Metagenomics: read length matters

High-Throughput Oxford Nanopore Sequencing Unveils Complex Viral Population in Kansas Wheat: Implications for Sustainable Virus Management

Wheat viruses are major yield-reducing factors, with mixed infections causing substantial economic losses. Determining field virus populations is crucial for effective management and developing virus-resistant cultivars. This study utilized the high-throughput Oxford Nanopore sequencing technique (ONT) to characterize wheat viral populations in major wheat-growing counties of Kansas from 2019 to 2021. Wheat leaves exhibiting virus-like symptoms were collected, total RNA was extracted, and cDNA libraries were prepared using a PCR-cDNA barcoding kit, then loaded onto ONT MinION flow cells. Sequencing reads aligned with cereal virus references identified eight wheat virus species. Tritimovirus tritici (wheat streak mosaic virus, WSMV), Poacevirus tritici (Triticum mosaic virus, TriMV), Bromovirus BMV (brome mosaic virus, BMV), as well as Emaravirus tritici, Luteovirus pavhordei, L. sgvhordei, Bymovirus tritici, and Furovirus tritici. Mixed infections involving two to five viruses in a single sample were common, with the most prevalent being WSMV + TriMV at 16.7% and WSMV + TriMV + BMV at 11.9%. Phylogenetic analysis revealed a wide distribution of WSMV isolates, including European and recombinant variants. A phylogenetic analysis of Emaravirus tritici based on RNA 3A and 3B segments and whole-genome characterization of Furovirus tritici were also conducted. These findings advance understanding of genetic variability, phylogenetics, and viral co-infections, supporting the development of sustainable management practices through host genetic resistance.

Metagenomic Analysis of Viromes of Aedes Mosquitoes across India

Metagenomic analysis of Aedes aegypti and Ae. albopictus mosquitoes from diverse geographical regions of India revealed the presence of several insect viruses of human interest. Most abundant reads found in Ae. aegypti mosquitoes were of Phasi Charoen-like virus (PCLV), Choristoneura fumiferana granulovirus (CfGV), Cell fusing agent virus (CFAV), and Wenzhou sobemo-like virus 4 (WSLV4), whereas WSLV4 and CfGV constituted the highest percentage of reads in Ae. albopictus viromes. Other reads that were of low percentage included Hubei mosquito virus 2 (HMV2), Porcine astrovirus 4 (PAstV4), and Wild Boar astrovirus (WBAstV). PCLV and CFAV, which were found to be abundant in Ae. aegypti viromes were absent in Ae. albopictus viromes. Among the viromes analyzed, Ae. aegypti sampled from Pune showed the highest percentage (79.82%) of viral reads, while Ae. aegypti mosquitoes sampled from Dibrugarh showed the lowest percentage (3.47%). Shamonda orthobunyavirus (SHAV), African swine fever virus (ASFV), Aroa virus (AROAV), and Ilheus virus (ILHV), having the potential to infect vertebrates, including humans, were also detected in both mosquito species, albeit with low read numbers. Reads of gemykibivirus, avian retrovirus, bacteriophages, herpesviruses, and viruses infecting protozoans, algae, etc., were also detected in the mosquitoes. A high percentage of reads in the Ae. albopictus mosquito samples belonged to unclassified viruses and warrant further investigation. The data generated in the present work may not only lead to studies to explain the influence of these viruses on the replication and transmission of viruses of clinical importance but also to find applications as biocontrol agents against pathogenic viruses.

Inter-kingdom interactions and stability of methanogens revealed by machine-learning guided multi-omics analysis of industrial-scale biogas plants

Multi-omics analysis is a powerful tool for the detection and study of inter-kingdom interactions, such as those between bacterial and archaeal members of complex biogas-producing microbial communities. In the present study, the microbiomes of three industrial-scale biogas digesters, each fed with different substrates, were analysed using a machine-learning guided genome-centric metagenomics framework complemented with metatranscriptome data. This data permitted us to elucidate the relationship between abundant core methanogenic communities and their syntrophic bacterial partners. In total, we detected 297 high-quality, non-redundant metagenome-assembled genomes (nrMAGs). Moreover, the assembled 16 S rRNA gene profiles of these nrMAGs showed that the phylum Firmicutes possessed the highest copy number, while the representatives of the archaeal domain had the lowest. Further investigation of the three anaerobic microbial communities showed characteristic alterations over time but remained specific to each industrial-scale biogas plant. The relative abundance of various microorganisms as revealed by metagenome data was independent from corresponding metatranscriptome activity data. Archaea showed considerably higher activity than was expected from their abundance. We detected 51 nrMAGs that were present in all three biogas plant microbiomes with different abundances. The core microbiome correlated with the main chemical fermentation parameters, and no individual parameter emerged as a predominant shaper of community composition. Various interspecies H₂/electron transfer mechanisms were assigned to hydrogenotrophic methanogens in the biogas plants that ran on agricultural biomass and wastewater. Analysis of metatranscriptome data revealed that methanogenesis pathways were the most active of all main metabolic pathways.

Genetic characterization of the rare Bruconha virus (Bunyavirales: Orthobunyavirus) isolated in Vale do Ribeira (Atlantic Forest biome), Southeastern Brazil

Brazil is a great source of arbovirus diversity, mainly in the Amazon region. However, other biomes, especially the Atlantic Forest, may also be a hotspot for emerging viruses, including Bunyaviruses (Negarnaviricota: Bunyavirales). For instance, Vale do Ribeira, located in the Southeastern region, has been widely studied for virus surveillance, where Flavivirus, Alphavirus and Bunyaviruses were isolated during the last decades, including Bruconha virus (BRCV), a member of Orthobunyavirus genus Group C, in 1976. Recently, a new isolate of BRCV named Span321532 was obtained from an adult sentinel mouse placed in Iguape city in 2011, and a full-length genome was generated with nucleotide differences ranging between 1.5%, 5.3% and 5% (L, M and S segments, respectively) from the prototype isolated 35 years earlier. In addition, each segment placed BRCV into different clusters, showing the high variety within Bunyavirales. Although no evidence for reassortants was detected, this finding reiterates the need for new surveillance and genomic studies in the area considering the high mutation rates of arbovirus, and also to identify the hosts capable of supporting the continuous circulation of Orthobunyavirus.

Unraveling potential enzymes and their functional role in fine cocoa beans fermentation using temporal shotgun metagenomics

Cocoa beans fermentation is a spontaneous process, essential for the generation of quality starting material for fine chocolate production. The understanding of this process has been studied by the application of high-throughput sequencing technologies, which grants a better assessment of the different microbial taxa and their genes involved in this microbial succession. The present study used shotgun metagenomics to determine the enzyme-coding genes of the microbiota found in two different groups of cocoa beans varieties during the fermentation process. The statistical evaluation of the most abundant genes in each group and time studied allowed us to identify the potential metabolic pathways involved in the success of the different microorganisms. The results showed that, albeit the distinction between the initial (0 h) microbiota of each varietal group was clear, throughout fermentation (24-144 h) this difference disappeared, indicating the existence of selection pressures. Changes in the microbiota enzyme-coding genes over time pointed to the distinct ordering of fermentation at 24-48 h (T1), 72-96 h (T2), and 120-144 h (T3). At T1, the significantly more abundant enzyme-coding genes were related to threonine metabolism and those genes related to the glycolytic pathway, explained by the abundance of sugars in the medium. At T2, the genes linked to the metabolism of ceramides and hopanoids lipids were clearly dominant, which are associated with the resistance of microbial species to extreme temperatures and pH values. In T3, genes linked to trehalose metabolism, related to the response to heat stress, dominated. The results obtained in this study provided insights into the potential functionality of microbial community succession correlated to gene function, which could improve cocoa processing practices to ensure the production of more stable quality end products.

