RNA-based tools in plant breedingilsi-india.org/PDF/international_conference_on_new_plant... ·...
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RNA-based tools in plant breeding
ALEXIOS N. POLIDOROS
Aristotle University of Thessaloniki
School of Agriculture
Department of Genetics and Plant Breeding, P.O. Box 261
54124, Thessaloniki, Greece
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RNA-Tools in…
• Transcriptional characterization of genes and genomes
• Biodiagnostic analysis of genetic diversity
• Regulatory transcripts in plant defense and development
• Implementation of RNA tools in genetic engineering and biotechnology
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RNA-Tools in…
• Transcriptional characterization of genes and genomes
• Biodiagnostic analysis of genetic diversity
• Regulatory transcripts in plant defense and development
• Implementation of RNA tools in genetic engineering and biotechnology
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Gene expression analysis
Control ExperimentControl Experiment
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Gene expression analysis
Polidoros and Scandalios, PhysiologiaPlantarum 106: 112–120. 1999
NORTHERN BLOT QUANTITATIVE Real-Time RT-PCR
Mhadhbi et al., Physiologia Plantarum141: 201–214. 2011
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Mylona et al., Journal of Experimental Botany, 58:1301–1312, 2007
Low-level integration of results
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What can you do with it in breeding?
Ask questions about relationships between specific genes and a
condition
Learn the expression ‘signature’ of
different genotypes in a condition
Classify genotypes according to their
‘signature’
Make functional hypotheses about the ideal genotype in the condition
Identify potential ideal genotypes and test their performance
Use identified gene(s) in genetic modification of target varieties
Breed Breed Breed Breed
elite elite elite elite
varietiesvarietiesvarietiesvarieties
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Success stories
VIRUS RESISTANCE NUTRITIONAL VALUE
INSECT RESISTANCE PLANT ARCHITECTURE
PESTICIDE TOLERANCE GENE STACKING (PYRAMIDING)
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Transcriptomics
1. Identify the transcriptome of a tissue under a condition
2. Choosing between different technologies
3. How to design an experiment
4. How to make sense of the data
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• The study of the complete set of RNAs (transcriptome) encoded by the genome of a specific cell or organism at a specific time or under a specific set of conditions
• Transcriptome includes:
mRNA
tRNA
rRNA
But also ncRNAs (non-coding RNAs)
miRNAsiRNApiRNAsnoRNAAnd many more being discovered
Transcriptomics
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cDNA library preparation
Rodrigues et al. 2014, Transcriptomics. In: Omics in Plant Breeding, pp33-57, ISBN: 9781118820995
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ESTs – Differential display
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Microarrays
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RNAseq
Kumar et al. : Front. Plant Sci., 28 August 2012 | doi: 10.3389/fpls.2012.00202
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Technology: Microarrays vs. RNAseq
• Microarrays = old
• RNAseq = the new hotness
• BUT it’s not that simple
It’s easy to think that:
Microarrays RNAseq
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Measure mRNA and ncRNA and everything else
Technology: Microarrays vs. RNAseqMicroarrays RNAseq
Detect transcripts hybridizing with probes spotted on the array
Detects every assembled transcript
Species specific (e.g. human, mouse, tobacco)
Similar experimental protocol for every sample type
Generally detect only one type of RNA (e.g. mRNA OR miRNA)
Mature technology: known, reliable ways to analyse data. No arguments
New technology: no-one is really sure how to analyse the data. Lots of arguments
Generally do not detect alternative splicing
Detect alternative splicing
Costs a lotAlso costs a lot (but getting rapidly cheaper)
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Technology: Microarrays vs. RNAseq
• When to use one or the other for a gene expression experiment?
