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1. 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, Adamski, Jerzy, Schwenk, Jochen M., Pearson, Ewan R., Dermitzakis, Emmanouil T., and Viñuela, Ana

Published
2023
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2. 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, Leal Rodríguez, Cristina, 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, and Brunak, Søren

Published
2023
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4. Author Correction: 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, Leal Rodríguez, Cristina, 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, and Brunak, Søren

Published
2023
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5. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Author

Wesolowska-Andersen, Agata, Brorsson, Caroline A., Bizzotto, Roberto, Mari, Andrea, Tura, Andrea, Koivula, Robert, Mahajan, Anubha, Vinuela, Ana, Tajes, Juan Fernandez, Sharma, Sapna, Haid, Mark, Prehn, Cornelia, Artati, Anna, Hong, Mun-Gwan, Musholt, Petra B., Kurbasic, Azra, De Masi, Federico, Tsirigos, Kostas, Pedersen, Helle Krogh, Gudmundsdottir, Valborg, Thomas, Cecilia Engel, Banasik, Karina, Jennison, Chrisopher, Jones, Angus, Kennedy, Gwen, Bell, Jimmy, Thomas, Louise, Frost, Gary, Thomsen, Henrik, Allin, Kristine, Hansen, Tue Haldor, Vestergaard, Henrik, Hansen, Torben, Rutters, Femke, Elders, Petra, t’Hart, Leen, Bonnefond, Amelie, Canouil, Mickaël, Brage, Soren, Kokkola, Tarja, Heggie, Alison, McEvoy, Donna, Hattersley, Andrew, McDonald, Timothy, Teare, Harriet, Ridderstrale, Martin, Walker, Mark, Forgie, Ian, Giordano, Giuseppe N., Froguel, Philippe, Pavo, Imre, Ruetten, Hartmut, Pedersen, Oluf, Dermitzakis, Emmanouil, Franks, Paul W., Schwenk, Jochen M., Adamski, Jerzy, Pearson, Ewan, McCarthy, Mark I., and Brunak, Søren

Published
2022
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6. The Association of Cardiometabolic, Diet and Lifestyle Parameters With Plasma Glucagon-like Peptide-1: An IMI DIRECT Study.

Author

Eriksen, Rebeca, White, Margaret C, Dawed, Adem Y, Perez, Isabel Garcia, Posma, Joram M, Haid, Mark, Sharma, Sapna, Prehn, Cornelia, Thomas, E Louise, Koivula, Robert W, Bizzotto, Roberto, Mari, Andrea, Giordano, Giuseppe N, Pavo, Imre, Schwenk, Jochen M, Masi, Federico De, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, and Mahajan, Anubha

Subjects
TYPE 2 diabetes, PEOPLE with diabetes, MULTIPLE regression analysis, INSULIN resistance, FOOD consumption
Abstract

Context The role of glucagon-like peptide-1 (GLP-1) in type 2 diabetes (T2D) and obesity is not fully understood. Objective We investigate the association of cardiometabolic, diet, and lifestyle parameters on fasting and postprandial GLP-1 in people at risk of, or living with, T2D. Methods We analyzed cross-sectional data from the two Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohorts, cohort 1 (n = 2127) individuals at risk of diabetes; cohort 2 (n = 789) individuals with new-onset T2D. Results Our multiple regression analysis reveals that fasting total GLP-1 is associated with an insulin-resistant phenotype and observe a strong independent relationship with male sex, increased adiposity, and liver fat, particularly in the prediabetes population. In contrast, we showed that incremental GLP-1 decreases with worsening glycemia, higher adiposity, liver fat, male sex, and reduced insulin sensitivity in the prediabetes cohort. Higher fasting total GLP-1 was associated with a low intake of wholegrain, fruit, and vegetables in people with prediabetes, and with a high intake of red meat and alcohol in people with diabetes. Conclusion These studies provide novel insights into the association between fasting and incremental GLP-1, metabolic traits of diabetes and obesity, and dietary intake, and raise intriguing questions regarding the relevance of fasting GLP-1 in the pathophysiology T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans-Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun-gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, Pomares-Millan, Hugo, Fitipaldi, Hugo, Mutie, Pascal, Klintenberg, Maria, Bergstrom, Margit, Groop, Leif, Ridderstrale, Martin, Atabaki Pasdar, Naeimeh, Deshmukh, Harshal, Heggie, Alison, Wake, Dianne, McEvoy, Donna, McVittie, Ian, Walker, Mark, Hattersley, Andrew, Hill, Anita, Jones, Angus, McDonald, Timothy, Perry, Mandy, Nice, Rachel, Hudson, Michelle, Thorne, Claire, Dermitzakis, Emmanouil, Viñuela, Ana, Cabrelli, Louise, Loftus, Heather, Dawed, Adem, Donnelly, Louise, Forgie, Ian, Pearson, Ewan, Palmer, Colin, Brown, Andrew, Koivula, Robert, Wesolowska-Andersen, Agata, Abdalla, Moustafa, McRobert, Nicky, Fernandez, Juan, Jiao, Yunlong, Robertson, Neil, Gough, Stephen, Kaye, Jane, Mourby, Miranda, Mahajan, Anubha, McCarthy, Mark, Shah, Nisha, Teare, Harriet, Holl, Reinhard, Koopman, Anitra, Rutters, Femke, Beulens, Joline, Groeneveld, Lenka, Bell, Jimmy, Thomas, Louise, Whitcher, Brandon, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Hingorani, Aroon D., Patel, Riyaz S., Hemingway, Harry, Franks, Paul W., Bell, Jimmy D., Banerjee, Rajarshi, and Yaghootkar, Hanieh

