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During the COVID-19 pandemic, SARS-CoV-2 surveillance efforts integrated genome sequencing of clinical samples to identify emergent viral variants and to support rapid experimental examination of genome-informed vaccine and therapeutic designs. Given the broad range of methods applied to generate new viral genomes, it is critical that consensus and variant calling tools yield consistent results across disparate pipelines. Here we examine the impact of sequencing technologies (Illumina and Oxford Nanopore) and 7 different downstream bioinformatic protocols on SARS-CoV-2 variant calling as part of the NIH Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Tracking Resistance and Coronavirus Evolution (TRACE) initiative, a public-private partnership established to address the COVID-19 outbreak. Our results indicate that bioinformatic workflows can yield consensus genomes with different single nucleotide polymorphisms, insertions, and/or deletions even when using the same raw sequence input datasets. We introduce the use of a specific suite of parameters and protocols that greatly improves the agreement among pipelines developed by diverse organizations. Such consistency among bioinformatic pipelines is fundamental to SARS-CoV-2 and future pathogen surveillance efforts. The application of analysis standards is necessary to more accurately document phylogenomic trends and support data-driven public health responses.

There is growing interest in reconstructing phylogenies from the copious amounts of genome sequencing projects that target related viral, bacterial or eukaryotic organisms. To facilitate the construction of standardized and robust phylogenies for disparate types of projects, we have developed a complete bioinformatic workflow, with a web-based component to perform phylogenetic and molecular evolutionary (PhaME) analysis from sequencing reads, draft assemblies or completed genomes of closely related organisms. Furthermore, the ability to incorporate raw data, including some metagenomic samples containing a target organism (e.g. from clinical samples with suspected infectious agents), shows promise for the rapid phylogenetic characterization of organisms within complex samples without the need for prior assembly.

To date, the potential impact of viral communities on biogeochemical cycles in soil has largely been inferred from indirect evidence, such as virus-driven changes in microbial abundances, viral auxiliary metabolic genes, and correlations with soil physiochemical properties. To more directly test the impact of soil viruses on carbon cycling during plant litter decomposition, we added concentrated viral community suspensions to complex litter decomposer communities in 40-day microcosm experiments. Microbial communities from two New Mexico alpine soils, Pajarito (PJ) and Santa Fe (SF), were inoculated onto grass litter on sand, and three treatments were applied in triplicate to each set of microcosms: addition of buffer (no added virus), addition of live virus (+virus), or killed virus (+killed-virus) fractions extracted from the same soil. Significant differences in respiration were observed between the +virus and +killed-virus treatments in the PJ, but not the SF microcosms. Bacterial and fungal community composition differed significantly by treatment in both PJ and SF microcosms. Combining data across both soils, viral addition altered links between bacterial and fungal diversity, dissolved organic carbon and total nitrogen. Overall, we demonstrate that increasing viral pressure in complex microbial communities can impact terrestrial biogeochemical cycling but is context-dependent.

Summary Genomics has become an essential technology for surveilling emerging infectious disease outbreaks. A range of technologies and strategies for pathogen genome enrichment and sequencing are being used by laboratories worldwide, together with different and sometimes ad hoc, analytical procedures for generating genome sequences. A fully integrated analytical process for raw sequence to consensus genome determination, suited to outbreaks such as the ongoing COVID-19 pandemic, is critical to provide a solid genomic basis for epidemiological analyses and well-informed decision making. We have developed a web-based platform and integrated bioinformatic workflows that help to provide consistent high-quality analysis of SARS-CoV-2 sequencing data generated with either the Illumina or Oxford Nanopore Technologies (ONT). Using an intuitive web-based interface, this workflow automates data quality control, SARS-CoV-2 reference-based genome variant and consensus calling, lineage determination and provides the ability to submit the consensus sequence and necessary metadata to GenBank, GISAID and INSDC raw data repositories. We tested workflow usability using real world data and validated the accuracy of variant and lineage analysis using several test datasets, and further performed detailed comparisons with results from the COVID-19 Galaxy Project workflow. Our analyses indicate that EC-19 workflows generate high-quality SARS-CoV-2 genomes. Finally, we share a perspective on patterns and impact observed with Illumina versus ONT technologies on workflow congruence and differences. Availability and implementation https://edge-covid19.edgebioinformatics.org, and https://github.com/LANL-Bioinformatics/EDGE/tree/SARS-CoV2. Supplementary information Supplementary data are available at Bioinformatics online.

