Assessing avian influenza surveillance intensity in wild birds using a One Health lens.

Wildlife disease surveillance, particularly for pathogens with zoonotic potential such as Highly Pathogenic Avian Influenza Virus (HPAIV), is critical to facilitate situational awareness, inform risk, and guide communication and response efforts within a One Health framework. This study evaluates the intensity of avian influenza virus (AIV) surveillance in Ontario's wild bird population following the 2021 H5N1 incursion into Canada. Analyzing 2562 samples collected between November 1, 2021, and October 31, 2022, in Ontario, Canada, we identify spatial variations in surveillance intensity relative to human population density, poultry facility density, and wild mallard abundance. Using the spatial scan statistic, we pinpoint areas where public engagement, collaborations with Indigenous and non-Indigenous hunter/harvesters, and working with poultry producers, could augment Ontario's AIV wild bird surveillance program. Enhanced surveillance at these human-domestic animal-wildlife interfaces is a crucial element of a One Health approach to AIV surveillance. Ongoing assessment of our wild bird surveillance programs is essential for strategic planning and will allow us to refine approaches and generate results that continue to support the program's overarching objective of safeguarding the health of people, animals, and ecosystems.

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database.

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

Optimal Selection of Sampling Points within Sewer Networks for Wastewater-Based Epidemiology Applications.

Wastewater-based epidemiology (WBE) has great potential to monitor community public health, especially during pandemics. However, it faces substantial hurdles in pathogen surveillance through WBE, encompassing data representativeness, spatiotemporal variability, population estimates, pathogen decay, and environmental factors. This paper aims to enhance the reliability of WBE data, especially for early outbreak detection and improved sampling strategies within sewer networks. The tool implemented in this paper combines a monitoring model and an optimization model to facilitate the optimal selection of sampling points within sewer networks. The monitoring model utilizes parameters such as feces density and average water consumption to define the detectability of the virus that needs to be monitored. This allows for standardization and simplicity in the process of moving from the analysis of wastewater samples to the identification of infection in the source area. The entropy-based model can select optimal sampling points in a sewer network to obtain the most specific information at a minimum cost. The practicality of our tool is validated using data from Hildesheim, Germany, employing SARS-CoV-2 as a pilot pathogen. It is important to note that the tool's versatility empowers its extension to monitor other pathogens in the future.

Genome-Based Characterization of Listeria monocytogenes, Costa Rica.

Genomic data on the foodborne pathogen Listeria monocytogenes from Central America are scarce. We analyzed 92 isolates collected during 2009-2019 from different regions in Costa Rica, compared those to publicly available genomes, and identified unrecognized outbreaks. Our findings suggest mandatory reporting of listeriosis in Costa Rica would improve pathogen surveillance.

Impact of the Application of Gaseous Ozone on Selected Pathogens Found in Animal Shelters and Other Facilities.

Correctly selecting disinfection procedures is crucial in facilities housing a high number of animals as it directly affects their health. The aim of this study was to verify the virucidal effect of gaseous ozone delivered by commercially available generators under controlled experimental conditions on a selection of viral pathogens (feline coronavirus, canine coronavirus, feline calicivirus, feline parvovirus) commonly found in shelters and other facilities. Two ozone generators with outputs of 3.5 g/h and 20 g/h were used to produce ozone. Virus viability after the application of ozone was evaluated by examining for typical pathogen-specific cytopathic effects on the CRFK (Crandell-Rees Feline Kidney) cell line post-incubation. No cytopathic effect was observed in feline coronavirus after the 2-h application of ozone; in canine coronavirus, the absence of a cytopathic effect was observed after the 4-h application of ozone. The absence of a cytopathic effect in feline calicivirus was observed after the 6-h application of ozone; the viability of feline parvovirus was not impaired even by the 6-h application of ozone. The results of the study confirm lower resistance to the application of gaseous ozone in enveloped viruses.

Assessment of the prevalence, serotype, and antibiotic resistance pattern of Salmonella enterica in integrated farming systems in the Maryland-DC area.

