Access to this full-text is provided by Springer Nature.
Content available from Nature Genetics
This content is subject to copyright. Terms and conditions apply.
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1579
nature genetics
https://doi.org/10.1038/s41588-023-01486-9
Article
Solanum americanum genome-assisted
discovery of immune receptors that detect
potato late blight pathogen effectors
Xiao Lin 1,2,12 , Yuxin Jia3,4,12, Robert Heal1, Maxim Prokchorchik 5,11,
Maria Sindalovskaya1, Andrea Olave-Achury 1, Moffat Makechemu1,
Sebastian Fairhead1, Azka Noureen1, Jung Heo6, Kamil Witek1, Matthew Smoker1,
Jodie Taylor1, Ram-Krishna Shrestha 1, Yoonyoung Lee5, Chunzhi Zhang 3,
Soon Ju Park 6,7, Kee Hoon Sohn 5,8,9,10 , Sanwen Huang 3 &
Jonathan D. G. Jones 1
Potato (Solanum tuberosum) and tomato (Solanum lycopersicon) crops
suer severe losses to late blight caused by the oomycete pathogen
Phytophthora infestans. Solanum americanum, a relative of potato
and tomato, is globally distributed and most accessions are highly
blight resistant. We generated high-quality reference genomes of four
S. americanum accessions, resequenced 52 accessions, and dened a
pan-NLRome of S. americanum immune receptor genes. We further
screened for variation in recognition of 315P. infestans RXLR eectors in
52 S. americanum accessions. Using these genomic and phenotypic data,
we cloned three NLR-encoding genes, Rpi-amr4, R02860 and R04373,
that recognize cognate P. infestans RXLR eectors PITG_22825 (AVRamr4),
PITG_02860 and PITG_04373. These genomic resources and methodologies
will support eorts to engineer potatoes with durable late blight resistance
and can be applied to diseases of other crops.
Potato is one of the most consumed nongrain crops worldwide. How-
ever, pests and diseases reduce global yields by ~17% (ref. 1). Potato
late blight, which is caused by the oomycete pathogen Phytophthora
infestans2, triggered the Irish famine in the 1840s and is still the most
damaging disease for global potato production1.
Plant immunity depends on pathogen recognition by both
cell-surface pattern recognition receptors (PRRs) and intracellular
immune receptors. Many R genes against P. infestans (Rpi genes)
were cloned from wild relatives of potato species, such as R2, R3a,
R8, Rpi-blb1, Rpi-blb2 and Rpi-vnt1 from Solanum demissum, Solanum
Received: 12 August 2022
Accepted: 21 July 2023
Published online: 28 August 2023
Check for updates
1The Sainsbury Laboratory, University of East Anglia, Norwich Research Park, Norwich, UK. 2State Key Laboratory of Plant Genomics, Institute of
Microbiology, Chinese Academy of Sciences, Beijing, China. 3Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome
Analysis Laboratory of the Ministry of Agriculture and Rural Area, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural
Sciences, Shenzhen, China. 4Key Laboratory for Potato Biology of Yunnan Province, The CAAS-YNNU-YINMORE Joint Academy of Potato Science,
Yunnan Normal University, Kunming, China. 5Department of Life Sciences, Pohang University of Science and Technology, Pohang, Republic of Korea.
6Department of Biological Science and Institute of Basic Science, Wonkwang University, Iksan, Republic of Korea. 7Division of Applied Life Sciences
and Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Gyeongsang National University, Jinju, Republic of Korea. 8School of
Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology, Pohang, Republic of Korea. 9Department of Agricultural
Biotechnology, Seoul National University, Seoul, Republic of Korea. 10Plant Immunity Research Center, Seoul National University, Seoul, Republic of
Korea. 11Present address: Plant Pathology Group, The Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany. 12These
authors contributed equally: Xiao Lin, Yuxin Jia. e-mail: linx@im.ac.cn; keehoon.sohn@snu.ac.kr; huangsanwen@caas.cn; jonathan.jones@tsl.ac.uk
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1580
Article https://doi.org/10.1038/s41588-023-01486-9
tree topology suggests that S. americanum is a sister species to the com-
mon ancestor of potato and tomato and diverged ~14.1 million years
ago (Ma; 95% highest posterior density interval, 11.7–17.2 Ma; Fig. 1a),
which is consistent with a former report based on plastid sequences
34
.
Chromosome rearrangement (CR) is an important evolutionary
process
35
. The reference-grade genome assemblies enabled us to explore
S. americanum chromosome evolution. We observed 45 large CRs (>1 Mb
in size), comprising 26 inversion and 19 inter-chromosome translocation
events, between the S. americanum and potato genomes (Fig. 1b and
Supplementary Fig. 5). In contrast, 67 large CRs (30 inversions and 37
inter-chromosome translocations) were found between S. americanum
and eggplant (Fig. 1b). Notably, CRs were not evenly distributed across the
genome. No CR was identified on chromosome 2 between S. americanum
and potato, while 11 CRs occurred on chromosome 11.
Structural variation between S. americanum genomes
Structural variations (SVs), including insertions, deletions, duplica-
tions, inversions and translocations, cause and maintain phenotypic
diversity36. The chromosome assemblies of three S. americanum
genomes enabled the analysis of large SVs (>1 Mb in size). Using SP1102
as the reference, we identified 56 large SVs in SP2271 (Supplementary
Fig. 6a), impacting ~256 Mb of the reference genome. However, only
14 large SVs were identified in SP2273, covering ~54 Mb of the refer-
ence genome (Supplementary Fig. 6b). Most of the SVs reside in single
contigs and are supported by the Hi-C interaction map, suggesting
the high reliability of SV identification (Supplementary Fig. 6c and
Supplementary Table 1). The large differences in SV numbers among
S. americanum genomes shed light on their complex evolutionary
history. We further characterized the small SVs (40 bp–1 Mb in size)
among S. americanum genomes and found that SVs might contribute to
the differential expression of 1,084 genes between SP1102 and SP2271
leaves (Supplementary Note 2 and Supplementary Figs. 7 and 8).
Defining the S. americanum pan-NLR repertoire
To understand NLR gene diversity, a phylogenetic tree was generated
using the NB-ARC domain of the NLR proteins from SP1102 (Fig. 2a) and
the position of these NLR genes in the SP1102 genome was visualized in
the physical map (Fig. 2b). We found that 71% of SP1102 NLR genes were
in clusters and the rest were singletons (Fig. 2c). Due to the complexity
bulbocastanum and Solanum venturii3–10. However, most cloned Rpi
genes have been overcome by the fast-evolving pathogen.
P. infestans effectors carry a signal peptide and an RXLR-EER motif
(where X represents any amino acid). In the P. infestans reference genome
(strain T30-4), 563 RXLR effectors were predicted, enabling screens for
recognition of these effectors (‘effectoromics’) in various plants11,12.
Reference genome sequences of potato, tomato, eggplant and
pepper have been determined13–16. Phased, chromosome-level genome
assemblies of heterozygous diploid and tetraploid potatoes are also
available17–19. Pan-genome studies of crop plants including potato
have also emerged that shed light on the extensive genetic variation
in these species
20–23
. Sequence capture methods have been developed
to sequence plant NLR (RenSeq) and PRR (RLP/KSeq) gene repertoires
that reduce the genomic complexity and sequencing costs24–26. These
methods have led to many important applications, such as AgRenSeq,
and defining the pan-NLRome of Arabidopsis27,28.
Diploid Solanum americanum is highly resistant to late blight.
Previously, our group cloned Rpi-amr1 and Rpi-amr3 from several
resistant S. americanum accessions along with their cognate effectors
AVRamr1 and AVRamr3 (refs. 25,29–31).
Here, we sequenced and assembled four high-quality genomes of
S. americanum, resequenced 52 accessions, and defined the
pan-NLRome of S. americanum. We also screened 315 P. infestans RXLR
effectors in 52 S. americanum accessions. These genomic resources
and functional data led to the rapid identification of three new
NLR-encoding genes, Rpi-amr4, R02860 and R04373, that are respon-
sible for PITG_22825 (AVRamr4), PITG_02860 and PITG_04373 recogni-
tion, respectively. This study unveils an effector-triggered immunity
(ETI) interaction landscape between S. americanum and P. infestans
that will enable us to clone more Rpi genes from the gene pool of wild
Solanum species and deepen our knowledge of late blight resistance in
wild relatives of potato. Potato genome design driven by potato genom-
ics that takes advantage of novel plant breeding technologies
32
will help
to develop better potato varieties with durable late blight resistance.
Results
Genome assembly and gene model prediction of S.
americanum
S. americanum is a globally distributed Solanaceae species that is
resistant to many pathogens, including P. infestans and Ralstonia
solanacearum25,29,33. Four S. americanum accessions SP1102, SP2271,
SP2273 and SP2275 were selected for sequencing based on their vari-
ation in resistance to late blight (Supplementary Fig. 1a,b). We gener-
ated PacBio high-fidelity, Oxford Nanopore and Illumina paired-end
reads and assembled the genomes of SP1102, SP2271, SP2273 and
SP2275 into contigs (Supplementary Note 1). We also generated Hi-C
data for SP1102, SP2271 and SP2273, and anchored the contigs into
12 pseudomolecules (Supplementary Note 1 and Supplementary
Figs. 2 and 3). The completeness of these assemblies was estimated to
be ~98.4% (single-copy and duplicated) by BUSCO, which indicates the
high quality of genome assembly (Supplementary Fig. 4a). To annotate
gene models, we applied EVidenceModeler or GeMoMa pipelines to
integrate the ab initio prediction, homology-based annotation and
transcriptome evidence for SP1102/SP2271 or SP2273/SP2275. In sum-
mary, we predicted an average of 34,193 gene models with an aver-
age of 98.1% BUSCO evaluation (single-copy and duplicated) for each
S. americanum genome (Supplementary Fig. 4b and Table 1).
Genome evolution of S. americanum
To investigate the evolution of S. americanum genomes, we clustered
the representative protein sequences from 15 genomes, comprising the
genomes from four S. americanum accessions, four potato accessions,
three tomato accessions, four additional Solanaceae species and an out-
group species of Arabidopsis thaliana, into 33,115 orthogroups, from
which we further identified 1,363 single-copy orthogroups. The species
Table 1 | Summary of genome assembly and annotation for
S. americanum
Genome features SP1102 SP2271 SP2273 SP2275
Sequencing method CCS, Hi-C CCS, Hi-C ONT,
Illumina,
Hi-C
ONT, Illumina
Estimated genome
size 1.15Gb 1.21Gb 1.31Gb 1.21Gb
Heterozygosity 0.34% 0.35% 0.05% 0.06%
Total assembly 1.07Gb 1.13Gb 1.02Gb 1.02Gb
Contigs 299 568 294 550
Contig N50 82.9Mb 55.2Mb 10.3Mb 4.8Mb
Contig L50 7 8 30 66
Chromosomes 12 12 12 –
Anchor rate 98.43% 97.60% 99.31% –
BUSCO for assembly 98.40% 98.30% 98.40% 98.40%
Gene models 35,654 36,073 31,976 33,051
Transcripts 35,654 36,073 42,391 44,027
BUSCO for
annotation 97.7 0% 97.90% 98.60% 98.00%
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1581
Article https://doi.org/10.1038/s41588-023-01486-9
of NLR gene clusters, most automatic annotation pipelines produce
incorrect gene models37. To generate better models of the NLR genes,
we manually annotated 528, 579 and 524 NLR genes from SP1102, SP2271
and SP2273 genomes by incorporating NLR-annotator results and cDNA
sequence data (Fig. 2d). Next, presence/absence (P/A) polymorphisms
of NLR genes among S. americanum accessions were compared (Fig. 2a).
Further, a pan-NLRome was built, which suggests that the accessions
in our research are representative of the S. americanum NLR repertoire
(Supplementary Note 3 and Supplementary Fig. 9).
