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1. TiPS: Rapidly simulating trajectories and phylogenies from compartmental models

Author

Gonché Danesh, Emma Saulnier, Olivier Gascuel, Marc Choisy, and Samuel Alizon

Subjects
compartmental models, phylogenies, population dynamics, R package, stochastic simulations, Ecology, QH540-549.5, Evolution, QH359-425
Abstract

Abstract Stochastic population dynamics simulations are essential for many ecological and epidemiological studies to generate time series and genealogies that capture the relatedness between individuals. Many software packages allow one to simulate phylogenetic trees but these tend to suffer from one or two major limitations. First, the underlying population dynamics model is often simplistic (e.g. constant population size or exponential growth). Second, the software packages are not appropriate to simulate a large number of trees. We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespie's exact or approximate stochastic simulation algorithm, or a newly proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards‐in‐time approach (i.e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function. TiPS is flexible, easy‐to‐use and available on the CRAN at https://cran.r‐project.org/package=TiPS. Plus, benchmarking analyses show that TiPS is faster than existing packages. This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies.

Published
2023
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2. Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data.

Author

Gonché Danesh, Victor Virlogeux, Christophe Ramière, Caroline Charre, Laurent Cotte, and Samuel Alizon

Subjects
Immunologic diseases. Allergy, RC581-607, Biology (General), QH301-705.5
Abstract

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health.

Published
2021
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3. Bacterial Transformation Buffers Environmental Fluctuations through the Reversible Integration of Mobile Genetic Elements

Author

Gabriel Carvalho, David Fouchet, Gonché Danesh, Anne-Sophie Godeux, Maria-Halima Laaberki, Dominique Pontier, Xavier Charpentier, and Samuel Venner

Subjects
natural transformation, horizontal gene transfer, mobile genetic elements, resistance genes, stochastic environment, Microbiology, QR1-502
Abstract

ABSTRACT Horizontal gene transfer (HGT) promotes the spread of genes within bacterial communities. Among the HGT mechanisms, natural transformation stands out as being encoded by the bacterial core genome. Natural transformation is often viewed as a way to acquire new genes and to generate genetic mixing within bacterial populations. Another recently proposed function is the curing of bacterial genomes of their infectious parasitic mobile genetic elements (MGEs). Here, we propose that these seemingly opposing theoretical points of view can be unified. Although costly for bacterial cells, MGEs can carry functions that are at points in time beneficial to bacteria under stressful conditions (e.g., antibiotic resistance genes). Using computational modeling, we show that, in stochastic environments, an intermediate transformation rate maximizes bacterial fitness by allowing the reversible integration of MGEs carrying resistance genes, although these MGEs are costly for host cell replication. Based on this dual function (MGE acquisition and removal), transformation would be a key mechanism for stabilizing the bacterial genome in the long term, and this would explain its striking conservation. IMPORTANCE Natural transformation is the acquisition, controlled by bacteria, of extracellular DNA and is one of the most common mechanisms of horizontal gene transfer, promoting the spread of resistance genes. However, its evolutionary function remains elusive, and two main roles have been proposed: (i) the new gene acquisition and genetic mixing within bacterial populations and (ii) the removal of infectious parasitic mobile genetic elements (MGEs). While the first one promotes genetic diversification, the other one promotes the removal of foreign DNA and thus genome stability, making these two functions apparently antagonistic. Using a computational model, we show that intermediate transformation rates, commonly observed in bacteria, allow the acquisition then removal of MGEs. The transient acquisition of costly MGEs with resistance genes maximizes bacterial fitness in environments with stochastic stress exposure. Thus, transformation would ensure both a strong dynamic of the bacterial genome in the short term and its long-term stabilization.

