Pathogen Genomes Research Articles

Large-scale genomic analyses with machine learning uncover predictive patterns associated with fungal phytopathogenic lifestyles and traits

Invasive plant pathogenic fungi have a global impact, with devastating economic and environmental effects on crops and forests. Biosurveillance, a critical component of threat mitigation, requires risk prediction based on fungal lifestyles and traits. Recent studies have revealed distinct genomic patterns associated with specific groups of plant pathogenic fungi. We sought to establish whether these phytopathogenic genomic patterns hold across diverse taxonomic and ecological groups from the Ascomycota and Basidiomycota, and furthermore, if those patterns can be used in a predictive capacity for biosurveillance. Using a supervised machine learning approach that integrates phylogenetic and genomic data, we analyzed 387 fungal genomes to test a proof-of-concept for the use of genomic signatures in predicting fungal phytopathogenic lifestyles and traits during biosurveillance activities. Our machine learning feature sets were derived from genome annotation data of carbohydrate-active enzymes (CAZymes), peptidases, secondary metabolite clusters (SMCs), transporters, and transcription factors. We found that machine learning could successfully predict fungal lifestyles and traits across taxonomic groups, with the best predictive performance coming from feature sets comprising CAZyme, peptidase, and SMC data. While phylogeny was an important component in most predictions, the inclusion of genomic data improved prediction performance for every lifestyle and trait tested. Plant pathogenicity was one of the best-predicted traits, showing the promise of predictive genomics for biosurveillance applications. Furthermore, our machine learning approach revealed expansions in the number of genes from specific CAZyme and peptidase families in the genomes of plant pathogens compared to non-phytopathogenic genomes (saprotrophs, endo- and ectomycorrhizal fungi). Such genomic feature profiles give insight into the evolution of fungal phytopathogenicity and could be useful to predict the risks of unknown fungi in future biosurveillance activities.

Olivar: automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens.

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer SNPs overlapping with primers and predicted PCR byproducts. We also compared Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluated Olivar on real wastewater samples and found that Olivar had up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

Biomaterials-mediated CRISPR/Cas9 delivery: recent challenges and opportunities in gene therapy.

The use of biomaterials in delivering CRISPR/Cas9 for gene therapy in infectious diseases holds tremendous potential. This innovative approach combines the advantages of CRISPR/Cas9 with the protective properties of biomaterials, enabling accurate and efficient gene editing while enhancing safety. Biomaterials play a vital role in shielding CRISPR/Cas9 components, such as lipid nanoparticles or viral vectors, from immunological processes and degradation, extending their effectiveness. By utilizing the flexibility of biomaterials, tailored systems can be designed to address specific genetic diseases, paving the way for personalized therapeutics. Furthermore, this delivery method offers promising avenues in combating viral illnesses by precisely modifying pathogen genomes, and reducing their pathogenicity. Biomaterials facilitate site-specific gene modifications, ensuring effective delivery to infected cells while minimizing off-target effects. However, challenges remain, including optimizing delivery efficiency, reducing off-target effects, ensuring long-term safety, and establishing scalable production techniques. Thorough research, pre-clinical investigations, and rigorous safety evaluations are imperative for successful translation from the laboratory to clinical applications. In this review, we discussed how CRISPR/Cas9 delivery using biomaterials revolutionizes gene therapy and infectious disease treatment, offering precise and safe editing capabilities with the potential to significantly improve human health and quality of life.

