Temporal dynamics of the fecal microbiome in female pigs from early life through estrus, parturition, and weaning of the first litter of piglets.

BACKGROUND: Age-associated changes in the gastrointestinal microbiome of young pigs have been robustly described; however, the temporal dynamics of the fecal microbiome of the female pig from early life to first parity are not well understood. Our objective was to describe microbiome and antimicrobial resistance dynamics of the fecal microbiome of breeding sows from early life through estrus, parturition and weaning of the first litter of piglets (i.e., from 3 to 53 weeks of age). RESULTS: Our analysis revealed that fecal bacterial populations in developing gilts undergo changes consistent with major maturation milestones. As the pigs progressed towards first estrus, the fecal bacteriome shifted from Rikenellaceae RC9 gut group- and UCG-002-dominated enterotypes to Treponema- and Clostridium sensu stricto 1-dominated enterotypes. After first estrus, the fecal bacteriome stabilized, with minimal changes in enterotype transition and associated microbial diversity from estrus to parturition and subsequent weaning of first litter piglets. Unlike bacterial communities, fecal fungal communities exhibited low diversity with high inter- and intra-pig variability and an increased relative abundance of certain taxa at parturition, including Candida spp. Counts of resistant fecal bacteria also fluctuated over time, and were highest in early life and subsequently abated as the pigs progressed to adulthood. CONCLUSIONS: This study provides insights into how the fecal microbial community and antimicrobial resistance in female pigs change from three weeks of age throughout their first breeding lifetime. The fecal bacteriome enterotypes and diversity are found to be age-driven and established by the time of first estrus, with minimal changes observed during subsequent physiological stages, such as parturition and lactation, when compared to the earlier age-related shifts. The use of pigs as a model for humans is well-established, however, further studies are needed to understand how our results compare to the human microbiome dynamics. Our findings suggest that the fecal microbiome exhibited consistent changes across individual pigs and became more diverse with age, which is a beneficial characteristic for an animal model system.

Age influences the temporal dynamics of microbiome and antimicrobial resistance genes among fecal bacteria in a cohort of production pigs.

BACKGROUND: The pig gastrointestinal tract hosts a diverse microbiome, which can serve to select and maintain a reservoir of antimicrobial resistance genes (ARG). Studies suggest that the types and quantities of antimicrobial resistance (AMR) in fecal bacteria change as the animal host ages, yet the temporal dynamics of AMR within communities of bacteria in pigs during a full production cycle remains largely unstudied. RESULTS: A longitudinal study was performed to evaluate the dynamics of fecal microbiome and AMR in a cohort of pigs during a production cycle; from birth to market age. Our data showed that piglet fecal microbial communities assemble rapidly after birth and become more diverse with age. Individual piglet fecal microbiomes progressed along similar trajectories with age-specific community types/enterotypes and showed a clear shift from E. coli/Shigella-, Fusobacteria-, Bacteroides-dominant enterotypes to Prevotella-, Megaspheara-, and Lactobacillus-dominated enterotypes with aging. Even when the fecal microbiome was the least diverse, the richness of ARGs, quantities of AMR gene copies, and counts of AMR fecal bacteria were highest in piglets at 2 days of age; subsequently, these declined over time, likely due to age-related competitive changes in the underlying microbiome. ARGs conferring resistance to metals and multi-compound/biocides were detected predominately at the earliest sampled ages. CONCLUSIONS: The fecal microbiome and resistome-along with evaluated descriptors of phenotypic antimicrobial susceptibility of fecal bacteria-among a cohort of pigs, demonstrated opposing trajectories in diversity primarily driven by the aging of pigs.

Prevalence of Antimicrobial Resistance in Select Bacteria From Retail Seafood-United States, 2019.

In 2019, the United States National Antimicrobial Resistance Monitoring System (NARMS) surveyed raw salmon, shrimp, and tilapia from retail grocery outlets in eight states to assess the prevalence of bacterial contamination and antimicrobial resistance (AMR) in the isolates. Prevalence of the targeted bacterial genera ranged among the commodities: Salmonella (0%-0.4%), Aeromonas (19%-26%), Vibrio (7%-43%), Pseudomonas aeruginosa (0.8%-2.3%), Staphylococcus (23%-30%), and Enterococcus (39%-66%). Shrimp had the highest odds (OR: 2.8, CI: 2.0-3.9) of being contaminated with at least one species of these bacteria, as were seafood sourced from Asia vs. North America (OR: 2.7; CI: 1.8-4.7) and Latin America and the Caribbean vs. North America (OR: 1.6; CI: 1.1-2.3) and seafood sold at the counter vs. sold frozen (OR: 2.1; CI: 1.6-2.9). Isolates exhibited pan-susceptibility (Salmonella and P. aeruginosa) or low prevalence of resistance (<10%) to most antimicrobials tested, with few exceptions. Seafood marketed as farm-raised had lower odds of contamination with antimicrobial resistant bacteria compared to wild-caught seafood (OR: 0.4, CI: 0.2-0.7). Antimicrobial resistance genes (ARGs) were detected for various classes of medically important antimicrobials. Clinically relevant ARGs included carbapenemases (bla IMI-2, bla NDM-1) and extended spectrum ß-lactamases (ESBLs; bla CTX-M-55). This population-scale study of AMR in seafood sold in the United States provided the basis for NARMS seafood monitoring, which began in 2020.

