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1. Parkinson’s disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions

Author

Baldini, Federico, Hertel, Johannes, Sandt, Estelle, Thinnes, Cyrille C., Neuberger-Castillo, Lorieza, Pavelka, Lukas, Betsou, Fay, Krüger, Rejko, Thiele, Ines, Aguayo, Gloria, Allen, Dominic, Ammerlann, Wim, Aurich, Maike, Balling, Rudi, Banda, Peter, Beaumont, Katy, Becker, Regina, Berg, Daniela, Binck, Sylvia, Bisdorff, Alexandre, Bobbili, Dheeraj, Brockmann, Kathrin, Calmes, Jessica, Castillo, Lorieza, Diederich, Nico, Dondelinger, Rene, Esteves, Daniela, Ferrand, Jean-Yves, Fleming, Ronan, Gantenbein, Manon, Gasser, Thomas, Gawron, Piotr, Geffers, Lars, Giarmana, Virginie, Glaab, Enrico, Gomes, Clarissa P. C., Goncharenko, Nikolai, Graas, Jérôme, Graziano, Mariela, Groues, Valentin, Grünewald, Anne, Gu, Wei, Hammot, Gaël, Hanff, Anne-Marie, Hansen, Linda, Hansen, Maxime, Haraldsdöttir, Hulda, Heirendt, Laurent, Herbrink, Sylvia, Herzinger, Sascha, Heymann, Michael, Hiller, Karsten, Hipp, Geraldine, Hu, Michele, Huiart, Laetitia, Hundt, Alexander, Jacoby, Nadine, Jarosław, Jacek, Jaroz, Yohan, Kolber, Pierre, Kutzera, Joachim, Landoulsi, Zied, Larue, Catherine, Lentz, Roseline, Liepelt, Inga, Liszka, Robert, Longhino, Laura, Lorentz, Victoria, Mackay, Clare, Maetzler, Walter, Marcus, Katrin, Marques, Guilherme, Martens, Jan, Mathay, Conny, Matyjaszczyk, Piotr, May, Patrick, Meisch, Francoise, Menster, Myriam, Minelli, Maura, Mittelbronn, Michel, Mollenhauer, Brit, Mommaerts, Kathleen, Moreno, Carlos, Mühlschlegel, Friedrich, Nati, Romain, Nehrbass, Ulf, Nickels, Sarah, Nicolai, Beatrice, Nicolay, Jean-Paul, Noronha, Alberto, Oertel, Wolfgang, Ostaszewski, Marek, Pachchek, Sinthuja, Pauly, Claire, Perquin, Magali, Reiter, Dorothea, Rosety, Isabel, Rump, Kirsten, Satagopam, Venkata, Schlesser, Marc, Schmitz, Sabine, Schmitz, Susanne, Schneider, Reinhard, Schwamborn, Jens, Schweicher, Alexandra, Simons, Janine, Stute, Lara, Trefois, Christophe, Trezzi, Jean-Pierre, Vaillant, Michel, Vasco, Daniel, Vyas, Maharshi, Wade-Martins, Richard, and Wilmes, Paul

Subjects
Male, Systemic disease, Parkinson's disease, Physiology, Luxembourg, Plant Science, Disease, Transsulfuration pathway, Biology, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Structural Biology, RNA, Ribosomal, 16S, Pantothenic acid, medicine, Humans, Microbiome, lcsh:QH301-705.5, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, Aged, Metabolic modelling, 0303 health sciences, Gut microbiome, Methionine, Dopaminergic, Parkinson Disease, Cell Biology, Middle Aged, medicine.disease, 3. Good health, Gastrointestinal Microbiome, RNA, Bacterial, chemistry, Computational modelling, lcsh:Biology (General), Case-Control Studies, Parkinson’s disease, Female, General Agricultural and Biological Sciences, 030217 neurology & neurosurgery, Developmental Biology, Biotechnology, Research Article
Abstract

