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1. Pre-hypertrophic chondrogenic enhancer landscape of limb and axial skeleton development

Author

Darbellay, Fabrice, Ramisch, Anna, Lopez-Delisle, Lucille, Kosicki, Michael, Rauseo, Antonella, Jouini, Zahra, Visel, Axel, and Andrey, Guillaume

Subjects
Biological Sciences, Genetics, Human Genome, Pediatric, Congenital Structural Anomalies, Arthritis, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Musculoskeletal, Animals, Enhancer Elements, Genetic, Humans, Chondrocytes, Mice, Chondrogenesis, Receptor, Fibroblast Growth Factor, Type 3, Collagen Type II, Gene Expression Regulation, Developmental, Bone Development, Extremities, Male, Cell Differentiation, Transcription Factors, Female
Abstract

Chondrocyte differentiation controls skeleton development and stature. Here we provide a comprehensive map of chondrocyte-specific enhancers and show that they provide a mechanistic framework through which non-coding genetic variants can influence skeletal development and human stature. Working with fetal chondrocytes isolated from mice bearing a Col2a1 fluorescent regulatory sensor, we identify 780 genes and 2'704 putative enhancers specifically active in chondrocytes using a combination of RNA-seq, ATAC-seq and H3K27ac ChIP-seq. Most of these enhancers (74%) show pan-chondrogenic activity, with smaller populations being restricted to limb (18%) or trunk (8%) chondrocytes only. Notably, genetic variations overlapping these enhancers better explain height differences than those overlapping non-chondrogenic enhancers. Finally, targeted deletions of identified enhancers at the Fgfr3, Col2a1, Hhip and, Nkx3-2 loci confirm their role in regulating cognate genes. This enhancer map provides a framework for understanding how genes and non-coding variations influence bone development and diseases.

Published
2024

2. Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases

Author

Carland, Corinne, Png, Grace, Malarstig, Anders, Kho, Pik Fang, Gustafsson, Stefan, Michaelsson, Karl, Lind, Lars, Tsafantakis, Emmanouil, Karaleftheri, Maria, Dedoussis, George, Ramisch, Anna, Macdonald-Dunlop, Erin, Klaric, Lucija, Joshi, Peter K., Chen, Yan, Björck, Hanna M., Eriksson, Per, Carrasco-Zanini, Julia, Wheeler, Eleanor, Suhre, Karsten, Gilly, Arthur, Zeggini, Eleftheria, Viñuela, Ana, Dermitzakis, Emmanouil T., Wilson, James F., Langenberg, Claudia, Thareja, Gaurav, Halama, Anna, Schmidt, Frank, Zanetti, Daniela, and Assimes, Themistocles

Published
2023
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5. Evolutionary dynamics of selfish DNA generates pseudo-linguistic features of genomes

Author

Sheinman, Michael, Ramisch, Anna, Massip, Florian, and Arndt, Peter F.

Subjects
Quantitative Biology - Populations and Evolution, Physics - Biological Physics, Quantitative Biology - Genomics
Abstract

Since the sequencing of large genomes, many statistical features of their sequences have been found. One intriguing feature is that certain subsequences are much more abundant than others. In fact, abundances of subsequences of a given length are distributed with a scale-free power-law tail, resembling properties of human texts, such as the Zipf's law. Despite recent efforts, the understanding of this phenomenon is still lacking. Here we find that selfish DNA elements, such as those belonging to the Alu family of repeats, dominate the power-law tail. Interestingly, for the Alu elements the power-law exponent increases with the length of the considered subsequences. Motivated by these observations, we develop a model of selfish DNA expansion. The predictions of this model qualitatively and quantitatively agree with the empirical observations. This allows us to estimate parameters for the process of selfish DNA spreading in a genome during its evolution. The obtained results shed light on how evolution of selfish DNA elements shapes non-trivial statistical properties of genomes., Comment: 9 pages, 5 figures

Published
2016
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9. Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases

Author

Carland, Corinne, primary, Png, Grace, additional, Malarstig, Anders, additional, Kho, Pik Fang, additional, Gustafsson, Stefan, additional, Michaelsson, Karl, additional, Lind, Lars, additional, Tsafantakis, Emmanouil, additional, Karaleftheri, Maria, additional, Dedoussis, George, additional, Ramisch, Anna, additional, Macdonald-Dunlop, Erin, additional, Klaric, Lucija, additional, Joshi, Peter K., additional, Chen, Yan, additional, Björck, Hanna M., additional, Eriksson, Per, additional, Carrasco-Zanini, Julia, additional, Wheeler, Eleanor, additional, Suhre, Karsten, additional, Gilly, Arthur, additional, Zeggini, Eleftheria, additional, Viñuela, Ana, additional, Dermitzakis, Emmanouil T., additional, Wilson, James F., additional, Langenberg, Claudia, additional, Thareja, Gaurav, additional, Halama, Anna, additional, Schmidt, Frank, additional, Consortium, SCALLOP, additional, Zanetti, Daniela, additional, and Assimes, Themistocles, additional

Published
2023
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10. Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

Author

Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J.M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., Viñuela, Ana, Brown, Andrew A., Fernandez-Tajes, Juan J., Hong, Mun gwan, Brorsson, Caroline A., Koivula, Robert W., Davtian, David, Dupuis, Théo, Sartori, Ambra, Michalettou, Theodora Dafni, Forgie, Ian M., Adam, Jonathan, Allin, Kristine H., Caiazzo, Robert, Cederberg, Henna, De Masi, Federico, Elders, Petra J.M., Giordano, Giuseppe N., Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison J., Howald, Cédric, Jones, Angus G., Kokkola, Tarja, Laakso, Markku, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Mourby, Miranda, Musholt, Petra B., Nilsson, Birgitte, Pattou, Francois, Penet, Deborah, Raverdy, Violeta, Ridderstråle, Martin, Romano, Luciana, Rutters, Femke, Sharma, Sapna, Teare, Harriet, ‘t Hart, Leen, Tsirigos, Konstantinos D., Vangipurapu, Jagadish, Vestergaard, Henrik, Brunak, Søren, Franks, Paul W., Frost, Gary, Grallert, Harald, Jablonka, Bernd, McCarthy, Mark I., Pavo, Imre, Pedersen, Oluf, Ruetten, Hartmut, Walker, Mark, Adragni, Kofi, Allesøe, Rosa Lundbye L., Artati, Anna A., Arumugam, Manimozhiyan, Atabaki-Pasdar, Naeimeh, Baltauss, Tania, Banasik, Karina, Barnett, Anna L., Baum, Patrick, Bell, Jimmy D., Beulens, Joline W., Bianzano, Susanna B., Bizzotto, Roberto, Bonnefond, Amelie, Cabrelli, Louise, Dale, Matilda, Dawed, Adem Y., de Preville, Nathalie, Dekkers, Koen F., Deshmukh, Harshal A., Dings, Christiane, Donnelly, Louise, Dutta, Avirup, Ehrhardt, Beate, Engelbrechtsen, Line, Eriksen, Rebeca, Fan, Yong, Ferrer, Jorge, Fitipaldi, Hugo, Forman, Annemette, Fritsche, Andreas, Froguel, Philippe, Gassenhuber, Johann, Gough, Stephen, Graefe-Mody, Ulrike, Grempler, Rolf, Groeneveld, Lenka, Groop, Leif, Gudmundsdóttir, Valborg, Gupta, Ramneek, Hennige, Anita M.H., Hill, Anita V., Holl, Reinhard W., Hudson, Michelle, Jacobsen, Ulrik Plesner, Jennison, Christopher, Johansen, Joachim, Jonsson, Anna, Karaderi, Tugce, Kaye, Jane, Kennedy, Gwen, Klintenberg, Maria, Kuulasmaa, Teemu, Lehr, Thorsten, Loftus, Heather, Lundgaard, Agnete Troen T., Mazzoni, Gianluca, McRobert, Nicky, McVittie, Ian, Nice, Rachel, Nicolay, Claudia, Nijpels, Giel, Palmer, Colin N., Pedersen, Helle K., Perry, Mandy H., Pomares-Millan, Hugo, Prehn, Cornelia P., Ramisch, Anna, Rasmussen, Simon, Robertson, Neil, Rodriquez, Marianne, Sackett, Peter, Scherer, Nina, Shah, Nisha, Sihinevich, Iryna, Slieker, Roderick C., Sondertoft, Nadja B., Steckel-Hamann, Birgit, Thomas, Melissa K., Thomas, Cecilia Engel E., Thomas, Elizabeth Louise L., Thorand, Barbara, Thorne, Claire E., Tillner, Joachim, Tura, Andrea, Uhlen, Mathias, van Leeuwen, Nienke, van Oort, Sabine, Verkindt, Helene, Vogt, Josef, Wad Sackett, Peter W., Wesolowska-Andersen, Agata, Whitcher, Brandon, White, Margaret W., Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Abstract

