# Analysis of Bacterial Community Composition and Ecological Function during Soft Rot Process in Pitaya (Hylocereus spp.) Stems

Authors: Zhijun Peng; Jingqiang Guan; Dan Mou; Xiao Zou; Bin Wang; Jilin Jin; Xingwu Zhang; Hui Luo
Journal: Journal of Chemistry
 (2022)

---

## Abstract

The soft rot in pitaya (Hylocereus spp.) stems seriously affected the harvest of pitaya fruits, but the dynamic change characteristics of bacterial community in pitaya stems during soft rot stages had not been revealed. In this study, we analyzed the bacterial community composition of different soft rot stages and visualized functional annotations of the core bacterial community in five soft rot periods via the Illumina high-throughput sequencing technology, MetaCoMET online network platform, and FAPROTAX database. The results showed that the dominant bacteria in healthy and diseased pitaya stems were Proteobacteria and Firmicutes. Pseudomonas, Enterobacter, Sphingomonas, and Sphingobacterium were the core bacteria microbiomes during the infection stages. Meanwhile, the ecological function analysis results showed that the Enterobacter and Pseudomonas bacteria may play an important role in causing soft rot of pitaya stems. Therefore, the results shown in this paper could provide a useful reference for the study on microecological mechanism of soft rot of pitaya stems.


---

## Body

## 1. Introduction

Pitaya (Hylocereus spp.) is often planted in tropical and subtropical regions and has high economic and nutritional value. Hainan Province in China began to grow pitaya in the late 1990s, followed by Fujian, Guangdong, Guangxi, Guizhou, Yunnan, and other southern provinces (regions) began to large-scale introduction. In recently years, Guizhou Province is one of the major pitaya producing areas in China, and pitaya in Luodian County has become a national geographic landmark product.At present, pitaya yield is threatened by many diseases; among which, soft rot is one of the main diseases. Various studies showed that a variety of fungi could produce soft rot on pitaya fruits, for example,Scytalidium dimidiatum, Fusarium spp., F. dimerum, Gilbertella persicaria, and F. oxysporum [1–6]. If the pitaya stem is infected by F. semitectum, F. oxysporum, F. moniliforme, and other Fusarium fungi, it will show symptoms of tissue softening, ulceration, and depression at the stem ridge [2, 7]. Bipolaris was a plant pathogenic fungus with strong distribution in the world, which could cause soft rot in cacti plants, for example, fruit rot of pitaya and stem rot of cactus [8, 9]. In addition, Liang et al. showed that B. cactivora could harm the stem of pitaya under natural conditions, which was the first report on the harm of stem rot of this fungus at home and abroad [10].However, there are few reports about the bacterial diseases of pitaya. Yuan et al. found thatErwinia sp. could make the nodes of pitaya stems infected with soft rot in western Guangdong Province [11]. Sang et al. also confirmed that the soft rot of pitaya stem was mainly caused by Erwenella [12]. Meanwhile, Masyahit et al. [13] first sampled the pitaya stems of soft rot from 11 sample areas in Malaysia and found that Enterobacter cloacae could cause soft rot and yellow-brown of pitaya stems. Lin et al. [14] isolated the endogenous bacteria in pitaya seedlings, and the identification results showed that Enterobacter bacteria induced typical soft rot symptoms in pitaya plants. In addition to Erwenella and Enterobacter, Zhang et al. [15] found that Paenibacillus polymyxa could also cause soft rot in the stem of pitaya. When plant stems are affected by soft rot pathogens, this type of disease will further spread and eventually affect the fruit yield [16]. However, the change characteristics of endogenous bacterial community during soft rot of pitaya stem are not clear.In this study, we analyzed the bacterial community composition in the pitaya stem tissue in different soft rot stages by the Illumina high-throughput sequencing technology and annotated the core bacterial microbiome to provide theoretical guidance for the prediction of pitaya soft rot disease in different soft rot stages.


