Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database

Background: Microbial inhabitants of soils are important to ecosystem and planetary functions, yet there are large gaps in our knowledge of their diversity and ecology. The 'Biomes of Australian Soil Environments' (BASE) project has generated a database of microbial diversity with associated metadata across extensive environmental gradients at continental scale. As the characterisation of microbes rapidly expands, the BASE database provides an evolving platform for interrogating and integrating microbial diversity and function. Findings: BASE currently provides amplicon sequences and associated contextual data for over 900 sites encompassing all Australian states and territories, a wide variety of bioregions, vegetation and land-use types. Amplicons target bacteria, archaea and general and fungal-specific eukaryotes. The growing database will soon include metagenomics data. Data are provided in both raw sequence (FASTQ) and analysed OTU table formats and are accessed via the project's data portal, which provides a user-friendly search tool to quickly identify samples of interest. Processed data can be visually interrogated and intersected with other Australian diversity and environmental data using tools developed by the 'Atlas of Living Australia'. Conclusions: Developed within an open data framework, the BASE project is the first Australian soil microbial diversity database. The database will grow and link to other global efforts to explore microbial, plant, animal, and marine biodiversity. Its design and open access nature ensures that BASE will evolve as a valuable tool for documenting an often overlooked component of biodiversity and the many microbe-driven processes that are essential to sustain soil function and ecosystem services

Size Matters: Assessing Optimum Soil Sample Size for Fungal and Bacterial Community Structure Analyses Using High Throughput Sequencing of rRNA Gene Amplicons

We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5 and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI) were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungal community structure, replicate dispersion and the number of operational taxonomic units (OTUs) retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation

Legislative Documents

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Comparative metatranscriptomics of wheat rhizosphere microbiomes in disease suppressive and non-suppressive soils for Rhizoctonia solani AG8.

<p>A comparative metatranscriptomic approach was applied to assess the taxonomic and functional characteristics of the rhizosphere microbiome of wheat plants grown in adjacent fields which are suppressive and non-suppressive to the plant pathogen, R. solani AG8.</p><p><br></p><p>The paper on this research has now been published: </p><p>Hayden, H. L., K. W. Savin, J. Wadeson, V. V. S. R. Gupta and P. M. Mele (2018). Comparative Metatranscriptomics of Wheat Rhizosphere Microbiomes in Disease Suppressive and Non-suppressive Soils for Rhizoctonia solani AG8. Frontiers in Microbiology 9(859).</p><p><a href="https://doi.org/10.3389/fmicb.2018.00859">https://doi.org/10.3389/fmicb.2018.00859</a><br></p><p><br></p><p><br></p><p> </p><p>The files in this set include:</p><p>1. Metatranscriptome assembly - TrinityAllMRZincrRNA.fasta.gz</p><p>2. Trinotate generated annotation file for the metatranscriptome assembly - Trinotate_report_wAnnotsOnly.txt<br></p><p>3. The count matrix generated in Trinity using RSEM for differential expression analysis.</p><p> </p><p>4. BLASTN of NCBI nt database (E ≤ 1e^-5) annotation file for differentially expressed genes as identified using EdgeR<br></p><p>- de.7219.Trinity.isoforms.srtXlogFC.nt.tophits.txt</p><p> </p><p>5. BLASTX of NCBI nr database (E ≤ 1e^-5) annotation file for differentially expressed genes as identified using EdgeR<br></p><p>- de.7219.Trinity.isoforms.srtXlogFC.nr.tophits.txt</p><p>6. Testing of differential expression software EdgeR compared to DeSeq2 on the metatranscriptome counts matrix<br></p><p>-EdgeR&DeSeq2Analysis.docx </p><p><br></p><p> </p><p> </p><p>Trinity (version 2.2.0) was used for <i>de novo</i> metatranscriptome assembly. A set of 348,722,194 quality filtered reads from 12 rhizosphere libraries (six suppressive soil, six non-suppressive soil) was combined into a single reference transcriptome assembly. </p><p><br></p><p>The assembly was annotated using Trinotate. Transcripts were subjected to a BLASTX search (E ≤ 1e-5) of the protein database Swiss-Prot downloaded from UniProt (http://www.uniprot.org/). The software Transdecoder (http://transdecoder.github.io) was used to predict likely coding regions within transcripts, and resulting protein products were subjected to a BLASTP search (E ≤ 1e-5) against the Swiss-Prot database. To identify conserved protein domains we used Hmmer software (http://hmmer.org/) and PFam. KEGG, Gene Ontology (GO), and Eggnog annotations were retrieved from Swiss-Prot where transcripts could be assigned to these databases. All results for the reference assembly annotation were parsed by Trinotate, stored in a SQLite database and then reported as a tab-delimited summary file. Only contigs with annotations are reported in the attached file.</p><p><br></p><p> </p><p>A count matrix produced in Trinity using RSEM was used for differential expression analysis in edgeR. For the assembly transcript abundance was filtered at a count per million (CPM) of 0.5 though expression was required in five of the six replicate samples. Normalisation to allow comparison between samples was performed for each count table in edgeR using TMM (trimmed mean of M-values). EdgeR settings included using the generalised linear model (GLM) likelihood ratio test with the contrast option (suppressive minus non-suppressive). Differentially expressed transcripts from the Trinity assembly were also subject to BLASTX and BLASTN searches of Genbank (E ≤ 1e^-5).</p><p><br></p><p>Exploration was done to examine the numbers and types of of contigs identified as being differentially expressed when the software DESeq2 was used for differential gene expression analysis, in comparison to the dataset above produced by EdgeR analyses. <br></p><p> </p><p> </p

