Abstract

   Plants grown under highly variable natural light regimes differ
   strongly from plants grown under constant light (CL) regimes. Plant
   phenotype and adaptation responses are important for plant biomass and
   fitness. However, the underlying regulatory mechanisms are still poorly
   understood, particularly from a transcriptional perspective. To
   investigate the influence of different light regimes on tomato plants,
   three dynamic light (DL) regimes were designed, using a CL regime as
   control. Morphological, photosynthetic, and transcriptional differences
   after five weeks of treatment were compared. Leaf area, plant height,
   shoot /root weight, total chlorophyll content, photosynthetic rate, and
   stomatal conductance all significantly decreased in response to DL
   regimes. The biggest expression difference was found between the
   treatment with the highest light intensity at the middle of the day
   with a total of 1080 significantly up-/down-regulated genes. A total of
   177 common differentially expressed genes were identified between DL
   and CL conditions. Finally, significant differences were observed in
   the levels of gene expression between DL and CL treatments in multiple
   pathways, predominantly of plant–pathogen interactions, plant hormone
   signal transductions, metabolites, and photosynthesis. These results
   expand the understanding of plant development and photosynthetic
   regulations under DL conditions by multiple pathways.

   Keywords: RNA sequencing, Solanum lycopersicum, different light
   regimes, differentially expressed genes, photosynthesis

1. Introduction

   Global agriculture faces an increasing demand due to growing
   population, climate change, and constraints of land, water, and rural
   farm labors [[34]1]. The light source is the main factor that directly
   impacts crop yield and crop performance by influencing photosynthesis
   and light-signaling metabolism processes. Agricultural production and
   the achieved crop quality can be influenced by many light-related
   factors, such as light intensity [[35]2,[36]3], light quality
   [[37]4,[38]5,[39]6], light period [[40]7,[41]8], and light source
   [[42]9,[43]10]. In the field, plants experience fluctuating sunlight
   conditions due to diurnal variations of light intensity, temporary
   shading by clouds, neighboring plants [[44]11], as well as the movement
   of leaves and branches by wind [[45]12]. Artificial light sources are
   widely used in modern crop cultivation systems, especially in a
   controlled environment, to increase the yield of agriculture products.
   Adjusting artificial light resource plays an important role in
   improving high-efficient productions of major crops. Lettuce fresh
   weight was shown to increase with light intensity level, except for the
   800 µmol m^-1 s^-1 treatment [[46]2,[47]3]. Many previous studies agree
   that red and blue light play an important role in yield
   [[48]4,[49]5,[50]6].

   Many researches have focused on the impact of the difference between
   constant light (CL) and fluctuating light (FL) conditions on plants
   [[51]13,[52]14,[53]15,[54]16]. For existence, the duration of light
   intensity switch can by ranged from seconds and minutes to hours
   [[55]12,[56]17]. The duration of light intensity period of common rays
   of sunlight upon the canopy of plants in natural conditions usually
   changes rapidly and irregularly
   [[57]16,[58]18,[59]19,[60]20,[61]21,[62]22].

   When plants receive the same daily light integral (DLI), photosynthesis
   can be significantly different under different light regimes due to
   different aspects: (1) The conversion efficiency of leaf photosynthesis
   decreases with increasing light intensity at specific light spectra
   [[63]23]. (2) The light energy absorbed by the chloroplast increases,
   and the excess light energy could damage the plants. Plants develop
   different photo protective mechanisms [[64]24], such as
   non-photochemical quenching (NPQ) in the chloroplast thylakoid
   membrane, light-harvesting complexes (LHCs), and de-epoxidantion of
   zeaxanthin [[65]25,[66]26,[67]27]. (3) The speed of opening stomata is
   lower than the initial up-regulation of photosynthetic electron
   transport, leading to an insufficient supply of CO[2] for the carbon
   cycle during transitions [[68]16]. (4) The rate of enzyme activation in
   the Calvin cycle also limits photosynthesis under FL conditions
   [[69]16,[70]28]. Plants grown under FL conditions have thinner leaves
   [[71]20,[72]21] and smaller total leaf areas [[73]12,[74]20].
   Interestingly, the responses of genes related to photosynthesis and
   vitamin metabolism are different between dynamic light duration
   occurring in the morning or at the end of the day. This shows that the
   circadian clock and the dynamic light signal work together and modulate
   related gene expressions during acclimation responses [[75]29].

   Compared with photoprotection and biochemical properties of leaves
   acclimated under dynamic conditions, much less is known about gene
   expression of acclamatory process. Our knowledge in the morphological,
   physiological, and transcriptional regulations of tomato plants under
   DL conditions is still limited. The question remains whether gene
   expressions and physiology characteristics remain the same when the
   period of light intensity changes during a day.

   In this study, we designed three different light intensity
   distributions and investigated their effects on the tomato plant’s
   morphological, physiological, and transcriptional levels during the
   early development stages of the tomato plants. This study provides
   useful knowledge for the improvement of both light-use efficiency and
   yield by using light source adjustments.

