f Serotyping forms the basis of national and international surveillance networks for Salmonella, one of the most prevalent foodborne pathogens worldwide (1-3). Public health microbiology is currently being transformed by whole-genome sequencing (WGS), which opens the door to serotype determination using WGS data. SeqSero (www.denglab.info/SeqSero) is a novel Webbased tool for determining Salmonella serotypes using high-throughput genome sequencing data. SeqSero is based on curated databases of Salmonella serotype determinants (rfb gene cluster, fliC and fljB alleles) and is predicted to determine serotype rapidly and accurately for nearly the full spectrum of Salmonella serotypes (more than 2,300 serotypes), from both raw sequencing reads and genome assemblies. The performance of SeqSero was evaluated by testing (i) raw reads from genomes of 308 Salmonella isolates of known serotype; (ii) raw reads from genomes of 3,306 Salmonella isolates sequenced and made publicly available by GenomeTrakr, a U.S. national monitoring network operated by the Food and Drug Administration; and (iii) 354 other publicly available draft or complete Salmonella genomes. We also demonstrated Salmonella serotype determination from raw sequencing reads of fecal metagenomes from mice orally infected with this pathogen. SeqSero can help to maintain the well-established utility of Salmonella serotyping when integrated into a platform of WGS-based pathogen subtyping and characterization. Salmonella is the most prevalent foodborne pathogen in the United States, causing 1.2 million cases of illness annually and the largest health burden among all bacterial pathogens (4). The U.S. National Salmonella Surveillance System has been built upon serotyping in public health laboratories, a subtyping method traditionally performed through the agglutination of Salmonella cells with specific antisera that detect lipopolysaccharide O antigen and flagellar H antigens. Specific combinations of O and H antigenic types represent serotypes (or serovars). More than 2,500 Salmonella serotypes have been described in the White-Kauffmann-Le Minor scheme (5, 6). The phenotypic determination of serotypes is labor-intensive and time-consuming (taking at least 2 days), which has led to the development of genetic methods for serotype determination (7,8). These methods generally use two categories of targets for serotype determination: (i) indirect targets, requiring the use of random surrogate genomic markers associated with particular serotypes, and (ii) direct targets, requiring the use of genetic determinants of serotypes, including the rfb gene cluster responsible for somatic (O) group synthesis (9, 10) and the fliC (11) and fljB (12) genes encoding the two flagellar antigens present in Salmonella. The latter approach has the advantage of determining serotypes using the same markers as the phenotypic method, providing continuity between the serotypes determined by phenotypic and genetic markers (13,14). While this approach may result in distinct genetic lineages bei...

SeqSero, launched in 2015, is a software tool for Salmonella serotype determination from whole-genome sequencing (WGS) data. Despite its routine use in public health and food safety laboratories in the United States and other countries, the original SeqSero pipeline is relatively slow (minutes per genome using sequencing reads), is not optimized for draft genome assemblies, and may assign multiple serotypes for a strain. Here, we present SeqSero2 (github.com/denglab/SeqSero2; denglab.info/SeqSero2), an algorithmic transformation and functional update of the original SeqSero. Major improvements include (i) additional sequence markers for identification of Salmonella species and subspecies and certain serotypes, (ii) a k-mer based algorithm for rapid serotype prediction from raw reads (seconds per genome) and improved serotype prediction from assemblies, and (iii) a targeted assembly approach for specific retrieval of serotype determinants from WGS for serotype prediction, new allele discovery, and prediction troubleshooting. Evaluated using 5,794 genomes representing 364 common U.S. serotypes, including 2,280 human isolates of 117 serotypes from the National Antimicrobial Resistance Monitoring System, SeqSero2 is up to 50 times faster than the original SeqSero while maintaining equivalent accuracy for raw reads and substantially improving accuracy for assemblies. SeqSero2 further suggested that 3% of the tested genomes contained reads from multiple serotypes, indicating a use for contamination detection. In addition to short reads, SeqSero2 demonstrated potential for accurate and rapid serotype prediction directly from long nanopore reads despite base call errors. Testing of 40 nanopore-sequenced genomes of 17 serotypes yielded a single H antigen misidentification. IMPORTANCE Serotyping is the basis of public health surveillance of Salmonella. It remains a first-line subtyping method even as surveillance continues to be transformed by whole-genome sequencing. SeqSero allows the integration of Salmonella serotyping into a whole-genome-sequencing-based laboratory workflow while maintaining continuity with the classic serotyping scheme. SeqSero2, informed by extensive testing and application of SeqSero in the United States and other countries, incorporates important improvements and updates that further strengthen its application in routine and large-scale surveillance of Salmonella by whole-genome sequencing.

The development of new materials/structures for efficient electrocatalytic water oxidation, which is a key reaction in realizing artificial photosynthesis, is an ongoing challenge. Herein, a Co(OH)F material as a new electrocatalyst for the oxygen evolution reaction (OER) is reported. The as-prepared 3D Co(OH)F microspheres are built by 2D nanoflake building blocks, which are further woven by 1D nanorod foundations. Weaving and building the substructures (1D nanorods and 2D nanoflakes) provides high structural void porosity with sufficient interior space in the resulting 3D material. The hierarchical structure of this Co(OH)F material combines the merits of all material dimensions in heterogeneous catalysis. The anisotropic low-dimensional (1D and 2D) substructures possess the advantages of a high surface-to-volume ratio and fast charge transport. The interconnectivity of the nanorods is also beneficial for charge transport. The high-dimensional (3D) architecture results in sufficient active sites per the projected electrode surface area and is favorable for efficient mass diffusion during catalysis. A low overpotential of 313 mV is required to drive an OER current density of 10 mA cm on a simple glassy carbon (GC) working electrode in a 1.0 m KOH aqueous solution.

In the past decade, the number of publicly available bacterial genomes has increased dramatically. These genomes have been generated for impactful initiatives, especially in the field of genomic epidemiology (Brown, Dessai, McGarry, & Gerner-Smidt, 2019; Timme et al., 2017). Genomes are sequenced, shared publicly, and subsequently analyzed for phylogenetic relatedness. If two genomes of epidemiological interest are found to be related, further investigation might be prompted. However, comparing the multitudes of genomes for phylogenetic relatedness is computationally expensive and, with large numbers, laborious. Consequently, there are many strategies to reduce the complexity of the data for downstream analysis, especially using nucleotide stretches of length k (kmers).

Increasingly, routine surveillance and monitoring of foodborne pathogens using whole-genome sequencing is creating opportunities to study foodborne illness epidemiology beyond routine outbreak investigations and case–control studies. Using a global phylogeny of Salmonella enterica serotype Typhimurium, we found that major livestock sources of the pathogen in the United States can be predicted through whole-genome sequencing data. Relatively steady rates of sequence divergence in livestock lineages enabled the inference of their recent origins. Elevated accumulation of lineage-specific pseudogenes after divergence from generalist populations and possible metabolic acclimation in a representative swine isolate indicates possible emergence of host adaptation. We developed and retrospectively applied a machine learning Random Forest classifier for genomic source prediction of Salmonella Typhimurium that correctly attributed 7 of 8 major zoonotic outbreaks in the United States during 1998–2013. We further identified 50 key genetic features that were sufficient for robust livestock source prediction.