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Article

Diversity Analysis and Biocontrol Potential of Cultivatable Terrestrial Bacterial Streptomyces in Southern China

by
Lifeng Zhou
†,
Fan Pei
†,
Wangling Pu
,
Chuang Zhang
,
Fei Chen
,
Yuechen Hu
,
Jie Chen
,
Haiping Lin
* and
Xudong Zhou
*
State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A & F University, Hangzhou 311300, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(10), 2500; https://doi.org/10.3390/agronomy13102500
Submission received: 27 August 2023 / Revised: 26 September 2023 / Accepted: 27 September 2023 / Published: 28 September 2023
(This article belongs to the Special Issue The Role of Symbiotic and Pathogenic Microbes in Insect-Host Plants Interactions)
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Abstract

:
Streptomyces are filamentous bacteria that are extensively present in soil, play an important role in carbon cycling, and produce a large number of highly valuable secondary metabolites. In this study, total number of 411 isolates of Streptomyces were collected from ecologically similar habitats from 13 provinces in Southern China. They were clustered into 96 operational taxonomic units including eight potentially new species based on their 16S rDNA sequences. Phylogenetic analysis disclosed that the isolates with similar geographical distributions clustered together. Beta diversity of Streptomyces showed it manifests in a latitudinal diversity gradient (R2 = 0.3828, p = 0.0113). The beta diversity did not change significantly with geographic distance, and this could be due to the large longitudinal and relatively smaller latitudinal sampling range, as the phylogenetic clustering of regions with higher annual average temperature was analyzed with the nearest taxon index (R = −0.23, p = 0.045). Result of the Streptomyces biogeography evaluation shows the diversity of the genus is controlled by many of the same processes. Bioassay results disclosed that there were 27 isolates strongly antagonistic to plant pathogenic fungi and 71 isolates with strong nematocidal activity against pine wood nematode. Our results provide significant insights into the diversity and biocontrol potential of cultivatable Streptomyces in Southern China.

1. Introduction

The genus Streptomyces (Actinobacteria) was first proposed by Waksman and Henrici in 1943, and was named after its morphological similarity to filamentous fungi [1]. Streptomyces are Gram-positive bacteria that are generally considered persistent soil saprophytes and are widespread in soil and play an important role in carbon cycling [2,3,4,5]. The complexity of Streptomyces is related to its complex developmental life cycle and its large genome with high G + C content exceeding 8 Mbp, which provides them the ability to adapt to a variety of complex environments [6,7,8]. Currently, there are 697 child taxa of Streptomyces with valid published and correct names (accessed on 10 August 2023, http://www.bacterio.net/streptomyces.html).
Streptomyces is widely studied because of its strong biological potential. The application of Streptomyces in agriculture and forestry is mainly focused on producing bioactive secondary metabolites and promoting plant growth [9,10,11]. Streptomyces can produce a variety of bioactive compounds, such as antifungal agents, antibacterial agents, antiviral agents, and immunosuppressants, which are widely used in various fields [12,13,14,15,16]. About two-thirds of the commercial antibiotics are developed and produced by Streptomyces [12]. Biocontrol metabolites of Streptomyces have been proved to be able to control a variety of plant pathogens and are biodegradable [17]. Actinomycete commercial product Mycostop could be used to prevent and cure various soil-borne diseases caused by Fusarium, Pythium, Rhizoctonia, and Phytophthora [18]. The Oligomycin produced by Streptomyces sp. was used to prevent Erwinia carotovora [19]. Meanwhile, the Cycle (l-Pro-l-Tyr) and Cycle (d-Pro-l-Tyr) from Streptomyces sp. were used to prevent Xanthomonas axonopodis, Ralstonia solanacearum, and Clavibacter michiganensis [20]. Proteases based on S. tsukiyonensis was used to prevent and cure the diseases caused by Colletotrichum dematium [21]. Furthermore, as an important component of soil microorganisms, Streptomyces can colonize different plant roots for saprophytic and endogenous life [22,23,24], and promote plant growth by stimulating and regulating the synthesis of plant growth hormone, alleviating various abiotic stresses and other ways [25]. Besides biological potential, macrolides, anthracyclines, aminoglycosides, ansamycins, β-lactam, and tetracycline, produced by Streptomyces, are also of vital importance in the medical field [26].
Southern China is located south of the Qinling-Huaihe River and east of the Qinghai-Tibet Plateau, bordering the Yellow Sea and East China Sea and South China Sea to the east and south, respectively. The terrain is complex and diverse, with obvious differences between the east and west. The west is dominated by plateaus and basins, while the east has staggered plains, low mountains, and hills, and there are large plains and deltas along the river. The research on actinomycetes in China mainly focuses on strains isolated under specific conditions or extreme environments, such as tropical rainforests, mangroves, and deep-sea [27,28,29]. In 1967, S. microaureus isolated from Taihe, Jiangxi Province, could produce the antibiotic Chunleimycin, which had significant inhibitory effects on rice blast, Pseudomonas aeruginosa, and some Bacillus subtilis strains [30]. Jinggangmycin, which was selected by Shanghai Institute of Pesticides in 1973, is still widely used to control rice sheath blight, cotton seedling damping off, and other diseases [31]. Later, Gongzhulingmycin, Zhongshengmycin, Liuyangmycin, Meilingmycin, Nikkomycin, and other antibiotics were successfully developed as biological control agents in China [32,33,34]. Although, many actinomycetes have been isolated in Southern China, the diversity of Streptomyces in this region is rarely known.
In this study, 411 isolates of Streptomyces were collected from 13 provinces in Southern China, and the geographical and climatic characteristics were used to explore the factors affecting the diversity of these isolates, and the results might provide a hint for the diversity of actinomycetes in China. Meanwhile, a preliminary study on these isolates against phytopathogenic fungi and nematode was conducted, which could provide a basis for the application of these isolates in agriculture and forestry.