Implementation of organic/pasture farming practices has been increasing in the USA regardless of official certification. These practices have created an increasingly growing demand for marketing safe products which are produced through these systems. Products from these farming systems have been reported to be at greater risk of transmitting foodborne pathogens because of current trends in their practices. Salmonella enterica (SE) is a ubiquitous foodborne pathogen that remains a public health issue given its prevalence in various food products, but also in the environment and as part of the microbial flora of many domestic animals. Monitoring antibiotic resistance and identifying potential sources contamination are increasingly important given the growing trend of organic/pasture markets. This study aimed to quantify prevalence of SE at the pre- and post-harvest levels of various integrated farms and sites in Maryland-Washington D.C. area, as well as identify the most prevalent serovars and antibiotic resistance patterns. Samples from various elements within the farm environment were collected and screened for SE through culture and molecular techniques, which served to identify and serotype SE, using species and serovar-specific primers, while antibiotic resistance was evaluated using an antibiogram assay. Results showed a prevalence of 7.80% of SE pre-harvest and 1.91% post-harvest. These results also showed the main sources of contamination to be soil (2.17%), grass (1.28%), feces (1.42%) and unprocessed produce (1.48%). The most commonly identified serovar was Typhimurium (11.32%) at the pre-harvest level, while the only identified serovar from post-harvest samples was Montevideo (4.35%). With respect to antibiotic resistance, out of the 13 clinically relevant antibiotics tested, gentamycin and kanamycin were the most effective, demonstrating 78.93 and 76.40% of isolates, respectively, to be susceptible. However, ampicillin, amoxicillin and cephradine had the lowest number of susceptible isolates with them being 10.95, 12.36, and 9.83%, respectively. These results help inform farms striving to implement organic practices on how to produce safer products by recognizing areas that pose greater risks as potential sources of contamination, in addition to identifying serotypes of interest, while also showcasing the current state of antibiotic efficacy and how this can influence antibiotic resistance trends in the future.

Aquaculture bacterial pathogen database: Pathogen monitoring and screening in coastal waters using environmental DNA.

Increasingly diverse pathogen occurrence in coastal and mariculture areas demands improved monitoring platforms to prevent economic and public health implications. Accessible databases with up-to-date knowledge and taxonomy are critical for detecting and screening environmental pathogens. Condensed from over 3000 relevant reports in peer reviewed articles, we constructed an aquaculture bacterial pathogen database that provides specialized curation of over 210 bacterial pathogenic species impacting aquaculture. Application of the aquaculture bacterial pathogen database to environmental DNA metabarcoding monitoring data in Hong Kong coastal and mariculture waters effectively characterized regional pathogen profiles over a one-year period and improved identification of new potential pathogen targets. The results highlighted the increase in potential pathogen abundance related to aquaculture activity and the associated inorganic nitrogen load, which was chiefly due to the enrichment of Vibrio during the atypical dry winter season. The value of the aquaculture bacterial pathogen database for empowering environmental DNA-based approaches in coastal marine pathogen surveillance benefits water resource management and aquaculture development on a global scale.

Ticks and Tick-Borne Pathogens from Wild Pigs in Northern and Central Florida.

Invasive wild pigs are distributed across much of the U.S. and are hosts to tick vectors of human disease. Herein, adult ticks were collected from 157 wild pigs in 21 northern and central Florida counties from 2019-2020 during removal efforts by USDA-APHIS Wildlife Services personnel and evaluated for their potential to be used as a method of tick-borne disease surveillance. Collected ticks were identified, screened for pathogens, and the effects of landscape metrics on tick community composition and abundance were investigated. A total of 1415 adult ticks of four species were collected. The diversity of tick species collected from wild pigs was comparable to collections made throughout the state with conventional surveillance methods. All species collected have implications for pathogen transmission to humans and other animals. Ehrlichia, Anaplasma-like, and Rickettsia spp. were detected in ticks collected from wild pigs. These results suggest that tick collection from wild pigs is a suitable means of surveillance for pathogens and vectors. The strongest drivers of variation in tick community composition were the developed open space and mixed forest landcover classes. Fragmented shrub/scrub habitat was associated with increased tick abundance. Similar models may be useful in predicting tick abundance and distribution patterns.

