Abstract

   The premature senescence of red raspberry leaves severely affects plant
   growth. In this study, the double-season red raspberry cultivar ‘Polka’
   was used, with N[150] (0.10 g N·kg^−1) selected as the treatment group
   (T150) and N[0] (0 g N·kg^−1) set as the control (CK). This study
   systematically investigated the mechanism of premature senescence in
   red raspberry leaves under different nitrogen application levels by
   measuring physiological parameters and conducting a combined
   multi-omics analysis of transcriptomics and metabolomics. Results
   showed that T150 plants had 8.34 cm greater height and 1.45 cm greater
   ground diameter than CK. The chlorophyll, carotenoid, soluble protein,
   and sugar contents in all leaf parts of T150 were significantly higher
   than those in CK, whereas soluble starch contents were lower.
   Malondialdehyde (MDA) content and superoxide anion (O[2]^−) generation
   rate in the lower leaves of T150 were significantly lower than those in
   CK. Superoxide sismutase (SOD) and peroxidase (POD) activities in the
   middle and lower functional leaves of T150 were higher than in CK,
   while catalase (CAT) activity was lower. Transcriptomic analysis
   identified 4350 significantly differentially expressed genes, including
   2062 upregulated and 2288 downregulated genes. Metabolomic analysis
   identified 135 differential metabolites, out of which 60 were
   upregulated and 75 were downregulated. Integrated transcriptomic and
   metabolomic analysis showed enrichment in the phenylpropanoid
   biosynthesis (ko00940) and flavonoid biosynthesis (ko00941) pathways,
   with the former acting as an upstream pathway of the latter. A
   premature senescence pathway was established, and two key metabolites
   were identified: chlorogenic acid content decreased, and naringenin
   chalcone content increased in early senescent leaves, suggesting their
   pivotal roles in the early senescence of red raspberry leaves.
   Modulating chlorogenic acid and naringenin chalcone levels could delay
   premature senescence. Optimizing fertilization strategies may thus
   reduce senescence risk and enhance the productivity, profitability, and
   sustainability of the red raspberry industry.

   Keywords: leaf senescence, metabolome, red raspberry, transcriptome

1. Introduction

   Raspberry (Rubus idaeus L.), a perennial deciduous shrub of the genus
   Rubus in the Rosaceae family, is referred to as raspberry in Chinese
   herbal medicine. Red raspberry is rich in ellagic acid, vitamins,
   anthocyanins, and other bioactive compounds. These help reduce the risk
   of chronic metabolic diseases through various mechanisms, including
   anti-cancer, antioxidant, and cardioprotective effects
   [[44]1,[45]2,[46]3,[47]4]. Thus, it holds significant medicinal and
   health value. The fruit can be processed into freeze-dried products,
   jam, fruit wine, and seed oil [[48]5,[49]6,[50]7,[51]8] and is
   particularly popular in developed countries. Red raspberry is also
   tolerant to low temperatures and insect pests, making it a rapidly
   expanding economic forest crop [[52]9,[53]10]. Current research mainly
   focuses on the functional components of its fruit and its physiological
   responses to abiotic stress, while studies on the physiological and
   molecular mechanisms of premature leaf senescence remain limited.

   Senescence is a critical phase in the plant life cycle. In rice,
   early-senescence mutants exhibit significantly reduced plant height and
   fruiting rates [[54]11,[55]12]. As leaves senesce, soluble protein
   content declines while total amino acid levels rise [[56]13]. Some
   researchers propose that chlorophyll degradation is the hallmark of
   senescence, with declining chlorophyll levels causing leaves to turn
   from green to yellow—an evident sign of premature senescence [[57]14].
   Studies show a positive correlation between chlorophyll content and
   plant vitality. In living cells, chlorophyll resides in the thylakoid
   membrane as part of chlorophyll–protein complexes. During senescence,
   the number and structure of grana alter, and the outer chloroplast
   membrane along with the thylakoid system membrane disintegrate
   [[58]15]. Moreover, excessive accumulation of O[2]^− is closely
   associated with reduced photosynthetic pigment content. These reactive
   oxygen species (ROS) specifically degrade chlorophyll a and lower the
   chlorophyll a/b ratio [[59]16].

   During leaf senescence, nucleic acid content decreases significantly,
   especially rRNA in chloroplasts and mitochondria, while mRNA levels of
   genes encoding proteases, nucleases, and related enzymes increase.
   Enzymatic activity leads to the degradation of chlorophyll,
   pigment-binding proteins, lipids, starch, and other cellular components
   [[60]15]. Abscisic acid (ABA), a plant growth inhibitor, induces the
   expression of senescence-associated genes (SAGs) when applied
   exogenously [[61]17,[62]18]. Ethylene, a gaseous hormone, promotes
   senescence by activating chloroplast-degradation-related genes through
   its response factor Ethylene Insensitive3 (EIN3) [[63]19].
   Myelocytomatosis oncogene 2 (MYC2), the core transcription factor in
   the jasmonic acid (JA) signaling pathway, suppresses Catalase Gene
   (CAT) expression, enhances H[2]O[2] accumulation, and thereby
   accelerates leaf senescence [[64]20]. When salicylic acid (SA) and
   ethylene act synergistically, EIN3 and NPR1 (Nonexpressor of
   pathogenesis-related genes 1), the central regulator of SA signaling,
   jointly upregulate senescence-related genes such as ORE1 (Oresara 1)
   and SAG29 (Senescence-Associated Gene 29), further promoting leaf
   senescence [[65]21]. However, at low concentrations, SA can delay
   methyl jasmonate (MeJA)-induced senescence by enhancing antioxidant
   capacity via nitric oxide signaling [[66]22].

   Plant growth, development, and responses to environmental stimuli
   involve complex regulatory networks. Single-omics approaches often fail
   to capture the full regulatory landscape. In contrast, multi-omics
   integration overcomes data limitations by linking molecular processes
   across different biological levels. Transcriptomics reveals
   differentially expressed genes, while metabolomics identifies the end
   products of gene expression. Together, they provide a more
   comprehensive bioinformatics framework, bridging causality and outcomes
   [[67]23]. Currently, integrated transcriptomic and metabolomic analyses
   are widely applied in studies of environmental responses, fruit
   quality, and color variation [[68]24,[69]25,[70]26]. For instance, Mei
   et al. (2021) used both approaches to investigate flower color
   differences between a rare mutant and the wild-type Camellia variety,
   revealing gene expression and metabolic pathway changes underlying the
   phenotype [[71]27]. Wang et al. (2021) demonstrated that high nitrogen
   fertilizer suppresses carbohydrate accumulation and flavonoid
   biosynthesis in apples through transcriptomic, proteomic, and
   metabolomic analyses [[72]28]. Similarly, Mehta et al. (2024) combined
   metabolomics, transcriptomics, and gene co-expression analyses to
   reconstruct the complete biosynthetic pathway of Amaryllidaceae
   alkaloids in daffodils [[73]29]. Han et al. (2024) employed integrated
   transcriptional and metabolic analyses to elucidate the role of the
   TAR1 (HIV-1 TAR RNA-binding protein 1) gene in trichome development and
   artemisinin biosynthesis in Artemisia annua [[74]30]. Such combined
   omics analyses have become increasingly prevalent in research on major
   crops.

