# Translatome analysis of an NB-LRR immune response identifies important contributors to plant immunity in Arabidopsis

Louis-Valentin Méteignier, Mohamed El Oirdi, Mathias Cohen, Teura Barff, Dominick Matteau, Jean-François Lucier, Sébastien Rodrigue, Pierre-Etienne Jacques, Keiko Yoshioka, Peter Moffett

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Louis-Valentin Méteignier, Mohamed El Oirdi, Mathias Cohen, Teura Barff, Dominick Matteau, et al. Translatome analysis of an NB-LRR immune response identifies important contributors to plant immunity in Arabidopsis. Journal of Experimental Botany, 2017, 68 (9), pp.2333-2344. 10.1093/jxb/erx078. hal-04160286

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RESEARCH PAPER

# Translatome analysis of an NB-LRR immune response identifies important contributors to plant immunity in Arabidopsis

Louis-Valentin Meteignier<fnref n="1" />, Mohamed El Oirdi<fnref n="1" />, Mathias Cohen<fnref n="1" />, Teura Barff<fnref n="1" />, Dominick Matteau<fnref n="1" />, Jean-François Lucier<fnref n="1" />, Sébastien Rodrigue<fnref n="1" />, Pierre-Etienne Jacques<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Keiko Yoshioka<fnref n="4" /> and Peter Moffett<fnref n="1" />,*

Received 3 October 2016; Editorial decision 20 February 2017; Accepted 20 February 2017

Editor: Björn Usadel, RWTH Aachen University

## Abstract

An important branch of plant immunity involves the recognition of pathogens by nucleotide-binding, leucine-rich repeat (NB-LRR) proteins. However, signaling events downstream of NB-LRR activation are poorly understood. We have analysed the Arabidopsis translatome using ribosome affinity purification and RNA sequencing. Our results show that the translational status of hundreds of transcripts is differentially affected upon activation of the NB-LRR protein RPM1, showing an overall pattern of a switch away from growth-related activities to defense. Among these is the central translational regulator and growth promoter, Target of Rapamycin (TOR) kinase. Suppression of TOR expression leads to increased resistance to pathogens while overexpression of TOR results in increased susceptibility, indicating an important role for translational control in the switch from growth to defense. Furthermore, we show that several additional genes whose mRNAs are translationally regulated, including BIG, CCT2, and CIPK5, are required for both NB-LRR-mediated and basal plant innate immunity, identifying novel actors in plant defense.

**Key words:** CIPK, ETI, NB-LRR, NLR, plant–microbe interactions, protein translation, PTI, Target of Rapamycin.

## Introduction

Plants rely on several surveillance mechanisms to defend against pathogen infection. These include pattern-recognition receptors at the plant cell surface, which detect the presence of pathogen-associated molecular patterns (PAMPs) and induce PAMP-triggered immunity (PTI) (Macho and Zipfel, 2014). To overcome the latter, pathogens deploy effector proteins, many of which interfere with PTI induction (Macho and Zipfel, 2015). Many effectors are delivered to the host cytoplasm,

where they can be recognized by specific nucleotide-binding, leucine-rich repeat (NB-LRR) proteins. This recognition leads to the induction of effector-triggered immunity (ETI), which often culminates in a type of cell death known as the hypersensitive response (HR). However, the HR is not required in many cases of ETI (Coll *et al.*, 2011) and although many studies have focused on recognition and activation of NB-LRR proteins, much about ETI signaling remains unknown (Li *et al.*, 2015).

<footnote n="1">¹ Université de Sherbrooke, Département de Biologie, 2500 Blvd. de l'Université, Sherbrooke, QC, J1K2R1, Canada</footnote>

<footnote n="2">² Université de Sherbrooke, Département d'informatique, 2500 Blvd. de l'Université, Sherbrooke, QC, J1K2R1, Canada</footnote>

<footnote n="3">³ Centre de recherche du centre hospitalier universitaire de Sherbrooke, Sherbrooke, QC, J1H5N4, Canada</footnote>

<footnote n="4">⁴ University of Toronto, Department of Cell and Systems Biology, 25 Willcocks St., Toronto, ON, Canada</footnote>

<footnote>* Correspondence: peter.moffett@usherbrooke.ca</footnote>
Several studies have shown that ETI and PTI responses
trigger qualitatively similar transcriptional responses, but
that these responses differ in terms of the timing and amp-
litude of gene expression changes (Tao *et al.*, 2003; Navarro
*et al.*, 2004; Tsuda and Katagiri, 2010). Studies on ETI have
shown that a significant modification of the proteome occurs
prior to changes to the transcriptome during *Pseudomonas*
infection (Jones *et al.*, 2004, 2006). At the same time, acti-
vation of the NB-LRR protein N results in a repression of
viral transcripts, but does not result in a global repression of
host translation (Meteignier *et al.*, 2016). However, the role
of translational control of host transcripts during an ETI
response has not been extensively studied. Recently, trans-
latome analyses of Arabidopsis in diverse conditions such as
heat stress (Yángüez *et al.*, 2013), photomorphogenesis (Liu
*et al.*, 2012, 2013), and pollen tube growth (Lin *et al.*, 2014)
have been reported. These studies have made use of trans-
genic Arabidopsis plants expressing a FLAG-tagged riboso-
mal protein L18 (FLAG-rpL18), which allows for translating
ribosome affinity purification (TRAP) (Zanetti *et al.*, 2005).
This method results in the isolation of intact, actively trans-
lating transcripts, which can then be converted to cDNA and
sequenced (TRAP-seq).

