The Future of Whole-Genome Sequencing for Public Health and the Clinic

CRISPR and CRISPR-MVLST reveal conserved spacer distribution and high similarity among Salmonella enterica serovar Infantis genomes from Brazil and other countries

Salmonella enterica serovar Infantis (S. Infantis) is a globally distributed non-typhoid serovar infecting humans and food-producing animals. Considering the zoonotic potential and public health importance of this serovar, strategies to characterizing, monitor and control this pathogen are of great importance. This study aimed to determine the genetic relatedness of 80 Brazilian S. Infantis genomes in comparison to 40 non-Brazilian genomes from 14 countries using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-Multi-Locus Virulence Sequence Typing (CRISPR-MVLST). CRISPR spacers were searched using CRISPR-Cas++ and fimH and sseL alleles using BLAST and MEGA X. Results were analyzed using BioNumerics 7.6 in order to obtain similarity dendrograms. A total of 23 CRISPR1 and 11 CRISPR2 alleles formed by 37 and 26 types of spacers, respectively, were detected. MVLST revealed the presence of five fimH and three sseL alleles. CRISPR's similarity dendrogram showed 32 strain subtypes, with an overall similarity ≥ 78.6. The CRISPR-MVLST similarity dendrogram showed 37 subtypes, with an overall similarity ≥ 79.2. In conclusion, S. Infantis strains isolated from diverse sources in Brazil and other countries presented a high genetic similarity according to CRISPR and CRISPR-MVLST, regardless of their source, year, and/or place of isolation. These results suggest that both methods might be useful for molecular typing S. Infantis strains using WGS data.

Graphical abstractGenotypic and Phenotypic Characterization of Antimicrobial and Heavy Metal tolerance in Salmonella enterica and Escherichia coli Isolates from Swine Feed Mills

Antimicrobials and heavy metals are commonly used in the animal feed industry. The role of in-feed antimicrobials on the evolution and persistence of resistance in enteric bacteria is not well described. Whole-Genome Sequencing (WGS) is widely used for genetic characterizations of bacterial isolates, including antimicrobial resistance, heavy metal tolerance, virulence factors, and relatedness to other sequenced isolates. The goals of this study were to i) use WGS to characterize Salmonella enterica (n = 33) and Escherichia coli (n = 30) isolated from swine feed and feed mill environments; and ii) investigate their genotypic and phenotypic antimicrobial and heavy metal tolerance. Salmonella isolates belonged to 10 serovars, the most common being Cubana, Senftenberg, and Tennessee. E. coli isolates were grouped into 22 O groups. Phenotypic resistance to at least one antimicrobial was observed in 19 Salmonella (57.6%) and 17 E. coli (56.7%) isolates, whereas multidrug resistance (resistant to ≥ 3 antimicrobial classes) was observed in four Salmonella (12%) and two E. coli (7%) isolates. Antimicrobial resistance genes were identified in 17 Salmonella (51%) and 29 E. coli (97%), with 11 and 29 isolates possessing genes conferring resistance to multiple antimicrobial classes. Phenotypically, 53% Salmonella and 58% E. coli presented resistance to copper and arsenic. All isolates that possessed the copper resistance operon were resistant to the highest concentration tested (40 mM). Heavy metal tolerance genes to copper and silver were present in 26 Salmonella isolates. Our study showed a strong agreement between predicted and measured resistances when comparing genotypic and phenotypic data for antimicrobial resistance, with an overall concordance of 99% and 98.3% for Salmonella and E. coli, respectively.

Genomic characterization and antimicrobial resistance profiles of Salmonella enterica serovar Infantis isolated from food, humans and veterinary-related sources in Brazil

AIMS

To characterize the genetic relatedness, phenotypic and genotypic antimicrobial resistance and plasmid content of 80 Salmonella Infantis strains isolated from food, humans and veterinary sources from 2013 to 2018 in Brazil.

METHODS AND RESULTS

Pulsed-field gel electrophoresis and single-nucleotide polymorphism analysis showed major clusters containing 50% and 38.8% of the strains studied respectively. Multilocus sequence typing assigned all strains to ST32. Disk-diffusion revealed that 90% of the strains presented resistant or intermediate resistant profiles and 38.8% displayed multidrug resistance. Resistance genes for aminoglycosides (aac(6')-Iaa; aadA12; aph(3″-Ib; aph(6)-Id), β-lactams (bla_TEM-1 ; bla_CTX-M-8 ; bla_CMY-2 ), trimethoprim (dfrA8), tetracycline (tet(A)), amphenicols (floR), sulfonamide (sul2), efflux pumps (mdsA; mdsB), chromosomal point mutations in gyrB, parC, acrB and pmrA were detected. Strains harboured IncI, IncF, IncX, IncQ, IncN and IncR plasmids.

