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MAP  >  Technology in Horticulture
2024 Volume 4
Article Contents
Abstract
Introduction
Materials and methods
Plant materials, growth conditions, and stress treatment
Vector construction
Chlorophyll and anthocyanin content determination
Physiological analysis
RNA isolation and qRT-PCR analysis
Results
Screening and characterization of lncRNAs
Generation of lncRNA overexpression lines
Effect of lncRNA overexpression on heat tolerance in Arabidopsis
Effect of lncRNA overexpression on the expression quantity of genes related to high-temperature response in Arabidopsis
Effect of lncRNA overexpression on drought tolerance in Arabidopsis
Discussion
Conclusions
Author contributions
Data availability
Acknowledgments
Conflict of interest
Supplementary information
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References
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ARTICLE   Open Access    

Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits

  • 1.

    Crop Production and Soil Science Department, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Nigeria

  • 2.

    Genetic Resources Unit, National Horticultural Research Institute, PMB 5432, Ibadan, Nigeria

  • 3.

    Product Development Programme, National Horticultural Research Institute, PMB 5432, Ibadan, Nigeria

More Information
  • The rising significance of tomato (Solanum lycopersicum L.) in human diet necessitates the continuous search for genotypes with favorable alleles for agronomic and nutritional properties from untapped genetic diversity. In this study, the phenotypic diversity of tomato accessions was assessed for agronomic and physico-chemical traits to identify accessions with potential horticultural traits that can be utilized in tomato improvement programs. A set of 23 accessions collected from the National Horticultural Research Institute (NIHORT), Ibadan, Nigeria, and two traditional varieties used as checks were evaluated in a 5 × 5 α-lattice design with three replicates at the Teaching and Research Farm of Ladoke Akintola University of Technology, Ogbomoso, Nigeria in the main cropping season of 2021. Data collected includes six physico-chemical parameters and 11 agronomic traits. Analysis of variance showed that accessions varied significantly (p < 0.001) for all of the traits measured. Wide variations were observed for some traits suggesting a considerable level of diversity among the accessions. Accession NHTO-0199, with the highest fruit weight, had a 59% yield advantage over the best traditional variety. The first two principal components accounted for 53% of the total variation among the tomato accessions. The patterns of variation were described by the phenological stages of flowering, fructifying, fruit maturation, plant height, fruit yield components, lycopene, and vitamin C content of the fruits. The cluster analysis delineated the accessions into three distinct clusters and hybridization between clusters may generate desired allelic combinations useful for developing unique variety. The following top five accessions: NHTO-0352, NHTO-0350, NHTO-0199, NHTO-0351, and NHTO-0346 had outstanding performances for fruit yield and physico-chemical traits based on Rank Summation Index. These superior accessions can be advanced for further improvement and may be used as sources of traits in crosses to develop new breeding lines.

  • Introduction

    Plants are continuously subjected to unpredictable environmental conditions and encounter a multitude of stressors throughout their growth and development, posing a significant challenge to global crop production and food security[1]. Heat and drought are undoubtedly the two most important stresses that have a huge impact on crops. Both elicit a wide array of biochemical, molecular, and physiological alterations and responses, impacting diverse cellular processes and ultimately influencing crop yield and quality[2].

    A primary physiological consequence of both stresses is the diminished photosynthetic capacity, partially resulting from the degradation of chlorophyll due to leaf senescence under stress conditions. Chlorophyll accumulation was diminished in numerous plants subjected to drought or heat stress conditions[3,4]. Various environmental stresses prompt excessive generation of reactive oxygen species (ROS), initiating oxidative damage that compromises lipids, and proteins, and poses a serious threat to cellular functions[2]. To mitigate oxidative stress and minimize damage, plants have developed various protective mechanisms to neutralize ROS. Several antioxidant enzymes, such as SOD, POD, and CAT, are integral to cellular antioxidative defense mechanisms. Additionally, antioxidants such as anthocyanins and proline serve as crucial ROS scavengers[5,6]. The elevation in temperature typically induces the transient synthesis of heat shock proteins (Hsps), which function as molecular chaperones in protecting proteins from denaturation and aggregation, with their activity primarily regulated at the transcriptional level by heat shock factors (Hsfs)[7]. The significance of Hsps and Hsfs in all organisms, including plants, has been assessed in various stress conditions that could disrupt cellular homeostasis and result in protein dysfunction[7]. Drought stress can also trigger the transcription of a suite of marker genes, including RD29A, RD29B, NCED3, AREB1, Rab18, etc., which assist plants in mitigating cellular damage during dehydration and bolstering their resilience to stress[8−10].

    Previous research efforts focusing on the regulatory control of stress-related genes have largely centered around protein-coding genes. In recent years, non-protein-coding transcripts have emerged as important regulatory factors in gene expression. Among them, long non-coding RNAs (lncRNAs) lncRNAs have been identified as implicated in various abiotic stresses[11,12]. LncRNAs are a class of non-coding RNAs (ncRNAs) exceeding 200 nucleotides in length. They possess minimal or no protein-coding potential[13]. In plants, lncRNAs are specifically transcribed by RNA polymerases Pol IV, Pol V, Pol II, and Pol III[14,15]. LncRNAs exhibit low abundance and display strong tissue and cellular expression specificity relative to mRNAs. Moreover, sequence conservation of lncRNAs is was very poor across different plant species[13,16,17]. The widespread adoption of high-throughput RNA sequencing technology has revealed lncRNAs as potential regulators of plant development and environmental responses. In cucumber, RNA-seq analysis has predicted 2,085 lncRNAs to be heat-responsive, with some potentially acting as competitive endogenous RNAs (ceRNAs) to execute their functions[18]. In radish, a strand-specific RNA-seq (ssRNA-seq) technique identified 169 lncRNAs that were differentially expressed following heat treatment[19]. In Arabidopsis, asHSFB2a, the natural antisense transcript of HSFB2a was massively induced upon heat stress and exhibited a counteracted expression trend relative to HSFB2a. Overexpression of asHSFB2a entirely suppressed the expression of HSFB2a and impacted the plant's response to heat stress[20]. For drought stress resistance, 244 lncRNAs were predicted in tomatoes to be drought responsive probably by interacting with miRNAs and mRNAs[21]. Under drought stress and rehydration, 477 and 706 lncRNAs were differentially expressed in drought-tolerant Brassica napus Q2 compared to drought-sensitive B. napus, respectively[22]. In foxtail millet and maize, 19 and 644 lncRNAs, respectively, were identified as drought-responsive[23,24]. Despite the identification of numerous lncRNAs by high-throughput sequencing, which suggests their potential involvement in various abiotic stress processes, only a minority have been experimentally validated for function.

    In our previous study, we characterized 1,229 differentially expressed (DE) lncRNAs in Chinese cabbage as heat-responsive, and subsequent bioinformatics analysis reduced this number to 81, which are more likely associated with heat resistance[25]. lnc000283 and lnc012465 were selected from among them for further functional investigation. The findings indicated that both lnc000283 and lnc012465 could be promptly induced by heat shock (HS). Overexpression of either lnc000283 or lnc012465 in Arabidopsis plants enhanced their capacity to tolerate heat stress. Additionally, both lnc000283 and lnc012465 conferred drought tolerance to transgenic Arabidopsis.

    Materials and methods

    Plant materials, growth conditions, and stress treatment

    The lncRNA sequences examined in this study were from Chiifu-401-42 Chinese cabbage and all Arabidopsis plants were of the Col-0 background. Transgenic plants expressing lnc000283 and lnc012465 were generated using the Agrobacterium tumefaciens-mediated floral dip method[26]. Single-copy and homozygous T3 plants were identified through genetic segregation on an agar medium supplemented with kanamycin. The T3 generation plants, or their homozygous progeny, were utilized in the experiments.

    For phenotypic assessment, Arabidopsis seeds were initially sown on filter paper moistened with ddH2O and placed in a 4 °C freezer for 2 d. Subsequently, they were evenly planted in nutrient-rich soil and transferred to a growth chamber operating a 16-h day/8-h night cycle, with day/night temperatures of 22 °C/18 °C and a light intensity of 250 μmol·m−2·s−1. After 10 d of growth, Arabidopsis plants with uniform growth were transferred to 50-hole plates. Arabidopsis plants grown in Petri dishes were firstly seed-sterilized and then sown on 1/2 MS medium supplemented with 10 g·L−1 sucrose. The seeds were then placed in a 4 °C refrigerator for 2 d in the dark before transferring them to a light incubator. The day/night duration was set to 16 h/8 h, the day/night temperature to 21 °C/18 °C, and the light intensity to 100 μmol·m−2·s−1.

    For heat treatment, 3-week-old seedlings were subjected to 38 °C for 4 d within a light incubator, subsequently transferred to their original growth conditions under the same light/dark cycles. For drought treatment, 3-week-old Arabidopsis seedlings were deprived of water for 10 d, followed by rehydration to facilitate a 2-d recovery period. Plants were photographed and surveyed both before and after treatment.

    Vector construction

    The lncRNA sequences (lnc000283 and lnc012465) were chemically synthesized based on RNA-seq data, with restriction sites for BamH1 and Kpn1 engineered upstream and downstream. The resultant lncRNA constructs were subcloned into the pCambia2301 binary vector, incorporating a cauliflower mosaic virus (CaMV) 35S promoter. The recombinant vectors were transformed into Escherichia coli TOP10 competent cells (Clontech), incubated at 37 °C overnight, after which single clones were selected for PCR verification, and the confirmed positive colonies were submitted for sequencing. Following verification, the correct plasmids were introduced into A. tumefaciens strain GV3101 using the freeze-thaw method and subsequently transformed into Arabidopsis wild-type (Col) plants.

