74 results on '"Barturen G"'
Search Results
2. POS0327 INTERFERON AND B LYMPHOCYTE DYSREGULATION PATTERNS DETERMINE DISTINCT LUPUS NEPHRITIS SUBGROUPS WITH DIFFERENTIAL ANTICIPATED RESPONSE TO TARGETED THERAPIES
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Parodis, I., primary, Lindblom, J., additional, Toro-Domínguez, D., additional, Beretta, L., additional, Borghi, M. O., additional, Castillo, J., additional, Carnero-Montoro, E., additional, Enman, Y., additional, Mohan, C., additional, Alarcon-Riquelme, M., additional, Barturen, G., additional, and Nikolopoulos, D., additional more...
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- 2024
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Catalog
3. OP0208 LYMPHOCYTE, NK CELL AND MITOCHONDRIAL GENE DYSREGULATION PATTERNS SEPARATE PATIENTS WITH NEUROPSYCHIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS INTO DISTINCT SUBGROUPS WITH DIFFERENTIAL ANTICIPATED RESPONSE TO TARGETED THERAPIES
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Lindblom, J., primary, Nikolopoulos, D., additional, Toro-Domínguez, D., additional, Carnero-Montoro, E., additional, Borghi, M. O., additional, Castillo, J., additional, Iacobaeus, E., additional, Enman, Y., additional, Mohan, C., additional, Beretta, L., additional, Alarcon-Riquelme, M., additional, Barturen, G., additional, and Parodis, I., additional more...
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- 2024
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4. POS0056 UNVEILING VASCULAR, PRO-FIBROTIC AND INTERFERON-RELATED ABNORMALITIES THROUGH AN EPIGENOME-WIDE ANALYSIS IN SYSTEMIC SCLEROSIS
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Martinez-Lopez, J., primary, Estupiñán-Moreno, E., additional, Ortiz-Fernández, L., additional, Kerick, M., additional, Andrés-León, E., additional, Terrón-Camero, L. C., additional, Carnero-Montoro, E., additional, Barturen, G., additional, Beretta, L., additional, Clinical Consortium, P., additional, Alarcon-Riquelme, M., additional, Acosta-Herrera, M., additional, Ballestar, E., additional, and Martin, J., additional more...
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- 2024
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5. Genetic evaluation of molecular traits in systemic lupus erythematosus
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Castellini-Pérez, Olivia, Iakovliev, Andrii, Martínez-Bueno, M, Barturen, G, Alarcón-Riquelme, ME, Carnero-Montoro, Elena, and Spiliopoulou, Athina
- Abstract
Purpose. Systemic Lupus Erythemathosus (SLE) is a prototypic systemic autoimmune disease characterized by a complex aetiology. Epigenetic alterations are known to be mediators of the environmental and genetic factors and to impact transcriptional programs. Here we aim to investigate genetic correlations between SLE and different molecular traits such as DNA methylation, gene expression and protein level by computing genotypic risk scores for the intermediate traits. Methods. We use genotypes for 13,482 European ancestry individuals obtained from pre-existing projects studying SLE genetics, i) 4,174 SLE patients from a collection of SLE cohorts and 4,048 healthy controls from the University of Michigan Health and Retirement Study, ii) 696 SLE patients and 304 healthy controls from the International Consortium for Systemic Lupus Erythematosus Genetics and iii) 397 SLE patients and 561 healthy controls from the PRECISESADS Consortium. We computed genotypic risk scores for biomarkers using the GENOSCORES platform and tested the association between scores and the SLE phenotype using a logistic regression model for each score separately and adjusting for sex and 20 genetic principal components. Results. We computed 1,716 locus-specific genotypic scores for loci affecting human plasma proteins (pQTLs). We detected 7 protein scores significantly associated with the SLE phenotype at Bonferroni correction. One of the 7 proteins, FCGR2B, is already known in SLE pathogenesis. Additionally, 4 protein scores were located within the HLA region in chromosome 6 (AMBN, ATF6, EDA, FIBCD1) and the remaining 2 (AXIN2, TREML4) scores were located in chromosome 14. Furthermore, we computed scores for the gene expression of these 7 proteins in different tissues and showed that scores for the gene expression of the AXIN2 gene were significantly associated with the SLE phenotype. Conclusions and Ongoing Analyses. This study expands the list of candidate proteins associated with SLE and regions that might contain novel genes implicated in the SLE phenotype. Our findings demonstrate how genotypic scores for molecular traits can be used to identify and characterize genetic associations with complex disease traits. We aim to further explore the detected associations by considering DNA methylation traits and their association with SLE. more...
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- 2022
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6. PO.1.5 Scoring personalized molecular portraits identify systemic lupus erythematosus subtypesand predict transcriptional drug responses, symptomatology and disease progression
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Toro Dominguez, D, primary, Martínez Bueno, M, additional, López Domínguez, R, additional, Martorell Marugan, J, additional, Carnero Montoro, E, additional, Barturen, G, additional, Goldman, D, additional, Petri, M, additional, Carmona Saez, P, additional, and Alarcón-Riquelme, M, additional more...
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- 2022
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7. S10.1 Genetic evaluation of molecular traits in systemic lupus erythematosus
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Castellini-Pérez, O, primary, Iakovliev, A, additional, Martínez-Bueno, M, additional, Barturen, G, additional, Alarcón-Riquelme, ME, additional, Carnero-Montoro, E, additional, and Spiliopoulou, A, additional more...
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- 2022
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8. PO.1.12 Transcriptome profiling and autoimmunity-related serological markers identify TP53 and C3aR as drug targets in neuropsychiatric systemic lupus erythematosus
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Lindblom, J, primary, Toro-Domínguez, D, additional, Carnero-Montoro, E, additional, Borghi, MO, additional, Castillo, J, additional, Iacobaeus, E, additional, Enman, Y, additional, Repsilber, D, additional, Mohan, C, additional, Alarcón-Riquelme, M, additional, Barturen, G, additional, and Parodis, I, additional more...
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- 2022
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9. PO.5.99 Drug repurposing for treating lupus nephritis based on transcriptome profiling and autoimmunity-related serological markers
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Parodis, I, primary, Lindblom, J, additional, Toro-Domínguez, D, additional, Carnero-Montoro, E, additional, Borghi, MO, additional, Castillo, J, additional, Enman, Y, additional, Repsilber, D, additional, Mohan, C, additional, Alarcón-Riquelme, M, additional, and Barturen, G, additional more...
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- 2022
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10. PO.1.4 The eIF4 translational inhibitor pateamine a improves immunological and neurological functions in BXSB.Yaa lupus mice
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Gómez-Hernández, G, primary, Varela, N, additional, Bagavant, H, additional, Barturen, G, additional, Alracón-Riquelme, M, additional, and Morell, M, additional
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- 2022
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11. PO.4.96 The epigenome of systemic lupus erythematosus: molecular subtypes, autoantibody profiles, and genetic influences
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Carnero-Montoro, E, primary, Castellini-Pérez, O, additional, Barturen, G, additional, Martínez-Bueno, G, additional, Kerick, M, additional, Iakovliev, A, additional, López-Dominguez, R, additional, Martin, J, additional, Spiliopoulou, A, additional, Derinaldis, E, additional, and Alarcón-Riquelme, ME, additional more...
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- 2022
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12. PO.8.165 Whole-blood dna methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection
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Barturen, G, primary, Carnero-Montoro, E, additional, Martínez-Bueno, M, additional, Rojo-Rello, S, additional, Sobrino, B, additional, Alcántara-Domínguez, C, additional, Bernardo, D, additional, and Alarcón-Riquelme, ME, additional more...
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- 2022
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13. DRUG REPURPOSING FOR TREATING LUPUS NEPHRITIS BASED ON TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS
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Parodis, Ioannis, Lindblom, J., Toro-Dominguez, D., Borghi, M. O., Enman, Y., Repsilber, Dirk, Mohan, C., Alarcon-Riquelme, M., Barturen, G., Parodis, Ioannis, Lindblom, J., Toro-Dominguez, D., Borghi, M. O., Enman, Y., Repsilber, Dirk, Mohan, C., Alarcon-Riquelme, M., and Barturen, G. more...
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- 2022
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14. TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS IDENTIFY TP53 AND C3AR AS DRUG TARGETS IN NEUROPSYCHIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS
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Lindblom, J., Toro-Dominguez, D., Borghi, M. O., Iacobaeus, E., Enman, Y., Repsilber, Dirk, Mohan, C., Alarcon-Riquelme, M., Barturen, G., Parodis, Ioannis, Lindblom, J., Toro-Dominguez, D., Borghi, M. O., Iacobaeus, E., Enman, Y., Repsilber, Dirk, Mohan, C., Alarcon-Riquelme, M., Barturen, G., and Parodis, Ioannis more...
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- 2022
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15. POS0187 DRUG REPURPOSING FOR TREATING LUPUS NEPHRITIS BASED ON TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS
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Parodis, I., primary, Lindblom, J., additional, Toro-Domínguez, D., additional, Borghi, M. O., additional, Enman, Y., additional, Repsilber, D., additional, Mohan, C., additional, Alarcon-Riquelme, M., additional, and Barturen, G., additional more...