2. Materials and Methods

2.1. Materials and Plant Growth Condition

   Tomato seeds (Solanum lycopersicum, Jinpeng No.1) were used as research
   material. Seeds were sown in a plastic seedling tray (53 × 27.5 × 4.5
   cm) filled with substrate (Pindstrup, Demark) within the artificial
   climate chamber at south campus, Northwest A&F University, Yangling,
   China. The pH of the nutrient solution was 5.5, with the concentration
   of N, P2O5, and K2O at 28, 76, and 132 mg/L, respectively. The
   temperature and relative humidity during the time were 28 °C and 65%,
   respectively, which decreased to 18 °C and 55% at night. Each group
   contained 144 seeds at the beginning of the experiment. Three weeks
   after sowing, 60 uniform seedlings with two fully expanded leaves were
   transplanted into 7 × 7 × 8 cm black plastic pots filled with substrate
   (Pindstrup, Demark). Then, plants were set 10–13 cm from each other.
   After three weeks’ irrigation with a half dose Yamazaki nutrient
   solution (EC 1.0 ± 0.2 mS/cm), the dose of the solution was doubled (pH
   6.5 ± 0.5, EC 2.0 ± 0.5 mS/cm) until the end of the experiment. The
   treatments were conducted after seedlings unearth using fluorescent
   light (CFLS; TL 5 Essential 21W/ 865, Philips, Shanghai, China), and
   the lighting array was fixed at 10 cm above the plant canopy. Plants
   were moved every three days at random to take into account any
   heterogeneity in the light intensity.

2.2. Light Intensity Distribution Design

   Three sinusoid types of dynamic light regimes with different phase
   positions over a day (treatments M, A and D) were designed to
   investigate their effects on the early-stage development of tomato
   plants. DL regimes were compared to a constant light condition, while
   the DL models were less dynamic compared to those experienced by crops
   grown in the field. The total daily light integral for each treatment
   and the control was the same during each day, with a total lighting
   period of 12 h. First, a constant light intensity (CL) of 200 μmol m^-1
   s^-1 was used for 12 h as control. The highest light intensity was 400
   μmol m^-1 s^-1 with a phase position at midday (M), advanced by 1.5 h
   compared with M (A), and delayed by 1.5 h compared with M (D)
   ([76]Figure 1A). Light intensity was measured using the PAR meter
   (Model MQ-100, Apogee Instruments Inc., Logan, UT, USA). The nutrient
   solution and environmental conditions, except the light regimes, were
   the same until the end of the experiment.

Figure 1.

   [77]Figure 1
   [78]Open in a new tab

   Experimental design and measurements of fresh and dry weight of shoots
   and roots, respectively. (A) Light distribution patterns of control
   (CL), treatment 1 (M), treatment 2 (A), and treatment 3 (D). (B)
   Morphological comparisons between different treatments after five weeks
   (n = 8). (C) Comparison of plant height between different treatments
   after five weeks. (D) Comparisons of shoot fresh weight, shoot dry
   weight, root fresh weight, and root dry weight between different
   treatments after five weeks. Different lowercase letters indicate a
   significant difference among the same treatments (p < 0.05). Each bar
   represents the mean ± SE (standard error) of eight biological
   replicates.

2.3. Morphological Measurements

   After five weeks of light treatments, fresh tomato plants were randomly
   divided into two groups. The fresh developed leaves from the first
   group with similar morphological shapes were immediately frozen with
   −80 °C liquid nitrogen for RNA sequencing. The remaining samples were
   used for measurements of some morphological traits, including plant
   height, shoot/ root fresh weight, which were quickly dried at 105 °C
   for 15 min and then kept at 60–80 °C for 48 h, until the samples were
   completely dried. The shoot/ root dry weight were then measured to
   compare with the fresh weight. Leaf areas were measured using the
   scanner (EPSON PERFECTION V700 PHOTO, Epson (China) Co., Ltd., Beijing,
   China). Eight plants of each treatment were selected for growth
   characteristic analysis.

2.4. Analysis of Gas Exchange Parameters and Chlorophyll Concentration

   All gas exchange and chlorophyll concentrations were measured on the
   fourth to fifth fully expanded leaf (counted from bottom of the plant),
   after five weeks of light treatments. All photosynthetic gas exchange
   was measured using a Li-cor 6400XT portable gas exchange system
   (LI-6400, LI-COR Inc., Lincoln, NE, USA) with a transparent leaf
   chamber. Measurements were conducted between 08:00 am and 20:00 pm. For
   the measurements, 10 plants were selected and two Li-cors were used,
   with measurements of photosynthetic parameters including net
   photosynthetic rate (Pn) and stomatal conductance (Gs) recorded every
   hour. The relative humidity in the assimilation chamber was maintained
   at 60–80%; the leaf temperature in the measurement chamber was
   maintained at 20 °C; the external CO[2] concentration was 400 ± 20 μmol
   mol^-1; the light intensity was measured according to the light
   treatment.

