genetic engineering for disease resistance in plants ... · 95 opportunities for engineering...
TRANSCRIPT
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Genetic Engineering for Disease Resistance in Plants: Recent Progress and Future 1
Perspectives 2
Oliver Xiaoou Dong1,2
, Pamela C. Ronald1,2
* 3
1. Department of Plant Pathology and the Genome Center, University of California, 4
Davis, CA 95616, USA. 5
2. Innovative Genomics Institute, Berkeley, CA 94704, USA 6
*For correspondence: [email protected] 7
One Sentence Summary: 8
A review of the recent progress in plant genetic engineering for disease resistance 9
highlights future challenges and opportunities in the field. 10
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Plant Physiology Preview. Published on March 13, 2019, as DOI:10.1104/pp.18.01224
Copyright 2019 by the American Society of Plant Biologists
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Background 15
Modern agriculture must provide sufficient nutrients to feed the world’s growing 16
population, which is projected to increase from 7.3 billion in 2015 to at least 9.8 billion 17
by 2050 (UN 2017). This goal is made even more challenging because of crop loss to 18
diseases. Bacterial and fungal pathogens reduce crop yields by about 15% and viruses 19
reduce yields by 3% (Oerke and Dehne, 2004). For some crops, such as potatoes, the loss 20
caused by microbial infection is estimated to be as high as 30% (Oerke and Dehne, 2004). 21
As an alternative to the application of chemical agents, researchers are altering plants’ 22
genetic composition to enhance resistance to microbial infection. 23
Conventional breeding plays an essential role in crop improvement, but usually 24
entails growing and examining large populations of crops over multiple generations, a 25
lengthy and labor-intensive process. Genetic engineering, which refers to the direct 26
alteration of an organism’s genetic material using biotechnology (Christou, 2013), 27
possesses several advantages compared with conventional breeding. First, it enables the 28
introduction, removal, modification or fine-tuning of specific genes of interest and causes 29
minimal undesired changes to the rest of the crop genome. As a result, crops specifically 30
exhibiting desired agronomic traits can be obtained in fewer generations compared with 31
conventional breeding. Second, genetic engineering allows interchange of genetic 32
material across species. Thus, the raw genetic materials that can be exploited for this 33
process is not restricted to the genes available within the species. Third, plant 34
transformation during genetic engineering allows the introduction of new genes into 35
vegetatively propagated crops such as banana (Musa sp.), cassava (Manihot esculenta), 36
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and potato (Solanum tuberosum). These features make genetic engineering a powerful 37
tool for enhancing resistance against plant pathogens. 38
Most cases of plant genetic engineering rely on conventional transgenic 39
approaches or the more recent genome editing technologies. In conventional transgenic 40
methods, genes that encode desired agronomic traits are inserted into the genome at 41
random locations through plant transformation (Lorence and Verpoorte, 2004). These 42
methods typically result in varieties containing foreign DNA. In contrast, genome editing 43
allows changes to the endogenous plant DNA, such as deletions, insertions, and 44
replacements of DNA of various lengths, at designated targets (Barrangou and Doudna, 45
2016). Depending on the type of edits introduced, the product may or may not contain 46
foreign DNA. In some areas of the world including the US (USDA, 2018), 47
Argentina (Secretariat of Agriculture, Livestock, Fishing and Food, Argentina, 2015), 48
and Brazil (CTNBio, 2018), genome-edited plants that do not contain foreign DNA are 49
regarded as equivalent to plants produced by conventional breeding, which exempts them 50
from additional regulatory measures applied to transgenic plants (Orozco, 2018). 51
Regardless of this distinction in regulatory policies, both conventional transgenic 52
techniques and genome editing continue to be powerful tools for crop improvement. 53
Plants have evolved multi-layered defense mechanisms against microbial 54
pathogens (Chisholm et al., 2006; Jones and Dangl, 2006). For example, pre-formed 55
physical and physiological barriers and their reinforcements prevent potential pathogens 56
from gaining access inside the cell (Collinge, 2009; Uma et al., 2011). Plasma 57
membrane-bound and intracellular immune receptors initiate defense responses upon the 58
perception of pathogens either directly through physically interacting with pathogen-59
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derived elicitors, or indirectly by monitoring modifications of host targets incurred by 60
pathogens (Bentham et al., 2017; Jones and Dangl, 2006; Kourelis and van der Hoorn, 61
2018; Zhang et al., 2017). Plant-derived anti-microbial peptides and other compounds can 62
suppress pathogenicity by direct detoxification or through inhibition of the activity of 63
virulence factors (Ahuja et al., 2012; Kitajima and Sato, 1999; Thomma et al., 2002). 64
Plants also employ RNA silencing (RNAi) to detect invading viral pathogens and target 65
the viral RNA for cleavage (Rosa et al., 2018). 66
On the other side, successful pathogens have evolved strategies to overcome the 67
defense responses of their plant hosts. For instance, many bacterial and fungal pathogens 68
produce and release cell wall-degrading enzymes (Kubicek et al., 2014). Pathogens can 69
also deliver effectors into the host cytoplasm (Franceschetti et al., 2017; Xin and He, 70
2013); some of these effectors suppress host defenses and the others promote 71
susceptibility. To counteract plant RNAi-based defenses, almost all plant viruses produce 72
viral suppressors of RNAi (Zamore, 2004). Some viruses can also hijack the host RNAi 73
system to silence host genes to promote viral pathogenicity (Wang et al., 2012). 74
These aspects of host-microbe interactions provide opportunities for genetic engineering 75
for disease resistance (Dangl et al., 2013). For example, genes can be introduced into 76
plants that encode proteins capable of breaking down mycotoxins (Karlovsky, 2011) or 77
inhibiting the activity of cell wall-degrading enzymes (Juge, 2006), or nucleic acid 78
species that can sequester viral suppressors of RNAi (Wang et al., 2012) to reduce 79
microbial virulence. Plants can be engineered to synthesize and secrete antimicrobial 80
peptides or compounds that directly inhibit colonization (Osusky et al., 2000). The RNAi 81
machinery can be exploited to confer robust viral immunity by targeting viral RNA for 82
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degradation (Rosa et al., 2018). Natural or engineered immune receptors that recognize 83
