Metagenomic Survey for Viruses in Western Arctic Caribou,Alaska, through Iterative Assembly of Taxonomic Units

Pathogen surveillance in animals does not provide a sufficient level of vigilance because it is generally confined to surveillance of pathogens with known economic impact in domestic animals and practically nonexistent in wildlife species. As most (re-)emerging viral infections originate from animal sources, it is important to obtain insight into viral pathogens present in the wildlife reservoir from a public health perspective. When monitoring living, free-ranging wildlife for viruses, sample collection can be challenging and availability of nucleic acids isolated from samples is often limited. The development of viral metagenomics platforms allows a more comprehensive inventory of viruses present in wildlife. We report a metagenomic viral survey of the Western Arctic herd of barren ground caribou (Rangifer tarandus granti) in Alaska, USA. The presence of mammalian viruses in eye and nose swabs of 39 free-ranging caribou was investigated by random amplification combined with a metagenomic analysis approach that applied exhaustive iterative assembly of sequencing results to define taxonomic units of each metagenome. Through homology search methods we identified the presence of several mammalian viruses, including different papillomaviruses, a novel parvovirus, polyomavirus, and a virus that potentially represents a member of a novel genus in the family Coronaviridae.     

Simultaneous virus identification and characterization of severe unexplained pneumonia cases using a metagenomics sequencing technique

Many viruses can cause respiratory diseases in humans. Although great advances have been achieved in methods of diagnosis, it remains challenging to identify pathogens in unexplained pneumonia (UP) cases. In this study, we applied next-generation sequencing (NGS) technology and a metagenomic approach to detect and characterize respiratory viruses in UP cases from Guizhou Province, China. A total of 33 oropharyngeal swabs were obtained from hospitalized UP patients and subjected to NGS. An unbiased metagenomic analysis pipeline identified 13 virus species in 16 samples. Human rhinovirus C was the virus most frequently detected and was identified in seven samples. Human measles virus, adenovirus B 55 and coxsackievirus A10 were also identified. Metagenomic sequencing also provided virus genomic sequences, which enabled genotype characterization and phylogenetic analysis. For cases of multiple infection, metagenomic sequencing afforded information regarding the quantity of each virus in the sample, which could be used to evaluate each viruses’ role in the disease. Our study highlights the potential of metagenomic sequencing for pathogen identification in UP cases.     

A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.     

Novel bacterial taxa in the human microbiome

The human gut harbors thousands of bacterial taxa. A profusion of metagenomic sequence data has been generated from human stool samples in the last few years, raising the question of whether more taxa remain to be identified. We assessed metagenomic data generated by the Human Microbiome Project Consortium to determine if novel taxa remain to be discovered in stool samples from healthy individuals. To do this, we established a rigorous bioinformatics pipeline that uses sequence data from multiple platforms (Illumina GAIIX and Roche 454 FLX Titanium) and approaches (whole-genome shotgun and 16S rDNA amplicons) to validate novel taxa. We applied this approach to stool samples from 11 healthy subjects collected as part of the Human Microbiome Project. We discovered several low-abundance, novel bacterial taxa, which span three major phyla in the bacterial tree of life. We determined that these taxa are present in a larger set of Human Microbiome Project subjects and are found in two sampling sites (Houston and St. Louis). We show that the number of false-positive novel sequences (primarily chimeric sequences) would have been two orders of magnitude higher than the true number of novel taxa without validation using multiple datasets, highlighting the importance of establishing rigorous standards for the identification of novel taxa in metagenomic data. The majority of novel sequences are related to the recently discovered genus Barnesiella, further encouraging efforts to characterize the members of this genus and to study their roles in the microbial communities of the gut. A better understanding of the effects of less-abundant bacteria is important as we seek to understand the complex gut microbiome in healthy individuals and link changes in the microbiome to disease.     