   Seven plants were selected for chlorophyll concentration analysis. The
   weighed fresh leaf tissue (0.1 g) was extracted in 96% alcohol/water
   (v/v). The extract was centrifuged (H2050R; Xiang Yi Centrifuge
   instrument, Co., Ltd., Changsha, China) at 10,000 g for 10 min. The
   supernatant was separated, and the absorbance was measured at 400–700
   nm using a spectrophotometer (UV-1800; Shimadzu Co., Kyoto, Japan) at
   wavelengths of 665 nm (A665), 649 nm (A649), and 470 nm (A470),
   respectively. The chlorophyll a, chlorophyll b, and total chlorophyll
   concentrations were measured by spectrophotometry and calculated
   according to the following equations of Lichtentaler and Wellburn
   [[79]30]: Chlorophyll a concentration = (13.95 × A665 – 6.88 × A649) ×
   20/(1000 × 0.1), chlorophyll b concentration = (24.96 × A649 –7.32 ×
   A665) × 20/(1000 × 0.1), and total chlorophyll concentrations =
   chlorophyll a concentration + chlorophyll b concentration. Chl a/b =
   chlorophyll a concentration/ chlorophyll b concentration.

2.5. RNA Extraction and Illumina Sequencing

   Total RNAs were extracted from the frozen fresh tomato leaves using the
   EASYspin Plus Kit according to the manufacturer’s instructions (Aidlab
   Biotechnologies Co. Ltd., Beijing, China). The quality and quantity of
   extracted RNAs was measured using agar gel electrophoresis and Nanodrop
   micro spectrophotometer (Thermo Scientific, Wilmington, DE, USA). RNAs
   from three biological repeats (0.5 g per sample) from at least five
   plants with the same concentration and volume were equally combined for
   RNA-seq. Library was constructed using the NEBNext Ultra RNA library
   prep kit (NEB#E7530, New England Biolabs, Ipswich, MA, USA). The
   quality of the cDNA library was measured using DNA 1000 assay Kit
   (5067-1504, Agilent Technologies, Santa Clara, CA, USA) before
   sequencing on an Illumina HiSeq TM 2500 by Gene Denovo Biotechnology
   Co. (Guangzhou, China).

2.6. Sequence Quality Control and De Novo Assembly

   Raw reads containing adapters with more than 10% of unknown nucleotides
   and with more than 50% of low quality (Q-value ≤ 20) bases were
   filtered before mapping to ribosome RNA (rRNA) database in Bowtie2
   [[80]31]. Mapped rRNA reads were removed before mapping to reference
   genome by TopHat2 (version 2.0.3.12) [[81]32]. The reconstruction of
   transcripts was carried out with software Cufflinks [[82]33], together
   with TopHat2. Gene abundances were quantified by software RSEM
   [[83]34]. The gene expression level was normalized by using the FPKM
   (Fragments Per Kilobase of transcript per Million mapped reads) method.
   Single-nucleotide polymorphism (SNP) was identified in GATK [[84]35]
   and SNP/InDel annotation was done using ANNOVAR [[85]36].

2.7. Differentially Expressed Genes (DEGs) Analysis

   Differentially expressed genes across treatments and control were
   identified using the edgeR package ([86]http://www.r-project.org/) in
   R. Genes with a fold change ≥2 and a false discovery rate (FDR) <0.05
   were treated as significant DEGs. DEGs were then subjected to
   enrichment analysis of GO functions and KEGG pathways.

   Gene Ontology (GO) enrichment analysis provides all GO terms that are
   significantly enriched in DEGs compared to the genome background. All
   DEGs were mapped to GO terms in the Gene Ontology database
   ([87]http://www.geneontology.org/). Significantly enriched GO terms
   (FDR correction p-value ≤ 0.05) were identified by hypergeometric test
   by comparing with the genome background. Pathway enrichment analysis
   was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG)
   database [[88]37]. Pathways with FDR-corrected p-values ≤ 0.05 were
   defined as significantly enriched pathways in DEGs.

3. Results

3.1. Experimental Design and Phenotypic Characterization

   After five weeks of treatment, plant heights of all three treatments
   were significantly lower compared with the control ([89]Figure 1B, C).
   In addition, both the fresh and dry weight of shoots and roots were
   also significantly reduced, compared with the control ([90]Figure 1D).
   Interestingly, among DL treatments, A showed a smaller difference
   compared with control with regard to plant height and biomass, but M
   and D treatments showed a bigger difference. These results indicate
   that the light intensity distributions during the day exerted a
   significant impact on the morphological development of tomato plants.

3.2. Changes in the Photosynthetic Characteristics of the Leaves

   Photosynthesis is an extremely important metabolic process in plants.
   The average value of the daily photosynthetic rate (Pn) of DL
   treatments was slightly lower than that of the CL condition. Plants
   grown under treatment D had the lowest value of Gs. Chlorophyll content
   is an important indicator of plant growing conditions and
   photosynthetic capacity. Plants grown under DL treatments displayed
   significantly lower total chlorophyll content compared with plants
   grown under the CL condition. Plants grown under DL conditions had
   significantly higher chlorophyll a/b ratios compared with plants grown
   under the CL condition. In addition, plants grown under M and D
   conditions had the smallest leaf areas compared with plants grown under
   other treatments. The leaf area of the M treatment was 17% less than
   that of the CL treatment ([91]Table 1). The results of the
   photosynthetic characteristics indicate that the plants grown under DL
   conditions had lower photosynthetic capability and light capture area
   compared with plants grown under the CL condition.

