in 28 breast cancer paired samples. We subsequently performed substantial technical and biological validation by Pyrosequencing, investigating the top qualifying 19 CpG regions in independent cohorts encompassing 47 basal-like, 44 ERBB2+ overexpressing, 48 luminal A and 48 luminal B paired breast cancer/adjacent tissues. Using all-subset selection method, we identified the most subtype predictive methylation profiles in multivariable logistic regression analysis.
RESULTS: The approach efficiently recognized 15 individual CpG loci differentially methylated in breast cancer tumor subtypes. We further identify novel subtype specific epigenotypes which clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors.
CONCLUSIONS: Our results provide evidence that well defined DNA methylation profiles enables breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.
10aAged10aBreast Neoplasms10aCpG Islands10aDNA Methylation10aEpigenesis, Genetic10aFemale10aGene Expression Profiling10aGenes, p5310aGenotype10aHumans10aKi-67 Antigen10aMiddle Aged10amutation10aNeoplasm Grading10aOligonucleotide Array Sequence Analysis10aReceptor, ErbB-210aTumor Suppressor Protein p531 aBediaga, Naiara, G1 aAcha-Sagredo, Amelia1 aGuerra, Isabel1 aViguri, Amparo1 aAlbaina, Carmen1 aDiaz, Irune, Ruiz1 aRezola, Ricardo1 aAlberdi, Maria, Jesus1 aDopazo, Joaquin1 aMontaner, David1 aRenobales, Mertxe1 aFernandez, Agustin, F1 aField, John, K1 aFraga, Mario, F1 aLiloglou, Triantafillos1 ade Pancorbo, Marian, M uhttp://clinbioinfosspa.es/content/dna-methylation-epigenotypes-breast-cancer-molecular-subtypes03015nas a2200541 4500008004100000022001400041245013100055210006900186260001300255300001100268490000700279520146000286653001501746653002301761653002701784653003001811653001301841653001101854653003001865653004401895653001401939653003001953653001301983653002001996100001102016700001902027700001802046700001302064700001802077700001802095700002102113700001402134700001602148700001802164700001202182700001402194700001202208700001202220700001102232700001402243700001802257700001702275700001702292700001202309700001102321700001602332856012502348 2010 eng d a1473-115000aFunctional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes.0 aFunctional analysis of multiple genomic signatures demonstrates c2010 Aug a310-230 v103 aGene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
10aAlgorithms10aDatabases, Genetic10aEndpoint Determination10aGene Expression Profiling10aGenomics10aHumans10aNeural Networks, Computer10aOligonucleotide Array Sequence Analysis10aPhenotype10aPredictive Value of Tests10aProteins10aQuality Control1 aShi, W1 aBessarabova, M1 aDosymbekov, D1 aDezso, Z1 aNikolskaya, T1 aDudoladova, M1 aSerebryiskaya, T1 aBugrim, A1 aGuryanov, A1 aBrennan, R, J1 aShah, R1 aDopazo, J1 aChen, M1 aDeng, Y1 aShi, T1 aJurman, G1 aFurlanello, C1 aThomas, R, S1 aCorton, J, C1 aTong, W1 aShi, L1 aNikolsky, Y uhttp://clinbioinfosspa.es/content/functional-analysis-multiple-genomic-signatures-demonstrates-classification-algorithms02384nas a2200301 4500008004100000022001400041245010100055210006900156260001600225300001100241490000600252520140600258653001601664653002301680653003001703653001301733653001101746653004401757100001801801700002001819700001801839700002001857700001901877700002401896700002001920700001801940856012401958 2010 eng d a1932-620300aFunctional genomics of 5- to 8-cell stage human embryos by blastomere single-cell cDNA analysis.0 aFunctional genomics of 5 to 8cell stage human embryos by blastom c2010 Oct 26 ae136150 v53 aBlastomere fate and embryonic genome activation (EGA) during human embryonic development are unsolved areas of high scientific and clinical interest. Forty-nine blastomeres from 5- to 8-cell human embryos have been investigated following an efficient single-cell cDNA amplification protocol to provide a template for high-density microarray analysis. The previously described markers, characteristic of Inner Cell Mass (ICM) (n = 120), stemness (n = 190) and Trophectoderm (TE) (n = 45), were analyzed, and