2. Materials and Methods

2.1. Sampling and Strain Collection

A total of more than 1000 soil samples were collected from 13 provinces in Southern China (Supplementary Figure S1 and Table S1), including Tibet (XZ), Guangxi (GX), Guangdong (GD), Hainan (HA), Hunan (HN), Jiangxi (JX), Fujian (FJ), Jiangsu (JS), Zhejiang (ZJ), Shanghai (SH), Yunnan (YN), Guizhou (GZ), and Sichuan (SC). Soil samples were collected only from areas dominated by perennial herbs with a neutral-to-acidic pH at a sampling depth of 0 to 5 cm, and the collected soil samples were air-dried at room temperature [35,36]. Precipitation and temperature data were obtained from China Meteorological Administration (accessed on 10 August 2023, http://www.cma.gov.cn). For the isolation of Streptomyces, 50 mg soil samples were diluted to 1:10,000 (w/v) in sterile water and shaken for 3 min, and the supernatant of 20 to 50 µL of the suspension was spread on 12 different culture mediums, ISP1 to ISP7, Streptomyces agar (SA), starch casein agar (SCA), actinomycetes isolation agar (AIA), nutrient agar (NA), and Luria–Bertani agar (LB) [37,38,39,40,41], and incubated at 28 °C for 5 to 7 days. Then, the colonies with Streptomyces characteristics were purified by streaking. The pure cultures were maintained on ISP2 agar slants at 28 °C and glycerol suspension (20%, v/v) at −20 °C.

2.2. 16S rDAN Sequences

Genomic DNA was extracted using the phenol–chloroform–isoamyl alcohol protocol [42]. PCR amplification of full length of 16S rDNA was performed in a 25 µL system using Taq PCR MasterMix (Tiangen, Beijing, China) and universal primers 27F (5′-AGAGTTGATCCTGGCTCA-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′) [43,44]. PCR amplifications were as follows: initial denaturation at 95 °C for 5 min, 94 °C for 30 s, 55 °C for 30 min, 72 °C for 1 min and 30 s for 35 cycles, and a final extension at 72 °C for 10 min. The amplified products were resolved by electrophoresis in agarose gel and sequenced at Sangon Biotech sequencing company (Shanghai, China).

2.3. Identification and Phylogenetic Analysis

The 16S rDNA sequences obtained from the sequencing company were manually trimmed using SeqMan sequence alignment software. Alignment was manually verified and adjusted by uniform pruning before rebuilding the phylogenetic tree. The sequences used in the phylogenetic analysis were first analyzed online with MAFFT (accessed on 10 August 2023, https://mafft.cbrc.jp/alignment/server/index.html) [45], and then formatted with Mesquite v3.51. Online construction of phylogenetic trees using maximum likelihood method in PhyML 3.0 (accessed on 10 August 2023, http://www.atgc-montpellier.fr/) [46], combining the estimated proportions of invariant loci and discrete gamma distributions (GTR + I + G), and the resultant tree topologies were analyzed through bootstrap analysis based on 1000. Tree roots were defined using Mycolicibacterium smegmatis as the outgroup.