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- 2022
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16. POS0188 TRANSCRIPTOME PROFILING AND AUTOIMMUNITY-RELATED SEROLOGICAL MARKERS IDENTIFY TP53 and C3aR AS DRUG TARGETS IN NEUROPSYCHIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS
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Lindblom, J., primary, Toro-Domínguez, D., additional, Borghi, M. O., additional, Iacobaeus, E., additional, Enman, Y., additional, Repsilber, D., additional, Mohan, C., additional, Alarcon-Riquelme, M., additional, Barturen, G., additional, and Parodis, I., additional more...
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- 2022
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17. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.
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UK Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Cancer Research UK Programme, Wellcome Trust and the Royal Society, Dutch Heart Foundation, Dutch Federation of University Medical Centres, Netherlands Organisation for Health Research and Development, Royal Netherlands Academy of Sciences, Economic and Social Research Council (grant no. ES/N000404/1), Austrian Academy of Sciences, Min, Josine L., Hemani, Gibran, Hannon, Eilis, Dekkers, Koen F., Castillo-Fernandez, Juan, Luijk, René, Carnero-Montoro, Elena, Lawson, Daniel J., Burrows, Kimberley, Suderman, Matthew, Bretherick, Andrew D., Richardson, Tom G., Klughammer, Johanna, Iotchkova, Valentina, Sharp, Gemma, Al Khleifat, Ahmad, Shatunov, Aleksey, Iacoangeli, Aldredo, McArdle, Wendy L., Ho, Karen M., Kumar, Ashish, Söderhäll, C., Soriano-Tárraga, Carolina, Giralt-Steinhauer, Eva, Kazmi, Nabila, Mason, Dan, McRae, Allan F., Corcoran, David L., Sugden, Karen, Kasela, Silva, Cardona, Alexia, Day, Felix R., Cugliari, Giovanni, Viberti, Clara, Guarrera, Simonetta, Lerro, Michael, Gupta, Richa, Bollepalli, Sailalita, Mandaviya, Pooja, Zeng, Yanni, Clarke, Toni-Kim, Walker, Rosie M., Schmoll, V., Czamara, D., Ruiz-Arenas, Carlos, Rezwan, F. I., Marioni, R. E., Lin, T., Awaloff, Y., Barturen, G., Català-Moll, Frances, Kerick, Martin, Jiménez-Conde, Jordi, Roquer, Jaume, Gonzalez, Juan Ramón, Bustamante, Mariona, Sunyer, Jordi, Alarcón-Riquelme, Marta, UK Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Cancer Research UK Programme, Wellcome Trust and the Royal Society, Dutch Heart Foundation, Dutch Federation of University Medical Centres, Netherlands Organisation for Health Research and Development, Royal Netherlands Academy of Sciences, Economic and Social Research Council (grant no. ES/N000404/1), Austrian Academy of Sciences, Min, Josine L., Hemani, Gibran, Hannon, Eilis, Dekkers, Koen F., Castillo-Fernandez, Juan, Luijk, René, Carnero-Montoro, Elena, Lawson, Daniel J., Burrows, Kimberley, Suderman, Matthew, Bretherick, Andrew D., Richardson, Tom G., Klughammer, Johanna, Iotchkova, Valentina, Sharp, Gemma, Al Khleifat, Ahmad, Shatunov, Aleksey, Iacoangeli, Aldredo, McArdle, Wendy L., Ho, Karen M., Kumar, Ashish, Söderhäll, C., Soriano-Tárraga, Carolina, Giralt-Steinhauer, Eva, Kazmi, Nabila, Mason, Dan, McRae, Allan F., Corcoran, David L., Sugden, Karen, Kasela, Silva, Cardona, Alexia, Day, Felix R., Cugliari, Giovanni, Viberti, Clara, Guarrera, Simonetta, Lerro, Michael, Gupta, Richa, Bollepalli, Sailalita, Mandaviya, Pooja, Zeng, Yanni, Clarke, Toni-Kim, Walker, Rosie M., Schmoll, V., Czamara, D., Ruiz-Arenas, Carlos, Rezwan, F. I., Marioni, R. E., Lin, T., Awaloff, Y., Barturen, G., Català-Moll, Frances, Kerick, Martin, Jiménez-Conde, Jordi, Roquer, Jaume, Gonzalez, Juan Ramón, Bustamante, Mariona, Sunyer, Jordi, and Alarcón-Riquelme, Marta more...
- Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15–17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype–phenotype map than previously anticipated. more...
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- 2021
18. THU0007 INDIVIDUALIZED PATHWAY ANALYSIS FROM WHOLE BLOOD TRANSCRIPTOMIC IN SSC PATIENTS DEMONSTRATES UNIQUE CORRELATIONS WITH DISEASE SEVERITY
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Bellocchi, C., primary, Rossato, M., additional, Barturen, G., additional, Segatto, G., additional, Vigone, B., additional, Makowska, Z., additional, Buttgereit, A., additional, Kerick, M., additional, Alarcon-Riquelme, M., additional, Martin Ibanez, J., additional, and Beretta, L., additional more...
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- 2020
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19. OP0137 GENOME-WIDE WHOLE-BLOOD TRANSCRIPTOME PROFILING IN A LARGE EUROPEAN COHORT OF SYSTEMIC SCLEROSIS PATIENTS
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Beretta, L., primary, Barturen, G., additional, Vigone, B., additional, Bellocchi, C., additional, Hunzelmann, N., additional, Delanghe, E., additional, Kovács, L., additional, Cervera, R., additional, Gerosa, M., additional, Ortega Castro, R., additional, Almeida, I., additional, Cornec, D., additional, Chizzolini, C., additional, Pers, J. O., additional, Makowska, Z., additional, Buttgereit, A., additional, Lesche, R., additional, Kerick, M., additional, Alarcon-Riquelme, M., additional, and Martin Ibanez, J., additional more...
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- 2020
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20. Molecular stratification of autoimmune diseases based on epigenetic profiles
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Barturen, G, Kerick, M, Alvarez-Errico, D, Quintares, R, Carnero, E, Gemperline, D, Dow, E, Beretta, Lorenzo, Pers, J.O., Renaudineau, Yves, Frostegard, J, Juárez, Manuela, Consortium, Clinical, Flow Cytometry, Group, Rao, S, Chamberlain, C, Wojcik, J., Segura, A, Martin, J, Ballestar, Esteban, Alarcon-Riquelme, Marta E., Centre for Genomics and Oncological Reearch (GENYO), Instituto de Parasitología y Biomedicina 'López-Neyra' (IPBLN), Consejo Superior de Investigaciones Científicas (CSIC), Institut d'Investigació Biomèdica de Bellvitge [Barcelone] (IDIBELL), Departmento de Quimica-Analitica, Granada, Eli Lilly, Indianapolis, USA, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Lymphocyte B et Auto-immunité (LBAI), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Laboratoire d'Immunologie et Immunothérapie [Brest], Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Unit of Chronic Diseases, Solna, Sweden, UCB Pharma Slough, Sanofi Genzyme, Quartzbio, Geneva, Departamento de Quimica-Analitica, Granada, Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), and Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM) more...
- Subjects
[SDV]Life Sciences [q-bio] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2018
21. S1A:5 Molecular stratification of autoimmune diseases based on epigenetic profiles
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Barturen, G, primary, Kerick, M, additional, Alvarez-Errico, D, additional, Quintares, R, additional, Carnero, E, additional, Gemperline, D, additional, Dow, E, additional, Beretta, L, additional, Pers, JO, additional, Renadineau, Y, additional, Frostegard, J, additional, Juarez, M, additional, Consortium, Clinical, additional, Group, Flow Cytometry, additional, Rao, S, additional, Chamberlain, C, additional, Wojcik, J, additional, Segura, A, additional, Martin, J, additional, Ballestar, E, additional, and Alarcón-Riquelme, ME, additional more...
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- 2018
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22. High-level organization of isochores into gigantic superstructures in the human genome
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Carpena, P., primary, Oliver, J. L., additional, Hackenberg, M., additional, Coronado, A. V., additional, Barturen, G., additional, and Bernaola-Galván, P., additional
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- 2011
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23. NGSmethDB: a database for next-generation sequencing single-cytosine-resolution DNA methylation data
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Hackenberg, M., primary, Barturen, G., additional, and Oliver, J. L., additional
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- 2010
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24. WordCluster: detecting clusters of DNA words and genomic elements
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Oliver José L, Alganza Ángel M, Bernaola-Galván Pedro, Barturen Guillermo, Carpena Pedro, and Hackenberg Michael
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Biology (General) ,QH301-705.5 ,Genetics ,QH426-470 - Abstract
Abstract Background Many k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds. Results We introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome. Conclusions WordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes. more...
