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Development of a Specific Nested PCR for the Detection of 16SrI and 16SrII Group Phytoplasmas Associated with Yellow Leaf Disease of Areca palm in Hainan, China

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21 April 2025

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22 April 2025

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Abstract
Yellow leaf disease (YLD), caused by areca palm yellow leaf phytoplasma (AYLP), is a devastating disease that severely impacts the sustainable development of the areca palm industry. Efficient and accurate detection methods are considered crucial for the diagnosis and management of YLD. To address issues like false positive results using universal nested PCR primers, this study designed specific outer and internal primers based on the conserved regions of the 16S rDNA sequence of phytoplasmas, resulting in a nested PCR primer set, HNP-1F/HNP-1R and HNP-2F/HNP-2R. This set consistently amplified a single specific target band of 429 bp from 16SrI and 16SrII groups of AYLP. The sensitivity threshold for the 16SrI group of AYLP was 7.5×10-7 ng/μL, while that for the 16SrII group of AYLP was 4 ×10-3 ng/μL. To verify and evaluate the efficiency, the developed nested PCR system was used to detect leaf samples collected from trees showing leaf yellowing symptoms in areca palm plantations, and the results showed that the primer set could specifically detect AYLP. This primer set is characterized by rapid and accurate detection, high specificity, and high sensitivity compared to the traditional universal primer set of P1/P7 and R16mF2/R16mR1 or R16mF2/R16mR1 and R16mF2n/R2, providing a scientific basis for the specific diagnosis and early control of yellow leaf disease of areca palm.
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1. Introduction

Areca palm (Areca catechu L.) is a perennial evergreen tree belonging to the genus Areca in the family Palmaceae. It is native to Malaysia and now widely distributed across South and Southeast Asia including 16 countries and regions such as India and Malaysia [1]. In China, areca palm is mainly cultivated in Hainan and Taiwan, with smaller amounts grown in Yunnan, Guangdong, and Guangxi [2]. The Areca palm holds significant dietary and medicinal value, ranking the first among the "Four Main Southern Medical Plants" (areca palm Areca catechu L., Alpinia oxyphylla Miq., Amomum villosum Lour., and Morinda officinalis How.). Its flowers and fruits are rich in various essential nutrients and its fruits, seeds, peels, and flowers can be used medicinally to treat conditions such as beriberi, swelling, and digestive issues [3]. The fruit of the areca palm, arecanut, is economically valuable and, when chewed by customers, promotes salivation, generates heat, and relieves fatigue, making it a popular chewing product. Areca palm is one of Hainan’s economically important "Six Trees" crops (coconut Cocos nucifera L., areca palm, oil palm Elaeis guineensis Jacq., rubber tree Hevea brasiliensis Muell. Arg., Dalbergia odorifera T. Chen, and agaru Aquilaria agallocha (Lour.) Roxb) in agriculture, accounting for over 95% of the national planting area [4]. With rising consumer demand and prices in China, the planting area and output value of areca palm in Hainan have increased annually. Currently, areca palm has become one of the key economic sources for more than 2.3 million farmers in Hainan [5], playing a crucial role in the provincial agricultural and rural development and in increasing farmers' income and wealth.
Yellow leaf disease of areca palm (YLD) caused by areca palm yellow leaf phytoplasma (AYLP) is a lethal disease first reported in the central region of Kerala, India in 1914 [6]. This disease was also observed in China in 1981 [7] and Sri Lanka in 2015 [8]. Since its initial discovery in Tunchang County, Hainan Province, China, in 1981, YLD has spread to nearly all areca palm growing areas in Hainan, causing severe damage to the local areca palm industry [5,9]. Due to the lack of effective control measures in production, pathogen detection remains the primary method for managing this disease.
Three phytoplasmas groups or subgroups responsible for YLD have been identified in India: 16SrI-B, 16SrXI-B, and 16SrXVI [10,12]. In China, earlier researches had indicated that the phytoplasmas causing YLD belong to the 16SrI-B and 16SrI-G subgroups [13,14]. Recently, our research team discovered new phytoplasmas, specifically from the 16SrII and 16SrXXXII groups, in YLD samples collected from Hainan [15,16].
To date, various detection methods have been developed for the YLD-causing phytoplasmas, including electron microscopy [18], enzyme-linked immunosorbent assays (ELISA) [19], nested PCR [14,20], loop-mediated isothermal amplification (LAMP) [21], droplet digital PCR (ddPCR) [22], and quantitative real-time PCR (qPCR) [10,23]. However, each of these methods has its advantages and limitations. For instance, electron microscopy assays require complex sample preparation, are time-consuming, and costly. Serological assays may face challenges in preparing antisera and risk of cross-reactivity with host antigens. Those molecular methods developed recently were used to detect AYLP in 16SrI group. Meanwhile, the LAMP technique, while capable of detection at a constant temperature, has a high primer design complexity [24] and is prone to contamination, leading to false positives. Compared to conventional PCR, qPCR and ddPCR involve higher reagent costs, more stringent laboratory conditions, and expensive equipment. Additionally, products from these methods (ELISA, LAMP, and qPCR) cannot be sequenced. In contrast, amplified products of nested PCR using universal phytoplasma primers can be sequenced, making it widely used for phytoplasma detection and classification research. However, nested PCR using universal phytoplasma primers may sometimes result in false-positive outcomes in the amplification of 16S rDNA sequences, significantly affecting detection efficiency [25]. In recent research, our team developed nested PCR primer sets F4/R1 and F2/R2 [20] to address detection challenges posed by the low titer and uneven distribution of AYLP in areca palm plants [4,26]. Though this primer set significantly improved the detection efficiency than universal primer sets like P1/P7 and R16mF2/R16mR1 or P1/P7 and R16mF2n/R2; subsequent studies showed that it, just like the universal primers, sometimes amplified non-specific fragments too (our lab’s unpublished data). In addition, research indicate that the 16SrI group is widely distributed in Hainan Province [17] and that there are many of cases where the 16SrII group has been identified in Wenchang City and Danzhou City (our unpublished data) , while only once case of the 16SrXXXII groups was observed [16]. Currently, there is no diagnostic technique available that can simultaneously detect both 16SrI and 16SrII groups of AYLP. To resolve this issue of non-specific amplification and to meet the needs for the detection of 16SrI group AYLP and novel identified 16SrII group AYLP, this study developed a nested PCR detection system comprising two specific primer pairs of four candidates, based on the 16S rDNA sequences of 16SrI and 16SrII group AYLPs and other areca palm-related pathogens. The goal is to establish a new specific nested PCR primers combination for accurate detection of AYLPs (16SrI and 16SrII groups) in Hainan, providing technical support for AYLP detection, identification, and early prevention of YLD in areca palm seedlings and infected plants in the field.

2. Results

2.1. Application of Universal Nested PCR for Phytoplasma Detection

A total of 335 areca palm genomic DNA samples were collectively tested using the universal nested PCR primer set targeting phytoplasmas. Among these samples, 50 exhibited amplification bands of approximately 1400 bp (Figure 1). The resulting PCR products from these 50 positive samples were subsequently sequenced by Sangon Biotech (Shanghai, China). The sequencing data revealed the following distribution: among the 50 samples, 16 samples showed amplification specific to areca chloroplast, 20 exhibited amplification bands corresponding to bacteria, only 10 displayed specific amplification related to phytoplasmas, and the remaining four samples yielded no detectable sequencing results (Figure 2).

2.2. Nested PCR Primer Test and Combination

By performing multiple sequence alignments of 16S rDNA from areca palm yellow leaf phytoplasma, areca chloroplasts, and endophytic bacteria, and following the principles of PCR primer design, we ultimately designed one pair of outer primer HNP-1F/R and three pairs of internal primers HNP-2F/2R, HNP-3F/3R, and HNP-4F/4R (Table 2).
These nested PCR primers were tested using genomic DNA samples from areca palm leaves infected with phytoplasma groups 16SrI and 16SrII, as well as genomic DNA samples from areca palm bacterial leaf blight pathogen and pineapple phytoplasma as references. Both HNP-2F/2R and HNP-3F/3R amplified target bands only in positive control and areca palm samples infected with phytoplasma groups 16SrI and 16SrII (Figure 3A and Figure 3B), indicating good performance. However, HNP-4F/4R also amplified a band of approximately 1500 bp in areca palm bacterial leaf blight pathogen DNA (Figure 3C). The combinations of HNP-1F/1R with either HNP-2F/2R or HNP-3F/3R were used to form a nested PCR in subsequent experiments.