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

Published
2023

11. Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Author

Allesøe, Rosa Lundbye, Lundgaard, Agnete Troen, Hernández Medina, Ricardo, Aguayo-Orozco, Alejandro, Johansen, Joachim, Nissen, Jakob Nybo, Brorsson, Caroline, Mazzoni, Gianluca, Niu, Lili, Biel, Jorge Hernansanz, Brasas, Valentas, Webel, Henry, Benros, Michael Eriksen, Pedersen, Anders Gorm, Chmura, Piotr Jaroslaw, Jacobsen, Ulrik Plesner, Mari, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Hong, Mun-Gwan, Musholt, Petra B., de Masi, Federico, Vogt, Josef, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, E. Louise, Frost, Gary, Thomsen, Henrik, Hansen, Elizaveta, Hansen, Tue Haldor, Vestergaard, Henrik, Muilwijk, Mirthe, Blom, Marieke T., ‘t Hart, Leen M., Pattou, Francois, Raverdy, Violeta, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Mourby, Miranda, Kaye, Jane, Hattersley, Andrew, McDonald, Timothy, Ridderstråle, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Hansen, Torben, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, McCarthy, Mark I., Pearson, Ewan, Banasik, Karina, Rasmussen, Simon, Brunak, S. ren, Froguel, Philippe, Thomas, Cecilia Engel, Haussler, Ragna, Beulens, Joline, Rutters, Femke, Nijpels, Giel, van Oort, Sabine, Groeneveld, Lenka, Elders, Petra, Giorgino, Toni, Rodriquez, Marianne, Nice, Rachel, Perry, Mandy, Bianzano, Susanna, Graefe-Mody, Ulrike, Hennige, Anita, Grempler, Rolf, Baum, Patrick, Stærfeldt, Hans-Henrik, Shah, Nisha, Teare, Harriet, Ehrhardt, Beate, Tillner, Joachim, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinevich, Iryna, Cabrelli, Louise, Loftus, Heather, Bizzotto, Roberto, Tura, Andrea, Dekkers, Koen, van Leeuwen, Nienke, Groop, Leif, Slieker, Roderick, Ramisch, Anna, Jennison, Christopher, McVittie, Ian, Frau, Francesca, Steckel-Hamann, Birgit, Adragni, Kofi, Thomas, Melissa, Pasdar, Naeimeh Atabaki, Fitipaldi, Hugo, Kurbasic, Azra, Mutie, Pascal, Pomares-Millan, Hugo, Bonnefond, Amelie, Canouil, Mickael, Caiazzo, Robert, Verkindt, Helene, Holl, Reinhard, Kuulasmaa, Teemu, Deshmukh, Harshal, Cederberg, Henna, Laakso, Markku, Vangipurapu, Jagadish, Dale, Matilda, Thorand, Barbara, Nicolay, Claudia, Fritsche, Andreas, Hill, Anita, Hudson, Michelle, Thorne, Claire, Allin, Kristine, Arumugam, Manimozhiyan, Jonsson, Anna, Engelbrechtsen, Line, Forman, Annemette, Dutta, Avirup, Sondertoft, Nadja, Fan, Yong, Gough, Stephen, Robertson, Neil, McRobert, Nicky, Wesolowska-Andersen, Agata, Brown, Andrew, Davtian, David, Dawed, Adem, Donnelly, Louise, Palmer, Colin, White, Margaret, Ferrer, Jorge, Whitcher, Brandon, Artati, Anna, Prehn, Cornelia, Adam, Jonathan, Grallert, Harald, Gupta, Ramneek, Sackett, Peter Wad, Nilsson, Birgitte, Tsirigos, Konstantinos, Eriksen, Rebeca, Jablonka, Bernd, Uhlen, Mathias, Gassenhuber, Johann, Baltauss, Tania, de Preville, Nathalie, Klintenberg, Maria, Abdalla, Moustafa, Lundgaard, Agnete Troen [0000-0001-7447-6560], Hernández Medina, Ricardo [0000-0001-6373-2362], Johansen, Joachim [0000-0001-7052-1870], Niu, Lili [0000-0003-4571-4368], Biel, Jorge Hernansanz [0000-0002-3125-2951], Benros, Michael Eriksen [0000-0003-4939-9465], Pedersen, Anders Gorm [0000-0001-9650-8965], Jacobsen, Ulrik Plesner [0000-0001-9181-6854], Koivula, Robert [0000-0002-1646-4163], Vinuela, Ana [0000-0003-3771-8537], Haid, Mark [0000-0001-6118-1333], Hong, Mun-Gwan [0000-0001-8603-8293], Kennedy, Gwen [0000-0002-9856-3236], Thomas, E Louise [0000-0003-4235-4694], Frost, Gary [0000-0003-0529-6325], Hansen, Tue Haldor [0000-0001-5948-8993], Kaye, Jane [0000-0002-7311-4725], Hattersley, Andrew [0000-0001-5620-473X], Ridderstråle, Martin [0000-0002-3270-9167], Pedersen, Oluf [0000-0002-3321-3972], Hansen, Torben [0000-0001-8748-3831], Schwenk, Jochen M [0000-0001-8141-8449], Rasmussen, Simon [0000-0001-6323-9041], Brunak, Søren [0000-0003-0316-5866], Apollo - University of Cambridge Repository, Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, General practice, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Graduate School, and APH - Methodology