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- 2011
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25. Prediction of CpG-island function: CpG clustering vs. sliding-window methods
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Luque-Escamilla Pedro L, Carpena Pedro, Barturen Guillermo, Hackenberg Michael, Previti Christopher, and Oliver José L
- Subjects
Biotechnology ,TP248.13-248.65 ,Genetics ,QH426-470 - Abstract
Abstract Background Unmethylated stretches of CpG dinucleotides (CpG islands) are an outstanding property of mammal genomes. Conventionally, these regions are detected by sliding window approaches using %G + C, CpG observed/expected ratio and length thresholds as main parameters. Recently, clustering methods directly detect clusters of CpG dinucleotides as a statistical property of the genome sequence. Results We compare sliding-window to clustering (i.e. CpGcluster) predictions by applying new ways to detect putative functionality of CpG islands. Analyzing the co-localization with several genomic regions as a function of window size vs. statistical significance (p-value), CpGcluster shows a higher overlap with promoter regions and highly conserved elements, at the same time showing less overlap with Alu retrotransposons. The major difference in the prediction was found for short islands (CpG islets), often exclusively predicted by CpGcluster. Many of these islets seem to be functional, as they are unmethylated, highly conserved and/or located within the promoter region. Finally, we show that window-based islands can spuriously overlap several, differentially regulated promoters as well as different methylation domains, which might indicate a wrong merge of several CpG islands into a single, very long island. The shorter CpGcluster islands seem to be much more specific when concerning the overlap with alternative transcription start sites or the detection of homogenous methylation domains. Conclusions The main difference between sliding-window approaches and clustering methods is the length of the predicted islands. Short islands, often differentially methylated, are almost exclusively predicted by CpGcluster. This suggests that CpGcluster may be the algorithm of choice to explore the function of these short, but putatively functional CpG islands. more...
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- 2010
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26. Une intégration multiomique révèle des altérations métaboliques dans les lymphocytes B en relation avec leur environnement dans la maladie de Sjögren.
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Iperi, C., Fernández-Ochoa, Á., Pers, J.O., Barturen, G., Alarcon-Riquelme, M.E., Precisesads, C.C., Quirantes-Piné, R., Borrás-Linares, I., Segura-Carretero, A., Cornec, D., Bordon, A., and Jamin, C. more...
- Abstract
Le syndrome de Sjögren (SjS) est une maladie auto-immune caractérisée par une sécheresse oculaire et buccale et des symptômes systémiques tels que la fatigue et des lésions spécifiques à certains organes. Dans cette étude, les données multiomiques (transcriptome, métabolome et méthylome) du sang total de patients SjS ont été analysées individuellement et intégrées en relation avec les données transcriptomiques des cellules B afin de déterminer les voies métaboliques altérées. Les analyses ont été réalisées à partir des données multi-omiques du consortium européen PRECISESADS à l'aide de BiomiX, un programme accessible pour l'intégration de données multi-omiques. Les transcriptomes des cellules B et des cellules du sang total ont été analysés à l'aide de DESeq et de GSEA. Les changements de pics métabolomiques dans le plasma et l'urine ont été quantifiés et annotés à l'aide de la base de données Ceu Mass Mediator. Les données méthylomiques ont été analysées à l'aide de ChAMP. Les sources communes de variation ont été identifiées à l'aide de l'analyse d'intégration MOFA. Des altérations au niveau de la voie de l'urée, en particulier des acides aminés glutamine et arginine ont été identifiées. Parmi les facteurs MOFA capables de distinguer les patients SjS des témoins, les facteurs associés à l'IFN ont montré une contribution clé des gènes WNT et de la déplétion en NAD+, ce qui suggère un contrôle de l'IFN sur ces altérations métaboliques. Les Résultats croisés de la métabolomique et de la transcriptomique le confirment. Enfin, a été observée une augmentation des acides lysophosphatidiques et de la sphingosine 1 phosphate dans le plasma des patients et leur association avec les facteurs MOFA sur la réponse à l'IFN et l'activation cellulaire. L'expression accrue du récepteur LPAR6 dans les lymphocytes B suggère également une action spécifique directe des acides lysophosphatidiques dans les lymphocytes B SjS. L'augmentation des acides lysophosphatidiques et l'expression spécifique du récepteur LPAR6 dans les lymphocytes B observées également dans le lupus érythémateux systémique, renforce l'idée que ces éléments peuvent jouer un rôle clé dans ces deux maladies autoimmunes. En outre, les Résultats suggèrent une association entre la voie WNT et la gravité de la maladie, ainsi qu'une élimination accrue des composés azotés par la voie métabolique de l'urée. Ces Résultats représentent une vision holistique des changements métaboliques chez les patients SjS, étayée par le chevauchement des données omiques. Ce nouveau type d'approche offre une compréhension plus globale de la pathologie en vue de traitements plus appropriés dans le cadre du développement de la médecine de précision. La recherche qui a conduit à ces Résultats a reçu le soutien du Innovative Medicines Initiative Joint de l'Union Européenne (FP7/2007-2013) et des entreprises de l'EFPIA (#115565). [ABSTRACT FROM AUTHOR] more...
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- 2024
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27. La stratification transcriptomique prédit la réponse au rituximab, à l'abatacept ou à l'association d'hydroxychloroquine-léflunomide dans 3 essais cliniques randomisés chez les patients atteints de la maladie de Sjögren
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Chevet, B., Devauchelle Pensec, V., Pontarini, E., Baloche, V., Bombardieri, M., Bowman, S., Barnes, M., Sreih, A., Liu, J., Kelly, S., Christodolou, A., Badani, H., Moingeon, P., Laigle, L., Soret, P., Le Dantec, C., Pers, J.O., Alarcon-Riquelme, M.E., Barturen, G., and Van Roon, J. more...
- Abstract
La maladie de Sjögren (SjD) est une maladie hétérogène sur le plan clinique et biologique. À ce jour, aucun essai de phase III n'a montré d'efficacité dans la réduction des symptômes ou de l'activité systémique de la maladie, et aucun médicament immunomodulateur n'a été commercialisé. Nous avons précédemment démontré, à partir des données de séquençage d'ARN du sang total du projet PRECISESADS, que les patients atteints de SjD peuvent être regroupés en 4 groupes distincts (endotypes) [1]. Le cluster 1 était caractérisé par une signature de l'interféron (IFN) uniquement, le cluster 2 comprenait des patients similaires à des individus sains, le cluster 3 était marqué par des signatures IFN et des lymphocytes B, et le cluster 4 était caractérisé par des signatures d'IFN, de lymphocytes B et de PNN. Dans cette étude, nous avons émis l'hypothèse que les patients des différents clusters ont des réponses différentes aux différents traitements, en raison de cibles thérapeutiques distinctes dans chacun des clusters. Les données cliniques, biologiques et transcriptomiques ont été obtenues à partir de trois essais contrôlés randomisés évaluant l'hydroxychloroquine et le leflunomide [2] (HCQ-LEF, incluant 13 patients sous HCQ-LEF et 5 sous placebo), le rituximab [3] (RTX, avec 31 patients sous RTX et 25 sous placebo inclus) ou l'abatacept [4] (ABA, incluant 58 patients sous ABA et 59 sous placebo) par rapport au placebo. Le séquençage ARN du sang total a été réalisé sur des échantillons collectés à l'inclusion dans les essais cliniques. Les patients ont été regroupés en 4 endotypes en utilisant un algorithme de réduction dimensionnelle semi-supervisé entraîné sur la base de nos travaux antérieurs. Nous avons comparé les caractéristiques démographiques et de la maladie entre les 4 clusters. Nous avons ensuite évalué la réponse aux traitements, définie par l'indice de réponse STAR [5] , dans les bras de traitement actif et placebo de chaque cluster. Quatre-vingt-un sujets ont été regroupés dans le cluster 1, 24 dans le cluster 2, 80 dans le cluster 3 et 6 patients dans le cluster 4. Les scores ESSPRI ne différaient pas entre les 4 clusters (de 5,71 à 7,22 ; p = 0,48). Cependant, l'ESSDAI était plus faible dans les clusters 1 et 2 (6,36 et 6,62, respectivement) et significativement plus élevé dans les clusters 3 et 4 (8,81 et 10,67 ; p = 0,003). Lorsque les 102 patients traités ont été regroupés et comparés aux 89 patients sous placebo, les patients traités dans le cluster 1 étaient plus susceptibles d'obtenir un score de réponse STAR que les patients témoins (60,5 % contre 23,7 %, p = 0,002) (Tableau 1). Une tendance similaire a été observée dans l'essai RTX. Dans l'essai HCQ-LEF, les patients du cluster 3 ayant reçu le traitement étaient plus susceptibles d'atteindre le score STAR. Que les études soient analysées ensemble ou séparément, aucun traitement par rapport au placebo n'a montré d'efficacité significative dans le cluster 2 (patients au transcriptome similaire à des individus sains). L'analyse des essais contrôlés avec des clusters basés sur la transcriptomique chez les patients atteints de SjD met en évidence l'absence de réponse à l'HCQ-LEF, au RTX et à l'ABA chez les patients similaires à des individus sains. Nous suggérons que ces patients, moins susceptibles de répondre selon le score STAR, ne devraient peut-être pas être inclus dans de futurs essais contrôlés. [ABSTRACT FROM AUTHOR] more...
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- 2024
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28. L'intégration d'une analyse multi-omique révèle des altérations métaboliques des lymphocytes B au cours du lupus érythémateux systémique.
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Iperi, C., Fernández-Ochoa, Á., Pers, J.O., Barturen, G., Alarcon-Riquelme, M.E., Precisesads, C.C., Quirantes-Piné, R., Borrás-Linares, I., Segura-Carretero, A., Cornec, D., Bordon, A., and Jamin, C. more...
