The shr mutant of Solanum lycopersicum cv Ailsa Craig (wild type- WT) was isolated from a γ-irradiated M₂ population of tomato as described in Negi et al. (2010). S. pennellii [LA 716] and S. pimpinellifolium [LA1589] (SP) seeds were obtained from Tomato Genetics Resource Center (UC, Davis, USA). The plants were grown in the greenhouse at Hyderabad under natural photoperiod (12–14 h day, 10–12 h night) at 28 ± 1°C during the day and ambient temperature (14–18°C) in the night. The RH in the greenhouse ranged from 45–70%.

A F₂ mapping population consisting of 69 plants was generated, segregating for short root phenotype, from an interspecific F₁ hybrid (S. lycopersicum shr/shr x S. pennellii SHR/SHR). Owing to self-incompatibility, the F₁ plants were selfed by manual sib mating. A second F₂ mapping population was generated which consist of 769 plants, segregating for short root phenotype, from an interspecific F₁ hybrid (S. lycopersicum shr/shr x S. pimpinellifolium SHR/SHR). The F₂ seedlings were scored for root length and NO levels as described in Negi et al. (2010). For NO determination detached roots were submerged in 10 μM of DAF-2 DA fluorescent probe in 10 mM MES-KCl (pH 7.0) buffer for 20 min. Thereafter the roots were washed with 10 mM MES-KCl (pH 7.0) buffer for 15 min. The NO level was examined by epi-fluorescence using the U-MWIB2 mirror unit (excitation 495 nm, emission 515 nm) in the Olympus BX-51 Microscope (Negi et al., 2010).

After scoring the seedling phenotypes, seedlings were transferred to the pots and plants were grown in the greenhouse. Wherever possible, F₃ seedlings were used to confirm the phenotype of F₂ plants. Chi-square tests were performed to determine the goodness of fit between the Mendelian ratio of the F₂ mapping population and the segregation data for the short root (shr) and the molecular markers.

For estimation of ethylene, fruits were harvested at different ripening stages viz., mature green (MG), breaker (BR) and red ripe (RR) stage. The ethylene emission from the harvested fruits was measured using a previously described procedure (Kilambi et al., 2013). Chlorophylls and carotenoids were extracted from leaves from 7–8 internodes of 45-day-old plants in 80% (v/v) acetone using the protocol of Makeen et al. (2007) and their amounts were calculated using the equation of Lichtenthaler (1987). Carotenoids were extracted from the pericarp of MG, BR and RR fruits using the procedure of Gupta et al. (2015). To avoid photooxidation, the entire procedure was performed under dim light. The carotenoids amount from the fruit tissue was calculated by comparing the peak area with the peak area obtained using pure standards of each carotenoid. For determination of sugars, the entire pericarp of fruit was homogenized, and values were recorded using PAL-1 refractometer. The firmness of fruits was measured three times at equatorial plane using Durofel DFT 100 (Gupta et al., 2014).

The endogenous levels of NO at MG and RR stage of fruits was determined by using EPR spectroscopy, and also the fruits cells were examined for DAF-2 DA fluorescence following the protocols described in Negi et al. (2010). However, both methods could not detect the NO indicating that NO level in fruits was below the limit of detection.

The metabolite profiling of fruits of WT and shr was essentially carried out by following the protocol of Roessner et al. (2000). The fruits from MG, BR, and RR stage were ground to a fine powder in liquid nitrogen. A 100 mg fresh weight of fruit powder was mixed with 1.4 mL 100% methanol and 60 μL of internal standard ribitol (0.2 mg/ml, w/v). After mixing, the sample was shaken at 70°C in a thermomixer for 15 min at 950 rpm. After that, 1.4 mL MilliQ water was added and after thorough mixing the sample was transferred in GL-14 Schott Duran glass vial and centrifuged at 2200 g for 15 min. An aliquot of polar phase (150 μL) was transferred in fresh Eppendorf tube and dried by vacuum centrifugation for 3–4 h. The dried sample was derivatized; first, it was dissolved in 80 μL of methoxyamine hydrochloride (20 mg/mL) and incubated at 37°C for 90 min at 600 rpm. Thereafter, 80 μL of MSTFA was added, and incubation was carried out at 37°C for 30 min at 600 rpm. The derivatized sample was transferred to a GC-MS injection vial and analyzed by Leco-PEGASUS GCXGC-TOF-MS system (Leco Corporation, USA) equipped with 30 m Rxi-5 ms column with 0.25 mm i.d. and 0.25 μm film thickness (Restek, USA). The injection temperature, interface, and ion source were set at 230°, 250°, and 200°C respectively. For the proper separation of groups of metabolites, the run program was set as following; isothermal heating at 70°C for 5 min, followed by 5°C min⁻¹ oven temperature ramp to 290°C and then final heating at 290°C for 5 min. The carrier gas (helium gas) flow rate was set to 1.5 mL/min. A 1 μL of sample was injected in split less mode and mass spectra were recorded at 2 scans/sec within a mass-to-charge ratio range 70–600.

The raw data were processed by ChromaTOF software 2.0 (Leco Corporation, USA) and further analyzed using the MetAlign software package (Lommen and Kools, 2012; www.metalign.nl) with a signal to noise ratio of ≥ 2, for base line correction, noise estimation, alignment and extraction of ion-wise mass signal. The mass signals that were present in less than three samples were discarded. The Metalign results were processed with MSClust software for reduction of data and compound mass extraction (Tikunov et al., 2012). The mass spectra extracted by MSClust were opened in NIST MS Search v 2.2 software for the identification of compound name within the NIST (National Institute of Standard and Technology) Library, and Golm Metabolome Database Library. The compound hits which showed maximum matching factor (MF) value (>600) and least deviation from the retention index (RI) was used for metabolite identity. The data was analyzed by normalizing with the internal standard ribitol.

