Transmission as a basic process in microbial biology. Lwoff Award Prize Lecture

Selection versus transmission: Quantitative and organismic biology in antibiotic resistance

We aimed to determine the importance of selection (mostly dependent on the anthropogenic use of antimicrobials) and transmission (mostly dependent on hygiene and sanitation) as drivers of the spread of antibiotic-resistant bacterial populations. The first obstacle to estimating the relative weight of both independent variables is the lack of detailed quantitative data concerning the number of bacterial cells, potentially either pathogenic or harmless, and bacterial species exposed to antimicrobial action in the microbiotas of specific environments. The second obstacle is the difficulty of considering the relative importance of the transmission and selection exerting their combined effects on antibiotic resistance across eco-biological levels. As a consequence, advances are urgently required in quantitative biology and organismic biology of antimicrobial resistance. The absolute number of humans exposed to antibiotics and the absolute number of potentially pathogenic and commensal bacteria in their microbiomes should influence both the selection and transmission of resistant bacterial populations. The "whole Earth" microbiome, with astonishingly high numbers of bacterial cells and species, which are also exposed to anthropogenic antimicrobials in various biogeographical spaces, shapes the antibiotic resistance landscape. These biogeographical spaces influence various intensities of selection and transmission of potentially pathogenic bacteria. While waiting for more precise data, biostatistics analysis and mathematical or computational modeling can provide proxies to compare the influence of selection and transmission in resistant bacteria. In European countries with lower sanitation levels, antibiotic consumption plays a major role in increasing antibiotic resistance; however, this is not the case in countries with high sanitation levels. Although both independent variables are linked, their relative influence on the level of antibiotic resistance varies according to the particular location. Therefore, interventions directed to decrease antibiotic resistance should be designed "a la carte" for specific locations with particular ecological conditions, including sanitation facilities.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Interplay between strain fitness and transmission frequency determines prevalence of antimicrobial resistance

The steep rise of infections caused by bacteria that are resistant to antimicrobial agents threatens global health. However, the association between antimicrobial use and the prevalence of resistance is not straightforward. Therefore, it is necessary to quantify the importance of additional factors that affect this relationship. We theoretically explore how the prevalence of resistance is affected by the combination of three factors: antimicrobial use, bacterial transmission, and fitness cost of resistance. We present a model that combines within-host, between-hosts and between-populations dynamics, built upon the competitive Lotka-Volterra equations. We developed the model in a manner that allows future experimental validation of the findings with single isolates in the laboratory. Each host may carry two strains (susceptible and resistant) that represent the host’s commensal microbiome and are not the target of the antimicrobial treatment. The model simulates a population of hosts who are treated periodically with antibiotics and transmit bacteria to each other. We show that bacterial transmission results in strain co-existence. Transmission disseminates resistant bacteria in the population, increasing the levels of resistance. Counterintuitively, when the cost of resistance is low, high transmission frequencies reduce resistance prevalence. Transmission between host populations leads to more similar resistance levels, increasing the susceptibility of the population with higher antimicrobial use. Overall, our results indicate that the interplay between bacterial transmission and strain fitness affects the prevalence of resistance in a non-linear way. We then place our results within the context of ecological theory, particularly on temporal niche partitioning and metapopulation rescue, and we formulate testable experimental predictions for future research.

Wildlife and Antibiotic Resistance

Antibiotic resistance is a major human health problem. While health care facilities are main contributors to the emergence, evolution and spread of antibiotic resistance, other ecosystems are involved in such dissemination. Wastewater, farm animals and pets have been considered important contributors to the development of antibiotic resistance. Herein, we review the impact of wildlife in such problem. Current evidence supports that the presence of antibiotic resistance genes and/or antibiotic resistant bacteria in wild animals is a sign of anthropic pollution more than of selection of resistance. However, once antibiotic resistance is present in the wild, wildlife can contribute to its transmission across different ecosystems. Further, the finding that antibiotic resistance genes, currently causing problems at hospitals, might spread through horizontal gene transfer among the bacteria present in the microbiomes of ubiquitous animals as cockroaches, fleas or rats, supports the possibility that these organisms might be bioreactors for the horizontal transfer of antibiotic resistance genes among human pathogens. The contribution of wildlife in the spread of antibiotic resistance among different hosts and ecosystems occurs at two levels. Firstly, in the case of non-migrating animals, the transfer will take place locally; a One Health problem. Paradigmatic examples are the above mentioned animals that cohabit with humans and can be reservoirs and vehicles for antibiotic resistance dissemination. Secondly, migrating animals, such as gulls, fishes or turtles may participate in the dissemination of antibiotic resistance across different geographic areas, even between different continents, which constitutes a Global Health issue.

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.

