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Using studies of gene expression to investigate species radiations in the New Zealand alpine flora Claudia Voelckel, Peter B Heenan, Peter J Lockhart Southern Connection 2010 Why Gene Expression Studies? Genomics DNA Transcription mRNA * Evolutionary Transcriptomics Translation proteins provide structure & drive metabolism substrate product Proteomics Metabolomics *Comparative transcript profiling within & between species 2 Outline 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 3 Outline 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 4 Pachycladon (Brassicaceae) enysii latisiliqua stellatum wallii fastigiatum enysii cheesemanii Pachycladon super-network, S. Joly, unpubl. novaezealandiae exile Diversification in New Zealand Alpine Cress Pachycladon fastigiatum vs. Pachycladon enysii Habitat Rosette Habitat Rosette Flowering Fruiting Flowering Fruiting 6 Sampling in the New Zealand Southern Alps P. fastigiatum P. enysii 7 Microarrays (DNA chips) DNA chip Sample 1 mRNA AAAAAA3’ AAAAAA3’ TTTTTT5’ green-labeled cDNA Sample 2 AAAAAA3’ with gene probes AAAAAA3’ TTTTTT5’ TTTTTT5’ TTTTTT5’ mRNA red-labeled cDNA DATA ANALYSIS Expression ratio: log intensity 1 intensity 2 8 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 Probability of differential expression ( log odds ratio) Results P. fastigiatum ESM1 P. enysii ESP Magnitude of differential expression (log fold change) Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753 9 (Aliphatic) Glucosinolates (GLS) – Synthesis and hydrolysis genes Chain elongation pathway MAM, MAM-I, MAM-D, BCAT4 Homomethionine (C3 GLS) Dihomomethionine (C4 GLS) CYP79, CYP83, C-S lyase, SGT, SOT GLS core pathway FMO Methylsulfinylalkyl GLS AOP2 Alkenyl GLS AOP3 Hydroxalkyl GLS GS-OH Methylthioalkyl GLS Side chain modification Methionine Hydroxalkenyl GLS Glucosinolate hydrolysis Thiocyanates ESM1 Isothiocyanates ESP Nitriles (Eithionitriles) myrosinase Oxazolidine-2-thione HPLC Test of Microarray Prediction Gene Regulation (log ratio) Prediction Test P. enysii ESP (At1g54040) 6.29 Nitriles in P. enysii HP (μ mol/g fw) 14 Allyl 3MTP 12 10 8 6 4 2 0 Isothiocyanates - 4.62 P. fastigiata Isothiocyanates in P. fastigiata HP (μ mol/g fw) ESM 1 (At3g14210) Nitriles/Epithionitriles 3MSOP 7 6 5 4 3 2 1 0 Isothiocyanates Nitriles/Epithionitriles Hypothesis: Role for herbivory in species diversification? Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753 Outline 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 12 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) Tag Profiling AAA3’ AAA3’ AAA3’ AAA3’ Sample 1 AAA3’ AAA3’ AAA3’ AAA3’ mRNA mRNA Solexa Genome Analyzer AAA3’ Sample 2 AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ 18 bp tag library 18 bp tag library TAG MAPPING Reference Sample 1 Sample 2 STATISTICAL ANALYSIS log count 1 count 2 1 2 2 1 1 1 14 Advantages & Challenges of Tag Profiling Advantages 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 Challenges mapping 18 bp tags (sequence differences Pachycladon/Arabidopsis) counting tags per gene (noise, location, abundance) statistical analysis of differential expression (proportion data) 15 Outline 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 16 Tag Profiling Results 17423 A. thaliana loci P. fastigiatum P. enysii (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)) 17 Microarrays (MA) vs. Tag Profiling (TP) MA: 20,468 genes TP: 17,423 genes 8863 11605 5818 PF MA 269 41 Locus AT3G14210, ESM1 2613 lfc MA -4.6 TP lfc TP -35.0 PE 310 up in PF 324 up in PE MA 274 50 1807 Locus AT1G54040, ESP lfc MA 6.3 TP 2654 up in PF 1857 up in PE lfc TP 7.0 more differentially expressed genes in TP (10.7-15.2% ) than with MA (1.5-1.6% ) 13.2% (PF) and 15.4 % (PE) of MA results confirmed by TP results biological inferences from both studies identical 18 Tag Profiling is dead, long live RNA-Seq! One year later: Tag profiling works for a non-model plant with a distant reference transcriptome! Let’s do more experiments! 