We also inspected the expression level of SP1102 NLR genes by
re-analyzing RenSeq cDNA data
25
. The transcripts per million (TPM)
values of NLR genes were visualized with a heatmap (Fig. 2a). Many
well-known NLR genes were relatively highly expressed, such as the
sensor NLR Rpi-amr3, and helper NLRs ADR1, NRG1, NRC4a, NRC2 and
NRC3 (Fig. 2f and Supplementary Table 2). Many Solanaceae coiled-coil
NLRs (CC-NLRs, or CNLs) require helper NLR NRCs that are phylogeneti-
cally related and we found that about 50% of the S. americanum NLRs
lie within the NRC superclade
38
(Fig. 2a). To investigate the NRC family,
we generated a phylogenetic tree for the NRC genes. We found NRC1,
NRC2, NRC3, NRC4a and NRC6 homologs, and two NRC5a homologs
in the S. americanum genome (Fig. 2b, e). Interestingly, NRC4b genes
(seven homologs) have expanded in the S. americanum genome com-
pared to Nicotiana benthamiana (two homologs) (Fig. 2e). Previously,
we reported that Rpi-amr3 and Rpi-amr1 require NbNRC2/NbNRC3/
NbNRC4 and NbNRC2/NbNRC3 in N. benthamiana, respectively
29,30
.
However, NRC1 is missing in N. benthamiana
39
. To test whether NRC1
from S. americanum can support Rpi-amr1/Rpi-amr3 function, we
cloned the SaNRC1 from SP1102 and showed that SaNRC1 enables
Rpi-amr3 but not Rpi-amr1 function in N. benthamiana nrc2/mc3/
mc4 knockout plants (Supplementary Fig. 10). This result indicates
A. thaliana
S. commersonii
S. pennellii
S. americanum (SP1102)
S. melongena
C. annuum
S. americanum (SP2275)
S. tuberosum Group Phureja (DM)
P. inflata
S. pimpinellifolium
S. americanum (SP2273)
S. chacoense
S. tuberosum Group Tuberosum (RH)
S. americanum (SP2271)
S. lycopersicum
0.02
100
100
100
100
22.0
16.2
14.1
7.8
Potato
(DM)
Sam
(SP1102)
Eggplant
(HQ-1315)
a
b
Chr. 1 Chr. 2 Chr. 3 Chr. 4 Chr. 5 Chr. 6 Chr. 7 Chr. 8 Chr. 9 Chr. 10 Chr. 11 Chr. 12
Fig. 1 | Genome evolution of Solanum americanum. a, Phylogenetic
relationship of S. americanum and neighboring species. The red number
indicates the bootstrap of each node. The black number denotes the estimated
divergence time (million years ago). The scale bar represents the number of
amino acid substitutions per site. b, Genome synteny of S. americanum, potato
and eggplant. Ribbons between chromosomes show syntenic regions. Large
chromosome rearrangements (>1 Mb in size) are marked in orange.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1582
Article https://doi.org/10.1038/s41588-023-01486-9
that distinct members in different plant species might enable NRC
functions.
The S. americanum genome and pan-NLRome also enabled us to
investigate the diversity of NLR genes. We found that sequence diversity
in NLR regions was significantly higher than in non-NLR regions (Sup-
plementary Fig. 11a), consistent with a previous report
37
. In addition, we
found extensive sequence diversity and copy number variation within
NLR clusters. For example, the Rpi-amr3 locus varied greatly among
the S. americanum genomes (Supplementary Fig. 11b), showing that
high-quality genomes are required for reliable NLRome annotation.
In summary, we generated a pan-NLRome of 20 S. americanum
accessions and manually annotated the NLR genes from three reference
genomes. This resource is important for the investigation of NLR gene
evolution and facilitates functional studies of ETI in S. americanum and
other Solanaceae species.
The ETI landscape of the S. americanum and P. infestans
interaction
There are 563 predicted RXLR effectors in the T30-4 P. infestans refer-
ence genome. In this study, we showed that there are ~550 NLR genes
in S. americanum reference genomes (Fig. 2d). To reveal one-to-one
effector-receptor interactions and clone more immune receptors, we
screened ~315 RXLR effectors on 52 S. americanum accessions (Fig. 3).
Based on cDNA PenSeq data, all these RXLR effectors are expressed
during colonization of a susceptible potato leaf31. We found that five
effectors triggered hypersensitive response (HR) on most S. america-
num accessions (≥ 50), including effectors from the AVRblb2 family,
while 185 effectors did not trigger HR in any S. americanum accessions,
71 effectors were recognized by fewer than five S. americanum acces-
sions and 54 effectors showed differential recognition by different
S. americanum accessions. AVRamr1 (36/52) and AVRamr3 (43/52)
were also widely recognized by different S. americanum accessions
(Fig. 3). The four reference accessions SP2271, SP2275, SP1102 and
SP2273 could recognize 25, 18, 30 and 30 RXLR effectors, respec-
tively, of which 5, 3, 7 and 9 effectors were specifically recognized in
each accession (Supplementary Fig. 12). Notably, accession SP2271
was susceptible to P. infestans in the detached leaf assay (DLA), but
susceptibility was age-dependent (Supplementary Fig. 1b), and this
accession is resistant to late blight in the field. As expected, SP2271 did
not recognize AVRamr1 and AVRamr3. We found premature stop codons
Chr. 1 Chr. 2 Chr. 3 Chr. 4 Chr. 5 Chr. 6 Chr. 7 Chr. 8 Chr. 9 Chr. 10 Chr. 11 Chr. 12
CNL TNL RNL
0
10
20
30
40
50
60
70
80
90
100
110
NRC1
NRG1
R3
ADR1
NRC3
R1
RB
Rpi-
vnt1
NRC2
Rpi-
chc1
Rpi-
amr3
Rpi-
amr1
Gpa2
NRC4a
NRC5
NRC6
NRC4b
log10(TPM)
4–6
3–4
2–3
1–2
0–1
Number of NLRs
0 50 100 150 200 250 300 350
98
22
325
185
12
c4.19 (SaADR1), SaNRG1,
c11.91 (Rpi-amr3)
c11.98 (SaNRC4a),
c10.43 (SaNRC2)
c5.63
(SaNRC3)
a b
d
f
SP1102 SP2271
SP2273 SP2275
641 NLRs 669 NLRs
608 NLRs 616 NLRs
528 579
524
ec
71%
29%
Singletons
In clusters
0.3
sp1102chr11_nlr_104
sp1102chr10_nlr_43
sp1102chr11_nlr_98
sp1102chr11_nlr_46
NbNRC8a
sp1102chr05_nlr_63
StNRC1a
NbNRCX
StNRC1b
sp1102chr11_nlr_97
NbNRC3
NbNRC2a
sp1102chr01_nlr_21
NbNRC2c
sp1102chr11_nlr_52
SlNRC4b
NbNRC2b
sp1102chr11_nlr_105
NbNRC7a
NbNRC4a
sp1102chr11_nlr_47
SlNRC1
Rpi-amr3
SlNRC2
NbNRC6
StNRC2
sp1102chr11_nlr_23
NbNRC5a
StNRC4b.1
SlNRC3
sp1102chr11_nlr_51
sp1102chr11_nlr_100
sp1102chr11_nlr_106
100
100
100
100
100
87
100
88
100
66
100
99
92
100
92
100
99
100
100
100
98
97
57
100
50
100
100
94
100
55
Tree scale: 1
CNL-8
CNL-6
CNL-13
CNL-14
CNL-9
CNL-1
CNL-3
CNL-2/12
log10(TPM)
1.0
3.0
5.0
0.0
NLR identity
71
76
88
100
Absent
Not available
CNL Rpi-vnt1.1
CNL R8
CNL StR1B-12-like
CNL Rpi-blb2
CNL Hero
CNL R1
CNL Prf
CNL SlNRC1
CNL Rpi-amr3
CNL Rpi-amr1
CNL SlR1B-12-like
CNL Rx
CNL BS2
TNL BS4
TNL N
TNL Rysto
TNL Roq1
TNL Gro1-4
TNL L6
c02.NRG1
CNL Pvr4
CNL RPS2
CNL StAt4g27190-like
CNL Rpi-blb1
CNL R3b
CNL R3a
CNL Rpi-chc1
CNL NtRPM1-like
CNL R2
CED-4 C. elegans
NLR expression
sp2271
sp2273
sp2275
sp1032
sp1034
sp1101
sp1123
sp2272
sp2298
sp2300
sp2308
sp2360
sp3370
sp3399
sp3400
sp3406
sp3408
sp3409
sp2307
CNL-5
CNL-16
CNL-4
CNL-10
CNL-1 1
CNL-1 1
TNL clade
RNL clade
NRC superclade
Fig. 2 | Pan-NLRome of S. americanum. a, The NB-ARC domains of S.
americanum SP1102 were predicted by NLR-annotator and used to generate a
maximum-likelihood tree using IQ-TREE with the JTT + F + R9 model. Known
NLR proteins from Solanaceae species were included (highlighted in red). The
NLRs are classified into different groups based on a previous report29. The RNL,
TNL and NRC superclade are shown. CED-4 from Caenorhabditis elegans was
used as the outgroup. The expression profile is shown by a heatmap (white to
red) based on the cDNA RenSeq data of SP1102. The P/A polymorphism of NLRs
from the three other S. americanum genomes and SMRT RenSeq assemblies of 16
additional accessions are shown by the heatmap (white to blue). The accession
order from top to bottom is SP3409, SP3408, SP3406, SP3400, SP3399, SP3370,
SP2360, SP2308, SP2307, SP2300, SP2298, SP2272, SP1123, SP1101, SP1034,
SP1032, SP2275, SP2273 and SP2271. The absent NLRs are shown by black blocks.
The scale bar represents the number of amino acid substitutions per site. b, The
physical map of NLR genes in the SP1102 genome. CNLs are shown by yellow
blocks, TNLs are shown by red blocks and RNLs are shown by pink blocks. Some
functionally characterized NLR clusters are noted on this map. Some previously
reported NLR clusters (NRC1, NRG1, R3, ADR1, NRC3, R1, RB, Rpi-vnt1, NRC2,
Rpi-chc1, Rpi-amr1, NRC6, NRC4b, NRC5, NRC4a, Rpi-amr3, Gpa2) are shown in
the physical map. (c) The proportion of NLR singletons and NLRs in clusters.
d, Number of manually curated NLR genes (red circle), and the number of NLR
genes predicted by NLR-annotator (yellow circle). Manual curation of NLR genes
from SP2275 was not performed. e, Phylogeny of the NRC helper NLR family. The
NRC homologs from potato, tomato and N. benthamiana are marked in orange,
red and green, respectively. The NRC proteins from S. americanum are in black.
The number indicates the bootstrap of each node. The scale bar represents the
number of amino acid substitutions per site. f, The log10 transformed TPM values
for NLR genes are classified into five groups, and some homologs of known R
genes are noted. The NLR IDs are shown in Supplementary Table 2.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1583
Article https://doi.org/10.1038/s41588-023-01486-9
in both Rpi-amr1 and Rpi-amr3 homologs from SP2271. Intriguingly,
25 RXLR effectors triggered HR in SP2271. These RXLR effector recog-
nitions might contribute to the age-dependent resistance and field
resistance of SP2271 to late blight. Taken together, these results reveal
the ETI landscape of S. americanum against the late blight pathogen.
Resequencing of S. americanum accessions
To investigate genetic diversity in the S. americanum accessions in
our collection, we performed PCR-free, 150 bp paired-end sequenc-
ing for 52 S. americanum accessions at 10× coverage. We constructed
a phylogenetic tree using all genic SNPs, and eggplant, potato, and
tomato were used as outgroups (Fig. 3 and Supplementary Fig. 13a).
Structural and inbreeding coefficient values were also analyzed (Sup-
plementary Fig. 13b, c). The 52 accessions can be assigned into four
groups (Supplementary Fig. 13a). All six accessions in group 1 lacked
Rpi-amr1 and Rpi-amr3 based on the effectoromics screening (Fig. 3 and
Supplementary Table 3). SP2275 and SP1102 were in group 2 and SP2273
was in group 3. No reference genome was available for group 4, but we
generated SMRT-RenSeq assemblies for several of these accessions.
