2024 Volume 4
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Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants

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  • The availability of soil phosphorus (P) is essential for crop cultivation and production. However, agronomic P management for tea crops remains unexplored. Herein, the effect of different P management practices viz. unfertilized (control), organic fertilizer (OF) application, OF + N application at 300 kg/ha (N300) (OF-P1), OF + N300 + P application at 45 kg/ha (OF-P2), OF + N300 + P application at 90 kg/ha (OF-P3), and OF + N300 + P application at 135 kg/ha (OF-P4) on soil nutrient acquisition, enzymatic activities, and physio-biochemical, and quality traits of tea plants are investigated in yield. The results showed that OF-P2 treatment had significantly higher soil N (30.5%), P (42.2%), and potash (1.6%) concentrations above the control. P concentrations had a linear positive correlation with the activities of acid-phosphatase and phytase. OF-P2 had the greatest effects on plant growth, chlorophyll and carotenoid contents, and antioxidative enzyme activities than other treatments. OF-P2 treatment had a two-fold decrease in hydrogen peroxide and dioxygen (singlet) compared to the control. It was further found that OF-P2 significantly increased amino acid content by 33.5%, 40.1%, and 31.9%, and decreased polyphenol content by 42.3%, 45.6%, and 25.7% in bud, first, and second leaf, respectively, above the control. Overall, the present findings suggest that low P application (OF-P2) can increase nutrient availability, bud quality, and yield by improving soil enzymatic activities, pigment contents, and antioxidative activities. Establishing this mode of low P application may provide an optimum strategy for enhancing crop performance in regions where unreasonable P application practices are common.
  • Columnar cacti are plants of the Cactaceae family distributed across arid and semi-arid regions of America, with ecological, economic, and cultural value[1]. One trait that makes it possible for the columnar cactus to survive in the desert ecosystem is its thick epidermis covered by a hydrophobic cuticle, which limits water loss in dry conditions[1]. The cuticle is the external layer that covers the non-woody aerial organs of land plants. The careful control of cuticle biosynthesis could produce drought stress tolerance in relevant crop plants[2]. In fleshy fruits, the cuticle maintains adequate water content during fruit development on the plant and reduces water loss in fruit during postharvest[3]. Efforts to elucidate the molecular pathway of cuticle biosynthesis have been carried out for fleshy fruits such as tomato (Solanum lycopersicum)[4], apple (Malus domestica)[5], sweet cherry (Prunus avium)[6], mango (Mangifera indica)[7], and pear (Pyrus 'Yuluxiang')[8].

    The plant cuticle is formed by the two main layers cutin and cuticular waxes[3]. Cutin is composed mainly of oxygenated long-chain (LC) fatty acids (FA), which are synthesized by cytochrome p450 (CYP) enzymes. CYP family 86 subfamily A (CYP86A) enzymes carry out the terminal (ω) oxidation of LC-FA[9]. Then, CYP77A carries out the mid-chain oxidation to synthesize the main cutin monomers. In Arabidopsis, AtCYP77A4 and AtCYP77A6 carry out the synthesis of mid-chain epoxy and mid-chain dihydroxy LC-FA, respectively[10,11]. AtCYP77A6 is required for the cutin biosynthesis and the correct formation of floral surfaces[10]. The expression of CYP77A19 (KF410855) and CYP77A20 (KF410856) from potato (Solanum tuberosum) restored the petal cuticular impermeability in Arabidopsis null mutant cyp77a6-1, tentatively by the synthesis of cutin monomers[12]. In eggplant (Solanum torvum), the over-expression of StoCYP77A2 leads to resistance to Verticillium dahlia infection in tobacco plants[13]. Although the function of CYP77A2 in cutin biosynthesis has not yet been tested, gene expression analysis suggests that CaCYP77A2 (A0A1U8GYB0) could play a role in cutin biosynthesis during pepper fruit development[14].

    It has been hypothesized that the export of cuticle precursors is carried out by ATP binding cassette subfamily G (ABCG) transporters. ABCG11/WBC11, ABCG12, and ABCG13 are required for the load of cuticle lipids in Arabidopsis[1517], but ABCG13 function appears to be specific to the flower epidermis[18]. The overexpression of TsABCG11 (JQ389853) from Thellungiella salsugineum increases cuticle amounts and promotes tolerance to different abiotic stresses in Arabidopsis[19].

    Once exported, the cutin monomers are polymerized on the surface of epidermal cells. CD1 code for a Gly-Asp-Ser-Leu motif lipase/esterase (GDSL) from tomato required for the cutin formation through 2-mono(10,16-dihydroxyhexadecanoyl)glycerol esterification[20]. GDSL1 from tomato carries out the ester bond cross-links of cutin monomers located at the cuticle layers and is required for cuticle deposition in tomato fruits[21]. It has been shown that the transcription factor MIXTA-like reduces water loss in tomato fruits through the positive regulation of the expression of CYP77A2, ABCG11, and GDSL1[22]. Despite the relevant role of cuticles in maintaining cactus homeostasis in desert environments[1], the molecular mechanism of cuticle biosynthesis has yet to be described for cactus fruits.

    Stenocereus thurberi is a columnar cactus endemic from the Sonoran desert (Mexico), which produces an ovoid-globose fleshy fruit named sweet pitaya[23]. In its mature state, the pulp of sweet pitaya contains around 86% water with a high content of antioxidants and natural pigments such as betalains and phenolic compounds, which have nutraceutical and industrial relevance[23]. Due to the arid environment in which pitaya fruit grows, studying its molecular mechanism of cuticle biosynthesis can generate new insights into understanding species' adaptation mechanisms to arid environments. Nevertheless, sequences of transcripts from S. thurberi in public databases are scarce.

    RNA-sequencing technology (RNA-seq) allows the massive generation of almost all the transcripts from non-model plants, even if no complete assembled genome is available[24]. Recent advances in bioinformatic tools has improved our capacity to identify long non-coding RNA (lncRNA), which have been showed to play regulatory roles in relevant biological processes, such as the regulation of drought stress tolerance in plants[25], fruit development, and ripening[2629].

    In this study, RNA-seq data were obtained for the de novo assembly and characterization of the S. thurberi fruit peel transcriptome. As a first approach, three transcripts, StCYP77A, StABCG11, and StGDSL1, tentatively involved in cuticle biosynthesis, were identified and quantified during sweet pitaya fruit development. Due to no gene expression analysis having been carried out yet for S. thurberi, stably expressed constitutive genes were identified for the first time.

    Sweet pitaya fruits (S. thurberi) without physical damage were hand harvested from plants in a native conditions field located at Carbó, Sonora, México. They were collocated in a cooler containing dry ice and transported immediately to the laboratory. The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their tentative stage of development defined by their visual characteristics, frozen in liquid nitrogen, and pulverized to create a single biological replicate. Four samples belonging to four different plants were analyzed. All fruits harvested were close to the ripening stage. Samples named M1 and M2 were turning from green to ripe [~35−40 Days After Flowering (DAF)], whereas samples M3 and M4 were turning from ripe to overripe (~40−45 DAF).

    Total RNA was isolated from the peels through the Hot Borate method[30]. The concentration and purity of RNA were determined in a spectrophotometer Nanodrop 2000 (Thermo Fisher) by measuring the 260/280 and 260/230 absorbance ratios. RNA integrity was evaluated through electrophoresis in agarose gel 1% and a Bioanalyzer 2100 (Agilent). Pure RNA was sequenced in the paired-end mode in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. Four RNA-seq libraries, each of them from each sample, were obtained, which include a total of 288,199,704 short reads with a length of 150 base pairs (bp). The resulting sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439. Libraries are named corresponding to the names of samples M1, M2, M3, and M4.

    FastQC software (www.bioinformatics.babraham.ac.uk/projects/fastqc) was used for short reads quality analysis. Short reads with poor quality were trimmed or eliminated by Trimmomatic (www.usadellab.org/cms/?page=trimmomatic) with a trailing and leading of 25, a sliding window of 4:25, and a minimum read length of 80 bp. A total of 243,194,888 reads with at least a 25 quality score on the Phred scale were used to carry out the de novo assembly by Trinity (https://github.com/trinityrnaseq/trinityrnaseq/wiki) with the following parameters: minimal k-mer coverage of 1, normalization of 50, and minimal transcript length of 200 bp.

    Removal of contaminating sequences and ribosomal RNA (rRNA) was carried out through SeqClean. To remove redundancy, transcripts with equal or more than 90% of identity were merged through CD-hit (www.bioinformatics.org/cd-hit/). Alignment and quantification in terms of transcripts per million (TPM) were carried out through Bowtie (https://bowtie-bio.sourceforge.net/index.shtml) and RSEM (https://github.com/deweylab/RSEM), respectively. Transcripts showing a low expression (TPM < 0.01) were discarded. Assembly quality was evaluated by calculating the parameters N50 value, mean transcript length, TransRate score, and completeness. The statistics of the transcriptome were determined by TrinityStats and TransRate (https://hibberdlab.com/transrate/). The transcriptome completeness was determined through a BLASTn alignment (E value < 1 × 10−3) by BUSCO (https://busco.ezlab.org/) against the database of conserved orthologous genes from Embryophyte.