A longitudinal investigation of the effects of age, dietary fiber type and level, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance of finisher pigs.

Age and diet are among the factors that influence the community composition of the fecal microbiome. Additionally, antimicrobial use can alter the composition of bacterial communities. An 86-d study with finisher pigs aimed to evaluate age-related dynamics (day 98 to 177 of age), effects of types and levels of dietary fiber, and injectable antimicrobials on the fecal microbiome and antimicrobial resistance (AMR) was conducted. A total of 287 pigs, housed in 36 pens, with 7 to 8 pigs per pen, fed a corn grain and soybean meal-based basal diet, formulated to contain 8.7% neutral detergent fiber (NDF), were randomly assigned to one of three treatments: 1) basal diet with no supplement, 2) basal diet supplemented with 20% distillers dried grains with solubles (DDGS) formulated to contain 13.6% NDF, or 3) basal diet supplemented with 14.5% sugar beet pulp (SBP) formulated to contain 13.6% NDF. Five finisher pigs from each treatment group were selected randomly, and fecal samples were collected on days 98, 110, 144, and 177 of age. In addition, fecal samples were collected from pigs that were injected intramuscularly ceftiofur hydrochloride or penicillin G on days 1 and 3 along with pen-mate-untreated controls on day 1. Fecal samples were subjected to 16S rRNA amplicon-based microbiome analysis and culture methods to quantify the abundance of total AMR coliforms and enterococci populations. The alpha-diversity, such as species richness, increased with age, and the overall bacterial composition changed with age (P =0.001) and diet (P = 0.001). Diet-associated shifts in the specific bacterial taxa were observed. The richness, diversity, and evenness of bacterial taxa did not differ between pigs that were injected with ceftiofur vs. their untreated pen mates or by dietary treatments but differed in pigs that received penicillin G injection. Both antimicrobial treatments contributed to changes in the overall fecal bacterial composition at the genus level. Collectively, the data demonstrate that both age and the diet (control vs. DDGS-, control vs. SBP-, or DDGS- vs. SBP-based diets) were associated with the overall bacterial community composition, and the impact of age on variations in fecal microbiome composition was greater than the diet. Antibiotic treatment had minimal effect on bacterial diversity and relative abundance of taxa. Furthermore, diets and antimicrobial treatment had minimal impact on the overall counts of AMR coliforms and enterococci populations in feces.

Bacterial communities in the gut and the feces are strongly influenced by a number of factors, particularly the age of the animal and the diet. In addition, antibiotic administration routinely used to treat bacterial diseases can also affect the community composition. A study with finisher pigs was conducted to evaluate age-related changes, effects of types­distiller's dried grains with solubles (DGGS) or sugar beet pulp (SBP)­and levels of dietary fiber, and injectable antibiotics on the fecal bacterial composition and antibiotic resistance in fecal bacteria. Fecal samples were collected from five pigs in each of the three dietary treatment groups, control diet with no supplement or supplemented with DDGS or SBP, on days 98, 110, 144, and 177 of age and on days 1 and 3 after the first injection of antibiotics, ceftiofur or penicillin G. Samples were analyzed to identify the bacterial community composition and prevalence of antibiotic resistance in fecal bacteria. Data generated suggested that the overall bacterial composition changed with age and diet, and age appeared to have a greater impact than diet. Antibiotics had only a modest impact on the bacterial community and had minimum impact on antibiotic resistance of fecal bacteria.

Modelling the antimicrobial pharmacodynamics for bacterial strains with versus without acquired resistance to fluoroquinolones or cephalosporins.