a housekeeping pattern of 46 genes was established. All the human blastomeres from the 5- to 8-cell stage embryo displayed a common gene expression pattern corresponding to ICM markers (e.g., DDX3, FOXD3, LEFTY1, MYC, NANOG, POU5F1), stemness (e.g., POU5F1, DNMT3B, GABRB3, SOX2, ZFP42, TERT), and TE markers (e.g., GATA6, EOMES, CDX2, LHCGR). The EGA profile was also investigated between the 5-6- and 8-cell stage embryos, and compared to the blastocyst stage. Known genes (n = 92) such as depleted maternal transcripts (e.g., CCNA1, CCNB1, DPPA2) and embryo-specific activation (e.g., POU5F1, CDH1, DPPA4), as well as novel genes, were confirmed. In summary, the global single-cell cDNA amplification microarray analysis of the 5- to 8-cell stage human embryos reveals that blastomere fate is not committed to ICM or TE. Finally, new EGA features in human embryogenesis are presented.
10aBlastomeres10aDNA, Complementary10aGene Expression Profiling10aGenomics10aHumans10aOligonucleotide Array Sequence Analysis1 aGalan, Amparo1 aMontaner, David1 aPóo, Eugenia1 aValbuena, Diana1 aRuiz, Veronica1 aAguilar, Cristóbal1 aDopazo, Joaquin1 aSimon, Carlos uhttp://clinbioinfosspa.es/content/functional-genomics-5-8-cell-stage-human-embryos-blastomere-single-cell-cdna-analysis01781nas a2200277 4500008004100000022001400041245009200055210006900147260001300216300001200229490000700241520089700248653001501145653003001160653001301190653001301203653001801216653004401234653001301278100002401291700002101315700002001336700002001356700001601376856011101392 2010 eng d a1362-496200aSerial Expression Analysis: a web tool for the analysis of serial gene expression data.0 aSerial Expression Analysis a web tool for the analysis of serial c2010 Jul aW239-450 v383 aSerial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.
10aAlgorithms10aGene Expression Profiling10aInternet10aKinetics10aLinear Models10aOligonucleotide Array Sequence Analysis10aSoftware1 aNueda, Maria, José1 aCarbonell, José1 aMedina, Ignacio1 aDopazo, Joaquin1 aConesa, Ana uhttp://clinbioinfosspa.es/content/serial-expression-analysis-web-tool-analysis-serial-gene-expression-data03500nas a2200373 4500008004100000022001400041245014500055210006900200260001300269300001200282490000800294520221900302653001302521653002002534653002102554653003002575653003002605653004202635653002102677653002102698653003302719653001502752653002402767653001302791653004402804653002902848653001402877653001902891100002102910700002502931700002002956700002302976856012702999 2009 eng d a1350-087200aExploring the antimicrobial action of a carbon monoxide-releasing compound through whole-genome transcription profiling of Escherichia coli.0 aExploring the antimicrobial action of a carbon monoxidereleasing c2009 Mar a813-8240 v1553 aWe recently reported that carbon monoxide (CO) has bactericidal activity. To understand its mode of action we analysed the gene expression changes occurring when Escherichia coli, grown aerobically and anaerobically, is treated with the CO-releasing molecule CORM-2 (tricarbonyldichlororuthenium(II) dimer). Microarray analysis shows that the E. coli CORM-2 response is multifaceted, with a high number of differentially regulated genes spread through several functional categories, namely genes involved in inorganic ion transport and metabolism, regulators, and genes implicated in post-translational modification, such as chaperones. CORM-2 has a higher impact in E. coli cells grown anaerobically, as judged by the repression of genes belonging to eight functional classes which are not seen in the response of aerobically CORM-2-treated cells. The biological relevance of the variations caused by CORM-2 was substantiated by studying the CORM-2 sensitivity of selected E. coli mutants. The results show that the deletion of redox-sensing regulators SoxS and OxyR increased the sensitivity to CORM-2 and suggest that while SoxS plays an important role in protection against CORM-2 under both growth conditions, OxyR seems to participate only in the aerobic CORM-2 