Transcript Slide 1
February 20, 2005 Sharp – what and why Davis – mapping genes Plant Genome & Gene Complexity, Gene Regulation, Clark - maize rootworm Bohnert - get genes/transcripts Tao – dynamics of genes Springer – making sense of all NSF DBI #0211842 Place & Timing, and how to sort the Players Valeriy Poroyko, Mark Fredricksen (Pinghua Li, Qingqiu Gong Linus Gog, Melanie Griffith) The Plant Genome Diploid Polyploid Chrysanthemum species illustrate the phenomenon. Monoploid number (the basic set) = 9 chromosomes In Chrysanthemum species, the number of chromosomes fall into 5 categories: 18 chromosomes = diploid (2 copies of the monoploid) 36 chromosomes = tetrapoid (4 copies of the monoploid) 54 chromosomes = hexapoid (6 copies of the monoploid) 72 chromosomes = octaploid (8 copies of the monploid) 90 chromosomes = decaploid (10 copies of the monoploid) 50% of all flowering plants are polyploid. • Ploidy changes - a recurring process • Many ‘diploid’ species have gone through ploidy changes • Fusions of related species new species See: Arabidopsis AQP are distributed over all Chromosomes - a few clusters, many duplications 5 Mb PIP1;3 10 20 TIP3;2 TIP2;xpseudo NIP3;1 Ch-1 Ch-2 NIP3;1pseudo TIP4;1NIP2;1pseudo NIP2;1 NIP6;1 SIP1;1 PIP2;6 PIP1;2 (4) rDNA TIP1;1 NIP7;1 TIP2;1 TIP1;2 TIP5;1 Ch-3 PIP2;2 PIP2;3 PIP2;1 SIP2;1 (14) PIP1;4 TIP1;3 NIP5;1 TIP2;2 NIP1;1 NIP1;2 PIP1;5 PIP2;7 PIP2;5 PIP1;1 (3) SIP1;2 Ch-5 TIP3;1 (15) PIP2;8 Ch-4 30 NIP4;1 NIP4;2 TIP2;3 PIP2;4 (12) - duplicated regions that include AQPs. Figure 3 Arabidopsis – model plant small, fast, prolific, mutants, lines, ecotypes, genome sequence Field on a dish! O3 control CO2 Columbia grown in Soy-FACE Arabidopsis growing in the field in high CO2 and/or ozone FACE-rings down there concept plant performance in the future earth’ atmosphere (~2040) also: soy, corn, weeds The Plant Genome Ecosystem – population – species – ecotype (breeding line) Organism – organ – tissue – cell – compartment Nucleus – envelope & pore – nucleoplasm, nucleolus & chromosomes Euchromatin & heterochromatin – gene islands – gene Promoters – 5’-regulatory – introns & exons - coding region – 3’-regulatory regions Plants in silico? Sure! And then: Plant Design from Scratch The Plant Genome • • • • • • • • • • • • • Controls for Gene Expression – many Switchboards Chromatin condensation state Local chromatin environment Transcription initiation Transcript elongation Levels of regulation that mRNA splicing mRNA export affect what we call mRNA place in the cell RNA half-life “gene expression” Killer microRNAs Ribosome loading Protein transport/targeting Protein modifications Protein turnover binding site for Pol-II sometimes recognizable promoter REs ABRE DREB ERE response elements for every condition number spacing 5’ splicing & alternative splicing 3’ – variable, condition-dependent Watch out – not only activators may bind, but repressors as well! think about: you may need an activator to make a protein that removes a repressor. The Plant Transcriptome 5 years ago, we did not know that Killer RNAs such a control system existed! (there are micro-genes) microRNAs no protein gene is essentially “silenced” The Plant Transcriptome How to sample the transcriptome? Morphological dissection remember Bob Sharp’s talk! (root, leaf, flower - epidermis, guard cell, etc.) Cell sorting make single cells, send through cell sorter (size, color, reporter gene) Laser ablation micromanipulation of laser to cut individual cells Painting cells with a reporter gene here this is GFP Biochemical dissection chloroplasts, mitochondria, ribosomes, other membranes Green Fluorescence Protein The Plant Transcriptome Painting tissues then isolating desired cells Enzymatic staining The Endodermis of the root tip is highlighted in transgenic plants using pSCR::mGFP5. Cell-Specific GFP Expression • Catalog of available transgenic Arabidopsis lines. • Lines are available from the stock centers. • However, the molecular basis for the observed phenotype is usually uncharacterized. The Plant Transcriptome cDNA – complementary DNA converts messenger