diverse strains of a pathogen can be introduced individually or in combination for robust, 84
broad-spectrum disease resistance (Fuchs, 2017). Essential defense hub regulatory genes 85
can be reprogramed to fine-tune defense responses (Pieterse and Van Loon, 2004). 86
Susceptible host targets can be modified to evade recognition and manipulation by 87
pathogens (Li et al., 2012). Similarly, host decoy proteins, which serve to trap pathogens, 88
can be modified through genetic engineering for altered specificity in pathogen 89
recognition (Kim et al., 2016). For a more comprehensive overview of various aspects of 90
engineered disease resistance plants, the readers are referred to previously published 91
review articles (Collinge et al., 2010; Cook et al., 2015). 92
Increased knowledge regarding the molecular mechanism underlying plant-93
pathogen interactions and advancements in biotechnology have provided new 94
opportunities for engineering resistance to microbial pathogens in plants. In the following 95
section of this review, we update recent breakthroughs in genetic engineering in plants 96
for enhanced disease resistance and highlight several strategies that have proven 97
successful in field trials. 98
Highlights of recent breakthroughs in genetic engineering for disease resistance in 99
plants 100
Pathogen-derived resistance and RNA silencing 101
Researchers have long observed that transgenic plants expressing genes derived 102
from viral pathogens often display immunity to the pathogen and its related 103
strains (Lomonossoff, 1995). These results led to the hypothesis that ectopic expression 104
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of genes encoding wild-type or mutant viral proteins could interfere with the viral life 105
cycle (Sanford and Johnston, 1985). More recent studies demonstrated that this immunity 106
is mediated by RNAi, which plays a major role in antiviral defense in plants. 107
The detailed molecular mechanism of RNAi in anti-viral defense has been 108
described in several excellent reviews (Galvez et al., 2014; Katoch and Thakur, 2013; 109
Wang et al., 2012). Activation of RNAi has proven to be an effective approach for 110
engineering resistance to viruses (Lindbo and Dougherty, 2005; Lindbo and Falk, 2017) 111
as they rely on the host cellular machinery to complete their life cycle. Most plant viruses 112
contain single-stranded RNA (ssRNA) as their genetic information. Double-stranded 113
RNA (dsRNA) replicative intermediates often form during the replication of the viral 114
genome, triggering RNAi in the host. 115
Transgenic over-expression of viral RNA often leads to the formation of dsRNA 116
mediated by RNA-dependent RNA polymerase, which in turn triggers RNAi (Galvez et 117
al., 2014). This process is often known as co-suppression (Box 1). By far, most existing 118
cases of genetically engineered crops with resistance to viral pathogens that have been 119
approved for cultivation and consumption were generated following this concept (Table 120
1). Notably, transgenic squash (Cucurbita sp.) and papaya (Carica papaya) varieties 121
created using this approach have been grown commercially in the US for more than 122
twenty years. Field data indicate that disease resistance achieved through RNAi-mediated 123
strategies is highly durable (Fuchs and Gonsalves, 2007). The RNAi strategy has also 124
been effective in generating squash varieties that are resistant to multiple viral species 125
(Table 1). These varieties were produced by gene stacking (i.e., transgenic expression of 126
two distinct RNAi constructs targeted to different viruses) (Tricoli et al., 1995). 127
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The knowledge that dsRNA effectively induces RNAi (Lindbo and Dougherty, 128
2005) inspired the design of transgenes encoding inverted repeat sequences, the 129
transcripts of which form dsRNA (Box 1) (Waterhouse et al., 1998). This strategy was 130
used to develop a transgenic common bean variety exhibiting resistance to the DNA virus 131
Bean golden mosaic virus (BGMV) (Bonfim et al., 2007). BGMV contains single-132
stranded DNA (ssDNA) as its genetic material, which is converted to double-stranded 133
DNA (dsDNA) in the host, transcribed, and translated to produce the essential proteins 134
required for its replication (Hanley-Bowdoin et al., 2013). To generate siRNA targeting 135
the viral transcripts, sense and anti-sense sequences of part of the BGMV replication gene 136
AC1 were directionally cloned into an intron-spliced hairpin RNA expression 137
cassette (Bonfim et al., 2007). The resulting genetically engineered bean exhibited strong 138
and robust resistance in greenhouse conditions (Bonfim et al., 2007) as well as field 139
conditions (Aragao and Faria, 2009). The BGMV-resistant bean was deregulated in 2011 140
in Brazil (Table 1), which allows farmers to grow the crop. It is to date the only example 141
of a deregulated genetically engineered crop showing resistance to a DNA virus. 142
Compared with the co-suppression strategy, transgenic expression of an artificially 143
designed inverted repeat allows simultaneous generation of heterogenous siRNAs 144
targeting multiple transcripts in a relatively simple manner. 145
The discovery of microRNAs (miRNAs), a class of endogenous noncoding 146
regulatory RNAs (Ambros, 2001; Reinhart et al., 2002) led to further refinements of 147
genetic engineering for viral resistance. The miRNA machinery (Xie et al., 2015) has 148
been exploited in engineering resistance to RNA viruses by replacing specific nucleotides 149
in the miRNA-encoding genes to alter targeting specificity (Box 1). Such artificial 150
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miRNAs (amiRNAs) have been used in many lab studies in engineering resistance to a 151
wide range of plant viral pathogens such as Turnip mosaic virus (Niu et al., 2006), 152
Cucumber mosaic virus (Duan et al., 2008; Qu et al., 2007; Zhang et al., 2011), Potato 153
virus X and Potato virus Y (Ai et al., 2011). These results suggest that amiRNA-based 154
anti-viral strategies are highly promising. Disease resistance engineered by this strategy 155
awaits future field tests. 156
157
Harnessing CRISPR-Cas to target viral pathogens directly 158
In recent years, the bacterially-derived Clustered Regularly Interspaced Short 159
Palindromic Repeats (CRISPR)-CRISPR-associated (Cas) technology has proven to be a 160
promising approach for engineering resistance against plant viruses (Box 2) (Wright et al., 161
2016). In many bacterial species, CRISPR-Cas acts as antiviral defense machinery. In 162
this process, an RNA-guided nuclease (often a Cas protein) cleaves at specific target sites 163
on the substrate viral DNA or RNA, leading to their degradation. The specificity of the 164