Subjects
Biomedical Engineering, Type 2 diabetes, Bioengineering, Applied Microbiology and Biotechnology, Deep Learning, SDG 3 - Good Health and Well-being, Diabetes Mellitus, Type 2, Machine learning, Molecular Medicine, Humans, Data integration, IMI DIRECT Consortium, Systems biology, Algorithms, Biotechnology
Abstract

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Published
2023

12. CRUP: a comprehensive framework to predict condition-specific regulatory units

Author

Ramisch, Anna, Heinrich, Verena, Glaser, Laura V., Fuchs, Alisa, Yang, Xinyi, Benner, Philipp, Schöpflin, Robert, Li, Na, Kinkley, Sarah, Römer-Hillmann, Anja, Longinotto, John, Heyne, Steffen, Czepukojc, Beate, Kessler, Sonja M., Kiemer, Alexandra K., Cadenas, Cristina, Arrigoni, Laura, Gasparoni, Nina, Manke, Thomas, Pap, Thomas, Pospisilik, John A., Hengstler, Jan, Walter, Jörn, Meijsing, Sebastiaan H., Chung, Ho-Ryun, and Vingron, Martin

Published
2019
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13. Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Author

Grasse, Sabrina, Lienhard, Matthias, Frese, Steffen, Kerick, Martin, Steinbach, Anne, Grimm, Christina, Hussong, Michelle, Rolff, Jana, Becker, Michael, Dreher, Felix, Schirmer, Uwe, Boerno, Stefan, Ramisch, Anna, Leschber, Gunda, Timmermann, Bernd, Grohé, Christian, Lüders, Heike, Vingron, Martin, Fichtner, Iduna, Klein, Sebastian, Odenthal, Margarete, Büttner, Reinhard, Lehrach, Hans, Sültmann, Holger, Herwig, Ralf, and Schweiger, Michal R.