RNA into > cDNA libraries • “neat” • normalized • subtracted > SAGE libraries double-stranded DNA “Normalization” removes mRNAs for which there are many copies in a cell – thus enriching for “rare mRNAs” (not so much sequencing to do) Subtraction removes cDNAs which you already know (less sequencing) The Plant Transcriptome cDNA Libraries Primary cDNA Library Library Normalization Total RNA Poly(A)+ RNA primary cDNA library make ss-DNA out of primary library 1st strand cDNA ds-cDNA Size-selected double stranded cDNA (>500 bp) Ligate to EcoRI adapters/digest NotI Clone (EcoRI/NotI) digested pBSII/SK+ & adaptored cDNA Primary (neat) library may be used for “normalization” ss-DNA DNA “tracer” PCR inserts by T7 and T3 standard primers tracer/driver hybridization Cloning of root RNAs from segments S1 – S4 root tip (Sharp lab) sequenced ~18,000 clones DNA “driver” column chromatogr. (double-strands stick) Non-hybridized DNA from flowthrough = normalized clones found ~8,000 unique and ~130 novel genes How many genes make a root? The Plant Transcriptome SAGE – Serial Analysis of Gene Expression – an Overview Isolate total RNA from cells or tissue Isolate small regions (SAGE tags) of each mRNA transcript in a cell Digest tags and ligate into concatamers for sequencing Reference sequencing results against public databases FR697, 48 h after transplanting (from cell length profiles) 1 -1 DISPLACEMENT VELOCITY (mm h ) 3.0 2 3 4 WW00 WS05 WS48 2.5 2.0 WW 4 segments each barcoded then normalized sequence 6,000 1.5 1.0 from ~17,000 seqs ~8,000 different mRNAs ~800 never found before in any organism WS 0.5 0.0 0 2 4 6 8 10 12 14 DISTANCE FROM ROOT APEX (mm) 16 18 20 SAGE tags and EST contigs recognized in three corn root libraries. Problem - how to understand WS05 WW00 SAGE: 969 ESTs: 1603 SAGE 417 ESTs 542 SAGE 461 ESTs 476 SAGE 700 ESTs 640 SAGE: 1031 ESTs: 2071 WS48 Figure 7 SAGE: 1076 ESTs: 1743 what the meaning of 8,000 genes might be! SAGE 477 ESTs 576 [J] Translation, ribosomal structure and biogenesis 25 Translation & ribosomes 20 15 10 5 0 s1 s2 s3 ww00 ws05 s4 ws48 [B] Chromatin structure and dynamics 3.5 Chromatin-associated functions 3 2.5 2 1.5 1 0.5 0 s1 s2 s3 ww00 ws05 s4 ws48 [D] Cell cycle control, cell division, chromosome partitioning 1.8 Cell cycle control & maintenance 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 s1 s2 s3 ww00 ws05 ws48 s4 [F] Nucleotide transport and metabolism 1.6 Nucleotide transport & metabolism 1.4 1.2 1 0.8 0.6 0.4 0.2 0 s1 s2 s3 ww00 ws05 s4 ws48 [Q] Secondary metabolites biosynthesis, transport and catabolism Secondary metabolite dynamics 3.5 3 2.5 2 1.5 1 0.5 0 s1 s2 s3 ww00 ws05 s4 ws48 [P] Inorganic ion transport and metabolism 3 Ion transport 2.5 2 1.5 1 0.5 0 s1 s2 s3 ww00 ws05 ws48 s4 Importing data into Pathways – biochemical, developmental, regulatory MapMan The Plant Transcriptome Taking SAGE & cDNA Quantitative PCR in 384-well plates (96 primer pairs, 3 repeats each) sequences together corn roots “express” 20-23,000 genes (i.e., mRNA is made) - The entire corn genome is expected to include ~50,000 genes Why are we doing this? • Genes expressed in well-watered conditions, how many, where and which? • Changes during drought episodes? • Variation in different lines or land races? • Breeders to cross and select for tolerance! • Proteins and substances (metabolites) made? (how to make a cell wall, how to defend against rootworms) • Make corn with thicker (modified) cell walls! Transcript Dynamics – Wenjing Tao is next mRNA 1 mRNA 2 DNA Array onto glass slides using Robotic Gridder Reverse Transcription labelling using Cy5 and Cy3 dyes Block reactive groups Fix & denature DNA cDNA 1- cDNA 2- cy3 cy5 Hybridize Measuring the ratio of expression (control to test Population) Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M. (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 37(6):914-39. http://gabi.rzpd.de/projects/MapMan/ The program can be downloaded from this site. As soon as I can, I will put together some real data (published data) that you could insert into the program and then manipulate.