The phytohormone extraction from the fruit sample of WT and shr was performed as described in Pan et al. (2004). A 100 mg homogenized powder from fruit sample was mixed with 500 μL of pre-chilled extraction solvent consisting of 2-propanol: MilliQ water: concentrated HCL in the ratio of 2:1:0.002 (v/v/v) respectively. After mixing, the extraction was carried out by shaking the sample for 30 min at 4°C at 500 rpm. Thereafter, 1 mL of dichloromethane (DCM) was added to the sample mix, and the incubation was continued for 30 min at 4°C at 500 rpm. After centrifugation at 13,000 g for 15 min at 4°C, the supernatant (~ 900 μL) was transferred to a fresh Eppendorf tubes and dried completely using the Speedvac (Thermo Scientific, USA). Before injection, the dried residue was dissolved in 70 μL of precooled 100% methanol followed by centrifugation at 13,000 g for 5 min.

The sample was transferred to an injection vial and analyzed using UPLC/ESI-MS (Waters, Milford, MA USA). The system consists of an Aquity UPLC™ System, quaternary pump, and autosampler. For separation of hormones, the sample was analyzed on a Hypercil GOLD C₁₈ (Thermo Scientific) column (2.1 × 75 mm, 2.7 μm). A gradient elution program was performed using two solvents system, solvent A- containing ultrapure water with 0.1% (v/v) formic acid, solvent B- containing acetonitrile with 0.1% (v/v) formic acid and run for 9 min at 20°C. The abscisic acid (ABA), jasmonic acid (JA), and salicylic acid (SA) detection was performed on Exactive^TMPlus Orbitrap mass spectrometer (Thermo Fisher Scientific, USA) in all ion fragmentation (AIF) mode (range of m/z 50–450) equipped with heated electrospray ionization (ESI) in negative ion mode. The zeatin, IAA, IBA, epibrassinosteroids (Epi-BR) and methyl jasmonate (MeJA) were analyzed in positive ion mode. For both modes, the following instruments setting were used, capillary temperature −350°C, sheath gas flow (N₂) 35 (arbitrary units), AUX gas flow rate (N₂) 10 (arbitrary units), collision gas (N₂) 4 (arbitrary units) and the capillary voltage 4.5 kV under ultra-high vacuum 4e⁻¹⁰ mbar. The hormones were analyzed from the >5 different fruits harvested from ripening stages viz. MG, BR, and RR of WT and shr. The quantification of each hormone was carried out by comparing the peak areas with those obtained for the respective hormone standards.

To obtain the overall clustering of the samples, we performed PCA using Metaboanalyst 3.0 (Xia et al., 2015; http://www.metaboanalyst.ca/). Firstly, we took the average of metabolites from at least three replicates and analyzed the differences in metabolites accumulation across the ripening stages in both WT and shr and then the results were presented in a two-dimensional graphical display.

All the metabolites measured using the GC-MS methods were mapped to the general metabolic pathways as described in the KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg) and LycoCyc (Sol Genomic networks, http://solcyc.solgenomics.net/). To compare levels of each metabolite across the ripening stages (MG, BR, and RR), we performed all pairwise multiple comparison procedures (Student-Newman-Keuls Method) by One-Way ANOVA using Sigma Plot version 11 with a significance threshold P ≤ 0.05 to highlight patterns of change across the ripening stages in shr compared to WT. Average fold change of metabolites occurring across the ripening stages in shr fruits compared to WT was shown on a primary metabolite pathway as presented in Do et al. (2010). A log2 fold of 0 means no difference, a log2-fold of 0.5 means 1or higher fold changes (equal to average means 1.5-fold) 1 means 2-fold or higher, a log2-fold of two means 4-fold or higher, and so on.

Networks for both WT and shr were created using Cytoscape software package (http://www.cytoscape.org/; Cline et al., 2007). Nodes represent the metabolites (circle), hormones (hexagon shape) and edges represent connectivity between the two metabolites. The connectivity between two nodes is drawn if the Pearson's Correlation Coefficient (PCC) value is larger than 0.9 either in positive or negative mode. New correlations in the shr network, which were insignificant in WT at all ripening stages were considered as new associations in shr or vice-versa.

Genomic DNA was isolated from young leaves (80–100 mg/well) in 96 well deepwell plates using a DNA extraction protocol developed for tomato (Sreelakshmi et al., 2010). The DNA was quantified using Nanodrop (ND-1000) spectrophotometer and DNA samples were diluted to final concentration of 5 ng/μL.

For mapping shr locus, we selected 129 SSR and 6 InDel markers that were evenly distributed across twelve chromosomes of the tomato. These markers were selected from the Solanaceae Genome Network (http://solgenomics.net/) and Tomato Mapping Resource Database (http://www.tomatomap.net/, Last accessed in 2013) and chosen based on their polymorphisms between tomato cultivar Ailsa Craig, S. pimpinellifolium and S. pennellii (Supplementary Table 1, for marker details). First, the location of mutation was determined to chromosome nine. Thereafter chromosome 9 was genotyped with 24 SSR and 7 CAPS markers. PCR amplification was in total volume of 20 μL containing 20 ng of genomic DNA, 1× PCR buffer (10 mM Tris, 5 mM KCl, 1.5 mM MgCl₂, 0.1% (w/v) gelatin, 0.005% (v/v) Tween-20, 0.005% (v/v) Np-40, pH 8.8, 0.2 mM dNTPs, 1 μL Taq polymerase and 5 pmoles each of forward and reverse primers. The cycling conditions for amplification were 94°C-5 min, followed by 35 cycles of 94°C-30 s, 57°C-30 s, 72°C-1 min, finally an extension step 72°C-8 min, and held at 4°C. The PCR products were size separated on 3.5% (w/v) agarose gels and gel images were collected with Alpha Imager™ gel documentation system.