CONCLUSIONS

The presence of a prevalent S. Infantis subtype in Brazil and the high antimicrobial resistance rates reinforced the potential hazard of this serovar for the public health and food safety fields.

SIGNIFICANCE AND IMPACT OF THE STUDY

This is the first study characterizing a large set of S. Infantis from Brazil by whole-genome sequencing, which provided a better local and global comprehension about the distribution and characteristics of this serovar of importance in the food, human and veterinary fields.

Complete microbial genomes for public health in Australia and the Southwest Pacific

Complete genomes of microbial pathogens are essential for the phylogenomic analyses that increasingly underpin core public health laboratory activities. Here, we announce a BioProject (PRJNA556438) dedicated to sharing complete genomes chosen to represent a range of pathogenic bacteria with regional importance to Australia and the Southwest Pacific; enriching the catalogue of globally available complete genomes for public health while providing valuable strains to regional public health microbiology laboratories. In this first step, we present 26 complete high-quality bacterial genomes. Additionally, we describe here a framework for reconstructing complete microbial genomes and highlight some of the challenges and considerations for accurate and reproducible genome reconstruction.

Antimicrobial Resistance Profiles and Phylogenetic Analysis of Campylobacter jejuni Strains Isolated in Brazil by Whole Genome Sequencing

Aims: The objectives of this work were to use whole genome sequencing (WGS) to determine the antimicrobial resistance genotypes of 116 Campylobacter jejuni strains isolated in Brazil and to compare it with the results obtained by antimicrobial susceptibility testing (AST). In addition, WGS was used to uncover the phylogenetic relationship among those strains. Results: By AST, the C. jejuni strains resistant to ciprofloxacin, tetracycline, doxycycline, and erythromycin were 51 (44%), 41 (35.3%), 41 (35.3%), and 6 (5.2%), respectively. By WGS, the genes aph(3')III, aadE, bla_OXA-449, bla_OXA-184, bla_OXA-61, and tet(O) were detected in 6 (5.2%), 3 (2.6%), 1 (0.9%), 10 (8.6%), 55 (47.4%), and 44 (38%) strains, respectively. Fifty-four (46.6%) strains showed the mutation T86I in the gyrA gene, and four (3.4%) strains presented the mutation A2075G in the 23S rRNA gene. The correlation between AST and WGS was 100% for ciprofloxacin, 97.5% for tetracyclines, and 66.7% for erythromycin. The whole genome single nucleotide polymorphism (SNP) tree clustered the C. jejuni strains into two clades comprising strains that were highly related from different sources, places, and years. Conclusion: The high rates of C. jejuni strains resistant to ciprofloxacin and tetracyclines are of concern and may represent a public health problem. WGS has a potential to be a powerful tool for the prediction of resistance of antibiotics used to treat campylobacteriosis. The results obtained by whole genome SNP analysis suggested the potential for transmission between clinical and nonclinical sources and between human and animal sources over the course of 20 years in Brazil.

Predicting antimicrobial susceptibility from the bacterial genome: A new paradigm for one health resistance monitoring

The laboratory identification of antibacterial resistance is a cornerstone of infectious disease medicine. In vitro antimicrobial susceptibility testing has long been based on the growth response of organisms in pure culture to a defined concentration of antimicrobial agents. By comparing individual isolates to wild-type susceptibility patterns, strains with acquired resistance can be identified. Acquired resistance can also be detected genetically. After many decades of research, the inventory of genes underlying antimicrobial resistance is well known for several pathogenic genera including zoonotic enteric organisms such as Salmonella and Campylobacter and continues to grow substantially for others. With the decline in costs for large scale DNA sequencing, it is now practicable to characterize bacteria using whole genome sequencing, including the carriage of resistance genes in individual microorganisms and those present in complex biological samples. With genomics, we can generate comprehensive, detailed information on the bacterium, the mechanisms of antibiotic resistance, clues to its source, and the nature of mobile DNA elements by which resistance spreads. These developments point to a new paradigm for antimicrobial resistance detection and tracking for both clinical and public health purposes.