Bioaerosols in the atmosphere at two sites in Northern Europe in spring 2021: Outline of an experimental campaign

A coordinated observational and modelling campaign targeting biogenic aerosols in the air was performed during spring 2021 at two locations in Northern Europe: Helsinki (Finland) and Siauliai (Lithuania), approximately 500 km from each other in north-south direction. The campaign started on March 1, 2021 in Siauliai (12 March in Helsinki) and continued till mid-May in Siauliai (end of May in Helsinki), thus recording the transition of the atmospheric biogenic aerosols profile from winter to summer. The observations included a variety of samplers working on different principles. The core of the program was based on 2- and 2.4--hourly sampling in Helsinki and Siauliai, respectively, with sticky slides (Hirst 24-h trap in Helsinki, Rapid-E slides in Siauliai). The slides were subsequently processed extracting the DNA from the collected aerosols, which was further sequenced using the 3-rd generation sequencing technology. The core sampling was accompanied with daily and daytime sampling using standard filter collectors. The hourly aerosol concentrations at the Helsinki monitoring site were obtained with a Poleno flow cytometer, which could recognize some of the aerosol types. The sampling campaign was supported by numerical modelling. For every sample, SILAM model was applied to calculate its footprint and to predict anthropogenic and natural aerosol concentrations, at both observation sites. The first results confirmed the feasibility of the DNA collection by the applied techniques: all but one delivered sufficient amount of DNA for the following analysis, in over 40% of the cases sufficient for direct DNA sequencing without the PCR step. A substantial variability of the DNA yield has been noticed, generally not following the diurnal variations of the total-aerosol concentrations, which themselves showed variability not related to daytime. An expected upward trend of the biological material amount towards summer was observed but the day-to-day variability was large. The campaign DNA analysis produced the first high-resolution dataset of bioaerosol composition in the North-European spring. It also highlighted the deficiency of generic DNA databases in applications to atmospheric biota: about 40% of samples were not identified with standard bioinformatic methods.

Marine viruses and climate change: Virioplankton, the carbon cycle, and our future ocean

Interactions between marine viruses and microbes are a critical part of the oceanic carbon cycle. The impacts of virus-host interactions range from short-term disruptions in the mobility of microbial biomass carbon to higher trophic levels through cell lysis (i.e., the viral shunt) to long-term reallocation of microbial biomass carbon to the deep sea through accelerating the biological pump (i.e., the viral shuttle). The biogeochemical backdrop of the ocean-the physical, chemical, and biological landscape-influences the likelihood of both virus-host interactions and particle formation, and the fate and flow of carbon. As climate change reshapes the oceanic landscape through large-scale shifts in temperature, circulation, stratification, and acidification, virus-mediated carbon flux is likely to shift in response. Dynamics in the directionality and magnitude of changes in how, where, and when viruses mediate the recycling or storage of microbial biomass carbon is largely unknown. Integrating viral infection dynamics data obtained from experimental models and field systems, with particle motion microphysics and global observations of oceanic biogeochemistry, into improved ecosystem models will enable viral oceanographers to better predict the role of viruses in marine carbon cycling in the future ocean.

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the "microbial dark matter" (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM): a method for detection of antimicrobial resistance in environmental samples

Effective monitoring of antibiotic resistance genes and their dissemination in environmental ecosystems has been hindered by the cost and efficiency of methods available for the task. We developed the Diversity of Antibiotic Resistance genes and Transfer Elements-Quantitative Monitoring (DARTE-QM), a method implementing TruSeq high-throughput sequencing to simultaneously sequence thousands of antibiotic resistant gene targets representing a full-spectrum of antibiotic resistance classes common to environmental systems. In this study, we demonstrated DARTE-QM by screening 662 antibiotic resistance genes within complex environmental samples originated from manure, soil, and livestock feces, in addition to a mock-community reference to assess sensitivity and specificity. DARTE-QM offers a new approach to studying antibiotic resistance in environmental microbiomes, showing advantages in efficiency and the ability to scale for many samples. This method provides a means of data acquisition that will alleviate some of the obstacles that many researchers in this area currently face.

Smith et al. present DARTE-QM, a highthroughput sequencing method for screening environmental DNA samples for antibiotic resistance genes on a broad scale. This method is demonstrated as effective on soil, manure and livestock fecal samples, as well as a synthetic mock-community reference.

An Introduction to Whole-Metagenome Shotgun Sequencing Studies

Microbial communities are found across diverse environments, including within and across the human body. As many microbes are unculturable in the lab, much of what is known about a microbiome-a collection of bacteria, fungi, archaea, and viruses inhabiting an environment--is from the sequencing of DNA from within the constituent community. Here, we provide an introduction to whole-metagenome shotgun sequencing studies, a ubiquitous approach for characterizing microbial communities, by reviewing three major research areas in metagenomics: assembly, community profiling, and functional profiling. Though not exhaustive, these areas encompass a large component of the metagenomics literature. We discuss each area in depth, the challenges posed by whole-metagenome shotgun sequencing, and approaches fundamental to the solutions of each. We conclude by discussing promising areas for future research. Though our emphasis is on the human microbiome, the methods discussed are broadly applicable across study systems.

An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome

The identification of bacterial pathogens to humans is critical for environmental microbial risk assessment. However, current methods for identifying pathogens in environmental samples are limited in their ability to detect highly diverse bacterial communities and accurately differentiate pathogens from commensal bacteria. In the present study, we suggest an improved approach using a combination of identification results obtained from multiple databases, including the multilocus sequence typing (MLST) database, virulence factor database (VFDB), and pathosystems resource integration center (PATRIC) databases to resolve current challenges. By integrating the identification results from multiple databases, potential bacterial pathogens in metagenomes were identified and classified into eight different groups. Based on the distribution of genes in each group, we proposed an equation to calculate the metagenomic pathogen identification index (MPII) of each metagenome based on the weighted abundance of identified sequences in each database. We found that the accuracy of pathogen identification was improved by using combinations of multiple databases compared to that of individual databases. When the approach was applied to environmental metagenomes, metagenomes associated with activated sludge were estimated with higher MPII than other environments (i.e., drinking water, ocean water, ocean sediment, and freshwater sediment). The calculated MPII values were statistically distinguishable among different environments (p<0.05). These results demonstrate that the suggested approach allows more for more accurate identification of the pathogens associated with metagenomes.

Evaluation of PCR conditions for characterizing bacterial communities with full-length 16S rRNA genes using a portable nanopore sequencer

MinION (Oxford Nanopore Technologies), a portable nanopore sequencer, was introduced in 2014 as a new DNA sequencing technology. MinION is now widely used because of its low initial start-up costs relative to existing DNA sequencers, good portability, easy-handling, real-time analysis and long-read output. However, differences in the experimental conditions used for 16S rRNA-based PCR can bias bacterial community assessments in samples. Therefore, basic knowledge about reliable experimental conditions is needed to ensure the appropriate use of this technology. Our study concerns the reliability of techniques for obtaining accurate and quantitative full-length 16S rRNA amplicon sequencing data for bacterial community structure assessment using MinION. We compared five PCR conditions using three independent mock microbial community standard DNAs and established appropriate, standardized, better PCR conditions among the trials. We then sequenced two mock communities and six environmental samples using Illumina MiSeq for comparison. Modifying the PCR conditions improved the sequencing quality; the optimized conditions were 35 cycles of 95 °C for 1 min, 60 °C for 1 min and 68 °C for 3 min. Our results provide important information for researchers to determine bacterial community using MinION accurately.