Surprisingly, four accessions (SP2303, SP2310, SP3393 and SP3051)
were not closely related to other groups and are highly heterozygous
(Supplementary Fig. 13c), suggesting that they may be polyploid spe-
cies like Solanum nigrum. Two other accessions, SP3052 and SP3376,
were also not closely related to the four S. americanum groups and
might belong to another Solanaceae species. These resequencing
data could be used for genome-wide association studies (GWAS) and
molecular marker development.
Cloning of Rpi-amr4 by GWAS and linkage analysis
During effectoromics screening, we found an effector, PITG_22825,
that triggered HR in 28 of 52 S. americanum accessions (Fig. 3 and
Supplementary Table 3), including SP1102 and SP2271 but not SP2298
(Fig. 4a). PITG_22825 is an RXLR effector with a signal peptide and RQLR
and EER motifs followed by the effector domain (Fig. 4a). This effector
had not received attention before our cDNA PenSeq study
31
. To map the
gene conferring its recognition, a GWAS analysis was performed, and a
strong signal was identified in an NLR singleton located on chromosome
01 of SP1102 (Figs. 2b and 4b). This gene encodes a CNL that belongs to
the CNL-13 Rpi-amr3 phylogenetic clade (Fig. 2a), although the Rpi-amr3
gene cluster locates on chromosome 11 (Fig. 2b). This indicates that
the candidate gene on chromosome 1 might have translocated from
the Rpi-amr3 locus on chromosome 11, which probably explains
another weaker GWAS signal in the Rpi-amr3 cluster of chromosome 11
(Fig. 4b). Based on cDNA RenSeq data from SP1102, the correspond-
ing NLR gene carries an extra exon compared to Rpi-amr3 (Fig. 4b). To
verify this GWAS signal, we performed a bulked segregant analysis and
resistance gene enrichment sequencing (BSA-RenSeq) in a segregat-
ing F2 population of SP2271 x SP2298 (Supplementary Fig. 14). The
PITG_22825 responsive gene from SP2271 was mapped to the same
position on chromosome 1 in both the SP2271 and SP1102 reference
genomes.
To test gene function, the ORFs of the candidate genes from SP2271
and SP1102 were PCR amplified and cloned into an over-expression
binary vector with the 35S promoter and Ocs terminator and the
resulting constructs were then transformed into Agrobacterium
0
Group 1
Group 2
Group 3
Group 4
0.1
N. benthamiana
sp3400
sp3052
sp3393
sp3051
sp2310
sp2303
sp2308
sp2305
sp3049
sp2307
sp2306
sp3050
sp2309
sp2304
sp2297
sp1032
sp3373
sp2302
sp2300
sp2301
sp2299
sp2298
sp3371
sp3399
sp2272
sp3402
sp3404
sp3409
sp3391
sp3387
sp2361
sp2360
sp3403
sp3392
sp3401
sp2273
sp3389
sp2268
sp1123
sp3390
sp3372
sp1102
sp3370
sp2275
sp1101
sp3406
sp3397
sp3396
sp3398
sp3405
sp2271
sp2269
AVRblb2 PITG_22825
AVRamr3
PpAVRamr1
AVR3b PITG_02860
PITG_04373
No. of S. americanum
accessions
(HR index ≥1) 10
20
30
40
50
HR index
21.5 10.5 0 NA
0 10 203040 50
+
No. of eectors
(HR index ≥1)
–
Fig. 3 | ETI landscape of S. americanum and P. infestans. A total of 315 RXLR
effectors were transiently expressed in 52 S. americanum accessions. The
HR index (2, strong HR; 1, weak HR; 0, no HR, NA, not available) was used for
the heatmap. These effectors were screened on N. benthamiana30, and their
recognitions are included in this heatmap. Empty PVX vector was used as
negative control, and co-expression of Rpi-amr3–HisFlag and AVRamr3–GFP
was used as positive control. The S. americanum accessions were ordered based
on the phylogenetic tree; SP3400 is not included in this tree. The scale bar
represents the number of amino acid substitutions per site. The S. americanum
accessions were classified into four groups (gray or yellow shading). The four
reference accessions are marked by red arrows. The effectors were ordered
based on the total HR index. For each effector, the numbers of responsive
S. americanum accessions is visualized by a bar chart on the top of the heatmap.
For each S. americanum accession, the numbers of recognized effectors is
visualized by a bar chart on the right of the heatmap. Some RXLR effectors
previously characterized or mentioned in this study are indicated by gray arrows.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1584
Article https://doi.org/10.1038/s41588-023-01486-9
tumefaciens. The candidate genes were expressed in N. benthami-
ana alone or co-expressed with PITG_22825 or AVRamr3-GFP as
the negative control. Rpi-amr3-HisFlag was used as a positive con-
trol. We found that the SP2271 allele (Rpi-amr4-2271 hereafter) was
auto-active in N. benthamiana, but when it was co-expressed with
PITG_22825 the HR was faster and stronger compared to the control
(Fig. 4c). In contrast, the SP1102 allele (Rpi-amr4-1102 hereafter) was not
auto-active in N. benthamiana. HR was triggered when Rpi-amr4-1102
was co-expressed with PITG_22825, but not AVRamr3–GFP (Fig. 4c).
There are only three amino-acid differences between the proteins
encoded by Rpi-amr4-1102 and Rpi-amr4-2271, and these differences
might cause the auto-activity of the SP2271 allele (Supplementary
Fig. 15). We also found that Rpi-amr4 was conserved in the PITG_22825
responsive accessions (Fig. 4d). To verify the function of Rpi-amr4,
we generated Rpi-amr4-knockout SP2271 lines by CRISPR-Cas9. In
total, 16 CRISPR-Cas9 knockout lines were generated (Supplemen-
tary Table 4) and 2 lines are shown in Fig. 4e. Wild-type SP2271 could
recognize PITG_22825, but the Rpi-amr4-knockout lines could not.
The HR phenotype could be complemented when Rpi-amr4-1102 was
co-expressed with PITG_22825 in the knockout lines (Fig. 4e). There-
fore, we conclude that Rpi-amr4 encodes the PITG_22825-recognizing
immune receptor and that PITG_22825 is Avramr4.
To test whether Rpi-amr4 confers late blight resistance, we tran-
siently expressed Rpi-amr4-1102 in N. benthamiana leaves and inocu-
lated the leaves with P. infestans zoospores. Rpi-amr3 was used as a
positive control and non-functional Rpi-amr3a from SP1102 was used
as a negative control (Fig. 4f). This assay showed that Rpi-amr4-1102
confers resistance against P. infestans isolate T30-4. However, the
resistance was weaker than that with Rpi-amr3 (Fig. 4f). We also gen-
erated stable Rpi-amr4-1102 transgenic N. benthamiana lines. As
expected, the T0 transgenic plants gained the capacity for PITG_22825
recognition, and were resistant to two P. infestans isolates T30-4 and
88069. We also verified this finding in the T1 Rpi-amr4 transgenic lines
(Supplementary Fig. 16a,b).
In summary, we successfully cloned a new Rpi gene Rpi-amr4
from S. americanum and defined its cognate effector gene Avramr4
Chromosome
1 2 3 4 5 6 7 8 9 10 11 12
–log10(P)
0
5
10
15
20
a
PITG_22825
SP2271 SP1102 SP2298
b
Rpi-amr4-1102
Rpi-amr4-2271
Rpi-amr3–HF
+ PITG_22825
+ AVRamr3–GFP
e f
21479187
21476388
SP1102
chr. 1 sp1102chr01_nlr39
(Rpi-amr4)
c
d
Rpi-amr3 (+) Rpi-amr3a (–) Rpi-amr4-1102
Rpi-amr3
Rpi-amr3a
Rpi-amr4
Rep 1
Rep 2
Rep 3
Rep 4
a
b
c
SP
1481
RQLR
55
N. benthamiana
Identity
82.4%
81.8%
82.5%
83.3%
83.3%
98.7%
99.7%
99.9%
99.9%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
HR
0.04
Rpi-amr4-1102
Rpi-amr4-3406
Rpi-amr4-1123
Rpi-amr4-2275
Rpi-amr4-3400
Rpi-amr4-2298
Rpi-amr4-2271
Rpi-amr4-2273
Rpi-amr4-3370
Rpi-amr4-2272
Rpi-amr4-2360
Rpi-amr4-3399
Rpi-amr4-1101
Rpi-amr3
Rpi-amr4-2300
Rpi-amr4-1032
Rpi-amr3 +
AVRamr3
Rpi-
amr4-1102
AVRamr3
PITG_22825
PITG_22825 +
Rpi-amr4-1102
sgRNA1 sgRNA2
CC NB-ARC LRR
SP2271
WT
SP2271
Rpi-amr4ko#1
SP2271
Rpi-amr4ko#2
SP2271 WT
sgRNA1 (5’ → 3’)
ko#1 hap1
ko#1 hap2
ko#2
+1 bp
–4 bp
+1 bp
Rpi-amr4-2271
Lesion diameter (mm)
0
5
10
15
20
2 cm
2 cm
2 cm
2 cm
CTACCTTTTGATCCGCGTATTAGGATTTTCAGTAGACTACCCATCTACCG
CTACCTTTTGATCCGCGTAATTAGGATTTTCAGTAGACTACCCATCTACCG
CTACCTTTTGATCCGCGTA----GATTTTCAGTAGACTACCCATCTACCG
CTACCTTTTGATCCGCGTAATTAGGATTTTCAGTAGACTACCCATCTACCG
Fig. 4 | Identification and characterization of Rpi-amr4 that recognizes
PITG_22825. a, PITG_22825 is an RXLR effector. 35S::PITG_22825 triggers cell
death on S. americanum SP2271 and SP1102 leaves, but not SP2298 leaves.
b, Manhattan plot of the GWAS of PITG_22825 recognition. The SNPs associated
with PITG_22825 recognition is located in an NLR singleton sp1102chr01_nlr39;
(red arrow). c, HR assay of candidate genes. Rpi-amr4-1102 and Rpi-amr4-2271
were expressed alone or co-expressed with either 35S::PITG_22825 or
35S::AVRamr3–GFP constructs in N. benthamiana leaves. Rpi-amr4-2271 is
auto-active in N. benthamiana, but when co-expressed with PITG_22825, the
HR was stronger. Rpi-amr4-1102 specifically recognizes PITG_22825. Rpi-amr3
was used as control. OD600 = 0.5. Four leaves from two plants were used for each
experiment and three biological replicates were performed with the same results.
HF, HisFlag. d, Phylogeny of Rpi-amr4 homologs in different S. americanum
accessions. Rpi-amr3 was used as an outgroup. PITG_22825-mediated HR is
shown by red (HR) or blue (no HR) circles. Percent identity of the amino acid
sequence relative to Rpi-amr4-1102 is shown. a,c,d–f, The scale bars represent
the number of amino acid substitutions per site. e, Rpi-amr4-knockout lines
lose the capacity for PITG_22825 recognition. Two sgRNAs (black arrows) were
designed on Rpi-amr4-2271. The genotype of the two knockout lines is shown.
Both lines failed to recognize PITG_22825, but HR could be complemented
when co-expressing PITG_22825 with Rpi-amr4-1102. Wild-type (WT) SP2271
plants were used as control plants. Rpi-amr3 and AVRamr3 were used as positive
control. OD600 = 0.5. f, DLA with 35S::Rpi-amr4-1102. 35S::Rpi-amr4-1102 (green),
Rpi-amr3 (positive control, red) and Rpi-amr3a (a non-functional Rpi-amr3
paralog, negative control, blue) were transiently expressed in N. benthamiana,
OD600 = 0.5. Zoospores from P. infes tans strain T30-4 were used to inoculate the
leaves 1 day post-infiltration (dpi). Lesion sizes were measured at 6 dpi. Four
biological replicates were performed, and all data points (74 data points per
treatment) were visualized as a box-and-whisker plot. Center line, median; box
limits, upper and lower quartiles. The whiskers (top and bottom) comprise values
within 1.5 times the interquartile range (IQR). The outliers are indicated by black
dots. Statistical differences were analyzed by one-way ANOVA with Tukey’s HSD
test (P < 0.001) and were indicated by the lower-case letters. Representative
leaves are shown.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1585
Article https://doi.org/10.1038/s41588-023-01486-9
(PITG_22825). Rpi-amr4 confers late blight resistance and may serve as
a resource for producing late blight-resistant potatoes.
