High Throughput DNA Sequencing and the Deployment of...
Transcript of High Throughput DNA Sequencing and the Deployment of...
WHAT ARE THE MOLECULAR BASES OF
SPECIFICITY BETWEEN HOST AND PATHOGEN? WHAT IS THE GENETIC BASIS FOR VARIATION
IN SUCH SPECIFICITY? HOW CAN WE ACHIEVE DURABLE RESISTANCE? WHAT NEW OPPORTUNITIES ARE PROVIDED BY
HIGH-THROUGHPUT DNA SEQUENCING AND MARKER TECHNOLOGIES?
LONG-STANDING QUESTIONS IN
PLANT-PATHOGEN INTERACTIONS:
High Throughput DNA Sequencing and the Deployment of Resistance Genes
Richard Michelmore The Genome Center & Dept. Plant Sciences
University of California, Davis
http://michelmorelab.ucdavis.edu
Lettuce genetics, genomics & breeding
The Compositae Genome Project
Molecular basis of disease resistance: CHARGE, Niblrrs
OVERLAP AND INTEGRATION OF PROJECTS IN MICHELMORE LAB
Bremia genomics
Technologies: Molecular markers High-throughput sequencing Gene silencing, RNAi Bioinformatics
Bremia lactucae
Gary Shroth (Illumina): “One HiSeq will be able to generate as much sequence as was in GenBank in 2009, every four days”.
Decreasing cost of sequencing (1990 – 2010)
DNA sequence becoming an inexpensive commodity. New paradigms as to how DNA sequence is generated, handled and valued.
The Economist, August 12th, 2010
Clone by clone, Sanger sequencing Long, high accuracy reads, ABI 3730 Massively parallel, shorter reads 454 Illumina Genome Analyzer & HiSeq 2000 ABI Solid Helicos (Complete Genomics) Imminent arrivals: Ion Torrent Pacific BioSciences Oxford Nanopore
The Revolution in DNA Sequencing: Identification of Variation
Illumina Sequencing Platforms
Feature GAiix HiSeq2000
Flowcells x Surface Imaging 1 x 1 2 x 2
Read length 2 x 150 2 x 100
Yield per run (PF data) 50 Gb 200 to 350 Gb
Data Rate 5 Gb / day 25 to 40 Gb / day
Human Genomes (30x) per run 0.5 >2
G. Shroth, Illumina
DNA (0.1-1.0 ug)
Sample preparation: Fragmentation &
addition of primers Cluster growth in flow cell
5’
5’ 3’
G
T
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1 2 3 7 8 9 4 5 6
Image acquisition Base calling
T G C T A C G A T …
Sequencing
Illumina Sequencing of Clusters of DNA Molecules Reversible Terminator Chemistry
Cycles of synthesis, imaging, & washing
G. Shroth, Illumina
Illumina HiSeq 2000
Dual surface imaging Fast scanning and imaging Two flow cells Initially capable of 200 Gb
per run -> 350 Gb Run time 7 to 8 days for
2x100 bp 25 Gb/day 2 billion paired-end reads <$10,000 per human
genome <$200 per transcriptome
G. Shroth, Illumina
Low instrument and reagent costs. Silicon chip technology. Single-use microchips Uses natural dNTPs and DNA polymerase Real-time detection of base incorporation No optics or enzymic amplification cascade Proprietary H+ ion sensitive layer
www.iontorrent.com
www.iontorrent.com
Sequencing determined by measuring H+ release following incorporation of nucleotide. Chip interrogated with cycles of A, T, C, G.
Interrogated with C
Interrogated with G (or A)
Interrogated with T
Simple kits and operation Benchtop convenience, small footprint
Fast. Single reads
Up to millions of reads 10s to 100s Mb output
Not single molecule sequencing
Compatible with preps for other libraries
www.iontorrent.com
Single Molecule Real Time (SMRT™) sequencing Recording natural DNA synthesis by DNA polymerase as it occurs Single molecule resolution Simple amplification-free sample prep Long reads, average read over 1kb Fast, 1 to 3 bases incorporated per second, Sample prep to data analysis in less than a day Low overall costs 80,000 Zero Mode Waveguides (ZMWs) monitored simultaneously, 2 sets per SMRT cell ~33% of ZMWs have only one polymerase Not for counting large numbers of tags
http://pacificbiosciences.com
Processive DNA Synthesis with Phospholinked Nucleotides
Incorporation of labeled nucleotide creates flash of light, captured by optics system and converted into base call with quality metrics
Base being incorporated held in detection volume for tens of milliseconds, producing a bright flash of light. Phosphate chain cleaved, releasing the attached dye molecule. Process repeats at 1 to 3 bases per second.