The effect of variant interference on de novo assembly for viral deep sequencing

BACKGROUND

Viruses have high mutation rates and generally exist as a mixture of variants in biological samples. Next-generation sequencing (NGS) approaches have surpassed Sanger for generating long viral sequences, yet how variants affect NGS de novo assembly remains largely unexplored.

RESULTS

Our results from > 15,000 simulated experiments showed that presence of variants can turn an assembly of one genome into tens to thousands of contigs. This "variant interference" (VI) is highly consistent and reproducible by ten commonly-used de novo assemblers, and occurs over a range of genome length, read length, and GC content. The main driver of VI is pairwise identities between viral variants. These findings were further supported by in silico simulations, where selective removal of minor variant reads from clinical datasets allow the "rescue" of full viral genomes from fragmented contigs.

CONCLUSIONS

These results call for careful interpretation of contigs and contig numbers from de novo assembly in viral deep sequencing.

Insights Into Chloroplast Genome Evolution Across Opuntioideae (Cactaceae) Reveals Robust Yet Sometimes Conflicting Phylogenetic Topologies

Chloroplast genomes (plastomes) are frequently treated as highly conserved among land plants. However, many lineages of vascular plants have experienced extensive structural rearrangements, including inversions and modifications to the size and content of genes. Cacti are one of these lineages, containing the smallest plastome known for an obligately photosynthetic angiosperm, including the loss of one copy of the inverted repeat (∼25 kb) and the ndh gene suite, but only a few cacti from the subfamily Cactoideae have been sufficiently characterized. Here, we investigated the variation of plastome sequences across the second-major lineage of the Cactaceae, the subfamily Opuntioideae, to address (1) how variable is the content and arrangement of chloroplast genome sequences across the subfamily, and (2) how phylogenetically informative are the plastome sequences for resolving major relationships among the clades of Opuntioideae. Our de novo assembly of the Opuntia quimilo plastome recovered an organelle of 150,347 bp in length with both copies of the inverted repeat and the presence of all the ndh gene suite. An expansion of the large single copy unit and a reduction of the small single copy unit was observed, including translocations and inversion of genes, as well as the putative pseudogenization of some loci. Comparative analyses among all clades within Opuntioideae suggested that plastome structure and content vary across taxa of this subfamily, with putative independent losses of the ndh gene suite and pseudogenization of genes across disparate lineages, further demonstrating the dynamic nature of plastomes in Cactaceae. Our plastome dataset was robust in resolving three tribes with high support within Opuntioideae: Cylindropuntieae, Tephrocacteae and Opuntieae. However, conflicting topologies were recovered among major clades when exploring different assemblies of markers. A plastome-wide survey for highly informative phylogenetic markers revealed previously unused regions for future use in Sanger-based studies, presenting a valuable dataset with primers designed for continued evolutionary studies across Cactaceae. These results bring new insights into the evolution of plastomes in cacti, suggesting that further analyses should be carried out to address how ecological drivers, physiological constraints and morphological traits of cacti may be related with the common rearrangements in plastomes that have been reported across the family.

Enabling Precision Oncology Through Precision Diagnostics

Genomic testing enables clinical management to be tailored to individual cancer patients based on the molecular alterations present within cancer cells. Genomic sequencing results can be applied to detect and classify cancer, predict prognosis, and target therapies. Next-generation sequencing has revolutionized the field of cancer genomics by enabling rapid and cost-effective sequencing of large portions of the genome. With this technology, precision oncology is quickly becoming a realized paradigm for managing the treatment of cancer patients. However, many challenges must be overcome to efficiently implement the transition of next-generation sequencing from research applications to routine clinical practice, including using specimens commonly available in the clinical setting; determining how to process, store, and manage large amounts of sequencing data; determining how to interpret and prioritize molecular findings; and coordinating health professionals from multiple disciplines.

Testing the advantages and disadvantages of short- and long- read eukaryotic metagenomics using simulated reads

BACKGROUND

The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. Illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities.

RESULTS

Here we compare simulated long reads from Oxford Nanopore and Pacific Biosciences (PacBio) with high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities.

CONCLUSIONS

This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.

MinION Sequencing of colorectal cancer tumour microbiomes-A comparison with amplicon-based and RNA-Sequencing

BACKGROUND

Recent evidence suggests a role for the gut microbiome in the development and progression of many diseases and many studies have been carried out to analyse the microbiome using a variety of methods. In this study, we compare MinION sequencing with meta-transcriptomics and amplicon-based sequencing for microbiome analysis of colorectal tumour tissue samples.

METHODS

DNA and RNA were extracted from 11 colorectal tumour samples. 16S rRNA amplicon sequencing and MinION sequencing was carried out using genomic DNA, and RNA-Sequencing for meta-transcriptomic analysis. Non-human MinION and RNA-Sequencing reads, and 16S rRNA amplicon sequencing reads were taxonomically classified using a database built from available RefSeq bacterial and archaeal genomes and a k-mer based algorithm in Kraken2. Concordance between the three platforms at different taxonomic levels was tested on a per-sample basis using Spearman's rank correlation.

RESULTS

The average number of reads per sample using RNA-Sequencing was greater than 129 times that generated using MinION sequencing. However, the average read length of MinION sequences was more than 13 times that of RNA or 16S rRNA amplicon sequencing. Taxonomic assignment using 16S sequencing was less reliable beyond the genus level, and both RNA-Sequencing and MinION sequencing could detect greater numbers of phyla and genera in the same samples, compared to 16S sequencing. Bacterial species associated with colorectal cancer, Fusobacterium nucleatum, Parvimonas micra, Bacteroides fragilis and Porphyromonas gingivalis, were detectable using MinION, RNA-Sequencing and 16S rRNA amplicon sequencing data.

CONCLUSIONS

Long-read sequences generated using MinION sequencing can compensate for low numbers of reads for bacterial classification. MinION sequencing can discriminate between bacterial strains and plasmids and shows potential as a cost-effective tool for rapid microbiome sequencing in a clinical setting.

Detection and characterization of Ilheus and Iguape virus genomes in historical mosquito samples from Southern Brazil

In Brazil, flaviviruses have caused massive outbreaks. Surveillance programs designed to monitor virus activity in vectors provides a system for mapping disease distribution and for identifying specific vector species for targeted control. The present study aimed to describe the detection, whole genome characterization and phylogenetic analysis of Ilheus virus (ILHV) and Iguape virus (IGUV) strains obtained from historical mosquito's samples. Twelve isolates of pooled mosquito specimens (inoculated in neonate mouse brain) collected in the state of São Paulo, Brazil, in 1993, 1994 and 1997 were investigated. Viral RNA was extracted and analyzed by qRT-PCR using Flavivirus genus-specific primers. Positive samples were sequenced and underwent phylogenetic analyses. Flavivirus was detected in 50% of the specimens. Positive samples were successfully Sanger sequenced. Three Anopholes cruzii pools collected in 1994 were positive for IGUV. One Culex sp. pool, one Anopheles triannulatus pool, and one Coquillettidia juxtamansonia pool, collected in 1994, were positive for ILHV. Metagenomic sequencing successfully characterize one ILHV and four IGUV full genomes, and revealed a high degree of homology between the Brazilian ILHV and IGUV strains and isolates available in GenBank. Phylogenetic analysis of partial ILHV NS5 gene revealed three distinct lineages (clades), an indication of genetic heterogeneity in strains circulating in Brazil. Nucleotide insertions and a high-level of nucleotide diversity were observed in the NS1 protein and capsid region of IGUV strains, respectively. Detection of ILHV and IGUV in mosquitoes from Southeastern Brazil confirms the historical circulation of these viruses in this area. Furthermore, this first evidence of ILHV in Anopheles triannulatus suggests the potential importance of Anopheles mosquitoes in the IGUV transmission cycle. Genomic and phylogenetic analysis of these viruses provided insights into their diversity and evolution, which are important for the emergence patterns of flaviviruses and their evolutionary trends in Brazil, an endemic country for several arbovirus. in In-depth studies of ILHV and IGUV including vector competence and molecular studies are needed to shed light on their epidemiology and potential risk of future emergence.

