%0 Journal Article
%T Stool metatranscriptomics: A technical guideline for mRNA stabilisation and isolation.
%A Reck M
%A Tomasch J
%A Deng Z
%A Jarek M
%A Husemann P
%A Wagner-Döbler I
%A
%J BMC Genomics
%V 16
%N 0
%D Jul 2015 4
%M 26140923
%F 4.547
%R 10.1186/s12864-015-1694-y
%X BACKGROUND: The complex microbiome of the gut has an enormous impact on human health. Analysis of the transcriptional activity of microorganisms through mRNA sequencing (metatranscriptomics) opens a completely new window into their activity in vivo, but it is highly challenging due to numerous technical and bioinformatical obstacles. Here we present an optimized pipeline for extraction of high quality mRNA from stool samples.
RESULTS: Comparison of three commercially available RNA extraction kits with the method of Zoetendal revealed that the Powermicrobiome Kit (MoBio) performed best with respect to RNA yield and purity. Next, the influence of the stabilization reagent during sample storage for up to 15 days was studied. RIN analysis and qRT-PCR of spiked-in and indigenous genes revealed that RNA Later preserved mRNA integrity most efficiently, while samples conserved in RNA Protect showed substantial mRNA decay. Using the optimized pipeline developed here, recovery rates for spiked-in E.coli cells expressing fluorescing proteins were 8.7-9.7% for SuperfolderGFP and 14.7-17.8% for mCherry. The mRNA of stabilized stool samples as well as of snap-frozen controls was sequenced with Illumina Hiseq, yielding on average 74 million reads per sample. PCoA analysis, taxonomic classification using Kraken and functional classification using bwa showed that the transcriptomes of samples conserved in RNA Later were unchanged for up to 6 days even at room temperature, while RNA Protect was inefficient for storage durations exceeding 24 h. However, our data indicate that RNA Later introduces a bias which is then maintained throughout storage, while RNA Protect conserved samples are initially more similar to the snap frozen controls. RNA Later conserved samples had a reduced abundance of e.g. Prevotellaceae transcripts and were depleted for e.g. COG category "Carbohydrate transport and metabolism".
CONCLUSIONS: Since the overall similarity between all stool transcriptional profiles studied here was >0.92, these differences are unlikely to affect global comparisons, but should be taken into account when rare but critically important members of the stool microbiome are being studied.