    To predict the proteins tentatively coded in the S. thurberi transcriptome, the best homology match of the assembled transcripts was found by alignment to the Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, and ITAG databases using the BLAST algorithm with an E value threshold of 1 × 10−10 for the nr-NCBI database and of 1 × 10−5 for the others[3134]. An additional alignment was carried out to the protein databases of commercial fruits Persea americana, Prunus persica, Fragaria vesca, Citrus cinensis, and Vitis vinifera to proteins of the cactus Opuntia streptacantha, and the transcriptomes of the cactus Hylocereus polyrhizus, Pachycereus pringlei, and Selenicereus undatus. The list of all databases and the database websites of commercial fruits and cactus are provided in Supplementary Tables S1 & S2. The open reading frame (ORF) of the transcripts and the protein sequences tentative coded from the sweet pitaya transcriptome was predicted by TransDecoder (https://github.com/TransDecoder/TransDecoder/wiki), considering a minimal ORF length of 75 amino acids (aa). The search for protein domains was carried out by the InterPro database (www.ebi.ac.uk/interpro). Functional categorization was carried out by Blast2GO based on GO terms and KEGG metabolic pathways[35].

    LncRNA were identified based on the methods reported in previous studies[25,29,36]. Transcripts without homology to any protein from Swiss-Prot, RefSeq, nr-NCBI, PlantTFDB, iTAK, TAIR, ITAG, P. americana, P. persica, F. vesca, C. cinensis, V. vinifera, and O. streptacantha databases, without a predicted ORF longer than 75 aa, and without protein domains in the InterPro database were selected to identify tentative lncRNA.

    Transcripts coding for signal peptide or transmembrane helices were identified by SignalP (https://services.healthtech.dtu.dk/services/SignalP-6.0/) and TMHMM (https://services.healthtech.dtu.dk/services/TMHMM-2.0/), respectively, and discarded. Further, transcripts corresponding to other non-coding RNAs (ribosomal RNA and transfer RNA) were identified through Infernal by using the Rfam database[37] and discarded. The remaining transcripts were analyzed by CPC[38], and CPC2[39] to calculate their coding potential. Transcripts with a coding potential score lower than −1 for CPC and a coding probability lower than 0.1 for CPC2 were considered lncRNA. To characterize the identified lncRNA, the length and abundance of coding and lncRNA were calculated. Bowtie and RSEM were used to align and quantify raw counts, respectively. The edgeR package[40] was used to normalize raw count data in terms of counts per million (CPM) for both coding and lncRNA.

    To obtain the transcript's expression, the aligning of short reads and quantifying of transcripts were carried out through Bowtie and RSEM software, respectively. A differential expression analysis was carried out between the four libraries by edgeR package in R Studio. Only the transcripts with a count equal to or higher than 0.5 in at least one sample were retained for the analysis. Transcripts with log2 Fold Change (log2FC) between +1 and −1 and with a False Discovery Rate (FDR) lower than 0.05 were taken as not differentially expressed (NDE).

    For the identification of the tentative reference genes two strategies were carried out as described below: i) The NDE transcripts were aligned by BLASTn (E value < 1 × 10−5) to 43 constitutive genes previously reported in fruits from the cactus H. polyrhizus, S. monacanthus, and S. undatus[4143] to identify possible homologous constitutive genes in S. thurberi. Then, the homologous transcripts with the minimal coefficient of variation (CV) were selected; ii) For all the NDE transcripts, the percentile 95 value of the mean CPM and the percentile 5 value of the CV were used as filters to recover the most stably expressed transcripts, based on previous studies[44]. Finally, transcripts to be tested by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were selected based on their homology and tentative biological function.

    The fruit harvesting was carried out as described above. Sweet pitaya fruit takes about 43 d to ripen, therefore, open flowers were tagged, and fruits with 10, 20, 30, 35, and 40 DAF were collected to cover the pitaya fruit development process (Supplementary Fig. S1). The superficial part of the peels (~1 mm deep) was removed carefully from the fruits using a scalpel. Peel samples from three fruits were pooled according to their stage of development defined by their DAF, frozen in liquid nitrogen, and pulverized to create a single biological replicate. One biological replicate consisted of peels from three fruits belonging to the same plant. Two to three biological replicates were evaluated for each developmental stage. Two technical replicates were analyzed for each biological replicate. RNA extraction, quantification, RNA purity, and RNA integrity analysis were carried out as described above.

    cDNA was synthesized from 100 ng of RNA by QuantiTect Reverse Transcription Kit (QIAGEN). Primers were designed using the PrimerQuest™, UNAFold, and OligoAnalyzer™ tools from Integrated DNA Technologies (www.idtdna.com/pages) and following the method proposed by Thornton & Basu[45]. Transcripts quantification was carried out in a QIAquant 96 5 plex according to the PowerUp™ SYBR™ Green Master Mix protocol (Applied Biosystems), with a first denaturation step for 2 min at 95 °C, followed by 40 cycles of denaturation step at 95 °C for 15 s, annealing and extension steps for 30 s at 60 °C.

    The Cycle threshold (Ct) values obtained from the qRT-PCR were analyzed through the algorithms BestKeeper, geNorm, NormFinder, and the delta Ct method[46]. RefFinder (www.ciidirsinaloa.com.mx/RefFinder-master/) was used to integrate the stability results and to find the most stable expressed transcripts in sweet pitaya fruit peel during development. The pairwise variation value (Vn/Vn + 1) was calculated through the geNorm algorithm in R Studio software[47].

    An alignment of 17 reported cuticle biosynthesis genes from model plants were carried out by BLASTx against the predicted proteins from sweet pitaya. Two additional alignments of 17 charaterized cuticle biosynthesis proteins from model plants against the transcripts and predicted proteins of sweet pitaya were carried out by tBLASTn and BLASTp, respectively. An E value threshold of 1 × 10−5 was used, and the unique best hits were recovered for all three alignments. The sequences of the 17 characterized cuticle biosynthesis genes and proteins from model plants are showed in Supplementary Table S3. The specific parameters and the unique best hits for all the alignments carried out are shown in Supplementary Tables S4S8.

    Cuticle biosynthesis-related transcripts tentatively coding for a cytochrome p450 family 77 subfamily A (CYP77A), a Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11) were identified by best bi-directional hit according to the functional annotation described above. Protein-conserved domains, signal peptide, and transmembrane helix were predicted through InterProScan, SignalP 6.0, and TMHMM, respectively. Alignment of the protein sequences to tentative orthologous of other plant species was carried out by the MUSCLE algorithm[48]. A neighbor-joining (NJ) phylogenetic tree with a bootstrap of 1,000 replications was constructed by MEGA11[49].

    Fruit sampling, primer design, RNA extraction, cDNA synthesis, and transcript quantification were performed as described above. Relative expression was calculated according to the 2−ΔΔCᴛ method[50]. The sample corresponding to 10 DAF was used as the calibrator. The transcripts StEF1a, StTUA, StUBQ3, and StEF1a + StTUA were used as normalizer genes.

    Normality was assessed according to the Shapiro-Wilk test. Significant differences in the expression of the cuticle biosynthesis-related transcripts between fruit developmental stages were determined by one-way ANOVA based on a completely randomized sampling design and a Tukey honestly significant difference (HSD) test, considering a p-value < 0.05 as significant. Statistical analysis was carried out through the stats package in R Studio.

    RNA was extracted from the peels of ripe sweet pitaya fruits (S. thurberi) from plants located in the Sonoran Desert, Mexico. Four cDNA libraries were sequenced in an Illumina NextSeq 500 platform at the University of Arizona Genetics Core Facility. A total of 288,199,704 reads with 150 base pairs (bp) in length were sequenced in paired-end mode. After trimming, 243,194,888 (84.38%) cleaned short reads with at least 29 mean quality scores per read in the Phred scale and between 80 to 150 bp in length were obtained to carry out the assembly. After removing contaminating sequences, redundancy, and low-expressed transcripts, the assembly included 174,449 transcripts with an N50 value of 2,110 bp. Table 1 shows the different quality variables of the S. thurberi fruit peel transcriptome. BUSCO score showed that 85.4% are completed transcripts, although out of these, 37.2% were found to be duplicated. The resulting sequence data can be accessed at the SRA repository of the NCBI through the BioProject ID PRJNA1030439.

    Table 1.  Quality metrics of the Stenocereus thurberi fruit peel transcriptome.
    Metric Data
    Total transcripts 174,449
    N50 2,110
    Smallest transcript length (bp) 200
    Largest transcript length (bp) 19,114
    Mean transcript length (bp) 1,198.69
    GC (%) 41.33
    Total assembled bases 209,110,524
    TransRate score 0.05
    BUSCO score (%) C: 85.38 (S:48.22, D:37.16),
    F: 10.69, M: 3.93.
    Values were calculated through the TrinityStats function of Trinity and TransRate software. Completeness analysis was carried out through BUSCO by aligning the transcriptome to the Embryophyte database through BLAST with an E value threshold of 1 × 10−3. Complete (C), single (S), duplicated (D), fragmented (F), missing (M).
     | Show Table
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    A summary of the homology search in the main public protein database for the S. thurberi transcriptome is shown in Supplementary Table S1. From these databases, the higher homologous transcripts were found in RefSeq with 93,993 (53.87 %). Based on the E value distribution, for 41,685 (44%) and 68,853 (49%) of the hits, it was found a strong homology (E value lower than 1 × 10−50) to proteins in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2a & b). On the other hand, 56,539 (52.34%) and 99,599 (71.11%) of the matches showed a percentage of identity higher than 60% in the Swiss-Prot and RefSeq databases, respectively (Supplementary Fig. S2c & d).

