Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities

Abstract

Background: Sequencing the expressed genetic information of an ecosystem (metatranscriptome) can provide information about the response of organisms to varying environmental conditions. Until recently, metatranscriptomics has been limited to microarray technology and random cloning methodologies. The application of high-throughput sequencing technology is now enabling access to both known and previously unknown transcripts in natural communities.

Methodology/principal findings: We present a study of a complex marine metatranscriptome obtained from random whole-community mRNA using the GS-FLX Pyrosequencing technology. Eight samples, four DNA and four mRNA, were processed from two time points in a controlled coastal ocean mesocosm study (Bergen, Norway) involving an induced phytoplankton bloom producing a total of 323,161,989 base pairs. Our study confirms the finding of the first published metatranscriptomic studies of marine and soil environments that metatranscriptomics targets highly expressed sequences which are frequently novel. Our alternative methodology increases the range of experimental options available for conducting such studies and is characterized by an exceptional enrichment of mRNA (99.92%) versus ribosomal RNA. Analysis of corresponding metagenomes confirms much higher levels of assembly in the metatranscriptomic samples and a far higher yield of large gene families with >100 members, approximately 91% of which were novel.

Conclusions/significance: This study provides further evidence that metatranscriptomic studies of natural microbial communities are not only feasible, but when paired with metagenomic data sets, offer an unprecedented opportunity to explore both structure and function of microbial communities--if we can overcome the challenges of elucidating the functions of so many never-seen-before gene families.

Figure 1. Relative abundance of sequence types identified for each sample.

Number of sequences per metabolism subsystem were normalised to sequencing effort for each sample and then relative abundance for each was calculated as a percentage.

Figure 2. Percentage taxonomic affiliation of sequences identified in each dataset by BLAST against the SEED database.

A – community at peak of the phytoplankton bloom (±1 SD). B - community after the phytoplankton bloom (±1 SD). Standard deviations are calculated from comparison of the different treatments. Data shown are for the high CO₂ treatment.