Individual variability in finger-to-finger transmission efficiency of Enterococcus faecium clones

Epidermis as a Platform for Bacterial Transmission

The epidermis constitutes a continuous external layer covering the body, offering protection against bacteria, the most abundant living organisms that come into contact with this barrier. The epidermis is heavily colonized by commensal bacterial organisms that help protect against pathogenic bacteria. The highly regulated and dynamic interaction between the epidermis and commensals involves the host’s production of nutritional factors promoting bacterial growth together to chemical and immunological bacterial inhibitors. Signal trafficking ensures the system’s homeostasis; conditions that favor colonization by pathogens frequently foster commensal growth, thereby increasing the bacterial population size and inducing the skin’s antibacterial response, eliminating the pathogens and re-establishing the normal density of commensals. The microecological conditions of the epidermis favors Gram-positive organisms and are unsuitable for long-term Gram-negative colonization. However, the epidermis acts as the most important host-to-host transmission platform for bacteria, including those that colonize human mucous membranes. Bacteria are frequently shared by relatives, partners, and coworkers. The epidermal bacterial transmission platform of healthcare workers and visitors can contaminate hospitalized patients, eventually contributing to cross-infections. Epidermal transmission occurs mostly via the hands and particularly through fingers. The three-dimensional physical structure of the epidermis, particularly the fingertips, which have frictional ridges, multiplies the possibilities for bacterial adhesion and release. Research into the biology of bacterial transmission via the hands is still in its infancy; however, tribology, the science of interacting surfaces in relative motion, including friction, wear and lubrication, will certainly be an important part of it. Experiments on finger-to-finger transmission of microorganisms have shown significant interindividual differences in the ability to transmit microorganisms, presumably due to genetics, age, sex, and the gland density, which determines the physical, chemical, adhesive, nutritional, and immunological status of the epidermal surface. These studies are needed to optimize interventions and strategies for preventing the hand transmission of microorganisms.

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.

Biology of Hand-to-Hand Bacterial Transmission

Numerous studies have demonstrated that adequate hand hygiene among hospital staff is the best measure to prevent hand-to-hand bacterial transmission. The skin microbiome is conditioned by the individual physiological characteristics and anatomical microenvironments. Furthermore, it is important to separate the autochthonous resident microbiota from the transitory microbiota that we can acquire after interactions with contaminated surfaces. Two players participate in the hand-to-hand bacterial transmission process: the bacteria and the person. The particularities of the bacteria have been extensively studied, identifying some genera or species with higher transmission efficiency, particularly those linked to nosocomial infections and outbreaks. However, the human factor remains unstudied, and intrapersonal particularities in bacterial transmission have not been yet explored. Herein we summarize the current knowledge on hand-to-hand bacterial transmission, as well as unpublished results regarding interindividual and interindividual transmission efficiency differences. We designed a simple in vivo test based on four sequential steps of finger-to-finger contact in the same person artificially inoculated with a precise bacterial inoculum. Individuals can be grouped into one of three observed transmission categories: high, medium, and poor finger-to-finger transmitters. Categorization is relevant to predicting the ultimate success of a human transmission chain, particularly for the poor transmitters, who have the ability to cut the transmission chain. Our model allowed us to analyze transmission rate differences among five bacterial species and clones that cause nosocomial infections, from which we detected that Gram-positive microorganisms were more successfully transmitted than Gram-negative.

Assessing the Likelihood of Hand-to-Hand Cross-Transmission of Bacteria: An Experimental Study

BACKGROUND Although the hands of healthcare workers (HCWs) are implicated in most episodes of healthcare-associated infections, the correlation between hand contamination and the likelihood of cross-transmission remains unknown. METHODS We conducted a laboratory-based study involving pairs of HCWs. The hands of a HCW (transmitter) were contaminated with Escherichia coli ATCC 10536 before holding hands with another HCW (host) for 1 minute. Meanwhile, the unheld hand of the transmitter was sampled. Afterward, the host's held hand was also sampled. Each experiment consisted of 4 trials with increasing concentrations of E. coli (103-106 colony-forming units [cfu]/mL). The primary outcome was the likelihood of transmission of at least 1 cfu from transmitter to host. We used a mixed logistic regression model with a random effect on the subject to assess the association between transmission and bacterial count on the transmitter's hands. RESULTS In total, 6 HCWs performed 30 experiments and 120 trials. The bacterial counts recovered from host hands were directly associated with the bacterial counts on transmitter hands (P1 and ≤3 log10 cfu compared to ≤1 log10. When transmitter contamination was <1 log10 cfu, no cross-transmission was detected. CONCLUSION There is a direct relationship between the bacterial burden on HCWs hands and the likelihood of cross-transmission. Under the described conditions, at least 1 log10 cfu must be present on HCW hands to be potentially transmitted. Further studies are needed at the low contamination range. Infect Control Hosp Epidemiol 2017;38:553-558.

Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage

BACKGROUND

Staphylococcus aureus is a common cause of bacterial infections worldwide. It is most commonly carried in and transmitted from the anterior nares. Hosts are known to vary in their proclivity for S. aureus nasal carriage and may be divided into persistent carriers, intermittent carriers, and noncarriers, depending on duration of carriage. Mathematical models of S. aureus to predict outcomes of interventions have, however, typically assumed that all individuals are equally susceptible to colonization.

OBJECTIVE

To characterize biases created by assuming a homogeneous host population in estimating efficacy of control interventions.

DESIGN

Mathematical model.

METHODS

We developed a model of S. aureus carriage in the healthcare setting under the homogeneous assumption as well as a heterogeneous model to account for the 3 types of S. aureus carriers. In both models, we calculated the equilibrium carriage prevalence to predict the impact of control measures (reducing contact and decolonization).

RESULTS

The homogeneous model almost always underestimates S. aureus transmissibility and overestimates the impact of intervention strategies in lowering carriage prevalence compared to the heterogeneous model. This finding is generally consistent regardless of changes in model setting that vary the proportions of various carriers in the population and the duration of carriage for these carrier types.

CONCLUSIONS

Not accounting for host heterogeneity leads to systematic and substantial biases in predictions of the effects of intervention strategies. Further understanding of the clinical impacts of heterogeneity through modeling can help to target control measures and allocate resources more efficiently.

Public health evolutionary biology of antimicrobial resistance: priorities for intervention

The three main processes shaping the evolutionary ecology of antibiotic resistance (AbR) involve the emergence, invasion and occupation by antibiotic-resistant genes of significant environments for human health. The process of emergence in complex bacterial populations is a high-frequency, continuous swarming of ephemeral combinatory genetic and epigenetic explorations inside cells and among cells, populations and communities, expanding in different environments (migration), creating the stochastic variation required for evolutionary progress. Invasion refers to the process by which AbR significantly increases in frequency in a given (invaded) environment, led by external invaders local multiplication and spread, or by endogenous conversion. Conversion occurs because of the spread of AbR genes from an exogenous resistant clone into an established (endogenous) bacterial clone(s) colonizing the environment; and/or because of dissemination of particular resistant genetic variants that emerged within an endogenous clonal population. Occupation of a given environment by a resistant variant means a permanent establishment of this organism in this environment, even in the absence of antibiotic selection. Specific interventions on emergence influence invasion, those acting on invasion also influence occupation and interventions on occupation determine emergence. Such interventions should be simultaneously applied, as they are not simple solutions to the complex problem of AbR.

Emergence and spread of antibiotic resistance: setting a parameter space

The emergence and spread of antibiotic resistance among human pathogens is a relevant problem for human health and one of the few evolution processes amenable to experimental studies. In the present review, we discuss some basic aspects of antibiotic resistance, including mechanisms of resistance, origin of resistance genes, and bottlenecks that modulate the acquisition and spread of antibiotic resistance among human pathogens. In addition, we analyse several parameters that modulate the evolution landscape of antibiotic resistance. Learning why some resistance mechanisms emerge but do not evolve after a first burst, whereas others can spread over the entire world very rapidly, mimicking a chain reaction, is important for predicting the evolution, and relevance for human health, of a given mechanism of resistance. Because of this, we propose that the emergence and spread of antibiotic resistance can only be understood in a multi-parameter space. Measuring the effect on antibiotic resistance of parameters such as contact rates, transfer rates, integration rates, replication rates, diversification rates, and selection rates, for different genes and organisms, growing under different conditions in distinct ecosystems, will allow for a better prediction of antibiotic resistance and possibilities of focused interventions.