Cloning of R02860 and R04373
Although Rpi-amr4 could be identified using a GWAS approach, the
number of effectors recognized by a few S. americanum accessions was
small and did not enable a clear GWAS signal. We therefore deployed
BSA-RenSeq to clone two more immune receptors.
PITG_02860 (Fig. 5a) targets the host protein NRL1 and attenuates
plant immunity and increases pathogen virulence, but the cognate
receptor was unknown
40
. We found that PITG_02860 triggered HR in 5
of 52 S. americanum accessions, including SP2271. We tested an F2 popu-
lation of SP2271 (PITG_02860 responsive, R) × SP2272 (PITG_02860
non-responsive, NR), and found that recognition of PITG_02860 segre-
gated according to a 3:1 ratio (104 R and 34 NR; χ
2
(1, Ν = 138) = 0.00966,
P = 0.92169) (Fig. 5b). The RenSeq pipeline was performed on the
F2 population, and most filtered SNPs were located within a 1-Mb region
on the top of chromosome 4 of SP2271 (Fig. 5c). SCAR markers were
designed based on the resequencing data and used for genotyping.
The candidate gene was mapped to a 295 kb region between markers
S42 and S36. Seven NLRs genes reside within the mapping interval and
all belong to the R3 family (Figs. 2a,b and 5c). To test these candidate
genes, the ORFs from four candidate genes (nlr13, nlr14, nlr16, nlr17)
were cloned into a binary vector under the control of the 35S promoter
and Ocs terminator and transformed into Agrobacterium for tran-
sient expression. The candidate genes were expressed alone or with
PITG_02860 or AVRamr3 in N. benthamiana and Nicotiana tabacum.
NLR16 and NLR17 were auto-active in N. benthamiana, but we found that
co-expression of NLR16 and PITG_02860 activated HR in N. tabacum
(Fig. 5d). To verify this finding, we generated nlr16 knockout SP2271
lines by using the CRISPR–Cas9 system. As expected, the knockout lines
lost recognition of PITG_02860 (Supplementary Fig. 17). Therefore,
we conclude that NLR16 (R02860 hereafter) is the immune receptor
for PITG_02860.
PITG_04373 (Fig. 5e) triggered HR in only 3 of 50 S. americanum
accessions including in SP2300, which carries both functional Rpi-amr1
and Rpi-amr3 (Fig. 3). To clone the corresponding immune receptor
gene, we first phenotyped a BC1 backcross population of SP2271
(NR) × SP2300 (R). The BC1 population segregated for PITG_04373
responsiveness with a 1:1 ratio (198 R and 182 NR; χ2 (1, N = 380)
= 0.67368, P = 0.41177) (Fig. 5f). The DNA from responsive or non-
responsive progenies was bulked for BSA-RenSeq and most linked
SNPs mapped to SP2271 chromosome 10 (Fig. 5g). SCAR markers were
designed and an F2 population of SP2271 × SP2300 was phenotyped and
genotyped. The PITG_04373 responsiveness was mapped to a 1.447-Mb
interval with eight genes based on the SP2271 genome (Fig. 5g). Most
candidates belong to the Rpi-chc1 family, except an R1 homolog
(Fig. 5g). In the absence of a reference genome for SP2300, we used the
SMRT-RenSeq assembly as the reference NLRome29. The SMRT-RenSeq
contigs mapped to this region of the SP2271 genome, and candidate
genes from SP2300 were cloned into a vector with the 35S promoter
and Ocs terminator for transient assays. Five candidate genes were
tested (C18.1, C18.2, C127, C168 and C829). We found that the can-
didate immune receptor C168 (R04373 hereafter) can specifically
recognize PITG_04373 after transient expression in N. benthamiana
(Fig. 5h). Therefore, we conclude R04373 is the immune receptor of
PITG_04373.
SP2300 also carries functional Rpi-amr1 and Rpi-amr3 homologs.
To test the function of R04373 in SP2300, we generated Rpi-amr1-
2300/Rpi-amr3-2300/R04373 triple knockout lines (Supplementary
Fig. 18a). Forty transgenic SP2300 knockout lines were generated
and phenotyped and 13 of these 40 knockout lines lost recognition
of the three effectors (PpAVRamr1, AVRamr3 and PITG_04373). Two
of these lines, SLJ25603#3 and SLJ25603#17, were genotyped and the
knockout events were confirmed (Supplementary Fig. 18b–e). We also
co-expressed R04373 with PITG_04373; however, the HR phenotype was
not restored in these knockout lines (Supplementary Fig. 18e) and we
hypothesized that the truncated R04373 fragment might produce inter-
fering RNAs. Interestingly, the triple knockout lines showed slightly
elevated susceptibility to P. infestans (Supplementary Fig. 18f, g)
compared to wild-type SP2300, but were more resistant than SP2271,
suggesting that there are additional Rpi genes in SP2300.
To test the late blight resistance conferred by R02860 and
R04373, we transiently expressed R02860, R04373, Rpi-amr4
and their combinations in N. benthamiana and measured
P. infestans growth. However, although we observed a slight sig-
nificant decrease in lesion size after transient expression of
R02860 and R04373, the pathogen could still infect the plants.
We also co-expressed Rpi-amr4 with R02860 or R04373 without
enhancing the resistance of Rpi-amr4 (Supplementary Fig. 19).
These results indicate that although R02860 and R04373 can
recognize the RXLR effectors PITG_02860 and PITG_04373 from
P. infestans, the resistance they confer can be overcome by
P. infestans.
In summary, by using BSA-RenSeq, SMRT-RenSeq and map-based
cloning strategies, we successfully cloned two new immune receptors,
R02860 and R04373, and defined their recognized RXLR effectors,
PITG_02860 and PITG_04373.
Discussion
Solanum is the largest genus of the Solanaceae family, comprising
more than 1,500 species, including many important crop plants such
as potato, tomato and eggplant for which extensive genome sequence
data are available. The S. nigrum complex is composed of many species
with different ploidy levels, including S. nigrum (6×), Solanum scabrum
(6×), Solanum villosum (4×) and S. americanum (2×). Some are regarded
as weeds, but others are consumed as food and medicine in various
countries41. Importantly, these species carry valuable genetic variation
for resistance to diseases, including, but not limited to, potato late
blight and bacterial wilt25,29,31. In this study, we sequenced and assembled
four S. americanum genomes, and generated multi-omics datasets.
These data enabled us to build an S. americanum pan-NLRome to study
the evolution and function of the NLR genes in S. americanum.
Potato late blight triggered the Irish famine in the 1840s and
remains a global challenge that greatly constrains potato production.
To understand ETI of S. americanum to P. infestans, we used ‘effec-
toromics’ to dissect the ETI interactions between them. We gener-
ated a matrix of 315 RXLR effectors × 52 S. americanum accessions.
Interestingly, AVRamr1 (36/52) and AVRamr3 (43/52) recognition
is widely distributed in S. americanum accessions, indicating that
Rpi-amr1 and Rpi-amr3 play important roles in the late blight resist-
ance of S. americanum. This finding is consistent with the conclusions
of a pan-genome ETI study of the interaction between Arabidopsis
and Pseudomonas syringae
42
. Some effectors induce cell death in all
S. americanum accessions, such as effectors in the AVRblb2 family
(Fig. 3). This observation is consistent with previous findings that AVR-
blb2 (PexRD39/PexRD40) induces cell death in all tested wild potato spe-
cies, but not in N. benthamiana43,44; this non-specific cell death might be
a result of the virulence activities of AVRblb2. Some resistant accessions
lack AVRamr1 and AVRamr3 recognition and thus are valuable sources
of novel Rpi genes.
Three new immune receptors Rpi-amr4, R02860 and R04373 were
cloned in this study (Figs. 4 and 5). We showed that Rpi-amr4 elevates
P. infestans resistance in N. benthamiana. PITG_02860 was reported
to promote host susceptibility by targeting the host protein NRL1
(ref.40), and the virulence functions and host targets of AVRamr4 and
PITG_04373 remain to be discovered. P. infes tans is a fast-evolving path-
ogen and may be able to overcome single Rpi genes in the field within a
few years. Resistance based on the recognition of a single effector can
be easily overcome by mutations or silencing, as shown for Avrvnt1
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1586
Article https://doi.org/10.1038/s41588-023-01486-9
(refs.45,46). R gene stacking is a better way to deploy cloned R genes in
the field47,48. Therefore, Rpi-amr4 can be stacked with other Rpi genes
to provide stronger and more durable potato late blight resistance.
The two other immune receptors R02860 and R04373, recogniz-
ing PITG_02860 and PITG_47373 were cloned from SP2271 and SP2300,
respectively. The resistance conferred by these genes might be too
weak to be applied in the field (Supplementary Fig. 19). Many effec-
tors are suppressors of host immunity, notably AVRcap1, which can
attenuate the function of the helper NLRs NRC2 and NRC3 (ref.49),
and this attenuation may explain why some plant immune receptors
that recognize effectors nevertheless do not confer strong disease
resistance. To understand the complex nature of plant–pathogen
interaction, our work provides an assay to identify the suppressor of
R02860 and R04373 in the future.
In summary, our study provides valuable genomic and genet-
ics tools that should accelerate the path to understanding and
achieving durable resistance against potato late blight and other
plant diseases and shows that S. americanum is an excellent model
plant to study molecular plant–microorganism interactions and
plant immunity.
PITG_02860
RKLLR
SP
1351 50
PITG_04373
SP
1411
RSLR
50
2271 (R) 2272 (NR)
×
F1
F2
2271 (NR) 2300 (R)
×
F1
F2
2271 (NR) ×
BC1
C985
C18.1
C829
C127
C829
C18.2
C965
C168
sp2271chr04
sp2271chr04
Nlr43 Nlr12 Nlr13 Nlr14 Nlr15 Nlr16 Nlr17
sp2271chr04
5.4815.1604.9324.8654.139
S31S36S35S42S30
1/66 1/66 0/66 2/66 3/66
C168
(R04373)
N. tabacum N. benthamiana
192 182
BSA-RenSeq
104 34
Bulk NRBulk R
BSA-RenSeq
a e
c g
d
0 91.927
218 SNPs
4.000
Bulk NR
Bulk R
sp2271chr10
b f
h
0 84.219
S42
4.932
S35
4.865
77.000
27 SNPs 30 SNPs
sp2271chr10
sp2271chr10
S11 S13 S5 S7 S16
9/676 4/676 0/676 1/676 8/676
SP2300
SMRT-RenSeq
C444
74.345 76.056 76.604 77.503 78.167
R02860 (R3 family)
12021
RX-CC NB-ARC LRR
R04373 (Rpi-chc1 family)
12591
RX-CC NB-ARC LRR
C168 (R04373) +
AVRamr3
C168 (R04373)
+ PITG_04373
Rpi-amr3
Rpi-amr3 +
AVRamr3
Rpi-amr3 +
PITG_04373
Rpi-amr3 +
AVRamr3
NLR16
(R02860)
AVRamr3
PITG_02860
NLR16 (R02860) +
PITG_02860
2 cm 2 cm
Fig. 5 | Identification of R02860 and R04373 that recognize the RXLR
effectors PITG_02860 and PITG_04373. a, PITG_02860 is an RXLR effector
from P. infestans. An illustration and predicted structure are shown. b, An
F2 population was generated from a cross between SP2271 (responds to
PITG_02860, R) and SP2272 (no response to PITG_02860, NR). The R bulk
(104 progenies) and NR bulk (34 progenies) were used for BSA-RenSeq. c, A
total of 218 linked SNPs (red dots) on the top of chromosome 4 of SP2271 were
identified. The gray bar represents the chromosome. The physical positions (in
Mb) are shown by number. Five molecular markers (S30, S42, S35, S36 and S31)
were used for the map-based cloning. The number of recombination events
per total tested gametes is shown. d, HR assay of the candidate PITG_02860
receptor. The candidate genes were expressed alone, or co-expressed with
PVX::PITG_02860 in N. tabacum leaves. Rpi-amr3 and AVRamr3 were used as
controls. NLR16 turned out to be the PITG_02860 receptor (R02860 hereafter).
