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Started on 1 January 2017, the INCEPTION project (Convergence Institute for the Emergence of Pathologies through Individuals and PopulatiONs) has launched several calls for projects to support interdisciplinary research projects.

Headed by the Institut Pasteur and co-directed by Olivier Gascuel, head of C3BI, and Thomas Bourgeron, head of the unit of Human Genetics and Cognitive Functions, this project fits into the context of biological big data around four axes of research:

  • Methods for integrative biology
  •  Emergence of diseases through populations
  • Emergence of diseases within individuals
  • Social science, emergence of diseases and prevention

Last year, the INCEPTION annual meeting, proposing several scientific presentations and the description of the first supported actions (Phd students, axes leaders, …), took place on Thursday, November 08, 2018, at the Institut Pasteur.


Resumes of talks :


Henrik Salje - Maximizing insights from antigenic and genetic data to understand pathogen spread


Understanding how infectious pathogens move around is critical for the development of intervention strategies, however, even with the best surveillance systems in place we typically only observe a small fraction of all infection events. Using dengue and Zika viruses as examples, we will demonstrate how we can use genetic information of the pathogen and antibody data from the host to infer patterns of spread, even in poorly observed settings. 


Sylvain Brisse - Understanding whooping cough resurgence in Europe by combining genomic, epidemiological and sociological approaches


Whooping cough, caused by the bacterium Bordetella pertussis (Bp), can lead to lethal infections in neonates. Although largely controlled by vaccination, the infection is resurging in several parts of the world, including Europe. To understand the causes of resurgence, it is essential to define B. pertussis population composition and evolutionary changes. This has so far been impossible because of a lack of good genetic data of Bp at the European level. The objectives of the project are to decipher pertussis re-emergence by a population genomics approach complemented by epidemiological modelling and social sciences. We will initiate genomic sequencing within EupertStrain, the European network of national reference centers, leading to the first large-scale genomic sequence dataset of B. pertussis isolates at European scale, and will analyze this unique resource (~2500 genomes and epidemiological surveillance data) to define inter-country dependencies in epidemiological patterns, strain transmission, evolutionary changes and vaccination strategies. We will also investigate historical or sociological factors, such as vaccine hesitancy, that may influence vaccination policy making. The integration of knowledge on pertussis epidemiology and population evolution with public health strategy build-up will contribute to a more efficient answer to the challenges of pertussis resurgence.


Olivier Gascuel – Renewing Felsenstein's phylogenetic bootstrap in the era of big data.


Felsenstein's application of the bootstrap method to evolutionary trees is one of the most cited scientific papers of all time. The bootstrap method, which is based on resampling and replications, is used extensively to assess the robustness of phylogenetic inferences. However, increasing numbers of sequences are now available for a wide variety of species, and phylogenies based on hundreds or thousands of taxa are becoming routine. With phylogenies of this size Felsenstein's bootstrap tends to yield very low supports, especially on deep branches. Here we propose a new version of the phylogenetic bootstrap in which the presence of inferred branches in replications is measured using a gradual 'transfer' distance rather than the binary presence or absence index used in Felsenstein's original version. The resulting supports are higher and do not induce falsely supported branches. The application of our method to large mammal, HIV and simulated datasets reveals their phylogenetic signals, whereas Felsenstein's bootstrap fails to do so.


Lemoine F, Domelevo Entfellner JB, Wilkinson E, Correia D, Dávila Felipe M, De Oliveira T, Gascuel O.

Nature. 2018 Apr;556(7702):452-456. doi: 10.1038/s41586-018-0043-0. Epub 2018 Apr 18.


Jérémy Choin - Population genetic approaches to understand common diseases: adaptation and maladaptation to new environments in Melanesia.


Adopting an evolutionary perspective has become highly complementary to clinical and epidemiological genetic studies, as population genetics can provide new insights into the genetic architecture of human disease. Specifically, the advent of high-throughput sequencing, combined with cutting-edge statistical and mathematical frameworks, provide useful information on the way in which selection removes deleterious variants from human populations and their potential to adapt to a broad range of climatic, nutritional, and pathogenic environments. Melanesia, a sub-region of Oceania, provides with an excellent model to test important hypotheses in population and medical genomics. Specifically, this project aims to (i) reconstruct the demographic history of Melanesian islanders, (ii) understand how such changes in human demography and environments have affected the efficacy of natural selection in the human genome and (iii) obtain insight into phenotypes having participated in human adaptation and maladaptation, thereby affecting human health. With these goals in mind, we have sequenced the whole genome of 300 individuals from different islands of Near and Remote Oceania. Finally, an integrated epidemiological approach will be used to investigate the genetic basis of a common infection in Melanesia caused by the herpes virus 8 (HHV-8) and to explore the co-evolution of the host and the virus. Together, this study will increase our understanding of how human populations have genetically adapted to the different environments they have encountered, as well as detect events of “maladaptation”, thus increasing knowledge on the genetic architecture of human pathologies.


Jonathan Bastard - Monitoring and controlling the spread of antimicrobial resistance in a farm network: a modelling study of Livestock-Associated Methicillin Resistant Staphylococcus aureus in French pig farms


Antibiotic resistance is a major concern in human and animal global health. Methicillin-resistantStaphylococcus aureus (MRSA) has been historically considered as a nosocomial and a human community associated pathogen. From the 2000’s, a new type of MRSA, referred to as Livestock-Associated MRSA (LA-MRSA), has been isolated from livestock animals and from humans in contact with them, such as farmers, vets and slaughterhouse workers. It was also suggested that zoonotic transmission of LA-MRSA could occur even in people not in contact with livestock. In Europe, the pig production sector seems to be an important reservoir of LA-MRSA, and its transmission between farms appears to be a determining factor of its epidemiology. This is why it is important to better understand how LA-MRSA spreads in a network of pig farms, in order to compare different strategies of control and surveillance.