Published
2019
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8. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, De Masi, Federico, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Gupta, Ramneek, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Hong, Mun-Gwan, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Mahajan, Anubha, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren

Published
2020
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10. Corrigendum: The aryl hydrocarbon receptor regulates lipid mediator production in alveolar macrophages

Author

Maier, Ann-Marie, primary, Huth, Karsten, additional, Alessandrini, Francesca, additional, Henkel, Fiona, additional, Schnautz, Benjamin, additional, Arifovic, Anela, additional, Riols, Fabien, additional, Haid, Mark, additional, Koegler, Anja, additional, Sameith, Katrin, additional, Schmidt-Weber, Carsten B., additional, Esser-von-Bieren, Julia, additional, and Ohnmacht, Caspar, additional

Published
2023
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11. The aryl hydrocarbon receptor regulates lipid mediator production in alveolar macrophages

Author

Maier, Ann-Marie, primary, Huth, Karsten, additional, Alessandrini, Francesca, additional, Schnautz, Benjamin, additional, Arifovic, Anela, additional, Riols, Fabien, additional, Haid, Mark, additional, Koegler, Anja, additional, Sameith, Katrin, additional, Schmidt-Weber, Carsten B., additional, Esser-von-Bieren, Julia, additional, and Ohnmacht, Caspar, additional

Published
2023
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12. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models:[with Author Correction]

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, Ridderstråle, Martin, Pedersen, Oluf, Hansen, Torben, Banasik, Karina, Rasmussen, Simon, and Brunak, Søren

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

13. Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study

Author

Molnos, Sophie, Wahl, Simone, Haid, Mark, Eekhoff, E. Marelise W., Pool, René, Floegel, Anna, Deelen, Joris, Much, Daniela, Prehn, Cornelia, Breier, Michaela, Draisma, Harmen H., van Leeuwen, Nienke, Simonis-Bik, Annemarie M. C., Jonsson, Anna, Willemsen, Gonneke, Bernigau, Wolfgang, Wang-Sattler, Rui, Suhre, Karsten, Peters, Annette, Thorand, Barbara, Herder, Christian, Rathmann, Wolfgang, Roden, Michael, Gieger, Christian, Kramer, Mark H. H., van Heemst, Diana, Pedersen, Helle K., Gudmundsdottir, Valborg, Schulze, Matthias B., Pischon, Tobias, de Geus, Eco J. C., Boeing, Heiner, Boomsma, Dorret I., Ziegler, Anette G., Slagboom, P. Eline, Hummel, Sandra, Beekman, Marian, Grallert, Harald, Brunak, Søren, McCarthy, Mark I., Gupta, Ramneek, Pearson, Ewan R., Adamski, Jerzy, and ’t Hart, Leen M.

Published
2017
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14. Trip13 Depletion in Liver Cancer Induces a Lipogenic Response Contributing to Plin2‐Dependent Mitotic Cell Death

Author

Rios Garcia, Marcos, primary, Meissburger, Bettina, additional, Chan, Jessica, additional, de Guia, Roldan M., additional, Mattijssen, Frits, additional, Roessler, Stephanie, additional, Birkenfeld, Andreas L., additional, Raschzok, Nathanael, additional, Riols, Fabien, additional, Tokarz, Janina, additional, Giroud, Maude, additional, Gil Lozano, Manuel, additional, Hartleben, Goetz, additional, Nawroth, Peter, additional, Haid, Mark, additional, López, Miguel, additional, Herzig, Stephan, additional, and Berriel Diaz, Mauricio, additional

Published
2022
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15. Cross-Laboratory Standardization of Preclinical Lipidomics Using Differential Mobility Spectrometry and Multiple Reaction Monitoring

Author

Ghorasaini, Mohan, primary, Mohammed, Yassene, additional, Adamski, Jerzy, additional, Bettcher, Lisa, additional, Bowden, John A., additional, Cabruja, Matias, additional, Contrepois, Kévin, additional, Ellenberger, Mathew, additional, Gajera, Bharat, additional, Haid, Mark, additional, Hornburg, Daniel, additional, Hunter, Christie, additional, Jones, Christina M., additional, Klein, Theo, additional, Mayboroda, Oleg, additional, Mirzaian, Mina, additional, Moaddel, Ruin, additional, Ferrucci, Luigi, additional, Lovett, Jacqueline, additional, Nazir, Kenneth, additional, Pearson, Mackenzie, additional, Ubhi, Baljit K., additional, Raftery, Daniel, additional, Riols, Fabien, additional, Sayers, Rebekah, additional, Sijbrands, Eric J. G., additional, Snyder, Michael P., additional, Su, Baolong, additional, Velagapudi, Vidya, additional, Williams, Kevin J., additional, de Rijke, Yolanda B., additional, and Giera, Martin, additional

