Prospective Genomic Surveillance Reveals Cryptic MRSA Outbreaks with Local to International Origins among NICU Patients.

Methicillin-resistant Staphylococcus aureus (MRSA) infections cause substantive morbidity and mortality in neonates. Using publicly available resources from the National Center of Biotechnology Information (NCBI) and Food and Drug Administration's (FDA) GalaxyTrakr pipeline, we illustrate the dynamics of MRSA colonization and infection in neonates. Over 217 days of prospective surveillance, analyses revealed concurrent MRSA transmission chains affecting 11 of 17 MRSA-colonized patients (65%), with two clusters that demonstrated intervals of more than a month among the appearance of isolates. All MRSA infected neonates (n = 3) showed previous colonization with the infecting strain. GalaxyTrakr clustering of the NICU strains, in the context of 21,521 international isolates deposited in NCBI's Pathogen Detection Resource, revealed NICU isolates to be distinct from adult MRSA strains seen locally and internationally. Clustering of the NICU strains within an international context enhanced the resolution of strain clusters and supported the rule-out of suspected, local transmission events within the NICU. Analyses also identified sequence type 1535 isolates, emergent in the Middle East, carrying a unique SCCmec with fusC and aac(6')-Ie/aph(2'')-1a that provided a multidrug-resistant phenotype. NICU genomic pathogen surveillance, leveraging public repositories and outbreak detection tools, supports rapid identification of cryptic MRSA clusters, and can inform infection prevention interventions for this vulnerable patient population. Results demonstrate that sporadic infections in the NICU may be indicative of hidden chains of asymptomatic transmission best identified with sequenced-based approaches.

Determination of Genomic Epidemiology of Historical Clostridium perfringens Outbreaks in New York State by Use of Two Web-Based Platforms: National Center for Biotechnology Information Pathogen Detection and FDA GalaxyTrakr.

Clostridium perfringens is the second leading cause of bacterial foodborne illness in the United States. The Wadsworth Center (WC) at the New York State Department of Health enumerates infectious dose from primary patient and food samples and, until recently, identified C. perfringens to the species level only. We investigated whether whole-genome sequence-based subtyping could benefit epidemiological investigations of this pathogen, as it has with other enteric organisms. We retrospectively sequenced 76 patient and food samples received between May 2010 and February 2020, including 52 samples linked epidemiologically to 13 outbreaks and 24 sporadic samples not linked to other samples. Phylogenetic trees were built using two Web-based platforms: National Centers for Biotechnology Information Pathogen Detection (NCBI-PD) and GalaxyTrakr (a Galaxy instance supported by the GenomeTrakr initiative). For GalaxyTrakr analyses, single nucleotide polymorphism (SNP) matrices and maximum-likelihood (ML) trees were generated using 3 different reference genomes. Across the four separate analyses, phylogenetic clustering was generally concordant with epidemiologically identified outbreaks. SNP diversity among phylogenetically linked samples from an outbreak ranged from 0 to 20 SNPs, excepting one outbreak ranging from 4 to 62 SNPs. Importantly, four of the 13 outbreak isolates harbored one or more samples that were phylogenetic outliers, and for two outbreaks, no samples were closely related. Two specimens were found harboring two distinct genotypes. For samples below CDC enumeration dose threshold, phylogenetic clustering was robust and linked patient and/or food samples. We concluded that WGS phylogenetic clusters (i) are largely concordant with epidemiologically defined outbreaks, irrespective of analysis platform or reference genome we employed; (ii) have limited pairwise SNP diversity, allowing phylogenetic clusters to be distinguished from sporadic cases; and (iii) can aid in epidemiological investigations by identifying outlier and polyclonal samples.

Optimizing open data to support one health: best practices to ensure interoperability of genomic data from bacterial pathogens.

The holistic approach of One Health, which sees human, animal, plant, and environmental health as a unit, rather than discrete parts, requires not only interdisciplinary cooperation, but standardized methods for communicating and archiving data, enabling participants to easily share what they have learned and allow others to build upon their findings. Ongoing work by NCBI and the GenomeTrakr project illustrates how open data platforms can help meet the needs of federal and state regulators, public health laboratories, departments of agriculture, and universities. Here we describe how microbial pathogen surveillance can be transformed by having an open access database along with Best Practices for contributors to follow. First, we describe the open pathogen surveillance framework, hosted on the NCBI platform. We cover the current community standards for WGS quality, provide an SOP for assessing your own sequence quality and recommend QC thresholds for all submitters to follow. We then provide an overview of NCBI data submission along with step by step details. And finally, we provide curation guidance and an SOP for keeping your public data current within the database. These Best Practices can be models for other open data projects, thereby advancing the One Health goals of Findable, Accessible, Interoperable and Re-usable (FAIR) data.