OD600 = 0.5. Four-week-old N. tabacum plants were used, and the photos were
taken at 4 dpi. Three biological replicates were performed with the same results.
e, PITG_04373 is an RXLR effector from P. infestans. An illustration and predicted
structure are shown. f, Both backcross (BC1) and F2 populations were generated
from SP2271 and SP2300. The BC1 population of 192 responsive plants and 182
non-responsive progenies were bulked for BSA-RenSeq. The F2 populations were
used for fine mapping. g, Informative SNPs (red dots) on the top of chromosome
10 of SP2271 were identified. Five molecular markers (S11, S13, S5, S7 and S16)
were used for fine mapping. The number of recombination gametes per total
tested gametes is shown. Nine genes from SP2300 SMRT-RenSeq assemblies
that mapped to the mapping interval of the SP2271 genome were regarded as
candidate genes. All candidates belong to the Rpi-chc1 family except C444.
h, HR assay of the PITG_04373 receptor candidates. The candidate genes were
expressed alone or co-expressed with 35S::PITG_04373 in N. benthamiana
leaves, Rpi-amr3 and AVRamr3 were used as controls. C168 turned out to
be the PITG_04373 receptor (R04373 hereafter). OD600 = 0.5. Four-week-old
N. benthamiana plants were used and photos were taken at 4 dpi. Three biological
replicates were performed with the same results.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1587
Article https://doi.org/10.1038/s41588-023-01486-9
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41588-023-01486-9.
References
1. Savary, S. et al. The global burden of pathogens and pests on
major food crops. Nat. Ecol. Evol. 3, 430–439 (2019).
2. Fry, W. Phytophthora infestans: the plant (and R gene) destroyer.
Mol. Plant Pathol. 9, 385–402 (2008).
3. Lokossou, A. A. et al. Exploiting knowledge of R/Avr genes to
rapidly clone a new LZ-NBS-LRR family of late blight resistance
genes from potato linkage group IV. Mol. Plant Microbe Interact.
22, 630–641 (2009).
4. Huang, S. et al. Comparative genomics enabled the isolation of
the R3a late blight resistance gene in potato. Plant J. 42,
251–261 (2005).
5. Vossen, J. H. et al. The Solanum demissum R8 late blight
resistance gene is an Sw-5 homologue that has been deployed
worldwide in late blight resistant varieties. Theor. Appl Genet 129,
1785–1796 (2016).
6. Song, J. et al. Gene RB cloned from Solanum bulbocastanum
confers broad spectrum resistance to potato late blight. Proc.
Natl Acad. Sci. USA 100, 9128–9133 (2003).
7. Vossen, E. et al. An ancient R gene from the wild potato species
Solanum bulbocastanum confers broad‐spectrum resistance to
Phytophthora infestans in cultivated potato and tomato. Plant J.
36, 867–882 (2003).
8. van der Vossen, E. A. G. et al. The Rpi-blb2 gene from
Solanum bulbocastanum is an Mi‐1 gene homolog conferring
broad-spectrum late blight resistance in potato. Plant J. 44,
208–222 (2005).
9. Foster, S. J. et al. Rpi-vnt1.1, a Tm-22 homolog from Solanum
venturii, confers resistance to potato late blight. Mol. Plant
Microbe Interact. 22, 589–600 (2009).
10. Pel, M. A. et al. Mapping and cloning of late blight resistance
genes from Solanum venturii using an interspeciic candidate
gene approach. Mol. Plant Microbe Interact. 22, 601–615 (2009).
11. Haas, B. J. et al. Genome sequence and analysis of the Irish potato
famine pathogen Phytophthora infestans. Nature 461, 393–398
(2009).
12. Vleeshouwers, V. G. A. A. et al. Understanding and exploiting late
blight resistance in the age of eectors. Annu. Rev. Phytopathol.
49, 507–531 (2011).
13. Xu, X. et al. Genome sequence and analysis of the tuber crop
potato. Nature 475, 189–195 (2011).
14. Tomato Genome Consortium. The tomato genome sequence
provides insights into leshy fruit evolution. Nature 485, 635–641
(2012).
15. Wei, Q. et al. A high-quality chromosome-level genome assembly
reveals genetics for important traits in eggplant. Hortic. Res. 7,
153 (2020).
16. Kim, S. et al. Genome sequence of the hot pepper provides
insights into the evolution of pungency in Capsicum species.
Nat. Genet. 46, 270–278 (2014).
17. Zhou, Q. et al. Haplotype-resolved genome analyses of a
heterozygous diploid potato. Nat. Genet. 52, 1018–1023 (2020).
18. Sun, H. et al. Chromosome-scale and haplotype-resolved
genome assembly of a tetraploid potato cultivar. Nat. Genet. 54,
342–348 (2022).
19. Hoopes, G. et al. Phased, chromosome-scale genome assemblies
of tetraploid potato reveal a complex genome, transcriptome,
and predicted proteome landscape underpinning genetic
diversity. Mol. Plant https://doi.org/10.1016/j.molp.2022.01.003
(2022).
20. Zhao, Q. et al. Pan-genome analysis highlights the extent of
genomic variation in cultivated and wild rice. Nat. Genet. 50,
278–284 (2018).
21. Liu, Y. et al. Pan-genome of wild and cultivated soybeans. Cell
182, 162–176 (2020).
22. Gao, L. et al. The tomato pan-genome uncovers new genes and a
rare allele regulating fruit lavor. Nat. Genet. 51, 1044–1051 (2019).
23. Tang, D. et al. Genome evolution and diversity of wild and
cultivated potatoes. Nature 606, 535–541 (2022).
24. Jupe, F. et al. Resistance gene enrichment sequencing (RenSeq)
enables reannotation of the NB-LRR gene family from sequenced
plant genomes and rapid mapping of resistance loci in
segregating populations. Plant J. 76, 530–544 (2013).
25. Witek, K. et al. Accelerated cloning of a potato late blight–
resistance gene using RenSeq and SMRT sequencing. Nat.
Biotechnol. 34, 656–660 (2016).
26. Lin, X. et al. RLP/K enrichment sequencing; a novel method to
identify receptor-like protein (RLP) and receptor-like kinase (RLK)
genes. New Phytol. 227, 1264–1276 (2020).
27. Arora, S. et al. Resistance gene cloning from a wild crop relative
by sequence capture and association genetics. Nat. Biotechnol.
37, 139–143 (2019).
28. Van de Weyer, A.-L. et al. A species-wide inventory of NLR genes
and alleles in Arabidopsis thaliana. Cell 178, 1260–1272 (2019).
29. Witek, K. et al. A complex resistance locus in Solanum
americanum recognizes a conserved Phytophthora eector.
Nat. Plants 7, 198–208 (2021).
30. Lin, X. et al. A potato late blight resistance gene protects
against multiple Phytophthora species by recognizing a broadly
conserved RXLR-WY eector. Mol. Plant 15, 1457–1469 (2022).
31. Lin, X. et al. Identiication of Avramr1 from Phytophthora infestans
using long read and cDNA pathogen‐enrichment sequencing
(PenSeq). Mol. Plant Pathol. 21, 1502–1512 (2020).
32. Zhang, C. et al. Genome design of hybrid potato. Cell 184,
3873–3883 (2021).
33. Moon, H. et al. Identiication of RipAZ1 as an avirulence
determinant of Ralstonia solanacearum in Solanum americanum.
Mol. Plant Pathol. 22, 317–333 (2021).
34. Särkinen, T., Bohs, L., Olmstead, R. G. & Knapp, S. A phylogenetic
framework for evolutionary study of the nightshades
(Solanaceae): a dated 1000-tip tree. BMC Evol. Biol. 13, 214 (2013).
35. Rieseberg, L. Chromosomal rearrangements and speciation.
Trends Ecol. Evol. 16, 351–358 (2001).
36. Yuan, Y., Bayer, P. E., Batley, J. & Edwards, D. Current status of
structural variation studies in plants. Plant Biotechnol. J. 19,
2153–2163 (2021).
37. Barragan, A. C. & Weigel, D. Plant NLR diversity: the known
unknowns of pan-NLRomes. Plant Cell 33, 814–831 (2021).
38. Wu, C.-H. et al. NLR network mediates immunity to diverse plant
pathogens. Proc. Natl Acad. Sci. USA 114, 8113–8118 (2017).
39. Adachi, H. et al. An atypical NLR protein modulates the NRC
immune receptor network in Nicotiana benthamiana. PLoS Genet.
19, e1010500 (2023).
40. Yang, L. et al. Potato NPH3/RPT2-like protein StNRL1, targeted by
a Phytophthora infestans RXLR eector, is a susceptibility factor.
Plant Physiol. 171, 645–657 (2016).
41. Sangija, F., Martin, H. & Matemu, A. African nightshades (Solanum
nigrum complex): the potential contribution to human nutrition
and livelihoods in sub-Saharan Africa. Compr. Rev. Food Sci. Food
Saf. 20, 3284–3318 (2021).
42. Lalamme, B. et al. The pan-genome eector-triggered immunity
landscape of a host–pathogen interaction. Science 367, 763–768
(2020).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics | Voume 55 | September 2023 | 1579–1588 1588
Article https://doi.org/10.1038/s41588-023-01486-9
43. Rietman, H. Putting the Phytophthora infestans genome sequence
at work: multiple novel avirulence and potato resistance gene
candidates revealed. PhD thesis (Wageningen University, 2011).
44. Oh, S.-K. et al. In planta expression screens of Phytophthora
infestans RXLR eectors reveal diverse phenotypes, including
activation of the Solanum bulbocastanum disease resistance
protein Rpi-blb2. Plant Cell 21, 2928–2947 (2009).
45. Vleeshouwers, V. G. A. A. & Oliver, R. P. Eectors as tools in
disease resistance breeding against biotrophic, hemibiotrophic,
and necrotrophic plant pathogens. Mol. Plant Microbe Interact.
27, 196–206 (2014).
46. Pais, M. et al. Gene expression polymorphism underpins evasion
of host immunity in an asexual lineage of the Irish potato famine
pathogen. BMC Evol. Biol. 18, 93 (2018).
47. Zhu, S., Li, Y., Vossen, J. H., Visser, R. G. F. & Jacobsen, E.
Functional stacking of three resistance genes against
Phytophthora infestans in potato. Transgenic Res. 21, 89–99
(2012).
48. Ghislain, M. et al. Stacking three late blight resistance genes
from wild species directly into African highland potato varieties
confers complete ield resistance to local blight races. Plant
Biotechnol. J. 17, 1119–1129 (2019).
49. Derevnina, L. et al. Plant pathogens convergently evolved
to counteract redundant nodes of an NLR immune receptor
network. PLoS Biol. 19, e3001136 (2021).
Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional ailiations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons license, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons license and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this license, visit http://creativecommons.
org/licenses/by/4.0/.
© The Author(s) 2023, corrected publication 2023
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01486-9
Methods
Sequencing and assembly of S. americanum genomes
Four representative S. americanum accessions, SP1102, SP2271, SP2273
and SP2275, were selected for sequencing. The Pacific Bioscience
Sequel II platform in the circular consensus sequencing (CCS) mode
was applied to sequence the genomes of SP1102 and SP2271 and gener-
ated 30.5 Gb and 28.5 Gb of high-fidelity (HiFi) reads, respectively. The
PromethION and GridION platforms of Oxford Nanopore Technologies
were applied to sequence the genomes of SP2273 and SP2275, and
generated ~81.1 Gb and ~114.5 Gb of data, respectively. To estimate the
genome heterozygosity and polish the raw assembled genomes, we
also prepared libraries for Illumina paired-end short-reads sequencing
following the standard protocol and generated an average of 99.2 Gb
of clean data for each S. americanum accession using the Illumina
Hiseq 2500 platform. Hi-C libraries of three S. americanum accessions,
SP1102, SP2271 and SP2273, were created from young seedlings based
on the restriction enzyme MboI. The Illumina Hiseq 2500 platform
was applied to generate 86.5, 81.8 and 54.8 Gb of paired-end reads for
SP1102, SP2271 and SP2273, respectively.
