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Supplemental Information

TomatoNet: An genome-wide co-functional network for unveiling complex traits of

tomato, a model crop for fleshy fruits

Hyojin Kim, Bong Suk Kim, Jung Eun Shim, Sohyun Hwang, Sunmo Yang, Eiru Kim, Anjali S. Iyer-Pascuzzi, and Insuk Lee

Contents

Supplemental Methods Supplemental Results Supplemental References Supplemental Figure 1 Supplemental Figure 2 Supplemental Figure 3 Supplemental Figure 4 Supplemental Table 1 Supplemental Table 2 Supplemental Table 3 Supplemental Table 4 Supplemental Table 5 Supplemental Table 6 Supplemental Table 7 Supplemental Table 8 Supplemental Table 9

1

Supplemental Methods

Reference genes and gold standard co-functional gene pairs for training a network of

tomato genes

Construction of TomatoNet is summarized in Supplemental Figure 1 and Supplemental

Table 1. We used 34,727 protein coding genes (http://solgenomics.net/) by The Tomato

Genome Consortium for the network construction. A set of gold-standard co-functional gene

pairs for training a tomato gene network was generated by pairing genes that share annotation

by either KEGG (Kanehisa et al., 2014) or MAPMAN (Thimm et al., 2004) pathway

databases. Pairs of genes that are annotated by either pathway database but no overlapped

annotation then would be gold-standard negative gene pairs. The resultant sets of gold

standard positive and negative pairs between tomato genes contain 190,125 and 24,299,376

pairs, respectively, for 6,999 genes (20.15 % of the coding genes).

Benchmarking and integrating gene networks

To integrate networks derived from heterogeneous data types that differ in data-intrinsic

score schemes, we re-evaluated the individual networks using a unified scoring scheme, log

likelihood scores (LLS) (Lee et al., 2004) of network links, based on a Bayesian statistics

framework. LLS was calculated as follows:

LLS=ln( P ( L|E )/ P(⌐ L∨E)P( L)/ P(⌐ L) )

,where P(L|E) and P(⌐L|E) represent the frequencies of positive (L) and negative (⌐L) gold

standard links observed in the given evidences (E), while P(L) and P(⌐L) represent the prior

expectations (i.e., the total frequencies of all positive and negative gold standard gene pairs,

respectively). We used ‘0.632 bootstrapping’ for all LLS calculations to avoid over-training

phenomenon.

Networks derived from different data types were integrated by naïve Bayes with weighted

sum (WS) scheme (Lee et al., 2004), which enables handling correlations among the networks

by optimizing two free parameters: T for the LLS threshold of network links to be integrated,

and D for the average dependence among networks:

2

http://solgenomics.net/

WS=Lo+∑n=0

∞ LiD ∙i

, for all L ≥T

where, L represents a LLS, L0 represents the maximum score among multiple LLSs for a

unique gene pair, and i is the rank order index among secondary scores.

Inferring co-functional networks by co-expression analysis of tomato genes

Genes that operate in the same cellular processes tend to be co-expressed across various

experimental conditions. We inferred co-functional gene pairs in tomato from diverse sets of

expression samples available from a public depository of microarray and RNA-seq data, the

Gene Expression Omnibus (GEO) (Barrett et al., 2013) database. We tested a total of 1,473

expression samples for 64 GEO data series that contain at least 12 samples, and found that 12

series comprising 276 samples show co-expression that correlates with the probability of gold

standard functional gene pairs (Supplemental Table 2). Microarray data based on

Affymetrix gene chip were normalized by MAS5, which was known to be more optimal for

co-expression analysis (Frost et al., 2012). Microarray data based on other platforms were

obtained as GEO ‘soft format’ data and analyzed. For RNA-seq data, short read data were

processed into reads per kilo-base per million (RPKM) values using edgeR software (Lim et

al., 2007). All the preprocessed expression data were analyzed for co-expression by Pearson’s

correlation coefficient (PCC). Twelve co-expression networks inferred from the 12 GSEs

were benchmarked by log likelihood score, and then integrated into a single co-expression

network for tomato (SL-CX of Supplemental Table 1) using the weighted sum method

described above.

Inferring co-functional networks by phylogenetic profiling method

Genes belong to the same pathways tend to be inherited together during speciation.

Therefore, we may infer co-functional gene pairs based on similarity between profiles of

presence or absence of orthologs of the target protein across species, which is represented as

the phylogenetic profile. We previously found that network inference using the phylogenetic

profiling method is most effective with profiles within the domains of life (Shin and Lee,

2015). We thus constructed phylogenetic profiles of 34,727 coding genes of tomato for three

domains of life, Archaea, Bacteria, and Eukaryota, based on –log(E-value) of blastp hit 3

scores. We then calculated mutual information between gene pairs with each of three profile

sets and found that a profile set based on 122 Archaea and one based on 1,626 Bacteria show

a correlation between mutual information score and co-functional likelihood score. These two

networks were integrated into a single network based on phylogenetic profiling method in

tomato (SL-PG of Supplemental Table 1).

Inferring co-functional networks by genomic neighborhood method

There are two approaches of gene neighborhood identification, distance-based gene

neighborhood (DGN) and probability-based gene neighborhood (PGN), and their inferred

network links are complementary (Shin et al., 2014). Therefore, we inferred functional

associations using each of the methods and integrated them into a single tomato network

based on gene neighborhood (SL-GN of Supplemental Table 1). The principle behind these

approaches is that two tomato genes of which bacterial orthologs are close to each other in

bacterial genomes have a higher inclination for being functionally associated. Gene

neighborhood analysis for tomato was conducted for 122 Archaeal bacteria and 1,626

Eubacteria genomes. The detailed methods of both gene neighborhood methods were

described in our earlier work (Shin et al., 2014).

Inferring co-functional networks from associalogs of other species

If orthologs of two tomato genes are functionally associated in other species, they are likely

to be co-functional in tomato as well. This co-functional link transferred from other species

by orthology-relationship is called associalog (Robinson et al., 2010). Due to the pervasive

paralogs in the tomato genome, we identified orthologs of tomato in other species based on

bidirectional best hit (BBH). Based on the orthology-based transfer of functional networks,

we could infer a total of 15 networks of tomato genes from seven different species networks

including not only plants but also animals: AraNet v2 (Lee et al., 2015b), YeastNet v3 (Kim

et al., 2014), HumanNet (Lee et al., 2011), WormNet v3 (Cho et al., 2014), RiceNet v2 (Lee

et al., 2015a), FlyNet (Shin et al., 2015), and an unpublished gene network for zebrafish

(Supplemental Table 1).