Viruses represent important test cases for data federation due to their genome size and the rapid increase in sequence data in publicly available databases. However, some consequences of previously decentralized (unfederated) data are lack of consensus or comparisons between feature annotations. Unifying or displaying alternative annotations should be a priority both for communities with robust entry representation and for nascent communities with burgeoning data sources. To this end, during this three-day continuation of the Virus Hunting Toolkit codeathon series (VHT-2), a new integrated and federated viral index was elaborated. This Federated Index of Viral Experiments (FIVE) integrates pre-existing and novel functional and taxonomy annotations and virus-host pairings. Variability in the context of viral genomic diversity is often overlooked in virus databases. As a proof-of-concept, FIVE was the first attempt to include viral genome variation for HIV, the most well-studied human pathogen, through viral genome diversity graphs. As per the publication of this manuscript, FIVE is the first implementation of a virus-specific federated index of such scope. FIVE is coded in BigQuery for optimal access of large quantities of data and is publicly accessible. Many projects of database or index federation fail to provide easier alternatives to access or query information. To this end, a Python API query system was developed to enhance the accessibility of FIVE. ispartof: VIRUSES-BASEL vol:12 issue:12 ispartof: location:Switzerland status: published

Non-typhoidal Salmonella (NTS) is a major global health concern that often causes bloodstream infections in areas of the world affected by malnutrition and comorbidities such as HIV and malaria. Developing a strategy to control the emergence and spread of highly invasive and antimicrobial resistant NTS isolates requires a comprehensive analysis of epidemiological factors and molecular pathogenesis. Here, we characterize 11 NTS isolates that caused bloodstream infections in pediatric patients in Siaya, Kenya from 2003–2010. Nine isolates were identified as S. Typhimurium sequence type 313 while the other two were S. Enteritidis. Comprehensive genotypic and phenotypic analyses were performed to compare these isolates to those previously identified in sub-Saharan Africa. We identified a S. Typhimurium isolate referred to as UGA14 that displayed novel plasmid, pseudogene and resistance features as compared to other isolates reported from Africa. Notably, UGA14 is able to ferment both lactose and sucrose due to the acquisition of insertion elements on the pKST313 plasmid. These findings show for the first time the co-evolution of plasmid-mediated lactose and sucrose metabolism along with cephalosporin resistance in NTS further elucidating the evolutionary mechanisms of invasive NTS phenotypes. These results further support the use of combined genomic and phenotypic approaches to detect and characterize atypical NTS isolates in order to advance biosurveillance efforts that inform countermeasures aimed at controlling invasive and antimicrobial resistant NTS., Author summary Non-typhoidal Salmonella (NTS) has been associated with life-threatening bacteremia in sub-Saharan Africa where co-morbidities such as HIV and malaria are highly prevalent. Children under the age of 5 are especially vulnerable to invasive NTS infections. The emergence and spread of multi-drug resistant invasive NTS isolates have limited the availability of effective treatment options. Understanding the molecular mechanisms that drive the evolution of invasive and antimicrobial resistant NTS strains is key to mitigating their impact on human health. In this study, we obtained 11 NTS isolates from the bloodstreams of children in Siaya, Kenya and performed both genotypic and phenotypic characterization compared to antimicrobial sensitive NTS strains. One strain, named UGA14, displayed a unique plasmid makeup compared to the other 10 isolates, which encoded for cephalosporin resistance as well as novel metabolic features allowing it to metabolize both lactose and sucrose. Not only was UGA14 multi-drug resistant but its unique metabolic profile made it indistinguishable from Escherichia coli on brilliant green agar indicating the failure of traditional culture-based techniques to inform diagnosis and treatment decisions. These findings highlight the importance of comparative genotypic and phenotypic analyses to understand the driving mechanisms of invasive and drug-resistant pathogens and support the development of effective countermeasures.