• Availability: If a commercial microarray exists, it’s an option. Otherwise it’s RNAseq
• Breadth: If alternative splicing or ncRNA are important, then RNAseqmight be a better choice
• Cost: Microarrays may cost less than RNAseq in model organisms. For organisms with a smaller transcriptome, the difference is less clear
• Complexity: Microarrays have standard protocols for normalization and analysis. RNAseq bioinformatics is still very new (~20 competing R packages doing the same job!)
• Comparison: Public databases with microarray experiments exist for many species and results may be comparable with existing data
• Future: RNAseq is likely to be around longer.
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Electronic Fluorescent Pictograph Browsers (eFP browsers) are online applications to build expression maps of your gene of interest based on transcript expression data. eFP browsers for Arabidopsis, poplar, Medicago truncatula, rice, barley, maize and soybean can be freely accessed at The Bio-Array Resource for Plant Biology http://www.bar.utoronto.ca.
Snapshoot from the The Bio-Array Resource for Plant Biology
The use of eFP browsers
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Ask questions about relationships between gene transcription and a ‘condition’
Learn the transcriptomic
‘signature’ of many genotypes in a
condition
Make functional hypotheses about the genetic makeup
of an ideal genotype
Develop selection criteria and tools to identify potential ideal genotypes
Transcriptomics in plant breeding: what can you do with it?
Combine data with other ‘omic’ technologies
Identify critical genes, determine their position and function, develop markers and high resolution maps.
understand the molecular basis of complex plant processes
Breed Breed Breed Breed
elite elite elite elite
varietiesvarietiesvarietiesvarieties
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An example…Resistance for Cassava Brown Streak Disease
Maruthi et. al. (2014) PLoS ONE 9(5): e96642. doi:10.1371/journal.pone.0096642
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RNA-Tools in…
• Transcriptional characterization of genes and genomes
• Biodiagnostic analysis of genetic diversity
• Regulatory transcripts in plant defense and development
• Implementation of RNA tools in genetic engineering and biotechnology
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cDNA - ESTs
EST-SSR markersEST- SNPsIntron targeted polymorphism
intron-flanking exon–exon based primersRNA-seq identified markers
The problem - huge plant genomes
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RNA-Tools in…
• Transcriptional characterization of genes and genomes
• Biodiagnostic analysis of genetic diversity
• Regulatory transcripts in plant defense and development
• Implementation of RNA tools in genetic engineering and biotechnology
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Breeding Targets
– Small RNAsDevelopment and Disease
-RNAiTranscription- translation Block
RdDmGene silencing
Regulatory RNAs
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Small RNAs repressing translation
Journal of Cellular Physiology Volume 213, Issue 2, pages 412-419, 2 AUG 2007
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RNA. Sep 2003; 9(9): 1034–1048.
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RNA directed DNA methylation
Nature Reviews Genetics 6, 24-35 (January 2005)
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NATURE REVIEWS | GENETICS VOLUME 14 | FEBRUARY 2013 |page 10
The RNA-dependent DNA methylationpathway in Arabidopsis thaliana.
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RNA-guided genome editing using a CRISPR-Cas system
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RNA-Tools in…
• Transcriptional characterization of genes and genomes
• Biodiagnostic analysis of genetic diversity
• Regulatory transcripts in plant defense and development
• Implementation of RNA tools in genetic engineering and biotechnology
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Biotechnological applications
• RNAi for Disease and Pathogen Resistance
• RNAi for Male Sterility
• RNAi and Plant Functional Genomics
• Engineering Plant Metabolic Pathways through RNAi
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RNAi Vector
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Some examples from the lab
1. Fungal disease resistance
2. Role of plant antioxidant genes in nodulation
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Fusarium wilt in tomato
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The role of SGE1
Michielse CB, et al. (2009) The Nuclear Protein Sge1 of Fusarium oxysporum Is Required for Parasitic Growth. PLoS Pathog 5(10): e1000637. doi:10.1371/journal.ppat.1000637
SGE1 (SIX Gene Expression 1) is essential for pathogenicity.
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…hopefully we expect such results!!