OBJECTIVES: Antimicrobial resistance threatens therapeutic options for human and animal bacterial diseases worldwide. Current antimicrobial treatment regimens were designed against bacterial strains that were fully susceptible to them. To expand the useable lifetime of existing antimicrobial drug classes by modifying treatment regimens, data are needed on the antimicrobial pharmacodynamics (PD) against strains with reduced susceptibility. In this study, we generated and mathematically modelled the PD of the fluoroquinolone ciprofloxacin and the cephalosporin ceftriaxone against non-typhoidal Salmonella enterica subsp. enterica strains with varying levels of acquired resistance. METHODS: We included Salmonella strains across categories of reduced susceptibility to fluoroquinolones or cephalosporins reported to date, including isolates from human infections, food-animal products sold in retail, and food-animal production. We generated PD data for each drug and strain via time-kill assay. Mathematical models were compared in their fit to represent the PD. The best-fit model's parameter values across the strain susceptibility categories were compared. RESULTS: The inhibitory baseline sigmoid Imax (or Emax) model was best fit for the PD of each antimicrobial against a majority of the strains. There were statistically significant differences in the PD parameter values across the strain susceptibility categories for each antimicrobial. CONCLUSION: The results demonstrate predictable multiparameter changes in the PD of these first-line antimicrobials depending on the Salmonella strain's susceptibility phenotype and specific genes conferring reduced susceptibility. The generated PD parameter estimates could be used to optimise treatment regimens against infections by strains with reduced susceptibility.

Faecal concentrations of ceftiofur metabolites in finisher pigs administered intramuscularly with ceftiofur.

The objective of this study was to determine the effects of dietary fibre level and source on faecal ceftiofur metabolites concentrations after intramuscular administration of therapeutic ceftiofur hydrochloride in finisher pigs. Pens of finisher pigs (n = 36), with an equal number of barrows and gilts, were randomly assigned to 1 of 3 dietary treatment groups: basal diet composed of corn grain and soy bean meal with no supplement and formulated to contain 8.7% neutral detergent fibre (NDF), supplemented with 20% distillers dried grains with solubles (a byproduct of the ethanol production from corn grain) formulated to contain 13.6% NDF, primarily insoluble fibre or supplemented with 14.5% sugar beet pulp formulated to contain 13.6% NDF. Faecal samples were collected 6-8 hr after ceftiofur injection from treated and untreated pen-mate pigs on days 1 and 3 of the 3-day treatment regimen. Faecal concentrations of ceftiofur metabolites, including the major metabolite, desfuroylceftiofur, were analysed by reverse-phase high pressure liquid chromatography with ultraviolet detection. Overall, the faecal concentrations of ceftiofur metabolites did not differ significantly between the dietary treatments. The mean concentrations of metabolites tended to be lower (p = .1) on day 3 compared to day 1 of the 3-day treatment regimen. Faecal concentrations of metabolites were not affected by the gender of the finisher pigs. The concentrations of ceftiofur metabolites in the faeces are likely reflective of the microbial activity in the hindgut. Our data suggest that the fibre level and source used in the study did not affect the faecal concentrations of ceftiofur metabolites.

Modeling Intervention Scenarios During Potential Foot-and-Mouth Disease Outbreaks Within U.S. Beef Feedlots.

Foot-and-mouth disease (FMD) is a highly contagious disease of livestock and has severely affected livestock industries during the past two decades in previously FMD-free countries. The disease was eliminated in North America in 1953 but remains a threat for re-introduction. Approximately 44% of the on-feed beef cattle in the U.S. are concentrated in feedlots <32,000 heads, but little information is available on dynamics of FMD in large feedlots. Therefore, there is a need to explore possible management and intervention strategies that might be implemented during potential FMD outbreaks on feedlots. We used a within home-pen stochastic susceptible-latent-infectious-recovered (SLIR) FMD dynamics model nested in a meta-population model of home-pens in a feedlot. The combinatory model was previously developed to simulate foot-and-mouth disease virus (FMDv) transmission within U.S. beef feedlots. We evaluated three intervention strategies initiated on the day of FMD detection: stopping movements of cattle between home-pens and hospital-pen(s) (NH), barrier depopulation combined with NH (NH-BD), and targeted depopulation of at-risk home-pens combined with NH (NH-TD). Depopulation rates investigated ranged from 500 to 4,000 cattle per day. We evaluated the projected effectiveness of interventions by comparing them with the no-intervention FMD dynamics in the feedlot. We modeled a small-size (4,000 cattle), medium-size (12,000 cattle), and large-size (24,000 cattle) feedlots. Implementation of NH delayed the outbreak progression, but it did not prevent infection of the entire feedlot. Implementation of NH-BD resulted in depopulation of 50% of cattle in small- and medium-size feedlots, and 25% in large-size feedlots, but the intervention prevented infection of the entire feedlot in 40% of simulated outbreaks in medium-size feedlots, and in 8% in large-size feedlots. Implementation of NH-TD resulted in depopulation of up to 50% of cattle in small-size feedlots, 75% in medium-size feedlots, and 25% in large-size feedlots, but rarely prevented infection of the entire feedlot. Number of hospital-pens in the feedlot was shown to weakly impact the success of NH-TD. Overall, the results suggest that stopping cattle movements between the home-pens and hospital-pens, without or with barrier or targeted cattle depopulation, would not be highly effective to interrupt FMDv transmission within a feedlot.