   ‘Polka’ has excellent storage and harvesting characteristics, a
   prolonged harvest period, and a high survival rate. Observations of its
   growth and assessments of fruit quality revealed strong tree vigor and
   vigorous branch development. Its fruit size and quality surpass those
   of ‘Heritage’, a variety widely cultivated in China. Currently, ‘Polka’
   is being extensively promoted and planted across China. Previous
   studies by our research group revealed that premature leaf senescence
   in red raspberry significantly impairs fruit development, leading to
   smaller fruits and fewer drupelets per fruit, thereby reducing overall
   fruit quality [[75]31]. Further investigations showed that most red
   raspberry cultivars exhibit premature aging during production, with
   ‘Polka’ displaying more pronounced symptoms than other varieties.
   Therefore, this dominant cultivar was selected as the focus of our
   study. Using integrated transcriptomic and metabolomic analyses, we
   identified key regulatory genes and metabolites associated with its
   leaf senescence. These findings offer technical support for developing
   preventive strategies to mitigate yield and quality losses caused by
   premature leaf aging in ‘Polka’.

2. Materials and Methods

2.1. Test Materials

   The experimental materials were tissue-cultured seedlings of
   double-season red raspberry ‘Polka’, provided by Hebei Zishui
   Technology Co., Ltd., Xingtai, China. After hardening, they were
   transplanted into pots. When the seedlings reached ~50 cm in height,
   samples were collected for testing.

2.2. Experimental Design

   The experiment was conducted at the nursery of the Forestry College,
   Hebei Agricultural University, West Campus, Baoding City, Hebei
   Province (115°45′40″ E, 39°23′03″ N). The soil background nitrogen
   content was 1.19 ± 0.04 g/kg. The site has an average annual
   precipitation of 532 mm, a frost-free period of 180 days, and a
   continental monsoon climate. A controlled pot experiment was carried
   out. When the seedlings reached 15 cm in height, they were transplanted
   into 35 cm × 35 cm pots. Each pot was filled with 4 kg of dry loam soil
   sieved through a 2 mm mesh, and two seedlings were planted per pot.
   Three nitrogen treatments were applied, with 15 pots per treatment: 0
   g, 0.10 g, and 0.15 g N·kg^−1 dry soil, equivalent to field application
   rates of 0 kg·hm^−2, 150 kg·hm^−2, and 225 kg·hm^−2, denoted as N[0],
   N[150], and N[225], respectively. Nitrogen fertilizer was applied in
   three stages: 50% as basal fertilizer before germination, 20% one week
   before flowering, and 30% during the fruit expansion period. Based on
   prior results, N[0] (no nitrogen) served as the control check (CK),
   while N[150] (optimal nitrogen level) was designated as T150. All other
   management practices were consistent across treatments. Each treatment
   comprised 15 pots, divided into plots of 5 pots, with 3 replicates.
   Sampling was conducted 90 days after transplanting ([76]Figure 1).

Figure 1.

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

   Experimental design process.

2.3. Determination Indicators and Methods

2.3.1. Determination of Differences in Tree Growth and Physiological and
Biochemical Indicators

Measurement of Tree Morphological Indicators

   To determine the growth indicators of the two red raspberry seedling
   phenotypes, three biological replicates were set for each phenotype to
   ensure sample representativeness. The specific measurement methods were
   as follows:

   Plant height, root length, and internode length were measured with a
   steel ruler (accuracy: 0.1 cm). The ground diameter was measured at the
   soil surface junction using a vernier caliper (accuracy: 0.01 mm). Root
   buds were counted manually by enumerating all visible buds on the roots
   of each seedling. Fresh weights were weighed using an electronic
   balance (accuracy: 0.001 g). Leaf thickness was measured using a YH-1
   leaf thickness meter (Zhejiang Top Cloud-Agri Technology Co., Ltd.,
   Hangzhou, China). The leaf SPAD value was measured using a YLS-4T
   chlorophyll detector (Sichuan Chengdu Yunzhice Technology Co., Ltd.,
   Chengdu, China). Root activity was measured using the 2,3,5-triphenyl
   tetrazolium chloride (TTC) method [[79]32].

Determination of Physiological and Biochemical Indicators

   Ninety days after treatment, plants with uniform growth vigor were
   selected for leaf sampling for subsequent analyses. For each treatment,
   three red raspberry plants with basically consistent growth were chosen
   as biological replicates. From each plant, leaves from the upper,
   middle, and lower parts were sampled separately (i.e., samples from
   each leaf position from each biological replicate), and these samples
   were used for the determination of all physiological indices. Each leaf
   position sample from each biological replicate was subjected to three
   technical replicates to ensure the reliability of the results. After
   sampling, all physiological and biochemical indices were determined
   using a UV-1800 PC-type UV-visible spectrophotometer (Aoyi Instruments
   Co., Ltd., Shanghai, China). The specific methods for each index were
   as follows:

   Chlorophyll and carotenoid contents: Determined via the acetone–ethanol
   mixture method (1:1 v/v). Fresh sampled leaves were rinsed with
   distilled water to remove surface impurities and blotted dry with
   filter paper, and 0.2 g of leaf tissue was weighed from each of the
   three biological replicates at the upper, middle, and lower leaf
   positions for each treatment. The leaf pieces were cut into small
   pieces, transferred to a centrifuge tube, and immersed in 20 mL of the
   acetone–ethanol mixture. They were then placed in a dark environment at
   4 °C for 48 h for extraction until the leaf pieces turned white.
   Absorbance was measured at 665 nm, 649 nm, and 470 nm, with three
   technical replicates per sample (mean absorbance used for
   calculations), and contents were calculated using corresponding
   standard formulas based on absorbance values [[80]32].

   Soluble protein content: Determined using Coomassie Brilliant Blue
   G-250 staining. A total of 0.2 g of red raspberry leaf tissue was
   weighed, and a small amount of quartz sand and distilled water were
   added for grinding (to fully disrupt the tissue) to form a homogenate.
   The homogenate was processed following the Coomassie Brilliant Blue
   G-250 staining protocol, and absorbance was measured at 595 nm. Soluble
   sugar and starch contents: Measured using the anthrone colorimetric
   method. A total of 0.5 g of crushed dry red raspberry leaf tissue was
   weighed and subjected to extraction and treatment following this
   method, and absorbance was measured at 620 nm [[81]33].

   MDA content: Determined using the thiobarbituric acid (TBA) method. A
   total of 0.5 g of fresh red raspberry leaf tissue was weighed and
   treated by the TBA method, with absorbance measured at 450 nm, 532 nm,
   and 600 nm for content calculation [[82]33].

   SOD activity, POD activity, CAT activity, and superoxide anion content:
   For each index, 0.5 g of fresh leaf tissue was used, collected from the
   upper, middle, and lower parts (consistent with the sampling positions
   above), with three biological replicates per part. Prior to subsequent
   analyses, the samples were ground under low temperature conditions to
   form a homogenate to prevent enzyme inactivation. The homogenate was
   then used for subsequent determinations, with the specific methods as
   follows: SOD activity: determined via the nitroblue tetrazolium (NBT)
   photoreduction method, with absorbance measured at 560 nm [[83]34]; POD
   activity: assayed using the guaiacol method; the increase in absorbance
   at 470 nm was recorded within 3 min, and activity was expressed as the
   change in absorbance per gram of fresh weight per minute [[84]33]; CAT
   activity: measured via the hydrogen peroxide method, based on the
   decrease in absorbance of hydrogen peroxide at 240 nm [[85]34];
   superoxide anion content: determined using the hydroxylamine oxidation
   method, with absorbance measured at 530 nm for quantification [[86]34].