    Figure 1 shows the homology between transcripts from S. thurberi and proteins of commercial fruits, as well as proteins and transcripts of cacti. Transcripts from S. thurberi homologous to proteins from fruits of commercial interest avocado (P. americana), peach (P. persica), strawberry (F. vesca), orange (C. sinensis), and grapefruit (V. vinifera) ranged from 77,285 (44.30%) to 85,421 (48.96%), with 70,802 transcripts homologous to all the five fruit protein databases (Fig. 1a).

    Transcripts homologous to transcripts or proteins from the cactus dragon fruit (H. polyrhizus), prickly pear cactus (O. streptacantha), Mexican giant cardon (P. pringlei), and pitahaya (S. undatus) ranged from 76,238 (43.70%) to 114,933 (65.88%), with 64,009 transcripts homologous to all the four cactus databases (Fig. 1b). Further, out of the total of transcripts, 44,040 transcripts (25.25%) showed homology only to sequences from cactus, but not for model plants Arabidopsis, tomato, or the commercial fruits included in this study (Fig. 1c).

    Figure 1.  Venn diagram of the homology search results against model plants databases, commercial fruits, and cactus. The number in the diagram corresponds to the number of transcripts from S. thurberi homologous to sequences from that plant species. (a) Homologous to sequences from Fragaria vesca (Fa), Persea americana (Pa), Prunus persica (Pp), Vitis vinifera (Vv), and Citrus sinensis (Cs). (b) Homologous to sequences from Opuntia streptacantha (Of), Selenicereus undatus (Su), Hylocereus polyrhizus (Hp), and Pachycereus pringlei (Pap). (c) Homologous to sequences from Solanum lycopersicum (Sl), Arabidopsis thaliana (At), from the commercial fruits (Fa, Pa, Pp, Vv, and Cs), or the cactus included in this study (Of, Su, Hp, and Pap). Homologous searching was carried out by BLAST alignment (E value < 1 × 10−5). The Venn diagrams were drawn by ggVennDiagram in R Studio.

    A total of 45,970 (26.35%), 58,704 (33.65%), and 48,186 (27.65%) transcripts showed homology to transcription factors, transcriptional regulators, and protein kinases in the PlantTFDB, iTAK-TR, and iTAK-PK databases, respectively (Supplementary Tables S1, S9S11). For the PlantTFDB, the homologous transcripts belong to 57 transcriptional factors (TF) families (Fig. 2 & Supplementary Table S9), from which, the most frequent were the basic-helix-loop-helix (bHLH), myeloblastosis-related (MYB-related), NAM, ATAF, and CUC (NAC), ethylene responsive factor (ERF), and the WRKY domain families (WRKY) (Fig. 2).

    Figure 2.  Transcription factor (TF) families distribution of S. thurberi fruit peel transcriptome. The X-axis indicates the number of transcripts with hits to each TF family. Alignment to the PlantTFDB database by BLASTx was carried out with an E value threshold of 1 × 10−5. The bar graph was drawn by ggplot2 in R Studio.

    Based on the homology found and the functional domain searches, gene ontology terms (GO) were assigned to 68,559 transcripts (Supplementary Table S12). Figure 3 shows the top 20 GO terms assigned to the S. thurberi transcriptome, corresponding to the Biological Processes (BP) and Molecular Function (MF) categories. For BP, organic substance metabolic processes, primary metabolic processes, and cellular metabolic processes showed a higher number of transcripts (Supplementary Table S13). Further, for MF, organic cyclic compound binding, heterocyclic compound binding, and ion binding were the processes with the higher number of transcripts. S. thurberi transcripts were classified into 142 metabolic pathways from the KEGG database (Supplementary Table S14). The pathways with the higher number of transcripts recorded were pyruvate metabolism, glycerophospholipid metabolism, glycolysis/gluconeogenesis, and citrate cycle. Further, among the top 20 KEEG pathways, the cutin, suberin, and wax biosynthesis include more than 30 transcripts (Fig. 4).

    Figure 3.  Top 20 Gene Ontology (GO) terms assigned to the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each GO term. Assignment of GO terms was carried out by Blast2GO with default parameters. BP and MF mean Biological Processes and Molecular Functions GO categories, respectively. The graph was drawn by ggplot2 in R Studio.
    Figure 4.  Top 20 KEGG metabolic pathways distribution in the S. thurberi fruit peel transcriptome. Bars indicate the number of transcripts assigned to each KEGG pathway. Assignment of KEGG pathways was carried out in the Blast2GO suite. The bar graph was drawn by ggplot2 in R Studio.

    Out of the total of transcripts, 43,391 (24.87%) were classified as lncRNA (Supplementary Tables S15 & S16). Figure 5 shows a comparison of the length (Fig. 5a) and expression (Fig. 5b) of lncRNA and coding RNA. Both length and expression values were higher in coding RNA than in lncRNA. In general, coding RNA ranged from 201 to 18,629 bp with a mean length of 1,507.18, whereas lncRNA ranged from 200 to 5,198 bp with a mean length of 481.51 (Fig. 5a). The higher expression values recorded from coding RNA and lncRNA were 12.83 and 9.45 log2(CPM), respectively (Fig. 5b).

    Figure 5.  Comparison of coding RNA and long non-coding RNA (lncRNA) from S. thurberi transcriptome. (a) Box plot of transcript length distribution. The Y-axis indicates the length of each transcript in base pairs. (b) Box plot of expression levels. The Y-axis indicates the log2 of the count per million of reads (log2(CPM)) recorded for each transcript. Expression levels were calculated by the edgeR package in R studio. (a), (b) The lines inside the boxes indicate the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. The box plots were drawn by ggplot2 in R Studio.

    To identify the transcripts without significant changes in expression between the four RNA-seq libraries, a differential expression analysis was carried out. Of the total of transcripts, 4,980 were not differentially expressed (NDE) at least in one paired comparison between the libraries (Supplementary Tables S17S20). Mean counts per million of reads (CPM) and coefficient of variation (CV)[44] were calculated for these NDE transcripts. Transcripts with a CV value lower than 0.113, corresponding with the percentile 5 of the CV, and a mean CPM higher than 1,138.06, corresponding with the percentile 95 of the mean CPM were used as filters to identify the most stably expressed transcripts (Supplementary Table S21). Based on its homology and its tentative biological function, five transcripts were selected to be tested as tentative reference genes. Besides, three NDE transcripts homologous to previously identified stable expressed reference genes in other species of cactus fruit[4143] were selected (Supplementary Table S22). Homology metrics for the eight tentative reference genes selected are shown in Supplementary Table S23. The primer sequences used to amplify the transcripts by qRT-PCR and their nucleotide sequence are shown in Supplementary Tables S24 & S25, respectively.

    The amplification specificity of the eight candidate reference genes determined by melting curves analysis is shown in Supplementary Fig. S3. For the eight tentative reference transcripts selected, the cycle threshold (Ct) values were recorded during sweet pitaya fruit development by qRT-PCR (Supplementary Table S26). The Ct values obtained ranged from 16.85 to 30.26 (Fig. 6a). Plastidic ATP/ADP-transporter (StTLC1) showed the highest Ct values with a mean of 27.34 (Supplementary Table S26). Polyubiquitin 3 (StUBQ3) showed the lowest Ct values in all five sweet pitaya fruit developmental stages (Fig. 6a).

    Figure 6.  Expression stability analysis of tentative reference genes. (a) Box plot of cycle threshold (Ct) distribution of candidate reference genes during sweet pitaya fruit development (10, 20, 30, 35, and 40 d after flowering). The black line inside the box indicates the median. The higher and lower box limits represent the 75th and 25th percentiles, respectively. (b) Bar chart of the geometric mean (geomean) of ranking values calculated by RefFinder for each tentative reference gene (X-axis). The lowest values indicate the best reference genes. (c) Bar chart of the pairwise variation analysis and determination of the optimal number of reference genes by the geNorm algorithm. A pairwise variation value lower than 0.15 indicates that the use of Vn/Vn + 1 reference genes is reliable for the accurate normalization of qRT-PCR data. The Ct data used in the analysis were calculated by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The box plot and the bar graphs were drawn by ggplot2 and Excel programs, respectively. Abbreviations: Actin 7 (StACT7), alpha-tubulin (StTUA), elongation factor 1-alpha (StEF1a), COP1-interactive protein 1 (StCIP1), plasma membrane ATPase 4 (StPMA4), BEL1-like homeodomain protein 1 (StBLH1), polyubiquitin 3 (StUBQ3), and plastidic ATP/ADP-transporter (StTLC1).

    The best stability values calculated by NormFinder were 0.45, 0.51, 0.97, and 0.99, corresponding to the transcripts elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), plastidic ATP/ADP-transporter (StTLC1), and actin 7 (StACT7), respectively (Supplementary Table S27). For BestKeeper, the most stable expressed transcripts were StUBQ3, StTUA, and StEF1a, with values of 0.72, 0.75, and 0.87, respectively. In the case of the delta Ct method[51], the transcripts StEF1a, StTUA, and StTLC1 showed the best stability.

