MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function.

Approaches in molecular biology, particularly those that deal with high-throughput sequencing of entire microbial communities (the field of metagenomics), are rapidly advancing our understanding of the composition and functional content of microbial communities involved in climate change, environmental pollution, human health, biotechnology, etc. Metagenomics provides researchers with the most complete picture of the taxonomic (i.e., what organisms are there) and functional (i.e., what are those organisms doing) composition of natively sampled microbial communities, making it possible to perform investigations that include organisms that were previously intractable to laboratory-controlled culturing; currently, these constitute the vast majority of all microbes on the planet. All organisms contained in environmental samples are sequenced in a culture-independent manner, most often with 16S ribosomal amplicon methods to investigate the taxonomic or whole-genome shotgun-based methods to investigate the functional content of sampled communities. Metagenomics allows researchers to characterize the community composition and functional content of microbial communities, but it cannot show which functional processes are active; however, near parallel developments in transcriptomics promise a dramatic increase in our knowledge in this area as well. Since 2008, MG-RAST (Meyer et al., BMC Bioinformatics 9:386, 2008) has served as a public resource for annotation and analysis of metagenomic sequence data, providing a repository that currently houses more than 150,000 data sets (containing 60+ tera-base-pairs) with more than 23,000 publically available. MG-RAST, or the metagenomics RAST (rapid annotation using subsystems technology) server makes it possible for users to upload raw metagenomic sequence data in (preferably) fastq or fasta format. Assessments of sequence quality, annotation with respect to multiple reference databases, are performed automatically with minimal input from the user (see Subheading 4 at the end of this chapter for more details). Post-annotation analysis and visualization are also possible, directly through the web interface, or with tools like matR (metagenomic analysis tools for R, covered later in this chapter) that utilize the MG-RAST API ( http://api.metagenomics.anl.gov/api.html ) to easily download data from any stage in the MG-RAST processing pipeline. Over the years, MG-RAST has undergone substantial revisions to keep pace with the dramatic growth in the number, size, and types of sequence data that accompany constantly evolving developments in metagenomics and related -omic sciences (e.g., metatranscriptomics).

The MG-RAST metagenomics database and portal in 2015.

MG-RAST (http://metagenomics.anl.gov) is an open-submission data portal for processing, analyzing, sharing and disseminating metagenomic datasets. The system currently hosts over 200,000 datasets and is continuously updated. The volume of submissions has increased 4-fold over the past 24 months, now averaging 4 terabasepairs per month. In addition to several new features, we report changes to the analysis workflow and the technologies used to scale the pipeline up to the required throughput levels. To show possible uses for the data from MG-RAST, we present several examples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence alignment tools.

Metagenomic analysis of planktonic microbial consortia from a non-tidal urban-impacted segment of James River.

Knowledge of the diversity and ecological function of the microbial consortia of James River in Virginia, USA, is essential to developing a more complete understanding of the ecology of this model river system. Metagenomic analysis of James River's planktonic microbial community was performed for the first time using an unamplified genomic library and a 16S rDNA amplicon library prepared and sequenced by Ion PGM and MiSeq, respectively. From the 0.46-Gb WGS library (GenBank:SRR1146621; MG-RAST:4532156.3), 4 × 10(6) reads revealed >3 × 10(6) genes, 240 families of prokaryotes, and 155 families of eukaryotes. From the 0.68-Gb 16S library (GenBank:SRR2124995; MG-RAST:4631271.3; EMB:2184), 4 × 10(6) reads revealed 259 families of eubacteria. Results of the WGS and 16S analyses were highly consistent and indicated that more than half of the bacterial sequences were Proteobacteria, predominantly Comamonadaceae. The most numerous genera in this group were Acidovorax (including iron oxidizers, nitrotolulene degraders, and plant pathogens), which accounted for 10 % of assigned bacterial reads. Polaromonas were another 6 % of all bacterial reads, with many assignments to groups capable of degrading polycyclic aromatic hydrocarbons. Albidiferax (iron reducers) and Variovorax (biodegraders of a variety of natural biogenic compounds as well as anthropogenic contaminants such as polycyclic aromatic hydrocarbons and endocrine disruptors) each accounted for an additional 3 % of bacterial reads. Comparison of these data to other publically-available aquatic metagenomes revealed that this stretch of James River is highly similar to the upper Mississippi River, and that these river systems are more similar to aquaculture and sludge ecosystems than they are to lakes or to a pristine section of the upper Amazon River. Taken together, these analyses exposed previously unknown aspects of microbial biodiversity, documented the ecological responses of microbes to urban effects, and revealed the noteworthy presence of 22 human-pathogenic bacterial genera (e.g., Enterobacteriaceae, pathogenic Pseudomonadaceae, and 'Vibrionales') and 6 pathogenic eukaryotic genera (e.g., Trypanosomatidae and Vahlkampfiidae). This information about pathogen diversity may be used to promote human epidemiological studies, enhance existing water quality monitoring efforts, and increase awareness of the possible health risks associated with recreational use of James River.

