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Genome-wide analysis uncovers tomato leaf lncRNAs transcriptionally active upon Pseudomonas syringae pv. tomato challenge

Plants rely on (in)direct detection of bacterial pathogens through plasma membrane-localized and intracellular receptor proteins. Surface pattern-recognition receptors (PRRs) participate in the detection of microbe-associated molecular patterns (MAMPs) and are required for the activation of pattern-triggered immunity (PTI). Pathogenic bacteria, such as Pseudomonas syringae pv. tomato (Pst) deploys ~ 30 effector proteins into the plant cell that contribute to pathogenicity. Resistant plants are capable of detecting the presence or activity of effectors and mount another response termed effector-triggered immunity (ETI). In order to investigate the involvement of tomato’s long non-coding RNAs (lncRNAs) in the immune response against Pst, we used RNA-seq data to predict and characterize those that are transcriptionally active in leaves challenged with a large set of treatments. Our prediction strategy was validated by sequence comparison with tomato lncRNAs described in previous works and by an alternative approach (RT-qPCR). Early PTI (30 min), late PTI (6 h) and ETI (6 h) differentially expressed (DE) lncRNAs were identified and used to perform a co-expression analysis including neighboring (± 100 kb) DE protein-coding genes. Some of the described networks could represent key regulatory mechanisms of photosynthesis, PRR abundance at the cell surface and mitigation of oxidative stress, associated to tomato-Pst pathosystem.

Plants are under the attack of different kind of pathogens and this provokes economical losses all around the world 1 . However, to defend themselves they possess a diversified innate immune system that consists in membrane and cytoplasmic receptors that are able to detect different pathogen features 2,3 . Pattern recognition receptors (PRRs) are on the surface of the plant cell and can recognize microbe-associated molecular patterns (MAMPs), activating an immune response named pattern-triggered immunity (PTI) 4 . This response includes production of reactive oxygen species (ROS), callose deposition into the apoplast, activation of MAP kinase cascades, increase of intracellular calcium concentration and transcriptional reprograming [5][6][7][8] .
Pathogenic bacteria such as Pseudomonas syringae pv. tomato (Pst) can introduce inside the plant cell cytoplasm effector proteins that are able to undermine PTI and also interfere with cellular processes for the promotion of their own growth, multiplication in the apoplast and virulence 9,10 . However, some plants have acquired resistance proteins (R proteins), most of them nucleotide-binding leucine-rich repeat proteins (NLRs) that can directly or indirectly detect some of these effectors. After this detection, they activate another immune response called effector-triggered immunity (ETI) or more recently named NLR-triggered immunity (NTI) 11,12 . ETI activation also includes ROS production, MAPK signaling, electrolyte leakage into the apoplast and transcriptional reprograming 13,14 , but it is characterized for the development of a hypersensitive response (HR) that culminates in programmed cell death (PCD) 15 . In addition, some effectors are involved in the suppression of this immune response 16 .
Tomato (Solanum lycopersicum) is an economically important crop that is produced all around the world. The interaction between tomato and Pseudomonas syringae pv. tomato (Pst), the causal agent of tomato speck disease,

Results
Tomato lncRNA identification. For our analysis we used a set of previously generated RNAseq data that includes 11 different treatments/controls (flg22, flgII-28 or different Pseudomonas spp. strains), each with three biological replicates (Table S1). The selection of these conditions was motivated by the fact that through certain comparisons we could capture lncRNA transcriptomic changes associated to early and late PTI (30 min and 6 h), ETI and the effect of two Pst DC3000 effectors (AvrPto and AvrPtoB) in suppressing PTI. We identified 22,595 novel tomato transcripts which were used as input for a pipeline (see "Materials and methods") that allowed the prediction of 2609 putative lncRNAs transcriptionally active in tomato leaves under these conditions (Table S2). We investigated the degree of overlap between our predicted lncRNAs for the tomato-Pst pathosystem and those available from previous works using tomato under different conditions. Already identified tomato leaf lncRNAs included those detected upon challenge with Tomato yellow curl leaf virus (TYCLV) 45,46,54 or Phytophthora infestans 48,55 , those available for tomato in CANTATA database 56 and four predicted tomato TRANS-ACTING SIRNA3 (TAS3) transcripts 57 . Performing a local blastn 58 using our predicted lncRNAs as query and those derived from previous works as database, we found that 1247 (47.7%) of query sequences had at least one hit, with an overall average identity of 97.6% (Table S3). The remaining transcripts without match to those previously identified could account for lncRNAs that are transcriptionally active upon elicitation of tomato immune response by Pseudomonas spp. or MAMPs challenges and time-points used in this work (30 min and 6 h). It is worth mentioning that we cannot assume these "novel" lncRNAs we identified are specific of the bacterial pathosystem under study. We continued our analysis with all the lncRNAs predicted in this work, regardless of their being previously identified.