To identify candidate genes involved in ETI responses, we
have used TRAP-seq to analyse the Arabidopsis translatome
upon expression of the *Pseudomonas* effector AvrRpm1,
which is recognized by the NB-LRR protein RPM1, from an
inducible promoter (Mackey *et al.*, 2002). Our results showed
that the ETI translatome showed significant differences
from the transcriptome. Many transcripts encoding proteins
involved in primary metabolism were down-regulated even
though their levels in total RNA did not change, whereas
many mRNAs encoding signaling-related proteins showed
a status indicating higher translational efficiency, suggest-
ing that ETI results in a highly active translational response
(or responses). Furthermore, we tested the requirement for
resistance to *Pseudomonas syringae* and *Hyaloperonospora
arabidopsidis* for a number of genes whose transcripts showed
large changes in translational efficiency after RPM1 activa-
tion. This analysis identified several genes required for ETI
and PTI, including *CCT2*, *BIG*, and *CIPK5*. At the same
time, we have found that Target of Rapamycin (*TOR*) is
down-regulated at the translational level upon NB-LRR acti-
vation. TOR plays a central role in regulating the translation
of growth-related genes (Ren *et al.*, 2012; Xiong *et al.*, 2013;
Dobrenel *et al.*, 2016). Disease resistance was enhanced by
underexpression of TOR and compromised by TOR overex-
pression. Our results suggest that TOR is involved in regulat-
ing the growth-to-defense switch necessary for immunity, and
demonstrate a central role for translational control in coordi-
nating defense responses.

**Materials and methods**

*Plant growth conditions and pathogen infection assays*

*Arabidopsis thaliana* plants were grown in soil under 16 h
light/8 h dark cycles at 22 °C. Arabidopsis TOR RNAi and OX

transgenic lines, as well as the *rrtf1* mutant (SALK_150614) have
been described previously (Deprost *et al.*, 2007; Schweizer *et al.*,
2013). Additional mutant lines were obtained from the Arabidopsis
Resource Center (Ohio State University), including *doc1-1*
(CS6204), *big* (SALK_1054950C), AT1G60000 (SALK_203629C),
*cipk5-1* (SALK_084455C), *cipk5-2* (SALK_063555C), *cct2*
(SALK_200207C), AT3G23900 (SALK_015201C), *ops*
(SALK_089722C), AT1G07280 (SALK_022732C), AT4G21020
(SALK_151565C), and *pgn* (SALK_034655C). Bacterial inocula-
tions were performed as described by González-Lamothe *et al.*
(2012). For *H. arabidopsidis* (*Hpa*) infection assays, plants were
grown in Sunshine Mix (http://www.sungro.com/) in a growth cham-
ber at 22 °C, 60% relative humidity (RH), with a 9-h photoperiod.
Four-week-old plants were infected with *Hpa* strain Noco2 accord-
ing to Yoshioka *et al.*, (2006). Plants were infected via spray inocula-
tion and left in a growth chamber with plastic cover at 16 °C, >90%
RH for 12 d before disease assessment. Spore counts of 2 × 10⁵
cells ml⁻¹ were used for TOR OX, TOR RNAi, *cct2*, and *doc1-1*,
and 4.5 × 10⁵ cells ml⁻¹ were used for *cipk5* analyses. Trypan Blue
staining of seedlings was performed as described by Yoshioka *et al.*
(2006).

*Ribosome immunopurification and RNA extraction*

Cellular extracts from 4-week-old Arabidopsis rosette leaves at 2 h
after spraying with DEX (30 µM) or water (mock) were prepared
for ribosome immunopurification (IP) followed by RNA purifica-
tion as previously described (Zanetti *et al.*, 2005; Mustroph *et al.*,
2009). Total RNA from the same samples was purified in parallel.
RNA integrity was analysed with a Bioanalyzer using an Agilent
RNA 6000 Nano kit following the manufacturer's procedure. For
details on quality control analysis see Supplementary Experimental
Procedures at JXB online.

*Sequencing*

RNA-seq libraries were prepared as described elsewhere (Carraro *et al.*, 2014). Paired-end sequencing was performed on an Illumina HiSeq 2000 system at the Massachusetts Institute of Technology BioMicro Center (40-bp reads). Total RNA and TRAP libraries were sequenced on two independent sequencing lanes as multiplexed samples as described previously (Carraro *et al.*, 2014). The data discussed in this paper have been deposited in NCBI's Gene Expression Omnibus (Edgar *et al.*, 2002) and are accessible through GEO Series accession number GSE75640 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75640).

*Gene expression analysis*

The quality of the raw reads was confirmed using FastQC (v0.10,
http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), fol-
lowed by alignment on the TAIR10 assembly of *A. thaliana* Col-0
genome using TopHat (v2.0.8) (Kim *et al.*, 2013) with default param-
eters, and with Bowtie 2 (v2.1.0) (Langmead and Salzberg, 2012).
Using Samtools (v0.1.18) (Li *et al.*, 2009), reads with a mapq score
>10 were kept for subsequent analysis. Cuffdiff (v2.1.1) (Trapnell
*et al.*, 2013) was then used with parameters frag-bias-correct, multi-
read-correct, and the genome annotations file from NCBI 2013-03-06
to identify ‘significant’ differentially expressed (RNA-seq) or trans-
lated (TRAP-seq) genes using comparisons described in the text.
Significance was defined as having a FDR-adjusted *P*-value <0.05
(Benjamini–Hochberg correction for multiple-testing). To conserve
visual representation accuracy in scatter-plots of translational effi-
ciency, the null FPKM (fragments per kilobase of exon per million
fragments mapped) values of genes with an ‘inf’ result were replaced
by the smallest FPKM value seen for the gene within each compari-
son, and the Log₂-fold change was recalculated to replace the ‘inf’
value (see Supplementary Table S5 at JXB online).
## Gene ontology analysis

The DAVID bioinformatics resource 6.7 (http://david.abcc.ncif-crf.gov) was used to analyse enriched gene ontology (GO) terms and protein domains in corresponding datasets compared to the Arabidopsis background, as described previously (Huang et al., 2009). Genes with Log₂-fold changes ≤-2 or ≥2 were used for this analysis, except for all down-regulated differentially expressed genes.