2.4. Diversity and Phylogeography of Streptomyces

Operational taxonomic units (OTUs) based on 16S rDNA sequences were defined at a 0.01 nucleotide dissimilarity cutoff using patristic distances as implemented in mothur v.1.45.3 [47]. Beta diversity among species communities was assessed with Bray–Crutis distance and Unifrac distance. The Bray–Crutis distance was calculated using the vegidst function in the R language vegan package [48], and the Unifrac distance is calculated using the R language phyloseq package [49]. Mantel correlations between geographic distance matrices and UniFrac and Bray–Curtis distances were tested for significance with 9999 permutations of the spearman correlation coefficient using the R language vegen package [48]. Principal coordinate analysis (PCA) was conducted using the vegen package in R language. The presence status of each OTUs in each plot was calculated in R language, and then imported into Cytoscape 2.8 [50] to construct the network. Phylogenetic diversity (PD) [51] and nearest taxon index (NTI) were both calculated using the picanta package in R [52]. For NTI, positive values indicate that co-existing taxa are more closely related than expected by chance (phylogenetic clustering), and negative values indicate that co-existing taxa are more distantly related than expected by chance (phylogenetic overdispersion).

2.5. Bioassay of Streptomyces

Ten common plant pathogenic fungi (Botryosphaeria dothidea, Fusarium graminearum, F. oxysporum, F. solani, Lasiodiplodia theobromae, Nigrospora oryzae, Pestalotiopsis disseminate, Pythium vexans, Rhizoctonia solani, and Sclerotinia sclerotiorum) and the worldwide quarantine pine-parasitic nematode (pine wood nematode, Bursaphelenchus xylophilus) [53] were used for bioassay. The phytopathogenic fungi and pine wood nematode used in the experiments were obtained from the Microbiology Laboratory of Zhejiang A & F University. The active compound of Streptomyces against phytopathogenic fungi were detected using plate confrontation assays. Streptomyces were cultured on Gao’s No. 1 medium at 28 °C for 7 to 15 days, and the pathogenic fungi were cultured on potato dextrose agar (PDA) at 25 °C for 7 days. The fungal block (diameter = 9 mm) was inoculated in the center of the PDA plate, and then the Streptomyces block (diameter = 9 mm) was inoculated symmetrically at 25 mm on both sides. To calculate the inhibitory activity, PDA plate with pathogenic fungus block was used as control, each treatment was replicated three times, and the colony diameter was measured after culturing at 25 °C for 7 days. The inhibitory activities of each isolate were computed as follows:
inhibitory activities = [(diameter in control group − diameter in treatment group)/diameter in control group] × 100%,
The nematocidal activity of Streptomyces fermentation broths were detected with dipping method. All isolates were inoculated into triangular bottles containing 50 mL of GGS broth and fermented at 200 rpm on a rotary shaker at 28 °C for 7 days to obtain the fermentation broths. The fermentation broths were centrifuged at 8000 rpm for 10 min to collect the supernatants, and then 2 mL of the supernatants were filtered through the 0.45 μm filter to obtain filtrates. A total of 450 μL of fermentation broth filtrates, 50 μL of nematode solutions (containing about 100 mixed-instar nematodes), and 100 μL of streptomycin solution (1200 ppm) were added into the 24-well cell plates. Sterile water and uninoculated fermentation medium supernatant filtrates were used as controls, and each treatment was repeated three times. After culturing at 28 °C for 24 h in the dark, the nematode mortalities were calculated under the microscope. The nematode-corrected mortality was calculated according to the Schneider–Orelli formula [54]. The nematocidal activity of each isolate was computed as follows:
corrected mortality = {[treatment mortality (%) − control mortality (%)]/[100 − control mortality (%)]} × 100.