Molecular complexity and diversity of persistent soil organic matter

Managing and increasing organic matter in soil requires greater understanding of the mechanisms driving its persistence through resistance to microbial decomposition. Conflicting evidence exists for whether persistent soil organic matter (SOM) is molecularly complex and diverse. As such, this study used a novel application of graph networks with pyrolysis-gas chromatography-mass spectrometry to quantify the complexity and diversity of persistent SOM, defined as SOM that persists through time (soil radiocarbon age) and soil depth. We analyzed soils from the Cooloola giant podzol chronosequence across a large gradient of soil depths (0–15 m) and SOM radiocarbon ages (modern to 19,000 years BP). We found that the most persistent SOM on this gradient was highly aromatic and had the lowest molecular complexity and diversity. By contrast, fresh surface SOM had higher molecular complexity and diversity, with high contributions of plant-derived lignins and polysaccharides. These findings indicate that persisting SOM declines in molecular complexity and diversity over geological timescales and soil depths, with aromatic SOM compounds persisting longer with mineral association

Biodiversity and ecosystem functioning in soil

We review the current knowledge on biodiversity in soils, its role in ecosystem processes, its importance for human purposes, and its resilience against stress and disturbance. The number of existing species is vastly higher than the number described, even in the macroscopically visible taxa, and biogeographical syntheses are largely lacking. A major effort in taxonomy and the training of a new generation of systematists is imperative. This effort has to be focussed on the groups of soil organisms that, to the best of our knowledge, play key roles in ecosystem functioning. To identify such groups, spheres of influence (SOI) of soil biota - such as the root biota, the shredders of organic matter and the soil bioturbators - are recognized that presumably control ecosystem processes, for example, through interactions with plants. Within those SOI, functional groups of soil organisms are recognized. Research questions of the highest urgency are the assignment of species to functional groups and determining the redundancy of species within functional groups. These priorities follow from the need to address the extent of any loss of functioning in soils, associated with intensive agriculture, forest disturbance, pollution of the environment, and global environmental change. The soil biota considered at present to be most at risk are species-poor functional groups among macrofaunal shredders of organic matter, bioturbators of soil, specialized bacteria like nitrifiers and nitrogen fixers, and fungiforming mycorrhizas. An experimental approach in addressing these research priorities is needed, using longterm and large-scale field experiments and modern methods of geostatistics and geographic information systems