Host Subtraction, Filtering and Assembly Validations for Novel Viral Discovery Using Next Generation Sequencing Data

Embracing Complexity: What Novel Sequencing Methods Are Teaching Us About Herpesvirus Genomic Diversity

The arrival of novel sequencing technologies throughout the past two decades has led to a paradigm shift in our understanding of herpesvirus genomic diversity. Previously, herpesviruses were seen as a family of DNA viruses with low genomic diversity. However, a growing body of evidence now suggests that herpesviruses exist as dynamic populations that possess standing variation and evolve at much faster rates than previously assumed. In this review, we explore how strategies such as deep sequencing, long-read sequencing, and haplotype reconstruction are allowing scientists to dissect the genomic composition of herpesvirus populations. We also discuss the challenges that need to be addressed before a detailed picture of herpesvirus diversity can emerge.

INSaFLU-TELEVIR: an open web-based bioinformatics suite for viral metagenomic detection and routine genomic surveillance

BACKGROUND

Implementation of clinical metagenomics and pathogen genomic surveillance can be particularly challenging due to the lack of bioinformatics tools and/or expertise. In order to face this challenge, we have previously developed INSaFLU, a free web-based bioinformatics platform for virus next-generation sequencing data analysis. Here, we considerably expanded its genomic surveillance component and developed a new module (TELEVIR) for metagenomic virus identification.

RESULTS

The routine genomic surveillance component was strengthened with new workflows and functionalities, including (i) a reference-based genome assembly pipeline for Oxford Nanopore technologies (ONT) data; (ii) automated SARS-CoV-2 lineage classification; (iii) Nextclade analysis; (iv) Nextstrain phylogeographic and temporal analysis (SARS-CoV-2, human and avian influenza, monkeypox, respiratory syncytial virus (RSV A/B), as well as a "generic" build for other viruses); and (v) algn2pheno for screening mutations of interest. Both INSaFLU pipelines for reference-based consensus generation (Illumina and ONT) were benchmarked against commonly used command line bioinformatics workflows for SARS-CoV-2, and an INSaFLU snakemake version was released. In parallel, a new module (TELEVIR) for virus detection was developed, after extensive benchmarking of state-of-the-art metagenomics software and following up-to-date recommendations and practices in the field. TELEVIR allows running complex workflows, covering several combinations of steps (e.g., with/without viral enrichment or host depletion), classification software (e.g., Kaiju, Kraken2, Centrifuge, FastViromeExplorer), and databases (RefSeq viral genome, Virosaurus, etc.), while culminating in user- and diagnosis-oriented reports. Finally, to potentiate real-time virus detection during ONT runs, we developed findONTime, a tool aimed at reducing costs and the time between sample reception and diagnosis.

CONCLUSIONS

The accessibility, versatility, and functionality of INSaFLU-TELEVIR are expected to supply public and animal health laboratories and researchers with a user-oriented and pan-viral bioinformatics framework that promotes a strengthened and timely viral metagenomic detection and routine genomics surveillance. INSaFLU-TELEVIR is compatible with Illumina, Ion Torrent, and ONT data and is freely available at https://insaflu.insa.pt/ (online tool) and https://github.com/INSaFLU (code).

Machine learning-based detection of adventitious microbes in T-cell therapy cultures using long-read sequencing

