Microtubules are major components of the eukaryotic cytoskeleton. They are assembled from dimers of a/ß-tubulin and involved in various key cellular processes. This functional diversity is regulated by the ‘tubulin code’ which combines the expression of different a/ß-tubulin isotypes with several reversible tubulin post-translational modifications. Among them, the importance of the a-tubulin tyrosination cycle, involving the cleavage and religation of a C-terminal tyrosine, has been clearly established in human health. This cycle only occurs on a-tubulin and serves as a readable code for the specific recruitment of microtubule-associated proteins. A unique feature in single-celled flagellated parasites from the trypanosomatid group is the presence of a non-canonical C-terminal tyrosine on the ß-tubulin gene. Leishmania spp., Trypanosoma cruzi and T. brucei, collectively known as the ‘TriTryps’, represent a strong human and economic burden which require new therapeutic approaches. Their cytoskeleton is essentially based on microtubules whose specialisation depend almost exclusively on post-translational modifications. The presence of the C-terminal tyrosine on ß-tubulin has the potential to reveal novel regulatory mechanisms important for all trypanosomatid infections. The goal of the TyrTRYPs project is to perform the first comprehensive study of ß-tubulin tyrosination as part of the trypanosomatid-specific ‘tubulin code’. This will be achieved through the following aims: (1) Explore the importance of ß-tubulin tyrosination and detyrosination for parasite development and virulence; (2) Define the ß-tubulin specific interactome depending on its tyrosination status. Altogether it will bring new insights into parasite-specific regulatory mechanisms that may be exploited to develop drugs against all medically important trypanosomatid parasites.

Understanding the relationships between wildlife biodiversity and zoonotic infectious diseases in a changing climate is a challenging issue that scientists must address to support further policy actions. Our project aims at tackling this challenge by focusing on rodent-borne diseases in European temperate forests and large urban green spaces. Rodents are important reservoirs of zoonotic agents; forests and green spaces are environments where rodents are abundant, human/domestic-wildlife interactions are plausible to occur, and efforts are undertaken to preserve biodiversity. The originality of this project is to extend previous research into four promising research directions: i) impact of coinfections on epidemiology, ii) interactions between gut microbiome and host susceptibility to infectious agents, iii) influence of socio-economic contexts on human exposure to wildlife and iv) temporal variability of biodiversity/health relationships. Using rodent sampling and large investigation of zoonotic agents and microbiome, we will establish an up-to-date, open database and maps of rodent-borne pathogens circulating in western-central European countries. We will apply eco-epidemiological approaches to enhance our understanding of the processes that influence zoonotic pathogen transmission in rodent populations. Mathematical models will be developed to analyse the influence of spatiotemporal scales and within-host interactions on the relationships between biodiversity (rodents and microbiome) and zoonotic diseases. Landscape features will be included in this modelling. Lastly, we will evaluate the impact of climatic change scenarios on zoonotic disease risk and rodent-microbiome biodiversity in forests and urban green spaces. Sociologists will be at the core of the project to help partners develop effective knowledge exchanges, what will enable transdisciplinary collaborations among scientists and with relevant stakeholders. A first circle of stakeholders will integrate collaborators from public health, biodiversity management and NGOs representing public at risk of rodent-borne zoonoses. These stakeholders will be strongly engaged throughout the project, as they will be informed, consulted and involved in project activities and dissemination. A second, larger, circle of stakeholders will be engaged mostly through knowledge exchanges, to guarantee that all organizations and public interested in, affected by rodent-borne diseases or involved in nature management are informed of our project. Overall, we aim to provide proof-of-concept that joint strategies between public health and conservation biology programs can help to prevent emergence of zoonotic pathogens from wildlife. In addition to protocols, maps and lists of zoonotic pathogens, an important outcome will be the improvement of zoonose prevention policies through dissemination of adapted surveillance, training and awareness campaigns designed with the active participation of stakeholders.

In view of the multiple pathogen evolution capabilities, the long-term efficacy of antimicrobials is a major public health problem. Defining sustainable strategies for managing antimicrobials efficiency, in space and time, must: (i) consider the continuous character of antimicrobial resistance -i.e. quantitative resistance- with varying degrees of intermediate resistance (called tolerance), and (ii) clarify the evolutionary relationships between virulence and quantitative resistance in colonizing parasites. Recommendations stemming from solid mathematical models are lacking to help fighting antimicrobial resistance. The QUASAR project will address this challenge through an approach combining predictive mathematical analysis, scientific computing, optimization-control of an integro-differential system with non-local terms, which is suitable to study evolutionary (epidemiological and within-host) dynamics of biological processes, without the time scale separation hypothesis.