2.3.2. Slice Production Method

   The leaves were cut into small pieces and fixed in
   formalin–aceto-alcohol (FAA) fixative [(50% ethanol, 90 mL) + glacial
   acetic acid (5 mL) + formaldehyde (5 mL)]. Plant tissues were embedded
   in paraffin, sectioned, and observed by double staining with
   safranin-fast green [[87]35].

2.3.3. Transcriptome Determination

RNA Extraction and Sequencing

   To ensure experimental accuracy, the same batch of plant material used
   for physiological and biochemical measurements was selected. Three
   biological replicates were prepared and sent to Shanghai Paisonno
   Biotechnology Co., Ltd., Shanghai, China. RNA was extracted using the
   Tiangen RNAprep Pure Polysaccharide and Polyphenol Plant Total RNA
   Extraction Kit. A 300 bp fragment was selected, and RNA served as a
   template for cDNA synthesis. The first-strand cDNA was synthesized
   using 6-base random primers and reverse transcriptase; the second
   strand was synthesized using the first strand as a template. Following
   library construction, PCR amplification was used to enrich the library
   fragments. The library size was assessed using an Agilent 2100
   Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and total
   concentration was measured via fluorescence quantification. The
   high-quality reads, obtained by filtering raw data, were de novo
   assembled into transcripts. The longest transcript for each gene was
   designated as the Unigene for subsequent database annotation,
   differential expression analysis, and enrichment analysis. Genes with
   |log[2](fold change)| ≥ 1 and p-adjust < 0.05 were considered
   significantly differentially expressed [[88]36].

Real-Time Quantitative PCR Validation

   To verify the analysis results, 12 differentially expressed genes were
   selected, and their expression levels were analyzed using qRT-PCR. A
   fluorescent dye was added to the PCR reaction system to monitor the
   fluorescence signal of each amplification cycle in real time,
   generating a fluorescence amplification curve used to quantify the
   initial template. Gene expression levels were calculated using the
   2^−△△Ct method [[89]37]. All samples were run in three technical
   replicates. The primers used are listed in [90]Table S8.

2.3.4. Metabolome Determination

Sample Extraction Method

   To ensure experimental accuracy, test materials from the same batch
   used for physiological and biochemical measurements were selected, with
   six biological replicates per batch. The samples were sent to Shanghai
   Paisono Biotechnology Co., Ltd. (Shanghai, China) for extraction and
   analysis. After gradual thawing at 4 °C, an appropriate amount of
   sample was mixed with pre-cooled methanol/acetonitrile/water (2:2:1,
   v/v), vortexed, sonicated at low temperature for 30 min, left at −20 °C
   for 10 min, and centrifuged at 14,000× g at 4 °C for 20 min, and the
   supernatant was vacuum dried. For mass spectrometry analysis, 100 μL of
   aqueous acetonitrile was added to the dried extract and the mixture was
   centrifuged, and then the supernatant was used for analysis. Separation
   was performed using an Agilent 1290 Infinity LC ultra-high performance
   liquid chromatography (UHPLC) system with a C-18 column, and data were
   collected in both positive and negative ion modes using an AB Triple
   TOF 6600 mass spectrometer (AB SCIEX, Framingham, MA, USA) [[91]38].

Data Analysis Process

   The original data in Wiff format were converted to the mzXML format
   using ProteoWizard and processed with MS-DIAL software (a tool for
   untargeted metabolomics data analysis, Kanazawa University, Kanazawa,
   Japan, Version 4.80). This included peak alignment, retention time
   correction, and peak area extraction. The data extracted by MS-DIAL
   were first used for metabolite structure identification, followed by
   data preprocessing, experimental data quality evaluation, and finally
   data analysis. Data analysis included univariate and multivariate
   statistical analysis, differential metabolite screening, correlation
   analysis of differential metabolites, and Kyoto Encyclopedia of Genes
   and Genomes (KEGG) pathway analysis.

2.4. Data Processing and Analysis

   The measured index data ([92]Figure 2) were analyzed using Microsoft
   Excel 2021 (Microsoft Corporation, Redmond, WA, USA) for membership
   function analysis and chart preparation. Significance analysis was
   conducted via one-way analysis of variance (ANOVA) using SPSS 26.0 (IBM
   Corporation, Armonk, NY, USA), and differences between T150 and CK were
   evaluated with the Tukey HSD multiple comparison method. Volcano and MA
   plots were generated using ggplot2 v3.2.1 (RStudio, Boston, MA, USA),
   and differential analysis was performed using DESeq 1.32 (Developed by
   EMBL, Heidelberg, Germany; and EBI, Cambridge, UK). Bar graphs were
   created using Origin 2024 (OriginLab Corporation, Northampton, MA,
   USA).

Figure 2.

   [93]Figure 2
   [94]Open in a new tab

   Relevant measurement indicators.

3. Results and Analysis

3.1. Growth of Red Raspberries at Three Nitrogen Application Levels

   The plant exhibiting the weakest growth—characterized by short height,
   sparse crown, yellow leaves, and red petiole and stem—was defined as
   the early senescence phenotype plant (CK), while the plant with the
   best growth was defined as the normal-type red raspberry (T150). A
   study on the effects of three nitrogen application levels on red
   raspberry growth showed ([95]Figure 3, [96]Table S1) that N[150]
   resulted in the highest plant height, ground diameter, crown size, root
   length, and SPAD (relative content of chlorophyll) value. The leaf
   thickness under N[150] was similar to that of N[0] and greater than
   that of N[225]. Under N[0], yellowing and early senescence of lower
   leaves occurred at the budding stage, and the plant height, ground
   diameter, and root length were the lowest. Based on the membership
   function method, the growth ranking under the three nitrogen levels was
   N[150] > N[0] > N[225]. The plant with the best growth was designated
   as the normal-type red raspberry (T150), while N[0], which showed
   premature leaf senescence and weak growth, was defined as the early
   senescence type red raspberry (CK).

Figure 3.

   [97]Figure 3
   [98]Open in a new tab

   Phenotypic differences between premature senescent and normal red
   raspberry seedlings. (A) Plant height; (B) ground diameter; (C) crown
   width; (D) root length; (E) leaf thickness; (F) SPAD; (G) the lower
   leaves and branches of N0 and N150; (H) premature senescence red
   raspberry (left) and normal red raspberry (right). Note: (G) scale bar
   = 50 mm; (H) scale bar = 100 mm. Note: Different lowercase letters (a,
   b, c) indicate significant differences at p < 0.05 as determined by
   one-way ANOVA.

3.2. Differences in Phenotypes and Physiological and Biochemical Parameters
Between T150 and CK Plants

3.2.1. Phenotypic Growth Differences Between T150 and CK

   Aboveground growth indices of T150 and CK plants were compared, and
   results showed that plant height, ground diameter, and number of buds
   were lower in CK than in T150. The external root morphology of T150 and
   CK plants was also analyzed ([99]Figure 4, [100]Table S2), revealing
   that the root length of CK was 4 cm longer than that of T150, but its
   fresh weight was 5.2 g lower. The root activity in CK was significantly
   lower than in T150, and the number of root buds in T150 was
   significantly higher than in CK.

