Soil metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA470990 | ENA |
REPOSITORIES: ENA
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Project description: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.
1000-01-01 | S-EPMC3498926 | BioStudies
Project description:Soil metagenomics has been touted as the "grand challenge" for metagenomics, as the high microbial diversity and spatial heterogeneity of soils make them unamenable to current assembly platforms. Here, we aimed to improve soil metagenomic sequence assembly by applying the Moleculo synthetic long-read sequencing technology. In total, we obtained 267 Gbp of raw sequence data from a native prairie soil; these data included 109.7 Gbp of short-read data (~100 bp) from the Joint Genome Institute (JGI), an additional 87.7 Gbp of rapid-mode read data (~250 bp), plus 69.6 Gbp (>1.5 kbp) from Moleculo sequencing. The Moleculo data alone yielded over 5,600 reads of >10 kbp in length, and over 95% of the unassembled reads mapped to contigs of >1.5 kbp. Hybrid assembly of all data resulted in more than 10,000 contigs over 10 kbp in length. We mapped three replicate metatranscriptomes derived from the same parent soil to the Moleculo subassembly and found that 95% of the predicted genes, based on their assignments to Enzyme Commission (EC) numbers, were expressed. The Moleculo subassembly also enabled binning of >100 microbial genome bins. We obtained via direct binning the first complete genome, that of "<i>Candidatus</i> Pseudomonas sp. strain JKJ-1" from a native soil metagenome. By mapping metatranscriptome sequence reads back to the bins, we found that several bins corresponding to low-relative-abundance <i>Acidobacteria</i> were highly transcriptionally active, whereas bins corresponding to high-relative-abundance <i>Verrucomicrobia</i> were not. These results demonstrate that Moleculo sequencing provides a significant advance for resolving complex soil microbial communities. <b>IMPORTANCE</b> Soil microorganisms carry out key processes for life on our planet, including cycling of carbon and other nutrients and supporting growth of plants. However, there is poor molecular-level understanding of their functional roles in ecosystem stability and responses to environmental perturbations. This knowledge gap is largely due to the difficulty in culturing the majority of soil microbes. Thus, use of culture-independent approaches, such as metagenomics, promises the direct assessment of the functional potential of soil microbiomes. Soil is, however, a challenge for metagenomic assembly due to its high microbial diversity and variable evenness, resulting in low coverage and uneven sampling of microbial genomes. Despite increasingly large soil metagenome data volumes (>200 Gbp), the majority of the data do not assemble. Here, we used the cutting-edge approach of synthetic long-read sequencing technology (Moleculo) to assemble soil metagenome sequence data into long contigs and used the assemblies for binning of genomes. <b>Author Video</b>: An author video summary of this article is available.
2016-01-01 | S-EPMC5069762 | BioStudies
Project description:Diaphorina citri (Hemiptera: Psyllidae), the Asian citrus psyllid, is the insect vector of Ca. Liberibacter asiaticus, the causal agent of citrus greening disease. Sequencing of the D. citri metagenome has been initiated to gain better understanding of the biology of this organism and the potential roles of its bacterial endosymbionts. To corroborate candidate endosymbionts previously identified by rDNA amplification, raw reads from the D. citri metagenome sequence were mapped to reference genome sequences. Results of the read mapping provided the most support for Wolbachia and an enteric bacterium most similar to Salmonella. Wolbachia-derived reads were extracted using the complete genome sequences for four Wolbachia strains. Reads were assembled into a draft genome sequence, and the annotation assessed for the presence of features potentially involved in host interaction. Genome alignment with the complete sequences reveals membership of Wolbachia wDi in supergroup B, further supported by phylogenetic analysis of FtsZ. FtsZ and Wsp phylogenies additionally indicate that the Wolbachia strain in the Florida D. citri isolate falls into a sub-clade of supergroup B, distinct from Wolbachia present in Chinese D. citri isolates, supporting the hypothesis that the D. citri introduced into Florida did not originate from China.
2012-01-01 | S-EPMC3500351 | BioStudies
Project description:The Anthropogenic Amazon Dark Earth soil is considered one of the world's most fertile soils. These soils differs from conventional Amazon soils because its higher organic content concentration. Here we describe the metagenome sequencing of microbial communities of two sites of Anthropogenic Amazon Dark Earth soils from Amazon Rainforest, Brazil. The raw sequence data are stored under Short Read Accession number: PRJNA344917.