Selected individuals of F₂ mapping populations of shr x S. pimpinellifolium and shr x S. pennellii were screened for polymorphism between bulks. DNA from fifteen shr and fifteen long root plants from shr x S. pimpinellifolium population, and twelve shr and twelve long root plants from shr x S. pennellii population were selected for bulks preparation. Short root and long root DNA bulks were prepared by pooling equivalent amount of DNA from each plant with specific phenotypic segregant of the F₂ mapping population. The parent lines and the bulks DNA were then subjected to BSA analysis for the identification of the tightly linked marker (Michelmore et al., 1991). Markers which corresponded to short root bulk and short root mutant and differed in the size of the PCR product with both long root parents (S. pennellii and S. pimpinellifolium) and long root bulks were considered to co-segregate with shr phenotype. Markers that were specific between bulks were assessed on debulks along with their parents.

For saturation of the shr locus, 14 SSR markers from Kazusa DNA Research Institute (http://www.kazusa.or.jp/tomato/, Shirasawa et al., 2010a,b),10 SSR markers from Veg Marks a DNA marker database for vegetables (http://vegmarks.nivot.affrc.go.jp, Last accessed in 2013) and 7 CAPS markers viz., At3g63190, C2_At4g02580, C2_At2g29210, C2_At4g02680, C2_At1g02910, C2_At4g03200, and U228448 (http://solgenomics.net) that were specific to chromosome 9 were selected and screened for polymorphism between the parental lines of mapping population (Supplementary Tables 2–4 for marker details). For amplification of CAPS region, 30 ng genomic DNA, 1 μL of 5 pM/ μL primer, 1X PCR buffer (10 mM Tris, 5 mM KCl, 1.5 mM MgCl₂, 0.1% (w/v) gelatin, 0.005% (v/v) Tween-20, 0.005% (v/v) Np-40, pH 8.8, 0.2 mM dNTPs and 1 μL Taq polymerase were used. After confirming PCR amplification for CAPS locus by agarose gel electrophoresis, the PCR amplicons of the CAPS markers were digested using ApoI, Hinf I, DraI and MspI (Fermentas) enzymes. Digestion reactions performed according to the supplier's manual and the products were separated on 3.5% (w/v) agarose gels.

Markers that showed bulk specific segregation along with shr phenotype were used for molecular mapping of the shr locus. Given the availability of the higher number of F₂ segregating progeny, we selected shr x S. pimpinellifolium mapping population for map construction for shr locus. Four SSR markers and one CAPS marker were chosen for molecular mapping of the shr locus. Total 769 F₂ plants of shr x S. pimpinellifolium were genotyped and analyzed by Chi-square test. Map construction was carried out using the MAPMAKER/EXE V.3.0 (Lander et al., 1987; Lincoln and Lander, 1992) program following Kosambi Function (Kosambi, 1943). Linkage groups were determined using “group” and “error detection on” commands with a LOD score of 3.0 and a recombination fraction of 0.5. The “compare” and “order” commands in Mapmaker were used to identify the most probable marker order within a linkage group. The “ripple” command was used to verify and confirm marker order as determined by multipoint analysis. Recombination frequencies were converted into map distances centi-Morgans (cM) using the Kosambi mapping function (Kosambi, 1943), and the linkage group maps were drawn using the MapChartv. 2.1 software (Voorrips, 2002).

The tomato genome, ITAG version 2.3 (SGN: http://solgenomics.net/gb2/gbrowse/ITAG2.3_genomic/) was used for overlaying the closest markers encompassing the shr locus. The predicted genes in the region encompassing shr locus were searched. The information on expression of the predicted genes was found by BLASTN searching of Tomato Expression Database (http://solgenomics.net/ted).

We crossed shr mutant with S. pimpinellifolium, a red fruited wild relative of tomato for mapping of the shr gene, and also compared the phenotypes of shr mutant plants and the parental lines at the different stages of development. Both light and dark grown seedlings of shr mutant showed extremely short roots compared to parental lines (Figures 1A–D). The F₁ seedlings of shr x S. pimpinellifolium grown in both light and dark conditions displayed elongated roots like WT and S. pimpinellifolium. Examination of root length of F₂ segregation mapping populations suggested that shr locus is encoded by a monogenic recessive locus (Supplementary Table 5). While seedlings of S. pimpinellifolium did not form lateral roots, however, light-grown seedlings of F₁ cross of shr x S. pimpinellifolium displayed lateral roots like WT (Figure 1A). The etiolated seedlings of WT lacked lateral roots and consequently etiolated seedlings of F₁ cross of shr x S. pimpinellifolium roots did not display lateral roots (Figures 1B,D). Interestingly, the lateral root formation in the F₂ population of shr x S. pimpinellifolium showed opposite segregation pattern of 1:3, indicating the presence of a locus in S. lycopersicum controlling lateral root initiation independently of shr locus (Supplementary Table 6).

