Metatranscriptomic Datasets Research Articles

Improving de novo metatranscriptome assembly via machine learning algorithms

In this paper, we present DNPipe, a Pipeline that processes and filters the transcript contigs reported by existing De Novo metatranscriptome assembly algorithms, aiming to improve the quality of de novo assembly of metatranscriptomic sequences. DNPipe consists of expectation-maximisation (EM) and sampling approaches that utilise abundance information of transcript contigs. We tested DNPipe on six metatranscriptomic datasets acquired from a mock microbial community dominated by three (among more than 15) known bacterial genomes. Results show that DNPipe can substantially improve the quality of metatranscriptome assembly, producing longer and more accurate transcripts. The N50 of the contigs increases by 19% (from around 1880 bps to 2250 bps), and the precision of the assembly improves by up to 8.7%, achieving about 81%. DNPipe assemblies are of higher quality than those assembled by Trinity as well. The DNPipe tool can be downloaded as open source software at https://sourceforge.net/projects/dnpipe/files/.

Improving de novo metatranscriptome assembly via machine learning algorithms

In this paper, we present DNPipe, a Pipeline that processes and filters the transcript contigs reported by existing De Novo metatranscriptome assembly algorithms, aiming to improve the quality of de novo assembly of metatranscriptomic sequences. DNPipe consists of expectation-maximisation (EM) and sampling approaches that utilise abundance information of transcript contigs. We tested DNPipe on six metatranscriptomic datasets acquired from a mock microbial community dominated by three (among more than 15) known bacterial genomes. Results show that DNPipe can substantially improve the quality of metatranscriptome assembly, producing longer and more accurate transcripts. The N50 of the contigs increases by 19% (from around 1880 bps to 2250 bps), and the precision of the assembly improves by up to 8.7%, achieving about 81%. DNPipe assemblies are of higher quality than those assembled by Trinity as well. The DNPipe tool can be downloaded as open source software at https://sourceforge.net/projects/dnpipe/files/.

Microbial metatranscriptomes from the thermally stratified Gulf of Aqaba/Eilat during summer

The water column in the oligotrophic Gulf of Aqaba/Eilat experiences distinct seasonal cycles with the cooling air and water temperatures of late fall and winter destabilizing the thermocline and forming mixed layer depths reaching 300 to 700m. As air temperatures warm thermal re-stratification results in a stable thermocline throughout the summer which physically separates a photic, nutrient-poor surface layer from an aphotic, nutrient-rich deep layer. Here we present the first metatranscriptome dataset, and its taxonomic assignments, sampled from three depths of the 700m deep Station A in the Gulf of Aqaba during the summer stratification (surface – 10m, deep chlorophyll maximum (DCM) – 85m, deep aphotic zone −500m). Intensive transcriptional activity was attributed to Prochlorococcus - the most abundant photosynthetic organism in the RNA-seq dataset - both at the surface and at the DCM. In contrast, cDNA reads related to picoeukaryotic algae were detected almost exclusively at the DCM. The metatranscriptomes presented here provide a basis for examining the seasonal differences in microbial gene expression by comparison with the published metatranscriptomes sampled during the winter deep-mixing from the same station.

Rumen metagenome and metatranscriptome analyses of low methane yield sheep reveals a Sharpea-enriched microbiome characterised by lactic acid formation and utilisation.

BackgroundEnteric fermentation by farmed ruminant animals is a major source of methane and constitutes the second largest anthropogenic contributor to global warming. Reducing methane emissions from ruminants is needed to ensure sustainable animal production in the future. Methane yield varies naturally in sheep and is a heritable trait that can be used to select animals that yield less methane per unit of feed eaten. We previously demonstrated elevated expression of hydrogenotrophic methanogenesis pathway genes of methanogenic archaea in the rumens of high methane yield (HMY) sheep compared to their low methane yield (LMY) counterparts. Methane production in the rumen is strongly connected to microbial hydrogen production through fermentation processes. In this study, we investigate the contribution that rumen bacteria make to methane yield phenotypes in sheep.ResultsUsing deep sequence metagenome and metatranscriptome datasets in combination with 16S rRNA gene amplicon sequencing from HMY and LMY sheep, we show enrichment of lactate-producing Sharpea spp. in LMY sheep bacterial communities. Increased gene and transcript abundances for sugar import and utilisation and production of lactate, propionate and butyrate were also observed in LMY animals. Sharpea azabuensis and Megasphaera spp. act as important drivers of lactate production and utilisation according to phylogenetic analysis and read mappings.ConclusionsOur findings show that the rumen microbiome in LMY animals supports a rapid heterofermentative growth, leading to lactate production. We postulate that lactate is subsequently metabolised mainly to butyrate in LMY animals, producing 2 mol of hydrogen and 0.5 mol of methane per mol hexose, which represents 24 % less than the 0.66 mol of methane formed from the 2.66 mol of hydrogen produced if hexose fermentation was directly to acetate and butyrate. These findings are consistent with the theory that a smaller rumen size with a higher turnover rate, where rapid heterofermentative growth would be an advantage, results in lower hydrogen production and lower methane formation. Together with previous methanogen gene expression data, this builds a strong concept of how animal traits and microbial communities shape the methane phenotype in sheep.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-016-0201-2) contains supplementary material, which is available to authorized users.

ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores

Functional annotation of metagenomic and metatranscriptomic data sets relies on similarity searches based on e-value thresholds resulting in an unknown number of false positive and negative matches. To overcome these limitations, we introduce ROCker, aimed at identifying position-specific, most-discriminant thresholds in sliding windows along the sequence of a target protein, accounting for non-discriminative domains shared by unrelated proteins. ROCker employs the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and calculate the best thresholds based on how simulated shotgun metagenomic reads of known composition map onto well-curated reference protein sequences and thus, differs from HMM profiles and related methods. We showcase ROCker using ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ) genes, mediating oxidation of ammonia and the reduction of the potent greenhouse gas, N2O, to inert N2, respectively. ROCker typically showed 60-fold lower FDR when compared to the common practice of using fixed e-values. Previously uncounted ‘atypical’ nosZ genes were found to be two times more abundant, on average, than their typical counterparts in most soil metagenomes and the abundance of bacterial amoA was quantified against the highly-related particulate methane monooxygenase (pmoA). Therefore, ROCker can reliably detect and quantify target genes in short-read metagenomes.

SAMSA: a comprehensive metatranscriptome analysis pipeline.

BackgroundAlthough metatranscriptomics—the study of diverse microbial population activity based on RNA-seq data—is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, designed specifically for metatranscriptome dataset analysis, which runs in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome transcription activity levels by organism or transcript function, and is fully open source. We used this new tool to evaluate best practices for sequencing stool metatranscriptomes.ResultsWorking with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes.Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100 bp) or joined paired-end reads. Each sample needs 40–50 million raw sequences, which can be expected to yield the 5–10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that overall organism transcriptional activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism transcriptional activity and specific cellular functions.ConclusionsBy making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100 bp paired end format with a minimum of 40 million reads per sample.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1270-8) contains supplementary material, which is available to authorized users.

Distribution and expression of microbial rhodopsins in the Baltic Sea and adjacent waters

Rhodopsins are light-driven ion-pumping membrane proteins found in many organisms and are proposed to be of global importance for oceanic microbial energy generation. Several studies have focused on marine environments, with less exploration of rhodopsins in brackish waters. We investigated microbial rhodopsins in the Baltic Sea using size-fractionated metagenomic and metatranscriptomic datasets collected along a salinity gradient spanning from ∼0 to 35 PSU. The normalised genomic abundance of rhodopsins in Bacteria, as well as rhodopsin gene expression, was highest in the smallest size fraction (0.1-0.8 μm), relative to the medium (0.8-3.0 μm) and large (>3.0 μm) size fractions. The abundance of rhodopsins in the two smaller size fractions displayed a positive correlation with salinity. Proteobacteria and Bacteroidetes rhodopsins were the most abundant while Actinobacteria rhodopsins, or actinorhodopsins, were common at lower salinities. Phylogenetic analysis indicated that rhodopsins have adapted independently to the marine-brackish transition on multiple occasions, giving rise to green light-adapted variants from ancestral blue light-adapted ones. A notable diversity of viral-like rhodopsins was also detected in the dataset and potentially linked with eukaryotic phytoplankton blooms. Finally, a new clade of likely proton-pumping rhodopsin with non-canonical amino acids in the spectral tuning and proton accepting site was identified.

Diversity and Expression of Bacterial Metacaspases in an Aquatic Ecosystem.