## Results

### TRAP enables the purification of high-quality translated mRNAs

We crossed plants with a DEX-inducible AvrRpm1 transgene (Mackey et al., 2002) with a transgenic line constitutively expressing a tagged cytosolic ribosomal protein, FLAG-rpL18, generating the Avr/L18 line, allowing for translating ribosome affinity purification (Zanetti et al., 2005), followed by sequencing (TRAP-seq) (Juntawong et al., 2014) (Fig. 1A). As in the parental DEX-AvrRpm1 plants (Mackey et al., 2002), a strong HR developed 4 h after spraying Avr/L18 plants with DEX (see Supplementary Fig. S1A). We also crossed the FLAG-rpL18 line with a *rpm1/rps2* double-mutant line expressing the DEX-AvrRpm1 transgene, but AvrRpm1 was not inducible by DEX in the progeny, probably due to transgene silencing, as previously reported (Geng and Mackey, 2011). To test the efficacy of the TRAP method, we immunopurified ribosomes with anti-FLAG beads. FLAG-rpL18 co-immunoprecipitated the small sub-unit ribosomal protein S14, except in the presence of EDTA, which che-lates the Mg²⁺ required for ribosome subunit association (Supplementary Fig. S1C). RT-PCR analysis of purified ribosomes showed that the chloroplastic *RBCL* transcript is largely depleted from the IP fraction, whereas cytosolic transcripts (*GAPDH*, *MEKK1*, *WRKY38*) are readily detectable (Supplementary Fig. S1D). Together, these results indicate

that TRAP allows for the purification of intact cytosolic ribosomes along with associated mRNAs with little background.

To analyse the defense translatome, we treated Avr/L18 plants with DEX or a mock treatment, followed by purification of total RNA and TRAP analysis at 2 h post-treatment. At this time-point, no visible phenotype is apparent on DEX-treated plants and it is well before the manifestation of any DEX- or AvrRpm1-induced phenotypes, which take days to weeks to appear (Kang et al., 1999; Geng et al., 2016). In parallel, we performed polysome profiling on plants treated the same way. No obvious reduction in total polysome abundance was observed in polysome profiles 2 h after DEX treatment (Fig. 1B), indicating that protein translation was not globally repressed upon NB-LRR activation. As shown by immune-blotting, anti-FLAG TRAP yielded approximately the same quantity of rpL18 at 2 h after DEX or mock treatment (Supplementary Fig. S1E, top panel). Likewise, Bioanalyzer analysis showed very similar amounts of high-quality rRNA, from cytosolic ribosomes only, both after mock and DEX treatment (see Supplementary Fig. S1E). RT-PCR confirmed that the *AvrRpm1* transcript was detectable in ribosome fractions only after DEX treatment, as was the *TBF1* transcript, a marker of the growth-to-defense transition (Pajerowska-Mukhtar et al., 2012). As shown above, chloroplastic *RBCL* transcripts were largely depleted in the IP fraction, whereas *RBCS* mRNAs are equally detectable in the total RNA versus the TRAP RNA fraction (Supplementary Fig. S1E). These samples were then used for the construction of sequencing libraries, in parallel with their total RNA fraction counterparts, followed by library quality assessment and paired-end sequencing (Supplementary Fig. S1E). The two techniques (RNA-seq and TRAP-seq) were conducted in biological triplicates under the two conditions (2 h after DEX or mock treatment) to generate twelve libraries.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Experimental design to study the NB-LRR translatome. (A) Schematic diagram of the experimental design. A transgenic Arabidopsis line carrying a DEX-inducible AvrRpm1 transgene together with a 35S:FLAG-rpL18 transgene, as well as the endogenous RPM1 gene was generated. Ribosome-bound RNA, isolated using an anti-FLAG antibody, and total RNA was purified 2 h after mock treatment or RPM1 activation by DEX treatment. Immunoprecipitated and total RNAs were then converted to cDNA sequencing libraries to generate TRAP-seq and RNA-seq datasets. Differentially expressed genes (DEGs) and differentially translated genes (DTGs) were identified by comparing DEX versus control from RNA-seq and TRAP-seq data, respectively. Genes showing uncoupled translation and transcription (GUTT) were identified by comparing translational efficiency in each condition. (B) Tissues from mock-treated plants or plants treated with DEX as in (A) were harvested and subjected to polysome profiling and measuring of O.D. 254 absorption of sucrose gradient fractions. The corresponding sucrose fractions were resolved on native 1.2% agarose gels and visualized by ethidium bromide staining. A representative profile from three biological replicates is shown.</figcaption></figure>
*Common and contrasting features between the
defense translatome and transcriptome*

We first assessed the quality of our libraries by looking
at the efficiency of rRNA depletion as well as biological
reproducibility. For each library, 20–40 million reads were
uniquely mapped on the TAIR10 assembly of *Arabidopsis*
*thaliana* Col-0 using TopHat (Trapnell *et al.*, 2009), with a
mean rRNA contamination of 5.9% (see Supplementary Fig.
S2A, C), and relative gene expression values (FPKM) were
obtained using Cufflinks (Trapnell *et al.*, 2010). The average
Pearson correlation values between biological replicates for
total RNA-seq datasets was 0.85 (Supplementary Fig. S2B),

whereas it was above 0.98 for TRAP-seq biological replicates
(Fig. S2D). This is consistent with previous reports showing
that transcription is inherently more ‘noisy’ than translation
(Keene, 2007; Joshi et al., 2011; Coate et al., 2014). Using
Cuffdiff (Trapnell et al., 2013), we identified 1292 differen-
tially expressed genes (DEGs) by comparing the expres-
sion levels in mock and DEX conditions from the RNA-seq
datasets, of which 25% showed decreased levels after DEX
treatment (Fig. 2A, Supplementary Table S1). We also iden-
tified 6225 differentially translated genes (DTGs) by com-
paring the TRA-Pseq datasets from the mock and DEX
treatments. Approximately equal numbers of DTGs were