Enteric Virome and Carcinogenesis in the Gut

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in both the USA and the world. Recent research has demonstrated the involvement of the gut microbiota in CRC development and progression. Microbial biomarkers of disease have focused primarily on the bacterial component of the microbiome; however, the viral portion of the microbiome, consisting of both bacteriophages and eukaryotic viruses, together known as the virome, has been lesser studied. Here we review the recent advancements in high-throughput sequencing (HTS) technologies and bioinformatics, which have enabled scientists to better understand how viruses might influence the development of colorectal cancer. We discuss the contemporary findings revealing modulations in the virome and their correlation with CRC development and progression. While a variety of challenges still face viral HTS detection in clinical specimens, we consider herein numerous next steps for future basic and clinical research. Clinicians need to move away from a single infectious agent model for disease etiology by grasping new, more encompassing etiological paradigms, in which communities of various microbial components interact with each other and the host. The reporting and indexing of patient health information, socioeconomic data, and other relevant metadata will enable identification of predictive variables and covariates of viral presence and CRC development. Altogether, the virome has a more profound role in carcinogenesis and cancer progression than once thought, and viruses, specific for either human cells or bacteria, are clinically relevant in understanding CRC pathology, patient prognosis, and treatment development.

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.

Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes

Background

Metagenomes can be analysed using different approaches and tools. One of the most important distinctions is the way to perform taxonomic and functional assignment, choosing between the use of assembly algorithms or the direct analysis of raw sequence reads instead by homology searching, k-mer analysys, or detection of marker genes. Many instances of each approach can be found in the literature, but to the best of our knowledge no evaluation of their different performances has been carried on, and we question if their results are comparable.

Results

We have analysed several real and mock metagenomes using different methodologies and tools, and compared the resulting taxonomic and functional profiles. Our results show that database completeness (the representation of diverse organisms and taxa in it) is the main factor determining the performance of the methods relying on direct read assignment either by homology, k-mer composition or similarity to marker genes, while methods relying on assembly and assignment of predicted genes are most influenced by metagenomic size, that in turn determines the completeness of the assembly (the percentage of read that were assembled).

Conclusions

Although differences exist, taxonomic profiles are rather similar between raw read assignment and assembly assignment methods, while they are more divergent for methods based on k-mers and marker genes. Regarding functional annotation, analysis of raw reads retrieves more functions, but it also makes a substantial number of over-predictions. Assembly methods are more advantageous as the size of the metagenome grows bigger.

The rare enterovirus c99 and echovirus 29 strains in Brazil: potential risks associated to silent circulation

Human enteroviruses (EVs) are associated with a wide spectrum of human diseases. Here we report the complete genome sequences of one EV-C99 strain and one E29 strain obtained from children suffering from acute gastroenteritis, without symptoms of enteroviral syndromes. This is the first report of EV-C99 in South America, and the second E29 genome described worldwide. Continuous surveillance on EVs is vital to provide further understanding of the circulation of new or rare EV serotypes in the country. The present study also highlights the capacity of EVs to remain in silent circulation in populations.

Metagenomic analysis and characterization of acidogenic microbiome and effect of pH on organic acid production

Organic acid production including lactate and acetate is an economically attractive technology that has gained momentum worldwide over the past years. These series of action need to be performed by an esoteric and complex microbial community, in which different members have distinct roles in the establishment of a collective organization. In this study, we analyzed the bioma from bioreactors with various pH conditions of 4.0, 5.0 and 6.0 (R₁, R₂ and R₃), respectively, involved in acidogenic digestion for stable production of various organic acids by means of high-throughput Illumina sequencing, disclosing thousands of genes and extracting more than 53 microbial genomes. At pH 5.0, the hydrolysis reaction was enhanced and thus the lactic acid fermentation was stably improved to 45.96 mm/L and acetic acid to 73.77 mm/L. R₂ was found with the most suitable pH condition for stable organic acids production as Lactobacilli and Bifidobacteria were the major members. Both the members have the key roles in heterofermentation and produce higher transcripts of key encoding enzymes involved in the dominant heterofermentation pathways.

Ultra-deep, long-read nanopore sequencing of mock microbial community standards

BACKGROUND

Long sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition.

FINDINGS

We sequenced 2 commercially available mock communities containing 10 microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the 10 individual species isolates were also sequenced with Illumina technology. We generated 14 and 16 gigabase pairs from 2 GridION flowcells and 150 and 153 gigabase pairs from 2 PromethION flowcells for the evenly distributed and log-distributed communities, respectively. Read length N50 ranged between 5.3 and 5.4 kilobase pairs over the 4 sequencing runs. Basecalls and corresponding signal data are made available (4.2 TB in total). Alignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning.

CONCLUSIONS

We present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current laboratory and software pipelines.

Applying Genome-Resolved Metagenomics to Deconvolute the Halophilic Microbiome

In the past decades, the study of microbial life through shotgun metagenomic sequencing has rapidly expanded our understanding of environmental, synthetic, and clinical microbial communities. Here, we review how shotgun metagenomics has affected the field of halophilic microbial ecology, including functional potential reconstruction, virus–host interactions, pathway selection, strain dispersal, and novel genome discoveries. However, there still remain pitfalls and limitations from conventional metagenomic analysis being applied to halophilic microbial communities. Deconvolution of halophilic metagenomes has been difficult due to the high G + C content of these microbiomes and their high intraspecific diversity, which has made both metagenomic assembly and binning a challenge. Halophiles are also underrepresented in public genome databases, which in turn slows progress. With this in mind, this review proposes experimental and analytical strategies to overcome the challenges specific to the halophilic microbiome, from experimental designs to data acquisition and the computational analysis of metagenomic sequences. Finally, we speculate about the potential applications of other next-generation sequencing technologies in halophilic communities. RNA sequencing, long-read technologies, and chromosome conformation assays, not initially intended for microbiomes, are becoming available in the study of microbial communities. Together with recent analytical advancements, these new methods and technologies have the potential to rapidly advance the field of halophile research.

CRISPR Spacers Indicate Preferential Matching of Specific Virioplankton Genes

Viral infection exerts selection pressure on marine microbes, as virus-induced cell lysis causes 20 to 50% of cell mortality, resulting in fluxes of biomass into oceanic dissolved organic matter. Archaeal and bacterial populations can defend against viral infection using the clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) system, which relies on specific matching between a spacer sequence and a viral gene. If a CRISPR spacer match to any gene within a viral genome is equally effective in preventing lysis, no viral genes should be preferentially matched by CRISPR spacers. However, if there are differences in effectiveness, certain viral genes may demonstrate a greater frequency of CRISPR spacer matches. Indeed, homology search analyses of bacterioplankton CRISPR spacer sequences against virioplankton sequences revealed preferential matching of replication proteins, nucleic acid binding proteins, and viral structural proteins. Positive selection pressure for effective viral defense is one parsimonious explanation for these observations. CRISPR spacers from virioplankton metagenomes preferentially matched methyltransferase and phage integrase genes within virioplankton sequences. These virioplankton CRISPR spacers may assist infected host cells in defending against competing phage. Analyses also revealed that half of the spacer-matched viral genes were unknown, some genes matched several spacers, and some spacers matched multiple genes, a many-to-many relationship. Thus, CRISPR spacer matching may be an evolutionary algorithm, agnostically identifying those genes under stringent selection pressure for sustaining viral infection and lysis. Investigating this subset of viral genes could reveal those genetic mechanisms essential to virus-host interactions and provide new technologies for optimizing CRISPR defense in beneficial microbes.IMPORTANCE The CRISPR-Cas system is one means by which bacterial and archaeal populations defend against viral infection which causes 20 to 50% of cell mortality in the ocean. We tested the hypothesis that certain viral genes are preferentially targeted for the initial attack of the CRISPR-Cas system on a viral genome. Using CASC, a pipeline for CRISPR spacer discovery, and metagenome data from oceanic microbes and viruses, we found a clear subset of viral genes with high match frequencies to CRISPR spacers. Moreover, we observed a many-to-many relationship of spacers and viral genes. These high-match viral genes were involved in nucleotide metabolism, DNA methylation, and viral structure. It is possible that CRISPR spacer matching is an evolutionary algorithm pointing to those viral genes most important to sustaining infection and lysis. Studying these genes may advance the understanding of virus-host interactions in nature and provide new technologies for leveraging CRISPR-Cas systems in beneficial microbes.

SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline

The improvement of sequencing technologies has facilitated generalization of metagenomic sequencing, which has become a standard procedure for analyzing the structure and functionality of microbiomes. Bioinformatic analysis of sequencing results poses a challenge because it involves many different complex steps. SqueezeMeta is a fully automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support that enables co-assembly of related metagenomes and retrieval of individual genomes via binning procedures. SqueezeMeta features several unique characteristics: co-assembly procedure or co-assembly of unlimited number of metagenomes via merging of individual assembled metagenomes, both with read mapping for estimation of the abundances of genes in each metagenome. It also includes binning and bin checking for retrieving individual genomes. Internal checks for the assembly and binning steps provide information about the consistency of contigs and bins. Moreover, results are stored in a MySQL database, where they can be easily exported and shared, and can be inspected anywhere using a flexible web interface that allows simple creation of complex queries. We illustrate the potential of SqueezeMeta by analyzing 32 gut metagenomes in a fully automatic way, enabling retrieval of several million genes and several hundreds of genomic bins. One of the motivations in the development of SqueezeMeta was producing a software capable of running in small desktop computers and thus amenable to all users and settings. We were also able to co-assemble two of these metagenomes and complete the full analysis in less than one day using a simple laptop computer. This reveals the capacity of SqueezeMeta to run without high-performance computing infrastructure and in absence of any network connectivity. It is therefore adequate for in situ, real time analysis of metagenomes produced by nanopore sequencing. SqueezeMeta can be downloaded from https://github.com/jtamames/SqueezeMeta.

Family A DNA Polymerase Phylogeny Uncovers Diversity and Replication Gene Organization in the Virioplankton

Shotgun metagenomics, which allows for broad sampling of viral diversity, has uncovered genes that are widely distributed among virioplankton populations and show linkages to important biological features of unknown viruses. Over 25% of known dsDNA phage carry the DNA polymerase I (polA) gene, making it one of the most widely distributed phage genes. Because of its pivotal role in DNA replication, this enzyme is linked to phage lifecycle characteristics. Previous research has suggested that a single amino acid substitution might be predictive of viral lifestyle. In this study Chesapeake Bay virioplankton were sampled by shotgun metagenomic sequencing (using long and short read technologies). More polA sequences were predicted from this single viral metagenome (virome) than from 86 globally distributed virome libraries (ca. 2,100, and 1,200, respectively). The PolA peptides predicted from the Chesapeake Bay virome clustered with 69% of PolA peptides from global viromes; thus, remarkably the Chesapeake Bay virome captured the majority of known PolA peptide diversity in viruses. This deeply sequenced virome also expanded the diversity of PolA sequences, increasing the number of PolA clusters by 44%. Contigs containing polA sequences were also used to examine relationships between phylogenetic clades of PolA and other genes within unknown viral populations. Phylogenic analysis revealed five distinct groups of phages distinguished by the amino acids at their 762 (Escherichia coli IAI39 numbering) positions and replication genes. DNA polymerase I sequences from Tyr762 and Phe762 groups were most often neighbored by ring-shaped superfamily IV helicases and ribonucleotide reductases (RNRs). The Leu762 groups had non-ring shaped helicases from superfamily II and were further distinguished by an additional helicase gene from superfamily I and the lack of any identifiable RNR genes. Moreover, we found that the inclusion of ribonucleotide reductase associated with PolA helped to further differentiate phage diversity, chiefly within lytic podovirus populations. Altogether, these data show that DNA Polymerase I is a useful marker for observing the diversity and composition of the virioplankton and may be a driving factor in the divergence of phage replication components.

Nanopore-based detection and characterization of yam viruses

We here assessed the capability of the MinION sequencing approach to detect and characterize viruses infecting a water yam plant. This sequencing platform consistently revealed the presence of several plant virus species, including Dioscorea bacilliform virus, Yam mild mosaic virus and Yam chlorotic necrosis virus. A potentially novel ampelovirus was also detected by a complimentary Illumina sequencing approach. The full-length genome sequence of yam chlorotic necrosis virus was determined using Sanger sequencing, which enabled determination of the coverage and sequencing accuracy of the MinION technology. Whereas the total mean sequencing error rate of yam chlorotic necrosis virus-related MinION reads was 11.25%, we show that the consensus sequence obtained either by de novo assembly or after mapping the MinION reads on the virus genomic sequence was >99.8% identical with the Sanger-derived reference sequence. From the perspective of potential plant disease diagnostic applications of MinION sequencing, these degrees of sequencing accuracy demonstrate that the MinION approach can be used to both reliably detect and accurately sequence nearly full-length positive-sense single-strand polyadenylated RNA plant virus genomes.

NanoAmpli-Seq: a workflow for amplicon sequencing for mixed microbial communities on the nanopore sequencing platform

Background

Amplicon sequencing on Illumina sequencing platforms leverages their deep sequencing and multiplexing capacity but is limited in genetic resolution due to short read lengths. While Oxford Nanopore or Pacific Biosciences sequencing platforms overcome this limitation, their application has been limited due to higher error rates or lower data output.

Results

In this study, we introduce an amplicon sequencing workflow, i.e., NanoAmpli-Seq, that builds on the intramolecular-ligated nanopore consensus sequencing (INC-Seq) approach and demonstrate its application for full-length 16S rRNA gene sequencing. NanoAmpli-Seq includes vital improvements to the INC-Seq protocol that reduces sample processing time while significantly improving sequence accuracy. The developed protocol includes chopSeq software for fragmentation and read orientation correction of INC-Seq consensus reads while nanoClust algorithm was designed for read partitioning-based de novo clustering and within cluster consensus calling to obtain accurate full-length 16S rRNA gene sequences.

Conclusions

NanoAmpli-Seq accurately estimates the diversity of tested mock communities with average consensus sequence accuracy of 99.5% for 2D and 1D² sequencing on the nanopore sequencing platform. Nearly all residual errors in NanoAmpli-Seq sequences originate from deletions in homopolymer regions, indicating that homopolymer aware base calling or error correction may allow for sequencing accuracy comparable to short-read sequencing platforms.

Do you cov me? Effect of coverage reduction on species identification and genome reconstruction in complex biological matrices by metagenome shotgun high-throughput sequencing

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms and viruses in a single experiment, with the possibility of reconstructing de novo the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. However, we demonstrate that-for some applications-it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. We measured the variation of several summary statistics simulating a decrease in sequencing depth by randomly subsampling a number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, detection threshold, and ability of reconstructing the metagenome in terms of length and completeness. Our results show that a classification of prokaryotic, eukaryotic and viral communities can be accurately performed even using very low number of reads, both in mock communities and in real complex matrices. With samples of 100,000 reads, the alpha diversity estimates were in most cases comparable to those obtained with the full sample, and the estimation of the abundance of all the present species was in excellent agreement with those obtained with the full sample. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1M reads). The length of the reconstructed assembly was smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct-even partially-the metagenome.