Exploring the diversity of plant DNA viruses and their satellites using vector-enabled metagenomics on whiteflies

Current knowledge of plant virus diversity is biased towards agents of visible and economically important diseases. Less is known about viruses that have not caused major diseases in crops, or viruses from native vegetation, which are a reservoir of biodiversity that can contribute to viral emergence. Discovery of these plant viruses is hindered by the traditional approach of sampling individual symptomatic plants. Since many damaging plant viruses are transmitted by insect vectors, we have developed "vector-enabled metagenomics" (VEM) to investigate the diversity of plant viruses. VEM involves sampling of insect vectors (in this case, whiteflies) from plants, followed by purification of viral particles and metagenomic sequencing. The VEM approach exploits the natural ability of highly mobile adult whiteflies to integrate viruses from many plants over time and space, and leverages the capability of metagenomics for discovering novel viruses. This study utilized VEM to describe the DNA viral community from whiteflies (Bemisia tabaci) collected from two important agricultural regions in Florida, USA. VEM successfully characterized the active and abundant viruses that produce disease symptoms in crops, as well as the less abundant viruses infecting adjacent native vegetation. PCR assays designed from the metagenomic sequences enabled the complete sequencing of four novel begomovirus genome components, as well as the first discovery of plant virus satellites in North America. One of the novel begomoviruses was subsequently identified in symptomatic Chenopodium ambrosiodes from the same field site, validating VEM as an effective method for proactive monitoring of plant viruses without a priori knowledge of the pathogens. This study demonstrates the power of VEM for describing the circulating viral community in a given region, which will enhance our understanding of plant viral diversity, and facilitate emerging plant virus surveillance and management of viral diseases.     

Is There Still Room for Novel Viral Pathogens in Pediatric Respiratory Tract Infections?

Viruses are the most frequent cause of respiratory disease in children. However, despite the advanced diagnostic methods currently in use, in 20 to 50% of respiratory samples a specific pathogen cannot be detected. In this work, we used a metagenomic approach and deep sequencing to examine respiratory samples from children with lower and upper respiratory tract infections that had been previously found negative for 6 bacteria and 15 respiratory viruses by PCR. Nasal washings from 25 children (out of 250) hospitalized with a diagnosis of pneumonia and nasopharyngeal swabs from 46 outpatient children (out of 526) were studied. DNA reads for at least one virus commonly associated to respiratory infections was found in 20 of 25 hospitalized patients, while reads for pathogenic respiratory bacteria were detected in the remaining 5 children. For outpatients, all the samples were pooled into 25 DNA libraries for sequencing. In this case, in 22 of the 25 sequenced libraries at least one respiratory virus was identified, while in all other, but one, pathogenic bacteria were detected. In both patient groups reads for respiratory syncytial virus, coronavirus-OC43, and rhinovirus were identified. In addition, viruses less frequently associated to respiratory infections were also found. Saffold virus was detected in outpatient but not in hospitalized children. Anellovirus, rotavirus, and astrovirus, as well as several animal and plant viruses were detected in both groups. No novel viruses were identified. Adding up the deep sequencing results to the PCR data, 79.2% of 250 hospitalized and 76.6% of 526 ambulatory patients were positive for viruses, and all other children, but one, had pathogenic respiratory bacteria identified. These results suggest that at least in the type of populations studied and with the sampling methods used the odds of finding novel, clinically relevant viruses, in pediatric respiratory infections are low.     

Estimating the total genome length of a metagenomic sample using k-mers

Background

Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results

As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions

We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

     

Discovery of a Novel Polyomavirus in Acute Diarrheal Samples from Children

Polyomaviruses are small circular DNA viruses associated with chronic infections and tumors in both human and animal hosts. Using an unbiased deep sequencing approach, we identified a novel, highly divergent polyomavirus, provisionally named MX polyomavirus (MXPyV), in stool samples from children. The ∼5.0 kB viral genome exhibits little overall homology (<46% amino acid identity) to known polyomaviruses, and, due to phylogenetic variation among its individual proteins, cannot be placed in any existing taxonomic group. PCR-based screening detected MXPyV in 28 of 834 (3.4%) fecal samples collected from California, Mexico, and Chile, and 1 of 136 (0.74%) of respiratory samples from Mexico, but not in blood or urine samples from immunocompromised patients. By quantitative PCR, the measured titers of MXPyV in human stool at 10% (weight/volume) were as high as 15,075 copies. No association was found between the presence of MXPyV and diarrhea, although girls were more likely to shed MXPyV in the stool than boys (p = 0.012). In one child, viral shedding was observed in two stools obtained 91 days apart, raising the possibility of chronic infection by MXPyV. A multiple sequence alignment revealed that MXPyV is a closely related variant of the recently reported MWPyV and HPyV10 polyomaviruses. Further studies will be important to determine the association, if any, of MXPyV with disease in humans.     

Joint analysis of multiple metagenomic samples

The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed "binning") algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough.     

