Plant Pathol J > Volume 41(1); 2025 > Article
Siddique, Xiaofeng, Zhe, Mingxiu, Dawei, Yuting, Naibo, Younis, Niaz, and Junhua: Genetic Diversity and Population Structure of Phyllosphere-Associated Xanthomonas euvesicatoria Bacteria in Physalis pubescens Based on BOX-PCR and ERIC-PCR in China

Abstract

Xanthomonas euvesicatoria has become a serious problem in Physalis pubescens, leading to substantial crop losses. In our previous investigation, we used rapid molecular detection techniques to identify X. euvesicatoria; however, this pathogen’s diversity and population structure remain poorly understood, despite their importance in disease management. To address this knowledge gap, we analyzed the diversity of X. euvesicatoria using BOX-PCR and ERIC-PCR fingerprinting techniques. A total of 103 isolates were collected from 13 counties across Heilongjiang province during the 2018 and 2019 growing seasons. Our findings revealed 635 unique genetic patterns from ERIC-PCR fingerprinting, compared to 360 patterns from BOX-PCR. BOX-PCR analysis identified 12 distinct genotypic clusters, whereas ERIC-PCR identified 14 clusters through unweighted pair group approach with arithmetic average analysis, demonstrating substantial genetic variability. STRUCTURE analysis further identified five distinct genetic clusters in the BOX-PCR data and two in the ERIC-PCR data. The Hailin isolates showed the highest level of diversification compared to other regional isolates. AMOVA results indicated that 85% of the genetic variation in BOX-PCR was attributable to within-population differences, while 78% of ERIC-PCR variation was due to differences across populations. In addition, a Mantel test demonstrated a tenuous correlation between BOX-PCR and ERIC-PCR genetic markers, indicating distinct genetic profiles. This extensive genetic information enhances our understanding of the epidemiology of bacterial leaf spot and its potential therapeutic prospects. These data can provide insights into Xanthomonas strains’ diversity and geographical dissemination.

The phyllosphere serves as a habitat for diverse microbial communities, where environmental factors and plant genotypes significantly influence microbial colonization and survival (Whipps et al., 2008). The genetic diversity of phyllosphere-associated Xanthomonas bacteria plays a crucial role in the health and productivity of ground cherry (Physalis pubescens). This diversity influences the pathogen’s ability to cause diseases such as bacterial leaf spot (BLS), which has been linked to significant economic losses in regions like Northeast China (Song et al., 2019). The symptoms of BLS include the formation of lesions, which are small spots or patches on the plant surface that are initially water-soaked. Over time, the lesions can grow and become necrotic, gradually turning brown and irregular. Eventually, the lesions become dark brown and greasy (Siddique et al., 2023). Consequently, these symptoms have an impact on both the quality and quantity of the fruit. Researchers have linked BLS disease to four genospecies of the bacterium Xanthomonas. These genospecies include X. vesicatoria, X. perforans, X. gardneri, and X. euvesicatoria (Osdaghi et al., 2021). Xanthomonas euvesicatoria is a Gram-negative rod-shaped bacterium, and it moves through its environment via aerobic means. It has a single flagellum polar to the rod. Some strains are very similar to other Xanthomonas species, like X. perforans (98% average nucleotide identity [ANI]) (Siddique et al., 2023). Understanding this genetic variation is essential for the development of effective detection and management strategies. Interactions between X. euvesicatoria and other phyllosphere microbes can affect plant health and disease dynamics, highlighting the importance of understanding these relationships. Although the genetic diversity of X. euvesicatoria is critical for understanding its pathogenicity, it is also essential to consider the broader microbial community in the phyllosphere.
Various genetic techniques, including 16S rRNA gene polymorphism, amplified fragment length polymorphism, multilocus sequence typing, random amplified polymorphic DNA, single nucleotide polymorphisms (SNPs), and repetitive sequence-based PCR (rep-PCR) have been utilized to establish pathotypes (Kebede et al., 2014; Moreira et al., 2010; Nunziata et al., 2020; Russi et al., 2022). These markers have been used in various studies to investigate the genetic diversity of X. euvesicatoria. Versalovic et al. (1991) developed the rep-PCR, which generates DNA fingerprints, including several DNA amplicons of diverse sizes. These amplicons consisted of distinct chromosomal segments located between repetitive sequences that are complementary to repetitive oligonucleotide sequence primers. There are different distances between the oligonucleotide primer binding sites at repetitive sequence targets in rep-PCR-based DNA fingerprints, and they are most sensitive at the subspecies or strain-specific levels (Versalovic et al., 1991, 1994). The rep-PCR approach utilizes primers specifically designed to target repetitive DNA sequences, including repetitive extragenic palindromic (REP), BOX elements, and enterobacterial repetitive intergenic consensus (ERIC). The ERIC sequences are composed of short tandem repeats of six nucleotides (5′-ATGTAx-3′), where “x” represents any nucleotide. These sequences are highly variable and are often used as molecular markers for bacterial strain typing and epidemiological studies. For example, Vancheva et al. (2018) used ERIC-PCR and BOX-PCR to analyze 161 different strains of Xanthomonas spp. from various pepper fields. The authors also found that the ERIC-PCR patterns of the isolates were highly diverse and that there was a significant correlation between them and geographic locations. Similarly, BOX-PCR is based on the amplification of BOX elements, which are short, conserved, and repetitive DNA sequences present in the genomes of most bacterial species. Unique genomic regions flank the BOX elements, and their amplification creates a distinctive banding pattern that aids in differentiating between different bacterial strains. Researchers have extensively used BOX-PCR to study the genetic diversity and population structure of plant pathogenic bacteria. Adhikari et al. (2019) found that the BOX-PCR was also a useful molecular marker for analyzing the genetic diversity within X. perforans populations in North Carolina. However, the principal advantages of rep-PCR-based chromosomal typing include its speed, reproducibility, ease of use due to the absence of radioisotopes, adaptability to intact cells and native tissue samples, and minimal resource requirements compared to typical molecular biology laboratory equipment.
Many researchers have already utilized genetic diversity and population structure analysis based on various markers (Chen et al., 2021; He et al., 2021; Nikolić et al., 2018; Sun et al., 2013; Wang et al., 2023), but in the past, no evidence was found of X. euvesicatoria population structure isolated from P. pubescens based on rep-PCR markers. Therefore, the primary objective of this study was to determine the genetic diversity of P. pubescens X. euvesicatoria strains based on rep-PCR markers using ERIC and BOX primers. In addition, we built a population structure analysis of X. euvesicatoria strains isolated from various locations in Heilongjiang province using a fingerprinting binary dataset to perform further clustering analysis. These findings can contribute to the development of improved disease management strategies and shed light on the evolutionary dynamics of X. euvesicatoria populations.

