Next generation gene mining to decipher CBSV resistance in cassava

1. “NRI's mission is to provide distinctive, high quality and relevant research, consultancy, teaching and advice in support of sustainable development, economic growth and poverty reduction.” Next generation gene mining to decipher CBSV resistance incassava Hale Ann Tufan Natural Resources Institute University of Greenwich

2. Outline • Introduction • Material and methods • General description of RNA-seq data • RNA-seq data analysis • Clustering and expression profiles • Gene ontology • Genes of interest • Conclusions www.iita.org

3. Threat of CBSD • Genus Ipomovirus, family Potyviridae • Losses of US$ 100 million annually • Serious threat to cassava production in Eastern and Central Africa • Spread mechanically and by whitefly vector • Pressing need for new sources of resistance Herrera Campo et al., (2011) Food Security, 3:329-345

4. Next generation sequencing for resistance gene discovery • For sequenced genomes, RNA-seq has potential to serve as a transcriptomics tool as well as marker development platform • Lower cost of sequencing enables use of this technology for resistance gene discovery Varshney et al. (2009). Trends in Biotechnology, 97: 522-530

6. Approach

7. Susceptible cv. Albert Leaves show severe symptoms and plants continue to show symptoms through development Roots show symptoms of rotting.

8. Resistant cv. Kaleso (Namikonga) Leaves show infection early but plant look and grow ‘normal’ thereafter Roots also show no sign of symptoms.

9. Methods • RNA isolated from 3 independent biological replicates each from 4 treatments: Albert Control, Albert CBSV, Kaleso Control, Kaleso CBSV • Pool replicates after quality control • RNA samples to GATC Biotech for sequencing • Illumina HiSeq 2000 platform, single-end 50 bp reads • Sequence reads mapped against reference genome with BWA aligner • The expression table buildup made by GATC in-house software

10. General description of data • ~50 million reads per sample, 50-60% of reads mapped per sample • 34,151 genes total

11. General description of data • 28,667 genes expressed in at least one of 4 treatments • Majority of these expressed in all treatments • High number of Kaleso-specific genes, compared to other treatments

12. Data analysis • Samples are pooled-limited options for data analysis • Genesis software used to analyze data http://genome.tugraz.at/genesisclient/genesisclient_description.shtml • CoV cutoff of 70% to identify genes with ‘significant’ gene induction between treatments • K-mean clustering to identify groups of genes with similar expression patterns (50 iterations, specify 5 clusters)

13. K-Means Clusters Expression Profiles Cluster 1 133 Genes Kaleso CBSV specific (highly expressed) Cluster 2 86 Genes Kaleso specific Cluster 5 670 Genes Mix/ some tendency for higher expression in Kaleso CBSV Cluster 4 4180 Genes Largely unchanged/ low expression (image truncated) Cluster 3 150 Genes Albert specific

14. Gene Ontology Cluster 1 Kaleso Control Kaleso Control Albert Control Albert Control Kaleso CBSV Kaleso CBSV Albert CBSV Albert CBSV Cluster 2

15. Gene Ontology Cluster 3 Kaleso Control Kaleso Control Albert Control Albert Control Kaleso CBSV Kaleso CBSV Albert CBSV Albert CBSV Cluster 5

16. Genes highly upregulated in Kaleso CBSD (Cluster 1) • Metabolism: sucrose synthase, Fatty acid hydroxylase, hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase • Transcription factors: MYB domain protein, zing finger domain protein, NAC transcription factor, WRKY protein • Signaling: MAPKK, MAPKKK, Leucine-rich repeat transmembrane protein kinase • Defence related: Seven transmembrane MLO family protein, peroxidase, pleiotropic drug resistance 1, Disease resistance-responsive (dirigent-like protein) family protein

17. Genes upregulated in Kaleso CBSD (Cluster 5) • Metabolism: Cinnamyl alcohol dehydrogenase 9 • Transcription factors: MYB domain protein, NAC domain protein, RWP-RK domain-containing protein, WRKY DNA-binding protein • Signaling: Protein kinase, receptor-like protein kinase 1, receptor serine/threonine kinase, Leucine-rich repeat protein kinase family protein, BAK1-interacting receptor-like kinase 1, cysteine-rich RLK (RECEPTOR-like protein kinase) • Defence related: disease resistance family protein, peroxidase, cellulose synthase, chitinase, beta glucosidase 11, Pathogenesis-related thaumatin, jasmonate-zim-domain protein 1, ethylene responsive element binding factor 4, ACC synthase 1, ethylene-responsive element binding factor 13, PR-1 • Other: RNA-dependent RNA polymerase 1, phloem protein 2-B15,

19. What’s next? Genes of interest

20. Model Gomez et al (2009) Eur. J. Plant. Path, 125: 1-22 Modified from Maule et al. (2007) Mol. Plant Path. 8: 223–231

21. Conclusions • Pooling samples yields good results for a snapshot study • Large number of genes specific to Kaleso CBSV treatment • Data analysis resulted in clusters of interesting genes, subset with large upregulation in response to Kaleso CBSV • Orthologues of genes well characterized to be involved in resistance responses are upregulated in response to Kaleso CBSV • Limitations in experimental design- focus on dominant resistance genes (NBS-LRR) for validation and further analysis • Knowledge can possibly be applied in the field- access to Albert x Kaleso cross progeny could yield very interesting results.

22. Please contact Dr. Maruthi Gowda at M.N.Maruthi@greenwich.ac.uk for further questions Thank you