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2. Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals.

Author

Bansal, Vikas, Gassenhuber, Johann, Phillips, Tierney, Oliveira, Glenn, Harbaugh, Rebecca, Villarasa, Nikki, Topol, Eric J, Seufferlein, Thomas, and Boehm, Bernhard O

Subjects
Humans, Diabetes Mellitus, Type 2, Prognosis, Case-Control Studies, Cohort Studies, Sequence Analysis, DNA, DNA Mutational Analysis, Phenotype, Mutation, Mutation, Missense, Adult, Female, Male, High-Throughput Nucleotide Sequencing, DNA pooling, High-throughput sequencing, MODY, Monogenic diabetes, Pathogenic variants, Targeted sequencing, Type 2 diabetes, General & Internal Medicine, Medical and Health Sciences
Abstract

BackgroundDiagnosis of monogenic as well as atypical forms of diabetes mellitus has important clinical implications for their specific diagnosis, prognosis, and targeted treatment. Single gene mutations that affect beta-cell function represent 1-2% of all cases of diabetes. However, phenotypic heterogeneity and lack of family history of diabetes can limit the diagnosis of monogenic forms of diabetes. Next-generation sequencing technologies provide an excellent opportunity to screen large numbers of individuals with a diagnosis of diabetes for mutations in disease-associated genes.MethodsWe utilized a targeted sequencing approach using the Illumina HiSeq to perform a case-control sequencing study of 22 monogenic diabetes genes in 4016 individuals with type 2 diabetes (including 1346 individuals diagnosed before the age of 40 years) and 2872 controls. We analyzed protein-coding variants identified from the sequence data and compared the frequencies of pathogenic variants (protein-truncating variants and missense variants) between the cases and controls.ResultsA total of 40 individuals with diabetes (1.8% of early onset sub-group and 0.6% of adult onset sub-group) were carriers of known pathogenic missense variants in the GCK, HNF1A, HNF4A, ABCC8, and INS genes. In addition, heterozygous protein truncating mutations were detected in the GCK, HNF1A, and HNF1B genes in seven individuals with diabetes. Rare missense mutations in the GCK gene were significantly over-represented in individuals with diabetes (0.5% carrier frequency) compared to controls (0.035%). One individual with early onset diabetes was homozygous for a rare pathogenic missense variant in the WFS1 gene but did not have the additional phenotypes associated with Wolfram syndrome.ConclusionTargeted sequencing of genes linked with monogenic diabetes can identify disease-relevant mutations in individuals diagnosed with type 2 diabetes not suspected of having monogenic forms of the disease. Our data suggests that GCK-MODY frequently masquerades as classical type 2 diabetes. The results confirm that MODY is under-diagnosed, particularly in individuals presenting with early onset diabetes and clinically labeled as type 2 diabetes; thus, sequencing of all monogenic diabetes genes should be routinely considered in such individuals. Genetic information can provide a specific diagnosis, inform disease prognosis and may help to better stratify treatment plans.

Published
2017

3. 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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4. 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

5. 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
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6. Effects on weight loss and glycemic control with SAR441255, a potent unimolecular peptide GLP-1/GIP/GCG receptor triagonist

Author

Bossart, Martin, Wagner, Michael, Elvert, Ralf, Evers, Andreas, Hubschle, Thomas, Kloeckener, Tim, Lorenz, Katrin, Moessinger, Christine, Eriksson, Olof, Velikyan, Irina, Pierrou, Stefan, Johansson, Lars, Dietert, Gabriele, Dietz-Baum, Yasmin, Kissner, Thomas, Nowotny, Irene, Einig, Christine, Jan, Christelle, Rharbaoui, Faiza, Gassenhuber, Johann, Prochnow, Hans-Peter, Agueusop, Inoncent, Porksen, Niels, Smith, William B., Nitsche, Almut, Konkar, Anish, Bossart, Martin, Wagner, Michael, Elvert, Ralf, Evers, Andreas, Hubschle, Thomas, Kloeckener, Tim, Lorenz, Katrin, Moessinger, Christine, Eriksson, Olof, Velikyan, Irina, Pierrou, Stefan, Johansson, Lars, Dietert, Gabriele, Dietz-Baum, Yasmin, Kissner, Thomas, Nowotny, Irene, Einig, Christine, Jan, Christelle, Rharbaoui, Faiza, Gassenhuber, Johann, Prochnow, Hans-Peter, Agueusop, Inoncent, Porksen, Niels, Smith, William B., Nitsche, Almut, and Konkar, Anish