Assuring that cell therapy products are safe before releasing them for use in patients is critical. Currently, compendial sterility testing for bacteria and fungi can take 7-14 days. The goal of this work was to develop a rapid untargeted approach for the sensitive detection of microbial contaminants at low abundance from low volume samples during the manufacturing process of cell therapies. We developed a long-read sequencing methodology using Oxford Nanopore Technologies MinION platform with 16S and 18S amplicon sequencing to detect USP <71> organisms and other microbial species. Reads are classified metagenomically to predict the microbial species. We used an extreme gradient boosting machine learning algorithm (XGBoost) to first assess if a sample is contaminated, and second, determine whether the predicted contaminant is correctly classified or misclassified. The model was used to make a final decision on the sterility status of the input sample. An optimized experimental and bioinformatics pipeline starting from spiked species through to sequenced reads allowed for the detection of microbial samples at 10 colony-forming units (CFU)/mL using metagenomic classification. Machine learning can be coupled with long-read sequencing to detect and identify sample sterility status and microbial species present in T-cell cultures, including the USP <71> organisms to 10 CFU/mL. IMPORTANCE This research presents a novel method for rapidly and accurately detecting microbial contaminants in cell therapy products, which is essential for ensuring patient safety. Traditional testing methods are time-consuming, taking 7-14 days, while our approach can significantly reduce this time. By combining advanced long-read nanopore sequencing techniques and machine learning, we can effectively identify the presence and types of microbial contaminants at low abundance levels. This breakthrough has the potential to improve the safety and efficiency of cell therapy manufacturing, leading to better patient outcomes and a more streamlined production process.

NanoViromics: long-read sequencing of dsRNA for plant virus and viroid rapid detection

There is a global need for identifying viral pathogens, as well as for providing certified clean plant materials, in order to limit the spread of viral diseases. A key component of management programs for viral-like diseases is having a diagnostic tool that is quick, reliable, inexpensive, and easy to use. We have developed and validated a dsRNA-based nanopore sequencing protocol as a reliable method for detecting viruses and viroids in grapevines. We compared our method, which we term direct-cDNA sequencing from dsRNA (dsRNAcD), to direct RNA sequencing from rRNA-depleted total RNA (rdTotalRNA), and found that it provided more viral reads from infected samples. Indeed, dsRNAcD was able to detect all of the viruses and viroids detected using Illumina MiSeq sequencing (dsRNA-MiSeq). Furthermore, dsRNAcD sequencing was also able to detect low-abundance viruses that rdTotalRNA sequencing failed to detect. Additionally, rdTotalRNA sequencing resulted in a false-positive viroid identification due to the misannotation of a host-driven read. Two taxonomic classification workflows, DIAMOND & MEGAN (DIA & MEG) and Centrifuge & Recentrifuge (Cent & Rec), were also evaluated for quick and accurate read classification. Although the results from both workflows were similar, we identified pros and cons for both workflows. Our study shows that dsRNAcD sequencing and the proposed data analysis workflows are suitable for consistent detection of viruses and viroids, particularly in grapevines where mixed viral infections are common.

Metagenomics studies for the diagnosis and treatment of prostate cancer

AIM

Mutation occurs in the prostate cell genes, leading to abnormal prostate proliferation and ultimately cancer. Prostate cancer (PC) is one of the most common cancers amongst men, and its prevalence worldwide increases relative to men's age. About 16% of the world's cancers are the result of microbes in the human body. Impaired population balance of symbiosis microbes in the human reproductive system is linked to PC development.

DISCUSSION

With the advent of metagenomics science, the genome sequence of the microbiota of the human body has been unveiled. Therefore, it is now possible to identify a higher range of microbiome changes in PC tissue via the Next Generation Technique, which will have positive consequences in personalized medicine. In this review, we intend to question the role of metagenomics studies in the diagnosis and treatment of PC.

CONCLUSION

The microbial imbalance in the men's genital tract might have an effect on prostate health. Based on next-generation sequencing-generated data, Proteobacteria, Firmicutes, Actinobacteria, and Bacteriodetes are the nine frequent phyla detected in a PC sample, which might be involved in inducing mutation in the prostate cells that cause cancer.

