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640 results on '"Costa, Tommaso"'

6. A bayesian reanalysis of the phase III aducanumab (ADU) trial

Author

Costa, Tommaso and Cauda, Franco

Subjects
Statistics - Applications
Abstract

In this article we have conducted a reanalysis of the phase III aducanumab (ADU) summary statistics announced by Biogen, in particular the result of the Clinical Dementia Rating-Sum of Boxes (CDR-SB). The results showed that the evidence on the efficacy of the drug is very low and a more clearer view of the results of clinical trials are presented in the Bayesian framework that can be useful for future development and research in the field.

Published
2021

27. Why classical receptor theory, which ignores allostery, can effectively measure the strength of an allosteric effect as agonist's efficacy.

Author

Onaran, H. Ongun and Costa, Tommaso

Subjects
*ACTION theory (Psychology), *CELLULAR signal transduction, *MATHEMATICAL analysis, *CELL communication, *COMPUTER simulation
Abstract

Background and Purpose: The classical theory of receptor action has been used for decades as a powerful tool to estimate molecular determinants of ligand‐induced receptor activation (i.e., affinity and efficacy) from experimentally observable biological responses. However, it is also a well‐recognized fact that the receptor‐binding and activation mechanisms, and the parameters thereof, described in the classical theory contradict with the modern view of receptor activation based on allosteric principles. Experimental Approach: We used mathematical analysis, along with some numerical simulations, to answer the key question as to what extent the classical theory is compatible—if at all—with the modern understanding of receptor activation. Key Results: Here, we showed conclusively that (1) receptor activation equations based on allosteric principles contain the logic of the classical theory in disguise, and therefore, (2) estimates of "intrinsic efficacy" (ε) obtained by means of classical techniques (i.e., null methods or fitting the operational model to concentration‐response data) are equivalent to the allosteric coupling factors that represent the molecular efficacy of ligands. Conclusion and Implications: Thus, we conclude that despite the justified criticisms it has received so far, the classical theory may continue to be useful in estimating ligand efficacy from experimental data, if used properly. Here, we also provide rigorous criteria for the proper use of the theory. These findings not only have implications for ligand classification but also resolve some long lasting discussions in the field of bias agonism in GPCR, which requires reasonable estimates of relative ligand efficacies at different signalling pathways. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Allosteric nanobodies reveal the dynamic range and diverse mechanisms of G-protein-coupled receptor activation.

Author

Staus, Dean P, Strachan, Ryan T, Manglik, Aashish, Pani, Biswaranjan, Kahsai, Alem W, Kim, Tae Hun, Wingler, Laura M, Ahn, Seungkirl, Chatterjee, Arnab, Masoudi, Ali, Kruse, Andrew C, Pardon, Els, Steyaert, Jan, Weis, William I, Prosser, R Scott, Kobilka, Brian K, Costa, Tommaso, and Lefkowitz, Robert J

Subjects
Humans, Isoproterenol, Receptors, Adrenergic, beta-2, Ligands, Crystallography, X-Ray, Nuclear Magnetic Resonance, Biomolecular, Allosteric Regulation, Allosteric Site, Protein Conformation, Models, Molecular, Drug Partial Agonism, Protein Stability, Adrenergic beta-2 Receptor Agonists, Single-Domain Antibodies, Crystallography, X-Ray, Models, Molecular, Nuclear Magnetic Resonance, Biomolecular, Receptors, Adrenergic, beta-2, General Science & Technology
Abstract