Marine methylotrophs revealed by stable-isotope probing, multiple displacement amplification and metagenomics

The concentrations of one-carbon substrates that fuel methylotrophic microbial communities in the ocean are limited and the specialized guilds of bacteria that use these molecules may exist at low relative abundance. As a result, these organisms are difficult to identify and are often missed with existing cultivation and gene retrieval methods. Here, we demonstrate a novel proof of concept: using environmentally-relevant substrate concentrations in stable-isotope probing (SIP) incubations to yield sufficient DNA for large-insert metagenomic analysis through multiple displacement amplification (MDA). A marine surface-water sample was labelled sufficiently by incubation with near in situ concentrations of methanol. Picogram quantities of labelled (13)C-DNA were purified from caesium chloride gradients, amplified with MDA to produce microgram amounts of high-molecular-weight DNA ( 10 000 clones. Denaturing gradient gel electrophoresis (DGGE) demonstrated minimal bias associated with the MDA step and implicated Methylophaga-like phylotypes with the marine metabolism of methanol. Polymerase chain reaction screening of 1500 clones revealed a methanol dehydrogenase (MDH) containing insert and shotgun sequencing of this insert resulted in the assembly of a 9-kb fragment of DNA encoding a cluster of enzymes involved in MDH biosynthesis, regulation and assembly. This novel combination of methodology enables future structure-function studies of microbial communities to achieve the long-desired goal of identifying active microbial populations using in situ conditions and performing a directed metagenomic analysis for these ecologically relevant microorganisms.     

A novel group of diverse Polinton-like viruses discovered by metagenome analysis

Background

The rapidly growing metagenomic databases provide increasing opportunities for computational discovery of new groups of organisms. Identification of new viruses is particularly straightforward given the comparatively small size of viral genomes, although fast evolution of viruses complicates the analysis of novel sequences. Here we report the metagenomic discovery of a distinct group of diverse viruses that are distantly related to the eukaryotic virus-like transposons of the Polinton superfamily.

Results

The sequence of the putative major capsid protein (MCP) of the unusual linear virophage associated with Phaeocystis globosa virus (PgVV) was used as a bait to identify potential related viruses in metagenomic databases. Assembly of the contigs encoding the PgVV MCP homologs followed by comprehensive sequence analysis of the proteins encoded in these contigs resulted in the identification of a large group of Polinton-like viruses (PLV) that resemble Polintons (polintoviruses) and virophages in genome size, and share with them a conserved minimal morphogenetic module that consists of major and minor capsid proteins and the packaging ATPase. With a single exception, the PLV lack the retrovirus-type integrase that is encoded in the genomes of all Polintons and the Mavirus group of virophages. However, some PLV encode a newly identified tyrosine recombinase-integrase that is common in bacteria and bacteriophages and is also found in the Organic Lake virophage group. Although several PLV genomes and individual genes are integrated into algal genomes, it appears likely that most of the PLV are viruses. Given the absence of protease and retrovirus-type integrase, the PLV could resemble the ancestral polintoviruses that evolved from bacterial tectiviruses. Apart from the conserved minimal morphogenetic module, the PLV widely differ in their genome complements but share a gene network with Polintons and virophages, suggestive of multiple gene exchanges within a shared gene pool.

Conclusions

The discovery of PLV substantially expands the emerging class of eukaryotic viruses and transposons that also includes Polintons and virophages. This class of selfish elements is extremely widespread and might have been a hotbed of eukaryotic virus, transposon and plasmid evolution. New families of these elements are expected to be discovered.

     

Identification of MW Polyomavirus, a Novel Polyomavirus in Human Stool

We have discovered a novel polyomavirus present in multiple human stool samples. The virus was initially identified by shotgun pyrosequencing of DNA purified from virus-like particles isolated from a stool sample collected from a healthy child from Malawi. We subsequently sequenced the virus' 4,927-bp genome, which has been provisionally named MW polyomavirus (MWPyV). The virus has genomic features characteristic of the family Polyomaviridae but is highly divergent from other members of this family. It is predicted to encode the large T antigen and small T antigen early proteins and the VP1, VP2, and VP3 structural proteins. A real-time PCR assay was designed and used to screen 514 stool samples from children with diarrhea in St. Louis, MO; 12 specimens were positive for MWPyV. Comparison of the whole-genome sequences of the index Malawi case and one St. Louis case demonstrated that the two strains of MWPyV varied by 5.3% at the nucleotide level. The number of polyomaviruses found in the human body continues to grow, raising the question of how many more species have yet to be identified and what roles they play in humans with and without manifest disease.     

VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment

Classification of viruses into their taxonomic ranks (e.g., order, family, and genus) provides a framework to organize an abundant population of viruses. Next-generation metagenomic sequencing technologies lead to a rapid increase in generating sequencing data of viruses which require bioinformatics tools to analyze the taxonomy. Many metagenomic taxonomy classifiers have been developed to study microbiomes, but it is particularly challenging to assign the taxonomy of diverse virus sequences and there is a growing need for dedicated methods to be developed that are optimized to classify virus sequences into their taxa. For taxonomic classification of viruses from metagenomic sequences, we developed VirusTaxo using diverse (e.g., 402 DNA and 280 RNA) genera of viruses. VirusTaxo has an average accuracy of 93% at genus level prediction in DNA and RNA viruses. VirusTaxo outperformed existing taxonomic classifiers of viruses where it assigned taxonomy of a larger fraction of metagenomic contigs compared to other methods. Benchmarking of VirusTaxo on a collection of SARS-CoV-2 sequencing libraries and metavirome datasets suggests that VirusTaxo can characterize virus taxonomy from highly diverse contigs and provide a reliable decision on the taxonomy of viruses.     

Pepper Mild Mottle Virus,a Plant Virus Associated with Specific Immune Responses,Fever, Abdominal Pains,and Pruritus in Humans

Background

Recently, metagenomic studies have identified viable Pepper mild mottle virus (PMMoV), a plant virus, in the stool of healthy subjects. However, its source and role as pathogen have not been determined.

Methods and Findings

21 commercialized food products containing peppers, 357 stool samples from 304 adults and 208 stool samples from 137 children were tested for PMMoV using real-time PCR, sequencing, and electron microscopy. Anti-PMMoV IgM antibody testing was concurrently performed. A case-control study tested the association of biological and clinical symptoms with the presence of PMMoV in the stool. Twelve (57%) food products were positive for PMMoV RNA sequencing. Stool samples from twenty-two (7.2%) adults and one child (0.7%) were positive for PMMoV by real-time PCR. Positive cases were significantly more likely to have been sampled in Dermatology Units (p<10⁻⁶), to be seropositive for anti-PMMoV IgM antibodies (p = 0.026) and to be patients who exhibited fever, abdominal pains, and pruritus (p = 0.045, 0.038 and 0.046, respectively).

Conclusions

Our study identified a local source of PMMoV and linked the presence of PMMoV RNA in stool with a specific immune response and clinical symptoms. Although clinical symptoms may be imputable to another cofactor, including spicy food, our data suggest the possibility of a direct or indirect pathogenic role of plant viruses in humans.     

Enteroaggregative and cell-detaching<Emphasis Type="Italic">Escherichia coli</Emphasis> strains among polish children with and without diarrhea

To determine the association of enteroaggregative (EAEC) and cell-detaching (CDEC)Escherichia coli with diarrhea of unknown origin among children from Wroc?aw (Poland),E. coli strains isolated from stool specimens of children with diarrhea were examined for mannose-resistant adherence to HEp-2 cells. EAEC were isolated from 10 of 39 (26%) children examined with diarrhea and 4 of 20 (20%) age-matched controls. CDEC were present in 14 (36%) cases of diarrhea and 7 (35%) healthy subjects. Cell-detaching activity was distinctly associated with hemolysin production. Among hemolytic CDEC strains cytotoxic necrotizing factor 1 (CNF1) synthesis prevailed among isolates obtained from cases of diarrhea (57%) in comparison with isolates obtained from healthy controls (14.3%). Although neither EAEC nor CDECE. coli strains were associated with diarrhea of children in this setting, there were differences among EAEC and CDEC strains isolated from children with and without diarrhea.     

Statistical Approach of Functional Profiling for a Microbial Community

Background

Metagenomics is a relatively new but fast growing field within environmental biology and medical sciences. It enables researchers to understand the diversity of microbes, their functions, cooperation, and evolution in a particular ecosystem. Traditional methods in genomics and microbiology are not efficient in capturing the structure of the microbial community in an environment. Nowadays, high-throughput next-generation sequencing technologies are powerfully driving the metagenomic studies. However, there is an urgent need to develop efficient statistical methods and computational algorithms to rapidly analyze the massive metagenomic short sequencing data and to accurately detect the features/functions present in the microbial community. Although several issues about functions of metagenomes at pathways or subsystems level have been investigated, there is a lack of studies focusing on functional analysis at a low level of a hierarchical functional tree, such as SEED subsystem tree.