The discovery of antibiotics such as penicillin and streptomycin marked a historic milestone in the 1940s and heralded a new era of antimicrobial therapy as the modern standard for medical treatment. Yet, even in those early days of discovery, it was noted that a small subset of cells (∼1 in 105) survived antibiotic treatment and continued to persist, leading to recurrence of chronic infection. These persisters are phenotypic variants that have modified their physiology to survive environmental stress. In this study, we have performed three transcriptomic screens to identify persistence genes that are common between three different stressor conditions. In particular, we identified genes that function in the synthesis of secondary metabolites, small molecules, and complex lipids, which are likely required to maintain the persistence state. Targeting universal persistence genes can lead to the development of clinically relevant antipersistence therapeutics for infectious disease management., Persister cells are genetically identical variants in a bacterial population that have phenotypically modified their physiology to survive environmental stress. In bacterial pathogens, persisters are able to survive antibiotic treatment and reinfect patients in a frustrating cycle of chronic infection. To better define core persistence mechanisms for therapeutics development, we performed transcriptomics analyses of Burkholderia thailandensis populations enriched for persisters via three methods: flow sorting for low proton motive force, meropenem treatment, and culture aging. Although the three persister-enriched populations generally displayed divergent gene expression profiles that reflect the multimechanistic nature of stress adaptations, there were several common gene pathways activated in two or all three populations. These include polyketide and nonribosomal peptide synthesis, Clp proteases, mobile elements, enzymes involved in lipid metabolism, and ATP-binding cassette (ABC) transporter systems. In particular, identification of genes that encode polyketide synthases (PKSs) and fatty acid catabolism factors indicates that generation of secondary metabolites, natural products, and complex lipids could be part of the metabolic program that governs the persistence state. We also found that loss-of-function mutations in the PKS-encoding gene locus BTH_I2366, which plays a role in biosynthesis of histone deacetylase (HDAC) inhibitors, resulted in increased sensitivity to antibiotics targeting DNA replication. Furthermore, treatment of multiple bacterial pathogens with a fatty acid synthesis inhibitor, CP-640186, potentiated the efficacy of meropenem against the persister populations. Altogether, our results suggest that bacterial persisters may exhibit an outwardly dormant physiology but maintain active metabolic processes that are required to maintain persistence. IMPORTANCE The discovery of antibiotics such as penicillin and streptomycin marked a historic milestone in the 1940s and heralded a new era of antimicrobial therapy as the modern standard for medical treatment. Yet, even in those early days of discovery, it was noted that a small subset of cells (∼1 in 105) survived antibiotic treatment and continued to persist, leading to recurrence of chronic infection. These persisters are phenotypic variants that have modified their physiology to survive environmental stress. In this study, we have performed three transcriptomic screens to identify persistence genes that are common between three different stressor conditions. In particular, we identified genes that function in the synthesis of secondary metabolites, small molecules, and complex lipids, which are likely required to maintain the persistence state. Targeting universal persistence genes can lead to the development of clinically relevant antipersistence therapeutics for infectious disease management.

To harness the potential of microbiome science across the broad range of relevant disciplines, new approaches to data infrastructure and transdisciplinary collaboration are necessary. The National Microbiome Data Collaborative (NMDC) is a new initiative to support microbiome data exploration and discovery through a collaborative, integrative data science ecosystem. To harness the potential of microbiome science across the broad range of relevant disciplines, new approaches to data infrastructure and transdisciplinary collaboration are necessary. The National Microbiome Data Collaborative is a new initiative to support microbiome data exploration and discovery through a collaborative, integrative data science ecosystem.