Distributed Reads: SMRT Strobe Sequencing
Standard 3.2kb Direct Read:
6.4kb Footprint: 19 x 170 bases Strobed Read (50% Duty Cycle)
• Converts long reads into even longer scaffolds
• Flexibility in gap lengths and strobe on-times
• No need to create multiple paired-end/mate-pair libraries
• Produces linked, single-molecule, kilobase observation windows of genomic structure
Strobe Sequencing:
Detection of DNA Methylation by SMRT Sequencing
70.5 71.0 71.5 72.0 72.5 73.0 73.5 74.0 74.50
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104.5 105.0 105.5 106.0 106.5 107.0 107.5 108.0 108.50
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T G A T C G T A C
mA A G T C T A A
G C C A A A A
Kinetic detection of methylated bases during sequencing E.g.: N6-methyladenosine (mA)
www.nanoporetech.com
www.nanoporetech.com
Exonuclease attached at entrance of α-hemolysin nanopore delivers individual DNA bases in order into the nanopore. Cyclodextrin attached to inside surface of nanopore acts as a binding site for individual DNA bases and allows accurate monitoring of their passage through the nanopore.
$1,000 ($100?) human genome coming => $1,000 genome for many animals and plants $100 genome for fungi $10 genome for bacteria en masse Not just genomic DNA sequence:
DNA modifications epigenomics & copy number variation (CNV) expression analysis
Enormous amounts of sequence data Need for major data handling capabilities Vital role for bioinformatics
The Challenge and Opportunity: How to utilize the deluge of sequence data?
In near future: DNA sequence = an inexpensive commodity generated on a variety of platforms
High-throughput sequencing Sequence assembly
Annotation Acquisition of other relevant data
Display in genome browser
UC Davis Sequencing & Gene Expression Service Cores 2011 ->
Tissue, DNA or RNA samples brought to Genome Center
Researcher queries samples versus existing information over web
Integrated activities of DNA Technology & Bioinformatics Cores
Genomic sequencing De novo Microbial, animal and plant diversity Novel & unculturable organisms Biomes (bacteria = 100x human) Novel genespace Re-sequencing SNP and CNV discovery, TILLING Gene cloning, novel allelic diversity Genome Wide Association Studies (GWAS) High resolution population genetics Mapping BSAseq
Gene regulation Transcriptome sequencing for gene models and splicing RNAseq for expression analysis Small and non-coding RNAs Ribosome profiling CHIPseq for DNA binding sites DNA modifications and epigenomics
Gene discovery from non-model organisms
E.g. Cloning of 10 genes from wheat rust, Puccinia striiformis f.sp. tritici (collaboration with J. Dubcovsky)
Whole genome sequencing (<100Mb genome) Two lanes Illumina GA to ~60x.
Quicker, cheaper, more informative than gene-by-gene. Permanent resource for other studies.
Genomic Encyclopedia of Bacteria & Archea www.jgi.doe.gov/programs/GEBA Using phyogenetic approach to sample diversity for genome sequencing.
1000 (Human) Genomes Project
http://www.1000genomes.org Extending hapmap. 2,000 anonymous individuals from 20 populations. 4x coverage originally planned; deeper, 30x now feasible Pilot studies: 15 M SNPs, 1 M small indels, 20 K structural variants
25,000 human genomes sequenced by end of 2011 by this & other projects 1001 Arabidopsis Genomes Project
http://www.1001genomes.org 1001 extensively characterized accessions July 2010: 158 finished, 94 released, 98 underway
1000 Plant & Animal Reference Genomes Project
http://ldl.genomics.org.cn BGI 1,000 economically and scientifically important species in two years
Analyses of Biological Diversity
Genome-Wide Association Studies (GWAS) Case and control comparisons to identify phenotype-marker associations Requires sufficient density of markers & numbers of individuals Currently based on SNP marker analysis Will be replaced by sequencing, at least in non-model species
False-positives due to multiple comparison issues Power issues due to loci of small effect
E.g. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. The Wellcome Trust Case Control Consortium. (2007) Nature 447:661-678.