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Analysis of the defense transcriptome and translatome. (A, B) Numbers of DEGs identified from the RNA-seq datasets (A) and DTGs from the TRAP-seq datasets (B), separated into up- (green) and down- (red) regulated genes. (C–E) Venn diagrams showing the overall overlap between the DEGs and DTGs (C), and between the subsets of up-regulated genes (D) and down-regulated genes (E). (F–H) Pie diagrams of the top10 enriched GO terms in the up-regulated DEGs (F) and DTGs (G) datasets, and the down-regulated DEGs (H) and DTGs (I) datasets as resolved by DAVID. The numbers shown at the bottom of the pie diagrams are the sums of the enrichment scores of each GO term indicated in one pie, as calculated by DAVID. Individual GO term percentages relative to the total enrichment score of each pie is indicated for each term.</figcaption></figure>
down- or up-regulated upon NB-LRR activation (Fig. 2B, Supplementary Table S2), and 62% of the DEGs were also identified as DTGs (Fig. 2C). Interestingly, the proportion of DEGs that are also DTGs increased to 77% for up-regulated genes (Fig. 2D), while only 20% of down-regulated DEGs were identified as DTGs (Fig. 2E). This indicates that, in many cases, transcription was uncoupled from translation, particularly for transcriptionally repressed genes. Functional annotation clustering using the DAVID bioinformatics resource revealed that the top 10 enriched GO terms included defense-related processes, including cell wall thickening and secondary metabolism, both at the transcriptional and translational levels (Fig. 2F and G, respectively). At the same time, cell death-related processes appeared in the top10 enriched GO terms only from the DTGs, suggesting a quick induction of HR-related proteins via translational regulation. Consistent with the GO terms, many up-regulated DEGs and DTGs possess TIR domains found in NB-LRR proteins (Supplementary Fig. S2E, F). In contrast to the overlap seen between up-regulated DEGs and DTGs, GO terms enriched in down-regulated genes showed major differences between the two sets of differentially regulated genes. For example, a number of genes belonging to the 'structural constituent of ribosome' GO term were down-regulated at the level of total RNA (RNA-seq DEGs; Fig. 2H, Table S3). However, no genes belonging to this GO term were significantly decreased at the translational level (TRAP-seq DTGs; Fig. 2I; Supplementary Table S3), possibly because certain highly translated mRNAs were not sufficiently turned over in their association with ribosomes within the time-frame of these experiments. GO terms enriched in the down-regulated DTGs largely belonged to primary metabolism and development, including photosynthesis, gravitropism, phototropism, auxin metabolism, and carbohydrate biosynthesis (Fig. 2J; Supplementary Table S3). Interestingly, RPM1 activation also induced a down-regulation of genes encoding proteins involved in carbohydrate biosynthetic process at the translational but not at the transcriptional level, albeit other photosynthesis-related GO terms appeared in the down-regulated DEGs as chlorophyll metabolic process (Supplementary Table S3). Together, these results further indicate that the repression of gene expression correlates poorly at the transcriptional and translational levels, but suggest a rapid growth-to-defense transition based on translational control.

## Identification of genes with uncoupled translation and transcription (GUTT) induced by NB-LRR activation

In a theoretical situation in which there is no translational control, all mRNAs should be represented comparably in both the RNA-seq and the TRAP-seq datasets. We used Cuffdiff to identify genes that were either over- or under-represented in the TRAP-seq dataset compared to the RNA-seq dataset. Analysing the control datasets (TRAP-seq versus total; no DEX), we identified 1169 genes showing uncoupled translation and transcription (GUTT), indicating that they were translated either more or less efficiently than the average mRNA. Interestingly, this number was divided by

approximately one half (541) 2 h after NB-LRR activation (defense), and only 115 transcripts were common to both control and defense GUTT datasets (Fig. 3A, Supplementary Table S4). This indicates a significant translational reprogramming in host cells upon RPM1 activation. The translational efficiency (TE) of each gene can be represented by the Log₂ ratio of the TRAP-seq FPKM value divided by the RNA-seq FPKM value, as calculated by Cuffdiff, with the expected ratio being 1. Graphic representation of the TE values for the 115 GUTT identified in both control and DEX conditions illustrated a positive correlation (r=0.81, P<0.0001) between control TEs and DEX TEs (Fig. 3B, Supplementary Table S5). That is, most genes that had a positive or negative TE in the absence of defense showed similarly positive or negative TE after NB-LRR activation. However, several genes deviated significantly from the 95% confidence interval boundaries, indicating that their translational status changed significantly after DEX induction. For example, these included genes that had a positive TE under normal conditions, but negative TE upon NB-LRR activation, including *CIPK5* (*CBL-Interacting Protein Kinase 5*) (Fig. 4A), *LBD1* (*LOB Domain-containing protein 1*), and *HB11* (*Homolog of Bee2 Interacting with IHB1*) (Fig. 3B), the latter having previously been shown to be involved in the plant growth-to-defense switch (Fan *et al.*, 2014; Malinovsky *et al.*, 2014). Conversely, *LEA* (*Late Embryogenesis Abundant*) and *LCR67* (*Low-molecular-weight Cysteine-Rich 67*), encoding a dehydrin and a defensin, respectively, showed negative TEs under normal conditions and positive TEs upon NB-LRR activation (Fig. 3B). At the same time, 426 transcripts showed a TE not significantly different from 0 (FDR<0.05) (which we consider as a normal TE, representative of the average mRNA) under control conditions, but either a positive or negative TE upon NB-LRR activation and are therefore designated as defense-specific GUTTs (Fig. 3A, Supplementary Table S4). For example, *TOR* (*Target of Rapamycin*) showed a normal TE under control conditions, but a significantly negative TE upon NB-LRR activation (Figs 3C and 4B, Table 1, and Supplementary Table S5). Likewise, *PGN* (*Pentatricopeptide repeat protein for Germination on NaCl*), which is required for defense against the necrotroph *Botrytis cinerea* (Laluk *et al.*, 2011), was strongly down-regulated (Fig. 3C, Table 1, and Supplementary Table S5). Conversely, *CCT2* (*Phosphorylcholine Cytidylyltransferase 2*) showed a normal TE under control conditions, but a positive TE upon NB-LRR activation (Figs 3C and 4C, Table 1, and Supplementary Table S5). To definitively attribute TE shifts between control and defense conditions to translational regulation and not to transcriptional effects, we systematically examined the expression data of DEGs and DTGs and compared these results to corresponding gene TEs in both treatments. This process revealed that for 32% of the 1595 GUTT (509 out of the sum of the three groups of GUTT) showing a TE differential between DEX and control, the differential was attributable to TRAP-seq changes but not RNA-seq changes (Supplementary Table S6). In other words, 32% of TE changes upon NB-LRR activation were attributable only to translational regulation. For example, *HB11* was well
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Identification of translationally controlled genes upon NB-LRR activation. (A) Asymmetric Venn diagram showing the number of genes with uncoupled translation and transcription (GUTT) after DEX treatment (defense) and their overlap with control GUTT (GUTT-common). (B) Scatter-plot of the translational efficiency (TE) [Log₂(FPKM-TRAPseq/FPKM-RNAseq)] in control versus defense conditions for the 115 GUTT-common. The linear regression line (solid black line) is shown together with the 95% confidence interval (dashed black lines). (C) Scatter-plot of the TE in control versus defense conditions for the 426 GUTT-defense. The positions of individual genes of interest are shown as colored dots in (B) and (C).</figcaption></figure>