Abstract

Unimolecular triple incretins, combining the activity of glucagon-like peptide-1 (GLP-1), glucose -dependent insulinotropic polypeptide (GIP), and glucagon (GCG), have demonstrated reduction in body weight and improved glucose control in rodent models. We developed SAR441255, a synthetic peptide agonist of the GLP-1, GCG, and GIP receptors, structurally based on the exendin-4 sequence. SAR441255 displays high potency with balanced activation of all three target receptors. In animal models, metabolic outcomes were superior to results with a dual GLP-1/GCG receptor agonist. Preclinical in vivo positron emission tomography imaging demonstrated SAR441255 binding to GLP-1 and GCG receptors. In healthy subjects, SAR441255 improved glycemic control during a mixed-meal tolerance test and impacted biomarkers for GCG and GIP receptor activation. Single doses of SAR441255 were well tolerated. The results demonstrate that integrating GIP activity into dual GLP-1 and GCG receptor agonism provides improved effects on weight loss and glycemic control while buffering the diabetogenic risk of chronic GCG receptor agonism.

Published
2022
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7. Effects on weight loss and glycemic control with SAR441255, a potent unimolecular peptide GLP-1/GIP/GCG receptor triagonist

Author

Bossart, Martin, primary, Wagner, Michael, additional, Elvert, Ralf, additional, Evers, Andreas, additional, Hübschle, Thomas, additional, Kloeckener, Tim, additional, Lorenz, Katrin, additional, Moessinger, Christine, additional, Eriksson, Olof, additional, Velikyan, Irina, additional, Pierrou, Stefan, additional, Johansson, Lars, additional, Dietert, Gabriele, additional, Dietz-Baum, Yasmin, additional, Kissner, Thomas, additional, Nowotny, Irene, additional, Einig, Christine, additional, Jan, Christelle, additional, Rharbaoui, Faiza, additional, Gassenhuber, Johann, additional, Prochnow, Hans-Peter, additional, Agueusop, Inoncent, additional, Porksen, Niels, additional, Smith, William B., additional, Nitsche, Almut, additional, and Konkar, Anish, additional

Published
2022
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9. 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
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10. 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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13. Additional file 2: of Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals

Author

Bansal, Vikas, Gassenhuber, Johann, Phillips, Tierney, Oliveira, Glenn, Harbaugh, Rebecca, Villarasa, Nikki, Topol, Eric, Seufferlein, Thomas, and Boehm, Bernhard

Abstract

Supplementary Methods: Description of methods for pooled variant calling, gene-level tests for rare coding variants, statistical analyses, comparison of pooled sequence data with population exome data, comparison of pooled allele counts with individual genotypes, and identification of the carriers of rare variants. (PDF 671 kb)

Published
2017
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14. Additional file 1: Table S1. of Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals

Author

Bansal, Vikas, Gassenhuber, Johann, Phillips, Tierney, Oliveira, Glenn, Harbaugh, Rebecca, Villarasa, Nikki, Topol, Eric, Seufferlein, Thomas, and Boehm, Bernhard

Abstract

List of 22 genes associated with monogenic forms of diabetes that were analyzed in this paper. Table S2. Criteria used to select genes for targeted sequencing. Table S3. Summary of samples sequenced in Stages 1, 2, and 3, and the coding variants identified in each stage. Table S4. Clinical data of the cases and controls for type 2 diabetes sequenced in this study. Table S5. List of all protein truncating mutations identified in the 22 monogenic diabetes genes. Table S6. Rare missense mutations in the HNF1A, HNF4A, HNF1B, ABCC8, and KCNJ11 genes predicted to be deleterious by PolyPhen2, SIFT, and MutationTaster. Table S7. Number of individuals with protein truncating variants and previously reported pathogenic missense variants in MODY genes. Table S8. List of exons with low sequence coverage in data from Stage 1 and 2 pools. Figure S1. Minor allele frequency distribution of variants identified from sequencing of pools in Stages 1 and 2. Figure S2. Pooled sequencing design of the study. Figure S3. Comparison of sequence coverage between cases and controls. (PDF 700 kb)

Published
2017
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15. Cloning and characterization of the ALG3 gene of Saccharomyces cerevisiae

Author

Aebi, Markus, Gassenhuber, Johann, Domdey, Horst, Heesen, Stephan te, Aebi, Markus, Gassenhuber, Johann, Domdey, Horst, and Heesen, Stephan te