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   Growth traits and root morphology of red raspberry seedlings with
   normal and premature senescent leaves. (A) Plant height; (B) ground
   diameter; (C) pitch distance; (D) root length; (E) fresh weight of
   root; (F) root/shoot; (G) root activity; (H) root system differences
   between red raspberries with normal leaves and those with premature
   senescent leaves; (I) root buds of red raspberries with premature
   senescent leaves and those with normal leaves. Note: (H) scale bar = 3
   cm; (I) scale bar = 1 cm. Note: Different lowercase letters (a, b)
   indicate significant differences at p < 0.05 as determined by one-way
   ANOVA.

3.2.2. Differences in Photosynthesis and Osmotic Regulation Substances in
Leaves of T150 and CK Plants

   The total chlorophyll, carotenoids, chlorophyll a, and chlorophyll b
   contents in leaves from different parts of T150 and CK plants are shown
   in [103]Table S3. The most pronounced differences between T150 and CK
   were observed in the middle and lower leaf sections. In the lower
   leaves, the total chlorophyll content in T150 was 0.68 mg/g higher than
   that in the lower leaves of the premature senescence phenotype
   ([104]Figure 5A,B, [105]Table S3), and carotenoid content was 0.29 mg/g
   higher than in the corresponding CK leaves. Among both phenotypes, the
   chlorophyll content was lowest in the lower leaves. The contents of
   carotenoid and chlorophyll a also showed significant differences across
   different leaf positions in both phenotypes, with distribution trends
   similar to that of total chlorophyll.

Figure 5.

   [106]Figure 5
   [107]Open in a new tab

   Contents of photosynthetic pigments and osmoregulatory substances in
   different parts of leaves of normal and premature senile raspberry. (A)
   Total chlorophyll; (B) carotenoid; (C) soluble protein content; (D)
   soluble sugar content; (E) starch content. Note: Capital letters
   represent the difference between normal type and premature senescence
   type, and lowercase letters represent the difference of all parts,
   which is significant at the level of p < 0.05. The same as below.

   Leaves from different parts of T150 and CK plants were also analyzed
   for soluble protein content. The results ([108]Figure 5C) show that
   soluble protein levels were highest in the upper young leaves for both
   phenotypes—4.31 mg/g in T150 and 3.99 mg/g in CK. In CK, the lower
   leaves contained 2.73 mg/g of soluble protein, significantly lower than
   in other parts. No significant difference was observed in the soluble
   protein content of the middle leaves between the two phenotypes.

   The soluble sugar contents in the upper, middle, and lower leaves of
   T150 were 0.32%, 0.15%, and 0.88% higher, respectively, than those in
   the corresponding parts of CK ([109]Figure 5D).

   The starch content was highest in the upper leaves and lowest in the
   lower leaves of both phenotypes ([110]Figure 5E). In T150, starch
   levels in the upper, middle, and lower leaves were 0.36%, 0.40%, and
   0.23% lower, respectively, than those in the same parts of CK.

3.2.3. Changes in Membrane Lipid Peroxidation and Antioxidant Enzyme
Activities in Leaves of T150 and CK Plants

   The MDA content of leaves from different parts of T150 and CK is shown
   in [111]Figure 6A. In T150, MDA content gradually decreased from the
   top to the bottom. In CK, the MDA content was lowest in the middle
   leaves and highest in the upper leaves. Comparing the two red raspberry
   phenotypes, the MDA content in the upper leaves of T150 was not
   significantly different from that of CK. However, the middle leaves of
   T150 showed an MDA content that was 1.13 μmol·g^−1 higher than CK,
   while the lower leaves were 1.41 μmol·g^−1 lower. The superoxide anion
   generation rate in different parts of T150 and CK is shown in
   [112]Figure 6B. In T150, the superoxide anion generation rate decreased
   progressively from the upper to the lower parts. In CK, the generation
   rate was highest in the middle leaves, not significantly different from
   the lower leaves, and lowest in the upper leaves. Compared with CK, the
   upper leaves of T150 showed a significantly higher superoxide anion
   generation rate, while the middle and lower leaves had significantly
   lower rates, by 0.55 and 1.23 nmol·min^−1·g^−1, respectively.

Figure 6.

   [113]Figure 6
   [114]Open in a new tab

   Changes in membrane lipid peroxidation and antioxidant enzyme
   activities in two phenotypes of red raspberry. (A) MDA content; (B)
   O^2− generation rate; (C) SOD activity; (D) POD activity; (E) CAT
   activity. Note: Capital letters represent the difference between normal
   type and premature senescence type, and lowercase letters represent the
   difference of all parts, which is significant at the level of p < 0.05.

   Leaves from various parts of T150 and CK were also analyzed for
   antioxidant enzyme activities. The results are shown in [115]Figure 6C,
   [116]Table S9. In T150, SOD activity was highest in the middle leaves
   at 3.90 U·min^−1·g^−1 FW, and lowest in the upper leaves. In CK, SOD
   activity was highest in the upper leaves at 3.09 U·min^−1·g^−1 FW, and
   lowest in the middle leaves. The SOD activity of the lower leaves in
   T150 was 1.24 U·min^−1·g^−1 FW, which was higher than that in CK. As
   shown in [117]Figure 6D, POD activity in the middle and lower leaves of
   T150 was significantly higher than in CK, while in the upper leaves, it
   was significantly lower. The CAT activity in T150 leaves gradually
   decreased from the upper to the lower part ([118]Figure 6E). The CAT
   activity in the middle leaves of T150 was slightly higher than in CK by
   0.03 U·min^−1·g^−1 FW, while in the lower leaves, it was 0.28
   U·min^−1·g^−1 FW lower than in CK.

3.2.4. Differences in Leaf Anatomical Structure Between T150 and CK Plants

   The anatomical structure of T150 and CK leaves is shown in [119]Figure
   7. The epidermis comprised a single layer of flat, tightly arranged
   thin-walled cells covering both the ventral and dorsal leaf surfaces.
   The outer wall of the upper epidermis was thicker, while that of the
   lower epidermis was thinner. In the palisade tissue of the upper and
   middle leaves of T150, chloroplasts were positioned close to the cell
   wall, whereas in CK, they were more diffusely arranged. The spongy
   tissue cells were irregular in shape, and in the lower leaves of CK,
   they were loosely arranged with large intercellular spaces.

Figure 7.

   [120]Figure 7
   [121]Open in a new tab

   Anatomical structure of normal and premature senescence raspberry
   leaves. Note: (a): normal upper part; (b): normal middle part; (c):
   normal lower part; (d): premature senescence upper part; (e): premature
   senescence middle part; (f): premature senescence lower part; scale bar
   = 20 µm.

3.3. Transcriptome Data Analysis of Lower Leaves of T150 and CK Plants

3.3.1. Transcriptome Sequencing Data Results and Unigene Functional
Annotation and Analysis

   The differences in physiological indicators between T150 and CK showed
   that the chlorophyll, carotenoids, soluble protein, sugar content, and
   SOD and POD activities in the lower leaves of T150 were significantly
   higher than those in CK red raspberries. In contrast, the starch, MDA
   content, superoxide anion generation rate, and CAT activity in CK
   leaves were significantly higher than those in T150. Based on these
   differences, the lower leaves of T150 and CK were selected for joint
   transcriptome and metabolome analysis.