4

Assessment of networks and network-based predictions

Tomato genes were ranked by sum of log likelihood scores of their links to known member

genes for each UniProtGOBP term. To reduce high variation of assessment scores by

pathways with only a few member genes, we excluded GOBP pathways with less than five

member genes. The exclusion of the large pathway terms is helpful to avoid functional bias

toward dominantly annotated pathways (Lee et al., 2007). Thus, we also excluded GOBP

pathways with more than 300 member genes. Therefore, we used only 813 GOBP pathways

that contain between five and 300 member genes. The functional links of the validation data

set were generated by pairing tomato genes that share annotations of 813 UniProtGOBP

terms. The final validation set contained 635,894 function links, of which only 4.4% (27,901

links) overlap with the training data, indicating independence of the validation data from the

training data. Then the retrieval rate of true positives and false positives were calculated to

measure precision for the given genome coverage. We also generated receiver operating

characteristic (ROC) curve, which was then summarized as area under the ROC curve (AUC)

to measure prediction capacity of TomatoNet for each GOBP term. If the member genes for a

GOBP pathway are well connected in the network, they will give a network edge score to

each other and rank them high, resulting in a high AUC. As a null model, we performed the

ROC analysis with randomized GOBP pathways in which original member genes were

replaced with random tomato genes.

Network algorithms to prioritize genes for complex traits of tomato

The major goal of TomatoNet was to provide network-based prediction tools for crop

scientists to prioritize genes for economic traits. We hence implemented two network-based

gene prioritization methods in a web-based software (www.inetbio.org/tomatonet). The first

gene prioritization option, pathway-centric prediction (see Supplemental Figure 1, STEP 4-

1), is based on the principle of guilt-by-association. The prediction system takes user-input

genes for a known trait, which serve as guide genes. If the given guide genes are well

connected by TomatoNet and elucidate a pathway (i.e., a connected network) for the trait,

other genes that connect to the guide genes are likely to be new members of the pathway and

involved in the trait. To prioritize candidate genes for the trait, we ranked other genes based

on the strength of network connection to the guide genes. TomatoNet takes into account edge

5

http://www.inetbio.org/tomatonet

weights of all connections to the guide genes as a sum of log likelihood scores. If a group of

genes for the trait of interest are connected as a pathway, pathway-centric prediction would

be an effective method of network-based gene prioritization.

However, for some traits, few guide genes are known, and pathway-centric prediction would

be less effective. Therefore, we implemented an alternative network-based prediction option,

context-centric prediction (see Supplemental Figure 2, STEP 4-2), which is based on gene

expression data. Transcriptome profiles for the trait-relevant context provides several

hundred genes that respond to the relevant trait. For example, we may observe differential

expression of many tomato genes that regulate biotic stress resistance upon pathogen

inoculation. We hypothesize that a gene that is connected to many of the differential

expressed genes (DEGs) in a trait-relevant context is likely associated with the trait. If a gene

that is connected to many of the DEGs upon pathogen inoculation, this might suggest that the

gene is likely involved in defense responses to the pathogen. For the analysis, we defined

subnetworks for each gene of TomatoNet, in which a gene of interest becomes a hub of all

connected neighbor genes. The association of each hub gene with the given trait was

measured by significance of overlap between neighbor genes of the hub and DEGs for the

trait-relevant context, based on Fisher’s exact test. We demonstrated the effectiveness of

these two network-based prediction methods in predicting genes related to fruit development

and resistance to a bacterial pathogen.

Network analysis of a fruit development and ripening pathway by TomatoNet

The genes associated with fruit development and ripening were compiled from the FR

database (Yue et al., 2015) that contains fruit development and ripening genes for various

plant and crop species, collected by manual curation of the literature. We obtained a total of

211 tomato FR genes, of which 96 genes are particularly related to the ripening process

(Supplemental Table 3). Significance of the connected network of 131 FR genes by

TomatoNet was measured by Z-test based on the number of within-group edges among FR

genes compared to the distribution of the number of within-group edges for 1000 sets of

randomly sampled 131 tomato genes.

Analysis of differential expressed genes for fruit development and ripening

6

FR-related DEGs were compiled from the tomato gene expression atlas using RNA-seq data

for different stages during fruit development: flower bud, flower, 1 cm fruit, 2 cm fruit, 3 cm

fruit, mature green fruit, breaker, 10 days after breaker stages (Tomato Genome, 2012). Short

read data were aligned by Rsubread package (Lee et al., 2008) and then converted into read

count data by featureCount (Liao et al., 2014) software. The probability of differential

expression was calculated by DESeq2 (Love et al., 2014) software using read counts between

two pairs of conditions: (i) gene expression at 10 days after breaker stage vs. those at flower

stage to find DEGs related to the entire process of fruit development and ripening; (ii) gene

expression at 10 days after breaker stage vs. those at mature green fruit to find DEGs

particularly related to the ripening process. For context-centric prediction, only the top 100

up-regulated DEGs from (i) and (ii) settings were used to predict fruit development-ripening

genes or fruit ripening genes, respectively.

TomatoNet prediction of candidate genes for tomato defense responses to R.

solanacearum

Of the 156 differentially expressed probesets that were identified in a microarray analysis of

tomato shoots after infection with R. solanacearum (Ishihara et al., 2012), 128 had Sol gene

IDs on the Sol Genomics Network (solgenomics.net). 119 of these 128 Sol gene IDs were

unique. These 119 genes were divided into 109 genes that were upregulated after infection

with R. solanacearum, and 10 that were downregulated (Ishihara et al., 2012) (Supplemental

Table 7). Each list of 109 and 10 genes was input into the pathway-centric algorithm of

TomatoNet. Eleven genes, none of which were in the list of input genes, were selected from

the top 100 predicted from pathway-centric analysis for experimental validation through

qRT-PCR (Supplemental Table 8).

Plant growth and R. solanacearum infection

Seeds of HA7996 (H7996) and WVa700 (WVa) were planted in soil of 36 cell square pots of

insert (Metromix, WA) and grown in 30 °C, 16 h light and 8 h dark periods of 60 %

humidity. Tomato plants at the three-leaf stage were root inoculated with Ralstonia

solanacearum, K60 strain (1.0 x 108 cfu/ml). The shoots of ten plants were harvested at 48

hours post inoculation. K60 was grown on Casamino Peptide Agar (CPG) plates for two days

7

at 28 °C and bacterial colonies were scraped from plates and resuspended in sterilized ddH2O.