Whole-genome sequencing of Shigella for surveillance purposes shows (inter)national relatedness and multidrug resistance in isolates from men who have sex with men.

In the Netherlands, more than half of domestic shigellosis cases are among men who have sex with men (MSM), particularly in the Amsterdam region. However, there is limited insight into which Shigella strains circulate in the Netherlands. Our objective was to assess the added value of whole-genome sequencing (WGS)-based surveillance for Shigella. To this end, we determined the relatedness among Shigella spp. isolates from patients in the Amsterdam region, as well as in an international context, including antimicrobial resistance markers, using WGS. The following criteria were used: it should provide insight into (1) clustering of shigellosis cases and the affected population, (2) the extent of admixture of MSM-associated isolates with those from the broader population and (3) the presence of antimicrobial resistance. It should then lead to more opportunities for targeted control measures. For this study, Shigella isolates from three laboratories in the Amsterdam region obtained between February 2019 and October 2021 were subjected to Illumina WGS at the National Institute for Public Health and the Environment (RIVM). Raw data were quality-checked and assembled, the Shigella serotype was determined with ShigaTyper, and antimicrobial resistance markers were detected using ResFinder and PointFinder. For Shigella sonnei, subclades were determined using Mykrobe. Relatedness of isolates, including 21 international reference genomes, was assessed with core genome multilocus sequence typing. In total, 109 isolates were included, of which 27 were from females (25 %) and 66 were from males (61 %), with which the majority (n=48, 73 %) being from MSM. No information on sex was available for the remaining 16 cases. The WGS data for all isolates, comprising 55 S. sonnei, 52 Shigella flexneri, 1 Shigella boydii and 1 Shigella dysenteriae, met the quality criteria. In total, 14 clusters containing 51 isolates (49 %) were identified, with a median cluster size of 2.5 cases (range: 2-15). Nine out of 14 clusters were MSM-associated, and 8 clusters (57 %) were travel-related. Six of the MSM clusters were related to international reference genomes. The prevalence of antimicrobial resistance markers was higher among isolates from MSM than non-MSM patients, particularly for ciprofloxacin (89 vs 33 %) and azithromycin (58 vs 17 %). In conclusion, about half of Shigella spp. patients were part of a cluster, of which a substantial part were related to international reference genomes, particularly among MSM, and a high prevalence of antimicrobial resistance markers was found. These findings indicate widespread international circulation of Shigella spp., particularly among MSM, with multidrug resistance that hampers treatment of patients. Moreover, the results of this study led to the implementation of a national WGS-based laboratory surveillance programme for Shigella spp. that started in April 2022.

Analysis of influenza surveillance results in Jingzhou， Hubei Province from 2016 to 2021 / 上海预防医学

ObjectiveTo understand the epidemiological characteristics of influenza in Jingzhou from 2016 to 2021， so as to provide scientific evidence for the formulation of influenza prevention and control policies in this region， and effectively protect people's health. MethodsData of influenza-like illness （ILI） and pathogen surveillance in Jingzhou during 2016‒2021 were collected and statistically analyzed. ResultsA total of 46 272 ILI cases were reported from two hospitals in Jingzhou City from 2016 to 2021. The difference in the constituent ratio of ILI was statistically significant among different age groups （P<0.05）. A total of 12 812 specimens were collected from two hospitals for influenza surveillance. A total of 1 513 cases were RNA positive，and the positive rate of influenza virus nucleic acid detection was 11.81％. The RNA positive specimens were mainly B （Victoria）， accounting for 39.33％. There were statistically significant differences in the positive rate of influenza virus nucleic acids and different types of influenza virus nucleic acids among different years （P<0.05）. ConclusionThe influenza epidemic in Jingzhou peaks in winter and spring， and the new A （H1），A （H3）， B （Victoria） and B （Yamagata） types alternate and mixed epidemics dominate.

