Next-generation sequencing capacity and capabilities within the National Animal Health Laboratory Network

With the cost of next-generation sequencing (NGS) decreasing, this technology is rapidly being integrated into the workflows of veterinary clinical and diagnostic laboratories nationwide. The mission of the U.S. Department of Agriculture-National Animal Health Laboratory Network (NAHLN) is in part to evaluate new technologies and develop standardized processes for deploying these technologies to network laboratories for improving detection and response to emerging and foreign animal diseases. Thus, in 2018, the NAHLN identified the integration of NGS into the network as a top priority. In order to assess the current state of preparedness across NAHLN laboratories and to identify which have the capability for performing NGS, a questionnaire was developed by the NAHLN Methods Technical Working Group and submitted to all NAHLN laboratories in December 2018. Thirty of 59 laboratories completed the questionnaire, of which 18 (60%) reported having some sequencing capability. Multiple sequencing platforms and reagents were identified, and limited standardized quality control parameters were reported. Our results confirm that NGS capacity is available within the NAHLN, but several gaps remain. Gaps include not having sufficient personnel trained in bioinformatics and data interpretation, lack of standardized methods and equipment, and maintenance of sufficient computing capacity to meet the growing demand for this technology

Metagenomic Sequencing for Identification of Xylella fastidiosa from Leaf Samples

International audienceXylella fastidiosa (Xf) is a globally distributed plant-pathogenic bacterium. The primary control strategy for Xf diseases is eradicating infected plants; therefore, timely and accurate detection is necessary to prevent crop losses and further pathogen dispersal. Conventional Xf diagnostics primarily relies on quantitative PCR (qPCR) assays. However, these methods do not consider new or emerging variants due to pathogen genetic recombination and sensitivity limitations. We developed and tested a metage-nomics pipeline using in-house short-read sequencing as a complementary approach for affordable, fast, and highly accurate Xf detection. We used metagenomics to identify Xf to the strain level in single-and mixed-infected plant samples at concentrations as low as 1 pg of bacterial DNA per gram of tissue. We also tested naturally infected samples from various plant species originating from Europe and the United States. We identified Xf sub-species in samples previously considered inconclusive with real-time PCR (quantification cycle [C-q], > 35). Overall, we showed the versatility of the pipeline by using different plant hosts and DNA extraction methods. Our pipeline provides taxonomic and functional infor-mation for Xf diagnostics without extensive knowledge of the disease. This pipeline demonstrates that metagenomics can be used for early detection of Xf and incorporated as a tool to inform disease management strategies.IMPORTANCE Destructive Xylella fastidiosa (Xf) outbreaks in Europe highlight this pathogen's capacity to expand its host range and geographical distribution. The current disease diag-nostic approaches are limited by a multiple-step process, biases to known sequences, and detection limits. We developed a low-cost, user-friendly metagenomic sequencing tool for Xf detection. In less than 3 days, we were able to identify Xf subspecies and strains in field-collected samples. Overall, our pipeline is a diagnostics tool that could be easily extended to other plant-pathogen interactions and implemented for emerging plant threat surveillance

Enhancing the one health initiative by using whole genome sequencing to monitor antimicrobial resistance of animal pathogens: Vet-LIRN collaborative project with veterinary diagnostic laboratories in United States and Canada

BACKGROUND: Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs). RESULTS: We found that most Salmonella isolates (46/69, 67%) had no known resistance genes. Several isolates from both food and companion animals, however, showed genetic relatedness to isolates from humans. For pathogenic E. coli, no resistance genes were identified in 60% (38/63) of the isolates. Diverse resistance patterns were observed, and one of the isolates had predicted resistance to fluoroquinolones and cephalosporins, important antibiotics in human and veterinary medicine. For S. pseudintermedius, we observed a bimodal distribution of resistance genes, with some isolates having a diverse array of resistance mechanisms, including the mecA gene (19/60, 32%). CONCLUSION: The findings from this study highlight the critical importance of veterinary diagnostic laboratory data as part of any national antimicrobial resistance surveillance program. The finding of some highly resistant bacteria from companion animals, and the observation of isolates related to those isolated from humans demonstrates the public health significance of incorporating companion animal data into surveillance systems. Vet-LIRN will continue to build the infrastructure to collect the data necessary to perform surveillance of resistant bacteria as part of fulfilling its mission to advance human and animal health. A One Health approach to AMR surveillance programs is crucial and must include data from humans, animals, and environmental sources to be effective

Enhancing the one health initiative by using whole genome sequencing to monitor antimicrobial resistance of animal pathogens: Vet-LIRN collaborative project with veterinary diagnostic laboratories in United States and Canada

Abstract Background Antimicrobial resistance (AMR) of bacterial pathogens is an emerging public health threat. This threat extends to pets as it also compromises our ability to treat their infections. Surveillance programs in the United States have traditionally focused on collecting data from food animals, foods, and people. The Veterinary Laboratory Investigation and Response Network (Vet-LIRN), a national network of 45 veterinary diagnostic laboratories, tested the antimicrobial susceptibility of clinically relevant bacterial isolates from animals, with companion animal species represented for the first time in a monitoring program. During 2017, we systematically collected and tested 1968 isolates. To identify genetic determinants associated with AMR and the potential genetic relatedness of animal and human strains, whole genome sequencing (WGS) was performed on 192 isolates: 69 Salmonella enterica (all animal sources), 63 Escherichia coli (dogs), and 60 Staphylococcus pseudintermedius (dogs). Results We found that most Salmonella isolates (46/69, 67%) had no known resistance genes. Several isolates from both food and companion animals, however, showed genetic relatedness to isolates from humans. For pathogenic E. coli, no resistance genes were identified in 60% (38/63) of the isolates. Diverse resistance patterns were observed, and one of the isolates had predicted resistance to fluoroquinolones and cephalosporins, important antibiotics in human and veterinary medicine. For S. pseudintermedius, we observed a bimodal distribution of resistance genes, with some isolates having a diverse array of resistance mechanisms, including the mecA gene (19/60, 32%). Conclusion The findings from this study highlight the critical importance of veterinary diagnostic laboratory data as part of any national antimicrobial resistance surveillance program. The finding of some highly resistant bacteria from companion animals, and the observation of isolates related to those isolated from humans demonstrates the public health significance of incorporating companion animal data into surveillance systems. Vet-LIRN will continue to build the infrastructure to collect the data necessary to perform surveillance of resistant bacteria as part of fulfilling its mission to advance human and animal health. A One Health approach to AMR surveillance programs is crucial and must include data from humans, animals, and environmental sources to be effective