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10 results on '"Giorgino, Toni"'

1. 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, Søren, Froguel, Philippe, Thomas, Cecilia Engel, Häussler, Ragna S., 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, 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, Søren, Froguel, Philippe, Thomas, Cecilia Engel, Häussler, Ragna S., 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, and Dekkers, Koen

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., Correction in DOI 10.1038/s41587-023-01805-9QC 20230626

Published
2023
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2. Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

Author

Majewski, Maciej, Pérez, Adrià, Thölke, Philipp, Doerr, Stefan, Charron, Nicholas E., Giorgino, Toni, Husic, Brooke E., Clementi, Cecilia, Noé, Frank, De Fabritiis, Gianni, Majewski, Maciej, Pérez, Adrià, Thölke, Philipp, Doerr, Stefan, Charron, Nicholas E., Giorgino, Toni, Husic, Brooke E., Clementi, Cecilia, Noé, Frank, and De Fabritiis, Gianni

Abstract

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Published
2022

3. GPCRmd uncovers the dynamics of the 3D-GPCRome

Author

Rodriguez-Espigares, Ismael, Torrens-Fontanals, Mariona, Tiemann, Johanna K. S., Aranda-Garcia, David, Manuel Ramirez-Anguita, Juan, Stepniewski, Tomasz Maciej, Worp, Nathalie, Varela-Rial, Alejandro, Morales-Pastor, Adrian, Medel-Lacruz, Brian, Pandy-Szekeres, Gaspar, Mayol, Eduardo, Giorgino, Toni, Carlsson, Jens, Deupi, Xavier, Filipek, Slawomir, Filizola, Marta, Carlos Gomez-Tamayo, Jose, Gonzalez, Angel, Gutiérrez-de-Terán, Hugo, Jimenez-Roses, Mireia, Jespers, Willem, Kapla, Jon, Khelashvili, George, Kolb, Peter, Latek, Dorota, Marti-Solano, Maria, Matricon, Pierre, Matsoukas, Minos-Timotheos, Miszta, Przemyslaw, Olivella, Mireia, Perez-Benito, Laura, Provasi, Davide, Rios, Santiago, Torrecillas, Ivan R., Sallander, Jessica, Sztyler, Agnieszka, Vasile, Silvana, Weinstein, Harel, Zachariae, Ulrich, Hildebrand, Peter W., De Fabritiis, Gianni, Sanz, Ferran, Gloriam, David E., Cordomi, Arnau, Guixa-Gonzalez, Ramon, Selent, Jana, Rodriguez-Espigares, Ismael, Torrens-Fontanals, Mariona, Tiemann, Johanna K. S., Aranda-Garcia, David, Manuel Ramirez-Anguita, Juan, Stepniewski, Tomasz Maciej, Worp, Nathalie, Varela-Rial, Alejandro, Morales-Pastor, Adrian, Medel-Lacruz, Brian, Pandy-Szekeres, Gaspar, Mayol, Eduardo, Giorgino, Toni, Carlsson, Jens, Deupi, Xavier, Filipek, Slawomir, Filizola, Marta, Carlos Gomez-Tamayo, Jose, Gonzalez, Angel, Gutiérrez-de-Terán, Hugo, Jimenez-Roses, Mireia, Jespers, Willem, Kapla, Jon, Khelashvili, George, Kolb, Peter, Latek, Dorota, Marti-Solano, Maria, Matricon, Pierre, Matsoukas, Minos-Timotheos, Miszta, Przemyslaw, Olivella, Mireia, Perez-Benito, Laura, Provasi, Davide, Rios, Santiago, Torrecillas, Ivan R., Sallander, Jessica, Sztyler, Agnieszka, Vasile, Silvana, Weinstein, Harel, Zachariae, Ulrich, Hildebrand, Peter W., De Fabritiis, Gianni, Sanz, Ferran, Gloriam, David E., Cordomi, Arnau, Guixa-Gonzalez, Ramon, and Selent, Jana