- Abstract
Le rôle des cellules B dans le lupus érythémateux systémique (LES) est connu depuis longtemps. Dans une nouvelle approche holistique, nous avons utilisé les données multi-omiques du consortium européen PRECISESADS afin d'établir un lien entre les modifications du transcriptome des cellules B de patients atteints de LES et celles de leur macroenvironnement, en incluant les composants cellulaires et fluidiques. L'objectif est d'identifier les altérations métaboliques spécifiques des lymphocytes B en relation avec le milieu environnant metabolomique au moyen d'une approche multi-omique. Il s'agit de permettre de développer une médecine de précision grâce à une compréhension globale des changements métaboliques pathologiques. L'analyse a porté sur 363 patients et 508 témoins, englobant la transcriptomique, la métabolomique et les données cliniques. Les transcriptomes des cellules B purifiées et des cellules du sang total ont été analysés à l'aide de DESeq et de GSEA. Les changements de pics métabolomiques dans le plasma et l'urine ont été quantifiés et annotés à l'aide de la base de données Ceu Mass Mediator. Les sources communes de variation ont été identifiées à l'aide de l'analyse d'intégration MOFA. Le macroenvironnement cellulaire était enrichi en cytokines, en réponses au stress, en voies de synthèse/mobilité lipidique et en dégradation des nucléotides. Les cellules B partagent ces voies, à l'exception de la dégradation des nucléotides qui est déviée vers la voie de récupération des nucléotides, et se distinguent par la glycosylation, et les protéines Schlafen. En outre, le récepteur LPAR6 a été identifié uniquement dans les lymphocytes B, et sa surexpression est en accord avec l'augmentation des acides lysophosphatidiques dans le plasma. Ces derniers sont en plus essentiels dans le calcul des facteurs MOFA, car ils permettent de distinguer les patients atteints de LES des témoins. Les cellules B ont montré des changements métaboliques partagés avec leur macroenvironnement et des changements uniques directement ou indirectement induits par la signalisation de l'IFN-α. Les Résultats mettent en évidence une importante déplétion en nucléotides dans les cellules immunitaires, suggérant que les lymphocytes B font face à cette déficience en activant des mécanismes de recyclage tels que la voie de récupération. De plus, la présence accrue de LPA dans le plasma, associée à l'augmentation du récepteur LPAR6 uniquement dans les lymphocytes, suggère une interaction entre eux, et donc un rôle potentiel dans la pathologie. Cette étude souligne l'importance de comprendre l'interaction entre les cellules B et leur macroenvironnement au cours du LES. Des altérations métaboliques systémiques et plus spécifiques des lymphocytes B ont été mises en évidence, ce qui suggère de nouvelles cibles métaboliques pour le traitement en rééduquant le métabolisme des patients. La recherche qui a conduit à ces Résultats a reçu le soutien du Innovative Medicines Initiative Joint de l'Union Européenne (FP7/2007-2013) et des entreprises de l'EFPIA (#115565). [ABSTRACT FROM AUTHOR] more...
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- 2024
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29. A comprehensive database for integrated analysis of omics data in autoimmune diseases
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Pedro Carmona-Sáez, Joaquín Dopazo, Víctor González-Rumayor, Adoracion Martin-Gomez, Fatima Al-Shahrour, Gonzalo Gómez-López, Juan Antonio Villatoro-García, Maria Peña-Chilet, Julio Saez-Rodriguez, Guillermo Barturen, Marta E. Alarcón-Riquelme, Jordi Martorell-Marugán, Kevin Troulé, Adrian Garcia-Moreno, Daniel Toro-Domínguez, Raúl López-Domínguez, European Commission, Junta de Andalucía, Innovative Medicines Initiative, Ministerio de Economía, Industria y Competitividad (España), [Martorell-Marugán,J, López-Domínguez,R, García-Moreno,A, Toro-Domínguez,D, Villatoro-García,JA, Carmona-Sáez,P] Bioinformatics Unit, GENYO. Centre for Genomics and Oncological Research: Pfzer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain. [Martorell-Marugán,J, González-Rumayor,V] Atrys Health S.A., Barcelona, Spain. [Toro-Domínguez,D, Barturen,G, Alarcón-Riquelme,ME] Genetics of Complex Diseases, GENYO. Centre for Genomics and Oncological Research: Pfzer/University of Granada/Andalusian Regional Government, PTS Granada, Granada, Spain. [Villatoro-García,JA, Carmona-Sáez,P] Department of Statistics, University of Granada, Granada, Spain. [Martín-Gómez,A] Nephrology Units, AADEA: Asociación Andaluza de Enfermedades Autoinmunes, Hospital de Poniente, Almería, Spain. [Troule,K, Gómez-López,G, Al-Shahrour,F] Bioinformatics Unit, Spanish National Cancer Center, CNIO, Madrid, Spain. [Peña-Chilet,M, Dopazo,J] Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocío, Seville, Spain. [Peña-Chilet,M, Dopazo,J] Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Seville, Spain. [Peña-Chilet,M, Dopazo,J] Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, Seville, Spain. [Dopazo,J] INB ELIXIR es, FPS, Hospital Virgen del Rocío, Seville, Spain. [Sáez-Rodríguez,J] Joint Research Centre for Computational Biomedicine (JRC COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany. [Sáez-Rodríguez,J] European Molecular Biology Laboratory-The European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, UK. [Sáez-Rodríguez,J] Institute for Computational Biomedicine, Bioquant Heidelberg, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany. [Alarcón-Riquelme,ME] Unit of Chronic Infammatory Diseases, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden., and This work is partially funded by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento (Grant CV20-36723), Consejería de Salud (Grant PI‐0173‐2017) and by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECIS‑ESADS (115565). JMM is partially funded by Ministerio de Economía, Industria y Competitividad. None of the funding bodies played any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript. more...
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Epigenomics ,Databases, Factual ,Computer science ,computer.software_genre ,Biochemistry ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::Gene Expression [Medical Subject Headings] ,Omics data ,Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings] ,0302 clinical medicine ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::DNA Methylation [Medical Subject Headings] ,Structural Biology ,Autoimmune disease ,Biology (General) ,Autoinmune disease ,0303 health sciences ,Database ,Conjunto de datos ,Applied Mathematics ,Pathway analysis ,Base de datos ,GEO ,Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Genetics::Genomics::Epigenomics [Medical Subject Headings] ,3. Good health ,Computer Science Applications ,Curation ,Meta‑analysis ,Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Computational Biology [Medical Subject Headings] ,Meta-analysis ,Information Science::Information Science::Information Storage and Retrieval::Databases as Topic::Databases, Factual [Medical Subject Headings] ,DNA microarray ,QH301-705.5 ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::Gene Expression::Transcription, Genetic::Transcriptome [Medical Subject Headings] ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Autoimmune Diseases ,03 medical and health sciences ,Enfermedades autoinmunes ,Diseases::Immune System Diseases::Autoimmune Diseases [Medical Subject Headings] ,medicine ,Humans ,Differential expression ,Metaanálisis ,Transcriptomics ,Molecular Biology ,Interferon signature ,030304 developmental biology ,030203 arthritis & rheumatology ,Computational Biology ,Omics ,medicine.disease ,Epigenómica ,Gene expression ,computer ,Expresión génica ,Dataset - Abstract
This work is partially funded by FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento (Grant CV20-36723), Consejeria de Salud (Grant PI-0173-2017) and by EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS (115565). JMM is partially funded by Ministerio de Economia, Industria y Competitividad. None of the funding bodies played any role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript., Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Results: Here, we present Autoimmune Diseases Explorer (https:// adex. genyo. es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. Conclusions: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies., FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento CV20-36723, Consejeria de Salud PI-0173-2017, EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565, Ministerio de Economia, Industria y Competitividad more...