3. Results

3.1. Identification and Phylogenetic Relationship of Streptomyces

Soil samples collected from 13 provinces and cities in Southern China were separated by unified conditions, and isolated strains with similar physiological characteristics were selected for subsequent diversity analysis. Full length 16S rDNA sequences of the isolates were amplified and sequenced (Supplementary Materials), and the obtained sequences were compared with the relevant strains in EzBioCloud (accessed on 10 August 2023, www.EzBioCloud.net) and NCBI (accessed on 10 August 2023, www.NCBI.nlm.nih.gov/). A total of 411 strains were identified as Streptomyces, and 16S rDNA sequences with similarity of more than 98.65% were considered to be of the known species; however, there were eight sequences were considered to be eight potentially new species. The phylogenetic tree of the 411 isolates was performed online using PhyML 3.0 with Mycolicibacterium smegmatis as the outgroup, A total of 411 Streptomyces strains were classified into 96 OTU on the basis of 16S rDNA sequence dissimilarity (Figure 1).
The maximum likelihood phylogenetic tree constructed on the basis of 16S rDNA sequence roughly showed the clustering of isolates among the 13 provinces in Southern China, and then principal coordinate analysis (PCoA) was performed to more directly show the phylogenetic relationship among different provinces (Figure 2). The analysis results showed that isolates from the 13 provinces were roughly divided into three groups. The first group comprises the sample plots in Southwestern China: Yunnan, Guizhou, Sichuan, and Tibet. The isolates from Tibet have a certain evolutionary distance from those from Yunnan, Guizhou, and Sichuan. The second group comprises the southeastern region of China: Fujian, Jiangxi, Hunan, Jiangsu, Shanghai, and Zhejiang. Furthermore, the third group comprises the Tropic of Cancer and the regions to the south: Guangdong, Guangxi, and Hainan.

3.2. Diversity Analysis of Streptomyces

The 411 isolates contained 190 unique 16S rDNA sequences, which were divided into 96 OTUs with a distance of 0.01 (Figure 2). Good’s coverage of each province was 0.87 (XZ), 0.33 (GX), 0.21 (HA), 0.33 (GX), 0.83 (HN), 0.81 (JX), 0.71 (FJ), 0.94 (JS), 0.66 (ZJ), 0.71 (SH), 0.64 (YN), 0.76 (GZ), and 0.88 (SC) (Supplementary Table S1). On average, there were 12.69 ± 6.21 OTUs in each province, and each OTU occurred in 1.72 ± 1.63 provinces on average. The same 16S rDNA sequence was obtained from XZ and SH, at a distance of 3000 km, indicating that Streptomyces has the ability of long-distance dispersal. The beta diversity of Streptomyces varies with the geographic distance of 1000 to 3000 km in space (Figure 3). The beta diversity of Streptomyces varies with geographical distance on a spatial scale of 1000 to 3000 km, despite the possibility of long-distance dispersal. Phylogenetic differentiation and Bray–Curtis difference (BCD) increased with the increase in geographical distance (Figure 3) (Unifrac distance, Mantel R2 = 0.1276, p = 0.2081; Bray–Curtis difference, Mantel R2 = 0.0016, p = 0.4953). Phylogenetic clustering showed that the phylogenetic relationships among taxa within provinces were closer than those from other provinces [55], indicating that the geographic range of Streptomyces population was larger.
Environmental characteristics of the sampling sites in the 13 provinces were used to detect the impact of the environment on the beta diversity of Streptomyces. The results showed that beta diversity of Streptomyces varies with latitude (Mantel R2 = 0.3828, p = 0.0113), but has no significant correlation with annual average temperature, longitude, and annual average precipitation (Table 1). These results indicated that environmental variables have little impact on beta diversity of Streptomyces, but the latitudinal variation is the most significant affecting factor. In the correlation coefficient analysis between the diversity of Streptomyces and environmental characteristics, there is no significant relationship between the phylogenetic diversity of Streptomyces and environmental variables, while the recent taxon index showed a significant negative correlation with the annual average temperature (R = −0.23, p = 0.045) (Figure 4).
Network analysis showed that the sharing of OTUs among the sampling sites, indicating that the clustering among sites was consistent with the geographical distribution (Figure 5). The network also showed that 62.5% (5/8) of the shared OTU observed in the southeast was also found in the southwest.

3.3. Biological Control Potential of Streptomyces

The bioassay results showed that there were 27 isolates (Table 2) with broad-spectrum antimicrobial activity, while there were 71 isolates (Table 3) with strong nematocidal activity against pine wood nematode. Among them, hn17 and jx27 have strong inhibitory activities against B. dothidea (inhibitory activity ≥ 70%), gx04 and sh05 against L. theobromae, zj25 against P. disseminate, gx10 against S. sclerotiorum, hn57, xz47, xz56, and fj07 against R. solani, hn27 against F. graminearum, hn57, jx23, and zj26 against P. vexans, zj25 against N. oryzae, hn23 against F. oxysporum, and gx10 against F. solani. The nematocidal activity of the fermentation broth filtrates of these isolates can reach to more than 80%, and even 23 (Table 3) of them can exceed 95%, providing a promising hint for biological control of pine wilt disease. Meanwhile, there are two isolates (jx25 and jx30) that have both antimycotic and nematocidal activities (Figure 6).