B-148 Analytical Validation and Performance Characteristics of A Targeted Metagenomics Next-generation Sequencing Respiratory Pathogen ID/AMR Panel

Abstract Background The COVID-19 global pandemic underscored the importance of access to diagnostic testing, broad community surveillance, and genomic analysis of pathogen variants. The dearth of testing in the initial days and months of the pandemic highlighted the need to improve public health infrastructure. The subsequent onset of viral evolution, producing new and functionally different CoV2 strains, increased the importance for the availability and scalability of pathogen genome sequencing and data sharing pipelines through national surveillance programs. As SARS-CoV-2 moves from a pandemic to endemic state, the scientific and medical community must not forget the lessons learned and continue to reinforce the value of ongoing monitoring of circulating pathogens. Methods Aegis Sciences performed an analytical validation of the Respiratory Pathogen ID/AMR Target Enrichment Panel (RPIP, Illumina RUO), a next-generation sequencing assay that uses target capture-based enrichment for detection, quantification, and characterization of 282 viral, bacterial, and fungal respiratory pathogens and over 2000 antimicrobial resistance (AMR) genes. A total of 1598 validation samples were sequenced via the NextSeq 550 Dx (Illumina) and demultiplexed and assembled FASTQ files were analyzed with Explify RPIP Analysis application in BaseSpace Sequencing Hub (Illumina). Samples tested were contrived specimens using 60 different pathogen isolates various concentrations and clinical comparative samples, and the study included the evaluation of concentration vs genome coverage, inclusivity, exclusivity, and orthogonal analyses. Results RPIP is a highly sensitive, robust sequencing assay that can detect organisms at very low concentrations. Evaluation of copy number vs genome coverage revealed that 53 out of 60 organisms were detected at 1 copy/µL (PPV = 86.67%), and 60 out of 60 were detected at 10 copies/µL (PPV = 98.33%) and 50 copies/µL (PPV = 96.67%). The average coverage across the genome improved with increasing concentration and was 59.97% at 1 copy/µL, 72.45% at 10 copies/µL, and >90% at 50 copies/µL. Inclusivity and exclusivity studies revealed an increase in false positives at low concentrations (1 copy/µL = 35.11%) and a reduction of false positives at higher concentrations (10 copies/µL = 20.3%) of isolates, and higher copy numbers of isolates allow for the true positives to be distinguished from contaminants and off-target organisms with very low coverage and quantification results. Orthogonal studies with upper respiratory clinical specimens tested previously by validated PCR methods revealed the following performance characteristics for respiratory pathogens, including SARS-CoV-2, influenza, parainfluenza, and rhinovirus: PPV = 81.11%, NPV = 99.66%, Sensitivity = 98.46%, and Specificity = 93.89%. Conclusions Completion of these studies demonstrated that next-generation sequencing is a cost effective, high-throughput option for broad pathogen tracking and surveillance. Laboratories, like Aegis, must continue to leverage collaborative relationships with public and private sector entities to expand resources, foster innovation, and broaden capabilities to quickly respond to biological threats.

Why pathogen genomics is crucial in Africa's public health.

No abstract available.

Identifying featured indels associated with SARS-CoV-2 fitness.

As an RNA virus, severe acute respiratory coronavirus 2 (SARS-CoV-2) is known for frequent substitution mutations, and substitutions in important genome regions are often associated with viral fitness. However, whether indel mutations are related to viral fitness is generally ignored. Here we developed a computational methodology to investigate indels linked to fitness occurring in over 9 million SARS-CoV-2 genomes. Remarkably, by analyzing 31,642,404 deletion records and 1,981,308 insertion records, our pipeline identified 26,765 deletion types and 21,054 insertion types and discovered 65 indel types with a significant association with Pango lineages. We proposed the concept of featured indels representing the population of specific Pango lineages and variants as substitution mutations and termed these 65 indels as featured indels. The selective pressure of all indel types is assessed using the Bayesian model to explore the importance of indels. Our results exhibited higher selective pressure of indels like substitution mutations, which are important for assessing viral fitness and consistent with previous studies in vitro. Evaluation of the growth rate of each viral lineage indicated that indels play key roles in SARS-CoV-2 evolution and deserve more attention as substitution mutations. IMPORTANCE The fitness of indels in pathogen genome evolution has rarely been studied. We developed a computational methodology to investigate the severe acute respiratory coronavirus 2 genomes and analyze over 33 million records of indels systematically, ultimately proposing the concept of featured indels that can represent specific Pango lineages and identifying 65 featured indels. Machine learning model based on Bayesian inference and viral lineage growth rate evaluation suggests that these featured indels exhibit selection pressure comparable to replacement mutations. In conclusion, indels are not negligible for evaluating viral fitness.