Transcriptome-Wide Comparisons and Virulence Gene Polymorphisms of Host-Associated Genotypes of the Cnidarian Parasite Ceratonova shasta in Salmonids

Ceratonova shasta is an important myxozoan pathogen affecting the health of salmonid fishes in the Pacific Northwest of North America. Ceratonova shasta exists as a complex of host-specific genotypes, some with low to moderate virulence, and one that causes a profound, lethal infection in susceptible hosts. High throughput sequencing methods are powerful tools for discovering the genetic basis of these host/virulence differences, but deep sequencing of myxozoans has been challenging due to extremely fast molecular evolution of this group, yielding strongly divergent sequences that are difficult to identify, and unavoidable host contamination. We designed and optimized different bioinformatic pipelines to address these challenges. We obtained a unique set of comprehensive, host-free myxozoan RNA-seq data from C. shasta genotypes of varying virulence from different salmonid hosts. Analyses of transcriptome-wide genetic distances and maximum likelihood multigene phylogenies elucidated the evolutionary relationship between lineages and demonstrated the limited resolution of the established Internal Transcribed Spacer marker for C. shasta genotype identification, as this marker fails to differentiate between biologically distinct genotype II lineages from coho salmon and rainbow trout. We further analyzed the data sets based on polymorphisms in two gene groups related to virulence: cell migration and proteolytic enzymes including their inhibitors. The developed single-nucleotide polymorphism-calling pipeline identified polymorphisms between genotypes and demonstrated that variations in both motility and protease genes were associated with different levels of virulence of C. shasta in its salmonid hosts. The prospective use of proteolytic enzymes as promising candidates for targeted interventions against myxozoans in aquaculture is discussed. We developed host-free transcriptomes of a myxozoan model organism from strains that exhibited different degrees of virulence, as a unique source of data that will foster functional gene analyses and serve as a base for the development of potential therapeutics for efficient control of these parasites.

k-mer-Based Metagenomics Tools Provide a Fast and Sensitive Approach for the Detection of Viral Contaminants in Biopharmaceutical and Vaccine Manufacturing Applications Using Next-Generation Sequencing

Adventitious agent detection during the production of vaccines and biotechnology-based medicines is of critical importance to ensure the final product is free from any possible viral contamination. Increasing the speed and accuracy of viral detection is beneficial as a means to accelerate development timelines and to ensure patient safety. Here, several rapid viral metagenomics approaches were tested on simulated next-generation sequencing (NGS) data sets and existing data sets from virus spike-in studies done in CHO-K1 and HeLa cell lines. It was observed that these rapid methods had comparable sensitivity to full-read alignment methods used for NGS viral detection for these data sets, but their specificity could be improved. A method that first filters host reads using KrakenUniq and then selects the virus classification tool based on the number of remaining reads is suggested as the preferred approach among those tested to detect nonlatent and nonendogenous viruses. Such an approach shows reasonable sensitivity and specificity for the data sets examined and requires less time and memory as full-read alignment methods.IMPORTANCE Next-generation sequencing (NGS) has been proposed as a complementary method to detect adventitious viruses in the production of biotherapeutics and vaccines to current in vivo and in vitro methods. Before NGS can be established in industry as a main viral detection technology, further investigation into the various aspects of bioinformatics analyses required to identify and classify viral NGS reads is needed. In this study, the ability of rapid metagenomics tools to detect viruses in biopharmaceutical relevant samples is tested and compared to recommend an efficient approach. The results showed that KrakenUniq can quickly and accurately filter host sequences and classify viral reads and had comparable sensitivity and specificity to slower full read alignment approaches, such as BLASTn, for the data sets examined.

Metagenomic Analysis of the Respiratory Microbiome of a Broiler Flock from Hatching to Processing

Elucidating the complex microbial interactions in biological environments requires the identification and characterization of not only the bacterial component but also the eukaryotic viruses, bacteriophage, and fungi. In a proof of concept experiment, next generation sequencing approaches, accompanied by the development of novel computational and bioinformatics tools, were utilized to examine the evolution of the microbial ecology of the avian trachea during the growth of a healthy commercial broiler flock. The flock was sampled weekly, beginning at placement and concluding at 49 days, the day before processing. Metagenomic sequencing of DNA and RNA was utilized to examine the bacteria, virus, bacteriophage, and fungal components during flock growth. The utility of using a metagenomic approach to study the avian respiratory virome was confirmed by detecting the dysbiosis in the avian respiratory virome of broiler chickens diagnosed with infection with infectious laryngotracheitis virus. This study provides the first comprehensive analysis of the ecology of the avian respiratory microbiome and demonstrates the feasibility for the use of this approach in future investigations of avian respiratory diseases.

