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1. Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease

Author

Mishra, Neha, Aden, Konrad, Blase, Johanna I, Baran, Nathan, Bordoni, Dora, Tran, Florian, Conrad, Claudio, Avalos, Diana, Jaeckel, Charlot, Scherer, Michael, Sørensen, Signe B, Overgaard, Silja H, Schulte, Berenice, Nikolaus, Susanna, Rey, Guillaume, Gasparoni, Gilles, Lyons, Paul A, Schultze, Joachim L, Walter, Jörn, Andersen, Vibeke, SYSCID Consortium, Dermitzakis, Emmanouil T, Schreiber, Stefan, Rosenstiel, Philip, Rosenstiel, Philip [0000-0002-9692-8828], and Apollo - University of Cambridge Repository

Subjects
Tumor Necrosis Factor-alpha, Research, Intestinal inflammation, Therapy response, Biomarker, Biologics, Inflammatory Bowel Diseases, Personalized medicine, Infliximab, Humans, RNA, Tumor Necrosis Factor Inhibitors, Interferons, Prospective Studies, Biomarkers
Abstract

Funder: Universitätsklinikum Schleswig-Holstein - Campus Kiel (6509), BACKGROUND AND AIMS: Treatment with tumor necrosis factor α (TNFα) antagonists in IBD patients suffers from primary non-response rates of up to 40%. Biomarkers for early prediction of therapy success are missing. We investigated the dynamics of gene expression and DNA methylation in blood samples of IBD patients treated with the TNF antagonist infliximab and analyzed the predictive potential regarding therapy outcome. METHODS: We performed a longitudinal, blood-based multi-omics study in two prospective IBD patient cohorts receiving first-time infliximab therapy (discovery: 14 patients, replication: 23 patients). Samples were collected at up to 7 time points (from baseline to 14 weeks after therapy induction). RNA-sequencing and genome-wide DNA methylation data were analyzed and correlated with clinical remission at week 14 as a primary endpoint. RESULTS: We found no consistent ex ante predictive signature across the two cohorts. Longitudinally upregulated transcripts in the non-remitter group comprised TH2- and eosinophil-related genes including ALOX15, FCER1A, and OLIG2. Network construction identified transcript modules that were coherently expressed at baseline and in non-remitting patients but were disrupted at early time points in remitting patients. These modules reflected processes such as interferon signaling, erythropoiesis, and platelet aggregation. DNA methylation analysis identified remission-specific temporal changes, which partially overlapped with transcriptomic signals. Machine learning approaches identified features from differentially expressed genes cis-linked to DNA methylation changes at week 2 as a robust predictor of therapy outcome at week 14, which was validated in a publicly available dataset of 20 infliximab-treated CD patients. CONCLUSIONS: Integrative multi-omics analysis reveals early shifts of gene expression and DNA methylation as predictors for efficient response to anti-TNF treatment. Lack of such signatures might be used to identify patients with IBD unlikely to benefit from TNF antagonists at an early time point.

Published
2022

2. Additional file 2 of Longitudinal multi-omics analysis identifies early blood-based predictors of anti-TNF therapy response in inflammatory bowel disease

Author

Mishra, Neha, Aden, Konrad, Blase, Johanna I., Baran, Nathan, Bordoni, Dora, Tran, Florian, Conrad, Claudio, Avalos, Diana, Jaeckel, Charlot, Scherer, Michael, Sørensen, Signe B., Overgaard, Silja H., Schulte, Berenice, Nikolaus, Susanna, Rey, Guillaume, Gasparoni, Gilles, Lyons, Paul A., Schultze, Joachim L., Walter, Jörn, Andersen, Vibeke, Dermitzakis, Emmanouil T., Schreiber, Stefan, and Rosenstiel, Philip

Abstract

Additional file 2: Figure S1. Baseline signatures of the discovery cohort. Figure S2. Transcriptomic changes in response to therapy and induction of remission in the discovery cohort. Figure S3. DNA methylation patterns in response to therapy and induction of remission in the discovery cohort. Figure S4. Integration of DNA methylation and transcriptome data. Figure S5. Molecular comparisons between discovery and replication cohorts and baseline signatures of replication cohort. Figure S6. Replication of molecular signatures. Figure S7. Comparison of IBD subtypes.