Sequencing-based analyses of microbiomes have traditionally focused on addressing the question of community membership and profiling taxonomic abundance through amplicon sequencing of 16 rRNA genes. More recently, shotgun metagenomics, which involves the random sequencing of all genomic content of a microbiome, has dominated this arena due to advancements in sequencing technology throughput and capability to profile genes as well as microbiome membership. While these methods have revealed a great number of insights into a wide variety of microbiomes, both of these approaches only describe the presence of organisms or genes, and not whether they are active members of the microbiome. To obtain deeper insights into how a microbial community responds over time to their changing environmental conditions, microbiome scientists are beginning to employ large-scale metatranscriptomics approaches. Here, we present a comprehensive review on computational metatranscriptomics approaches to study microbial community transcriptomes. We review the major advancements in this burgeoning field, compare strengths and weaknesses to other microbiome analysis methods, list available tools and workflows, and describe use cases and limitations of this method. We envision that this field will continue to grow exponentially, as will the scope of projects (e.g. longitudinal studies of community transcriptional responses to perturbations over time) and the resulting data. This review will provide a list of options for computational analysis of these data and will highlight areas in need of development.

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps, (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node, (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience, and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

Many of the sequenced bacterial and archaeal genomes encode regions of viral provenance. Yet, not all of these regions encode bona fide viruses. Gene transfer agents (GTAs) are thought to be former viruses that are now maintained in genomes of some bacteria and archaea and are hypothesized to enable exchange of DNA within bacterial populations. In Alphaproteobacteria, genes homologous to the ‘head-tail’ gene cluster that encodes structural components of the Rhodobacter capsulatus GTA (RcGTA) are found in many taxa, even if they are only distantly related to Rhodobacter capsulatus. Yet, in most genomes available in GenBank RcGTA-like genes have annotations of typical viral proteins, and therefore are not easily distinguished from their viral homologs without additional analyses. Here, we report a ‘support vector machine’ classifier that quickly and accurately distinguishes RcGTA-like genes from their viral homologs by capturing the differences in the amino acid composition of the encoded proteins. Our open-source classifier is implemented in Python and can be used to scan homologs of the RcGTA genes in newly sequenced genomes. The classifier can also be trained to identify other types of GTAs, or even to detect other elements of viral ancestry. Using the classifier trained on a manually curated set of homologous viruses and GTAs, we detected RcGTA-like ‘head-tail’ gene clusters in 57.5% of the 1,423 examined alphaproteobacterial genomes. We also demonstrated that more than half of the in silico prophage predictions are instead likely to be GTAs, suggesting that in many alphaproteobacterial genomes the RcGTA-like elements remain unrecognized.Data depositionSequence alignments and phylogenetic trees are available in a FigShare repository at DOI 10.6084/m9.figshare.8796419. The Python source code of the described classifier and additional scripts used in the analyses are available via a GitHub repository at https://github.com/ecg-lab/GTA-Hunter-v1