Published
2021
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17. Inflammatory macrophage memory in nonsteroidal anti-inflammatory drug–exacerbated respiratory disease

Author

Haimerl, Pascal, primary, Bernhardt, Ulrike, additional, Schindela, Sonja, additional, Henkel, Fiona D.R., additional, Lechner, Antonie, additional, Zissler, Ulrich M., additional, Pastor, Xavier, additional, Thomas, Dominique, additional, Cecil, Alexander, additional, Ge, Yan, additional, Haid, Mark, additional, Prehn, Cornelia, additional, Tokarz, Janina, additional, Heinig, Matthias, additional, Adamski, Jerzy, additional, Schmidt-Weber, Carsten B., additional, Chaker, Adam M., additional, and Esser-von Bieren, Julia, additional

Published
2021
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18. Multi-omics analysis of diabetic pig lungs reveals molecular derangements underlying pulmonary complications of diabetes mellitus

Author

Shashikadze, Bachuki, Flenkenthaler, Florian, Kemter, Elisabeth, Franzmeier, Sophie, Stöckl, Jan B., Haid, Mark, Riols, Fabien, Rothe, Michael, Pichl, Lisa, Renner, Simone, Blutke, Andreas, Wolf, Eckhard, and Fröhlich, Thomas

Abstract

Growing evidence shows that the lung is an organ prone to injury by diabetes mellitus. However, the molecular mechanisms of these pulmonary complications have not yet been characterized comprehensively. To systematically study the effects of insulin deficiency and hyperglycaemia on the lung, we combined proteomics and lipidomics with quantitative histomorphological analyses to compare lung tissue samples from a clinically relevant pig model for mutant INS gene-induced diabetes of youth (MIDY) with samples from wild-type littermate controls. Among others, the level of pulmonary surfactant-associated protein A (SFTPA1), a biomarker of lung injury, was moderately elevated. Furthermore, key proteins related to humoral immune response and extracellular matrix organization were significantly altered in abundance. Importantly, a lipoxygenase pathway was dysregulated as indicated by 2.5-fold reduction of polyunsaturated fatty acid lipoxygenase ALOX15 levels, associated with corresponding changes in the levels of lipids influenced by this enzyme. Our multi-omics study points to an involvement of reduced ALOX15 levels and an associated lack of eicosanoid switching as mechanisms contributing to a proinflammatory milieu in the lungs of subjects with diabetes mellitus.

Published
2024
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19. Metabolite Shifts Induced by Marathon Race Competition Differ between Athletes Based on Level of Fitness and Performance: A Substudy of the Enzy-MagIC Study

Author

Schader, Jana F, Haid, Mark, Cecil, Alexander, Schoenfeld, Julia, Halle, Martin, Pfeufer, Arne, Prehn, Cornelia, Adamski, Jerzy, Nieman, David C, Scherr, Johannes, University of Zurich, and Schader, Jana F

Subjects
amino acids, 1303 Biochemistry, exercise, lcsh:QR1-502, 610 Medicine & health, Metabolism, Biomarker, Exercise, Amino Acids, Fatty Acids, Urea Cycle, fatty acids, Article, lcsh:Microbiology, 2712 Endocrinology, Diabetes and Metabolism, 1312 Molecular Biology, urea cycle, biomarker, 10046 Balgrist University Hospital, Swiss Spinal Cord Injury Center, human activities, metabolism
Abstract

This study compared metabolite shifts induced by training for, participation in, and recovery from a marathon race competition among athletes divided into three groups based on fitness (relative maximum oxygen uptake (VO2max)) and performance levels (net running time). Plasma samples from 76 male runners participating in the Munich Marathon were analyzed for metabolite shifts using a targeted metabolomics panel. For the entire cohort of runners, pronounced increases were measured immediately after the race for plasma concentrations of acylcarnitines (AC), the ratio (palmitoylcarnitine + stearoylcarnitine)/free carnitine that is used as a proxy for the activity of the mitochondrial enzyme carnitine palmitoyltransferase, and arginine-related metabolites, with decreases in most amino acids (AA) and phospholipids. Plasma levels of AA and phospholipids were strongly increased 24 and 72 h post-race. Post-race plasma concentrations of AC and arginine-related metabolites were higher in the low compared to top performers, indicating an accumulation of fatty acids and a reliance on protein catabolism to provide energy after the marathon event. This study showed that marathon race competition is associated with an extensive and prolonged perturbation in plasma metabolite concentrations with a strong AC signature that is greater in the slower, less aerobically fit runners. Furthermore, changes in the arginine-related metabolites were observed.

Published
2020

20. Additional file 1 of Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, Masi, Federico De, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Ramneek Gupta, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Mun-Gwan Hong, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Anubha Mahajan, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Jagadish Vangipurapu, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren

Abstract

Additional file 1. Supplementary Figures. This file contains Fig. S1 – S13.