translated before DEX treatment, with a TE of 0.7 compared to a TE of -3 after DEX treatment. Likewise, the association of *HBII* transcripts with ribosomes was reduced 38-fold upon DEX treatment (TRAP-seq Log₂-fold change = -5.26) while no significant change was seen at the level of total RNA (RNA-seq). Thus, *HBII* appeared to be strongly repressed at the translational level upon DEX treatment (Table 1). In contrast, *TBF1* was induced at the total RNA level and at the translational level (see Supplementary Fig. S3). Table 1 lists a number of additional genes of interest that showed a significant alteration of TE upon DEX treatment. Overall, these results suggest that the translational status of many individual transcripts can be affected either positively or negatively upon activation of ETI responses without necessarily involving transcriptional changes.

## Validation of candidate genes

To validate the involvement of translational control in defense responses, we tested Arabidopsis mutant lines for altered responses to infection by *Pseudomonas syringae* pv. tomato (*Pst*). We selected genes showing TE differentials during a NB-LRR response without significant transcriptional changes (GUTT-Defense or GUTT-Common; Fig. 3B and 3C; Supplementary Table S6). From these, we prioritized those with the highest TE differentials and for which confirmed homozygous knock-out lines were available from TAIR, including: AT5G10930 (*CIPK5*),

AT4G15130 (*CCT2*), AT1G56570 (*PGN*), AT3G02260 (*BIG*), AT1G60000 (unknown RNABP), AT4G21020 (*LEA*), AT3G09070 (*OPS*; *Octopus*), AT3G23900 (unknown RNABP), AT1G07280 (unknown TPR-containing protein), and AT4G34410 (*RRTF1*; *Redox Responsive Transcription Factor 1*). Additionally, we tested the effect of TOR perturbation on plant immunity because TOR is directly involved in translational regulation, and because the translational status of genes involved in carbohydrate anabolism are altered both after NB-LRR activation (Fig. 2F, G, I) and by TOR inhibition (Menand et al., 2002; Ren et al., 2012; Xiong et al., 2013). Null mutants for TOR are not viable, but we obtained a TOR knock-down transgenic line (TOR RNAi) (Menand et al., 2002; Schepetilnikov et al., 2011) as well as a transgenic line that overexpresses TOR (TOR OX) (Deprost et al., 2007).

Mutant Arabidopsis lines were challenged with *Pst* DC3000 (AvrRpm1). Monitoring the number of bacteria at 0 and 3 d post-infiltration (dpi) revealed that the *big* mutant, and to a lesser degree, the *cct2* and *rrtf1* mutant lines allowed more growth of avirulent bacteria, indicating that these genes are required for optimal RPM1-mediated resistance (Fig. 5A). In contrast, in a separate set of experiments, two independent *CIPK5* mutant lines (*cipk5-1* and *cipk5-2*) showed less bacterial growth at 3 dpi (Fig. 5B), suggesting that CIPK5 is a negative regulator of defense. Interestingly, the most closely related homolog of *CIPK5*, *CIPK25*, showed a highly similar TE pattern (5.07 Log₂-fold difference between control and
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Visual representation of RNA-seq and TRAP-seq data for individual genes. Raw-read distributions and numbers of hits from the RNA-seq and TRAP-seq datasets were visualized with the UCSC genome browser for the *CIPK5* (A), *BIG* (B), *CCT2* (C), and *TOR* (D) genes. Each panel is representative of the three biological replicates. The y-axis maximum is set at 2900 (*CIPK5*), 400 (*BIG*), 600 (*CCT2*), and 400 (*TOR*) fragments.</figcaption></figure>

defense TE) (see Supplementary Table S5). However, infec-
tion of a *cipk25* mutant did not result in increased resistance
(Fig. 5B). This may be due to a lack of functional redundancy
or to the fact that *CIPK25* is expressed at much lower levels
compared to *CIPK5* (see Supplementary Table S4). The TOR
RNAi line was also more resistant to *Pst* DC3000 (AvrRpm1)
(Fig. 5C). Likewise, this line was also more resistant to *Pst*
DC3000 (EV) (Fig. 5D). These results suggest that down-reg-
ulation of TOR results in a faster and/or more effective dis-
ease resistance response. Infection of the TOR OX line with
*Pst* DC3000 (EV) or (AvrRpm1), however, did not lead to a
significant difference compared to Col-0 (Fig. 5C, D).