Bacterial concentration was determined by O.D. 600 = 0.1 as 108 CFU/ml.

RNA isolation and cleanup.

Total RNA from leaves or roots were isolated with Trizol according to the manufacturer’s

protocol (Invitrogen, CA). Approximately 100 mg of plant tissue was used to isolate RNA. 1

ml of Trizol was added to the ground tissue, the slurry was vortexed for 15 sec, and then

incubated at room temperature for 5 min. Chloroform (200 µl) was added to the mixture and

the sample was mixed for 15 sec, and incubated for 5 min at room temperature. The sample

was then centrifuged at 14,000 x g for 15 min at 4°C. The aqueous phase was recovered and

500 µl of isopropanol was added, and the samples were mixed gently by hand. After 10 min

incubation at room temperature, the sample was centrifuged at 14,000 x g for 10 min at 4° C.

The supernatant was removed and the pellet containing RNA was washed by 5 min spin at

10,000 rpm at 4°C in 1ml of ice-cold 70% ethanol. The pellet was dried at room temperature,

resuspended in 30 µl of DEPC-treated water. The isolated total RNA was further DNase

treated and cleaned with the column to remove residual genomic DNA using Norgen RNA

isolation kit according to manufacturer’s procedures. Total RNAs were eluted with 30 µl of

RNase-free water and were stored at -80° C until use.

Generation of cDNA.

cDNA was synthesized from the purified total RNA with the first cDNA kit (NEB, MA)

according to the manufacture’s protocol. Briefly, 1.0µg of total RNA was added with 1 µl of

oligo d(T) (200 pg/µl) and ddH2O to a final volume of 8 µl. The total RNA was incubated at

70° C for 5 min and the tubes were placed on ice for 5 min. 10 µl of reaction buffer and 2 µl

of M-MuLV reverse transcriptase (NEB, MA) was added to the tubes to a final volume of

20µl. For the negative control, the reaction above was prepared without the addition of the

reverse transcriptase. The samples were incubated for 60 min at 42°C, and 5 min at 85°C.

Real-time PCR.

8

Quantitative RT-PCR was performed by adding 1µl of cDNA, 5 µl of AB master mix (AB

biosystem, CA), forward and reverse primers at 10µM (0.5 µl each), and the volume was

adjusted to 10µl with DNase Free water. Two negative control reactions were prepared, one

without the addition of primers and another without template. Primers were used to this study

are listed in Supplemental Table 9. GAPDH primers were used as an internal control. The

Roche Light Cycler was used (Roche, CA) and the following protocol was used for

amplification: 50°C for 2 min and 95°C for 2 min followed by 40 cycles of 95°C for 15 sec

and 60°C for 1 min. PCR efficiency of the primers ranged from 95 % to 105 %. All

experiments were performed in triplicate and bar graphs were generated using the delta Ct

method and expressed as a fold change relative to the internal control and the mock-

inoculated control plant.

9

Supplemental Results

Literature survey of the eleven novel candidate genes for immune response against a

bacterial pathogen Ralstonia solanacearum

Two genes, an E3 ubiquitin ligase (E3UL) and a receptor like kinase (RLK), were

significantly upregulated more than 2-fold in the resistant variety, H7996. Seven of the genes

were repressed more than 2-fold in the susceptible line West Virginia 700 (WVa) or the

resistant line HA. These included Mitogen Activated Protein Kinase 3 (MAPK3), Ethylene

Response Factor (ERF), Phytophthora-Inhibited Protease 1 (PIP1), TolB, Temperature

Induced Lipocalin (TIL), and Vacuolar Sorting Receptor 6/7 (VSR6/7) in WVa, and a kinase

in HA (Supplemental Figure 4). Of these, MAPK3 was of particular interest because it also

showed a slight increase in expression in the resistant variety and is known to have a

significant role in plant immunity in Arabidopsis (Pitzschke et al., 2009; Tena et al., 2011)

(Meng and Zhang, 2013). Indeed, consistent with the prediction from TomatoNet, Virus

Induced Gene Silencing (VIGS) of MAPK3 in a resistant tomato line led to an increase in

bacterial colonization in stems (Chen et al., 2009). Two additional genes that were strongly

repressed in WVa also have known roles in plant defense pathways. These include ERF and

PIP1. Ethylene promotes defense responses to biotrophic pathogens and is known to function

in defense pathways to R. solanacearum (Chen et al., 2009; Ishihara et al., 2012). PIP1

encodes a Pathogenesis-Related (PR) protein that accumulates to high levels after infection

with bacterial, oomycete, and fungal pathogens (Shabab et al., 2008; Tian et al., 2007; Zhao

et al., 2003). The strong down-regulation of PIP expression in WVa suggests strong

suppression of defense responses in the susceptible line, consistent with its phenotype. The

other two genes examined, PM23 and DH, were also repressed in both WVa and H7996, and

have no known role in defense or disease pathways. Together, these data show that

TomatoNet was able to predict genes with known roles in resistance or disease pathways, as

well as predict novel candidates for these pathways.

10

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Supplemental Figure 3. Overview of TomatoNet construction and network-based

prediction methods. Individual gene networks were inferred from different data types and

network inference algorithms (STEP 1). The inferred networks were evaluated using

reference co-functional links derived from pathway annotations (STEP 2), and then integrated

into a single network (STEP 3), which turns out to be more accurate and comprehensive. The

integrated tomato gene network is used to predict new genes for the trait of interest with two

alternative network algorithms (STEP 4): pathway-centric prediction and context-centric

prediction. TomatoNet web server can run both network-based predictions with different

types of user-input genes.

14

Supplemental Figure 2. Benchmarking of component functional networks derived from

18 distinct data types and the integrated network, TomatoNet. The x-axis of the graph

indicates the coverage of the ~35000 tomato coding genes by networks and the y-axis is log

likelihood score (LLS) of functional coupling. Bootstrapping sampling was used to avoid

overtraining for each network. The integrated tomato network provides 721,626 links

mapping approximately 65% to 34,727 tomato-related genes. Overall, the integrated data

outperforms all individual component networks. TomatoNet w/o SL-CX: TomatoNet with

excluding functional links derived from SL-CX.