Table 1.

   Comparison of Pn, Gs, total chlorophyll content, Chl a/b, and leaf area
   in different light regimes. (n = 10 for Pn, Gs, and leaf area. n = 7
   for total chlorophyll and Chl a/b.)
   Treatments Pn
   (μmol·m^-2·s^-1) Gs
   (mmol·m^-2·s^-1) Total Chlorophyll
   (mg g^-1 FW) Chl a/b Leaf Area
   (cm^2)
   CL 4.94 ± 0.11 a 0.38 ± 0.03 a 2.75 ± 0.02 a 2.76 ± 0.01 c 123.70 ±
   5.14 a
   M 4.78 ± 0.13 ab 0.31 ± 0.01 ab 2.53 ± 0.05 b 2.83 ± 0.02 b 102.32 ±
   4.15 b
   A 4.71 ± 0.08 ab 0.38 ± 0.04 a 2.54 ± 0.04 b 2.82 ± 0.02 b 115.16 ±
   2.33 ab
   D 4.56 ± 0.08 b 0.29 ± 0.03 b 2.39 ± 0.08 b 2.87 ± 0.01 a 103.26 ± 4.74
   b
   [92]Open in a new tab

   Note: Net photosynthetic rate (Pn); stomatal conductance (Gs).
   Different lowercase letters indicate significant difference (p < 0.05).

3.3. Transcriptome Sequencing, Assembly, and Annotation

   To understand the mechanisms of the effects of light intensity
   distributions on the development of tomato plants, RNA-seq was
   performed based on deep transcriptome sequencing analysis after five
   weeks of treatments. The sequencing quality for all the treatments and
   control was quite high, after discarding the raw sequencing data. The
   percentage of bases with Q20 (high sequencing quality) was close to
   100% ([93]Figure 2A). Gene coverage ranged from 80 to 100%, accounting
   for approximately 80% of the total genes ([94]Figure 2B). Within each
   control or treatment, the correlation coefficient between replicates
   exceeded 99.5%, indicating high consistency between replicates
   ([95]Figure 2C). Summaries of raw sequence quality before and after
   filtering and the number of reads mapped to the reference genome
   (version 3.0) are provided in [96]Supplementary Table S1. These results
   show that the transcriptome sequencing quality was sufficient for
   further analyses.

Figure 2.

   [97]Figure 2
   [98]Open in a new tab

   Transcriptome sequencing quality analysis between treatments and
   control. (A) Base composition and quality distributions. (B) Statistic
   maps of gene coverage. (C) Correlation coefficient maps between
   different sequencing repeats.

3.4. Gene Expression Difference Analysis

   Differentially expressed genes (DEGs) between treatments and control
   were identified using edgeR software [[99]38,[100]39]. FDR and log2FC
   were used to screen for DEGs. The screening conditions were FDR < 0.05
   and |log2FC| > 1. Hundreds of genes were up- or down-regulated between
   control and treatments, as well as between different treatments
   ([101]Supplementary Tables S2–S8). The total number of significantly
   regulated genes differed in different comparisons ([102]Figure 3A). The
   largest difference was found between treatment M and control (1080
   significant up-/down-regulated genes), followed by treatment D and
   control (1032 significant up-/down-regulated genes). In general, the
   number of up-regulated genes was lower than that of down-regulated
   genes, with the only exception between treatment M and control. In
   particular, the largest difference between the numbers of
   down-regulated and up-regulated genes was found between treatments M
   and D, reaching a total of 416 genes ([103]Figure 3A). The smallest
   number of significantly regulated genes was found between treatment A
   and D, with 66 and 154 significantly up- and down-regulated genes,
   respectively. Volcano plots show that the number of up-and
   down-regulated genes had a distinct distribution pattern between the
   three DL treatments and control ([104]Figure 3B). For example, the
   distribution pattern of down-regulated genes of treatment A was much
   higher than the patterns of treatments A and D, respectively. Although
   the number between down- and up-regulated genes was low between
   treatments A and D compared with other treatments, the distribution
   patterns were quite similar ([105]Figure 3B). These results show clear
   global gene expression patterns between different treatments and
   control.

Figure 3.

   [106]Figure 3
   [107]Open in a new tab

   Difference analysis of gene expression between treatments. (A)
   Comparison of the number of up- and down-regulated genes. (B) Volcano
   plots between treatments and control. Red and green points represent
   up- and down-regulated genes, respectively. Block points represent no
   difference genes.