## 2. Materials and Methods

### 2.1. Sample Collection

In July 2019, the stems of pitaya (variety “Purple Red Dragon”) in five different soft rot stages were sampled from Luodian County, Guizhou Province, China. According to the methods of Masyahit et al. [13], the soft rot disease grade of pitaya stem was divided into five grades, which were normal (CK), early soft rot grade (B-1), middle soft rot grade (B-2), middle and late soft rot grade (B-3), and late soft rot grade (B-4), respectively. Three stems were collected at each period. The samples were encapsulated in sterile ziplock bags, stored in ice bags, and transported to the laboratory for further processing. Tissue blocks with a size of 5mm×5mm were cut at the junction of the disease with a sterile scalpel, 3 tissue blocks were cut from each stem, and 9 tissue blocks were mixed evenly in each period. According to the treatment method of Xu et al. [17], tissue blocks in each period were disinfected: first soaked in 75% alcohol for 40 s, then soaked in 5% sodium hypochlorite solution for 1 min, and finally washed with sterile water for 4 times. The dried tissue was encapsulated in a sterile ziplock bag and stored in a -80°C refrigerator.


### 2.2. DNA Extraction, PCR Amplification, and Sequencing

Under aseptic conditions, plant tissue blocks were ground into a fine powder with liquid nitrogen. Approximately, 50 mg tissue powder was taken at each onset stage, and total microbial genomic DNA was extracted according to the instructions in the E.Z.N.A.® SoiL DNA Kit (Omega Bio-Tek, Norcross, GA, USA). According to the method of Wei et al. [18], the V3-V4 region of bacterial 16S rDNA was amplified by ABI Gene Amp®9700 (ABI, CA, USA). Polymerase chain reaction (PCR) reaction system is as follows: 0.4 μL of TransStart Fastpfu DNA Polymerase (2.5 U·μ/L), 4 μL of Fastpfu Buffer, 2 μL of dNTPs (2.5 mol/L), 0.8 μL of forward and reverse primers of 338F (5 μmol/L) and 806R (5 μmol/L), 0.2 μL of BSA (0.8 μg·μ/L), 10 μL of DNA template (1 ng/μL), and 20 μL of ddH2O. PCR reaction procedure is as follows: predenaturation at 95°C for 3 min; denaturation at 98°C for 30 s, 55°C for 30 s, 72°C for 45 s, a total of 30 cycles; extended at 72°C for 10 min [19, 20]. PCR products were detected by 1% agarose gel electrophoresis and sequenced using the Illumina Miseq platform.


### 2.3. Data Analysis

In order to obtain effective Tags data, the sequence of each sample was splicing and quality optimization (Tags interception, filtering, and chimeric removal) by referring to the processing method of Robert [21]. The UPARSE software was used to cluster the 97% nonrepeating sequences (excluding single sequences) into operational taxonomic units (OTUs). Mothur method was used to compare OTUs with species in SSU rRNA database. Finally, α diversity analysis was performed on the obtained data.The species abundance of each sample was counted at the taxonomic level of phylum and genus, and the bacterial communities of samples in each period were analyzed by using the Origin Pro 2018C software. The distribution differences of OTUs in different soft decay stages were analyzed by Venn diagram. At the genus level, the species annotation and abundance of each sample were analyzed. The top 26 groups with relative abundance were selected, and the changes of dominant bacterial groups in samples at different soft rot stages were analyzed by heat map.Core microbiome is a key component of Holobionts, which is of great significance for the study of symbiotic and pathogenic microorganisms [22]. OTUs were uploaded to the MetaCoMET platform (http://probes.pw.usda.gov/MetaCoMETT). The membership definition method was used to obtain the core bacterial microbiomes of different soft decay stages.FAPROTAX is a kind of database collected for culturable bacteria and is often used to predict the ecological function of the microbiome. The database has included more than 4600 culturable bacteria, including more than 80 functional groups and 7600 functional annotations, which can be applied to predict the ecological function of culturable bacteria [23]. Through the FAPROTAX database, the OTU function of the core bacterial microbiome in different soft decay stages was predicted.