Within L. sativa types: Crisphead x Crisphead Romaine x Romaine Leafy x Leafy + Butterhead
Between cultivated lettuce types: L. sativa x L. sativa
Between & within primary genepool: L. sativa x L. serriola L. serriola x L. serriola
Between & within secondary genepool: L. sativa x L. saligna L. saligna x L. saligna
For each population: Intercross 20 genotypes for 3 generations to breakdown LD & population substructure. Self to produce 200 F7:8 RILs. Phenotype & sequence F7:8 RILs.
PLANNED ASSOCIATION MAPPING POPULATIONS IN LETTUCE
Cultivated lettuce
Primary genepool
Secondary genepool
Old paradigm (slow and inflexible): One-by-one marker development Utilization of core set of reference markers
Current paradigm (faster but specific to populations): Sequence transcriptome of parents to id. 1,000s of SNPs Develop informative SNP panel for specific sets of crosses Run SNPs on segregating individuals
Future paradigm (fast, flexible,& highly informative): Sequence transcriptome or genespace of segregating individuals
Rate limiting steps:
Informative populations Accurate phenotyping Library preparation Sequencing not limiting Data analysis?
Genetic Mapping
Parent-independent genotyping for constructing an ultrahigh-density linkage map based on population sequencing. Xie et al., 2010. PNAS 2010.
238 rice RILs each sequenced to 0.055x, 13x in aggregate. Barcoded and multiplexed. 2x 36 nt paired-end reads, 20.6 Mb total single run. Genotypes inferred from RILs using maximum parsimony of recombination & HMM. New capabilities => any species tractable in a single run.
E.g. BSAseq: High-throughput sequencing of the 52b2 mutant of Arabidopsis to identify the causal mutation using sequence-based mapping of a bulked segregating population. Cuperus et al. (2010) PNAS 107:466-471.
©2010 by National Academy of Sciences
Transcriptome Profiling using Illumina RNAseq
• Rapidly replacing chip hybridizations • Analysis of unknown genomes/genes, no a priori knowledge required • No limitation on number of genes assayed • Digital, unambiguous readout • High sensitivity, specificity, dynamic range • Opportunity to progressively sequence to greater resolution • Detection few mRNA per cell possible • Low cost (vs. SAGE, MPSS, = chips) • Mulitplex samples 12 per lane, 192 libraries per run on HiSeq2000 • Quantification of splice variants • Quantification of allele specific expression • Currently limited by speed (& cost) of library preparation ->
automation
Human Body Map 2.0 Project
16 Tissues: • Adrenal • Adipose • Brain • Breast • Colon • Heart • Kidney • Liver • Lung • Lymph Node • Ovary • Prostate • Skeletal Muscle • Testis • Thyroid • White Blood Cells
Illumina RNAseq whole transcriptome, HiSeq2000 5 billion reads, 300 Gb, 14 days total run time
G. Shroth, Illumina
Illumina RNA-Seq Protocols
Conventional mRNA-Seq Purified Total RNA
Poly-A Selection
RNA Fragmentation cDNA Synthesis
Adapter Ligation & PCR
Total RNA-Seq + DSN Purified Total RNA
RNA Fragmentation cDNA Synthesis
Adapter Ligation & PCR
DSN Normalization:
Denaturation @ 98oC
Renaturation @ 68oC
Cleavage with DSN
PCR of intact fragments (Duplex Specific Nuclease
from Evrogen)
www.illumina.com
mRNA seq
mRNAseq + DSN
Total RNAseq + DSN
DSN treatment does not affect relative abundance of rare transcripts & reveals non-poly A snoRNAs
THE ‘BOOM-AND-BUST’ CYCLE OF DISEASE CONTROL The Agricultural Consequences of Pathogen Evolution
INTRODUCTION OF RESISTANT VARIETY
INCREASE IN ACREAGE
PATHOGEN CHANGES TO RENDER RESISTANCE INEFFECTIVE
DECREASE IN ACREAGE DEVELOPMENT OF
RESISTANT VARIETY (Suneson, 1960)
Aim: Durable Disease Resistance
“Resistance that remains effective over long periods of widespread agricultural use”
Pragmatic but elusive goal
Success can only be judged retrospectively
Strategies to maximize likelihood that will be durable
Diversify selection pressures on pathogen
Roy Johnson (1984). A critical analysis of durable resistance. Ann. Rev. Phytopathol. 22:309-30.
