Rationale. Recommended infection control precautions for COVID-19 include the use surgical mask in most care situations, for limiting the contamination of mucosa by large droplets. However, several invasive respiratory procedures can generate aerosols of small particles, requiring the use of a N95 or FFP2 mask. High flow nasal oxygen (HFNO) is increasingly used in place of conventional oxygen mask (COM) for the care of patients with de novo respiratory failure, but its high gas flow raises concerns about aerosolization of small infectious particles and spread of infection. Project. We aim at studying the environmental contamination by SARS-CoV-2 in relation to oxygen therapy modality and patients’ room characteristics. We will take the opportunity of the recently funded COVIDICUS clinical trial, in which 600 patients will be included to study the efficacy of various oxygen therapy modalities and of corticosteroids in severe COVID-19 patients. Methods. Environmental samples, including air and surfaces from the COVID-19 patients’ room, will be collected at 6 occasions over the 24 first hours using a BioSampler or VIVAS device (for air) and Dacron swabs pre-moistened for surfaces. Virological analyses will be performed in order to detect and quantify the presence of the SARS-CoV-2, and evaluate its infectivity by viral culture. Specific analytical methods will be developed. The contamination of the patients’ room will be studied using mixed effect statistical modeling of the data. Expected results. This project will provide important data about the viral risk for the healthcare workers, and therefore for other hospitalized patients, that will be useful to compare in other settings. By comparing data from several settings and ICU practices will be of valuable help to identify the best practice for preventing the expansion of SARS-CoV-2 in the hospital and staff protection. Consortium. This project will be conducted in partnership with internationally renowned French research teams. The project will be led by L. Bouadma (Bichat intensive Care Unit, University of Paris & APHP) and A. Mekontso Dessap (Henri Mondor intensive Care Unit, Paris-East University & APHP). The Principal Investigator of the COVIDICUS trial, which has already been funded by the Ministry of Health and APHP and will begin shortly, is JF. Timsit (Bichat intensive Care Unit, University of Paris & APHP). The environmental sampling will be performed by Jean-Christophe Lucet (Bichat Infection Control Unit, University of Paris & APHP), the virological analysis of the samples will be led by B. Visseaux (Bichat Virology Laboratory, University of Paris & APHP). Statistical analysis will be led by C. Burdet (Bichat Epidemiology, Biostatistics and Clinical Research Department, University of Paris & APHP).

Escherichia coli SOS response is an archetype of the DNA damage response regulon, which hugely contributed to the understanding of how different DNA damage repair and tolerance functions are coordinated as a function of the nature, quantity and persistence of DNA damages. Because SOS regulon was first identified as a response to DNA damages, the role of SOS genes that are not directly involved in the DNA repair was largely ignored. By using a multidisciplinary approach based on phylogeny, natural isolates, genetics, molecular biology and animal models, this project aims at investigating how SOS-controlled toxins, which modulate cellular energy production and translation capacity, are functionally and temporally integrated in cellular strategy for dealing with DNA damages. Identifying factors that contribute to bacterial robustness to environmental perturbations may provide new targets for antibacterial drugs and can also be useful for improvement of industrial microorganisms.

The vulnerability of our society to life-threatening emerging viruses with epidemic potential is largely due to the lack of appropriate preparedness including access to an effective prophylactic and therapeutic mean to protect the population. We aim to develop an innovative treatment strategy based on the broad-spectrum antiviral drug Favipiravir. The drug will be tested for the first time in human healthy volunteers given orally to evaluate its safety at high doses and its potential to achieve high level of protection against HFV and other emerging RNA viruses. We will employ mathematical modelling techniques to integrate these data with our current knowledge on Favipiravir efficacy to propose individualized treatment strategy that can provide protection and/or high chance of cure against highly pathogenic viruses.