    According to geNorm analysis, the most stable expressed transcripts were StTUA, StEF1a, StUBQ3, and StACT7, with values of 0.74, 0.74, 0.82, and 0.96, respectively. All the pairwise variation values (Vn/Vn + 1) were lower than 0.15, ranging from 0.019 for V2/V3 to 0.01 for V6/V7 (Fig. 6c). The V value of 0.019 obtained for V2/V3 indicates that the use of the best two reference genes StTUA and StEF1a is reliable enough for the accurate normalization of qRT-PCR data, therefore no third reference gene is required[47]. Except for BestKeeper analysis, StEF1a and StTUA were the most stable transcripts for all of the methods carried out in this study (Supplementary Table S27). The comprehensive ranking analysis indicates that StEF1a, followed by StTUA and StUBQ3, are the most stable expressed genes and are stable enough to be used as reference genes in qRT-PCR analysis during sweet pitaya fruit development (Fig. 6b).

    Three cuticle biosynthesis-related transcripts TRINITY_DN17030_c0_g1_i2, TRINITY_DN15394_c0_g1_i1, and TRINITY_DN23528_c1_g1_i1 tentatively coding for the enzymes cytochrome p450 family 77 subfamily A (CYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (GDSL1), and an ATP binding cassette transporter subfamily G member 11 (ABCG11/WBC11), respectively, were identified and quantified. The nucleotide sequence and predicted amino acid sequences of the three transcripts are shown in Supplementary File 1. The best homology match for StCYP77A (TRINITY_DN17030_c0_g1_i2) was for AtCYP77A4 (AT5G04660) from Arabidopsis and SmCYP77A2 (P37124) from eggplant (Solanum melongena) in the TAIR and Swiss-Prot databases, respectively (Supplementary Table S23).

    TransDecoder, InterPro, and TMHMM analysis showed that StCYP77A codes a polypeptide of 518 amino acids (aa) in length that comprises a cytochrome P450 E-class domain (IPR002401) and a transmembrane region (residues 10 to 32). The phylogenetic tree constructed showed that StCYP77A is grouped in a cluster with all the CYP77A2 proteins included in this analysis, being closer to CYP77A2 (XP_010694692) from B. vulgaris and Cgig2_012892 (KAJ8441854) from Carnegiea gigantean (Supplementary Fig. S4).

    StGDSL1 (TRINITY_DN15394_c0_g1_i1) alignment showed that it is homologous to a GDSL esterase/lipase from Arabidopsis (Q9LU14) and tomato (Solyc03g121180) (Supplementary Table S23). TransDecoder, InterPro, and SignalP analysis showed that StGDSL1 codes a polypeptide of 354 aa in length that comprises a GDSL lipase/esterase domain IPR001087 and a signal peptide with a cleavage site between position 25 and 26 (Supplementary Fig. S5).

    Supplementary Figure S6 shows the analysis carried out on the predicted amino acid sequence of StABCG11 (TRINITY_DN23528_c1_g1_i1). The phylogenetic tree constructed shows three clades corresponding to the ABCG13, ABCG12, and ABCG11 protein classes with bootstrap support ranging from 40% to 100% (Supplementary Fig. S6a). StABCG11 is grouped with all the ABCG11 transporters included in this study in a well-separated clade, being closely related to its tentative ortholog from C. gigantean Cgig2_004465 (KAJ8441854). InterPro and TMHMM results showed that the StABCG11 sequence contains an ABC-2 type transporter transmembrane domain (IPR013525; PF01061.27) with six transmembrane helices (Supplementary Fig. S6b).

    The predicted protein sequence of StABCG11 is 710 aa in length, holding the ATP binding domain (IPR003439; PF00005.30) and the P-loop containing nucleoside triphosphate hydrolase domain (IPR043926; PF19055.3) of the ABC transporters of the G family. Multiple sequence alignment shows that the Walker A and B motif sequence and the ABC signature[15] are conserved between the ABCG11 transporters from Arabidopsis, tomato, S. thurberi, and C. gigantean (Supplementary Fig. S6c).

    According to the results of the expression stability analysis (Fig. 6), four normalization strategies were tested to quantify the three cuticle biosynthesis-related transcripts during sweet pitaya fruit development. The four strategies consist of normalizing by StEF1a, StTUA, StUBQ3, or StEF1a+StTUA. Primer sequences used to quantify the transcripts StCYP77A (TRINITY_DN17030_c0_g1_i2), StGDSL1 (TRINITY_DN15394_c0_g1_i1), and StABCG11 (TRINITY_DN23528_c1_g1_i1) by qRT-PCR during sweet pitaya fruit development are shown in Supplementary Table S24.

    The three cuticle biosynthesis-related transcripts showed differences in expression during sweet pitaya fruit development (Supplementary Table S28). The same expression pattern was recorded for the three cuticle biosynthesis transcripts when normalization was carried out by StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). A higher expression of StCYP77A and StGDSL1 are shown at the 10 and 20 DAF, showing a decrease at 30, 35, and 40 DAF. StABCG11 showed a similar behavior, with a higher expression at 10 and 20 DAF and a reduction at 30 and 35 DAF. Nevertheless, unlike StCYP77A and StGDSL1, a significant increase at 40 DAF, reaching the same expression as compared with 10 DAF, is shown for StABCG11 (Fig. 7).

    Figure 7.  Expression analysis of cuticle biosynthesis-related transcripts StCYP77A, StGDSL1, and StABCG11 during sweet pitaya (Stenocereus thurberi) fruit development. Relative expression was calculated through the 2−ΔΔCᴛ method using elongation factor 1-alpha (StEF1a), alpha-tubulin (StTUA), polyubiquitin 3 (StUBQ3), or StEF1a + StTUA as normalizing genes at 10, 20, 30, 35, and 40 d after flowering (DAF). The Y-axis and error bars represent the mean of the relative expression ± standard error (n = 4−6) for each developmental stage in DAF. The Ct data for the analysis was recorded by qRT-PCR in a QIAquant 96 5 plex (QIAGEN) according to the manufacturer's protocol. The graph line was drawn by ggplot2 in R Studio. Abbreviations: cytochrome p450 family 77 subfamily A (StCYP77A), Gly-Asp-Ser-Leu motif lipase/esterase 1 (StGDSL1), and ATP binding cassette transporter subfamily G member 11 (StABCG11).

    Characteristics of a well-assembled transcriptome include an N50 value closer to 2,000 bp, a high percentage of conserved transcripts completely assembled (> 80%), and a high proportion of reads mapping back to the assembled transcripts[52]. In the present study, the first collection of 174,449 transcripts from S. thurberi fruit peel are reported. The generated transcriptome showed an N50 value of 2,110 bp, a TransRate score of 0.05, and a GC percentage of 41.33 (Table 1), similar to that reported for other de novo plant transcriptome assemblies[53]. According to BUSCO, 85.4% of the orthologous genes from the Embryophyta databases completely matched the S. thurberi transcriptome, and only 3.9% were missing (Table 1). These results show that the S. thurberi transcriptome generated is not fragmented, and it is helpful in predicting the sequence of almost all the transcripts expressed in sweet pitaya fruit peel[24].

    The percentage of transcripts homologous found, E values, and identity distribution (Supplementary Tables S1 & S2; Supplementary Fig. S2) were similar to that reported in the de novo transcriptome assembly for non-model plants and other cactus fruits[4143,54] and further suggests that the transcriptome assembled of S. thurberi peel is robust[52]. Of the total of transcripts, 70,802 were common to all the five commercial fruit protein databases included in this study, which is helpful for the search for conserved orthologous involved in fruit development and ripening (Fig. 2a). A total of 34,513 transcripts (20%) show homology only to sequences in the cactus's databases, but not in the others (Supplementary Tables S1 & S2; Fig. 1c). This could suggest that a significant conservation of sequences among plants of the Cactaceae family exists which most likely are to have a function in this species adaptation to desert ecosystems.

    To infer the biological functionality represented by the S. thurberi fruit peel transcriptome, gene ontology (GO) terms and KEGG pathways were assigned. Of the main metabolic pathways assigned, 'glycerolipid metabolism' and 'cutin, suberine, and wax biosynthesis' suggests an active cuticle biosynthesis in pitaya fruit peel (Fig. 4). In agreement with the above, the main GO terms assigned for the molecular function (MF) category were 'organic cyclic compound binding', 'transmembrane transporter activity', and 'lipid binding' (Fig. 3). For the biological processes (BP) category, the critical GO terms for the present research are 'cellular response to stimulus', 'response to stress', 'anatomical structure development', and 'transmembrane transport', which could suggest the active development of the fruit epidermis and cuticle biosynthesis for protection to stress.

    The most frequent transcription factors (TF) families found in S. thurberi transcriptome were NAC, WRKY, bHLH, ERF, and MYB-related (Fig. 2), which had been reported to play a function in the tolerance to abiotic stress in plants[55,56]. Although the role of NAC, WRKY, bHLH, ERF, and MYB TF in improving drought tolerance in relevant crop plants has been widely documented[57,58], their contribution to the adaptation of cactus to arid ecosystems has not yet been elucidated and further experimental pieces of evidence are needed.

    It has been reported that the heterologous expression of ERF TF from Medicago truncatula induces drought tolerance and cuticle wax biosynthesis in Arabidopsis leaf[59]. In tomato fruits, the gene SlMIXTA-like which encodes a MYB transcription factor avoids water loss through the positive regulation of genes related to the biosynthesis and transport of cuticle compounds[22]. Despite the relevant role of cuticles in maintaining cactus physiology in desert environments, experimental evidence showing the role of the different TF-inducing cuticle biosynthesis has yet to be reported for cactus fruits.

