Metagenomic communities inhabiting Phragmites australis litter
In this study, the intermittent lake is taken up as a model for changes in water availability and how they affect the functional microbial communities decomposing Phragmites australis litter. The original material (source material) was exposed to decomposition at a predominately wet site (wet; 45°43'38.30" N, 14°24'16,59" E) or dry site (dry; 45°43'38.29" N, 14°24'16,72" E) for a duration of 45 days. The overall aim of our study was to evaluate the effect of different conditions due to fluctuating water levels on the decomposition of P. australis litter. To predict litter decomposition dynamics in intermittent lakes, it is critical to understand how the functional microbial communities (present in the decomposing leaves) change under different habitat conditions. First, we hypothesized that functional communities from fresh leaves would change due to the start of the decomposition processes. Second, we hypothesized that the response of communities in different habitats would differ relative to the submergence level, showing differences in the metabolic processes. Our last hypothesis was that changes at the level of functional genes would be more pronounced in bacterial than in fungal functional communities. To evaluate our hypotheses, we analyzed functional microbial communities in fresh P. australis leaves and leaves decomposing in either wet or dry habitats that are characterized by different flooding patterns.
Steps to reproduce
For the current dataset, the whole community DNA was extracted from common reed leaves using the GenElute® Plant Genomic DNA isolation kit (Sigma), following the step-by-step procedures from the manufacturer's manual. Shotgun metagenomic sequencing was done via the Illumina HiSeqX platform (2 × 150 pair-ends) according to the manufacturer's guidelines using the TruSeq Nano kit (Illumina). Analysis and annotation of output data were performed through Metagenomics rapid annotation (MG-RAST) online server version 4.0.3 (Meyer et al., 2008) with the default parameters. Following quality control (QC), sequences were annotated using BLAT algorithm (Kent, 2002) against M5nr and M5rna databases (Wilke et al., 2012), which offers non-redundant integration of numerous databases. Functional classification was performed using the SEED Subsystems database, and taxonomic annotations up to genus level were performed using the RefSeq database with a maximum e value cut-off of 10−5, minimum identity cut-off of 60% and a minimum length of sequence alignment of 15 nucleotides. Analysis was conducted on two layers, i) taxonomy with abundance tables for bacterial and fungal communities and ii) functional gene categories.
Javna Agencija za Raziskovalno Dejavnost RS