Surveillance trends for SARS-CoV-2 and other respiratory pathogens among U.S. military health system beneficiaries, 27 September 2020­2 October 2021

Laboratory-based respiratory pathogen surveillance for SARS-CoV-2 and other respiratory pathogens was conducted in the 2020-2021 surveillance season among U.S. Military Health System (MHS) beneficiaries through the Department of Defense Global Respiratory Pathogen Surveillance Program (DoDGRPSP). Sentinel and participating sites submitted 96,660 specimens for clinical diagnostic testing. A total of 12,282 SARS-CoV-2 positive cases were identified, and 7,286 of the associated viruses were successfully sequenced. Two overlapping waves of SARS-CoV-2 activity were observed during the season. The B.1.1.7 (Alpha) lineage was dominant during February 2021 through May 2021. By July 2021, and continuing through the rest of the season, B.1.617.2/AY.x (Delta) lineage predominated and by September 2021 comprised 100% of identified SARS-CoV-2 lineages. The emergence of SARS-CoV-2 coincided with substantial reductions in the circulation of seasonal influenza viruses and most other non-SARS-CoV-2 respiratory pathogens. A total of 4,426 non-SARS-CoV-2 respiratory pathogens were identified, including 71 influenza. Of the 71 influenza positives, 64 were successfully sequenced. The majority of influenza strains sequenced belonged to influenza A(H3N2) clades 3C.2a1b.2a2. The most common non-SARSCoV-2 respiratory pathogen detected was rhinovirus/enterovirus (n=3,058).

High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when using surveillance data.

Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. However, we lack a systematic understanding of (i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models (ii) what types of biases may be associated with these conclusions and (iii) how explicitly invoking causal or mechanistic modelling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference about pathogen persistence and spillover in multihost-parasite systems, with clear implications for human and wildlife health. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed multihost-parasite models to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model-driven inference of maintenance species can minimize and eliminate emergent biases. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modelling. Using our model, we demonstrate how the community, spatial and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model-driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. Model-driven inference is an essential, yet underused, component of multidisciplinary studies that make inference about host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.

Virus-virus interactions of febrile respiratory syndrome among patients in China based on surveillance data from February 2011 to December 2020.

The burden of acute respiratory infections is still considerable, and virus-virus interactions may affect their epidemics, but previous evidence is inconclusive. To quantitatively investigate the interactions among respiratory viruses at both the population and individual levels, we use data from the pathogen surveillance for febrile respiratory syndrome (FRS) in China from February 2011 to December 2020. Cases tested for influenza virus (IV), respiratory syncytial virus (RSV), human parainfluenza virus (PIV), human Adenovirus (AdV), human coronavirus (CoV), human bocavirus (BoV), and rhinovirus (RV) were collected. We used spearman's rank correlation coefficients and binary logistic regression models to analyze the interactions between any two of the viruses at the population and individual levels, respectively. Among 120 237 cases, 4.5% were coinfected with two or more viruses. Correlation coefficients showed seven virus pairs were positively correlated, namely: IV and RSV, PIV and AdV, PIV and CoV, PIV and BoV, PIV and RV, AdV and BoV, and CoV and RV. Regression models showed positive interactions for all virus pairs, except for the negative interaction between IV and RV (odds ratio = 0.70, 95% confidence interval: 0.61-0.81). Most of the respiratory viruses interact positively, while IV and RV interact negatively.

Discovering disease-causing pathogens in resource-scarce Southeast Asia using a global metagenomic pathogen monitoring system.