3.5. DEGs GO/Pathway Enrichment Analysis

   GO and pathway enrichment analyses were performed for all significant
   DEGs ([108]Figure 4). Different comparisons show similar distribution
   patterns with regard to the numbers and types of enriched pathways,
   which can be divided into three main functional groups, including 19
   biological processes, 11 molecular functions, and 11 cellular
   components ([109]Figure 4A). However, the enrich level (Q-value) for
   each functional group varied ([110]Figure 4B). The Q-value is the
   p-value after multiple hypothesis test corrections, which ranges from 0
   to 1. The closer it is to zero, the more significant the enrichment.
   Notably, most of the functional groups that were significantly enriched
   were involved in different cellular metabolic pathways, such as
   monoterpenoid, cellular, nitrate, and pigment metabolic processes
   ([111]Figure 4B).

Figure 4.

   [112]Figure 4
   [113]Open in a new tab

   DEGs GO/Pathway enrichment analyses between different light regimes.
   (A) Summary of the distribution and number of DEGs in three ontology
   classes, including molecular function, cellular component, and
   biological process. (B) Q-value heatmap of the GO pathway enrichment of
   the three main ontology classes. The color scale indicates the Q-value.
   Darker coloration indicates more significant enrichment.

3.6. KEGG Enrichment Analysis

   The gene clustering heatmap shows a distinct global gene expression
   pattern between control and DL treatments ([114]Figure 5A). The
   expression patterns of most DEGs under treatments were completely
   opposite. Most of the genes with higher expression levels under CL had
   lower expression levels under DL, and vice versa. Moreover, most of the
   DEGs showed large differences in expression profiles under the three DL
   treatments. KEGG expression enrichment analysis ([115]Figure 5C) shows
   that in the comparison of DL treatments versus CK, DEGs were most
   highly enriched in plant–pathogen interaction, plant hormone signal
   transduction, diterpenoid biosynthesis, sesquiterpenoid and
   triterpenoid biosynthesis, phenylpropanoid biosynthesis, and
   biosynthesis of secondary metabolites. In DL comparisons, DEGs were
   most highly enriched in plant–pathogen interaction, the MAPK signaling
   pathway, phenylpropanoid biosynthesis, diterpenoid biosynthesis, and
   biosynthesis of secondary metabolites. In addition, the Q-value of the
   KEGG enrichment indicates that the largest number of enriched genes was
   involved in the biosynthesis of secondary metabolites, although the
   degree of enrichment might not be the highest compared with the other
   top enriched pathways ([116]Figure 5B).

Figure 5.

   [117]Figure 5
   [118]Open in a new tab

   Gene expression patterns between DL treatments and control and KEGG
   enrichment analysis. (A) Global gene expression patterns between
   control and treatments. (B) Q-value heatmap of KEGG enrichment. (C)
   KEGG pathway enrichment analysis between control and DL treatments.
   RichFactor refers to the ratio of the number of transcripts in the
   pathway entry in the differentially expressed transcript to the total
   number of transcripts in the transcript that are located in the pathway
   entry. The larger the RichFactor, the higher the degree of enrichment
   is. The dot size indicates the number of DEGs of the pathway, and the
   dot colour indicates the Q-value.

3.7. Identification of Common DEGs Under Dynamic Light Conditions

   In this study, three out of four treatments were characterized with DL
   conditions. We further quantified the common DEGs compared with the CL
   ([119]Figure 6), and 177 common DEGs were identified ([120]Figure 6A).
   KEGG pathway annotation showed that these genes were involved in
   different metabolic processes, such as terpenoids, vitamins, amino
   acids, and lipids ([121]Figure 6B). Among the top 20 most enriched
   pathways, biosynthesis of metabolic pathways represented the most
   significantly enriched ([122]Figure 6C). In addition, a distinct
   correlation pattern was found between the CL condition and DL
   conditions ([123]Figure 6D). Although differences among different DL
   treatments were also identified, the differences were less important
   compared with that of the CL condition ([124]Figure 6D).

Figure 6.

   [125]Figure 6
   [126]Open in a new tab

   Common DEGs under fluctuating light treatments. (A) Venn diagram of
   common genes detected in three DL treatments compared with the CL
   treatment. (B) KEGG pathway enrichment for the 177 common genes. (C)
   Top 20 enriched KEGG pathways. (D) Hierarchical clustering of common
   genes based on normalized FPKM values under the four tested light
   regimes. Blue indicates lower expression, and red indicates higher
   expression.

3.8. Specific Up- and Down-Regulated Genes

   The tomato plant development is a very complex process. The RNA-seq
   data shows that common DEGs were involved in many relevant pathways
   where they modulate metabolic processes, such as the plant–pathogen
   interaction, phenylpropanoid biosynthesis, plant hormone signal
   transduction, biosynthesis of terpenoids, and photosynthetic metabolic
   processes ([127]Figure 7). Three of the DEGs involved in plant
   –pathogen interaction belonged to the R2R3-MYB transcription factor
   family, which is involved in stress responses and biosynthesis of
   secondary metabolites. In particular, one gene (Solyc02g087960.3) was
   down-regulated and two genes (Solyc06g083900.3 and Solyc08g008480.3)
   were up-regulated compared with the CL. In addition, heat shock protein
   (Solyc07g047790.3), calcium-binding protein (Solyc06g073830.1), and
   pathogenesis-related protein 1 (Solyc00g174340.2) were all up-regulated
   in response to M treatment. However, 3-ketoacyl-coA synthase
   (Solyc03g078330.1) was down-regulated under all DL treatments.