cleavage is governed by base complementarity between the CRISPR RNA (crRNA) and 165
the target DNA or RNA molecules. A number of Cas proteins with sequence-specific 166
nuclease activity have been identified (Wu et al., 2018). For example, the RNA-guided 167
endonuclease Cas9 from Streptococcus pyogenes (SpCas9) induces double-stranded 168
breaks in DNA in vivo (Jinek et al., 2012) and the RNA-guided RNases Cas13a from 169
Leptotrichia shahii (LshCas13a) or Leptotrichia wadei (LwaCas13a) target RNA in 170
vivo (Abudayyeh et al., 2017; Cox et al., 2017). Cas9 from Francisella novicida (FnCas9) 171
exhibits in vivo activity in cleaving both DNA and RNA (Price et al., 2015). 172
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The ability of CRISPR-Cas to act like molecular scissors, creating breaks at 173
sequence-specific targets in the substrate DNA or RNA molecules makes it an excellent 174
candidate tool for engineering for anti-viral defense in plants. CRISPR-Cas platforms 175
based on Cas9 or Cas13a have been successfully harnessed to engineer resistance to 176
DNA viruses or RNA viruses in planta (Figure 1). 177
The Tomato Yellow Leaf Curl Virus (TYLCV) belongs to the Geminiviridae, a 178
major family of plant viruses with single-stranded circular DNA genomes (Dry et al., 179
1993). The viral genome forms double-stranded intermediates during replication inside 180
the host cell nuclei. Overexpression of SpCas9 and artificially designed guide RNAs 181
targeting various regions of TYLCV conferred resistance to the virus in Nicotiana 182
benthamiana and tomato (Solanum lycopersicum) (Ali et al., 2015; Mahfouz et al., 2017). 183
One potential caveat of this approach is that alterations to the viral DNA sequence may 184
occur near the cleavage target due to DNA repair within the host cell. These mutations 185
could shield the viral DNA from recognition by the guide RNA. Among all the guide 186
RNAs used against TYLCV, the ones targeting the stem-loop sequence within the 187
replication of origin were the most effective, possibly because of the reduced occurrence 188
of viable escapee variants of the virus with mutations in this region (Ali et al., 2015; Ali 189
et al., 2016). A separate lab study demonstrated that overexpression of SpCas9 and guide 190
RNAs in Nicotiana benthamiana and Arabidopsis conferred resistance to the geminivirus 191
family member Beet Severe Curly Top Virus (BSCTV) (Ji et al., 2015). CRISPR-Cas has 192
also been used to engineer resistance to RNA viruses, which comprise most known plant 193
viral pathogens. 194
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Targeting viral RNA using CRISPR-Cas has broad applicability since the 195
majority of known plant viruses contain RNA as their genetic material (Mahas and 196
Mahfouz, 2018). For example, stable expression of the RNA-targeting nuclease Cas13a 197
and the corresponding guide RNA in Nicotiana benthamiana conferred resistance to the 198
RNA virus Turnip Mosaic Virus (TuMV) (Aman et al., 2018). Using Cas13a to target 199
viral RNA substrates does not induce DNA breakage, and thus would not introduce 200
undesired off-target mutations to the host genome (Abudayyeh et al., 2017). Similarly, 201
FnCas9 has been used to demonstrate engineered resistance against RNA viruses 202
Cucumber Mosaic Virus (CMV) and Tobacco Mosaic Virus (TMV) in Nicotiana 203
benthamiana and Arabidopsis (Zhang et al., 2018). 204
Although a field test of CRISPR-Cas-based anti-viral resistance on crop species 205
has not been reported, the above lab studies have demonstrated its potential as an anti-206
viral tool in plant genetic engineering. To ensure durable resistance, it is important to 207
consider potential viral evasions from the surveillance by the specific guide RNA used. 208
Choosing genomic targets essential for the replication or movement of the viral pathogen 209
minimizes viral evasions (Ali et al., 2016). Multiplexing the guide RNAs can also 210
improve the robustness. In addition, it has been hypothesized that the used of Cas12a 211
(also known as Cpf1) may reduce the occurrence of escapee viral variants because 212
mutations caused by CRISPR-Cas12a tend to be located outside the critical region for 213
guide RNA recognition (Ali et al., 2016). Future efforts on improving CRISPR-Cas for 214
anti-virus resistance may also be focused on establishing an in planta adaptive immunity 215
by exploiting the spacer acquisition machinery in the CRISPR-Cas adaptive immune 216
system in prokaryotes (McGinn and Marraffini, 2019). 217
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Deploying resistance genes for broad-spectrum and durable resistance 219
Pioneering genetic studies in plant-pathogen interaction using flax and flax rust 220
fungus by Flor in the early 1940s (Flor, 1956) established the classic “gene-for-gene” 221
theory, which states that the outcome of any given plant-pathogen interaction is largely 222
determined by a resistance (R) locus from the host and the matching avirulence factor 223
(avr) from the pathogen (Flor, 1971). When both the R gene in the plant host and the 224
cognate avr in the pathogen are present, the plant-pathogen interaction is incompatible 225
and the host exhibits full resistance to the pathogen (Flor, 1971). The effectiveness of R 226
gene-mediated resistance was first demonstrated by British scientist Rowland Biffen in 227
wheat (Triticum sp.) breeding in the early twentieth century (Biffen, 1905). 228
Since that time numerous R genes have been cloned and introduced into varieties 229
of the same species (Dewit et al., 1985), across species boundaries (Song et al., 1995) and 230
across genera (Tai et al., 1999). For example, the introduction of the maize (Zea mays) R 231
gene Rxo1 into rice (Oryza sativa) conferred resistance to bacterial streak under lab 232
conditions (Zhao et al., 2005). In tomato, multi-year fields trials under commercial type 233
growth conditions have demonstrated that tomatoes expressing the pepper Bs2 R gene 234
confer robust resistance to bacterial spot disease (Horvath et al., 2015; Kunwar et al., 235
2018). Wheat transgenically expressing various alleles of the wheat resistance locus Pm3 236
exhibited race-specific resistance to stem rust in the field (Brunner et al., 2012). Potatoes 237
transgenically expressing wild potato R gene RB or Rpi-vnt1.1 display strong field 238
resistance to Phytophthora infestans, the causal agent of potato late blight (Bradeen et al., 239
2009; Foster et al., 2009; Halterman et al., 2008; Jones et al., 2014). Notably, transgenic 240
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potato expressing Rpi-vnt1.1 developed by J. R. Simplot is to date the only case of a 241
genetically engineered crop with enhanced resistance to a non-viral pathogen that has 242
been approved for commercial use (Table 1). 243