response. Under anaerobic conditions, we found that the heat-shock proteins IbpA and IbpB contribute to CORM-2 defence since the deletion of these genes increases the sensitivity of the strain. The induction of several met genes and the hypersensitivity to CORM-2 of the DeltametR, DeltametI and DeltametN mutant strains suggest that CO has effects on the methionine metabolism of E. coli. CORM-2 also affects the transcription of several E. coli biofilm-related genes and increases biofilm formation in E. coli. In particular, the absence of tqsA or bhsA increases the resistance of E. coli to CORM-2, and deletion of tsqA leads to a strain that has lost its capacity to form biofilm upon treatment with CORM-2. In spite of the relatively stable nature of the CO molecule, our results show that CO is able to trigger a significant alteration in the transcriptome of E. coli which necessarily has effects in several key metabolic pathways.
10aBiofilms10aCarbon Monoxide10aEscherichia coli10aEscherichia coli Proteins10aGene Expression Profiling10aGene Expression Regulation, Bacterial10aGenes, Bacterial10aGenes, Regulator10aGenetic Complementation Test10aMethionine10aMicrobial Viability10amutation10aOligonucleotide Array Sequence Analysis10aOrganometallic Compounds10aPhenotype10aRNA, Bacterial1 aNobre, Lígia, S1 aAl-Shahrour, Fátima1 aDopazo, Joaquin1 aSaraiva, Lígia, M uhttp://clinbioinfosspa.es/content/exploring-antimicrobial-action-carbon-monoxide-releasing-compound-through-whole-genome-002713nas a2200265 4500008004100000022001400041245005800055210005700113260001600170300000700186490001500193520188200208653002402090653003002114653004402144653001702188100002402205700002502229700002002254700002902274700002002303700002002323700001602343856008802359 2009 eng d a1471-210500aFunctional assessment of time course microarray data.0 aFunctional assessment of time course microarray data c2009 Jun 16 aS90 v10 Suppl 63 aMOTIVATION: 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.
METHODS: 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.
RESULTS: 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.
10aComputer Simulation10aGene Expression Profiling10aOligonucleotide Array Sequence Analysis10aTime Factors1 aNueda, Maria, José1 aSebastián, Patricia1 aTarazona, Sonia1 aGarcia-Garcia, Francisco1 aDopazo, Joaquin1 aFerrer, Alberto1 aConesa, Ana uhttp://clinbioinfosspa.es/content/functional-assessment-time-course-microarray-data02417nas a2200469 4500008004100000022001400041245007800055210006900133260001300202300001300215490000700228520094000235653001001175653002101185653003001206653004101236653002601277653001101303653002101314653002201335653004401357653001901401653001801420653002701438100002001465700003001485700002701515700002501542700002101567700002401588700002801612700002601640700002701666700002401693700002201717700002101739700001501760700002001775700002301795700002101818856010801839 2009 eng d a1029-240300aFunctional signatures identified in B-cell non-Hodgkin lymphoma profiles.0 aFunctional signatures identified in Bcell nonHodgkin lymphoma pr c2009 Oct a1699-7080 v503 aGene-expression profiling in B-cell lymphomas has provided crucial data on specific lymphoma types, which can contribute to the identification of essential lymphoma survival genes and pathways. In this study, the gene-expression profiling data of all major B-cell lymphoma types were analyzed by unsupervised clustering. The transcriptome classification so obtained, was explored using gene set enrichment analysis generating a heatmap for B-cell lymphoma that identifies common lymphoma survival mechanisms and potential therapeutic targets, recognizing sets of coregulated genes and functional pathways expressed in different lymphoma types. Some of the most relevant signatures (stroma, cell cycle, B-cell receptor (BCR)) are shared by multiple lymphoma types or subclasses. A specific attention was paid to the analysis of BCR and coregulated pathways, defining molecular heterogeneity within multiple B-cell lymphoma types.
