Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio (NST), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic (NST = 21%) to more stochastic (NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic (NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.

Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available at https://github.com/dib-lab/SSUsearch under a BSD license.

The Amazon Rainforest plays major roles in global carbon and nitrogen cycling. Despite this region’s immense importance, deforestation and pasture creation are still occurring at alarming rates. In this study, we investigated the effects of land-use change on aerobic ammonia-oxidizers in primary rainforest, young and old pasture, and secondary forest in Rondônia, Brazil. Forest-to-pasture conversion decreased soil nitrate, phosphorus, and exchangeable acidity contents that recovered to pre-disturbance levels as pastures aged or were abandoned and formed secondary forest. The ammonia-oxidizing community, numerically dominated by thaumarchaea, shifted due to land-use change, both in terms of gene abundance and community structure. However, thaumarchaeal ammonia monooxygenase gene abundances did not correlate with any measured soil physicochemial parameters. Phylogenetic analyses showed that community structural changes in ammonia-oxidizing thaumarchaea are driven by a shift away from primary rainforest, old pasture, and secondary forest clusters to separate clusters for young pasture. Additionally, the nearly complete disappearance in young pasture, old pasture, and secondary forest sites of a thaumarchaeal genus, the Nitrosotalea, indicates that land-use change can have long lasting effects on large portions of the thaumarchaeal community. The results of this study can be used as a conceptual foundation for determining how ammonia-oxidizers become altered by land-use change in South American tropical forests.

In bacterial populations, subtle expressional differences may promote ecological specialization through the formation of distinct ecotypes. In a barrier-free habitat, this process most likely precedes population divergence and may predict speciation events. To examine this, we used four sequenced strains of the bacterium Shewanella baltica, OS155, OS185, OS195, and OS223, as models to assess transcriptional variation and ecotype formation within a prokaryotic population. All strains were isolated from different depths throughout a water column of the Baltic Sea, occupying different ecological niches characterized by various abiotic parameters. Although the genome sequences are nearly 100% conserved, when grown in the laboratory under standardized conditions, all strains exhibited different growth rates, suggesting significant expressional variation. Using the Ecotype Simulation algorithm, all strains were considered to be discrete ecotypes when compared to 32 other S. baltica strains isolated from the same water column, suggesting ecological divergence. Next, we employed custom microarray slides containing oligonucleotide probes representing the core genome of OS155, OS185, OS195, and OS223 to detect natural transcriptional variation among strains grown under identical conditions. Significant transcriptional variation was noticed among all four strains. Differentially expressed gene profiles seemed to coincide with the metabolic signatures of the environment at the original isolation depth. Transcriptional pattern variations such as the ones highlighted here may be used as indicators of short-term evolution emerging from the formation of bacterial ecotypes. IMPORTANCE Eukaryotic studies have shown considerable transcriptional variation among individuals from the same population. It has been suggested that natural variation in eukaryotic gene expression may have significant evolutionary consequences and may explain large-scale phenotypic divergence of closely related species, such as humans and chimpanzees (M.-C. King and A. C. Wilson, Science 188:107-116, 1975, http://dx.doi.org/10.1126/science.1090005; M. F. Oleksiak, G. A. Churchill, and D. L. Crawford, Nat Genet 32:261-266, 2002, http://dx.doi.org/10.1038/ng983). However, natural variation in gene expression is much less well understood in prokaryotic organisms. In this study, we used four sequenced strains of the marine bacterium Shewanella baltica to better understand the natural transcriptional divergence of a stratified prokaryotic population. We found substantial low-magnitude expressional variation among the four S. baltica strains cultivated under identical laboratory conditions. Collectively, our results indicate that transcriptional variation is an important factor for ecological speciation.

The large volumes of sequencing data required to sample complex environments deeply pose new challenges to sequence analysis approaches. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires significant computational resources. We apply two pre-assembly filtering approaches, digital normalization and partitioning, to make large metagenome assemblies more comput\ ationaly tractable. Using a human gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes from matched Iowa corn and native prairie soils. The predicted functional content and phylogenetic origin of the assembled contigs indicate significant taxonomic differences despite similar function. The assembly strategies presented are generic and can be extended to any metagenome; full source code is freely available under a BSD license.

The large volumes of sequencing data required to sample deeply the microbial communities of complex environments pose new challenges to sequence analysis. De novo metagenomic assembly effectively reduces the total amount of data to be analyzed but requires substantial computational resources. We combine two preassembly filtering approaches--digital normalization and partitioning--to generate previously intractable large metagenome assemblies. Using a human-gut mock community dataset, we demonstrate that these methods result in assemblies nearly identical to assemblies from unprocessed data. We then assemble two large soil metagenomes totaling 398 billion bp (equivalent to 88,000 Escherichia coli genomes) from matched Iowa corn and native prairie soils. The resulting assembled contigs could be used to identify molecular interactions and reaction networks of known metabolic pathways using the Kyoto Encyclopedia of Genes and Genomes Orthology database. Nonetheless, more than 60% of predicted proteins in assemblies could not be annotated against known databases. Many of these unknown proteins were abundant in both corn and prairie soils, highlighting the benefits of assembly for the discovery and characterization of novelty in soil biodiversity. Moreover, 80% of the sequencing data could not be assembled because of low coverage, suggesting that considerably more sequencing data are needed to characterize the functional content of soil.

Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO₂ sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment.IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs' β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests.

Background

Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes.

Results

We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.

Conclusion

Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

Studying how bacterial strains diverge over space and time and how divergence leads to ecotype formation is important for understanding structure and dynamics of environmental communities. Here we assess the ecological speciation and temporal dynamics of a collection of Shewanella baltica strains from the redox transition zone of the central Baltic Sea, sampled at three time points over a course of 12 years, with a subcollection containing 46 strains subjected to detailed genetic and physiological characterization. Nine clades were consistently recovered by three different genotyping approaches: gyrB gene sequencing, multilocus sequence typing (MLST) and whole genome clustering of data from comparative genomic hybridization, and indicated specialization according to nutrient availability, particle association and temporal distribution. Genomic analysis suggested higher intra- than inter-clade recombination that might result from niche partitioning. Substantial heterogeneity in carbon utilization and respiratory capabilities suggested rapid diversification within the same 'named' species and physical habitat and showed consistency with genetic relatedness. At least two major ecotypes, represented by MLST clades A and E, were proposed based on genetic, ecological and physiological distinctiveness. This study suggests that genetic analysis in conjunction with phenotypic evaluation can provide better understanding of the ecological framework and evolutionary trajectories of microbial species.

Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth.