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Public health and regulatory agencies worldwide sequence all Listeria monocytogenes isolates obtained as part of routine surveillance and outbreak investigations. Many of these entities submit the sequences to the National Center for Biotechnology Information Pathogen Detection (NCBI PD) database, which groups the L. monocytogenes isolates into single nucleotide polymorphism (SNP) clusters based on a pairwise SNP difference threshold of 50 SNPs. Our goal was to assess whether isolates with metadata that suggest different sources or locations could show evidence for close genetic relatedness indicating a recent common ancestor and a possible unknown common source. We compared the whole genome sequencing (WGS) data of 249 L. monocytogenes isolates sequenced here, which have detailed metadata, with WGS data of nonclinical isolates on NCBI PD. The 249 L. monocytogenes isolates originated from natural environments (n = 91) as well as from smoked fish (n = 62), dairy (n = 56), and deli meat (n = 40) operations in the United States. Using a combination of subtyping by core genome multilocus sequence typing and high-quality SNP, we observed five SNP clusters in which study isolates and SNP cluster isolates seemed to be closely related and either (i) shared the same geolocation but showed different source types (one SNP cluster); (ii) shared the same source type but showed different geolocations (two SNP clusters); or (iii) shared neither source type nor geolocation (two SNP clusters). For one of the two clusters under (iii), there was, however, no strong bootstrap support for a common ancestor shared between the study isolates and SNP cluster isolates, indicating the value of in-depth evolutionary analyses when WGS data are used for traceback and epidemiological investigations. Overall, our results demonstrate that some L. monocytogenes subtypes may be associated with specific locations or commodities; these associations can help in investigations involving multi-ingredient foods such as sandwiches. However, at least some L. monocytogenes subtypes can be widespread geographically and can be associated with different sources, which may present a challenge to traceback investigations involving these subtypes.

As microplastic pollution continues to increase, an emerging threat is the potential for microplastics to act as novel substrates and/or carriers for pathogens. This is of particular concern for aquatic product safety given the growing evidence of microplastic ingestion by aquaculture species. However, the potential risks of pathogens associated with microplastics in mariculture remain poorly understood. Here, an in situ incubation experiment involving three typical microplastics including polyethylene terephthalate (PET), polyethylene (PE), and polypropylene (PP) was conducted during the summer–autumn period in a mariculture cage. The identification of potential pathogens based on the 16S rRNA gene amplicon sequencing and a custom-made database for pathogenic bacteria involved in aquatic environments, was performed to assess the risks of different microplastics attaching potential pathogens. The enrichment of pathogens was not observed in microplastic-associated communities when compared with free-living and particle-attached communities in surrounding seawater. Despite the lower relative abundance, pathogens showed different preferences for three microplastic substrates, of which PET was the most favored by pathogens, especially potentially pathogenic members of Vibrio, Tenacibaculum, and Escherichia. Moreover, the colonization of these pathogens on microplastics was strongly affected by environmental factors (e.g., temperature, nitrite). Our results provide insights into the ecological risks of microplastics in mariculture industry.

Bovine respiratory disease (BRD) is the most common economically important disease affecting cattle. For developing accurate diagnostics that can predict disease susceptibility/resistance and stratification, it is necessary to identify the molecular mechanisms that underlie BRD. To study the complex interactions among the bovine host and the multitude of viral and bacterial pathogens, as well as the environmental factors associated with BRD etiology, genome-scale high-throughput functional genomics methods such as microarrays, RNA-seq, and proteomics are helpful. In this review, we summarize the progress made in our understanding of BRD using functional genomics approaches. We also discuss some of the available bioinformatics resources for analyzing high-throughput data, in the context of biological pathways and molecular interactions. Although resources for studying host response to infection are available, the corresponding information is lacking for majority of BRD pathogens, impeding progress in identifying diagnostic signatures for BRD using functional genomics approaches.

As an application instructional course for Aquatic Animal Medicine (AAM), Aquatic Animal Pathogen Biology needs to be guided by a large number of examples and cases, but the current case database construction faces many urgent problems. In view of this, this paper analyzes the characteristics of professional education of AAM postgraduate students. With the goal of cultivating applied personnel that meet the requirements of the times, and the guiding ideology of strengthening the reform of the education model in colleges and universities, and improving the quantity and quality of personnel training, it builds a case database for the course Aquatic Animal Pathogen Biology in accordance with modern postgraduate teaching needs.