Advances and Discoveries in Myxozoan Genomics

Myxozoans are highly diverse and globally distributed cnidarian endoparasites in freshwater and marine habitats. They have adopted a heteroxenous life cycle, including invertebrate and fish hosts, and have been associated with diseases in aquaculture and wild fish stocks. Despite their importance, genomic resources of myxozoans have proven difficult to obtain due to their miniaturized and derived genome character and close associations with fish tissues. The first 'omic' datasets have now become the main resource for a better understanding of host-parasite interactions, virulence, and diversity, but also the evolutionary history of myxozoans. In this review, we discuss recent genomic advances in the field and outline outstanding questions to be answered with continuous and improved efforts of generating myxozoan genomic data.

MegaPath: sensitive and rapid pathogen detection using metagenomic NGS data

Background

Next-generation sequencing (NGS) enables unbiased detection of pathogens by mapping the sequencing reads of a patient sample to the known reference sequence of bacteria and viruses. However, for a new pathogen without a reference sequence of a close relative, or with a high load of mutations compared to its predecessors, read mapping fails due to a low similarity between the pathogen and reference sequence, which in turn leads to insensitive and inaccurate pathogen detection outcomes.

Results

We developed MegaPath, which runs fast and provides high sensitivity in detecting new pathogens. In MegaPath, we have implemented and tested a combination of polishing techniques to remove non-informative human reads and spurious alignments. MegaPath applies a global optimization to the read alignments and reassigns the reads incorrectly aligned to multiple species to a unique species. The reassignment not only significantly increased the number of reads aligned to distant pathogens, but also significantly reduced incorrect alignments. MegaPath implements an enhanced maximum-exact-match prefix seeding strategy and a SIMD-accelerated Smith-Waterman algorithm to run fast.

Conclusions

In our benchmarks, MegaPath demonstrated superior sensitivity by detecting eight times more reads from a low-similarity pathogen than other tools. Meanwhile, MegaPath ran much faster than the other state-of-the-art alignment-based pathogen detection tools (and compariable with the less sensitivity profile-based pathogen detection tools). The running time of MegaPath is about 20 min on a typical 1 Gb dataset.

Tissue-associated microbial detection in cancer using human sequencing data

Cancer is one of the leading causes of morbidity and mortality in the globe. Microbiological infections account for up to 20% of the total global cancer burden. The human microbiota within each organ system is distinct, and their compositional variation and interactions with the human host have been known to attribute detrimental and beneficial effects on tumor progression. With the advent of next generation sequencing (NGS) technologies, data generated from NGS is being used for pathogen detection in cancer. Numerous bioinformatics computational frameworks have been developed to study viral information from host-sequencing data and can be adapted to bacterial studies. This review highlights existing popular computational frameworks that utilize NGS data as input to decipher microbial composition, which output can predict functional compositional differences with clinically relevant applicability in the development of treatment and prevention strategies.

Discovery and surveillance of viruses from salmon in British Columbia using viral immune-response biomarkers, metatranscriptomics, and high-throughput RT-PCR