Using bacteria to transform reactive corrosion products into stable compounds represents an alternative to traditional methods employed in iron conservation. Two environmental Aeromonas strains (CA23 and CU5) were used to transform ferric iron corrosion products (goethite and lepidocrocite) into stable ferrous iron-bearing minerals (vivianite and siderite). A genomic and transcriptomic approach was used to analyze the metabolic traits of these strains and to evaluate their pathogenic potential. Although genes involved in solid-phase iron reduction were identified, key genes present in other environmental iron-reducing species are missing from the genome of CU5. Several pathogenicity factors were identified in the genomes of both strains, but none of these was expressed under iron reduction conditions. Additional in vivo tests showed hemolytic and cytotoxic activities for strain CA23 but not for strain CU5. Both strains were easily inactivated using ethanol and heat. Nonetheless, given a lesser potential for a pathogenic lifestyle, CU5 is the most promising candidate for the development of a bio-based iron conservation method stabilizing iron corrosion. Based on all the results, a prototype treatment was established using archaeological items. On those, the conversion of reactive corrosion products and the formation of a homogenous layer of biogenic iron minerals were achieved. This study shows how naturally occurring microorganisms and their metabolic capabilities can be used to develop bio-inspired solutions to the problem of metal corrosion.IMPORTANCE Microbiology can greatly help in the quest for a sustainable solution to the problem of iron corrosion, which causes important economic losses in a wide range of fields, including the protection of cultural heritage and building materials. Using bacteria to transform reactive and unstable corrosion products into more-stable compounds represents a promising approach. The overall aim of this study was to develop a method for the conservation and restoration of corroded iron items, starting from the isolation of iron-reducing bacteria from natural environments. This resulted in the identification of a suitable candidate (Aeromonas sp. strain CU5) that mediates the formation of desirable minerals at the surfaces of the objects. This led to the proof of concept of an application method on real objects.

Gene transfer agents (GTAs) are phage-like particles that can package and transfer a random piece of the producing cell’s genome, but are unable to transfer all the genes required for their own production. As such, GTAs represent an evolutionary conundrum: are they selfish genetic elements propagating through an unknown mechanism, defective viruses, or viral structures “repurposed” by cells for gene exchange, as their name implies? In Rhodobacter capsulatus, production of the R. capsulatus GTA (RcGTA) particles is associated with a cluster of genes resembling a small prophage. Utilizing transcriptomic, genetic and biochemical approaches, we report that the RcGTA “genome” consists of at least 24 genes distributed across five distinct loci. We demonstrate that, of these additional loci, two are involved in cell recognition and binding and one in the production and maturation of RcGTA particles. The five RcGTA “genome” loci are widespread within Rhodobacterales, but not all loci have the same evolutionary histories. Specifically, two of the loci have been subject to frequent, probably virus-mediated, gene transfer events. We argue that it is unlikely that RcGTA is a selfish genetic element. Instead, our findings are compatible with the scenario that RcGTA is a virus-derived element maintained by the producing organism due to a selective advantage of within-population gene exchange. The modularity of the RcGTA “genome” is presumably a result of selection on the host organism to retain GTA functionality.

Microbiology can greatly help in the quest for a sustainable solution to the problem of iron corrosion, which causes important economic losses in a wide range of fields, including the protection of cultural heritage and building materials. Using bacteria to transform reactive and unstable corrosion products into more-stable compounds represents a promising approach. The overall aim of this study was to develop a method for the conservation and restoration of corroded iron items, starting from the isolation of iron-reducing bacteria from natural environments. This resulted in the identification of a suitable candidate (Aeromonas sp. strain CU5) that mediates the formation of desirable minerals at the surfaces of the objects. This led to the proof of concept of an application method on real objects., Using bacteria to transform reactive corrosion products into stable compounds represents an alternative to traditional methods employed in iron conservation. Two environmental Aeromonas strains (CA23 and CU5) were used to transform ferric iron corrosion products (goethite and lepidocrocite) into stable ferrous iron-bearing minerals (vivianite and siderite). A genomic and transcriptomic approach was used to analyze the metabolic traits of these strains and to evaluate their pathogenic potential. Although genes involved in solid-phase iron reduction were identified, key genes present in other environmental iron-reducing species are missing from the genome of CU5. Several pathogenicity factors were identified in the genomes of both strains, but none of these was expressed under iron reduction conditions. Additional in vivo tests showed hemolytic and cytotoxic activities for strain CA23 but not for strain CU5. Both strains were easily inactivated using ethanol and heat. Nonetheless, given a lesser potential for a pathogenic lifestyle, CU5 is the most promising candidate for the development of a bio-based iron conservation method stabilizing iron corrosion. Based on all the results, a prototype treatment was established using archaeological items. On those, the conversion of reactive corrosion products and the formation of a homogenous layer of biogenic iron minerals were achieved. This study shows how naturally occurring microorganisms and their metabolic capabilities can be used to develop bio-inspired solutions to the problem of metal corrosion. IMPORTANCE Microbiology can greatly help in the quest for a sustainable solution to the problem of iron corrosion, which causes important economic losses in a wide range of fields, including the protection of cultural heritage and building materials. Using bacteria to transform reactive and unstable corrosion products into more-stable compounds represents a promising approach. The overall aim of this study was to develop a method for the conservation and restoration of corroded iron items, starting from the isolation of iron-reducing bacteria from natural environments. This resulted in the identification of a suitable candidate (Aeromonas sp. strain CU5) that mediates the formation of desirable minerals at the surfaces of the objects. This led to the proof of concept of an application method on real objects.