A Meta-Population Model of Potential Foot-and-Mouth Disease Transmission, Clinical Manifestation, and Detection Within U.S. Beef Feedlots.

Foot-and-mouth disease (FMD) has not been reported in the U.S. since 1929. Recent outbreaks in previously FMD-free countries raise concerns about potential FMD introductions in the U.S. Mathematical modeling is the only tool for simulating infectious disease outbreaks in non-endemic territories. In the majority of prior studies, FMD virus (FMDv) transmission on-farm was modeled assuming homogenous animal mixing. This assumption is implausible for U.S. beef feedlots which are divided into multiple home-pens without contact between home-pens except fence line with contiguous home-pens and limited mixing in hospital pens. To project FMDv transmission and clinical manifestation in a feedlot, we developed a meta-population stochastic model reflecting the contact structure. Within a home-pen, the dynamics were represented assuming homogenous animal mixing by a modified SLIR (susceptible-latent-infectious-recovered) model with four additional compartments tracing cattle with subclinical or clinical FMD and infectious status. Virus transmission among home-pens occurred via cattle mixing in hospital-pen(s), cowboy pen rider movements between home-pens, airborne, and for contiguous home-pens fence-line and via shared water-troughs. We modeled feedlots with a one-time capacity of 4,000 (small), 12,000 (medium), and 24,000 (large) cattle. Common cattle demographics, feedlot layout, endemic infectious and non-infectious disease occurrence, and production management were reflected. Projected FMD-outbreak duration on a feedlot ranged from 49 to 82 days. Outbreak peak day (with maximum number of FMD clinical cattle) ranged from 24 (small) to 49 (large feedlot). Detection day was 4-12 post-FMD-introduction with projected 28, 9, or 4% of cattle already infected in a small, medium, or large feedlot, respectively. Depletion of susceptible cattle in a feedlot occurred by day 23-51 post-FMD-introduction. Parameter-value sensitivity analyses were performed for model outputs. Detection occurred sooner if there was a higher initial proportion of latent animals in the index home-pen. Shorter outbreaks were associated with a shorter latent period and higher bovine respiratory disease morbidity (impacting the in-hospital-pen cattle mixing occurrence). This first model of potential FMD dynamics on U.S. beef feedlots shows the importance of capturing within-feedlot cattle contact structure for projecting infectious disease dynamics. Our model provides a tool for evaluating FMD outbreak control strategies.

Mathematical modeling of the 'inoculum effect': six applicable models and the MIC advancement point concept.

Antimicrobial treatment regimens against bacterial pathogens are designed using the drug's minimum inhibitory concentration (MIC) measured at a bacterial density of 5.7 log10(colony-forming units (CFU)/mL) in vitro. However, MIC changes with pathogen density, which varies among infectious diseases and during treatment. Incorporating this into treatment design requires realistic mathematical models of the relationships. We compared the MIC-density relationships for Gram-negative Escherichia coli and non-typhoidal Salmonella enterica subsp. enterica and Gram-positive Staphylococcus aureus and Streptococcus pneumonia (for n = 4 drug-susceptible strains per (sub)species and 1-8 log10(CFU/mL) densities), for antimicrobial classes with bactericidal activity against the (sub)species: ß-lactams (ceftriaxone and oxacillin), fluoroquinolones (ciprofloxacin), aminoglycosides (gentamicin), glycopeptides (vancomycin) and oxazolidinones (linezolid). Fitting six candidate mathematical models to the log2(MIC) vs. log10(CFU/mL) curves did not identify one model best capturing the relationships across the pathogen-antimicrobial combinations. Gompertz and logistic models (rather than a previously proposed Michaelis-Menten model) fitted best most often. Importantly, the bacterial density after which the MIC sharply increases (an MIC advancement-point density) and that density's intra-(sub)species range evidently depended on the antimicrobial mechanism of action. Capturing these dependencies for the disease-pathogen-antimicrobial combination could help determine the MICs for which bacterial densities are most informative for treatment regimen design.