Analysis of the number of isoforms, indicated that the large majority of lncRNA genes (2141; 82%) encoded for a single isoform (Fig. S1). We defined a lncRNA as "expressed" if it had ≥ 3 FPKM (fragments per kilobase per million mapped reads) in at least one of the 11 conditions analyzed. From the 2609 lncRNAs we predicted with our pipeline, only these "expressed" lncRNAs (2048 ; Table S4) were considered from this point on, unless stated otherwise. www.nature.com/scientificreports/ Protein-coding transcripts' distribution shifted towards larger sizes with a median value that almost doubled the observed for lncRNAs (Fig. 1C).
To further characterize the identified tomato lncRNAs we studied the number of exons for transcripts in each category (Fig. 1D). For categories i, u and x most transcripts contained a single exon while for those in categories j and o, 2-exon transcripts were most abundant. Overall, 1-exon transcripts accounted for 1174 lncRNAs (57% of all expressed lncRNAs).
The distribution of lncRNAs in tomato chromosomes did not differ from the one observed for protein-coding genes (Fig. S2), ranging from 7 to 12% for chromosomes 1-12. We then analyzed the position within each chromosome where protein-coding (Fig. 2, lane B) and lncRNAs (Fig. 2, lane C) reside. Both types of transcripts are encoded mainly at the beginning and ending of each chromosome. These findings indicate there are no obvious lncRNA-specific hot spots in the genome and that these are encoded in the same regions as protein-coding genes.
LncRNAs' expression changes associated to tomato immunity. Setting a cut-off of q-value < 0.05 and |log2 fold-change| ≥ 1, we established differentially expressed lncRNAs (DELs) for the comparisons of interest (Table S4 and Fig. 3). Early flg22-associated PTI induction lead to a small set of DELs (26 up-and 8 down-regulated). Contrastingly, for flgII-28 challenge at 6 h time-point the number of DELs was clearly larger, suggesting a stronger immune response at the transcriptional level (118 up-and 82 down-regulated). Leaf infiltration with the strong PTI inducer, Pseudomonas fluorescens 55, lead to a number of DELs that were fewer than those identified for flgII-28. This finding is in agreement with the data that derives from the same treatments, but for protein-coding transcripts differentially expressed genes (DEGs) 7 . We compared DC3000 vs DC3000 ΔavrPtoΔavrPtoB in RG-prf3 susceptible plants. With this comparison, which accounts for the effect of AvrPto and AvrPtoB effectors at the transcriptional level, we observed that up-regulation of lncRNAs prevailed over down-regulation. This same trend had been previously found for protein-coding genes 7 .
A group of transcripts of particular interest are those induced by flgII-28 treatment and suppressed by AvrPto and/or AvrPtoB effectors (DC3000 < DC3000 ΔavrPtoΔavrPtoB in RG-prf3 susceptible plants). This group of transcripts were previously termed FIRE (flagellin-induced, repressed by effectors) which allowed the identification of a tomato wall associated kinase, SlWak1, that participates in the immunity against Pst 7,60 . We were able to identify a set of 20 FIRE lncRNAs (Table S4 and Fig. 3) which accounts for ~ 57% of those suppressed by AvrPto and/or AvrPtoB in RG-prf3. In contrast, in the case of protein-coding genes, this percentage is considerably higher (~ 91%) 7 .