## 2.1. Sample Collection

In July 2019, the stems of pitaya (variety “Purple Red Dragon”) in five different soft rot stages were sampled from Luodian County, Guizhou Province, China. According to the methods of Masyahit et al. [13], the soft rot disease grade of pitaya stem was divided into five grades, which were normal (CK), early soft rot grade (B-1), middle soft rot grade (B-2), middle and late soft rot grade (B-3), and late soft rot grade (B-4), respectively. Three stems were collected at each period. The samples were encapsulated in sterile ziplock bags, stored in ice bags, and transported to the laboratory for further processing. Tissue blocks with a size of 5mm×5mm were cut at the junction of the disease with a sterile scalpel, 3 tissue blocks were cut from each stem, and 9 tissue blocks were mixed evenly in each period. According to the treatment method of Xu et al. [17], tissue blocks in each period were disinfected: first soaked in 75% alcohol for 40 s, then soaked in 5% sodium hypochlorite solution for 1 min, and finally washed with sterile water for 4 times. The dried tissue was encapsulated in a sterile ziplock bag and stored in a -80°C refrigerator.


## 2.2. DNA Extraction, PCR Amplification, and Sequencing

Under aseptic conditions, plant tissue blocks were ground into a fine powder with liquid nitrogen. Approximately, 50 mg tissue powder was taken at each onset stage, and total microbial genomic DNA was extracted according to the instructions in the E.Z.N.A.® SoiL DNA Kit (Omega Bio-Tek, Norcross, GA, USA). According to the method of Wei et al. [18], the V3-V4 region of bacterial 16S rDNA was amplified by ABI Gene Amp®9700 (ABI, CA, USA). Polymerase chain reaction (PCR) reaction system is as follows: 0.4 μL of TransStart Fastpfu DNA Polymerase (2.5 U·μ/L), 4 μL of Fastpfu Buffer, 2 μL of dNTPs (2.5 mol/L), 0.8 μL of forward and reverse primers of 338F (5 μmol/L) and 806R (5 μmol/L), 0.2 μL of BSA (0.8 μg·μ/L), 10 μL of DNA template (1 ng/μL), and 20 μL of ddH2O. PCR reaction procedure is as follows: predenaturation at 95°C for 3 min; denaturation at 98°C for 30 s, 55°C for 30 s, 72°C for 45 s, a total of 30 cycles; extended at 72°C for 10 min [19, 20]. PCR products were detected by 1% agarose gel electrophoresis and sequenced using the Illumina Miseq platform.


## 2.3. Data Analysis

In order to obtain effective Tags data, the sequence of each sample was splicing and quality optimization (Tags interception, filtering, and chimeric removal) by referring to the processing method of Robert [21]. The UPARSE software was used to cluster the 97% nonrepeating sequences (excluding single sequences) into operational taxonomic units (OTUs). Mothur method was used to compare OTUs with species in SSU rRNA database. Finally, α diversity analysis was performed on the obtained data.The species abundance of each sample was counted at the taxonomic level of phylum and genus, and the bacterial communities of samples in each period were analyzed by using the Origin Pro 2018C software. The distribution differences of OTUs in different soft decay stages were analyzed by Venn diagram. At the genus level, the species annotation and abundance of each sample were analyzed. The top 26 groups with relative abundance were selected, and the changes of dominant bacterial groups in samples at different soft rot stages were analyzed by heat map.Core microbiome is a key component of Holobionts, which is of great significance for the study of symbiotic and pathogenic microorganisms [22]. OTUs were uploaded to the MetaCoMET platform (http://probes.pw.usda.gov/MetaCoMETT). The membership definition method was used to obtain the core bacterial microbiomes of different soft decay stages.FAPROTAX is a kind of database collected for culturable bacteria and is often used to predict the ecological function of the microbiome. The database has included more than 4600 culturable bacteria, including more than 80 functional groups and 7600 functional annotations, which can be applied to predict the ecological function of culturable bacteria [23]. Through the FAPROTAX database, the OTU function of the core bacterial microbiome in different soft decay stages was predicted.