Figure 7.

   [128]Figure 7
   [129]Open in a new tab

   Heatmap of transcriptional levels for specific up- and down-regulated
   27 DEGs enriched in DL compared with CL. In this heatmap, the columns
   represent tomato leaf samples treated with different light regimes, and
   the rows represent DEGS enriched in 10 KEGG pathways. Relative
   expression levels were normalized based on the Z-score and are shown as
   a color gradient from low (blue) to high (red).

   With regard to the DEGs involved in the phenylpropanoid biosynthesis,
   four peroxidase-related genes were detected which were involved in the
   biosynthesis of lignin. Solyc03g044100.3, Solyc04g071890.3,
   Solyc02g092580.3, and LECEVI16G peroxidase precursor were all
   up-regulated in respond to M treatment. In contrast, cytochrome P450
   (Solyc10g078220.2), which played an important role in preventing plant
   injury, was down-regulated under DL treatment. For DEGs involved in
   plant hormone signal transduction, four genes, including two
   IAA-regulated genes (Solyc06g084070.3 and Solyc07g063850.3) and one
   ABA-regulated gene (Solyc06g051940.3), showed decreased expression in
   response to DL treatment. In addition, one terpene synthesis-related
   gene showed decreased expression in DL treatments, except for the
   Gibberellin 2 oxidase gene (Solyc12g0006530.2).

3.9. SNP/InDel Annotations

   Transcriptome sequencing also identified various single nucleotide
   polymorphisms (SNPs) ([130]Figure 8). Up to nine types of functional
   variations were identified for each control and DL treatment, such as
   frameshift/nonframeshift deletion/insertion, and
   synonymous/nonsynonymous single nucleotide variants (SNV) ([131]Figure
   8A). Among these, nonsynonymous SNV and synonymous SNV represented
   dominant functional variations, with overall similar trends for all
   types of functional variations ([132]Figure 8A). In addition, these
   SNPs were located in different locations, with dominant locations in
   exonic and intronic locations, both of which were highest between all
   DL treatments and control ([133]Figure 8B). All mutations were
   identified in each treatment, with transition and transversion as the
   two dominant types ([134]Figure 8C). These results demonstrate a
   comprehensive transcriptional regulation in tomato under different
   light intensity regimes.

Figure 8.

   [135]Figure 8
   [136]Open in a new tab

   SNP/InDel Annotations in terms of function (A), location (B), and type
   (C).

4. Discussion

4.1. Dynamic Light Affects the Growth and Photosynthetic Characteristics of
Tomato Plants

   The light environment acts not only as a photosynthetic driving force,
   but also as a signal for plant morphological and physiological
   adaptions in response to different environmental changes
   [[137]40,[138]41,[139]42,[140]43,[141]44,[142]45]. Plants experience
   constantly changing light conditions under the natural environment. A
   more recent study explored the responses of photosynthesis
   [[143]13,[144]15,[145]16,[146]28,[147]46] and morphology [[148]21] in
   response to DL conditions. A better understanding of the physiological,
   photosynthetic, and transcriptional responses to DL may provide a new
   stimulus to improve photosynthesis for crop growth in the field.

   Under DL regimes, photosynthesis rates were lower compared with plants
   grown under CL. The major reason was that the efficiency of radiation
   declined with increasing light intensity. The efficiency of radiation
   use was high under low light. Under CL treatment, the light intensity
   always remained at 200 μmol m^-2 s^-1, while the efficiency was low
   under 400 μmol m^-2 s^-1 light period, and the quantum yield of CO[2]
   fixation was reduced under DL treatments. Furthermore, changes in
   photosynthesis-related enzyme activities also regulated photosynthesis
   [[149]47,[150]48]. This study showed that the daily photosynthetic rate
   was lower under DL conditions compared to CL regime, and stomatal
   conductance followed the same trend ([151]Table 1). However, in this
   experiment, the photosynthesis parameters were measured only once per
   hour, thus losing much transient changes between two measurement
   points. In addition, the chlorophyll content also decreased under DL
   treatments, while chlorophyll a/b increased, which is consistent with
   previous studies [[152]12,[153]17,[154]20].

   When plants are grown under high light conditions, they accumulate less
   chlorophyll content and have smaller light-harvesting antennae compared
   with plants in low light conditions [[155]49,[156]50,[157]51]. The
   down-regulated chlorophyll content prevents the excess light from
   damaging the photosynthetic metabolic process, which enhanced plant
   fitness under DL conditions. The results of this study show that plants
   grown under DL condition had a less-expanding leaf area, which is
   consistent with previous research [[158]12,[159]20,[160]21]. Results
   from this study also demonstrated that DL inhibited plant height. Yang
   (2018) showed that light intensity played a vital role in regulating
   soybean seedling height and leaf morphology. Previous studies also
   reported that leaf morphology [[161]52] and stem elongation [[162]53]
   were significantly affected by reduced light intensities. This finding
   suggests that DL had the same effect in response to high light to plant
   morphology and physiology.