    Out of the transcripts, 43,391 were classified as lncRNA (Supplementary Tables S15 & S16). This is the first report of lncRNA identification for the species S. thurberi. In fruits, 3,679 lncRNA has been identified from tomato[26], 3,330 from peach (P. persica)[29], 3,857 from melon (Cucumis melo)[28], 2,505 from hot pepper (Capsicum annuum)[27], and 3,194 from pomegranate (Punica granatum)[36]. Despite the stringent criteria to classify the lncRNA of sweet pitaya fruit (S. thurberi), a higher number of lncRNAs are shown when compared with previous reports. This finding is most likely due to the higher level of redundancy found during the transcriptome analysis. To reduce this redundancy, further efforts to achieve the complete genome assembly of S. thurberi are needed.

    Previous studies showed that lncRNA is shorter and has lower expression levels than coding RNA[6062]. In agreement with those findings, both the length and expression values of lncRNA from S. thurberi were lower than coding RNA (Fig. 5). It has been suggested that lncRNA could be involved in the biosynthesis of cuticle components in cabbage[61] and pomegranate[36] and that they could be involved in the tolerance to water deficit through the regulation of cuticle biosynthesis in wild banana[60]. Nevertheless, the molecular mechanism by which lncRNA may regulate the cuticle biosynthesis in S. thurberi fruits has not yet been elucidated.

    A relatively constant level of expression characterizes housekeeping genes because they are involved in essential cellular functions. These genes are not induced under specific conditions such as biotic or abiotic stress. Because of this, they are very useful as internal reference genes for qRT-PCR data normalization[63]. Nevertheless, their expression could change depending on plant species, developmental stages, and experimental conditions[64]. Reliable reference genes for a specific experiment in a given species must be identified to carry out an accurate qRT-PCR data normalization[63]. An initial screening of the transcript expression pattern through RNA-seq improves the identification of stably expressed transcripts by qRT-PCR[44,64].

    Identification of stable expressed reference transcripts during fruit development has been carried out in blueberry (Vaccinium bracteatum)[65], kiwifruit (Actinidia chinensis)[66], peach (P. persica)[67], apple (Malus domestica)[68], and soursop (Annona muricata)[69]. These studies include the expression stability analysis through geNorm, NormFinder, and BestKeeper algorithms[68,69], some of which are supported in RNA-seq data[65,66]. Improvement of expression stability analysis by RNA-seq had led to the identification of non-previously reported reference genes with a more stable expression during fruit development than commonly known housekeeping genes in grapevine (V. vinifera)[44], pear (Pyrus pyrifolia and P. calleryana)[64], and pepper (C. annuum)[70].

    For fruits of the Cactaceae family, only a few studies identifying reliable reference genes have been reported[4143]. Mainly because gene expression analysis has not been carried out previously for sweet pitaya (S. thurberi), the RNA-seq data generated in this work along with geNorm, NormFinder, BestKeeper, and RefFinder algorithms were used to identify reliable reference genes. The comprehensive ranking analysis showed that out of the eight candidate genes tested, StEF1a followed by StTUA and StUBQ3 were the most stable (Fig. 6b). All the pairwise variation values (Vn/Vn + 1) were lower than 0.15 (Fig. 6c), which indicates that StEF1a, StTUA, and StUBQ3 alone or the use of StEF1a and StTUA together are reliable enough to normalize the gene expression data generated by qRT-PCR.

    The genes StEF1a, StTUA, and StUBQ3 are homologous to transcripts found in the cactus species known as dragonfruit (Hylocereus monacanthus and H. undatus)[41], which have been tested as tentative reference genes during fruit development. EF1a has been proposed as a reliable reference gene in the analysis of changes in gene expression of dragon fruit (H. monacanthus and H. undatus)[41], peach (P. persica)[67], apple (M. domestica)[68], and soursop (A. muricata)[69]. According to the expression stability analysis carried out in the present study (Fig. 6) four normalization strategies were designed. The same gene expression pattern was recorded for the three target transcripts evaluated when normalization was carried out by the genes StEF1a, StTUA, StUBQ3, or StEF1a + StTUA (Fig. 7). Further, these data indicates that these reference genes are reliable enough to be used in qRT-PCR experiments during fruit development of S. thurberi.

    The plant cuticle is formed by two main layers: the cutin, composed mainly of mid-chain oxygenated LC fatty acids, and the cuticular wax, composed mainly of very long-chain (VLC) fatty acids, and their derivates VLC alkanes, VLC primary alcohols, VLC ketones, VLC aldehydes, and VLC esters[3]. In Arabidopsis CYP77A4 and CYP77A6 catalyze the synthesis of midchain epoxy and hydroxy ω-OH long-chain fatty acids, respectively[10,11], which are the main components of fleshy fruit cuticle[3].

    The functional domain search carried out in the present study showed that StCYP77A comprises a cytochrome P450 E-class domain (IPR002401) and a membrane-spanning region from residues 10 to 32 (Supplementary Fig. S4). This membrane-spanning region has been previously characterized in CYP77A enzymes from A. thaliana and Brassica napus[11,71]. It suggests that the protein coded by StCYP77A could catalyze the oxidation of fatty acids embedded in the endoplasmic reticulum membrane of the epidermal cells of S. thurberi fruit. Phylogenetic analysis showed that StCYP77A was closer to proteins from its phylogenetic-related species B. vulgaris (BvCYP772; XP_010694692) and C. gigantea (Cgig2_012892) (Supplementary Fig. S4). StCYP77A, BvCYP77A2, and Cgig2_012892 were closer to SlCYP77A2 and SmCYP77A2 than to CYP77A4 and CYP77A6 proteins, suggesting that StCYP77A (TRINITY_DN17030_c0_g1_i2) could correspond to a CYP77A2 protein.

    Five CYP77A are present in the Arabidopsis genome, named CYP77A4, CYP77A5, CYP77A6, CYP77A7, and CYP77A9, but their role in cuticle biosynthesis has only been reported for CYP77A4 and CYP77A6[72]. It has been suggested that CYP77A2 from eggplant (S. torvum) could contribute to the defense against fungal phytopathogen infection by the synthesis of specific compounds[13]. In pepper fruit (C. annuum), the expression pattern of CYP77A2 (A0A1U8GYB0) and ABCG11 (LOC107862760) suggests a role of CYP77A2 and ABCG11 in cutin biosynthesis at the early stages of pepper fruit development[14].

    In the case of the protein encoded by StGDSL1 (354 aa), the length found in this work is similar to the length of its homologous from Arabidopsis (AT3G16370) and tomato (Solyc03g121180) (Supplementary Fig. S5). A GDSL1 protein named CD1 polymerizes midchain oxygenated ω-OH long-chain fatty acids to form the cutin polyester in the extracellular space of tomato fruit peel[20,21]. It has been suggested that the 25-amino acid N-signal peptide found in StGDSL1 (Supplementary Fig. S5), previously reported in GDSL1 from Arabidopsis, B. napus, and tomato, plays a role during the protein exportation to the extracellular space[21,73].

    A higher expression of StCYP77A, StGDSL1, and StABCG11 is shown at the 10 and 20 DAF of sweet pitaya fruit development (Fig. 7), suggesting the active cuticle biosynthesis at the early stages of sweet pitaya fruit development. In agreement with that, two genes coding for GDSL lipase/hydrolases from tomato named SGN-U583101 and SGN-U579520 are highly expressed in the early stages and during the expansion stages of tomato fruit development, but their expression decreases in later stages[74]. It has been shown that the expression of GDSL genes, like CD1 from tomato, is higher in growing fruit[20,21]. Like tomato, the increase in expression of StCYP77A and StGDSL1 shown in pitaya fruit development could be due to an increase in cuticle deposition during the expansion of the fruit epidermis[20].

    The phylogenetic analysis, the functional domains, and the six transmembrane helices found in the StABCG11 predicted protein (Supplementary Fig. S6), suggests that it is an ABCG plasma membrane transporter of sweet pitaya[15]. Indeed, an increased expression of StABCG11 at 40 DAF was recorded in the present study (Fig. 7). Further, this data strongly suggests that it could be playing a relevant role in the transport of cuticle components at the beginning and during sweet pitaya fruit ripening.

    In Arabidopsis, ABCG11 (WBC11) exports cuticular wax and cutin compounds from the plasma membrane[15,75]. It has been reported that a high expression of the ABC plasma membrane transporter from mango MiWBC11 correlates with a higher cuticle deposition during fruit development[7]. The expression pattern for StABCG11, StCYP77A, and StGDSL1 suggests a role of StABCG11 as a cutin compound transporter in the earlier stages of sweet pitaya fruit development (Fig. 7). Further, its increase at 40 DAF suggests that it could be transporting cuticle compounds other than oxygenated long-chain fatty acids, or long-chain fatty acids that are not synthesized by StCYP77A and StGDSL1 in the later stages of fruit development.

    Like sweet pitaya, during sweet cherry fruit (Prunus avium) development, the expression of PaWCB11, homologous to AtABCG11 (AT1G17840), increases at the earlier stages of fruit development decreases at the intermediate stages, and increases again at the later stages[76]. PaWCB11 expression correlated with cuticle membrane deposition at the earlier and intermediate stages of sweet cherry fruit development but not at the later[76]. The increased expression of StABCG11 found in the present study could be due to the increased transport of cuticular wax compounds, such as VLC fatty acids and their derivates, in the later stages of sweet pitaya development[15,75].