## 3. Results

### 3.1. Sequence Data

As shown in Table1, a total of 178,745 valid sequences and 516 OUTs were obtained, with an average of 103 OUTs per sample. Sequencing results showed that the coverage rate of each sample was above 99%, which could accurately reflect the real situation of the bacterial community of the sample [24]. According to the sobs index differences shown in Table 1, bacterial abundance in different onset periods was B-4 > B-3 > B-2 > B-1 > CK, indicating that the abundance and diversity of bacteria in stem tissue of pitaya changed with the progression of disease degree. Meanwhile, based on Simpson and Shannon indexes in Table 1, Simpson index was the lowest (0.090), and Shannon index was the highest (3.42) in B-4 stage (late onset), indicating that the bacterial community gradually tended to diversify during the soft rot of pitaya stem tissue. In addition, Shannon-Wiener index can reflect the relationship between species diversity and sequencing amount [25, 26]. As can be seen from Figure 1, Shannon-Wiener index curve becomes flatter with the increase of sample sequence number, indicating that the data depth of this experiment can fully reflect the microbial information in the sequenced samples. Analysis of OTUs of samples in 5 periods (Figure 2) revealed that 2, 4, 5, 20, and 34 endemic OTUs (Figure 2) were shared by the five samples of CK, B-1, B-2, B-3, and B-4, respectively.Table 1
Statistics of sequencing date and alpha diversity index in different soft rot stages.
SamplesRaw readsEffective readsNumbersCoverage (%)Simpson indexShannon indexSobs indexAce indexChao1 indexCK36580360245699.9530.6270.7054967.36068.429B-141511407715699.9680.6900.6325068.98957.800B-241016400028099.9550.5460.8977086.78682.750B-3369483574913999.9640.5661.130121127.709128.091B-4388983604418599.9890.0903.287152153.213152.667Figure 1
The Shannon-Wiener curves in different soft rot stages.Figure 2
Analysis of OTUs from different soft rot process with Venn diagram.


### 3.2. Composition of Bacterial Community in Different Soft Rot Stages

As shown in Figure3, except Proteobacteria and Firmicutes, Proteobacteria and Firmicutes have always been at the level of dominant phyla, while bacteria at other phyla levels have undergone great changes. From CK to B-4, Proteobacteria accounted for more than 70%, which was the dominant flora in the whole pathological process (>1%). Firmicutes were the dominant bacteria next to Proteobacteria; the abundance of Firmicutes was more than 2% in all periods. From B-2 to B-4, the abundance of Actinobacteria and Bacteroidetes showed an increasing trend and gradually established a dominant position in B-3 and B-4. Verrucomicrobia belonged to the dominant group in B-1 and B-2 stages, but with the development of disease (B-3 to B-4 stage), it gradually changed to nondominant group (<1%).Figure 3
The stacked area graph of bacteria on phylum level.As shown in Figure4, a total of 116 genera of bacteria were detected in five samples. In the CK group, Pseudomonas was the main dominant groups accounting for 93.75%. From B-1 to B-4, Pseudomonas, Enterobacter, Sphingomonas, and Sphingobacterium were the dominant genera accounting for more than 2%. The bacterial community structure became more complex, and the species diversity became richer when the soft rot disease of pitaya stem was aggravated.Figure 4
Histogram of sample’s relative abundance on genus level.


### 3.3. Dynamic Analysis of Bacterial Community in Different Soft Rot Stages

As shown in Figure5, in the CK group, Pseudomonas of Bacteroidetes and Enterococcus of Firmicutes were obviously dominant. From B-1 to B-2, the abundance of Pseudomonas, Enterobacter, Sphingosphinx, and Sphingosphinx showed an increasing trend. With the soft rot grade rising to B-3 stage, Sphingosphinx of Bacteroides and Enterobacter of Firmicutes were the dominant genera. In B-4 stage, Pseudomonas, Sphingomonas, Methylobacterium, Rhizobium, Devosia, Kineococcus, Enterobacter, and Aureimonas had obvious dominant characteristics.Figure 5
Heat map of the species abundance during different soft rot stages.