2.3. Specificity Validation of Nested PCR

Using the outer primers HNP-1F/1R and internal primers HNP-2F/2R and HNP-3F/3R, the specificity of detection of areca palm DNA infected with phytoplasmas from the 16SrI and 16SrII groups, as well as DNA from five other pathogens was conducted. The results indicated that HNP-2F/2R only amplified a target band of approximately 429 bp in positive control and in areca palm samples infected with phytoplasmas from the 16SrI and 16SrII groups (Figure 4A). However, HNP-3F/3R, in addition to the three mentioned samples, also amplified a 652bp band in healthy areca palm samples and samples infected with phytoplasmas from the 16SrXXXII group (Figure 4B). Consequently, HNP-2F/2R was selected as the internal primer set for subsequent experiments.

2.4. Optimization of PCR Annealing Temperatures

In the nested PCR reaction, the annealing temperatures for both the outer and internal primers were systematically optimized within the range of 40°C to 60°C. For the outer primers targeting HNP-1F/1R, there was no significant difference in band intensity between annealing temperatures of 40.0°C to 53.6°C. However, as the annealing temperature increased from 56.0°C to 60.0°C, the band intensity decreased (Figure 5A). Therefore, we selected 53.6°C as the optimal annealing temperature.
Regarding the internal primers targeting HNP-2F/2R, a gradient annealing temperature approach was employed. Bands corresponding to the target were consistently visible within the temperature range of 46.4°C to 51.3°C, while no amplification occurred at 60.0°C (Figure 5B). Consequently, we determined the annealing temperature for HNP-2F/2R to be 51.3°C.

2.5. Sensitivity Determination of Nested PCR

To assess the sensitivity of this method for detecting the two phytoplasma groups, nested PCR using the outer primers HNP-1F/1R and internal primers HNP-2F/2R was conducted on recombinant plasmids containing 16S rDNA segments from the 16SrI and 16SrII phytoplasma groups.
Stable amplification of a single specific target band was achieved for the 16SrI group across plasmid concentrations ranging from 7.5 ng/μL to 7.5 × 10-7 ng/μL. However, as the concentration decreased, the amplification band gradually became fainter and disappeared at 7.5 × 10-7 ng/μL (Figure 6A). Similarly, for the 16SrII group, a single specific target band (429 bp) was consistently yielded across plasmid concentrations ranging from 4 ng/μL to 4 × 10-3 ng/μL. The amplification band intensity decreased with decreasing plasmid concentration and disappeared at 4 × 10-3 ng/μL (Figure 6B).
These results indicate that the nested PCR method has a minimum detection limit of 7.5 × 10-7 ng/μL for the 16SrI phytoplasma group and 4 × 10-3 ng/μL for the 16SrII phytoplasma group.

2.6. Comparison of The Newly Developed Nested PCR with Universal Nested PCR

To compare the detection efficiency of the nested PCR method developed in this study with that of the universal nested PCR, 30 field-collected areca samples were tested.
The universal nested PCR method failed to detect phytoplasma, except in the positive control (Figure 7A). In contrast, our newly developed nested PCR method successfully detected phytoplasma in 10 of the tested areca samples (Figure 7B). Sequencing confirmed that all 10 PCR products corresponded to the 16SrI group phytoplasma. This study highlights the effectiveness of our specific nested PCR approach for detecting AYLP and provides valuable insights for routine diagnosis of yellow leaf disease of areca palm research.

2.7. Construction and Analysis of Phylogenetic Trees

A phylogenetic tree was constructed using sequences of representative strains from the phytoplasma groups/subgroups, based on the respective amplified fragments of R16mF2/R1 and HNP-2F/2R, using MEGA 11 software. In both phylogenetic trees, phytoplasma of the 16SrI and 16SrII groups were each clustered into a separate branch. Additionally, the phylogenetic tree constructed from the sequence fragments from HNP-2F/2R showed differences in the subdivision of 16SrI subgroups compared to the tree from the R16mF2/R1 sequence fragments. This indicates that the nested PCR method established in this study can identify and group the phytoplasmas associated with YLD in the 16SrI and 16SrII groups but cannot differentiate at the subgroup level within the 16SrI group (Figures 8A and Figure 8B). The multiple sequence alignment also indicates that the fragments amplified by HNP-2F/2R exhibit high similarity to the 16SrI group AYLP (Figure 8C).

3. Discussion

Phytoplasmas, originally known as mycoplasma-like organisms (MLOs) [27], belong to the prokaryote group without cell walls [28,29]. Phytoplasmas are prokaryotic microorganisms that specifically parasitize the sieve tube cells in the phloem of plants. Researches have shown that their titer within plants is uneven and varies with temperature [30,31]. Unfortunately, phytoplasmas are difficult to cultivate artificially [32], which hinders their systematic identification and classification using traditional isolation and culture methods commonly employed for other prokaryotic and eukaryotic organisms (such as plant pathogenic bacteria and fungi) [33,34]. Therefore, the establishment of specific and sensitive detection methods is crucial for the study of aetiology, epidemiology, and control of phytoplasma diseases, including areca palm yellow leaf disease. In this study, internal and outer primers based on the 16S rDNA specific regions of AYLP were designed. A primer set, HNP-1F/HNP-1R and HNP-2F/HNP-2R, was ultimately screened for specific amplification of AYLP from the novel designed primers. The annealing temperature was optimized, and then the optimal nested PCR system for detecting areca palm yellow leaf phytoplasma was established.
The universal primers P1/P7 and R16mF2/R1 are commonly used for detecting phytoplasmas from different hosts. However, their non-specific amplification is quite notable. In this study, these universal primers were used for the amplification of 16S rDNA of 335 areca palm leaf samples. The result showed that among the 50 samples that amplified with the target size bands (about 1400 bp), only 10 (20%) were ultimately confirmed as areca palm yellow leaf phytoplasma (AYLP) according to the results of sequences alignment; 16 (32%) were identified as areca palm chloroplast, and 20 (40%) were other bacteria, with the remaining samples showing no signal. This result indicated that the specificity of the universal primers P1/P7 and R16mF2/R1 for the amplification of AYLP is relatively poor, resulting in a high false-positive rate (72% to 80%). A similar result was found in the detection of the pathogenic phytoplasma in sisal purple leafroll disease [35]. In our preliminary studies during 2018 and 2019, 100 leaf samples were detected using universal primer sets P1/P7 and R16mF2/R16mR1, only one sample was successfully amplified, its sequence was verified as AYLP. To solve the problem of the low detection efficiency of AYLP, a nested PCR method had been developed with novel primer sets F4/R1 and F2/R2 [20] with a target band of 525 bp had been designed based on 16S rDNA gene sequences of AYLP. Though this primer set significantly improved detection efficiency (80 out of 482, 17%) than universal primer set P1/P7 and R16mF2/R16mR1 (1 out of 100, 1%), subsequent studies showed that this primer set also led to non-specific amplification of sequences (for example, Bacillus sp.) in the areca leaf and suspected vector insects (our lab unpublished data). The newly nested PCR detection method was developed by performing multiple sequence alignments of the 16S rDNA sequences of the reported areca palm yellow leaf phytoplasma and other bacteria. This approach was used to select specific regions of the phytoplasma for primer design and screening, ensuring high specificity. Specificity tests demonstrated that this method could detect the 16SrI and 16SrII groups of phytoplasma currently found in areca palms, while other groups of phytoplasma and other bacteria were not detected as positive.
In terms of detection sensitivity, the newly developed nested PCR detection method has a minimum detection limit of 16 copies/μL (7.5×10-7 ng/μL) for the 16SrI group of AYLP. In comparison, the existing TaqMan qPCR detection method has a limit of 1.16 copies/μL [23], and ddPCR has a limit of 0.07 copies/μL [22]. However, the nested PCR detection method developed in this study can also simultaneously detect the 16SrII group of phytoplasmas, with a minimum detection limit of 1.3×10³ copies/μL (4 ×10-3 ng/μL).
Regarding cost and detection efficacy, this newly established method is more cost-effective and less prone to contamination compared to qPCR and ddPCR. Additionally, all amplified fragments from the samples sequenced thus far have been identified as phytoplasmas. This method also allows for the construction of phylogenetic trees from the sequencing data to identify positive samples, a capability that current qPCR and ddPCR methods lack.
The nested PCR detection method developed in this study can be used for the detection and identification of areca palm yellow leaf phytoplasma (16SrI and 16SrII groups). It provides technical support for subsequent AYLP detection, identification, and early prevention and control of areca palm yellow leaf disease.