Published
2020
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21. Predicting and elucidating the etiology of fatty liver disease:A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J M, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, Adamski, Jerzy, Musholt, Petra B, Brage, Søren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, and Pedersen, Oluf

Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning.METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.

Published
2020

22. Identifizierung eigenschaftsrelevanter Metabolitencluster

Author

Haid, Mark

Abstract

In dieser Arbeit wurde eine in-silico basierte Methode zur Identifizierung von Bioaktivitäts-relevanten Metaboliten in Naturstoffextrakten entwickelt. Die s.g. Activity-Correlation-Analysis Methode (AcorA) basiert auf der Korrelation zwischen massenspektrometrischen Signalen und Bioaktivitätsdaten von Naturstoffextrakten. Nach der erfolgreichen Testung in einem Proof of Concept Experiment, wurde AcorA zusammen mit einer Reihe von Machine Learning Methoden (PCA, PCR, PLSR, QPAR, Ridge Regression, Lasso, Elastic Net, Random Forest) in Hinblick auf die Qualität zur Identifizierung von bekannten Antibiotika in fungalen Extrakten untersucht. Anschließend wurde die Praktikabilität von AcorA in einem realitätsnahen Experiment unter Beweis gestellt. Mit AcorA gelang die in-silico Identifizierung von zwölf potenziell zytotoxisch wirksamen Peptaibolen in Extrakten der Spezies Sepedonium ampullosporum. Für Ampullosporin A wurde ein EC50-Wert von 4,5 µM gegen HT-29 Zellen bestimmt. Die Primärstrukturen elf weiterer, bislang unbekannter Peptaibole wurden mittels LC-MS/MS ermittelt und aufgrund dessen ihre Bioaktivität diskutiert., Within this thesis, a method for an in-silico based identification of bioactive metabolites in natural products extracts was developed. The so called Activity-Correlation-Analysis method (AcorA) is based on the correlation between mass spectrometric signals and bioactivity data from natural products extracts. After successful testing in a proof of concept experiment, AcorA was analyzed together with a number of machine learning methods (PCA, PCR, PLSR, QPAR, Ridge Regression, Lasso, Elastic Net, Random Forest) with regard to their performance for the identification of known antibiotics in fungal extracts. Subsequently, the applicability of AcorA was proven in a realistic experiment. AcorA allowed the in-silico identification of twelve potentially cytotoxic peptaibols in extracts of Sepedonium ampullosporum. An EC50-value of 4.5 µM against HT-29 cells was determined for Ampullosporin A. The primary structures of eleven other, so far unknown peptaibols were elucidated by LC-MS/MS analysis and their bioactivity was discussed.

Published
2020
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23. Additional file 2 of Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Gudmundsdottir, Valborg, Pedersen, Helle Krogh, Mazzoni, Gianluca, Allin, Kristine H., Artati, Anna, Beulens, Joline W., Banasik, Karina, Brorsson, Caroline, Cederberg, Henna, Chabanova, Elizaveta, Masi, Federico De, Elders, Petra J., Forgie, Ian, Giordano, Giuseppe N., Grallert, Harald, Ramneek Gupta, Haid, Mark, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew T., Heggie, Alison, Mun-Gwan Hong, Jones, Angus G., Koivula, Robert, Kokkola, Tarja, Laakso, Markku, Løngreen, Peter, Anubha Mahajan, Mari, Andrea, McDonald, Timothy J., McEvoy, Donna, Musholt, Petra B., Pavo, Imre, Prehn, Cornelia, Ruetten, Hartmut, Ridderstråle, Martin, Rutters, Femke, Sharma, Sapna, Slieker, Roderick C., Syed, Ali, Tajes, Juan Fernandez, Thomas, Cecilia Engel, Thomsen, Henrik S., Jagadish Vangipurapu, Vestergaard, Henrik, Viñuela, Ana, Wesolowska-Andersen, Agata, Walker, Mark, Adamski, Jerzy, Schwenk, Jochen M., McCarthy, Mark I., Pearson, Ewan, Dermitzakis, Emmanouil, Franks, Paul W., Pedersen, Oluf, and Brunak, Søren

Subjects
Data_FILES
Abstract

Additional file 2. Supplementary Methods. This file contains methods descriptions for omics data generation and preprocessing.