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Role of plant’s antioxidant defense in nodulation
New Phytologist (2010) 188: 960–976
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Role of catalase in bacteroids
Single katA- or katC- Normal
Double katA-katC-
katB-katC-Drastic reduction in N2-fixing activity
Double katA-katB- Non-viable
In Sinorhizobium (Ensifer) meliloti there are three catalase genes encoding two monofunctional (KatA and KatC) and one bifunctional (KatB) catalase-peroxidase enzyme.
MUTANTS
Jamet et al., 2003. MPMI 16: 217–225
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Do plant’s antioxidant enzymes play any role?
Examine catalase in Medicago truncatula
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0
500
1000
1500
2000
2500
3000
GM +R GM -R Cont +R Cont -R
CA
T A
cti
vit
y(U
/mg
)
Genotype
Con Man
Reduction of CAT activity
CAT activity
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Effects of lower catalase activity on
nodulation
Nodule number
b
a
a
0
20
40
60
80
100
120
140
160
GM GM-Control Control
No
du
le n
um
ber
Γενότυπος
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Nodule number in relation to plant size
c
c
b
a
a
-20
0
20
40
60
80
100
120
140
160
GM Small GM Medium GM Large GM Control Control
No
du
le n
um
ber
Genotype
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RNA-Tools …
• Transcriptional characterization of genes and genomes• Biodiagnostic analysis of genetic diversity • Regulatory transcripts in plant defense and development • Implementation of RNA tools in genetic engineering and biotechnology
…and ring-shaped objects
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3’ RACE
+ pool of cDNAs
- single site specific PCR
5’RACE
- only one cDNA each time
- single site specific PCR (Initrogen,Roche)
- modified gene specific primer (5 end phosphate, TAKARA)
RNA sequencing
RNA ligase-mediated rapid amplification reaction - RLM
+ pool of cDNAs
+ full length cDNAs
+ 5 and 3 end isolation possible
- 5 and 3 end isolation in separate reactions
- pool of cDNAs not amplifiable
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The infinity of ring-shaped objects
Dalí’s ‘Exploding Raphaelesque Head’ (1951)
…spherical object resembling a female head that blows up into the numberless similar fragments rotating around some central point.
Demidov V.: Expert Rev. Mol. Diagn. 2(6), (2002)
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Rolling Cycle Amplification (RCA)
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Rolling-circle amplification of viral DNA genomes using phi29 polymerase
Trends in Microbiology, Volume 17, Issue 5, 2009, 205 - 211
Principle of rolling-circle
amplification
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The RCA-RACE method
5’ Poly(A)
5’ (A)n3’ (T)17
Φ29 polymerase
RNaseH
CircLigaseTM3’ (T)17
5’
5’
3’
3’
Rolling Circle Amplification
PCR, Cloning, Sequencing
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+ pool of cDNAs+ gene-specific primers+ simultaneous isolation of 3 and 5 ends + amplification of rare transcripts+ No need for a 5->3 exonuclease minus polymerase
-less sequence information compared to RLM(isolation of longest amplification product necessary)
RCA-RACECircular RACE after rolling circle amplification
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Application in single cell research
We are entering an era of single-cell transcriptomics that holds promise to substantially impact biology and medicine.