4. Materials and Methods

4.1. Materials

Areca leaf samples showing yellow symptoms collected from the field and stored in our laboratory were used in this study (Table 1).

4.2. Extraction of Total DNA from Areca Plam Leaf Samples Showing Yellow

Leaf samples of areca palm weighing approxima was 0.1 g were taken and cut into 1 mm pieces. DNA was extracted following the instructions provided by TianGen Biotech Co., Ltd. (Beijing, China), using the plant genomic DNA extraction kit, quantified using Nanodrop 2000, and stored at -20 °C for subsequent use.

4.3. Construction of Recombinant Plasmids for 16S rDNA of Areca Palm Yellow Leaf Phytoplasma

Yellow leaf disease (YLD) is a major disease affecting areca palm plants. Rapid detection of the pathogen responsible for YLD is crucial for effective management. In this study, universal nested PCR primers P1/P7 and R16mF2/R16mR1 (Table 2) targeting the 16S rDNA of phytoplasmas were employed to amplify the total DNA extracted from Areca leaf showing YLD. The reaction mixture consisted of 2 μL of DNA template, 12.5 μL of 2 × Taq PCR Master Mix (Aidlab Biotechnologies Co., Ltd, Beijing, China, PC0902), and 1 μL of each primer (10 μM), with ddH2O added to a final volume of 25 μL. The PCR procedure included an initial denaturation at 94 °C for 3 minutes, followed by 35 cycles of denaturation at 94 °C for 30 seconds, annealing at 55 °C for 30 seconds, extension at 72 °C for 1 minute, and a final extension at 72 °C for 5 minutes.
After the first round of PCR using external primers P1/P7, the product was diluted 20-fold, and a second round of amplification was performed using internal primers R16mF2/R16mR1. The procedure was like the first round, except for an annealing temperature of 50 °C. The amplified products were analyzed by electrophoresis on a 1% agarose gel (120 V, 35 minutes) and visualized using a gel imaging system (Synoptics Ltd, USA, GBOX-F3-LFB).
To construct recombinant plasmids, the PCR products were purified using a gel extraction kit (Tiangen Biotech, Beijing, China, DP209-02). Subsequently, a rapid ligation kit (Sangon Biotech, Shanghai, China, B522214-0020) was used to ligate the purified products into the T-vector. The resulting plasmids were transformed into DH5α competent cells (Sangon Biotech, Shanghai, China, B528413-0010). Positive clones were selected in LB sterile liquid medium supplemented with 50 ng/μL ampicillin, and plasmid DNA was extracted using a plasmid mini-prep kit (Tiangen Biotech, Beijing, China, DP103-02) and sequenced by Sangon Biotech (Shanghai, China).

4.4. Design and Primary Screening of Specific Primers

In this study, outer and internal primers were designed and screened for detecting the 16S rDNA of phytoplasmas associated with yellow leaf disease of areca palm (YLD). For this, previously reported 16S rDNA sequences from the 16SrI-B subgroup and the 16SrI-G subgroup of YLD phytoplasmas were utilized, along with the 16S rDNA sequence of phytoplasmas from the 16SrII subgroup, which was previously characterized in our laboratory. To enhance specificity, these 16S rDNA sequences were also aligned and compared with those from Areca chloroplasts, areca bacterial leaf spot pathogens, and other bacterial species (Table 3). Based on sequence variations, several nested PCR primers were designed and synthesized in Sangon Biotech (Shanghai, China).
In the first round of amplification, genomic DNA samples from areca palm leaves infected with phytoplasma groups 16SrI and 16SrII, as well as DNA of areca bacterial leaf blight pathogen (Burkholderia andropogonis) and Pantoea ananatis, were amplified using the primers HNP-1F/1R. Chrysanthemum genomic DNA infected with phytoplasma group 16SrI was used as a positive control, and H2O served as the negative control. The first-round PCR products were diluted 20 times, and three pairs of nested primers (HNP-2F/2R, HNP-3F/3R, and HNP-4F/4R) were used to amplify the first-round PCR products, aiming to select the better nested PCR primer combination.

4.5. Specificity Verification of Nested PCR

The specificity of the method developed in the current study was validated by using external primers HNP-1F/1R to amplify genomic DNA from various sources, including healthy areca palm leaves, areca palm bacterial leaf spot pathogens, pineapple pan-genus bacteria, YLD phytoplasmas (16SrI and 16SrII), and kenaf plants infected with 16SrXXXII phytoplasmas. Additionally, the complementary DNA was included from areca yellow leaf virus 1 (Table 1). After diluting the first round PCR product 20-fold and using it as a template, a second-round amplification was performed with the internal primers pairs mentioned above. The nested PCR reaction system and cycling conditions were identical to those described in section 1.3. The amplified products were analyzed by electrophoresis on a 1% agarose gel (120 V, 35 minutes) and visualized using a gel imaging system (Synoptics Ltd, USA, GBOX-F3-LFB).

4.6. Optimization of Annealing Temperatures for Nested PCR with Internal Primers

To optimize the annealing temperatures for the nested PCR using internal primers in the detection of phytoplasmas, the HNP-1F/1R + HNP-2F/2R primer set was utilized, targeting the positive Chrysanthemum phytoplasma genomic DNA samples (Table 1).
The first round of amplification using primers HNP-1F/1R, the reaction mixture was same as in section 1.3. The PCR cycling conditions included an initial denaturation at 94 °C for 3 minutes, followed by 35 cycles of denaturation at 94 °C for 30 seconds, annealing with a temperature gradient ranging from 40 °C to 60 °C (in 12 steps), annealing for 30 seconds, extension at 72 °C for 1 minute, and a final extension at 72 °C for 5 minutes.
After the completion of the first PCR round, the product was diluted 20-fold, and the second round of amplification using primers HNP-2F/2R was performed. The reaction mixture for the second round was the same as the first, with a temperature gradient ranging from 40 °C to 60 °C (also in 12 steps). The cycling conditions remained consistent, and the amplified products were analyzed by electrophoresis on a 1% agarose gel (120 V, 35 minutes) and visualized using a gel imaging system.

4.7. Establishment of Nested PCR Reaction System

In this study, a nested PCR reaction system using external primers HNP-1F/1R was established for the first round of amplification. After completing the first PCR round, the resulting product was diluted 20-fold, and the second round of amplification was performed using internal primers HNP-2F/2R. The mixture and PCR cycling condition for the two rounds of reactions were the same as in section 1.6, but the annealing temperature was determined to be 42.5 °C (while for the second PCR round, the annealing temperature was 46 °C).

4.8. Sensitivity Testing of Newly Developed Nested PCR

Sensitivity testing was conducted for the nested PCR developed in this study, using recombinant plasmids S97-1 and W4-1, each containing 16S rDNA segments from 16SrI and 16SrII of AYLP, respectively. These plasmids were subjected to a 10-fold serial dilution. S97-1 was diluted from 7.5 ng/μL to 7.5×10-8 ng/μL. W4-1 was diluted from 4 ng/μL to 400 zg/μL. The HNP-1F/1R + HNP-2F/2R nested PCR primer set was employed to amplify the diluted S97-1 and W4-1 recombinant plasmids. The nested PCR reaction system and cycling conditions were consistent with those described in section 1.6.

