Metatranscriptomic analyses of chlorophototrophs of a hot-spring microbial mat

Abstract

The phototrophic microbial mat community of Mushroom Spring, an alkaline siliceous hot spring in Yellowstone National Park, was studied by metatranscriptomic methods. RNA was extracted from mat specimens collected at four timepoints during light-to-dark and dark-to-light transitions in one diel cycle, and these RNA samples were analyzed by both pyrosequencing and SOLiD technologies. Pyrosequencing was used to assess the community composition, which showed that B84% of the rRNA was derived from members of four kingdoms Cyanobacteria, Chloroflexi, Chlorobi and Acidobacteria. Transcription of photosynthesis-related genes conclusively demonstrated the phototrophic nature of two newly discovered populations; these organisms, which were discovered through metagenomics, are currently uncultured and previously undescribed members of Chloroflexi and Chlorobi. Data sets produced by SOLiD sequencing of complementary DNA provided 4100-fold greater sequence coverage. The much greater sequencing depth allowed transcripts to be detected from B15 000 genes and could be used to demonstrate statistically significant differential transcription of thousands of genes. Temporal differences for in situ transcription patterns of photosynthesis-related genes suggested that the six types of chlorophototrophs in the mats may use different strategies for maximizing their solar-energy capture, usage and growth. On the basis of both temporal pattern and transcript abundance, intra-guild gene expression differences were also detected for two populations of the oxygenic photosynthesis guild. This study showed that, when community-relevant genomes and metagenomes are available, SOLiD sequencing technology can be used for metatranscriptomic analyses, and the results suggested that this method can potentially reveal new insights into the ecophysiology of this model microbial community.

Community ecology of hot spring cyanobacterial mats: predominant populations and their functional potential

“…We detected the 16S rRNA sequences of these populations (Supplementary Figure 4 and Liu et al, 2011) but were unable to infer from them a phototrophic phenotype, as these lineages of Kingdom Chloroflexi had not been known previously to contain phototrophic organisms. The novel population forms an out-group to the currently known filamentous anoxygenic chlorophototrophs within Order Chloroflexales and sequences of non-phototrophic Chloroflexi (Supplementary Figure 11).…”

A novel metatranscriptomic approach to identify gene expression dynamics during extracellular electron transfer

“…We applied DNA sequencing with metagenomic assembly, constructed draft genomes, annotated the taxonomy and function of the ORFs, and subsequently performed read mapping of mRNA sequences to the metagenomic ORFs. Generally, this approach improves the number of mRNA reads used for analyses because they are mapped to an identical DNA template [31][32][33][34] . Additionally, this procedure allowed us to correlate gene expression results to specific strains in the community.…”

“…The success of this approach relies heavily on using simplified EET stimuli to induce gene expression changes, and the strength of using the same genetic background for metatranscriptomic analyses. Generally, metatranscriptomic analyses have demonstrated between 0.5 and 40% of mRNA reads mapped 15,16,[31][32][33][34][35][36][37] . We found that over 60% of mRNA reads were mapped to the reference metagenomic contigs (Supplementary Table S2), which indicates a higher representation of overall gene expression profiles within the microbial community.…”

Sizing up metatranscriptomics

“…For example, bacterial mRNA pools have provided assays of the bioreactive components of dissolved organic carbon pools based on transcriptome changes in amended seawater (McCarren et al, 2010;Poretsky et al, 2010;Shi et al, 2012); identified bacterial degradation pathways based on shifts in mRNA composition with increased substrate concentrations (Vila-Costa et al, 2010); characterized short-term reactions to altered CO 2 (Gilbert et al, 2008) and pollutant concentrations (de Menezes et al, 2012); and revealed niche differentiation among co-occurring autotrophs (Liu et al, 2012) and heterotrophs (Gifford et al, 2012). Changes in transcript inventories provide a sensitive window into the fluctuating cues perceived by microbes in their environment, and therefore the signals that drive changes in ecosystem function.…”