MOLECULAR IDENTIFICATION & USE OF DISEASE RESISTANCE GENES
PHENOTYPIC LEVEL Resistant accessions Lab & field screens
MOLECULAR LEVEL Candidate genes from ESTs
Ultra-dense map from GeneChip
GENETIC ANALYSES Coincident position on chromosome?
Targeted association studies of diversity panel Fine mapping with high-resolution recombinants
FUNCTIONAL ANALYSES VIGS / RNAi: Gene family
EMS mutation,transgenic complementation: Specific gene
BREEDING Marker-assisted selection Introgression & pyramiding
BASIC STUDIES Molecular basis of specificity
Genetic basis of evolution of specificity
Resistance candidate genes:
Pathogen recognition
Resistance signaling pathway
Defense response
Susceptibility factor
Quantitative trait loci
Resistance phenotypes: Downy mildew
Virus
Anthracnose
Root aphid
Plasmopora lactucae-radicis
Corky root
Fusarium wilt
Verticillium wilt
Effector HR
CLX_S3_7961 CLX_S3_9182
CLX_S3_790 CLX_S3_6420
CLX_S3_12147 CLX_S3_6892
QGD8I16 QGB24A18
QGG16O14 CLX_S3_8436 CLX_S3_1125
RGC12E QGD13P19
RGC11B CLX_S3_15206 CLX_S3_13702
CLX_S3_6762 QGC18F20
CLX_S3_6208 CLX_S3_2669 CLX_S3_2959
QGF9F09 CLX_S3_15211
QGG16P08 CLX_S3_427
QGG19L12
0
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40
60
80
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120
R39
RB
Q3
AvrPto-HR
9 6 CLSX2101
CLX_S3_6010 CLX_S3_10810 CLX_S3_12870 CLX_S3_14206 CLX_S3_12264
CLX_S3_8454 CLX_S3_5158
CLX_S3_10491 CLX_S3_12430
QGJ5F12 CLSY5163
CLX_S3_6910 CLX_S3_6962 CLX_S3_3891
QGF18C23 CLX_S3_9497
CLX_S3_14773 QGF16L07 QGF17L05
CLX_S3_13566
CLX_S3_374 .
0
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40
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100
120
QGD5O22 CLSY3114
CLX_S3_14061 CLX_S3_12044 CLX_S3_14050 CLX_S3_12906 CLX_S3_1517 CLX_S3_1173
QGG19N22 CLX_S3_1404
QGJ12P21 QGG21B18
CLX_S3_4405 RGC15A
CLX_S3_1446 QGG13J04
CLX_S3_13603 QGG10J06
CLX_S3_14099 QGA13C08
CLX_S3_357 RGC4O
QGH8M10 RGC4E RGC4T RGC4V RGC4F RGC4X RGC4G RGC4C RGC4R
QGG13M01 LserNBS16
RGC4S RGC4Q
RGC20A RGC20C RGC20B
CLX_S3_8418 CLX_S3_6808
RGC5A LsatNBS11
CLX_S3_11489 CLX_S3_821
CLX_S3_12882 CLRX4778
CLX_S3_5632
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AvrPpiC-H
R
AvrRps4-H
R
8
AN
T3
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QGH3f04
CLX_S3_10156
QGG13J02 CLX_S3_1659
CLX_S3_2349 QGF6A06
CLSM11140 CLX_S3_13351
CLX_S3_2031 CLX_S3_15074 CLX_S3_12667 CLX_S3_13025
QGF25M24 RGC4K
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RB
Q1
FUS3
QGF3e10 CLX_S3_10033 CLX_S3_1274
QGF24O17 CLSM3058
CLX_S3_5128 CLX_S3_6332
QGC16I05 QGC20G02
QGG8P13 CLX_S3_1736
CLRY2600 CLX_S3_2780 CLX_S3_8398
