1. Using studies of gene expression to investigate species radiations in the New Zealand alpine flora Southern Connection 2010 Claudia Voelckel, Peter B Heenan, Peter J Lockhart

2. DNA mRNA proteins Transcription Translation provide structure & drive metabolism substrateproduct Why Gene Expression Studies? Genomics Transcriptomics Proteomics Metabolomics * Comparative transcript profiling within & between species * Evolutionary 2

3. 1.Transcriptomics and species radiation – a case study 2.New tool in town – sequencing based methods replace microarrays 3.Putting the new tool to the test – case study revisited 4.Systems biology and species radiation Outline 3

4. 1.Transcriptomics and species radiation – a case study 2.New tool in town – sequencing based methods replace microarrays 3.Putting the new tool to the test – case study revisited 4.Systems biology and species radiation Outline 4

5. Pachycladon (Brassicaceae) Pachycladon super-network, S. Joly, unpubl. stellatum fastigiatum enysii cheesemaniiexile novae- zealandiae wallii latisiliqua

6. Diversification in New Zealand Alpine Cress HabitatRosette Flowering Fruiting HabitatRosette Flowering Fruiting vs. 6 Pachycladon fastigiatumPachycladon enysii

7. Sampling in the New Zealand Southern Alps 7 P. enysii P. fastigiatum

8. 8 DNA chip with gene probes AAAAAA3’ TTTTTT5’ green-labeled cDNA AAAAAA3’ TTTTTT5’ red-labeled cDNA Microarrays (DNA chips) Sample 1 AAAAAA3’ mRNA Sample 2 AAAAAA3’ mRNA DATA ANALYSIS intensity 1 intensity 2 Expression ratio:log

9. P. enysiiP. fastigiatum Probability of differential expression ( log odds ratio) Magnitude of differential expression (log fold change) ESM1 ESP  Arabidopsis microarray (20,468 genes)  310 genes (1.5%) up in P. fastigiatum 324 genes (1.6%) up in P. enysii  up-regulation of ESM1 and ESP predict P. fastigiatum to produce isothiocyanates and P. enysii to produce nitriles Results Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753 9

10. Methionine Chain elongation pathway Homomethionine (C3 GLS) Dihomomethionine (C4 GLS) Methylthioalkyl GLS Methylsulfinylalkyl GLS Alkenyl GLSHydroxalkyl GLS Hydroxalkenyl GLS GLS core pathway Glucosinolate hydrolysis ThiocyanatesNitriles (Eithionitriles) IsothiocyanatesOxazolidine-2-thione Side chain modification (Aliphatic) Glucosinolates (GLS) – Synthesis and hydrolysis genes MAM, MAM-I, MAM-D, BCAT4 CYP79, CYP83, C-S lyase, SGT, SOT FMO AOP2AOP3 GS-OH myrosinase ESM1ESP

11. ESP (At1g54040) ESM 1 (At3g14210) 6.29 - 4.62 Nitriles in P. enysii Isothiocyanates in P. fastigiata P. enysii P. fastigiata HP (μ mol/g fw) GenePredictionRegulation (log ratio) Test HPLC Test of Microarray Prediction Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753 Hypothesis: Role for herbivory in species diversification?

12. 1.Transcriptomics and species radiation – a case study 2.New tool in town – sequencing based methods replace microarrays 3.Putting the new tool to the test – case study revisited 4.Systems biology and species radiation Outline 12

13. NEXT-GEN Sequencing  Inexpensive production of large volumes of sequence data  Several platforms (Roche/454, Illumina/Solexa, ABI/SOLiD)  Many applications (de-novo assembly, re-sequencing, epigenetics and chromatin structure, metagenomics)  Revolutionary tools for gene expression analysis (e.g. Tag profiling, RNA-seq)

14. 14 Tag Profiling If needed – build reference transcriptome through RNA seq 1212 2121 1111 count 1 count 2 log STATISTICAL ANALYSIS Solexa Genome Analyzer Sample 1mRNA AAA3’ Sample 2mRNA AAA3’ 18 bp tag library AAA3’ 18 bp tag library AAA3’ Sample 1 Sample 2 Reference TAG MAPPING

15. Advantages & Challenges of Tag Profiling  open to any organism  any expressed transcript detectable (1 copy/cell)  less RNA needed (tag profiling = 1µg, microarrays = 100 µg)  minor data normalization/no background Advantages Challenges  mapping 18 bp tags (sequence differences Pachycladon/Arabidopsis)  counting tags per gene (noise, location, abundance)  statistical analysis of differential expression (proportion data) 15

16. 1.Transcriptomics and species radiation – a case study 2.New tool in town – sequencing based methods replace microarrays 3.Putting the new tool to the test – case study revisited 4.Systems biology and species radiation Outline 16

17. Tag Profiling Results  17423 A. thaliana loci (noise filter 10, count most abundant tag per gene)  2654 genes (15.2%) up in P. fastigiatum 1857 genes (10.7%) up in P. enysii (tagwise normalization, -log2(1.5) < logfc < log2 (1.5)) P. enysiiP. fastigiatum 17