4.9. Comparison of Newly Developed Nested PCR Method with Universal Nested PCR Method

In this study, 30 samples of areca palm showing yellow symptoms were simultaneously tested to evaluate the performance of two detection methods using universal primers and the newly developed nested PCR primer set.
The universal nested PCR method followed the reaction steps described in section 1.3. The newly established nested PCR method followed the reaction steps outlined in section 1.6.

4.10. Phylogenetic Analysis of the Fragment Amplified from Primers HNP1F/1R and HNP-2F/2R

The sequence fragments obtained from the aforementioned sequencing and assembly process, along with 16S rDNA sequences of different groups of plant pathogens from GenBank, were used to construct a phylogenetic tree. The Neighbor-Joining (NJ) method in MEGA 11 was employed with a bootstrap value of 1000 [35].

Author Contributions

Conceptualization, Q.H.T. and X.Q.Z.; methodology, H.Y.G., Q.H.T. and X.Q.Z.; validation, H.Y.G.; formal analysis, H.Y.G.; in-vestigation, X.L.M, Z.W.L., W.W.S., S.J., and W.Q.Q.; data curation, H.Y.G., Q.H.T. and X.Q.Z.; writing—original draft preparation, H.Y.G.; writing—review and editing, H.Y.G., Q.H.T. and X.Q.Z.; review, Q.H.T. and X.Q.Z.; supervision, Q.H.T. and X.Q.Z.; and project administration, Q.H.T. and X.Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hainan Key Project (ZDYF2025XDNY118 and ZDYF2022XDNY208) and the Project of Yazhouwan Scientific and Technological Administration of Sanya (SYND-2022-36).

Data Availability Statement

DNA sequences are available in the GenBank database, with the accession numbers listed in the Results. All other relevant data are within the paper and Supplementary Materials.

Acknowledgments

We sincerely thank Graduate student Wang Yuhang and Liu Yueyue, undergraduate interns Wang Huiqing, Xu Caide, Deng Ting and Zheng Xingxing participated in patial sample collections and DNA extraction from 2020 to 2022, and scientific research as-sistants Ma Luping, Ma Pei, Guo Chunping and Long Shida participated in patial DNA extraction during 2019 and 2024. 16SrI Group DNA sample of PT-1 from Paulownia sp. and 16SrIV Group DNA sample of ZF-1 from Jujube Ziziphus jujuba were generously provided by Dr. Gao Rui in Shandong Institute of Pomology of Shandong Academy of Agricultural Sciences. The oringinal manuscript was grammar proofreading by Dr Syed Majid of Rasheed Department of Agriculture Entomology Section, Bacha Khan University, Charsadda, Pakistan. We would like to express our heartfelt thanks to them. We also thank our laboratory partners for their technical assistance.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
YLD Yellow leaf disease of areca palm
AYLP areca palm yellow leaf phytoplasma
LAMP loop-mediated isothermal amplification
qPCR quantitative real-time PCR
ddPCR droplet digital PCR