Age Dependence of Antimicrobial Resistance Among Fecal Bacteria in Animals: A Scoping Review.

Introduction: A phenomenon of decreasing antimicrobial resistance (AMR) among fecal bacteria as food animals age has been noted in multiple field studies. We conducted a scoping review to summarize the extent, range, and nature of research activity and the data for the following question: "does AMR among enteric/fecal bacteria predictably shift as animals get older?". Methods: This review followed a scoping review methodology framework. Pertinent literature published up until November 2018 for all animals (except humans) was retrieved using keyword searches in two online databases, namely, PubMed® and the Web of Science™ Core Collection, without filtering publication date, geographic location, or language. Data were extracted from the included studies, summarized, and plotted. Study quality was also assessed using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) guidelines for all included papers. Results: The publications with detailed relevant data (n = 62) in food animals, poultry, and dogs were identified. These included longitudinal studies (n = 32), cross-sectional studies of different age groups within one food animal production system or small-animal catchment area (n = 16), and experimental or diet trials (n = 14). A decline in host-level prevalence and/or within-host abundance of AMR among fecal bacteria in production beef, dairy cattle, and swine was reported in nearly two-thirds (65%) of the identified studies in different geographic locations from the 1970's to 2018. Mixed results, with AMR abundance among fecal bacteria either increasing or decreasing with age, have been reported in poultry (broiler chicken, layer, and grow-out turkey) and dogs. Conclusions: Quantitative synthesis of the data suggests that the age-dependent AMR phenomenon in cattle and swine is observed irrespective of geographic location and specific production practices. It is unclear whether the phenomenon predates or is related to antimicrobial drug use. However, almost 50% of the identified studies predate recent changes in antimicrobial drug use policy and regulations in food animals in the United States and elsewhere.

Expanding behavior pattern sensitivity analysis with model selection and survival analysis.

BACKGROUND: Sensitivity analysis is an essential step in mathematical modeling because it identifies parameters with a strong influence on model output, due to natural variation or uncertainty in the parameter values. Recently behavior pattern sensitivity analysis has been suggested as a method for sensitivity analyses on models with more than one mode of output behavior. The model output is classified by behavior mode and several behavior pattern measures, defined by the researcher, are calculated for each behavior mode. Significant associations between model inputs and outputs are identified by building linear regression models with the model parameters as independent variables and the behavior pattern measures as the dependent variables. We applied the behavior pattern sensitivity analysis to a mathematical model of tetracycline-resistant enteric bacteria in beef cattle administered chlortetracycline orally. The model included 29 parameters related to bacterial population dynamics, chlortetracycline pharmacokinetics and pharmacodynamics. The prevalence of enteric resistance during and after chlortetracycline administration was the model output. Cox proportional hazard models were used when linear regression assumptions were not met. RESULTS: We have expanded the behavior pattern sensitivity analysis procedure by incorporating model selection techniques to produce parsimonious linear regression models that efficiently prioritize input parameters. We also demonstrate how to address common violations of linear regression model assumptions. Finally, we explore the semi-parametric Cox proportional hazards model as an alternative to linear regression for situations with censored data. In the example mathematical model, the resistant bacteria exhibited three behaviors during the simulation period: (1) increasing, (2) decreasing, and (3) increasing during antimicrobial therapy and decreasing after therapy ceases. The behavior pattern sensitivity analysis identified bacterial population parameters as high importance in determining the trajectory of the resistant bacteria population. CONCLUSIONS: Interventions aimed at the enteric bacterial population ecology, such as diet changes, may be effective at reducing the prevalence of tetracycline-resistant enteric bacteria in beef cattle. Behavior pattern sensitivity analysis is a useful and flexible tool for conducting a sensitivity analysis on models with varied output behavior, enabling prioritization of input parameters via regression model selection techniques. Cox proportional hazard models are an alternative to linear regression when behavior pattern measures are censored or linear regression assumptions cannot be met.

Clinical and infection dynamics of foot-and-mouth disease in beef feedlot cattle: An expert survey.