Conducting metagenomic studies in microbiology and clinical research

Owing to the increased cost-effectiveness of high-throughput technologies, the number of studies focusing on the human microbiome and its connections to human health and disease has recently surged. However, best practices in microbiology and clinical research have yet to be clearly established. Here, we present an overview of the challenges and opportunities involved in conducting a metagenomic study, with a particular focus on data processing and analytical methods.

The North Sea goes viral: Occurrence and distribution of North Sea bacteriophages

Marine viruses are dominated by phages and have an enormous influence on microbial population dynamics, due to lysis and horizontal gene transfer. The aim of this study is to analyze the occurrence and diversity of phages in the North Sea, considering the virus-host interactions and biogeographic factors. The virus community of four sampling stations were described using virus metagenomics (viromes). The results show that the virus community was not evenly distributed throughout the North Sea. The dominant phage members were identified as unclassified phage group, followed by Caudovirales order. Myoviridae was the dominant phage family in the North Sea, which occurrence decreased from the coast to the open sea. In contrast, the occurrence of Podoviridae increased and the occurrence of Siphoviridae was low throughout the North Sea. The occurrence of other groups such as Phycodnaviridae decreased from the coast to the open sea. The coastal virus community was genetically more diverse than the open sea community. The influence of riverine inflow and currents, for instance the English Channel flow affects the genetic virus diversity with the community carrying genes from a variety of metabolic pathways and other functions. The present study offers the first insights in the virus community in the North Sea using viromes and shows the variation in virus diversity and the genetic information moved from coastal to open sea areas.

Spatial Distribution and Diverse Metabolic Functions of Lignocellulose-Degrading Uncultured Bacteria as Revealed by Genome-Centric Metagenomics

The mechanisms by which specific anaerobic microorganisms remain firmly attached to lignocellulosic material, allowing them to efficiently decompose organic matter, have yet to be elucidated. To circumvent this issue, microbiomes collected from anaerobic digesters treating pig manure and meadow grass were fractionated to separate the planktonic microbes from those adhered to lignocellulosic substrate. Assembly of shotgun reads, followed by a binning process, recovered 151 population genomes, 80 out of which were completely new and were not previously deposited in any database. Genome coverage allowed the identification of microbial spatial distribution in the engineered ecosystem. Moreover, a composite bioinformatic analysis using multiple databases for functional annotation revealed that uncultured members of the Bacteroidetes and Firmicutes follow diverse metabolic strategies for polysaccharide degradation. The structure of cellulosome in Firmicutes species can differ depending on the number and functional roles of carbohydrate-binding modules. In contrast, members of the Bacteroidetes are able to adhere to and degrade lignocellulose due to the presence of multiple carbohydrate-binding family 6 modules in beta-xylosidase and endoglucanase proteins or S-layer homology modules in unknown proteins. This study combines the concept of variability in spatial distribution with genome-centric metagenomics, allowing a functional and taxonomical exploration of the biogas microbiome.IMPORTANCE This work contributes new knowledge about lignocellulose degradation in engineered ecosystems. Specifically, the combination of the spatial distribution of uncultured microbes with genome-centric metagenomics provides novel insights into the metabolic properties of planktonic and firmly attached to plant biomass bacteria. Moreover, the knowledge obtained in this study enabled us to understand the diverse metabolic strategies for polysaccharide degradation in different species of Bacteroidetes and Clostridiales Even though structural elements of cellulosome were restricted to Clostridiales species, our study identified a putative mechanism in Bacteroidetes species for biomass decomposition, which is based on a gene cluster responsible for cellulose degradation, disaccharide cleavage to glucose, and transport to cytoplasm.

Overview of Virus Metagenomic Classification Methods and Their Biological Applications

Metagenomics poses opportunities for clinical and public health virology applications by offering a way to assess complete taxonomic composition of a clinical sample in an unbiased way. However, the techniques required are complicated and analysis standards have yet to develop. This, together with the wealth of different tools and workflows that have been proposed, poses a barrier for new users. We evaluated 49 published computational classification workflows for virus metagenomics in a literature review. To this end, we described the methods of existing workflows by breaking them up into five general steps and assessed their ease-of-use and validation experiments. Performance scores of previous benchmarks were summarized and correlations between methods and performance were investigated. We indicate the potential suitability of the different workflows for (1) time-constrained diagnostics, (2) surveillance and outbreak source tracing, (3) detection of remote homologies (discovery), and (4) biodiversity studies. We provide two decision trees for virologists to help select a workflow for medical or biodiversity studies, as well as directions for future developments in clinical viral metagenomics.

MinION™ nanopore sequencing of environmental metagenomes: a synthetic approach

Environmental metagenomic analysis is typically accomplished by assigning taxonomy and/or function from whole genome sequencing or 16S amplicon sequences. Both of these approaches are limited, however, by read length, among other technical and biological factors. A nanopore-based sequencing platform, MinION™, produces reads that are ≥1 × 104 bp in length, potentially providing for more precise assignment, thereby alleviating some of the limitations inherent in determining metagenome composition from short reads. We tested the ability of sequence data produced by MinION (R7.3 flow cells) to correctly assign taxonomy in single bacterial species runs and in three types of low-complexity synthetic communities: a mixture of DNA using equal mass from four species, a community with one relatively rare (1%) and three abundant (33% each) components, and a mixture of genomic DNA from 20 bacterial strains of staggered representation. Taxonomic composition of the low-complexity communities was assessed by analyzing the MinION sequence data with three different bioinformatic approaches: Kraken, MG-RAST, and One Codex. Results: Long read sequences generated from libraries prepared from single strains using the version 5 kit and chemistry, run on the original MinION device, yielded as few as 224 to as many as 3497 bidirectional high-quality (2D) reads with an average overall study length of 6000 bp. For the single-strain analyses, assignment of reads to the correct genus by different methods ranged from 53.1% to 99.5%, assignment to the correct species ranged from 23.9% to 99.5%, and the majority of misassigned reads were to closely related organisms. A synthetic metagenome sequenced with the same setup yielded 714 high quality 2D reads of approximately 5500 bp that were up to 98% correctly assigned to the species level. Synthetic metagenome MinION libraries generated using version 6 kit and chemistry yielded from 899 to 3497 2D reads with lengths averaging 5700 bp with up to 98% assignment accuracy at the species level. The observed community proportions for “equal” and “rare” synthetic libraries were close to the known proportions, deviating from 0.1% to 10% across all tests. For a 20-species mock community with staggered contributions, a sequencing run detected all but 3 species (each included at <0.05% of DNA in the total mixture), 91% of reads were assigned to the correct species, 93% of reads were assigned to the correct genus, and >99% of reads were assigned to the correct family. Conclusions: At the current level of output and sequence quality (just under 4 × 103 2D reads for a synthetic metagenome), MinION sequencing followed by Kraken or One Codex analysis has the potential to provide rapid and accurate metagenomic analysis where the consortium is comprised of a limited number of taxa. Important considerations noted in this study included: high sensitivity of the MinION platform to the quality of input DNA, high variability of sequencing results across libraries and flow cells, and relatively small numbers of 2D reads per analysis limit. Together, these limited detection of very rare components of the microbial consortia, and would likely limit the utility of MinION for the sequencing of high-complexity metagenomic communities where thousands of taxa are expected. Furthermore, the limitations of the currently available data analysis tools suggest there is considerable room for improvement in the analytical approaches for the characterization of microbial communities using long reads. Nevertheless, the fact that the accurate taxonomic assignment of high-quality reads generated by MinION is approaching 99.5% and, in most cases, the inferred community structure mirrors the known proportions of a synthetic mixture warrants further exploration of practical application to environmental metagenomics as the platform continues to develop and improve. With further improvement in sequence throughput and error rate reduction, this platform shows great promise for precise real-time analysis of the composition and structure of more complex microbial communities.