15

Supplemental Figure 3. TomatoNet is highly predictive for pathways in tomato. (A)

Assessment of TomatoNet based on precision-recall analysis, in which we measured

precision by percentage of tomato gene pairs that share UniProtGOBP annotations (y-axis)

for given coverage of the genome by the network (x-axis). A random model was generated by

pairing randomly selected two genes out of all tomato genes. TomatoNet shows substantially

higher precision for the entire range of the genome coverage. For example, TomatoNet links

show over 50% precision for gene pairs that share UniProtGOBP annotations for the network

covering ~20% of coding genome, while randomized model shows ~5% precision for the

same genome coverage. (B) Since the network connects genes that belong to the same GOBP

pathways, we may be able to predict tomato genes for each GOBP pathway by guilt-by-

association (GBA). To assess the GBA-based predictions for GOBP pathways by TomatoNet,

we employed receiver operating characteristic (ROC) analysis, which can be summarized as

area under the ROC curve (AUC) scores for each GOBP pathway. We found that the median

AUC of GOBP pathway predictions by TomatoNet is approximately 0.7; 425 of 813 (~52%)

tested GOBP pathways were predicted with an AUC of 0.7 or higher. This demonstrated that

TomatoNet is highly predictive for the majority of the pathways in tomato. We considered

only 813 GOBP terms with no less than five member genes. Random analysis was done by

using 813 gene sets of the same size of random tomato genes. As expected, the majority of

the AUCs by random samples show ~0.5.

16

Supplemental Figure 4: qRT-PCR analysis of the 11 genes predicted by TomatoNet to

have roles in tomato immune responses to R. solanacearum. A) H7996. Of the 11 genes,

two were upregulated in the resistant cultivar H7996 more than 2-fold at 48 hpi, while eight

of the other nine were significantly repressed 48 hpi, B) WVa. Nine of the 11 genes were

significantly repressed in WVa 48 hpi. Stars indicate significance at p < 0.05 for a given gene

compared to its mock-inoculated plant.

Supplemental Table 1. TomatoNet and component networks inferred from 18 distinct data types17

Network Description Genes Links

TomatoNet Integrated network 22,549 721,626Component networks

SL-CX By co-expression of tomato genes 17,167 170,530

SL-GN By gene neighborhood of two bacterial orthologs of tomato genes in prokaryotic genomes 3,209 36,109

SL-PG By phylogenetic profile similarity across species 3,980 121,494

AT-CC By co-citation of (A. thaliana) orthologs in Pubmed articles 3,598 19,727

AT-CX By co-expression of arabidopsis orthologs 10,237 132,506

AT-HT By high-throughput arabidopsis orthologous 1,903 2,650

AT-LC By literature curated arabidopsis orthologous 1,294 1,615

CE-CX By co-expression of worm (C. elegans) orthologs 2,097 14,665

DM-CX By co-expression of fly (D. melanogaster) orthologs 2,205 26,097

DR-CX By co-expression of zebrafish (D. rerio) orthologs 1,842 24,100

HS-CX By co-expression of human orthologs 1,317 10,842

HS-HT By high-throughput human orthologous PPIs 1,578 5,624

HS-LC By literature curated human orthologous PPIs 2,768 15,776

SC-CX By co-expression of yeast (S. cerevisiae) orthologs 2,113 48,149

SC-GT By genetic interactions of yeast orthologs 1,514 24,574

SC-HT By high-throughput yeast orthologous PPIs 2,044 36,576

SC-LC By literature curated yeast orthologous PPIs 1,952 14,316

OS-CX By co-expression of rice (O. sativa) orthologs 8,300 77,496

18

Supplemental Table 2. Gene expression data used for inferring co-expression networks.

GSE ID Data type GSE Title # Samples

GSE19326 microarray Expression data fromvarious tomato plant tissues 67

GSE22304 microarray Expression data in response to abiotic stresses in tomato at flowering stage 24

GSE22803 microarray

Expression data from rootsand first two leaves of tomato seedlings growing on regular MS medium or MS medium supplemented with multi-wall carbon nanotubes(0, 50, 100, 200 ug/ml) or activated carbon (50 mg/ml)

20

GSE23562 RNA-seqSmall RNA profiling Tomato,wild tomato relative and seriesof introgression lines

14

GSE28564 microarrayGlobal gene expression analysis of normal and ripening inhibitor (rin) mutant tomatoes during ripening

12

GSE30270 microarrayTranscriptomic and metabolic responses of mycorrhizal roots to nitrogen patches under field conditions

30

GSE35020 microarray Microarray analysis of gene expression under different light conditions 20

GSE39894 microarray

Comparative transcriptomeanalysis of responses to water deficit in Solanum lycopersicumand S. pimpinellifolium roots

24

GSE41135 microarray

Expression analysis oftomato plants TYLCV resistant, susceptible and resistant line silenced in the hexose transporter (LeHT1) gene before and 7 days after inoculation of tomato with Tomato yellow leaf curl virus (TYLCV).

16

GSE43492 microarray The effects of a novel agrochemical in alleviating salinity stress 12

GSE45774 RNA-seq Transcriptome Sequencing in wild and domesticated tomato species 19

GSE49289 RNA-seqTranscriptome profiling of tomato fruits of a FRUITFULL-suppressed line and rin mutant by next generation RNA sequencing

18

GSE: Gene expression sample series of GEO database

19

Supplemental Table 3. Fruit development and fruit ripening (grey for ripening) genes in tomato by the FR database