Published
2018
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15. Genetic Landscape of the ACE2 Coronavirus Receptor

Author

Yang, Zhijian, Macdonald-Dunlop, Erin, Chen, Jiantao, Zhai, Ranran, Li, Ting, Richmond, Anne, Klaric, Lucija, Pirastu, Nicola, Ning, Zheng, Zheng, Chenqing, Wang, Yipeng, Huang, Tingting, He, Yazhou, Guo, Huiming, Ying, Kejun, Gustafsson, Stefan, Prins, Bram, Ramisch, Anna, Dermitzakis, Emmanouil T., Png, Grace, Eriksson, Niclas, Haessler, Jeffrey, Hu, Xiaowei, Zanetti, Daniela, Boutin, Thibaud, Hwang, Shih-Jen, Wheeler, Eleanor, Pietzner, Maik, Raffield, Laura M., Kalnapenkis, Anette, Peters, James E., Vinuela, Ana, Gilly, Arthur, Elmstahl, Solve, Dedoussis, George, Petrie, John R., Polasek, Ozren, Folkersen, Lasse, Chen, Yan, Yao, Chen, Vosa, Urmo, Pairo-Castineira, Erola, Clohisey, Sara, Bretherick, Andrew D., Rawlik, Konrad, Esko, Tonu, Enroth, Stefan, Johansson, Åsa, Gyllensten, Ulf B., Langenberg, Claudia, Levy, Daniel, Hayward, Caroline, Assimes, Themistocles L., Kooperberg, Charles, Manichaikul, Ani W., Siegbahn, Agneta, Wallentin, Lars, Lind, Lars, Zeggini, Eleftheria, Schwenk, Jochen M., Butterworth, Adam S., Michaëlsson, Karl, Pawitan, Yudi, Joshi, Peter K., Baillie, J. Kenneth, Malarstig, Anders, Reiner, Alexander P., Wilson, James F., Shen, Xia, Yang, Zhijian, Macdonald-Dunlop, Erin, Chen, Jiantao, Zhai, Ranran, Li, Ting, Richmond, Anne, Klaric, Lucija, Pirastu, Nicola, Ning, Zheng, Zheng, Chenqing, Wang, Yipeng, Huang, Tingting, He, Yazhou, Guo, Huiming, Ying, Kejun, Gustafsson, Stefan, Prins, Bram, Ramisch, Anna, Dermitzakis, Emmanouil T., Png, Grace, Eriksson, Niclas, Haessler, Jeffrey, Hu, Xiaowei, Zanetti, Daniela, Boutin, Thibaud, Hwang, Shih-Jen, Wheeler, Eleanor, Pietzner, Maik, Raffield, Laura M., Kalnapenkis, Anette, Peters, James E., Vinuela, Ana, Gilly, Arthur, Elmstahl, Solve, Dedoussis, George, Petrie, John R., Polasek, Ozren, Folkersen, Lasse, Chen, Yan, Yao, Chen, Vosa, Urmo, Pairo-Castineira, Erola, Clohisey, Sara, Bretherick, Andrew D., Rawlik, Konrad, Esko, Tonu, Enroth, Stefan, Johansson, Åsa, Gyllensten, Ulf B., Langenberg, Claudia, Levy, Daniel, Hayward, Caroline, Assimes, Themistocles L., Kooperberg, Charles, Manichaikul, Ani W., Siegbahn, Agneta, Wallentin, Lars, Lind, Lars, Zeggini, Eleftheria, Schwenk, Jochen M., Butterworth, Adam S., Michaëlsson, Karl, Pawitan, Yudi, Joshi, Peter K., Baillie, J. Kenneth, Malarstig, Anders, Reiner, Alexander P., Wilson, James F., and Shen, Xia