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.

Integrative Molecular Approaches to Plant Disease: A Review

Modern molecular and bioinformatics technologies have made understanding host-pathogen interactions easier. Plants have many ways to protect themselves from microbial diseases, such as physical barriers, PAMP detection, and R genes that recognize pathogen-effector proteins and turn on effector-triggered immunity. Plant pathogen genome databases provide genomic and phenotypic data on plant pathogen species and information on plant-pathogen interactions. Map-based or positional gene cloning is improving our understanding of plant-pathogen interactions, with R genes being used to develop resistance to pathogens. Plant genomes typically contain several hundred nucleotide-binding site-leucine-rich repeats (NLRs), with their number, arrangement, and domain combinations varying by species. Bacterial blight (BB) severely impacts rice production, and about 37 of 44 resistance genes have been mapped and 15 cloned. Many disease-resistant wheat cultivars have been developed using powdery mildew leaf rust (Lr) resistance genes from wild relatives of T. aestivum. Over 140 genes are linked to powdery mildew resistance in T. aestivum and MutChromSeq have found new target genes. Cloning Arabidopsis resistance genes is essential for developing resistant cultivars and understanding R gene evolution. Some R genes encode proteins with nucleotide-binding site (NBS) motifs, and an LRR protects against Erysiphe cruciferarum powdery mildew. CRISPR/Cas9 gene editing is a major tool in plant genome editing, efficiently introducing target site mutations and improving plant immunity. High-throughput sequencing can identify and clone candidate resistance genes in different plant species, and gene editing technologies like CRISPR/Cas have illuminated site-specific mutagenesis and durable resistance.

Plant and pathogen genomics: essential approaches for stem rust resistance gene stacks in wheat.

The deployment of disease resistance genes is currently the most economical and environmentally sustainable method of crop protection. However, disease resistance genes can rapidly break down because of constant pathogen evolution, particularly when they are deployed singularly. Polygenic resistance is, therefore, considered the most durable, but combining and maintaining these genes by breeding is a laborious process as effective genes are usually unlinked. The deployment of polygenic resistance with single-locus inheritance is a promising innovation that overcomes these difficulties while enhancing resistance durability. Because of major advances in genomic technologies, increasing numbers of plant resistance genes have been cloned, enabling the development of resistance transgene stacks (RTGSs) that encode multiple genes all located at a single genetic locus. Gene stacks encoding five stem rust resistance genes have now been developed in transgenic wheat and offer both breeding simplicity and potential resistance durability. The development of similar genomic resources in phytopathogens has advanced effector gene isolation and, in some instances, enabled functional validation of individual resistance genes in RTGS. Here, the wheat stem rust pathosystem is used as an illustrative example of how host and pathogen genomic advances have been instrumental in the development of RTGS, which is a strategy applicable to many other agricultural crop species.

Genomic characterization of respiratory syncytial virus genotypes circulating in the paediatric population of Sydney, NSW, Australia.