Abstract

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Published
2020
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4. TorchMD: A deep learning framework for molecular simulations

Author

Doerr, Stefan, Majewsk, Maciej, Pérez, Adrià, Krämer, Andreas, Clementi, Cecilia, Noe, Frank, Giorgino, Toni, De Fabritiis, Gianni, Doerr, Stefan, Majewsk, Maciej, Pérez, Adrià, Krämer, Andreas, Clementi, Cecilia, Noe, Frank, Giorgino, Toni, and De Fabritiis, Gianni

Abstract

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}.

Published
2020
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5. GPCRmd uncovers the dynamics of the 3D-GPCRome

Author

Rodríguez-Espigares, Ismael, Torrens-Fontanals, Mariona, Tiemann, Johanna K. S., Aranda-García, David, Ramírez-Anguita, Juan Manuel, Stepniewski, Tomasz Maciej, Worp, Nathalie, Varela-Rial, Alejandro, Morales-Pastor, Adrián, Medel-Lacruz, Brian, Pándy-Szekeres, Gáspár, Mayol, Eduardo, Giorgino, Toni, Carlsson, Jens, Deupi, Xavier, Filipek, Slawomir, Filizola, Marta, Gómez-Tamayo, José Carlos, Gonzalez, Angel, Gutiérrez-de-Terán, Hugo, Jiménez-Rosés, Mireia, Jespers, Willem, Kapla, Jon, Khelashvili, George, Kolb, Peter, Latek, Dorota, Marti-Solano, Maria, Matricon, Pierre, Matsoukas, Minos-Timotheos, Miszta, Przemyslaw, Olivella, Mireia, Perez-Benito, Laura, Provasi, Davide, Ríos, Santiago, Torrecillas, Iván R., Sallander, Jessica, Sztyler, Agnieszka, Vasile, Silvana, Weinstein, Harel, Zachariae, Ulrich, Hildebrand, Peter W., De Fabritiis, Gianni, Sanz, Ferran, Gloriam, David E., Cordomi, Arnau, Guixà-González, Ramon, Selent, Jana, Rodríguez-Espigares, Ismael, Torrens-Fontanals, Mariona, Tiemann, Johanna K. S., Aranda-García, David, Ramírez-Anguita, Juan Manuel, Stepniewski, Tomasz Maciej, Worp, Nathalie, Varela-Rial, Alejandro, Morales-Pastor, Adrián, Medel-Lacruz, Brian, Pándy-Szekeres, Gáspár, Mayol, Eduardo, Giorgino, Toni, Carlsson, Jens, Deupi, Xavier, Filipek, Slawomir, Filizola, Marta, Gómez-Tamayo, José Carlos, Gonzalez, Angel, Gutiérrez-de-Terán, Hugo, Jiménez-Rosés, Mireia, Jespers, Willem, Kapla, Jon, Khelashvili, George, Kolb, Peter, Latek, Dorota, Marti-Solano, Maria, Matricon, Pierre, Matsoukas, Minos-Timotheos, Miszta, Przemyslaw, Olivella, Mireia, Perez-Benito, Laura, Provasi, Davide, Ríos, Santiago, Torrecillas, Iván R., Sallander, Jessica, Sztyler, Agnieszka, Vasile, Silvana, Weinstein, Harel, Zachariae, Ulrich, Hildebrand, Peter W., De Fabritiis, Gianni, Sanz, Ferran, Gloriam, David E., Cordomi, Arnau, Guixà-González, Ramon, and Selent, Jana

Abstract

G-protein-coupled receptors (GPCRs) are involved in numerous physiological processes and are the most frequent targets of approved drugs. The explosion in the number of new three-dimensional (3D) molecular structures of GPCRs (3D-GPCRome) over the last decade has greatly advanced the mechanistic understanding and drug design opportunities for this protein family. Molecular dynamics (MD) simulations have become a widely established technique for exploring the conformational landscape of proteins at an atomic level. However, the analysis and visualization of MD simulations require efficient storage resources and specialized software. Here we present GPCRmd (http://gpcrmd.org/), an online platform that incorporates web-based visualization capabilities as well as a comprehensive and user-friendly analysis toolbox that allows scientists from different disciplines to visualize, analyze and share GPCR MD data. GPCRmd originates from a community-driven effort to create an open, interactive and standardized database of GPCR MD simulations.