Published
2022
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3. Identification of evolutionarily conserved gene networks mediating neurodegenerative dementia

Author

Swarup, Vivek, Hinz, Flora I., Rexach, Jessica E., Noguchi, Ken-ichi, Toyoshiba, Hiroyoshi, Oda, Akira, Hirai, Keisuke, Sarkar, Arjun, Seyfried, Nicholas T., Cheng, Chialin, Haggarty, Stephen J., Ferrari, Raffaele, Rohrer, Jonathan D., Ramasamy, Adaikalavan, Hardy, John, Hernandez, Dena G., Nalls, Michael A., Singleton, Andrew B., Kwok, John B. J., Dobson-Stone, Carol, Brooks, William S., Schofield, Peter R., Halliday, Glenda M., Hodges, John R., Piguet, Olivier, Bartley, Lauren, Thompson, Elizabeth, Haan, Eric, Hernández, Isabel, Ruiz, Agustín, Boada, Mercè, Borroni, Barbara, Padovani, Alessandro, Cairns, Nigel J., Cruchaga, Carlos, Binetti, Giuliano, Ghidoni, Roberta, Benussi, Luisa, Forloni, Gianluigi, Albani, Diego, Galimberti, Daniela, Fenoglio, Chiara, Serpente, Maria, Scarpini, Elio, Clarimón, Jordi, Lleó, Alberto, Blesa, Rafael, Waldö, Maria Landqvist, Nilsson, Karin, Nilsson, Christer, Mackenzie, Ian R. A., Hsiung, Ging-Yuek R., Mann, David M. A., Grafman, Jordan, Morris, Christopher M., Attems, Johannes, Griffiths, Timothy D., McKeith, Ian G., Thomas, Alan J., Jaros, Evelyn, Pietrini, Pietro, Huey, Edward D., Wassermann, Eric M., Tierney, Michael C., Baborie, Atik, Pastor, Pau, Ortega-Cubero, Sara, Razquin, Cristina, Alonso, Elena, Perneczky, Robert, Diehl-Schmid, Janine, Alexopoulos, Panagiotis, Kurz, Alexander, Rainero, Innocenzo, Rubino, Elisa, Pinessi, Lorenzo, Rogaeva, Ekaterina, George-Hyslop, Peter St., Rossi, Giacomina, Tagliavini, Fabrizio, Giaccone, Giorgio, Rowe, James B., Schlachetzki, Johannes C. M., Uphill, James, Collinge, John, Mead, Simon, Danek, Adrian, Van Deerlin, Vivianna M., Grossman, Murray, Trojanowski, John Q., Pickering-Brown, Stuart, Momeni, Parastoo, van der Zee, Julie, Cruts, Marc, Van Broeckhoven, Christine, Cappa, Stefano F., Leber, Isabelle, Brice, Alexis, Hannequin, Didier, Golfier, Véronique, Vercelletto, Martine, Nacmias, Benedetta, Sorbi, Sandro, Bagnoli, Silvia, Piaceri, Irene, Nielsen, Jørgen E., Hjermind, Lena E., Riemenschneider, Matthias, Mayhaus, Manuel, Gasparoni, Gilles, Pichler, Sabrina, Ibach, Bernd, Rossor, Martin N., Fox, Nick C., Warren, Jason D., Spillantini, Maria Grazia, Morris, Huw R., Rizzu, Patrizia, Heutink, Peter, Snowden, Julie S., Rollinson, Sara, Gerhard, Alexander, Richardson, Anna, Bruni, Amalia C., Maletta, Raffaele, Frangipane, Francesca, Cupidi, Chiara, Bernardi, Livia, Anfossi, Maria, Gallo, Maura, Conidi, Maria Elena, Smirne, Nicoletta, Rademakers, Rosa, Baker, Matt, Dickson, Dennis W., Graff-Radford, Neill R., Petersen, Ronald C., Knopman, David, Josephs, Keith A., Boeve, Bradley F., Parisi, Joseph E., Miller, Bruce L., Karydas, Anna M., Rosen, Howard, Seeley, William W., van Swieten, John C., Dopper, Elise G. P., Seelaar, Harro, Pijnenburg, Yolande A. L., Scheltens, Philip, Logroscino, Giancarlo, Capozzo, Rosa, Novelli, Valeria, Puca, Annibale A., Franceschi, Massimo, Postiglione, Alfredo, Milan, Graziella, Sorrentino, Paolo, Kristiansen, Mark, Chiang, Huei-Hsin, Graff, Caroline, Pasquier, Florence, Rollin, Adeline, Deramecourt, Vincent, Lebouvier, Thibaud, Ferrucci, Luigi, Kapogiannis, Dimitrios, Lah, James