Validation of two ribosomal RNA removal methods for microbial metatranscriptomics

Abstract

The predominance of rRNAs in the transcriptome is a major technical challenge in sequence-based analysis of cDNAs from microbial isolates and communities. Several approaches have been applied to deplete rRNAs from (meta)transcriptomes, but no systematic investigation of potential biases introduced by any of these approaches has been reported. Here we validated the effectiveness and fidelity of the two most commonly used approaches, subtractive hybridization and exonuclease digestion as well as combinations of these treatments, on two synthetic five-microorganism metatranscriptomes using massively parallel sequencing. We found that the effectiveness of rRNA removal was a function of community composition and RNA integrity for these treatments. Subtractive hybridization alone introduced the least bias in relative transcript abundance, whereas exonuclease and in particular combined treatments greatly compromised mRNA abundance fidelity. Illumina sequencing itself also can compromise quantitative data analysis by introducing a G+C bias between runs.Rapid technological advances in ultra-high-throughput sequencing are making de novo sequencing of transcriptomes (RNA-seq) a viable alternative to microarray analysis of microbial isolates and communities 1 . A major technical challenge for de novo transcriptome sequencing is the low relative abundance of mRNAs in total cellular RNA (1-5%; ref.2), the bulk of which is rRNAs and tRNAs 3 . Unlike eukaryotic mRNAs, which can be selectively synthesized into cDNA by virtue of their poly(A) tails 4 , bacterial and archaeal cDNAs are predominantly rRNA -2-sequences 5,6 . Therefore, prokaryotic rRNAs are often removed before sequencing to improve mRNA detection sensitivity. Different methods have been used to eliminate prokaryotic rRNA, including subtractive hybridization with rRNA-specific probes 7,8 , digestion with exonuclease that preferentially acts on rRNA, poly(A) tail addition to discriminate against rRNA 9,10 , reverse transcription with rRNA-specific primers followed by RNase H digestion to degrade rRNA:DNA hybrids 11 , and gel electrophoresis size separation and extraction of non-rRNA bands 12 .Among these methods, subtractive hybridization and exonuclease digestion have become the most popular owing to the availability of commercial kits from Ambion (MICROBExpress Bacterial mRNA Enrichment kit) and Epicentre (mRNA-ONLY Prokaryotic mRNA Isolation kit). The former kit uses a subtractive hybridization with capture oligonucleotides specific to 16S and 23S rRNAs. It has been applied to both bacterial isolates and environmental samples, in one or two rounds 6,[13][14][15][16][17][18] . The Epicentre kit uses exonuclease to preferentially degrade processed RNAs with 5′ monophosphate (the majority of which are believed to be rRNAs) 19,20 . In some instances, these methods have been used in combination to improve rRNA removal [21][22][23] . There is no consensus, however, on the best approach, and existing data are anecdotal. Here we validated the performance of these kits wit...

Elucidating the impact of microbial community biodiversity on pharmaceutical biotransformation during wastewater treatment

“…DNA samples were prepared for shotgun metagenomic sequencing at the University of Michigan DNA Sequencing Core (details provided in Appendix S1) and sequenced on a 100‐cycle paired end run on a HiSeq 2500 (Illumina). RNA samples were prepared for shotgun metatranscriptomic sequencing by first enriching the mRNA from the total RNA extracts using the MICROBExpress Bacterial mRNA Enrichment Kit (Invitrogen, Carlsbad, CA) based on previous studies (He et al ., 2010; Mettel et al ., 2010). Individual libraries were prepared for each sample as for the DNA samples, and the samples were multiplexed using sample‐specific adaptors on a single lane of a HiSeq Flow Cell (Illumina).…”

Next-generation transcriptome assembly

“…Poly(A) selection is very effective at enriching mRNAs in eukaryotes, but this selection approach will miss noncoding RNAs (ncRNA) and mRNAs that lack a poly(A) tail. In order to include RNAs without a poly(A) tail in the assembled transcriptome, rRNA contamination can be removed by hybridization-based depletion methods 29,30 . These normalization techniques can reduce the representation of highly abundant transcripts by many fold 31 , thereby increasing the opportunity for assembling rare transcripts.…”

A note on statistical repeatability and study design for high-throughput assays

“…The two technical replicates, each comprising measurements from multiple analytes, are plotted against each other, one point per analyte, and the corresponding sample correlation coefficient, r , is reported as a measure of experimental precision; see for example 9, 10, 11, 12, 13, 14, 15. As illustration, Figure 1a–d displays this method applied to a pair of replicates from each of four representative high‐throughput assays 20, 21, 22, 23.…”

“…While there has been extensive work on post‐experimental statistical procedures for controlling false discovery rates 6, 7, 8, little guidance exists on how to assess the precision of multivariate assays and incorporate this into experimental study design and the planning of experiments. Here, we critically review the current standard practice of quantifying assay performance, which is to calculate the sample correlation of measurements across a pair of multivariate technical replicates 9, 10, 11, 12, 13, 14, 15. We highlight important flaws in this approach and present an alternative framework based on statistical repeatability (also known as the intraclass correlation coefficient), for communicating assay precision and for integrating it into the planning of high‐throughput experiments 16.…”