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- 2021
30. Integrative epigenomics in Sjögren´s syndrome reveals novel pathways and a strong interaction between the HLA, autoantibodies and the interferon signature
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Teruel, María, Barturen, Guillermo, Martínez Bueno, Manuel, Castellini Pérez, Olivia, Barroso Gil, Miguel, Povedano, Elena, Kerick, Martin, Català Moll, Francesc, Makowska, Zuzanna, Buttgereit, Anne, Beretta, Lorenzo, Chizzolini, Carlo, Zuber, Aleksandra, Wynar, Donatienne, Kovács, Laszló, Balog, Attila, Deák, Magdolna, Bocskai, Márta, Dulic, Sonja, Kádár, Gabriella, Hiepe, Falk, Gerl, Velia, Thiel, Silvia, Rodriguez Maresca, Manuel, López Berrio, Antonio, Aguilar Quesada, Rocío, Navarro Linares, Héctor, Alvarez, Montserrat, Álvarez Errico, Damiana, Azevedo, Nancy, Barbarroja, Nuria, Cheng, Qingyu, Cremer, Jonathan, Groof, Aurélie de, Langhe, Ellen de, Ducreux, Julie, Dufour, Aleksandra, Hernández Fuentes, María, Khodadadi, Laleh, Kniesch, Katja, Li, Tianlu, López Pedrera, Chary, Marañón, Concepción, Muchmore, Brian, Neves, Esmeralda, Rouvière, Bénédicte, Simon, Quentin, Trombetta, Elena, Varela, Nieves, Witte, Torsten, Pers, Jacques-olivier, Ballestar, Esteban, Martin, Javier, Carnero Montoro, Elena, Alarcón Riquelme, Marta, Precisesads Clinical Consortium, Precisesads Flow Cytometry Study Group, Vigone, Barbara, Pers, Jacques Olivier, Saraux, Alain, Devauchelle-Pensec, Valérie, Cornec, Divi, Jousse-Joulin, Sandrine, Lauwerys, Bernard, Maudoux, Anne-lise, Vasconcelos, Carlos, Tavares, Ana, Faria, Raquel, Brandão, Mariana, Campar, Ana, Marinho, António, Farinha, Fátima, Almeida, Isabel, Gonzalez-Gay Mantecón, Miguel Ángel, Blanco Alonso, Ricardo, Corrales Martínez, Alfonso, Cervera, Ricard, Rodríguez Pintó, Ignasi, Espinosa, Gerard, Lories, Rik, Hunzelmann, Nicolas, Belz, Doreen, Baerlecken, Niklas, Stummvoll, Georg, Zauner, Michael, Lehner, Michaela, Collantes, Eduardo, Ortega Castro, Rafaela, Aguirre Zamorano, Mª Angeles, Escudero Contreras, Alejandro, Castro Villegas, Mª Carmen, Ortego, Norberto, Fernández Roldán, María Concepción, Raya, Enrique, Jiménez Moleón, Inmaculada, Ramon, Enrique de, Díaz Quintero, Isabel, Meroni, Pier Luigi, Gerosa, Maria, Schioppo, Tommaso, Artusi, Carolina, PRECISESADS Clinical Consortium, PRECISESADS Flow Cytometry Study Group, [Teruel,M, Barturen,G, Martínez-Bueno,M, Castellini-Pérez,O, Barroso-Gil,M, Povedano,E, Marañón,C, Carnero-Montoro,E, Alarcón-Riquelme,ME] GENYO, Center for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government, Granada, Spain. [Kerick,M, Martin,J] IPBLN-CSIC, Instituto de Parasitología y Biomedicina López-Neyra, Consejo Superior de Investigaciones Científcas, Granada, Spain. [Català-Moll,F, Ballestar,E] Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Badalona, Barcelona, Spain. [Català-Moll,F, Ballestar,E] IDIBELL, Bellvitge Biomedical Research Institute L’Hospitalet de Llobregat, Barcelona, Spain. [Makowska,Z, Buttgereit,A] Pharmaceuticals Division, Bayer Pharma Aktiengesellschaft, Berlin, Germany. [Pers,JO] Université de Brest, INSERM, Labex IGO, CHU de Brest, Brest, France.[Alarcón-Riquelme,ME] Institute for Environmental Medicine, Karolinska Institutet, Solna, Sweden., and Funding for the preparation of this manuscript has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement nº 115,565, resources composed of the financial contribution from the European Union's Seventh Framework Program (FP7/2007-2013) and the EFPIA companies’ in kind contribution. MT is supported by a Spanish grant from Health Department, Junta de Andalucía (PI/0017/2016) and through the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806975. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. EC-M was funded by the Postdoctoral Training Subprogramme Juan de la Cierva-Ministry of Economy and Competitiveness (FJCI_2014_20652). We thank Ralf Lesche for the production of RNASeq data and Marc Torres Ciuró for design support. more...
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Epigenomics ,Male ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::Gene Expression Regulation [Medical Subject Headings] ,Autoimmune diseases ,Gene Expression ,Quantitative trait ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::Gene Expression [Medical Subject Headings] ,Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans [Medical Subject Headings] ,Chemicals and Drugs::Amino Acids, Peptides, and Proteins::Peptides::Intercellular Signaling Peptides and Proteins::Interferons [Medical Subject Headings] ,0302 clinical medicine ,Rheumatic diseases ,HLA Antigens ,Genetics ,Regulation of gene expression ,0303 health sciences ,education.field_of_study ,Multidisciplinary ,Malalties autoimmunitàries ,Molecular medicine ,Epigenetic ,Autoanticuerpos ,Genomics ,Disciplines and Occupations::Natural Science Disciplines::Biological Science Disciplines::Biology::Genetics::Genomics::Epigenomics [Medical Subject Headings] ,3. Good health ,Sjogren's Syndrome ,DNA methylation ,Phenomena and Processes::Chemical Phenomena::Biochemical Phenomena::Biochemical Processes::DNA Methylation [Medical Subject Headings] ,Medicine ,Chemicals and Drugs::Biological Factors::Antigens::Antigens, Surface::Histocompatibility Antigens::HLA Antigens [Medical Subject Headings] ,Epigenetics ,Female ,Phenomena and Processes::Genetic Phenomena::Genetic Variation [Medical Subject Headings] ,Extracellular matrix organization ,Science ,Population ,Check Tags::Male [Medical Subject Headings] ,Human leukocyte antigen ,Biology ,Variación genética ,Article ,03 medical and health sciences ,Rheumatology ,Enfermedades autoinmunes ,Diseases::Immune System Diseases::Autoimmune Diseases [Medical Subject Headings] ,Immunogenetics ,Diseases::Immune System Diseases::Autoimmune Diseases::Arthritis, Rheumatoid::Sjogren's Syndrome [Medical Subject Headings] ,Humans ,Chemicals and Drugs::Amino Acids, Peptides, and Proteins::Proteins::Blood Proteins::Immunoproteins::Immunoglobulins::Antibodies [Medical Subject Headings] ,education ,Gene ,030304 developmental biology ,Autoantibodies ,030203 arthritis & rheumatology ,Phenomena and Processes::Genetic Phenomena::Genetic Processes::Gene Expression Regulation::Epigenesis, Genetic [Medical Subject Headings] ,Genetic Variation ,DNA Methylation ,Epigenètica ,Check Tags::Female [Medical Subject Headings] ,Gene Expression Regulation ,Epigenómica ,Síndrome de Sjögren ,Interferons ,Expresión génica - Abstract
Funding for the preparation of this manuscript has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no 115,565, resources composed of the financial contribution from the European Union's Seventh Framework Program (FP7/2007-2013) and the EFPIA companies' in kind contribution. MT is supported by a Spanish grant from Health Department, Junta de Andalucia (PI/0017/2016) and through the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806975. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. EC-M was funded by the Postdoctoral Training Subprogramme Juan de la Cierva-Ministry of Economy and Competitiveness (FJCI_2014_20652). We thank Ralf Lesche for the production of RNASeq data and Marc Torres Ciuro for design support., Primary Sjögren’s syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population., Innovative Medicines Initiative Joint Undertaking from the European Union's Seventh Framework Program (FP7/2007-2013) 115,565, EFPIA companies, Junta de Andalucia PI/0017/2016, Innovative Medicines Initiative 2 Joint Undertaking 806975 European Union's Horizon 2020 research and innovation programme, EFPIA, Postdoctoral Training Subprogramme Juan de la Cierva-Ministry of Economy and Competitiveness FJCI_2014_20652 more...
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- 2021
31. Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.
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Leist IC, Rivas-Torrubia M, Alarcón-Riquelme ME, Barturen G, Consortium PC, Gut IG, and Rueda M
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- Humans, Information Storage and Retrieval methods, Computational Biology methods, Software, Phenotype, Genomics methods
- Abstract
Background: Phenotypic data comparison is essential for disease association studies, patient stratification, and genotype-phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness., Results: Here, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient's profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker's performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research., Conclusions: Pheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool's source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker ., Competing Interests: Declarations. Ethics approval and consent to participate: The Ethical Review Boards of the 18 participating institutions approved the protocol of the cross-sectional study (see Additional File 4: Part B for the names of the ethics committees). In addition, the boards of the 6 sites involved approved the inception study protocol. The studies adhered to the standards set by the International Conference on Harmonization and Good Clinical Practice (ICH-GCP), and to the ethical principles that have their origin in the Declaration of Helsinki (2013). All study participants provided written informed consent prior to their enrolment in the PRECISESADS project. The protection of the confidentiality of records that could identify the included subjects is ensured as defined by the EU Directive 2001/20/EC and the applicable national and international requirements relating to data protection in each participating country. The CS study is registered with number NCT02890121, and the inception study with number NCT02890134 in ClinicalTrials.gov. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interest., (© 2024. The Author(s).) more...
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32. Revisiting the heterogeneity of interferon-related autoimmune diseases.
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Barturen G and Alarcón-Riquelme ME
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Competing Interests: Competing interests: The authors declare no competing interests.
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- 2024
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33. A strong Dysregulated Myeloid Component in the Epigenetic Landscape of Systemic Sclerosis: An Integrated DNA Methylome and Transcriptome Analysis.
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Martínez-López J, Ortiz-Fernandez L, Estupiñán-Moreno E, Kerick M, Andrés-León E, Terron-Camero LC, Carnero-Montoro E, Barturen G, Beretta L, Almeida I, Alarcón-Riquelme ME, Ballestar E, Acosta-Herrera M, and Martín J more...
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Objective: Nongenetic factors influence systemic sclerosis (SSc) pathogenesis, underscoring epigenetics as a relevant contributor to the disease. We aimed to unravel DNA methylation abnormalities associated with SSc through an epigenome-wide association study., Methods: We analyzed DNA methylation data from whole-blood samples in 179 patients with SSc and 241 unaffected individuals to identify differentially methylated positions (DMPs) with a false discovery rate (FDR) <0.05. These results were further integrated with RNA sequencing data from the same patients to assess their functional consequence. Additionally, we examined the impact of DNA methylation changes on transcription factors and analyzed the relationship between alterations of the methylation and gene expression profile and serum proteins levels., Results: This analysis yielded 525 DMPs enriched in immune-related pathways, with leukocyte cell-cell adhesion being the most significant (FDR = 4.91 × 10
-9 ), prioritizing integrins as they were exposed by integrating methylome and transcriptome data. Furthermore, through this integrative approach, we observed an enrichment of neutrophil-related pathways, highlighting this myeloid cell type as a relevant contributor in SSc pathogenesis. In addition, we uncovered novel profibrotic and proinflammatory mechanisms involved in the disease. Finally, the altered epigenetic and transcriptomic signature revealed an increased activity of CCAAT/enhancer-binding protein transcription factor family in SSc, which is crucial in the myeloid lineage development., Conclusion: Our findings uncover the impaired epigenetic regulation of the disease and its impact on gene expression, identifying new molecules for potential clinical applications and improving our understanding of SSc pathogenesis., (© 2024 The Author(s). Arthritis & Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.) more...- Published
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34. Molecular subtypes explain lupus epigenomic heterogeneity unveiling new regulatory genetic risk variants.