J., Levey, Allan I., Kondou, Shinichi, Geschwind, Daniel H., Int Frontotemporal Dementia Gen, Swarup, Vivek, Hinz, Flora I., Rexach, Jessica E., Noguchi, Ken-ichi, Toyoshiba, Hiroyoshi, Oda, Akira, Hirai, Keisuke, Sarkar, Arjun, Seyfried, Nicholas T., Cheng, Chialin, Haggarty, Stephen J., Ferrari, Raffaele, Rohrer, Jonathan D., Ramasamy, Adaikalavan, Hardy, John, Hernandez, Dena G., Nalls, Michael A., Singleton, Andrew B., Kwok, John B. J., Dobson-Stone, Carol, Brooks, William S., Schofield, Peter R., Halliday, Glenda M., Hodges, John R., Piguet, Olivier, Bartley, Lauren, Thompson, Elizabeth, Haan, Eric, Hernández, Isabel, Ruiz, Agustín, Boada, Mercè, Borroni, Barbara, Padovani, Alessandro, Cairns, Nigel J., Cruchaga, Carlo, Binetti, Giuliano, Ghidoni, Roberta, Benussi, Luisa, Forloni, Gianluigi, Albani, Diego, Galimberti, Daniela, Fenoglio, Chiara, Serpente, Maria, Scarpini, Elio, Clarimón, Jordi, Lleó, Alberto, Blesa, Rafael, Waldö, Maria Landqvist, Nilsson, Karin, Nilsson, Christer, Mackenzie, Ian R. A., Hsiung, Ging-Yuek R., Mann, David M. A., Grafman, Jordan, Morris, Christopher M., Attems, Johanne, Griffiths, Timothy D., Mckeith, Ian G., Thomas, Alan J., Jaros, Evelyn, Pietrini, Pietro, Huey, Edward D., Wassermann, Eric M., Tierney, Michael C., Baborie, Atik, Pastor, Pau, Ortega-Cubero, Sara, Razquin, Cristina, Alonso, Elena, Perneczky, Robert, Diehl-Schmid, Janine, Alexopoulos, Panagioti, Kurz, Alexander, Rainero, Innocenzo, Rubino, Elisa, Pinessi, Lorenzo, Rogaeva, Ekaterina, George-Hyslop, Peter St., Rossi, Giacomina, Tagliavini, Fabrizio, Giaccone, Giorgio, Rowe, James B., Schlachetzki, Johannes C. M., Uphill, Jame, Collinge, John, Mead, Simon, Danek, Adrian, Van Deerlin, Vivianna M., Grossman, Murray, Trojanowski, John Q., Pickering-Brown, Stuart, Momeni, Parastoo, van der Zee, Julie, Cruts, Marc, Van Broeckhoven, Christine, Cappa, Stefano F., Leber, Isabelle, Brice, Alexi, Hannequin, Didier, Golfier, Véronique, Vercelletto, Martine, Nacmias, Benedetta, Sorbi, Sandro, Bagnoli, Silvia, Piaceri, Irene, Nielsen, Jørgen E., Hjermind, Lena E., Riemenschneider, Matthia, Mayhaus, Manuel, Gasparoni, Gille, Pichler, Sabrina, Ibach, Bernd, Rossor, Martin N., Fox, Nick C., Warren, Jason D., Spillantini, Maria Grazia, Morris, Huw R., Rizzu, Patrizia, Heutink, Peter, Snowden, Julie S., Rollinson, Sara, Gerhard, Alexander, Richardson, Anna, Bruni, Amalia C., Maletta, Raffaele, Frangipane, Francesca, Cupidi, Chiara, Bernardi, Livia, Anfossi, Maria, Gallo, Maura, Conidi, Maria Elena, Smirne, Nicoletta, Rademakers, Rosa, Baker, Matt, Dickson, Dennis W., Graff-Radford, Neill R., Petersen, Ronald C., Knopman, David, Josephs, Keith A., Boeve, Bradley F., Parisi, Joseph E., Miller, Bruce L., Karydas, Anna M., Rosen, Howard, Seeley, William W., van Swieten, John C., Dopper, Elise G. P., Seelaar, Harro, Pijnenburg, Yolande A. L., Scheltens, Philip, Logroscino, Giancarlo, Capozzo, Rosa, Novelli, Valeria, Puca, Annibale A., Franceschi, Massimo, Postiglione, Alfredo, Milan, Graziella, Sorrentino, Paolo, Kristiansen, Mark, Chiang, Huei-Hsin, Graff, Caroline, Pasquier, Florence, Rollin, Adeline, Deramecourt, Vincent, Lebouvier, Thibaud, Ferrucci, Luigi, Kapogiannis, Dimitrio, Lah, James J., Levey, Allan I., Kondou, Shinichi, and Geschwind, Daniel H.

