Comparison of different NGS approaches to characterize bacterial pathogens

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1 Cmparisn f different NGS appraches t characterize bacterial pathgens Jurnées Annuelles du grupe Rngeurs du CBGP Maria RAZZAUTI maria.razzauti@supagr.inra.fr Centre de Bilgie pur la Gestin des Ppulatins, Mntferrier-sur-Lez, France Zntic pathgens Rdents are reservirs and/r vectrs f many human and livestck pathgens. Mst f these pathgens are vectred by ticks.

2 PATHO-ID prject Describe the «pathbime» (= pathgenic agents in the micrbilgic envirnment) f ticks and rdents: 1) T build an inventry f micrrganisms recvered frm ticks and rdents 2) T identify pathgens distributin and prevalence in tick and rdent ppulatins 3) T seek fr evidence f interactins between pathgens Sampling f rdents and ticks RNA extractin DNA extractin Catalg f pathbime f a pl f samples RNA-sequencing Large-scale survey f bacteria in each samples 16S-metabarcding Cmparisn f tw NGS appraches Interactin between pathgens

3 Sampling Ardennes 192 bank vles studied (Guivier et al. 2011) Mydes glarelus Sample cllectin Sample preservatin RNA RNA extractin Samples pling RNA fragmentatin RNA cdna syntesis NNNNNN + adaptrs cdna RNA-sequencing cleaning hst ribsmal RNA De nv assembling RNA-seq reads NNNNNN Jin adaptrs = NNNNNN 16S meta-barcding adaptr A DNA extractin in 96-well plates PCR f the gene cding fr the 16S rrna MID Fwd Primer Fwd Emultin PCR Primer Rev Multiplex f tagged amplicns Purificatin by gel excitin template quantificatn MID Rev Sequencing by 454 GS-FLX TM Sequencing by MiSeq adaptr B Barcding Bacteria detectin

4 Raw results f the cmparisn f RNA-Seq and the 16S meta-barcding Number f bacterial reads Number f bacterial genera Number f pathgenic bacteria 16S meta-barcding 16S meta-barcding 16S meta-barcding Number f reads detected fr each pathgenic bacteria 45 pathgenic bacteria detected 16S S-MiSeq

5 Crrelatin between number f reads (Lg-scale) generated fr each bacteria genera RNA-Seq 16S-MiSeq 16S S-454 RNA-Seq 16S-MiSeq Crrelatin between number f reads (Lg-scale) and the prevalence fr each bacteria genera RNA-Seq 16S-MiSeq 16S

6 prs and cns f bth appraches RNA-seq 16S meta-barcding N. f bacteria detected higher lwer A priri dcumentatin nne universal primers Taxnmical level species genera Reslutin f sample sequencing pl f samples individual sample Prevalence estimatins null allwed Bacteria interactin studies null allwed prs and cns f the sequencers Sequencers HiSeq 2000 (Illumina) MiSeq (Illumina) 454 GS-FLX (Rche) Price / run Price / Mb Output data / run 600 Gb 6 Gb 0.7 Gb Reads / run 3000 millins 12 millins 1 millins Errr with hmplymers Read length lw lw high 2x100 bp paired end (shrt reads assembly) 2x250 bp paired end 400 bp Time/run 8 day 26 hurs 10 hurs

7 Cnclusins RNA-Sequencing: mre sensitive t bacteria detectin (higher sequencing depth) deeper level f taxnmy nly allws infrmatin fr pl f samples arduus data analysis 16S meta-barcding: may be influenced by the perfrmance f the universal primers might be influenced by the number f cpies f the gene cding fr the 16S ribsmal RNA allws multiplexing f samples, and hence further analysis n cmmunity r a particular sample accessible data analysis Cllabratrs: The Centre fr Bilgy and Management f Ppulatins (CBGP-INRA), Mntpellier - GALAN Maxime - CHARBONNEL Nathalie - COSSON Jean-Françis INRA, GABI, Sigenae, Juy-en-Jsas - BERNARD Maria Animal Epidemilgy Unit, INRA, Clermnt-Ferrand - VOURC H Gwenaël - BAILLY Xavier Ecle Natinale Vétérinaire d Alfrt, ENVA, INRA, ANSES, Maisns-Alfrt - VAYSSIER-TAUSSAT Muriel PATHOQUEST, Institute Pasteur, Paris - CHEVAL Justine - ELOIT Marc Micrbial Eclgy Unit, University f Lyn (CNRS), Lyn - MAVINGUI Patrick Cntrl f emerging and extic animal diseases, INRA CMAEE/CIRAD Guadelupe - ALBINA Emmanuel Emergent Extic Diseases Unit (CMAEE), Mntpellier - LEFRANÇOIS Thierry