Microbial Metabolism Shifts Towards an Adverse Profile with Supplementary Iron in the TIM-2 In vitro Model of the Human Colon.

Oral iron administration in African children can increase the risk for infections. However, it remains unclear to what extent supplementary iron affects the intestinal microbiome. We here explored the impact of iron preparations on microbial growth and metabolism in the well-controlled TNO's in vitro model of the large intestine (TIM-2). The model was inoculated with a human microbiota, without supplementary iron, or with 50 or 250 µmol/L ferrous sulfate, 50 or 250 µmol/L ferric citrate, or 50 µmol/L hemin. High resolution responses of the microbiota were examined by 16S rDNA pyrosequencing, microarray analysis, and metagenomic sequencing. The metabolome was assessed by fatty acid quantification, gas chromatography-mass spectrometry (GC-MS), and (1)H-NMR spectroscopy. Cultured intestinal epithelial Caco-2 cells were used to assess fecal water toxicity. Microbiome analysis showed, among others, that supplementary iron induced decreased levels of Bifidobacteriaceae and Lactobacillaceae, while it caused higher levels of Roseburia and Prevotella. Metagenomic analyses showed an enrichment of microbial motility-chemotaxis systems, while the metabolome markedly changed from a saccharolytic to a proteolytic profile in response to iron. Branched chain fatty acids and ammonia levels increased significantly, in particular with ferrous sulfate. Importantly, the metabolite-containing effluent from iron-rich conditions showed increased cytotoxicity to Caco-2 cells. Our explorations indicate that in the absence of host influences, iron induces a more hostile environment characterized by a reduction of microbes that are generally beneficial, and increased levels of bacterial metabolites that can impair the barrier function of a cultured intestinal epithelial monolayer.

Bacterial community structure and functional contributions to emergence of health or necrotizing enterocolitis in preterm infants.

BACKGROUND: Preterm infants represent a unique patient population that is born functionally immature and must accomplish development under the influence of a hospital environment. Neonatal necrotizing enterocolitis (NEC) is an inflammatory intestinal disorder affecting preterm infants. The purpose of this study was to evaluate the progression of intestinal microbiota community development between preterm infants who remained healthy compared to preterm infants who developed NEC. RESULTS: Weekly fecal samples from ten preterm infants, five with NEC and five matched healthy controls were obtained. Bacterial DNA from individual fecal samples was subjected to sequencing of 16S rRNA-based inventories using the 454 GS-FLX platform. Fecal samples from control infants demonstrated a temporal pattern in their microbiota, which converged toward that of a healthy full term breast-fed infant. Microbiota development in NEC patients diverged from controls beginning three weeks prior to diagnosis. Shotgun metagenomic sequencing was performed to identify functional differences in the respective microbiota of fecal samples from a set of twins in which one twin developed NEC and one did not. The majority of the differentially abundant genes in the NEC patient were associated with carbohydrate metabolism and mapped to members of the family Enterobacteriaceae. This may indicate an adaptation of the community to an altered profile of substrate availability for specific members as a first step towards the development of NEC. We propose that the microbial communities as a whole may metabolize milk differently, resulting in differential substrate availability for specific microbial groups. Additional differentially represented gene sets of interest were related to antibiotic resistance and vitamin biosynthesis. CONCLUSIONS: Our results suggest that there is a temporal component to microbiome development in healthy preterm infants. Thus, bacteriotherapy for the treatment or prevention of NEC must consider this temporal component of the microbial community in addition to its taxonomic composition and functional content.

Short-read reading-frame predictors are not created equal: sequence error causes loss of signal.