The emergence of infectious agents poses a continual economic and environmental challenge to aquaculture production, yet the diversity, abundance, and epidemiology of aquatic viruses are poorly characterised. In this study, we applied salmon host transcriptional biomarkers to identify and select fish in a viral disease state, but only those that were negative for known viruses based on RT-PCR screening. These fish were selected for metatranscriptomic sequencing to discover potential viral pathogens of dead and dying farmed Atlantic (Salmo salar) and Chinook (Oncorhynchus tshawytscha) salmon in British Columbia (BC). We found that the application of the biomarker panel increased the probability of discovering viruses in aquaculture populations. We discovered two viruses that have not previously been characterised in Atlantic salmon farms in BC (Atlantic salmon calicivirus and Cutthroat trout virus-2), as well as partially sequenced three putative novel viruses. To determine the epidemiology of the newly discovered or emerging viruses, we conducted high-throughput reverse transcription polymerase chain reaction (RT-PCR) and screened over 9,000 farmed and wild salmon sampled over one decade. Atlantic salmon calicivirus and Cutthroat trout virus-2 were in more than half of the farmed Atlantic salmon we tested. Importantly we detected some of the viruses we first discovered in farmed Atlantic salmon in Chinook salmon, suggesting a broad host range. Finally, we applied in situ hybridisation to determine infection and found differing cell tropism for each virus tested. Our study demonstrates that continual discovery and surveillance of emerging viruses in these ecologically important salmon will be vital for management of both aquaculture and wild resources in the future.

High-throughput sequencing (HTS) for the analysis of viral populations

The development of High-Throughput Sequencing (HTS) technologies is having a major impact on the genomic analysis of viral populations. Current HTS platforms can capture nucleic acid variation across millions of genes for both selected amplicons and full viral genomes. HTS has already facilitated the discovery of new viruses, hinted new taxonomic classifications and provided a deeper and broader understanding of their diversity, population and genetic structure. Hence, HTS has already replaced standard Sanger sequencing in basic and applied research fields, but the next step is its implementation as a routine technology for the analysis of viruses in clinical settings. The most likely application of this implementation will be the analysis of viral genomics, because the huge population sizes, high mutation rates and very fast replacement of viral populations have demonstrated the limited information obtained with Sanger technology. In this review, we describe new technologies and provide guidelines for the high-throughput sequencing and genetic and evolutionary analyses of viral populations and metaviromes, including software applications. With the development of new HTS technologies, new and refurbished molecular and bioinformatic tools are also constantly being developed to process and integrate HTS data. These allow assembling viral genomes and inferring viral population diversity and dynamics. Finally, we also present several applications of these approaches to the analysis of viral clinical samples including transmission clusters and outbreak characterization.

SEPATH: benchmarking the search for pathogens in human tissue whole genome sequence data leads to template pipelines

BACKGROUND

Human tissue is increasingly being whole genome sequenced as we transition into an era of genomic medicine. With this arises the potential to detect sequences originating from microorganisms, including pathogens amid the plethora of human sequencing reads. In cancer research, the tumorigenic ability of pathogens is being recognized, for example, Helicobacter pylori and human papillomavirus in the cases of gastric non-cardia and cervical carcinomas, respectively. As of yet, no benchmark has been carried out on the performance of computational approaches for bacterial and viral detection within host-dominated sequence data.

RESULTS

We present the results of benchmarking over 70 distinct combinations of tools and parameters on 100 simulated cancer datasets spiked with realistic proportions of bacteria. mOTUs2 and Kraken are the highest performing individual tools achieving median genus-level F1 scores of 0.90 and 0.91, respectively. mOTUs2 demonstrates a high performance in estimating bacterial proportions. Employing Kraken on unassembled sequencing reads produces a good but variable performance depending on post-classification filtering parameters. These approaches are investigated on a selection of cervical and gastric cancer whole genome sequences where Alphapapillomavirus and Helicobacter are detected in addition to a variety of other interesting genera.

CONCLUSIONS

We provide the top-performing pipelines from this benchmark in a unifying tool called SEPATH, which is amenable to high throughput sequencing studies across a range of high-performance computing clusters. SEPATH provides a benchmarked and convenient approach to detect pathogens in tissue sequence data helping to determine the relationship between metagenomics and disease.