   The plant performance under DL had disadvantages to maximize the light
   utilized for carbon fixation. In brief, plants grown under CL regime
   had higher photosynthetic capacity and larger leaf area to fully
   utilize the absorbed light for carbon fixation, which resulted in a
   higher dry mass compared with plants grown under DL conditions. In this
   experiment, the distribution of different light periods affected the
   growth and development of the tomato plants. Biomass was significantly
   lower under M and D treatments compared with A treatment ([163]Figure
   1). This interesting phenomenon can be explained by the time of day,
   which possibly regulates the expression of related genes, such as genes
   related to the circadian clock, to affect photosynthesis and plant
   hormone signal, thus ultimately affecting plant morphology and biomass.
   In general, many genes that are involved in the hormone metabolism are
   regulated in a circadian pattern. Abscisic acid, auxin, and cytokinins
   are strongly regulated by the circadian clock [[164]54]. Additionally,
   photosynthesis-related genes are also regulated by the circadian clock
   [[165]55].

4.2. Transcriptional Regulations in Response to Dynamic Light

   A highly variable light environment changes the plant performance and
   regulates related gene expressions to improve the fitness in the field
   [[166]29]. The obtained data showed transcriptome features and
   identified candidate genes that are likely responsible for plant
   adaption to different light regimes at different levels. The results
   not only provide useful information to predict gene expression in
   tomato plants grown in the field, but also help to understand the
   transcriptional regulation of plant developmental plasticity.

   In this study, based on KEGG and GO pathway annotations, under DL
   conditions, DEGs were most highly enriched in plant–pathogen
   interaction, plant hormone signal transduction, diterpenoid
   biosynthesis, sesquiterpenoid and triterpenoid biosynthesis,
   phenylpropanoid biosynthesis, and biosynthesis of secondary metabolites
   ([167]Figure 7 and [168]Figure 8).

   Plant–pathogen interactions. The R2R3MYB proteins form one of the
   largest families of transcription factors and play a crucial role in
   developmental processes [[169]56,[170]57] and responses to biotic and
   abiotic stresses [[171]58,[172]59,[173]60]. In this study, three genes
   related to R2R3MYB transcription factors were identified, the
   expression levels of R2R3MYB transcription factor 13 (Solyc06g083900.3)
   and R2R3MYB transcription factor 4 (Solyc08g008480.3) were more highly
   expressed under DL conditions than the CL condition. Under cold
   stresses, MYB15 gene transcription was up-regulated, and the MYB15
   protein interacted with ICE1 and bound to Myb recognition sequences in
   the promoters of CBF genes. The CBF genes activate many downstream
   genes that have been connected to freezing tolerance in plants
   [[174]61]. PacMYBA, a sweet cherry R2R3-MYB transcription factor,
   enhanced salt stress tolerance and pathogen resistance in transgenic
   Arabidopsis plants. This increased stress tolerance may be due to
   increased anthocyanin accumulation [[175]62].

   Considerable research has indicated that heat shock proteins (Hsp)
   [[176]63,[177]64], as molecular chaperones, are involved in many
   biological activities by folding, transporting, translocating,
   assembling, or degrading client proteins [[178]65,[179]66]. In this
   study, the expression levels of heat shock protein (Solyc07g047790.3),
   calcium-binding protein (Solyc06g073830.1), and pathogenesis-related
   protein 1 (Solyc00g174340.2) were also significantly higher under DL
   conditions rather than the CL condition. Calcium-binding protein and
   pathogenesis-related protein were induced by R2R3MYB transcription
   factors [[180]67]. These results suggest that DL stimulates plants
   defense responses likely via R2R3MYB transcription factors in tomato
   plants.

   Hormones. Plant hormones induce plant growth and development in
   response to environmental signals [[181]68,[182]69]. Under DL
   conditions, four common DEGs were found to encode proteins related to
   plant hormones, including auxin-regulated IAA2 (Solyc06g084070.3),
   IAA-amido synthetase 3-9 (Solyc07g063850.3), protein phosphatase 2c
   (Solyc06g051940.3), and pathogenesis-related protein 1
   (Solyc00g174340.2) ([183]Figure 7). IAA affected the plant phenotype by
   regulating several genes [[184]70,[185]71], all of which control plant
   cell division and elongation. Light conditions controlled the
   elongation of stem cells by an auxin-responsive GH3 gene homologue
   [[186]72]. In this experiment, the expression trends of auxin gene
   expression levels of tomato seedlings under different treatments were
   consistent with the elongation of both plant height and leaves. These
   results also imply that hormonal crosstalk plays a vital role in the
   effect of a DL environment on plant morphology [[187]73].