The shortening of root in the shr mutant is associated with hyperaccumulation of NO; therefore, cosegregation of short root phenotype and accumulation of NO was examined by staining the primary root tip with NO-sensitive fluorophore 4, 5-diaminofluorescein diacetate (DAF-2DA) (Correa-Aragunde et al., 2004). In vivo imaging of NO levels in parental lines and shr mutant showed stronger fluorescence of DAF-2DA in shr mutant root tips, while the level of DAF-2 DA fluorescence in root tips of parental lines was nearly similar. The F₁ plants of shr x S. pimpinellifolium showed DAF-2 DA fluorescence level that was similar to parental lines indicating the recessive nature of shr locus. The imaging of F₂ population of shr x S. pimpinellifolium showed DAF-2 DA fluorescence pattern consistent with above results, showing a 3:1 segregation pattern in root tips. The segregation pattern of NO accumulation as visualized by DAF-2 DA fluorescence in F₂ mapping population was consistent with shr phenotype and indicated the cosegregation of NO hyperaccumulation with the short root locus (Figure 1E).

The F₁ seedlings of shr x S. pimpinellifolium were slightly taller and had longer internodes than either WT or S. pimpinellifolium (Supplementary Figures 1A,B). Similarly, the leaf of F₁plant was longer than shr mutant and possessed chlorophylls and carotenoids similar to WT and S. pimpinellifolium (Supplementary Figures 1C,D). However, F₁ plants showed an intermediate phenotype than either of its progenitors in the number of flowers and the shape of inflorescence (Figure 1F). On the contrary, the RR fruits of F₁ hybrid (shr x S. pimpinellifolium) emitted less ethylene (3.18 ± 0.2120 nL/h/g FW) than S. pimpinellifolium (14.59 ± 1.11 nL/h/g FW) and shr fruits.

To map the shr gene, the SSR markers described in Tomato Mapping Resource Database (http://www.tomatomap.net/, Last accessed in 2013) and Solanaceae Genome Network (http://solgenomics.net) were screened using the bulk segregation analysis (BSA). Out of 135 markers used, only 69 were polymorphic between the shr and S. pimpinellifolium. These 69 polymorphic markers were used for BSA of shr x S. pimpinellifolium populations. Among these, two markers, SSR19, and SSR110 showed polymorphism and mapping results indicated that the shr locus was located in a region intervening between SSR19-SSR110 on chromosome 9 of tomato. To develop high-resolution molecular map and saturate the region around the shr locus, additional markers for chromosome 9 were selected and analyzed for polymorphism between shr mutant and S. pimpinellifolium (Ohyama et al., 2009; Shirasawa et al., 2010a). The bulk segregation analysis with TGS0213 and C2_At3g63190 markers showed strong linkage with shr locus, and these were used for genotyping of entire mapping population (Supplementary Figures 2A–C).

A total of six polymorphic markers around shr locus, including one CAPS (C2_At3g63190), one InDel (Cosi52) and four SSRs (SSR19, SSR110, SSR383, TGS0213) markers, were genotyped on 769 F₂shr x S. pimpinellifolium mapping population. Out of these, five markers showed satisfactorily expected ratio for the co-dominant inheritance of 1:2:1 and were used for mapping the shr locus (Supplementary Table 7). Using MAPMAKER3.0 program, the shr locus was mapped at 0.2 cM from TGS0213 and 2.8 cM from C2_At3g63190, on chromosome 9 (Figure 2). To identify the candidate gene encoding shr locus, the sequence of SSR markers tightly linked to shr was searched with BLAST against the tomato genome sequence release ITAG 2.3 Release SL2.40ch09:54045071.58939091(http://solgenomics.net/gb2/gbrowse/ITAG2.3_genomic). The genomic region flanked by two markers was about 4.89 Mb (4894020 bp) and contained 197 genes, which were examined as candidate genes for NO hyperaccumulation. However, the above genomic region is not completely sequenced and consists of two major gaps of size 35116 bp (SL2.40ch09:56426627…56461743) and 31568 bp (SL2.40ch09:56795954…56827522). Currently, it is not known whether these two gaps also harbors functional genes or consist of repetitive DNA sequences. Out of the 197 genes, 139 genes showed high to low expression in tomato root and remaining showed no expression (Supplementary Table 8).

Out of 139 genes showing root specific expression, only three genes were reported to be associated with modulation of cellular NO levels; alcohol dehydrogenase III (ADH3)/GSNO reductase (GSNOR1/HOT5/PAR2, Solyc09g064370 alcohol dehydrogenase III gene), CUE domain containing protein 2 (Solyc09g064860CUE domain containing protein), and glutathione S-transferase (Solyc09g063150 Glutathione S-transferase) (Li et al., 2008; Chen et al., 2009; Lok et al., 2012). The presence of the shr mutation in these three genes was examined by amplifying complete ORF of genes from WT and shr mutant using PCR and detection of mutation in heteroduplexed DNA using mismatch endonuclease assay (Sreelakshmi et al., 2010). However, no mutation was detected in any of these three genes, thus ruling them out as candidate genes.

The shr mutant shows sluggish growth, prolonged life cycle (shr-150 ± 10 days, WT-110 ± 10 days) and a diminutive phenotype with pale green leaves with reduced level of photosynthetic pigments compared to WT (Negi et al., 2010; Supplementary Figure 1D). The pleiotropic effect of shr mutation also manifests during reproductive phase. Compared to WT, the initiation of the first inflorescence in the mutant was delayed by nearly 3 weeks (Figures 3A,B). The shr mutant made fewer inflorescences with smaller flowers and inflorescence had ca. 50% less flowers than the WT (Figures 3C–F).

