AVISPA: a web tool for the prediction and analysis of alternative splicing

Transcriptome complexity and its relation to numerous diseases underpins the need to predict in silico splice variants and the regulatory elements that affect them. Building upon our recently described splicing code, we developed AVISPA, a Galaxy-based web tool for splicing prediction and analysis. Given an exon and its proximal sequence, the tool predicts whether the exon is alternatively spliced, displays tissue-dependent splicing patterns, and whether it has associated regulatory elements. We assess AVISPA's accuracy on an independent dataset of tissue-dependent exons, and illustrate how the tool can be applied to analyze a gene of interest. AVISPA is available at http://avispa.biociphers.org

Genes associated with metabolic syndrome predict disease-free survival in stage II colorectal cancer patients. A novel link between metabolic dysregulation and colorectal cancer

Producción CientíficaStudies have recently suggested that metabolic syndrome and its components increase the risk of colorectal cancer. Both diseases are increasing in most countries, and the genetic association between them has not been fully elucidated. The objective of this study was to assess the association between genetic risk factors of metabolic syndrome or related conditions (obesity, hyperlipidaemia, diabetes mellitus type 2) and clinical outcome in stage II colorectal cancer patients. Expression levels of several genes related to metabolic syndrome and associated alterations were analysed by real-time qPCR in two equivalent but independent sets of stage II colorectal cancer patients. Using logistic regression models and cross-validation analysis with all tumour samples, we developed a metabolic syndrome-related gene expression profile to predict clinical outcome in stage II colorectal cancer patients. The results showed that a gene expression profile constituted by genes previously related to metabolic syndrome was significantly associated with clinical outcome of stage II colorectal cancer patients. This metabolic profile was able to identify patients with a low risk and high risk of relapse. Its predictive value was validated using an independent set of stage II colorectal cancer patients. The identification of a set of genes related to metabolic syndrome that predict survival in intermediate-stage colorectal cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy and avoid the toxic and unnecessary chemotherapy in patients classified as low risk. Our results also confirm the linkage between.Ministerio de Ciencia, Innovación y Universidades (AGL2010-21565, RyC 2008-03734, IPT-2011-1248-060000),y la Comunidad de Madrid (ALIBIRD, S2009/AGR-1469

The ellagic acid derivative 4,4′-Di-O-methylellagic acid efficiently inhibits colon cancer cell growth through a mechanism involving WNT16

Producción CientíficaEllagic acid (EA) and some derivatives have been reported to inhibit cancer cell proliferation, induce cell cycle arrest, and modulate some important cellular processes related to cancer. This study aimed to identify possible structure-activity relationships of EA and some in vivo derivatives in their antiproliferative effect on both human colon cancer and normal cells, and to compare this activity with that of other polyphenols. Our results showed that 4,4′-di-O-methylellagic acid (4,4′-DiOMEA) was the most effective compound in the inhibition of colon cancer cell proliferation. 4,4′-DiOMEA was 13-fold more effective than other compounds of the same family. In addition, 4,4′-DiOMEA was very active against colon cancer cells resistant to the chemotherapeutic agent 5-fluoracil, whereas no effect was observed in nonmalignant colon cells. Moreover, no correlation between antiproliferative and antioxidant activities was found, further supporting that structure differences might result in dissimilar molecular targets involved in their differential effects. Finally, microarray analysis revealed that 4,4′-DiOMEA modulated Wnt signaling, which might be involved in the potential antitumor action of this compound. Our results suggest that structural-activity differences between EA and 4,4′-DiOMEA might constitute the basis for a new strategy in anticancer drug discovery based on these chemical modifications.Ministerio de Economía, Industria y Competitividad (AGL2013-48943-C2-2-R and IPT-2011-1248-060000)Comunidad de Madrid [Grant P2013/ABI-2728 ALIBIRD-CM

ColoLipidGene: Signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolismrelated genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk groupThis work was supported by Ministerio de Ciencia e Innovación del Gobierno de España (Plan Nacional I + D + i AGL2013–48943-C2–2-R and IPT-2011–1248-060000), Comunidad de Madrid (P2013/ABI-2728. ALIBIRDCM) and European Union Structural Funds. CIBEREHD is funded by the Instituto de Salud Carlos III. This is a collaborative study between the Molecular Oncology Unit of The Institute of Advanced Studies of Madrid IMDEA Food and the Grupo Español Multidisciplinar en Cáncer Digestivo (GEMCAD

Use of ChIP-Seq data for the design of a multiple promoter-alignment method

We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments

Expression of Interest: The Canfranc Axion Detection Experiment (CADEx)

Trabajo presentado al 29th Meeting of the LSC Scientific Committee, celebrado online del 30 de noviembre al 1 de diciembre de 2021.Peer reviewe

Software development and analysis of high throughput sequencing data for genomic enhancer prediction