    Chlorophyll and anthocyanin content determination

    To quantify the chlorophyll content, the aerial portions of wild-type and transgenic Arabidopsis plants, grown in Petri dishes were weighed, minced, and then subjected to boiling in 95% ethanol until fully decolorized. Aliquots of 200 μL from the extract were transferred to a 96-well plate and the absorbance at 663 nm and 645 nm was measured via spectrophotometry by a microplate reader (Multiskan GO, Thermo Scientific, Waltham, MA, USA). Three biological replicates were analyzed for WT and each transgenic line. Chlorophyll content was determined according to the formula of the Arnon method[27]: Chlorophyll a = (12.72A663 − 2.59A645) v/w, Chlorophyll b = (22.88A645 − 4.67A663) v/w, Total chlorophyll = (20.29A645 + 8.05A663) v/w.

    The quantification of anthocyanin was performed as follows: aerial parts of wild-type and transgenic Arabidopsis plants, cultivated in Petri dishes, were weighed and ground to powder in liquid nitrogen. Subsequently, the samples were incubated in 600 μL of acidified methanol (containing 1% HCl) at 70 °C for 1 h. Following this, 1 mL of chloroform was added, and the mixture was vigorously shaken to remove chlorophyll. The mixture was then centrifuged at 12,000 rpm for 5 min, after which the absorbance of the aqueous phase was determined at 535 nm using a spectrophotometer (Shimadzu, Kyoto, Japan). Three biological replicates were analyzed for WT and each transgenic line. The relative anthocyanin content was calculated according to anthocyanin concentration and extraction solution volume. One anthocyanin unit is defined as an absorption unit at a wavelength of 535 nm in 1 mL of extract solution. In the end, the quantity was normalized to the fresh weight of each sample.

    Physiological analysis

    Three-week-old transgenic and WT A. thaliana plants, subjected to normal conditions or varying durations of heat or drought stress, were utilized for subsequent physiological assessments. All assays were performed in accordance with the method described by Chen & Zhang[28]. In brief, 0.1 g of fresh leaf tissue was homogenized in 500 μL of 100 mM PBS (pH 7.8) while chilled on ice. The homogenate was then centrifuged at 4 °C, and the resultant supernatant was employed for further analysis. For the determination of MDA content, 100 μL of the supernatant was combined with 500 μL of a 0.25% thiobarbituric acid (TBA) solution (which was prepared by dissolving 0.125 g of TBA in 5 mL of 1 mol·L−1 NaOH before being added to 45 mL of 10% TCA) and boiled for 15 min. Following a 5 min cooling period on ice, the absorbance was measured at 532 nm and 600 nm. The activity of POD was determined as follows: initially, 28 μL of 0.2% guaiacol and 19 μL of 30% H2O2 were sequentially added to 50 mL of 10mM PBS (pH 7.0), after thorough heating and mixing, 1 mL was transferred into a cuvette, then 50 μL of the supernatant was added to the cuvette and the absorbance at 470 nm was monitored every 15 s for 1 min. To determine the proline content, a reaction solution was prepared by mixing 3% sulfosalicylic acid, acetic acid, and 2.5% acidic ninhydrin in a ratio of 1:1:2, then 50 μL of the supernatant was added to 1 mL of the reaction solution, which was then subjected to a boiling water bath for 15 min (the solution turned red after the boiling water bath). Following cooling on ice, the absorbance at 520 nm was recorded. For the quantification of proline, an L-proline standard curve was prepared by dissolving 0, 5, 10, 15, 20, 25, and 30 μg of L-proline in 0.5 mL of ddH2O, followed by the addition of 1 mL of the reaction solution and measuring the absorbance at 520 nm. The proline content in the samples was then determined based on the L-proline standard curve.

    RNA isolation and qRT-PCR analysis

    Total RNA was isolated from the aerial parts of Arabidopsis using the TaKaRa MiniBEST Plant RNA Extraction Kit, followed by purification and reverse transcription using the PrimeScript RT reagent Kit with gDNA Eraser (Takara). The cDNA product was diluted 10 times and real-time PCR was conducted in triplicate for each biological replicate using SYBR PCR Master Mix (Applied Biosystems) on the ABI 7500 system under the following conditions: 98 °C for 3 min, followed by 40 cycles of 98 °C for 2 s and 60 °C for 30 s. The relative expression levels of each gene were normalized against the transcript abundance of the endogenous control UBC30 (At5g56150) and calculated using the 2−ΔCᴛ method. The specific primers employed for qRT-PCR are detailed in Supplemental Table S1.

    Results

    Screening and characterization of lncRNAs

    In our prior investigation, dozens of lncRNAs associated with the heat stress response in Chinese cabbage were identified through informatics analysis. Two lncRNAs (lnc000283 and lnc012465) were chosen for genetic transformation in Arabidopsis to elucidate their functions comprehensively. Transcriptome data analysis indicated that the expression of lnc000283 and lnc012465 in Chinese cabbage were both induced by HS. To verify the accuracy, the expression patterns of lnc000283 and lnc012465 were confirmed through quantitative real-time PCR (qRT-PCR), and the results from qRT-PCR were consistent with those obtained from RNA-seq (Fig. 1a). The corresponding homologous genes in Arabidopsis were identified as CNT2088434 and CNT2088742, exhibiting sequence similarities of 88% and 87%, respectively (Supplemental Fig. S1). Subcellular localization predictions using the lnclocator database (www.csbio.sjtu.edu.cn/bioinf/lncLocator) suggested that both lncRNAs are localized within the nucleus (Supplemental Table S2). Bioinformatics analysis was conducted using the CPC tool (http://cpc.cbi.pku.edu.cn/) indicated that lnc000283 and lnc012465 are noncoding sequences, with coding probabilities of 0.0466805 and 0.0432148, respectively comparable to the well-characterized lncRNAs COLDAIR and Xist, but significantly lower than those of the protein-coding genes UBC10 and ACT2 (Fig. 1b).

    Figure 1.  Characteristics of lnc000283 and lnc012465. (a) Expression level of lnc000283 and lnc012465 in Chinese cabbage leaves treated at 38 °C at different time points, as determined by qRT-PCR and RNA-seq. CK is a representative plant before heating, and T1, T4, T8, and T12 denote plants that were subjected to 38 °C for 1, 4, 8, and 12 h, respectively. The expression levels were normalized to the expression level of Actin. (b) Analysis of coding potential for lnc000283 and lnc012465. The coding potential scores were calculated using the CPC program. UBC10 (At5g53300) and ACT2 (At3g18780) are positive controls that encode proteins. COLDAIR (HG975388) and Xist (L04961) serve as negative controls, exhibiting minimal protein-coding potential.
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    Generation of lncRNA overexpression lines

    To elucidate the role of lnc000283 and lnc012465 in response to abiotic stress, overexpression vectors were constructed for these lncRNAs, driven by the CaMV 35S promoter, and they were introduced into Arabidopsis thaliana (Col-0 ecotype). Through PCR identification and generational antibiotic screening, two homozygous positive lines for lnc012465 and lnc000283 were obtained. The relative expression levels of these lncRNAs were assessed using qRT-PCR (Fig. 2a). When plants were grown in 1/2 MS medium, with the consumption of nutrients, and reduction of water, the leaves of WT began to turn yellow, but the lnc000283 and lnc012465 overexpression lines developed a deep purple color of leaf veins (Fig. 2b). Examination of chlorophyll and anthocyanin contents in the plants revealed that both overexpression lines had higher levels of chlorophyll and anthocyanin compared to the WT, suggesting that the transgenic plants might possess enhanced resistance to nutritional or water stress (Fig. 2c, d).

    Figure 2.  Arabidopsis plants overexpressing lnc000283 and lnc012465 had higher anthocyanins and chlorophyll content. (a) The relative expression level of lnc000283 and lnc012465 in WT and different transgenic lines. UBC10 (At5g53300) was used as an internal control. Each value is mean ± sd (n = 3). (b) The phenotype of WT and Arabidopsis overexpressing lnc000283 or lnc012465 grown on 1/2 MS medium 50 d after sowing. The (c) anthocyanin and (d) chlorophyll content of WT and transgenic Arabidopsis overexpressing lnc000283 or lnc012465. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01).
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    Effect of lncRNA overexpression on heat tolerance in Arabidopsis

    Given that lnc000283 and lnc012465 were highly induced by heat, the thermotolerance of the overexpressing (OE) plants were compared to that of the wild type. Arabidopsis plants were initially exposed to a an HS treatment at 38 °C for 4 d, followed by recovery at room temperature. The death caused by HS was processive. Post-severe HS challenge for 4 d, OE plants initially appeared similar to WT, but upon recovery, their leaves started to fold or curl, followed by a transition to yellow, white, and eventually drying out (Fig. 3a). OE lnc000283 and OE lnc012465 plants exhibited enhanced thermotolerance compared to WT, with lnc012465 showing particularly strong tolerance (Fig. 3a; Supplemental Fig. S2a). After 5 d of recovery, leaf coloration indicated that transgenic plants maintained a significantly higher percentage of green leaves and a lower percentage of bleached leaves compared to WT (Fig. 3b; Supplemental Fig. S2b). Under non-heat-stress conditions, WT and OE plants possessed comparable water content. However, following heat stress, the fresh-to-dry weight ratio of OE lnc000283 and lnc012465 lines was significantly greater than that of WT (Fig. 3c; Supplemental Fig. S2c). Abiotic stresses frequently trigger the production of excessive reactive oxygen species (ROS), which are believed to cause lipid peroxidation of membrane lipids, leading to damage to macromolecules. Leaf MDA content is commonly used as an indicator of lipid peroxidation under stress conditions; therefore, the MDA content in both transgenic and WT plants was assessed. Figure 3d shows that the MDA content in WT plants progressively increased after heat treatment, whereas in the two lines overexpressing lnc012465, the MDA content increased only slightly and remained significantly lower than that in WT at all time points. In plants overexpressing lnc000283, the MDA content did not significantly differ from that of WT before heat stress. However, after 4 d of heat treatment, the MDA content was significantly lower compared to WT (Supplemental Fig. S2d). The results suggested that the expression of both lnc012465 and lnc000283 can mitigate injury caused by membrane lipid peroxidation under heat-stress conditions. Peroxidase (POD) is a crucial antioxidant enzyme involved in ROS scavenging. Figure 3e and Supplemental Fig. S2e demonstrate that POD activity increased in both transgenic and WT plants after heat treatment. However, the increase in WT plants was modest, whereas OE lnc000283 and OE lnc012465 plants exhibited consistently higher POD activity. As anticipated, proline levels were induced in response to stress in all studied plants (Fig. 3f; Supplemental Fig. S2f). However, under normal conditions and 2 d post-heat stress treatment, the proline content in OE lnc000283 and OE lnc012465 plants did not exhibit significant changes compared to WT (Fig. 3f; Supplemental Fig. S2f). Moreover, after 4 d of heat stress, the proline content in OE lnc012465 lines was significantly lower than in WT, and the OE lnc000283 transgenic line 12-6 also showed a marked decrease in proline content compared to WT (Fig. 3f; Supplemental Fig. S2f). The results indicated that the thermotolerance of plants overexpressing either lnc000283 or lnc012465 was independent of proline accumulation.