Background Parkinson’s disease (PD) is a systemic disease clinically defined by the degeneration of dopaminergic neurons in the brain. While alterations in the gut microbiome composition have been reported in PD, their functional consequences remain unclear. Herein, we addressed this question by an analysis of stool samples from the Luxembourg Parkinson’s Study (n = 147 typical PD cases, n = 162 controls). Results All individuals underwent detailed clinical assessment, including neurological examinations and neuropsychological tests followed by self-reporting questionnaires. Stool samples from these individuals were first analysed by 16S rRNA gene sequencing. Second, we predicted the potential secretion for 129 microbial metabolites through personalised metabolic modelling using the microbiome data and genome-scale metabolic reconstructions of human gut microbes. Our key results include the following. Eight genera and seven species changed significantly in their relative abundances between PD patients and healthy controls. PD-associated microbial patterns statistically depended on sex, age, BMI, and constipation. Particularly, the relative abundances of Bilophila and Paraprevotella were significantly associated with the Hoehn and Yahr staging after controlling for the disease duration. Furthermore, personalised metabolic modelling of the gut microbiomes revealed PD-associated metabolic patterns in the predicted secretion potential of nine microbial metabolites in PD, including increased methionine and cysteinylglycine. The predicted microbial pantothenic acid production potential was linked to the presence of specific non-motor symptoms. Conclusion Our results suggest that PD-associated alterations of the gut microbiome can translate into substantial functional differences affecting host metabolism and disease phenotype.

Published
2020
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2. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

Author

Topalovic, Marko, Das, Nilakash, Burgel, Pierre-Regis, Daenen, Marc, Derom, Eric, Haenebalcke, Christel, Janssen, Rob, Kerstjens, Huib AM, Liistro, Giuseppe, Louis, Renaud, Ninane, Vincent, Pison, Christophe, Schlesser, Marc, Vercauter, Piet, Vogelmeier, Claus F, Wouters, Emiel, Wynants, Jokke, Janssens, Wim, De Pauw, R, Depuydt, C, Haenebalcke, C, Muyldermans, S, Ringoet, V, Stevens, D, Bayat, S, Benet, J, Catho, E, Claustre, J, Fedi, A, Ferjani, MA, Guzun, R, Isnard, M, Nicolas, S, Pierret, T, Pison, C, Rouches, S, Wuyam, B, Corhay, JL, Guiot, J, Ghysen, K, Renaud, L, Sibille, A, De La Barriere, H, Charpentier, C, Corhut, S, Hamdan, KA, Schlesser, M, Wirtz, G, Alabadan, E, Birsen, G, Burgel, PR, Chohra, A, Hamard, C, Lemarie, B, Lothe, MN, Martin, C, Sainte-Marie, AC, Sebane, L, Berk, Y, de Brouwer, B, Janssen, R, Kerkhoff, J, Spaanderman, A, Stegers, M, Termeer, A, van Grimbergen, I, van Veen, A, van Ruitenbeek, L, Vermeer, L, Zaal, R, Zijlker, M, Aumann, J, Cuppens, K, Degraeve, D, Demuynck, K, Dieriks, B, Pat, K, Spaas, L, Van Puijenbroek, R, Weytjens, K, Wynants, J, Adam, V, Berendes, BJ, Hardeman, E, Jordens, P, Munghen, E, Tournoy, K, Vercauter, P, Alame, T, Bruyneel, M, Gabrovska, M, Muylle, I, Ninane, V, Rozen, D, Rummens, P, Van den Broecke, S, Froidure, A, Gohy, S, Liistro, G, Pieters, T, Pilette, C, Pirson, F, Kerstjens, H, Van den Berge, M, Ten Hacken, N, Duiverman, M, Koster, D, Vosse, B, Conemans, L, Maus, M, Bischoff, M, Rutten, M, Agterhuis, D, Sprooten, R, Beutel, B, Jerrentrup, A, Klemmer, A, Viniol, C, Vogelmeier, C, Bode, H, Dooms, C, Gullentops, D, Janssens, W, Nackaerts, K, Rutens, D, Wauters, E, Wuyts, W, Derom, E, Dobbelaere, S, Loof, S, Serry, G, Putman, B, Van Acker, L, Vandeweygaerde, Y, Criel, M, Daenen, M, Gubbelmans, R, Klerkx, S, Michiels, E, Thomeer, M, Vanhauwaert, A, UCL - (SLuc) Service de pneumologie, Groningen Research Institute for Asthma and COPD (GRIAC), Lifestyle Medicine (LM), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Universitaire [Grenoble] (CHU), Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée (LBFA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), RS: NUTRIM - R3 - Respiratory & Age-related Health, MUMC+: MA Longziekten (3), Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), and MUMC+: MA Med Staf Artsass Longziekten (9)