The genome size and heterozygosity were estimated using a
k-mer-based approach by KAT
50
and GenomeScope
51
. The estimated
genome size was calculated as the total number of k-mers divided
by the estimated sequencing coverage. The total number of k-mers
could be calculated from sequencing data, and sequencing coverage
could be assessed based on k-mer distribution frequency. In this study,
we applied KAT to calculate k-mer frequency with k = 19 and the Perl
script estimate_genome_size.pl (https://github.com/josephryan/esti-
mate_genome_size.pl) to estimate the genome size of S. americanum.
Hifiasm
52
was applied to assemble assemble SP1102 and SP2271 de novo
using default parameters. To assemble the genomes of SP2273 and
SP2275, we first corrected ONT reads using Canu
53
with parameters ‘cor-
rect corOutCoverage=500 corMinCoverage=2 minReadLength=2000
genomeSize=1g -nanopore-raw’. The corrected reads were then assem-
bled into raw contigs by SMARTdenovo
54
with the following command
line arguments ‘perl smartdenovo.pl -c 1 -t 24 -k 17’. The raw assemblies
were then iteratively polished using Illumina short reads. Reads were
aligned to the raw assemblies using BWA
55
, and resulting bam files were
passed to Pilon
56
for polishing. Pseudo-chromosomes were built by
using the juicer57 and 3d-DNA58 pipeline with parameters ‘-m haploid
-i 15000 -r 0’. The quality of the assemblies was assessed by BUSCO
(Benchmarking Universal Single-Copy Orthologs)
59
with the solana-
les_odb10 database.
Protein-coding genes prediction
For SP1102 and SP2271, to help with gene model prediction, the tran-
scriptomes of S. americanum whole seedlings, roots, stems, leaves,
flowers and fruits were sequenced by using the Illumina HiSeq 2500
platform with three replications for each tissue and 4 Gb of clean data
for each sample. The reads were aligned to the genome by HISAT
60
,
transcripts were assembled using StringTie
61
, Cufflinks
62
and Trinity
63
and the assemblies were then imported into PASA
64
for protein-coding
gene prediction. Ab initio and homologous protein search strategies
were also performed by using SNAP65, AUGUSTUS66, GlimmerHMM67
and exonerate68. All predicted evidence was integrated using EVM64. To
predict gene models in SP2273 and SP2275, we used the ITAG4.0 (ref.14)
and SolTub_3.0 (ref.13) datasets for homology-based gene prediction
in the GeMoMa program69. RNA-seq data, obtained from SP2273 were
also incorporated for splice site prediction.
Phylogenetic analysis of S. americanum
The representative protein sequences of Arabidopsis thaliana,
Petunia inflata, Capsicum annuum, Solanum melongena, Solanum
tuberosum Group Phureja (DM1-3 516 R44), S. tuberosum Group Tubero
-
sum (RH89-039-16), Solanum commersonii, Solanum chacoense,
Solanum pennellii, Solanum pimpinellifolium, Solanum lycopersicum
and four S. americanum accessions (SP1102, SP2271, SP2273 and SP2275)
were extracted and input into OrthoFinder
70
to cluster orthogroups
using the MCL algorithm. The protein sequences of 1,363 single-copy
orthogroups were extracted to infer the phylogenetic relationship
following the supermatrix method. Sequences from 15 genomes were
aligned using MAFFT71 with parameter ‘--auto’ and trimmed using tri-
mAl with parameters ‘-phylip -gt 0.8’. IQ-TREE
72
was applied to infer the
phylogenetic relationships with parameters ‘--alrt 1000 -B 1000’. We
used BASEML and MCMCTREE from the PAML software package
73
to
estimate the divergence time. The coding sequence (CDS) sequences
of 1,363 single-copy orthogroups were extracted for a rough estimation
of the substitution rate using BASEML with model = 7. MCMCTREE with
parameters ‘model = 7, burnin = 5,000,000, sampfreq = 300, nsample
= 20,000’ was applied to estimate the divergence time. The diver-
gence times of potato–tomato (7–10 Ma)
34
and potato–Arabidopsis
(111–131 Ma; http://www.timetree.org/) were used for calibration. Two
rounds of estimation were performed with similar results.
Genomic alignments of S. americanum and neighboring
species
Genomic alignment between SP1102 and eggplant/potato was per-
formed using MUMMER
74
with parameters ‘ --maxmatch -c 100 -b 500 -l
50 ‘. The alignment was further filtered with parameters ‘ -1 -i 80 -l 100’.
The delta format was then converted to PAF format using the paftools.
js script75 and passed to D-Genies76 for dot plot visualization.
Syntenic analysis of S. americanum and neighboring species
We applied the Python-based program MCscan (v1.1.8) (ref. 77) to
perform the syntenic analysis. The representative protein sequences
and corresponding gene model annotations in BED format of potato
(DMv6.1), eggplant (HQ-1315) and four S. americanum genomes were
extracted to search for homologous with parameters ‘-m jcvi.compara.
catalog ortholog --cscore = .99’. Syntenic regions were identified with
‘-m jcvi.compara.synteny screen --minspan=30’ and visualized with ‘-m
jcvi.graphics.karyotype’ parameters.
Identification of SVs
To identify large SVs (>1 Mb in length), we aligned the chromosome-
grade assembly (SP2271 and SP2273) to the SP1102 reference genome
by using MUMMER with parameters: ‘--batch 1 -t 20 -l 100 -c 500’ and
further filtered the alignment with parameters: ‘-i 90 -l 100’. SyRI v1.4
was adopted to identify SVs based on the alignment delta files; only
large SVs were kept for further analysis. We adopted the Hi-C interac-
tion map and SV location to validate the large SVs. Of the 70 SVs iden-
tified in SP2271 and SP2273, 68 SVs reside in single contig, suggesting
high reliability. Of these, 40 SVs could be verified by a Hi-C interaction
map.
The assembly-based approach was applied to identify SVs (> 40 bp
in length) among S. americanum genomes following the pipeline of
SVIM-asm
78
. The contig assemblies of SP2271, SP2273 and SP2275 were
aligned to the SP1102 reference using minimap2 (ref.76) with the fol-
lowing parameters ‘--paf-no-hit -a -x asm5 --cs -r2k’. SVs, which consist
of insertions, deletions, duplications and inversions were identified
using SVIM-asm with ‘haploid’ mode. The SVs were further annotated
by SnpEff79.
Calculation of sequence diversity across the S. americanum
genome
We used the SV information generated by SVIM-asm and a
sliding-window (window = 500 kb, step = 50 kb) method to calculate
sequence diversity across the S. americanum genome. The diversity
of a window was calculated as follows: diversity = (sum of SV length in
a window) / window length. The final diversity value of each window
was generated from the average of SP2271, SP2273 and SP2275. Higher
diversity values refer to higher variation levels of a window. If a window
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01486-9
overlapped an NLR gene, this window was counted as an NLR-region and
a total of 2,304 NLR-regions were extracted from the SP1102 genome.
To compare the variations between NLR-region and non-NLR-regions,
we randomly selected 2,304 non-NLR-regions and compared their
diversity values with those for NLR-regions by Wilcoxon rank-sum test.
Ten rounds of random selection and comparisons were done between
non-NRL-regions and NLR-regions.
Annotation of NLR gene models
The NLR genes from the four S. americanum reference genomes were
predicted by NLR-annotator
80
. To obtain a better gene model for these
NLR genes, all the NLR genes from SP2271, SP1102 and SP2273 genomes
were manually curated. In brief, the outputs of NLR-annotator were
imported into Geneious (v10.2.6) (ref. 81) as annotations of the refer-
ence genomes and the predicted NLR fragments with 2 kb flanking
sequences from both sides were extracted. Augustus66 was then used
to predict the gene model based on the trained dataset of tomato. The
gene models were curated based on functionally validated NLR genes
from public databases and cDNA RenSeq data were also used to assist
the manual annotation.
Phylogenetic analysis of NLRs in S. americanum genomes
To infer the phylogeny of NLRs, the protein sequences for the NB-ARC
domain found using NLR-annotator were aligned using MAFFT71 and
IQ-TREE was used to build a phylogenetic tree. The JTT + F + R9 sub-
stitution model was selected by ModelFinder82 and used to infer the
maximum-likelihood tree. Ultrafast bootstrap (UFBoot)
83
was set to
1,000. CED-4 from C. elegans was selected as an outgroup.
To analyze the NLR presence and absence in S. americanum
genomes, we collected 13 previously reported SMRT RenSeq assem-
blies37 and generated 3 new assemblies from accessions SP2298, SP3370
and SP2308. NLR-annotator was used to annotate the NLR genes in the
SMRT-RenSeq dataset. We used GMAP84 to predict the NLR homologs
among the S. americanum genomes and SMRT-RenSeq assemblies.
The CDS sequences of manually curated NLRs in the SP1102 genome
were extracted and mapped to the three S. americanum genomes
and 16 SMRT RenSeq assemblies using GMAP with parameters ‘-f 2 -n
1 --min-trimmed-coverage=0.70 --min-identity=0.70’. NLRs that failed
to align were marked as absent. In the v4 RenSeq library85, more baits
were included compared to the v3 RenSeq library; thus, if a certain
NLR was absent in all v3 RenSeq assemblies but present in v4 assem-
blies, the absence might be a false-positive and was marked as NA. To
calculate the expression level of NLR genes in SP1102, RNA was isolated
from young leaves of SP1102 and cDNA RenSeq was done as described
previously86. We mapped the reads from SP1102 cDNA RenSeq to its
genome using STAR (2.6.0c) (ref. 87), the BAM files were imported
into Geneious (v10.2.6) (ref. 81) and the TPM values for NLR genes were
calculated using the ‘Calculate Expression Levels’ function. The NLR
phylogeny, TPM and PAV results were passed to the online software
iTOL88 for final visualization.
Analysis of the S. americanum pan-NLRome
The NLR protein sequences from 4 genome assemblies as well as
16 SMRT-RenSeq assemblies were classified into orthogroups by
OrthoFinder using the MCL algorithm. The orthogroups matrix was
then processed with PanGP (v.1.0.1) (ref. 89) using the random algo-
rithm. The sample size and sample repeat parameters were set to 500
and 30, respectively. These parameters indicate that at each given
accession number (n), n accessions will be randomly selected for pan-
and core-NLR analysis. A 500 times random selection was performed
with 30 replicates. The estimated size of pan- and core-NLRomes were
illustrated with a box plot and fitted with exponential models. The
orthogroups were classified into three categories according to their
frequency of occurrence: core (orthogroups present in all 18–20 acces-
sions); dispensable (orthogroups that were missed in more than 3
accessions and present in at least 2 accessions); and unique (ortho-
groups present in only 1 accession). For each accession, the numbers
of NLRs in different categories were summarized and illustrated with
a stacked bar chart.
Resequencing of 52 S. americanum accessions
The genomic DNA of 4-week-old young leaves from 52 S. americanum
accessions was sampled and isolated using a Qiagen DNeasy plant kit
(Qiagen, 69104). A whole-genome PCR-free, 2 × 150 bp paired-end
Illumina library was generated and sequenced by Novogene (Beijing,
China), generating ~10 Gb of data for each S. americanum accession.
The raw reads were trimmed using trimmomatic v0.36 (ref. 90). The
clean reads of each accession were mapped to the SP1102 reference
genome with minimap2 (v2.16) (ref. 75), and converted to BAM format
with samtools (v1.9). SNP calling was carried out with samtools and
bcftools (v1.9).