The assembly of high-quality genomes from mixed microbial samples is a long-standing challenge in genomics and metagenomics. Here, we describe the application of ProxiMeta, a Hi-C-based metagenomic deconvolution method, to deconvolve a human fecal metagenome. This method uses the intra-cellular proximity signal captured by Hi-C as a direct indicator of which sequences originated in the same cell, enabling culture-free de novo deconvolution of mixed genomes without any reliance on a priori information. We show that ProxiMeta deconvolution provides results of markedly high accuracy and sensitivity, yielding 50 near-complete microbial genomes (many of which are novel) from a single fecal sample, out of 252 total genome clusters. ProxiMeta outperforms traditional contig binning at high-quality genome reconstruction. ProxiMeta shows particularly good performance in constructing high-quality genomes for diverse but poorly-characterized members of the human gut. We further use ProxiMeta to reconstruct genome plasmid content and sharing of plasmids among genomes—tasks that traditional binning methods usually fail to accomplish. Our findings suggest that Hi-C-based deconvolution can be useful to a variety of applications in genomics and metagenomics.

Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data ( Li et al., 2017 ). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.

Several bacterial and archaeal lineages produce nanostructures that morphologically resemble small tailed viruses, but, unlike most viruses, contain apparently random pieces of the host genome. Since these elements can deliver the packaged DNA to other cells, they were dubbed Gene Transfer Agents (GTAs). Because many genes involved in GTA production have viral homologs, it has been hypothesized that the GTA ancestor was a virus. Whether GTAs represent an atypical virus, a defective virus, or a virus co-opted by the prokaryotes for some function, remains to be elucidated. To evaluate these possibilities, we examined the distribution and evolutionary histories of genes that encode a GTA in the α-proteobacteriumRhodobacter capsulatus(RcGTA). We report that although homologs of many individual RcGTA genes are abundant across bacteria and their viruses, RcGTA-like genomes are mainly found in one subclade of α-proteobacteria. When compared to the viral homologs, genes of the RcGTA-like genomes evolve significantly slower, and do not have higher %A+T nucleotides than their host chromosomes. Moreover, they appear to reside in stable regions of the bacterial chromosomes that are generally conserved across taxonomic orders. These findings argue against RcGTA being an atypical or a defective virus. Our phylogenetic analyses suggest that RcGTA ancestor likely originated in the lineage that gave rise to contemporary α-proteobacterial ordersRhizobiales, Rhodobacterales, Caulobacterales, Parvularculales, and Sphingomonadales,and since that time the RcGTA-like element has co-evolved with its host chromosomes. Such evolutionary history is compatible with maintenance of these elements by bacteria due to some selective advantage. As for many other prokaryotic traits, horizontal gene transfer played a substantial role in the evolution of RcGTA-like elements, not only in shaping its genome components within the orders, but also in occasional dissemination of RcGTA-like regions across the orders and even to different bacterial phyla.

