Project description:Global warming has shifted climate zones poleward or upward. However, understanding the responses and mechanism of microbial community structure and functions relevant to natural climate zone succession is challenged by the high complexity of microbial communities. Here, we examined soil microbial community in three broadleaved forests located in the Wulu Mountain (WLM, temperate climate), Funiu Mountain (FNM, at the border of temperate and subtropical climate zones), or Shennongjia Mountain (SNJ, subtropical climate).Soils were characterized for geochemistry, Illumina sequencing was used to determine microbial taxonomic communities and GeoChips 5.0 were used to determine microbial functional genes.
Project description:A functional biodiversity microarray (EcoChip) prototype has been developed to facilitate the analysis of fungal communities in environmental samples with broad functional and phylogenetic coverage and to enable the incorporation of nucleic acid sequence data as they become available from large-scale (next generation) sequencing projects. A dual probe set (DPS) was designed to detect a) functional enzyme transcripts at conserved protein sites and b) phylogenetic barcoding transcripts at ITS regions present in precursor rRNA. Deviating from the concept of GeoChip-type microarrays, the presented EcoChip microarray phylogenetic information was obtained using a dedicated set of barcoding microarray probes, whereas functional gene expression was analyzed by conserved domain-specific probes. By unlinking these two target groups, the shortage of broad sequence information of functional enzyme-coding genes in environmental communities became less important. The novel EcoChip microarray could be successfully applied to identify specific degradation activities in environmental samples at considerably high phylogenetic resolution. Reproducible and unbiased microarray signals could be obtained with chemically labeled total RNA preparations, thus avoiding the use of enzymatic labeling steps. ITS precursor rRNA was detected for the first time in a microarray experiment, which confirms the applicability of the EcoChip concept to selectively quantify the transcriptionally active part of fungal communities at high phylogenetic resolution. In addition, the chosen microarray platform facilitates the conducting of experiments with high sample throughput in almost any molecular biology laboratory. In this study, two independent RNA samples from a pine forest soil were labelled and hybridised to a custom-made EcoChip microarray consisting of about 9000 probes targeting expressed fungals genes and about 5000 probes targeting the precursor-rRNA of different fungal lineages
Project description:Despite the global importance of forests, it is virtually unknown how their soil microbial communities adapt at the phylogenetic and functional level to long term metal pollution. Studying twelve sites located along two distinct gradients of metal pollution in Southern Poland revealed that both community composition (via MiSeq Illumina sequencing of 16S rRNA genes) and functional gene potential (using GeoChip 4.2) were highly similar across the gradients despite drastically diverging metal contamination levels. Metal pollution level significantly impacted microbial community structure (p = 0.037), but not bacterial taxon richness. Metal pollution altered the relative abundance of specific bacterial taxa, including Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes and Proteobacteria. Also, a group of metal resistance genes showed significant correlations with metal concentrations in soil, although no clear impact of metal pollution levels on overall functional diversity and structure of microbial communities was observed. While screens of phylogenetic marker genes, such as 16S rRNA, provided only limited insight into resilience mechanisms, analysis of specific functional genes, e.g. involved in metal resistance, appeared to be a more promising strategy. This study showed that the effect of metal pollution on soil microbial communities was not straightforward, but could be filtered out from natural variation and habitat factors by multivariate statistical analysis and spatial sampling involving separate pollution gradients.