BACKGROUND: Gene prediction algorithms (or gene callers) are an essential tool for analyzing shotgun nucleic acid sequence data. Gene prediction is a ubiquitous step in sequence analysis pipelines; it reduces the volume of data by identifying the most likely reading frame for a fragment, permitting the out-of-frame translations to be ignored. In this study we evaluate five widely used ab initio gene-calling algorithms-FragGeneScan, MetaGeneAnnotator, MetaGeneMark, Orphelia, and Prodigal-for accuracy on short (75-1000 bp) fragments containing sequence error from previously published artificial data and "real" metagenomic datasets. RESULTS: While gene prediction tools have similar accuracies predicting genes on error-free fragments, in the presence of sequencing errors considerable differences between tools become evident. For error-containing short reads, FragGeneScan finds more prokaryotic coding regions than does MetaGeneAnnotator, MetaGeneMark, Orphelia, or Prodigal. This improved detection of genes in error-containing fragments, however, comes at the cost of much lower (50%) specificity and overprediction of genes in noncoding regions. CONCLUSIONS: Ab initio gene callers offer a significant reduction in the computational burden of annotating individual nucleic acid reads and are used in many metagenomic annotation systems. For predicting reading frames on raw reads, we find the hidden Markov model approach in FragGeneScan is more sensitive than other gene prediction tools, while Prodigal, MGA, and MGM are better suited for higher-quality sequences such as assembled contigs.

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

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

Structure, fluctuation and magnitude of a natural grassland soil metagenome.

The soil ecosystem is critical for human health, affecting aspects of the environment from key agricultural and edaphic parameters to critical influence on climate change. Soil has more unknown biodiversity than any other ecosystem. We have applied diverse DNA extraction methods coupled with high throughput pyrosequencing to explore 4.88 × 10(9) bp of metagenomic sequence data from the longest continually studied soil environment (Park Grass experiment at Rothamsted Research in the UK). Results emphasize important DNA extraction biases and unexpectedly low seasonal and vertical soil metagenomic functional class variations. Clustering-based subsystems and carbohydrate metabolism had the largest quantity of annotated reads assigned although <50% of reads were assigned at an E value cutoff of 10(-5). In addition, with the more detailed subsystems, cAMP signaling in bacteria (3.24±0.27% of the annotated reads) and the Ton and Tol transport systems (1.69±0.11%) were relatively highly represented. The most highly represented genome from the database was that for a Bradyrhizobium species. The metagenomic variance created by integrating natural and methodological fluctuations represents a global picture of the Rothamsted soil metagenome that can be used for specific questions and future inter-environmental metagenomic comparisons. However, only 1% of annotated sequences correspond to already sequenced genomes at 96% similarity and E values of <10(-5), thus, considerable genomic reconstructions efforts still have to be performed.

Connecting genotype to phenotype in the era of high-throughput sequencing.

BACKGROUND: The development of next generation sequencing technology is rapidly changing the face of the genome annotation and analysis field. One of the primary uses for genome sequence data is to improve our understanding and prediction of phenotypes for microbes and microbial communities, but the technologies for predicting phenotypes must keep pace with the new sequences emerging. SCOPE OF REVIEW: This review presents an integrated view of the methods and technologies used in the inference of phenotypes for microbes and microbial communities based on genomic and metagenomic data. Given the breadth of this topic, we place special focus on the resources available within the SEED Project. We discuss the two steps involved in connecting genotype to phenotype: sequence annotation, and phenotype inference, and we highlight the challenges in each of these steps when dealing with both single genome and metagenome data. MAJOR CONCLUSIONS: This integrated view of the genotype-to-phenotype problem highlights the importance of a controlled ontology in the annotation of genomic data, as this benefits subsequent phenotype inference and metagenome annotation. We also note the importance of expanding the set of reference genomes to improve the annotation of all sequence data, and we highlight metagenome assembly as a potential new source for complete genomes. Finally, we find that phenotype inference, particularly from metabolic models, generates predictions that can be validated and reconciled to improve annotations. GENERAL SIGNIFICANCE: This review presents the first look at the challenges and opportunities associated with the inference of phenotype from genotype during the next generation sequencing revolution. This article is part of a Special Issue entitled: Systems Biology of Microorganisms.

Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset.