Subjects
Proteomics, Genetics and Molecular Biology (all), 0301 basic medicine, Messenger, Gene regulatory network, Inbred C57BL, Biochemistry, Transgenic, Mice, 0302 clinical medicine, Gene Regulatory Networks, Regulation of gene expression, Cell Death, Drug discovery, Neurodegeneration, Neurodegenerative Diseases, General Medicine, Chemistry, Frontotemporal Dementia, 030220 oncology & carcinogenesis, Frontotemporal dementia, Evolution, Systems biology, Genetic Vectors, Animals, Dementia, Disease Models, Animal, Gene Expression Regulation, Genetic Predisposition to Disease, Humans, Mice, Inbred C57BL, Mice, Transgenic, MicroRNAs, RNA, Messenger, Reproducibility of Results, Transcriptome, tau Proteins, Evolution, Molecular, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, microRNA, medicine, Biochemistry, Genetics and Molecular Biology (all), Animal, Molecular, medicine.disease, 030104 developmental biology, Disease Models, RNA, Human medicine
Abstract

Identifying the mechanisms through which genetic risk causes dementia is an imperative for new therapeutic development. Here, we apply a multistage, systems biology approach to elucidate the disease mechanisms in frontotemporal dementia. We identify two gene coexpression modules that are preserved in mice harboring mutations in MAPT, GRN and other dementia mutations on diverse genetic backgrounds. We bridge the species divide via integration with proteomic and transcriptomic data from the human brain to identify evolutionarily conserved, disease-relevant networks. We find that overexpression of miR-203, a hub of a putative regulatory microRNA (miRNA) module, recapitulates mRNA coexpression patterns associated with disease state and induces neuronal cell death, establishing this miRNA as a regulator of neurodegeneration. Using a database of drug-mediated gene expression changes, we identify small molecules that can normalize the disease-associated modules and validate this experimentally. Our results highlight the utility of an integrative, cross-species network approach to drug discovery.

Published
2018

4. Additional file 1 of Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Author

Scherer, Michael, Gasparoni, Gilles, Rahmouni, Souad, Shashkova, Tatiana, Arnoux, Marion, Louis, Edouard, Nostaeva, Arina, Avalos, Diana, Dermitzakis, Emmanouil T., Aulchenko, Yurii S., Lengauer, Thomas, Lyons, Paul A., Georges, Michel, and Walter, Jörn

Abstract

Additional file 1: Additional text including method descriptions and additional figures S1-S10.

Published
2021
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5. Additional file 1 of DNA methylation analysis on purified neurons and glia dissects age and Alzheimer’s disease-specific changes in the human cortex

Author

Gasparoni, Gilles, Bultmann, Sebastian, Lutsik, Pavlo, Kraus, Theo F. J., Sordon, Sabrina, Vlcek, Julia, Dietinger, Vanessa, Steinmaurer, Martina, Haider, Melanie, Mulholland, Christopher B., Arzberger, Thomas, Roeber, Sigrun, Riemenschneider, Matthias, Kretzschmar, Hans A., Giese, Armin, Leonhardt, Heinrich, and Walter, Jörn

Abstract

Additional file 1. Supplementary Figures.

Published
2020
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6. Additional file 1 of A cell-of-origin epigenetic tracer reveals clinically distinct subtypes of high-grade serous ovarian cancer

Author

Riso, Pietro Lo, Villa, Carlo Emanuele, Gasparoni, Gilles, Vingiani, Andrea, Luongo, Raffaele, Manfredi, Anna, Jungmann, Annemarie, Bertolotti, Alessia, Borgo, Francesca, Garbi, Annalisa, Lupia, Michela, Laise, Pasquale, Das, Vivek, Pruneri, Giancarlo, Viale, Giuseppe, Colombo, Nicoletta, Manzo, Teresa, Nezi, Luigi, Cavallaro, Ugo, Cacchiarelli, Davide, Walter, Jörn, and Testa, Giuseppe

Subjects
endocrine system
Abstract

Additional file 1: Figure S1. The variance in global DNA methylation does not allow to classify HGSOC according to its cell of origin. Figure S2. OriPrint CpGs map preferentially to intergenic regions. Figure S3. OriPrint allow stratification of tumor samples. Figure S4. The cell of origin is the main determinant of global variance in DNA methylation for HGSOC. Figure S5. Diffusion pseudotime on global DNA methylome does not allow to derive an evolutionary line from FI and OSE to tumors. Figure S6. Survival curves are consistent after machine learning and show similar results in published cohorts. Figure S7. OSE-like tumors show a reduced copy number burden. Figure S8. OSE-like tumors present an increased fraction of Memory Resting T cells and M2 macrophages. Figure S9. Validation of DEGs between FI-like and OSE-like tumors. Figure S10. DNA methylation of promoters in FI-like and OSE-like tumors. (PPTX 6504 kb)

