Download P0196 Poster Session I Basic science: pathogenesis of

P0196
Poster Session I
Basic science: pathogenesis of staphylococci
COMPARISON OF TRANSCRIPTOMICS TECHNOLOGIES FOR ASSESSMENT OF STAPHYLOCOCCUS
AUREUS GENE EXPRESSION
D. Hernandez1, C. Le Priol2, D. Baud1, A. Fischer1, F. Reynier2, G. Gervasi2, S. Chatellier2, J. Veyrieras2,
P. François1, J. Schrenzel1
1
Genomic Research Laboratory Division of Infectious Diseases, Geneva University Hospitals, Geneva, Switzerland
2
Technology Research Department Innovation and Systems Unit, BioMérieux, Marcy l'Etoile, France
Objectives
Today, high-throughput shotgun sequencing of transcriptomes (RNA-seq) in prokaryote seems to be an appealing
alternative to well-established transcriptomics technologies such as microarray. While this later technology provides
an analogical quantification of individual genes transcription (via the fluorescent intensity measuring the amount of
hybridization between capture probes and their complementary cDNA fragments), RNA-seq methods make it
possible to get a comprehensive digital quantification of transcribed regions (as done by counting the number of
sequenced reads that map onto the corresponding genomic regions). Besides, contrary to existing digital
technologies like the NanoString nCounter platform (and contrary to microarrays too), RNA-seq approaches do not
require the prior design of probes and can then be used to get simultaneously the transcriptomic profile of prokaryote
strains at both known and unknown transcribed regions. Nevertheless, analytical performances of RNA-seq
approaches in prokaryotes have not been yet so far investigated. Here, we compared two RNA-seq solutions
(Illumina MiSeq and Ion Torrent PGM) with Agilent microarrays and the NanoString nCounter system from
Staphylococcus aureus total RNA samples.
Methods
We extracted four total RNA samples from the Staphylococcus aureus strain NCTC 8325. Samples were obtained
at 3h and 5h of growth from a wild-type strain, as well as from a GdpS mutant. Each sample has been depleted from
structural RNAs by using MicrobEnrich method (Ambion). The samples were then subjected to the different methods.
RNA-seq data were mapped onto the reference genome sequence using BWA and converted to gene counts using
Bedtools software applications. Statistical analysis was performed using the software R, with both the DESeq R
package, as well as home-made scripts.
Results
Both Illumina and Ion-Torrent RNA-seq experiments displayed an average variation coefficient of about 25%
between individual triplicates. However, at the gene level, the variation is strongly correlated with the individual
coverage. Microarray and NanoString nCounter showed better reproducibility with Pearson correlation coefficients
> 0.99.
Conclusions
RNA-seq, which is likely to become the standard approach in prokaryote transcriptomics, requires sufficient coverage
for the results to be reliable. Since individual gene counts are not independent, highly expressed genes are detected
at the expense of weakly covered genes for which reads counts may be insufficient for a reliable expression
measurement. Both sequencing technologies are affected by sequence-related biases (such as %GC content),
which may prevent comparing expression levels between individual genes. However, the sequence bias is strongly
correlated for each sequencing technologies, which allows for differential expression measurements. The probebased NanoString nCounter system provides the most accurate expression measure and remarkable correlation
between replicates. However, it only allows querying for a number of 800 targeted genes whose sequences have to
be known.