Published
2020

6. Promoting transparency and reproducibility in enhanced molecular simulations

Author

Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Tribello, Gareth A., Banas, Pavel, Barducci, Alessandro, Bernetti, Mattia, Bolhuis, Peter G., Bottaro, Sandro, Branduardi, Davide, Capelli, Riccardo, Carloni, Paolo, Ceriotti, Michele, Cesari, Andrea, Chen, Haochuan, Chen, Wei, Colizzi, Francesco, De, Sandip, De La Pierre, Marco, Donadio, Davide, Drobot, Viktor, Ensing, Bernd, Ferguson, Andrew L., Filizola, Marta, Fraser, James S., Fu, Haohao, Gasparotto, Piero, Gervasio, Francesco Luigi, Giberti, Federico, Gil-Ley, Alejandro, Giorgino, Toni, Heller, Gabriella T., Hocky, Glen M., Iannuzzi, Marcella, Invernizzi, Michele, Jelfs, Kim E., Jussupow, Alexander, Kirilin, Evgeny, Laio, Alessandro, Limongelli, Vittorio, Lindorff-Larsen, Kresten, Lohr, Thomas, Marinelli, Fabrizio, Martin-Samos, Layla, Masetti, Matteo, Meyer, Ralf, Michaelides, Angelos, Molteni, Carla, Morishita, Tetsuya, Nava, Marco, Paissoni, Cristina, Papaleo, Elena, Parrinello, Michele, Pfaendtner, Jim, Piaggi, Pablo, Piccini, GiovanniMaria, Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Provasi, Davide, Quigley, David, Raiteri, Paolo, Raniolo, Stefano, Rydzewski, Jakub, Salvalaglio, Matteo, Sosso, Gabriele Cesare, Spiwok, Vojtech, Sponer, Jiri, Swenson, David W. H., Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A., White, Andrew, Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Tribello, Gareth A., Banas, Pavel, Barducci, Alessandro, Bernetti, Mattia, Bolhuis, Peter G., Bottaro, Sandro, Branduardi, Davide, Capelli, Riccardo, Carloni, Paolo, Ceriotti, Michele, Cesari, Andrea, Chen, Haochuan, Chen, Wei, Colizzi, Francesco, De, Sandip, De La Pierre, Marco, Donadio, Davide, Drobot, Viktor, Ensing, Bernd, Ferguson, Andrew L., Filizola, Marta, Fraser, James S., Fu, Haohao, Gasparotto, Piero, Gervasio, Francesco Luigi, Giberti, Federico, Gil-Ley, Alejandro, Giorgino, Toni, Heller, Gabriella T., Hocky, Glen M., Iannuzzi, Marcella, Invernizzi, Michele, Jelfs, Kim E., Jussupow, Alexander, Kirilin, Evgeny, Laio, Alessandro, Limongelli, Vittorio, Lindorff-Larsen, Kresten, Lohr, Thomas, Marinelli, Fabrizio, Martin-Samos, Layla, Masetti, Matteo, Meyer, Ralf, Michaelides, Angelos, Molteni, Carla, Morishita, Tetsuya, Nava, Marco, Paissoni, Cristina, Papaleo, Elena, Parrinello, Michele, Pfaendtner, Jim, Piaggi, Pablo, Piccini, GiovanniMaria, Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Provasi, Davide, Quigley, David, Raiteri, Paolo, Raniolo, Stefano, Rydzewski, Jakub, Salvalaglio, Matteo, Sosso, Gabriele Cesare, Spiwok, Vojtech, Sponer, Jiri, Swenson, David W. H., Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A., and White, Andrew