No. FDR gene list Symbol Description by FR Database 1.0

1 Solyc00g136560.2 During fruit development and ripening

2 Solyc01g006560.2 During fruit ripening

3 Solyc01g008710.2 MAN4 Fruit ripening

4 Solyc01g009170.2 Fruit ripening

5 Solyc01g010970.2 In early stage of fruit

6 Solyc01g056340.2 Det1 During fruit ripening

7 Solyc01g057270.2 During tomato fruit development and ripening


9 Solyc01g065980.2 ERF6 During fruit ripening

10 Solyc01g067710.2 nhx3 During fruit development

11 Solyc01g067890.2 dxs1 During fruit ripening


13 Solyc01g080010.2 Xegip Fruit development

14 Solyc01g081610.2 Post-ripening

15 Solyc01g091160.2 ARG1 At the mature green stage

16 Solyc01g091170.2 ARG2 At the mature green stage

17 Solyc01g095080.2 Tomato ripening stages

18 Solyc01g096750.1 In tomato developmental processes

19 Solyc01g096810.2 EIL3 Fruit ripening

20 Solyc01g097290.2 Early fruit

21 Solyc01g097340.2 During fruit development


23 Solyc01g098190.2 nhx4 During fruit development

24 Solyc01g099190.2 LOXB Mature fruit

25 Solyc01g099630.2 XTH1 During fruit growth and ripening

26 Solyc01g104950.2 XYL2 Fruit development and ripening





31 Solyc02g021650.2 DDB1 Mature green

32 Solyc02g037530.2 Fruit initiation

33 Solyc02g065750.1 Fruit development

34 Solyc02g065770.2 Fruit development and ripening

35 Solyc02g067180.2 Tomato fruit ripening

36 Solyc02g070580.1 SD1 During fruit maturation

20


38 Solyc02g077560.2 ARF3 During fruit development

39 Solyc02g079220.2 ht1 During tomato fruit development



42 Solyc02g080800.2 DHS Fruit softening and development

43 Solyc02g081170.2 ChrC Red-ripe

44 Solyc02g083950.2 wus Early developmental stages

45 Solyc02g084720.2 TBG6 Fruit development




49 Solyc02g089160.2 Flower and fruit production

50 Solyc02g089200.2 Fruit and floral development



53 Solyc02g091970.2 Through fruit development

54 Solyc02g092980.2 cycD3 During fruit development


56 Solyc03g006860.2 Frk1 Early in fruit development


58 Solyc03g007960.2 CrtR-b2 During fruit ripening


60 Solyc03g026270.1 Early developmental stages

61 Solyc03g026280.2 CBF1 Mature green

62 Solyc03g031860.2 Psy1 Fruit ripening


64 Solyc03g082420.2 HSP21 Fruit ripening

65 Solyc03g083910.2 Plant and fruit development


67 Solyc03g093610.1 ERF1 During fruit ripening and softening


69 Solyc03g114340.2 DXR Fruit ripening

70 Solyc03g114840.2 In fruit maturation

71 Solyc03g118290.2 ARF2 During fruit development

72 Solyc03g120500.2 IAA6 During fruit initiation and development

73 Solyc03g123630.2 PMEU1 During fruit ripening


75 Solyc04g009440.2 Salt stress

21

76 Solyc04g009800.2 Tomato ripening

77 Solyc04g009900.2 From 8 to 20 days post anthesis



80 Solyc04g056270.2 ER66 During fruit development and ripening

81 Solyc04g056280.2 CDKC In developing tomato fruit

82 Solyc04g056600.2 NHX2 During fruit development

83 Solyc04g071880.2 During fruit early development




87 Solyc04g078840.2 During different developmental stages

88 Solyc04g078900.2 CYP707A1 Fruit set

89 Solyc04g081000.2 During ovary growth


91 Solyc04g082840.2 cdkB2 In developing tomato fruit

92 Solyc05g005710.2 spdsyn During fruit ripening

93 Solyc05g007190.2 SUT2 Fruit and seed development

94 Solyc05g008060.2 Fruit set

95 Solyc05g012020.2 In fruit maturation and during fruit ripening

96 Solyc05g014280.2 vis1 Fruit ripening

97 Solyc05g015750.2 LeMADS5 In fruit maturation


99 Solyc05g053550.2 CHS2 Fruit development

100 Solyc05g054410.2 TBP1 Seed and fruit development

101 Solyc06g005150.2 APX2 Flower and fruit development

102 Solyc06g008820.2 NHX1 During fruit development

103 Solyc06g036260.2 Pigmentation of flowers and fruits

104 Solyc06g049050.2 EXP2 Early tomato fruit growth

105 Solyc06g051400.2 During fruit maturation

106 Solyc06g051800.2 EXP1 Fruit ripening

107 Solyc06g053620.2 PPCK2 From 8 to 20 days post anthesis

108 Solyc06g053710.2 ETR4 During fruit development



111 Solyc06g065630.2 FZY During fruit ripening

112 Solyc06g066440.2 Hxk2 Fruit development

113 Solyc06g068090.2 PLDa1 Flower development and fruit development and ripening

114 Solyc06g068860.2 Post-ripening

22


116 Solyc06g073190.2 FRK2 Early tomato seed development


118 Solyc06g073720.1 EIL1 During ripening

119 Solyc06g073730.1 EIL4 Fruit ripening

120 Solyc06g074730.2 In tomato developmental processes

121 Solyc06g076520.1 Post-harvest fruit


123 Solyc07g006900.1 At 4 days after anthesis

124 Solyc07g007600.2 During fruit early development


126 Solyc07g042260.2 Fruit set and development

127 Solyc07g043310.2 GABA-TP1 During fruit development and ripening

128 Solyc07g049530.2 ACO1 Post-harvest fruit

129 Solyc07g049690.2 HPL During fruit ripening

130 Solyc07g055290.2 Rab11a During the development and ripening

131 Solyc07g055920.2 TAGL1 During fruit development and ripening

132 Solyc07g056570.1 Fruit set

133 Solyc07g056580.2 ETR2 During fruit ripening

134 Solyc07g064170.2 PME1.9 Post harvest

135 Solyc07g064180.2 PME2.1 Post harvest



138 Solyc08g060810.2 EBF2 During fruit ripening


140 Solyc08g065790.2 vpe Fruit development

141 Solyc08g066330.1 During early tomato fruit development




145 Solyc08g080640.1 NP24 Ripening of tomato fruit

146 Solyc08g081190.2 Post-anthesis development and during ripening

147 Solyc08g081530.2 MDHAR During fruit development and ripening

148 Solyc08g081540.2 ACS1A During fruit ripening

149 Solyc08g082250.2 Cel8 Fruit ripening


151 Solyc09g010080.2 lin5 During fruit development and ripening

152 Solyc09g010090.2 LIN7 During fruit ripening

153 Solyc09g010210.2 Cel2 Fruit abscission

23


155 Solyc09g061280.2 krp2 Tomato fruit developing

156 Solyc09g065640.2 EOL1 Full ripe stage of fruit and fruit ripening

157 Solyc09g074520.2 Anthesis stages

158 Solyc09g074830.2 wee1 Early fruit development

159 Solyc09g075440.2 NR During fruit development

160 Solyc09g075820.2 During tomato fruit development

161 Solyc09g082690.2 Tomato fruit ripening

162 Solyc09g082990.2 In red fruits


164 Solyc09g090100.2 Flowering and fruit development

165 Solyc09g091510.2 CHS1 Fruit development


167 Solyc09g091780.2 krp1 Tomato fruit developing


169 Solyc10g007600.2 Expressed at 15 days after flowering


171 Solyc10g047030.1 XYL1 Fruit development and ripening

172 Solyc10g052470.1 Very early stages of tomato fruit development

173 Solyc10g054590.1 Fruit and seed development