Materials and Methods

Sample collection

In China, Heilongjiang province plays the most crucial role in the cultivation of P. pubescens. In 2018 and 2019, a survey was conducted in five main cities (Harbin, Mudanjiang, Qiqihar, Jiamusi, and Suihua) of the Heilongjiang province and further BLS disease samples of 103 isolates were collected from 13 counties, including [Hailun (Sh), Qinggang (Sq), Zhaodong (Sz), Mulan (Hm), Gannan (Qg), Fuyu (Qf), Baiquan (Qb), Fangzheng (Hf), Shuang cheng (Hs), Fujin (Jf), Huanan (Jh), Hailin (Mh) and Ningan (Mn)] Heilongjiang, China (Supplementary Fig. 1). The infected leaves showed signs of BLS infection, such as circular water-soaked spots, leaf spots with dark brown spots, and decaying stages with chlorotic edges. The leaves were gathered from each field and placed in a sealed plastic bag. These bags were labeled with the date, location, and cultivar before being transported to the laboratory for isolation. After being brought back to the laboratory, it was maintained at −20°C.

Isolation and DNA extraction

To isolate the bacterium, infected samples were primarily cut into small pieces, then immersed in 2% sodium hypochlorite, moved to phosphate buffered saline for 1 day, and finally made into a suspension (1 × 105 cfu solution). The bacteria were then grown on nutrient agar (NA) in Petri dishes for 3 days at 28°C. A single colony was selected to cultivate a homogeneous culture, and subsequently, many streaks were produced on NA. Finally, to preserve the bacterial isolates for long-term storage, they were mixed with a solution of 15% glycerol and frozen at −80°C. The cetyltrimethylammonium bromide (CTAB) technique was used for DNA extraction from plant tissues for molecular studies. X. euvesicatoria was identified in our earlier investigation through the amplification of hrpb1, hrpb2, and recQ genes using PCR and loop-mediated isothermal amplification (LAMP) methodologies. The full experimental protocol is detailed in Siddique et al. (2023).