Successful pathogens often evade detection by host R genes (Jones and Dangl, 244
2006). Thus, disease resistance conferred by a single R gene often lacks durability in the 245
field because pathogens can evolve to evade recognition by mutating the corresponding 246
avr gene. For improved durability and widened spectrum of disease resistance, multiple R 247
genes are often introduced simultaneously, which is commonly known as stacking (Fuchs, 248
2017; Li et al., 2001; Mundt, 2018). Resistance conferred by stacked R genes is predicted 249
to be long-lasting, as the evolution of a pathogen strain that could overcome resistance 250
conferred by multiple R genes simultaneously is a low occurrence event. One approach to 251
stack R genes is by cross-breeding of pre-existing R loci (Figure 2). Breeders can then use 252
marker-assisted selection to identify the progeny with the desired R gene 253
composition (Das and Rao, 2015). For example, bacterial blight in rice, caused by the 254
bacterial pathogen Xanthomonas oryzae pv. oryzae (Xoo), is a severe disease in much of 255
Asia and parts of Africa (Nino-Liu et al., 2006). Through cross-breeding and marker-256
assisted selection, three R genes that confer resistance to bacterial blight in rice, Xa21, 257
Xa5, and Xa13 were introduced into a deepwater rice cultivar called Jalmagna (Pradhan 258
et al., 2015). The resulting line with the stacked R genes exhibited a higher level of field 259
resistance to eight Xoo isolates tested (Pradhan et al., 2015). Although marker-assisted 260
selection has largely improved the efficiency of the selection process, combining a large 261
number of loci through this approach can still be highly time-consuming. 262
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As an alternative to stacking through marker-assisted selection, molecular 263
stacking refers to the assembly of multiple R gene cassettes onto one plasmid and 264
introduction of the R gene cluster en bloc at a single genetic locus through plant 265
transformation (Figure 2) (Dafny-Yelin and Tzfira, 2007; Que et al., 2010). This 266
simplifies the selection process, as all the R genes introduced this way are inherited as a 267
single genetic locus. As an example, Zhu et al. in 2012 demonstrated molecular stacking 268
of three broad-spectrum potato late blight R genes Rpi-sto1, Rpi-vnt1.1, and Rpi-blb3 269
through Agrobacterium-based transformation of a susceptible potato cultivar (Zhu et al., 270
2012). The resulting triple gene transformants displayed broad-spectrum resistance 271
equivalent to the sum of the strain-specific resistance conferred by all three individual 272
Rpi genes under greenhouse conditions (Zhu et al., 2012). In a related study, a single 273
DNA fragment harboring Rpi-vnt1.1 and Rpi-sto1 were introduced into three different 274
potato varieties through Agrobacterium-mediated transformation (Jo et al., 2014). The R 275
gene-stacked lines showed broad-spectrum late blight resistance because of the 276
introduction of both R genes (Jo et al., 2014). Notably, no foreign DNA such as a 277
selectable marker gene was included in the inserted DNA fragment in this study (Jo et al., 278
2014), which gave potential regulatory advantages for the resulting products. Various R 279
gene-transformed potato lines exhibiting robust disease resistance in the greenhouse were 280
further evaluated in field trials, where the resistance was confirmed (Haverkort et al., 281
2016). It is estimated that proper spatial and temporal deployment of late blight R gene 282
stacks in potatoes can reduce the use of fungicide by over 80% (Haverkort et al., 2016). 283
In a recent study of African highland potato varieties in Uganda, Ghislain et al. reported 284
that significant field resistance to the late blight pathogen could be achieved by molecular 285
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stacking of three R genes (RB, Rpi-blb2 and Rpi-vnt1.1) (Ghislain et al., 2018). The 286
yields of the triple R gene stacked potato varieties were three times higher than the 287
national average. These results demonstrate that resistance achieved by this strategy does 288
not negatively affect yield (Ghislain et al., 2018). The above studies show the simplicity 289
and effectiveness of molecular stacking in genetic engineering for broad-spectrum 290
disease resistance, especially in vegetatively propagated crop species, where breeding 291
stacks are not practical. 292
Despite the advantages of molecular stacking, the number of genes that can be 293
introduced through molecular stacking is often restrained by the capacity of the 294
vector (Que et al., 2010). The ability to insert exogenous DNA sequences at designated 295
targets in the plant genome would enable additional R gene cassettes to be introduced 296
nearby an existing R gene cluster (Ainley et al., 2013). Recent breakthroughs in genome 297
editing technologies in plants have allowed such targeted insertion of DNA fragments to 298
be achieved in diverse crop species (Figure 2) (Kumar et al., 2016; Puchta and Fauser, 299
2013; Rinaldo and Ayliffe, 2015; Voytas, 2017). As genome editing platforms in plants 300
continue to be improved, the efficiency of targeted insertion, the size limit of the DNA 301
inserts and the number of applicable plant species are increasing. Future advancements in 302
targeted gene insertion would offer new opportunities for stacking of large numbers of R 303
genes and engineered viral resistance at a single locus for broad-spectrum, durable 304
disease resistance and convenience in breeding. 305
Enriching the known repertoire of immune receptors 306
Plants possess immune receptors which perceive pathogens and trigger cellular 307
defense responses. Discovery of novel immune receptors recognizing major virulence 308
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factors will enrich the repertoire of known immune receptor genes that may be deployed 309
in the field. The nucleotide-binding leucine-rich repeats (NLR) family proteins comprise 310
a major category of intracellular immune receptors conserved across the plant and animal 311
kingdoms (Bentham et al., 2017; Li et al., 2015; Maekawa et al., 2011; Zhang et al., 312
2017). A distinct hallmark for NLR-mediated defense is the onset of localized 313
programmed cell death known as the hypersensitive response (HR), which plays a crucial 314
role in restricting the movement of pathogens (del Pozo and Lam, 1998; Pontier et al., 315
1998). The HR is often used as a marker to screen diverse plant germplasm for novel 316
functional NLRs recognizing known effectors (Vleeshouwers and Oliver, 2014). During 317
the screening, core virulence factors are delivered into plant leaves either as transiently-318
expressed genes through Agro-infiltration (Du et al., 2014) or as peptides carried by 319