Human Virome and Disease: High-Throughput Sequencing for Virus Discovery, Identification of Phage-Bacteria Dysbiosis and Development of Therapeutic Approaches with Emphasis on the Human Gut

The virome is comprised of endogenous retroviruses, eukaryotic viruses, and bacteriophages and is increasingly being recognized as an essential part of the human microbiome. The human virome is associated with Type-1 diabetes (T1D), Type-2 diabetes (T2D), Inflammatory Bowel Disease (IBD), Human Immunodeficiency Virus (HIV) infection, and cancer. Increasing evidence also supports trans-kingdom interactions of viruses with bacteria, small eukaryotes and host in disease progression. The present review focuses on virus ecology and biology and how this translates mostly to human gut virome research. Current challenges in the field and how the development of bioinformatic tools and controls are aiding to overcome some of these challenges are also discussed. Finally, the present review also focuses on how human gut virome research could result in translational and clinical studies that may facilitate the development of therapeutic approaches.

DNAscan: personal computer compatible NGS analysis, annotation and visualisation

BACKGROUND

Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step.

RESULTS

We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub.

CONCLUSIONS

DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available.

Addressing Research Needs in the Field of Plant Virus Ecology by Defining Knowledge Gaps and Developing Wild Dicot Study Systems

Viruses are ubiquitous within all habitats that support cellular life and represent the most important emerging infectious diseases of plants. Despite this, it is only recently that we have begun to describe the ecological roles of plant viruses in unmanaged systems and the influence of ecosystem properties on virus evolution. We now know that wild plants frequently harbor infections by diverse virus species, but much remains to be learned about how viruses influence host traits and how hosts influence virus evolution and vector interactions. To identify knowledge gaps and suggest avenues for alleviating research deficits, we performed a quantitative synthesis of a representative sample of virus ecology literature, developed criteria for expanding the suite of pathosystems serving as models, and applied these criteria through a case study. We found significant gaps in the types of ecological systems studied, which merit more attention. In particular, there is a strong need for a greater diversity of logistically tractable, wild dicot perennial study systems suitable for experimental manipulations of infection status. Based on criteria developed from our quantitative synthesis, we evaluated three California native dicot perennials typically found in Mediterranean-climate plant communities as candidate models: Cucurbita foetidissima (buffalo gourd), Cucurbita palmata (coyote gourd), and Datura wrightii (sacred thorn-apple). We used Illumina sequencing and network analyses to characterize viromes and viral links among species, using samples taken from multiple individuals at two different reserves. We also compared our Illumina workflow with targeted RT-PCR detection assays of varying costs. To make this process accessible to ecologists looking to incorporate virology into existing studies, we describe our approach in detail and discuss advantages and challenges of different protocols. We also provide a bioinformatics workflow based on open-access tools with graphical user interfaces. Our study provides evidence that dicot perennials in xeric habitats support multiple, asymptomatic infections by viruses known to be pathogenic in related crop hosts. Quantifying the impacts of these interactions on plant performance and virus epidemiology in our logistically tractable host systems will provide fundamental information about plant virus ecology outside of crop environments.

Analysis of the Aedes albopictus C6/36 genome provides insight into cell line utility for viral propagation

Background

The 50-year-old Aedes albopictus C6/36 cell line is a resource for the detection, amplification, and analysis of mosquito-borne viruses including Zika, dengue, and chikungunya. The cell line is derived from an unknown number of larvae from an unspecified strain of Aedes albopictus mosquitoes. Toward improved utility of the cell line for research in virus transmission, we present an annotated assembly of the C6/36 genome.

Results

The C6/36 genome assembly has the largest contig N50 (3.3 Mbp) of any mosquito assembly, presents the sequences of both haplotypes for most of the diploid genome, reveals independent null mutations in both alleles of the Dicer locus, and indicates a male-specific genome. Gene annotation was computed with publicly available mosquito transcript sequences. Gene expression data from cell line RNA sequence identified enrichment of growth-related pathways and conspicuous deficiency in aquaporins and inward rectifier K⁺ channels. As a test of utility, RNA sequence data from Zika-infected cells were mapped to the C6/36 genome and transcriptome assemblies. Host subtraction reduced the data set by 89%, enabling faster characterization of nonhost reads.

Conclusions

The C6/36 genome sequence and annotation should enable additional uses of the cell line to study arbovirus vector interactions and interventions aimed at restricting the spread of human disease.