Nature Methods 11, 22–24 (2014)
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10 20 30 40 50 60 70 80 90 100 110
....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|
APMADS1_Agapa MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAREISVLCESQVSVVIFSSCGKMSEYCSPNTSFPRILERYQHNCGKKLWDAKHENLNAQIDRVKKENDNMQIELRHLKG 110
CsPIA1.PRO, 2 MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAREISVLCESQVSVVIFSNSGKLSDYCSSNTSLPKILERYQLNCGKKLWDAKHENLSAQIDRIKKENDNMQIELRHLKG 110
FEG1_Elaeis_g MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAREISVLCDAQVSVVIFSSSGKMSEYCSPSTTLSRILERYQHNSGKKLWDAKHESLSAEIDRIKKENDNMQIELRHLKG 110
HPI1.PRO, 212 MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAKEISVLCESEIAIVVFSSLSKMSEFCSPNTTFPKMLEKYQQHSGKKLWDAKHENLSAEIDRIKRENDNMQIELRHLKG 110
HPI2.PRO, 212 MGRGKIEIKRIENSTNRQVTFSKRLNGIIKKAKEISVLCESEIAIVIFSSLNKISDFCSPNTSLPKMLEKYQQHSGKKLWDAKHENLSAEIDRIKRENDNMQIELRRLKG 110
LRGLOA.PRO, 2 MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAREISVLCEAQVSVVIFSSSGKMSEYCSPSTSLPKILERYQVNCGKKIWDPKHEHLSAEIDRIKKENDNMQIQLRHLKG 110
LRGLOB.PRO, 2 MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKARELSVLCEAHVSVVIFSSSGKMSEYCSPSTSLPKILERYQLNSGKKIWDAKHEHLSAEIDRIKKENDNMQIELRHLKG 110
MADS_RICE.PRO MGRGKIEIKRIENSTNRQVTFSKRRSGILKKAREISVLCDAEVGVVIFSSAGKLYDYCSPKTSLSRILEKYQTNSGKILWDEKHKSLSAEIDRIKKENDNMQIELRHLKG 110
MADS4_RICE.PR MGRGKIEIKRIENSTNRQVTFSKRRSGILKKAREIGVLCDREVGVVIFSSAGKLSDYCTPKTTLSRILEKYQTNSGKILWDEKHKSLSAEIDRVKKENDNMQIELRHMKG 110
PI-GLO-like.P MGRGNTEIKRIENSTNRQVTFSKRRSGIIKKAREISVLCDAQVSLVIFSSLGKLSEYCSPSTTLSKMLERYQQNSGKKLWDATHENLSAEIDRIKKENDTMQIELR.LKG 109
similar_to_PI .........................NGIIKKAREISVLCDAQVSVVIFSSSGKMSEYCSPSTSLSKMLEKYQQNSGKKLWDAKHENLSAEIDRMKKENDNMQIELRHLKG 85
similar_to_PI ..........IENSTNRQVTFSKRRNGIVKKAKEITVLCDAKVSFIIFSTTGKMFEFVSPSTTLMDMLERYQTNSGKRLWDAKHERLSAELDRIKKENDSMQIELRHLKG 100
TGGLO.PRO, 21 MGRGKIEIKRIENSTNRQVTFSKRRNGIIKKAREISVLCDAWVSVVIFSSSGKMSEYCSPTITLPKMLDKYQQNCGNKLWDAKHQNLSEEIDRIKKENDNMQIELRHLKG 110
120 130 140 150 160 170 180 190 200 210
....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|....|..
