For centuries, Biomedical Research has been based on the observation of disease to describe physiopathological mechanisms, a strategy that has proved fruitful in gaining information about Human Physiology. However, with the commoditization of new technological tools and high- throughput methods of measurement, this historical research paradigm encounters a profound mutation. Biology has now access to substantial data collections, genomic data that contain obviously essential information with multiple applications in the health field. Like other disciplines before it, Biology is becoming a Computational Science, increasingly appealing to mathematical modelling, algorithms and computers. The area of bioinformatics (in the broad sense, including biostatistics and modelling of living systems) is therefore experiencing a rapid expansion, with massive recruitment of researchers and engineers in public and the private sectors, as exemplified by the recent involvement of IT companies (e.g. Google, Dassault systems) in Life Sciences, and the creation of large research centres, with sufficient critical mass.
Beyond the wet lab fundamental science, the processing of new biological data will definitely play a crucial role in medicine, as the need urges for highly refined diagnostic tools and personalized treatments. This radical change expected in medicine, where each patient will soon be accompanied throughout its existence and its fitness trail, while accumulating considerable amounts of data, implies a deep reflection about the social impacts of these new approaches.
The INCEPTION project aims to cover the whole spectrum of this research, with a focus on the emergence of diseases, from raw data acquisition (genomes, biological and medical images, patient data, environment, social context) to health policy definition. It will be carried out with the essential participation of basic research teams in biology, but also in mathematics, statistics and computer science -due to the complexity of data and questions- and the clear commitment to address the issues in their entirety, in particular from a sociological perspective.
Methods for integrative biology
The integrative approach in biology consists in considering and combining the various levels of living organisms, from molecules to individuals, populations and ecosystems, including numerous intermediaries, such as genes and genomes, organelles and cells, as well as organs and systems. A time dimension enhances this spatial dimension, with, once again, highly varying scales: from chemical reaction times to those of biological evolution. Proper understanding of living organisms involves integrative steps, in which we establish links between these various levels, notably between genotypes and phenotypes.
The data associated with these different spatial and temporal scales are heterogeneous, voluminous, and of high dimensionality. For example, when the goal is to explore genotype- phenotype relationships, it is important to have access to numerous individuals and collect for each of them genetic and phenotypic data. The latter can be very diverse and range from molecular phenotypes (transcriptomics, proteomics, metabolomics, etc.) to organismal phenotypes (physiological, morphological, etc.). Methods are thus confronted with a dual challenge: that of modelling, in order to account for the complexity of living organisms; and that of algorithmics, required to process the massive quantities of data now available, in reasonable computing time. To these, one must add the need for novel statistical methods, with the challenge of learning large-scale models in a robust manner, and characterizing their statistical meaning and properties. Resolving the challenges of integrative biology thus requires the joint efforts of many mathematics, statistics and computer science disciplines.
Emergence of diseases in populations
Emerging and re-emerging infectious diseases pose a major threat to public health as was exemplified by the H1N1pdm09 influenza pandemic in 2009, the recent Ebola outbreak in West Africa, or the ongoing Zika epidemic in the Americas. The frequency of travels and the increased global economic interdependence have only added layers of complexity to the containment of these threats. There is also increasing exposure to multidrug resistant bacterial strains, like MRSA, Escherichia coli or Salmonella, which can generate large nosocomial or foodborne disease outbreaks with severe public health and economic consequences. Numerous challenges need to be addressed to quickly detect, monitor and characterize emerging threats, and determine the factors (of the host and of the pathogen) affecting the risk and outcome of infection. It also proves difficult to evaluate the impact of the epidemic, design optimal control strategies and appropriately advise populations and policy makers. The recent Ebola epidemic in West Africa demonstrated the difficulty of the task and the dramatic consequences of a delay or failure in tackling these challenges. A prerequisite is that emergences can rapidly be documented in the field, wherever they occur, via the collection of high-quality samples and data including detailed epidemiological investigations, clinical description of cases, surveillance in humans or in the animal host, genetic sequences, and others. Further, the analysis of these complex data requires the development of sophisticated statistical, mathematical, and bioinformatics methods by inter- disciplinary teams as described in 1.2.1. These methods must then be integrated in standardized protocols by platforms able to rapidly cope with massive influx of data during outbreaks. Lastly, this work must translate into concrete improvements in the management and control of outbreaks, and the development of new diagnostic tests, treatments or vaccines.The goal of this task in the project is to develop a novel coherent and integrative framework to tackle the emergence of diseases, being infectious or not, in human populations. Its ultimate purpose is to impact Public Health by radically improving the way we detect, study and respond to emergence.
As part of this research, INCEPTION supports the Outbreak Investigation Taskforce created to respond to local support and investigation of infectious disease outbreaks.