Published
2020
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7. Sodium chloride is an ionic checkpoint for human Th2 cell responses and shapes the atopic skin microenvironment

Author

Julia Matthias, Rebecca Noster, Natalie Garzorz-Stark, H. Gerstenberg, Joern Walter, Florian Jeschke, Percy A. Knolle, Anna Welle, Gasparoni Gilles, Tilo Biedermann, Karl Nordstroem, Dirk Baumjohann, D. Renisch, Stephan Grabbe, Kilian Eyerich, Sainitin Donakonda, Klaus Eberhardt, Christina E. Zielinski, Dominik Soll, Julia Maul, Hanna Meinl, and Ying-Yin Chao

Subjects
Naive T cell, Chemistry, Kinase, Effector, T cell, Sodium, Cell, chemistry.chemical_element, Atopic dermatitis, medicine.disease, medicine.anatomical_structure, Immunology, medicine, Transcription factor
Abstract

One sentence summarySodium is an ionic checkpoint for the induction and amplification of human Th2 cell responses and shapes the atopic skin microenvironment, where it could serve as a novel therapeutic target for Th2 mediated diseases.AbstractThere has been a strong increase in the incidence of allergic diseases over the last 50 years. Environmental factors most likely account for this phenomenon. However, the nature of these factors and the mode of action by which they induce the type 2 immune deviation, which is characteristic of atopic diseases, remains unclear. It has previously been reported that dietary sodium chloride promotes the polarization of Th17 cells with implications for autoimmune diseases such as multiple sclerosis. Here, we demonstrate that sodium chloride also potently promotes Th2 cell responses on multiple regulatory levels. Sodium chloride enhanced IL-4 and IL-13 production while suppressing IFN-γproduction in effector T cells. It diverted alternative T cell fates into the Th2 cell phenotype and also inducedde novoTh2 cell polarization from naïve T cell precursors. Mechanistically, it exerted its effects via the osmosensitive transcription factor NFAT-5 and the kinase SGK-1, which regulated Th2 signature cytokines and master transcription factors in hyperosmolar salt conditions. The skin of patients suffering from atopic dermatitis contained highly elevated amounts of sodium compared to non-lesional atopic and healthy skin. This demonstrates that sodium chloride represents a so far overlooked cutaneous microenvironmental factor in atopic dermatitis that can induce Th2 cell responses, the orchestrators of allergic diseases. Together, our data propose ionic signaling through sodium chloride as a novel checkpoint and potential therapeutic target for type 2 immunity and its associated allergic diseases.

Published
2018

8. MOESM1 of DNA methylation analysis on purified neurons and glia dissects age and Alzheimerâ s disease-specific changes in the human cortex

Author

Gasparoni, Gilles, Bultmann, Sebastian, Lutsik, Pavlo, Kraus, Theo, Sordon, Sabrina, Vlcek, Julia, Dietinger, Vanessa, Steinmaurer, Martina, Haider, Melanie, Mulholland, Christopher, Arzberger, Thomas, Roeber, Sigrun, Riemenschneider, Matthias, Kretzschmar, Hans, Giese, Armin, Leonhardt, Heinrich, and JĂśrn Walter

Abstract

Additional file 1. Supplementary Figures.

Published
2018
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9. Additional file 2 of A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains

Author

Abdulrahman Salhab, NordstrĂśm, Karl, Gasparoni, Gilles, Kattler, Kathrin, Ebert, Peter, Ramirez, Fidel, Arrigoni, Laura, MĂźller, Fabian, Polansky, Julia K., Cadenas, Cristina, G.Hengstler, Jan, Lengauer, Thomas, Manke, Thomas, and JĂśrn Walter

Abstract

Supplementary materials. PDF document with supplementary figures and supplementary methods. (PDF 11,526 kb)

Published
2018
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10. Additional file 2 of A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains

Author

Abdulrahman Salhab, NordstrĂśm, Karl, Gasparoni, Gilles, Kattler, Kathrin, Ebert, Peter, Ramirez, Fidel, Arrigoni, Laura, MĂźller, Fabian, Polansky, Julia, Cadenas, Cristina, G.Hengstler, Jan, Lengauer, Thomas, Manke, Thomas, and JĂśrn Walter

Abstract

Supplementary materials. PDF document with supplementary figures and supplementary methods. (PDF 11,526 kb)

Published
2018
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11. Additional file 8: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Abstract