laboratory bacterial strains with a functional secretion system (Zhang and Coaker, 2017). 320
Once a collection of germplasm exhibiting various degrees of resistance to a 321
particular pathogen strain are identified, comparative genomics tools such as resistance 322
gene enrichment sequencing (RenSeq) (Jupe et al., 2014; Jupe et al., 2013) can be applied 323
to identify genomic variants in NLR genes that are linked to disease phenotypes (Box 3). 324
This leads to the cloning of new NLR genes and their potential deployment in crop 325
protection through genetic engineering. For example, RenSeq was successfully applied in 326
the accelerated identification and cloning of an anti-P. infestans NLR gene Rpi-amr3i 327
from Solanum americanum, a wild potato relative (Witek et al., 2016). Transgenic 328
expression of Rpi-amr3i in potato conferred full resistance to P. infestans in greenhouse 329
conditions (Witek et al., 2016). In a more recent study, Chen et al. described the rapid 330
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mapping of a newly identified anti-potato late blight NLR gene Rpi-ver1 by RenSeq in the 331
wild potato species Solanum verrucosum (Chen et al., 2018). 332
In addition to its application in potato research, RenSeq has also been employed 333
in the cloning of wheat NLR resistance genes against the stem rust fungus Puccinia 334
graminis f. sp tritici. As an example, Arora et al. used RenSeq to survey accessions of a 335
wild progenitor species of bread wheat, Ae. Tauschii ssp. strangulata for trait-linked 336
polymorphisms (Arora et al., 2019). This led to the rapid cloning of four stem rust (Sr) 337
resistance genes (Arora et al., 2019). In a related study, Steuernagel et al. applied RenSeq 338
to a mutagenized hexaploid bread wheat population to identify mutations disrupting 339
resistance to the stem rust fungus (Steuernage et al., 2016). The study revealed the 340
identity of two stem rust NLR genes, Sr22 and Sr45, which confer resistance to 341
commercially important races of the stem rust pathogen (Steuernage et al., 2016). 342
Although future field experiments are required to evaluate the potential of the 343
deployment of these newly identified NLR genes, the above lab studies demonstrate that 344
RenSeq is a powerful tool to rapidly identify novel NLR genes. 345
In addition to the identification of useful immune receptors from diverse plant 346
germplasm, researchers have attempted to engineer known immune receptors for new 347
ligand specificity. For example, the fusion of the ectodomain of the Arabidopsis thaliana 348
pattern recognition receptor EF-Tu Receptor (EFR) to the intracellular domain of the 349
phylogenetically-related rice receptor Xa21 yielded a functional chimeric receptor in both 350
rice and Arabidopsis (Holton et al., 2015; Schwessinger et al., 2015). This receptor 351
triggers defense markers when transgenic tissues are treated with elf18, the ligand of EFR, 352
although whole-plant resistance to the microbe was weak or undetectable (Holton et al., 353
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2015; Schwessinger et al., 2015). Conversely, expression of a fusion receptor consisting 354
of the ectodomain of Xa21 and the cytoplasmic domain of EFR in rice conferred robust 355
resistance to Xoo expressing the cognate ligand of Xa21 (Thomas et al., 2018). Two 356
related studies reported that chimeric receptors generated by fusing the rice chitin-357
binding protein Chitin Elicitor-Binding Protein (CEBiP) and the intracellular protein 358
kinase domain of the rice receptor-like kinase Xa21 or Pi-d2 confers disease resistance to 359
the rice blast fungus (Kishimoto et al., 2010; Kouzai et al., 2013). Although these studies 360
have not been advanced to field trials, they demonstrate that domain swapping among 361
immune receptors may be an attractive approach in engineering broadened recognition 362
specificity. 363
NLRs often perceive pathogens indirectly by monitoring the modification of host 364
target proteins caused by pathogen-derived virulence factors (Jones and Dangl, 2006). 365
Accordingly, many host target proteins have evolved as specialized decoys to facilitate 366
the perception of virulent factors by NLRs (van der Hoorn and Kamoun, 2008). In 367
Arabidopsis, the NLR protein RESISTANCE TO PSEUDOMONAS SYRINGAE 5 368
(RPS5) activates defense responses when the decoy protein AVRPPHB SUSCEPTIBLE 369
1 (PBS1), is cleaved by the virulence factor AvrPphB from the bacterial pathogen 370
Pseudomonas syringae (Simonich and Innes, 1995). A recent study reported the 371
modification of this decoy protein to widen the recognition specificity of the NLR protein 372
guarding it (Kim et al., 2016). Converting the decoy cleavage target of PBS1 into ones 373
that are recognized by other pathogen-secreted proteases conferred disease resistance to 374
the corresponding pathogens in the host plant (Kim et al., 2016). These results make 375
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18
decoy modification an attractive approach for engineering disease resistance (Kourelis et 376
al., 2016). 377
Altered specificity and activity of immune receptors can also be achieved by 378
directed molecular evolution (Lassner and Bedbrook, 2001). During this process, immune 379
receptor genes are subjected to mutagenesis to generate multiple variants, which are 380
screened for their immune functions. The process can be performed iteratively for 381
continuous improvement in the activity of the gene product. Two pioneering studies 382
demonstrated the potential value of directed molecular evolution in expanding the pool of 383
available immune receptor genes. In a search for gain-of-function mutants of the potato 384
late blight NLR gene R3a, Segretin et al. screened a library of R3a variants generated 385
through PCR-based mutagenesis and identified R3a mutants recognizing additional 386
Avr3a isoforms from P. infestans and the related blight pathogen P. capsici (Segretin et 387
al., 2014). In a follow-up study, the newly identified mutation in R3a was transferred into 388
its tomato ortholog I2 and expanded the recognition spectrum of the NLR encoded when 389
transiently expressed in Nicotiana benthaminana (Giannakopoulou et al., 2015). 390
Recently, a CRISPR-based mutagenesis platform known as EvolvR was 391
developed for the directed evolution of a precisely-defined window of genomic DNA in 392
vivo (Halperin et al., 2018; Sadanand, 2018). EvolvR employs the mutagenesis activity of 393
a fusion of the Cas9 nickase and an error-prone DNA polymerase to introduce nucleotide 394
diversification at specific genomic regions defined by guide RNAs (Halperin et al., 2018). 395
For example, EvolvR may be used for developing novel alleles of the rice immune 396
receptor Xa21 with the ability to recognize a broader array of Xoo strains (Pruitt et al., 397
2015). Variants of Xa21 receptors carrying amino acid substitutions predicted to alter 398