AvrPto-and/or AvrPtoB-induced ETI (DC3000 ΔfliCΔavrPtoΔavrPtoB vs. DC3000 ΔfliC in PtoR background) lead to the highest numbers of DELs of all treatments analyzed, both up-and down-regulated, while flagellin-associated PTI was associated to a milder transcriptional response (Table S4 and Fig. 3). A similar behavior was observed for protein-coding genes under the same challenges 52 .
In order to globally analyze lncRNA transcriptional changes, we performed separate clustering analysis data deriving from RG-prf3 and RG-PtoR tomato lines' challenges. Treatments in RG-prf3 plants formed three clear www.nature.com/scientificreports/ clusters that can be categorized as 30 min time-points, PTI-inducing treatments and mock treatments; these last two, at 6 hai ( Fig. 4A). It is worth mentioning that grouping along with mock treatments, was DC3000 challenge in RG-prf3 plants, which can be associated to the effect of AvrPto and/or AvrPtoB in suppressing PTI response at the transcriptional level of protein coding genes 7 . Distinct transcript clusters included: 1, PTI induced at 6 hai (including some FIRE lncRNAs); 2, early flg22 induced; 3, PTI suppressed at 6 hai ( Fig. 4A). Within cluster 1, some transcripts following FIRE transcriptional changes can be visualized. In the case of treatments in RG-PtoR background clustering (PtoR_DC3000ΔΔΔ grouping with PtoR_DC3000ΔΔ) was in agreement with having found a larger number of DELs for the comparison PtoR_DC3000ΔΔΔ vs PtoR_DC3000Δ, than in PtoR_DC3000ΔΔΔ vs PtoR_DC3000ΔΔ (Figs. 3 and 4B). Transcript clustering allowed the identification of groups of lncRNAs associated to strong PTI/ETI suppression/induction (Fig. 4B).

Evaluation of lncRNAs' expression by RT-qPCR.
For this purpose we selected several lncRNAs: two induced by P. fluorescens 55 treatment (PTI, prf3_mock_bacteria_6h vs prf3_Pf_6h), two induced by ETI (DC3000 ΔfliCΔavrPtoΔavrPtoB vs. DC3000 ΔfliC in PtoR background) and one induced by both immune responses. We challenged an independent set of RG-PtoR plants to induce PTI (mock vs P. fluorescens 55) and ETI (DC3000 ΔavrPtoΔavrPtoB vs DC3000) and sampled at the same time-point as the one used for the RNAseq experiment (6 hai). For all selected lncRNAs we were able to detect their corresponding transcripts and to confirm their transcriptional changes upon PTI and/or ETI activation (Fig. 5).

Gene ontology (GO) term analysis and co-expression networks.
Transcriptional co-regulation of lncRNAs and neighboring protein-coding genes could help identify networks that are modulated by lncR-NAs. Such regulation represents one of lncRNAs' mechanisms to control gene expression (cis-action) 31 . In our case we were interested in identifying lncRNAs that modulate key protein-coding genes involved in tomato defense response against Pst. For up-regulated DELs identified in the comparisons mock_flgII-28_6h vs flgII-28_6h (PTI-flgII-28) and DC3000 ΔfliCΔavrPtoΔavrPtoB vs. DC3000 ΔfliC in PtoR background (ETI-AvrPto/ AvrPtoB), we identified those neighboring (within a 100 Kb genome region 48,61-63 ) protein-coding genes whose transcriptional behavior was the same (up-regulated, positive co-regulation) or opposite (down-regulated, negative co-regulation) for these same comparisons, using |log2 fold-change| ≥ 1 and q-value < 0.05 as cut-offs. This set of genes was termed neighboring protein-coding co-regulated genes (NCG). Though gene ontology (GO) term analysis of PTI-flgII-28 induced DELs' NCG with positive co-regulation (121 coding genes) resulted in no enrichment, "kinase activity" was one terms with the lowest p-value and was assigned to 10 NCGs (Table S5). Negatively co-regulated NCGs (82 total coding genes) were enriched in the term "photosynthesis" (Table S5), www.nature.com/scientificreports/ indicating that the corresponding DELs could be controlling transcript abundance of these NCGs and consequently leading to a suppression of the photosynthesis-related genes during PTI activation. The analysis of the 221 NCGs with positive co-regulation with ETI up-regulated DELs, revealed that "transcription factor activity, sequence-specific DNA binding" was one of the terms with lowest p-value (Table S6). Again for negatively co-regulated NCGs we found an enrichment of "photosynthesis" term. Indicating that lncRNAs may participate in the negative modulation of photosynthesis-related genes during both PTI and ETI induction.