Parameterizing mathematical models of foot-and-mouth disease virus (FMDv) transmission is challenging due to knowledge gaps on the variable dynamics in susceptible populations. Expert opinion surveys are an approach to gather data on topics where no data have been reported. The objective of this study was to collect-via an expert-opinion survey-key parameter values of the potential FMD natural history and transmissibility in beef feedlot cattle in the U.S. Experts with experience working with FMD in endemic and non-endemic settings were targeted. Parameters surveyed were: duration of infection and disease stages, proportions of animals with specific clinical manifestations, duration and extent of the reduction in feed consumption, and probabilities of severe clinical disease and FMDv transmission. We surveyed the parameter values for infections by strains of different virulence, different infection doses, and routes of transmission. Twenty-seven experts from around the world agreed to participate and 16 (59%) completed the survey. The expert responses to individual questions were resampled via Monte Carlo simulations; to the resulting distributions, candidate theoretical distributions were fitted using the maximum likelihood method and the sought parameter values estimated based on the best-fit distributions. Of the infection stages, the estimates of the expected FMD latent period in beef feedlot ranged from 1.7 to 5.3 days and the infectious period from 5.6 to 10.9 days. Of the disease stages, the estimated incubation period ranged from 2.9 to 6.1 days, subclinical period from 1.2 to 2.8 days, and clinical period from 4.2 to 7.5 days. Probability of developing clinical disease after infection varied from 82% (IQ range 90-70%) with high-virulent to 63% (IQ range 89-60%) with low-virulent strains. Reduction in feed consumption was estimated to last 5 (SD ± 2) days in cattle infected by a low-virulent FMDv strain and 7 (SD ± 2) days for high virulent strains. The study results can be used in combination with experimental and outbreak investigation data to parameterize FMDv-transmission models to evaluate intervention responses during hypothetical FMD epidemics in beef feedlot populations in the U.S.

Variation in fluoroquinolone pharmacodynamic parameter values among isolates of two bacterial pathogens of bovine respiratory disease.

To design an antimicrobial treatment regimen for a bacterial disease, data on the drug pharmacodynamics (PD) against selected drug-susceptible strains of the pathogen are used. The regimen is applied across such strains in the field, assuming the PD parameter values remain the same. We used time-kill experiments and PD modeling to investigate the fluoroquinolone enrofloxacin PD against different isolates of two bovine respiratory disease pathogens: four Mannheimia haemolytica and three Pasteurella multocida isolates. The models were fitted as mixed-effects non-linear regression; the fixed-effects PD parameter values were estimated after accounting for random variation among experimental replicates. There was both inter- and intra- bacterial species variability in the PD parameters Hill-coefficient and Emax (maximal decline of bacterial growth rate), with more variable PD responses among M. haemolytica than among P. multocida isolates. Moreover, the Hill-coefficient was correlated to the isolate's maximal population growth rate in the absence of antimicrobial exposure (a.k.a. specific growth rate; Spearman's ρ = 0.98, p-value = 0.003, n = 6 isolates excluding one outlier). Thus, the strain's properties such as growth potential may impact its PD responses. This variability can have clinical implications. Modifying the treatment regimen depending on phenotypic properties of the pathogen strain causing disease may be a precision medicine approach.

Corrigendum: Monte Carlo Simulations Suggest Current Chlortetracycline Drug-Residue Based Withdrawal Periods Would Not Control Antimicrobial Resistance Dissemination from Feedlot to Slaughterhouse.

[This corrects the article on p. 1753 in vol. 8, PMID: 29033901.].

Establishing Statistical Equivalence of Data from Different Sampling Approaches for Assessment of Bacterial Phenotypic Antimicrobial Resistance.

To assess phenotypic bacterial antimicrobial resistance (AMR) in different strata (e.g., host populations, environmental areas, manure, or sewage effluents) for epidemiological purposes, isolates of target bacteria can be obtained from a stratum using various sample types. Also, different sample processing methods can be applied. The MIC of each target antimicrobial drug for each isolate is measured. Statistical equivalence testing of the MIC data for the isolates allows evaluation of whether different sample types or sample processing methods yield equivalent estimates of the bacterial antimicrobial susceptibility in the stratum. We demonstrate this approach on the antimicrobial susceptibility estimates for (i) nontyphoidal Salmonella spp. from ground or trimmed meat versus cecal content samples of cattle in processing plants in 2013-2014 and (ii) nontyphoidal Salmonella spp. from urine, fecal, and blood human samples in 2015 (U.S. National Antimicrobial Resistance Monitoring System data). We found that the sample types for cattle yielded nonequivalent susceptibility estimates for several antimicrobial drug classes and thus may gauge distinct subpopulations of salmonellae. The quinolone and fluoroquinolone susceptibility estimates for nontyphoidal salmonellae from human blood are nonequivalent to those from urine or feces, conjecturally due to the fluoroquinolone (ciprofloxacin) use to treat infections caused by nontyphoidal salmonellae. We also demonstrate statistical equivalence testing for comparing sample processing methods for fecal samples (culturing one versus multiple aliquots per sample) to assess AMR in fecal Escherichia coli These methods yield equivalent results, except for tetracyclines. Importantly, statistical equivalence testing provides the MIC difference at which the data from two sample types or sample processing methods differ statistically. Data users (e.g., microbiologists and epidemiologists) may then interpret practical relevance of the difference.IMPORTANCE Bacterial antimicrobial resistance (AMR) needs to be assessed in different populations or strata for the purposes of surveillance and determination of the efficacy of interventions to halt AMR dissemination. To assess phenotypic antimicrobial susceptibility, isolates of target bacteria can be obtained from a stratum using different sample types or employing different sample processing methods in the laboratory. The MIC of each target antimicrobial drug for each of the isolates is measured, yielding the MIC distribution across the isolates from each sample type or sample processing method. We describe statistical equivalence testing for the MIC data for evaluating whether two sample types or sample processing methods yield equivalent estimates of the bacterial phenotypic antimicrobial susceptibility in the stratum. This includes estimating the MIC difference at which the data from the two approaches differ statistically. Data users (e.g., microbiologists, epidemiologists, and public health professionals) can then interpret whether that present difference is practically relevant.