    Figure 3.  Overexpressing lnc012465 lines are more tolerant to heat stress. (a) Phenotypes of WT and OE lnc012465 plants were assessed before and after exposure to heat stress. The heat treatment was applied to 25-day-old Arabidopsis plants. (b) The percentage of leaves with different colors in Arabidopsis after heat treatment and recovery for 5 d. (c) The fresh-to-dry weight ratio of Arabidopsis leaves was measured before and after 38 °C heat treatment. (d)−(f) depict the MDA content, POD activity, and proline content in Arabidopsis leaves at varying durations of heat stress. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Effect of lncRNA overexpression on the expression quantity of genes related to high-temperature response in Arabidopsis

    To elucidate the molecular mechanisms by which lncRNAs enhance thermotolerance in Arabidopsis, the expression of the Hsf gene HsfA7a and three Hsps (Hsp25.3, Hsa32, and Hsp18.1-CI) in OE lnc000283, OE lnc012465, and WT Arabidopsis plants were investigated at various time points following heat treatment. As shown in Fig. 4 and Supplemental Fig. S3, both Hsf and Hsps exhibited a rapid response to heat stress with strong induction. Notably, the transcripts of HsfA7a and Hsp25.3 were significantly upregulated at 1 h after heat exposure, then experienced a sharp decrease. Hsa32 and Hsp18.1-CI were highly induced at 1 h and, unlike the other proteins, sustained high expression levels at 3 h (Fig. 4; Supplemental Fig. S3). At 1 h post-heat treatment, the transcript levels of Hsa32 and HsfA7a in OE lnc000283 did not significantly differ from those in WT. However, by 3 h, Hsa32 expression was roughly 50% of the WT level, while HsfA7a expression was approximately double that of WT (Supplemental Fig. S3). The overexpression of lnc000283 did not significantly affect the transcript level of Hsp25.3 at any of the tested time points. Notably, Hsp18.1-CI expression in both lines overexpressing lnc000283 was significantly induced at all three detection points post-heat treatment, reaching approximately 4-9-fold higher levels than in the WT (Supplemental Fig. S3). In Arabidopsis plants with elevated expression of lnc012465, the expression patterns of all Hsp and Hsf genes were similar to those in plants overexpressing lnc000283, with the notable exception of Hsa32. Unlike the WT, Hsa32 did not show a trend of down-regulation at 3 h post-heat treatment (Fig. 4). The findings suggest that the substantial induction of Hsp18.1-CI may play a role in enhancing the thermotolerance of Arabidopsis plants overexpressing lnc000283 and lnc012465.

    Figure 4.  The expression of HSF and HSP genes in lnc012465 overexpressing lines before and after different heat treatment times. Gene expression levels were quantified using RT-qPCR and normalized to UBC10 (At5g53300). Each value represents the mean ± standard deviation (n = 3). The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Effect of lncRNA overexpression on drought tolerance in Arabidopsis

    Prior research has implicated a significant proportion of genes in conferring resistance to various abiotic stresses. To elucidate the functions of lnc000283 and lnc012465 more thoroughly, WT and transgenic plants were subjected to drought stress by depriving them of water for 9 d. It was noted that the majority of leaves in WT plants withered and dried, whereas the OE lnc000283 and OE lnc012465 plants exhibited reduced withering, with only a minority displaying dryness (Fig. 5a; Supplemental Fig. S4a). Eight days post-rewatering, a negligible fraction of WT seedlings exhibited recovery, whereas the overwhelming majority of transgenic plants regained vigorous growth (Fig. 5a; Supplemental Fig. S4a). The transgenic plants demonstrated a significantly higher survival rate compared to the WT plants. Following 9 d of water deficit treatment, less than 40% of the WT plants survived, whereas the OE 012465 lines 8-7 and 9-1 exhibited survival rates of 100% and 95%, respectively, and the OE 000283 lines 11-10 and 12-6 had survival rates of 87% each. (Fig. 5b; Supplemental Fig. S4b). Water loss serves as a critical metric for assessing plant drought tolerance, hence the fresh-to-dry weight ratio of excised leaves was assessed via desiccation analysis. Following 4 d of drought treatment, the fresh-to-dry weight ratio for WT plants was reduced to 43%, whereas for OE lnc000283 lines 11-10 and 12-6, it was reduced to 73% and 75%, respectively. For OE 012465 lines 8-7 and 9-1, the ratios were reduced to 67% and 62%, respectively (Fig. 5c; Supplemental Fig. S4c). The findings indicated that lnc000283 and lnc012465 endow the transgenic plants with drought tolerance.

    Figure 5.  Overexpressing lnc012465 lines are more tolerant to drought stress. (a) Phenotype of WT and OE lnc012465 plants before and after subjecting to drought stress. Drought treatment was carried out on 20-day-old Arabidopsis plants. (b) The percentage of leaves with different colors in Arabidopsis after heat treatment and recovery for 5 d. (c) The fresh weight to dry weight ratio of Arabidopsis leaves before and after undergoing 38 °C heat treatment. (d)−(f) MDA content, POD activity, and proline content in Arabidopsis leaves under different times of heat stress. The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001)
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    MDA content in leaves is a standard biomarker for assessing the extent of drought stress-induced damage. Prior to drought stress exposure, MDA levels in WT and transgenic plants were comparable. However, following 7 and 9 d of water deficit, the MDA content in the transgenic plants was markedly reduced compared to the WT, suggesting a less severe degree of membrane lipid peroxidation in the transgenic plants (Fig. 5d; Supplemental Fig. S4d). Oxidative stress frequently coincides with drought stress, hence the activity of POD was assessed to evaluate the ROS scavenging ability. The findings indicated that as the duration of drought treatment increased, POD activity progressively rose. Before drought exposure, the POD activity in lines 11-10 and 12-6 of OE 000283 was 2.4-fold and 2.2-fold higher than that of the WT, respectively (refer to Supplemental Fig. S4e). Following drought treatment, the POD activity in the transgenic lines remained significantly elevated compared to the wild type, although the enhancement was less pronounced than before the treatment (Supplemental Fig. S4e). In the OE 012465 plants, the POD activity in lines 8-7 and 9-1 significantly surpassed that of the wild type, with the discrepancy being more pronounced during drought stress (Fig. 5e). The proline content in WT and OE 000283 plants exhibited no significant differences before and after 7 d of treatment. However, after 9 d of drought, the proline content in OE 000283 plants was significantly lower compared to that in the WT (Supplemental Fig. S4f). OE 000465 plants showed no significant difference from the wild type before and after drought treatment (Fig. 5f). The findings were consistent with those under heat stress, indicating that the enhanced stress resistance due to the overexpression of lnc000283 and lnc012465 in Arabidopsis is not reliant on proline accumulation.

    Following drought stress treatment, the expression levels of drought-related genes such as RD29A, RD29B, NCED3, AREB1, and Rab18 were significantly elevated in plants overexpressing lnc000283 and lnc012465 compared to WT plants. These findings suggest that lnc000283 and lnc012465 modulate Arabidopsis drought tolerance by regulating the expression of genes associated with the drought stress response (Fig. 6; Supplemental Fig. S5).

    Figure 6.  The expression of drought-responsive genes in lnc012465 overexpressing lines before and after different drought treatment time. Gene expression levels were determined by qRT-PCR normalized against UBC10 (At5g53300). Each value is mean ± sd (n = 3). The asterisks above the bars indicate statistical significance using Student's t-test (*, p < 0.05; **, p < 0.01; ***, p < 0.001).
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    Discussion

    The integrity of global food security is under threat due to the confluence of rapid population expansion and profound climatic shifts[29]. Amidst the shifting climatic landscape, heat and drought stress have emerged as primary limitations to crop yield and global food security. Understanding how plants detect stress cues and acclimate to challenging conditions is a pivotal biological inquiry. Moreover, enhancing plant resilience to stress is essential for maintaining agricultural productivity and fostering environmental sustainability[2]. Concurrently, the advancement of next-generation sequencing (NGS) technology has led to the identification of a substantial number of lncRNAs that participate in diverse stress responses, with functional analyses having been conducted on several of these molecules.[30] For instance, in the case of potatoes, the lncRNA StFLORE has been identified to modulate water loss through its interaction with the homologous gene StCDF1[31]. LncRNA TCONS_00021861 can activate the IAA biosynthetic pathway, thereby endowing rice with resistance to drought stress[32]. In wheat, the expression of TalnRNA27 and TalnRNA5 was upregulated in response to heat stress[33]. Our prior investigation identified a total of 81 lncRNAs in Chinese cabbage that engage in intricate interactions with their respective mRNA targets across various phases of heat treatment[25]. Two lncRNAs, lnc000283 and lnc012465, were chosen for subsequent functional analysis. Findings confirmed that these lncRNAs endow transgenic Arabidopsis plants with enhanced tolerance to both heat and drought, thereby offering novel resources for enhancing stress resistance through genetic engineering.