SignificanceMetagenomic pathogen sequencing offers an unbiased approach to characterizing febrile illness. In resource-scarce settings with high biodiversity, it is critical to identify disease-causing pathogens in order to understand burden and to prioritize efforts for control. Here, metagenomic next-generation sequencing (mNGS) characterization of the pathogen landscape in Cambodia revealed diverse vector-borne and zoonotic pathogens irrespective of age and gender as risk factors. Identification of key pathogens led to changes in national program surveillance. This study is a "real world" example of the use of mNGS surveillance of febrile individuals, executed in-country, to identify outbreaks of vector-borne, zoonotic, and other emerging pathogens in a resource-scarce setting.

Longitudinal analysis of SARS-CoV-2 spike and RNA-dependent RNA polymerase protein sequences reveals the emergence and geographic distribution of diverse mutations.

Amid the ongoing COVID-19 pandemic, it has become increasingly important to monitor the mutations that arise in the SARS-CoV-2 virus, to prepare public health strategies and guide the further development of vaccines and therapeutics. The spike (S) protein and the proteins comprising the RNA-Dependent RNA Polymerase (RdRP) are key vaccine and drug targets, respectively, making mutation surveillance of these proteins of great importance. Full protein sequences were downloaded from the GISAID database, aligned, and the variants identified. 437,006 unique viral genomes were analyzed. Polymorphisms in the protein sequence were investigated and examined longitudinally to identify sequence and strain variants appearing between January 5th, 2020 and January 16th, 2021. A structural analysis was also performed to investigate mutations in the receptor binding domain and the N-terminal domain of the spike protein. Within the spike protein, there were 766 unique mutations observed in the N-terminal domain and 360 in the receptor binding domain. Four residues that directly contact ACE2 were mutated in more than 100 sequences, including positions K417, Y453, S494, and N501. Within the furin cleavage site of the spike protein, a high degree of conservation was observed, but the P681H mutation was observed in 10.47% of sequences analyzed. Within the RNA dependent RNA polymerase complex proteins, 327 unique mutations were observed in Nsp8, 166 unique mutations were observed in Nsp7, and 1157 unique mutations were observed in Nsp12. Only 4 sequences analyzed contained mutations in the 9 residues that directly interact with the therapeutic Remdesivir, suggesting limited mutations in drug interacting residues. The identification of new variants emphasizes the need for further study on the effects of the mutations and the implications of increased prevalence, particularly for vaccine or therapeutic efficacy.

Quantifying human-animal contact rates in Malaysian Borneo: Influence of agricultural landscapes on contact with potential zoonotic disease reservoirs.

Changing landscapes across the globe, but particularly in Southeast Asia, are pushing humans and animals closer together and may increase the likelihood of zoonotic spillover events. Malaysian Borneo is hypothesized to be at high risk of spillover events due to proximity between reservoir species and humans caused by recent deforestation in the region. However, the relationship between landscape and human-animal contact rates has yet to be quantified. An environmentally stratified cross-sectional survey was conducted in Sabah, Malaysia in 2015, collecting geolocated questionnaire data on potential risk factors for contact with animals for 10,100 individuals. 51% of individuals reported contact with poultry, 46% with NHPs, 30% with bats, and 2% with swine. Generalised linear mixed models identified occupational and demographic factors associated with increased contact with these species, which varied when comparing wildlife to domesticated animals. Reported contact rates with each animal group were integrated with remote sensing-derived environmental data within a Bayesian framework to identify regions with high probabilities of contact with animal reservoirs. We have identified high spatial heterogeneity of contact with animals and clear associations between agricultural practices and high animal rates. This approach will help inform public health campaigns in at-risk populations and can improve pathogen surveillance efforts on Malaysian Borneo. This method can additionally serve as a framework for researchers looking to identify targets for future pathogen detection in a chosen region of study.