2016-01-01 | S-EPMC5121143 | BioStudies
Project description:Using two forest soils, we previously constructed two fosmid libraries containing 113,700 members in total. The libraries were screened to select active antifungal clones using Saccharomyces cerevisiae as a target fungus. One clone from the Yuseong pine tree rhizosphere soil library, pEAF66, showed S. cerevisiae growth inhibition. Despite an intensive effort, active chemicals were not isolated. DNA sequence analysis and transposon mutagenesis of pEAF66 revealed 39 open reading frames (ORFs) and indicated that eight ORFs, probably in one transcriptional unit, might be directly involved in the expression of antifungal activity in Escherichia coli. The deduced amino acid sequences of eight ORFs were similar to those of the core genes encoding type II family polyketide synthases, such as the acyl carrier protein (ACP), ACP synthases, aminotransferase, and ACP reductase. The gene cluster involved in antifungal activity was similar in organization to the putative antibiotic production locus of Pseudomonas putida KT2440, although we could not select a similar active clone from the KT2440 genomic DNA library in E. coli. ORFs encoding ATP binding cassette transporters and membrane proteins were located at both ends of the antifungal gene cluster. Upstream ORFs encoding an IclR family response regulator and a LysR family response regulator were involved in the positive regulation of antifungal gene expression. Our results suggested the metagenomic approach as an alternative to search for novel antifungal antibiotics from unculturable soil bacteria. This is the first report of an antifungal gene cluster obtained from a soil metagenome using S. cerevisiae as a target fungus.
2008-01-01 | S-EPMC2227728 | BioStudies
Project description:Soil microbial communities contain the highest level of prokaryotic diversity of any environment, and metagenomic approaches involving the extraction of DNA from soil can improve our access to these communities. Most analyses of soil biodiversity and function assume that the DNA extracted represents the microbial community in the soil, but subsequent interpretations are limited by the DNA recovered from the soil. Unfortunately, extraction methods do not provide a uniform and unbiased subsample of metagenomic DNA, and as a consequence, accurate species distributions cannot be determined. Moreover, any bias will propagate errors in estimations of overall microbial diversity and may exclude some microbial classes from study and exploitation. To improve metagenomic approaches, investigate DNA extraction biases, and provide tools for assessing the relative abundances of different groups, we explored the biodiversity of the accessible community DNA by fractioning the metagenomic DNA as a function of (i) vertical soil sampling, (ii) density gradients (cell separation), (iii) cell lysis stringency, and (iv) DNA fragment size distribution. Each fraction had a unique genetic diversity, with different predominant and rare species (based on ribosomal intergenic spacer analysis [RISA] fingerprinting and phylochips). All fractions contributed to the number of bacterial groups uncovered in the metagenome, thus increasing the DNA pool for further applications. Indeed, we were able to access a more genetically diverse proportion of the metagenome (a gain of more than 80% compared to the best single extraction method), limit the predominance of a few genomes, and increase the species richness per sequencing effort. This work stresses the difference between extracted DNA pools and the currently inaccessible complete soil metagenome.
2011-01-01 | S-EPMC3067229 | BioStudies
Project description:Areas within an agricultural field in the same season often differ in crop productivity despite having the same cropping history, crop genotype, and management practices. One hypothesis is that abiotic or biotic factors in the soils differ between areas resulting in these productivity differences. In this study, bulk soil samples collected from a high and a low productivity area from within six agronomic fields in Illinois were quantified for abiotic and biotic characteristics. Extracted DNA from these bulk soil samples were shotgun sequenced. While logistic regression analyses resulted in no significant association between crop productivity and the 26 soil characteristics, principal coordinate analysis and constrained correspondence analysis showed crop productivity explained a major proportion of the taxa variance in the bulk soil microbiome. Metagenome-wide association studies (MWAS) identified more Bradyrhizodium and Gammaproteobacteria in higher productivity areas and more Actinobacteria, Ascomycota, Planctomycetales, and Streptophyta in lower productivity areas. Machine learning using a random forest method successfully predicted productivity based on the microbiome composition with the best accuracy of 0.79 at the order level. Our study showed that crop productivity differences were associated with bulk soil microbiome composition and highlighted several nitrogen utility-related taxa. We demonstrated the merit of MWAS and machine learning for the first time in a plant-microbiome study.