    Abiotic stresses frequently trigger the synthesis of anthocyanins, serving as natural antioxidants that mitigate oxidative damage by neutralizing surplus reactive oxygen species (ROS), thereby protecting plants from growth inhibition and cell death, allowing plants to adapt to abiotic stresses[34,35]. For instance, during chilling stress, the accumulation of anthocyanins within leaves can mitigate oxidative damage, thereby enhancing the photosynthetic rate[36]. Consequently, the level of abiotic stress tolerance can be inferred from the concentration of anthocyanins. The reduction of photosynthetic ability is one of the key physiological phenomena of stresses, which is partly due to the degradation of chlorophyll caused by leaf senescence during stress. The reduced accumulation of chlorophyll in the plants was seen in many plants when exposed to drought or heat stress conditions. The current investigation revealed that lncRNA-overexpressing plants cultivated in Petri dishes exhibited increased accumulation of both chlorophyll and anthocyanins in advanced growth phases, indicating that these transgenic plants, overexpressing lnc000283 and lnc012465, demonstrated enhanced stress tolerance and superior growth performance relative to WT (Fig. 2c, d).

    Upon exposure to heat stress, there is a marked induction of transcription for numerous genes that encode molecular chaperones in plants, with the vast majority of these genes contributing to the prevention of protein denaturation-related damage and the augmentation of thermotolerance[37−39]. The present investigation identified multiple heat-inducible genes in plants overexpressing lnc000283 and lnc012465, as well as in WT (Fig. 4; Supplemental Fig. S3). The findings indicated that of the four HSP or HSF genes examined, Hsp18.1-CI exhibited a significantly greater abundance in both OE lnc000283 and OE lnc012465 plants compared to the WT following heat treatment for several days. Hsp18.1-CI, formerly referred to as Hsp18.2 has been the subject of investigation since 1989.[40] Following the fusion of the 5' region of Hsp18.2 in frame with the uidA gene of Escherichia coli, the activity of GUS, serving as the driver gene was observed to increase upon exposure to HS[40]. The Arabidopsis hsfA2 mutant exhibited diminished thermotolerance after heat acclimation, with the transcript levels of Hsp18.1-CI being substantially reduced compared to those in wild-type plants following a 4-h recovery period[41]. The findings revealed that the upregulation of Hsp18.1-CI protein is a critical mechanism by which plants achieve enhanced protection against heat stress in adverse environmental conditions, thereby bolstering their thermotolerance.

    Plants cultivated in natural settings are often subjected to concurrent multiple abiotic stresses, which can exacerbate threats to their routine physiological functions, growth, and developmental processes[42,43]. Elucidating the molecular mechanisms underlying plant responses to abiotic stress is crucial for the development of new crop varieties with enhanced tolerance to multiple abiotic stresses. Previous research has indicated that the overexpression of certain protein-coding genes can endow plants with resistance to a variety of abiotic stresses. For instance, tomatoes with robust expression of ShCML44 demonstrated significantly enhanced tolerance to drought, cold, and salinity stresses[44]. Overexpression of PeCBF4a in poplar plants confers enhanced tolerance to a range of abiotic stresses[45]. With respect to lncRNAs, transgenic Arabidopsis plants that overexpress lncRNA-DRIR displayed marked increased tolerance to salt and drought stresses compared to the wild-type[46]. In the present study, both overexpression lines of lnc000283 and lnc012465 exhibited resistance to heat and drought stresses, thereby contributing to the enhancement of plant resilience against multiple stresses (Figs 3, 5; Supplemental Figs S2, S4).

    The number of genes implicated in plant drought resistance is regulated by both ABA-dependent and ABA-independent pathways[47,48]. It is well established that the expression of RD29A exhibits a high level of responsiveness to drought stress, operating through both ABA-dependent and ABA-independent mechanisms[49]. RD29B, AREB1, and RAB18 are governed by an ABA-dependent regulatory pathway[10,49,50]. NCED3 is involved in ABA biosynthesis[51]. In the present study, the transcript levels of RD29A, RD29B, NCED3, AREB1, and RAB18 were significantly elevated in OE lnc000283 and OE lnc012465 plants compared to those in the WT plants (Fig. 6; Supplemental Fig. S5). The findings indicated that the drought tolerance imparted by OE lnc000283 and OE lnc012465 plants is contingent upon an ABA-dependent mechanism.

    Prior research has indicated that certain long non-coding RNAs (lncRNAs) can assume analogous roles across diverse biological contexts. For example, the lncRNA bra-miR156HG has been shown to modulate leaf morphology and flowering time in both B. campestris and Arabidopsis[52]. Heterogeneous expression of MSL-lncRNAs in Arabidopsis has been associated with the promotion of maleness, and similarly, it is implicated in the sexual lability observed in female poplars[53]. In the present study, lnc000283 and lnc012465 were induced by heat in Chinese cabbage, and their heterologous expression was found to confer heat tolerance in Arabidopsis. Additionally, sequences homologous to lnc000283 and lnc012465 were identified in Arabidopsis (Supplemental Fig. S1). The data suggest that these sequences may share a comparable function to that of heat-inducible sequences, potentially accounting for the conservation of lnc000283 and lnc012465'os functionality across various species.

    Conclusions

    In conclusion, the functions of two heat-inducible lncRNAs, lnc000283 and lnc012465 have been elucidated. Transgenic Arabidopsis lines overexpressing these lncRNAs accumulated higher levels of anthocyanins and chlorophyll at a later stage of growth compared to the WT when grown on Petri dishes. Furthermore, under heat and drought stress conditions, these OE plants exhibited enhanced stress tolerance, with several genes related to the stress resistance pathway being significantly upregulated. Collectively, these findings offer novel insights for the development of new varieties with tolerance to multiple stresses.

    Author contributions

    The authors confirm contribution to the paper as follows: study conception and supervision: Li N, Song X; experiment performing: Wang Y, Sun S; manuscript preparation and revision: Wang Y, Feng X, Li N. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    All data generated or analyzed during this study are included in this published article and its supplementary information files.

    Acknowledgments

    This work was supported by the National Natural Science Foundation of China (32172583), the Natural Science Foundation of Hebei (C2021209019), the Natural Science Foundation for Distinguished Young Scholars of Hebei (C2022209010), and the Basic Research Program of Tangshan (22130231H).

  • The authors declare that they have no conflict of interest.

  • Supplemental Table S1 Mean performance for agronomic traits of tomato accessions and the checks evaluated.
    Supplemental Table S2 Mean performance of the evaluated tomato accessions and the checks for physico-chemical traits.
    Supplemental Table S3 Principal component analysis of the contributions of agronomic and physico-chemical traits to total variation among the tomato accession and checks evaluated.
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  • Cite this article

    Olayinka AO, lbitoye DO, Aderibigbe OR. 2024. Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits. Technology in Horticulture 4: e021 doi: 10.48130/tihort-0024-0018
    Olayinka AO, lbitoye DO, Aderibigbe OR. 2024. Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits. Technology in Horticulture 4: e021 doi: 10.48130/tihort-0024-0018
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Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits

  • 1.

    Crop Production and Soil Science Department, Ladoke Akintola University of Technology, PMB 4000, Ogbomoso, Nigeria

  • 2.

    Genetic Resources Unit, National Horticultural Research Institute, PMB 5432, Ibadan, Nigeria

  • 3.

    Product Development Programme, National Horticultural Research Institute, PMB 5432, Ibadan, Nigeria

  • Received: 14 May 2024
  • Revised: 10 August 2024
  • Accepted: 22 August 2024
  • Published online: 12 September 2024
Technology in Horticulture  4,  Article number: e021  (2024)  |  Cite this article

Abstract: The rising significance of tomato (Solanum lycopersicum L.) in human diet necessitates the continuous search for genotypes with favorable alleles for agronomic and nutritional properties from untapped genetic diversity. In this study, the phenotypic diversity of tomato accessions was assessed for agronomic and physico-chemical traits to identify accessions with potential horticultural traits that can be utilized in tomato improvement programs. A set of 23 accessions collected from the National Horticultural Research Institute (NIHORT), Ibadan, Nigeria, and two traditional varieties used as checks were evaluated in a 5 × 5 α-lattice design with three replicates at the Teaching and Research Farm of Ladoke Akintola University of Technology, Ogbomoso, Nigeria in the main cropping season of 2021. Data collected includes six physico-chemical parameters and 11 agronomic traits. Analysis of variance showed that accessions varied significantly (p < 0.001) for all of the traits measured. Wide variations were observed for some traits suggesting a considerable level of diversity among the accessions. Accession NHTO-0199, with the highest fruit weight, had a 59% yield advantage over the best traditional variety. The first two principal components accounted for 53% of the total variation among the tomato accessions. The patterns of variation were described by the phenological stages of flowering, fructifying, fruit maturation, plant height, fruit yield components, lycopene, and vitamin C content of the fruits. The cluster analysis delineated the accessions into three distinct clusters and hybridization between clusters may generate desired allelic combinations useful for developing unique variety. The following top five accessions: NHTO-0352, NHTO-0350, NHTO-0199, NHTO-0351, and NHTO-0346 had outstanding performances for fruit yield and physico-chemical traits based on Rank Summation Index. These superior accessions can be advanced for further improvement and may be used as sources of traits in crosses to develop new breeding lines.