DNA methylation levels of SNCA intron 1 in pluripotent stem cells (ESC line I3 and I6 (n = 1 each), iPSC line I3 (n = 3), ES- and iPS-NSC (line I3, n = 1 and n = 3, respectively), and ES- and iPS-Neurons (line I3, n = 1 and n = 3, respectively). ESC versus iPS-Neurons p = 0.03, iPSC vs. iPS-Neurons p = 0.02, and iPS-NSC vs. iPS-Neurons p = 0.02. (PDF 41 kb)

Published
2018
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12. Additional file 1: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Subjects
nervous system
Abstract

A–C Interindividual analysis of DMCG comparing hES-Neurons to each iPSC-derived neuronal clone. D X chromosomal-based DMCG analysis using Fisher’s test. Methylation deltas were calculated as indicated and CpGs above applying a moderate (0.2) or high (0.5) threshold. (PDF 437 kb)

Published
2018
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13. Additional file 7: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Subjects
nervous system, biological phenomena, cell phenomena, and immunity, reproductive and urinary physiology
Abstract

Mean DNA methylation levels (%) of single genes (APP, GNAS, MIR886, PMP22, SNCA promoter, SNCA intron 1) of hESC-derived and iPSC-derived NSC and neurons. Analysis is based on individual samples. Data is shown as mean ± SD. (PDF 40 kb)

Published
2018
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14. Additional file 2 of A comprehensive analysis of 195 DNA methylomes reveals shared and cell-specific features of partially methylated domains

Author

Abdulrahman Salhab, NordstrĂśm, Karl, Gasparoni, Gilles, Kattler, Kathrin, Ebert, Peter, Ramirez, Fidel, Arrigoni, Laura, MĂźller, Fabian, Polansky, Julia, Cadenas, Cristina, G.Hengstler, Jan, Lengauer, Thomas, Manke, Thomas, and JĂśrn Walter

Abstract

Supplementary materials. PDF document with supplementary figures and supplementary methods. (PDF 11,526 kb)

Published
2018
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15. MOESM1 of Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

Author

Ardakani, Fatemeh Behjati, Kattler, Kathrin, NordstrÜm, Karl, Gasparoni, Nina, Gasparoni, Gilles, Fuchs, Sarah, Sinha, Anupam, Barann, Matthias, Ebert, Peter, Fischer, Jonas, Hutter, Barbara, Zipprich, Gideon, Imbusch, Charles, BäRbel Felder, Jßrgen Eils, Brors, Benedikt, Lengauer, Thomas, Manke, Thomas, Rosenstiel, Philip, JÜrn Walter, and Schulz, Marcel

Abstract

Additional file 1. Supplementary methods and results.

Published
2018
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16. Additional file 3: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Subjects
nervous system, embryonic structures, biological phenomena, cell phenomena, and immunity, reproductive and urinary physiology, nervous system diseases
Abstract

Comparison of mean Pearson DNA methylation correlation coefficients. Comparisons were made including all ESC- and iPSC-derived NSC and neurons from the human isogenic stem cell system (isogenic hESC- and iPSC-derived NSC and neurons), all samples from the study of Fernández-Santiago et al. [11] except fibroblasts, and all samples from the study of Kim et al. [15]. iPSC, induced pluripotent stem cell; LRRK2, leucin-rich repeat kinase 2; PD, Parkinson’s disease; iPD, idiopathic PD; DAn, dopaminergic neuron; n, number; NSC, neural stem cell; hES(C), human embryonic stem (cell); I3, ESC-line I3; H9, ESC-line H9. (PDF 49 kb)

Published
2018
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17. Additional file 5: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Abstract

Mean DNA methylation levels of different genomic regions based on the genome-wide 450 K array data [AU ± SD]. AU, arbitrary unit; SD, standard deviation; hES, human embryonic stem cell; NSC, neural stem cell; iPS, induced pluripotent stem cell. (PDF 39 kb)

Published
2018
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19. Additional file 9: of DNA methylation alterations in iPSC- and hESC-derived neurons: potential implications for neurological disease modeling

Author

Boni, Laura De, Gasparoni, Gilles, Haubenreich, Carolin, Tierling, Sascha, Schmitt, Ina, Peitz, Michael, Koch, Philipp, Walter, Jörn, Wüllner, Ullrich, and Brüstle, Oliver

Subjects
nervous system, embryonic structures, reproductive and urinary physiology
Abstract

A–C Immunocytochemical analysis of iPSC generated from hESC-derived NSC staining positive for the pluripotency-associated markers Oct4, Tra-1-60, and Tra-1-81. D–F Upon differentiation, these cells give rise to all three germ layers and express appropriate markers for endoderm (D; AFP), mesoderm (E, SMA), and ectoderm (F; TUBB3). G, H iPSC-derived NSC expressing the early neuroectodermal markers nestin, Dach1, ZO1, and PLZF. I, J 6-week-old iPSC-derived neurons staining for the neuronal marker MAP2 (I) and the neurotransmitter GABA (J). Nuclei are counterstained with DAPI. Scale bars: A–F, 75 μm; G–J, 10 μm. (PDF 3601 kb)

Published
2018
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20. MOESM7 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Subjects
genetic structures, bacterial infections and mycoses
Abstract

Additional file 7: Figure S4. Distance of DMRs to the transcription start sites (TSS) of their associated genes (GREAT).

