1. Next generation gene mining to decipher CBSV resistance in cassava
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Next generation gene mining to decipher CBSV resistance in cassava
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

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:

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:

6. Approach Resistant and Susceptible lines
Inoculate with virulent CBSV isolate
Collect RNA from Control and CBSV infected plants
Library construction and sequencing
Data analysis
Candidate genes
Validation
Test on cross progeny

7. Susceptible cv. Albert
Where does Albert come from?
Note: CBSD symptoms are usually absent on top leaves even in susceptible varieties
Leaves show severe symptoms and plants continue to show symptoms through development
Roots show symptoms of rotting.

8. Resistant cv. Kaleso (Namikonga)
Is Kaleso also resistant in the field? Where did Kaleso come from? Landrace? SA introgression? Source?
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
Nampula in Mozambique

10. General description of data
~50 million reads per sample, 50-60% of reads mapped per sample
34,151 genes total
Albert Control
Albert CBSV
Kaleso Control
Kaleso CBSV
Number of Reads
Percentage
All
54,045,667 -
60,070,579
38,949,010
49,681,907
Mapping to whole genome
31,632,660 59
35,964,664 60
20,946,755 54
29,534,087
Non uniquely mapped
8.674,373 27
10,282,664 29
5,526,455 26
7,563,418
Uniquely mapped
23,261,749 74
26,036,303 72
15,618,148 75
22,243,065
Resulting Reads
Number of reads used in this analysis. Number of reads mapped to whole genome. *1) Number of reads mapped to more than one site of the genome. *2)*4) Number of reads mapped to exactly one site of the genome. *2) Number of reads as result of mapping/preprocessing. *2)
*1) Percentage is calculated based on all reads used in this analysis. *2) Percentage is calculated based on the number of reads mapping to whole genome. *3) Percentage is calculated based on the number of reads mapped uniquely. *4) Reads have been excluded from analysis.

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
Number of reads used in this analysis. Number of reads mapped to whole genome. *1) Number of reads mapped to more than one site of the genome. *2)*4) Number of reads mapped to exactly one site of the genome. *2) Number of reads as result of mapping/preprocessing. *2)
*1) Percentage is calculated based on all reads used in this analysis. *2) Percentage is calculated based on the number of reads mapping to whole genome. *3) Percentage is calculated based on the number of reads mapped uniquely. *4) Reads have been excluded from analysis.

12. Data analysis Samples are pooled-limited options for data analysis
Genesis software used to analyze data
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)
Min
Mean
Max
StDev
% CoV
3.1
3.32
3.52
0.17
5.11
2.7
3.34
4.37
0.73
21.96
0.24
0.39
0.65
0.19
47.69
0.009
0.02
0.04
77.92
0.16
1.63
3.19
1.65
101.29

13. K-Means Clusters Expression Profiles
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 Cluster 2 Kaleso Control Albert Control
Albert CBSV
Kaleso CBSV
Kaleso Control
Cluster 2
Albert Control
Albert CBSV
Kaleso CBSV
Kaleso Control

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

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
UniqueID
best arabidopsis TAIR10 hit name
best arabidopsis TAIR10 hit symbol
best arabidopsis TAIR10 hit defline
cassava4.1_001246m|PACid:
AT3G
RPM1,RPS3
NB-ARC domain-containing disease resistance protein
cassava4.1_025993m|PACid:
AT3G
cassava4.1_000944m|PACid:
AT4G
Disease resistance protein (TIR-NBS-LRR class) family
cassava4.1_021672m|PACid:
AT1G
LRR and NB-ARC domains-containing disease resistance protein
cassava4.1_000627m|PACid:
AT5G
disease resistance protein (TIR-NBS-LRR class), putative

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. Thank you
Please contact Dr. Maruthi Gowda at for further questions