QGF7C21 CLX_S3_1042 CLX_S3_1828
QGG11K24 CLX_S3_579
CLRX8688 CLX_S3_15606 CLX_S3_10397 CLX_S3_9834 CLX_S3_3539
RGC16E QGF17I06
CLX_S3_3224 CLX_S3_8409
CLSM741 QGD7B12
LserNBS03 QGG8K23
RGC16B QGC12K15 CLSY4446
RGC16A QGE3a07
CLRX1526 RGC16F
CLRX9010 CLX_S3_6708
QGF20G21 CLSX3769
RGC6A CLX_S3_3390 CLX_S3_1499 CLX_S3_9641
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o2 D
m10
plr R
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m43
AvrB - HR
AvrRpt2 - HR
AvrR
pm1 - HR
1 QGI9H24 CLX_S3_15048 CLX_S3_5843
CLX_S3_14085 QGB19M14
CLX_S3_15781 CLSS12467
CLX_S3_7798 CLX_S3_1242 CLX_S3_9203
QGG4d04 CLX_S3_11134 CLX_S3_3740 CLX_S3_535
QGG14D23 CLSX5517 CLRY4117
CLX_S3_2774 QGI13M08
CLX_S3_15772
CLSY483 RGC1P
LserNBS10 RGC4A
LserNBS24 RGC1O
CLSX9287 CLRX8858
RGC1Q CLX_S3_2706 CLX_S3_2010
CLX_S3_11722 CLX_S3_11849
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Dm
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3b
QGH6P20
CLX_S3_6776 CLX_S3_4579
CLX_S3_15049
CLX_S3_4740
QGC5C13 QGA17D17
QGF6A22 CLRY8019
CLX_S3_2939 CLX_S3_826
CLX_S3_13487 CLX_S3_2221
QGB7L15
CLX_S3_1356 QGF5E16
CLX_S3_15192 QGJ7D09
QGG21F05
CLX_S3_12280 QGB4d05
CLX_S3_15257 CLX_S3_4305
CLX_S3_13459
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CLSX3763 CLSX3674
CLX_S3_11945 QGG26B23
CLX_S3_13611 CLSY4807 CLRY3021
RGC11A RGC12G
CLX_S3_2930 CLX_S3_7341 CLX_S3_8995 CLX_S3_9470 CLX_S3_7346 CLX_S3_7363 CLX_S3_4627
QGC10F01 CLX_S3_15389
CLRY544 CLX_S3_14751 CLX_S3_3791
CLRY9160 CLX_S3_1701 CLX_S3_6039 CLX_S3_1415
QGF13H12 QGI4E15
QGA12L23 QGG11A07
CLX_S3_12210 CLSM14946
CLX_S3_9579 CLX_S3_6780
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m4
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mo1
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VRT1
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2 Dm
6 Dm
14 Dm
15 Dm
16 Dm
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FUS2
RGC2F RGC2A
AY153845 RGC2X
QGB28O08 RGC2J
RGC2M RGC2B RGC2S
CLSM16923 RGC2Y RGC2U RGC4U
CLX_S3_4455 QGD10N11
CLX_S3_14182 CLX_S3_1771 CLX_S3_9978
LsatNBS05 CLX_S3_13590 CLX_S3_11288 CLX_S3_12930
CLX_S3_3045 CLX_S3_4550 CLX_S3_3943
CLX_S3_15473 CLX_S3_6671
QGI11K10 CLX_S3_7649 CLX_S3_4292
CLX_S3_15231 QGC22B19
CLX_S3_4116 CLX_S3_1216
CLX_S3_12591 CLX_S3_4001 CLX_S3_7749 CLX_S3_8485
CLX_S3_891 CLX_S3_5919
QGF20E14 CLX_S3_7847
RGC1B CLX_S3_6313
0
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120
140
Dm
3 Tvr
Dm
45
Dm
36
52 PHENOTYPES MAPPED RELATIVE TO 289 RESISTANCE GENE CANDIDATES
Nearly all resistance phenotypes segregate with a cluster of NBS-LRR encoding genes. 100s of resistance genes in all genotypes.