BOX-PCR fingerprinting pattern assay

BOX-PCR possesses the capacity to accurately distinguish between pathogenic microorganisms. The primer BOXA1R (5′-CTACGGCAAGGCGACGCTGACG-3′) was used for BOX-PCR fingerprinting. Each PCR tube had a total of 25 μl of reaction, which included 2 μl of template DNA, 0.4 μM of primer, and 12.5 μl of 2× Taq Master Mix (CoWin Biosciences, Beijing, China), with the remaining volume covered with dd water. The cycling conditions for the BOXA1R were 7 min at 95°C, 35 cycles of denaturation at 94°C for 1 min, annealing at 52°C for 1 min, elongation at 65°C for 8 min, and a final elongation at 65°C for 15 min (Versalovic et al., 1994).

ERIC-PCR fingerprinting pattern assay

ERIC has been effectively employed to characterize a substantial number of bacterial strains and distinguish between genetically identical strains. The isolates were analyzed using a set of primers ERIC1R (5′-ATGTAAGCTCCTGGGGATTCAC-3′) and ERIC2 (5′-AAGTAAGTGACTGGGGTGAGCG-3′). Each PCR tube contained a total of 25 μl of reaction, which comprised template DNA, 0.4 μM of ERIC1R primer, 0.4 μM of ERIC2 primer, 2× Taq Master Mix (CoWin Biosciences), and double distilled water. For PCR amplification, primer annealing gradients ranging from 46 to 52°C were initially established. Subsequently, a temperature of 46°C was chosen for a duration of 30 s. The cycling conditions were 5 min at 95°C, 35 cycles of denaturation at 95°C for 50 s and annealing at 46°C for 30 s, elongation at 72°C for 1 min, and a final elongation at 72°C for 10 min.

Agarose gel electrophoresis

To separate the restriction products, electrophoretic separation was performed in a 1.5% agarose gel in Tris-borate-EDTA buffer for two hours at a voltage of 80 volts. The gel was stained with ethidium bromide (EtBr), a Super DNA marker (CW2583) was used, and the genetic marker ranged from 100 base pairs to 10,000 base pairs.

Banding patterns analysis

The banding patterns resulting from BOX-PCR and ERIC-PCR were examined using PyElph v.1.3. This was executed following the instructions previously released by Pavel and Vasile (2012). This approach was selected for its streamlined process of converting banded fingerprinting into binary data, utilizing a minimal number of steps. In this system, positive results are denoted by 1, while negative results are represented by 0.

Primers efficiency and diversity index analysis

The iMEC tool (https://irscope.shinyapps.io/iMEC/) calculated polymorphic information content (PIC), discriminating power, arithmetic mean heterozygosity (Havp), resolving power (Rp), and expected heterozygosity (H) for each primer to assess their efficacy. In this study, we also used the diversity index to quantify the biodiversity of communities of each fingerprinting pattern among the 103 isolates through genotypic binary data created by ERIC and BOX primers. This was done to better understand the similarities and differences between communities. One of the most well-known statistics, the Shannon diversity index, was used, which is referred to as the Shannon-Wiener diversity index {H = −∑[(pi) × log(pi)]} (where H - Shannon diversity index, pi - the proportion of individuals of i-th species in a whole community, and log - usually the natural logarithm, but the base of the logarithm is arbitrary).

Clustering and population structure analysis

For clustering analysis, 103 isolates were analyzed using the unweighted pair group approach with arithmetic average (UPGMA) approach with the Jaccard coefficient in PAST v. 4.1. We also used the Bayesian clustering method in STRUCTURE v. 2.3.4, for the population structure analysis. An admixture model with strain frequencies was selected to determine the proportion of ancestral contributions for each entry. It was conducted with a burn-in period of 100,000 generations and a Markov chain Monte Carlo repetition count of 200,000 times. K-values ranging from two to ten were tested to determine the most appropriate K-value for the dataset. STRUCTURE HARVESTER was used to evaluate the findings and determine the most probable K-value using the K technique proposed by Evanno et al. (2005), as well as the Q value (standard Q > 0.60 Q admixture).

