Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: a modelling study applied to nosocomial pathogen control.
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SUBMITTER: Smith DRM
PROVIDER: S-EPMC8560094 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionAccording to the Centers for Disease Control's 2015 Hospital Acquired Infection Hospital Prevalence Survey, 1 in 31 hospital patients was infected with at least one nosocomial pathogen while being treated for unrelated issues. Many studies associate antibiotic administration with nosocomial infection occurrence. However, to our knowledge, there is little to no direct evidence of antibiotic administration selecting for nosocomial opportunistic pathogens.AimThis study aims to confirm gut microbiota shifts in an animal model of antibiotic treatment to determine whether antibiotic use favors pathogenic bacteria.MethodologyWe utilized next-generation sequencing and in-house metagenomic assembly and taxonomic assignment pipelines on the fecal microbiota of a urinary tract infection mouse model with and without antibiotic treatment.ResultsAntibiotic therapy decreased the number of detectable species of bacteria by at least 20-fold. Furthermore, the gut microbiota of antibiotic treated mice had a significant increase of opportunistic pathogens that have been implicated in nosocomial infections, like Acinetobacter calcoaceticus/baumannii complex, Chlamydia abortus, Bacteroides fragilis, and Bacteroides thetaiotaomicron. Moreover, antibiotic treatment selected for antibiotic resistant gene enriched subpopulations for many of these opportunistic pathogens.ConclusionsOral antibiotic therapy may select for common opportunistic pathogens responsible for nosocomial infections. In this study opportunistic pathogens present after antibiotic therapy harbored more antibiotic resistant genes than populations of opportunistic pathogens before treatment. Our results demonstrate the effects of antibiotic therapy on induced dysbiosis and expansion of opportunistic pathogen populations and antibiotic resistant subpopulations of those pathogens. Follow-up studies with larger samples sizes and potentially controlled clinical investigations should be performed to confirm our findings.
| S-EPMC7885457 | biostudies-literature
Project description:Broad-spectrum antibiotic therapy decimates the gut microbiome, resulting in a variety of negative health consequences. Debio 1452 is a staphylococcus-selective enoyl-acyl carrier protein reductase (FabI) inhibitor under clinical development and was used to determine whether treatment with pathogen-selective antibiotics would minimize disturbance to the microbiome. The effect of oral Debio 1452 on the microbiota of mice was compared to the effects of four commonly used broad-spectrum oral antibiotics. During the 10 days of oral Debio 1452 treatment, there was minimal disturbance to the gut bacterial abundance and composition, with only the unclassified S24-7 taxon reduced at days 6 and 10. In comparison, broad-spectrum oral antibiotics caused ?100- to 4,000-fold decreases in gut bacterial abundance and severely altered the microbial composition. The gut bacterial abundance and composition of Debio 1452-treated mice were indistinguishable from those of untreated mice 2 days after the antibiotic treatment was stopped. In contrast, the bacterial abundance in broad-spectrum-antibiotic-treated mice took up to 7 days to recover, and the gut composition of the broad-spectrum-antibiotic-treated mice remained different from that of the control group 20 days after the cessation of antibiotic treatment. These results illustrate that a pathogen-selective approach to antibiotic development will minimize disturbance to the gut microbiome.
| S-EPMC4914625 | biostudies-literature
Project description:Surveillance of nosocomial infection is the foundation of infection control. Nosocomial infection surveillance data ought to be summarized, reported, and fed back to health care personnel for corrective action. Using the Japanese Nosocomial Infection Surveillance (JANIS) data, we determined the incidence of nosocomial infections in intensive care units (ICUs) of Japanese hospitals and assessed the impact of nosocomial infections on mortality and length of stay. We also elucidated individual and environmental factors associated with nosocomial infections, examined the benchmarking of infection rates and developed a practical tool for comparing infection rates with case-mix adjustment. The studies carried out to date using the JANIS data have provided valuable information on the epidemiology of nosocomial infections in Japanese ICUs, and this information will contribute to the development of evidence-based infection control programs for Japanese ICUs. We conclude that current surveillance systems provide an inadequate feedback of nosocomial infection surveillance data and, based on our results, suggest a methodology for assessing nosocomial infection surveillance data that will allow infection control professionals to maintain their surveillance systems in good working order.