Published
2016
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21. MOESM3 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 3: Table S2. Regulation of miRNAs during differentiation.

Published
2016
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22. MOESM9 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 9: Figure S5. Time course of DNA demethylation. Comparative methylation plots obtained by targeted deep bisulfite sequencing of 10 DMRs show a rapid decline in DNA methylation during monocyte-to-macrophage differentiation (time points: 0 h, 12 h, 24 h, 36 h, 48 h, 60 h and 72 h). Samples sorted by differentiation time.

Published
2016
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23. MOESM2 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 2: Table S1. List of statistically significantly overrepresented GO terms (GO biological process data set) for genes up-regulated during ​monocyte-to-macrophage differentiation (log2Fc > 2) in the Blueprint study (Saeed et al. 2014) but with unchanged expression in the present study (log2Fc 2 are shown. In the reverse analysis (genes up-regulated in the present study, but unchanged in the Blueprint study), no significant enrichment of GO terms was found.

Published
2016
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24. MOESM6 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 6: Figure S3. Validation of DMRs by deep bisulfite sequencing. Comparative methylation plots of monocytes and macrophage from 4 independent donor samples (Hm03, Hm04, Hm05 and Hm06). Samples sorted by overall methylation. DMR33 is shown in the main text. Related to Fig. 2d and 2e.

Published
2016
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25. MOESM14 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 14: Figure S7. Time course of 5hmC (5-hydroxymethylcytosine) and 5mC (5-methylcytosine) in 2 DMRs during monocyte-to-macrophage differentiation. Average cytosine variant levels in Hm06 (A and B) and Hm10 (C) donor monocytes (0 h) and cells collected at different time points during differentiation into macrophages. (A) DMR33, (B and C) DMR13. Related to Fig. 5c.

Published
2016
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26. MOESM12 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 12: Table S6. Primer sequences and PCR conditions for targeted bisulfite sequencing and NOMe-Seq analysis.

Published
2016
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27. MOESM4 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Subjects
sense organs, skin and connective tissue diseases
Abstract

Additional file 4: Figure S2. Correlation plot of gene expression changes and DNA methylation changes around the transcription start site (± 1 kb). Each dot represents a gene. The x-coordinate shows the mean change of DNA methylation during monocyte-to-macrophage differentiation in the regions ± 1 kb around the TSS of the corresponding genes. The y-coordinate represents the log2 fold change in its expression.

Published
2016
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28. MOESM13 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 13: Table S7. Antibodies used for flow cytometry of surface antigens.

Published
2016
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29. MOESM10 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Abstract

Additional file 10: Figure S6 Time course of chromatin accessibility (GpC methylation) and DNA CpG methylation in 4 DMRs during monocyte-to-macrophage differentiation. The transition from lower to higher GpC methylated fraction is indicative of an increase in chromatin accessibility. Average CpG and GpC methylated fractions in Hm10 donor monocytes (0 h) and cells collected at different time points during differentiation into macrophages (6 h, 12 h, 18 h, 36 h, 48 h and 72 h). GCG motifs were excluded due to ambiguity between CpG- endogenous- and GpC-enzymatic-methylation. Related to Fig. 5b.

Published
2016
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30. MOESM1 of Epigenetic dynamics of monocyte-to-macrophage differentiation

Author

Wallner, Stefan, Schröder, Christopher, Leitão, Elsa, Berulava, Tea, Haak, Claudia, Beißer, Daniela, Rahmann, Sven, Richter, Andreas, Manke, Thomas, Bönisch, Ulrike, Arrigoni, Laura, Fröhler, Sebastian, Klironomos, Filippos, Chen, Wei, Rajewsky, Nikolaus, Müller, Fabian, Ebert, Peter, Lengauer, Thomas, Barann, Matthias, Rosenstiel, Philip, Gasparoni, Gilles, Nordström, Karl, Walter, Jörn, Brors, Benedikt, Zipprich, Gideon, Felder, Bärbel, Klein-Hitpass, Ludger, Attenberger, Corinna, Schmitz, Gerd, and Horsthemke, Bernhard

Subjects
sense organs, skin and connective tissue diseases
Abstract

Additional file 1: Figure S1. Scatter plot of the mean log2 fold changes obtained by BLUEPRINT for three pairs of monocytes/macrophages vs. the mean log2 fold changes of the two pairs of monocytes/macrophages analyzed by DEEP (r = 0.68).