Statistical correction for functional metagenomic profiling of a microbial community with short NGS reads

By sequence homology search, the list of all the functions found and the counts of reads being aligned to them present the functional profile of a metagenomic sample. However, a significant obstacle has been observed in this approach due to the short read length associated with many next generation sequencing technologies. This includes artificial families, cross-annotations, length bias and conservation bias. The widely applied cutoff methods, such as BLAST E-value, are not able to solve the problems. Following the published successful procedures on the artificial families and the cross-annotation issue, we propose in this paper to use zero-truncated Poisson and Binomial (ZTP-Bin) hierarchical modelling to correct the length bias and the conservation bias. Goodness-of-fit of the modelling and cross-validation for the prediction using a bioinformatic simulated sample show the validity of this approach. Evaluated on an in vitro-simulated data set, the proposed modelling method outperforms other traditional methods. All three steps were then sequentially applied on real-life metagenomic samples to show that the proposed framework will lead to a more accurate functional profile of a short read metagenomic sample.

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Mycobacterium tuberculosis). As a result, the substantially higher sensitivity of SVM and BLAST was observed compared with that of PNN and KNN. However, the machine learning algorithms (PNN, KNN and SVM) were found capable of substantially reducing the false discovery rate (SVM < PNN < KNN). In sum, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Modulation of the orodigestive tract microbiome in HIV-infected patients

More than 37 million people are living with human immunodeficiency virus 1 (HIV), and more people than ever received lifesaving antiretroviral therapy worldwide. HIV-1 infection disrupts the intestinal immune system, leading to microbial translocation and systemic immune activation. We investigated the impact of HIV-1 infection on the GI microbiome and its association with host immune activation. The data indicated that the microbiome was different in HIV-positive and HIV-negative individuals. The initial sequence analysis of saliva indicated that there were major differences in the phyla of Bacteroidetes, Firmicutes, Proteobacteria, and TM7. Phylum Tenericutes was only seen in HIV-positive saliva. At the family level, we identified differences in Streptococcacea, Prevotellaceae, Porphyromonadaceae, and Neisseriaceae, whereas data from various sites in GI tract indicated that Prevotella melaninigencia, Fusobacterium necrophorum, Burkholderia, Bradyrhizobium, Ralstonia, and Eubacterium biforme were predominant but differentially present at various sites. Furthermore, there was a decrease in seven proteins associated with the alternative complement pathway and an increase in 6 proteins associated with the lectin and classical complement pathways. The correlation with a shift in complement pathways suggests that compromised immunity could be responsible for the observed dysbiosis in the GI microbiome.

Mushroom Emergence Detected by Combining Spore Trapping with Molecular Techniques

Obtaining reliable and representative mushroom production data requires time-consuming sampling schemes. In this paper, we assessed a simple methodology to detect mushroom emergence by trapping the fungal spores of the fruiting body community in plots where mushroom production was determined weekly. We compared the performance of filter paper traps with that of funnel traps and combined these spore trapping methods with species-specific quantitative real-time PCR and Illumina MiSeq to determine the spore abundance. Significantly more MiSeq proportional reads were generated for both ectomycorrhizal and saprotrophic fungal species using filter traps than were obtained using funnel traps. The spores of 37 fungal species that produced fruiting bodies in the study plots were identified. Spore community composition changed considerably over time due to the emergence of ephemeral fruiting bodies and rapid spore deposition (lasting from 1 to 2 weeks), which occurred in the absence of rainfall events. For many species, the emergence of epigeous fruiting bodies was followed by a peak in the relative abundance of their airborne spores. There were significant positive relationships between fruiting body yields and spore abundance in time for five of seven fungal species. There was no relationship between fruiting body yields and their spore abundance at plot level, indicating that some of the spores captured in each plot were arriving from the surrounding areas. Differences in fungal detection capacity by spore trapping may indicate different dispersal ability between fungal species. Further research can help to identify the spore rain patterns for most common fungal species.IMPORTANCE Mushroom monitoring represents a serious challenge in economic and logistical terms because sampling approaches demand extensive field work at both the spatial and temporal scales. In addition, the identification of fungal taxa depends on the expertise of experienced fungal taxonomists. Similarly, the study of fungal dispersal has been constrained by technological limitations, especially because the morphological identification of spores is a challenging and time-consuming task. Here, we demonstrate that spores from ectomycorrhizal and saprotrophic fungal species can be identified using simple spore traps together with either MiSeq fungus-specific amplicon sequencing or species-specific quantitative real-time PCR. In addition, the proposed methodology can be used to characterize the airborne fungal community and to detect mushroom emergence in forest ecosystems.

Current Advances on Virus Discovery and Diagnostic Role of Viral Metagenomics in Aquatic Organisms

The global expansion of the aquaculture industry has brought with it a corresponding increase of novel viruses infecting different aquatic organisms. These emerging viral pathogens have proved to be a challenge to the use of traditional cell-cultures and immunoassays for identification of new viruses especially in situations where the novel viruses are unculturable and no antibodies exist for their identification. Viral metagenomics has the potential to identify novel viruses without prior knowledge of their genomic sequence data and may provide a solution for the study of unculturable viruses. This review provides a synopsis on the contribution of viral metagenomics to the discovery of viruses infecting different aquatic organisms as well as its potential role in viral diagnostics. High throughput Next Generation sequencing (NGS) and library construction used in metagenomic projects have simplified the task of generating complete viral genomes unlike the challenge faced in traditional methods that use multiple primers targeted at different segments and VPs to generate the entire genome of a novel virus. In terms of diagnostics, studies carried out this far show that viral metagenomics has the potential to serve as a multifaceted tool able to study and identify etiological agents of single infections, co-infections, tissue tropism, profiling viral infections of different aquatic organisms, epidemiological monitoring of disease prevalence, evolutionary phylogenetic analyses, and the study of genomic diversity in quasispecies viruses. With sequencing technologies and bioinformatics analytical tools becoming cheaper and easier, we anticipate that metagenomics will soon become a routine tool for the discovery, study, and identification of novel pathogens including viruses to enable timely disease control for emerging diseases in aquaculture.

Toward Accurate and Quantitative Comparative Metagenomics

Shotgun metagenomics and computational analysis are used to compare the taxonomic and functional profiles of microbial communities. Leveraging this approach to understand roles of microbes in human biology and other environments requires quantitative data summaries whose values are comparable across samples and studies. Comparability is currently hampered by the use of abundance statistics that do not estimate a meaningful parameter of the microbial community and biases introduced by experimental protocols and data-cleaning approaches. Addressing these challenges, along with improving study design, data access, metadata standardization, and analysis tools, will enable accurate comparative metagenomics. We envision a future in which microbiome studies are replicable and new metagenomes are easily and rapidly integrated with existing data. Only then can the potential of metagenomics for predictive ecological modeling, well-powered association studies, and effective microbiome medicine be fully realized.