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    Introduction

    • Tomato (Solanum lycopersicum L.) is one of the most important horticultural crops in the Solanaceae family with numerous uses and health benefits. Tomato fruits are rich sources of minerals, vitamins (A, B, and C), antioxidant compounds (lycopene and ascorbic acid) and carotenoids (β-carotene) which are essential for the usual physiological activities of the human body[1]. Tomatoes are enjoyed in various forms: fresh fruits are commonly eaten in salads and sandwiches while processed varieties are consumed dried or in the form of pastes, sauces, soups and juices[2]. Its contributions to food and nutrition security has led to a rise in cultivation and consumption over the years[3]. Thus, it is imperative to preserve tomato biodiversity and also promote new germplasm with highly marketable values.

      Collection, evaluation, and exploitation of different tomato genetic resources have become a necessity and precondition for successful breeding, considering the increase in the loss of global crop biodiversity. The diversity in crop species depends on mutation, recombination, selection, introduction, and genetic drift[4]. The continuous search for diversity in newly collected germplasm is a way of identifying desirable genes for future utilization in breeding programs. Genetic resources are important reservoirs for variability that can be exploited to enhance the genetic improvement of crops. Adequate characterization of gene bank accessions is therefore needed to facilitate the utilization of germplasm by end users.

      The examination of genetic diversity within a crop species can be undertaken through various methodologies, including morphological or phenotypical, biochemical, and molecular markers[5]. Morphological characterization influenced by environmental factors offers insights into crop species based on their observable traits in field conditions and has proven effectives in examining genetic diversity in crops[6]. Morphological characterization of newly introduced accessions is a quick and inexpensive effective method for maintenance and utilization of genetic resources[7]. A similar method was found suitable for detailed accession characterization in this study.

      With the availability of diverse tomato varieties, both qualitative and quantitative traits have been successfully used to study diversity in tomato[2]. In the past, tomato producers, consumers, and breeders were mainly concerned about the yield and phenotypic appeal of the fruit. However, for sustainable improvement and production of elite tomato, the genetic background and breeding values of newly collected tomato accessions should be investigated not only for morphological or agronomic traits, but also for nutritional parameters which has become essential in the identification of the best parental combinations[8]. The physico-chemical properties of the tomato fruits are valuable for the selection of superior genotypes with improved quality and flavor.

      To ensure the success of a breeding program, it is imperative to establish a robust pre-breeding gene pool, which will facilitate the generation of genetic recombinants for future selection[9]. Building such a gene pool necessitates thorough characterization of germplasm harboring traits of breeding significance. The approach used in this study provides an opportunity to identify variability among new collections of tomato accessions and identify superior accessions for breeding improved quantitative traits. Thus, the objectives of this study are to (i) assess the variability in agronomic and physico-chemical traits of tomato accessions, and (ii) identify complementary parents with high-quality fruits that can be utilized in tomato improvement programs.

    Materials and methods

      Plant materials, experimental site, and cultivation

    • A total of 25 genotypes consisting of 23 accessions collected from the genetic resources unit of the National Horticultural Research Institute (NIHORT), Ibadan, Nigeria, and two traditional varieties used as local checks (Table 1) were evaluated at the Teaching and Research Farm of Ladoke Akintola University of Technology, Ogbomoso (8°10'N, 4°10'E, and altitude 341 m above sea level). Tomato seeds were sown in perforated nursery trays filled with sterilized soil and grown for three weeks in the greenhouse. All plant protection measures and cultural practices were observed during the nursery growth period. Seedlings were later transplanted to the open field in a single row plot (one row per single bed) that was 4 m long with a spacing of 0.5 m between rows and 0.5 m between plants within a row. The beds were 1 m apart. The seedlings were arranged in a 5 × 5 α-lattice design with three replications. The recommended dose of N, P2O5, and K2O, fertilizers in the form of urea (46% N), single super phosphate (16% P2O5), and murate of potash (60% K2O) was applied three weeks after transplanting. The plants were supported with trellises to prevent lodging and reduce the risk of fruit loss due to diseases and pests. Weeding was manually performed every two weeks. To protect the leaves from pests that cause defoliation, plants were treated with the pyrethroid insecticide Cymbush, which contains cypermethrin. The insecticide was applied at 2, 6, and 9 weeks after transplanting, using a knapsack sprayer at a rate of 450 mL of active ingredient per 100 L of water per hectare. Throughout the experiment, no disease infestations were observed. Data collection was conducted on five randomly selected plants per plot for each accession per replicate. Other field management activities, including staking, weeding, and pest protection, were carried out during the crop's growth period.

      Table 1.  Tomato accessions and local checks evaluated in the study.

      S/N Accession Local name Collection source Growth habit
      1 NHTO-0199 Dan Biu Maiduguri, Borno state Semi-determinate
      2 NHTO-0239 UC Funtua, Katsina state Indeterminate
      3 NHTO-0259 Tomato Babura, Jigawa state Indeterminate
      4 NHTO-0264 Tomato Babura, Jigawa state Indeterminate
      5 NHTO-0340 Tima Kano state Indeterminate
      6 NHTO-0342 Tomato Babura, Jigawa state Semi-determinate
      7 NHTO-0346 Ex-Babura Babura, Jigawa state Indeterminate
      8 NHTO-0350 Tomato Makarfi, Kaduna state Indeterminate
      9 NHTO-0351 Dan Batanas Makarfi, Kaduna state Determinate
      10 NHTO-0352 Dan India Makarfi, Kaduna state Indeterminate
      11 NHTO-0353 Tomato Bauchi State Semi-determinate
      12 NHTO-0368 Dan Gombe Dadin kowa, Gombe state Semi-determinate
      13 NHTO-0388 Heinz 2274 Kano state Semi-determinate
      14 NHTO-0389 Tomato Maiduguri, Borno state Indeterminate
      15 NHTO-0390 Girafto Babura, Jigawa state Semi-determinate
      16 NHTO-0400 Bakin iri Bomo, Zaria state Indeterminate
      17 NHTO-0568 Dan Gombe Dadin kowa, Gombe state Determinate
      18 NHTO-0569 Dan Baga Maiduguri, Borno state Indeterminate
      19 NHTO-0570 Tomato Makarfi, Kaduna state Determinate
      20 NHTO-0571 Tomato Makarfi, Kaduna state Semi-determinate
      21 NHTO-0572 Dan Syria Maiduguri, Borno state Semi-determinate
      22 NHTO-0573 Dallaji Bauchi state Indeterminate
      23 NHTO-0574 Tomato Maiduguri, Borno state Determinate
      24 LC CHK-Y Tomato Yoruba Ogbomoso, Oyo State Semi-determinate
      25 LC CHK-H Timo Hausa Ogbomoso, Oyo State Semi-determinate
      NHTO = NIHORT Tomato; LC CHK-H = Local check-Hausa; LC CHK-Y = Local check-Yoruba.

      Data collection for agronomic traits and physico-chemical assessments

    • The International Plant Genetic Resources Institute[10] tomato descriptors were considered in collecting data in the field as well as in the laboratory. Quantitative agronomic data were collected on number of branches (NOB), number of days to flowering (DTF), number of days to maturity (DTM), fruit length (FL), fruit width (FWD), number of flowers per cluster (NFPC), number of fruits per cluster (FPC), plant height (PH), number of days to first harvest (DTH), number of fruits per plot (FPP) and fruit weight per plot (TFW). Days to flowering was recorded as the number of days from sowing to when 50% of the plants in each plot had flowered. Manual branch counting was used to determine the number of branches, days to maturity was recorded from sowing until 50% of plants had at least one ripened fruit. Fruit length and width were measured at physiological maturity. Fruit length was recorded from stem end to blossom end using a meter rule (cm) while fruit width was recorded at the largest diameter of cross-sectioned fruits using a digital calipers-515 (cm). The total number of fruits per plot was determined at physiological maturity and a digital weighing machine was used to obtain the total fruit weight per plot.

      For physico-chemical parameters, tomatoes were harvested at red ripe stage on five plants per genotype. Total soluble solids (TSS) content which gives information on the percentage of sugars present in the tomato juice was measured using a digital refractometer (Model, PAL-Tea, ATAGO, Tokyo, Japan), and the results were expressed as °Brix in accordance with the Association of Official Analytical Chemists[11] methods at room temperature. The fruit juice pH was determined using a pH meter and the titratable acidity was determined following the method of AOAC and expressed in percentage. Simple sugars such as ascorbic acid (vitamin C) and carotenoids (β-carotene and lycopene) were also analyzed according to standard laboratory procedures and expressed in mg 100 g−1. All analyses were done in triplicate for each representative fruit sample at the Product Development Laboratory of NIHORT.

      Statistical data analysis

    • All collected data was entered into Microsoft Excel 2019 before analysis. Analysis of variance (ANOVA) was performed with the General Linear Model (GLM) procedure in Statistical Analysis System (SAS) software version 9.4[12] to examine differences among the accessions. To avoid Type I error rates across multiple comparisons, Tukey's honestly significant difference (HSD) test was applied to determine trait means significant differences among the evaluated accessions at 5% probability level using the R statistical software.

      The linear model used in this study was: yij=μ+bj+αi+eij, where yij is the observation value of response trait obtained from i-th accession in j-th block, µ is the overall mean, bj is the effect of j-th block, αi is the effect of i-th accession and eij the error associated with yij.