Published
2019

7. 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, B., Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, A., Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, I., Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Froguel, Philippe, Brorsson, Caroline, Brunak, S., De Masi, Federico, Pedersen, H., Banasik, Karina, Thomas, Cecilia, Lundgaard, Agnete, Nielsen, A., Mazzoni, Gianluca, Karaderi, T., Rasmussen, S., Johansen, Joachim, Allesøe, Rosa, Arumugam, Manimozhiyan, Allin, Kristine, Hansen, Torben, Hansen, Tue, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, J., Vestergaard, Henrik, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Jennison, Christopher, Ehrhardt, B., Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, A., Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, I., Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Froguel, Philippe, Brorsson, Caroline, Brunak, S., De Masi, Federico, Pedersen, H., Banasik, Karina, Thomas, Cecilia, Lundgaard, Agnete, Nielsen, A., Mazzoni, Gianluca, Karaderi, T., Rasmussen, S., Johansen, Joachim, Allesøe, Rosa, Arumugam, Manimozhiyan, Allin, Kristine, Hansen, Torben, Hansen, Tue, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, J., and Vestergaard, Henrik

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 <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and he

Published
2019

8. Promoting transparency and reproducibility in enhanced molecular simulations

Author

Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Tribello, Gareth A., Banas, Pavel, Barducci, Alessandro, Bernetti, Mattia, Bolhuis, Peter G., Bottaro, Sandro, Branduardi, Davide, Capelli, Riccardo, Carloni, Paolo, Ceriotti, Michele, Cesari, Andrea, Chen, Haochuan, Chen, Wei, Colizzi, Francesco, De, Sandip, De La Pierre, Marco, Donadio, Davide, Drobot, Viktor, Ensing, Bernd, Ferguson, Andrew L., Filizola, Marta, Fraser, James S., Fu, Haohao, Gasparotto, Piero, Gervasio, Francesco Luigi, Giberti, Federico, Gil-Ley, Alejandro, Giorgino, Toni, Heller, Gabriella T., Hocky, Glen M., Iannuzzi, Marcella, Invernizzi, Michele, Jelfs, Kim E., Jussupow, Alexander, Kirilin, Evgeny, Laio, Alessandro, Limongelli, Vittorio, Lindorff-Larsen, Kresten, Lohr, Thomas, Marinelli, Fabrizio, Martin-Samos, Layla, Masetti, Matteo, Meyer, Ralf, Michaelides, Angelos, Molteni, Carla, Morishita, Tetsuya, Nava, Marco, Paissoni, Cristina, Papaleo, Elena, Parrinello, Michele, Pfaendtner, Jim, Piaggi, Pablo, Piccini, GiovanniMaria, Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Provasi, Davide, Quigley, David, Raiteri, Paolo, Raniolo, Stefano, Rydzewski, Jakub, Salvalaglio, Matteo, Sosso, Gabriele Cesare, Spiwok, Vojtech, Sponer, Jiri, Swenson, David W. H., Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A., White, Andrew, Bonomi, Massimiliano, Bussi, Giovanni, Camilloni, Carlo, Tribello, Gareth A., Banas, Pavel, Barducci, Alessandro, Bernetti, Mattia, Bolhuis, Peter G., Bottaro, Sandro, Branduardi, Davide, Capelli, Riccardo, Carloni, Paolo, Ceriotti, Michele, Cesari, Andrea, Chen, Haochuan, Chen, Wei, Colizzi, Francesco, De, Sandip, De La Pierre, Marco, Donadio, Davide, Drobot, Viktor, Ensing, Bernd, Ferguson, Andrew L., Filizola, Marta, Fraser, James S., Fu, Haohao, Gasparotto, Piero, Gervasio, Francesco Luigi, Giberti, Federico, Gil-Ley, Alejandro, Giorgino, Toni, Heller, Gabriella T., Hocky, Glen M., Iannuzzi, Marcella, Invernizzi, Michele, Jelfs, Kim E., Jussupow, Alexander, Kirilin, Evgeny, Laio, Alessandro, Limongelli, Vittorio, Lindorff-Larsen, Kresten, Lohr, Thomas, Marinelli, Fabrizio, Martin-Samos, Layla, Masetti, Matteo, Meyer, Ralf, Michaelides, Angelos, Molteni, Carla, Morishita, Tetsuya, Nava, Marco, Paissoni, Cristina, Papaleo, Elena, Parrinello, Michele, Pfaendtner, Jim, Piaggi, Pablo, Piccini, GiovanniMaria, Pietropaolo, Adriana, Pietrucci, Fabio, Pipolo, Silvio, Provasi, Davide, Quigley, David, Raiteri, Paolo, Raniolo, Stefano, Rydzewski, Jakub, Salvalaglio, Matteo, Sosso, Gabriele Cesare, Spiwok, Vojtech, Sponer, Jiri, Swenson, David W. H., Tiwary, Pratyush, Valsson, Omar, Vendruscolo, Michele, Voth, Gregory A., and White, Andrew