Comparison of Whole Genome (wg-) and Core Genome (cg-) MLST (BioNumericsTM) Versus SNP Variant Calling for Epidemiological Investigation of Pseudomonas aeruginosa

Whole genome sequencing (WGS) is increasingly used for epidemiological investigations of pathogens. While SNP variant calling is currently considered as the most suitable method, the choice of a representative reference genome and the isolate dependency of results limit standardization and affect resolution in an unknown manner. Whole or core genome Multi Locus Sequence Typing (wg-, cg-MLST) represents an attractive alternative. Here, we assess the accuracy of wg- and cg-MLST by comparing results of four Pseudomonas aeruginosa datasets for which epidemiological and genomic data were previously described. Three datasets included 155 isolates from three different sequence types (ST) of P. aeruginosa collected in our ICUs over a 5-year period. The fourth dataset consisted of 10 isolates from an investigation of P. aeruginosa contaminated hand soap. All isolates were previously analyzed by a core SNP approach. In this study, wg- and cg-MLST were performed in BioNumerics^TM using a scheme developed by Applied-Maths. Correlation between SNP calling and wg- or cg-MLST results were evaluated by calculating linear regressions and their coefficient of correlations (R²) between the number of SNPs and the number of allele differences in pairwise comparison of isolates. The number of SNPs and allele difference between isolates with close epidemiological linkage varies between 0–26 and 0–13, respectively. When compared to core-SNP calling, a higher coefficient of correlation was obtained with cgMLST (R² of 0.92–0.99) than with wgMLST (0.78–0.99). In one dataset, a putative homologous recombination of a large DNA fragment (202 loci) was identified among these isolates, affecting its phylogeny, but with no impact on the epidemiological analysis of outbreak isolates. In conclusion, we showed that the P. aeruginosa wgMLST scheme in BioNumerics^TM is as discriminatory as the core-SNP calling approach and apparently useful for outbreak investigations. We also showed that epidemiological linked isolates showed less than 26 SNPs or 13 allele differences. These are important figures for the distinction between outbreak and non-outbreak isolates when interpreting WGS results. However, as P. aeruginosa is highly recombinant, a cgMLST approach is preferable and caution should be addressed to possible recombination of large DNA fragments.

A genoserotyping system for a fast and objective identification of Salmonella serotypes commonly isolated from poultry and pork food sectors in Belgium

Humans are mostly contaminated by Salmonella through the consumption of pork- and poultry-derived food products. Therefore, a strict monitoring of Salmonella serotypes in food-producing animals is needed to limit the transmission of the pathogen to humans. Additionally, Salmonella can lead to economic loss in the food sector. Previously, a genoserotyping method using the MOL-PCR and Luminex technology was developed for the identification of the 6 Salmonella serotypes, and their variants, subjected to an official control in the Belgian food sector. In this study, 3 additional assays using the same technology were developed for the rapid and cost-effective detection of 13 dangerous highly invasive serotypes or other serotypes frequently isolated from the Belgian poultry and pork sector, i.e. Agona, Anatum, Brandenburg, Choleraesuis, Derby, Enteritidis vaccine strains, Gallinarum var. Gallinarum/Pullorum, Livingstone, Mbandaka, Minnesota, Ohio, Rissen and Senftenberg. Moreover, the previously developed first MOL-PCR assay was improved for S. Paratyphi B and serogroup O:3 detection. Finally, a Decision Support System hosted by a web application was created for an automatic and objective interpretation of the Luminex raw data. The 3 new assays and the modifications of the first assay were validated with a 100% accuracy, using 553 Salmonella and non-Salmonella strains in total.

Poultry processing and the application of microbiome mapping

Chicken is globally one of the most popular food animals. However, it is also one of the major reservoirs for foodborne pathogens, annually resulting in continued morbidity and mortality incidences worldwide. In an effort to reduce the threat of foodborne disease, the poultry industry has implemented a multifaceted antimicrobial program that incorporates not only chemical compounds, but also extensive amounts of water application and pathogen monitoring. Unfortunately, the pathogen detection methods currently used by the poultry industry lack speed, relying on microbiological plate methods and molecular detection systems that take time and lack precision. In many cases, the time to data acquisition can take 12 to 24 h. This is problematic if shorter-term answers are required which is becoming more likely as the public demand for chicken meat is only increasing, leading to new pressures to increase line speed. Therefore, new innovations in detection methods must occur to mitigate the risk of foodborne pathogens that could result from faster slaughter and processing speeds. Future technology will have 2 tracks: rapid methods that are meant to detect pathogens and indicator organisms within a few hours, and long-term methods that use microbiome mapping to evaluate sanitation and antimicrobial efficacy. Together, these methods will provide rapid, comprehensive data capable of being applied in both risk-assessment algorithms and used by management to safeguard the public.