From data from the French pig production sector, a model was built, combining within-farm (breeding practices) and between-farms (pig movements) dynamics. Two situations were considered: i) the introduction of LA-MRSA in a free network, and ii) an endemic presence of LA-MRSA in the pig industry (5% of farms contaminated). Our objectives were to determine what farms should be preferentially targeted to i) ensure an efficient surveillance of LA-MRSA introduction, and ii) to apply within-farm control measures.

Our results suggest that LA-MRSA spread is more important if it is introduced in Nucleus or Multiplier farms, or in farms exporting pigs to a high number of other sites. To ensure an efficient surveillance of LA-MRSA introduction, in term of sensitivity and detection speed, the farms importing pigs from the highest number of other sites should be monitored. In the endemic scenario, if efficient within-farm control measures can be applied to a limited number of farms (n=100), the farms exporting pigs to the highest number of other farms should be targeted, to allow a reduction in LA-MRSA prevalence. If the control measures are applied to randomly selected farms, no change in LA-MRSA prevalence is observed.

Our model allows to determine what farms of the French pig industry should be preferentially targeted for optimizing the efficiency of surveillance and control strategies. It can be applied to other agents affecting the pig farming sector.


Marie Morel - Evolutionary Trajectories of Viruses : Adaptation, Convergence and Dynamics.


Environmental adaptation of viruses could cause major outbreaks and represents a concerning issue. In this project, we will first focus on detecting evolutionary convergence in viral genomes. To do so, we develop new phenotype independent methods that we will run on large data sets such as HIV sequences to study drug resistance mutations. Besides, we will investigate adaptation of Dengue virus to host constraints, thanks to a collaboration with the G5 group of Etienne Simon Loriere.


Robin chalumeau - Next-generation structured illumination microscopy for biological imaging.


The projet consists in the implementation of reconstruction algorithms and an experimental set-up for live cells Structured Illumination Microscopy (SIM), as well as its application to relevant biological questions, such as the intrusion of a pathogen agent in epithelial cells.  The most important part of this project will be to develop new algorithms to improve images quality, reduce acquisition time and the reduce the amount of data to acquire.  These improvements of SIM should lead to a significant increase of the acquisition rate of the microscope while maintaining the same super-resolution factor, leading to better dynamic imaging for biological samples.


Tamara Giles-Vernick - Rethinking Contact: People, nonhuman primates and microbial ecologies in the Congo River basin 


Zoonotic transmissions are a major global health risk, and human-animal contact is frequently raised as an important driver of transmission. A literature examining zooanthroponosis largely agrees that more human-animal contact leads to more risk. Our analyses, however, have shown that the term “contact” is employed inconsistently and imprecisely, overlooking the range of pathogens, their transmission routes and directions, and most crucially, the historical, social and environmental processes that bring people and nonhuman primates into engagement. Building on these insights, we present our multi-disciplinary research in Cameroon on human-nonhuman primate engagements and their implications for microbial sharing and zoonotic transmission. Our anthropological, historical, and geographical analyses reveal wide-ranging, changing interactions between human beings and nonhuman primates over the past century; in the present, we find more frequent human physical contact with monkeys than with great apes. We also present preliminary findings of our analyses of gut microbial ecologies of people and great apes living in close proximity. Not only do these insights shed further light on where, when and how pathogenic transmissions between people and NHPs may occur, but also demonstrate the fruitful dialogue that can develop between the social sciences and integrative biology.


Roberto Toro - Challenges in the analysis of massive, multimodal, neurodevelopmental data.


The study of rare mutations with large effect sizes has provided invaluable information to understand neurodevelopmental disorders. Recent research shows that in addition to this type of genetic architecture, the aggregation of highly polygenic, small size effects, is responsible for a substantial proportion of the risk, in particular for neurodevelopmental psychiatric disorders such as schizophrenia and autism. The study of this type of genetic architecture requires extremely large populations, and the development of a different kind of data analysis approaches. In absence of major genes, brain imaging endophenotypes can provide an alternative, rich source of biological information. Brain imaging phenotypes are also strongly polygenic, and their variability is affected by some of the same genomic regions that determine the risk to neurodevelopmental disorders. Whereas large shared databases provide rich information on various aspects of specific regions of the genome, similar sources of information are less developed at the level of the different regions of our nervous system. I will present our recent work on the analysis of large neuroimaging and genetics datasets, the autism-related project ABIDE, and the UK Biobank project. I will provide an overview of the challenges involved – concerning in particular the analysis and annotation of large imaging-genetics cohorts; the way in which we are addressing them; and perspective on our future work.


Christophe Zimmer - Deep learning massively accelerates super-resolution localization microscopy.


The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in 3 h, yielding an image spanning spatial scales from 20 nm to 2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.


Ouyang W, Aristov A, Lelek M, Hao X, Zimmer C.

Nat Biotechnol. 2018 Jun;36(5):460-468. doi: 10.1038/nbt.4106. Epub 2018 Apr 16.