   Cutadapt software (Version 4.4, University of Lausanne, Lausanne,
   Switzerland) was used to remove reads containing 3′ adapter sequences
   and those with an average quality score < Q20. As shown in [122]Figure
   S1A and [123]Table S4, the percentage of Clean Reads across the six
   samples ranged from 90.16% to 91%. Q20 values were above 97%, and Q30
   values exceeded 94%, indicating high sequencing accuracy. Gene function
   annotation was performed using Unigene ([124]Figure S1B), with
   reference to several databases, including NR, GO, KEGG, eggNOG,
   Swiss-Prot, and Pfam. The highest number of annotated unigenes was
   found in the NR database (25,123; 67.35%), followed by eggNOG (22,975;
   61.59%), Swiss-Prot (20,379; 54.63%), Pfam (16,023; 42.95%), and GO
   (14,786; 39.64%). The fewest annotations were in KEGG, with 11,014
   unigenes (29.52%). Only 5843 unigenes (15.66%) were annotated across
   all databases. Annotation using the NR database also enabled comparison
   of gene sequence similarity with closely related species, providing
   functional insights into the genes of this species. As shown in
   [125]Figure S1C, 54.66% of unigene sequences matched Rosa chinensis,
   followed by matches with Fragaria vesca subsp. vesca, Carpinus
   fangiana, and other species.

3.3.2. Analysis of Differentially Expressed Genes

   The Pearson correlation coefficient was used to assess gene expression
   similarity between samples; values closer to 1 indicate higher
   similarity ([126]Figure 8A). Principal component analysis (PCA) was
   used to cluster similar samples, with shorter distances indicating
   greater similarity ([127]Figure 8B).

Figure 8.

   [128]Figure 8
   [129]Open in a new tab

   Correlation analysis and characterization of DEGs. (A) Sample
   correlation test; (B) PCA principal components; (C) differentially
   expressed gene clustering; (D) volcano plot of differentially expressed
   genes. Note: T = T150. (C) Red indicates high-expression genes and
   green indicates low-expression genes; (D) red dots indicate upregulated
   genes in this group, blue dots indicate downregulated genes in this
   group, and grey dots indicate non-significant differentially expressed
   genes.

   Clustering was performed based on the expression levels of the same
   gene across different samples and the expression patterns of different
   genes within the same sample. Euclidean distance was used for
   calculating similarity, and hierarchical clustering was conducted using
   the complete linkage (longest distance) method. The results are shown
   in [130]Figure 8C. Differentially expressed genes (DEGs) were
   identified using the criteria |log[2]FoldChange| > 1 and p-value <
   0.05. A volcano plot of DEGs was generated using the ggplot2 package in
   R. In the plot, the two vertical dotted lines represent the twofold
   expression difference threshold, while the horizontal dotted line marks
   the p-value = 0.05 threshold. The blue dots indicate downregulated
   genes compared to CK, red dots represent upregulated genes, and gray
   dots denote genes without significant differential expression. The
   results are shown in [131]Figure 8D, where a total of 4350 DEGs were
   identified, including 2062 upregulated and 2288 downregulated genes,
   accounting for 47.4% and 52.6% of the total, respectively.

3.3.3. KEGG and GO Enrichment Analysis of Differentially Expressed Genes

   To clarify the main pathways involved in leaf senescence in red
   raspberry seedlings, the DEGs were mapped to the KEGG database
   ([132]Figure 9A and [133]Table S10). An enrichment scatter plot of the
   top 20 KEGG pathways was generated ([134]Figure 9B). The annotation
   results indicated that the most enriched pathway was ribosome, followed
   by photosynthesis, photosynthesis-antenna proteins, plant hormone
   signal transduction, and porphyrin and chlorophyll metabolism. In the
   ribosome pathway, the number of DEGs was highest, with 281 genes—49
   upregulated and 232 downregulated.

Figure 9.

   [135]Figure 9
   [136]Open in a new tab

   KEGG and GO enrichment analysis of DEGs between two red raspberry
   phenotypes. (A) KEGG pathway enrichment histogram; (B) KEGG pathway
   enrichment bubble plot of DEGs between the two phenotypes; (C) GO term
   enrichment histogram; (D) GO term enrichment scatter plot of DEGs
   between the two phenotypes. Note: T = T150.

   To explore the main biological functions of DEGs, topGO was used to
   perform GO enrichment analysis on the DEGs in normal and prematurely
   aged leaves of red raspberry seedlings (significant enrichment was
   defined as p < 0.05). As shown in [137]Figure 9C, these genes were
   categorized into three groups based on GO analysis: cellular component
   (CC), molecular function (MF), and biological process (BP). In terms of
   cellular components, chloroplast thylakoid, plastid thylakoid, and its
   thylakoid were significantly enriched. For molecular function, the DEGs
   were mainly associated with structural constituent of ribosome,
   followed by chlorophyll binding and tetrapyrrole binding. Within
   biological processes, the genes were primarily enriched in
   photosynthesis, light reaction, and the secondary metabolite
   biosynthetic process ([138]Figure 9D).

3.3.4. Physiological and Biochemical Indicators and Hormone-Related
Differentially Expressed Genes

   DEGs related to physiological indicators of early senescence in red
   raspberry leaves were identified, and the results are shown in
   [139]Figure 10A. Four DEGs were related to senescence—one was
   upregulated and three were downregulated. Fourteen DEGs were related to
   peroxidase activity, with seven upregulated and seven downregulated.
   One catalase-related gene was identified and was upregulated. Three
   starch-related genes were detected, all of which were upregulated.
   Twenty-eight chlorophyll-related genes were identified, of which 25
   were upregulated and 3 were downregulated.

Figure 10.

   [140]Figure 10
   [141]Open in a new tab

   Heatmap of physiological- and hormone-related DEGs between two red
   raspberry phenotypes. (A) Differential expression genes related to
   physiological indicators of two phenotypes of red raspberries. a:
   senescence-related. b: POD-related. c: CAT-related. d: starch-related.
   e: chlorophyll-related; (B) hormone related differentially expressed
   genes in two phenotypes of red raspberries. a: auxin-related. b:
   cytokinins-related. c: gibberellin-related. d: ABA-related. e:
   ethylene-related. f: brassinolide-related; Note: Red indicates
   upregulated genes, and blue indicates downregulated genes.

   DEGs associated with hormone regulation were also screened, and the
   results are presented in [142]Figure 10B. Twenty-eight DEGs were
   related to auxin, with 20 upregulated and 8 downregulated. Five DEGs
   were related to cytokinin, including three upregulated and two
   downregulated. One gibberellin-related gene was found to be
   downregulated. Three genes were associated with abscisic acid, with two
   upregulated and one downregulated. Eight DEGs were related to ethylene,
   including five upregulated and three downregulated. Two
   brassinolide-related DEGs were identified, both of which were
   downregulated.

   To verify the results of DEGs involved in red raspberry leaf
   senescence, 12 were selected for quantitative fluorescence PCR
   validation, including 6 upregulated and 6 downregulated genes. As shown
   in [143]Figure S1E and [144]Table S11E, the ordinate represents the
   RNA-seq expression levels, and the abscissa represents the qRT-PCR
   expression levels. The results showed that the expression trends of
   these 12 DEGs were consistent with the transcriptome sequencing data,
   confirming that transcriptome sequencing is efficient and accurate.
   This approach can therefore be used to screen DEGs and analyze red
   raspberry leaf senescence, enhancing the reliability of the experiment.

3.4. Metabolome Data Analysis of Lower Leaves of T150 and CK Plants

3.4.1. Metabolite PCA and OPLS-DA Analysis

   A total of 1292 metabolites were identified in this experiment,
   including 793 in positive ion mode and 499 in negative ion mode. The
   metabolites were classified and quantified based on their chemical
   categories, and the proportion of each type is shown in [145]Figure
   S1D.