10aAdult10aCluster Analysis10aGene Expression Profiling10aGene Expression Regulation, Leukemic10aGenetic Heterogeneity10aHumans10aLymphoma, B-Cell10aNeoplasm Proteins10aOligonucleotide Array Sequence Analysis10aRNA, Messenger10aRNA, Neoplasm10aTranscription, Genetic1 aAggarwal, Mohit1 aSánchez-Beato, Margarita1 aGómez-López, Gonzalo1 aAl-Shahrour, Fátima1 aMartínez, Nerea1 aRodríguez, Antonia1 aRuiz-Ballesteros, Elena1 aCamacho, Francisca, I1 aPérez-Rosado, Alberto1 ade la Cueva, Paloma1 aArtiga, María, J1 aPisano, David, G1 aKimby, Eva1 aDopazo, Joaquin1 aVilluendas, Raquel1 aPiris, Miguel, A uhttp://clinbioinfosspa.es/content/functional-signatures-identified-b-cell-non-hodgkin-lymphoma-profiles03382nas a2200325 4500008004100000022001400041245007200055210006900127260001600196300000800212490000700220520234600227653001502573653002102588653004402609653002602653653002802679653001102707653003002718653001302748653001102761653004402772653003002816653003102846100002002877700001902897700002502916700002002941856009502961 2009 eng d a1471-216400aGene set internal coherence in the context of functional profiling.0 aGene set internal coherence in the context of functional profili c2009 Apr 27 a1970 v103 aBACKGROUND: Functional profiling methods have been extensively used in the context of high-throughput experiments and, in particular, in microarray data analysis. Such methods use available biological information to define different types of functional gene modules (e.g. gene ontology -GO-, KEGG pathways, etc.) whose representation in a pre-defined list of genes is further studied. In the most popular type of microarray experimental designs (e.g. up- or down-regulated genes, clusters of co-expressing genes, etc.) or in other genomic experiments (e.g. Chip-on-chip, epigenomics, etc.) these lists are composed by genes with a high degree of co-expression. Therefore, an implicit assumption in the application of functional profiling methods within this context is that the genes corresponding to the modules tested are effectively defining sets of co-expressing genes. Nevertheless not all the functional modules are biologically coherent entities in terms of co-expression, which will eventually hinder its detection with conventional methods of functional enrichment.
RESULTS: Using a large collection of microarray data we have carried out a detailed survey of internal correlation in GO terms and KEGG pathways, providing a coherence index to be used for measuring functional module co-regulation. An unexpected low level of internal correlation was found among the modules studied. Only around 30% of the modules defined by GO terms and 57% of the modules defined by KEGG pathways display an internal correlation higher than the expected by chance.This information on the internal correlation of the genes within the functional modules can be used in the context of a logistic regression model in a simple way to improve their detection in gene expression experiments.
CONCLUSION: For the first time, an exhaustive study on the internal co-expression of the most popular functional categories has been carried out. Interestingly, the real level of coexpression within many of them is lower than expected (or even inexistent), which will preclude its detection by means of most conventional functional profiling methods. If the gene-to-function correlation information is used in functional profiling methods, the results obtained improve the ones obtained by conventional enrichment methods.