The influence of shr mutation was examined on the chronological development of fruit from anthesis (days post anthesis- DPA) to RR stage. Analogous to delayed inflorescence initiation, the fruit development was slower, and the ripened shr fruits were smaller in size than WT (Figures 4A,B). In addition, the shr mutation influenced the transition phases of ripening. The time period to reach the MG stage was longer in shr fruits (35–37 days shr, 30–33 days WT) (Figure 4C). The transition from MG to RR Stage was 7–8 days slower in shr fruits than WT. Though shr fruits were smaller in size at RR stage, they emitted nearly two-fold higher ethylene than WT (Figure 7). Among shr and WT fruits, no obvious difference was found in TSS level (Brix) except at RR stage (Supplementary Figure 3A). During ripening, the loss of firmness in shr fruits was similar to WT (Supplementary Figure 3B). The pH of WT and shr fruits was almost similar (Supplementary Figure 3C). Unlike reduced photosynthetic pigments in shr leaves, the accumulation of lycopene and β-carotene in shr fruits was only mildly affected. However, the level of carotenoids precursors, phytoene and phytofluene were higher in shr fruits than WT (Supplementary Table 9).

A total of 96 metabolites were identified in WT and shr fruits at three ripening stages, MG, BR, and RR. The levels of several metabolites in shr fruits were significantly different from WT at one or more stages (Supplementary Table 10). Principal component analysis (PCA) and the correlation variances explained by the two principal components clearly revealed two clusters of WT and shr metabolites (Figure 5). Based on chemical nature the metabolites were grouped as amino acids and amines, sugars, organic acids, fatty acids, and miscellaneous (Supplementary Table 10). Only those metabolites which showed up-regulation or down-regulation >1.5-fold (Log2 shr/WT value 0.5) in shr fruits than WT were mapped on the metabolic networks (Figure 6, Supplementary Figures 4A–E).

In shr fruits, only 14 out of 25 amino acids/amines were differentially regulated at one or more stages. Among these, tyrosine was detected only in shr fruits, and its levels progressively declined during ripening. The upregulation of asparagine, tryptophan, alanine 3-cyano, and ornithine 1-5-lactam in shr fruits was discernible at all stages with maxima at BR (Figure 6, Supplementary Table 10). The glutamine level in shr fruits was significantly higher at MG and BR but was similar to WT at RR. Contrastingly, glutamate was downregulated in shr fruits, most significantly at MG and RR (Figure 6, Supplementary Table 10). The hydroxylamine consistently showed higher levels in shr fruits. The amino acids derived from the 3-phosphoglycerate and pyruvate showed no or little change in shr fruits. Polyamine, putrescine showed significantly high level at MG stage. Considering tyrosine, asparagine, and glutamine showed substantial upregulation (~ 10-fold) and glutamate showed downregulation, these amino acids may have a key role in cellular homeostasis of shr fruits.

A total of 27 sugars and their derivatives were identified in shr and WT fruits; however only a few were up- or down-regulated in shr fruit (Figure 6, Supplementary Table 10). The glucose 6-phosphate derived metabolite ribofuranose was downregulated at all stages in shr fruits. Similarly, arabinopyranose and threose (immediate precursor glycerol-3-phosphate) were downregulated in shr fruits. While glucopyranose levels were high at BR and RR in shr fruits, it was undetectable in WT at same stages.

In shr fruits, out of 6 TCA cycle components, citrate and cis-aconitate were upregulated while succinate and methyl succinate were downregulated at all stages. Isocitrate at BR, RR, and malate at RR were upregulated, and fumarate was downregulated at BR. Lactate increased considerably at MG and BR. Acetyl-CoA derived compound- acetate significantly declined during ripening (Figure 6). Interestingly, dehydroascorbate dimer and tartarate were upregulated at all stages. Caffeate (a chlorogenic acid metabolites) and nicotinate, derived from the shikimate pathway considerably increased at all stages. In addition to TCA cycle components nucleic acid metabolites; guanidine and adenosine were also upregulated at MG-RR and BR-RR stage respectively (Figure 6, Supplementary Table 10).

Interestingly in shr fruits, all fatty acids were significantly downregulated at MG and BR except myristate that was downregulated at all stages. Only linolate exhibited no alterations in shr compared to WT. During ripening, free fatty acid metabolites in WT progressively declined from a high level at MG, while in shr though the level was half of WT, it remained unchanged during ripening.

Among the plant hormones, GA and Epi-BR were below the detectable level, and SA, zeatin, MeJA, and JA levels were similar in WT and shr fruits. In shr fruits, ABA level was low at MG and BR but attained level similar to WT at RR (Figure 7). Conversely, IAA content was high at BR and RR whereas IBA was high at MG in shr fruits. The shr fruits also emitted higher ethylene at RR (Figure 7). These results indicated that shr mutation influenced the temporal changes in ethylene, auxins, and ABA during ripening.

The regulatory network involved in shr fruit ripening was identified by constructing correlation network of significantly different (P < 0.05) metabolites and hormones at all stages. The network comprised of 28 metabolites and 2 hormones (ABA and JA) for WT and 16 metabolites and hormone JA for shr. In both WT and shr network, 3 clusters (I, II, and III) could be distinguished (Figure 8) of which cluster II was most dense with a maximum node connectivity while cluster I and III were sparse with less connectivity.

The PCA of shr and WT revealed that the collective complement of metabolites in shr fruits was distinctly different from the WT at all stages of fruit ripening. Consistent with this the correlation network of shr fruits was distinctly different from WT. First, the network density in shr fruits (0.65) was less than the WT (0.71) (Supplementary Table 11). The number of interactions in shr was about 1/4th of the WT. Unlike in WT where positive and negative interactions were about 329 and 170 respectively, these were nearly equal in shr (+37 and −41). Most importantly there was only a little overlap in the interactions between WT and shr. Among 78 interactions that were present in shr only 14 were common with WT and positive to the WT. Moreover, WT network showed 31 unique nodes and had only 9 nodes common with shr. Similarly, shr also showed 8 unique nodes in its correlation network.