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19
ligand specificity can be selected and introduced into susceptible rice cultivars. Future 399
development of high-throughput functional screening methods to quickly identify gene 400
variants conferring resistance phenotypes would broaden the application of directed 401
molecular evolution for enhanced disease resistance in plants. 402
Modifying susceptibility genes to attenuate pathogenicity 403
Many plant pathogens hijack host genes to facilitate the infection process. These 404
targeted host genes are often referred to as susceptibility genes (van Schie and Takken, 405
2014). The modification or removal of host susceptibility genes may, therefore, be 406
effective strategies to achieve resistance by preventing their manipulation by the 407
pathogens. RNAi and genome editing platforms (Box 2) enable efficient modification of 408
endogenous susceptibility genes in a relatively simple manner (Rinaldo and Ayliffe, 409
2015). 410
The highly conserved eukaryotic translation Initiation Factor 4E (eIF4E) in many 411
plant species is manipulated by a number of plant viruses to facilitate the viral infection 412
process (Robaglia and Caranta, 2006). Naturally occurring recessive alleles of eIF4E 413
confer viral resistance due to abolished protein-protein interaction with a specialized viral 414
virulence protein (Cavatorta et al., 2008). Based on this knowledge, Cavatorta et al. 415
demonstrated that transgenic overexpression of a site-directed-mutagenized viral-resistant 416
allele of the eIF4E conferred resistance to Potato virus Y in potato (Cavatorta et al., 417
2011). In a more recent study, Chandrasekaran et al. knocked out the eIF4E gene in 418
cucumber using CRISPR-Cas and obtained homozygous mutant plants that are resistant 419
to a variety of viral pathogens under greenhouse conditions (Chandrasekaran et al., 2016). 420
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The SWEET family genes encode cross-membrane sugar transporters, many of 421
which are exploited by plant pathogens for virulence (Chen et al., 2010; Streubel et al., 422
2013). Very often, when SWEET genes are activated, more sugar is transported outside of 423
the cell and made available to the bacteria (Chen et al., 2010; Chen et al., 2012). For 424
example, the promoters of rice SWEET11 and SWEET14 are targets of various 425
transcriptional activator-like (TAL) effectors from Xoo (Antony et al., 2010; Yang et al., 426
2006a). Li et al. used TAL effector-like nuclease (TALEN) to mutate predicted TAL 427
effector-binding sites within the SWEET11 promoter (Li et al., 2012). The resulting rice 428
exhibits enhanced resistance to at least two strains of Xoo due to the lack of induction of 429
SWEET11 by the pathogens (Li et al., 2012). In a follow-up study, Zhou et al. 430
successfully generated rice lines carrying mutations in the promoter of SWEET11 using 431
CRISPR-Cas (Zhou et al., 2014). Mutations within the SWEET14 promoter in rice 432
protoplasts by CRISPR-Cas have also been demonstrated (Jiang et al., 2013). Besides 433
genome editing tools, RNAi has been employed to knock down SWEET11 in rice for 434
enhanced resistance to an Xoo strain with the cognate TAL effector (Yang et al., 2006b). 435
As a disease susceptibility gene for citrus bacterial canker disease, Citrus sinensis 436
Lateral Organ Boundary 1 (CsLOB1) encodes a transcription factor that regulates plant 437
growth and development (Hu et al., 2014). Deletion of the effector binding element 438
within the promoter of the susceptibility gene CsLOB1 by CRISPR-Cas conferred 439
resistance to the bacterial canker pathogen Xanthomonas citri spp. citri (Xcc) in orange 440
(Citrus sinensis) (Peng et al., 2017). Similarly, disrupting the coding region of CsLOB1 441
by CRISPR-Cas in grapefruit (Citrus paradisi) resulted in enhanced resistance to Xcc (Jia 442
et al., 2017). In both studies, the plants were raised in controlled environmental 443
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21
conditions. The effectiveness of disease resistance gained through mutating CsLOB1 in 444
citrus plants awaits further evaluation by field experiments. 445
Susceptibility genes are often conserved among plant species (Huibers et al., 446
2013). Therefore, knowledge of susceptibility genes in one plant species can facilitate the 447
discovery of new susceptibility genes in another plant species. The Arabidopsis 448
susceptibility gene DOWNY MILDEW RESISTANT 6 (DMR6) encodes an oxygenase and 449
is up-regulated during pathogen infection (Van Damme et al., 2005; van Damme et al., 450
2008; Zeilmaker et al., 2015). Through phylogeny and gene expression analysis, 451
Thomazella et al. identified a single DMR6 ortholog in tomato (SlDMR6-1) (Thomazella 452
et al., 2016). Knocking out SlDMR6-1 in tomato using CRISPR-Cas led to increased 453
resistance to the bacterial pathogen Pseudomonas syringae under greenhouse 454
conditions (Thomazella et al., 2016). In another study in 2016, Sun et al. selected eleven 455
Arabidopsis genes that had been previously shown to correlate with susceptibility to one 456
or more of six common plant pathogens (Sun et al., 2016). In a search for gene products 457
with similar amino acid sequences within the potato genome, they identified eleven 458
candidate susceptibility genes in potato (Sun et al., 2016). Silencing of six of these 459
candidate genes individually in potato using RNAi conferred enhanced or complete 460
resistance to the late blight pathogen in controlled environmental conditions (Sun et al., 461
2016). These studies show that advancements in genomics, as well as the increasing 462
number of susceptibility genes documented, enables the discovery of additional 463
susceptibility genes in a large variety of crop species. 464
Modifying susceptibility genes to thwart virulence strategies of pathogens holds 465
great potential in crop protection. Because a given allele that confers susceptibility to one 466
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22
pathogen may confer resistance to another or have other essential biological 467
functions (Lorang et al., 2012; McGrann et al., 2014), it is important to evaluate the 468
potential side-effects of modification of susceptibility genes. It remains to be determined 469
whether the observed resistance in the instances discussed in the above lab studies is 470
robust or durable under field conditions or if the plants perform well in the field. 471
472
Concluding remarks 473
Since the first report of a genetically engineered crop conferring resistance to 474
disease in 1986, a virus-resistant tobacco expressing a viral coat protein gene (Abel et al., 475
1986), there have been tremendous breakthroughs both in labs and in the field. In terms 476