18. Microarrays (MA) vs. Tag Profiling (TP)  more differentially expressed genes in TP (10.7-15.2% ) than with MA (1.5-1.6% ) 310 up in PF 324 up in PE 2654 up in PF 1857 up in PE PF MA TP 41269 2613 PE 502741807 MA TP  13.2% (PF) and 15.4 % (PE) of MA results confirmed by TP results 18  biological inferences from both studies identical MA: 20,468 genes TP: 17,423 genes 116058863 5818

19. “...not a popular product, too expensive, tricky chemistry.. instead use: RNA-Seq!” Tag Profiling is dead, long live RNA-Seq! 2 Oct 09: “Illumina is discontinuing the support of Tag Profiling and will no longer be manufacturing the reagent kits for this application.”  One year later: Tag profiling works for a non-model plant with a distant reference transcriptome! Let’s do more experiments! 19

20. 20 RNA-Sequencing If needed – build reference transcriptome through RNA seq Sample 1 AAA3’ Sample 2mRNA Solexa Genome Analyzer AAA3’ cDNA library Sample 1 Sample 2 Reference READ MAPPING 1212 2121 1111 count 1 count 2 log STATISTICAL ANALYSIS gene length

21.  read mapping (reference transcriptome)  quantification of reads (lack of software, but packages evolve: e.g. edgeR) Advantages & Challenges of RNA-Seq Advantages Challenges  whole transcriptome coverage and longer reads  large dynamic range of expression levels  base-resolution expression profiles for each gene  multiplex-compatible  sequence variation in transcribed regions (e.g. SNPs)  splicing isoforms, gene boundaries, novel transcribed regions 21 Great for non-model organisms!

22. Planned RNA Sequencing Projects EST library for Pachcladon fastigiatum (31,116 genes, 79% of Arabidopsis) 22 Allopolyploidy and genome bias in Pachycladon Adaptation to warmer climates in Pachycladon SNP development in Pachycladon

23. 1.Transcriptomics and species radiation – a case study 2.New tool in town – sequencing based methods replace microarrays 3.Putting the new tool to the test – case study revisited 4.Systems biology and species radiation Outline 23

24. DNA mRNA proteins Transcription Translation provide structure & drive metabolism substrateproduct How about System Biology? Genomics Transcriptomics Proteomics Metabolomics * Evolutionary 24 * Evolutionary *C omparative transcript, protein and metabolite profiling within & between species

25. Q: Ecological drivers of diversification? A: Comparative gene and protein expression profiling in common gardens FA ST EN LA CH EX NZ WA Questions & Approach P. cheesemanii (CH) P. exile (EX) P. novae-zelandiae (NZ) CH EX NZ

26. Lincoln Plant growth Peter Heenan Murray Dawson Auckland Microarray analysis Bart Janssen Luke Luo Silvia Schmidt Jena Glucosinolate analysis Michael Reichelt Palmy Link all data Claudia Voelckel Pete Lockhart Sydney Protein analysis Paul A. Haynes Mehdi Mirzai Dana Pascovici People who helped: Submitted

27. 9601 loci1489 loci Overall correlation: 85274151074 TPTP CHEXNZ CH0.520.430.30 EX0.470.450.32 NZ0.400.360.34 T P T = transcript profiling, P = protein profiling similar to other non-plant systems (0.2-0.5)

28. Interconversion of carbon dioxide and bicarbonate (carbonic anhydrase) Draught response Serine racemase Vegetative storage proteins 976129* 18814 142288 TPTP TP TP EX+NZ CH+NZ CH+EX 23% 32% 18% 4% 36% 3% CH EX NZ Specific Genes Found by T AND P

29. EX+NZ CH- CH+NZ EXiso CH+EX NZ- EX+NZ CH- CH+NZ EX- CH+EX NZnitriles Testing Predictions from T & P: Glucosinolate Hydrolysis Prediction P. cheesemanii P. novae-zelandiae Test

30. TCHEXNZ CH10.910.74 EX10.83 NZ1 PCHEXNZ CH10.750.59 EX10.72 NZ1 == Profiling Patterns Through the Phylogenetic Lens: CH EX NZ ≠ Glucosinolates

31. Thanks to: YOU! Funding Marsden & Humboldt Foundation New Zealand Landcare: Peter Heenan, Kerry Ford, Murray Dawson, Kat Trought Plant and Food: Bart Janssen, Luke Luo, Silvia Schmidt AWC Genome Service: Pete Lockhart, Patrick Biggs, Lorraine Berry, Lesley Collins, Maurice Collins Students: Christine Reinsch, Hanna Daniel, Helene Kretzmer Germany MPICE: Michael Reichelt, Jonathan Gershenzon Australia Macquarie University: Mehdi Mirzai, Dana Pascovici, Paul Haynes, Mark Westoby