Published
2020
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24. High levels of modified ceramides are a defining feature of murine and human cancer cachexia

Author

Morigny, Pauline, primary, Zuber, Julia, additional, Haid, Mark, additional, Kaltenecker, Doris, additional, Riols, Fabien, additional, Lima, Joanna D.C., additional, Simoes, Estefania, additional, Otoch, José Pinhata, additional, Schmidt, Sören Fisker, additional, Herzig, Stephan, additional, Adamski, Jerzy, additional, Seelaender, Marilia, additional, Berriel Diaz, Mauricio, additional, and Rohm, Maria, additional

Published
2020
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25. Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI DIRECT study

Author

Eriksen, Rebeca, primary, Perez, Isabel Garcia, additional, Posma, Joram M., additional, Haid, Mark, additional, Sharma, Sapna, additional, Prehn, Cornelia, additional, Thomas, Louise E., additional, Koivula, Robert W., additional, Bizzotto, Roberto, additional, Mari, Andrea, additional, Giordano, Giuseppe N., additional, Pavo, Imre, additional, Schwenk, Jochen M., additional, De Masi, Federico, additional, Tsirigos, Konstantinos D., additional, Brunak, Søren, additional, Viñuela, Ana, additional, Mahajan, Anubha, additional, McDonald, Timothy J., additional, Kokkola, Tarja, additional, Rutter, Femke, additional, Teare, Harriet, additional, Hansen, Tue H., additional, Fernandez, Juan, additional, Jones, Angus, additional, Jennison, Chris, additional, Walker, Mark, additional, McCarthy, Mark I., additional, Pedersen, Oluf, additional, Ruetten, Hartmut, additional, Forgie, Ian, additional, Bell, Jimmy D., additional, Pearson, Ewan R., additional, Franks, Paul W., additional, Adamski, Jerzy, additional, Holmes, Elaine, additional, and Frost, Gary, additional

Published
2020
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26. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G., additional, Thomas, E. Louise, additional, Koivula, Robert W., additional, Kurbasic, Azra, additional, Mutie, Pascal M., additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y., additional, Giordano, Giuseppe N., additional, Forgie, Ian M., additional, McDonald, Timothy J., additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, Masi, Federico De, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H., additional, Hansen, Tue H., additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra J. M., additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S., additional, Sharma, Sapna, additional, Thomsen, Henrik S., additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M., additional, Adamski, Jerzy, additional, Musholt, Petra B., additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T., additional, Walker, Mark, additional, Beulens, Joline W. J., additional, Mari, Andrea, additional, Schwenk, Jochen M., additional, Gupta, Ramneek, additional, McCarthy, Mark I., additional, Pearson, Ewan R., additional, Bell, Jimmy D., additional, Pavo, Imre, additional, and Franks, Paul W., additional

Published
2020
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28. Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study

Author

Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G, additional, Thomas, E Louise, additional, Koivula, Robert W, additional, Kurbasic, Azra, additional, Mutie, Pascal M, additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y, additional, Giordano, Giuseppe N, additional, Forgie, Ian M, additional, McDonald, Timothy J, additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, De Masi, Federico, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H, additional, Hansen, Tue H, additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra JM, additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S, additional, Sharma, Sapna, additional, Thomsen, Henrik S, additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M, additional, Adamski, Jerzy, additional, Musholt, Petra B, additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T, additional, Walker, Mark, additional, Beulens, Joline WJ, additional, Mari, Andrea, additional, Schwenk, Jochen M, additional, Gupta, Ramneek, additional, McCarthy, Mark I, additional, Pearson, Ewan R, additional, Bell, Jimmy D, additional, Pavo, Imre, additional, and Franks, Paul W, additional

Published
2020
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29. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Jennison, Christopher, Ehrhardt, Beate, Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, Anita, Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, Iryna, Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Roberto, Froguel, Philippe, Bonneford, Amelie, Canouil, Mickael, Dhennin, Veronique, Brorsson, Caroline, Brunak, Soren, De Masi, Federico, Gudmundsdóttir, Valborg, Pedersen, Helle, Banasik, Karina, Thomas, Cecilia, Sackett, Peter, Staerfeldt, Hans Henrik, Lundgaard, Agnete, Nilsson, Birgitte, Nielsen, Agnes, Mazzoni, Gianluca, Karaderi, Tugce, Rasmussen, Simon, Johansen, Joachim, Allesøe, Rosa, Fritsche, Andreas, Thorand, Barbara, Adamski, Jurek, Grallert, Harald, Haid, Mark, Sharma, Sapna, Troll, Martina, Adam, Jonathan, Ferrer, Jorge, Eriksen, Heather, Frost, Gary, Haussler, Ragna, Hong, Mun gwan, Schwenk, Jochen, Uhlen, Mathias, Nicolay, Claudia, Pavo, Imre, Steckel-Hamann, Birgit, Thomas, Melissa, Adragni, Kofi, Wu, Han, Hart, Leen't, Roderick, Slieker, van Leeuwen, Nienke, Dekkers, Koen, Frau, Francesca, Gassenhuber, Johann, Jablonka, Bernd, Musholt, Petra, Ruetten, Hartmut, Tillner, Joachim, Baltauss, Tania, Bernard Poenaru, Oana, de Preville, Nathalie, Rodriquez, Marianne, Arumugam, Manimozhiyan, Allin, Kristine, Engelbrechtsen, Line, Hansen, Torben, Hansen, Tue, Forman, Annemette, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, Josef, Vestergaard, Henrik, Laakso, Markku, Kokkola, Tarja, Kuulasmaa, Teemu, Franks, Paul, Giordano, Nick, and Pomares-Millan, Hugo

Subjects
Genome-wide association study, Magnetic resonance imaging, Metabolism, Iron, Genetics, Metabolic syndrome
Abstract

Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p

Published
2019

30. Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Author

Wilman, Henry R., primary, Parisinos, Constantinos A., additional, Atabaki-Pasdar, Naeimeh, additional, Kelly, Matt, additional, Thomas, E. Louise, additional, Neubauer, Stefan, additional, Mahajan, Anubha, additional, Hingorani, Aroon D., additional, Patel, Riyaz S., additional, Hemingway, Harry, additional, Franks, Paul W., additional, Bell, Jimmy D., additional, Banerjee, Rajarshi, additional, Yaghootkar, Hanieh, additional, Jennison, Christopher, additional, Ehrhardt, Beate, additional, Baum, Patrick, additional, Schoelsch, Corinna, additional, Freijer, Jan, additional, Grempler, Rolf, additional, Graefe-Mody, Ulrike, additional, Hennige, Anita, additional, Dings, Christiane, additional, Lehr, Thorsten, additional, Scherer, Nina, additional, Sihinecich, Iryna, additional, Pattou, Francois, additional, Raverdi, Violeta, additional, Caiazzo, Robert, additional, Torres, Fanelly, additional, Verkindt, Helene, additional, Mari, Andrea, additional, Tura, Andrea, additional, Giorgino, Toni, additional, Bizzotto, Roberto, additional, Froguel, Philippe, additional, Bonneford, Amelie, additional, Canouil, Mickael, additional, Dhennin, Veronique, additional, Brorsson, Caroline, additional, Brunak, Soren, additional, De Masi, Federico, additional, Gudmundsdóttir, Valborg, additional, Pedersen, Helle, additional, Banasik, Karina, additional, Thomas, Cecilia, additional, Sackett, Peter, additional, Staerfeldt, Hans-Henrik, additional, Lundgaard, Agnete, additional, Nilsson, Birgitte, additional, Nielsen, Agnes, additional, Mazzoni, Gianluca, additional, Karaderi, Tugce, additional, Rasmussen, Simon, additional, Johansen, Joachim, additional, Allesøe, Rosa, additional, Fritsche, Andreas, additional, Thorand, Barbara, additional, Adamski, Jurek, additional, Grallert, Harald, additional, Haid, Mark, additional, Sharma, Sapna, additional, Troll, Martina, additional, Adam, Jonathan, additional, Ferrer, Jorge, additional, Eriksen, Heather, additional, Frost, Gary, additional, Haussler, Ragna, additional, Hong, Mun-gwan, additional, Schwenk, Jochen, additional, Uhlen, Mathias, additional, Nicolay, Claudia, additional, Pavo, Imre, additional, Steckel-Hamann, Birgit, additional, Thomas, Melissa, additional, Adragni, Kofi, additional, Wu, Han, additional, Hart, Leen't, additional, Roderick, Slieker, additional, van Leeuwen, Nienke, additional, Dekkers, Koen, additional, Frau, Francesca, additional, Gassenhuber, Johann, additional, Jablonka, Bernd, additional, Musholt, Petra, additional, Ruetten, Hartmut, additional, Tillner, Joachim, additional, Baltauss, Tania, additional, Bernard Poenaru, Oana, additional, de Preville, Nathalie, additional, Rodriquez, Marianne, additional, Arumugam, Manimozhiyan, additional, Allin, Kristine, additional, Engelbrechtsen, Line, additional, Hansen, Torben, additional, Hansen, Tue, additional, Forman, Annemette, additional, Jonsson, Anna, additional, Pedersen, Oluf, additional, Dutta, Avirup, additional, Vogt, Josef, additional, Vestergaard, Henrik, additional, Laakso, Markku, additional, Kokkola, Tarja, additional, Kuulasmaa, Teemu, additional, Franks, Paul, additional, Giordano, Nick, additional, Pomares-Millan, Hugo, additional, Fitipaldi, Hugo, additional, Mutie, Pascal, additional, Klintenberg, Maria, additional, Bergstrom, Margit, additional, Groop, Leif, additional, Ridderstrale, Martin, additional, Atabaki Pasdar, Naeimeh, additional, Deshmukh, Harshal, additional, Heggie, Alison, additional, Wake, Dianne, additional, McEvoy, Donna, additional, McVittie, Ian, additional, Walker, Mark, additional, Hattersley, Andrew, additional, Hill, Anita, additional, Jones, Angus, additional, McDonald, Timothy, additional, Perry, Mandy, additional, Nice, Rachel, additional, Hudson, Michelle, additional, Thorne, Claire, additional, Dermitzakis, Emmanouil, additional, Viñuela, Ana, additional, Cabrelli, Louise, additional, Loftus, Heather, additional, Dawed, Adem, additional, Donnelly, Louise, additional, Forgie, Ian, additional, Pearson, Ewan, additional, Palmer, Colin, additional, Brown, Andrew, additional, Koivula, Robert, additional, Wesolowska-Andersen, Agata, additional, Abdalla, Moustafa, additional, McRobert, Nicky, additional, Fernandez, Juan, additional, Jiao, Yunlong, additional, Robertson, Neil, additional, Gough, Stephen, additional, Kaye, Jane, additional, Mourby, Miranda, additional, McCarthy, Mark, additional, Shah, Nisha, additional, Teare, Harriet, additional, Holl, Reinhard, additional, Koopman, Anitra, additional, Rutters, Femke, additional, Beulens, Joline, additional, Groeneveld, Lenka, additional, Bell, Jimmy, additional, Thomas, Louise, additional, and Whitcher, Brandon, additional