174 Solyc10g074720.1 cdkB1 In developing tomato fruit

175 Solyc10g078550.1 gdh1 During fruit ripening

176 Solyc10g079240.1 10, 20, 33 DPA

177 Solyc10g080210.1 Tomato fruit ripening process of softening

178 Solyc10g081120.1 ARF1 Fruit development and ripening

179 Solyc10g081470.1 The onset of fruit ripening

180 Solyc10g081650.1 CRTISO Fruit maturation


182 Solyc10g085150.1 CPT4 During fruit development and ripening


184 Solyc10g086260.1 ANT1 During fruit development and ripening

185 Solyc11g010570.1 Flower abscission

186 Solyc11g011260.1 During fruit-set and development

187 Solyc11g017010.1 SUT1 Fruit development


189 Solyc11g069190.1 ARF4 During fruit development and ripening




24

193 Solyc11g072630.1 MAPK4 Green mature and post-harvest

194 Solyc12g005950.1 COP1 Fruit development and ripening

195 Solyc12g006380.1 ODD Expressed at 15 days after flowering



198 Solyc12g008840.1 Fruit ripening and softening

199 Solyc12g009300.1 Fruit set and development

200 Solyc12g009560.1 EBF1 During fruit ripening

201 Solyc12g009570.1 cipk During fruit development


203 Solyc12g015860.1 FPS1 During early fruit development




207 Solyc12g056600.2 Breaker stage


209 Solyc12g095860.1 During the early development of tomato fruit



25

Supplemental Table 4. Five validated fruit development and ripening genes among top 100 context-centric predictions using 100 up-regulated genes of ‘10 days after breaker stage’ compared to ‘flower stage’

Rank Gene ID Symbol TF DEG

PMID Validation method

Description

8 Solyc03g082420.2 HSP21 15879560 Overexpression [FR Database 1.0 base] It plays a role in plant development under normal growth conditions, in addition to its protective effect under stress conditions. Also, it affects fruit color changes.[Arabidopsis ortholog GOBP] protein folding, response to heat, response to high light intensity, heat acclimation, response to hydrogen peroxide

53 Solyc05g014280.2 vis1 DEG

12586896 Northern; Southern

[FR Database 1.0 base] It plays a role in pectin depolymerization to contribute to juice viscosity.

54 Solyc07g055920.2 TAGL1 TF 20335407; 21203447; 19880793; 19891701

RNAi [FR Database 1.0 base] It regulates tomato flower and fruit development and ripening.[AgriGO] regulation of transcription, DNA-dependent

76 Solyc01g065980.2 ERF6 TF 22111515 Southern [FR Database 1.0 base] It affects on trans-lycopene and beta-carotene accumulation.[Uniprot2GOBP] regulation of transcription, DNA-templated,heat acclimation,positive regulation of transcription, DNA-templated,ethylene-activated signaling pathway,cell death,transcription, DNA-templated[AgriGO] regulation of transcription, DNA-dependent[Arabidopsis ortholog GOBP] cell death, response to ethylene, response to cytokinin, response to jasmonic acid, ethylene-activated signaling pathway, heat acclimation, positive regulation of transcription & DNA-templated, response to other organism

91 Solyc05g015750.2 LeMADS5 TF 20946942 Northern; Southern

[FR Database 1.0 base] It plays crucial roles in organ and cell differentiation.[AgriGO] regulation of transcription, DNA-dependent[Arabidopsis ortholog GOBP] cell fate specification, regulation of transcription & DNA-templated, flower development, specification of floral organ identity, carpel development, ovule development, meristem development, specification of floral organ number

26

Supplemental Table 5. Five validated fruit ripening genes among top 100 context-centric predictions using 100 up-regulated genes of ‘10 days after breaker stage’ compared to ‘mature green stage’

Rank Gene ID Symbol TF DEG PMID Validation method

Description

17 Solyc05g014280.2 vis1 DEG 12586896

Northern; Southern

[FR Database 1.0 base] It plays a role in pectin depolymerization to contribute to juice viscosity.

27 Solyc03g082420.2 HSP21 15879560

Overexpression [FR Database 1.0 base] It plays a role in plant development under normal growth conditions,

in addition to its protective effect under stress conditions. Also, it affects fruit color changes.[Arabidopsis ortholog GOBP] protein folding, response to heat, response to high light intensity, heat acclimation, response to hydrogen peroxide

72 Solyc01g067890.2 dxs1 20591838

Western [FR Database 1.0 base] It is involved in the MEP pathway to increase monoterpene β-phellandrene and decrease two sesquiterpenes in trichomes.[AgriGO] metabolic process[Arabidopsis ortholog GOBP] sulfur amino acid metabolic process, glycine catabolic process, unsaturated fatty acid biosynthetic process, phosphatidylglycerol biosynthetic process, oxidoreduction coenzyme metabolic process, vitamin metabolic process, cellular amino acid biosynthetic process, aromatic amino acid family metabolic process, aromatic amino acid family biosynthetic process, lipoate metabolic process, coenzyme biosynthetic process, nucleotide metabolic process, response to light stimulus, jasmonic acid biosynthetic process, leaf morphogenesis, regulation of proton transport, chlorophyll metabolic process, chlorophyll biosynthetic process, carotenoid biosynthetic process, regulation of lipid metabolic process, isopentenyl diphosphate biosynthetic process & methylerythritol 4-phosphate pathway, cysteine biosynthetic process, secondary metabolic process, cell differentiation, oxylipin biosynthetic process, regulation of protein localization, sulfur compound biosynthetic process, positive regulation of transcription & DNA-templated, protein autophosphorylation

89 Solyc08g060810.2 EBF2 19903730

RNAi [FR Database 1.0 base] It negatively regulates the ethylene signalling pathway via mediating the degradation of EIN3/EIL proteins to trigger fruit ripening.[Arabidopsis ortholog GOBP] ubiquitin-dependent protein catabolic process, response to ethylene, negative regulation of ethylene-activated signaling pathway, regulation of circadian rhythm

97 Solyc10g085150.1 CPT4 23134568

Clone [FR Database 1.0 base] It is involved in the synthesis of long-chain polyisoprenoids.[Uniprot2GOBP] metabolic process[Arabidopsis ortholog GOBP] ubiquinone biosynthetic process, dolichol biosynthetic process

27

Supplemental Table 6. 12 implicated genes for fruit development and ripening among top 100 candidates by context-centric prediction using 100 up-regulated genes in ‘10 days after breaker stage’ compared to ‘mature green stage’. Annotations related to fruit development and ripening are highlighted.

