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1. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning

Author

Philippe Faure, Jean-Christophe Olivo-Marin, Fabrice de Chaumont, Nicolas Torquet, Albane Imbert, Stephane Dallongeville, Anne-Marie Le Sourd, Thomas Bourgeron, Elodie Ey, Thierry Legou, Thibault Lagache, Analyse d'images biologiques - Biological Image Analysis (BIA), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Neurosciences Paris Seine (NPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche et Innovation Technologique (CITECH), Institut Pasteur [Paris], Laboratoire Parole et Langage (LPL), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), This work was partially funded by the Institut Pasteur, the Bettencourt-Schueller Foundation, the Cognacq–Jay Foundation, the Conny–Maeva Foundation, the ERANET–NEURON SYNPATHY program, the Agence Nationale de la Recherche through grant number ANR-10-LABX-62-IBEID, France-BioImaging infrastructure through grant number ANR-10-INBS-04 and the INCEPTION program through grant number ANR-16-CONV-0005, the Centre National de la Recherche Scientifique, the University Paris Diderot, the BioPsy Labex, the Institut National du Cancer through grant number TABAC-16–022, the Foundation for Medical Research (Equipe DEQ20130326488), the Innovative Medicines Initiative Joint Undertaking through grant agreement number 115300, resources of which are composed of financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies in kind contribution., The authors thank Y. Archambeau and P. Ollivon at the workshop of the Institut Pasteur for building the first 12 setups and advising on hardware, W. Meiniel for the mathematical proof for decisions of head/tail probability, Microsoft France for their technical support, P. Spinicelli for optical engineering and reading of the paper, R. Marée for machine learning support, B. König for advice and reading of biological experiments, J. N. Crawley for reading and providing comments on the manuscript, A. Barmpoutis for providing us with the early Kinect 2 driver and support, N. Chenouard for driving the use of the machine learning solution, P. Dugast for drawing the mice in the different behavioural events, A. Engelberg for checking the English, S. Wagner and R. Accardi for RFID advice, M. Marim for website development, and X. Montagutelli and M. Bérard for animal facility support., Analyse d'images biologiques - BioImage Analysis (AIQ), Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut Pasteur [Paris], Neuroscience Paris Seine (NPS), Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur [Paris], and Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Male, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Biomedical Engineering, Video Recording, Medicine (miscellaneous), Bioengineering, Nerve Tissue Proteins, Mouse tracking, Biology, Machine learning, computer.software_genre, Machine Learning, 03 medical and health sciences, Mice, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Animals, Autistic Disorder, Real time analysis, Social Behavior, Mice, Knockout, Behavior, Animal, business.industry, Microfilament Proteins, Computer Science Applications, Disease Models, Animal, 030104 developmental biology, Phenotype, Mutation, Identification (biology), Female, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, Biotechnology, Behavioral Research
Abstract

Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations—mutations that are associated with autism—on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions. A method that combines a depth-sensing camera and machine learning can track the movements of up to four mice in real time and for several days, extracting both individual and group behavioural traits.

Published
2019

2. Valorisation of local agro-industrial processing waters as growth media for polyhydroxyalkanoates (PHA) production

Author

Patrick Loulergue, Stéphane Bruzaud, Nicolas Hachet, Jean-Luc Audic, Yves-Marie Corre, Adélaïde Le Grand, Véronique Le Tilly, Anne Elain, Magali Le Fellic, Laboratoire d'Ingénierie des Matériaux de Bretagne (LIMATB), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université de Brest (UBO), Plateforme technologique ProDiaBio (PFT ProDiaBio), Institut des Sciences Chimiques de Rennes (ISCR), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), The authors acknowledge the regions of Bretagne and Pays de Loire for funding. We are grateful to Breizpack and Valorial pole for their help and to the industrial partners of PHApack consortium: Europlastiques, Séché Environnement, Triballat Noyal and Cap Ouest, for their contribution to this research program. We are also thankful to Mélanie Blanche Barbat and Laureen Van Steenberge for their contribution to this work. We thank Maja Bumke Scheer for taking time to help us improve the paper., Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Nationale Supérieure de Chimie de Rennes (ENSCR)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0106 biological sciences, 02 engineering and technology, Bacterial growth, Batch fermentation, 01 natural sciences, Polyhydroxyalkanoates, Stachyose, chemistry.chemical_compound, Marine bacterium, 010608 biotechnology, [CHIM]Chemical Sciences, Food science, Growth medium, Halomonas, biology, business.industry, 021001 nanoscience & nanotechnology, biology.organism_classification, 6. Clean water, Biotechnology, Agro-industrial processing waters, chemistry, Fermentation, Biodegradable plastic, Valorisation, 0210 nano-technology, business, Agronomy and Crop Science
Abstract

