Polymyxins are polycationic antimicrobial peptides that are currently the last-resort antibiotics for the treatment of multidrug-resistant, Gram-negative bacterial infections. The reintroduction of polymyxins for antimicrobial therapy has been followed by an increase in reports of resistance among Gram-negative bacteria. Some bacteria, such as Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii, develop resistance to polymyxins in a process referred to as acquired resistance, whereas other bacteria, such as Proteus spp., Serratia spp., and Burkholderia spp., are naturally resistant to these drugs. Reports of polymyxin resistance in clinical isolates have recently increased, including acquired and intrinsically resistant pathogens. This increase is considered a serious issue, prompting concern due to the low number of currently available effective antibiotics. This review summarizes current knowledge concerning the different strategies bacteria employ to resist the activities of polymyxins. Gram-negative bacteria employ several strategies to protect themselves from polymyxin antibiotics (polymyxin B and colistin), including a variety of lipopolysaccharide (LPS) modifications, such as modifications of lipid A with phosphoethanolamine and 4-amino-4-deoxy-L-arabinose, in addition to the use of efflux pumps, the formation of capsules and overexpression of the outer membrane protein OprH, which are all effectively regulated at the molecular level. The increased understanding of these mechanisms is extremely vital and timely to facilitate studies of antimicrobial peptides and find new potential drugs targeting clinically relevant Gram-negative bacteria.

Over the past decade, a significant increase in the circulation of infectious agents was observed. With the spread and emergence of epizootics, zoonoses, and epidemics, the risks of pandemics became more and more critical. Human and animal health has also been threatened by antimicrobial resistance, environmental pollution, and the development of multifactorial and chronic diseases. This highlighted the increasing globalization of health risks and the importance of the human–animal–ecosystem interface in the evolution and emergence of pathogens. A better knowledge of causes and consequences of certain human activities, lifestyles, and behaviors in ecosystems is crucial for a rigorous interpretation of disease dynamics and to drive public policies. As a global good, health security must be understood on a global scale and from a global and crosscutting perspective, integrating human health, animal health, plant health, ecosystems health, and biodiversity. In this study, we discuss how crucial it is to consider ecological, evolutionary, and environmental sciences in understanding the emergence and re-emergence of infectious diseases and in facing the challenges of antimicrobial resistance. We also discuss the application of the “One Health” concept to non-communicable chronic diseases linked to exposure to multiple stresses, including toxic stress, and new lifestyles. Finally, we draw up a list of barriers that need removing and the ambitions that we must nurture for the effective application of the “One Health” concept. We conclude that the success of this One Health concept now requires breaking down the interdisciplinary barriers that still separate human and veterinary medicine from ecological, evolutionary, and environmental sciences. The development of integrative approaches should be promoted by linking the study of factors underlying stress responses to their consequences on ecosystem functioning and evolution. This knowledge is required for the development of novel control strategies inspired by environmental mechanisms leading to desired equilibrium and dynamics in healthy ecosystems and must provide in the near future a framework for more integrated operational initiatives.

1. In a rapidly changing world, ecology has the potential to move from empirical and conceptual stages to application and management issues. It is now possible to make large-scale predictions up to continental or global scales, ranging from the future distribution of biological diversity to changes in ecosystem functioning and services. With these recent developments, ecology has a historical opportunity to become a major actor in the development of a sustainable human society. With this opportunity, however, also comes an important responsibility in developing appropriate predictive models, correctly interpreting their outcomes and communicating their limitations. There is also a danger that predictions grow faster than our understanding of ecological systems, resulting in a gap between the scientists generating the predictions and stakeholders using them (conservation biologists, environmental managers, journalists, policymakers). 2. Here, we use the context provided by the current surge of ecological predictions on the future of biodiversity to clarify what prediction means, and to pinpoint the challenges that should be addressed in order to improve predictive ecological models and the way they are understood and used.3. Synthesis and applications. Ecologists face several challenges to ensure the healthy development of an operational predictive ecological science: (i) clarity on the distinction between explanatory and anticipatory predictions; (ii) developing new theories at the interface between explanatory and anticipatory predictions; (iii) open data to test and validate predictions; (iv) making predictions operational; and (v) developing a genuine ethics of prediction. Supporting InformationAdditional Supporting Information may be found in the online version of this article.Appendix S1. Characteristics of mechanistic and phenomenological models in ecology.Appendix S2. Non-exhaustive list, of international initiatives of the scientific community aiming for sharing ecological data.

While the epidemic of SARS-CoV-2 has spread worldwide, there is much concern over the mortality rate that the infection induces. Available data suggest that COVID-19 case fatality rate had varied temporally (as the epidemic has progressed) and spatially (among countries). Here, we attempted to identify key factors possibly explaining the variability in case fatality rate across countries. We used data on the temporal trajectory of case fatality rate provided by the European Center for Disease Prevention and Control, and country-specific data on different metrics describing the incidence of known comorbidity factors associated with an increased risk of COVID-19 mortality at the individual level. We also compiled data on demography, economy and political regimes for each country. We found that temporal trajectories of case fatality rate greatly vary among countries. We found several factors associated with temporal changes in case fatality rate both among variables describing comorbidity risk and demographic, economic and political variables. In particular, countries with the highest values of DALYs lost to cardiovascular, cancer and chronic respiratory diseases had the highest values of COVID-19 CFR. CFR was also positively associated with the death rate due to smoking in people over 70 years. Interestingly, CFR was negatively associated with share of death due to lower respiratory infections. Among the demographic, economic and political variables, CFR was positively associated with share of the population over 70, GDP per capita, and level of democracy, while it was negatively associated with number of hospital beds ×1000. Overall, these results emphasize the role of comorbidity and socio-economic factors as possible drivers of COVID-19 case fatality rate at the population level.

Comparative analysis methods control for the variation linked to phylogeny before attempting to correlate the remaining variation of a trait to present‐day conditions (i.e., ecology and/or environment). A portion of the phylogenetic variation of the trait may be related to ecology, however; this portion is called “phylogenetic niche conservatism. We propose a method of variation partitioning that allows users to quantify this portion of the variation, called the “phylogenetically structured environmental variation. The new method is applied to published data to study, in a phylogenetic framework, the link between body mass and population density in 79 species of mammals. The results suggest that an important part of the variation of mammal body mass is related to the common influence of phylogeny and population density.

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Field parasitological studies consistently demonstrate the reality of polyparasitism in natural systems. However, only recently, studies from ecological and evolutionary fields have emphasised a broad spectrum of potential multiple infections-related impacts. The main goal of our review is to reunify the different approaches on the impacts of polyparasitism, not only from laboratory or human medical studies but also from field or theoretical studies. We put forward that ecological and epidemiological determinants to explain the level of polyparasitism, which regularly affects not only host body condition, survival or reproduction but also host metabolism, genetics or immune investment. Despite inherent limitations of all these studies, multiple infections should be considered more systematically in wildlife to better appreciate the importance of parasite diversity in wildlife, cumulative effects of parasitism on the ecology and evolution of their hosts.

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