10aAlgorithms10aBreast Neoplasms10aCarcinoma, Intraductal, Noninfiltrating10aComputational Biology10aDatabases, Nucleic Acid10aFemale10aGene Expression Profiling10aGenomics10aHumans10aOligonucleotide Array Sequence Analysis10aPapillomavirus Infections10aReproducibility of Results1 aMontaner, David1 aMinguez, Pablo1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/gene-set-internal-coherence-context-functional-profiling01564nas a2200229 4500008004100000022001400041245003200055210003100087260000900118300001100127490000800138520093500146653001201081653002601093653002001119653003001139653001101169653004401180100002001224700002501244856006501269 2008 eng d a1064-374500aExpression and microarrays.0 aExpression and microarrays c2008 a245-550 v4533 aHigh throughput methodologies have increased by several orders of magnitude the amount of experimental microarray data available. Nevertheless, translating these data into useful biological knowledge remains a challenge. There is a risk of perceiving these methodologies as mere factories that produce never-ending quantities of data if a proper biological interpretation is not provided. Methods of interpreting these data are continuously evolving. Typically, a simple two-step approach has been used, in which genes of interest are first selected based on thresholds for the experimental values, and then enrichment in biologically relevant terms in the annotations of these genes is analyzed in a second step. For various reasons, such methods are quite poor in terms of performance and new procedures inspired by systems biology that directly address sets of functionally related genes are currently under development.
10aAnimals10aComputational Biology10agene expression10aGene Expression Profiling10aHumans10aOligonucleotide Array Sequence Analysis1 aDopazo, Joaquin1 aAl-Shahrour, Fátima uhttp://clinbioinfosspa.es/content/expression-and-microarrays02498nas a2200385 4500008004100000022001400041245007700055210006900132260001600201300001200217490000700229520130100236653002201537653003701559653003001596653001301626653001301639653004401652653001301696100002301709700002001732700002101752700002401773700001901797700001601816700002501832700002801857700001801885700001901903700002901922700001601951700002001967700002001987856010502007 2008 eng d a1362-496200aGEPAS, a web-based tool for microarray data analysis and interpretation.0 aGEPAS a webbased tool for microarray data analysis and interpret c2008 Jul 01 aW308-140 v363 aGene 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.
10aComputer Graphics10aDose-Response Relationship, Drug10aGene Expression Profiling10aInternet10aKinetics10aOligonucleotide Array Sequence Analysis10aSoftware1 aTárraga, Joaquín1 aMedina, Ignacio1 aCarbonell, José1 aHuerta-Cepas, Jaime1 aMinguez, Pablo1 aAlloza, Eva1 aAl-Shahrour, Fátima1 aVegas-Azcárate, Susana1 aGoetz, Stefan1 aEscobar, Pablo1 aGarcia-Garcia, Francisco1 aConesa, Ana1 aMontaner, David1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/gepas-web-based-tool-microarray-data-analysis-and-interpretation-003267nas a2200517 4500008004100000022001400041245013100055210006900186260001600255300001200271490000700283520168900290653001201979653001501991653001002006653000902016653002202025653001402047653002502061653002502086653001102111653003002122653004302152653001102195653000902206653001602215653004402231653001402275653005202289653001802341653002402359653002202383100002102405700002502426700001302451700001502464700002902479700001702508700001602525700002002541700001402561700001602575700001502591700001602606856012702622 2008 eng d a1476-559400aMolecular profiling related to poor prognosis in thyroid carcinoma. Combining gene expression data and biological information.0 aMolecular profiling related to poor prognosis in thyroid carcino c2008 Mar 06 a1554-610 v273 aUndifferentiated 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.