The Pasteur OITF, created in 2015, comprises staff in the Institut Pasteur International Network (IPIN) who are committed to participating in outbreak responses, through the World Health Organization’s Global Outbreak Alert and Response Network (GOARN) or in support of requests for assistance from other institutes in the IPIN. The OITF Program also supports collaborative projects on emerging infections and outbreak-related research collaborations between Institut Pasteur, the IPIN, and collaborating institutions. The mission of the OITF is to increase capacity for outbreak response locally and internationally by providing training and hands-on field experience, promote collaboration within the IPIN and partners to enhance knowledge about epidemic-prone infectious diseases, and contribute to the detection, response, and control of public health threats worldwide.
To control and minimise the impact of infectious disease outbreaks, emerging infectious disease threats need to be quickly detected and characterised, and an effective, multidisciplinary response must be mobilized. As part of outbreak response, risk factors for infection need to be determined to ensure prompt control of the outbreak and inform future prevention and control efforts. This requires comprehensive and coordinated multidisciplinary response, drawing on expertise from a diverse range of fields, including epidemiology, clinical care, microbiology, entomology, ecology, anthropology, mathematical modelling, communications, and logistics. Traditional methods of field epidemiology are complemented by innovative approaches in epidemiologic analysis, advanced microbial detection and phylogenetics – all core activities of the wider INCEPTION project. Epidemiologic, clinical, and laboratory data can be used to perform a wide range of biomedical research, including development of diagnostic tests, therapeutics and vaccines, in addition to strengthening the capacity to detect and contain outbreaks more rapidly in the future.
The INCEPTION funding awarded to the OITF in 2018 has increased the OITF’s impact in international outbreak response preparedness and improved its capacity for outbreak response and research.
Emergence of diseases in individuals
How diseases emerge in individuals? Why some individuals infected and/or carrying a deleterious mutation are severely affected while others have mild to no sign of the disease? The INCEPTION project will address these questions by collecting and analyzing multi-scale datasets coming from large cohorts of affected and healthy individuals.
The driving vision of INCEPTION is to take into account the individual in his/her environment in medical care. This effort is complementary to the objectives of the Laboratories of Excellence (LabEx) included in INCEPTION. INCEPTION will explore the emergence of a broad range of human diseases including infectious diseases, immune deficiency and neurodevelopmental disorders. While these pathological and/or developmental processes have distinct causes and clinical symptoms, clinicians and researchers working in these apparently separate fields are using similar approaches. A major asset of INCEPTION is therefore to combine methods from integrative biology and well-structured datasets to address major public health issues. In addition, this global approach should shed light on unpredicted links between diseases, e.g., infectious diseases & immune deficiency; neurodevelopmental disorders & microbiota.
Social science and disease emergence and prevention
Social sciences offer a critical perspective on emerging diseases. The INCEPTION approach will not be limited to a narrow understanding of social studies of health as mainly concerned with the societal and cultural “consequences” of techno-scientific innovation or epidemic emergence. Far from understanding society and citizens as mere obstacles to disease prevention, as passive recipients of medical innovation, or as simple channels of disease transmission, our vision is that social sciences must be built into epidemiological, biological and computational research on emerging diseases. Based on a long experience of collaborations between history, anthropology, virology and epidemiology, we propose to develop within INCEPTION an innovative framework for ecological perspectives on disease emergence and prevention. Our approach is both interdisciplinary and multi-disciplinary. Disciplinary tools of medical anthropology, geography, and history will be integrated to produce a social, economic, political analysis of emerging disease outbreaks and epidemics. The INCEPTION participants in social science will work in close collaboration with epidemiologists, geneticists, virologists, evolutionary biologists, and other biomedical researchers, bringing into conversation multiple disciplinary analyses of emerging infectious diseases. Our cutting-edge research on the social, political economic, and spatial practices will shed new light on these dynamics, by providing robust analysis of the dynamics of human-animal-pathogen contacts. The idea is also to bring innovative, information-based approaches to lay public knowledge -not always verifiable from a biomedical perspective- but which can in all its complexity enrich Emerging Infectious Diseases (EID) surveillance.
Our main objective is to embed social sciences within the investigations on the dynamics of disease emergence and prevention.
Secondary objectives are to: Illuminate disease emergence from the perspectives of populations, bringing lay public’s explanations for disease emergence and “local knowledge” into conversation with biomedical investigations and surveillance.
- Aggregate and use historical data to understand past and present emergences, together with phylogenetic and computational studies,
- Develop innovative research on “prevention 2.0” including vaccination uptake, personalized prevention and social networks analysis,
- Offer an experimental model for the integration of social, biological and computational sciences in the study of emergence.