International audience; Polyhydroxyalkanoates (PHA) are bacterial polyesters usually produced from costly sugars or volatile fatty acids (VFAs). In this work, two processing waters rich in vegetable proteins and reducing sugars, i.e., a mixture of saccharose and stachyose in Leguminous Processing Water (LPW) and a mixture of glucose and fructose in Fruit Processing Water (FPW), were tested as growth medium for PHA production in a two-stage fermentation with a unique marine bacterial species: Halomonas i4786. In preliminary shake flask experiments, it was shown that the two media can effectively support the bacterial growth and the accumulation of PHA (evaluated using Nile Red staining). In batch cultivation mode in a 5-L fermentor, PHA productivities of 1.6 g L−1 and 1.8 g L−1 were further achieved within 72 h, in LPW and FPW respectively. Polymer characterization by Differential Scanning Calorimetry and Steric Exclusion Chromatography indicated that the two substrates led to the biosynthesis of polymers with different chain length, distribution and crystallinity. To summarize, these results show that by-products derived from local agri-food industry can be used as a user-adapted and cost-effective source to produce bio-sourced and biodegradable plastic materials

Published
2016

3. RNA sequencing revealed novel actors of the acquisition of drug resistance in Candida albicans

Author

Jean-Yves Coppée, Gaëlle Lelandais, Odile Sismeiro, Sophie Lemoine, Frédéric Devaux, Stéphane Le Crom, Rajendra Prasad, Maria Bernard, Sanjiveeni Dhamgaye, Membrane Biology Laboratory, Jawaharlal Nehru University (JNU), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Dynamique des Structures et Interactions des Macromolécules Biologiques - Pôle de La Réunion (DSIMB Réunion), Biologie Intégrée du Globule Rouge (BIGR (UMR_S_1134 / U1134)), Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université des Antilles (UA), Transcriptome et Epigénome (PF2), Institut Pasteur [Paris], Génomique des Microorganismes (LGM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), The work presented in this paper has been supported in part by grants to R.P. from Indo-French Center for the Promotion of Advanced Scientific Research (IFC-3403-2/2006) and DST INT/SWISS/P-31/2009., Institut de biologie de l'ENS Paris (IBENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Institut Pasteur [Paris] (IP), BMC, Ed., Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Transfusion Sanguine [Paris] (INTS)-Université Paris Diderot - Paris 7 (UPD7)-Université de La Réunion (UR)-Université des Antilles (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Candida albicans, beta-Glucans, lcsh:QH426-470, lcsh:Biotechnology, Repressor, RNA-Seq, Drug resistance, Multidrug resistance, Fungal Proteins, 03 medical and health sciences, Drug Resistance, Fungal, lcsh:TP248.13-248.65, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Transcriptional regulation, Genetics, CZF1, Gene, 030304 developmental biology, 0303 health sciences, biology, 030306 microbiology, Sequence Analysis, RNA, Gene Expression Profiling, biology.organism_classification, Yeast, 3. Good health, Multiple drug resistance, Gene expression profiling, lcsh:Genetics, [SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], RNA-seq, Biotechnology, Transcription Factors, Research Article
Abstract

Background Drug susceptible clinical isolates of Candida albicans frequently become highly tolerant to drugs during chemotherapy, with dreadful consequences to patient health. We used RNA sequencing (RNA-seq) to analyze the transcriptomes of a CDR (Candida Drug Resistance) strain and its isogenic drug sensitive counterpart. Results RNA-seq unveiled differential expression of 228 genes including a) genes previously identified as involved in CDR, b) genes not previously associated to the CDR phenotype, and c) novel transcripts whose function as a gene is uncharacterized. In particular, we show for the first time that CDR acquisition is correlated with an overexpression of the transcription factor encoding gene CZF1. CZF1 null mutants were susceptible to many drugs, independently of known multidrug resistance mechanisms. We show that CZF1 acts as a repressor of β-glucan synthesis, thus negatively regulating cell wall integrity. Finally, our RNA-seq data allowed us to identify a new transcribed region, upstream of the TAC1 gene, which encodes the major CDR transcriptional regulator. Conclusion Our results open new perspectives of the role of Czf1 and of our understanding of the transcriptional and post-transcriptional mechanisms that lead to the acquisition of drug resistance in C. albicans, with potential for future improvements of therapeutic strategies.

Published
2012