2017-01-01 | S-EPMC5378059 | BioStudies
Project description:Purpose: Deconstructing the soil microbiome into reduced-complexity functional modules represents a novel method of microbiome analysis. The goals of this study are to confirm differences in transcriptomic patterns among five functional module consortia. Methods: mRNA profiles of 3 replicates each of functional module enrichments of soil inoculum in M9 media with either 1) xylose, 2) n-acetylglucosamine, 3) glucose and gentamycin, 4) xylan, or 5) pectin were generated by sequencing using an Illumina platform (GENEWIZ performed sequencing). Sequence reads that passed quality filters were aligned to a soil metagenome using Burrows Wheeler Aligner. Resulting SAM files were converted to raw reads using HTSeq, and annotated using Uniref90 or EGGNOG databases. Results: To reduce the size of the RNA-Seq counts table and increase its computational tractability, transcripts containing a minimum of 75 total counts, but no more than 3 zero counts, across the 15 samples were removed. The subsequent dataset was normalized using DESeq2, resulting in a dataset consisting of 6947 unique transcripts across the 15 samples, and 185,920,068 reads. We identified gene categories that were enriched in a sample type relative to the overall dataset using Fisher’s exact test. Conclusions: our dataset confirms that the functional module consortia generated from targeted enrichments of a starting soil inoculum had distinct functional trends by enrichment type. Overall design: mRNA profiles of enriched functional consortia were sequenced using an Illumina HiSeq platform.
2020-01-14 | GSE143587 | GEO
Project description:The microbiome associated with an animal's gut and other organs is considered an integral part of its ecological functions and adaptive capacity. To better understand how microbial communities influence activities and capacities of the host, we need more information on the functions that are encoded in a microbiome. Until now, the information about soil invertebrate microbiomes is mostly based on taxonomic characterization, achieved through culturing and amplicon sequencing. Using shotgun sequencing and various bioinformatics approaches we explored functions in the bacterial metagenome associated with the soil invertebrate Folsomia candida, an established model organism in soil ecology with a fully sequenced, high-quality genome assembly. Our metagenome analysis revealed a remarkable diversity of genes associated with antimicrobial activity and carbohydrate metabolism. The microbiome also contains several homologs to F. candida genes that were previously identified as candidates for horizontal gene transfer (HGT). We suggest that the carbohydrate- and antimicrobial-related functions encoded by Folsomia's metagenome play a role in the digestion of recalcitrant soil-born polysaccharides and the defense against pathogens, thereby significantly contributing to the adaptation of these animals to life in the soil. Furthermore, the transfer of genes from the microbiome may constitute an important source of new functions for the springtail.
2019-01-01 | S-EPMC6513849 | BioStudies
Project description:<h4>Background</h4>The human microbiota are complex systems with important roles in our physiological activities and diseases. Sequencing the microbial genomes in the microbiota can help in our interpretation of their activities. The vast majority of the microbes in the microbiota cannot be isolated for individual sequencing. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes. However, these results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10-100?kb), thus improving metagenome assembly. However, it is not clear how the choices for several parameters in the use of linked-read sequencing affect the assembly quality.<h4>Results</h4>We first examined the effects of read depth (C) on metagenome assembly from linked-reads in simulated data and a mock community. The results showed that C positively correlated with the length of assembled sequences but had little effect on their qualities. The latter observation was corroborated by tests using real data from the human gut microbiome, where C demonstrated minor impact on the sequence quality as well as on the proportion of bins annotated as draft genomes. On the other hand, metagenome assembly quality was susceptible to read depth per fragment (C<sub>R</sub>) and DNA fragment physical depth (C<sub>F</sub>). For the same C, deeper C<sub>R</sub> resulted in more draft genomes while deeper C<sub>F</sub> improved the quality of the draft genomes. We also found that average fragment length (?<sub>FL</sub>) had marginal effect on assemblies, while fragments per partition (N<sub>F/P</sub>) impacted the off-target reads involved in local assembly, namely, lower N<sub>F/P</sub> values would lead to better assemblies by reducing the ambiguities of the off-target reads. In general, the use of linked-reads improved the assembly for contig N50 when compared to Illumina short-reads, but not when compared to PacBio CCS (circular consensus sequencing) long-reads.<h4>Conclusions</h4>We investigated the influence of linked-read sequencing parameters on metagenome assembly comprehensively. While the quality of genome assembly from linked-reads cannot rival that from PacBio CCS long-reads, the case for using linked-read sequencing remains persuasive due to its low cost and high base-quality. Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient C<sub>R</sub> but a smaller amount of input DNA. Video Abstract.
2020-01-01 | S-EPMC7659138 | BioStudies