Respiratory syncytial virus (RSV), or human orthopneumovirus, is a major cause of acute lower respiratory infection (ALRI), particularly in young children, causing significant morbidity and mortality. We used pathogen genomics to characterize the population structure and genetic signatures of RSV isolates circulating in children in New South Wales between 2016 and 2018 and to understand the evolutionary dynamics of these strains in the context of publicly available RSV genomes from the region and globally. Whole-genome phylogenetic analysis demonstrated the co-circulation of a few major RSV clades in the paediatric population from Sydney. The whole-genome-based genotypes A23 (RSV-A ON1-like genotype) and B6 (RSV-B BA9-like genotype) were the predominant RSV-A and RSV-B genotypes circulating during the study period, respectively. These genotypes were characterized with high levels of diversity of predicted N- and O-linked glycosylation patterns in both the G and F glycoproteins. Interestingly, a novel 72-nucleotide triplication in the sequence that corresponds to the C-terminal region of the G gene was identified in four of the A23 genotype sequenced in this study. Consistently, the population dynamics analysis demonstrated a continuous increase in the effective population size of A23 and B6 genotypes globally. Further investigations including functional mapping of mutations and identifying the impact of sequence changes on virus fitness are highly required. This study highlights the potential impact of an integrated approach that uses WG-based phylogeny and studying selective pressure events in understanding the emergence and dissemination of RSV genotypes.

A novel culture-enriched metagenomic sequencing strategy effectively guarantee the microbial safety of drinking water by uncovering the low abundance pathogens

Assessing the presence of waterborne pathogens and antibiotic resistance genes (ARGs) is crucial for managing the environmental quality of drinking water sources. However, detecting low abundance pathogens in such settings is challenging. In this study, a workflow was developed to enrich for broad spectrum pathogens from drinking water samples. A mock community was used to evaluate the effectiveness of various enrichment broths in detecting low-abundance pathogens. Monthly metagenomic surveillance was conducted in a drinking water source from May to September 2021, and water samples were subjected to five enrichment procedures for 6 h to recover the majority of waterborne bacterial pathogens. Oxford Nanopore Technology (ONT) was used for metagenomic sequencing of enriched samples to obtain high-quality pathogen genomes. The results showed that selective enrichment significantly increased the proportions of targeted bacterial pathogens. Compared to direct metagenomic sequencing of untreated water samples, targeted enrichment followed by ONT sequencing significantly improved the detection of waterborne pathogens and the quality of metagenome-assembled genomes (MAGs). Eighty-six high-quality MAGs, including 70 pathogen MAGs, were obtained from ONT sequencing, while only 12 MAGs representing 10 species were obtained from direct metagenomic sequencing of untreated water samples. In addition, ONT sequencing improved the recovery of mobile genetic elements and the accuracy of phylogenetic analysis. This study highlights the urgent need for efficient methodologies to detect and manage microbial risks in drinking water sources. The developed workflow provides a cost-effective approach for environmental management of drinking water sources with microbial risks. The study also uncovered pathogens that were not detected by traditional methods, thereby advancing microbial risk management of drinking water sources.

BinaRena: a dedicated interactive platform for human-guided exploration and binning of metagenomes

BackgroundExploring metagenomic contigs and “binning” them into metagenome-assembled genomes (MAGs) are essential for the delineation of functional and evolutionary guilds within microbial communities. Despite the advances in automated binning algorithms, their capabilities in recovering MAGs with accuracy and biological relevance are so far limited. Researchers often find that human involvement is necessary to achieve representative binning results. This manual process however is expertise demanding and labor intensive, and it deserves to be supported by software infrastructure.ResultsWe present BinaRena, a comprehensive and versatile graphic interface dedicated to aiding human operators to explore metagenome assemblies via customizable visualization and to associate contigs with bins. Contigs are rendered as an interactive scatter plot based on various data types, including sequence metrics, coverage profiles, taxonomic assignments, and functional annotations. Various contig-level operations are permitted, such as selection, masking, highlighting, focusing, and searching. Binning plans can be conveniently edited, inspected, and compared visually or using metrics including silhouette coefficient and adjusted Rand index. Completeness and contamination of user-selected contigs can be calculated in real time.In demonstration of BinaRena’s usability, we show that it facilitated biological pattern discovery, hypothesis generation, and bin refinement in a complex tropical peatland metagenome. It enabled isolation of pathogenic genomes within closely related populations from the gut microbiota of diarrheal human subjects. It significantly improved overall binning quality after curating results of automated binners using a simulated marine dataset.ConclusionsBinaRena is an installation-free, dependency-free, client-end web application that operates directly in any modern web browser, facilitating ease of deployment and accessibility for researchers of all skill levels. The program is hosted at https://github.com/qiyunlab/binarena, together with documentation, tutorials, example data, and a live demo. It effectively supports human researchers in intuitive interpretation and fine tuning of metagenomic data.85aKgQNhGdzniqM56hYkLtVideo

Public health implementation of pathogen genomics: the role for accreditation and application of ISO standards.

Pathogen genomics has transitioned rapidly from the research setting into a powerful tool now routinely used in public health microbiology, for surveillance, outbreak investigations and disease control. As these investigations can have significant public health, treatment and legal impacts, we must ensure the accuracy of these results through validation of testing processes. For laboratories working in this space, it is important to approach this work with a quality and accreditation framework in mind, working towards implementation of quality systems and test validation that meet international regulatory standards. Here we outline the key international standards and processes that lead toward accreditation for pathogen genomics.

Evolutionarily conserved heat shock protein, HtpX, as an adjunct target against antibiotic-resistant Neisseria gonorrhoeae.

The emergence of multiple drug resistance and extreme drug resistance pathogens necessitates the continuous evaluation of the pathogenic genome to identify conserved molecular targets and their respective inhibitors. In this study, we mapped the global mutational landscape of Neisseria gonorrhoeae (an intracellular pathogen notoriously known to cause the sexually transmitted disease gonorrhoea). We identified highly variable amino acid positions in the antibiotic target genes like the penA, ponA, 23s rRNA, rpoB, gyrA, parC, mtrR and porB. Some variations are directly reported to confer resistance to the currently used front-line drugs like ceftriaxone, cefixime, azithromycinand ciprofloxacin. Further, by whole genome comparison and Shannon entropy analysis, we identified a completely conserved protein HtpX in the drug-resistant as well as susceptible isolates of N. gonorrhoeae (NgHtpX). Comparison with the only available information of Escherichia coli HtpX suggested it to be a transmembrane metalloprotease having a role in stress response. The critical zinc-binding residue of NgHtpX was mapped to E141. By applying composite high throughput screening followed by MD simulations, we identified pemirolast and thalidomide as high-energy binding ligands of NgHtpX. Following cloning and expression of the purified metal-binding domain of NgHtpX (NgHtpXd), its Zn2+ -binding (Kd = 0.4 µM) and drug-binding (pemirolast, Kd = 3.47 µM; and thalidomide, Kd = 1.04 µM) potentials were determined using in-vitro fluorescence quenching experiment. When tested on N. gonorrhoeae cultures, both the ligands imposed a dose-dependent reduction in viability. Overall, our results provide high entropy positions in the targets of presently used antibiotics, which can be further explored to understand the AMR mechanism. Additionally, HtpX and its specific inhibitors identified can be utilised effectively in managing gonococcal infections.

PCR diagnostics are insufficient for the detection of Diarrhoeagenic Escherichia coli in Ibadan, Nigeria.

Understanding the contribution of different diarrhoeagenic Escherichia coli pathotypes to disease burden is critical to mapping risk and informing vaccine development. Targeting select virulence genes by PCR is the diagnostic approach of choice in high-burden, least-resourced African settings. We compared the performance of a commonly-used multiplex protocol to whole genome sequencing (WGS). PCR was applied to 3,815 E. coli isolates from 120 children with diarrhoea and 357 healthy controls. Three or more isolates per specimen were also Illumina-sequenced. Following quality assurance, ARIBA and Virulencefinder database were used to identify virulence targets. Root cause analysis of deviant PCR results was performed by examining target sensitivity using BLAST, Sanger sequencing false-positive amplicons, and identifying lineages prone to false-positivity using in-silico multilocus sequence typing and a Single Nucleotide Polymorphism phylogeny constructed using IQTree. The sensitivity and positive predictive value of PCR compared to WGS ranged from 0-77.8% while specificity ranged from 74.5-94.7% for different pathotypes. WGS identified more enteroaggregative E. coli (EAEC), fewer enterotoxigenic E. coli (ETEC) and none of the Shiga toxin-producing E. coli detected by PCR, painting a considerably different epidemiological picture. Use of the CVD432 target resulted in EAEC under-detection, and enteropathogenic E. coli eae primers mismatched more recently described intimin alleles common in our setting. False positive ETEC were over-represented among West Africa-predominant ST8746 complex strains. PCR precision varies with pathogen genome so primers optimized for use in one part of the world may have noticeably lower sensitivity and specificity in settings where different pathogen lineages predominate.