Abstract

Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. Conclusions: Human plasma ACE2 shares a genetic basis with cardi

Published
2022
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18. MOESM1 of CRUP: a comprehensive framework to predict condition-specific regulatory units

Author

Ramisch, Anna, Heinrich, Verena, Glaser, Laura, Fuchs, Alisa, Xinyi Yang, Benner, Philipp, Schöpflin, Robert, Li, Na, Kinkley, Sarah, Römer-Hillmann, Anja, Longinotto, John, Heyne, Steffen, Czepukojc, Beate, Kessler, Sonja, Kiemer, Alexandra, Cadenas, Cristina, Arrigoni, Laura, Gasparoni, Nina, Manke, Thomas, Pap, Thomas, Pospisilik, John, Hengstler, Jan, Walter, Jörn, Meijsing, Sebastiaan, Ho-Ryun Chung, and Vingron, Martin

Subjects
Data_FILES
Abstract

Additional file 1 This file contains additional information about the origin and quality of the data used in this study as well as complementary results.

Published
2019
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19. Enhancer Prediction Based on Epigenomic Data

Author

Ramisch, Anna

Subjects
classification, epigenetics, enhancer prediction, histone modifications, 500 Naturwissenschaften und Mathematik::570 Biowissenschaften, Biologie::570 Biowissenschaften, Biologie, 500 Naturwissenschaften und Mathematik::500 Naturwissenschaften::500 Naturwissenschaften und Mathematik, random forest
Abstract

In this thesis, we show how to exploit the current knowledge of enhancers, and integrate different types of epigenomic data to make condition-specific predictions on the location of active enhancers. First, we introduce a novel method for genome-wide enhancer prediction which is solely based on histone modification data. Our method is a combination of two random forest classifiers, where one classifier learns the difference between active and inactive genomic regions and the other concentrates on the more difficult task to distinguish active enhancers from active promoters. We model and optimize the corresponding features taking into account the local chromatin structure. For an active enhancer, this is in essence an accessible region flanked by nucleosomes with specific histone modifications. To avoid circular reasoning, our training enhancers are defined by feature set-independent characteristics: accessibility and bidirectional transcription. We thoroughly validate our method on mouse embryonic stem cell data and achieve very good performances on a constructed test set as well as on a validated set of enhancers. Moreover, our genome-wide enhancer predictions have a high spatial resolution. We also cluster proximal enhancers and show that the resulting regions of high enhancer density are in good agreement with a published list of super-enhancers in mouse embryonic stem cells. In contrast to many other methods, we offer a pre-trained classifier with integrated data normalization that can be used to reliably predict enhancers across different cell types and species. This classifier is superior to the prominent unsupervised method ChromHMM, and shows similar results as the recent supervised REPTILE approach when applied in the same cell type. In terms of transferability to other conditions, our method outperforms REPTILE. Finally, we demonstrate how our pre-trained classifier can be embedded into a comprehensive framework to predict condition-specific regulatory units (pairs of enhancers and putative target genes) of histone modification and gene expression data., In dieser Doktorarbeit zeigen wir, wie man die aktuellen Enhancer-Kentnisse nutzen und verschiedene epigenetische Datensätze integrieren kann um die Postition aktiver Enhancer unter spezifischen Bedingungen vorherzusagen. Zuerst stellen wir eine neue Methode zur genomweiten Enhancer-Vorhersage basierend auf Histonmodifikationsdaten vor. Unsere Methode kombiniert zwei Random Forest Klassifikationsverfahren zur Unterscheidung von aktiven und inaktiven genomischen Regionen und zur schwierigeren Unterscheidung von aktiven Enhancern und aktiven Promotoren. Beim Modellieren und Optimieren der Klassifikationsmerkmale (Feature) berücksichtigen wir die lokale Chromatinstruktur. Kennzeichnend für einen aktiven Enhancer ist imWesentlichen ein Abschnitt zugänglichen Chromatins, umgeben von Nukleosomen mit spezifischen Histonmodifikationen. Unsere Trainings-Enhancer sind so definiert, dass sie offene Chromatinregionen umfassen und nachweislich bidirektionale Transkripte herstellen. Diese Enhancer-Charakteristiken haben wir möglichst unabhängig von den Klassifikationsmerkmalen gewählt um Zirkelschlüsse zu vermeiden. Wir haben unsere Methode in embryonalen Stammzellen der Maus validiert und sehr gute Vorhersagergebnisse auf ausgewählten Testsets erzielt. Außerdem haben wir vorhergesagte, beieinanderliegende Enhancer in Regionen hoher Enhancer-Dichte zusammengefasst, für die wir eine gute Übereinstimmung mit veröffentlichten Superenhancern feststellen konnten. Im Gegensatz zu vielen Methoden zur Enhancer-Vorhersage bieten wir ein trainiertes Modell mit integriereter Datennormalisierung an, dass zuverlässig auf neue Datensätze anderer Zelltypen und Spezies angewendet werden kann. Unser Modell zeigt bessere Ergenisse als die viel genutzte Methode ChromHMM, und ist bei Anwendung innerhalb eines Zelltyps vergleichbar mit der REPTILE-Methode. Für die Anwendung auf neue Datensätze ist unsere Methode besser geeignet. Schließlich zeigen wir, wie unser trainiertes Modell als Basis eines Frameworks fungieren kann um bedingungsspezifische regulatorische Einheiten (Enhancer-Gen-Paare) von Histonmodifikations- und Genexpressionsdaten vorherzusagen.