To infer the phylogenetic relationships of S. americanum acces-
sions, we selected the genomes of potato (DM 1-3 516 R44 v6.1),
tomato (Heinz 1706 v4.0) and eggplant (v3) as an outgroup. Wgsim
(https://github.com/lh3/wgsim) was used to simulate whole-genome
sequencing reads from the potato, tomato and eggplant genomes with
parameters: ‘-e 0 -d 350 -N 500000000 -1 150 -2 150 -r 0 -R 0 -X 0’. The
simulated reads mapping and SNP calling were performed using the
same approaches. Bedtools (v2.17) was used to extract SNPs in coding
regions. The SNP-based phylogenetic tree was inferred by IQ-TREE
with UFBoot set to 1,000 and the TVMe+R2 best-fit model, which was
automatically selected by ModelFinder. The phylogenetic tree was
visualized with FigTree (v1.4.4).
GWAS analysis
For the GWAS analysis, all the SNPs residing in NLR gene regions, as
well as the 3 kb upstream and 1 kb downstream regions, were extracted
with bedtools (v2.17). The SNPs were filtered and processed using Plink
(v1.90) with parameters ‘--make-bed --allow-extra-chr --allow-no-sex
--mind 1 --maf 0.05 -geno 0.05 --recode --out’. The responsiveness scores
of each effector were used as the phenotype and passed to Plink for
association analysis with parameters ‘--allow-extra-chr --allow-no-sex
--assoc --bfile --pheno’. The Manhattan plot was created using the R
package qqman (v0.1.8).
Effectoromics screening
An RXLR effector library of 311 RXLR effectors was used in the effecto-
romics screening. The signal peptides were removed, and the effec-
tor domains were cloned into overexpression vectors (pMDC32 or
pICSL86977) or PVX vectors. S. americanum plants were grown in a
containment glasshouse. Four- or 5-week-old plants were used for the
agroinfiltration. For the overexpression vectors, cell death was scored
at 4 dpi; for the PVX vectors, cell death was scored at 7 dpi. OD
600
= 0.5.
The cell death phenotype was scored (2, strong HR; 1, weak HR; 0, no
HR). Two leaves each from two plants were used for each experiment.
Constructs for transient overexpression
To verify the candidate genes, the ORF of each candidate genes was
amplified by Phusion high-fidelity DNA polymerase (NEB, M0530S)
or KAPA HiFi Uracil+ DNA polymerase (Roche, 07959052001) and then
cloned into the pICLS86922 overexpression vector with the 35S pro-
moter and Ocs terminator using BsaI (NEB, #R3733) or a USER cloning
vector (pICSLUS0004OD) with the 35S promoter and Ocs terminator
using USER enzyme (NEB, #M5508). The verified constructs were
transformed into Agrobacterium for transient expression in plants.
Gene knockout with the CRISPR–Cas9 system
For the knockout constructs, guide RNAs were designed in Geneious
(v10.2.6) using the ‘Find CRISPR Site’ function with parameters:
‘Maximum mismatches allowed against off-targets = 3; Maximum
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01486-9
mismatches allowed to be indels = 0; pair CRISPR Sites: Maximum
overlap of paired sites = 100; Maximum allowed space between paired
sites = 300’. The reference genome or SMRT-RenSeq assembly was used
for scoring of off-target activity. The selected guide RNAs are shown in
Supplementary Table 3. Two guide RNAs for each candidate gene were
amplified with the sgRNA scaffold by Q5 high-fidelity DNA polymerase
(NEB, M0491S) and the pICSL70001 vector was used as the template.
The fragments were then fused with an Arabidopsis U6-26 promoter
(pICL90002) and cloned into level 1 vectors at different positions (posi-
tion 3, pICH47751; position 4, pICH47761; position 5, pICH47772; posi-
tion 6, pICH47772). For the final level 2 constructs, Cas9 with introns
(position 1, pICSL11197), NPTII (position 2, pICSL11055), an end linker
(pICH41922) and the guide RNAs were assembled into pICSL4723_OD.
The final constructs were then transformed into S. americanum acces-
sions from gene knockout. After transformation, the T
0
lines were
moved into a containment glasshouse for phenotyping and genotyping.
Agroinfiltration of the corresponding effector was used for the pheno-
typing. Genomic DNA from the individual T0 lines was isolated, and
specific primers were designed for the Cas9 gene and the target genes.
Amplicons from the target genes were sub-cloned into the pGEM-Teasy
TA cloning vector (Promega, A1360) or pICSL86977 for sequencing. The
sequencing data were analyzed in Geneious (v10.2.6).
Plant growth and transformation
The N. benthamiana and N. tabacum cv. Petit Gerard plants were sowed
and grown in a controlled environment room (CER) at 22 °C and 45–65%
humidity with a 16-h photoperiod. Four-week-old plants were used for
the HR assay.
For the S. americanum transformation, sterilized seeds (SP2271
and SP2300) were sown in MS medium (2% sucrose). Leaf disks were cut
from 4 to 6-week-old in-vitro plants. Overnight Agrobacterium (AGL1)
culture (100 µl) and 200 µM acetosyringone were added to 20 ml of
LSR broth and the leaf discs were gently dipped into the solution using
sterile forceps for 20 min. The leaf discs were then removed from the
Agrobacterium suspension, blotted dry, and incubated under low light
conditions at 18–24 °C for 3 days. The dried leaf discs were plated on
LSR1 + 200 µM Acetosyringone solid medium. Co-cultivated explants
were transferred to LSR1 medium in petri dishes with selection antibiot-
ics (about seven leaf discs per plate). Explants were subcultured onto
fresh LSR1 medium approximately every 14 days. Once the calli were
sufficiently developed they were transferred onto LSR2 medium. Sub-
culturing continued every 14 days when shoots started to appear. The
shoots were removed with a sharp scalpel and planted into MS2R solid
medium with selection antibiotics. Transgenic plants harboring appro-
priate antibiotic or herbicide resistance genes should root normally by
the fourth week and can then be weaned out of tissue culture into sterile
peat blocks before being transferred to the glasshouse. Media used had
the following components: LSR broth (1× MS medium, 3% sucrose, pH
5.7); LSR1 medium (1× MS medium, 3% sucrose, 2.0 mg L-1 zeatin riboside,
0.2 mg L
-1
NAA, 0.02 mg L
-1
GA3, 0.6% agarose, pH 5.7); LSR2 medium
(1× MS medium, 3% sucrose, 2.0 mg L
-1
zeatin riboside, 0.02 mg L
-1
GA3,
0.6% agarose, pH 5.7); MS2R (1× MS medium, 2% Sucrose, 100 mg L
-1
myo-inositol, 2.0 mg L-1 glycine, 0.2% Gelrite, pH 5.7).
Disease assay
P. infestans isolates T30-4 and 88069 were used for the disease test and
were maintained on rye sucrose agar (RSA) medium in an 18 °C incuba-
tor. To induce zoospores, ice-cold water was added to the plates after
10–14 days. The plates were then incubated at 4 °C for 1–2 h and a hemo-
cytometer was used to count the number of zoospores. The zoospore
suspension was used for the DLA (100–500 zoospores per droplet).
BSA-RenSeq and map-based cloning
Three mapping populations were used in this study: (1) F2 popu-
lations of SP2271 × SP2272 and (2) BC1 and (3) F2 populations of
SP2271 × SP2300. The populations were phenotyped by agroinfiltra-
tion of RXLR effectors. A cork borer was used for sampling, and the
leaf discs from the responsive and non-responsive progenies were
pooled. The Genomic DNA was isolated using the Qiagen DNeasy plant
kit (Qiagen, 69104). RenSeq libraries were then prepared, as described
previously
24
. The libraries were sequenced (Illumina 2 × 250-bp reads)
by Novogene (Beijing, China). The SNP filtering and calling steps were
described previously26.
To design molecular markers, the 10× PCR-free resequencing
reads were mapped to the SP2271 S. americanum reference genome.
Then SCAR markers that linked with the BSA-RenSeq signals were
designed; amplicons should only be present in the non-responsive
allele. The SCAR markers were first tested on the parental lines and
the verified markers were then used on genomic DNA from individ-
ual non-responsive plants. GoTaq G2 DNA polymerase (Promega,
0000066542) was used for genotyping.
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
The raw sequencing data and genome assemblies of SP1102, SP2271,
SP2273 and SP2275 genomes have been deposited at the National
Center for Biotechnology Information (NCBI) Sequence Read Archive
(SRA) with BioProject accession number PRJNA845062; The raw SMRT
RenSeq data were deposited in ENA under project number PRJEB38240.
The whole-genome resequencing data were deposited in ENA under
project number PRJEB57057. The BSA-RenSeq data were deposited in
ENA under project numbers PRJEB57070 and PRJEB57074. The assem-
bled genomes, gene structure annotations, SMRT-RenSeq assemblies
and manually annotated NLR genes as well as variation information
are available at Figshare (https://figshare.com/projects/The_Sola-
num_americanum_pangenome_and_effectoromics_reveals_new_resist-
ance_genes_against_potato_late_blight/145449). The SaNRC1-1102,
SaNRC2-1102, SaNRC3-1102, Rpi-amr4-1102, Rpi-amr4-2271, R02860
(Rpi-amr16) and R04373 (Rpi-amr17) sequences were deposited at NCBI
GenBank under accession number OP918030–OP918036. Source data
are provided with this paper.
Code availability
Custom scripts and codes used in this study are available at Zenodo
(https://doi.org/10.5281/zenodo.7928678)91.
References
50. Mapleson, D., Garcia Accinelli, G., Kettleborough, G., Wright, J. &
Clavijo, B. J. KAT: a K-mer analysis toolkit to quality control NGS
datasets and genome assemblies. Bioinformatics 33, 574–576 (2017).
51. Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C.
GenomeScope 2.0 and Smudgeplot for reference-free proiling of
polyploid genomes. Nat. Commun. 11, 1432 (2020).
52. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H.
Haplotype-resolved de novo assembly using phased assembly
graphs with hiiasm. Nat. Meth.18, 170–175 (2021).
53. Koren, S. et al. Canu: scalable and accurate long-read assembly
via adaptive k-mer weighting and repeat separation. Genome Res.
27, 722–736 (2017).
54. Liu, H., Wu, S., Li, A. & Ruan, J. SMARTdenovo: a de novo
assembler using long noisy reads. GigaByte https://doi.
org/10.46471/gigabyte.15 (2021).
55. Li, H. & Durbin, R. Fast and accurate short read alignment with
Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
56. Walker, B. J. et al. Pilon: an integrated tool for comprehensive
microbial variant detection and genome assembly improvement.
PLoS ONE 9, e112963 (2014).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01486-9
57. Durand, N. C. et al. Juicer provides a one-click system for
analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98
(2016).
58. Dudchenko, O. et al. De novo assembly of the Aedes aegypti
genome using Hi-C yields chromosome-length scaolds. Science
356, 92–95 (2017).
59. Simão, F. A., Waterhouse, R. M. & Ioannidis, P. BUSCO: assessing
genome assembly and annotation completeness with
single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
60. Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced
aligner with low memory requirements. Nat. Meth. 12, 357–360
(2015).
61. Pertea, M. et al. StringTie enables improved reconstruction of a
transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295
(2015).
62. Roberts, A. et al. Dierential gene and transcript expression
analysis of RNA-seq experiments with TopHat and Culinks. Nat.
Protoc. 7, 562–578 (2012).
63. Grabherr, M. G. et al. Full-length transcriptome assembly from
RNA-Seq data without a reference genome. Nat. Biotechnol. 29,
644–652 (2011).
64. Haas, B. J. et al. Automated eukaryotic gene structure annotation
using EVidenceModeler and the program to assemble spliced
alignments. Genome Biol. 9, R7 (2008).
65. Korf, I. Gene inding in novel genomes. BMC Bioinforma. 5, 59
(2004).
66. Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene
prediction in eukaryotes that allows user-deined constraints.
Nucleic Acids Res. 33, W465–W467 (2005).
67. Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and
GlimmerHMM: two open source ab initio eukaryotic gene-inders.
Bioinformatics https://doi.org/10.1093/bioinformatics/bth315
(2004).