    Cuticular waxes make up the smallest amount of the fruit cuticle. Even so, they mainly contribute to the impermeability of the fruit's epidermis[3]. An increase in the transport of cuticular waxes at the beginning of the ripening stage carried out by ABCG transporters could be due to a greater need to avoid water loss and to maintain an adequate amount of water during the ripening of the sweet pitaya fruit. Nevertheless, further expression analysis of cuticular wax biosynthesis-related genes, complemented with chemical composition analysis of cuticles could contribute to elucidating the molecular mechanism of cuticle biosynthesis in cacti and their physiological contribution during fruit development.

    In this study, the transcriptome of the sweet pitaya (S. thurberi) fruit peel was assembled for the first time. The reference genes found here are a helpful tool for further gene expression analysis in sweet pitaya fruit. Transcripts tentatively involved in cuticle compound biosynthesis and transport are reported for the first time in sweet pitaya. The results suggest a relevant role of cuticle compound biosynthesis and transport at the early and later stages of fruit development. The information generated will help to improve the elucidation of the molecular mechanism of cuticle biosynthesis in S. thurberi and other cactus species in the future. Understanding the cuticle's physiological function in the adaptation of the Cactaceae family to harsh environmental conditions could help design strategies to increase the resistance of other species to face the increase in water scarcity for agricultural production predicted for the following years.

    The authors confirm contribution to the paper as follows: study conception and design: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; data collection: Tiznado-Hernández ME, Tafolla-Arellano JC, García-Coronado H, Hernández-Oñate MÁ; analysis and interpretation of results: Tiznado-Hernández ME, García-Coronado H, Hernández-Oñate MÁ, Burgara-Estrella AJ; draft manuscript preparation: Tiznado-Hernández ME, García-Coronado H. All authors reviewed the results and approved the final version of the manuscript.

    All data generated or analyzed during this study are included in this published article and its supplementary information files. The sequence data can be accessed at the Sequence Read Archive (SRA) repository of the NCBI through the BioProject ID PRJNA1030439.

    The authors wish to acknowledge the financial support of Consejo Nacional de Humanidades, Ciencias y Tecnologías de México (CONAHCYT) through project number 579: Elucidación del Mecanismo Molecular de Biosíntesis de Cutícula Utilizando como Modelo Frutas Tropicales. We appreciate the University of Arizona Genetics Core and Illumina for providing reagents and equipment for library sequencing. The author, Heriberto García-Coronado (CVU 490952), thanks the CONAHCYT (acronym in Spanish) for the Ph.D. scholarship assigned (749341). The author, Heriberto García-Coronado, thanks Dr. Edmundo Domínguez-Rosas for the technical support in bioinformatics for identifying long non-coding RNA.

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

  • Supplemental Fig. S1 Effect of phosphorus based on organic fertilize on basic physical and chemical properties of tea plantation soil. water content (a), bulk density (b), total porosity (c), total nitrogen (d), total phosphorus (e), total potassium (f), available phosphorus (g), available aluminum (h), exchangeable calcium (i), soil acid phosphatase activity (j), phytase activity (k), phosphomonesterase activity contents (l).
    Supplemental Fig. S2 Effect of phosphorus based on organic fertilize on chlorophyll content in different leaf positions in Camellia sinensis. Chlorophyll a (a), chlorophyll b (b), carotenoid content (c) in bud, chlorophyll a (d), chlorophyll b (e), carotenoid content (f) in first leaf, chlorophyll a (g), chlorophyll b (h), carotenoid content (i) in second leaf.
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  • Cite this article

    Wang Y, Shi R, Deng S, Wang H, Hussain S, et al. 2024. Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants. Beverage Plant Research 4: e037 doi: 10.48130/bpr-0024-0027
    Wang Y, Shi R, Deng S, Wang H, Hussain S, et al. 2024. Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants. Beverage Plant Research 4: e037 doi: 10.48130/bpr-0024-0027

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Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants

Beverage Plant Research  4 Article number: e037  (2024)  |  Cite this article

Abstract: The availability of soil phosphorus (P) is essential for crop cultivation and production. However, agronomic P management for tea crops remains unexplored. Herein, the effect of different P management practices viz. unfertilized (control), organic fertilizer (OF) application, OF + N application at 300 kg/ha (N300) (OF-P1), OF + N300 + P application at 45 kg/ha (OF-P2), OF + N300 + P application at 90 kg/ha (OF-P3), and OF + N300 + P application at 135 kg/ha (OF-P4) on soil nutrient acquisition, enzymatic activities, and physio-biochemical, and quality traits of tea plants are investigated in yield. The results showed that OF-P2 treatment had significantly higher soil N (30.5%), P (42.2%), and potash (1.6%) concentrations above the control. P concentrations had a linear positive correlation with the activities of acid-phosphatase and phytase. OF-P2 had the greatest effects on plant growth, chlorophyll and carotenoid contents, and antioxidative enzyme activities than other treatments. OF-P2 treatment had a two-fold decrease in hydrogen peroxide and dioxygen (singlet) compared to the control. It was further found that OF-P2 significantly increased amino acid content by 33.5%, 40.1%, and 31.9%, and decreased polyphenol content by 42.3%, 45.6%, and 25.7% in bud, first, and second leaf, respectively, above the control. Overall, the present findings suggest that low P application (OF-P2) can increase nutrient availability, bud quality, and yield by improving soil enzymatic activities, pigment contents, and antioxidative activities. Establishing this mode of low P application may provide an optimum strategy for enhancing crop performance in regions where unreasonable P application practices are common.

    • Tea is a valuable economic crop in China that requires adequate fertilization to support its optimal growth and development[1]. Since the mid-twentieth century, the use of chemical fertilizers has significantly enhanced tea plantation production and profitability[2]. However, the expansion of tea gardens and subsequent increase in production have resulted in a significant escalation in fertilizer usage, leading to more severe consequences[3]. According to an estimate, approximately 30% of tea plantations in China are experiencing excessive use of chemical fertilizers, with 80% opting for compound fertilizers with an equal nutrient ratio, highlighting the increasing resilience of P fertilizers[1]. Studies have shown that soil P levels decrease under no P fertilization but increase after the application of P fertilizers[4]. However, excessive utilization of chemical fertilizers can result in soil compaction and environmental contamination[5]. On the other hand, organic fertilizers offer significant benefits to soil fertility by decreasing bulk density and enhancing soil texture[6]. Additionally, it is evident that the inclusion of organic fertilizer along with chemical sources improves crop quality, as observed in tea[7]. Some studies have also stated that the combined use of organic and compound fertilizers can lead to a substantial increase in soil total N, alkali-hydrolyzed N, available P, and available K[8]. Excessive active P in high-P conditions can accumulate in the soil[9], elevating P levels in the surface and lower layers[4]. This accumulation alters the soil's physical and chemical properties, thus affecting water and nutrient movement and transformation[10]. For tea plantations, it has been well reported that long-term excessive utilization of phosphate fertilizer does not effectively increase the soil's available P content. Instead, it leads to the inefficient use of fertilizer resources and exacerbates soil environmental pollution. Thus, alternative approaches to enhance fertilization use efficiency in tea plantation systems and the achievement of sustainable development goals are needed.

      Phosphorus, an indispensable nutrient for plant growth and development[11], offer numerous crucial roles in metabolic processes, including photosynthesis, respiration[12], and mineral metabolism[13]. Many studies have shown that P application can increase antioxidant enzyme activities in P-deficient plants, thus protecting them from photooxidation damage[14]. Previous studies have also observed that adequate P application significantly enhances various the growth and development of various crops. Working with strawberries, Zheng et al. reported that adequate P supplementation can elevate plant height, petiole length, and leaf area while boosting the activities of antioxidative enzymes[15]. In rice, P fertilizer has been reported to improve grain quality traits, such as starch content[16]. Recently, Yan et al. documented improved wheat yield under P application, which was associated with high P accumulation and distribution in plant organs[17,18]. However, excessive P fertilizer can negatively affect plant growth and developmental processes. Higher P application results in fewer tillers, which, in turn, can harm crop yield in rice[19]. P application above the optimum level results in stunted root growth[20]. In addition, phosphorus deficiency in the soil can impact chlorophyll synthesis, hinder dry matter accumulation, and reduce sugar content in plant leaf[21]. Although the content of P is low in the soil of tea plantations, many physiological processes, such as photosynthesis and respiration, are highly dependent on P in tea plant. P affects the decomposition and metabolism of minerals and metabolites, thereby affecting the yield and quality of tea[22]. Both P-deficiency and excess reduced the syntheses of flavonoids and phosphorylated metabolites[13,23]. Thus, the judicious application of P at an agronomic optimum is urgently needed to promote plant performance and improve crop quality.

      Given the current research status, although published studies have focused on the detrimental impacts of soil P deficiency on tea quality, further in-depth exploration is needed on the optimal application of P fertilizer in high-P environments, particularly when combined with organic fertilizers. Moreover, the effects of combined chemical and organic P application on soil properties, plant health, tea yield, and quality are yet to be fully explored. Here, we hypothesized that a low dose of chemical P, combined with an organic source, would be beneficial in improving soil properties, nutrient acquisition, growth, quality, and yield of tea. This study was conducted to determine the impact of varying levels of P fertilizer application, using both organic and inorganic sources, on soil physicochemical properties, enzymatic activities, plant growth, physiology, stress resistance ability, and quality of tea crop. This study would offer more precise guidance on P application for sustainable tea gardens. Additionally, it would also help prevent the negative outcomes of excessive or inadequate P application and achieve sustainable developmental goals for ecological restoration.