Subjects
Pulmonary and Respiratory Medicine, Adult, Male, Pulmonary function, STRATEGIES, Pulmonary Function Study Investigators, Context (language use), DIAGNOSIS, GUIDELINES, [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, Pulmonary function testing, 03 medical and health sciences, 0302 clinical medicine, Clinical history, Artificial Intelligence, Pulmonary Medicine, Medicine, Humans, 030212 general & internal medicine, Prospective Studies, Medical diagnosis, Pulmonologists, Aged, Aged, 80 and over, Interpretation (logic), business.industry, Gold standard (test), STANDARDIZATION, PERFORMANCE, Middle Aged, 3. Good health, Respiratory Function Tests, Clinical Practice, 030228 respiratory system, Female, Artificial intelligence, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Software
Abstract

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p

Published
2018
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3. Incidence of SARS-CoV-2 in people with cystic fibrosis in Europe between February and June 2020

Author

Halyna Makukh, Andrea Dugac Vukic, Meir Mei-Zahav, Tsitsino Parulava, Harriet Corvol, Godfrey Fletcher, Rita Padoan, S. Fustik, Silke van Koningsbruggen-Rietschel, Fiona Dunlevy, Andreas Jung, Hana Kayserova, Satenik Harutyunyan, Siobhán B. Carr, Domenique Zomer-van Ommen, Łukasz Woźniacki, Svetlana Keegan, Elena Kondratyeva, Jacqui G. van Rens, Elpis Hatziagorou, Carla Colombo, Pedro Mondejar-Lopez, Vincent Gulmans, Anita Senstad Wathne, María Dolores Pastor-Vivero, Milan Macek, Egil Bakkeheim, Milan Rodić, Sevgi Pekcan, Filia Diamantea, Elliott McClenaghan, Géraldine Daneau, Guergana Petrova, Kęstutis Malakauskas, Marko Krasnyk, Yasemin Gokdemir, Panayiotis K. Yiallouros, Elise Lammertyn, Marc Schlesser, Edward F. McKone, Andreas Pfleger, Olga I. Simonova, Luísa Pereira, Elena Zhekaite, Anders Lindblad, Oxana Turcu, Andrea Párniczky, Isabelle de Monestrol, Duška Tješić-Drinković, Elina Aleksejeva, Uro Krivecs, Lutz Naehrlich, Elena Amelina, A. Zolin, Pierre-Régis Burgel, Vladimir Bobrovnichy, Annalisa Orenti, Alena Bilkova, Deniz Dogru, Liviu Pop, Marco Salvatore, Nataliya Kashirskaya, Irena Kasmi, Keith G. Brownlee, Pavel Drevinek, Hanne Vebert Olesen, Rebecca Cosgriff, Lydie Lemonnier-Videau, A. Fox, Ivan Bambir, Naehrlich, Lutz, Orenti, Annalisa, Dunlevy, Fiona, Kasmi, Irena, Harutyunyan, Satenik, Pfleger, Andreas, Keegan, Svetlana, Daneau, Geraldine, Petrova, Guergana, Tjesic-Drinkovic, Duska, Yiallouros, Panayioti, Bilkova, Alena, Olesen, Hanne Vebert, Burgel, Pierre-Regis, Parulava, Tsitsino, Diamantea, Filia, Parniczky, Andrea, McKone, Edward F., Mei-Zahav, Meir, Salvatore, Marco, Colombo, Carla, Aleksejeva, Elina, Malakauskas, Kestutis, Schlesser, Marc, Fustik, Stojka, Turcu, Oxana, Zomer-van Ommen, Domenique, Wathne, Anita Senstad, Wozniacki, Lukasz, Pereira, Luisa, Pop, Liviu, Kashirskaya, Nataliya, Rodic, Milan, Kayserova, Hana, Krivecs, Uro, Mondejar-Lopez, Pedro, de Monsterol, Isabelle, Dogru, Deniz, Makukh, Halyn, Cosgriff, Rebecca, van Koningsbruggen-Rietschel, Silke, Jung, Andreas, Bobrovnichy, Vladimir, Bambir, Ivan, Vukic, Andrea Dugac, Drevinek, Pavel, Macek, Milan, Jr., Corvol, Harriet, Lemonnier-Videau, Lydie, Hatziagorou, Elpis, Fletcher, Godfrey, Padoan, Rita, Gulmans, Vincent, Bakkeheim, Egil, Kondratyeva, Elena, Amelina, Elena, Zhekaite, Elena, Simonova, Olga, Pastor-Vivero, Maria Dolores, Lindblad, Anders, Gokdemir, Yasemin, Pekcan, Sevgi, Brownlee, Keith, McClenaghan, Elliot, Carr, Siobhan, Lammertyn, Elise, Zolin, Anna, Fox, Alice, Krasnyk, Marko, and Van Rens, Jacqui