Recommendations for performance optimizations when using GATK3.8 and GATK4

BACKGROUND

Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance.

RESULTS

We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU.

CONCLUSIONS

In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Managing genomic variant calling workflows with Swift/T

Bioinformatics research is frequently performed using complex workflows with multiple steps, fans, merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Scientific workflow management systems could help with that. Many are now being proposed, but is there yet the “best” workflow management system for bioinformatics? Such a system would need to satisfy numerous, sometimes conflicting requirements: from ease of use, to seamless deployment at peta- and exa-scale, and portability to the cloud. We evaluated Swift/T as a candidate for such role by implementing a primary genomic variant calling workflow in the Swift/T language, focusing on workflow management, performance and scalability issues that arise from production-grade big data genomic analyses. In the process we introduced novel features into the language, which are now part of its open repository. Additionally, we formalized a set of design criteria for quality, robust, maintainable workflows that must function at-scale in a production setting, such as a large genomic sequencing facility or a major hospital system. The use of Swift/T conveys two key advantages. (1) It operates transparently in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.), thus a single workflow is trivially portable across numerous clusters. (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, which makes it easy to maintain and to request resources optimal for each stage of the pipeline. While Swift/T’s data-level parallelism eliminates the need to code parallel analysis of multiple samples, it does make debugging more difficult, as is common for implicitly parallel code. Nonetheless, the language gives users a powerful and portable way to scale up analyses in many computing architectures. The code for our implementation of a variant calling workflow using Swift/T can be found on GitHub at https://github.com/ncsa/Swift-T-Variant-Calling, with full documentation provided at http://swift-t-variant-calling.readthedocs.io/en/latest/.

Cyberbiosecurity Challenges of Pathogen Genome Databases

Pathogen detection, identification, and tracking is shifting from non-molecular methods, DNA fingerprinting methods, and single gene methods to methods relying on whole genomes. Viral Ebola and influenza genome data are being used for real-time tracking, while food-borne bacterial pathogen outbreaks and hospital outbreaks are investigated using whole genomes in the UK, Canada, the USA and the other countries. Also, plant pathogen genomes are starting to be used to investigate plant disease epidemics such as the wheat blast outbreak in Bangladesh. While these genome-based approaches provide never-seen advantages over all previous approaches with regard to public health and biosecurity, they also come with new vulnerabilities and risks with regard to cybersecurity. The more we rely on genome databases, the more likely these databases will become targets for cyber-attacks to interfere with public health and biosecurity systems by compromising their integrity, taking them hostage, or manipulating the data they contain. Also, while there is the potential to collect pathogen genomic data from infected individuals or agricultural and food products during disease outbreaks to improve disease modeling and forecast, how to protect the privacy of individuals, growers, and retailers is another major cyberbiosecurity challenge. As data become linkable to other data sources, individuals and groups become identifiable and potential malicious activities targeting those identified become feasible. Here, we define a number of potential cybersecurity weaknesses in today's pathogen genome databases to raise awareness, and we provide potential solutions to strengthen cyberbiosecurity during the development of the next generation of pathogen genome databases.

Advances and challenges in barcoding of microbes, parasites, and their vectors and reservoirs

DNA barcoding is now a common tool in parasitology and epidemiology, which require good methods for identification not only of parasites and pathogens but vectors and reservoirs. This special issue presents some advances and challenges in barcoding of microbes, parasites, and their vectors and reservoirs. DNA barcoding found new applications in disease ecology, conservation parasitology, environmental parasitology and in paleoparasitology. New technologies such as next-generation sequencing and matrix-assisted laser desorption-ionization time-of-flight have made it now possible to investigate large samples of specimens. By allowing the investigation of parasites at the interface between environment, biodiversity, animal and human health, barcoding and biobanking have important policy outcomes as well as ethics and legal implications. The special issue 'Advances and challenges in the barcoding of parasites, vectors and reservoirs' illustrates some recent advances and proposes new avenues for research in barcoding in parasitology.