of successful field applications, anti-viral resistance has had a clear head start with most 477
of the commercialized genetically engineered crops for disease resistance thus far falling 478
into this category. However, with new technologies that enable rapid identification of 479
novel immune receptor genes such as RenSeq and directed molecular evolution, the pool 480
of deployable genes for enhanced resistance to other microbes has expanded substantially. 481
In parallel, advancements in molecular stacking and targeted gene insertion through 482
genome editing are expected to play a major role in generating broad-spectrum resistance 483
against both viral and non-viral pathogens in the near future. Furthermore, the 484
increasingly versatile, accurate and efficient genome editing tools such as CRISPR-Cas 485
enable accurate modification of endogenous genes for disease resistance, including but 486
not limited to, susceptibility and the decoy genes. 487
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23
The challenges that we face in the future in genetic engineering for resistance 488
provide opportunities for further advancements in sustainable crop protection. In the 489
Outstanding Questions Box, we present some examples of the pending issues. 490
On the one hand, limitation in our knowledge of the underlying mechanism 491
governing plant innate immunity restricts the development of novel crop protection 492
strategies. For example, what amino acid residues determine the ligand specificity in 493
various immune receptors? What are the hub genes involved in plant defense that can be 494
over-expressed in various species to effectively confer broad-spectrum resistance without 495
compromising yield? On the other hand, many novel scientific discoveries are not 496
possible without high-throughput technologies with high accuracy. For example, how to 497
perform a high-throughput screen for sophisticated traits such as disease resistance? How 498
do we improve the efficiency and accuracy of CRISPR-Cas genome editing in plants, 499
especially for targeted insertion or gene replacement? How do we effectively screen and 500
regenerate diverse genetically engineered crop species and elite crop varieties? How do 501
we determine among the numerous genetically engineered individuals generated in the 502
laboratories which ones should be carried on for field studies? In addition, how do we 503
ensure the safety of the commercially grown genetically engineered food without 504
compromising innovative research? The answers to such questions are highly valuable to 505
genetic engineering for plant disease resistance in the future. 506
507
Acknowledgments 508
We thank Dr. Mawsheng Chern and Dr. Michael Steinwand for valuable suggestions on 509
the manuscript. This work was supported by research grants from the Innovative 510
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24
Genomics Institute (49411), the National Institutes of Health (R01GM122968) and the 511
United States Department of Agriculture (NIFA 2017-67013-26590) to P.C.R. 512
513
514
515
516
517
518
519
520
521
522
Figure 1. CRISPR-Cas platforms for engineering viral resistance in plants. Plant 523
viruses complete their life cycles in the host cells. Viral particles with geminate capsids 524
and rod-shaped capsids are used as examples to illustrate DNA viruses and RNA viruses, 525
respectively. One of the Cas nuclease genes (SpCas9, FnCas9 or LshCas13a) and the 526
matching guide RNA(s) are expressed transgenically to generate sequence-specific 527
ribonucleoproteins which target viral DNA or RNA substrates for degradation. SpCas9 528
targets nuclear dsDNA while FnCas9 and LshCas13a have been used to target ssRNA 529
substrates in the cytoplasm. For simplicity, only one generic Cas nuclease is drawn. 530
531
Figure 2. Methods of R gene stacking. a) Stacking by marker-assisted breeding is 532
performed by cross-pollinating individuals with existing trait loci followed by marker-533
assisted selection for progeny with combined trait loci. b) Stacking can be completed by 534
combining multiple genes into a single stack vector and introduce them together as a 535
single transgenic event. c) Stacking by targeted insertion aims at placing new gene(s) 536
adjacent to an existing locus. This process can be performed iteratively to stack large 537
numbers of genes. In b) and c), the stacked genes are genetically linked and thus can be 538
easily introduced into a different genetic background as a single locus through breeding. 539
540
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25
Table 1. Events of genetically engineered food crops with enhanced disease 541
resistance to microbial pathogens that have been approved for commercial 542
production* 543
544
Crop
Species Developer
Initial
Approval Target Pathogen Gene(s) Expressed** References
Squash Seminis and
Monsanto 1994 USDA
Watermelon Mosaic Virus 2 and
Zucchini Yellow Mosaic Virus Coat Proteins Tricoli et al., 1995
Squash Seminis and
Monsanto 1996 USDA
Cucumber Mosaic Virus,
Watermelon Mosaic Virus 2 and
Zucchini Yellow Mosaic Virus
Coat Proteins Tricoli et al., 1995
Papaya
Cornell
University and
the University
of Hawaii
1996 USDA Papaya Ringspot Virus Coat Protein Gonsalves, 1998;
Gonsalves, 2006
Potato Monsanto 1998 USDA Potato Leafroll Virus Replicase and Helicase Kaniewski et al., 2004;
Thomas et al., 1997
Sweet
Pepper
Beijing
University 1998 MOA Cucumber Mosaic Virus Coat Protein Zhu et al., 1996
Potato Monsanto 1999 USDA Potato Virus Y Coat Protein Kaniewski et al., 2004;
Newell et al., 1991
Tomato Beijing
University 1999 MOA Cucumber Mosaic Virus Coat Protein Yang et al., 1995
Papaya
South China
Agricultural
University
2006 MOA Papaya Ringspot Virus Replicase Ye et al., 2010
Plum USDA/ARS 2007 USDA Plum Pox Virus Coat Protein Ilardi et al., 2015;
Scorza et al., 1994
Papaya University of
Florida 2009 USDA Papaya Ringspot Virus Coat Protein Davis et al., 2004
Bean EMBRAPA 2011 CTNBio Bean Golden Mosaic Virus +, - RNA of Viral
Replication Protein
Bonfim et al., 2007;
Tollefson, 2011
Potato J.R. Simplot 2015 USDA Phytophthora infestans (Late
Blight) Resistance protein Foster et al., 2009
545
* Data collected from The International Service for the Acquisition of Agri-biotech Applications (ISAAA) 546 and the United States Department of Agriculture-Animal and Plant Health Inspection Service (USDA-547 APHIS). 548
**Only genes relevant to pathogen resistance are listed. 549
USDA, the U.S. Department of Agriculture. MOA, Ministry of Agriculture, China. ARS, Agriculture 550 Research Service. EMBRAPA, the Brazilian Agricultural Research Corporation. CTNBio, the Brazilian 551 National Technical Commission on Biosafety. 552
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ADVANCES
• Many aspects of plants’ multi-layered defenses against microbial pathogens can be genetically engineered to enhance disease resistance.