For further validation, we tested candidate genes with the
most pronounced phenotypes in the *Pst* assay (*cct2*, *big*, and
*cipk5*), including an additional point-mutant allele (*doc1-1*).

of *BIG* (Gil *et al.*, 2001), as well as the lines with altered TOR
expression, using the oomycete pathogen, *Hyaloperonospora
arabidopsidis* (*Hpa*) strain Noco2, which is virulent on WT
Col-0. Consistent with the results seen with *Pst*, spore counts
from infected leaves revealed that the TOR RNAi line was less
susceptible to *Hpa* (Fig. 5E). Unlike *Pst*, however, TOR OX
plants were significantly more susceptible to *Hpa* (Fig. 5E).
Likewise, *cct2* and *big* mutants showed a dramatic increase in
susceptibility (Fig. 5F) and *cipk5* mutants showed decreased
susceptibility (Fig. 5G). This was also supported by Trypan
Blue staining, which clearly showed increased hyphal growth
and oospore generation in TOR OX, *cct2*, and *big*, and a
decrease in TOR RNAi and *cipk5* plants (Fig. 5H, I).

Although the direct targets of translational control of
TOR in plants are unknown, we nonetheless compared our
defense transcriptome analysis with transcriptomic data
produced from plants in which TOR was pharmacologic-
ally inhibited (Ren *et al.*, 2012). Interestingly, up-regulated
genes in the transcriptome of TOR-inhibited plants were
significantly enriched for ‘defense response’, ‘innate immune
response’, and other GO terms related to immunity, such as
‘response to oxidative stress’ and ‘secondary metabolites’ (see
Supplementary Fig. S4A). However, the overlap between up-
regulated genes in the transcriptome of TOR-inhibited plants
and our transcriptome (total RNA-seq) accounted for only
80 genes (Fig. S4B). Not surprisingly, these 80 genes mainly
belong to immune-related GO terms (Fig. S4C), suggesting
an overlap between TOR and NB-LRR signaling and par-
tially explaining the resistance phenotypes of plants with
altered TOR expression.

Discussion

TRAP-seq using FLAG-rpL18 has been used to identify
genes expressed in specific cell types (Reynoso et al., 2015)
and related translational profiling methods have been used to
characterize plant responses to abiotic stresses (Juntawong
and Bailey-Serres, 2012; Yángüez et al., 2013; Juntawong
et al., 2014; Vragovic et al., 2015) as well as virus infec-
tion (Moeller et al., 2012). We found that defense-induced
changes in the translational status of a number of individ-
ual transcripts, as defined by ribosome association, was sig-
nificantly different from what would be predicted from their
total abundance. These alterations appeared to be selective
as NB-LRR activation did not trigger a global reduction in
polysomes (Fig. 1B), as is seen during heat stress (Yángüez
et al., 2013), hypoxia (Juntawong et al., 2014), or UV expos-
ure (Meteignier et al., 2016). The reasons for alteration in the
relative association of different transcripts with ribosomes
could be due to various mechanisms and the TEs calculated
for individual mRNAs may not correlate perfectly with trans-
lational activity in all cases. Nevertheless, the translational
status of a large number of transcripts did change during ETI
and we suggest that the TE values obtained are in most cases
indicative of their translational status. Other methods, such
as ribosome footprinting, can provide additional information
on how mRNAs are regulated. However, studies using TRAP
<table><caption>**Table 1.** Genes tested or mentioned in this study and their corresponding Log₂-fold changes</caption><thead><tr><th>TAIR ID</th><th>Gene name</th><th>TE DEX</th><th>TE Control</th><th>TRAP-seq (DTG)</th><th>RNA-seq (DEG)</th></tr></thead><tbody><tr><td>AT2G18300</td><td>HBI1</td><td>-3.05 (0.12)</td><td>0.77 (1.70)</td><td>-5.26 (0.02)</td><td>ns</td></tr><tr><td>AT3G02260</td><td>BIG</td><td>-1.99 (0.25)</td><td>-2.92 (0.13)</td><td>1.07 (2.10)</td><td>ns</td></tr><tr><td>AT4G15130</td><td>CCT2</td><td>3.10 (8.57)</td><td>ns</td><td>3.01 (8.05)</td><td>ns</td></tr><tr><td>AT1G50030</td><td>TOR</td><td>-3.19 (0.11)</td><td>ns</td><td>ns</td><td>ns</td></tr><tr><td>AT5G10930</td><td>CIPK5</td><td>-3.83 (0.07)</td><td>1.23 (2.34)</td><td>-4.38 (0.05)</td><td>ns</td></tr><tr><td>AT3G09070</td><td>OPS</td><td>+inf</td><td>ns</td><td>1.44 (2.71)</td><td>-inf</td></tr><tr><td>AT1G60000</td><td>Unknown</td><td>-4.37 (0.05)</td><td>ns</td><td>-3.6 (0.08)</td><td>ns</td></tr><tr><td>AT3G23900</td><td>Unknown</td><td>-2.84 (0.14)</td><td>ns</td><td>-1.1 (0.46)</td><td>ns</td></tr><tr><td>AT4G21020</td><td>LEA</td><td>+inf</td><td>-inf</td><td>+inf</td><td>-inf</td></tr><tr><td>AT1G56570</td><td>PGN</td><td>-inf</td><td>ns</td><td>-inf</td><td>ns</td></tr><tr><td>AT4G34410</td><td>RRTF1</td><td>1.73 (3.32)</td><td>ns</td><td>7.78 (219.80)</td><td>ns</td></tr><tr><td>AT1G07280</td><td>Unknown</td><td>-4.49 (0.04)</td><td>ns</td><td>-4.45 (0.04)</td><td>ns</td></tr><tr><td>AT1G07900</td><td>LBD1</td><td>-inf</td><td>+inf</td><td>-inf</td><td>+inf</td></tr><tr><td>AT5G14740</td><td>CA2</td><td>-2.35 (0.20)</td><td>-0.94 (0.52)</td><td>-2.19 (0.22)</td><td>ns</td></tr><tr><td>AT3G16640</td><td>TCTP</td><td>ns</td><td>-1.43 (0.37)</td><td>0.84 (1.79)</td><td>ns</td></tr><tr><td>AT4G36990</td><td>TBF1</td><td>ns</td><td>ns</td><td>4.95 (30.91)</td><td>5.79 (55.33)</td></tr><tr><td>AT1G75830</td><td>LCR67</td><td>+inf</td><td>-inf</td><td>+inf</td><td>ns</td></tr></tbody></table>

