Submitted to: Advances in Microbiology
Publication Type: Peer reviewed journal
Publication Acceptance Date: 9/24/2013
Publication Date: 10/1/2013
Citation: Bhagwat, A.A., Ying, I., Karns, J.S., Smith, A.D. 2013. Determining RNA quality for NextGen sequencing: some exceptions to the gold standard rule of 23S to 16S rRNA ratio. Advances in Microbiology. 1:10. Interpretive Summary: Next generation sequencing is a powerful technology. It can provide insights into the physiology of food borne human pathogens, and in particular their response to environmental stimuli, not previously attainable. The first step in this process is to isolate high quality, intact ribonucleic acids (RNA) from pathogenic bacteria. Currently, automated platforms use internal algorithms to evaluate the integrity (degree of intactness) of RNA molecules, based on ribosomal RNA from E. coli strains. We observed that these automated methods consistently generated low integrity scores (indicating degradation) for several Citrobacter rodentium (a mouse pathogen) and Salmonella sp. (human pathogen), isolates. We investigated the cause for these low scores and determined that it was not due to RNA degradation. Rather, these strains produce ribosomal RNA fragments that differ in size than those from E. coli. Therefore, the results from the automated platforms were incorrect and misleading. As next generation sequencing technology is applied to more non-model organisms (such as foodborne pathogens) caution needs to be exercised in relying soley on scores generated by these automated platforms. This information will be useful to other scientists and will accelerate the adoption of next generation sequencing analyses for RNA preparations of mouse and human pathogens.
Technical Abstract: Using next-generation-sequencing technology to assess entire transcriptomes requires high quality starting RNA. Currently, RNA quality is routinely judged using automated microfluidic gel electrophoresis platforms and associated algorithms. Here we report that such automated methods generate false-negative RNA integrity scores for those microorganisms which do not carry intact 23S ribosomal RNA molecules. For RNA-sequencing projects involving non-model organisms, relying fully on automated methods could be misleading as high-quality RNA preparations may be judged as ‘degraded’ or as having poor 23S to 16S ratios.