   To understand the overall distribution trends of the two red raspberry
   phenotypes, PCA was conducted. The two treatments formed two distinct
   clusters ([146]Figure S2A,B), effectively differentiating the groups.
   This result indicates good experimental repeatability and significant
   differences between the two sample sets.

   The data from T150 and CK treatments were further analyzed using
   orthogonal partial least squares discriminant analysis (OPLS-DA), which
   filters out irrelevant variation and accurately highlights intergroup
   differences. The resulting score plots ([147]Figure S2C,D) illustrate
   both inter- and intra-group variation and clearly separate the two
   groups. Evaluation parameters R^2 X, R^2 Y, and Q^2 were obtained via
   cross-validation. The closer these values are to 1, the more stable and
   reliable the model. Generally, Q^2 > 0.5 indicate an effective model,
   while Q^2 < 0.3 suggest low reliability. In this analysis, Q^2 values
   were 0.993 in positive ion mode and 0.996 in negative ion mode,
   indicating highly robust models.

   A permutation test was performed to validate the model. In the plot,
   the horizontal axis represents permutation order, the vertical axis
   shows R^2 (green dots) and Q^2 (blue dots), and the dotted line
   represents the regression line for both parameters. As shown in
   [148]Figure S2E,F, R^2 and Q^2 values of the randomized models
   decreased as permutation order decreased, confirming the strong
   robustness of the original model.

3.4.2. Screening of Differentially Expressed Metabolites (DEMs)

   Metabolites associated with premature senescence in red raspberry
   leaves were screened using OPLS-DA, with variable importance in
   projection (VIP) > 1 and p-value < 0.05 as selection criteria. A total
   of 135 significantly differential metabolites were identified (87 in
   positive ion mode and 48 in negative ion mode), including 51 lipids and
   lipid-like molecules; 27 phenylpropanoids and polyketides; 13 organic
   oxygen compounds; 9 organic heterocyclic compounds; 7 organic acids and
   their derivatives; 5 benzene ring compounds; 2 organic nitrogen
   compounds; 2 alkaloids and their derivatives; 2 lignin, neolignin, and
   related compounds; and 17 other substances. Among these, 60 metabolites
   (19 negative and 41 positive ions) were significantly upregulated in
   T150, accounting for 44.44%, while 75 metabolites (29 negative and 46
   positive ions) were significantly downregulated, accounting for 55.56%.
   [149]Tables S5 and S6 list the top 20 differential metabolites based on
   fold change in expression for positive and negative ion modes,
   respectively.

   The samples and differential metabolites were clustered, with red
   indicating significantly upregulated metabolites and blue indicating
   significantly downregulated metabolites. As shown in [150]Figure 11,
   the two phenotypes of red raspberry samples clustered separately,
   indicating distinct metabolic profiles between the two groups.

Figure 11.

   [151]Figure 11
   [152]Open in a new tab

   Hierarchical clustering heatmap of significantly different metabolites
   between two red raspberry phenotypes. (A) Positive ion mode; (B)
   negative ion mode. Note: red indicates upregulated metabolites, blue
   indicates downregulated metabolites; T = T150.

3.4.3. KEGG Pathway Analysis

   To further investigate metabolic differences, pathway enrichment
   analysis was performed using the KEGG database on the differential
   metabolites of the two phenotypes. As shown in [153]Figure 12A, six
   pathways with p-value < 0.05 were identified: Glycine, serine and
   threonine metabolism; glycerophospholipid metabolism; phenylpropanoid
   biosynthesis; flavonoid biosynthesis; axon regeneration; and
   cholinergic synapse.

Figure 12.

   [154]Figure 12
   [155]Open in a new tab

   KEGG enrichment pathway diagram and differential metabolite cluster
   heat map. (A) KEGG enrichment pathway diagram of differential
   metabolites of two phenotypic red raspberries; (B) biosynthesis of
   secondary metabolites; (C) metabolic pathways. Note: T = T150.

   The differential metabolites from both positive and negative ion modes
   were merged and annotated using the KEGG database (VIP > 1, p-value <
   0.05). The identified metabolites were mapped to 34 pathways, with the
   largest number annotated to secondary metabolite biosynthesis and
   general metabolic pathways (six metabolites each). These were
   visualized using a heatmap, where each row represents a differential
   metabolite and each column represents a sample. As shown in [156]Figure
   12B,C, CK and T150 samples clustered together. Compared with CK,
   chlorogenic acid and choline were significantly upregulated in T150,
   while naringenin chalcone, erythromycin, and tryptophan were
   significantly downregulated.

3.5. Joint Analysis of Metabolome and Transcriptome of Lower Leaves of T150
and CK Plants

   For the joint analysis of the metabolome and transcriptome, we first
   obtained quantitative detection and analysis results for both datasets
   and mapped the transcripts corresponding to differential metabolites
   and related enzymes to the KEGG database. Transcriptomic and
   metabolomic data were cross-validated to identify metabolic pathways
   with significant changes. The results ([157]Table S7) showed that the
   differential metabolites and transcripts were annotated to the
   phenylpropanoid biosynthesis (ko00940) and flavonoid biosynthesis
   (ko00941) pathways. In the phenylpropanoid biosynthesis pathway, 31
   genes were differentially expressed, including 10 upregulated and 21
   downregulated genes. Two differential metabolites—chlorogenic acid and
   stenophylline A—were identified. In the flavonoid biosynthesis pathway,
   eight genes were differentially expressed, with five upregulated and
   three downregulated. Two differential metabolites—chlorogenic acid and
   naringenin chalcone—were identified.

   The metabolic pathway analysis results were visualized, with
   differential metabolite expression indicated by a color gradient from
   blue (low) to orange (high), and transcript expression by a gradient
   from green (low) to red (high). The phenylpropanoid biosynthesis
   pathway serves as a key upstream pathway of flavonoid biosynthesis. As
   shown in [158]Figure 13, in CK leaves, the contents of phenylalanine
   ammonia lyase (PAL), shikimate O-hydroxycinnamoyltransferase (HCT), and
   5-O-(4-coumaryl)-D-quinic acid 3′-monooxygenase (C3H) increased, while
   the contents of trans-cinnamate 4-monooxygenase (C4H) and
   4-carboxymethyl-CoA ligase (4CL) decreased. The naringenin chalcone
   content increased, whereas that of chlorogenic acid decreased.

Figure 13.

   [159]Figure 13
   [160]Open in a new tab

   Key pathway of red raspberry premature senescence.

4. Discussion

4.1. Apparent Differences and Physiological and Biochemical Changes Between
T150 and CK

   Plants undergo dramatic changes in external morphology as well as
   internal physiological and biochemical processes during senescence. The
   green-type wheat maintains higher chlorophyll content, net
   photosynthetic rate, and antioxidant enzyme activity during the late
   stages of leaf senescence [[161]39]. Low nitrogen levels induce
   senescence in maize seedlings, leading to reduced leaf area,
   chlorophyll content, and net photosynthetic rate, as well as decreased
   accumulation of soluble sugars and starch [[162]40]. The results of
   this experiment are consistent with the findings reported above.
   Compared with normal leaves, prematurely senescent raspberry seedlings
   show significantly reduced plant height, ground diameter, and number of
   buds, indicating that the seedlings may lack sufficient nutrients to
   support aboveground growth. The chlorophyll content decreases
   progressively from the upper to the lower parts of the plant, and the
   lower leaves turn yellow, indicating an aging state. In some
   prematurely aging trees, both upper and lower leaves fall off, further
   indicating weakened photosynthetic capacity, which impairs the
   formation of photosynthetic products.