Published
2019
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31. Characterization of Bulk Phosphatidylcholine Compositions in Human Plasma Using Side-Chain Resolving Lipidomics

Author

Quell, Jan D., primary, Römisch-Margl, Werner, additional, Haid, Mark, additional, Krumsiek, Jan, additional, Skurk, Thomas, additional, Halama, Anna, additional, Stephan, Nisha, additional, Adamski, Jerzy, additional, Hauner, Hans, additional, Mook-Kanamori, Dennis, additional, Mohney, Robert P., additional, Daniel, Hannelore, additional, Suhre, Karsten, additional, and Kastenmüller, Gabi, additional

Published
2019
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32. House dust mite drives pro‐inflammatory eicosanoid reprogramming and macrophage effector functions

Author

Henkel, Fiona D. R., primary, Friedl, Antonie, additional, Haid, Mark, additional, Thomas, Dominique, additional, Bouchery, Tiffany, additional, Haimerl, Pascal, additional, de los Reyes Jiménez, Marta, additional, Alessandrini, Francesca, additional, Schmidt‐Weber, Carsten B., additional, Harris, Nicola L., additional, Adamski, Jerzy, additional, and Esser‐von Bieren, Julia, additional

Published
2018
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35. Scientific Reports / Diacetin, a reliable cue and private communication channel in a specialized pollination system : signalling between oil flowers and oil bees

Author

Schäffler, Irmgard, Steiner, Kim E., Dötterl, Stefan, Haid, Mark, Berkel, Sander S. van, Gerlach, Günter, Johnson, Steven D., and Wessjohann, Ludger

Subjects
Plant signalling, fungi, food and beverages, Chemical ecology
Abstract

The interaction between floral oil secreting plants and oil-collecting bees is one of the most specialized of all pollination mutualisms. Yet, the specific stimuli used by the bees to locate their host flowers have remained elusive. This study identifies diacetin, a volatile acetylated glycerol, as a floral signal compound shared by unrelated oil plants from around the globe. Electrophysiological measurements of antennae and behavioural assays identified diacetin as the key volatile used by oil-collecting bees to locate their host flowers. Furthermore, electrophysiological measurements indicate that only oil-collecting bees are capable of detecting diacetin. The structural and obvious biosynthetic similarity between diacetin and associated floral oils make it a reliable cue for oil-collecting bees. It is easily perceived by oil bees, but cant be detected by other potential pollinators. Therefore, diacetin represents the first demonstrated private communication channel in a pollination system. Irmgard Schäffler, Kim E. Steiner, Mark Haid, Sander S. van Berkel, Günter Gerlach, Steven D. Johnson, Ludger Wessjohann, and Stefan Dötterl

Published
2015

36. House dust mite drives proinflammatory eicosanoid reprogramming and macrophage effector functions.

Author

Henkel, Fiona D. R., Friedl, Antonie, Haid, Mark, Thomas, Dominique, Bouchery, Tiffany, Haimerl, Pascal, Reyes Jiménez, Marta, Alessandrini, Francesca, Schmidt‐Weber, Carsten B., Harris, Nicola L., Adamski, Jerzy, and Esser‐von Bieren, Julia

Subjects
HOUSE dust mites, NEMATODE infections, HELMINTHIASIS, IMMUNE response, LIPID metabolism
Abstract

Background: Eicosanoid lipid mediators play key roles in type 2 immune responses, for example in allergy and asthma. Macrophages represent major producers of eicosanoids and they are key effector cells of type 2 immunity. We aimed to comprehensively track eicosanoid profiles during type 2 immune responses to house dust mite (HDM) or helminth infection and to identify mechanisms and functions of eicosanoid reprogramming in human macrophages. Methods: We established an LC‐MS/MS workflow for the quantification of 52 oxylipins to analyze mediator profiles in human monocyte‐derived macrophages (MDM) stimulated with HDM and during allergic airway inflammation (AAI) or nematode infection in mice. Expression of eicosanoid enzymes was studied by qPCR and western blot and cytokine production was assessed by multiplex assays. Results: Short (24 h) exposure of alveolar‐like MDM (aMDM) to HDM suppressed 5‐LOX expression and product formation, while triggering prostanoid (thromboxane and prostaglandin D2 and E2) production. This eicosanoid reprogramming was p38‐dependent, but dectin‐2‐independent. HDM also induced proinflammatory cytokine production, but reduced granulocyte recruitment by aMDM. In contrast, high levels of cysteinyl leukotrienes (cysLTs) and 12‐/15‐LOX metabolites were produced in the airways during AAI or nematode infection in mice. Conclusion: Our findings show that a short exposure to allergens as well as ongoing type 2 immune responses are characterized by a fundamental reprogramming of the lipid mediator metabolism with macrophages representing particularly plastic responder cells. Targeting mediator reprogramming in airway macrophages may represent a viable approach to prevent pathogenic lipid mediator profiles in allergy or asthma. [ABSTRACT FROM AUTHOR]