Results

A two-step statistical procedure (metaFunction) is proposed to detect all possible functional roles at the low level from a metagenomic sample/community. In the first step a statistical mixture model is proposed at the base of gene codons to estimate the abundances for the candidate functional roles, with sequencing error being considered. As a gene could be involved in multiple biological processes the functional assignment is therefore adjusted by utilizing an error distribution in the second step. The performance of the proposed procedure is evaluated through comprehensive simulation studies. Compared with other existing methods in metagenomic functional analysis the new approach is more accurate in assigning reads to functional roles, and therefore at more general levels. The method is also employed to analyze two real data sets.

Conclusions

metaFunction is a powerful tool in accurate profiling functions in a metagenomic sample.     

The challenges of defining the human nasopharyngeal resistome

The nasopharynx is an important microbial reservoir for the emergence and spread of antibiotic-resistant organisms. The nasopharyngeal resistome is an extensive, adaptable reservoir of antibiotic-resistance genes (ARGs) within this niche. Metagenomic sequencing decodes the genetic material of all organisms within a sample using next-generation technologies, permitting unbiased discovery of novel ARGs and associated mobile genetic elements (MGEs). The challenges of sequencing a low-biomass bacterial sample have limited exploration of the nasopharyngeal resistome. Here, we explore the current understanding of the nasopharyngeal resistome, particularly the role of MGEs in propagating antimicrobial resistance (AMR), explore the advantages and limitations of metagenomic sequencing technologies and bioinformatic pipelines for nasopharyngeal resistome analysis, and highlight the key outstanding questions for future research.     

A Statistical Framework for Accurate Taxonomic Assignment of Metagenomic Sequencing Reads

The advent of next-generation sequencing technologies has greatly promoted the field of metagenomics which studies genetic material recovered directly from an environment. Characterization of genomic composition of a metagenomic sample is essential for understanding the structure of the microbial community. Multiple genomes contained in a metagenomic sample can be identified and quantitated through homology searches of sequence reads with known sequences catalogued in reference databases. Traditionally, reads with multiple genomic hits are assigned to non-specific or high ranks of the taxonomy tree, thereby impacting on accurate estimates of relative abundance of multiple genomes present in a sample. Instead of assigning reads one by one to the taxonomy tree as many existing methods do, we propose a statistical framework to model the identified candidate genomes to which sequence reads have hits. After obtaining the estimated proportion of reads generated by each genome, sequence reads are assigned to the candidate genomes and the taxonomy tree based on the estimated probability by taking into account both sequence alignment scores and estimated genome abundance. The proposed method is comprehensively tested on both simulated datasets and two real datasets. It assigns reads to the low taxonomic ranks very accurately. Our statistical approach of taxonomic assignment of metagenomic reads, TAMER, is implemented in R and available at http://faculty.wcas.northwestern.edu/hji403/MetaR.htm.     

Amplification of uncultured single-stranded DNA viruses from rice paddy soil

Viruses are known to be the most numerous biological entities in soil; however, little is known about their diversity in this environment. In order to explore the genetic diversity of soil viruses, we isolated viruses by centrifugation and sequential filtration before performing a metagenomic investigation. We adopted multiple-displacement amplification (MDA), an isothermal whole-genome amplification method with phi29 polymerase and random hexamers, to amplify viral DNA and construct clone libraries for metagenome sequencing. By the MDA method, the diversity of both single-stranded DNA (ssDNA) viruses and double-stranded DNA viruses could be investigated at the same time. On the contrary, by eliminating the denaturing step in the MDA reaction, only ssDNA viral diversity could be explored selectively. Irrespective of the denaturing step, more than 60% of the soil metagenome sequences did not show significant hits (E-value criterion, 0.001) with previously reported viral sequences. Those hits that were considered to be significant were also distantly related to known ssDNA viruses (average amino acid similarity, approximately 34%). Phylogenetic analysis showed that replication-related proteins (which were the most frequently detected proteins) related to those of ssDNA viruses obtained from the metagenomic sequences were diverse and novel. Putative circular genome components of ssDNA viruses that are unrelated to known viruses were assembled from the metagenomic sequences. In conclusion, ssDNA viral diversity in soil is more complex than previously thought. Soil is therefore a rich pool of previously unknown ssDNA viruses.