APMADS1_Agapa EDLNSLNPKELIPIEEALENGLNGVRAKQMEYLKMLKKNERLLEEENKRLTYILRHQQL.AMEGNVRQLDLGYHQREREFAAQMPMAFRVQPIHPNLQQNK. 210
CsPIA1.PRO, 2 EDLNSLNPKELIPIEEALTNGLTSVQDKQMDYLKMLKKNERLLEEENKRLTYILHHQQL.AMEGNMRELDLGHQHEDREHATQMPMAFTVQPFQPNLQGNK. 210
FEG1_Elaeis_g EDLNSLSPKELIPIEDALQNGLISVRDKQMEFLKKLKKNERLLEEENKHLTYLLHQQEL.AMDANVRELELGYPSKDRDFASHMPLAFHVQPIQPNLQENN. 210
HPI1.PRO, 212 EDLSSLNPRELIPIEEALQNGVTGARAKQMEFLKMMKLNGKLLEDENKKLAYLLHHQEL.AMDGSR.......HQRGTEYASEIPMALRVQPVQPNLQEA.. 202
HPI2.PRO, 212 DDLTSLNPRELIPIEEALQNGVTGACAKQMEFLKMMKLNGKLLEDENKKLAYFLHHQEL.AMDGNR.......HQRGTEYASEIPMALRVQPVQPNLQEA.. 202
LRGLOA.PRO, 2 EDLNSLQPKELIPIEEALENGIRGVREKQNDFLRMLKKNERILEEDNKRLTYILHHQQL.AMDENMRNLEFAYHHKDGDFSSQMPMAFRVQPIQPNLHEDK. 210
LRGLOB.PRO, 2 EDLNSLQPKELIPIEEALENGVRGVREKQNDVLRMLKKNERILEEDNKRLAYILHHHQL.TR...............GEYEGH..............GTCM. 181
MADS_RICE.PRO EDLNSLQPKELIMIEEALDNGIVNVNDKLMDHWERHVRTDKMLEDENKLLAFKLHQQDIA.LSGSMRDLELGYHPD.RDFAAQMPITFRVQPSHPNLQENN. 209
MADS4_RICE.PR EDLNSLQPKELIAIEEALNNGQANLRDKMMDHWRMHKRNEKMLEDEHKMLAFRVHQQEVE.LSGGIRELELGYHHDDRDFAASMPFTFRVQPSHPNLQQEK. 210
PI-GLO-like.P EDLNSLTPKELIPIEEGLQNGLTSVREKQMDFLKMLKKNERMLEEENKRLKYLLQHQQL.AIEGSMRELEISYHQKDPEYADQMPMTFRVQPFQPNLHGNN. 209
similar_to_PI EDLNQLNAKELIPIEDALHNGLTNVREKQMDFLKMLKKNERLLEEENKRLTYILHHQQL.AMNGNVREMDLAYHQKDREYPPQMPLAFRVQPLQPNLQEDKQ 186
similar_to_PI EDINSLHPKELIPIEEALQSGLTNVRAKQMDFLKMLKKNERTLEDENKRLSYILHHQQL.ALDGNMRDLDNGFHPKERDYSSQMPFIFRVQPIQPNLQQSK. 200
TGGLO.PRO, 21 EDLNSLQPKELIPIEEALENGFRSVREKQDDVLMTRKKNMRLMEEDNKRLNYVLHHQQQQAMDENIRDMELAYHQKHREFNSQMPMTFRVQPIQPNLHENK. 211
Application in multigenefamilies
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Fig. 3 Schematic representation of immuno-RCA, surface-RCA, and SNP detection. (A) A C-probe binds to its target and both ends
of the C-probe are ligated to form a closed circular probe that can be amplified by RCA or RAM. Only those ends that match perfec...David Zhang , Josephine Wu , Fei Ye , Tao Feng , Ivy Lee , Bingjiao Yin
Amplification of circularizable probes for the detection of target nucleic acids and proteins
Clinica Chimica Acta, Volume 363, Issues 1–2, 2006, 61 - 70
http://dx.doi.org/10.1016/j.cccn.2005.05.039
Circularization and biodiagnostic technologies
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Geminivirus detection in a single sequencing reaction
Complete genomic sequences of Tomato Yellow Leaf
Curl Virus (TYLCV) isolate infecting tomato from
Northern Greece.
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What is plant breeding??
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Ideal genotype!!
BREEDER
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After many years of crossing,
selection, evaluation e.t.c…
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Sometimes the outcome…
…is beyond expactations!
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THANK YOU!!
CollaboratorsPhotini Mylona, Agricultural research Center of Northern Greece, HAO DmeterPanos Madesis, Institute of Applied Biosciences, CERTHKostas Pasentsis, Institute of Applied Biosciences, CERTHIrini Nianiou-Obedat, Aristotle Univ. Thessaloniki
StudentsNikos AnagnostopoulosLefki KarapetsaNikos Fikas