### 3.4. Core Bacterial Group and Functional Analysis during Soft Decay Period

MetaCoMET analysis of data sets from B-1 to B-4, as shown in Figure6, showed that there were five core bacterial microbiomes at the genus level, namely, Pseudomonas, Sphingomonas, Enterobacter, and Sphingobacterium. Meanwhile, Figure 7 showed that the main functional groups of the core bacterial groups in the different soft rot periods were chemoheterotrophy, aerobic chemoheterotrophy, animal parasites or symbiotics symbionts, and plant pathogens, among which chemoheterotrophs and oxidative functional groups dominated. This suggested that microbes needed to break down organic matter in the stem tissue to obtain a supply of nutrients. In the whole soft rot process, Pseudomonas played an important role in chemoheterotrophy, heterotrophy, animal parasitism, and symbiosis. It is noteworthy that, combined with the change process of species abundance, the change of species abundance of Enterobacter was synchronized with the change of plant pathogenic functional groups. With the increase of species abundance, the role of Enterobacter in plant pathogenic functional groups became more and more prominent. Therefore, the Enterobacter sequenced in this study may be a class of important plant pathogens, with certain functional potential for the occurrence of soft rot diseases.Figure 6
Core bacterial microbiota compositions during different soft rot stages.Figure 7
Changes of the functional groups of the core bacterial microbiota at different soft rot stages.


## 3.1. Sequence Data

As shown in Table1, a total of 178,745 valid sequences and 516 OUTs were obtained, with an average of 103 OUTs per sample. Sequencing results showed that the coverage rate of each sample was above 99%, which could accurately reflect the real situation of the bacterial community of the sample [24]. According to the sobs index differences shown in Table 1, bacterial abundance in different onset periods was B-4 > B-3 > B-2 > B-1 > CK, indicating that the abundance and diversity of bacteria in stem tissue of pitaya changed with the progression of disease degree. Meanwhile, based on Simpson and Shannon indexes in Table 1, Simpson index was the lowest (0.090), and Shannon index was the highest (3.42) in B-4 stage (late onset), indicating that the bacterial community gradually tended to diversify during the soft rot of pitaya stem tissue. In addition, Shannon-Wiener index can reflect the relationship between species diversity and sequencing amount [25, 26]. As can be seen from Figure 1, Shannon-Wiener index curve becomes flatter with the increase of sample sequence number, indicating that the data depth of this experiment can fully reflect the microbial information in the sequenced samples. Analysis of OTUs of samples in 5 periods (Figure 2) revealed that 2, 4, 5, 20, and 34 endemic OTUs (Figure 2) were shared by the five samples of CK, B-1, B-2, B-3, and B-4, respectively.Table 1
Statistics of sequencing date and alpha diversity index in different soft rot stages.
SamplesRaw readsEffective readsNumbersCoverage (%)Simpson indexShannon indexSobs indexAce indexChao1 indexCK36580360245699.9530.6270.7054967.36068.429B-141511407715699.9680.6900.6325068.98957.800B-241016400028099.9550.5460.8977086.78682.750B-3369483574913999.9640.5661.130121127.709128.091B-4388983604418599.9890.0903.287152153.213152.667Figure 1
The Shannon-Wiener curves in different soft rot stages.Figure 2
Analysis of OTUs from different soft rot process with Venn diagram.


## 3.2. Composition of Bacterial Community in Different Soft Rot Stages

As shown in Figure3, except Proteobacteria and Firmicutes, Proteobacteria and Firmicutes have always been at the level of dominant phyla, while bacteria at other phyla levels have undergone great changes. From CK to B-4, Proteobacteria accounted for more than 70%, which was the dominant flora in the whole pathological process (>1%). Firmicutes were the dominant bacteria next to Proteobacteria; the abundance of Firmicutes was more than 2% in all periods. From B-2 to B-4, the abundance of Actinobacteria and Bacteroidetes showed an increasing trend and gradually established a dominant position in B-3 and B-4. Verrucomicrobia belonged to the dominant group in B-1 and B-2 stages, but with the development of disease (B-3 to B-4 stage), it gradually changed to nondominant group (<1%).Figure 3
The stacked area graph of bacteria on phylum level.As shown in Figure4, a total of 116 genera of bacteria were detected in five samples. In the CK group, Pseudomonas was the main dominant groups accounting for 93.75%. From B-1 to B-4, Pseudomonas, Enterobacter, Sphingomonas, and Sphingobacterium were the dominant genera accounting for more than 2%. The bacterial community structure became more complex, and the species diversity became richer when the soft rot disease of pitaya stem was aggravated.Figure 4
Histogram of sample’s relative abundance on genus level.


