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

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

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

Gene expression analysis

Control ExperimentControl Experiment

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

Mylona et al., Journal of Experimental Botany, 58:1301–1312, 2007

Low-level integration of results

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

Success stories

VIRUS RESISTANCE NUTRITIONAL VALUE

INSECT RESISTANCE PLANT ARCHITECTURE

PESTICIDE TOLERANCE GENE STACKING (PYRAMIDING)

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

• 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

cDNA library preparation

Rodrigues et al. 2014, Transcriptomics. In: Omics in Plant Breeding, pp33-57, ISBN: 9781118820995

ESTs – Differential display

Microarrays

RNAseq

Kumar et al. : Front. Plant Sci., 28 August 2012 | doi: 10.3389/fpls.2012.00202

Technology: Microarrays vs. RNAseq

• Microarrays = old

• RNAseq = the new hotness

• BUT it’s not that simple

It’s easy to think that:

Microarrays RNAseq

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)

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.

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

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

An example…Resistance for Cassava Brown Streak Disease

Maruthi et. al. (2014) PLoS ONE 9(5): e96642. doi:10.1371/journal.pone.0096642

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

cDNA - ESTs

EST-SSR markersEST- SNPsIntron targeted polymorphism

intron-flanking exon–exon based primersRNA-seq identified markers

The problem - huge plant genomes

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

Breeding Targets

– Small RNAsDevelopment and Disease

-RNAiTranscription- translation Block

RdDmGene silencing

Regulatory RNAs

Small RNAs repressing translation

Journal of Cellular Physiology Volume 213, Issue 2, pages 412-419, 2 AUG 2007

RNA. Sep 2003; 9(9): 1034–1048.

RNA directed DNA methylation

Nature Reviews Genetics 6, 24-35 (January 2005)

NATURE REVIEWS | GENETICS VOLUME 14 | FEBRUARY 2013 |page 10

The RNA-dependent DNA methylationpathway in Arabidopsis thaliana.

RNA-guided genome editing using a CRISPR-Cas system

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

Biotechnological applications

• RNAi for Disease and Pathogen Resistance

• RNAi for Male Sterility

• RNAi and Plant Functional Genomics

• Engineering Plant Metabolic Pathways through RNAi

RNAi Vector

Some examples from the lab

1. Fungal disease resistance

2. Role of plant antioxidant genes in nodulation

Fusarium wilt in tomato

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.

…hopefully we expect such results!!

Role of plant’s antioxidant defense in nodulation

New Phytologist (2010) 188: 960–976

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

Do plant’s antioxidant enzymes play any role?

Examine catalase in Medicago truncatula

0

500

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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

Effects of lower catalase activity on

nodulation

Nodule number

b

a

a

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GM GM-Control Control

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du

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Γενότυπος

Nodule number in relation to plant size

c

c

b

a

a

-20

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GM Small GM Medium GM Large GM Control Control

No

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Genotype

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

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

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)

Rolling Cycle Amplification (RCA)

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

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

+ 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

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)

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

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

Geminivirus detection in a single sequencing reaction

Complete genomic sequences of Tomato Yellow Leaf

Curl Virus (TYLCV) isolate infecting tomato from

Northern Greece.

What is plant breeding??

Ideal genotype!!

BREEDER

After many years of crossing,

selection, evaluation e.t.c…

Sometimes the outcome…

…is beyond expactations!

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