G-protein-coupled receptors (GPCRs) modulate many physiological processes by transducing a variety of extracellular cues into intracellular responses. Ligand binding to an extracellular orthosteric pocket propagates conformational change to the receptor cytosolic region to promote binding and activation of downstream signalling effectors such as G proteins and β-arrestins. It is well known that different agonists can share the same binding pocket but evoke unique receptor conformations leading to a wide range of downstream responses (‘efficacy’). Furthermore, increasing biophysical evidence, primarily using the β2-adrenergic receptor (β2AR) as a model system, supports the existence of multiple active and inactive conformational states. However, how agonists with varying efficacy modulate these receptor states to initiate cellular responses is not well understood. Here we report stabilization of two distinct β2AR conformations using single domain camelid antibodies (nanobodies)—a previously described positive allosteric nanobody (Nb80) and a newly identified negative allosteric nanobody (Nb60). We show that Nb60 stabilizes a previously unappreciated low-affinity receptor state which corresponds to one of two inactive receptor conformations as delineated by X-ray crystallography and NMR spectroscopy. We find that the agonist isoprenaline has a 15,000-fold higher affinity for β2AR in the presence of Nb80 compared to the affinity of isoprenaline for β2AR in the presence of Nb60, highlighting the full allosteric range of a GPCR. Assessing the binding of 17 ligands of varying efficacy to the β2AR in the absence and presence of Nb60 or Nb80 reveals large ligand-specific effects that can only be explained using an allosteric model which assumes equilibrium amongst at least three receptor states. Agonists generally exert efficacy by stabilizing the active Nb80-stabilized receptor state (R80). In contrast, for a number of partial agonists, both stabilization of R80 and destabilization of the inactive, Nb60-bound state (R60) contribute to their ability to modulate receptor activation. These data demonstrate that ligands can initiate a wide range of cellular responses by differentially stabilizing multiple receptor states.

Published
2016

46. An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers

Author

Premi, Enrico, Costa, Tommaso, Moreno, Fermin, Panman, Jessica, Papma, Janne, Pievani, Michela, Pijnenburg, Yolande, Polito, Cristina, Prioni, Sara, Prix, Catharina, Rademakers As London Ontario Geneticist, Rosa, Redaelli, Veronica, Rittman, Tim, Santana, Isabel, Rogaeva, Ekaterina, Rosa-Neto, Pedro, Rossi, Giacomina, Rossor, Martin, Santiago, Beatriz, Scarpini, Elio, Schönecker, Sonja, Semler, Elisa, Shafei, Rachelle, Shoesmith, Christen, Laforce, Robert, Tábuas-Pereira, Miguel, Tainta, Mikel, Taipa, Ricardo, Tang-Wai, David, L Thomas, David, Thompson, Paul, Thonberg, Hakan, Timberlake, Carolyn, Tiraboschi, Pietro, Van Damme, Philip, Ducharme, Simon, Vandenbulcke, Mathieu, Veldsman, Michele, Verdelho, Ana, Villanua, Jorge, Warren, Jason, Wilke, Carlo, Woollacott, Ione, Wlasich, Elisabeth, Zetterberg, Henrik, Zulaica, Miren, Graff, Caroline, Galimberti, Daniela, Masellis, Mario, Tartaglia, Carmela, Rowe, James B, Finger, Elizabeth, Gazzina, Stefano, Tagliavini, Fabrizio, de Mendonça, Alexandre, Vandenberghe, Rik, Gerhard, Alexander, Butler, Chris R, Danek, Adrian, Synofzik, Matthis, Levin, Johannes, Otto, Markus, Ghidoni, Roberta, Benussi, Alberto, Frisoni, Giovanni B, Sorbi, Sandro, Peakman, Georgia, Todd, Emily, Bocchetta, Martina, Rohrer, Johnathan D, Borroni, Barbara, Members, GENFI Consortium, Afonso, Sónia, Rosario Almeida, Maria, Cauda, Franco, Anderl-Straub, Sarah, Andersson, Christin, Antonell, Anna, Arighi, Andrea, Balasa, Mircea, Barandiaran, Myriam, Bargalló, Nuria, Bartha, Robart, Bender, Benjamin, Benussi, Luisa, Gasparotti, Roberto, Bessi, Valentina, Binetti, Giuliano, Black, Sandra, Borrego-Ecija, Sergi, Bras, Jose, Bruffaerts, Rose, Caroppo, Paola, Cash, David, Castelo-Branco, Miguel, Convery, Rhian, Archetti, Silvana, Cope, Thomas, de Arriba, María, Di Fede, Giuseppe, Díaz, Zigor, Duro, Diana, Fenoglio, Chiara, Ferrari, Camilla, B Ferreira, Catarina, Fox, Nick, Freedman, Morris, Alberici, Antonella, Fumagalli, Giorgio, Gabilondo, Alazne, Gauthier, Serge, Giaccone, Giorgio, Gorostidi, Ana, Greaves, Caroline, Guerreiro, Rita, Heller, Carolin, Hoegen, Tobias, Indakoetxea, Begoña, van Swieten, John C, Jelic, Vesna, Jiskoot, Lize, Karnath, Hans Otto, Keren, Ron, Langheinrich, Tobias, João Leitão, Maria, Lladó, Albert, Lombardi, Gemma, Loosli, Sandra, Maruta, Carolina, Sanchez-Valle, Raquel, Mead, Simon, Meeter, Lieke, Miltenberger, Gabriel, van Minkelen, Rick, Mitchell, Sara, Moore, Katrina, Nacmias, Benedetta, Nicholas, Jennifer, Öijerstedt, Linn, Olives, Jaume, GENFI Consortium Members, Neurology, Rowe, James [0000-0001-7216-8679], and Apollo - University of Cambridge Repository