    • The experiment utilized the tea cultivar 'Baiye 1' and was conducted in Daping village, Shangnan County, Shangluo City, Shaanxi Province, China (Fig. 1). Fertilization experiments were carried out under suitable weather conditions. Soil samples were collected under the 0−20 cm layer. The soil was placed in a ventilated, dry place to avoid direct light and dried, and then the soil nutrient content and enzyme activity were analyzed through 16, 60, and 100 mesh screens[24]. Fresh tea leaves (including bud head, first leaf, and second leaf position) were collected and crushed with a plant crusher, and then vacuum freeze-dried for the determination of the biochemical quality of tea[9]. Hydroponic tea plants were maintained in the laboratory of Northwest A&F University in Shaanxi Province. Daytime and nighttime temperature of 25 °C/18 °C, with a photoperiod of 14 h of light and 10 h of darkness, and a relative humidity of 75% ± 5%.

      Figure 1. 

      (a) Location of the study area, and (b) test design site.

      A randomized block design was used and different P management practices viz. unfertilized (control), sole organic fertilizer (OF) application, OF + N application at 300 kg/ha (N300) (OF-P1), OF + N300 + P application at 45 kg/ha (OF-P2), OF + N300 + P application at 90 kg/ha (OF-P3), and OF + N300 + P application at 135 kg/ha (OF-P4) were maintained. Each treatment was replicated three times. Each plot size had a 25 m2 area. The application rate for organic fertilizer was consistent across treatments at organic fertilizer 7,500 kg/ha. Sixty percent of the N fertilizer was applied as a basal dressing, with the remaining 40% applied as topdressing. Phosphorus, P fertilizer, and organic fertilizer were combined and applied as basal dressing.

    • Tea plants were initially cultured in deionized water for 3 d, followed by 1/2 nutrient solution for 1 week, and then transitioned to full strength nutrient solution for further cultivation. The concentrations of KH2PO4 and K2SO4 in the medium were adjusted to create P concentration gradients of 0, 0.01, 0.02, and 0.03 mM according to Wang's method in two tea cultivars 'Longjing 43' and 'Shuchazao'[25]. The nutrient solution was refreshed weekly. Photographs of the tea seedling were taken and stored for subsequent observation of root development[26].

    • Soil moisture content and bulk density were determined using the drying method[27]. Total porosity was calculated based on bulk density. Total phosphorus and total potassium contents in the soil were determined using resistance colorimetry and flame spectrophotometry, respectively. Total nitrogen content was analyzed using a continuous flow analyzer[28]. Available phosphorus content in the soil was determined using the hydrochloric acid-ammonium fluoride method[29]. Trace elements aluminum and calcium were determined using inductively coupled plasma mass spectrometry. Acid phosphatase activity was assessed using p-nitrobenzene disodium phosphate colorimetry, while phytase activity was determined using the molybdenum blue method. Phosphomonesterase activity was measured using the p-nitrobenzene phosphate method.

    • The relative chlorophyll content of leaves was determined using the ethanol extraction method. Chlorophyll fluorescence was observed using the hexagon imaging PAM (WALZ)[30]. The contents of water extract, tea polyphenol, free amino acid, and caffeine in the fresh parts of tea tree (bud head, first leaf, and second leaf) were determined, with five replicates for each sample. The content of water extract was determined by boiling water reflux method. The content of tea polyphenols was determined by the ferrous tartrate colorimetric method. The content of free amino acids was determined by ninhydrin colorimetry. Caffeine is easily soluble in water, first extracted by boiling water reflux, and then determined after removing the interfering substances[31].

    • The determination of superoxide anion contents followed the methods outlined by Tan et al.[32] and Sui et al.[33]. Hydrogen peroxide contents were estimated using the method described by Moloi & van der Westhuizen[34]. Additionally, antioxidant enzyme activities were determined using the modified method as outlines by Chen et al.[35].

    • All data was processed using Origin 2021 and SPSS Statistics 25.0. All drawings were made using GraphPad prism 8.0 and the chiplot website (www.chiplot.online), including principal component analysis and correlation analysis. All data were measured three times under each treatment.

    • Soil water content and porosity increased first and then decreased with increasing P rates. However, these treatments showed no significant effect (p > 0.05) on soil bulk density. Compared with CK, all treatments increased soil water retention capacity; however, the maximum values were recorded for OF-P2. A similar trend was observed for soil total N, total P, and total K, where values increased initially and then decreased with increasing P rates. The maximum values were recorded for the OF-P2 treatment, which increased total N, total P, and total K content by 30.5%, 45.2%, and 1.6%, respectively, over the control. There was no significant (p > 0.05) change in available P content between the unfertilized control and sole organic fertilizer (OF) application. Additionally, OF-P2 increased available aluminum content by 45.6% higher than the control, whereas there was a significant decrease in exchangeable calcium content under the same treatment. The activity of phosphatase in the soil directly influences the conversion of organic P and enhances the absorption of inorganic P. There were significant differences in phosphatase activity among different treatments, with maximum values recorded for the OF-P2 treatment, whereas the CK treatment depicted significantly lower values than other treatments. Phytase activity increased significantly with increasing P rate. Compared with the CK treatment, the effect of low P on soil enzyme activity was small. There was no significant difference in phosphomonesterase activity among the treatments (Supplemental Fig. S1). Correlation analysis showed that total soil P had a significant effect on water content, bulk density, and total porosity (p < 0.01) (Fig. 2), whereas it had a highly significant effect on soil aluminum content (p < 0.001). The content of inorganic P in the soil was affected by available P and available K (p < 0.05). Linear analysis of soil phosphatase activity and total P and available P contents showed that the correlation reached 0.5395 and 0.6057, respectively (Fig. 2be). Soil phytase activity was linearly correlated with total P and available P contents, reaching 0.4002 and 0.523. The results of soil physical and chemical properties of tea garden with different P application rates showed that the suitable P rate could accelerate the decomposition of P and enhance the absorption and utilization of soil nutrients by tea seedlings.

      Figure 2. 

      (a) Correlation analysis among soil physical and chemical properties. (b) Linear relationship between acid phosphatase and total phosphorus, (c) acid phosphatase and available phosphorus, (d) phytase and total phosphorus, (e) phytase and available phosphorus. TN: total nitrogen, TP: total phosphorus, TK: total potassium, AN: available nitrogen, AP: available phosphorus, AK: available potassium. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

    • The P management treatments significantly affected the 100-bud weight of one bud (Fig 3a), with the OF-P2 showing significantly (p < 0.05) higher values (15.3% higher than the CK) than other treatments. For the 100-bud weight of one bud, the treatments were ordered as OF-P2 > OF-P3 > OF-P1 > CK > OF > OF-P4. Bud and leaf density also differed significantly among the treatments, with OF-P1, OF-P2, OF-P3, and OF-P4 demonstrating an increase of 35.4%, 50.2%, 48.3%, and 44.2%, respectively, over the CK. For bud density per 0.04 m2, these treatments were ordered as OF-P2 > OF-P3 > OF-P4 > OF-P1 > OF > CK. Overall, OF-P2 outperformed the other treatments in improving bud density per 0.04 m2 and 100-bud weight of one bud.

      Figure 3. 

      (a) Effect of phosphorus based on organic fertilizer on 100-bud weight of one bud of tea plant and (b) bud density per 0.04 m2. Different * indicates the level of difference between treatments: * p < 0.05, ** p < 0.01, *** p < 0.001.

    • Different P rates had a significant effect on the root growth of tea (Fig. 4). Compared with the treatment without P (0 mM KH2PO4), the root length of tea trees treated with 0.01 mM KH2PO4 was significantly increased and reached the maximum for a P concentration of 0.01 mM KH2PO4. These hydroponics results showed that low P promoted root growth, whereas high rates showed inhibitory effects on plant roots.

      Figure 4. 

      Effect of phosphorus on root growth in Camellia sinensis. (a) Root phenotype before phosphorus treatment, (b) root phenotype after phosphorus treatment.

    • There were significant changes in the contents of polyphenols and free amino acids under different treatments (p < 0.05) (Fig. 5a & b), but the effect on water extract and caffeine content was nonsignificant (p > 0.05) (Fig. 5c & d). The polyphenol content of tea in different organs showed a decreasing trend with increasing P rate, with maximum reduction recorded for the OF-P2 treatment, which decreased the values by 42.3%, 45.6%, and 25.7% in the bud, first leaf, and second leaf, respectively, compared to the CK. The changes in polyphenol content in tea under different organs were as follows: OF-P2 > OF-P1 > OF-P3 > OF > CK > OF-P4. An opposite trend was observed for free amino acids, where values increased with increasing P rates. The OF-P2 treatment recorded significantly higher values by 33.5%, 40.1%, and 31.4% in the bud, first leaf, and second leaf, respectively, compared to the CK. For free amino acid contents, the treatments were arranged as OF-P2 > OF-P1 > OF-P3 > OF-P4 > CK > OF.

      Figure 5. 