Bacterial Human Virulence Genes across Diverse Habitats As Assessed by In silico Analysis of Environmental Metagenomes

The occurrence and distribution of clinically relevant bacterial virulence genes across natural (non-human) environments is not well understood. We aimed to investigate the occurrence of homologs to bacterial human virulence genes in a variety of ecological niches to better understand the role of natural environments in the evolution of bacterial virulence. Twenty four bacterial virulence genes were analyzed in 46 diverse environmental metagenomic datasets, representing various soils, seawater, freshwater, marine sediments, hot springs, the deep-sea, hypersaline mats, microbialites, gutless worms and glacial ice. Homologs to 16 bacterial human virulence genes, involved in urinary tract infections, gastrointestinal diseases, skin diseases, and wound and systemic infections, showed global ubiquity. A principal component analysis did not demonstrate clear trends across the metagenomes with respect to occurrence and frequency of observed gene homologs. Full-length (>95%) homologs of several virulence genes were identified, and translated sequences of the environmental and clinical genes were up to 50-100% identical. Furthermore, phylogenetic analyses indicated deep branching positions of some of the environmental gene homologs, suggesting that they represent ancient lineages in the phylogeny of the clinical genes. Fifteen virulence gene homologs were detected in metatranscriptomes, providing evidence of environmental expression. The ubiquitous presence and transcription of the virulence gene homologs in non-human environments point to an important ecological role of the genes for the activity and survival of environmental bacteria. Furthermore, the high degree of sequence conservation between several of the environmental and clinical genes suggests common ancestral origins.

Pharmacometabolomics Informs Viromics toward Precision Medicine

Nowadays, we are experiencing the big data era with the emerging challenge of single data interpretation. Although the advent of high-throughput technologies as well as chemo- and bio-informatics tools presents pan-omics data as the way forward to precision medicine, personalized health care and tailored-made therapeutics can be only envisaged when interindividual variability in response to/toxicity of xenobiotics can be interpreted and thus, predicted. We know that such variability is the net outcome of genetics (host and microbiota) and environmental factors (diet, lifestyle, polypharmacy, and microbiota) and for this, tremendous efforts have been made to clarify key-molecules from correlation to causality to clinical significance. Herein, we focus on the host–microbiome interplay and its direct and indirect impact on efficacy and toxicity of xenobiotics and we inevitably wonder about the role of viruses, as the least acknowledged ones. We present the emerging discipline of pharmacometabolomics-informed viromics, in which pre-dose metabotypes can assist modeling and prediction of interindividual response to/toxicity of xenobiotics. Such features, either alone or in combination with host genetics, can power biomarker discovery so long as the features are variable among patients, stable enough to be of predictive value, and better than pre-existing tools for predicting therapeutic efficacy/toxicity.

Direct 16S rRNA-seq from bacterial communities: a PCR-independent approach to simultaneously assess microbial diversity and functional activity potential of each taxon

The analysis of environmental microbial communities has largely relied on a PCR-dependent amplification of genes entailing species identity as 16S rRNA. This approach is susceptible to biases depending on the level of primer matching in different species. Moreover, possible yet-to-discover taxa whose rRNA could differ enough from known ones would not be revealed. DNA-based methods moreover do not provide information on the actual physiological relevance of each taxon within an environment and are affected by the variable number of rRNA operons in different genomes. To overcome these drawbacks we propose an approach of direct sequencing of 16S ribosomal RNA without any primer- or PCR-dependent step. The method was tested on a microbial community developing in an anammox bioreactor sampled at different time-points. A conventional PCR-based amplicon pyrosequencing was run in parallel. The community resulting from direct rRNA sequencing was highly consistent with the known biochemical processes operative in the reactor. As direct rRNA-seq is based not only on taxon abundance but also on physiological activity, no comparison between its results and those from PCR-based approaches can be applied. The novel principle is in this respect proposed not as an alternative but rather as a complementary methodology in microbial community studies.

Droplet barcoding for massively parallel single-molecule deep sequencing

The ability to accurately sequence long DNA molecules is important across biology, but existing sequencers are limited in read length and accuracy. Here, we demonstrate a method to leverage short-read sequencing to obtain long and accurate reads. Using droplet microfluidics, we isolate, amplify, fragment and barcode single DNA molecules in aqueous picolitre droplets, allowing the full-length molecules to be sequenced with multi-fold coverage using short-read sequencing. We show that this approach can provide accurate sequences of up to 10 kb, allowing us to identify rare mutations below the detection limit of conventional sequencing and directly link them into haplotypes. This barcoding methodology can be a powerful tool in sequencing heterogeneous populations such as viruses.

The ability to accurately sequence long DNA molecules is important across biology. Here, Lan et al. report a droplet-based method that barcodes single DNA molecules, allowing the full-length molecules to be sequenced with multi-fold coverage using short-read next-generation sequencing.

Moleculo Long-Read Sequencing Facilitates Assembly and Genomic Binning from Complex Soil Metagenomes

Soil microorganisms carry out key processes for life on our planet, including cycling of carbon and other nutrients and supporting growth of plants. However, there is poor molecular-level understanding of their functional roles in ecosystem stability and responses to environmental perturbations. This knowledge gap is largely due to the difficulty in culturing the majority of soil microbes. Thus, use of culture-independent approaches, such as metagenomics, promises the direct assessment of the functional potential of soil microbiomes. Soil is, however, a challenge for metagenomic assembly due to its high microbial diversity and variable evenness, resulting in low coverage and uneven sampling of microbial genomes. Despite increasingly large soil metagenome data volumes (>200 Gbp), the majority of the data do not assemble. Here, we used the cutting-edge approach of synthetic long-read sequencing technology (Moleculo) to assemble soil metagenome sequence data into long contigs and used the assemblies for binning of genomes.

Soil metagenomics has been touted as the “grand challenge” for metagenomics, as the high microbial diversity and spatial heterogeneity of soils make them unamenable to current assembly platforms. Here, we aimed to improve soil metagenomic sequence assembly by applying the Moleculo synthetic long-read sequencing technology. In total, we obtained 267 Gbp of raw sequence data from a native prairie soil; these data included 109.7 Gbp of short-read data (~100 bp) from the Joint Genome Institute (JGI), an additional 87.7 Gbp of rapid-mode read data (~250 bp), plus 69.6 Gbp (>1.5 kbp) from Moleculo sequencing. The Moleculo data alone yielded over 5,600 reads of >10 kbp in length, and over 95% of the unassembled reads mapped to contigs of >1.5 kbp. Hybrid assembly of all data resulted in more than 10,000 contigs over 10 kbp in length. We mapped three replicate metatranscriptomes derived from the same parent soil to the Moleculo subassembly and found that 95% of the predicted genes, based on their assignments to Enzyme Commission (EC) numbers, were expressed. The Moleculo subassembly also enabled binning of >100 microbial genome bins. We obtained via direct binning the first complete genome, that of “Candidatus Pseudomonas sp. strain JKJ-1” from a native soil metagenome. By mapping metatranscriptome sequence reads back to the bins, we found that several bins corresponding to low-relative-abundance Acidobacteria were highly transcriptionally active, whereas bins corresponding to high-relative-abundance Verrucomicrobia were not. These results demonstrate that Moleculo sequencing provides a significant advance for resolving complex soil microbial communities.

IMPORTANCE Soil microorganisms carry out key processes for life on our planet, including cycling of carbon and other nutrients and supporting growth of plants. However, there is poor molecular-level understanding of their functional roles in ecosystem stability and responses to environmental perturbations. This knowledge gap is largely due to the difficulty in culturing the majority of soil microbes. Thus, use of culture-independent approaches, such as metagenomics, promises the direct assessment of the functional potential of soil microbiomes. Soil is, however, a challenge for metagenomic assembly due to its high microbial diversity and variable evenness, resulting in low coverage and uneven sampling of microbial genomes. Despite increasingly large soil metagenome data volumes (>200 Gbp), the majority of the data do not assemble. Here, we used the cutting-edge approach of synthetic long-read sequencing technology (Moleculo) to assemble soil metagenome sequence data into long contigs and used the assemblies for binning of genomes.

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