Appendix

Table A1. Areca palm leaf samples used in the detection assays using novel nested PCR in this study.
Table A1. Areca palm leaf samples used in the detection assays using novel nested PCR in this study.
No Sample Result City/County Time No Sample Result City/County Time
1 J069 Positive Wenchang 2022 169 ZB1-2 Negative Wenchang 2022
2 A056 Positive Wenchang 2022 170 MQR4 Negative Ledong 2021
3 T40 Positive Wenchang 2022 171 HL3-4 Negative Ledong 2021
4 W4 Positive Wenchang 2022 172 HL3-1 Negative Ledong 2021
5 S97 Positive Wenchang 2022 173 HL1-2 Negative Ledong 2021
6 M25 Positive Wenchang 2023 174 HZ3-2 Negative Ledong 2021
7 H61 Positive Wenchang 2023 175 HZ5-2 Negative Ledong 2021
8 H64 Positive Wenchang 2023 176 T52 Negative Ledong 2021
9 R71 Positive Waning 2022 177 NY1-2 Negative Wenchang 2022
10 R72 Positive Waning 2022 178 ZB2-2 Negative Wenchang 2022
11 R61 Negative Waning 2022 179 M22 Negative Wenchang 2022
12 R62 Negative Waning 2022 180 M23 Negative Wenchang 2022
13 R63 Negative Waning 2022 181 M25 Negative Wenchang 2022
14 R64 Negative Waning 2022 182 M26 Negative Wenchang 2022
15 R65 Negative Waning 2022 183 M27 Negative Wenchang 2022
16 R66 Negative Waning 2022 184 M30 Negative Wenchang 2022
17 R67 Negative Waning 2022 185 M31 Negative Wenchang 2022
18 R68 Negative Waning 2022 186 L30 Negative Wenchang 2021
19 R69 Negative Waning 2022 187 L32 Negative Wenchang 2021
20 R70 Negative Waning 2022 188 L33 Negative Wenchang 2021
21 R73 Negative Waning 2022 189 S40 Negative Wenchang 2023
22 R74 Negative Waning 2022 190 S41 Negative Wenchang 2023
23 R75 Negative Waning 2022 191 S42 Negative Wenchang 2023
24 R76 Negative Waning 2022 192 S43 Negative Wenchang 2023
25 R77 Negative Waning 2022 193 S44 Negative Wenchang 2023
26 R78 Negative Waning 2022 194 S45 Negative Wenchang 2023
27 R79 Negative Waning 2022 195 S46 Negative Wenchang 2023
28 R80 Negative Waning 2022 196 S47 Negative Wenchang 2023
29 R81 Negative Waning 2022 197 S50 Negative Wenchang 2023
30 R82 Negative Waning 2022 198 W11 Negative Wenchang 2023
31 R83 Negative Waning 2022 199 W12 Negative Wenchang 2023
32 R84 Negative Waning 2022 200 W13 Negative Wenchang 2023
33 R85 Negative Waning 2022 201 W14 Negative Wenchang 2023
34 R86 Negative Waning 2022 202 W15 Negative Wenchang 2023
35 R87 Negative Waning 2022 203 W16 Negative Wenchang 2023
36 R88 Negative Waning 2022 204 W17 Negative Wenchang 2023
37 R89 Negative Waning 2022 205 W18 Negative Wenchang 2023
38 R90 Negative Waning 2022 206 W19 Negative Wenchang 2023
39 W1 Negative Waning 2022 207 W20 Negative Wenchang 2023
40 W2 Negative Wenchang 2022 208 W21 Negative Wenchang 2023
41 W3 Negative Wenchang 2022 209 W22 Negative Wenchang 2023
42 W3-4 Negative Wenchang 2022 210 W23 Negative Wenchang 2023
43 A09 Negative Wenchang 2022 211 W24 Negative Wenchang 2023
44 A12 Negative Wenchang 2022 212 W25 Negative Wenchang 2023
45 A13 Negative Wenchang 2022 213 W26 Negative Wenchang 2023
46 A14 Negative Wenchang 2022 214 W27 Negative Wenchang 2023
47 A16 Negative Wenchang 2022 215 W28 Negative Wenchang 2023
48 A19 Negative Wenchang 2022 216 W29 Negative Wenchang 2023
49 A22 Negative Wenchang 2022 217 W30 Negative Wenchang 2023
50 A24 Negative Wenchang 2022 218 W31 Negative Wenchang 2023
51 A27 Negative Wenchang 2022 219 W32 Negative Wenchang 2023
52 A30 Negative Wenchang 2022 220 W33 Negative Wenchang 2023
53 A31 Negative Wenchang 2022 221 W34 Negative Wenchang 2023
54 A32 Negative Wenchang 2022 222 W35 Negative Wenchang 2023
55 A34 Negative Wenchang 2022 223 W36 Negative Wenchang 2023
56 A36 Negative Wenchang 2022 224 W37 Negative Wenchang 2023
57 A37 Negative Wenchang 2022 225 W38 Negative Wenchang 2023
58 A39 Negative Wenchang 2022 226 W39 Negative Wenchang 2023
59 A49 Negative Wenchang 2022 227 B2-1 Negative Baoting 2021
60 A50 Negative Wenchang 2022 228 B2-2 Negative Baoting 2021
61 A51 Negative Wenchang 2022 229 B2-4 Negative Baoting 2021
62 A55 Negative Wenchang 2022 230 B2-5 Negative Baoting 2021
63 A57 Negative Wenchang 2022 231 B2-6 Negative Baoting 2021
64 A59 Negative Wenchang 2022 232 B2-7 Negative Baoting 2021
65 A60 Negative Wenchang 2022 233 B2-9 Negative Baoting 2021
66 B13 Negative Baoting 2020 234 B4-1 Negative Baoting 2021
67 B4 Negative Baoting 2020 235 B4-2 Negative Baoting 2021
68 B17 Negative Baoting 2020 236 B4-3 Negative Baoting 2021
69 B20 Negative Baoting 2020 237 B4-4 Negative Baoting 2021
70 B21 Negative Baoting 2020 238 B4-5 Negative Baoting 2021
71 B23 Negative Baoting 2020 239 B4-6 Negative Baoting 2021
72 B24 Negative Baoting 2020 240 B4-7 Negative Baoting 2021
73 B25 Negative Baoting 2020 241 B4-8 Negative Baoting 2021
74 B27 Negative Baoting 2020 242 B4-9 Negative Baoting 2021
75 B30 Negative Baoting 2020 243 B4-10 Negative Baoting 2021
76 B31 Negative Baoting 2020 244 B4-11 Negative Baoting 2021
77 B32 Negative Baoting 2020 245 B4-12 Negative Baoting 2021
78 B33 Negative Baoting 2020 246 B4-13 Negative Baoting 2021
79 B37 Negative Baoting 2020 247 B4-14 Negative Baoting 2021
80 B41 Negative Baoting 2020 248 B4-15 Negative Baoting 2021
81 B44 Negative Baoting 2020 249 B4-16 Negative Baoting 2021
82 B46 Negative Baoting 2020 250 B4-17 Negative Baoting 2021
83 B47 Negative Baoting 2020 251 B4-18 Negative Baoting 2021
84 B49 Negative Baoting 2020 252 B4-19 Negative Baoting 2021
85 B53 Negative Baoting 2020 253 B5-4 Negative Baoting 2021