Published
2019

20. Additional file 2: of Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Author

Grasse, Sabrina, Lienhard, Matthias, Frese, Steffen, Kerick, Martin, Steinbach, Anne, Grimm, Christina, Hussong, Michelle, Rolff, Jana, Becker, Michael, Dreher, Felix, Schirmer, Uwe, Boerno, Stefan, Ramisch, Anna, Leschber, Gunda, Timmermann, Bernd, Grohé, Christian, Lüders, Heike, Vingron, Martin, Iduna Fichtner, Klein, Sebastian, Odenthal, Margarete, Büttner, Reinhard, Lehrach, Hans, Sültmann, Holger, Herwig, Ralf, and Schweiger, Michal

Abstract

Additional Methods. Figure S1. Region used for the MSP of LRP12. Figure S2. MeDIP-Seq statistics of 54 samples including primary tumor tissues (P) and PDXs (X). Table S3. MeDIP-Sequencing statistics. Table S4 Methyl-Sequencing statistics. Figure S3. Correlation of methylation of overlapping DMRs in primary NSCLC and patient-derived xenografts. Table S5. Tumor content of primary tumors and genome-wide Spearman correlation of DMRs of primary tissue to the PDXs. Figure S4. Patientwise circos plots of overlapping DMRs of primary NSCLC and PDX. Figure S5. Comparison of methylation values of primary NSCLCs and PDXs. Figure S6. Methylation differences between non-responders and responders in large hypomethylated blocks (LHBs) on chromosomes 1, 2, and 4 as examples. Table S6. Histopathologic evaluation of primary tumor and PDX tumor. Figure S7. Ingenuity pathway and upstream regulator analyses of the 2380 genes differentially methylated. Figure S8. LRP12 knockdown induces carboplatin resistance. Table S9. Patient’s data and clinical characteristics of the validation cohort. Figure S9. LRP12 DNA hypermethylation as independent factor predictive for clinical outcome in NSCLC. Figure S10 LRP12 DNA hypermethylation as independent predictive factor for clinical outcome in 449 NSCLC patients from the TCGA data set. Additional references. (XLSX 141 kb)

Published
2018
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21. CRUP: A comprehensive framework to predict condition-specific regulatory units