68. Slater, G. S. C. & Birney, E. Automated generation of heuristics
for biological sequence comparison. BMC Bioinformatics. 6, 31
(2005).
69. Keilwagen, J., Hartung, F., Paulini, M., Twardziok, S. O. & Grau, J.
Combining RNA-seq data and homology-based gene prediction
for plants, animals and fungi. BMC Bioinformatics. 19, 189 (2018).
70. Emms, D. M. & Kelly, S. OrthoFinder: solving fundamental biases
in whole genome comparisons dramatically improves orthogroup
inference accuracy. Genome Biol. 16, 157 (2015).
71. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment
software version 7: improvements in performance and usability.
Mol. Biol. Evol. 30, 772–780 (2013).
72. Minh, B. Q. et al. IQ-TREE 2: new models and eicient methods
for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37,
1530–1534 (2020).
73. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood.
Mol. Biol. Evol. 24, 1586–1591 (2007).
74. Marçais, G. et al. MUMmer4: a fast and versatile genome
alignment system. PLoS Comput. Biol. 14, e1005944 (2018).
75. Li, H. Minimap2: pairwise alignment for nucleotide sequences.
Bioinformatics 18, 3094–3100 (2018).
76. Cabanettes, F. & Klopp, C. D-GENIES: dot plot large genomes in
an interactive, eicient and simple way. PeerJ 6, e4958
(2018).
77. Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary
analysis of gene synteny and collinearity. Nucleic Acids Res. 40,
e49 (2012).
78. Heller, D. & Vingron, M. SVIM-asm: structural variant detection
from haploid and diploid genome assemblies. Bioinformatics
https://doi.org/10.1093/bioinformatics/btaa1034 (2020).
79. Cingolani, P. et al. A program for annotating and predicting the
eects of single nucleotide polymorphisms, SnpE: SNPs in the
genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly
6, 80–92 (2012).
80. Steuernagel, B. et al. The NLR-Annotator tool enables annotation
of the intracellular immune receptor repertoire. Plant Physiol. 183,
468–482 (2020).
81. Kearse, M. et al. Geneious Basic: an integrated and extendable
desktop software platform for the organization and analysis of
sequence data. Bioinformatics 28, 1647–1649 (2012).
82. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. &
Jermiin, L. S. ModelFinder: fast model selection for
accurate phylogenetic estimates. Nat. Meth. 14, 587–589
(2017).
83. Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh,
L. S. UFBoot2: improving the ultrafast bootstrap approximation.
Mol. Biol. Evol. 35, 518–522 (2018).
84. Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and
alignment program for mRNA and EST sequences. Bioinformatics
21, 1859–1875 (2005).
85. Seong, K., Seo, E., Witek, K., Li, M. & Staskawicz, B. Evolution of
NLR resistance genes with noncanonical N-terminal domains in
wild tomato species. New Phytol. 227, 1530–1543 (2020).
86. Andolfo, G. et al. Deining the full tomato NB-LRR resistance gene
repertoire using genomic and cDNA RenSeq. BMC Plant Biol. 14,
120 (2014).
87. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner.
Bioinformatics 29, 15–21 (2013).
88. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online
tool for phylogenetic tree display and annotation. Nucleic Acids
Res. 49, W293–W296 (2021).
89. Zhao, Y. et al. PanGP: a tool for quickly analyzing bacterial
pan-genome proile. Bioinformatics 30, 1297–1299 (2014).
90. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a lexible
trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120
(2014).
91. Jia Y. Codes of Solanum americanum genome-assisted discovery
of immune receptors that detect potato late blight pathogen
eectors. Zenodo https://doi.org/10.5281/zenodo.7928678
(2023).
Acknowledgements
This research was inanced by BBSRC grants BB/P021646/1 (J.D.G.J.),
BB/S018832/1 (J.D.G.J.), BB/W017423/1 (J.D.G.J.), the Gatsby Charitable
Foundation (J.D.G.J.), National Key Research and Development
Program of China (2019YFA0906200, S.H.), Guangdong Major
Project of Basic and Applied Basic Research (2021B0301030004,
S.H.), Agricultural Science and Technology Innovation Program
(CAAS-ZDRW202101, S.H.), Shenzhen Outstanding Talents
Training Fund (S.H.), National Research Foundation of Korea
(2018R1A5A1023599 and 2023R1A2C3002366, K.H.S.) and
New Breeding Technologies Development Program (PJ015799
and PJ016538, S.J.P.). We thank TSL transformation team
(A. Wawryk-Khamdavong), SynBio team (M. Youles and L. Egan),
media services (N. Stammars), bioinformatics team (D. MacLean and
C. Jégousse) and horticultural team (S. Perkins, J. Smith, L. Phillips,
C. Taylor, T. Wells, D. Alger and S. Able) for their support. We thank
P. Robinson (JIC) for the scientiic photography. We thank
S. Marillonnet (Icon Genetics GmbH, Halle/Saale, Germany) for
sharing the Cas9 construct (pAGM47523). We thank Experimental
Garden and Genebank of Radboud University, Nijmegen, the
Netherlands, IPK Gatersleben, Germany and S. Knapp (Natural History
Museum, London, UK) for access to S. americanum genetic diversity.
We thank V. G. A. A. Vleeshouwers at Wageningen University and
Research, P. Birch, I. Hein and B. Harrower at James Hutton Institute for
making available clones of some eectors. We thank B. B.H. Wul,
S. Arora and K. Gaurav (JIC) for helpful discussions.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature Genetics
Article https://doi.org/10.1038/s41588-023-01486-9
Author contributions
X.L. Y.J., K.H.S., S.H. and J.D.G.J. conceived and designed the
project. X.L. and Y.J. wrote the irst draft with input from all
the authors. X.L., Y.J., K.H.S., S.H. and J.D.G.J. reviewed and
edited the manuscript. Y.J., M.P., X.L., R.K.S. and J.H. performed
the bioinformatics analyses. X.L. and M.M. performed the
eectoromics screening. X.L., R.H., M.S., A.C.O.A., S.F. and
A.N. contributed to cloning and characterization of Rpi-amr4,
R02860 and R04373. M. Smoker and J.T. performed the plant
transformation. K.W., Y.L., C.Z., S.J.P., K.H.S., S.H. and J.D.G.J.
contributed resources.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41588-023-01486-9.
Correspondence and requests for materials should be addressed to
Xiao Lin, Kee Hoon Sohn, Sanwen Huang or Jonathan D. G. Jones.
Peer review information Nature Genetics thanks Doil Choi,
Xiu-Fang Xin, and Jack H. Vossen for their contribution to the peer
review of this work. Peer reviewer reports are available.
Reprints and permissions information is available at
www.nature.com/reprints.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1
nature portfolio | reporting summary March 2021
Corresponding author(s):
Xiao Lin, Kee Hoon Sohn, Sanwen Huang,
Jonathan D. G. Jones
Last updated by author(s): May 22, 2023
Reporting Summary
Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency
in reporting. For further information on Nature Portfolio policies, see our Editorial Policies and the Editorial Policy Checklist.
Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement
A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly
The statistical test(s) used AND whether they are one- or two-sided
Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested
A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons
A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient)
AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)
For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted
Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes
Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated
Our web collection on statistics for biologists contains articles on many of the points above.
Software and code
Policy information about availability of computer code
Data collection No software was used for data collection.
Data analysis KAT (v2.4.2), GenomeScope (v2.0), Hifiasm (v.0.13), juicer (v.1.5), 3d-DNA (v.180922), Canu (v1.8), SMARTdenovo (v8488de9), BWA (0.7.5a-
r405), Pilon (v.1.23), BUSCO (), HISAT (v.2.0.1-beta), StringTie (v.1.3.3b), Cufflinks (v2.2.1), Trinity (v2.10.0), PASA (v2.4.1), SNAP
(v.2013-02-16), AUGUSTUS (v.3.4.0), GlimmerHMM (v3.0.4), exonerate (v2.2), EVM (v1.1.1), GeMoMa (v1.7.1), OrthoFinder (v2.5.2), MAFFT
(v.7.471), trimAl (v1.4.1), IQ-TREE (v2.1.4-beta), BASEML (v.4.9), MCMCTREE (v.4.9), MUMMER (v4.0.0rc1), D-Genies (v1.2.0), MCscan (Python
version), Python (v3.5.6), minimap2 (v2.17-r941), SVIM-asm (v1.0.2), SnpEff (v5.0e), Geneious (v10.2.6), GMAP (v2020-10-14), STAR (2.6.0c),
iTOL (v5), trimmomatic (v0.36), samtools (v1.9), bcftools (v1.9), Wgsim (2011 version), Bedtools (v2.17) , FigTree (v1.4.4), Plink (v1.90), R
package qqman (v0.1.8), clinker (2020 version).
Some customized Python scripts were used to process the data generated by each software, which parameters were described in Methods
section. All codes are available from the corresponding author upon request.
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and
reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio guidelines for submitting code & software for further information.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
2
nature portfolio | reporting summary March 2021
Data
Policy information about availability of data
All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:
- Accession codes, unique identifiers, or web links for publicly available datasets
- A description of any restrictions on data availability
- For clinical datasets or third party data, please ensure that the statement adheres to our policy
The raw sequencing data for SP1102, SP2271, SP2273 and SP2275 genomes have been deposited at the National Center for Biotechnology Information (NCBI)
Sequence Read Archive (SRA) with BioProject accession number PRJNA845062 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA845062?
reviewer=hliiufd2hm679172evsbdgcr69); The raw SMRT RenSeq data were deposit in ENA under project number: PRJEB38240; The whole genome resequencing
data were deposit in ENA under project number: PRJEB57057; The BSA-RenSeq data were deposit in ENA under project number: PRJEB57070 and PRJEB57074. The
assembled genomes, gene structure annotations, SMRT RenSeq assemblies, manually annotated NLR genes as well as variation information are available at Figshare
(https://figshare.com/projects/The_Solanum_americanum_pangenome_and_effectoromics_reveals_new_resistance_genes_against_potato_late_blight/145449).
The SaNRC1-1102, SaNRC2-1102, SaNRC3-1102, Rpi-amr4-1102, Rpi-amr4-2271, R02860 (Rpi-amr16) and R04373 (Rpi-amr17) sequences were deposited at NCBI
GenBank under accession number: OP918030-OP918036.
Human research participants
Policy information about studies involving human research participants and Sex and Gender in Research.
Reporting on sex and gender N/A
Population characteristics N/A
Recruitment N/A
Ethics oversight N/A
Note that full information on the approval of the study protocol must also be provided in the manuscript.
Field-specific reporting
Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.
Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf
Life sciences study design
All studies must disclose on these points even when the disclosure is negative.
Sample size No statistical analysis was used to determine the sample size. The sample size is widely used and accepted in the field, the details were
described in the Methods section.
Data exclusions For the disease assay in Figure 4d, the preliminary disease assay was not blinded and with a small sample size. The results were consistent
with the 4 blind replicates, but the preliminary result was not included in the final statistical analysis and figure.
Replication For Fig. 3: 3 biological replicates were performed for all the responsive effectors.
For the HR assays in Fig. 4 and Fig. 5: 3 biological replicates were performed.
For the disease assay in Figure 4d: 4 biological replicates were performed.
For the disease assay in Figure S19: 4 biological replicates were performed.
Randomization All the plants in the same experiment were grew in the same condition. All samplings were randomized.
Blinding For the disease assay in Figure 4d: Blind tests were performed for all the 4 replicates. For each replicate, a colleague streaked out the
constructs (Rpi-amr3, Rpi-amr3a and Rpi-amr4) with a random code "A", "B" or "C". Another researcher performed the agroinfiltration,
detached leaf assays, and scoring. The genotype codes were revealed after scoring.
Reporting for specific materials, systems and methods
Content courtesy of Springer Nature, terms of use apply. Rights reserved
3
nature portfolio | reporting summary March 2021
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material,
system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
Materials & experimental systems
n/a Involved in the study
Antibodies
Eukaryotic cell lines
Palaeontology and archaeology
Animals and other organisms
Clinical data
Dual use research of concern
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.