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Castellini-Pérez O, Povedano E, Barturen G, Martínez-Bueno M, Iakovliev A, Kerick M, López-Domínguez R, Marañón C, Martín J, Ballestar E, Borghi MO, Qiu W, Zhu C, Shankara S, Spiliopoulou A, de Rinaldis E, Carnero-Montoro E, and Alarcón-Riquelme ME more...
- Abstract
The heterogeneity of systemic lupus erythematosus (SLE) can be explained by epigenetic alterations that disrupt transcriptional programs mediating environmental and genetic risk. This study evaluated the epigenetic contribution to SLE heterogeneity considering molecular and serological subtypes, genetics and transcriptional status, followed by drug target discovery. We performed a stratified epigenome-wide association studies of whole blood DNA methylation from 213 SLE patients and 221 controls. Methylation quantitative trait loci analyses, cytokine and transcription factor activity - epigenetic associations and methylation-expression correlations were conducted. New drug targets were searched for based on differentially methylated genes. In a stratified approach, a total of 974 differential methylation CpG sites with dependency on molecular subtypes and autoantibody profiles were found. Mediation analyses suggested that SLE-associated SNPs in the HLA region exert their risk through DNA methylation changes. Novel genetic variants regulating DNAm in disease or in specific molecular contexts were identified. The epigenetic landscapes showed strong association with transcription factor activity and cytokine levels, conditioned by the molecular context. Epigenetic signals were enriched in known and novel drug targets for SLE. This study reveals possible genetic drivers and consequences of epigenetic variability on SLE heterogeneity and disentangles the DNAm mediation role on SLE genetic risk and novel disease-specific meQTLs. Finally, novel targets for drug development were discovered., (© 2024. The Author(s).) more...
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- 2024
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35. Integration of multi-omics analysis reveals metabolic alterations of B lymphocytes in systemic lupus erythematosus.
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Iperi C, Fernández-Ochoa Á, Pers JO, Barturen G, Alarcón-Riquelme M, Quirantes-Piné R, Borrás-Linares I, Segura-Carretero A, Cornec D, Bordron A, and Jamin C
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- Humans, Female, Adult, Male, Transcriptome, Middle Aged, Gene Expression Profiling, Multiomics, Lupus Erythematosus, Systemic immunology, Lupus Erythematosus, Systemic metabolism, B-Lymphocytes immunology, B-Lymphocytes metabolism, Metabolomics
- Abstract
Objective: To link changes in the B-cell transcriptome from systemic lupus erythematosus (SLE) patients with those in their macroenvironment, including cellular and fluidic components., Methods: Analysis was performed on 363 patients and 508 controls, encompassing transcriptomics, metabolomics, and clinical data. B-cell and whole-blood transcriptomes were analysed using DESeq and GSEA. Plasma and urine metabolomics peak changes were quantified and annotated using Ceu Mass Mediator database. Common sources of variation were identified using MOFA integration analysis., Results: Cellular macroenvironment was enriched in cytokines, stress responses, lipidic synthesis/mobility pathways and nucleotide degradation. B cells shared these pathways, except nucleotide degradation diverted to nucleotide salvage pathway, and distinct glycosylation, LPA receptors and Schlafen proteins., Conclusions: B cells showed metabolic changes shared with their macroenvironment and unique changes directly or indirectly induced by IFN-α signalling. This study underscores the importance of understanding the interplay between B cells and their macroenvironment in SLE pathology., Competing Interests: Declaration of competing interest The authors have declared that no conflict of interest exists., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.) more...
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- 2024
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36. Molecular characterisation of lupus low disease activity state (LLDAS) and DORIS remission by whole-blood transcriptome-based pathways in a pan-European systemic lupus erythematosus cohort.
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Parodis I, Lindblom J, Barturen G, Ortega-Castro R, Cervera R, Pers JO, Genre F, Hiepe F, Gerosa M, Kovács L, De Langhe E, Piantoni S, Stummvoll G, Vasconcelos C, Vigone B, Witte T, Alarcón-Riquelme ME, and Beretta L more...
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- Humans, Female, Adult, Male, Middle Aged, Severity of Illness Index, Cohort Studies, Lupus Erythematosus, Systemic drug therapy, Lupus Erythematosus, Systemic blood, Lupus Erythematosus, Systemic genetics, Remission Induction, Transcriptome
- Abstract
Objectives: To unveil biological milieus underlying low disease activity (LDA) and remission versus active systemic lupus erythematosus (SLE)., Methods: We determined differentially expressed pathways (DEPs) in SLE patients from the PRECISESADS project (NTC02890121) stratified into patients fulfilling and not fulfilling the criteria of (1) Lupus LDA State (LLDAS), (2) Definitions of Remission in SLE remission, and (3) LLDAS exclusive of remission., Results: We analysed data from 321 patients; 40.8% were in LLDAS, and 17.4% in DORIS remission. After exclusion of patients in remission, 28.3% were in LLDAS. Overall, 604 pathways differed significantly in LLDAS versus non-LLDAS patients with an false-discovery rate-corrected p (q)<0.05 and a robust effect size (dr)≥0.36. Accordingly, 288 pathways differed significantly between DORIS remitters and non-remitters (q<0.05 and dr≥0.36). DEPs yielded distinct molecular clusters characterised by differential serological, musculoskeletal, and renal activity. Analysis of partially overlapping samples showed no DEPs between LLDAS and DORIS remission. Drug repurposing potentiality for treating SLE was unveiled, as were important pathways underlying active SLE whose modulation could aid attainment of LLDAS/remission, including toll-like receptor (TLR) cascades, Bruton tyrosine kinase (BTK) activity, the cytotoxic T lymphocyte antigen 4 (CTLA-4)-related inhibitory signalling, and the nucleotide-binding oligomerization domain leucine-rich repeat-containing protein 3 (NLRP3) inflammasome pathway., Conclusions: We demonstrated for the first time molecular signalling pathways distinguishing LLDAS/remission from active SLE. LLDAS/remission was associated with reversal of biological processes related to SLE pathogenesis and specific clinical manifestations. DEP clustering by remission better grouped patients compared with LLDAS, substantiating remission as the ultimate treatment goal in SLE; however, the lack of substantial pathway differentiation between the two states justifies LLDAS as an acceptable goal from a biological perspective., Competing Interests: Competing interests: IP has received research funding and/or honoraria from Amgen, AstraZeneca, Aurinia, Elli Lilly, Gilead, GlaxoSmithKline, Janssen, Novartis, Otsuka, and Roche., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.) more...
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- 2024
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37. Interferon and B-cell Signatures Inform Precision Medicine in Lupus Nephritis.
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Parodis I, Lindblom J, Toro-Domínguez D, Beretta L, Borghi MO, Castillo J, Carnero-Montoro E, Enman Y, Mohan C, Alarcón-Riquelme ME, Barturen G, and Nikolopoulos D
- Abstract
Introduction: Current therapeutic management of lupus nephritis (LN) fails to induce long-term remission in over 50% of patients, highlighting the urgent need for additional options., Methods: We analyzed differentially expressed genes (DEGs) in peripheral blood from patients with active LN ( n = 41) and active nonrenal lupus ( n = 62) versus healthy controls (HCs) ( n = 497) from the European PRECISESADS project (NTC02890121), and dysregulated gene modules in a discovery ( n = 26) and a replication ( n = 15) set of active LN cases., Results: Replicated gene modules qualified for correlation analyses with serologic markers, and regulatory network and druggability analysis. Unsupervised coexpression network analysis revealed 20 dysregulated gene modules and stratified the active LN population into 3 distinct subgroups. These subgroups were characterized by low, intermediate, and high interferon (IFN) signatures, with differential dysregulation of the "B cell" and "plasma cells/Ig" modules. Drugs annotated to the IFN network included CC-motif chemokine receptor 1 (CCR1) inhibitors, programmed death-ligand 1 (PD-L1) inhibitors, and irinotecan; whereas the anti-CD38 daratumumab and proteasome inhibitor bortezomib showed potential for counteracting the "plasma cells/Ig" signature. In silico analysis demonstrated the low-IFN subgroup to benefit from calcineurin inhibition and the intermediate-IFN subgroup from B-cell targeted therapies. High-IFN patients exhibited greater anticipated response to anifrolumab whereas daratumumab appeared beneficial to the intermediate-IFN and high-IFN subgroups., Conclusion: IFN upregulation and B and plasma cell gene dysregulation patterns revealed 3 subgroups of LN, which may not necessarily represent distinct disease phenotypes but rather phases of the inflammatory processes during a renal flare, providing a conceptual framework for precision medicine in LN., (© 2024 International Society of Nephrology. Published by Elsevier Inc.) more...
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- 2024
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38. Response to the letter 'testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis'.