Next-generation sequencing has dramatically changed the landscape of microbial ecology, large-scale and in-depth diversity studies being now widely accessible. However, determining the accuracy of taxonomic and quantitative inferences and comparing results obtained with different approaches are complicated by incongruence of experimental and computational data types and also by lack of knowledge of the true ecological diversity. Here we used highly diverse bacterial and archaeal synthetic communities assembled from pure genomic DNAs to compare inferences from metagenomic and SSU rRNA amplicon sequencing. Both Illumina and 454 metagenomic data outperformed amplicon sequencing in quantifying the community composition, but the outcome was dependent on analysis parameters and platform. New approaches in processing and classifying amplicons can reconstruct the taxonomic composition of the community with high reproducibility within primer sets, but all tested primers sets lead to significant taxon-specific biases. Controlled synthetic communities assembled to broadly mimic the phylogenetic richness in target environments can provide important validation for fine-tuning experimental and computational parameters used to characterize natural communities.

Diverse microbial communities thrive on and in deep-sea hydrothermal vent mineral deposits. However, our understanding of the inter-field variability in these communities is poor, as limited sampling and sequencing efforts have hampered most previous studies. To explore the inter-field variability in these communities, we used barcoded pyrosequencing of the variable region 4 (V4) of the 16S rRNA gene to characterize the archaeal and bacterial communities of over 30 hydrothermal deposit samples from six vent fields located along the Eastern Lau Spreading Center. Overall, the bacterial and archaeal communities of the Eastern Lau Spreading Center are similar to other active vent deposits, with a high diversity of Epsilonproteobacteria and thermophilic Archaea. However, the archaeal and bacterial communities from the southernmost vent field, Mariner, were significantly different from the other vent fields. At Mariner, the epsilonproteobacterial genus Nautilia and the archaeal family Thermococcaceae were prevalent in most samples, while Lebetimonas and Thermofilaceae were more abundant at the other vent fields. These differences appear to be influenced in part by the unique geochemistry of the Mariner fluids resulting from active degassing of a subsurface magma chamber. These results show that microbial communities associated with hydrothermal vent deposits in back-arc basins are taxonomically similar to those from mid-ocean ridge systems, but differences in geologic processes between vent fields in a back-arc basin can influence microbial community structure.

Background In the last 5 years, the rapid pace of innovations and improvements in sequencing technologies has completely changed the landscape of metagenomic and metagenetic experiments. Therefore, it is critical to benchmark the various methodologies for interrogating the composition of microbial communities, so that we can assess their strengths and limitations. The most common phylogenetic marker for microbial community diversity studies is the 16S ribosomal RNA gene and in the last 10 years the field has moved from sequencing a small number of amplicons and samples to more complex studies where thousands of samples and multiple different gene regions are interrogated. Results We assembled 2 synthetic communities with an even (EM) and uneven (UM) distribution of archaeal and bacterial strains and species, as metagenomic control material, to assess performance of different experimental strategies. The 2 synthetic communities were used in this study, to highlight the limitations and the advantages of the leading sequencing platforms: MiSeq (Illumina), The Pacific Biosciences RSII, 454 GS-FLX/+ (Roche), and IonTorrent (Life Technologies). We describe an extensive survey based on synthetic communities using 3 experimental designs (fusion primers, universal tailed tag, ligated adaptors) across the 9 hypervariable 16S rDNA regions. We demonstrate that library preparation methodology can affect data interpretation due to different error and chimera rates generated during the procedure. The observed community composition was always biased, to a degree that depended on the platform, sequenced region and primer choice. However, crucially, our analysis suggests that 16S rRNA sequencing is still quantitative, in that relative changes in abundance of taxa between samples can be recovered, despite these biases. Conclusion We have assessed a range of experimental conditions across several next generation sequencing platforms using the most up-to-date configurations. We propose that the choice of sequencing platform and experimental design needs to be taken into consideration in the early stage of a project by running a small trial consisting of several hypervariable regions to quantify the discriminatory power of each region. We also suggest that the use of a synthetic community as a positive control would be beneficial to identify the potential biases and procedural drawbacks that may lead to data misinterpretation. The results of this study will serve as a guideline for making decisions on which experimental condition and sequencing platform to consider to achieve the best microbial profiling. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2194-9) contains supplementary material, which is available to authorized users.