| S-EPMC2698243 | biostudies-other
Project description:BackgroundCirculation of multidrug-resistant bacteria (MRB) in healthcare facilities is a major public health problem. These settings have been greatly impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, notably due to surges in COVID-19 caseloads and the implementation of infection control measures. We sought to evaluate how such collateral impacts of COVID-19 impacted the nosocomial spread of MRB in an early pandemic context.Methods and findingsWe developed a mathematical model in which Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and MRB cocirculate among patients and staff in a theoretical hospital population. Responses to COVID-19 were captured mechanistically via a range of parameters that reflect impacts of SARS-CoV-2 outbreaks on factors relevant for pathogen transmission. COVID-19 responses include both "policy responses" willingly enacted to limit SARS-CoV-2 transmission (e.g., universal masking, patient lockdown, and reinforced hand hygiene) and "caseload responses" unwillingly resulting from surges in COVID-19 caseloads (e.g., abandonment of antibiotic stewardship, disorganization of infection control programmes, and extended length of stay for COVID-19 patients). We conducted 2 main sets of model simulations, in which we quantified impacts of SARS-CoV-2 outbreaks on MRB colonization incidence and antibiotic resistance rates (the share of colonization due to antibiotic-resistant versus antibiotic-sensitive strains). The first set of simulations represents diverse MRB and nosocomial environments, accounting for high levels of heterogeneity across bacterial parameters (e.g., rates of transmission, antibiotic sensitivity, and colonization prevalence among newly admitted patients) and hospital parameters (e.g., rates of interindividual contact, antibiotic exposure, and patient admission/discharge). On average, COVID-19 control policies coincided with MRB prevention, including 28.2% [95% uncertainty interval: 2.5%, 60.2%] fewer incident cases of patient MRB colonization. Conversely, surges in COVID-19 caseloads favoured MRB transmission, resulting in a 13.8% [-3.5%, 77.0%] increase in colonization incidence and a 10.4% [0.2%, 46.9%] increase in antibiotic resistance rates in the absence of concomitant COVID-19 control policies. When COVID-19 policy responses and caseload responses were combined, MRB colonization incidence decreased by 24.2% [-7.8%, 59.3%], while resistance rates increased by 2.9% [-5.4%, 23.2%]. Impacts of COVID-19 responses varied across patients and staff and their respective routes of pathogen acquisition. The second set of simulations was tailored to specific hospital wards and nosocomial bacteria (methicillin-resistant Staphylococcus aureus, extended-spectrum beta-lactamase producing Escherichia coli). Consequences of nosocomial SARS-CoV-2 outbreaks were found to be highly context specific, with impacts depending on the specific ward and bacteria evaluated. In particular, SARS-CoV-2 outbreaks significantly impacted patient MRB colonization only in settings with high underlying risk of bacterial transmission. Yet across settings and species, antibiotic resistance burden was reduced in facilities with timelier implementation of effective COVID-19 control policies.ConclusionsOur model suggests that surges in nosocomial SARS-CoV-2 transmission generate selection for the spread of antibiotic-resistant bacteria. Timely implementation of efficient COVID-19 control measures thus has 2-fold benefits, preventing the transmission of both SARS-CoV-2 and MRB, and highlighting antibiotic resistance control as a collateral benefit of pandemic preparedness.
| S-EPMC10241372 | biostudies-literature
Project description:Corynebacterium striatum is a nosocomial opportunistic pathogen increasingly associated with a wide range of human infections and is often resistant to several antibiotics. We investigated the susceptibility of 63 C. striatum isolated at the Farhat-Hached hospital, Sousse (Tunisia), during the period 2011-2014, to a panel of 16 compounds belonging to the main clinically relevant classes of antimicrobial agents. All strains were susceptible to vancomycin, linezolid, and daptomycin. Amikacin and gentamicin also showed good activity (MICs90 = 1 and 2 mg/L, respectively). High rates of resistance to penicillin (82.5%), clindamycin (79.4%), cefotaxime (60.3%), erythromycin (47.6%), ciprofloxacin (36.5%), moxifloxacin (34.9%), and rifampicin (25.4%) were observed. Fifty-nine (93.7%) out of the 63 isolates showed resistance to at least one compound and 31 (49.2%) were multidrug-resistant. Twenty-nine resistance profiles were distinguished among the 59 resistant C. striatum. Most of the strains resistant to fluoroquinolones showed a double mutation leading to an amino acid change in positions 87 and 91 in the quinolone resistance-determining region of the gyrA gene. The 52 strains resistant to penicillin were positive for the gene bla, encoding a class A β-lactamase. Twenty-two PFGE patterns were identified among the 63 C. striatum, indicating that some clones have spread within the hospital.
| S-EPMC5573724 | biostudies-literature
Project description:The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in the growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in the death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental, and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified two genetic hot spots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied.
| S-EPMC9934204 | biostudies-literature
Project description:Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.
| S-EPMC2222825 | biostudies-literature
Project description:The rise of multi-drug resistance in bacterial pathogens imposes the need to study these organisms from new angles. A little explored outset is to scrutinize bacterial niche adaptations and interactions among pathogenic and commensal bacteria, because they can provide a better understanding of the fitness of pathogens in their human host. We have previously shown that co-culturing of the pathogen Staphylococcus aureus with co-resident Klebsiella oxytoca or Bacillus thuringiensis wound isolates resulted in reduced levels of virulence factor secretion, suggesting that the presence of these co-resident bacteria would modulate S. aureus virulence. In the present study, we performed an in-depth investigation of changes in S. aureus gene expression upon co-cultivation with K. oxytoca and B. thuringiensis under infection-mimicking conditions. To this end, we profiled the cellular proteomes of the co-existing bacteria with special focus on S. aureus. In parallel, we employed RNA sequencing to highlight global changes in staphylococcal behaviour. The results imply that co-colonizing bacteria from chronic wounds can pacify S. aureus, and this conclusion was verified in a Galleria mellonella infection model. Altogether, our findings show that the presence of K. oxytoca and B. thuringiensis leads to massive rearrangements in S. aureus physiology and substantial reduction in virulence.
2020-04-22 | PXD017208 | Pride
Project description:Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): a significant development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and fundamental improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based split gene drive system intended to drive a disease-refractory gene into a population in a confinable and reversible manner, incorporating time-varying temperature and rainfall data. The simulations also evaluate impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is freely available as an open-source R package on CRAN (https://CRAN.R-project.org/package=MGDrivE2). We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.
| S-EPMC8186770 | biostudies-literature
Project description:BackgroundIntestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.Methodology/principal findingsIn this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.Conclusion/significanceThe model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.
| S-EPMC4760737 | biostudies-literature