Towards a post-pandemic future for global pathogen genome sequencing.

Pathogen genome sequencing has become a routine part of our response to active outbreaks of infectious disease and should be an important part of our preparations for future epidemics. In this Essay, we discuss the innovations that have enabled routine pathogen genome sequencing, as well as how genome sequences can be used to understand and control the spread of infectious disease. We also explore the impact of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic on the field of pathogen genomics and outline the challenges we must address to further improve the utility of pathogen genome sequencing in the future.

Whole genome sequence and diversity in multigene families of Babesia ovis.

Ovine babesiosis, caused by Babesia ovis, is an acute, lethal, and endemic disease worldwide and causes a huge economic loss to animal industry. Pathogen genome sequences can be utilized for selecting diagnostic markers, drug targets, and antigens for vaccine development; however, those for B. ovis have not been available so far. In this study, we obtained a draft genome sequence for B. ovis isolated from an infected sheep in Turkey. The genome size was 7.81 Mbp with 3,419 protein-coding genes. It consisted of 41 contigs, and the N50 was 526 Kbp. There were 259 orthologs identified among eight Babesia spp., Plasmodium falciparum, and Toxoplasma gondii. A phylogeny was estimated on the basis of the orthologs, which showed B. ovis to be closest to B. bovis. There were 43 ves genes identified using hmm model as well. They formed a discriminating cluster to other ves multigene family of Babesia spp. but showed certain similarities to those of B. bovis, B. caballi, and Babesia sp. Xinjiang, which is consistent with the phylogeny. Comparative genomics among B. ovis and B. bovis elucidated uniquely evolved genes in these species, which may account for the adaptation.

Genomic epidemiology of dengue in Shantou, China, 2019.

Dengue has been endemic in Southeast Asian countries for decades. There are few reports tracing the dynamics of dengue in real time. In this study, we generated hundreds of pathogen genomes to understand the genomic epidemiology of an outbreak in a hyper-endemic area of dengue. We leveraged whole-genome short-read sequencing (PE150) to generate genomes of the dengue virus and investigated the genomic epidemiology of a dengue virus transmission in a mesoscale outbreak in Shantou, China, in 2019. The outbreak was sustained from July to December 2019. The total accumulated number of laboratory-confirmed cases was 944. No gender bias or fatalities were recorded. Cambodia and Singapore were the main sources of imported dengue cases (74.07%, n = 20). A total of 284 dengue virus strains were isolated, including 259 DENV-1, 24 DENV-2, and 1 DENV-3 isolates. We generated the entire genome of 252 DENV isolates (229 DENV-1, 22 DENV-2, and 1 DENV-3), which represented 26.7% of the total cases. Combined epidemiological and phylogenetic analyses indicated multiple independent introductions. The internal transmission evaluations and transmission network reconstruction supported the inference of phylodynamic analysis, with high Bayes factor support in BSSVS analysis. Two expansion founders and transmission chains were detected in CCH and LG of Shantou. We observed the instant effects of genomic epidemiology in monitoring the dynamics of DENV and highlighted its prospects for real-time tracing of outbreaks of other novel agents in the future.

Metagenomic Surveillance of Viral Gastroenteritis in a Public Health Setting.