BACKGROUND: The world's oceans are home to a diverse array of microbial life whose metabolic activity helps to drive the earth's biogeochemical cycles. Metagenomic analysis has revolutionized our access to these communities, providing a system-scale perspective of microbial community interactions. However, while metagenome sequencing can provide useful estimates of the relative change in abundance of specific genes and taxa between environments or over time, this does not investigate the relative changes in the production or consumption of different metabolites. RESULTS: We propose a methodology, Predicted Relative Metabolic Turnover (PRMT) that defines and enables exploration of metabolite-space inferred from the metagenome. Our analysis of metagenomic data from a time-series study in the Western English Channel demonstrated considerable correlations between predicted relative metabolic turnover and seasonal changes in abundance of measured environmental parameters as well as with observed seasonal changes in bacterial population structure. CONCLUSIONS: The PRMT method was successfully applied to metagenomic data to explore the Western English Channel microbial metabalome to generate specific, biologically testable hypotheses. Generated hypotheses linked organic phosphate utilization to Gammaproteobactaria, Plantcomycetes, and Betaproteobacteria, chitin degradation to Actinomycetes, and potential small molecule biosynthesis pathways for Lentisphaerae, Chlamydiae, and Crenarchaeota. The PRMT method can be applied as a general tool for the analysis of additional metagenomic or transcriptomic datasets.

Locomotor activity level monitoring using the Drosophila Activity Monitoring (DAM) System.

Adult behavioral assays have been used with great success in Drosophila melanogaster to identify circadian rhythm genes. In particular, the locomotor activity assay can identify altered behavior patterns over the course of several days in small populations, or even individual flies. Commercially available, highly efficient automated systems allow for continuous data collection from large numbers of individuals, and analytical tools make it possible to quickly analyze multiple aspects of circadian behavior from each experiment. These features make the locomotor activity assay useful for high-throughput analyses, leading to the rapid discovery and functional characterization of many Drosophila circadian rhythm genes. The locomotor assay described here can simultaneously assess both circadian and sleep behavior, and several methods can be used to analyze the data generated from such assays. This protocol details the use of the Drosophila Activity Monitoring (DAM) System from TriKinetics. Briefly, the system records activity from individual flies maintained in sealed tubes placed in activity monitors. An infrared beam directed through the midpoint of each tube measures an "activity event" each time a fly crosses the beam. Events detected over the course of each consecutive sampling interval are summed and recorded over the course of the experiment for each fly. The general approaches described here can be applied to a wide range of behavioral activity experiments, including sleep deprivation analyses and general studies of hypoactivity and hyperactivity.

Processing circadian data collected from the Drosophila Activity Monitoring (DAM) System.

Adult behavioral assays have been used with great success in Drosophila melanogaster to identify circadian rhythm genes. In particular, the locomotor activity assay can identify altered behavior patterns over the course of several days in small populations, or even individual flies. Generally, circadian behavior is assayed during a period of 12 h light:12 h dark cycling (LD entrainment) followed by conditions of constant darkness (DD). LD activity profiles provide a qualitative image of daily activity bouts, and the data can be used to quantitatively assess the phase and/or amplitude of particular bouts. Additional activity assessments made from entrained flies that have been shifted to constant darkness can provide insight into the state of internal clocks and the ability of these clocks to drive rhythmic outputs. Typical LD DD runs assess both free-running rhythmicity and period length with χ2 periodogram (P'gram) analysis. This protocol describes the use of ClockLab (a MATLAB-based program) and the Counting Macro (an Excel-based program) to analyze circadian locomotor activity data collected using the Drosophila Activity Monitoring (DAM) System from TriKinetics. Specific procedures are described to analyze free-running rhythmicity and period length for individual flies, and assess group activity plots during both entrainment and constant conditions.

Processing sleep data created with the Drosophila Activity Monitoring (DAM) System.

Adult behavioral assays have been used with great success in Drosophila melanogaster to identify circadian rhythm genes. In particular, the locomotor activity assay can identify altered behavior patterns over the course of several days in small populations, or even individual flies. Sleep is a highly conserved behavior that is required for optimal performance and, in many cases, life of an organism. Drosophila demonstrate a behavioral state that shows traits consistent with sleep: periods of relative behavioral immobility that coincide with an increased arousal threshold after ~5 min of inactivity, regulated by circadian and homeostatic mechanisms. However, because flies do not produce brain waves recordable by electroencephalography, sleep researchers use behavior-based paradigms to infer when a fly is asleep, as opposed to awake but immobile. Data on Drosophila activity can be collected using an automated monitoring system to provide insight into sleep duration, consolidation, and latency, as well as sleep deprivation and rebound. This protocol details the use of Counting Macro, an Excel-based program, to process data created with the Drosophila Activity Monitoring (DAM) System from TriKinetics for sleep analyses. Specifically, it details the steps necessary to convert the raw data created by the DAM System into sleep duration and consolidation data, broken down into the light (L), dark (D), light:dark cycling (LD), and constant darkness (DD) phases of a behavior experiment.