Published
2020
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4. TiPS : Rapidly simulating trajectories and phylogenies from compartmental models

Author

Emma Saulnier, Marc Choisy, Olivier Gascuel, Gonché Danesh, Samuel Alizon, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM), Institut de Systématique, Evolution, Biodiversité (ISYEB ), Muséum national d'Histoire naturelle (MNHN)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA), Oxford University Clinical Research Unit [Ho Chi Minh City] (OUCRU), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Theoretical computer science, Computer science, Population, Population genetics, computer.software_genre, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Set (abstract data type), Software, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, education, Ecology, Evolution, Behavior and Systematics, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, education.field_of_study, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], business.industry, Ecological Modeling, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Ode, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Gillespie algorithm, Simulation software, Viral phylodynamics, Ordinary differential equation, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, computer, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespie’s exact or approximate stochastic simulation algorithm, or a newly-proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards-in-time approach (i.e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function. Furthermore, the package allows defining time periods in which the model’s parameters can take different values. This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies. This package is available on the CRAN at https://cran.r-project.org/package=TiPS.

Published
2022

5. COVFlow: virus phylodynamics analyses from selected SARS-CoV-2 sequences

Author

Gonché Danesh, Corentin Boennec, Laura Verdurme, Mathilde Roussel, Sabine Trombert-Paolantoni, Benoit Visseaux, Stéphanie Haim-Boukobza, and Samuel Alizon

Abstract

Phylodynamic analyses generate important and timely data to optimise public health response to SARS-CoV-2 outbreaks and epidemics. However, their implementation is hampered by the massive amount of sequence data and the difficulty to parameterise dedicated software packages. We introduce theCOVFlowpipeline, accessible athttps://gitlab.in2p3.fr/ete/CoV-flow, which allows a user to select sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID) database according to user-specified criteria, to perform basic phylogenetic analyses, and to produce an XML file to be run in theBeast2software package. We illustrate the potential of this tool by studying two sets of sequences from the Delta variant in two French regions. This pipeline can facilitate the use of virus sequence data at the local level, for instance, to track the dynamics of a particular lineage or variant in a region of interest.

Published
2022

6. Spread of SARS-CoV-2 Delta variant infections bearing the S:E484Q and S:T95I mutations in July and August 2021 in France

Author

Brigitte Montes, Mircea T. Sofonea, Gonché Danesh, Laura Verdume, Bénédicte Roquebert, Stéphanie Haim-Boukobza, Edouard Tuaillon, Vincent Foulongne, Samuel Alizon, Sabine Trombert, and Valérie Noël

Subjects
Delta, Whole genome sequencing, Genetics, 2019-20 coronavirus outbreak, Phylogenetic tree, Screening test, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Mutation (genetic algorithm), Biology, Disease cluster
Abstract

Analysing 92,598 variant screening tests performed on SARS-CoV-2 positive samples collected in France between 1 July and 31 August 2021 shows an increase of Kappa-like infections. Full genome sequencing reveals that these correspond to Delta variants bearing the S:E484Q mutation. Most of these sequences belong to a phylogenetic cluster and also bear the S:T95I mutation. Further monitoring is needed to determine if this trend is driven by undocumented superspreading events or an early signal of future viral evolutionary dynamics.