Potential Information Loss Due to Categorization of Minimum Inhibitory Concentration Frequency Distributions.

A bacterial isolate's susceptibility to antimicrobial is expressed as the lowest drug concentration inhibiting its visible growth, termed minimum inhibitory concentration (MIC). The susceptibilities of isolates from a host population at a particular time vary, with isolates with specific MICs present at different frequencies. Currently, for either clinical or monitoring purposes, an isolate is most often categorized as Susceptible, Intermediate, or Resistant to the antimicrobial by comparing its MIC to a breakpoint value. Such data categorizations are known in statistics to cause information loss compared to analyzing the underlying frequency distributions. The U.S. National Antimicrobial Resistance Monitoring System (NARMS) includes foodborne bacteria at the food animal processing and retail product points. The breakpoints used to interpret the MIC values for foodborne bacteria are those relevant to clinical treatments by the antimicrobials in humans in whom the isolates were to cause infection. However, conceptually different objectives arise when inference is sought concerning changes in susceptibility/resistance across isolates of a bacterial species in host populations among different sampling points or times. For the NARMS 1996-2013 data for animal processing and retail, we determined the fraction of comparisons of susceptibility/resistance to 44 antimicrobial drugs of twelve classes of a bacterial species in a given animal host or product population where there was a significant change in the MIC frequency distributions between consecutive years or the two sampling points, while the categorization-based analyses concluded no change. The categorization-based analyses missed significant changes in 54% of the year-to-year comparisons and in 71% of the slaughter-to-retail within-year comparisons. Hence, analyses using the breakpoint-based categorizations of the MIC data may miss significant developments in the resistance distributions between the sampling points or times. Methods considering the MIC frequency distributions in their entirety may be superior for epidemiological analyses of resistance dynamics in populations.

Monte Carlo Simulations Suggest Current Chlortetracycline Drug-Residue Based Withdrawal Periods Would Not Control Antimicrobial Resistance Dissemination from Feedlot to Slaughterhouse.

Antimicrobial use in beef cattle can increase antimicrobial resistance prevalence in their enteric bacteria, including potential pathogens such as Escherichia coli. These bacteria can contaminate animal products at slaughterhouses and cause food-borne illness, which can be difficult to treat if it is due to antimicrobial resistant bacteria. One potential intervention to reduce the dissemination of resistant bacteria from feedlot to consumer is to impose a withdrawal period after antimicrobial use, similar to the current withdrawal period designed to prevent drug residues in edible animal meat. We investigated tetracycline resistance in generic E. coli in the bovine large intestine during and after antimicrobial treatment by building a mathematical model of oral chlortetracycline pharmacokinetics-pharmacodynamics and E. coli population dynamics. We tracked three E. coli subpopulations (susceptible, intermediate, and resistant) during and after treatment with each of three United States chlortetracycline indications (liver abscess reduction, disease control, disease treatment). We compared the proportion of resistant E. coli before antimicrobial use to that at several time points after treatment and found a greater proportion of resistant enteric E. coli after the current withdrawal periods than prior to treatment. In order for the proportion of resistant E. coli in the median beef steer to return to the pre-treatment level, withdrawal periods of 15 days after liver abscess reduction dosing (70 mg daily), 31 days after disease control dosing (350 mg daily), and 36 days after disease treatment dosing (22 mg/kg bodyweight for 5 days) are required in this model. These antimicrobial resistance withdrawal periods would be substantially longer than the current U.S. withdrawals of 0-2 days or Canadian withdrawals of 5-10 days. One published field study found similar time periods necessary to reduce the proportion of resistant E. coli following chlortetracycline disease treatment to those suggested by this model, but additional carefully designed field studies are necessary to confirm the model results. This model is limited to biological processes within the cattle and does not include resistance selection in the feedlot environment or co-selection of chlortetracycline resistance following other antimicrobial use.