Review of molecular subtyping methodologies used to investigate outbreaks due to multidrug-resistant enteric bacterial pathogens in sub-Saharan Africa

Background

In sub-Saharan Africa, molecular epidemiological investigation of outbreaks caused by antimicrobial-resistant enteric bacterial pathogens have mostly been described for Salmonella species, Vibrio cholerae, Shigella species and Escherichia coli. For these organisms, I reviewed all publications describing the use of molecular subtyping methodologies to investigate outbreaks caused by multidrug-resistant (MDR) enteric bacterial infections.

Objectives

To describe the use of molecular subtyping methodologies to investigate outbreaks caused by MDR enteric bacterial pathogens in sub-Saharan Africa and to describe the current status of molecular subtyping capabilities in the region.

Methods

A PubMed database literature search (English language only) was performed using the search strings: ‘Africa outbreak MDR’, ‘Africa outbreak multi’, ‘Africa outbreak multidrug’, ‘Africa outbreak multi drug’, ‘Africa outbreak resistance’, ‘Africa outbreak resistant’, ‘Africa outbreak drug’, ‘Africa outbreak antibiotic’, ‘Africa outbreak antimicrobial’. These search strings were used in combination with genus and species names of the organisms listed above. All results were included in the review.

Results

The year 1991 saw one of the first reports describing the use of molecular subtyping methodologies in sub-Saharan Africa; this included the use of plasmid profiling to characterise Salmonella Enteritidis. To date, several methodologies have been used; pulsed-field gel electrophoresis analysis and multilocus sequence typing have been the most commonly used methodologies. Investigations have particularly highlighted the emergence and spread of MDR clones; these include Salmonella Typhi H58 and Salmonella Typhimurium ST313 clones. In recent times, whole-genome sequencing (WGS) analysis approaches have increasingly been used.

Conclusion

Traditional molecular subtyping methodologies are still commonly used and still have their place in investigations; however, WGS approaches have increasingly been used and are slowly gaining a stronghold. African laboratories need to start adapting their molecular surveillance methodologies to include WGS, as it is foreseen that WGS analysis will eventually replace all traditional methodologies.

Validation of a Bioinformatics Workflow for Routine Analysis of Whole-Genome Sequencing Data and Related Challenges for Pathogen Typing in a European National Reference Center: Neisseria meningitidis as a Proof-of-Concept

Despite being a well-established research method, the use of whole-genome sequencing (WGS) for routine molecular typing and pathogen characterization remains a substantial challenge due to the required bioinformatics resources and/or expertise. Moreover, many national reference laboratories and centers, as well as other laboratories working under a quality system, require extensive validation to demonstrate that employed methods are "fit-for-purpose" and provide high-quality results. A harmonized framework with guidelines for the validation of WGS workflows does currently, however, not exist yet, despite several recent case studies highlighting the urgent need thereof. We present a validation strategy focusing specifically on the exhaustive characterization of the bioinformatics analysis of a WGS workflow designed to replace conventionally employed molecular typing methods for microbial isolates in a representative small-scale laboratory, using the pathogen Neisseria meningitidis as a proof-of-concept. We adapted several classically employed performance metrics specifically toward three different bioinformatics assays: resistance gene characterization (based on the ARG-ANNOT, ResFinder, CARD, and NDARO databases), several commonly employed typing schemas (including, among others, core genome multilocus sequence typing), and serogroup determination. We analyzed a core validation dataset of 67 well-characterized samples typed by means of classical genotypic and/or phenotypic methods that were sequenced in-house, allowing to evaluate repeatability, reproducibility, accuracy, precision, sensitivity, and specificity of the different bioinformatics assays. We also analyzed an extended validation dataset composed of publicly available WGS data for 64 samples by comparing results of the different bioinformatics assays against results obtained from commonly used bioinformatics tools. We demonstrate high performance, with values for all performance metrics >87%, >97%, and >90% for the resistance gene characterization, sequence typing, and serogroup determination assays, respectively, for both validation datasets. Our WGS workflow has been made publicly available as a "push-button" pipeline for Illumina data at https://galaxy.sciensano.be to showcase its implementation for non-profit and/or academic usage. Our validation strategy can be adapted to other WGS workflows for other pathogens of interest and demonstrates the added value and feasibility of employing WGS with the aim of being integrated into routine use in an applied public health setting.