Malaria, caused by Plasmodium, is the deadliest parasitic disease in humans. The World Health Organization reports an impressive improvement of the situation in recent years, with a ~30-40% decrease in mortality rates in the 2000-2016 period. Nonetheless, malaria is still causing ~200 million clinical cases and ~500,000 deaths worldwide every year. This progress is mostly due to the combination of the massive use of artemisinin derivatives (ART) and the large-scale distribution of insecticide-treated bednets. ART is currently the last approved anti-malarial drug against which resistance has not yet spread widely. Therefore, ongoing malaria control and possible elimination efforts heavily rely on ART efficacy. Worryingly, since 2008 the efficacy of ART-base combination therapies has decreased in South-East Asia, due to the emergence and spread of ART resistance (ARTR). Spread of ARTR to sub-Saharan Africa, where most clinical cases and deaths occur, would be catastrophic and threaten the world’s malaria control and elimination efforts. ARTR was first defined by the delayed clearance time of P. falciparum parasites from the peripheral blood of Cambodian patients treated with ART monotherapy, associated with an increase in treatment failures. Strikingly, these ‘resistant’ parasites were fully or only slightly less sensitive to ART in vitro, as defined by standard growth inhibition assays under continuous drug pressure. Delayed parasite clearance in patients, however, correlated with the ring-stage survival assay (RSA), which measures the percentage of early ring-stage parasites surviving a clinically relevant 6 h-pulse of DHA. Parasites ‘resisting’ a first RSA display a similar survival rate when subjected to a second RSA. Clearly, such resistance does not respond to a classical resistance phenotype. Rather, they suggested that parasites subjected to ART at early developmental stages (rings) could escape aggression by entering in some form of dormancy or cell cycle dysregulation. The first and currently only ARTR markers are all mutations that cluster in the Kelch domain of the K13 protein. These K13 mutations associate with delayed parasite clearance in patients across South-East Asia. The K13 C580Y substitution – accounting for 80% of resistant cases – was shown to be sufficient for conferring resistance in Cambodian and laboratory P. falciparum strains using genome-editing technology. K13 is member of a class of ‘Kelch-like’ proteins that function as substrate adaptors that facilitate ubiquitin ligation by cullin-3 ligases. Mutations in Kelch domains are thought to decrease substrate binding, and thus substrate ubiquitination and degradation. Our understanding of ARTR is still hampered by the lack of a formal investigation of activation and nature of cell cycle modulation/dormancy pathway(s). In this project, we will address this challenge first by generating new tools and using new technologies to identify and sort the small population of parasites able to survive ART injury. We will then characterize this population by: (i) timing the cell cycle and its alteration at an unprecedented time resolution (Task 1 and Task 2); (ii) identifying transcriptomic (Task 2) and proteomic (Task 3) signals associated with this population; and (iii) providing a comprehensive and molecular view ofits ability to resist injury (Task 1, Task2 and Task 3). A deep understanding of the cellular and molecular basis of resistance is central to our capacity to fully circumvent the issue of recrudescence, and better grasp how P. falciparum adapts its growth to hostile environments such as drug-induced oxidative stress. As well as new knowledge, our project will deliver new tools and datasets for exploring the P. falciparum cell cycle, which should help the design of improved drugs and strategies to treat and eliminate ART-resistant parasites.

Understanding the spread of viral infectious diseases is central to informing public health decisions. Classical mathematical epidemiology primarily relies on incidence data (number of new cases per week) and contact tracing data. However, thanks to technological advances made in recent years, viral genetic sequence data from infected patients can now be obtained readily and quickly. These sequences contain rich information about the transmission network, as studied by the emerging field of phylodynamics. Currently, most approaches, whether in mathematical epidemiology or in phylodynamics, use only part of the available data: the former field ignores sequence data, while the latter tends to neglect incidence data. From a public health perspective, it is important to extract as much information as possible by combining heterogeneous data. This is all the more important that each type of data has its strengths and weaknesses. For example, incidence data is easy to collect, but has the disadvantage that it is often aggregated and highly sensitive to sampling bias. Conversely, sequence data is more expensive to generate but contains a lot of information and is somewhat less sensitive to sampling bias. We propose to extend a method that we have already validated and that is based on regression-Approximate Bayesian Computation (ABC) to combine heterogeneous data, in particular genetic sequences and incidence, to analyze viral epidemics. Our preliminary results show that this project is feasible. From a conceptual point of view, since our method is based on summary statistics, combining heterogeneous data is easy as long as it is possible to simulate in silico data with the same structure as the biological data. From a technical point of view, we have already developed an R package that allows to quickly simulate phylogenies and time series for any compartmental model. By analyzing the COVID-19 epidemic in different contexts, including that of France, we will be able to validate this combination of heterogeneous data for the analysis of viral epidemic outbeaks. We will also be able to obtain more precise information on the epidemiological parameters of the epidemic (notably the basic reproduction number R0), but also on biological parameters such as the length of the infectious period or the heterogeneity between infections. From a more applied point of view, we will develop a pipeline that will ensure repeatabilty but also transposability of the analyses to different contexts. This will be implemented via a dedicated R package.