      Effect of phosphorus based on organic fertilizer on metabolites of different leaf positions in C. sinensis. (a) Water extract, (b) free amino acid, (c) total polyphenols, (d) caffeine.

    • There were significant differences in chlorophyll (Chl) content among different treatments. The Chl a, b, and carotenoid contents under all treatments were significantly higher than those in the control (CK) (Fig. 6a). The Chl content of the first leaf increased first and then decreased with increasing P rates, reaching the maximum values in the OF-P3 treatment (Supplemental Fig. S2). The Chl content in the second leaf followed the same trend as the first leaf and bud. A similar trend was observed for chlorophyll fluorescence. Correlation analysis showed that Chl content in the bud exhibited a trend of first increasing and then decreasing with increasing P rates (Fig. 6b). However, the relationship between Chl content and soil P content was not significant for the first leaf and second leaf (Fig. 6c & d). The results indicated that the bud head was more responsive to P content compared to the first-leaf and second-leaf. Overall, the Chl content after P application was higher than that of the CK, and the Chl content under low P conditions was higher than that under high P conditions.

      Figure 6. 

      (a) Effect of phosphorus based on organic fertilizer on chlorophyll content in C. sinensis chlorophyll fluorescence of different leaf positions in C. sinensis. Relationship between total phosphorus and chlorophyll a/b content on (b) bud, (c) first leaf and (d) second leaf in C. sinensis.

    • Compared with the control, P treatments significantly decreased the H2O2 content. In the bud and second leaf, the H2O2 contents first decreased and then increased with increasing P rates (Fig. 7ac). The lowest values were recorded for the OF-P2 treatment. The results of DAB staining of the first leaf (Fig. 7d) showed that damage caused by applying P fertilizer to the tea plant was minimal. Compared with the CK, the O2 content in the first and second leaf decreased under different P fertilizer treatments. In the bud, first and second leaf, O2 content showed a decreasing trend with increasing P rates. The maximum reduction was recorded for the OF-P2 treatment, which decreased the values by 50.1%, 42.8%, and 14.3% in the bud, first and second leaf, respectively, lower than the control (Fig. 7eg). A leaf stained with NBT also showed the same trend (Fig. 7h).

      Figure 7. 

      Effect of phosphorus based on organic fertilizer on hydrogen peroxide and superoxide anion contents in C. sinensis. hydrogen peroxide content on (a) bud, (b) first leaf, and (c) second leaf, (d) DAB staining in first leaf, (e) superoxide anion content on bud, (f) first leaf, and (g) second leaf, (h) NBT staining in first leaf. Different * indicates the level of difference between treatments: * p < 0.05, ** p < 0.01, *** p < 0.001.

      The experimental results showed that, except for the CK, the SOD activity in different leaves was significantly increased under different P amendments (Fig. 8). Maximum values for SOD were recorded for the OF-P2 treatment. POD activity in different leaves increased initially and then decreased with increasing P rates. Compared with the CK, low P treatment significantly increased POD activity compared to the higher rates. APX activity was significantly increased under different treatments compared with the control (p < 0.05). Maximum CAT activity in the first and second leaf was recorded for the OF-P2 treatment. However, the difference between the first and second leaf was not significant for CAT activity. The OF-P2 treatment increased CAT activity in the bud head, first leaf, and second leaf by 16.6%, 23.5%, and 22.9%, respectively, higher than the CK. Further PCA analysis showed that the control and OF-P2 were significantly different in the three different leaves (p < 0.05).

      Figure 8. 

      Principal components analysis of phosphorus based on organic fertilizer on antioxidant enzymes of different leaf positions in C. sinensis.

    • The use of organic fertilizer can significantly stimulate and increase soil P availability[36]. The present findings demonstrated that combining organic fertilizer with low P increases total N, P, and K in the soil. These findings are in line with the research by Vu et al., which reported a positive effect of organic fertilizer on the availability of soil P[37]. Moreover, in this study, adding low P also increased aluminum levels in the soil, which demonstrated a significant improvement in aluminum availability. We also noted that low P facilitated soil enzymatic activity, including acid phosphatase and phytase, aiding in organophosphorus breakdown and enhancing plants' P absorption. Additionally, the combined application of chemical and organic P was also reported to enhance phosphatase activity, speed up the transformation of soil P, and increase soil available P and nutrient contents. Excessive P application wastes soil P resources and reduces the availability of available P, as reported previously[38]. The combined use of organic fertilizer and low chemical P markedly enhanced tea bud weight, bud density per 0.04 m2, and promoted root growth. Similar results were reported by Kirchgesser and colleagues, who demonstrated that P deficiency leads to smaller root systems, whereas adequate P application substantially improved crop performance[39]. In contrast to this study, some authors have also reported that high P inhibits the root system, possibly due to Polygonum's strong P tolerance compared to weak-tolerant crop species, including tea. In another study, similar results were reported, where peanut exhibited increased total root length, root volume, and surface area under appropriate P fertilization[18]. Furthermore, Chen and colleagues also revealed that optimal P levels significantly improved tea root vitality, while excess P had inhibitory effects[40]. The positive influences of combined organic fertilizer and chemical P on tea quality are well-documented. P deficiency results in decreased tea water extract, free amino acids, and caffeine contents[14]. Meanwhile, a sufficient supply of P has been reported to improve quality traits, for example in peanut[18]. In conclusion, combined organic and chemical P fertilizer in appropriate amounts guarantees meeting the tea plants' growth requirements and optimizing tea quality characteristics.

      Under low P conditions, chlorophyll content in tea plants significantly increased compared to the control and other treatment groups. Simultaneously, heightened antioxidant enzyme activity assisted in eliminating excess free radicals in tea plants, leading to decreased hydrogen peroxide and superoxide anion levels, thereby improving plant resistance. These results align with previous studies where phosphate fertilizer application initially boosted chlorophyll content and antioxidant enzyme activity, followed by a decline[41]. Examination of chlorophyll and fluorescence parameters in Chinese cabbage under varied P levels revealed that with P fertilizer addition, the content initially stabilized, then peaked, and eventually decreased[42]. In the study of the combined application of P fertilizer on citrus, it was found that a high concentration of P would reduce the antioxidant capacity of leaves and roots, whereas leaves and roots have a stronger antioxidant capacity when P is low. Proper concentration of phosphorus can improve the physiological characteristics of citrus[43].

      Therefore, it is crucial to implement effective strategies to reduce P inputs and minimize environmental losses comprehensively[9]. The present research has revealed that maintaining a low P level can enhance the available P content in the soil of tea plantation. This enhancement boosts the activity of phosphatase and phytase, thereby improving phosphorus uptake by the tea plants. In situations of low P levels, the growth of tea plant roots are stimulated, leading to an increased 100-bud weight of one bud and bud density per 0.04 m2, reduced polyphenol content in the tea, higher levels of free amino acids, and an overall improvement in tea quality. Furthermore, the increase in antioxidant enzyme activity plays a vital role in combating the adverse effects of reactive oxygen species production in plants, ultimately strengthening the resilience of tea plants.

    • The study explored the relationship between soil nutrient status and above-ground performance of tea seedlings under different P fertilizer rates from both organic and inorganic sources. Results showed that low P application (45 kg/ha) improved soil nutrient levels, leading to significant improvements in the 100-bud weight of one bud, bud density per 0.04 m2, plant resistance, and overall tea quality. This research provides valuable insights into the proper use of P fertilizers in southern Shaanxi, emphasizing the importance of determining the adequate dosage for optimal fertilizer application.

    • The authors confirm contribution to the paper as follows: study conception and design: Bai J, Wang Y, Shi R, Deng S, Wang H, Wang C, Gong C; experiments performed: Wang Y, Shi R, Deng S, Wang H; data analysis: Wang Y, Shi R; draft manuscript preparation: Wang Y, Bai J, Gong C, Hussain S. All authors reviewed the results and approved the final version of the manuscript.

    • Due to administrative requirements, the original data of the experiments during the research period of the project are not available to the public, but available from the corresponding author or the first author upon reasonable request.

      • This research was supported by the Key Research and Development Project of Shaanxi Province (No: 2022NY-167).

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

      • Supplemental Fig. S1 Effect of phosphorus based on organic fertilize on basic physical and chemical properties of tea plantation soil. water content (a), bulk density (b), total porosity (c), total nitrogen (d), total phosphorus (e), total potassium (f), available phosphorus (g), available aluminum (h), exchangeable calcium (i), soil acid phosphatase activity (j), phytase activity (k), phosphomonesterase activity contents (l).
      • Supplemental Fig. S2 Effect of phosphorus based on organic fertilize on chlorophyll content in different leaf positions in Camellia sinensis. Chlorophyll a (a), chlorophyll b (b), carotenoid content (c) in bud, chlorophyll a (d), chlorophyll b (e), carotenoid content (f) in first leaf, chlorophyll a (g), chlorophyll b (h), carotenoid content (i) in second leaf.
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Cite this article
    Wang Y, Shi R, Deng S, Wang H, Hussain S, et al. 2024. Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants. Beverage Plant Research 4: e037 doi: 10.48130/bpr-0024-0027
    Wang Y, Shi R, Deng S, Wang H, Hussain S, et al. 2024. Application of low phosphorus on the basis of organic fertilizer can effectively improve yield and quality of tea plants. Beverage Plant Research 4: e037 doi: 10.48130/bpr-0024-0027

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