• RNA interference (RNAi) has been exploited for anti-viral defense by transgenic expression of the plus or minus strand of the viral genome, inverted repeats or artificial microRNAs (amiRNAs).
• CRISPR-Cas is useful in engineering resistance against DNA and RNA viruses.
• Robust, broad-spectrum disease resistance can be achieved through R gene stacking. Molecular stacking or targeted gene insertion allows multiple R genes to be clustered, thus simplifying downstream genetic segregation.
• Comparative genomics tools such as R gene enrichment sequencing (RenSeq) facilitate high-throughput screening of germplasms or mutants to rapidly identify candidate NLR genes recognizing core effectors.
• Susceptibility genes can be modified to reduce pathogenicity.
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OUTSTANDING QUESTIONS
What hub genes can be over-expressed to confer broad-spectrum resistance without compromising yield?
What residues/motifs of known NLRs/decoys determine their recognition specificities to core virulence factors?
How can scientists best perform high-throughput screens to quickly identify novel resistance genes?
Which genomic regions can transgenes be inserted and expressed in without incurring yield penalty (genomic safe harbors)?
How can researchers reduce off-target mutations caused by genome editing reagents such as CRISPR-Cas?
How can researchers deliver the genome editing reagents into elite crop varieties and regenerate plants at high efficiency?
What is the most efficient approach to sort promising strategies and advance them to field trials?
What is the most effective approach to ensure that genetically improved crops are safely deployed without impeding innovation?
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BOX 1. Various RNAi-based strategies for anti-
viral resistance
Co-suppression: Transgenic over-expressing genes encoding viral RNAs to trigger RNAi leading to the silencing of the viral RNAs. Inverted repeats: Nucleotide sequences followed by its reverse complement sequence downstream, which are transcribed to produce transcripts with self-complementary. These transcripts are processed to form double-stranded RNA molecules, which triggers RNAi leading to the silencing of the corresponding viral RNAs. Artificial microRNA (amiRNA): A type of engineered RNA exhibiting anti-viral resistance. It is designed by replacing nucleotide sequences of the intrinsic plant microRNA to achieve altered target specificity.
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BOX 2. CRISPR-Cas genome editing
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) - CRISPR-associated (Cas) is a widely-used genome editing platform. CRISPR-Cas originates from a prokaryotic acquired immune system against viruses (Hille et al., 2018). The CRISPR-Cas genome editing platform consists of a sequence-specific endonuclease such as SpCas9, and RNA molecules that determine the target specificity of the nucleases, often known as guide RNAs (Jinek et al., 2012). SpCas9 recognize and cuts at DNA targets complementary to the variable region on the guide RNA with an adjacent protospacer adjacent motif (PAM) (Jinek et al., 2012). The variable regions of guide RNA can thus be programmed to direct Cas9 to generate double-stranded breaks at specific targets in a given genome (Jinek et al., 2012). Subsequent cellular DNA repair processes often introduce modifications at the DNA breakpoint.
Repair through non-homologous end-joining (NHEJ) frequently leads to small insertions or deletions, often creating knocking-out alleles. Homology-directed repair (HDR) enables gene replacements or gene insertions at the DNA breakpoint when an appropriate repair template is supplied (Rinaldo and Ayliffe, 2015). Besides SpCas9, other Cas family nucleases that require different PAM sequences have been identified or developed (Wu et al., 2018). Some Cas nucleases recognize RNA as their substrates (Abudayyeh et al., 2017; Cox et al., 2017; Price et al., 2015). These expand the range of targets that can be edited by CRISPR-Cas. Furthermore, fusing the nuclease-deactivated Cas9 (dCas9) with diverse proteins allows additional applications at specific genomic regions, such as epigenetic modification and transcriptional activation or repression (Mahas et al., 2018).
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BOX 3. Resistance gene enrichment
sequencing (RenSeq)
RenSeq is a comparative genomic tool used to identify nucleotide-binding leucine-rich repeats (NLR) gene family members that play a role in disease resistance. For RenSeq, a cDNA library of all NLRs of a given reference genome is used to capture genomic DNA encoding NLRs from the germplasm with novel disease resistance phenotypes (Jupe et al., 2014). The enriched NLR-encoding DNA is sequenced and compared with the reference genome to identify the polymorphisms in the NLR genes that potentially account for the observed disease resistance in the germplasm of interest (Jupe et al., 2013).
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ssRNA ssDNA
dsDNAdsRNA
Viral Nucleic Acid CRISPR-Cas
Guide RNA
DNA Viruses RNA Viruses
Cas Guide RNA
CytoplasmNucleus
and
Cas nuclease
Figure 1. CRISPR-Cas platforms for engineering viral resistance in plants. Plant viruses complete their life cycles in the host cells. Viral particles with geminate capsids and rod-shaped capsids are used as examples to illustrate DNA viruses and RNA viruses, respectively. One of the Cas nuclease genes (SpCas9, FnCas9 or LshCas13a) and the matching guide RNA(s) are expressed transgenically to generate sequence-specific ribonucleoproteins which target viral DNA or RNA substrates for degradation. SpCas9 targets nuclear dsDNA while FnCas9 and LshCas13a have been used to target ssRNA substrates in the cytoplasm. For simplicity, only one generic Cas nuclease is drawn.
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a) Stacking by marker-assisted breeding
b) Stacking by introducing a vector with multiple genes
c) Stacking by targeted gene insertion
Event A Event B
Marker-assisted selection
Transformation by a stack vector containing multiple genes
Insertion of additional gene(s) adjacent to
an existing locus
Cross-pollination
Insertion of additional gene(s) adjacent to
an existing locus
Figure 2. Methods of R gene stacking. a) Stacking by marker-assisted breeding is performed by cross-pollinating individuals with existing trait loci followed by marker-assisted selection for progeny with combined trait loci. b) Stacking can be completed by combining multiple genes into a single stack vector and introduce them together as a single transgenic event. c) Stacking by targeted insertion aims at placing new gene(s) adjacent to an existing locus. This process can be performed iteratively to stack large numbers of genes. In b) and c), the stacked genes are genetically linked and thus can be easily introduced into a different genetic background as a single locus through breeding.
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