Published
2016
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31. Frontotemporal dementia and its subtypes: a genome-wide association study

Author

Ferrari, Raffaele, Hernandez, Dena G, Nalls, Michael A, Rohrer, Jonathan D, Ramasamy, Adaikalavan, Kwok, John BJ, Dobson-Stone, Carol, Brooks, William S, Schofield, Peter R, Halliday, Glenda M, Hodges, John R, Piguet, Olivier, Bartley, Lauren, Thompson, Elizabeth, Haan, Eric, Hernández, Isabel, Ruiz, Agustín, Boada, Mercè, Borroni, Barbara, Padovani, Alessandro, Cruchaga, Carlos, Cairns, Nigel J, Benussi, Luisa, Binetti, Giuliano, Ghidoni, Roberta, Forloni, Gianluigi, Galimberti, Daniela, Fenoglio, Chiara, Serpente, Maria, Scarpini, Elio, Clarimón, Jordi, Lleó, Alberto, Blesa, Rafael, Waldö, Maria Landqvist, Nilsson, Karin, Nilsson, Christer, Mackenzie, Ian RA, Hsiung, Ging-Yuek R, Mann, David MA, Grafman, Jordan, Morris, Christopher M, Attems, Johannes, Griffiths, Timothy D, McKeith, Ian G, Thomas, Alan J, Pietrini, P, Huey, Edward D, Wassermann, Eric M, Baborie, Atik, Jaros, Evelyn, Tierney, Michael C, Pastor, Pau, Razquin, Cristina, Ortega-Cubero, Sara, Alonso, Elena, Perneczky, Robert, Diehl-Schmid, Janine, Alexopoulos, Panagiotis, Kurz, Alexander, Rainero, Innocenzo, Rubino, Elisa, Pinessi, Lorenzo, Rogaeva, Ekaterina, St George-Hyslop, Peter, Rossi, Giacomina, Tagliavini, Fabrizio, Giaccone, Giorgio, Rowe, James B, Schlachetzki, Johannes CM, Uphill, James, Collinge, John, Mead, Simon, Danek, Adrian, Van Deerlin, Vivianna M, Grossman, Murray, Trojanowski, John Q, van der Zee, Julie, Deschamps, William, Van Langenhove, Tim, Cruts, Marc, Van Broeckhoven, Christine, Cappa, Stefano F, Le Ber, Isabelle, Hannequin, Didier, Golfier, Véronique, Vercelletto, Martine, Brice, Alexis, Nacmias, Benedetta, Sorbi, Sandro, Bagnoli, Silvia, Piaceri, Irene, Nielsen, Jørgen E, Hjermind, Lena E, Riemenschneider, Matthias, Mayhaus, Manuel, Ibach, Bernd, Gasparoni, Gilles, Pichler, Sabrina, Gu, Wei, and Rossor, Martin N

Subjects
Adult, Male, Aging, Genotype, Clinical Sciences, Neurodegenerative, Rare Diseases, Clinical Research, 80 and over, Genetics, Acquired Cognitive Impairment, Humans, 2.1 Biological and endogenous factors, Aetiology, Alzheimer's Disease Related Dementias (ADRD), Aged, Neurology & Neurosurgery, Human Genome, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Middle Aged, Brain Disorders, Frontotemporal Dementia (FTD), Frontotemporal Dementia, Neurological, Female, Dementia, Genome-Wide Association Study
Abstract

BackgroundFrontotemporal dementia (FTD) is a complex disorder characterised by a broad range of clinical manifestations, differential pathological signatures, and genetic variability. Mutations in three genes-MAPT, GRN, and C9orf72--have been associated with FTD. We sought to identify novel genetic risk loci associated with the disorder.MethodsWe did a two-stage genome-wide association study on clinical FTD, analysing samples from 3526 patients with FTD and 9402 healthy controls. To reduce genetic heterogeneity, all participants were of European ancestry. In the discovery phase (samples from 2154 patients with FTD and 4308 controls), we did separate association analyses for each FTD subtype (behavioural variant FTD, semantic dementia, progressive non-fluent aphasia, and FTD overlapping with motor neuron disease [FTD-MND]), followed by a meta-analysis of the entire dataset. We carried forward replication of the novel suggestive loci in an independent sample series (samples from 1372 patients and 5094 controls) and then did joint phase and brain expression and methylation quantitative trait loci analyses for the associated (p

Published
2014

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