The Origin of Niches and Species in the Bacterial World

Niches are spaces for the biological units of selection, from cells to complex communities. In a broad sense, "species" are biological units of individuation. Niches do not exist without individual organisms, and every organism has a niche. We use "niche" in the Hutchinsonian sense as an abstraction of a multidimensional environmental space characterized by a variety of conditions, both biotic and abiotic, whose quantitative ranges determine the positive or negative growth rates of the microbial individual, typically a species, but also parts of the communities of species contained in this space. Microbial organisms ("species") constantly diversify, and such diversification (radiation) depends on the possibility of opening up unexploited or insufficiently exploited niches. Niche exploitation frequently implies "niche construction," as the colonized niche evolves with time, giving rise to new potential subniches, thereby influencing the selection of a series of new variants in the progeny. The evolution of niches and organisms is the result of reciprocal interacting processes that form a single unified process. Centrifugal microbial diversification expands the limits of the species' niches while a centripetal or cohesive process occurs simultaneously, mediated by horizontal gene transfers and recombinatorial events, condensing all of the information recovered during the diversifying specialization into "novel organisms" (possible future species), thereby creating a more complex niche, where the selfishness of the new organism(s) establishes a "homeostatic power" limiting the niche's variation. Once the niche's full carrying capacity has been reached, reproductive isolation occurs, as no foreign organisms can outcompete the established population/community, thereby facilitating speciation. In the case of individualization-speciation of the microbiota, its contribution to the animal' gut structure is a type of "niche construction," the result of crosstalk between the niche (host) and microorganism(s). Lastly, there is a parallelism between the hierarchy of niches and that of microbial individuals. The increasing anthropogenic effects on the biosphere (such as globalization) might reduce the diversity of niches and bacterial individuals, with the potential emergence of highly transmissible multispecialists (which are eventually deleterious) resulting from the homogenization of the microbiosphere, a possibility that should be explored and prevented.

Antibiotic Resistance: Moving From Individual Health Norms to Social Norms in One Health and Global Health

Antibiotic resistance is a problem for human health, and consequently, its study had been traditionally focused toward its impact for the success of treating human infections in individual patients (individual health). Nevertheless, antibiotic-resistant bacteria and antibiotic resistance genes are not confined only to the infected patients. It is now generally accepted that the problem goes beyond humans, hospitals, or long-term facility settings and that it should be considered simultaneously in human-connected animals, farms, food, water, and natural ecosystems. In this regard, the health of humans, animals, and local antibiotic-resistance-polluted environments should influence the health of the whole interconnected local ecosystem (One Health). In addition, antibiotic resistance is also a global problem; any resistant microorganism (and its antibiotic resistance genes) could be distributed worldwide. Consequently, antibiotic resistance is a pandemic that requires Global Health solutions. Social norms, imposing individual and group behavior that favor global human health and in accordance with the increasingly collective awareness of the lack of human alienation from nature, will positively influence these solutions. In this regard, the problem of antibiotic resistance should be understood within the framework of socioeconomic and ecological efforts to ensure the sustainability of human development and the associated human-natural ecosystem interactions.

Gene Transmission in the One Health Microbiosphere and the Channels of Antimicrobial Resistance

Antibiotic resistance is a field in which the concept of One Health can best be illustrated. One Health is based on the definition of communication spaces among diverse environments. Antibiotic resistance is encoded by genes, however, these genes are propagated in mobile genetic elements (MGEs), circulating among bacterial species and clones that are integrated into the multiple microbiotas of humans, animals, food, sewage, soil, and water environments, the One Health microbiosphere. The dynamics and evolution of antibiotic resistance depend on the communication networks linking all these ecological, biological, and genetic entities. These communications occur by environmental overlapping and merging, a critical issue in countries with poor sanitation, but also favored by the homogenizing power of globalization. The overwhelming increase in the population of highly uniform food animals has contributed to the parallel increase in the absolute size of their microbiotas, consequently enhancing the possibility of microbiome merging between humans and animals. Microbial communities coalescence might lead to shared microbiomes in which the spread of antibiotic resistance (of human, animal, or environmental origin) is facilitated. Intermicrobiome communication is exerted by shuttle bacterial species (or clones within species) belonging to generalist taxa, able to multiply in the microbiomes of various hosts, including humans, animals, and plants. Their integration into local genetic exchange communities fosters antibiotic resistance gene flow, following the channels of accessory genome exchange among bacterial species. These channels delineate a topology of gene circulation, including dense clusters of species with frequent historical and recent exchanges. The ecological compatibility of these species, sharing the same niches and environments, determines the exchange possibilities. In summary, the fertility of the One Health approach to antibiotic resistance depends on the progress of understanding multihierarchical systems, encompassing communications among environments (macro/microaggregates), among microbiotas (communities), among bacterial species (clones), and communications among MGEs.

Defining and combating antibiotic resistance from One Health and Global Health perspectives

Several interconnected human, animal and environmental habitats can contribute to the emergence, evolution and spread of antibiotic resistance, and the health of these contiguous habitats (the focus of the One Health approach) may represent a risk to human health. Additionally, the expansion of resistant clones and antibiotic resistance determinants among human-associated, animal-associated and environmental microbiomes have the potential to alter bacterial population genetics at local and global levels, thereby modifying the structure, and eventually the productivity, of microbiomes where antibiotic-resistant bacteria can expand. Conversely, any change in these habitats (including pollution by antibiotics or by antibiotic-resistant organisms) may influence the structures of their associated bacterial populations, which might affect the spread of antibiotic resistance to, and among, the above-mentioned microbiomes. Besides local transmission among connected habitats-the focus of studies under the One Health concept-the transmission of resistant microorganisms might occur on a broader (even worldwide) scale, requiring coordinated Global Health actions. This Review provides updated information on the elements involved in the evolution and spread of antibiotic resistance at local and global levels, and proposes studies to be performed and strategies to be followed that may help reduce the burden of antibiotic resistance as well as its impact on human and planetary health.

Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membrane-surrounded entities" able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes.IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples-just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power.