Published
2021

7. Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data

Author

Christophe Ramière, Gonché Danesh, Victor Virlogeux, Caroline Charre, Laurent Cotte, Samuel Alizon, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), and Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, RNA viruses, Epidemiology, Hepacivirus, medicine.disease_cause, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Geographical locations, 0302 clinical medicine, Epidemiological Statistics, Medicine and Health Sciences, 030212 general & internal medicine, Biology (General), Phylogeny, Pathology and laboratory medicine, Data Management, 0303 health sciences, Hepatitis C virus, Transmission (medicine), [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], virus diseases, Phylogenetic Analysis, Medical microbiology, Hepatitis C, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Phylogenetics, Europe, Viruses, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Epidemiological Methods and Statistics, Female, France, Pathogens, Approximate Bayesian computation, Research Article, Computer and Information Sciences, medicine.medical_specialty, Sexual transmission, QH301-705.5, Sexual Behavior, Immunology, Men WHO Have Sex with Men, Biology, Microbiology, Virus, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Virology, Genetics, medicine, Humans, Evolutionary Systematics, European Union, Homosexuality, Male, Epidemics, Molecular Biology, Taxonomy, 030304 developmental biology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, Evolutionary Biology, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Flaviviruses, Public health, Organisms, Viral pathogens, Biology and Life Sciences, RC581-607, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Hepatitis viruses, Microbial pathogens, Viral phylodynamics, People and Places, Population Groupings, Parasitology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Immunologic diseases. Allergy, Sexuality Groupings, Demography, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Opioid substitution and syringes exchange programs have drastically reduced hepatitis C virus (HCV) spread in France but HCV sexual transmission in men having sex with men (MSM) has recently arisen as a significant public health concern. The fact that the virus is transmitting in a heterogeneous population, with different transmission routes, makes prevalence and incidence rates poorly informative. However, additional insights can be gained by analyzing virus phylogenies inferred from dated genetic sequence data. By combining a phylodynamics approach based on Approximate Bayesian Computation (ABC) and an original transmission model, we estimate key epidemiological parameters of an ongoing HCV epidemic among MSMs in Lyon (France). We show that this new epidemic is largely independent of the previously observed non-MSM HCV epidemics and that its doubling time is ten times lower (0.44 years versus 4.37 years). These results have practical implications for HCV control and illustrate the additional information provided by virus genomics in public health., Author summary Lyon (France) is witnessing a new epidemic of hepatitis C virus infection, which appears to be fuelled by sexual transmission. Upon detection, patients are found to belong to two main risk groups. The first group is referred to as non-MSM and typically corresponds to HIV-negative patients infected through nosocomial transmission or with a history of opioid intravenous drug use or blood transfusion or patients with haemophilia. The second group is more recent and mainly corresponds to Men Having Sex with Men (MSM) who are HIV-infected or HIV-negative MSMs. They tend to be detected during or shortly after the acute HCV infection phase and to use recreational drugs such as cocaine or cathinones. By taking advantage of recent developments in the emerging field of phylodynamics, we combine this patient information with virus sequence data to estimate key properties of the epidemics. We show that the current HCV spread via sexual transmission and MSM hosts is comparable to that before the advent of third-generation detection tests. We also find that the duration of the effective infectious period in MSM hosts is comparable to that of the acute phase. These results have timely public health implications, one of which is that treatment upon detection is necessary to slow down the ongoing HCV epidemics in Lyon.

Published
2021

8. Early phylodynamics analysis of the COVID-19 epidemic in France

Author

Jean-Christophe Plantier, Antonin Bal, Gonché Danesh, Anne-Geneviève Marcelin, Laurence Josset, Sonia Burrel, Sylvie Behillil, Sylvie van der Werf, Etienne Simon-Loriere, Samuel Alizon, Grégory Destras, Yannis Michalakis, Vincent Enouf, Vincent Thibault, Bruno Lina, Baptiste Elie, David Boutolleau, Mircea T. Sofonea, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Institut des Agents Infectieux [Lyon] (IAI), Hospices Civils de Lyon (HCL), Hôpital de la Croix-Rousse [CHU - HCL], Virology and human respiratory Pathologies - Virology and human respiratory Pathologies (VirPath), Centre International de Recherche en Infectiologie - UMR (CIRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Génétique Moléculaire des Virus à ARN - Molecular Genetics of RNA Viruses (GMV-ARN (UMR_3569 / U-Pasteur_2)), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Centre National de Référence des Virus des Infections Respiratoires (dont la Grippe) [Lyon] (CNR), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Hôpital Charles Nicolle [Rouen], CHU Pontchaillou [Rennes], Génomique évolutive des virus à ARN - Evolutionary genomics of RNA viruses, Institut Pasteur [Paris], Pasteur International Bioresources network (PIBNet), Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), ANR-20-COVI-0098,PHYEPI,Intégration de données de séquences et d'incidence pour analyser et contrôler les épidémies virales(2020), Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR - laboratoire coordonnateur), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), Centre National de Référence des Virus des Infections Respiratoires (dont la Grippe) [Lyon] (CNR - laboratoire associé), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), CHU Rouen, Normandie Université (NU)-Normandie Université (NU), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-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 - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-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)-Institut Pasteur [Paris]-Université de Paris (UP), Génétique et évolution des maladies infectieuses (GEMI), Centre National de Référence des virus des infections respiratoires (dont la grippe) - National Reference Center Virus Influenzae [Paris] (CNR), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Génétique et évolution des maladies infectieuses (GEMI), Alizon, Samuel, and Intégration de données de séquences et d'incidence pour analyser et contrôler les épidémies virales - - PHYEPI2020 - ANR-20-COVI-0098 - COVID-19 - VALID