Statistics analysis

The scoreable bands for each isolate were analyzed as binary data using genetics based on a haploid organism. The number of polymorphic loci, effective alleles (Ne), heterozygosity, Shannon’s information index, and unbiased diversity were calculated among geographic populations using GenAlEx v. 6.5. Furthermore, an analysis of molecular variance (AMOVA) was performed through GenAlEx v. 6.5 to analyze the genetic diversity within and among population strains of regional isolates. Principal component analysis (PCA) was performed for the association of 13 main geographical groups and 103 isolated within groups using PAST v. 4.1. Genetic distance matrices of populations acquired from the ERIC− and BOX-PCR datasets were compared using the Mantel test (Mantel, 1967) in GenAIEx v. 6.5. This test determines whether bacterial populations are inter-correlated with one another by computing the linear correlation between two proximity matrices.

Results

Isolation and identification of pathogen

BLS disease was observed in certain locations of Heilongjiang province, such as Hailun, Qinggang, Zhaodong, Mulan, Gannan, Fuyu, Baiquan, Fangzheng, Shuang cheng, Fujin, Huanan, Hailin, and Ningan. In every surveyed field, symptoms of BLS disease were present. A total of 103 samples were taken, the majority of which came from the Xinte variety. Infected tissues of P. pubescens were used to isolate 103 bacterial strains, which were then cultivated on NA medium at 28°C for 48 h. Previous research (Siddique et al., 2023) confirmed that X. euvesicatoria was the causative agent of BLS disease, identified through PCR and LAMP analysis targeting the hrpb1, hrpb2, and recQ genes (Supplementary Table 1).

BOX-PCR fingerprinting analysis

BOX-PCR analysis, employing the BOXAIR primer, was conducted on the isolated samples to distinguish among different strains. Distinct band patterns were separated using gel electrophoresis, and further analyzed with PyElph v.1.3 and GenAlEx v. 6.5, to assess the genetic relatedness across strains. The BOXAIR primer produced fingerprint bands ranging from 100 bp to 3 kb in size (Supplementary Fig. 2A), with each profile containing between 1 and 13 band patterns. The study revealed a total of 360 bands across 103 bacterial isolates. To explore additional variations, band patterns within specific populations were also examined. Among the isolates, Jf, Mh, and Mn from Fujin, Hailin, and Ningan had the largest strain populations (Supplementary Fig. 3A). The UPGMA dendrogram constructed from the BOX-PCR data displayed 12 unique clusters at a similarity distance threshold of 0.3, labeled clusters I to XII, each containing different numbers of strains (Fig. 1B). Cluster I included eight strains, while Cluster II comprised five. Clusters VI and VIII, containing 16 strains each, indicated these clusters are more prevalent than the smaller ones, suggesting a greater abundance and diversity within these groups. Furthermore, based on genetic similarity, 10 pairs of closely related clades were identified among the isolates, including (Sq2 and Sq7), (Qg4 and Qg6), (Qb2 and Qb4), (Mn1 and Mn3), (Hm1 and Hm3), (Hm2 and Hm9), (Hs1 and Qf2), (Qb1 and Qb3), (Mh2 and Mh6), and (Qf5 and Jf6) (Fig. 1B). Isolates within each clade shared a common ancestor, reflecting genetic similarities within the groups.

ERIC-PCR fingerprinting analysis

To differentiate the different strains, the isolated samples were analyzed using ERIC-PCR with specific ERIC primers. The PCR products were separated into different band patterns by gel electrophoresis, with fingerprint band sizes ranging from 100 bp to more than 5 kb (Supplementary Fig. 2B). Each profile exhibited between one to 20 bands, and 635 bands were detected across the 103 bacterial isolates. To identify additional variances, band patterns within populations were also analyzed. The Jf and Mh isolates, derived from Fujin and Hailin respectively, exhibited the highest strain diversity (Supplementary Fig. 3B). A dendrogram was constructed using the UPGMA method and the Jaccard coefficient in PAST v. 4.1. This ERIC-PCR-based dendrogram identified 14 distinct genotypic clusters at a coefficient level of 3.0. Cluster I comprised 7 strains, whereas clusters II, IV, V, VI, VII, VIII, IX, X, XI, XII, XIII, and XIV consisted of 4, 7, 4, 13, 6, 14, 4, 6, 10, 2, 5, and 3 strains, respectively (Fig. 1A). Cluster III had 18 strains, suggesting that this group comprises a larger collection of genetically similar bacterial strains with less diversity than other clusters. Furthermore, it was noted that isolates (Sh6 and Sh8) and (Sh3 and Sh1)were clustered together in a single clade, suggesting a close genetic connection between these isolates (Fig. 1A).