Complex clusters of phenotypes & candidate genes
Leah McHale et al., 2009. Theor. Appl. Genet. 118:1223-4. Maria Truco, Oswaldo Ochoa, Kirsten Lahre, et al. unpublished
Cf-2 Cf-4 Cf-5 Cf-9 RPP27
Xa21 FLS2
Pto L6 M N RPS4 RPP1 RPP5 RRS1
RPM1 RPS2 Prf I2 Mi Dm3 RPP8 Bs2
Nucleotide Binding Site Leucine-Rich Repeat Protein Kinase Toll-Interleukin Homology Coiled-coil domain C terminal domain
RPP13 RPS5 Xa1 Pi-B Rx1 Rx2 Rp1 Gpa2
MAJOR PROTEIN MOTIFS SHARED BETWEEN PLANT DISEASE
RESISTANCE GENE PRODUCTS ACTIVE AGAINST DIVERSE
PATHOGENS & PESTS
RPW8
Rpg1
35S ocs
RGC2B = Dm3
~500bp pdk intron ~400bp GUS
• Generate stable transgenic plants with RNAi construct. • Test for silencing of GUS using Agrobacterium-‐mediated transient assays with 35S::GUS. • Test plants exhibiCng silencing for disease resistance to isolates expressing avirulence to mulCple Dm genes in cluster.
LG2
FUNCTIONAL ANALYSIS OF CANDIDATE GENES USING RNAi: SILENCING RGC2 GENE FAMILY WITH GUS REPORTER FRAGMENT FOR SILENCING
Wroblewski et al. 2007. Plant J. 51:803-818
RNAi using the NBS region of RGC12G silences the linked Dm7, Dm4 and Dm11 genes
LSE57/15 21 F1s 15 F1s CG R4T57 27 F1s 23 F1s CG Capitan 25 F1s 24 F1s CG
R = Resistance S = Susceptible
GUS
Avr7
Silencing
Dm7
CG w/ RNAi x LSE57/15 R4T57 x CG w/ RNAi Capitan x CG w/ RNAi (T-DNA/-)(dm0) (Dm7) (Dm4) (T-DNA/-)(dm0) (Dm11) (T-DNA/-)(dm0)
+ + - - R R S S no no yes yes
+ + - - R R S S no no yes yes
GUS
Avr4
Silencing
Dm4
GUS
Avr11
Silencing
Dm11
+ + - - R R S S no no yes yes
Marilena Christopoulou
Conclusion: Dm4, Dm7 and Dm11 are encoded by RGC12G or related sequence(s)
On-going: making RNAi lines for most candidate resistance genes
= excellent markers for breeding resistance
COMBINING RESISTANCES TO FUSARIUM AND DIEBACK IN CRISPHEAD AND ROMAINE
In collaboration with Ivan Simko, USDA, Salinas
Ra
Dm
1
AN
T2
Dm
2 Dm
6 Dm
14 Dm
15 Dm
16 Dm
18 FUS2
20
40
60
80
100
120
140
2 RGC2F
RGC2A
AY153845
RGC2X
QGB28O08
RGC2J
RGC2M
RGC2B
RGC2S
CLSM16923
RGC2Y
RGC2U
RGC4U
CLX_S3_4455
QGD10N11
CLX_S3_14182
CLX_S3_1771
CLX_S3_9978
LsatNBS05
CLX_S3_13590
CLX_S3_11288
CLX_S3_12930
CLX_S3_3045
CLX_S3_4550
CLX_S3_3943
CLX_S3_15473
CLX_S3_6671
QGI11K10
CLX_S3_7649
CLX_S3_4292
CLX_S3_15231
QGC22B19
CLX_S3_4116
CLX_S3_1216
CLX_S3_12591
CLX_S3_4001
CLX_S3_7749
CLX_S3_8485
CLX_S3_891
CLX_S3_5919
QGF20E14
CLX_S3_7847
RGC1B
CLX_S3_6313
0 Dm
3 Tvr
Dieback resistance from crisphead
Fusarium resistance from romaine
Danger that transfer of resistance to one disease will introduce susceptibility to the other.
Broke linkage. Have lines resistant to both diseases.