      To examine the proportion of the total variance of a trait that is due to genetic differences among the tomato accessions, repeatability was computed. The GLM procedure of SAS was used to estimate the variances and the repeatability of the traits was computed only for all the agronomic traits measured. The Rank summation index (RSI) of Mulumba & Mock[13] was used to rank the performance of the tomato accessions based on four selected economically important traits (number of fruits per plot, fruit weight per plot, β-carotene, and lycopene). The accessions were ranked for these traits and the rankings were then combined to create an index for each accession. The accession with the lowest RSI value was considered the best, while the one with the highest RSI value had poor performance.[14] Principal Component Analysis (PCA) was performed to determine the traits that account for most of the variations among the accessions using R statistical software (version 4.2.2) and was plotted using the package 'FactoMineR'. The PCs with Eigenvalues > 1 were selected[15] and the first two PCs which explained maximum total variations were plotted on a two-dimensional plot for all the accessions. For the grouping of similar accessions based on agronomic and physico-chemical traits, cluster analysis was computed. Distinct clusters were established using Ward's coefficient of agglomerative hierarchical clustering in R statistical software version 4.2.2[16]. Pearson's correlation analysis was computed to determine associations among all traits measured using the 'metan' package in R[17].

    Results

      Variability among tomato accessions

    • The tomato leaves and fruits showed a large range of phenotypic variation among the 23 accessions and two traditional varieties used as checks (Fig. 1). In addition to obvious differences in leaf and fruit shapes, results from analysis of variance revealed that the accessions showed significantly (p < 0.001, p < 0.01, and p < 0.05) different mean squares for all the measured agronomic and physico-chemical traits (Table 2). Coefficients of variation (CV) were below 20% for most of the measured traits but appeared excessively high (21%−80%) for numbers of branches, flowers per cluster, fruit per cluster, fruit per plot, plant height, and fruit weight. The low CV observed for most traits implies the precision of the experiment and reliability of the data collection procedure. The magnitude of the coefficient of determination (R2) was high (70%−99%) for all traits measured, indicating the reliability of the statistical analysis to capture variability among the tomato accessions. Repeatability estimates for agronomic traits ranged from 0.39 (fruit length) to 0.66 (number of branches). Only the latter trait showed high magnitude as other traits had moderate estimates which is an indication of the effects of the test environment on the performance of the tomato accessions.

      Figure 1. 

      Tomato accession diversity of leaf and fruit morphology within each distinct cluster. Each accession's leaf and fruit are accompanied by the accession name.

      Table 2.  Mean squares of agronomic and physico-chemical traits of the tomato accessions evaluated.

      Source df No. of branches No. of
      days to
      flowering (d)      		 No. of
      days to
      maturity (d)      		 Fruit length (cm) Fruit
      width
      (cm)      		 No. of
      flowers
      per
      cluster      		 No. of
      fruits
      per cluster      		 Plant height (cm) No. of days
      to first
      harvest (d)      		 No. of
      fruits
      per plot      		 Fruit weight
      per plot (kg)      		 Vitamin C (mg
      100 g−1)      		 β-carotene (mg 100 g−1) Lycopene (mg
      100 g−1)      		 Titratable acidity (%) Fruit juice pH Total soluble solids (°Brix)
      Replication (Rep) 2 39.52** 24.16 86.44 0.82 0.67* 0.65 2.28 116.76 10.33 9,268.93 1.42 0.44 0.02** 2.22*** 0.03 0.01*** 0.00
      Block (Rep) 12 10.66 38.87 47.36 0.10 0.09 1.57 1.96 353.90* 28.50 4,766.78 3.65 0.32 0.01 0.18 0.02 0.00 0.03***
      Accession 24 12.42* 106.19*** 223.06** 3.55*** 1.11*** 3.72*** 3.58*** 651.85*** 154.51*** 36,185.35*** 8.73*** 13.67*** 1.10*** 297.40*** 0.36*** 0.22*** 0.06***
      Error 36 6.01 28.29 73.73 0.30 0.17 1.33 1.40 166.38 46.2 5,525.12 1.80 0.31 0.01 0.23 0.02 0.00 0.01
      CV (%) 31.27 13.49 11.44 18.42 14.53 28.64 21.95 21.05 9.03 80.10 54.04 5.33 4.06 3.09 6.25 0.59 1.53
      R2 (%) 75 78 73 91 85 73 70 78 76 84 79 97 99 99 94 99 95
      Repeatability 0.66 0.51 0.55 0.39 0.44 0.57 0.59 0.50 0.53 0.44 0.47 − − − − − −
      *, **, *** significant at 0.05, 0.01 and 0.001 probability levels, respectively. CV = coefficient of variation, R2 = coefficient of determination.

      The significant differences among accessions for all measured traits enabled grouping into different classes and the identification of outstanding accessions. The Tukey's HSD separated the trait means into two classes for the numbers of branches, days to maturity, flowers per cluster, plant height, and number of days to first harvest (Supplemental Table S1). The other measured agronomic and nutritional traits were separated into three or more classes and the means having 'a' were considered the best. The greatest magnitude of variation was observed in the number of fruits per plot which varied from 10 (NHTO-0568) to 476 (NHTO-0259), followed by plant height in the range of 34.3 (NHTO-0568) to 93.7 cm (NHTO-0259). The number of branches varied from 4 (NHTO-0351) to 13 (NHTO-0572), number of days to flowering ranged from 30 (NHTO-0389) to 55 d (NHTO-0569), number of days to maturity was between 65 (NHTO-0350) to 98 d (NHTO-0574), fruit length varied from 1.2 (NHTO-0572) to 5.8 cm (NHTO-0368), fruit width ranged from 1.3 (NHTO-0572) to 4.1 cm (NHTO-0568), number of flowers per cluster was between 2 (NHTO-0570) and 7 (NHTO-0259), the number of fruits per cluster was between 4 (LC CHK-H) and 8 (NHTO-0572), number of days to first harvest ranged from 68 (NHTO-0350) to 97 d (NHTO-0569) and fruit weight per plot varied from 0.3 (NHTO-0569) to 6.5 kg (NHTO-0199). The grand mean values were 7.8 for number of branches, 39 d for number of days to flowering, 75 d for number of days to maturity, 3.0 cm for fruit length, 2.8 cm for fruit width, 4.0 for number of flowers per cluster, 5.4 for number of fruits per cluster, 61.3 cm for plant height, 75 d for number of days to first harvest, 92.8 for number of fruits per plot and 2.5 kg for fruit weight per plot.

      In comparison to the traditional varieties used as checks, the two checks (LC CHK-Y and LC CHK-H) evaluated in this study were comparable with the tomato accessions for most traits. Only one accession (NHTO-0568) with a fruit width of 4.1 cm was significantly (p < 0.05) different from the checks (Supplemental Table S1). NHTO-0569 took significantly longer days (97 d) to harvesting. The number of fruits per plot of NHTO-0259 and NHTO-0572 were significantly (p < 0.05) different from the checks. The fruit weight of NHTO-0199 surpasses that of the LC CHK-H significantly (p < 0.05) and out-yielded the best check (LC CHK-Y) by 59%.

      Furthermore, the potential of the fruit quality determines their utilization in value addition industries. Considering the nutritional profile of the evaluated tomato accessions, a higher value in total soluble solids content (5.4 °Brix) was found in NHTO-0332 (Supplemental Table S2). The lowest value was recorded in NHTO-0368 (4.5 °Brix). Higher value in titratable acidity (3.0%) which surpasses the checks significantly (p < 0.05) was observed in NHTO-0199 while the lowest value (1.6%) was recorded in NHTO-0353. The pH showed the ideal range, ranging from 2.4 (NHTO-0389) to 4.1 (NHTO-0573). Three accessions (NHTO-0573, NHTO-0574 and NHTO-0569) had significant (p < 0.05) higher pH values than the checks. The lycopene content varied from 5 mg 100 g−1 (NHTO-0389) to 38 mg 100 g−1 (NHTO-0568). Only NHTO-0568 and NHTO-0351 were higher in lycopene (the compound providing the red color to the fruits) than the superior check (LC CHK-H) which has 32.4 mg 100 g−1 lycopene content. A higher value in β-carotene content (3.4 mg 100 g−1) was found in NHTO-0350. The lowest value was recorded in NHTO-0390 (0.8 mg 100 g−1). About 30% of the accessions notably; NHTO-0350, NHTO-0400, NHTO-0352, NHTO-0571, NHTO-0340, NHTO-0346 and NHTO-0388 had significantly (p < 0.05) higher β-carotene content (precursor of vitamin A) than the checks. The vitamin C content varied from 7.7 mg 100 g−1 (NHTO-0346) to 14.0 mg 100 g−1 (NHTO-0353) and was at par with the checks. The grand mean values were 10.4 mg 100 g−1 for vitamin C, 1.8 mg 100 g−1 for β-carotene, 15.4 mg 100 g−1 for lycopene, 2.2% for titratable acidity, 3.4 for fruit juice pH and 5.0 °Brix for total soluble solids.

      Additionally, the tomato accessions performance was ranked based on four economic traits namely: number of fruits per plot, fruit weight, β-carotene, and lycopene content of the tomato fruits. The accessions were ranked for each of these traits and the ranks for each trait were summed up to obtain an index for each accession. The best accession had the least RSI value (20), whereas the worst one had the highest RSI value (93). The top five accessions with high fruit yield and quality were NHTO-0352, NHTO-0350, NHTO-0199, NHTO-0351, and NHTO-0346 (Table 3). However, NHTO-0353 ranked first based on the number of desirable 'a's it had across all agronomic andphysico-chemical traits, according to Tukey's HSD ranking. NHTO-0199, NHTO-0346, NHTO-0350, and NHTO-0352 identi-fied as superior by RSI, ranked second, as well as NHTO-0259, NHTO-0340, NHTO-0353, NHTO-0400, and NHTO-0573 indicating adaptability to the test environment.

      Table 3.  Fruit yield and quality of top and bottom five tomato accessions based on Rank Summation Index.