86 B54 Negative Baoting 2020 254 B5-5 Negative Baoting 2021
87 B56 Negative Baoting 2020 255 B5-6 Negative Baoting 2021
88 B57 Negative Baoting 2020 256 B5-10 Negative Baoting 2021
89 B61 Negative Baoting 2020 257 B6-1 Negative Baoting 2021
90 B63 Negative Baoting 2020 258 B6-2 Negative Baoting 2021
91 B76 Negative Baoting 2020 259 B6-4 Negative Baoting 2021
92 C21 Negative Qionghai 2020 260 B6-5 Negative Baoting 2021
93 C26 Negative Qionghai 2020 261 B6-6 Negative Baoting 2021
94 C27 Negative Qionghai 2020 262 B6-8 Negative Baoting 2021
95 C41 Negative Qionghai 2020 263 B6-10 Negative Baoting 2021
96 C47 Negative Qionghai 2020 264 B6-11 Negative Baoting 2021
97 C61 Negative Qionghai 2020 265 F04-2 Negative Tunchang 2020
98 C68 Negative Qionghai 2020 266 F05-2 Negative Tunchang 2020
99 C69 Negative Qionghai 2020 267 F05-1 Negative Tunchang 2020
100 C70 Negative Qionghai 2020 268 F06-1 Negative Tunchang 2020
101 C73 Negative Qionghai 2020 269 F07-1 Negative Tunchang 2020
102 C85 Negative Qionghai 2020 270 F022-1 Negative Tunchang 2020
103 C87 Negative Qionghai 2020 271 F028-2 Negative Tunchang 2020
104 C89 Negative Qionghai 2020 272 F023-2 Negative Tunchang 2020
105 C90 Negative Qionghai 2020 273 F035-1 Negative Tunchang 2020
106 C91 Negative Qionghai 2020 274 F036-1 Negative Tunchang 2020
107 C95 Negative Qionghai 2020 275 F036-2 Negative Tunchang 2020
108 C99 Negative Qionghai 2020 276 F037-1 Negative Tunchang 2020
109 C100 Negative Qionghai 2020 277 F037-2 Negative Tunchang 2020
110 BSL-1 Negative Ding'an 2023 278 F038-1 Negative Tunchang 2020
111 BSL-2 Negative Ding'an 2023 279 F038-2 Negative Tunchang 2020
112 BSL-3 Negative Ding'an 2023 280 F039-1 Negative Tunchang 2020
113 BSL-4 Negative Ding'an 2023 281 F040-1 Negative Tunchang 2020
114 BSL-5 Negative Ding'an 2023 282 F040-2 Negative Tunchang 2020
115 BSL-6 Negative Ding'an 2023 283 F041-2 Negative Tunchang 2020
116 BSL-7 Negative Ding'an 2023 284 F044-1 Negative Tunchang 2020
117 BSL-8 Negative Ding'an 2023 285 F044-2 Negative Tunchang 2020
118 BSL-9 Negative Ding'an 2023 286 F045-1 Negative Tunchang 2020
119 BSL-10 Negative Ding'an 2023 287 F045-2 Negative Tunchang 2020
120 BSL-11 Negative Ding'an 2023 288 F049-2 Negative Tunchang 2020
121 BSL-12 Negative Ding'an 2023 289 F055-2 Negative Tunchang 2020
122 BSL-13 Negative Ding'an 2023 290 F099-1 Negative Tunchang 2020
123 BSL-14 Negative Ding'an 2023 291 C002-1 Negative Qionghai 2020
124 BSL-15 Negative Ding'an 2023 292 C007-2 Negative Qionghai 2020
125 BSL-16 Negative Ding'an 2023 293 C013-2 Negative Qionghai 2020
126 BSL-17 Negative Ding'an 2023 294 C018Y-1 Negative Qionghai 2020
127 BSL-18 Negative Ding'an 2023 295 C018Y-2 Negative Qionghai 2020
128 BSL-19 Negative Ding'an 2023 296 C021-1 Negative Qionghai 2020
129 BSL-20 Negative Ding'an 2023 297 C023-1 Negative Qionghai 2020
130 XS-1 Negative Ding'an 2023 298 C026-1 Negative Qionghai 2020
131 XS-2 Negative Ding'an 2023 299 C026-2 Negative Qionghai 2020
132 XS-3 Negative Ding'an 2023 300 C031-2 Negative Qionghai 2020
133 XS-4 Negative Ding'an 2023 301 C048-1 Negative Qionghai 2020
134 XS-5 Negative Ding'an 2023 302 C064-1 Negative Qionghai 2020
135 XS-6 Negative Ding'an 2023 303 C065-2 Negative Qionghai 2020
136 XS-7 Negative Ding'an 2023 304 C071-2 Negative Qionghai 2020
137 XS-8 Negative Ding'an 2023 305 C072-2 Negative Qionghai 2020
138 XS-9 Negative Ding'an 2023 306 C076-2 Negative Qionghai 2020
139 XS-10 Negative Ding'an 2023 307 C077-2 Negative Qionghai 2020
140 XS-11 Negative Ding'an 2023 308 C078-1 Negative Qionghai 2020
141 LK-1 Negative Ding'an 2023 309 C080-2 Negative Qionghai 2020
142 LK-2 Negative Ding'an 2023 310 C056-2 Negative Qionghai 2020
143 LK-3 Negative Ding'an 2023 311 C065-2 Negative Qionghai 2020
144 LK-4 Negative Ding'an 2023 312 D008-2 Negative Ding’an 2020
145 LK-5 Negative Ding'an 2023 313 D039-1 Negative Ding’an 2020
146 LK-6 Negative Ding'an 2023 314 D062-2 Negative Ding’an 2020
147 LK-7 Negative Ding'an 2023 315 D093-2 Negative Ding’an 2020
148 LK-8 Negative Ding'an 2023 316 ZY2-1 Negative Wenchang 2020
149 LK-9 Negative Ding'an 2023 317 7-4-2 Negative Wenchang 2020
150 LK-10 Negative Ding'an 2023 318 003-2 Negative Wenchang 2020
151 LK-11 Negative Ding'an 2023 319 B021-2 Negative Baoting 2020
152 LK-12 Negative Ding'an 2023 320 B027-2 Negative Baoting 2020
153 LK-13 Negative Ding'an 2023 321 B029-2 Negative Baoting 2020
154 LK-14 Negative Ding'an 2023 322 B030-1 Negative Baoting 2020
155 LK-15 Negative Ding'an 2023 323 B032-1 Negative Baoting 2020
156 LK-16 Negative Ding'an 2023 324 B037-2 Negative Baoting 2020
157 LK-17 Negative Ding'an 2023 325 B038-2 Negative Baoting 2020
158 LK-18 Negative Ding'an 2023 326 B042-1 Negative Baoting 2020
159 LK-19 Negative Ding'an 2023 327 B042-2 Negative Baoting 2020
160 LK-20 Negative Ding'an 2023 328 B055-2 Negative Baoting 2020
161 I027 Negative Qionghai 2022 329 B059-1 Negative Baoting 2020
162 N01 Negative Lingshui 2022 330 B059-2 Negative Baoting 2020
163 N06 Negative Lingshui 2022 331 B056-1 Negative Baoting 2020
164 N07 Negative Lingshui 2022 332 B056-2 Negative Baoting 2020
165 N012 Negative Lingshui 2022 333 B061-1 Negative Baoting 2020
166 N013 Negative Lingshui 2022 334 B069-1 Negative Baoting 2020
167 ZH-40 Negative Lingshui 2022 335 B069-2 Negative Baoting 2020
168 NLT Negative Lingshui 2022
Note: a total of 10 leaf samples were positive in the detection using our novel nest PCR.