   Carbon metabolic process-related genes. Photosynthetic pigments form
   the beginning of reception, transferal and capture of light energy. The
   light-harvesting efficiency of the photosystem is assumed to be largely
   dependent on the size of its photosynthetic antenna [[188]74], which is
   controlled by the biosynthesis of chlorophyll b [[189]75,[190]76].
   Light-harvesting antenna systems play the dual role of gathering and
   dissipating light energy to transfer just enough light energy to the
   reaction centers. Chlorophyll b is synthesized from chlorophyll a by
   chlorophyllide a oxygenase (CAO) [[191]77]. Phosphoenolpyruvate
   carboxylase (PEPC) is one of the CO[2]-fixing enzymes, which forms
   oxaloacetate from phosphoenolpyruvate (PEP) and bicarbonate (HCO3-),
   releasing inorganic phosphate (Pi) in the presence of Mg^2+ or Mn^2+
   [[192]78]. In the leaves of C3 plants, PEPC participates in a variety
   of biosynthetic pathways and in nitrogen assimilation, where it acts as
   anaplerotic to replenish the tricarboxylic acid (TCA) cycle [[193]79].
   PEPC is activated by glucose-6-phosphate and inhibited by L-malate or
   aspartic acid (Asp) [[194]80,[195]81]. Trehalose-6-phosphate (T6P), a
   crucial regulator of sugar metabolism, growth, and development is
   widely distributed in higher plants [[196]82,[197]83,[198]84]. T6P not
   only acts as a signaling metabolite of starch synthesis [[199]85], but
   also as an effector to inhibit the hexokinase and control glycolytic
   flux [[200]86]. The biosynthesis of trehalose-6-phosphate involves
   glucose-6-phosphate and UDP-glucose by the enzyme trehalose-6-phosphate
   synthase (TPS) [[201]87]. Moreover, trehalose-6-phosphate phosphatase
   (TPP) catalyzes the dephosphorylation of T6P to trehalose [[202]87].
   Trehalose plays an important role in protecting bioactive substances
   and cell structures under various stress environments, such as drought,
   freezing, high temperature, and salt [[203]88,[204]89,[205]90,[206]91].
   Aldose 1-epimerase is a key regular of lactose metabolic processes.
   Lactose is hydrolyzed into D-glucose and β-D-galactose under
   β-galactosidases (= lactases). Then, β-D-galactose is catabolized via
   the Leloir pathway [[207]92], but galactokinase, the first enzyme of
   the Leloir pathway, accepts only α-D-galactose and cannot act on the
   β-anomer in prokaryotes, yeasts, and mammals. In the present study, the
   down-regulations of CAO (Solyc11g012850.2), PEPC (Solyc04g006970.3),
   TPS (Solyc07g006500.3), and aldose 1-epimerase (Solyc02g087800.3)
   suggest that the DL light condition might not benefit chlorophyll b
   synthesis, CO[2] assimilation, sugar formation, and sugar metabolism.

   The regulation among different pathways and their gene expression
   levels are important factors for plant development and interactions
   with the different light regimes. The changes in gene expressions
   varied in pathways of diterpenoid biosynthesis, sesquiterpenoid and
   triterpenoid biosynthesis, indicating the influences of light regimes
   on secondary metabolites. The gene expression levels of different DL
   regimes imply an influence of the circadian clock and the light
   intensity in coordinating the acclamatory responses of
   functionally-related genes.

   Mitogen-activated protein kinasse signaling pathway. Mitogen-activated
   protein kinases (MAPKs) are serine/threonine protein kinases in
   eukaryotes. MAPKs are components of MAPK cascades, which are involved
   in the transduction of extracellular signals to intracellular targets
   and regulate the expression of special genes. MAPKs induce activation
   of defense responses in response to different extra cellular stimuli
   [[208]93]. Recent findings clearly demonstrate that the auxin signal
   transduction is mediated by MAPKs [[209]94]. Thus, auxin may promote
   plant defense responses by regulating the pathway of MAPK signaling,
   specially increasing the expression of pathogenesis-related protein 1.

   As a central mediator for the coordination of metabolism, the circadian
   clock in higher plants maintains homeostasis under a predictable,
   albeit changing, environment [[210]57], which is involved in dynamic
   regulations of diverse physiological processes [[211]13]. However, the
   highest light intensity of the treatments still did not reach the
   tomato light saturation point, which is limited by the number of lamps
   in the artificial growth box. Further investigations are needed to
   identify the key regulated metabolites and the relationships between
   transcriptome and metabolome [[212]58]. Furthermore, the influence of
   light intensity distributions during a complete tomato life circle
   should be investigated, especially the key metabolic differences of
   tomato fruit quality at the red-ripe stage.

5. Conclusions

   DL regimes affected the early development stage of tomato plants at the
   morphological, photosynthetic, and transcriptional levels. It slowed
   the plant development and regulated many regulatory pathways, such as
   plant–pathogen genes, heat shock protein, lignin biosynthesis genes,
   and auxin-related genes. DL regimes also suppressed the expression of
   photosynthesis related genes, such as chlorophyllide a oxygenase,
   phosphoenolpyruvate carboxylas, and trehalose-6-phosphate synthase 1,
   resulting in a decrease of photosynthetic rate, especially in the M
   treatment, which had the highest light intensity during midday. This
   analysis also showed that light intensity regulated many circadian
   clock-related genes, which could be useful for the utilization of light
   source under artificial light sources. This study provides new
   morphological, photosynthetic, and transcriptional regulations that
   underlie light regimes for plant development.

Acknowledgments