A proposed analytic framework for determining the impact of an antimicrobial resistance intervention.

Antimicrobial use (AMU) is increasingly threatened by antimicrobial resistance (AMR). The FDA is implementing risk mitigation measures promoting prudent AMU in food animals. Their evaluation is crucial: the AMU/AMR relationship is complex; a suitable framework to analyze interventions is unavailable. Systems science analysis, depicting variables and their associations, would help integrate mathematics/epidemiology to evaluate the relationship. This would identify informative data and models to evaluate interventions. This National Institute for Mathematical and Biological Synthesis AMR Working Group's report proposes a system framework to address the methodological gap linking livestock AMU and AMR in foodborne bacteria. It could evaluate how AMU (and interventions) impact AMR. We will evaluate pharmacokinetic/dynamic modeling techniques for projecting AMR selection pressure on enteric bacteria. We study two methods to model phenotypic AMR changes in bacteria in the food supply and evolutionary genotypic analyses determining molecular changes in phenotypic AMR. Systems science analysis integrates the methods, showing how resistance in the food supply is explained by AMU and concurrent factors influencing the whole system. This process is updated with data and techniques to improve prediction and inform improvements for AMU/AMR surveillance. Our proposed framework reflects both the AMR system's complexity, and desire for simple, reliable conclusions.

Limitations of MIC as sole metric of pharmacodynamic response across the range of antimicrobial susceptibilities within a single bacterial species.

The minimum inhibitory concentration (MIC) of an antimicrobial drug for a bacterial pathogen is used as a measure of the bacterial susceptibility to the drug. However, relationships between the antimicrobial concentration, bacterial susceptibility, and the pharmacodynamic (PD) inhibitory effect on the bacterial population are more complex. The relationships can be captured by multi-parameter models such as the Emax model. In this study, time-kill experiments were conducted with a zoonotic pathogen Pasteurella multocida and the fluoroquinolone enrofloxacin. Pasteurella multocida isolates with enrofloxacin MIC of 0.01 µg/mL, 1.5 µg/mL, and 2.0 µg/mL were used. An additive inhibitory Emax model was fitted to the data on bacterial population growth inhibition at different enrofloxacin concentrations. The values of PD parameters such as maximal growth inhibition, concentration achieving a half of the maximal inhibition, and Hill coefficient that captures steepness of the relationships between the concentration and effect, varied between the isolate with low MIC and less susceptible isolates. While enrofloxacin PD against the isolate with low MIC exhibited the expected concentration-dependent characteristics, the PD against the less susceptible isolates demonstrated time-dependent characteristics. The results demonstrate that bacterial antimicrobial susceptibility may need to be described by a combination of parameters rather than a single parameter of the MIC.

Exploring Post-Treatment Reversion of Antimicrobial Resistance in Enteric Bacteria of Food Animals as a Resistance Mitigation Strategy.

Antimicrobial drug use in food animals is associated with an elevation in relative abundance of bacteria resistant to the drug among the animal enteric bacteria. Some of these bacteria are potential foodborne pathogens. Evidence suggests that at least in the enteric nontype-specific Escherichia coli, after treatment the resistance abundance reverts to the background pre-treatment levels, without further interventions. We hypothesize that it is possible to define the distribution of the time period after treatment within which resistance to the administered drug, and possibly other drugs in case of coselection, in fecal bacteria of the treated animals returns to the background pre-treatment levels. Furthermore, it is possible that a novel resistance mitigation strategy for microbiological food safety could be developed based on this resistance reversion phenomenon. The strategy would be conceptually similar to existing antimicrobial drug withdrawal periods, which is a well-established and accepted mitigation strategy for avoiding violative drug residues in the edible products from the treated animals. For developing resistance-relevant withdrawals, a mathematical framework can be used to join the necessary pharmacological, microbiological, and animal production components to project the distributions of the post-treatment resistance reversion periods in the production animal populations for major antimicrobial drug classes in use. The framework can also help guide design of empirical studies into the resistance-relevant withdrawal periods and development of mitigation approaches to reduce the treatment-associated elevation of resistance in animal enteric bacteria. We outline this framework, schematically and through exemplar equations, and how its components could be formulated.