Genetic population structure and diversity analysis of Xanthomonas strains

This study aimed to characterize the genetic diversity and structure of a population of 103 Xanthomonas isolates using STRUCTURE software. Results from the BOX-PCR, performed with K-values ranging from 2 to 10, showed that the population was optimally divided into five distinct clusters (K = 5) (Fig. 2), supported by a high delta K-value of 99.6, underscoring the stability of this clustering. Membership likelihood (Q) analysis highlighted variations among strains, reflecting differing levels of population admixture. ERIC-PCR analysis, in contrast, identified an optimal K-value of 2, with a delta K of 49.65 (Fig. 3), indicating a purer population structure than the more admixed clusters found by BOX-PCR. According to these findings, more than 35 strains exhibited high ancestry consistency. The fixation index (Fst) results showed two separate genetic clusters, cluster 1 had a mean Fst (0.2493), which indicated a greater degree of genetic differentiation, and cluster 2 had a lower Fst (0.0270), which indicated closer genetic ties among strains.

Spatial genetic patterns of Xanthomonas isolates

PCA was conducted to investigate the genetic connections between the Xanthomonas isolates collected from 13 locations. PCA effectively highlighted similarities and distinctions within the bacterial populations using ERIC− and BOX-PCR profiles analyzed with PAST v. 4.1. Regarding the ERIC-PCR analysis, it was noted that the isolates from Qinggang, Zhaodong, and Hailun locales had a close clustering pattern in PC1. Conversely, the isolates obtained from Fujin, Shuang cheng, and Hailin locations had positive correlations with PC2 (Fig. 4). However, it was also found that isolates Sz6 from location Zhaodong and Sq3 from Qinggang had the highest PC1 scores, which indicates that they have a significant genetic link with one another. Isolates Hm3 (Mulan) and Mn9 (Ningan), on the other hand, had the lowest PC1 and PC2 values, which indicates that they had various genetic differences based on their respective characteristics (Fig. 4). Similarly, the results of the BOX-PCR showed that the isolates from locations Qinggang, Zhaodong, Mulan, and Hailun clustered together on PC1, whereas the isolates from locations Fangzheng and Gannan exhibited positive correlations on PC2 (Fig. 4). However, this was the case even though some of the isolates from these four groups clustered together on PC1. The results of this study indicate that isolates from the same area have a propensity to have genetic profiles that are comparable to one another.

Genetic diversity assessment in Xanthomonas isolates

The genetic diversity within and between Xanthomonas isolates collected from 13 different regions was evaluated using a variety of metrics, including the number of alleles (Na), effective alleles (Ne), heterozygosity, Shannon’s information index, unbiased diversity, and polymorphic band percentage (PBP%). According to the results from the BOX-PCR analysis, the PBP ranged from 30% in Shuang cheng isolates to 65% in Hailin and Fujin isolates (Table 1). Hailin isolates showed the highest level of genetic diversity, with Ne at 1.427, a Shannon index of 0.337, and heterozygosity at 0.227. Conversely, the lowest diversity values were consistently found in isolates from Baiquan (Supplementary Table 2). The smallest genetic distance was observed between the Huanan (Jh) and Ningan (Mn) isolates (0.032), indicating the strongest relationship, while the largest genetic distance was found between Fujin (Jf) and Gannan (Qg) isolates (0.183), suggesting the weakest relationship between them (Supplementary Table 3). Similarly, according to the ERIC-PCR analysis, Hailin (Mh) isolates displayed the highest genetic diversity (Ne = 1.461, Shannon’s Information Index = 0.405, and Diversity = 0.270) (Supplementary Table 4). The PBP in ERIC-PCR ranged from 46.15% to 76.92% (Table 1). The greatest genetic distance in this analysis was between the Hailun (Sh) and Gannan (Qg) isolates, while the smallest was between the Mh and Qg isolates (Supplementary Table 5). The AMOVA analysis indicated that variations across strain populations accounted for 85% of the genetic variation observed in BOX-PCR, whereas 78% of the variation was identified in ERIC-PCR (Fig. 5). The PhiPT test further confirmed significant genetic variation across locations, with BOX-PCR showing lower diversity (PhiPT = 0.146) compared to ERIC-PCR (PhiPT = 0.219), underscoring the higher strain diversity detected by the latter method.