Revisiting Polymorphic Diversity of Aminoglycoside N-Acetyltransferase AAC(6')-Ib Based on Bacterial Genomes of Human, Animal, and Environmental Origins

The prevalence of aac(6')-Ib variants has been demonstrated in numerous epidemiological studies. We revisited the polymorphic diversity of aminoglycoside 6'-N-acetyltransferase gene [aac(6')-Ib] in the bacterial genome databases based on One Health perspectives. aac(6')-Ib was searched against bacterial complete and draft genome databases of NCBI. Based on the major polymorphic residues 102, 117, and 179, taxonomy, ecology, and temporal emergence of bacterial isolates harboring variants of aac(6')-Ib gene were evaluated using whole-genome sequences available in the databases. A total of 3,964 aac(6')-Ib sequences were found to be present in the genomes of 34 bacterial genera, mostly found in Gammaproteobacteria. Among these, aac(6')-Ib-cr variant, known to confer fluoroquinolone resistance, were increasingly detected in bacterial genomes and most abundant in the genera Klebsiella and Escherichia, thereby suggesting that these genera were the major reservoirs of the plasmid-mediated quinolone resistance (PMQR) determinant. The proportions of the cr variant were higher in animal and environmental isolates than in human isolates, among which the variant was dominant (>50%) in the genomes of intestinal, rectal, and fecal origins. In addition, our study suggested that the prevalence of the cr variant was associated with the occurrence of a variant with the mutation L117 (IbL). An integrated surveillance system for antimicrobial resistance in human, animal, and environmental sectors, based on whole-genome sequencing, would provide a better insight into the evolution, ecology, and epidemiology of antimicrobial-resistant bacteria.

The Present and Future of Whole Genome Sequencing (WGS) and Whole Metagenome Sequencing (WMS) for Surveillance of Antimicrobial Resistant Microorganisms and Antimicrobial Resistance Genes across the Food Chain

Antimicrobial resistance (AMR) surveillance is a critical step within risk assessment schemes, as it is the basis for informing global strategies, monitoring the effectiveness of public health interventions, and detecting new trends and emerging threats linked to food. Surveillance of AMR is currently based on the isolation of indicator microorganisms and the phenotypic characterization of clinical, environmental and food strains isolated. However, this approach provides very limited information on the mechanisms driving AMR or on the presence or spread of AMR genes throughout the food chain. Whole-genome sequencing (WGS) of bacterial pathogens has shown potential for epidemiological surveillance, outbreak detection, and infection control. In addition, whole metagenome sequencing (WMS) allows for the culture-independent analysis of complex microbial communities, providing useful information on AMR genes occurrence. Both technologies can assist the tracking of AMR genes and mobile genetic elements, providing the necessary information for the implementation of quantitative risk assessments and allowing for the identification of hotspots and routes of transmission of AMR across the food chain. This review article summarizes the information currently available on the use of WGS and WMS for surveillance of AMR in foodborne pathogenic bacteria and food-related samples and discusses future needs that will have to be considered for the routine implementation of these next-generation sequencing methodologies with this aim. In particular, methodological constraints that impede the use at a global scale of these high-throughput sequencing (HTS) technologies are identified, and the standardization of methods and protocols is suggested as a measure to upgrade HTS-based AMR surveillance schemes.

Rodent-borne diseases and their public health importance in Iran

Background

Rodents are reservoirs and hosts for several zoonotic diseases such as plague, leptospirosis, and leishmaniasis. Rapid development of industry and agriculture, as well as climate change throughout the globe, has led to change or increase in occurrence of rodent-borne diseases. Considering the distribution of rodents throughout Iran, the aim of this review is to assess the risk of rodent-borne diseases in Iran.

Methodology/Principal finding

We searched Google Scholar, PubMed, Science Direct, Scientific Information Database (SID), and Magiran databases up to September 2016 to obtain articles reporting occurrence of rodent-borne diseases in Iran and extract information from them. Out of 70 known rodent-borne diseases, 34 were reported in Iran: 17 (50%) parasitic diseases, 13 (38%) bacterial diseases, and 4 (12%) viral diseases. Twenty-one out of 34 diseases were reported from both humans and rodents. Among the diseases reported in the rodents of Iran, plague, leishmaniasis, and hymenolepiasis were the most frequent. The most infected rodents were Rattus norvegicus (16 diseases), Mus musculus (14 diseases), Rattus rattus (13 diseases), Meriones persicus (7 diseases), Apodemus spp. (5 diseases), Tatera indica (4 diseases), Meriones libycus (3 diseases), Rhombomys opimus (3 diseases), Cricetulus migratorius (3 diseases), and Nesokia indica (2 diseases).