   Appropriate nitrogen fertilizer supplementation can improve leaf
   function. High-temperature stress causes premature senescence in wheat
   flag leaves, reducing starch accumulation and the activity of key
   enzymes involved in starch synthesis. Nitrogen application can enhance
   photosynthetic efficiency in leaves, accelerate starch synthesis in
   grains, and thereby increase yield [[163]41]. Plants use photosynthesis
   to synthesize starch, which is stored as a polysaccharide in fruits,
   seeds, and rhizomes. In this experiment, leaf starch content was lowest
   and decreased from the upper to the lower part of the plant. This was
   likely due to the reduced photosynthetic capacity of lower leaves,
   resulting in decreased starch synthesis. The starch content in CK
   leaves was higher than that in the corresponding parts of T150, while
   soluble sugar content was lower. This may have been due to reduced
   metabolic consumption in the plant, preventing timely starch
   degradation and utilization, thus causing accumulation. Alternatively,
   it may reflect the transport of soluble sugars to other organs, where
   they are converted into starch and stored.

   Topdressing with nitrogen fertilizer can increase the activity of SOD
   and CAT in leaves, reduce membrane lipid peroxidation and
   malondialdehyde (MDA) accumulation, maintain flag leaf greenness for a
   longer period, and enhance production potential [[164]42]. The SOD and
   POD activities in the middle and lower leaves of T150 were
   significantly higher than those in CK, while CAT activity in CK leaves
   was significantly higher than in T150. MDA and superoxide anion
   contents in the lower leaves of CK were significantly higher than those
   in T150, indicating that the antioxidant system in the middle and lower
   leaves of T150 removed harmful substances more efficiently than they
   were produced, thereby protecting cellular structure and function while
   delaying premature senescence. In conclusion, the premature senescence
   of red raspberries may be due to the excessive accumulation of MDA and
   O[2]^−, reduced antioxidant enzyme activity, and impaired
   self-regulation resulting from malnutrition.

   Studies have shown that an unbalanced root-to-shoot relationship can
   contribute to plant aging [[165]43]. When stimulated, plant roots can
   adjust to environmental changes by increasing the root-to-shoot ratio
   [[166]44], as well as improving root quality [[167]45]. Low nitrogen
   levels induced senescence in maize seedlings, yet still led to
   increases in root dry weight, total root length, and average root
   diameter [[168]40]. Under low nitrogen conditions, the elongation and
   maturation zones of rapeseed roots exhibit active cell division and
   elongation, while the root-to-shoot ratio and nitrogen use efficiency
   also increase significantly [[169]46,[170]47]. Wheat root activity is
   strongly correlated with leaf senescence index [[171]48], indicating
   that root systems may influence leaf senescence. In this experiment,
   the roots of CK seedlings were significantly longer than those of T150,
   indicating that CK seedlings allocated more photosynthates to root
   growth, which promoted preferential root development and improved
   access to water and nitrogen in the soil, thereby enhancing nutrient
   use efficiency. However, the number of root buds in CK was lower than
   in T150, possibly because nutrients were primarily allocated for root
   growth and partially reserved for future bud germination. The root
   activity in the premature senescence phenotype (CK) was lower than in
   T150, suggesting that the CK root system had a weaker capacity to
   absorb soil water and nutrients, resulting in insufficient supply to
   the aboveground parts of the plant.

   Leaf senescence is regulated internally, beginning at the base of the
   leaves and gradually progressing to the upper leaves [[172]49]. In this
   experiment, the chloroplasts in the palisade tissue of the upper and
   middle leaves of T150 were located near the cell wall, whereas in CK,
   the chloroplasts were more diffusely distributed. This distribution
   pattern in T150 reflects its stronger regulatory capacity. In contrast,
   the chloroplasts in the lower leaves of both T150 and CK were dispersed
   throughout the cytoplasm, allowing more effective use of scattered
   light to enhance photosynthesis. The spongy tissue cells in the lower
   leaves of CK were loosely arranged, with large intercellular spaces
   that facilitated gas exchange with the external environment.

4.2. Analysis of Differentially Expressed Genes in Premature Leaf Senescence
of Red Raspberry

   Plant senescence involves physiological, biochemical, and molecular
   biological processes, regulated by multiple genes. In this study, lower
   leaves displaying premature senescence and normal phenotypes in
   raspberry were analyzed de novo, yielding 37,304 transcripts. The
   majority of unigenes were annotated in the NR database. Annotation
   results indicated functional similarity with roses and wild
   strawberries within the Rosaceae family. A total of 4350 DEGs were
   identified, including 2062 upregulated and 2288 downregulated. GO
   enrichment analysis showed that DEGs were associated with chloroplast
   thylakoids, plastid thylakoids, and thylakoids in the cellular
   component category; ribosomal structural components, chlorophyll
   binding, and tetrapyrrole binding in the molecular function category;
   and photosynthesis, light reactions, and secondary metabolite
   biosynthesis in the biological process category. These findings suggest
   that premature senescence in red raspberries primarily affects
   chloroplast regulation, as well as transcriptional and translational
   processes.

   In this experiment, the senescence-related genes TRINITY_DN1158_c0_g1,
   TRINITY_DN3133_c0_g1, and TRINITY_DN1122_c0_g1 were upregulated in
   prematurely senescent red raspberry. In contrast, in the normal
   phenotype, the upregulated expression of chlorophyll-binding protein
   genes TRINITY_DN557_c1_g1, TRINITY_DN5422_c0_g1, and
   TRINITY_DN1024_c0_g1; chlorophyll reductase genes TRINITY_DN8732_c0_g1
   and TRINITY_DN7630_c0_g1; the chlorophyll oxygenase gene
   TRINITY_DN5083_c0_g1; and the light-harvesting chlorophyll protein
   complex gene TRINITY_DN6259_c0_g1 supported normal photosynthesis.
   Additionally, peroxidase-related genes TRINITY_DN7508_c0_g1,
   TRINITY_DN14477_c0_g1, TRINITY_DN3369_c0_g1, and others were
   upregulated, helping reduce oxidative damage to cells. In this
   experiment, the starch synthase genes TRINITY_DN10439_c0_g1,
   TRINITY_DN3486_c0_g1, and TRINITY_DN10383_c0_g1 were upregulated in the
   normal phenotype, ensuring stable photosynthesis and starch synthesis.

4.3. Analysis of Differential Metabolites in Premature Senescence of Red
Raspberry Leaves

   Metabolomics was used to quantitatively analyze all metabolites in the
   plants, revealing their relationship to physiological changes. A total
   of 1292 metabolites were identified, including 793 positive ions and
   499 negative ions. Among these, 135 significantly different metabolites
   (87 positive ions and 48 negative ions) were identified based on VIP >
   1 and p < 0.05. These included 51 lipids and lipid molecules, 27
   phenylpropanes and polyketides, 13 organic oxygen compounds, 9 organic
   heterocyclic compounds, 7 organic acids and derivatives, 5 benzene ring
   compounds, 2 organic nitrogen compounds, 2 alkaloids and derivatives, 2
   lignin- and neolignin-related compounds, and 17 other substances.
   Clustering analysis of all samples and differential metabolites showed
   that the T150 group and CK group formed distinct clusters, indicating
   that the identified differential metabolites can effectively
   distinguish between the two groups. These metabolites were annotated to
   34 pathways. Notably, the secondary metabolite biosynthesis (ko01110)
   and metabolic pathways (ko01100) each contained six annotated
   differential metabolites, suggesting their important roles in the
   premature senescence of red raspberry.