Primer efficiency and Mantel test assay

The iMEC tool provides a simple and efficient way to calculate each primer’s polymorphism efficiency. PIC reflects population allele diversity and frequency. The BOXA1R primer exhibited the lowest average PIC at 0.2485, while the ERIC primer set had the highest PIC at 0.3011, representing the maximum value. The measured parameters included gene diversity (H), average heterozygosity (Havp), effective multiplex ratio (EMR), marker index (MI), diversity index (D), and Rp. For the BOXA1R primer, the average values were 0.2908 for H, 0.00057 for Havp, 3.708 for EMR, 0.002 for MI, 0.969 for D, and 6.083 for Rp. While, the ERIC primers showed mean values of 0.369 for H, 0.00054 for Havp, 6.6 for EMR, 0.003 for MI, 0.940 for D, and 13.2 for Rp. Additionally, the Shannon index (H) was used to measure bacterial population fluctuations, with isolates Hf1 and Jf1 displaying more BOX-PCR strains (Fig. 6A). In the ERIC-PCR analysis, isolate Sz6 had more strains than isolate Mn6, as shown in Fig. 6B. A Mantel test was performed on the ERIC and BOX distance matrices to assess correlation using GenAlEx 6.5. This non-parametric approach randomly rearranges the rows and columns of one input distance matrix to estimate the significance of the correlation. In Fig. 7, BOX-PCR is shown on the y-axis, and ERIC-PCR on the x-axis. The R-squared value of 0.0016 indicated a poor association between BOX and ERIC genetic markers, suggesting a weak connection and a nonlinear regression line between the variables.