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Toro-Domínguez D, Martorell-Marugán J, Martinez-Bueno M, López-Domínguez R, Carnero-Montoro E, Barturen G, Goldman D, Petri M, Carmona-Sáez P, and Alarcón-Riquelme ME
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- Humans, Lupus Nephritis
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- 2023
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39. Corrigendum to 'Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus' [J. Autoimmun. 136 (April 2023) 103025].
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Lindblom J, Toro-Domínguez D, Carnero-Montoro E, Beretta L, Borghi MO, Castillo J, Enman Y, Mohan C, Alarcón-Riquelme ME, Barturen G, and Parodis I
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- 2023
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40. Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus.
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Lindblom J, Toro-Domínguez D, Carnero-Montoro E, Beretta L, Borghi MO, Castillo J, Enman Y, Mohan C, Alarcón-Riquelme ME, Barturen G, and Parodis I
- Subjects
- Humans, Transcriptome, Gene Regulatory Networks, Interferons genetics, Precision Medicine, Lupus Erythematosus, Systemic diagnosis, Lupus Erythematosus, Systemic drug therapy, Lupus Erythematosus, Systemic genetics
- Abstract
Objectives: We aimed at investigating the whole-blood transcriptome, expression quantitative trait loci (eQTLs), and levels of selected serological markers in patients with SLE versus healthy controls (HC) to gain insight into pathogenesis and identify drug targets., Methods: We analyzed differentially expressed genes (DEGs) and dysregulated gene modules in a cohort of 350 SLE patients and 497 HC from the European PRECISESADS project (NTC02890121), split into a discovery (60%) and a replication (40%) set. Replicated DEGs qualified for eQTL, pathway enrichment, regulatory network, and druggability analysis. For validation purposes, a separate gene module analysis was performed in an independent cohort (GSE88887)., Results: Analysis of 521 replicated DEGs identified multiple enriched interferon signaling pathways through Reactome. Gene module analysis yielded 18 replicated gene modules in SLE patients, including 11 gene modules that were validated in GSE88887. Three distinct gene module clusters were defined i.e., "interferon/plasma cells", "inflammation", and "lymphocyte signaling". Predominant downregulation of the lymphocyte signaling cluster denoted renal activity. By contrast, upregulation of interferon-related genes indicated hematological activity and vasculitis. Druggability analysis revealed several potential drugs interfering with dysregulated genes within the "interferon" and "PLK1 signaling events" modules. STAT1 was identified as the chief regulator in the most enriched signaling molecule network. Drugs annotated to 15 DEGs associated with cis-eQTLs included bortezomib for its ability to modulate CTSL activity. Belimumab was annotated to TNFSF13B (BAFF) and daratumumab was annotated to CD38 among the remaining replicated DEGs., Conclusions: Modulation of interferon, STAT1, PLK1, B and plasma cell signatures showed promise as viable approaches to treat SLE, pointing to their importance in SLE pathogenesis., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Ioannis Parodis reports a relationship with Amgen, AstraZeneca, Aurinia Pharmaceuticals, Elli Lilly and Company, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis, Otsuka Pharmaceutical, and F. Hoffmann-La Roche AG that includes: consulting or advisory and funding grants., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.) more...
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- 2023
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41. Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression.
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Toro-Domínguez D, Martorell-Marugán J, Martinez-Bueno M, López-Domínguez R, Carnero-Montoro E, Barturen G, Goldman D, Petri M, Carmona-Sáez P, and Alarcón-Riquelme ME
- Subjects
- Disease Progression, Gene Regulatory Networks, Humans, Quality of Life, Autoimmune Diseases, Lupus Erythematosus, Systemic drug therapy, Lupus Erythematosus, Systemic genetics
- Abstract
Objectives: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions., Methods: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores., Results: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes., Conclusions: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions., (© The Author(s) 2022. Published by Oxford University Press.) more...
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- 2022
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42. Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection.
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Barturen G, Carnero-Montoro E, Martínez-Bueno M, Rojo-Rello S, Sobrino B, Porras-Perales Ó, Alcántara-Domínguez C, Bernardo D, and Alarcón-Riquelme ME
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- Cytokine Release Syndrome, Cytokines, DNA Methylation genetics, Humans, SARS-CoV-2 genetics, COVID-19 genetics
- Abstract
SARS-CoV-2 infection can cause an inflammatory syndrome (COVID-19) leading, in many cases, to bilateral pneumonia, severe dyspnea, and in ~5% of these, death. DNA methylation is known to play an important role in the regulation of the immune processes behind COVID-19 progression, however it has not been studied in depth. In this study, we aim to evaluate the implication of DNA methylation in COVID-19 progression by means of a genome-wide DNA methylation analysis combined with DNA genotyping. The results reveal the existence of epigenomic regulation of functional pathways associated with COVID-19 progression and mediated by genetic loci. We find an environmental trait-related signature that discriminates mild from severe cases and regulates, among other cytokines, IL-6 expression via the transcription factor CEBP. The analyses suggest that an interaction between environmental contribution, genetics, and epigenetics might be playing a role in triggering the cytokine storm described in the most severe cases., (© 2022. The Author(s).) more...
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- 2022
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43. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.
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Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, and Relton CL more...
- Subjects
- Chromosome Mapping, Epigenesis, Genetic genetics, Genome-Wide Association Study, Humans, Multifactorial Inheritance genetics, Polymorphism, Single Nucleotide genetics, Quantitative Trait, Heritable, Transcriptome genetics, DNA metabolism, DNA Methylation genetics, Gene Expression Regulation genetics, Genetic Predisposition to Disease genetics, Quantitative Trait Loci genetics
- Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.) more...
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- 2021
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44. Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children.
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Beckmann ND, Comella PH, Cheng E, Lepow L, Beckmann AG, Tyler SR, Mouskas K, Simons NW, Hoffman GE, Francoeur NJ, Del Valle DM, Kang G, Do A, Moya E, Wilkins L, Le Berichel J, Chang C, Marvin R, Calorossi S, Lansky A, Walker L, Yi N, Yu A, Chung J, Hartnett M, Eaton M, Hatem S, Jamal H, Akyatan A, Tabachnikova A, Liharska LE, Cotter L, Fennessy B, Vaid A, Barturen G, Shah H, Wang YC, Sridhar SH, Soto J, Bose S, Madrid K, Ellis E, Merzier E, Vlachos K, Fishman N, Tin M, Smith M, Xie H, Patel M, Nie K, Argueta K, Harris J, Karekar N, Batchelor C, Lacunza J, Yishak M, Tuballes K, Scott I, Kumar A, Jaladanki S, Agashe C, Thompson R, Clark E, Losic B, Peters L, Roussos P, Zhu J, Wang W, Kasarskis A, Glicksberg BS, Nadkarni G, Bogunovic D, Elaiho C, Gangadharan S, Ofori-Amanfo G, Alesso-Carra K, Onel K, Wilson KM, Argmann C, Bunyavanich S, Alarcón-Riquelme ME, Marron TU, Rahman A, Kim-Schulze S, Gnjatic S, Gelb BD, Merad M, Sebra R, Schadt EE, and Charney AW more...
- Subjects
- Adolescent, CD56 Antigen metabolism, CD57 Antigens metabolism, CD8-Positive T-Lymphocytes metabolism, COVID-19 genetics, Child, Child, Preschool, Down-Regulation, Female, Humans, Infant, Infant, Newborn, Killer Cells, Natural immunology, Killer Cells, Natural metabolism, Male, Mucocutaneous Lymph Node Syndrome genetics, Mucocutaneous Lymph Node Syndrome immunology, SARS-CoV-2 pathogenicity, Systemic Inflammatory Response Syndrome genetics, Young Adult, CD8-Positive T-Lymphocytes immunology, COVID-19 immunology, Systemic Inflammatory Response Syndrome immunology, Transcriptome immunology
- Abstract
Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56
dim CD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C., (© 2021. The Author(s).) more...- Published
- 2021
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45. Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology.
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Kerick M, González-Serna D, Carnero-Montoro E, Teruel M, Acosta-Herrera M, Makowska Z, Buttgereit A, Babaei S, Barturen G, López-Isac E, Lesche R, Beretta L, Alarcon-Riquelme ME, and Martin J
- Subjects
- Adult, Aged, Basic Helix-Loop-Helix Transcription Factors genetics, Female, Genetic Association Studies, Humans, Inhibitor of Differentiation Proteins genetics, Kruppel-Like Factor 4, Kruppel-Like Transcription Factors genetics, Male, Middle Aged, Molecular Targeted Therapy, Nuclear Proteins genetics, Polymorphism, Single Nucleotide, Quantitative Trait Loci, T-Box Domain Proteins genetics, Transcription Factors genetics, Gene Expression Regulation genetics, Scleroderma, Systemic genetics
- Abstract
Objective: To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease., Methods: We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc., Results: We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (P
GWAS < 10-5 ). A total of 1,436 genes were within 1 Mb of the 4,539 SSc-associated SNPs. Of those 1,436 genes, 565 were detected as having ≥1 eQTL with an SSc-associated SNP. We developed a strategy to prioritize disease-associated genes based on their expression variance explained by SSc eQTLs (r2 > 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc., Conclusion: The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease., (© 2021, American College of Rheumatology.) more...- Published
- 2021
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46. A comprehensive database for integrated analysis of omics data in autoimmune diseases.