TE, Log₂(FPKM<sub>TRAP</sub>/FPKM<sub>RNA</sub>); (x), absolute fold-change (FPKM<sub>TRAP</sub>/FPKM<sub>RNA</sub>); ns, not significant; +inf, denominator is not detected; -inf, numerator is not detected.

and ribosome footprinting have shown that the two methods identify similar sets of regulated transcripts during hypoxia in plants (Juntawong et al., 2014), indicating that TRAP-seq is a robust method for identifying translationally regulated transcripts. Some of these changes could be induced by AvrRpm1, which could not be tested as discussed above. However, previous studies suggest that, at early time-points, AvrRpm1 has no effect on the transcriptional response to defense signaling independent of RPM1 (de Torres et al., 2003). Given that the transcriptional response in our experimental system strongly resembles the expected transcriptional response of NB-LRR activation, it is reasonable assume that the vast majority of the translational responses, and the overall pattern of regulation, are attributable to NB-LRR activation. Most importantly, we have validated the involvement of a number of genes identified herein by showing their involvement in plant defenses against pathogens. Indeed, although the number of examples are limited for the time, translational up- and down-regulation correlated with positive and negative contributions to defense, suggesting that these responses are induced by NB-LRR signaling. These genes are involved in both ETI and PTI (Fig. 5) and thus, like transcriptional changes, translationally regulated transcripts may be involved in both types of resistance.

The TOR protein appears to be required for the translation of certain viral RNAs (Schepetilnikov et al., 2011; Ouibrahim et al., 2015). In addition to this, our results suggest an important role (or roles) for TOR in immunity as its TE is reduced approximately 10-fold (Log₂ = -3.1) upon NB-LRR activation (Table 1, Fig. 4D). Previous studies in Arabidopsis have characterized the transcriptome and the metabolome profiles in conditions of decreased TOR activity, either by chemical inhibition or by inhibiting TOR expression using RNAi. These studies showed that TOR

reduction leads to a metabolic switch resulting in reduced anabolic activities, such as photosynthesis, and an increase in catabolism (Ren et al., 2012; Caldana et al., 2013). Plant defense responses result in a similar metabolic reprogramming, presumably because these responses are energetically demanding (Rojas et al., 2014). Our datasets cannot be directly compared to all of these studies due to differences in experimental design, although the transcriptome of TOR-inhibited plants is significantly enriched in immune-related genes (see Supplementary Fig. S4). However, the similarity in metabolic switches suggests that NB-LRR activation induces a reorientation of cellular activities that allows the cell to generate energy and use it for defense-related responses, in part by down-regulating TOR activity. Thus, we suggest that TOR RNAi plants are more resistant to avirulent and virulent *Pst* and *Hpa* (Fig. 5) because lower TOR levels allow the plant cell to more rapidly switch from a growth to defense program. In contrast, TOR OX plants are more susceptible to virulent *Hpa* (Fig. 5D, F) due to a less efficient switch. Although we did not see increased susceptibility to *Pst* in the TOR OX lines, it has been reported that the Cauliflower Mosaic Virus P6 protein, which strongly activates TOR, causes increased susceptibility to *Pst* (Zvereva et al., 2016). Thus, the differences seen between the two pathosystems with respect to the TOR OX line may be due to differences in thresholds. Further work will be required to determine whether TOR regulates immunity by regulating the translation of proteins involved in defense, and/or whether it contributes more broadly by altering the metabolic state of the cell. Alternatively, or in parallel, down-regulation of TOR contributes to defense through its role in suppressing autophagy (Liu and Bassham, 2010), which has been implicated in plant immunity (Hofius et al., 2011; Zvereva et al., 2016).
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Candidate gene validation. (A) Corresponding mutants of selected translationally regulated genes during defense were challenged with *Pseudomonas syringae* DC3000 (AvrRpm1). The graph indicates bacterial counts at 0 and 3 d post-infection (dpi) on a Log₁₀ scale. Mean values (±SEM) were calculated for three independent biological replicates. Statistically significant differences were determined using two-way ANOVA and Holm-Sidak's multiple comparison test with Col-0 as a control: \*P<0.05, \*\*P<0.01, \*\*\*P<0.001, \*\*\*\*P<0.0001). (B) Col-0, *cipk5-1*, *cipk5-2*, and *cipk25* mutant lines were infected and analysed as in (A). (C) Col-0, TOR RNAi, and TOR OX plants were infected and analysed as in (A). (D) Same as (C) but with *Pseudomonas syringae* DC3000 carrying an empty vector (EV) instead of AvrRpm1. (E-G) Spore numbers counted on the indicated plant lines infected with *Hyaloperonospora arabidopsidis* (Hpa) strain Noco2, 12 d after inoculation. Data are the mean (±SEM; n = 3). Significant differences were detected between mutant/transgenic plants and Col-0 using non-parametric Kruskal-Wallis and Dunn's multiple comparison tests: \*P<0.05, \*\*P<0.01. (H, I) Trypan Blue staining of infected leaves, revealing hyphae and oospores from the indicated infected plants 12 d after infection with *Hpa* strain Noco2. Scale bar =250 µm.</figcaption></figure>