QTL for Fusarium resistance close to resistance to dieback
Chr 2
Large cluster of NBS-LRR encoding genes and resistance phenotypes
CONSEQUENCES OF RESISTANCE GENE GENETICS & MOLECULAR BIOLOGY TO PLANT IMPROVEMENT
All genotypes have large numbers (100s) of resistance genes. Backcrossing often introgresses clusters not single resistance genes. Introgression will replace resistance genes in recurrent parent. Clustering implies pyramiding some combinations of genes will be very difficult. Large number and wide variety of recognition specificities possible. Only finite number of specificities against a particular pathogen?? Possibility for repeated introgression of same resistance specificity. Some non-host resistance may be pyramids of specific genes. Quantitative resistance can be conferred by single NBS-LRR genes. Divergent selection using multiple/different resistances optimal. Opportunities for transfer across sexual compatibility barriers.
HOW CAN WE ACHIEVE DURABLE RESISTANCE?
Strategies to enhance durability of resistance
Use knowledge of pathogen variability to inform strategies for resistance gene deployment. New opportunities from new
technologies for rapid and comprehensive genotyping
Pyramid/stack multiple genes – increase evolutionary hurdle
Diversify selection pressure on pathogen
Utilize ‘durable’ resistance – empirical rather than mechanistic?
Match life expectancy of resistance and cultivar
DIRECT OR INDIRECT PATHOGEN PRODUCTS
SIGNAL CASCADE, RESISTANCE RESPONSE & RAPID CELL DEATH
SELECTION PRESSURE ON PATHOGEN FOR LACK OF ACTIVITY OF MULTIPLE AVIRULENCE FACTORS
PYRAMIDING RESISTANCE GENES USING MARKER-ASSISTED SELECTION OF CAUSAL GENES
X
MULTIPLE RECOGNITION EVENTS
Diversify selection pressure on pathogen
Heterogeneity of resistance genes in space and/or time:
diversify resistance sources,
pipeline with different resistance genes from other programs,
multilines and cultivar mixtures,
syn- and allo-patric gene deployment,
Roy Johnson (1984). A critical analysis of durable resistance. Ann. Rev. Phytopathol. 22:309-30.
IMPACT OF HIGH THROUGHPUT SEQUENCING & MASSIVELY PARALLEL GENOTYPING ON RESISTANCE STRATEGIES
• Global rather then gene-by-gene analysis • Saturation of identification of candidate genes Recognition, signal transduction, response SNPs in causal genes • Characterization of germplasm Full genome resequencing of 10 – 100 genotypes Gobal genotyping of 1000 - 10,000 genotypes Natural variation in 1o, 2o, & 3o genepools Vast numbers of resistance genes available § Characterization of pathogens (A)virulence factors Pathogen variability § Gene deployment Marker assisted selection of causal genes Pyramids of multiple genes, conventional & transgenic Heterogeneity between genotypes in space & time Fragment selection pressure on pathogen populations Manage pathogen evolution
PATHOGEN POPULATION GENETICS SHOULD DRIVE DEPLOYMENT OF RESISTANCE GENES
Influenza paradigm
Continual sampling of pathogen Virulence phenotyping Gene-space sequencing (10s) SNP genotyping (1000s)
Resistance gene discovery pipeline Germplasm screens Mapping, molecular markers Molecular characterization
Deployment of effective resistance genes Pyramiding, MAS or effector-driven selection Allo- and sympatric diversity Temporal adjustment of resistance genes deployed Transgenic approaches for novel resistance strategies
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Potential Challenges to Implementation of the Influenza Paradigm for Resistance Gene Deployment
Collection of pathogen samples from diverse, low-tech locations. Global coordination of pathogen sampling.
Data processing and interpretation.
Data-driven consensus building and decision making.
Sufficient numbers of genes for pipeline (conventional &/or transgenic).
Persuading breeders (public and private) to participate and coordinate.
Providing agronomically acceptable options to famers and consumers.
Revision/adaption of regulatory/registration requirements to accommodate
agronomically equivalent cultivars with different resistance genes.
SUMMARY DNA sequence becoming an inexpensive commodity generated on a variety of platforms. New paradigms as to how DNA sequence is generated, handled and valued. Enormous amounts of sequence data imminent. Need for major bioinformatics capabilities: data into knowledge. Unprecedented opportunities for discovery and manipulation of variation in plants and pathogens. Durable resistance: old ideas, new opportunities. Diversify sources and types of resistance. Pyramid to increase evolutionary hurdle for pathogen. Fragment/diversify selection pressure on pathogen. Pathogen population genetics should drive deployment of resistance genes: the influenza paradigm.