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Martorell-Marugán J, López-Domínguez R, García-Moreno A, Toro-Domínguez D, Villatoro-García JA, Barturen G, Martín-Gómez A, Troule K, Gómez-López G, Al-Shahrour F, González-Rumayor V, Peña-Chilet M, Dopazo J, Sáez-Rodríguez J, Alarcón-Riquelme ME, and Carmona-Sáez P more...
- Subjects
- Databases, Factual, Humans, Autoimmune Diseases epidemiology, Autoimmune Diseases genetics, Computational Biology
- Abstract
Background: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field., Results: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis., Conclusions: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies. more...
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- 2021
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47. A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.
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Soret P, Le Dantec C, Desvaux E, Foulquier N, Chassagnol B, Hubert S, Jamin C, Barturen G, Desachy G, Devauchelle-Pensec V, Boudjeniba C, Cornec D, Saraux A, Jousse-Joulin S, Barbarroja N, Rodríguez-Pintó I, De Langhe E, Beretta L, Chizzolini C, Kovács L, Witte T, Bettacchioli E, Buttgereit A, Makowska Z, Lesche R, Borghi MO, Martin J, Courtade-Gaiani S, Xuereb L, Guedj M, Moingeon P, Alarcón-Riquelme ME, Laigle L, and Pers JO more...
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- Adult, Autoantibodies blood, Biomarkers blood, Chemokines analysis, Chemokines genetics, Chemokines metabolism, Cohort Studies, Computational Biology, Computer Simulation, Cross-Sectional Studies, Cytokines analysis, Cytokines genetics, Databases, Genetic, Databases, Protein, Female, Flow Cytometry, Genome-Wide Association Study, Humans, Inflammation genetics, Inflammation immunology, Inflammation metabolism, Interferons genetics, Male, Middle Aged, Multigene Family, Polymorphism, Single Nucleotide, Proteome genetics, RNA-Seq, Sjogren's Syndrome blood, Sjogren's Syndrome genetics, Sjogren's Syndrome physiopathology, Cytokines blood, DNA Methylation genetics, Interferons blood, Proteome metabolism, Sjogren's Syndrome immunology, Transcriptome genetics
- Abstract
There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials. more...
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- 2021
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48. Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases.
- Author
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Barturen G, Babaei S, Català-Moll F, Martínez-Bueno M, Makowska Z, Martorell-Marugán J, Carmona-Sáez P, Toro-Domínguez D, Carnero-Montoro E, Teruel M, Kerick M, Acosta-Herrera M, Le Lann L, Jamin C, Rodríguez-Ubreva J, García-Gómez A, Kageyama J, Buttgereit A, Hayat S, Mueller J, Lesche R, Hernandez-Fuentes M, Juarez M, Rowley T, White I, Marañón C, Gomes Anjos T, Varela N, Aguilar-Quesada R, Garrancho FJ, López-Berrio A, Rodriguez Maresca M, Navarro-Linares H, Almeida I, Azevedo N, Brandão M, Campar A, Faria R, Farinha F, Marinho A, Neves E, Tavares A, Vasconcelos C, Trombetta E, Montanelli G, Vigone B, Alvarez-Errico D, Li T, Thiagaran D, Blanco Alonso R, Corrales Martínez A, Genre F, López Mejías R, Gonzalez-Gay MA, Remuzgo S, Ubilla Garcia B, Cervera R, Espinosa G, Rodríguez-Pintó I, De Langhe E, Cremer J, Lories R, Belz D, Hunzelmann N, Baerlecken N, Kniesch K, Witte T, Lehner M, Stummvoll G, Zauner M, Aguirre-Zamorano MA, Barbarroja N, Castro-Villegas MC, Collantes-Estevez E, de Ramon E, Díaz Quintero I, Escudero-Contreras A, Fernández Roldán MC, Jiménez Gómez Y, Jiménez Moleón I, Lopez-Pedrera R, Ortega-Castro R, Ortego N, Raya E, Artusi C, Gerosa M, Meroni PL, Schioppo T, De Groof A, Ducreux J, Lauwerys B, Maudoux AL, Cornec D, Devauchelle-Pensec V, Jousse-Joulin S, Jouve PE, Rouvière B, Saraux A, Simon Q, Alvarez M, Chizzolini C, Dufour A, Wynar D, Balog A, Bocskai M, Deák M, Dulic S, Kádár G, Kovács L, Cheng Q, Gerl V, Hiepe F, Khodadadi L, Thiel S, de Rinaldis E, Rao S, Benschop RJ, Chamberlain C, Dow ER, Ioannou Y, Laigle L, Marovac J, Wojcik J, Renaudineau Y, Borghi MO, Frostegård J, Martín J, Beretta L, Ballestar E, McDonald F, Pers JO, and Alarcón-Riquelme ME more...
- Subjects
- Adult, Aged, Antiphospholipid Syndrome genetics, Antiphospholipid Syndrome immunology, Arthritis, Rheumatoid genetics, Arthritis, Rheumatoid immunology, Autoimmune Diseases immunology, Case-Control Studies, Cluster Analysis, Cross-Sectional Studies, Epigenomics, Female, Humans, Inflammation immunology, Interferons immunology, Lupus Erythematosus, Systemic genetics, Lupus Erythematosus, Systemic immunology, Male, Middle Aged, Mixed Connective Tissue Disease genetics, Mixed Connective Tissue Disease immunology, Scleroderma, Systemic genetics, Scleroderma, Systemic immunology, Sjogren's Syndrome genetics, Sjogren's Syndrome immunology, Undifferentiated Connective Tissue Diseases genetics, Undifferentiated Connective Tissue Diseases immunology, Autoimmune Diseases classification, Autoimmune Diseases genetics, Epigenome, Gene Expression Profiling
- Abstract
Objective: Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis., Methods: Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time., Results: Four clusters were identified and validated. Three were pathologic, representing "inflammatory," "lymphoid," and "interferon" patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse-remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient., Conclusion: Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases., (© 2020, American College of Rheumatology.) more...
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- 2021
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49. geno 5 mC: A Database to Explore the Association between Genetic Variation (SNPs) and CpG Methylation in the Human Genome.
- Author
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Gómez-Martín C, Aparicio-Puerta E, Medina JM, Barturen G, Oliver JL, and Hackenberg M
- Subjects
- Humans, Inflammatory Bowel Diseases genetics, Promoter Regions, Genetic, Search Engine, CpG Islands genetics, DNA Methylation genetics, Databases, Genetic, Genome, Human, Polymorphism, Single Nucleotide genetics
- Abstract
Genetic variation, gene expression and DNA methylation influence each other in a complex way. To study the impact of sequence variation and DNA methylation on gene expression, we generated geno erscript>5 mC, a database that contains statistically significant SNP-CpG associations that are biologically classified either through co-localization with known regulatory regions (promoters and enhancers), or through known correlations with the expression levels of nearby genes. The SNP rs727563 can be used to illustrate the usefulness of this approach. This SNP has been associated with inflammatory bowel disease through GWAS, but it is not located near any gene related to this phenotype. However, geno
5 mC reveals that rs727563 is associated with the methylation state of several CpGs located in promoter regions of genes reported to be involved in inflammatory processes. This case exemplifies how geno5 mC can be used to infer relevant and previously unknown interactions between described disease-associated SNPs and their functional targets., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.) more...- Published
- 2021
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50. An elevated polyclonal free light chain level reflects a strong interferon signature in patients with systemic autoimmune diseases.
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Bettacchioli E, Le Gaffric C, Mazeas M, Borghi MO, Frostegard J, Barturen G, Makowska Z, Babei S, Lesche R, Meroni PL, Alarcon-Riquelme ME, and Renaudineau Y
- Abstract
High amount of polyclonal free light chains (FLC) are reported in systemic autoimmune diseases (SAD) and we took advantage of the PRECISESADS study to better characterize them. Serum FLC levels were explored in 1979 patients with SAD (RA, SLE, SjS, Scl, APS, UCTD, MCTD) and 614 healthy controls. Information regarding clinical parameters, disease activity, medications, autoantibodies (Ab) and the interferon α and/or γ scores were recorded. Among SAD patients, 28.4% had raised total FLC (from 12% in RA to 30% in SLE and APS) with a normal kappa/lambda ratio. Total FLC levels were significantly higher in SAD with inflammation, active disease in SLE and SjS, and an impaired pulmonary functional capacity in SSc, while independent from kidney impairment, infection, cancer and treatment. Total FLC concentrations were positively correlated among the 10/17 (58.8%) autoantibodies (Ab) tested with anti-RNA binding protein Ab (SSB, SSA-52/60 kDa, Sm, U1-RNP), anti-dsDNA/nucleosome Ab, rheumatoid factor and negatively correlated with complement fractions C3/C4. Finally, examination of interferon (IFN) expression as a potential driver of FLC overexpression was tested showing an elevated level of total FLC among patients with a high IFNα and IFNγ Kirou's score, a strong IFN modular score, and the detection in the sera of B-cell IFN dependent factors, such as TNF-R1/TNFRSF1A and CXCL10/IP10. In conclusion, an elevated level of FLC, in association with a strong IFN signature, defines a subgroup of SAD patients, including those without renal affectation, characterized by increased disease activity, autoreactivity, and complement reduction., Competing Interests: All authors declare that they have no conflict of interest., (© 2021 The Author(s).) more...
- Published
- 2021
- Full Text
- View/download PDF
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