To further understand the relationships between lncRNAs and their co-regulated coding genes we performed a network analysis including PTI-flgII-28-and ETI-AvrPto/AvrPtoB-induced NCGs. Complete networks can be found in Figs. S3-S6, while selected ones are shown in Fig. 6. We could identify networks that are exclusive Pst DC3000 mutants' nomenclature is described in Fig. 3 legend.  www.nature.com/scientificreports/ of PTI-flgII-28 (Fig. 6A) or ETI-AvrPto/AvrPtoB (Fig. 6E), but also common ones (Fig. 6B-D). MSTRG.4157.1, a category x and PTI-flgII-28-induced lncRNA, was member of one of the largest networks found. In this case NCGs included transcripts up-and down-regulated by PTI-flgII-28 activation. Four up-regulated NCGs encode for cystein-rich receptor-like kinases and one for a chaperone, while down-regulated NCGs included transcripts coding for cell wall degrading enzymes, a photosystem II subunit and a sulfite transporter (Fig. 6A). The network of MSTG.18961.1 (Fig. 6B), an intronic lncRNA of Solyc10g081980.2, contained Solyc10g081850.2 whose closest protein in Arabidopsis thaliana is AT3G11820 (Penetration 1, PEN1) that has been recently proposed to have role in the accumulation of the receptor FLS2 at the plasma membrane 64 . Two Arabidopsis non-race specific disease resistance gene 1/Harpin-induced gene 1 (NDR1/HIN1)-like transcripts also belong to this network. Particularly interesting is Solyc10g081980.1 whose closest protein in A. thaliana is AT5G06320 (NDR1/HIN1-like 3, NHL3), a membrane-localized protein that when overexpressed leads to plants with increased resistance to pathogenic Pst DC3000 65 .
Three PTI-flgII-28-and ETI-AvrPto/AvrPtoB-suppressed NCGs coding for chlorophyll a/b-binding proteins (CBP) form a network with MSTRG.5380.1, a DEL induced by these same immune responses (Fig. 6C). In this network we also identified a transcript encoding for a purine permease. Another lncRNA, but only induced by www.nature.com/scientificreports/ ETI-AvrPto/AvrPtoB (Fig. 6E), shares a network with a transcript coding for CRR21 (Chlororespiratory reduction 21) which plays a role in chloroplast RNA editing of a subunit of the NAD(P)H complex, which is key for its function 66 . In addition, a transcript coding for PSAN (Photosystem I reaction center subunit PSI-N) was found to be part of this network. These lncRNAs could negatively impact the abundance of transcripts coding for key photosynthesis-related proteins. Six transcripts coding for glutathione S-transferases (GST) that are induced by PTI-flgII-28-and/or ETI-AvrPto/AvrPtoB, belong to the network of MSTRG.16534.1, an intergenic lncRNA that may modulate these coding genes' transcript abundance (Fig. 6D). GSTs have been shown to participate in plant immunity against different types of pathogens 67 .

Discussion
Taking advantage of a large set of RNA-seq data we were able to identify lncRNAs transcriptionally active in tomato leaves challenged with MAMPs and bacterial strains. Input RNA-seq data for prediction of novel transcripts represented 25× coverage of tomato genome and a stringent pipeline was used for lncRNA identification. The reliability of our approach was confirmed by comparing our set of lncRNAs with others described for tomato in previous publications and by analyzing gene expression by another methodology (RT-qPCR) the predicted transcriptional changes associated to different bacterial challenges.
Differential gene expression analysis of lncRNAs revealed that challenges with fewest and largest number of up-/down-regulated transcripts were mostly in agreement with what was observed for protein-coding genes 7,52 . This analysis allowed the identification of FIRE lncRNAs, which represent a promising set of candidates for studying their involvement in tomato immunity against Pst.