Published
2019

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, B., Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, A., Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, I., Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Froguel, Philippe, Brorsson, Caroline, Brunak, S., De Masi, Federico, Pedersen, H., Banasik, Karina, Thomas, Cecilia, Lundgaard, Agnete, Nielsen, A., Mazzoni, Gianluca, Karaderi, T., Rasmussen, S., Johansen, Joachim, Allesøe, Rosa, Arumugam, Manimozhiyan, Allin, Kristine, Hansen, Torben, Hansen, Tue, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, J., Vestergaard, Henrik, Wilman, Henry R., Parisinos, Constantinos A., Atabaki-Pasdar, Naeimeh, Kelly, Matt, Thomas, E. Louise, Neubauer, Stefan, Jennison, Christopher, Ehrhardt, B., Baum, Patrick, Schoelsch, Corinna, Freijer, Jan, Grempler, Rolf, Graefe-Mody, Ulrike, Hennige, A., Dings, Christiane, Lehr, Thorsten, Scherer, Nina, Sihinecich, I., Pattou, Francois, Raverdi, Violeta, Caiazzo, Robert, Torres, Fanelly, Verkindt, Helene, Mari, Andrea, Tura, Andrea, Giorgino, Toni, Bizzotto, Froguel, Philippe, Brorsson, Caroline, Brunak, S., De Masi, Federico, Pedersen, H., Banasik, Karina, Thomas, Cecilia, Lundgaard, Agnete, Nielsen, A., Mazzoni, Gianluca, Karaderi, T., Rasmussen, S., Johansen, Joachim, Allesøe, Rosa, Arumugam, Manimozhiyan, Allin, Kristine, Hansen, Torben, Hansen, Tue, Jonsson, Anna, Pedersen, Oluf, Dutta, Avirup, Vogt, J., and Vestergaard, Henrik

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 <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and he

Published
2019

10. Exploring the Role of Interdisciplinarity in Physics: Success, Talent and Luck

Author

Pluchino, Alessandro, Burgio, Giulio, Rapisarda, Andrea, Biondo, Alessio Emanuele, Pulvirenti, Alfredo, Ferro, Alfredo, Giorgino, Toni, Pluchino, Alessandro, Burgio, Giulio, Rapisarda, Andrea, Biondo, Alessio Emanuele, Pulvirenti, Alfredo, Ferro, Alfredo, and Giorgino, Toni

Abstract

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counter-intuitive effect indicating that the most talented authors are not necessarily the most successful ones., Comment: 21 pages, 19 figures

Published
2019
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