These differences between WT and shr indicate that the shr mutation causes a massive shift in metabolic interaction during the fruit ripening. Most of the interactions that were present in WT were not observed in shr. In addition, shr showed several unique interactions that were not present in WT. For several metabolites, the interactions were opposite in nature, for example, the interaction of tyrosine with other metabolites (Supplementary Table 11). Interestingly, most of the fatty acids metabolites showed negative interaction with group I (citrate and cis-Aconitate) and positive interaction with group II (acetate, methylsuccinate, and succinate) in WT network, while none of the fatty acids metabolites showed interaction with group I and II metabolites in the shr network. These results indicated the metabolites were regulated in a different fashion in shr fruits than in the WT.

Examination of WT and shr network revealed that TCA pathway metabolites (citrate, cis-aconitate, succinate, methylsuccinate, and acetate) were interconnected and also had maximum connectivity with the other metabolites mostly positioned in cluster II (Figure 8, Supplementary Table 11). On the basis of interactions, two groups were discernible in WT and shr. In WT the group I (citrate and cis-acotinate) positively correlated with each other and negatively correlated with group II (succinate, methylsuccinate, and acetate) and vice-versa. Similarly, in shr the group I (citrate) negatively correlated with group II (succinate, and acetate) and vice-versa. The phytohormones ABA and JA showed maximum connectivity with cluster II (Figure 8). In WT, ABA, and JA positively correlated with group II and negatively correlated with group I. Similarly in shr, JA negatively correlated with the group I and positively with group II.

In both WT and shr, the cluster I and III were populated with few metabolites. The tyrosine amino acid that specifically is accumulated at a high level in shr fruits was positioned in cluster III and it positively correlated with aspartate (Figure 8, Supplementary Table 11). However, tyrosine negatively correlated with most metabolites in shr fruit. In addition, several significantly different out-class metabolites were identified that were present only in shr network. Taken together the network analysis indicated that the shr mutation distinctly influences the regulation of metabolites during fruit ripening.

The genetic analysis of shr segregation indicated that the shr locus is encoded by a single recessive gene located on chromosome nine and it co-segregates with hyperaccumulation of NO.

Using the advantage of the availability of the complete genome sequence of tomato, we overlaid the shr locus on to the tomato physical map. The shr locus was located within 4.89 Mb (4894020 bp) region of genome scaffold SL2.40ch09:54045071.58939091 (http://solgenomics.net/gb2/gbrowse/ITAG2.3_genomic). Among the known genes regulating NO levels in plants, only one gene was found in the region encompassing shr locus. In Arabidopsis, the null alleles of the HOT5 locus (GSNOR1/HOT5/PAR2) show increase in in vivo levels of NO (Lee et al., 2008; Chen et al., 2009). Based on their reported role in regulating NO level in the mammalian system, glutathione S-transferase (Lok et al., 2012) and CUE domain containing protein (Li et al., 2008) were also examined as potential candidate genes. However, these three most obvious candidate genes did not show a mutation in their respective ORFs. Considering that the tomato genome sequence encompassing shr locus region has two major unfilled gaps of 35116 bp and 31568 bp size, it could be possible that these gaps may have additional genes and one of them may be encoding for shr mutation. Since shr mutant was obtained from γ-irradiated population, the possibility remains that rather than a single gene mutation, the chromosomal rearrangement, and/or deletion may have contributed to the phenotype attributed to shr locus.

Although the source of in vivo NO production (Domingos et al., 2015) remains to be fully deciphered, endogenous NO regulates several facets of higher plant development. The observed diminutive size, sluggish growth and delayed life cycle of the shr mutant is consistent with the reports that high endogenous NO level reduces the growth and prolongs the life cycle (Morot-Gaudry-Talarmain et al., 2002). One distinct effect of shr locus was on the onset and progression of the reproductive phase. In Arabidopsis, NO overproducer mutant, nox1 shows delayed flowering (He et al., 2004) whereas NO under-producer mutant nos1/noa1 shows earlier flowering (Guo et al., 2003). Consistent with this, shr mutant displayed delayed development of inflorescence(s) with smaller and fewer flowers than the parental WT.

Compared to vegetative development, little is known about the role of endogenous NO in fruit development and quality. So far the information is largely derived by the application of exogenous NO donors to detached fruits with an aim to extend the postharvest shelf life (Manjunatha et al., 2010; Lai et al., 2011). Post-anthesis, the fruit development in shr was sluggish with 5–7 days delay in attaining MG stage than the WT. Consistent with the reduction in root and leaf size due to high endogenous NO levels, the MG fruits of shr mutant too were half in size than the WT. Even post-MG stage, the transition to different ripening stages was much slower in shr fruits than the WT. Attainment of RR stage in shr fruit was delayed by ca. 9 days compared to WT. Though hyperaccumulation of NO slowed ripening of fruits, the on vine shelf life of shr fruit post-RR stage the was similar to WT. Considering that the carotenoids levels, firmness, and brix of shr fruits were similar to WT, it can be assumed that these responses were not affected by NO hyperaccumulation.

Tomato being a climacteric fruit, its ripening is strongly enhanced by the emission of the plant hormone ethylene before the onset of the ripening. The reduction in ethylene biosynthesis by transgenic means also delays tomato ripening (Oeller et al., 1991). Considering that shr fruits emitted a higher amount of ethylene than WT, the post MG-delay in ripening is apparently not linked to ethylene biosynthesis. Moreover, our results are not in conformity with the reports that NO downregulates ethylene biosynthesis (Eum et al., 2009; Lai et al., 2011), presumably by S-nitrosylation-mediated inhibition of enzymes regulating ethylene synthesis (Abat and Deswal, 2009). Conversely, our results indicate that higher endogenous NO likely extends the shelf life by delaying the ripening process from MG to RR stage. In several species such as banana, tomato, and strawberries, the application of NO donor SNP (sodium nitroprusside) to detached fruits extended postharvest life (Manjunatha et al., 2010, 2012; Lai et al., 2011). It can be surmised that exogenous NO donors may be extending the fruit shelf life by delaying the overall ripening process.