Effectors AvrPto and AvrPtoB have the capacity to suppress early recognition stages of PTI 68 . In agreement with this most of the genes suppressed by these effectors (DC3000 < DC3000 ΔavrPtoΔavrPtoB in RG-prf3 plants) should also be induced by PTI (mock < flgII-28). That is the case for protein-coding genes, with a 91% of genes suppressed by AvrPto/AvrPtoB that are also induced by flgII-28 7 . This percentage is clearly lower (57%) for the case of lncRNAs analyzed in this work using the same exact challenges. This means that there is a larger proportion of lncRNAs that are suppressed by these effectors that are not modulated by PTI activation. This may be evidence of virulence exerted by AvrPto and AvrPtoB through manipulation of key lncRNAs' abundance, independently of their effect on PTI suppression.
To identify putative lncRNAs that may participate in plant immunity activation by modulating transcript abundance of neighboring protein-coding genes 30 , we performed a network analysis that included PTI-flgII-28and ETI-AvrPto/AvrPtoB-induced NCGs. This analysis revealed several interesting groups of transcripts whose abundance could be modulated by lncRNAs. Nuclear encoded chloroplast-targeted genes (NECGs) have been shown to be down-regulated upon activation of PTI, ETI or challenge with a pathogenic bacterial strain 7,69,70 , though a reduction in photosynthetic activity is only observed in the last two situations 71 . We found at least one network with photosynthesis-related coding genes suppressed by ETI and not by PTI that could contribute to the differences observed in the status of chloroplast physiology between these immune responses.
Control of the abundance of membrane-localized of FLS2 receptor is key for modulating the perception of flg22. Several components of this control system have been identified [72][73][74] , including degradation of FLS2 through selective autophagy, mediated by ATG8 and orosomucoid proteins 75 . Recently, subunits EXO70B1/2 of exocyst complex have been shown to modulate trafficking of FLS2 to the plasma membrane and PEN1 may independently participate in this process 64 . We identified a PTI-and ETI-induced lncRNA whose NCGs included PEN1. It is possible that this lncRNA modulates the abundance of PEN1 transcript and consequently affects the levels of FLS2 at the plasma membrane. This up-regulation of a typically PTI-associated gene upon PTI and ETI induction is consistent with fairly recent findings that indicate there is a crosstalk between these two responses 76,77 .
Members of the GST protein family have been found to be transcriptionally induced upon PTI and ETI activation and contribute to mitigating oxidative stress 67 . We identified a set of 6 GSTs encoded in chromosome 9 of tomato, induced by PTI and/or ETI, that could potentially be transcriptionally regulated by MSTRG.16534.1. Tomato glutarredoxin SlGRX, which also contributes to preventing oxidative damage and promote resistance to Phytophthora infestans, can be induced by the neighboring lncRNA16397 48 . Further exploration of MSTRG.16534.1 network may shed light on a similar lncRNA-based control of oxidative damage.
To our knowledge our work represents the first report on tomato lncRNAs' participation against a bacterial pathogen, such as Pst. We believe the generated information will contribute to finding key regulatory modules controlling important processes during plant-pathogen interactions.