Discussion

The occurrence of phyllosphere-associated BLS diseases caused by Xanthomonas species is a significant concern in various crops, impacting agricultural productivity. BLS is caused by four different species of the genus Xanthomonas, specifically X. euvesicatoria, X. gardneri, X. vesicatoria, and X. perforans. According to Dhakal et al. (2019), these four species can decrease tomato and pepper plant yields by up to 50% under unfavorable conditions. In a recent study, Song et al. (2019) identified X. euvesicatoria pv. euvesicatoria, thereby affecting P. pubescens. Their research also highlights the considerable economic impact of this discovery. In our previous study (Siddique et al., 2023), we used the loop-mediated isothermal amplification method to identify X. euvesicatoria on P. pubescens, targeting the unique ATP-dependent DNA helicase recQ gene, but little is known about the population structure and genetic diversity of X. euvesicatoria on P. pubescens. However, more extensive investigation is required to examine the genetic variation among populations of X. euvesicatoria strains, which would aid in the effective control of the BLS pathogen. The genetic diversity and population structure of X. euvesicatoria have been studied using various molecular markers (Vancheva et al., 2021). One of these markers is rep-PCR, a widely used molecular typing method that detects repetitive DNA elements in the genome. The rep-PCR can be considered a form of fingerprint analysis with multiple genomes. This technique amplifies highly conserved repetitive sequences found throughout the bacterial genome at various locations (Shin et al., 2023; Spigaglia and Mastrantonio, 2003; Versalovic et al., 1994). According to Vancheva et al. (2018), rep-PCR employing ERIC and BOX A1R primers can be used to assess strain diversity in X. euvesicatoria. The researchers demonstrated the effectiveness of this technique for characterizing variations among strains of the bacterium.
This study investigated the genetic diversity and population structure of 103 Xanthomonas isolates using rep-PCR-based analysis and STRUCTURE software. BOX-PCR results showed the population optimally clustered into five groups (K = 5) with high admixture, while ERIC-PCR revealed a simpler structure with two clusters (K = 2), indicating distinct genetic groupings. Bayesian analysis of core gene SNPs clustered X. euvesicatoria strains into five distinct groups, indicating a complex population structure that can adapt to environmental pressures (Parajuli et al., 2024). Chen et al. (2021) conducted a recent study on the genetic variation and population structure of X. campestris pv. campestris strains. They used rep-PCR-based genotyping in their research. They noted that the X. campestris pv. campestris cluster showed a 90% similarity coefficient with 14 other groups. Furthermore, our finding on spatial genetic patterns analyzed by PCA revealed regional clustering, with isolates from areas like Qinggang, Zhaodong, and Hailun closely associated, indicating genetic linkages. Genetic diversity metrics, such as Shannon’s index and heterozygosity, varied across locations, with Hailin and Fujin showing the highest diversity in BOX-PCR, and ERIC-PCR. PBP also varied, reflecting genetic richness. The application of ERIC-PCR has highlighted the geographical diversity of Xanthomonas strains, with studies showing that strains from different regions exhibit significant genetic differences (Keshavarz et al., 2011). A comprehensive study conducted in Florida’s pepper cultivation regions identified four distinct races of Xe, with the P1 race being the predominant variety, consequently underscoring the genetic diversity among local populations (Subedi et al., 2024).
In the current study, the BOX-PCR method identified between 1 to 13 distinct band patterns, whereas the ERIC-PCR method detected between 1 to 20 patterns. Utilizing UPGMA clustering, dendrograms were constructed to assess genetic similarities, revealing clearly defined clusters of bacterial isolates. The ERIC-PCR method classified the isolates into 14 distinct clusters, with cluster III containing the largest number of strains (18), suggesting a higher prevalence and diversity within this particular group. The BOX-PCR method established 12 clusters based on a similarity threshold of 0.3, thereby emphasizing the genetic variation evident among the isolates. BOX-PCR was able to distinguish bacterial spot pathogens by cluster analysis using the UPGMA method and BOX-A1R primer produced 0.2-1 kb fragment (Osdaghi et al., 2018). In another study by Popović et al. (2019), genetic relatedness between X. campestris pv. campestris isolates were analyzed using ERIC-PCR and BOX-PCR analysis.
Generally, current study results showed that ERIC-PCR had greater genetic diversity among bacterial populations than BOX-PCR did. The efficiency analysis revealed that ERIC primers exhibited superior values in terms of PIC, heterozygosity, EMR, MI, discriminative power, and Rp compared to the BOXA1R primer. ERIC-PCR proved an effective technique for evaluating genetic diversity among Xanthomonas bacteria and their association with xanthan synthesis (Asgarani et al., 2015). The ERIC-PCR and VNTRs are the most effective markers for determining the genetic variability of the Xanthomonas phaseoli pv. manihotis populations (De Oliveira et al., 2023). According to the findings of Bilung et al. (2018), although ERIC-PCR had a higher discriminatory index (0.826) than BOX-PCR (0.809), both methods successfully clustered pathogenic Leptospira isolates, highlighting their complementary utility in revealing genetic heterogeneity. This enhanced discriminatory power of ERIC-PCR is likely due to the use of two primers, which facilitated a more comprehensive amplification of target sequences. In contrast, the discriminatory index of BOX-PCR was slightly higher (0.06) than that of ERIC-PCR (0.03) for X. euvesicatoria (Vancheva et al., 2018). However, ERIC-PCR only identified two profiles, organizing the majority of strains into a single dominant profile, whereas BOX-PCR revealed four profiles with superior strain discrimination, particularly for Bulgarian populations (Vancheva et al., 2018). Louws et al. (1994) also highlighted the utility of ERIC-PCR, noting that the fingerprints of Xanthomonas campestris pv. campestris isolates were highly diverse when analyzed using ERIC-PCR, whereas BOX-PCR produced more analogous profiles. On the other hand, Louws et al. (1995) found that BOX-PCR was selected for genetic diversity analysis because of its robustness and greater amplification yield than ERIC-PCR and REP-PCR, further indicating its complementary role in genetic studies. Similarly, another study by Ahamdi et al. (2023) reported that BOX-PCR produced a greater number of distinct patterns than ERIC-PCR for Enterococcus faecium isolates, indicating that BOX-PCR may be more effective in certain contexts. In conclusion, our findings indicate that ERIC-PCR demonstrates greater discriminatory capability while also revealing significant genetic variability among the samples, with distinct groups identified across different geographical areas. The combination of both techniques gives a comprehensive understanding of the genetic diversity and distribution patterns of X. euvesicatoria. Both ERIC and BOX-PCR techniques are reliable for assessing genetic relationships; however, the selection of methods may be influenced by the specific bacterial species under study and the required level of differentiation.

Notes

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Acknowledgments

This research was funded by “Precision poverty alleviation project of planting industry science and technology, special project of central leading local science and technology development, grant number ZY18C08”, “Isolation and identification of candidate genes related to response to the stress of Magnaporthe grisea in Rice, nature fund project, Heilongjiang, China, grant number C2017032”, and “Integration and extension of green control techniques for rice diseases in main rice production areas of Heilongjiang, grant number GA19B104”.

Electronic Supplementary Material

Supplementary materials are available at The Plant Pathology Journal website (http://www.ppjonline.org/).