## 3.3. Dynamic Analysis of Bacterial Community in Different Soft Rot Stages

As shown in Figure5, in the CK group, Pseudomonas of Bacteroidetes and Enterococcus of Firmicutes were obviously dominant. From B-1 to B-2, the abundance of Pseudomonas, Enterobacter, Sphingosphinx, and Sphingosphinx showed an increasing trend. With the soft rot grade rising to B-3 stage, Sphingosphinx of Bacteroides and Enterobacter of Firmicutes were the dominant genera. In B-4 stage, Pseudomonas, Sphingomonas, Methylobacterium, Rhizobium, Devosia, Kineococcus, Enterobacter, and Aureimonas had obvious dominant characteristics.Figure 5
Heat map of the species abundance during different soft rot stages.


## 3.4. Core Bacterial Group and Functional Analysis during Soft Decay Period

MetaCoMET analysis of data sets from B-1 to B-4, as shown in Figure6, showed that there were five core bacterial microbiomes at the genus level, namely, Pseudomonas, Sphingomonas, Enterobacter, and Sphingobacterium. Meanwhile, Figure 7 showed that the main functional groups of the core bacterial groups in the different soft rot periods were chemoheterotrophy, aerobic chemoheterotrophy, animal parasites or symbiotics symbionts, and plant pathogens, among which chemoheterotrophs and oxidative functional groups dominated. This suggested that microbes needed to break down organic matter in the stem tissue to obtain a supply of nutrients. In the whole soft rot process, Pseudomonas played an important role in chemoheterotrophy, heterotrophy, animal parasitism, and symbiosis. It is noteworthy that, combined with the change process of species abundance, the change of species abundance of Enterobacter was synchronized with the change of plant pathogenic functional groups. With the increase of species abundance, the role of Enterobacter in plant pathogenic functional groups became more and more prominent. Therefore, the Enterobacter sequenced in this study may be a class of important plant pathogens, with certain functional potential for the occurrence of soft rot diseases.Figure 6
Core bacterial microbiota compositions during different soft rot stages.Figure 7
Changes of the functional groups of the core bacterial microbiota at different soft rot stages.