   Secondary metabolites are small organic compounds synthesized via
   complex secondary metabolic pathways during plant growth and
   development. They exist in various forms but are generally low in
   concentration [[173]50]. Terpenoids refer broadly to all isoprene
   polymers and their derivatives [[174]51]. Among terpenes, monoterpenes
   and sesquiterpenes are the main components of volatile oils, diterpenes
   are the primary constituents of resins, and triterpenes commonly occur
   as saponins [[175]52]. Saponins are the main active components of many
   Chinese herbal medicines (such as ginseng, licorice, bupleurum, etc.),
   known for their antibacterial, antipyretic, and anticancer properties.
   Studies have shown that triterpenoid compounds in raspberries possess
   antioxidant and antibacterial activities [[176]53]. Phenols include
   both flavonoids and non-flavonoids. Flavonoids comprise flavones,
   dihydroflavonoids, chalcones, anthocyanidins, etc. [[177]54].
   Nitrogen-containing compounds mainly consist of alkaloids, non-protein
   amino acids, and cyanogenic glycosides, most of which serve defensive
   roles. In this experiment, metabolomic analysis identified 20
   triterpenoid metabolites, 19 of which were upregulated in CK
   raspberries. Additionally, 19 flavonoids were detected, of which 15
   were downregulated. These results suggest that different premature
   senescence phenotypes in red raspberries employ distinct strategies to
   mitigate environmental stress. CK may enhance its antibacterial and
   antiviral capacity by generating terpenoid metabolites, while T150
   appears to bolster its antioxidant defenses via flavonoid synthesis,
   thereby alleviating premature senescence.

4.4. Analysis of Key Pathways in Premature Senescence of Red Raspberry Leaves

   Phenylpropanoid biosynthesis is an important secondary metabolic
   pathway in plants [[178]55]. Red pear enhances its antioxidant capacity
   through phenolic compounds produced in this pathway [[179]56]. The
   phenylpropanoid biosynthesis pathway also serves as a key upstream
   pathway of flavonoid biosynthesis [[180]57]. Studies have shown that
   overexpression of phenylalanine ammonia-lyase (PAL), cinnamate
   4-hydroxylase (C4H), 4-coumarate:coenzyme A ligase (4CL),
   hydroxycinnamoyl transferase (HCT), hydroxycinnamoyl-CoA:quinate
   hydroxycinnamoyl transferase (HQT), and coumarate 3-hydroxylase
   (C3H)-related genes can significantly increase the chlorogenic acid
   content in cells [[181]58].

   Chlorogenic acid, a caffeoylquinic acid compound, is widely found in
   higher dicotyledonous plants and ferns [[182]59]. Chlorogenic acid
   possesses antibacterial properties and is widely used in medicine to
   reduce inflammation [[183]60]. One of chlorogenic acid’s most important
   biological activities is antioxidant activity [[184]61]. In this
   experiment, senescent leaves had lower chlorogenic acid content
   compared to normal leaves, and thus had reduced capacity to eliminate
   O[2]^−, ultimately leading to cell damage and subsequent leaf
   senescence.

   The flavonoid biosynthesis pathway is ubiquitous in plants and produces
   various secondary metabolites, known as flavonoid compounds [[185]62],
   which contribute significantly to combating chronic diseases. This
   pathway also plays an important role in plant growth and development.
   In plants, flavonoids have antioxidant functions; scavenging ROS, they
   enhance stress resilience and influence growth and development through
   the synthesis and distribution of auxins [[186]63]. In Chinese cabbage,
   melatonin treatment significantly delays leaf senescence, preserving
   high quality and maintaining elevated total phenolic and flavonoid
   contents [[187]64].

   Naringenin chalcone belongs to the flavonoid class of phytochemicals
   and is found in the peel of red tomatoes. In a study investigating the
   effect of potassium humate on waterlogged barnyard grass, it was
   observed that potassium humate influences the flavonoid biosynthesis
   pathway. Specifically, p-coumaryl-CoA is converted to naringenin
   chalcone by chalcone synthase (CHS), which is then transformed into
   naringenin by chalcone isomerase (CHI). These flavonoid metabolites
   were significantly downregulated following the application of potassium
   humate [[188]65]. Additionally, some studies have shown that the
   addition of naringenin chalcone to cancer cells induces chromatin
   condensation, DNA fragmentation, and vacuole formation [[189]66],
   leading to the production of apoptotic bodies and autophagic vacuoles.
   Naringenin chalcone can inhibit cancer cell growth by inducing early
   and late apoptosis and promoting autophagy in animals. However, few
   studies have investigated its role in plants. It is therefore
   speculated that naringenin chalcone may have a similar function in
   plants. In this experiment, naringenin chalcone levels significantly
   increased in prematurely senescent raspberries. Its upregulation may
   trigger apoptosis in senescent cells and redirect nutrients to other
   organs, but it also inhibits the growth of new cells, ultimately
   causing the leaves to display signs of aging ([190]Figure 14).

Figure 14.

   [191]Figure 14
   [192]Open in a new tab

   Joint transcriptome and metabolome results.

5. Conclusions

   Compared with T150 plants, CK plants exhibited distinct differences in
   phenotype, physiology, and leaf structure: CK showed reduced plant
   height, stem diameter, bud number, and root activity, alongside
   increased root-to-crown ratio and root length. Additionally, CK had
   thicker lower leaves, while the upper and middle leaves displayed
   loosely arranged chloroplasts. The chlorophyll, soluble protein, and
   soluble sugar contents in CK leaves were lower than those in T150. In
   contrast, T150 plants showed higher SOD and POD activities in lower
   leaves, lower CAT activity, and significantly lower MDA and O[2]^−
   contents in lower leaves compared to CK.

   Transcriptomic analysis identified 2062 upregulated and 2288
   downregulated genes between the two types, with enrichment in pathways
   such as ribosome and photosynthesis. Specifically, T150 downregulated
   senescence-related genes while upregulating genes involved in
   chlorophyll metabolism and antioxidant enzyme synthesis. Metabolomic
   analysis detected 135 differential metabolites, mainly enriched in
   phenylpropanoid and flavonoid biosynthesis pathways: CK upregulated 19
   triterpenoids, whereas T150 upregulated 15 flavonoids. Multi-omics
   integration highlighted the phenylpropanoid and flavonoid biosynthesis
   pathways as core regulatory hubs. T150 delays leaf senescence by
   maintaining flavonoid-mediated redox balance, while CK undergoes leaf
   senescence due to altered contents of chlorogenic acid and naringenin
   chalcone.

   These findings link phenotypic plasticity to molecular reprogramming,
   demonstrating that adequate nitrogen supply delays leaf senescence
   through multiple mechanisms (e.g., maintaining photosynthetic
   efficiency, enhancing antioxidant capacity, regulating flavonoid
   metabolism). This underscores precision nitrogen management as a key
   strategy for modulating senescence in red raspberry leaves.

Acknowledgments