A large part of the patients infected by the SARS-CoV-2 present persistent symptoms following the infection, sometimes several months after the initial symptoms onset. This is particularly the case for severe COVID-19 cases requiring hospitalization. Some studies show that around 75% of COVID-19 patients requiring a hospitalization report persistent symptoms up to 6 months following their discharge. The associated clinical picture is variable, including fatigue, dyspnea, chest pain, muscle fatigue, sleep disorders, anxiety or depressive disorders. This persistence of symptoms following the infection, known as “long COVID”, has become an increasingly subject of concern, with the World Health Organization, as well as the Haute Autorité de Santé, calling for research to better understand the causes and consequences of this condition. Indeed, these “long COVIDs" can imply a major economic and public health burden, and we need to study this phenomenon to get the full picture of the public health challenge imposed by the pandemic. In this proposal, we want to use health insurance databases to describe the healthcare consumption of patients following a COVID-19 requiring hospitalization in France, identify individuals outside the normal healthcare consumption, and characterize them. We will use the Echantillon Généraliste des Bénéficiaires (EGB), which represents 1/97th of the population insured by the public health insurance system in France. This database includes sociodemographic data, the existence of long-lasting conditions, date of hospitalization (if any) and diagnosis associated, and the healthcare consumption among liberal physician, in hospitals and in pharmacies. We will identify hospitalized COVID-19 cases in this database (around 2,300 individuals) and match each of them with 4 controls sharing the same gender, birth year, insurance scheme, geographical area and with the same long-lasting conditions. We will use sequence analysis and unsupervised classification methods to assign each case to a subgroup corresponding to pattern of healthcare consumption in the 6 months following the hospital discharge. By describing the healthcare pathways in each subgroup and among the matched controls, we will be able to identify profiles with deviance from the usual healthcare consumption and quantify the overconsumption among a part of the cases, which we assume it will be due to persistent symptoms. Finally, we will perform the same analysis for each subsequence of the healthcare consumption one-by-one: the number of general practitioner visits, the number of visits to a medical specialist, the number of visits to a paramedical specialist, the number of hospitalization days and the prescription drugs consumption. This study will constitute a first step in the evaluation of economic long-term consequences of COVID-19 infections in France. The results will allow to better take into account in public health measures se long COVID. More particularly, they could be used in effectiveness and cost-effectiveness modelling of SARS-CoV-2 prevention and treatments currently in clinical trials.

Bacterial infections are a leading cause of morbidity and mortality. The increasing resistance to antibiotics among pathogenic bacteria is a major health concern. These infections are often chronic and due to the presence of self-structured multicellular communities called biofilms developing on medical devices or mucosa. In biofilms, bacteria undergo specific physiological changes and display characteristic but ill-understood high level of tolerance to both antimicrobial agents and host immune defences. As a consequence, there is currently no fully efficient method to prevent or eradicate biofilms. Whereas enhanced tolerance of biofilms towards antibiotics is a multifactorial process, relapse of infection is mainly explained by the presence within biofilms of high levels of so called persister bacteria that can sustain extremely high concentration of antibiotics but can regrow as biofilms when treatment is stopped. Persister bacteria are proposed to serve as a potential evolutionary reservoir from which resistance could emerge. The presence of these persister bacteria can be mainly explained by quiescence and stress-response caused by high physico-chemical heterogeneity and localized biofilm area in which bacteria have limited access to, for instance, nutrients or oxygen. The impact of bacterial persisters in clinical situations has been largely overlooked and evolution of tolerance due to persisters is not understood. However, recent studies have demonstrated that high-levels of antibiotic tolerance, but not resistance, could be rapidly achieved by exposure of batch planktonic cultures of E. coli and other pathogens to cyclic treatments of lethal concentration of antibiotics. Considering the importance of biofilms in chronic infections and the failure of their treatment there is an urgent need to characterize evolution of persistence and resistance within biofilms. We have developed an in vitro model of pathogenic E. coli biofilms on silicone disks and perform a first series of Adaptive Laboratory Evolution experiments (ALE), which mimic relevant clinical situations of biofilm-associated chronic infection that are treated by intermittent exposure of lethal antibiotic concentrations. We have shown that E. coli biofilms could rapidly evolved increased resistance towards 2 lethal concentration of the aminoglycoside, amikacin, through accumulation of mutations known to cause resistance. These promising preliminary experiments strongly suggest that the evolutionary path followed by biofilms and planktonic bacteria are different, with biofilms strongly favoring rapid emergence of genetic resistance, may be because of the high frequency of persister bacteria present in biofilms and increased mutability. These first results urge for a deepened analysis of emergence of antibiotic resistance within biofilms. Using ALE experiments, the developed in vitro biofilm model and a clinically relevant in vivo rat model of catheter-related infection, we will i/ pursue the analysis of the evolutionary trajectories that can lead to increased levels of tolerance and then resistance towards lethal concentration of different antibiotics in pathogenic E. coli within clinically relevant biofilm environments; ii/ determine whether the emergence of resistance within biofilms is directly link to the frequency of persister bacteria ; ii/ analyze, using DNA barcoding tagging, the dynamic of dissemination of emerged tolerant/resistant clones and repopulation of biofilms during intermittent antibiotic treatment. This project should contribute to improve our understanding of the dynamic relationships between biofilm tolerance and emergence of antibiotic resistance, as well as of the dissemination of this tolerance/resistance in highly structured environments. This could lead to the design of relevant strategies or clinical treatment protocols to mitigate the emergence of high tolerance and subsequent antibiotic resistance in clinically relevant situations.