High Throughput Sequencing technologies (HTS) are becoming the standard in genomic regulation analysis. During my thesis I developed software for the analysis of HTS data. Through collaborations with other research groups, I specialized in the analysis of ChIP-Seq short mapped reads. For instance, I collaborated in the analysis of the effect of Hog1 stress induced response in Yeast and helped in the design of a multiple promoter-alignment method using ChIP-Seq data, among other collaborations. Making use of expertise and the software developed during this time, I analyzed ENCODE datasets in order to detect active genomic enhancers. Genomic enhancers are regions in the genome known to regulate transcription levels of close by or distant genes. Mechanism of activation and silencing of enhancers is still poorly understood. Epigenomic elements, like histone modifications and transcription factors play a critical role in enhancer activity. Modeling epigenomic signals, I predicted active and silenced enhancers in two cell lines and studied their effect in splicing and transcription initiation.Las tecnologías High Throughput Sequencing (HTS) se están convirtiendo en el método standard de análisis de la regulación genómica. Durante mi tesis, he desarrollado software para el análisis de datos HTS. Mediante la colaboración con otros grupos de investigaci n, me he especializado ́ en el análisis de datos de ChIP-Seq. Por ejemplo, colaborado en el análisis del efecto de Hog1 en células de levadura afectadas por stress, colaboré en el diseño de un m ́ todo para el alineamiento m ́ ltiple de promotores usando datos de ChIP-Seq, entre otras colaboraciones. Usando el conocimiento y el software desarrollados durante este tiempo, analicé datos producidos por el proyecto ENCODE para detectar enhancers genómicos activos. Los enhancers son areas del genoma conocidas por regular la transcripción de genes cercanos y lejanos. Los mecanismos de activación y silenciamiento de enhancers son aún poco entendidos. Elementos epigenómicos, como las modificaciones de histonas y los factores de transcripción juegan un papel crucial en la actividad de enhancers. Construyendo un modelo con estas señales epigen ́ micas, predije enhancers activos y silenciados en dos lineas celulares y estudié su efecto sobre splicing y sobre la iniciacion de la transcripción

A semi-supervised approach uncovers thousands of intragenic enhancers differentially activated in human cells

Background. Transcriptional enhancers are generally known to regulate gene transcription from afar. Their activation involves a series of changes in chromatin marks and recruitment of protein factors. These enhancers may also occur inside genes, but how many may be active in human cells and their effects on the regulation of the host gene remains unclear./nResults. We describe a novel semi-supervised method based on the relative enrichment of chromatin signals between 2 conditions to predict active enhancers. We applied this method to the tumoral K562 and the normal GM12878 cell lines to predict enhancers that are differentially active in one cell type. These predictions show enhancer-like properties according to positional distribution, correlation with gene expression and production of enhancer RNAs. Using this model, we predict 10,365 and 9777 intragenic active enhancers in K562 and GM12878, respectively, and relate the differential activation of these enhancers to expression and splicing differences of the host genes./nConclusions. We propose that the activation or silencing of intragenic transcriptional enhancers modulate the regulation of the host gene by means of a local change of the chromatin and the recruitment of enhancer-related factors that may interact with the RNA directly or through the interaction with RNA binding proteins. Predicted enhancers are available at http://regulatorygenomics.upf.edu/Projects/enhancers.html.The authors would like to thank E. Furlong, Y. Barash, B. Blencowe and U. Braunschweig for useful discussions. This work was supported by grants from Plan Nacional I + D (BIO2011-23920) and Consolider (CSD2009-00080) from MINECO (Spanish Government), and by the Sandra Ibarra Foundation for Cancer (FSI 2013). JGV and BS were supported FPI grants from the MINECO (Spanish Government) BES-2009-018064 and BES-2012-052683, respectively

Clinical relevance of the differential expression of the glycosyltransferase gene GCNT3 in colon cancer

et al.Altered glycosylation is considered a universal cancer hallmark. Mucin-type core 2 1,6-N-acetylglucosaminyltransferase enzyme (C2GnT-M), encoded by the GCNT3 gene, has been reported to be altered in tumours and to possess tumour suppressor properties. In this work, we aimed to determine the possible role of GCNT3 gene expression as prognostic marker in colon cancer. We investigated the differential expression of GCNT3 gene among tumour samples from stage II colon cancer patients by quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Univariate and Multivariate Cox regression analyses were used to determine the correlation between GCNT3 expression and disease-free survival. The risk of relapse in GCNT3 low-expressing cancer patients was significantly higher than that in GCNT3 high-expressing patients in both training (Hazard Ratio (HR) 4.26, p = 0.002) and validation (HR 3.06, p = 0.024) series of patients, and this association was independent of clinical factors. Additionally, qRT-PCR was used to explore the modulation of GCNT3 expression by different antitumour drugs. Three chemotherapeutic agents with different mechanism of action (5-fluorouracil, bortezomib and paclitaxel) significantly induced GCNT3 expression in several cancer cells, being observed the correlation between antitumour action and GCNT3 modulation, whereas this gene was not modulated in cells that do not respond to treatment. Overall, these results indicate that low GCNT3 expression is a promising prognostic biomarker for colon cancer that could be used to identify early-stage colon cancer patients at high risk of relapse. Additionally, our results suggest that this enzyme might also constitute a biomarker to monitor tumour response to chemotherapy in cancer patients.This work has been supported by the Spanish Ministry of Science and Innovation (Plan Nacional I+D+i AGL2010-21565, AGL2013-48943-C2-2-R; RyC 2008-03734, IPT-2011-1248-060000); Comunidad de Madrid (P2013/ABI-2728. ALIBIRD-CM); and European Union Structural Funds.Peer Reviewe