Background: Commitment to food safety is evidenced by high profile governmental initiatives around the globe. To measure progress towards targets, policy makers need to know the baseline from which they started. Aim: To describe the burden (mortality, morbidity, new presentations to general practice, hospital admissions, and hospital occupancy) and trends of indigenous foodborne disease (IFD) in England and Wales between 1992 and 2000. Methods: Routinely available surveillance data, special survey data, and hospital episode statistics were collated and arithmetic employed to estimate the burden and trends of IFD in England and Wales. Adjustments were made for underascertainment of disease through national surveillance and for foreign travel. The final estimates were compared with those from the USA. Results: In 1995 there were an estimated 2 365 909 cases, 21 138 hospital admissions, and 718 deaths in England and Wales due to IFD. By 2000 this had fallen to 1 338 772 cases, 20 759 hospital admissions, and 480 deaths. In terms of disease burden the most important pathogens were campylobacters, salmonellas, Clostridium perfringens, verocytotoxin producing Escherichia coli (VTEC) O157, and Listeria monocytogenes. The ratio of food related illness in the USA to IFD in England and Wales in 2000 was 57:1. Taking into account population rates, this ratio fell to 11:1 and converged when aetiology and disease severity were considered. Conclusion: Reducing IFD in England and Wales means tackling campylobacter. Lowering mortality rates however also requires better control and prevention of salmonellas, Cl perfringens, L monocytogenes, and VTEC O157.

Host-pathogen interaction is the most crucial factor that evokes the host immune system to fight against pathogens. In contrast to specialized immune cells present in humans and animals, plants have disease resistance (R-) and disease susceptibility (S-) genes. R-genes confer disease resistance and are generally introgressed from wild crop relatives to cultivated crops. S-genes, on the other hand, assist pathogens in establishing contact, displaying counter-defense measures, and spreading the infection. To achieve resistance in a variety of crops, researchers are now focusing on the identification, silencing, editing, or elimination of crucial S-genes. To aid in this field, we created the first curated database of disease susceptibility genes in plants (DSP), with the simple and advanced search tool that allows researchers to restrict the query and mining of specified hits. SSR marker identification and primer designing could be performed with the help of MISA and Primer3 software, respectively. The DSP database is available at http://45.248.163.60/bic/sgenos/ and http://14.139.62.220/sgenos/ .

As viruses continue to pose risks to global health, having a better understanding of virus–host protein–protein interactions aids in the development of treatments and vaccines. Here, we introduce Viruses.STRING, a protein–protein interaction database specifically catering to virus–virus and virus–host interactions. This database combines evidence from experimental and text-mining channels to provide combined probabilities for interactions between viral and host proteins. The database contains 177,425 interactions between 239 viruses and 319 hosts. The database is publicly available at viruses.string-db.org, and the interaction data can also be accessed through the latest version of the Cytoscape STRING app.

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Cellular receptors usually contain a designated sensory domain that recognizes the signal. Per/Arnt/Sim (PAS) domains are ubiquitous sensors in thousands of species ranging from bacteria to humans. Although PAS domains were described as intracellular sensors, recent structural studies revealed PAS-like domains in extracytoplasmic regions in several transmembrane receptors. However, these structurally defined extracellular PAS-like domains do not match sequence-derived PAS domain models, and thus their distribution across the genomic landscape remains largely unknown. Here we show that structurally defined extracellular PAS-like domains belong to the Cache superfamily, which is homologous to, but distinct from the PAS superfamily. Our newly built computational models enabled identification of Cache domains in tens of thousands of signal transduction proteins including those from important pathogens and model organisms. Furthermore, we show that Cache domains comprise the dominant mode of extracellular sensing in prokaryotes.

Background The reliability of culture-independent pathogen detection in foods using metagenomics is contingent on the quality and composition of the reference database. The inclusion of microbial sequences from a diverse representation of taxonomies in universal reference databases is recommended to maximize classification precision for pathogen detection. However, these sizable databases have high memory requirements that may be out of reach for some users. In this study, we aimed to assess the performance of a foodborne pathogen (FBP)-specific reference database (taxon-specific) relative to a universal reference database (taxon-agnostic). We tested our FBP-specific reference database's performance for detecting Listeria monocytogenes in two complex food matrices—ready-to-eat (RTE) turkey deli meat and prepackaged spinach—using three popular read-based DNA-to-DNA metagenomic classifiers: Centrifuge, Kraken 2 and KrakenUniq. Results In silico host sequence removal led to substantially fewer false positive (FP) classifications and higher classification precision in RTE turkey deli meat datasets using the FBP-specific reference database. No considerable improvement in classification precision was observed following host filtering for prepackaged spinach datasets and was likely a consequence of a higher microbe-to-host sequence ratio. All datasets classified with Centrifuge using the FBP-specific reference database had the lowest classification precision compared to Kraken 2 or KrakenUniq. When a confidence-scoring threshold was applied, a nearly equivalent precision to the universal reference database was achieved for Kraken 2 and KrakenUniq. Recall was high for both reference databases across all datasets and classifiers. Substantially fewer computational resources were required for metagenomics-based detection of L. monocytogenes using the FBP-specific reference database, especially when combined with Kraken 2. Conclusions A universal (taxon-agnostic) reference database is not essential for accurate and reliable metagenomics-based pathogen detection of L. monocytogenes in complex food matrices. Equivalent classification performance can be achieved using a taxon-specific reference database when the appropriate quality control measures, classification software, and analysis parameters are applied. This approach is less computationally demanding and more attainable for the broader scientific and food safety communities.