Augmented surveillance of antimicrobial resistance with high-throughput robotics detects transnational flow of fluoroquinolone-resistant Escherichia coli strain into poultry.

BACKGROUND: Food animal AMR surveillance programs assess only small numbers of Escherichia coli (from 100 to 600 per animal class) nationally each year, severely limiting the evaluation of public health risk(s). Here we demonstrate an affordable approach for early detection of emerging resistance on a broad scale that can also accurately characterize spatial and temporal changes in resistance. METHODS: Caecal samples (n = 295) obtained from 10 meat poultry were screened using high-throughput robotics. Initial screening via agar dilution (5310 plates) quantified AMR carriage (cfu/g) for each sample. Ciprofloxacin-resistant isolates (n = 91) proceeded to downstream broth microdilution susceptibility testing. A subset of 28 ciprofloxacin-resistant isolates underwent WGS and phylogenetic analysis. RESULTS: Intra- and inter-flock carriage of resistance varied with drug class. Ampicillin and tetracycline resistance was ubiquitous to most birds in all flocks with an average carriage rate of 5.8 log10 cfu/g. Gentamicin and ciprofloxacin-resistant E. coli colonized fewer birds, and had an average carriage rate of 1.2 log10 cfu/g and 1.0 log10 cfu/g of faeces, respectively. Resistance to extended-spectrum cephalosporins was absent. ST354 was the dominant ST among the WGS isolates, but they demonstrated markedly lower resistance gene carriage than their international counterparts. CONCLUSIONS: These data amply demonstrate the ineffectiveness of commonly relied-on approaches to AMR surveillance for achieving early detection of emergence, or for measuring spatial and temporal resistance trends. Genetic analysis suggested there has been transnational flow of a ciprofloxacin-resistant strain into Australian poultry flocks, explaining their detection in a nation that prohibits fluoroquinolone use in poultry.

Robotic Antimicrobial Susceptibility Platform (RASP): a next-generation approach to One Health surveillance of antimicrobial resistance.

BACKGROUND: Surveillance of antimicrobial resistance (AMR) is critical to reducing its wide-reaching impact. Its reliance on sample size invites solutions to longstanding constraints regarding scalability. A robotic platform (RASP) was developed for high-throughput AMR surveillance in accordance with internationally recognized standards (CLSI and ISO 20776-1:2019) and validated through a series of experiments. METHODS: Experiment A compared RASP's ability to achieve consistent MICs with that of a human technician across eight replicates for four Escherichia coli isolates. Experiment B assessed RASP's agreement with human-performed MICs across 91 E. coli isolates with a diverse range of AMR profiles. Additionally, to demonstrate its real-world applicability, the RASP workflow was then applied to five faecal samples where a minimum of 47 E. coli per animal (239 total) were evaluated using an AMR indexing framework. RESULTS: For each drug-rater-isolate combination in Experiment A, there was a clear consensus of the MIC and deviation from the consensus remained within one doubling dilution (the exception being gentamicin at two dilutions). Experiment B revealed a concordance correlation coefficient of 0.9670 (95% CI: 0.9670-0.9670) between the robot- and human-performed MICs. RASP's application to the five faecal samples highlighted the intra-animal diversity of gut commensal E. coli, identifying between five and nine unique isolate AMR phenotypes per sample. CONCLUSIONS: While adhering to internationally accepted guidelines, RASP was superior in throughput, cost and data resolution when compared with an experienced human technician. Integration of robotics platforms in the microbiology laboratory is a necessary advancement for future One Health AMR endeavours.

Antimicrobial Resistance of and Genomic Insights into Pasteurella multocida Strains Isolated from Australian Pigs.

Infection with Pasteurella multocida represents a significant economic threat to Australian pig producers, yet our knowledge of its antimicrobial susceptibilities is lagging, and genomic characterization of P. multocida strains associated with porcine lower respiratory disease is internationally scarce. This study utilized high-throughput robotics to phenotypically and genetically characterize an industry-wide collection of 252 clinical P. multocida isolates that were recovered between 2014 and 2019. Overall, antimicrobial resistance was found to be low, with clinical resistance below 1% for all tested antimicrobials except those from the tetracycline class. Five dominant sequence types, representing 64.8% of all isolates, were identified; they were disseminated across farms and had previously been detected in various animal hosts and countries. P. multocida in Australian farms remain controllable via current antimicrobial therapeutic protocols. The identification of highly dominant, interspecies-infecting strains provides insight into the epidemiology of the opportunistic pathogen, and it highlights a biosecurity threat to the Australian livestock industry. IMPORTANCE Pasteurellosis is rated by the World Animal Health Organisation (OIE) as a high-impact disease in livestock. Although it is well understood in many host-disease contexts, our understanding of the organism in porcine respiratory disease is limited. Given its high frequency of involvement in porcine respiratory disease complex (PRDC), it is important that we are aware of its antimicrobial susceptibilities so that we can respond quickly and appropriately with antimicrobial therapy. Genetic insights about the organism can help us to better understand its epidemiology and inform our biosecurity practices and prophylactic management.

Primer ID ultra-deep sequencing reveals dynamics of drug resistance-associated variants in breakthrough hepatitis C viruses: relevance to treatment outcome and resistance screening.

BACKGROUND: Use of direct-acting antiviral drugs (DAAs) that target HCV may be hampered by the rapid selection of viral strains that harbour drug resistance-associated variants (RAVs). These RAVs are often associated with a fitness cost and tend to occur on low-frequency strains within treatment-naive subjects. To address the clinical relevance of low frequency RAVs in the setting of DAAs, this study utilized a Primer ID ultra-deep sequencing approach to mitigate PCR errors and bias to accurately quantify viral sequences in subjects that failed DAA treatment. METHODS: Subjects were enrolled in the follow-up study P05063, and had previous treatment with boceprevir and all had detectable RAVs at virological failure (VF) based on Sanger-based population sequencing. Twelve subjects had three time points available: baseline, VF and follow-up (median 830.5 days). Viral RNA was amplified using unique primer identifiers (Primer IDs) and sequenced using 454 ultra-deep sequencing. RESULTS: The sequencing strategy used in this study improved the detection of clinically relevant low frequency strains bearing RAVs compared to population sequencing and showed that these strains can persist for up to 2 years post-treatment failure. Strains carrying multiple RAVs were common in breakthrough viruses. Putative compensatory mutations were identified. CONCLUSIONS: The Primer ID ultra-deep sequencing approach identifies RAVs that can reduce drug sensitivity at levels below the detection threshold for population sequencing. The approach also removes PCR errors and biases, suggesting this sequencing strategy should become the standard approach by which to perform temporal quasispecies studies and resistance screening. ClinicalTrials.gov NCT00689390.