Metacaspases are distant homologs of metazoan caspase proteases, implicated in stress response, and programmed cell death (PCD) in bacteria and phytoplankton. While the few previous studies on metacaspases have relied on cultured organisms and sequenced genomes, no studies have focused on metacaspases in a natural setting. We here present data from the first microbial community-wide metacaspase survey; performed by querying metagenomic and metatranscriptomic datasets from the brackish Baltic Sea, a water body characterized by pronounced environmental gradients and periods of massive cyanobacterial blooms. Metacaspase genes were restricted to ~4% of the bacteria, taxonomically affiliated mainly to Bacteroidetes, Alpha- and Betaproteobacteria and Cyanobacteria. The gene abundance was significantly higher in larger or particle-associated bacteria (>0.8 μm), and filamentous Cyanobacteria dominated metacaspase gene expression throughout the bloom season. Distinct seasonal expression patterns were detected for the three metacaspase genes in Nodularia spumigena, one of the main bloom-formers. Clustering of normalized gene expression in combination with analyses of genomic and assembly data suggest functional diversification of these genes, and possible roles of the metacaspase genes related to stress responses, i.e., sulfur metabolism in connection to oxidative stress, and nutrient stress induced cellular differentiation. Co-expression of genes encoding metacaspases and nodularin toxin synthesis enzymes was also observed in Nodularia spumigena. The study shows that metacaspases represent an adaptation of potentially high importance for several key organisms in the Baltic Sea, most prominently Cyanobacteria, and open up for further exploration of their physiological roles in microbes and assessment of their ecological impact in aquatic habitats.

Characterization of CRISPR RNA transcription by exploiting stranded metatranscriptomic data.

CRISPR–Cas systems are bacterial adaptive immune systems, each typically composed of a locus of cas genes and a CRISPR array of spacers flanked by repeats. Processed transcripts of CRISPR arrays (crRNAs) play important roles in the interference process mediated by these systems, guiding targeted immunity. Here we developed computational approaches that allow us to characterize the expression of many CRISPRs in their natural environments, using community RNA-seq (metatranscriptomic) data. By exploiting public human gut metatranscriptomic data sets, we studied the expression of 56 repeat-sequence types of CRISPRs, revealing that most CRISPRs are transcribed in one direction (producing crRNAs). In rarer cases, including a type II system associated with Bacteroides fragilis, CRISPRs are transcribed in both directions. Type III CRISPR–Cas systems were found in the microbiomes, but metatranscriptomic reads were barely found for their CRISPRs. We observed individual-level variation of the crRNA transcription, and an even greater transcription of a CRISPR from the antisense strand than the crRNA strand in one sample. The orientations of CRISPR expression implicated by metatranscriptomic data are largely in agreement with prior predictions for CRISPRs, with exceptions. Our study shows the promise of exploiting community RNA-seq data for investigating the transcription of CRISPR–Cas systems.

Metatranscriptomics: Monitoring Gut Microbiome Activity in Response to Diet

The study of the gut microbiome—the collection of microflora within the intestinal tract—is growing in popularity as associations are found between diet and nutrition, gut microflora activity, and host health and disease. However, current metagenomic and ribosomal profiling approaches are unable to capture changes in activity within the gut microbiome populations. In this environment, shifts in diet or nutritional intake may not result in microbiome population changes, but may instead influence the activity of the existing microbial community. Metatranscriptomics—the study of microbial population activity based on RNA‐seq data—offers detailed data on the gene expression of all microbes within the sampled community. Unfortunately, current approaches for processing raw metatranscriptome data rely either on restricted databases, a fully in‐house analysis server, or use metagenome‐based approaches that have not been fully evaluated for use in processing metatranscriptomic datasets.We have created a new bioinformatics pipeline, SAMSA (Simple Analysis of Metatranscriptome Sequence Annotations), designed specifically for metatranscriptome dataset analysis, with options for either in‐house or external server‐based computational processing. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome activity by organism or transcript function, and is fully open source. We have also determined a series of “best practices” for metatranscriptome preprocessing and sequencing for the most accurate analysis results. Using publicly available data, we have demonstrated that SAMSA offers summary analysis of both the organism activity and transcript‐level expression within a metatranscriptome, as well as identifying the most significant expression changes between control and experimental populations. We expect that SAMSA will shed new light on expression changes within the gut microbiome, providing a deeper understanding of how this community reacts to dietary modulation.Support or Funding InformationFunded in part by the Peter J. Shields Endowed Chair in Dairy Food Science (D.M.) S.W. was supported by a fellowship under the Training Program in Biomolecular Technology (NIGMS‐NIH T32‐GM008799) at the University of California, Davis.