2 Oct 09: “Illumina is discontinuing the support of Tag Profiling and will no longer be manufacturing the reagent kits for this application.” “...not a popular product, too expensive, tricky chemistry.. instead use: RNA-Seq!” 19 RNA-Sequencing AAA3’ AAA3’ AAA3’ AAA3’ Sample 1 AAA3’ AAA3’ AAA3’ AAA3’ mRNA mRNA Sample 2 Solexa Genome Analyzer cDNA library cDNA library READ MAPPING Reference Sample 1 Sample 2 STATISTICAL ANALYSIS log count 1 gene count 2 length 1 2 2 1 1 1 20 Advantages & Challenges of RNA-Seq Advantages 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 Great for non-model organisms! Challenges read mapping (reference transcriptome) quantification of reads (lack of software, but packages evolve: e.g. edgeR) 21 Planned RNA Sequencing Projects EST library for Pachcladon fastigiatum (31,116 genes, 79% of Arabidopsis) Adaptation to warmer climates in Pachycladon SNP development in Pachycladon Allopolyploidy and genome bias in Pachycladon 22 Outline 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 23 How about System Biology? Genomics DNA Transcription mRNA * Evolutionary Transcriptomics Translation proteins provide structure & drive metabolism substrate product * Evolutionary Proteomics * Evolutionary Metabolomics *Comparative transcript, protein and metabolite profiling within & between species 24 Questions & Approach EN Q: Ecological drivers of diversification? ST A: Comparative gene and protein expression profiling in common gardens FA WA NZ EN CH P. cheesemanii (CH) LA P. exile (EX) EX P. novae-zelandiae (NZ) People who helped: Peter Heenan Murray Dawson Lincoln Plant growth Michael Reichelt Jena Glucosinolate analysis Sydney Protein analysis Auckland Microarray analysis Paul A. Haynes Mehdi Mirzai Dana Pascovici Palmy Link all data Claudia Voelckel Pete Lockhart Bart Janssen Luke Luo Silvia Schmidt Submitted Overall correlation: T = transcript profiling, P = protein profiling 9601 loci 1489 loci T TP P 8527 1074 P CH 415 EX NZ 0.52 0.43 0.30 EX 0.47 0.45 0.32 NZ 0.40 0.36 0.34 T CH similar to other non-plant systems (0.2-0.5) Specific Genes Found by T AND P EX+NZ CH T 97 TP 29* P 61 23% 32% Interconversion of carbon dioxide and bicarbonate (carbonic anhydrase) Draught response Serine racemase CH+NZ EX T 18 TP 4 P 81 Draught response P 228 Interconversion of carbon dioxide and bicarbonate (carbonic anhydrase) 18% 4% CH+EX NZ T 14 TP 8 36% 3% Vegetative storage proteins Testing Predictions from T & P: Glucosinolate Hydrolysis Prediction EX+NZ CH Test P. cheesemanii - CH+NZ EX iso CH+EX NZ - EX+NZ CH CH+NZ EX CH+EX NZ nitriles P. novae-zelandiae Profiling Patterns Through the Phylogenetic Lens: T CH EX NZ P CH EX NZ CH 1 0.91 0.74 CH 1 0.75 0.59 1 0.83 1 0.72 EX NZ = 1 EX NZ = 1 Glucosinolates EX ≠ CH* NZ 3MSOP 4MSOB 3-Butenyl 4MTB 8MSOO 6MSOH 7MSOH 4OHI3M 7MTH 1MOI3M 4MOI3M 3MTP S-2OH3-But. Allyl NZ* CH EX Thanks to: 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 Australia Macquarie University: Mehdi Mirzai, Dana Pascovici, Paul Haynes, Mark Westoby Germany MPICE: Michael Reichelt, Jonathan Gershenzon Funding Marsden & Humboldt Foundation YOU!