Rank Gene ID Symbol

TF Description

15 Solyc02g092860.2 Uniprot2GOBP oxidation-reduction processAgriGO oxidation reductionArabidopsis ortholog GOBP response to ethylene, heat acclimation, response to karrikin

27 Solyc04g082960.1 Arabidopsis ortholog GOBP respiratory burst involved in defense response, ethylene-activated signaling pathway, response to chitin, intracellular signal transduction

41 Solyc01g104050.2 Uniprot2GOBP phosphorylation, ethylene biosynthetic process, response to ethylene, intracellular signal transduction, protein phosphorylation, abscisic acid-activated signaling pathwayAgriGO protein amino acid phosphorylationArabidopsis ortholog GOBP ethylene biosynthetic process, response to ethylene, abscisic acid-activated signaling pathway, intracellular signal transduction

45 Solyc02g087210.2 Uniprot2GOBP signal transduction, response to cyclopentenone, response to ethylene, toxin catabolic process, protein targeting to membrane, response to wounding, salicylic acid mediated signaling pathway, hyperosmotic salinity response, response to jasmonic acid, jasmonic acid mediated signaling pathway, response to auxin, abscisic acid-activated signaling pathway, response to water deprivation, heat acclimation, regulation of plant-type hypersensitive responseArabidopsis ortholog GOBP protein targeting to membrane, signal transduction, toxin catabolic process, response to water deprivation, response to wounding, response to ethylene, response to auxin, response to abscisic acid, abscisic acid-activated signaling pathway, response to jasmonic acid, salicylic acid mediated signaling pathway, jasmonic acid mediated signaling pathway, response to chitin, heat acclimation, regulation of plant-type hypersensitive response, response to cyclopentenone, hyperosmotic salinity response

48 Solyc06g071920.2 Uniprot2GOBP oxidation-reduction process, glucose metabolic processAgriGO oxidation reductionArabidopsis ortholog GOBP glucose catabolic process, gluconeogenesis, glycolytic process, pentose-phosphate shunt, water transport, hyperosmotic response, response to oxidative stress, cytoskeleton organization, Golgi organization, aerobic respiration, response to temperature stimulus, response to heat, response to salt stress, response to sucrose, fruit development, proteasomal protein catabolic process, response to endoplasmic reticulum stress, response to hydrogen peroxide, response to cadmium ion, seed development, response to redox state

69 Solyc06g073830.1 Arabidopsis ortholog GOBP response to wounding, response to fungus, jasmonic acid biosynthetic process, response to ethylene, abscisic acid-activated signaling pathway, response to jasmonic acid, ethylene-activated signaling pathway, response to chitin, intracellular signal transduction

75 Solyc11g066440.1 AgriGO oxidation reductionArabidopsis ortholog GOBP cell death, embryo development ending in seed dormancy, defense response to bacterium & incompatible interaction, flower development, leaf morphogenesis, thylakoid membrane organization, fruit development, vegetative to reproductive phase transition of meristem, chlorophyll catabolic process, iron-sulfur cluster assembly, cell differentiation, positive regulation of transcription & DNA-templated, ovule development

76 Solyc01g065980.2 ERF6 TF Uniprot2GOBP regulation of transcription, DNA-templated, heat acclimation, positive regulation of transcription, DNA-templated, ethylene-activated signaling pathway, cell death,transcription, DNA-templatedAgriGO regulation of transcription, DNA-dependentArabidopsis ortholog GOBP cell death, response to ethylene, response to cytokinin, response to jasmonic acid, ethylene-activated signaling pathway, heat acclimation, positive regulation of transcription & DNA-templated, response to other organism

80 Solyc04g077980.1 TF Arabidopsis ortholog GOBP respiratory burst involved in defense response, response to oxidative stress, signal transduction, response to cold, response to water deprivation, response to wounding, response to mechanical stimulus, response to fungus, response to high light intensity, response to salt stress, jasmonic acid biosynthetic process, response to ethylene, response to auxin, response to abscisic acid, abscisic acid-activated signaling pathway, response to jasmonic acid, ethylene-activated signaling pathway, photoprotection, response to chitin, photosynthesis, multicellular organism growth, intracellular signal transduction, hyperosmotic salinity response, negative regulation of transcription & DNA-templated

87 Solyc06g068460.2 TF AgriGO regulation of transcription, DNA-dependentArabidopsis ortholog GOBP MAPK cascade, response to molecule of bacterial origin, respiratory burst involved in defense response, regulation of transcription & DNA-templated, protein targeting to membrane, detection of biotic stimulus, response to wounding, response to mechanical stimulus, response to fungus, response to absence of

28

http://solcyc.solgenomics.net/LYCO/NEW-IMAGE?type=GENE-IN-CHROM-BROWSER&object=Solyc06g068460.2










light, jasmonic acid biosynthetic process, salicylic acid biosynthetic process, response to ethylene, abscisic acid-activated signaling pathway, response to salicylic acid, response to jasmonic acid, systemic acquired resistance & salicylic acid mediated signaling pathway, salicylic acid mediated signaling pathway, jasmonic acid mediated signaling pathway, ethylene-activated signaling pathway, response to chitin, regulation of hydrogen peroxide metabolic process, regulation of plant-type hypersensitive response, detection of bacterium, endoplasmic reticulum unfolded protein response, regulation of defense response, negative regulation of defense response, intracellular signal transduction, defense response to bacterium, negative regulation of programmed cell death, regulation of multi-organism process, regulation of defense response to virus by host, regulation of immune response, defense response to fungus

89 Solyc01g104740.2 Arabidopsis ortholog GOBP transcription & DNA-templated, protein folding, response to heat, response to water deprivation, response to high light intensity, response to ethylene, response to abscisic acid, ethylene-activated signaling pathway, response to endoplasmic reticulum stress, response to hydrogen peroxide, positive regulation of transcription & DNA-templated

100 Solyc07g026650.2 aco5 AgriGO oxidation reductionArabidopsis ortholog GOBP ethylene biosynthetic process, xylem development, cell wall macromolecule metabolic process, cellular response to potassium ion starvation, cellular response to iron ion, cellular response to fatty acid, cellular response to nitric oxide