Author

Ramisch, Anna, primary, Heinrich, Verena, additional, Glaser, Laura V., additional, Fuchs, Alisa, additional, Yang, Xinyi, additional, Benner, Philipp, additional, Schöpflin, Robert, additional, Li, Na, additional, Kinkley, Sarah, additional, Hillmann, Anja, additional, Longinotto, John, additional, Heyne, Steffen, additional, Czepukojc, Beate, additional, Kessler, Sonja M., additional, Kiemer, Alexandra K., additional, Cadenas, Cristina, additional, Arrigoni, Laura, additional, Gasparoni, Nina, additional, Manke, Thomas, additional, Pap, Thomas, additional, Pospisilik, Andrew, additional, Hengstler, Jan, additional, Walter, Jörn, additional, Meijsing, Sebastiaan H., additional, Chung, Ho-Ryun, additional, and Vingron, Martin, additional

Published
2018
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22. Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Author

Volkswagen Foundation, Federal Ministry of Education and Research (Germany), University of Cologne, Staatskanzlei des Landes Nordrhein-Westfalen, German Research Foundation, Grasse, Sabrina, Lienhard, Matthias, Frese, Steffen, Kerick, Martin, Steinbach, Anne, Grimm, Christina, Hussong, Michelle, Rolff, Jana, Becker, Michael, Dreher, Felix, Schirmer, Uwe, Boerno, Stefan, Ramisch, Anna, Leschber, Gunda, Timmermann, Bernd, Grohé, Christian, Lüders, Heike, Vingron, Martin, Fichtner, Iduna, Klein, Sebastian, Odenthal, Margarete, Büttner, Reinhard, Lehrach, Hans, Sültmann, Holger, Herwig, Ralf, Schweiger, Michal R., Volkswagen Foundation, Federal Ministry of Education and Research (Germany), University of Cologne, Staatskanzlei des Landes Nordrhein-Westfalen, German Research Foundation, Grasse, Sabrina, Lienhard, Matthias, Frese, Steffen, Kerick, Martin, Steinbach, Anne, Grimm, Christina, Hussong, Michelle, Rolff, Jana, Becker, Michael, Dreher, Felix, Schirmer, Uwe, Boerno, Stefan, Ramisch, Anna, Leschber, Gunda, Timmermann, Bernd, Grohé, Christian, Lüders, Heike, Vingron, Martin, Fichtner, Iduna, Klein, Sebastian, Odenthal, Margarete, Büttner, Reinhard, Lehrach, Hans, Sültmann, Holger, Herwig, Ralf, and Schweiger, Michal R.

Abstract

[Background] Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed., [Methods] Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort., [Results] Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma)., [Conclusions] Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models.

Published
2018

25. Genetic Landscape of the ACE2 Coronavirus Receptor.

Author

Yang Z, Macdonald-Dunlop E, Chen J, Zhai R, Li T, Richmond A, Klarić L, Pirastu N, Ning Z, Zheng C, Wang Y, Huang T, He Y, Guo H, Ying K, Gustafsson S, Prins B, Ramisch A, Dermitzakis ET, Png G, Eriksson N, Haessler J, Hu X, Zanetti D, Boutin T, Hwang SJ, Wheeler E, Pietzner M, Raffield LM, Kalnapenkis A, Peters JE, Viñuela A, Gilly A, Elmståhl S, Dedoussis G, Petrie JR, Polašek O, Folkersen L, Chen Y, Yao C, Võsa U, Pairo-Castineira E, Clohisey S, Bretherick AD, Rawlik K, Esko T, Enroth S, Johansson Å, Gyllensten U, Langenberg C, Levy D, Hayward C, Assimes TL, Kooperberg C, Manichaikul AW, Siegbahn A, Wallentin L, Lind L, Zeggini E, Schwenk JM, Butterworth AS, Michaëlsson K, Pawitan Y, Joshi PK, Baillie JK, Mälarstig A, Reiner AP, Wilson JF, and Shen X

Subjects
Cross-Sectional Studies, Genome-Wide Association Study, Humans, Receptors, Coronavirus, SARS-CoV-2, Angiotensin-Converting Enzyme 2 genetics, COVID-19 genetics
Abstract

Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood., Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data., Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P =0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P =0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P =0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells., Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Published
2022
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