      Accession Fruit weight per plot (kg) Number of fruits per plot β-carotene (mg
      100 g−1)      		 Lycopene (mg
      100 g−1)      		 Rank Summation Index
      Top 5
      NHTO-0352 4.2 80.5 2.8 30.3 20
      NHTO-0350 3.3 65.7 3.4 9.2 32
      NHTO-0199 6.5 92.0 1.5 8.6 35
      NHTO-0351 2.3 71.1 1.8 34.7 35
      NHTO-0346 2.8 159.8 2.2 8.4 37
      Mean of Top 5 3.8 93.8 2.3 18.2
      Grand mean 2.5 92.8 1.8 15.4
      Selection differential (%) 53.3 1.1 31.5 18.2
      Bottom 5
      NHTO-0342 1.8 38.2 1.6 5.5 69
      NHTO-0389 0.7 91.3 1.3 4.9 74
      NHTO-0569 0.3 14.8 1.6 9.4 74
      NHTO-0573 0.5 11.9 1.6 6.6 77
      NHTO-0574 0.3 15.0 0.9 5.3 93
      Mean of bottom 5 0.7 34.3 1.4 6.3
      Grand mean 2.5 92.8 1.8 15.4
      Selection differential (%) −71.8 −63.1 −21.8 −58.9

      Multivariate analysis of tomato accessions

    • The correlogram illustrates the strength and direction of the linear relationships between pairs of traits (Fig. 2). The number of fruits per cluster had a linear positive strong and significant (p < 0.01) relationships with number of flowers per cluster (r = 0.55) and number of fruits per plot (r = 0.72). Similarly, the number of branches had a linear positive strong and significant (p < 0.01) relationships with number of flowers per cluster (r = 0.60), number of fruits per plot (r = 0.64) and plant height (r = 0.60). The number of fruits per cluster and the number of fruits per plot have a statistically significant linear relationship (r = 0.74, p < 0.001), but a negative correlation with fruit width (r = −0.64). The number of fruits per plot had a strong positive and significant correlations with fruit width (r = 0.71) and plant height (r = 0.52). Plant height had a negative and significant correlations with fruit width (r = −0.63), number of days to first harvest (r = −0.57), number of days to maturity (r = −0.69), number of days to flowering (r = −0.60) and fruit juice pH (r = −0.65). On the other hand, plant height and total fruit weight per plot have a positive significant linear relationship (r = 0.54, p < 0.01). Total fruit weight per plot had a negative and significant correlations with number of days to first harvest (r = −0.60) and number of days to maturity (r = −0.64). The number of days to flowering had a strong positive and significant association with the number of days to maturity (r = 0.84) and number of days to first harvest (r = 0.79). The number of days to maturity and number of days to first harvest have a statistically strong significant linear relationship (r = 0.93, p < 0.001). Total fruit weight per plot was negatively correlated with number of days to first harvest (r = −0.48). Similarly, the number of days to first harvest has a negative and significant association with number of fruits per plot (r = −0.29). Considering the physico-chemical properties measured, vitamin C had a positive association with fruit length (r = 0.59, p < 0.01) and lycopene (r = 0.60, p < 0.01). Fruit juice pH had a positive and significant (p < 0.001) association with number of days to flowering (r = 0.70), number of days to maturity (r = 0.68) and number of days to first harvest (r = 0.64). The number of fruits per plot had a negative and significant association with vitamin C (r = −0.36). Vitamin C had a positive and significant correlation with lycopene (r = 0.57) but a negative correlation with β-carotene (r = −0.27). β-carotene showed a negative and significant association with titratable acidity (r = −0.33).

      Figure 2. 

      Correlogram showing the relationship between average values of agronomic and physico-chemical traits of tomato accessions. Dark blue denotes a high negative correlation, whereas dark red represents a high positive correlation. The cell value denotes correlation coefficient (r) values. NOB = number of branches, DTF = number of days to flowering (d), DTM = number of days to maturity (d), FL = fruit length (cm), FWD = fruit width (cm), FPC = number of fruits per cluster, NFPC = number of flowers per cluster, PLTHT = plant height (cm), TFW = total fruit weight per plot (kg), DTH = number of days to first harvest (d), FPP = number of fruits per plot, VITC = vitamin C (mg 100 g−1), BETAC = β-carotene (mg 100 g−1), LCOP = lycopene (mg 100 g−1), TTA = titratable acidity (%), TSS = total soluble solid (°Brix), pH = fruit juice pH. *,**,*** significant at 0.05, 0.01 and 0.001 probability levels, respectively. ns = nonsignificant.

      Principal component analysis (PCA) was based on the measured agronomic and physico-chemical traits (Supplemental Table S3). The first four principal components (PCs) with eigenvalues > 1 accounted for approximately 73% of the total variation among the accessions. The first and second PCs explained 37% and 16% of the total variation among the accessions, respectively. The proportion of variance explained by the third PC was 12% and the fourth PC accounted for 8% of the total variation. The PCs loading visualized by the PCA biplot shows the contributions of the measured traits to PC1 and PC2 (Fig. 3). The vectors of fruit width, fruit juice pH, total soluble solid, titratable acidity number of days to first harvest, flowering, and maturity points in the direction of PC1. The strength of vectors of these traits denotes a strong positive influence on PC1. Conversely, the vectors of number of branches, plant height, number of fruits per plot and fruits per cluster points to the negative side of PC1, indicating a strong negative influence on PC1. Vitamin C, fruit length, total fruit weight per plot, β-carotene, and lycopene had a strong influence on PC2. Besides, the color gradient shows the contribution of each trait to the PCs. The traits with vector of lighter blue color indicates higher contributions to the PCA model while the traits with a vector of darker blue color indicates lower contributions. In agreement with Pearson's correlation coefficients illustrated in Fig. 2, vectors of numbers of days to flowering, maturity, first harvest, and fruit juice pH pointing in the same direction with acute angles indicate a positive correlation among them. Likewise, the clustering of the vectors of numbers of branches, fruits per cluster, flowers per cluster and fruits per plot suggest a positive correlation among them. On the other hand, the vector of fruit width pointing in the opposite directions of the numbers of branches, fruits per cluster, flowers per cluster, and fruits per plot with obtuse angles suggest negative correlations. Superimposing the accessions on the trait plots (Fig. 3, biplot on the left) showed that NHTO-0569 is unique for late flowering and harvesting combination while NHTO-0259 was superior in numbers of fruits per cluster and fruits per plot. Similarly, NHTO-0572 is unique for numbers of branches and flowers per cluster in agreement with Supplemental Table S1.

      Figure 3. 

      A two-dimensional principal component analysis (PCA) showing the relationships among the 17 agronomic and physico-chemical traits and the 25 tomato accessions and checks evaluated. The first two components, PC1 (37%) and PC2 (16%) explaining the highest variance were plotted on the x-axis and y-axis, respectively. The arrows indicate traits contributing to the respective PCs and the correlation between traits can be determined by the close arrow proximity. NOB = number of branches, DTF = number of days to flowering (d), DTM = number of days to maturity (d), FL = fruit length (cm), FWD = fruit width (cm), FPC = number of fruits per cluster, NFPC = number of flowers per cluster, PHT = plant height (cm), TFW = total fruit weight per plot (kg), DTH = number of days to first harvest (d), FPP = number of fruits per plot, VITC = vitamin C (mg 100 g−1), BETAC = β-carotene (mg 100 g−1), LCOP = lycopene (mg 100 g−1), TTA = titratable acidity (%), TSS = total soluble solid (°Brix), PH = fruit juice pH.

      The heatmap and dendrogram provided additional support to the PCA by arranging the measured traits into distinct clusters based on their correlation. The dendrogram represents similarity in the performance of the accessions based on the selected traits, showing diversity among the tomato accessions. The tomato accessions were classified into two main groups. Cluster I consisted of four accessions and cluster II had 21 accessions which was further divided into two sub-clusters (II 'a' and II 'b'). The first sub-cluster (II 'a') had 10 accessions including the accessions identified by RSI as superior for the number of fruits per plot, fruit weight, lycopene, and β-carotene while Cluster II 'b' comprises 11 accessions including the checks. Accessions that were tall with high numbers of fruits per cluster, flowers per cluster, fruits per plot, and branches clustered together (Figs 1 & 4). The four accessions in cluster I, had traits associated with fruit yield in common (number of fruits per plot). The accessions in cluster II 'a' and II 'b' had few traits in common and differed for other traits. Traits of the accessions in cluster II 'a' were a high count of branches, tall plants, high fruit weight per plot, and considerable physico-chemical traits. The accessions in this cluster strike a balance between agronomic traits and tomato fruit nutritional quality. The accessions in cluster II 'b' were either elongated or round, late flowering, late maturing and late harvesting with a substantial amount of lycopene and vitamin C content.

      Figure 4. 

      Hierarchical clustering and heatmap of tomato accessions and checks based on the scaled values of the measured traits. Each row represents an accessions, and each column indicates a measured trait. Accessions are clustered based on their measured traits, and the traits groups are clustered based on their correlation. The traits that are clustered together have a high positive correlation. Cells with red and blue colours have high and low relative appearances, respectively. NOB = number of branches, DTF = number of days to flowering (d), DTM = number of days to maturity (d), FL = fruit length (cm), FWD = fruit width (cm), FPC = number of fruits per cluster, NFPC = number of flowers per cluster, PHT = plant height (cm), TFW = total fruit weight per plot (kg), DTH = number of days to first harvest (d), FPP = number of fruits per plot, VITC = vitamin C (mg 100 g−1), BETAC = β-carotene (mg 100 g−1), LCOP = lycopene (mg 100 g−1), TTA = titratable acidity (%), TSS = total soluble solid (°Brix), pH = fruit juice pH.