## 4. Discussion

Microbial invasion is often the main cause of fruit and vegetable quality degradation. In natural ecosystems, the quality of fruit and vegetable crops is affected by soft rot pathogens from planting, harvesting to storage. Bruises, cuts, and insect bites of plants can increase the advantages of microbial colonization [27]. At present, there are few reports on the change of bacterial community in pitaya stem tissue during soft rot. Therefore, exploring and revealing the relationship between bacteria and stem soft rot can provide important reference value for the maintenance of pitaya fruit quality and disease prediction.The occurrence of soft rot in pitaya stem changed the composition and distribution of bacterial community in stem tissue. Alpha diversity analysis showed that bacterial community richness and diversity in stem tissues gradually increased with the deepening of the degree of soft rot disease. Welington et al. [28] believed that the healthy plants could only be colonized by a few dominant microorganisms, while the diseased plants without obvious diseases could recruit more community richness. This study also found a similar phenomenon: Pseudomonas and Enterococcus were the dominant bacteria in the stem tissue of the healthy pitaya. In the early stage of soft rot, there were not only Pseudomonas bacteria but also abundant Enterobacter and Sphingomonas. At the end of soft rot, the abundance and diversity of bacteria in stem tissue reached the maximum. The distribution differences of OTUs in different soft rot stages indicated that there was a transient succession process in the bacterial community in the stem tissue of pitaya at different soft rot stages: the bacterial community structure was most complex when the bacterial community in the stem tissue succeeded to the end of soft rot. It can be seen that plants are a complex microecosystem, in which bacterial communities occupy a certain ecological niche and constantly compete with each other for nutrients and water in host tissues [29].MetaCoMET analysis and FAPROTAX functional analysis showed that the functional groups of the core bacterial groups in the five soft decay stages were mainly dominated by chemoheterotrophic and oxidation-requiring functional groups. These results suggested that microorganisms need to decompose organic matter in the stem tissue to obtain a supply of nutrients. It should be noted thatPseudomonas and Enterobacter may play an important role in the occurrence of pitaya stem soft rot disease by analyzing the changes of bacterial community composition and the functional changes of core bacterial group. In healthy pitaya stem tissue, Pseudomonas was the dominant bacterium with the highest species abundance, accounting for 93.75%. However, with the aggravation of soft rot, the species abundance of Pseudomonas decreased gradually and reached the lowest value (26.8%) at the end of soft rot. Studies had showed that after inoculation with Pseudomonas, the fruit quality of grape and cotton can be well maintained and improved [30]. Based on this research significance, Pseudomonas bacteria can be used as an important indicator for the prediction of soft rot in pitaya in subsequent studies.In the early stage of soft rot disease,Enterobacter began to colonize in the stem tissue, and its species abundance increased to the maximum (13.79%) in the late stage of soft rot disease. FAPROTAX functional analysis showed that Enterobacter played an increasingly important role in plant pathogenic functional groups with the increase of species abundance. Enterobacter was a common human pathogen in Enterobacteriaceae, which could often activate pectinase regulation pathway and cause the occurrence of plant soft rot. At present, Enterobacter cloacae, Enterobacter nimipressuralis, and Enterobacter pyrinus could cause soft rot in plants [14]. In recent years, studies on the harm of Enterobacter bacteria to fruits, vegetables, and other plants had been reported. Masyahit et al. [13] first found that Enterobacter cloacae could cause bacterial soft rot in stem segments of pitaya fruit and causes the disease in 36% of pitaya fruit. Lin et al. [14] first discovered Enterobacter bacteria in pitaya stems from Taiwan, China. According to morphological characteristics, molecular identification, pathogenicity determination, and other methods, the bacteria causing the soft rot of pitaya stem was Enterobacter group. Oniha and Egwari [16] isolated Enterobacter spp. bacteria that could cause soft rot from the fruit and stem of Carica papaya L. In addition to the harmful effects of Enterobacter bacteria on fruits, researchers had observed the symptoms of decay in cucumbers, carrots, cabbage, and onions inoculated with Enterobacter spp. [31]. The Enterobacter bacteria obtained in this test may play an important role in the occurrence of soft rot of pitaya stem, but whether it has the above soft rot pathogenic ability is not clear and needs to be isolated and identified. It is worth discussing that studying the change characteristics of bacterial community in plants can not only provide help for predicting plant diseases but also reveal the role of dominant microorganisms in orchard ecosystem.


## 5. Conclusion

In conclusion, the dominant bacterial genera with the stem samples of healthy pitaya werePseudomonas and Enterococcus. From the initial infection stage to the late infection stage, the dominant bacteria in pitaya stems were Pseudomonas, Enterobacter, Sphingomonas, and Sphingobacterium. At the terminal stage, the bacterial genera with dominance in soft rot pitaya stems were Pseudomonas, Sphingomonas, Sphingobacterium, Microbacterium, Methylobacterium, Rhizobium, Devosia, Kineococcus, Enterobacter, and Aureimonas, of which Pseudomonas, Enterobacter, Sphingomonas, and Sphingobacterium were the core microbiota of bacteria during the stages of infection. Meanwhile, the functional groups of these core microbiomes were chemoheterotrophy, aerobic chemoheterotrophy, animal parasites or symbionts, and plant pathogen. The Enterobacter and Pseudomonas bacteria may play an important role in causing soft rot disease of pitaya stems.



---

*Source: 9169433-2022-05-06.xml*