Comprehensive detection of bacterial carbohydrate-active enzyme coding genes expressed in cow rumen.

To find the abundant and characteristic fibrolytic enzyme-coding gene expressed in fiber-associating microbiota, a metatranscriptomic data set was obtained from fiber-associating microbiota, and it was compared with that of rumen fluid-floating microbiota and two metagenomic data sets. Fibrolytic rumen bacteria associate with plant polysaccharide and hydrolyze it in the rumen. We obtained a metatranscriptomic assembly from fiber-associating microbiota in three ruminally fistulated Holstein cows fed timothy (Phleum pratense) hay. Each metatranscriptomic data set involved over a thousand of the glycoside hydrolase (GH) gene transcripts that accounted for about 1% of total protein coding gene transcripts. Three-quarters of the total GH gene transcripts were dominated by non-structural oligosaccharide-acting hydrolase gene transcripts. In the fiber-associating microbiota, endo-cellulase coding gene families, especially GHs 9 and 5, were abundantly detected, and GHs 9, 11, 30 and 43, carbohydrate esterase 8 and carbohydrate-binding module 6 were characteristically detected. Most fibrolytic gene transcripts assigned to Fibrobacter succinogenes were detected in fiber-associating sections, and GHs 45, 44, 74, 11, 30 and 16 were Fibrobacter-characteristically detected. The metatranscriptomic assembly highlighted the characteristic fibrolytic enzymes expressed in the fiber-associated rumen microbiota and offered access to the fibrolytic activities in each fibrolytic bacteria.

Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality.

BackgroundMetatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function.ResultsHere, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76 % of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions.ConclusionsThe application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0146-x) contains supplementary material, which is available to authorized users.

Functional Profiling of Unfamiliar Microbial Communities Using a Validated De Novo Assembly Metatranscriptome Pipeline.

BackgroundMetatranscriptomic landscapes can provide insights in functional relationships within natural microbial communities. Analysis of complex metatranscriptome datasets of these communities poses a considerable bioinformatic challenge since they are non-restricted with a varying number of participating strains and species. For RNA-Seq data a standard approach is to align the generated reads to a set of closely related reference genomes. This only works well for microbial communities for which a near complete catalogue of reference genomes is available at a small evolutionary distance. In this study, we focus on the design of a validated de novo metatranscriptome assembly pipeline for single-end Illumina RNA-Seq data to obtain functional and taxonomic profiles of murine microbial communities.ResultsThe here developed de novo assembly metatranscriptome pipeline combined rRNA removal, IDBA-UD assembler, functional annotation and taxonomic classification. Different assemblers were tested and validated using RNA-Seq data from an in silico generated mock community and in vivo RNA-Seq data from a restricted microbial community taken from a mouse model colonized with Altered Schaedler Flora (ASF). Precision and recall of resulting gene expression, functional and taxonomic profiles were compared to those obtained with a standard alignment method. The validated pipeline was subsequently used to generate expression profiles from non-restricted cecal communities of four C57BL/6J mice fed on a high-fat high-protein diet spiked with an RNA-Seq data set from a well-characterized human sample. The spike in control was used to estimate precision and recall at assembly, functional and taxonomic level of non-restricted communities.ConclusionsA generic de novo assembly pipeline for metatranscriptome data analysis was designed for microbial ecosystems, which can be applied for microbial metatranscriptome analysis in any chosen niche.

Genetic Diversity Affects the Daily Transcriptional Oscillations of Marine Microbial Populations.

Marine microbial communities are genetically diverse but have robust synchronized daily transcriptional patterns at the genus level that are similar across a wide variety of oceanic regions. We developed a microarray-inspired gene-centric approach to resolve transcription of closely-related but distinct strains/ecotypes in high-throughput sequence data. Applying this approach to the existing metatranscriptomics datasets collected from two different oceanic regions, we found unique and variable patterns of transcription by individual taxa within the abundant picocyanobacteria Prochlorococcus and Synechococcus, the alpha Proteobacterium Pelagibacter and the eukaryotic picophytoplankton Ostreococcus. The results demonstrate that marine microbial taxa respond differentially to variability in space and time in the ocean. These intra-genus individual transcriptional patterns underlie whole microbial community responses, and the approach developed here facilitates deeper insights into microbial population dynamics.