Meta-analysis of Drosophila circadian microarray studies identifies a novel set of rhythmically expressed genes.

Five independent groups have reported microarray studies that identify dozens of rhythmically expressed genes in the fruit fly Drosophila melanogaster. Limited overlap among the lists of discovered genes makes it difficult to determine which, if any, exhibit truly rhythmic patterns of expression. We reanalyzed data from all five reports and found two sources for the observed discrepancies, the use of different expression pattern detection algorithms and underlying variation among the datasets. To improve upon the methods originally employed, we developed a new analysis that involves compilation of all existing data, application of identical transformation and standardization procedures followed by ANOVA-based statistical prescreening, and three separate classes of post hoc analysis: cross-correlation to various cycling waveforms, autocorrelation, and a previously described fast Fourier transform-based technique. Permutation-based statistical tests were used to derive significance measures for all post hoc tests. We find application of our method, most significantly the ANOVA prescreening procedure, significantly reduces the false discovery rate relative to that observed among the results of the original five reports while maintaining desirable statistical power. We identify a set of 81 cycling transcripts previously found in one or more of the original reports as well as a novel set of 133 transcripts not found in any of the original studies. We introduce a novel analysis method that compensates for variability observed among the original five Drosophila circadian array reports. Based on the statistical fidelity of our meta-analysis results, and the results of our initial validation experiments (quantitative RT-PCR), we predict many of our newly found genes to be bona fide cyclers, and suggest that they may lead to new insights into the pathways through which clock mechanisms regulate behavioral rhythms.

Clockwork orange encodes a transcriptional repressor important for circadian-clock amplitude in Drosophila.

Gene transcription is a central timekeeping process in animal clocks. In Drosophila, the basic helix-loop helix (bHLH)-PAS transcription-factor heterodimer, CLOCK/CYCLE (CLK/CYC), transcriptionally activates the clock components period (per), timeless (tim), Par domain protein 1 (Pdp1), and vrille (vri), which feed back and regulate distinct features of CLK/CYC function. Microarray studies have identified numerous rhythmically expressed transcripts, some of which are potential direct CLK targets. Here we demonstrate a circadian function for one such target, a bHLH-Orange repressor, CG17100/CLOCKWORK ORANGE (CWO). cwo is rhythmically expressed, and levels are reduced in Clk mutants, suggesting that cwo is CLK activated in vivo. cwo mutants display reduced-amplitude molecular and behavioral rhythms with lengthened periods. Molecular analysis suggests that CWO acts, in part, by repressing CLK target genes. We propose that CWO acts as a transcriptional and behavioral rhythm amplifier.

A dynamic role for the mushroom bodies in promoting sleep in Drosophila.

The fruitfly, Drosophila melanogaster, exhibits many of the cardinal features of sleep, yet little is known about the neural circuits governing its sleep. Here we have performed a screen of GAL4 lines expressing a temperature-sensitive synaptic blocker shibire(ts1) (ref. 2) in a range of discrete neural circuits, and assayed the amount of sleep at different temperatures. We identified three short-sleep lines at the restrictive temperature with shared expression in the mushroom bodies, a neural locus central to learning and memory. Chemical ablation of the mushroom bodies also resulted in reduced sleep. These studies highlight a central role for the mushroom bodies in sleep regulation.

Drosophila clock can generate ectopic circadian clocks.

Circadian rhythms of behavior, physiology, and gene expression are present in diverse tissues and organisms. The function of the transcriptional activator, Clock, is necessary in both Drosophila and mammals for the expression of many core clock components. We demonstrate in Drosophila that Clock misexpression in nai;ve brain regions induces circadian gene expression. This includes major components of the pacemaker program, as Clock also activates the rhythmic expression of cryptochrome, a gene that CLOCK normally represses. Moreover, this ectopic clock expression has potent effects on behavior, radically altering locomotor activity patterns. We propose that Clock is uniquely able to induce and organize the core elements of interdependent feedback loops necessary for circadian rhythms.