Fig. 1
The dendrograms of 103 isolates were generated using the unweighted pair group approach with arithmetic average clustering approach, utilizing ERIC-PCR (A) and BOX-PCR (B) fingerprinting patterns. The ERIC-PCR analysis revealed 14 distinct clusters with a coefficient of 3.0. Cluster III, with 18 strains, formed a significant group of genetically uniform isolates. The significant clades included closely related isolates, specifically (Sh6 and Sh8) and (Sh3 and Sh1). (B) The BOX-PCR analysis identified 12 separate clusters with a similarity coefficient of 0.3. Clusters VI and VIII, each consisting of 16 strains, showed a higher prevalence of these strains. There were a total of 10 pairs of isolates that had similar genetic traits. These pairs included the pairings of Qg4 and Qg6, as well as Qb2 and Qb4.
ppj-oa-09-2024-0138f1.jpg
Fig. 2
Population structure analysis using BOX-PCR. (A) The rate of change in probability distributions as well as the Delta K-values for a variety of K-values, which were computed with the help of structural equation modeling software. In the course of the investigation, a high Delta K-value of 99.6 was discovered at K = 5, which provided substantial evidence in favor of the classification of the 103 Xanthomonas isolates into five separate genetic clusters. (B) A visual representation of the five clusters, each of which is portrayed and colored differently. The number of isolates is displayed along the vertical axis, and this is accompanied by changes in membership likelihood (Q) amongst strains.
ppj-oa-09-2024-0138f2.jpg
Fig. 3
Population Structure analysis using ERIC-PCR. (A) The plot illustrates the rate of change in likelihood distributions as well as Delta K-values for a variety of K-values. A Delta K-value of 49.65 was found to be ideal, and the optimal K-value was found to be 2. (B) The population structure is depicted by two unique clusters, each of which is displayed in a different color. The vertical axis indicates the number of isolates that are present. By analyzing the population structure with ERIC-PCR, it was discovered that the population structure was more distinct, with over 35 strains demonstrating high ancestry consistency.
ppj-oa-09-2024-0138f3.jpg
Fig. 4
Xanthomonas isolates from 13 different locations were subjected to principal component analysis using PAST 4.1 software to analyze their ERIC and BOX-PCR profiles. (A) ERIC-PCR showed that Qing gang, Zhaodong, and Hailun isolates clustered on PC1, while Fu Jin, Shuang Cheng, and Hai Lin correlated on PC2. Sz6 (Zhaodong) and Sq3 (Qing gang) had the highest PC1 scores, indicating a significant genetic relationship. (B) The BOX-PCR analysis revealed that the Qing gang, Zhaodong, Mulan, and Hailun isolates clustered together on the first principal component (PC1), whereas the Fangzheng and Gannan isolates showed a positive correlation on the second principal component (PC2).
ppj-oa-09-2024-0138f4.jpg
Fig. 5
Genetic variation analysis of strain populations. (A) BOX-PCR, AMOVA demonstrated that strain population fluctuations caused 85% of genetic variance and ERIC-PCR study found 78% of genetic variance. (B) The PhiPT test showed genetic variation decreasing in different regions (0.146) in BOX-PCR. A PhiPT number of 0.219 showed that ERIC-PCR had more strain variety than BOX-PCR.
ppj-oa-09-2024-0138f5.jpg
Fig. 6
The Shannon diversity index (H) was computed using the BOX-PCR (A) and ERIC-PCR (B) profile to identify the variations among the 103 identified strains in the community.
ppj-oa-09-2024-0138f6.jpg
Fig. 7
Mantel tests were performed on BOX and ERIC marker. Generated a regression line and assessed rep-PCR marker correlation using a genetic distance matrix. It was analyzed with GenAlEx 6.5. Box-PCR is on the y-axis and ERIC-PCR on the x-axis.
ppj-oa-09-2024-0138f7.jpg
Table 1
Polymorphic band percentage of regional isolates through BOX and ERIC
Population ERIC (%) BOX (%)
Hailun (Sh) 46.15 55.00
Qinggang (Sq) 57.69 55.00
Zhaodong (Sz) 69.23 60.00
Mulan (Hm) 57.69 55.00
Fangzheng (Hf) 57.69 50.00
Shuang cheng (Hs) 69.23 30.00
Gannan (Qg) 65.38 45.00
Fuyu (Qf) 57.69 55.00
Baiquan (Qb) 65.38 35.00
Fujin (Jf) 73.08 65.00
Huanan (Jh) 53.85 55.00
Hailin (Mh) 76.92 65.00
Ningan (Mn) 46.15 60.00
Mean 61.24 52.69
SE 2.67 2.92

SE, standard error.

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