Conclusions/Significance

The results of this review indicate the importance of rodent-borne diseases in Iran. Considering notable diversity of rodents and their extensive distribution throughout the country, it is crucial to pay more attention to their role in spreading infectious diseases for better control of the diseases.

This review showed that approximately half of the known rodent-borne diseases have been reported in Iran, half of which were reported both in humans and rodents. Most of the diseases were bacterial and parasitic. Plague, leishmaniasis, and hymenolepiasis were the most frequent diseases among rodent populations. Also, this review showed that among the rodent species, three commensal ones—R. norvegicus, M. musculus, and R. rattus—play an important role in the transmission of diseases to humans in Iran. Considering repeated reports of many of these diseases in humans and rodents, and the notable diversity and extensive distribution of rodents throughout Iran, it is crucial to pay adequate attention to rodents as a source of zoonotic infectious diseases in the country.

Exploitation of microbial forensics and nanotechnology for the monitoring of emerging pathogens

Emerging infectious diseases remain among the leading causes of global mortality. Traditional laboratory diagnostic approaches designed to detect and track infectious disease agents provide a framework for surveillance of bio threats. However, surveillance and outbreak investigations using such time-consuming approaches for early detection of pathogens remain the major pitfall. Hence, reasonable real-time surveillance systems to anticipate threats to public health and environment are critical for identifying specific aetiologies and preventing the global spread of infectious disease. The current review discusses the growing need for monitoring and surveillance of pathogens with the same zeal and approach as adopted by microbial forensics laboratories, and further strengthening it by integrating with the innovative nanotechnology for rapid detection of microbial pathogens. Such innovative diagnostics platforms will help to track pathogens from high risk areas and environment by pre-emptive approach that will minimize damages. The various scenarios with the examples are discussed where the high risk associated human pathogens in particular were successfully detected using various nanotechnology approaches with potential future prospects in the field of microbial forensics.

Comprehensive assessment of the quality of Salmonella whole genome sequence data available in public sequence databases using the Salmonella in silico Typing Resource (SISTR)

Public health and food safety institutions around the world are adopting whole genome sequencing (WGS) to replace conventional methods for characterizing Salmonella for use in surveillance and outbreak response. Falling costs and increased throughput of WGS have resulted in an explosion of data, but questions remain as to the reliability and robustness of the data. Due to the critical importance of serovar information to public health, it is essential to have reliable serovar assignments available for all of the Salmonella records. The current study used a systematic assessment and curation of all Salmonella in the sequence read archive (SRA) to assess the state of the data and their utility. A total of 67 758 genomes were assembled de novo and quality-assessed for their assembly metrics as well as species and serovar assignments. A total of 42 400 genomes passed all of the quality criteria but 30.16 % of genomes were deposited without serotype information. These data were used to compare the concordance of reported and predicted serovars for two in silico prediction tools, multi-locus sequence typing (MLST) and the Salmonella in silico Typing Resource (SISTR), which produced predictions that were fully concordant with 87.51 and 91.91 % of the tested isolates, respectively. Concordance of in silico predictions increased when serovar variants were grouped together, 89.25 % for MLST and 94.98 % for SISTR. This study represents the first large-scale validation of serovar information in public genomes and provides a large validated set of genomes, which can be used to benchmark new bioinformatics tools.

Advances in the Microbiome: Applications to Clostridium difficile Infection

Clostridium difficile is a major cause of morbidity and mortality worldwide, causing over 400,000 infections and approximately 29,000 deaths in the United States alone each year. C. difficile is the most common cause of nosocomial diarrhoea in the developed world, and, in recent years, the emergence of hyper-virulent (mainly ribotypes 027 and 078, sometimes characterised by increased toxin production), epidemic strains and an increase in the number of community-acquired infections has caused further concern. Antibiotic therapy with metronidazole, vancomycin or fidaxomicin is the primary treatment for C. difficile infection (CDI). However, CDI is unique, in that, antibiotic use is also a major risk factor for acquiring CDI or recurrent CDI due to disruption of the normal gut microbiota. Therefore, there is an urgent need for alternative, non-antibiotic therapeutics to treat or prevent CDI. Here, we review a number of such potential treatments which have emerged from advances in the field of microbiome research.