Although the signaling pathways regulating TOR activity
are not as well defined in plants as in other systems, it is inter-
esting to note that our study has identified CBL-interacting

protein kinase 5 (CIPK5) as an important regulator of defense.
This protein, also known as SNF1-related protein kinase 3.24
(SnRK3.24), is part of a large family SNF1-related kinases,
including SnRK1, SnRK2, and SnRK3 proteins. SnRK1 proteins are most closely related to the SNF1 and AMPK kinases that negatively regulate TOR activity in yeast and mammals, respectively, while several SnRK2 and SnRK3 proteins have been shown to be important in the responses to different abiotic stresses (Hrabak et al., 2003; Kolukisaoglu et al., 2004; Crozet et al., 2014). Interestingly, CIPK5 transcripts are less abundant after prolonged TOR inhibition in Arabidopsis (Dong et al., 2015). The CIPK5 mRNA showed a decrease in TE upon DEX treatment, and ablation of this gene resulted in a more effective resistance response to both virulent and avirulent pathogens (Fig. 5). Thus, CIPK5 appears to be a negative regulator of defense that is down-regulated upon NB-LRR activation in order to permit a more effective growth-to-defense transition. It is interesting to speculate that CIPK5 might positively regulate TOR and that a lack of this activity in the *cipk5* mutant results in decreased TOR activity, leading to a more efficient growth-to-defense transition, similar to the TOR RNAi line.

The importance of a switch from growth to defense for resistance to pathogens has been inferred from a number of studies (Lozano-Durán and Zipfel, 2015). Notably, the HBI1 protein negatively regulates plant defenses. Knock-down of *HBI1* expression leads to decreased susceptibility to virulent *Pst* and HBI1 overexpression leads to increased susceptibility and increased growth (Fan et al., 2014; Malinovsky et al., 2014). Consistent with these reports, we found that the *HBI1* transcript had a significantly higher than average TE (Log₂ = 0.77) under normal conditions but that its translational status was 9-fold lower than expected (Log₂ = -3.05) upon activation of RPM1 (Fig. 3B). Thus, in addition to transcriptional down-regulation induced by PAMPs (Fan et al., 2014; Malinovsky et al., 2014), it would appear that NB-LRR signaling leads to a strong repression of HBI1 translation.

Our study also identified several genes not previously implicated in ETI or PTI responses, including *BIG* and *CCT2*, whose TEs increase upon NB-LRR activation (Fig. 5A, B). These mutants also showed increased susceptibility to *Hpa* (Fig. 5E, F), indicating that they are required for both NB-LRR and basal immunity. The *BIG* gene encodes a 5078aa protein with multiple functional domains and has been implicated in several hormonal and light responses, possibly due to a role in endocytosis (Kanyuka et al., 2003; Paciorek et al., 2005). This function appears to result in a pronounced auxin-related phenotype (Gil et al., 2001), which, given the antagonistic relationship between auxin and defense (Robert-Seilaniantz et al., 2011), may contribute to the *big* defense phenotype. The CCT2 protein is involved in the production of phosphatidylcholine (Liao et al., 2014) and its role in defense remains to be elucidated.

The mechanisms of translational status regulation by NB-LRR signaling are probably diverse, given the number of transcripts affected and the instances of both positive and negative regulation. Nonetheless, we have shown that some of the most strongly regulated genes are involved in pathogen defense, indicating that this approach is an effective method for identifying new players in plant immunity. Our results suggest that certain proteins (*BIG*, *CCT2*, *RRTF1*) are rapidly up-regulated

at the translational level because they are required for disease resistance. Conversely, other transcripts (*HBII*, *TOR*, *CIPK5*) show decreased TE upon RPM1 activation because their encoded proteins are normally suppressors of defense and/or promoters of growth. Future studies on the involvement of additional candidates, as well as the mechanisms by which they are regulated, will lead to a greater understanding of how plants defend themselves against pathogens and help to clarify the general mechanisms of translational control in plants.

**Supplementary data**

Fig. S1. Translating ribosome affinity purification (TRAP) followed by sequencing (TRAP-seq) quality controls.

Fig. S2. Technical features of RNA-seq and TRAP-seq libraries and protein domain enrichment analysis.

Fig. S3. Read accumulation of the TBF1 transcript as an example of a transcriptionally and translationally induced gene after DEX treatment.

Fig. S4. TOR inhibition leads to increased abundance of defense-related transcripts.

Table S1. List of genes with significantly increased or decreased abundance at the level of total RNA (DEGs) after DEX treatment.

Table S2. List of genes with significantly increased or decreased abundance on ribosomes after DEX treatment (DTGs).

Table S3. GO term enrichment analysis of DEGs and DTGs.

Table S4. List of significant (FDR<0.05) control, DEX, and common GUTTs.

Table S5. List of significant (FDR<0.05) common and defense GUTTs with their associated TE differentials upon DEX treatment.

Table S6. Raw Cuffdiff output of the systematic analysis allowing the identification of translationally regulated genes.

Supplementary Experimental Procedures. Details of the RT-PCR experiment, the sucrose cushion concentration and sucrose gradient fractionation, and immune-blotting and silver staining.

**Acknowledgments**

We are grateful to Christophe Robaglia for sharing the RNAi 35-7 TOR line, to Christian Meyer for the G548 TOR OX line, to Philippe Reymond for the *rrtf1* mutant and to David Mackey for the DEX:AvrRpm1 transgenics. We are particularly grateful to Julia Bailey-Serres for the FLAG-rpL18 transgenic plants and for technical advice and helpful discussions. This work was supported by funding from the National Science and Engineering Council of Canada (NSERC).

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