COGNIZER: A Framework for Functional Annotation of Metagenomic Datasets

BackgroundRecent advances in sequencing technologies have resulted in an unprecedented increase in the number of metagenomes that are being sequenced world-wide. Given their volume, functional annotation of metagenomic sequence datasets requires specialized computational tools/techniques. In spite of having high accuracy, existing stand-alone functional annotation tools necessitate end-users to perform compute-intensive homology searches of metagenomic datasets against "multiple" databases prior to functional analysis. Although, web-based functional annotation servers address to some extent the problem of availability of compute resources, uploading and analyzing huge volumes of sequence data on a shared public web-service has its own set of limitations. In this study, we present COGNIZER, a comprehensive stand-alone annotation framework which enables end-users to functionally annotate sequences constituting metagenomic datasets. The COGNIZER framework provides multiple workflow options. A subset of these options employs a novel directed-search strategy which helps in reducing the overall compute requirements for end-users. The COGNIZER framework includes a cross-mapping database that enables end-users to simultaneously derive/infer KEGG, Pfam, GO, and SEED subsystem information from the COG annotations.ResultsValidation experiments performed with real-world metagenomes and metatranscriptomes, generated using diverse sequencing technologies, indicate that the novel directed-search strategy employed in COGNIZER helps in reducing the compute requirements without significant loss in annotation accuracy. A comparison of COGNIZER's results with pre-computed benchmark values indicate the reliability of the cross-mapping database employed in COGNIZER.ConclusionThe COGNIZER framework is capable of comprehensively annotating any metagenomic or metatranscriptomic dataset from varied sequencing platforms in functional terms. Multiple search options in COGNIZER provide end-users the flexibility of choosing a homology search protocol based on available compute resources. The cross-mapping database in COGNIZER is of high utility since it enables end-users to directly infer/derive KEGG, Pfam, GO, and SEED subsystem annotations from COG categorizations. Furthermore, availability of COGNIZER as a stand-alone scalable implementation is expected to make it a valuable annotation tool in the field of metagenomic research.Availability and ImplementationA Linux implementation of COGNIZER is freely available for download from the following links: http://metagenomics.atc.tcs.com/cognizer, https://metagenomics.atc.tcs.com/function/cognizer.

Bifidobacteria exhibit social behavior through carbohydrate resource sharing in the gut.

Bifidobacteria are common and frequently dominant members of the gut microbiota of many animals, including mammals and insects. Carbohydrates are considered key carbon sources for the gut microbiota, imposing strong selective pressure on the complex microbial consortium of the gut. Despite its importance, the genetic traits that facilitate carbohydrate utilization by gut microbiota members are still poorly characterized. Here, genome analyses of 47 representative Bifidobacterium (sub)species revealed the genes predicted to be required for the degradation and internalization of a wide range of carbohydrates, outnumbering those found in many other gut microbiota members. The glycan-degrading abilities of bifidobacteria are believed to reflect available carbon sources in the mammalian gut. Furthermore, transcriptome profiling of bifidobacterial genomes supported the involvement of various chromosomal loci in glycan metabolism. The widespread occurrence of bifidobacterial saccharolytic features is in line with metagenomic and metatranscriptomic datasets obtained from human adult/infant faecal samples, thereby supporting the notion that bifidobacteria expand the human glycobiome. This study also underscores the hypothesis of saccharidic resource sharing among bifidobacteria through species-specific metabolic specialization and cross feeding, thereby forging trophic relationships between members of the gut microbiota.

Strand-specific community RNA-seq reveals prevalent and dynamic antisense transcription in human gut microbiota.

Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts. Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts). Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment. As a demonstration of utilizing metatranscriptomics beyond these common analyses, we developed a computational and statistical procedure to analyze the antisense transcripts in strand-specific metatranscriptomic datasets. Antisense RNAs encoded on the DNA strand opposite a gene’s CDS have the potential to form extensive base-pairing interactions with the corresponding sense RNA, and can have important regulatory functions. Most studies of antisense RNAs in bacteria are rather recent, are mostly based on transcriptome analysis, and have been applied mainly to single bacterial species. Application of our approaches to human gut-associated metatranscriptomic datasets allowed us to survey antisense transcription for a large number of bacterial species associated with human beings. The ratio of protein coding genes with antisense transcription ranges from 0 to 35.8% (median = 10.0%) among 47 species. Our results show that antisense transcription is dynamic, varying between human individuals. Functional enrichment analysis revealed a preference of certain gene functions for antisense transcription, and transposase genes are among the most prominent ones (but we also observed antisense transcription in bacterial house-keeping genes).

Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis.

Motivation: Metagenomics research has accelerated the studies of microbial organisms, providing insights into the composition and potential functionality of various microbial communities. Metatranscriptomics (studies of the transcripts from a mixture of microbial species) and other meta-omics approaches hold even greater promise for providing additional insights into functional and regulatory characteristics of the microbial communities. Current metatranscriptomics projects are often carried out without matched metagenomic datasets (of the same microbial communities). For the projects that produce both metatranscriptomic and metagenomic datasets, their analyses are often not integrated. Metagenome assemblies are far from perfect, partially explaining why metagenome assemblies are not used for the analysis of metatranscriptomic datasets.Results: Here, we report a reads mapping algorithm for mapping of short reads onto a de Bruijn graph of assemblies. A hash table of junction k-mers (k-mers spanning branching structures in the de Bruijn graph) is used to facilitate fast mapping of reads to the graph. We developed an application of this mapping algorithm: a reference-based approach to metatranscriptome assembly using graphs of metagenome assembly as the reference. Our results show that this new approach (called TAG) helps to assemble substantially more transcripts that otherwise would have been missed or truncated because of the fragmented nature of the reference metagenome.Availability and implementation: TAG was implemented in C++ and has been tested extensively on the Linux platform. It is available for download as open source at http://omics.informatics.indiana.edu/TAG.Contact: yye@indiana.edu

Mining of luxS genes from rumen microbial consortia by metagenomic and metatranscriptomic approaches.

Although rumen bacterial communities vary depending on many factors such as diet, age and physiological conditions, a core microbiota exists within the rumen. In many natural environments, some bacteria use a quorum-sensing (QS) system to regulate their physiological activities. However, very limited information is available about QS systems in rumen. To investigate the autoinducer 2 (AI-2)-mediated QS system in rumen, we detected genes (luxS) encoding the AI-2 synthase (LuxS), from three datasets embedded in metagenomics RAST server (MG-RAST) and from a metatranscriptome dataset. We collected 135 luxS genes from the metagenomic datasets, which were presumed to originate from Bacteroidetes, Firmicutes, Fusobacteria and Actinobacteria, and 34 luxS genes from the metatranscriptome dataset, which probably originated from Bacteroidetes, Firmicutes and Spirochaetes. Because the essential amino acids for LuxS activity were conserved in the LuxS homologues predicted from luxS gene sequences from both datasets, the LuxS homologues probably function in the rumen. Since the largest number of sequences of luxS genes were collected from the genera Prevotella, Ruminococcus and Eubacterium, which include many fibrolytic bacteria and constituent members of biofilm on feed particles, an AI-2-mediated QS system is likely involved in biofilm formation and fibrolytic activity in the rumen.

The green impact: bacterioplankton response toward a phytoplankton spring bloom in the southern North Sea assessed by comparative metagenomic and metatranscriptomic approaches.

Phytoplankton blooms exhibit a severe impact on bacterioplankton communities as they change nutrient availabilities and other environmental factors. In the current study, the response of a bacterioplankton community to a Phaeocystis globosa spring bloom was investigated in the southern North Sea. For this purpose, water samples were taken inside and reference samples outside of an algal spring bloom. Structural changes of the bacterioplankton community were assessed by amplicon-based analysis of 16S rRNA genes and transcripts generated from environmental DNA and RNA, respectively. Several marine groups responded to bloom presence. The abundance of the Roseobacter RCA cluster and the SAR92 clade significantly increased in bloom presence in the total and active fraction of the bacterial community. Functional changes were investigated by direct sequencing of environmental DNA and mRNA. The corresponding datasets comprised more than 500 million sequences across all samples. Metatranscriptomic data sets were mapped on representative genomes of abundant marine groups present in the samples and on assembled metagenomic and metatranscriptomic datasets. Differences in gene expression profiles between non-bloom and bloom samples were recorded. The genome-wide gene expression level of Planktomarina temperata, an abundant member of the Roseobacter RCA cluster, was higher inside the bloom. Genes that were differently expressed included transposases, which showed increased expression levels inside the bloom. This might contribute to the adaptation of this organism toward environmental stresses through genome reorganization. In addition, several genes affiliated to the SAR92 clade were significantly upregulated inside the bloom including genes encoding for proteins involved in isoleucine and leucine incorporation. Obtained results provide novel insights into compositional and functional variations of marine bacterioplankton communities as response to a phytoplankton bloom.