29



Supplemental Table 7A. List of 109 up-regulated genes in Ishihara et al. (2012)

Solyc01g005470.2 Solyc03g095770.2 Solyc06g050930.2 Solyc09g082270.2Solyc01g008620.2 Solyc03g098730.1 Solyc06g071280.2 Solyc09g089910.1Solyc01g081310.2 Solyc03g113220.2 Solyc06g071810.1 Solyc09g090070.1Solyc01g086680.2 Solyc03g114600.2 Solyc06g075690.2 Solyc09g090470.2Solyc01g095080.2 Solyc03g114890.2 Solyc06g076300.2 Solyc09g090730.1Solyc01g095170.2 Solyc03g115920.2 Solyc07g040710.2 Solyc09g090980.2Solyc01g097240.2 Solyc03g115930.1 Solyc07g045030.2 Solyc09g090990.2Solyc01g098590.2 Solyc03g117860.2 Solyc07g049530.2 Solyc09g097960.2Solyc01g100010.2 Solyc04g005050.1 Solyc07g049660.2 Solyc10g012370.2Solyc01g107390.2 Solyc04g009440.2 Solyc07g055710.2 Solyc10g050880.1Solyc01g107780.2 Solyc04g009860.2 Solyc07g056200.2 Solyc10g052880.1Solyc01g107820.2 Solyc04g016470.2 Solyc08g006470.2 Solyc10g055200.1Solyc01g109140.2 Solyc04g040180.2 Solyc08g007790.2 Solyc10g055760.1Solyc01g112220.2 Solyc04g048900.2 Solyc08g008280.2 Solyc10g079860.1Solyc02g061770.2 Solyc04g064870.2 Solyc08g028780.1 Solyc10g083290.1Solyc02g064830.2 Solyc04g071890.2 Solyc08g068700.1 Solyc10g083690.2Solyc02g069800.1 Solyc04g074950.2 Solyc08g075540.2 Solyc10g085010.1Solyc02g080070.2 Solyc04g078290.2 Solyc08g075550.2 Solyc11g011330.1Solyc02g082920.2 Solyc04g078660.1 Solyc08g080130.2 Solyc11g011340.1Solyc02g082930.2 Solyc05g005460.2 Solyc09g011590.2 Solyc12g006380.1Solyc02g086270.2 Solyc05g041910.2 Solyc09g011630.2 Solyc12g007030.1Solyc02g090490.2 Solyc05g050130.2 Solyc09g011870.1 Solyc12g008960.1Solyc02g092860.2 Solyc05g050800.2 Solyc09g014990.2 Solyc12g014010.1Solyc02g093050.2 Solyc05g056400.2 Solyc09g057960.1 Solyc12g042480.1Solyc03g025670.2 Solyc06g009040.2 Solyc09g061840.2 Solyc12g045030.1Solyc03g025720.2 Solyc06g009110.2 Solyc09g075020.2Solyc03g033840.2 Solyc06g034370.1 Solyc09g075820.2Solyc03g080190.2 Solyc06g036310.2 Solyc09g082240.2

Supplemental Table 7B. List of 10 down-regulated genes in Ishihara et al. (2012)

Solyc11g072310.1 Solyc11g045100.1 Solyc02g084950.2 Solyc03g111130.1

Solyc05g007210.2 Solyc06g075660.2 Solyc08g005680.2 Solyc03g111140.2

Solyc04g080540.2 Solyc01g096940.2 　　

30

Supplemental Table 8. TomatoNet predicted genes used for qRT-PCR validation

Ranka ID Gene and abbreviation Inputb

1 Solyc02g077040.2 Phytophthora-inhibited protease (PIP1), a cysteine protease UP

6 Solyc01g057080.1 Ethylene Response Factor (ERF), a transcription factor UP

7 Solyc09g008010.2 Probable receptor-like kinase (Kinase) UP

13 Solyc03g112340.1 E3 Ubiquitin protein ligase (E3UL) UP

14 Solyc06g005170.2 Mitogen Activated Protein Kinase 3 (MAPK3) UP

16 Solyc02g081970.2 Vacuolar Sorting Receptor (VSR6/7) UP

17 Solyc08g066210.2 Receptor like kinase (RLK) UP

47 Solyc06g008620.1 TolB-protein related (TolB) UP

3 Solyc12g010320.1 Temperature induced lipocalin (TIL) DN

35 Solyc01g006510.2 L-idonate 5-dehydrogenase (DH) DN

62 Solyc01g107380.2 Fragment of seed maturation protein (PM23) DN

Gene and abbreviation are from Sol Genomics Network: solcyc.solgenomics.net

a. Rank in pathway prediction method

b. Refers to the input gene list used for prediction. UP, up regulated DEGs; DN down regulated DEGs;

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Supplemental Table 9. List of qRT-PCR primers

ID Gene Direction Seq (5' --- 3')

Solyc01g057080.1 ERF For CCTTCCATGTTACCGCCTAA

Rev AGCATGTTGTTGAGCAGGTG

Solyc08g066210.2 RLK For ACCTTGCACCTCTGTTGCTT

Rev CAGTGTTCCCTTCTCCGTGT

Solyc06g005170.2 MAPK3 For CTGAGCTTCTTGGCACTCCT

Rev TGGATTCACATGAGGGAACA

Solyc03g112340.1 E3UL For TGTAATGGACCGGGCTCTAC

Rev ACCTTGCACCTCTGTTGCTT

Solyc02g077040.2 PIP1 For CGAACAACCAGCAGCAGTTA

Rev GCCGCAATACCAACAGAAAT

Solyc01g107380.2 PM23 For TTGGCTCGTCTGTTGTTTTG

Rev TTAACCCACATGGCAAGGAT

Solyc01g006510 DH For AATCGACGCGAGTTTTGACT

Rev GCAGCTGGAGTGAGAGGAAC

Solyc12g010320.1 TIL For TGCTTTGATTGGTCAGCCTA

Rev GGCGTCTTGTGGAGCTTACT

Solyc02g081970.2 VSR6/7 For GTTCGGTAAACAACGGAGGA

Rev GCCATCCCCTTTAAAACCAT

Solyc09g008010.2 Kinase For GAACCATGGGATATGCTGCT

Rev TGCTCACTCCTAGGCCTGTT

Solyc06g008620.1 TolB For TGGATTGATACGATGCCAAA

Rev ATATCCTCCGAAGCCCTGAT

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