Apart from their antagonistic interactions in several developmental processes of plants, ethylene and ABA, synergistically promote the ripening process in climacteric fruits (Sun et al., 2012). ABA acts as a principal signal for the onset of ripening, and a decline in ABA levels precedes the climacteric ethylene production in tomato fruit. Considering that the shr mutation upregulated ethylene emission at RR stage, it may have affected the endogenous ABA levels. Consistent with this ABA levels in shr fruit at MG and BR stages were lower than the WT. The smaller size of shr fruits appears to be related to lower ABA levels as ABA deficiency in tomato leads to a reduction in fruit size (Galpaz et al., 2008; Nitsch et al., 2012; Sun et al., 2012). Tomato fruits harvested at the pink stage from ABA deficient plants showed significantly extended shelf life (Sun et al., 2012). Analogously, the slower development of shr fruits and prolonged post-MG ripening period is likely related to reduced ABA levels. However, unlike ABA-deficient plants (Galpaz et al., 2008; Sun et al., 2012), carotenoids levels and firmness is not higher in shr fruits. Thus, the observed effects of NO on above processes can also arise from a mechanism other than ABA.

In tomato fruits, the endogenous level of free IAA massively declines before the onset of ripening at MG stage followed by a minor rise at RR stage (Böttcher et al., 2010). Contrarily IAA level declined in WT fruits post-MG stage, whereas in shr fruits it increased at RR stage. Conversely, shr fruits showed higher IBA levels at MG stage than WT. While the role of IBA per se is not yet established in fruit ripening, it is well established that auxin-mediated gene expression strongly influences the ripening process, and excess auxin levels cause parthenocarpy in tomato fruits (de Jong et al., 2009). Tomato WT/35S::IPT plants showed 1.5-2 fold higher zeatin levels in ripe fruit accompanied with higher fruit weight (Ghanem et al., 2011). Though shr fruit had 4-fold higher levels of zeatin than WT, it had no effect on fruit weight. While MeJa level in shr was nearly similar to WT, it had nearly 4-fold less JA level at MG stage. Though JA-deficient tomato mutants show a reduction in lycopene level (Liu et al., 2012), shr fruit showed no such decline in lycopene. It remains to be established how shr mutation affected multiple hormonal responses during ripening. However, the observed changes may result from cross talk between NO and phytohormone(s) as NO is the part of signal transduction chain triggered by several hormones. Such a cross talk has been recently reported in developing tomato fruits where AUXIN RESPONSE FACTOR 2A homodimerizes with ABA STRESS RIPENING (ASR1) protein, thus linking ABA and ethylene-dependent ripening. (Breitel et al., 2016).

Tricarboxylic acid (TCA) cycle, at the center of cellular metabolism, is interconnected to wider metabolic network contributing to a plethora of pathways such as amino acid biosynthesis (Mackenzie and McIntosh, 1999), regulation of carbon/nitrogen balance (Noguchi and Terashima, 2006), isoprenoid synthesis (Fatland et al., 2005) and cellular redox control (Scheibe et al., 2005) etc. The profiling of proteins from capsicum fruit exposed to NO revealed nitrosylation of a substantial number of enzymes involved in photosynthesis, glycolysis, oxidative/redox metabolism, amino acid biosynthesis, and proteolysis (Chaki et al., 2015). Therefore, it can be presumed that the increased level of NO in shr mutant alters the cellular homeostasis by modifying the activity of enzymes involved in metabolic pathways, consequently affecting the fruit size and prolonging the ripening of fruits. This presumption is in consonance with a previous report wherein enhanced levels of central carbon metabolites are associated with reduced fruit size in tomato (Schauer et al., 2006).

Considering that the observed shifts in metabolite levels may arise from multiple factors, we focused only on those metabolites that were significantly different in shr from WT at all three stages of fruit development. In shr fruits among the intermediates of TCA cycle, the citrate and cis-aconitate levels were high and succinate and its derivative, methylsuccinate were low. In tobacco leaf extracts addition of a NO donor inhibited aconitase activity by forming a metal-nitrosyl complex with the Fe-S cluster of the enzyme (Navarre et al., 2000). The higher levels of TCA cycle intermediates in shr fruits appears to be related to NO-mediated inhibition of aconitase activity. The leaves of aconitase deficient mutant of Lycopersicon pennellii (Solanum pennellii) show a similar increase in citrate levels (Carrari et al., 2003). Likewise, hypoxia induced NO accumulation in Arabidopsis roots concomitantly reduced aconitase activity and increased the citrate and malate levels (Gupta et al., 2012). Similarly, a reduction in aconitase activity in a tomato introgression line increased citrate levels and reduced succinate level in fruits (Morgan et al., 2013). Thus, it can be assumed that enhanced citrate and reduced succinate levels in shr fruit may have resulted from inhibition of aconitase. Considering that the tomato non-ripening mutants rin, Nr, and nor also display reduced level of succinate during ripening (Osorio et al., 2011), the reduced succinate level in shr may be linked to prolonged ripening period. However, nitric oxide also affects the activity of other TCA cycle constituents; succinate dehydrogenase (Simonin and Galina, 2013) and cytochrome C oxidase (Millar and Day, 1996). The observed shift in TCA cycle intermediates and ensuing metabolome may thus represent a cumulative effect of NO on a plethora of enzymes and proteins. From the foregoing, it is apparent that the reduction in fruit size and prolonged ripening of shr fruits may have a relationship with alteration in central carbon metabolism.