Abstract

The Saccharomyces cerevisiae alg3-1 mutant is descilbed as defective in the biosynthesis of dolichol-linked oligosaccharides (Huffaker and Robbins, Proc. Natl. Acad. Sci. USA, 80, 7466-7470, 1983). Man5GlcNAc2-PP-Dol accumulates in alg3 cells and Endo H resistant carbohydrates are transferred to protein by the oligosaccharyltransferase complex. In this study, we describe the cloning of the ALG3 locus by complementation of the temperature sensitive growth defect of the alg3 stt3 double mutant. The isolated ALG3 gene complements both the defect in the biosynthesis of lipidlinked oligosaccharides of the alg3-mutant and the underglycosylation of secretory proteins. The inactivation of the nonessential ALG3 gene results in the accumulation of lipid-linked Man5GlcNAc2 and protein-bound carbohydrates which are completely Endo H resistant. The ALG3 locus encodes a potential ER-transmembrane protein of 458 amino acids (53 kDa) with a C-terminal KKXX-retrieval sequence

Published
2017

16. Cathepsin A inhibition attenuates myocardial infarction-induced heart failure on the functional and proteomic levels

Author

Petrera, Agnese, Gassenhuber, Johann, Ruf, Sven, Gunasekaran, Deepika, Esser, Jennifer, Shahinian, Jasmin Hasmik, Hübschle, Thomas, Rütten, Hartmut, Sadowski, Thorsten, and Schilling, Oliver

Subjects
Medicine(all), Heart Failure, Male, Proteomics, Proteome, Biochemistry, Genetics and Molecular Biology(all), Research, Heart Ventricles, Myocardial Infarction, Cathepsin A, Organ Size, Peptide Mapping, Mouse model, Cell Line, Rats, Mice, Inbred C57BL, Disease Models, Animal, Cardiovascular diseases, cardiovascular system, Animals, Protease Inhibitors, Drug therapy, Ligation
Abstract

Background Myocardial infarction (MI) is a major cause of heart failure. The carboxypeptidase cathepsin A is a novel target in the treatment of cardiac failure. We aim to show that recently developed inhibitors of the protease cathepsin A attenuate post-MI heart failure. Methods Mice were subjected to permanent left anterior descending artery (LAD) ligation or sham operation. 24 h post–surgery, LAD-ligated animals were treated with daily doses of the cathepsin A inhibitor SAR1 or placebo. After 4 weeks, the three groups (sham, MI-placebo, MI-SAR1) were evaluated. Results Compared to sham-operated animals, placebo-treated mice showed significantly impaired cardiac function and increased plasma BNP levels. Cathepsin A inhibition prevented the increase of plasma BNP levels and displayed a trend towards improved cardiac functionality. Proteomic profiling was performed for the three groups (sham, MI-placebo, MI-SAR1). More than 100 proteins were significantly altered in placebo-treated LAD ligation compared to the sham operation, including known markers of cardiac failure as well as extracellular/matricellular proteins. This ensemble constitutes a proteome fingerprint of myocardial infarction induced by LAD ligation in mice. Cathepsin A inhibitor treatment normalized the marked increase of the muscle stress marker CA3 as well as of Igγ 2b and fatty acid synthase. For numerous further proteins, cathepsin A inhibition partially dampened the LAD ligation-induced proteome alterations. Conclusions Our proteomic and functional data suggest that cathepsin A inhibition has cardioprotective properties and support a beneficial effect of cathepsin A inhibition in the treatment of heart failure after myocardial infarction. Electronic supplementary material The online version of this article (doi:10.1186/s12967-016-0907-8) contains supplementary material, which is available to authorized users.

Published
2016

22. 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

23. Cloning and characterization of the ALG3 gene of Saccharomyces cerevisiae

Author

Aebi, Markus, Gassenhuber, Johann, Domdey, Horst, Heesen, Stephan te, Aebi, Markus, Gassenhuber, Johann, Domdey, Horst, and Heesen, Stephan te

Abstract

The Saccharomyces cerevisiae alg3-1 mutant is descilbed as defective in the biosynthesis of dolichol-linked oligosaccharides (Huffaker and Robbins, Proc. Natl. Acad. Sci. USA, 80, 7466-7470, 1983). Man5GlcNAc2-PP-Dol accumulates in alg3 cells and Endo H resistant carbohydrates are transferred to protein by the oligosaccharyltransferase complex. In this study, we describe the cloning of the ALG3 locus by complementation of the temperature sensitive growth defect of the alg3 stt3 double mutant. The isolated ALG3 gene complements both the defect in the biosynthesis of lipidlinked oligosaccharides of the alg3-mutant and the underglycosylation of secretory proteins. The inactivation of the nonessential ALG3 gene results in the accumulation of lipid-linked Man5GlcNAc2 and protein-bound carbohydrates which are completely Endo H resistant. The ALG3 locus encodes a potential ER-transmembrane protein of 458 amino acids (53 kDa) with a C-terminal KKXX-retrieval sequence

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