A draft genome sequence for the Ixodes scapularis cell line, ISE6

Background: The tick cell line ISE6, derived from Ixodes scapularis, is commonly used for amplification and detection of arboviruses in environmental or clinical samples.

Methods: To assist with sequence-based assays, we sequenced the ISE6 genome with single-molecule, long-read technology.

Results: The draft assembly appears near complete based on gene content analysis, though it appears to lack some instances of repeats in this highly repetitive genome. The assembly appears to have separated the haplotypes at many loci. DNA short read pairs, used for validation only, mapped to the cell line assembly at a higher rate than they mapped to the Ixodes scapularis reference genome sequence.

Conclusions: The assembly could be useful for filtering host genome sequence from sequence data obtained from cells infected with pathogens.

A host subtraction database for virus discovery in human cell line sequencing data

The human cell lines HepG2, HuH-7, and Jurkat are commonly used for amplification of the RNA viruses present in environmental samples. To assist with assays by RNAseq, we sequenced these cell lines and developed a subtraction database that contains sequences expected in sequence data from uninfected cells. RNAseq data from cell lines infected with Sendai virus were analyzed to test host subtraction. The process of mapping RNAseq reads to our subtraction database vastly reduced the number non-viral reads in the dataset to allow for efficient secondary analyses.

A viral metagenomic approach on a non-metagenomic experiment: Mining next generation sequencing datasets from pig DNA identified several porcine parvoviruses for a retrospective evaluation of viral infections

Shot-gun next generation sequencing (NGS) on whole DNA extracted from specimens collected from mammals often produces reads that are not mapped (i.e. unmapped reads) on the host reference genome and that are usually discarded as by-products of the experiments. In this study, we mined Ion Torrent reads obtained by sequencing DNA isolated from archived blood samples collected from 100 performance tested Italian Large White pigs. Two reduced representation libraries were prepared from two DNA pools constructed each from 50 equimolar DNA samples. Bioinformatic analyses were carried out to mine unmapped reads on the reference pig genome that were obtained from the two NGS datasets. In silico analyses included read mapping and sequence assembly approaches for a viral metagenomic analysis using the NCBI Viral Genome Resource. Our approach identified sequences matching several viruses of the Parvoviridae family: porcine parvovirus 2 (PPV2), PPV4, PPV5 and PPV6 and porcine bocavirus 1-H18 isolate (PBoV1-H18). The presence of these viruses was confirmed by PCR and Sanger sequencing of individual DNA samples. PPV2, PPV4, PPV5, PPV6 and PBoV1-H18 were all identified in samples collected in 1998-2007, 1998-2000, 1997-2000, 1998-2004 and 2003, respectively. For most of these viruses (PPV4, PPV5, PPV6 and PBoV1-H18) previous studies reported their first occurrence much later (from 5 to more than 10 years) than our identification period and in different geographic areas. Our study provided a retrospective evaluation of apparently asymptomatic parvovirus infected pigs providing information that could be important to define occurrence and prevalence of different parvoviruses in South Europe. This study demonstrated the potential of mining NGS datasets non-originally derived by metagenomics experiments for viral metagenomics analyses in a livestock species.

Culture-Independent Diagnostics for Health Security

The past decade has seen considerable development in the diagnostic application of nonculture methods, including nucleic acid amplification-based methods and mass spectrometry, for the diagnosis of infectious diseases. The implications of these new culture-independent diagnostic tests (CIDTs) include bypassing the need to culture organisms, thus potentially affecting public health surveillance systems, which continue to use isolates as the basis of their surveillance programs and to assess phenotypic resistance to antimicrobial agents. CIDTs may also affect the way public health practitioners detect and respond to a bioterrorism event. In response to a request from the Department of Homeland Security, Los Alamos National Laboratory and the Centers for Disease Control and Prevention cosponsored a workshop to review the impact of CIDTs on the rapid detection and identification of biothreat agents. Four panel discussions were held that covered nucleic acid amplification-based diagnostics, mass spectrometry, antibody-based diagnostics, and next-generation sequencing. Exploiting the extensive expertise available at this workshop, we identified the key features, benefits, and limitations of the various CIDT methods for providing rapid pathogen identification that are critical to the response and mitigation of a bioterrorism event. After the workshop we conducted a thorough review of the literature, investigating the current state of these 4 culture-independent diagnostic methods. This article combines information from the literature review and the insights obtained at the workshop.