10aAdenoma10aAdolescent10aAdult10aAged10aBiomarkers, Tumor10aCarcinoma10aCarcinoma, Papillary10aCell Differentiation10aFemale10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aHumans10aMale10aMiddle Aged10aOligonucleotide Array Sequence Analysis10aPrognosis10aReverse Transcriptase Polymerase Chain Reaction10aRNA, Neoplasm10aSignal Transduction10aThyroid Neoplasms1 aMontero-Conde, C1 aMartín-Campos, J, M1 aLerma, E1 aGimenez, G1 aMartínez-Guitarte, J, L1 aCombalía, N1 aMontaner, D1 aMatías-Guiu, X1 aDopazo, J1 ade Leiva, A1 aRobledo, M1 aMauricio, D uhttp://clinbioinfosspa.es/content/molecular-profiling-related-poor-prognosis-thyroid-carcinoma-combining-gene-expression-002675nas a2200385 4500008004100000022001400041245016600055210006900221260001300290300001000303490000700313520140700320653002201727653001201749653001801761653002601779653003001805653001001835653001301845653001101858653001301869653004401882653002601926653001301952653002401965653002601989100002502015700001902040700002302059700002002082700001602102700002002118700002002138856013102158 2007 eng d a1362-496200aFatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments.0 aFatiGO a functional profiling tool for genomic data Integration c2007 Jul aW91-60 v353 aThe ultimate goal of any genome-scale experiment is to provide a functional interpretation of the data, relating the available information with the hypotheses that originated the experiment. Thus, functional profiling methods have become essential in diverse scenarios such as microarray experiments, proteomics, etc. We present the FatiGO+, a web-based tool for the functional profiling of genome-scale experiments, specially oriented to the interpretation of microarray experiments. In addition to different functional annotations (gene ontology, KEGG pathways, Interpro motifs, Swissprot keywords and text-mining based bioentities related to diseases and chemical compounds) FatiGO+ includes, as a novelty, regulatory and structural information. The regulatory information used includes predictions of targets for distinct regulatory elements (obtained from the Transfac and CisRed databases). Additionally FatiGO+ uses predictions of target motifs of miRNA to infer which of these can be activated or deactivated in the sample of genes studied. Finally, properties of gene products related to their relative location and connections in the interactome have also been used. Also, enrichment of any of these functional terms can be directly analysed on chromosomal coordinates. FatiGO+ can be found at: http://www.fatigoplus.org and within the Babelomics environment http://www.babelomics.org.
10aAmino Acid Motifs10aAnimals10aBinding Sites10aComputational Biology10aGene Expression Profiling10aGenes10aGenomics10aHumans10aInternet10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aTranscription Factors1 aAl-Shahrour, Fátima1 aMinguez, Pablo1 aTárraga, Joaquín1 aMedina, Ignacio1 aAlloza, Eva1 aMontaner, David1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/fatigo-functional-profiling-tool-genomic-data-integration-functional-annotation-regulatory-002054nas a2200385 4500008004100000022001400041245012300055210006900178260001300247300001000260490000700270520079500277653001201072653002101084653002601105653002201131653003001153653001101183653001301194653002001207653003101227653004401258653002601302653001301328653002401341653002801365100001701393700002001410700002701430700002301457700002001480700002501500700002001525856012301545 2007 eng d a1362-496200aISACGH: a web-based environment for the analysis of Array CGH and gene expression which includes functional profiling.0 aISACGH a webbased environment for the analysis of Array CGH and c2007 Jul aW81-50 v353 aWe present the ISACGH, a web-based system that allows for the combination of genomic data with gene expression values and provides different options for functional profiling of the regions found. Several visualization options offer a convenient representation of the results. Different efficient methods for accurate estimation of genomic copy number from array-CGH hybridization data have been included in the program. Moreover, the connection to the gene expression analysis package GEPAS allows the use of different facilities for data pre-processing and analysis. A DAS server allows exporting the results to the Ensembl viewer where contextual genomic information can be obtained. The program is freely available at: http://isacgh.bioinfo.cipf.es or within http://www.gepas.org.
10aAnimals10aCluster Analysis10aComputational Biology10aComputer Graphics10aGene Expression Profiling10aHumans10aInternet10aModels, Genetic10aNucleic Acid Hybridization10aOligonucleotide Array Sequence Analysis10aProgramming Languages10aSoftware10aSystems Integration10aUser-Computer Interface1 aConde, Lucia1 aMontaner, David1 aBurguet-Castell, Jordi1 aTárraga, Joaquín1 aMedina, Ignacio1 aAl-Shahrour, Fátima1 aDopazo, Joaquin uhttp://clinbioinfosspa.es/content/isacgh-web-based-environment-analysis-array-cgh-and-gene-expression-which-includes-0