Methods
Tomato leaf transcript prediction and quantification. Raw RNA-seq reads from 33 samples of tomato leaves challenged with flg22, flgII-28 or different Pseudomonas spp. strains detailed in Table S1 were retrieved from Sequence Read Archive (SRA; https:// www. ncbi. nlm. nih. gov/ sra) available at NCBI. The complete set used accounted for 475 M reads (~ 21 Gb; 25×, 828 Mb genome). Reads were aligned to tomato rRNA sequences retrieved from SILVA database 78 using Bowtie 79 (v1.2.2) with the option-v 3 to allow a thorough removal of rRNA contamination. Clean reads were mapped to the tomato genome (assembly 3.00) 80 with Hisat2 program 81 (v2.1.0). Transcript assemblies and quantification were performed using Stringtie 81 (v1.3.3). Each alignment file was used to generate individual transcript assemblies, with the default setting of minimal transcript length of 200 bp, that were then merged into a single assembly by setting the option -merge. This merged assembly was used to estimate transcript abundance for each sample. Cuffcompare 82  LncRNA identification and global characterization. Novel transcripts falling in the categories mentioned above were used as input for getorf stand-alone tool from EMBOSS (v6.6.0.0) which allowed the identification of 4,397 that had no open reading frame larger than 300 nt. From these, 2,677 had no homology to any peptide present in Pfam database (v31.0) 84 using blastx (Expect-value > 1e − 3). We employed CPC2 tool 85 to identify 2,668 transcripts with low coding potential. Finally to further remove transcripts that would not qualify as lncRNAs, we used batch sequence search tool from Rfam database 86 to filter other types of genomic and plastidial RNAs. After this stringent pipeline we kept 2,609 transcripts as putative tomato lncRNAs. We used local blastn (-evalue 1e − 10 -soft_masking 'false' -num_alignments 1) to compare our predicted lncRNA with those available from previous works in tomato: leaves challenged with Tomato yellow curl leaf virus (TYCLV) 45,46,54 or Phytophthora infestans 48,55 ; fruit pericarp tissue, roots infected with Meloidogyne incognita and leaves inoculated with Potato spindle tuber viroid from CANTATA database 56 ; and four predicted tomato TRANS-ACTING SIRNA3 (TAS3) transcripts 57 .
To generate a graphical representation of the genomic distribution of protein-coding and lncRNAs we used software package Circos 87 (v0.69-8).
Neighboring co-regulated genes' identification, network generation and GO term analysis. Considering lncRNAs may modulate the expression of genes within a 100 kb up/down-stream region we identified their corresponding neighboring coding genes region 48,[61][62][63] . Then for each DEL found in the comparisons mock_flgII-28_6h vs flgII-28_6h (PTI-flgII-28) and DC3000 ΔfliCΔavrPtoΔavrPtoB vs DC3000 ΔfliC at 6 h in PtoR plants (ETI-AvrPto/AvrPtoB) we identified those neighboring protein-coding co-regulated genes (NCG), defined as having the same or opposite trend (|log2 fold-change| ≥ 1, q-value < 0.05). This information was used to generate NCG networks with Cytoscape program 88 (v3.8.2). We subjected the lists of NCGs identified for PTI-flgII-28 and ETI-AvrPto/AvrPtoB comparisons, to a GO term analysis using AgriGO v2.0 89 with default settings, ITAG3.2 as background and Plant GO Slim as gene ontology type. We analyzed separately those NCGs positively and negatively co-regulated in each comparison.
Clustering analysis. Given that the RNA-seq data used in this work derives from two separate experiments using different Rio Grande tomato backgrounds (RG-PtoR and RG-prf3, see Table S1), we performed two independent clustering analyses for each of them with Heatmapper online tool 90 , using average linkage (clustering method) and Spearman rank correlation (distance measurement method). Input data in both cases were FPKM values of those expressed lncRNA (≥ 3 FPKM in at least one condition) with at least one q-value < 0.05 in any of the comparisons of interest.
Bacterial challenge and RT-qPCR assay. Four-week old Rio Grande PtoR (RG-PtoR) tomato plants, kindly provided by Prof. Gregory B. Martin, were syringe infiltrated on leaflets of their third true leaves, with mock solution (10 mM MgCl 2 ) or the following suspensions: 10 8 colony forming units (cfu)/mL of Pseudomonas fluorescens 55 (Pf 55), 5 × 10 6 cfu/mL Pst DC3000 (DC3000) and 5 × 10 6 cfu/mL Pst DC3000 ΔavrPtoΔavrPtoB (DC3000 ΔΔ). Mock vs Pf 55 accounts for PTI induction, while DC3000 ΔΔ vs DC3000 for AvrPto/AvrPtoB ETI induction. Six hours post infiltration (hpi) leaf tissue was frozen with N 2 (l) and stored at -80 °C until use. Total RNA isolation and cDNA synthesis were performed as described previously 91  . Two reference genes (ARD2 and VIN3) were used for normalization 53 . A list of primers used in this work can be found in Table S7. This study complied with local and national regulations.