References

  1. Kumar, S.N.; Bai, K.V.K.; Rajagopal, V.; Aggarwal, P.K. Simulating coconut growth, development and yield with the infocrop-coconut model. Tree Physiology 2008, 28. [Google Scholar] [CrossRef] [PubMed]
  2. Sun, H.J.; Gong, M. Current Development status and countermeasures of Arecanut planting and processing industry in Hainan. Chinese Journal of Tropical Agriculture 2019, 39, 91–94. [Google Scholar]
  3. Chen, F.; Liu, T.; L, J.J.; Chen, J.X. The medical value of areca nut. China Tropical Medicine 2014, 14, 243–245. [Google Scholar]
  4. Tang, Q.H.; Meng, X.L.; Yu, S.S.; Lin, Z.W.; Niu, X.Q.; Song, W.W.; Qin, W.Q. Forty years of research on “yellow leaf disease of areca palm” in China: New progress of the causal agent and the management. Chinese Journal of Tropical Crops 2022, 43, 1010–1022. [Google Scholar]
  5. Tang, Q.H.; Song, W.W.; Yu, S.S.; Niu, X.Q.; Qin, W.Q. Questions and foresight on the causal agent of Arecanut yellow leaf disease researches and it’s management Plant Protection 2021, 47, 6–11.
  6. Abeysinghe, S.; Abeysinghe, P.D.; Kanatiwela-de Silva, C.; Udagama, P.; Warawichanee, K.; Aljafar, N.; Kawicha, P.; Dickinson, M. Refinement of the taxonomic structure of 16SrXI and 16SrXIV phytoplasmas of gramineous plants using multilocus sequence typing. Plant Disease 2016, 100, 2001–2010. [Google Scholar] [CrossRef]
  7. Jin, K.X.; Sun, F.S.; Chen, M.R.; Luo, D.Q.; Cai, X.Z. Yellows disease of betel nut palm in Hainan, China. Scientia Silvae Sinicae 1995, 31, 556–558. [Google Scholar]
  8. Kanatiwela-de Silva, C.; Damayanthi, M.; de Silva, R.; Dickinson, M.; de Silva, N.; Udagama, P. Molecular and scanning electron microscopic proof of phytoplasma associated with areca palm yellow leaf disease in Sri Lanka. Plant Disease 2015, 99, 1641–1641. [Google Scholar] [CrossRef]
  9. Lin, Z.W.; Tang, Q.H.; Meng, X.L.; Song, W.W.; Yu, F.Y.; Huang, S.C.; Niu, X.Q.; Qin, W.Q. Occurrence and distribution of areca pathological yellowing in Hainan, China and Its phytoplasma detection analysis. Chinese Journal of Tropical Crops 2022, 43, 2106–2113. [Google Scholar]
  10. Nair, S.; Roshna, O.M.; Soumya, V.P.; Hegde, V.; Suresh Kumar, M.; Manimekalai, R.; Thomas, G.V. Real-Time PCR technique for detection of Arecanut yellow leaf disease phytoplasma. Australasian Plant Pathology 2014, 43, 527–529. [Google Scholar] [CrossRef]
  11. Chaithra, M.; Priya, M.; Kumar, S.; Manimekalai, R.; Rao, G. Detection and characterization of 16SrI-B phytoplasmas associated with yellow leaf disease of Arecanut palm in India. Phytopathogenic Mollicutes 2015, 4, 77–82. [Google Scholar]
  12. Sumi, K.; Priya, M.; Kumar, S.; Rao, G.; Rao, K. Molecular confirmation and interrelationship of phytoplasmas associated with diseases of palms in South India. Phytopathogenic Mollicutes 2015, 4, 41–52. [Google Scholar] [CrossRef]
  13. Zhou, Y.K.; Gan, B.C.; Zhang, Z.; Sui, C.; Wei, J.H.; Yang, Y.; Yang, X.Q. Detection of the phytoplasmas associated with yellow leaf disease of Areca catechu L. in Hainan province of China by Nested PCR. Chinese Agricultural Science Bulletin 2010, 26, 381–384. [Google Scholar]
  14. Che, H.Y.; Wu, C.T.; Fu, R.Y.; Wen, Y.S.; Ye, S.B.; Luo, D.Q. Molecular identification of pathogens from Areca nut yellow leaf disease in Hainan. Chinese Journal of Tropical Crops 2010, 31, 83–87. [Google Scholar]
  15. Lin, Z.; Song, W.; Tang, Q.; Meng, X. First Report of 16SrII group-related phytoplasma associated with areca palm yellow leaf disease on Areca catechu in China. Plant Disease 2023, 107, 3275. [Google Scholar] [CrossRef]
  16. Yu, S.S.; Zhu, A.N.; Che, H.Y.; Song, W.W. Molecular identification of ‘Candidatus phytoplasma Malaysianum’-related strains associated with Areca Catechu palm yellow leaf disease and phylogenetic diversity of the phytoplasmas within the 16SrXXXII group. Plant Disease 2024, 108, 1331–1343. [Google Scholar] [CrossRef]
  17. Lin, Z.W.; Tang, Q.H.; Long, S.D.; Niu, X.Q.; Liu, F.; Song, W.W. Establishment of Real-time fluorescent quantitative PCR method for detection of areca yellows phytoplasma 16SrI group. Molecular Plant Breeding 2024, 1–15. [Google Scholar]
  18. Nayar, R.; Seliskar, C.E. Mycoplasma like organisms associated with yellow leaf disease of Areca Catechu L. Forest Pathology 1978, 8, 125–128. [Google Scholar] [CrossRef]
  19. Rajeev, G.; Vijayamma Ramakrishnan Nair, P.; Vaganan, M.; Sasikala, M.; Solomon, J.; Nair, G. Microscopic and polyclonal antibody-based detection of yellow leaf disease of Arecanut (Areca Catechu L.). Archives of Phytopathology and Plant Protection 2011, 44, 1093–1104. [Google Scholar] [CrossRef]
  20. Meng, X.L.; Tang, Q.H.; Lin, Z.W.; Niu, X.Q.; Liu, B.; Song, W.W. Developing efficient primers to detect phytoplasmas in areca palms infected with yellow leaf disease. Molecular Plant Breeding 2022, 20, 4624–4633. [Google Scholar]
  21. Yu, S.S.; Che, H.Y.; Wang, S.J.; Lin, C.L.; Lin, M.X.; Song, W.W.; Tang, Q.H.; Yan, W.; Qin, W.-Q. Rapid and efficient detection of 16SrI group areca palm yellow leaf phytoplasma in China by Loop-Mediated Isothermal Amplification. Journal of Plant Pathology 2020, 36, 459–467. [Google Scholar] [CrossRef]
  22. Yu, S.; Zhang, X.; Song, W.; Qin, W. Accurate and sensitive detection of areca palm yellow leaf phytoplasma in China by Droplet Digital PCR targeting tuf gene sequence. Annals of Applied Biology 2022, 181, 152–159. [Google Scholar] [CrossRef]
  23. Lin, Z.W.; Meng, X.L.; Tang, Q.H.; Niu, X.Q.; Song, W.W. Establishment of TaqMan probe real-time fluorescent quantitative PCR detection method for areca palm yellow leaf phytoplasma. Chinese Journal of Tropical Crops 2023, 1–8. [Google Scholar]
  24. Fan, X.X.; Zhao, Y.G.; Li, L.; Wu, X.D.; Wang, Z.L. Research progress of recombinant Enzyme Polymerase Amplification (RPA) in rapid detection of diseases. China Animal Health Inspection 2016, 33, 72–77. [Google Scholar]
  25. Lu, P.P.; Wu, W.H.; Zheng, J.L.; Wang, G.H.; He, C.P.; Lin, P.Q.; Huang, X.; Liang, Y.Q.; Yi, K.X. Establishment and optimization of single-tube Nested PCR detection technique for phytoplasma related to sisal purple leafroll disease. Journal of Agricultural Biotechnology 2021, 29(7), 1426–1434. [Google Scholar]
  26. Lin, Z.W.; Long, S.D.; Meng, X.L.; Tang, Q.H.; Niu, X.Q.; Wang, Y.N.; Song, W.W. Cloning and sequence analysis of tuf, secA and rp genes of areca yellows phytoplasma. Molecular Plant Breeding 2024, 1–15. [Google Scholar]
  27. Mou, H.Q.; Zhu, S.F.; Xu, X.; Zhao, W.J. An overview of research on phytoplasma-induced diseases. Plant Protection 2011, 37, 17–22. [Google Scholar]
  28. Bertaccini, A. Plants and phytoplasmas: when bacteria modify plants. Plants (Basel) 2022, 11, 1425. [Google Scholar] [CrossRef]
  29. Wei, W.; Zhao, Y. Phytoplasma taxonomy: nomenclature, classification, and identification. Biology (Basel) 2022, 11, 1119. [Google Scholar] [CrossRef]
  30. Dai, Q.; Liu, B.S.; He, F.T.; Chen, Z.W. Relationship between the seasonal temperature variation and the phytoplasmal amounts in mulberry trees. Scientia Silvae Sinicae 1998, 34, 76–80. [Google Scholar]
  31. Duan, Y.W.; Yang, Y.; Lu, Q.L.; Xiao, Z.L.; Li, D.Y.; Zhang, W.Z.; Zhao, W.J. Molecular identification of beggarweed witches′-broom phytoplasma. Plant Quarantine 2019, 33, 29–32. [Google Scholar]
  32. Song, X.B.; Huang, F.; Tang, Y.F.; Cui, Y.P.; Ling, J.F.; Chen, X. First report of; little leaf disease caused by phytoplasma on Breynia disticha in China. Plant Protection 2024, 50, 272 277+292. [Google Scholar]
  33. Yang, Y.; Jiang, L.; Li, S.F. Research progress on phytoplasma classification and identification. Plant Quarantine 2020, 34, 13–20. [Google Scholar]
  34. Lai, F.; Li, Y.; Xu, Q.C.; Tian, G.Z. The present status on classification of phytoplasmas. Microbiology China 2008, 291–295. [Google Scholar]
  35. Wang, G.; Wu, W.; Tan, S.; Liang, Y.; He, C.; Chen, H.; Huang, X.; Yi, K. Development of a specific Nested PCR assay for the detection of 16SrI group phytoplasmas associated with sisal purple leafroll disease in sisal plants and mealybugs. Plants 2022, 11, 2817. [Google Scholar] [CrossRef]
  36. Deng, S.; Hiruki, C. Amplification of 16S rRNA genes from culturable and nonculturable mollicutes. Journal of Microbiological Methods 1991, 14, 53–61. [Google Scholar] [CrossRef]
  37. Gundersen, D.E.; Lee, I.M.; Gundersen, D.E.; Lee, I.M. Ultrasensitive detection of phytoplasmas by Nested-PCR assays using two universal primer pairs. Phytopathologia Mediterranea 1996, 35, 144–151. [Google Scholar]
  38. Zhu, A.N.; Yu, S.S.; Su, L.H.; Liu, L.; Song, W.W.; Yan, W. Molecular detection and genetic variation of phytoplasmas from eight plants in garden of areca palm with yellow leaf disease in China. Chinese Journal of Tropical Crops 2023, 44, 1190–1202. [Google Scholar]
Figure 1. Profile of 16S rDNA sequences amplified by nested PCR with universal primer P1/P7 followed by R16mF2/R16mR1, from areca samples. M: DL2000 DNA Marker; 1-19: areca samples; NC1: blank control of the first round of PCR; NC2: blank control of the second round of PCR; PC: positive control, periwinkle DNA, the same for the following figures.
Figure 1. Profile of 16S rDNA sequences amplified by nested PCR with universal primer P1/P7 followed by R16mF2/R16mR1, from areca samples. M: DL2000 DNA Marker; 1-19: areca samples; NC1: blank control of the first round of PCR; NC2: blank control of the second round of PCR; PC: positive control, periwinkle DNA, the same for the following figures.
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Figure 2. Ratios of different sequences in 50 samples.