The shr mutation had a broad spectrum effect on the cellular aminome eliciting significant changes in levels of several amino acids during fruit ripening. The NO-mediated aconitase inhibition reportedly activates the alternate oxidase pathway and shifts the metabolism toward upregulation of amino acids (Gupta et al., 2012). Among the upregulated amino acids, the high level of hydroxylamine in shr fruits may have a relationship to NO biosynthesis as tobacco cell suspensions reportedly convert hydroxylamine to NO (Rümer et al., 2009). Little is known about the role of 3-cyanoalanine in fruit ripening except that it is a byproduct in detoxification of HCN produced during ethylene emission from fruits. Though ornithine,1-5 lactam levels were higher in shr fruits, it had no significant effect on polyamines levels which are implicated for longer shelf life of fruits (Mehta et al., 2002), except putrescine at MG stage.

The strong upregulation of tyrosine in shr fruit is intriguing. The increased level of tyrosine may signify a block in its downstream metabolism or strong upregulation of its biosynthesis. Considering that the activity of arogenate dehydrogenase that converts arogenate to tyrosine is strongly inhibited by tyrosine (Rippert and Matringe, 2002), the upregulation of biosynthesis is unlikely. Alternately tyrosine can be synthesized from prephenate by the action of prephenate dehydrogenase which lacks feedback regulation by tyrosine via 4-hydroxyphenylpyruvate (Schenck et al., 2015). However, this pathway is reported only in legumes. Nonetheless, upregulation of tyrosine level represents a very specific modulation of a metabolite level by shr mutation.

Considering that asparagine is derived from glutamine and aspartate, the high level of glutamine may have correspondingly increased the asparagine levels, indicating a co-ordinated upregulation of these two amino acids. This is also corroborated by the reduced level of aspartate in shr fruits. In tomato, arbuscular mycorhizzal association specifically upregulates asparagine and glutamine levels in fruits, presumably by promoting their transport from root to the fruits (Salvioli et al., 2012). Considering that shr mutation strongly influences the root phenotype, it may have also influenced the mobilization of these amino acids to the fruit. The reduced level of the glutamate may have a relationship with the prolonged period of ripening of shr fruit. A comparison of glutamate levels in rin and nor non-ripening mutants of tomato with a normal cultivar revealed a significant negative correlation between fruit glutamate levels and shelf life, with lower glutamate levels being associated with a longer shelf life (Pratta et al., 2004). Thus, the lower level of glutamate in shr fruit is consistent with its slower ripening.

The progression of fruit ripening in tomato is associated with a steady decline in the fatty acid levels. However, in shr fruits, the levels of most fatty acids were much lower even at MG stage and for some even at BR stage. By RR stage, due to a continual decline in fatty acids levels in WT, their levels became nearly equal to shr fruits. In leaves of Arabidopsis ssi2 mutant, the reduction in oleic acid (18:1) level has been shown to induce NO production in chloroplast (Mandal et al., 2012). Considering that level of oleate in shr fruits is lower it may have a linkage with the shr mutation.

The sizable shift in metabolomic interactions, loss of nodes present in WT and appearance of new nodes in shr likely reflects a broad spectrum action of shr mutation. A large number of proteins regulating a range of metabolic and developmental processes are known to be targets of NO (Hu et al., 2015). Assuming that the observed shift is related to hyperaccumulation of NO in shr mutant, it is plausible that it may have affected the activity of several key proteins regulating cellular metabolism. This presumption is consistent with the report that exogenous application of NO to pepper fruits delayed fruit ripening which may be related to protein nitration of key enzymes (Chaki et al., 2015). One of the distinct responses related to protein nitrosylation pertains to ABA signaling. Plants deficient in NO are hypersensitive to ABA and tyrosine nitration of ABA receptor by NO inhibits ABA signaling (Castillo et al., 2015). The absence of ABA in the correlation network of shr fruits may reflect such a negative effect of the shr mutation on ABA-triggered signal transduction. The effect of NO was not restricted to ABA alone. Though JA mapped on the shr network, it interacted with a different metabolite sets than in WT.

Currently, little is known how developmental mutants regulate the metabolic shifts. Similar to shr mutant, tomato sun mutant also showed massive shifts in metabolite interactions, with the loss of several interactions and appearance of unique interactions compared to WT (Clevenger et al., 2015). While shr had 64 unique interaction pairs and lost 485 interaction pairs present in the WT, sun fruits had 151 unique interaction pairs and lost 273 interaction pairs. Consistent with SUN being a protein with calmodulin recruitment domains, the mutation in it affects the calcium related processes; the major metabolic shifts in sun mutant were related to calcium signaling (Clevenger et al., 2015). Likewise, it can be assumed that analogous to sun mutant, the metabolic shifts in shr mutant may be related to its modulation of NO level. The shift in shr correlation networks probably stems from a requirement to sustain the metabolomic homeostasis affected by the shr mutation. The altered interactions between different metabolites likely arise from the need to maintain the cellular homeostasis to continue the normal process of ripening (Fares, 2015; Ho and Zhang, 2016), though the overall duration of ripening in shr is prolonged. While it can be presumed that the observed loss and gain of metabolite interactions in shr represents the process of metabolic compensation, the mechanisms underlying this process are yet to be deciphered.

In summary, the characterization of shr mutant indicated that hyperaccumulation of NO slows the on-vine process of fruit ripening in tomato, possibly by altering the overall cellular homeostasis. Our results have an implication for increasing the shelf life of tomato, as selective manipulation of NO levels during ripening can keep the fruits fresh for a longer duration.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.