Subjects
0106 biological sciences, Coronavirus disease 2019 (COVID-19), Genomic data, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Context (language use), [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Incidence data, Pandemic, [SDV.BID.SPT] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [SDV.BID.EVO] Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.EE.ECO] Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.EE.SANT] Life Sciences [q-bio]/Ecology, environment/Health, 030304 developmental biology, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [SDV.EE.IEO] Life Sciences [q-bio]/Ecology, environment/Symbiosis, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Geography, Viral phylodynamics, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.GEN.GPO] Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Contact tracing, Demography, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

France was one of the first countries to be reached by the COVID-19 pandemic. Here, we analyse 196 SARS-Cov-2 genomes collected between Jan 24 and Mar 24 2020, and perform a phylodynamics analysis. In particular, we analyse the doubling time, reproduction number (ℛt) and infection duration associated with the epidemic wave that was detected in incidence data starting from Feb 27. Different models suggest a slowing down of the epidemic in Mar, which would be consistent with the implementation of the national lock-down on Mar 17. The inferred distributions for the effective infection duration andℛtare in line with those estimated from contact tracing data. Finally, based on the available sequence data, we estimate that the French epidemic wave originated between mid-Jan and early Feb. Overall, this analysis shows the potential to use sequence genomic data to inform public health decisions in an epidemic crisis context and calls for further analyses with denser sampling.

Published
2021

9. Rt2: computing and visualising COVID-19 epidemics temporal reproduction number

Author

Bastien Reyné, Gonché Danesh, Mircea T. Sofonea, Samuel Alizon, Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-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), and Région Occitanie et Agence Nationale de la Recherche (projet PHYEPI)

Subjects
medicine.medical_specialty, Context monitoring, Coronavirus disease 2019 (COVID-19), Reproduction (economics), Secondary infection, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 03 medical and health sciences, 0302 clinical medicine, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Sliding window protocol, Statistics, medicine, 030212 general & internal medicine, 030304 developmental biology, [SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, 0303 health sciences, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], Public health, Incidence (epidemiology), [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], 3. Good health, Geography, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Time variations, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Analysing the spread of COVID-19 epidemics in a timely manner is essential for public health authorities. However, raw numbers may be misleading because of spatial and temporal variations. We introduce Rt2, an R-program with a shiny interface, which uses incidence data, i.e. number of new cases per day, to compute variations in the temporal reproduction number (R t), which corresponds to the average number of secondary infections caused by an infected person. This number is computed with the R0 package, which better captures past variations, and the EpiEstim package, which provides a more accurate estimate of current values. R t can be computed in different countries using either the daily number of new cases or of deaths. For France, these numbers can also be computed at the regional and departmental level using also daily numbers of hospital and ICU admissions. Finally, in addition to R t , we represent the incidence using a one-week sliding window to buffer daily variations. Overall, Rt2 provides an accurate and timely overview of the state and speed of spread of COVID-19 epidemics at different scales, using different metrics. Context Monitoring the state and speed of spread of COVID-19 epidemics at the national and regional levels is crucial to implement non-pharmaceutical interventions (Flaxman et al., 2020). Every day, public health agencies communicate key figures to monitor the epidemic, especially incidences, which correspond to the number of new cases detected. These incidences are typically related to four variables, which are PCR-based detection, deaths, hospitalisations, and ICU admissions. The statistical analysis of time variations in these time series can inform us about epidemiological dynamics.