Expression of eukaryotic-like protein in the microbiome of sponges

Eukaryotic-like proteins (ELPs) are classes of proteins that are found in prokaryotes, but have a likely evolutionary origin in eukaryotes. ELPs have been postulated to mediate host-microbiome interactions. Recent work has discovered that prokaryotic symbionts of sponges contain abundant and diverse genes for ELPs, which could modulate interactions with their filter-feeding and phagocytic host. However, the extent to which these ELP genes are actually used and expressed by the symbionts is poorly understood. Here, we use metatranscriptomics to investigate ELP expression in the microbiomes of three different sponges (Cymbastella concentrica, Scopalina sp. and Tedania anhelens). We developed a workflow with optimized rRNA removal and in silico subtraction of host sequences to obtain a reliable symbiont metatranscriptome. This showed that between 1.3% and 2.3% of all symbiont transcripts contain genes for ELPs. Two classes of ELPs (cadherin and tetratricopeptide repeats) were abundantly expressed in the C. concentrica and Scopalina sp. microbiomes, while ankyrin repeat ELPs were predominant in the T. anhelens metatranscriptome. Comparison with transcripts that do not encode ELPs indicated a constitutive expression of ELPs across a range of bacterial and archaeal symbionts. Expressed ELPs also contained domains involved in protein secretion and/or were co-expressed with proteins involved in extracellular transport. This suggests these ELPs are likely exported, which could allow for direct interaction with the sponge. Our study shows that ELP genes in sponge symbionts represent actively expressed functions that could mediate molecular interaction between symbiosis partners.

Challenges in the analysis of viral metagenomes

Genome sequencing technologies continue to develop with remarkable pace, yet analytical approaches for reconstructing and classifying viral genomes from mixed samples remain limited in their performance and usability. Existing solutions generally target expert users and often have unclear scope, making it challenging to critically evaluate their performance. There is a growing need for intuitive analytical tooling for researchers lacking specialist computing expertise and that is applicable in diverse experimental circumstances. Notable technical challenges have impeded progress; for example, fragments of viral genomes are typically orders of magnitude less abundant than those of host, bacteria, and/or other organisms in clinical and environmental metagenomes; observed viral genomes often deviate considerably from reference genomes demanding use of exhaustive alignment approaches; high intrapopulation viral diversity can lead to ambiguous sequence reconstruction; and finally, the relatively few documented viral reference genomes compared to the estimated number of distinct viral taxa renders classification problematic. Various software tools have been developed to accommodate the unique challenges and use cases associated with characterizing viral sequences; however, the quality of these tools varies, and their use often necessitates computing expertise or access to powerful computers, thus limiting their usefulness to many researchers. In this review, we consider the general and application-specific challenges posed by viral sequencing and analysis, outline the landscape of available tools and methodologies, and propose ways of overcoming the current barriers to effective analysis.

Recovering full-length viral genomes from metagenomes

Infectious disease metagenomics is driven by the question: “what is causing the disease?” in contrast to classical metagenome studies which are guided by “what is out there?” In case of a novel virus, a first step to eventually establishing etiology can be to recover a full-length viral genome from a metagenomic sample. However, retrieval of a full-length genome of a divergent virus is technically challenging and can be time-consuming and costly. Here we discuss different assembly and fragment linkage strategies such as iterative assembly, motif searches, k-mer frequency profiling, coverage profile binning, and other strategies used to recover genomes of potential viral pathogens in a timely and cost-effective manner.