Published
2019
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38. Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

Author

Molnos, Sophie, Wahl, Simone, Haid, Mark, Eekhoff, E. Marelise W., Pool, René, Floegel, Anna, Deelen, Joris, Much, Daniela, Prehn, Cornelia, Breier, Michaela, Draisma, Harmen H., van Leeuwen, Nienke, Simonis-Bik, Annemarie M. C., Jonsson, Anna, Willemsen, Gonneke, Bernigau, Wolfgang, Wang-Sattler, Rui, Suhre, Karsten, Peters, Annette, and Thorand, Barbara

Abstract

Aims/hypothesis: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. Methods: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data ( n = 340) and epidemiological case-control studies of prevalent ( n = 4925) and incident ( n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. Results: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance ( p ≤ 5.4 × 10) and prevalent type 2 diabetes (OR 2.64 [β 0.97 ± 0.09], p = 1.0 × 10). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HR 1.57 [β 0.45 ± 0.06]; p = 1.3 × 10), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). Conclusions/interpretation: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors. [ABSTRACT FROM AUTHOR]

Published
2018
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43. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H, Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra JM, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, 'T Hart, Leen M, Adamski, Jerzy, Musholt, Petra B, Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T, Walker, Mark, Beulens, Joline WJ, Mari, Andrea, Schwenk, Jochen M, Gupta, Ramneek, McCarthy, Mark I, Pearson, Ewan R, Bell, Jimmy D, Pavo, Imre, and Franks, Paul W

Subjects
Diabetes Complications, Fatty Liver, Machine Learning, Male, Models, Statistical, Humans, Reproducibility of Results, Female, Prospective Studies, Middle Aged, Risk Assessment, 3. Good health
Abstract

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

44. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, ‘T Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, and Franks, Paul W.

Subjects
Medicine and health sciences, Research and analysis methods, FOS: Computer and information sciences, Computer and information sciences, Biology and life sciences, 3. Good health, Research Article
Abstract

Funder: Henning och Johan Throne-Holsts, Funder: Hans Werthén, Funder: Swedish Foundation for Strategic Research, Funder: NIHR clinical senior lecturer fellowship, Funder: Wellcome Trust Senior Investigator, Funder: NIHR Exeter Clinical Research Facility, Funder: Science for Life Laboratory (Plasma Profiling Facility), Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas), Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine), Background: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (

45. Machine-Learning Algorithm Predicts Early Type 2 Diabetes Remission following Roux-en-Y Gastric Bypass

Author

Thomas, Cecilia E., Raverdi, Violeta, Pedersen, Helle Krogh, Gassenhuber, Johann, Brorsson, Caroline, Gudmundsdottir, Valborg, Vinuela, Ana, Howald, Cedric, Wu, Han, Karina Banasik, Yengo, Loic, Haid, Mark, Thomas, Melissa K., Hinterholzer, Michaela, Canouil, Mickael, Hong, Mun Gwan, Davidsen, Peter, Gupta, Ramneek, Sharma, Sapna, Wahl, Simone, Dermitzakis, Emmanouil T., Grallert, Harald, Schwenk, Jochen M., Farmen, Mark, Brunak, Soren, and Pattou, Francois

46. Characterization of O-phosphohydroxyproline in rat {alpha}-crystallin A.

Author

Kühlberg A, Haid M, and Metzger S

Subjects
Animals, Eye chemistry, Hydroxylation physiology, Hydroxyproline chemistry, Muscle Proteins chemistry, Myocardium chemistry, Phosphorylation physiology, Rats, Spectrometry, Mass, Electrospray Ionization, alpha-Crystallin A Chain chemistry, Eye metabolism, Hydroxyproline metabolism, Muscle Proteins metabolism, Myocardium metabolism, alpha-Crystallin A Chain metabolism
Abstract

Post-translational modifications have major importance for the structure and function of a multiplicity of proteins. Phosphorylation is a widespread phenomenon among eukaryotic proteins. Whereas O-phosphorylation on the side chains of serine, threonine, and tyrosine in proteins is well known and has been studied extensively, to our knowledge the endogenous phosphorylation of hydroxyproline has not previously been reported. In the present work, we provide evidence for the first time that O-phosphohydroxyproline (Hyp(P)) is a proteinogenic amino acid. To detect Hyp(P) in proteins we generated a Hyp(P)-specific polyclonal antibody. We could identify Hyp(P) in various proteins by Western blot analysis, and we characterized the sequence position of Hyp(P) in the protein α-crystallin A by electrospray ionization-tandem mass spectrometry. Our experiments clearly demonstrate hydroxylation and subsequent phosphorylation of a proline residue in α-crystallin A in the eye as well as in heart tissue of rat.

Published
2010
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