Published
2020

10. Phylodynamique du COVID-19 en France : variations temporelles de la croissance épidémique

Author

Gonché Danesh, Baptiste Elie, Samuel Alizon, Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Evolution Théorique et Expérimentale (MIVEGEC-ETE), Perturbations, Evolution, Virulence (PEV), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Maladies infectieuses et vecteurs : écologie, génétique, évolution et contrôle (MIVEGEC), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud]), Centre interdisciplinaire de recherche en biologie (CIRB), Labex MemoLife, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Collège de France (CdF (institution))-Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-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), Fondation pour la Recherche Médicale, Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD), Université de Montpellier (UM), FRA., and Travail du groupe de modélisation de l'équipe ETE

Subjects
[SDV.EE.SANT]Life Sciences [q-bio]/Ecology, environment/Health, R0, [SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE], [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], COVID-19, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], génomique, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, épidémiologie, phylogénie, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, France, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.EE.IEO]Life Sciences [q-bio]/Ecology, environment/Symbiosis
Abstract

Travail du groupe de modélisation de l'équipe ETE

Published
2020

11. Bacterial Transformation Buffers Environmental Fluctuations through the Reversible Integration of Mobile Genetic Elements

Author

David Fouchet, Gabriel Carvalho, Maria-Halima Laaberki, Dominique Pontier, Xavier Charpentier, Samuel Venner, Anne-Sophie Godeux, Gonché Danesh, Transferts horizontaux de gènes et bactéries pathogènes - Horizontal gene transfer in bacterial pathogens (HORIGENE), Centre International de Recherche en Infectiologie - UMR (CIRI), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre International de Recherche en Infectiologie (CIRI), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Gene Transfer, Horizontal, [SDV]Life Sciences [q-bio], Bacterial genome size, Computational biology, Ecological and Evolutionary Science, Biology, Genome, natural transformation, Microbiology, 03 medical and health sciences, Virology, Environmental Microbiology, Computer Simulation, Gene, 030304 developmental biology, Genetic diversity, 0303 health sciences, Bacteria, 030306 microbiology, Mechanism (biology), resistance genes, mobile genetic elements, QR1-502, Interspersed Repetitive Sequences, Transformation (genetics), stochastic environment, Horizontal gene transfer, horizontal gene transfer, Transformation, Bacterial, Mobile genetic elements, Function (biology), Genome, Bacterial, Research Article
Abstract

Natural transformation is the acquisition, controlled by bacteria, of extracellular DNA and is one of the most common mechanisms of horizontal gene transfer, promoting the spread of resistance genes. However, its evolutionary function remains elusive, and two main roles have been proposed: (i) the new gene acquisition and genetic mixing within bacterial populations and (ii) the removal of infectious parasitic mobile genetic elements (MGEs). While the first one promotes genetic diversification, the other one promotes the removal of foreign DNA and thus genome stability, making these two functions apparently antagonistic. Using a computational model, we show that intermediate transformation rates, commonly observed in bacteria, allow the acquisition then removal of MGEs. The transient acquisition of costly MGEs with resistance genes maximizes bacterial fitness in environments with stochastic stress exposure. Thus, transformation would ensure both a strong dynamic of the bacterial genome in the short term and its long-term stabilization., Horizontal gene transfer (HGT) promotes the spread of genes within bacterial communities. Among the HGT mechanisms, natural transformation stands out as being encoded by the bacterial core genome. Natural transformation is often viewed as a way to acquire new genes and to generate genetic mixing within bacterial populations. Another recently proposed function is the curing of bacterial genomes of their infectious parasitic mobile genetic elements (MGEs). Here, we propose that these seemingly opposing theoretical points of view can be unified. Although costly for bacterial cells, MGEs can carry functions that are at points in time beneficial to bacteria under stressful conditions (e.g., antibiotic resistance genes). Using computational modeling, we show that, in stochastic environments, an intermediate transformation rate maximizes bacterial fitness by allowing the reversible integration of MGEs carrying resistance genes, although these MGEs are costly for host cell replication. Based on this dual function (MGE acquisition and removal), transformation would be a key mechanism for stabilizing the bacterial genome in the long term, and this would explain its striking conservation.

Published
2020

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