    Discussion

    • The efficacy of genetic resources in breeding depends on its capacity to boost productivity and diversity. To promote newly collected germplasm and identify novel source of traits; evaluation and characterization becomes essential for appropriate utilization in varietal development[3]. The 23 tomato accessions and two traditional varieties evaluated in this study performed differently in the same environment. The tomato leaves displayed slight variations in size, shape, and serration patterns. Based on leaf morphology, 88% of the accessions had regular leaves with serrated edges, while 12% had large, broad, and smooth edges (potato leaves). The tomato fruits fell into categories of flattened/ribbed, round, and elongated types. The traditional varieties were mainly elongated types, as they are well-suited to the local climate, and soil conditions, and the yield recorded by farmers also contribute to their popularity. Significant differences observed among the accessions for all measured traits indicates that selection of superior accessions in relation to fruit yield and physico-chemical traits for further improvement is realistic. This result corroborates the reports of Kumar et al.[18] & Tembe et al.[19] who reported significant variation in days to maturity, the number of fruits per plant and average fruit weight among tomato accessions evaluated. The variability observed among the tomato accessions may be attributed to their genetic make-up in consensus with previous studies[7,20] and this variability can be exploited for developing improved varieties for peculiar horticultural traits.

      Regarding the time it takes for the accessions to reach flowering, fruiting, and fruit maturity among the accessions, the findings of this study exceeded those reported by Chávez-Servia et al.[21], who focused on tomato accession characterization. However, they were lower than the values reported by Fanedoul et al.[22], though they aligned with the findings of Kathimba et al.[23]. The association between earliness and maturity was further highlighted in this study because NHTO-0569 flowered latest (55 d) and also took the longest to mature (97 d). The variations in agronomic performance among the tomato accessions is mainly attributed to their genetic composition, aligning with the findings of Shah et al.[24]. The performance of some accessions were significantly superior to the traditional varieties used as check for majority of the traits measured. Some accessions were comparable to the checks for few agronomic traits, vitamin C, total soluble solid, and titratable acidity. This indicates that the evaluated accessions can be utilized for increasing the genetic potential of tomato to earn financial profits. Few accessions combines superiority for diverse traits; NHTO-0572 was outstanding for numbers of branches and flowers per cluster, likewise NHTO-0259, was outstanding for plant height, numbers of fruits per cluster and fruits per plot. NHTO-0568 had wide fruits with high lycopene content and NHTO-0199 with the highest fruit weight had 59% yield advantage over the best check (LC CHK-Y) with the high titratable acidity. Hence, these accessions have potential to meet the demand for improved fruit yield.

      Carotenoids, vitamins, and antioxidant content of tomato fruits are of interest because of their nutritional value which offer health benefits to the consumers[25]. Variations in the physico-chemical traits among the accessions could stem from factors such as the genetic makeup of the accessions, the environmental conditions, or the stage of ripeness when harvested[26]. The vitamin C (antioxidant) contents in the fruits of the evaluated accessions were similar to the report of Shah et al.[24]. The lycopene content range observed in the assessed accessions exceeded the report of Srivalli et al.[27], who noted a range of 4.1 to 5.5 mg 100 g−1. The lycopene content in the evaluated accessions surpassed that of β-carotene, affirming the predominance of lycopene over β-carotene in red tomato fruits, as noted by Viskelis et al.[28]. The amounts of β-carotene in the evaluated accessions were comparable to the report of Agarwal & Rao[29]. The high (4.5−5.4 °Brix) total soluble solid (indicator for fruit sweetness) content of the accessions evaluated depict their suitability for the preparation of processed products. The present results confirmed that smaller tomato fruits had high concentrations of total soluble solid. The variation in total soluble solid content among these accessions was similar to the findings of Ali et al.[30] & Hossain et al.[31], who reported total soluble solid ranging from 3.7 to 5.4 °Brix in tomato varieties. The fruit juice pH was below 4.2 for all the accessions indicating their suitability for fresh consumption and industrial processing. According to Tigist et al.[32] a pH value below 4.5 is desirable in tomato, because it halts the proliferation of microorganisms. It is important to mention that the fruits with high titratable acidity which depicts higher flavour also had a lower pH which is desirable because low pH eliminates the risk of pathogen growth (Bacillus coagulans) in tomato fruits[33]. Furthermore, accessions NHTO-0352, NHTO-0350, NHTO-0199, NHTO-0351, and NHTO-0346 were identified as outstanding for combinations of agronomic and physico-chemical parameters.

      For direct and indirect selection towards genetic improvement of crops, correlation analysis is inevitable. In this study, the correlation coefficient between traits ranged from weak, moderate and strong positives to negatives. The highest positive and significant correlation coefficient observed was between the number of days to maturity and the number of days to first harvest followed by number of days to flowering and number of days to maturity, number of flowers per cluster and number of fruits per plot which has similar direction and strength with number of fruits per cluster and number of fruits per plot. The days to flowering determines either earliness or lateness of an accession and it has been reported to be closely associated with the maturity of the tomato accession[23]. As both traits tend to increase together, this strong correlations implies the potential for improving both traits simultaneously. Conversely, significant negative correlations were noted between the number of fruits per plot and fruit width, the number of fruits per plot and fruit length, while a positive correlation was observed with plant height. This implies that fruit yield is influenced by the interaction of multiple traits, with tall plants exhibiting a greater number of fruits per plot compared to short plants. This relationship could be attributed to the taller plants' capability to capture more light energy, which is crucial for efficient photosynthesis and subsequent partitioning towards fruit production. The correlations among these traits may save breeding time as easily measurable traits could be useful in selection[34].

      The PCA identified four PCs explaining 73% of the total variations observed. The traits that mostly responsible for variation in the tomato accessions were outlined. The phenological stages of tomato, which influence productivity, contributed to the observed variations. Key traits driving these variations included the number of days to flowering, maturity, and first harvest, along with fruit width, plant height, number of fruits per plot, fruit weight, lycopene and vitamin C contents. Most of these traits were associated with PC1 and PC2. Notably, PC1 and PC2, accounting for 37% and 16% of the variation, respectively, made the most significant contributions to the overall variance in this study. Hence, these highly discriminating traits may be considered as descriptors for phenotypic characterization of tomato germplasm. The current findings align with those of Chernet et al.[35], who identified six PCs, collectively explaining 83% of the total variation.

      The cluster analysis grouped the 23 tomato accessions and two traditional varieties used as checks into distinct clusters. Grouping into different clusters was associated with their shared similarities in agronomic and physico-chemical traits. The pattern of clusters revealed that the phenotypic diversity was not associated with the collection source because some accessions collected from the same source were grouped in different clusters. These findings are supported by the findings of Hussain et al.[36] & Kiran et al.[37]. It is important to mention that four accessions out of the top five identified by RSI were all grouped together. Therefore, simultaneous improvement in fruit yield, titratable acidity, total soluble solid, lycopene, Vitamin C, and β-carotene contents could be possible by selecting promising parental lines from the diverse clusters. Parental line selected from these dissimilar clusters will vary in the number of favourable alleles for a specific trait; hence hybridization will exploit heterosis to produce desired (high-yielding and quality) allelic recombinants[38]. Therefore, the heat map analysis effectively grouped tomato accessions according to their trait expression patterns. In contrast, PCA facilitated the plotting of trait relationships and the identification of accessions based on the trends in trait combinations.

    Conclusions

    • The performance of the agronomic and physico-chemical traits of the evaluated tomato accessions varied. Accessions NHTO-0352, NHTO-0350, NHTO-0199, NHTO-0351, and NHTO-0346 stood out for their exceptional performance for fruit yield and physico-chemical traits. The strong correlations among traits gave valuable insights into the associations among the measured agronomic and physico-chemical traits. The strength of correlated traits would help in improvement of more than one trait at a time. The PCA and heatmap clustering analyses sheds light on how various growth, physiological, and phenological traits impact tomato fruit yield and physico-chemical traits. The clustering of the accessions was majorly based on genetic relatedness. The degree of similarity and divergence observed among the tomato accessions is essential for selection and hybridization plan in tomato breeding programmes. These results present useful genetic variations for tomato breeding programmes. However, the yield and quality traits of tomato are highly influenced by the environment. The superior accessions identified in this study will be subjected to multi-environment evaluation for confirmation of fruit yield and quality potentials.

    Author contributions

    • The authors confirm contribution to the paper as follows: study conception and design: Olayinka AO, Ibitoye DO; data collection: Olayinka AO, Ibitoye DO; analysis and interpretation of results: Olayinka AO, Aderibigbe OR; draft manuscript preparation: Olayinka AO. All authors reviewed the results and approved the final version of the manuscript.

    Data availability

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

    Acknowledgements

    • We appreciate the genetic resources unit of National Horticultural Research Institute, (NIHORT) Ibadan, Nigeria for providing the tomato accessions used in this study. We are grateful to the students of the Department of Crop Production and Soil Science, Faculty of Agricultural Sciences, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, for their technical assistance.

      Conflict of interest

      • The authors declare that they have no conflict of interest.

      Supplementary information
      • Supplemental Table S1 Mean performance for agronomic traits of tomato accessions and the checks evaluated.
      • Supplemental Table S2 Mean performance of the evaluated tomato accessions and the checks for physico-chemical traits.
      • Supplemental Table S3 Principal component analysis of the contributions of agronomic and physico-chemical traits to total variation among the tomato accession and checks evaluated.
      Rights and permissions
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (4)  Table (3) References (38)
  • About this article
    Cite this article
    Olayinka AO, lbitoye DO, Aderibigbe OR. 2024. Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits. Technology in Horticulture 4: e021 doi: 10.48130/tihort-0024-0018
    Olayinka AO, lbitoye DO, Aderibigbe OR. 2024. Unveiling phenotypic diversity among tomato (Solanum lycopersicum L.) accessions: a comprehensive analysis of agronomic and physico-chemical traits. Technology in Horticulture 4: e021 doi: 10.48130/tihort-0024-0018
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