Norovirus is the primary cause of viral gastroenteritis (GE). To investigate norovirus epidemiology, there is a need for whole-genome sequencing and reference sets consisting of complete genomes. To investigate the potential of shotgun metagenomic sequencing on the Illumina platform for whole-genome sequencing, 71 reverse transcriptase quantitative PCR (RT-qPCR) norovirus positive-feces (threshold cycle [CT], <30) samples from norovirus surveillance within The Netherlands were subjected to metagenomic sequencing. Data were analyzed through an in-house next-generation sequencing (NGS) analysis workflow. Additionally, we assessed the potential of metagenomic sequencing for the surveillance of off-target viruses that are of importance for public health, e.g., sapovirus, rotavirus A, enterovirus, parechovirus, aichivirus, adenovirus, and bocaparvovirus. A total of 60 complete and 10 partial norovirus genomes were generated, representing 7 genogroup I capsid genotypes and 12 genogroup II capsid genotypes. In addition to the norovirus genomes, the metagenomic approach yielded partial or complete genomes of other viruses for 39% of samples from children and 6.7% of samples from adults, including adenovirus 41 (N = 1); aichivirus 1 (N = 1); coxsackievirus A2 (N = 2), A4 (N = 2), A5 (N = 1), and A16 (N = 1); bocaparvovirus 1 (N = 1) and 3 (N = 1); human parechovirus 1 (N = 2) and 3 (N = 1); Rotavirus A (N = 1); and a sapovirus GI.7 (N = 1). The sapovirus GI.7 was initially not detected through RT-qPCR and warranted an update of the primer and probe set. Metagenomic sequencing on the Illumina platform robustly determines complete norovirus genomes and may be used to broaden gastroenteritis surveillance by capturing off-target enteric viruses. IMPORTANCE Viral gastroenteritis results in significant morbidity and mortality in vulnerable individuals and is primarily caused by norovirus. To investigate norovirus epidemiology, there is a need for whole-genome sequencing and reference sets consisting of full genomes. Using surveillance samples sent to the Dutch National Institute for Public Health and the Environment (RIVM), we compared metagenomics against conventional techniques, such as RT-qPCR and Sanger-sequencing, with norovirus as the target pathogen. We determined that metagenomics is a robust method to generate complete norovirus genomes, in parallel to many off-target pathogenic enteric virus genomes, thereby broadening our surveillance efforts. Moreover, we detected a sapovirus that was not detected by our validated gastroenteritis RT-qPCR panel, which exemplifies the strength of metagenomics. Our study shows that metagenomics can be used for public health gastroenteritis surveillance, the generation of reference-sets for molecular epidemiology, and how it compares to current surveillance strategies.

Improving Protection to Prevent Bacterial Infections: Preliminary Applications of Reverse Vaccinology against the Main Cystic Fibrosis Pathogens.

Reverse vaccinology is a powerful tool that was recently used to develop vaccines starting from a pathogen genome. Some bacterial infections have the necessity to be prevented then treated. For example, individuals with chronic pulmonary diseases, such as Cystic Fibrosis, are prone to develop infections and biofilms in the thick mucus that covers their lungs, mainly caused by Burkholderia cepacia complex, Haemophilus influenzae, Mycobacterium abscessus complex, Pseudomonas aeruginosa and Staphylococcus aureus. These infections are complicated to treat and prevention remains the best strategy. Despite the availability of vaccines against some strains of those pathogens, it is necessary to improve the immunization of people with Cystic Fibrosis against all of them. An effective approach is to develop a broad-spectrum vaccine to utilize proteins that are well conserved across different species. In this context, reverse vaccinology, a method based on computational analysis of the genome of various microorganisms, appears as one of the most promising tools for the identification of putative targets for broad-spectrum vaccine development. This review provides an overview of the vaccines that are under development by reverse vaccinology against the aforementioned pathogens, as well as the progress made so far.