Subjects
0301 basic medicine, Male, COVID-19/diagnosis, IMPACT, Epidemiology, Cystic fibrosis, 0302 clinical medicine, Cystic Fibrosis/complications, Case fatality rate, Medicine, Registries, Child, education.field_of_study, Incidence (epidemiology), Incidence, Middle Aged, Vaccination, Europe, Hospitalization, medicine.anatomical_structure, Child, Preschool, Female, Covid-19, Lung Transplantation, Pulmonary and Respiratory Medicine, Adult, medicine.medical_specialty, Adolescent, Critical Care, Population, Article, Europe/epidemiology, 03 medical and health sciences, Young Adult, Intensive care, Internal medicine, Humans, education, Retrospective Studies, Lung, business.industry, SARS-CoV-2, Infant, Newborn, Infant, medicine.disease, cystic fibrosis, incidence, epidemiology, Transplantation, 030104 developmental biology, 030228 respiratory system, Pediatrics, Perinatology and Child Health, business
Abstract

Background: Viral infections can cause significant morbidity in cystic fibrosis (CF). The current Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic could therefore have a serious impact on the health of people with CF (pwCF). Methods: We used the 38-country European Cystic Fibrosis Society Patient Registry (ECFSPR) to collect case data about pwCF and SARS-CoV-2 infection. Results: Up to 30 June 2020, 16 countries reported 130 SARS-CoV-2 cases in people with CF, yielding an incidence of 2.70/10 0 0 pwCF. Incidence was higher in lung-transplanted patients (n = 23) versus non transplanted patients (n = 107) (8.43 versus 2.36 cases/10 0 0). Incidence was higher in pwCF versus the age-matched general population in the age groups < 15, 15-24, and 25-49 years (p < 0.001), with similar trends for pwCF with and without lung transplant. Compared to the general population, pwCF (regardless of transplantation status) had significantly higher rates of admission to hospital for all age groups with available data, and higher rates of intensive care, although not statistically significant. Most pwCF recovered (96.2%), however 5 died, of whom 3 were lung transplant recipients. The case fatality rate for pwCF (3.85%, 95% CI: 1.26-8.75) was non-significantly lower than that of the general population (7.46%; p = 0.133). Conclusions: SARS-CoV-2 infection can result in severe illness and death for pwCF, even for younger patients and especially for lung transplant recipients. PwCF should continue to shield from infection and should be prioritized for vaccination. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of European Cystic Fibrosis Society. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Published
2021
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