Subjects
genetics [Granulins], Frontotemporal dementia, granulin, magnetic resonance imaging, mutation, preclinical, presymptomatic, Atrophy, Brain, Granulins, Humans, Magnetic Resonance Imaging, Mutation, Progranulins, Frontotemporal Dementia, Medizin, genetics [Mutation], diagnostic imaging [Frontotemporal Dementia], frontotemporal dementia, genetics [Progranulins], methods [Magnetic Resonance Imaging], SDG 3 - Good Health and Well-being, pathology [Brain], Settore BIO/13 - Biologia Applicata, ddc:610, diagnostic imaging [Brain], genetics [Frontotemporal Dementia], pathology [Atrophy], Frontotemporal dementia, granulin, magnetic resonance imaging, mutation, preclinical, presymptomatic, General Neuroscience, General Medicine, Psychiatry and Mental health, Clinical Psychology, pathology [Frontotemporal Dementia], Geriatrics and Gerontology
Abstract

Background:Magnetic resonance imaging (MRI) measures may be used as outcome markers in frontotemporal dementia (FTD). Objectives:To predict MRI cortical thickness (CT) at follow-up at the single subject level, using brain MRI acquired at baseline in preclinical FTD. Methods:84 presymptomatic subjects carrying Granulin mutations underwent MRI scans at baseline and at follow-up (31.2±16.5 months). Multivariate nonlinear mixed-effects model was used for estimating individualized CT at follow-up based on baseline MRI data. The automated user-friendly preGRN-MRI script was coded. Results:Prediction accuracy was high for each considered brain region (i.e., prefrontal region, real CT at follow-up versus predicted CT at follow-up, mean error ≤1.87%). The sample size required to detect a reduction in decline in a 1-year clinical trial was equal to 52 subjects (power = 0.80, alpha = 0.05). Conclusion:The preGRN-MRI tool, using baseline MRI measures, was able to predict the expected MRI atrophy at follow-up in presymptomatic subjects carrying GRN mutations with good performances. This tool could be useful in clinical trials, where deviation of CT from the predicted model may be considered an effect of the intervention itself. Swedish Frontotemporal Dementia Initiative Schörling Foundation; Swedish Research Council: JPND Prefrontals, 2015-02926 ,2018-02754; Swedish Alzheimer foundation; Swedish Brain Foundation; Karolinska Institutet Doctoral Funding; KI StratNeuro; Swedish Dementia foundation and Stockholm County Council ALF/Region Stockholm.

Published
2022

50. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects

Author

Gray, Jodie P., primary, Manuello, Jordi, additional, Alexander-Bloch, Aaron F., additional, Leonardo, Cassandra, additional, Franklin, Crystal, additional, Choi, Ki Sueng, additional, Cauda, Franco, additional, Costa, Tommaso, additional, Blangero, John, additional, Glahn, David C., additional, Mayberg, Helen S., additional, and Fox, Peter T., additional

Published
2022
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