Microarray analysis of defined Mycobacterium tuberculosis populations using RNA amplification strategies.

BMC Genomics

Medical Microbiology, Centre for Infection, Division of Cellular & Molecular Medicine, St, George's University of London, Cranmer Terrace, Tooting, London, UK.

Published: February 2008

Background: The amplification of bacterial RNA is required if complex host-pathogen interactions are to be studied where the recovery of bacterial RNA is limited. Here, using a whole genome Mycobacterium tuberculosis microarray to measure cross-genome representation of amplified mRNA populations, we have investigated two approaches to RNA amplification using different priming strategies. The first using oligo-dT primers after polyadenylation of the bacterial RNA, the second using a set of mycobacterial amplification-directed primers both linked to T7 polymerase in vitro run off transcription.

Results: The reproducibility, sensitivity, and the representational bias introduced by these amplification systems were examined by contrasting expression profiles of the amplified products from inputs of 500, 50 and 5 ng total M. tuberculosis RNA with unamplified RNA from the same source. In addition, as a direct measure of the effectiveness of bacterial amplification for identifying biologically relevant changes in gene expression, a model M. tuberculosis system of microaerophilic growth and non-replicating persistence was used to assess the capability of amplified RNA microarray comparisons. Mycobacterial RNA was reproducibly amplified using both methods from as little as 5 ng total RNA (~equivalent to 2 x 105 bacilli). Differential gene expression patterns observed with unamplified RNA in the switch from aerobic to microaerophilic growth were also reflected in the amplified expression profiles using both methods.

Conclusion: Here we describe two reproducible methods of bacterial RNA amplification that will allow previously intractable host-pathogen interactions during bacterial infection to be explored at the whole genome level by RNA profiling.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2276497PMC
http://dx.doi.org/10.1186/1471-2164-9-94DOI Listing

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