Table S12. Rarefaction curves for all platforms. (PDF 107 kb)

Figure S7. Taxonomic profiling of different platforms. (PDF 7 kb)

Figure S2. Read quality distribution for PacBio. (PDF 5 kb)

Figure S5. Impact of library preparation method on barcode switching probability. (ZIP 16 kb)

Figure S6. Heatmap of observed species in the single-species libraries. (PDF 32 kb)

Figure S8. OTU comparison for all platforms. (PDF 8 kb)

Despite over three decades of progress, extraction of high molecular weight (HMW) DNA from high clay soils or iron oxide cemented clay has remained challenging. HMW DNA is desirable for next generation sequencing as it yields the most comprehensive coverage. Several DNA extraction procedures were compared from samples that exhibit strong nucleic acid adsorption. pH manipulation or use of alternative ion solutions offered no improvement in nucleic acid recovery. Lysis by liquid N2 grinding in concentrated guanidine followed by concentrated sodium phosphate extraction supported HMW DNA recovery from clays high in iron oxides. DNA recovered using 1 M sodium phosphate buffer (PB) as a competitive desorptive wash was 15.22±2.33 µg DNA/g clay, with most DNA consisting of >20 Kb fragments, compared to 2.46±0.25 µg DNA/g clay with the Powerlyzer system (MoBio). Increasing PB concentration in the lysis reagent coincided with increasing DNA fragment length during initial extraction. Rarefaction plots of 16S rRNA (V1–V3 region) pyrosequencing from A-horizon and clay soils showed an ∼80% and ∼400% larger accessed diversity compared to the Powerlyzer soil DNA system, respectively. The observed diversity from the Firmicutes showed the strongest increase with >3-fold more operational taxonomic units (OTU) recovered.

Bacterial and fungal communities associated with plant roots are central to the host health, survival and growth. However, a robust understanding of the root-microbiome and the factors that drive host associated microbial community structure have remained elusive, especially in mature perennial plants from natural settings. Here, we investigated relationships of bacterial and fungal communities in the rhizosphere and root endosphere of the riparian tree species Populus deltoides, and the influence of soil parameters, environmental properties (host phenotype and aboveground environmental settings), host plant genotype (Simple Sequence Repeat (SSR) markers), season (Spring vs. Fall) and geographic setting (at scales from regional watersheds to local riparian zones) on microbial community structure. Each of the trees sampled displayed unique aspects to its associated community structure with high numbers of Operational Taxonomic Units (OTUs) specific to an individual trees (bacteria >90%, fungi >60%). Over the diverse conditions surveyed only a small number of OTUs were common to all samples within rhizosphere (35 bacterial and 4 fungal) and endosphere (1 bacterial and 1 fungal) microbiomes. As expected, Proteobacteria and Ascomycota were dominant in root communities (>50%) while other higher-level phylogenetic groups (Chytridiomycota, Acidobacteria) displayed greatly reduced abundance in endosphere compared to the rhizosphere. Variance partitioning partially explained differences in microbiome composition between all sampled roots on the basis of seasonal and soil properties (4% to 23%). While most variation remains unattributed, we observed significant differences in the microbiota between watersheds (Tennessee vs. North Carolina) and seasons (Spring vs. Fall). SSR markers clearly delineated two host populations associated with the samples taken in TN vs. NC, but overall host genotypic distances did not have a significant effect on corresponding communities that could be separated from other measured effects.