Application of nanomaterial-based biosensors in airborne pathogen surveillance / 中华微生物学和免疫学杂志

COVID-19 is a novel respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The continuous emergence of SARS-CoV-2 variants poses a serious threat to the public health. A bedside device with high specificity and sensitivity for real-time and rapid detection is of great value for the prevention and control of COVID-19. Moreover, understanding the transmission behavior of airborne pathogens such as SARS-CoV-2 is also vital for scientific disease prevention. Biosensors are devices that can convert biomolecular reaction signals into detectable physical or chemical signals and have been increasingly used for the detection and analysis of pathogenic microorganisms. Biosensors bases on the conventional detection methods such as colony counting, immunoassays and molecular techniques are often time-consuming, labor-intensive and complicated. Nanomaterial-based biosensors are more portable and can be used for rapid on-site detection. This review summarized the progress in biosensors for pathogen detection and the application of nanomaterial based-biosensors in the surveillance of airborne pathogens.

Bacterial genomic epidemiology with mixed samples.

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

Genomics of Environmental Salmonella: Engaging Students in the Microbiology and Bioinformatics of Foodborne Pathogens.

We have developed and implemented an undergraduate microbiology course in which students isolate, characterize, and perform whole genome assembly and analysis of Salmonella enterica from stream sediments and poultry litter. In the development of the course and over three semesters, successive teams of undergraduate students collected field samples and performed enrichment and isolation techniques specific for the detection of S. enterica. Eighty-eight strains were confirmed using standard microbiological methods and PCR of the invA gene. The isolates' genomes were Illumina-sequenced by the Center for Food Safety and Applied Nutrition at the FDA and the Virginia state Division of Consolidated Laboratory Services as part of the GenomeTrakr program. Students used GalaxyTrakr and other web- and non-web-based platforms and tools to perform quality control on raw and assembled sequence data, assemble, and annotate genomes, identify antimicrobial resistance and virulence genes, putative plasmids, and other mobile genetic elements. Strains with putative plasmid-borne antimicrobial resistance genes were further sequenced by students in our research lab using the Oxford Nanopore MinIONTM platform. Strains of Salmonella that were isolated include human infectious serotypes such as Typhimurium and Infantis. Over 31 of the isolates possessed antibiotic resistance genes, some of which were located on large, multidrug resistance plasmids. Plasmid pHJ-38, identified in a Typhimurium isolate, is an apparently self-transmissible 183 kb IncA/C2 plasmid that possesses multiple antimicrobial resistance and heavy-metal resistance genes. Plasmid pFHS-02, identified in an Infantis isolate, is an apparently self-transmissible 303 kb IncF1B plasmid that also possesses numerous heavy-metal and antimicrobial resistance genes. Using direct and indirect measures to assess student outcomes, results indicate that course participation contributed to cognitive gains in relevant content knowledge and research skills such as field sampling, molecular techniques, and computational analysis. Furthermore, participants self-reported a deeper interest in scientific research and careers as well as psychosocial outcomes (e.g., sense of belonging and self-efficacy) commonly associated with student success and persistence in STEM. Overall, this course provided a powerful combination of field, wet lab, and computational biology experiences for students, while also providing data potentially useful in pathogen surveillance, epidemiological tracking, and for the further study of environmental reservoirs of S. enterica.

Global genomic pathogen surveillance to inform vaccine strategies: a decade-long expedition in pneumococcal genomics.

Vaccines are powerful agents in infectious disease prevention but often designed to protect against some strains that are most likely to spread and cause diseases. Most vaccines do not succeed in eradicating the pathogen and thus allow the potential emergence of vaccine evading strains. As with most evolutionary processes, being able to capture all variations across the entire genome gives us the best chance of monitoring and understanding the processes of vaccine evasion. Genomics is being widely adopted as the optimum approach for pathogen surveillance with the potential for early and precise identification of high-risk strains. Given sufficient longitudinal data, genomics also has the potential to forecast the emergence of such strains enabling immediate or pre-emptive intervention. In this review, we consider the strengths and challenges for pathogen genomic surveillance using the experience of the Global Pneumococcal Sequencing (GPS) project as an early example. We highlight the multifaceted nature of genome data and recent advances in genome-based tools to extract useful information relevant to inform vaccine strategies and treatment options. We conclude with future perspectives for genomic pathogen surveillance.