Figure 2. Ratios of different sequences in 50 samples.
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Figure 3. The preliminary screening of three pairs of internal primers designed in this study. A: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-2F/2R; B: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-3F/3R; C: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-4F/4R. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR; PC: positive control.
Figure 3. The preliminary screening of three pairs of internal primers designed in this study. A: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-2F/2R; B: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-3F/3R; C: Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-4F/4R. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR; PC: positive control.
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Figure 4. Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-2F/2R and HNP-3F/3R respective for the specificity validation. A: HNP-2F/2R, B: HNP-3F/3R. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR; PC: positive control; 1: healthy areca leaf sample; 2: Burkholderia andropogonis (Robbsia andropogonis); 3: Pantoea ananatis; 4: Jujube witches-broom phytoplasma; 5: AYLP of 16SrI group; 6: AYLP of 16SrII group; 7: Trema tomentosa witches'-broom phytoplasma; 8: leaf sample infected by areca palm velarivirus 1.
Figure 4. Profile of 16S rDNA sequences amplified by nested PCR with primer HNP-1F/1R followed by HNP-2F/2R and HNP-3F/3R respective for the specificity validation. A: HNP-2F/2R, B: HNP-3F/3R. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR; PC: positive control; 1: healthy areca leaf sample; 2: Burkholderia andropogonis (Robbsia andropogonis); 3: Pantoea ananatis; 4: Jujube witches-broom phytoplasma; 5: AYLP of 16SrI group; 6: AYLP of 16SrII group; 7: Trema tomentosa witches'-broom phytoplasma; 8: leaf sample infected by areca palm velarivirus 1.
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Figure 5. A: Annealing temperature optimized for the outer primer HNP-1F/1R. M: marker DL2000; NC: blank control. M: marker DL2000; NC: blank control. B: Annealing temperature optimized for the inner primer HNP-2F/2R. M: marker DL2000; NC: blank control.
Figure 5. A: Annealing temperature optimized for the outer primer HNP-1F/1R. M: marker DL2000; NC: blank control. M: marker DL2000; NC: blank control. B: Annealing temperature optimized for the inner primer HNP-2F/2R. M: marker DL2000; NC: blank control.
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Figure 6. Sensitivity test for nested PCR primer set of HNP-1F/1R and HNP-2F/2R. A: Gel electrophoresis for sensitivity of 16SrI phytoplasma using novel nested PCR. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR. B: Gel electrophoresis results for the sensitivity of 16SrII phytoplasma using novel nested PCR M: marker DL2000; NC: blank control.
Figure 6. Sensitivity test for nested PCR primer set of HNP-1F/1R and HNP-2F/2R. A: Gel electrophoresis for sensitivity of 16SrI phytoplasma using novel nested PCR. M: marker DL2000; NC1: blank control for the first round of PCR; NC2: blank control for the second round of PCR. B: Gel electrophoresis results for the sensitivity of 16SrII phytoplasma using novel nested PCR M: marker DL2000; NC: blank control.
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Figure 7. Comparison of Profile of 16S rDNA sequences amplified using universal primers with the novel primers developed in this study. M: marker DL2000; 1: blank control for the first round of PCR; 2: blank control for the second round of PCR; 3: healthy areca leaf sample; 4: positive control; 5-34: areca leaf samples collected in the fields.
Figure 7. Comparison of Profile of 16S rDNA sequences amplified using universal primers with the novel primers developed in this study. M: marker DL2000; 1: blank control for the first round of PCR; 2: blank control for the second round of PCR; 3: healthy areca leaf sample; 4: positive control; 5-34: areca leaf samples collected in the fields.
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Figure 8. Phylogenetic tree constructed based on 16S rDNA gene sequences of phytoplasmas. A: Phylogenetic tree constructed from the amplified fragments of universal primers targeting the 16S rDNA gene in phytoplasmas. B: Phylogenetic tree constructed from the amplified fragments of nested primers established in this study for the 16S rDNA gene in phytoplasmas. C: Multiple sequence alignment of 10 positive PCR product sequences and the T40 sequence used for constructing the phylogenetic tree with 16SrI-B subgroup AYLP.
Figure 8. Phylogenetic tree constructed based on 16S rDNA gene sequences of phytoplasmas. A: Phylogenetic tree constructed from the amplified fragments of universal primers targeting the 16S rDNA gene in phytoplasmas. B: Phylogenetic tree constructed from the amplified fragments of nested primers established in this study for the 16S rDNA gene in phytoplasmas. C: Multiple sequence alignment of 10 positive PCR product sequences and the T40 sequence used for constructing the phylogenetic tree with 16SrI-B subgroup AYLP.
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Table 1. Areca leaf samples used in this study.
Table 1. Areca leaf samples used in this study.
Samples Taxonomy Host plant Location
J069 16SrI Group Areca palm Areca catechu Wenchang, Hainan
S97 16SrI Group Areca palm A. catechu Wenchang, Hainan
T40 16SrI Group Areca palm A. catechu Wenchang, Hainan
H-9 16SrI Group Periwinkle Catharanthus roseus Wenchang, Hainan
A056 16SrII Group Areca palm A. catechu Wenchang, Hainan
W4 16SrII Group Areca palm A. catechu Wenchang, Hainan
MW1-1 Burkholderia andropogonis Areca palm A. catechu Wenchang, Hainan
I027 Areca palm velarivirus 1 Areca palm A. catechu Wenchang, Hainan
TC-1 Pantoea ananatis Areca palm A. catechu Tunchang, Hainan
SHM-1 16SrXXXII Group Trema tomentosa Qionghai, Hainan
PT-1 16SrI Group Paulownia sp. Taian, Shandong
ZF-1 16SrIV Group Jujube Ziziphus jujuba Taian, Shandong
Table 2. Primers for amplification of 16S rDNA gene using nested-PCR.
Table 2. Primers for amplification of 16S rDNA gene using nested-PCR.
Primer Primer sequence (5’- 3’) Annealing temperature (℃) Target fragment length (bp) References
P1 AAGAATTTGATCCTGGCTCAGGATT 55 1800 [36]
P7 CGTCCTTCATCGGCTCTT
R16mF2 CATGCAAGTCGAACGGA 50 1500 [37]
R16mR1 CTTAACCCCAATCATCGA
HNP-1F TTCTTGTTTTTAAAAGACCT 44 1072 This study
HNP-1R AAACTTGCGCTTCAGCT
HNP-2F TGTGGTCTAAGTGCAAT 48 429 This study
HNP-2R CTGATAACCTCCACTGTGTT
HNP-3F TTCTTGTTTTTAAAAGACCT 50 837 This study
HNP-3R ATAACCTCCACTGTGTTTCT
HNP-4F AATGCTCAACATTGTGATGCT 48 652 This study
HNP-4R AAACTTGCGCTTCAGCT
Table 3. Reference sequences used in this study.
Table 3. Reference sequences used in this study.
NO. Samples GenBank accession number Application
1 16Sr I-B SubGroup AYLP FJ998269 Primer Design/Phylogenetic analysis
2 16Sr I-G SubGroup AYLP FJ694685 Primer Design/Phylogenetic analysis
3 16Sr II-A SubGroup AYLP OQ586085 Primer Design
4 Areca Catechu Chloroplast NC_050163 Primer Design
5 Burkholderia andropogonis NR_104960 Primer Design
6 Pantoea ananatis MW174802 Primer Design
7 Chrysophyllum albidum LC110196 Primer Design
8 Curtobacterium citreum MF319766 Primer Design
9 Curtobacterium luteum JX437941 Primer Design
10 Sphingomonas yantingensis MF101149 Primer Design
11 Bacillus cereus
(Robbsia andropogonis)
HQ833025 Primer Design
12 Staphylococcus epidermidis CP040883 Primer Design
13 Xanthomonas sacchari MN889285 Primer Design
14 Xanthomonas campestris JX415480 Primer Design
15
16 16SrI-C SubGroup AF222065 Phylogenetic analysis
17 16SrI-D SubGroup AY265206 Phylogenetic analysis
18 16SrI-E SubGroup AY265213 Phylogenetic analysis
19 16SrI-F SubGroup AY265211 Phylogenetic analysis
20
21 16SrII-A SubGroup L33765 Phylogenetic analysis
22 16SrII-B SubGroup U15442 Phylogenetic analysis
23 16SrII-C SubGroup AJ293216 Phylogenetic analysis
24 16SrII-D SubGroup Y10097 Phylogenetic analysis
25 16SrIII-A SubGroup L04682 Phylogenetic analysis
26 16SrIV-A SubGroup AF498307 Phylogenetic analysis
27 16SrV-A SubGroup AY197655 Phylogenetic analysis
28 16SrVI-A SubGroup AY390261 Phylogenetic analysis
29 16SrVII-A SubGroup AF092209 Phylogenetic analysis
30 16SrVIII-A SubGroup AF353090 Phylogenetic analysis
21 16SrIX-A SubGroup AF248957 Phylogenetic analysis
32 16SrX-A SubGroup AJ542541 Phylogenetic analysis
33 16SrXI-A SubGroup AB052873 Phylogenetic analysis
34 16SrXII-A SubGroup AJ964960 Phylogenetic analysis
35 16SrXIII Group AF248960 Phylogenetic analysis
36 16SrXIV-A SubGroup AJ550984 Phylogenetic analysis
37 16SrXV-A SubGroup AF147708 Phylogenetic analysis
38 16SrXVI-A SubGroup AY725228 Phylogenetic analysis
39 16SrXVII-A SubGroup AY725234 Phylogenetic analysis
40 16SrXVIII-A SubGroup DQ174122 Phylogenetic analysis
41 16SrXIX-A SubGroup AB054986 Phylogenetic analysis
42 16SrXX-A SubGroup X76431 Phylogenetic analysis
43 16SrXXI-A SubGroup AJ632155 Phylogenetic analysis
44 16SrXXII-A SubGroup Y14175 Phylogenetic analysis
45 16SrXXIII-A SubGroup AY083605 Phylogenetic analysis
46 16SrXXIV-A SubGroup AF509322 Phylogenetic analysis
47 16SrXXV-A SubGroup AF521672 Phylogenetic analysis
48 16SrXXVI-A SubGroup AJ539179 Phylogenetic analysis
49 16SrXXVII-A SubGroup AJ539180 Phylogenetic analysis
50 16SrXXIX Group EF666051 Phylogenetic analysis
51 16SrXXX-A SubGroup FJ432664 Phylogenetic analysis
52 16SrXXXI Group HQ225630 Phylogenetic analysis
53 16SrXXXII Group EU371934 Phylogenetic analysis
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