4. Discussion

A total of 411 isolates of Streptomyces were collected from 13 provinces in Southern China in this study. According to the phylogenetic cluster analysis, isolates from the southeast (Hunan, Jiangxi, Fujian, Jiangsu, Zhejiang, and Shanghai provinces), southwest (Tibet, Yunnan, Guizhou, and Sichuan provinces) and Tropic of Cancer and the regions to the south (Guangdong, Guangxi, and Hainan provinces) were separated completely. In the cluster of Southwest China, there is a certain evolutionary distance between isolates in Tibet and those in Yunnan, Guizhou, and Sichuan. Streptomyces and other soil microorganisms showed regional specificity [56,57,58], which corresponds to the overall complex environment in Southern China. The overall terrain of Southern China is higher in the west and lower in the east, and the southwest has a higher altitude (especially the Qinghai Tibet Plateau).
Selecting similar habitats for sample collection could reduce the possibility of environmental selection [59]. Phylogenetic clustering in spatial geography showed that the clustering of isolates collected had no significant relationship with geographical distance. Further analysis of the impact of environmental characteristics on the diversity of Streptomyces showed that environmental variables had little impact on the beta diversity of Streptomyces, and the impact was most significant due to latitude, while the phylogenetic diversity of Streptomyces (as defined by PD and NTI) was significantly related to the annual average temperature. Talbot et al. also found that the impact of environmental change on spatial scale was small when conducting biogeographical analysis of fungi in soil with single vegetation [58]. Latitude diversity gradient has been a basic model in ecology since Wallace Period, and widely exists in the classification of animals and plants. However, there are few reports on latitude diversity gradient in bacterial research [35,60]. Nitrogen, carbon, phosphate, and other sources are essential elements required for bacterial growth [26]. These essential elements’ diversity along with the latitude gradient’s diversity may be two of the most significant events impacting the beta diversity of Streptomyces. The ecological hypothesis explains that the difference in species richness has an influence on ecological factors, such as carrying capacity and productivity, which change with the climate gradient. The hypothesis of historical contingency shows that the gradient of diversity is caused by ecological, demographic events, and historical geology that affect diffusion and diversification.
The evaluation of the formation of microbial diversity depends on the limitations of sampling design and culture methods, and the taxa with low relative abundance lack sensitivity. In the current study, the sampling was concentrated in Southern China, but the corresponding sampling latitude span was around 11.60°, while the longitude span was around 33.47°. The culture methods used to isolate Streptomyces are very limited, and the vast majority of Streptomyces are still not culturable. However, the taxon-specific method can be used to determine the physiological characterization and genotype of discrete microbial taxon, and these data can be used to describe and assume microbial diversity patterns.
More than 34,000 natural active substances have been isolated from microorganisms [61], of which 35% are from actinomycetes and mainly from Streptomyces. The various primary and secondary metabolites produced by Streptomyces are widely used in various fields such as medical, industrial, agricultural, and forestry practices. Avermectin, a 16-membered pentacyclic lactone compounds derived from polyketide and linked to a disaccharide of the methylated deoxysugar L-oleandrose, produced by S. avermitilis, is a highly efficient commercial antiparasitic agent in the field of agriculture and forestry [62]. Twenty years after it was discovered, Avermectin is still the best nematicide for field application in the control of pine wood nematode [63]. In Cuba, Streptomyces sp. CBQ-EA2 and CBQ-B-8 used as biocontrol agents against root rot complex disease of Phaseolus vulgaris, caused by Macrophomina phaseolina and R. solani, in the field [64]. S. aureoverticillatus HN6 has significant effects on disease prevention and growth promotive, which could improve the rhizosphere fertility of plants and regulate the rhizosphere microbial community of plants [65]. In maize plants, Streptomyces promote their growth and induce disease resistance through the regulation of auxin signaling and archetypal defense pathways [66]. In the current study, we found that there were 27 isolates with broad-spectrum antimicrobial activity, and 71 isolates showed strong nematocidal activity against pine wood nematode. However, the chemical composition of these compounds and the mechanisms underlying the phenomena are still unclear.

5. Conclusions

This study showed that the diversity of Streptomyces in Southern China presents a gradient of latitude diversity, but it may not change significantly with geographical distance because the sampling latitude span is relatively small. Habitat filtration is usually used to explain biogeography, but it cannot be used to explain the temperature gradient of NTI. These results provide important ideas for the study of terrestrial bacterial diversity and even terrestrial microorganisms in Southern China. In addition, the preliminarily screen of the collected Streptomyces resources in terms of biological control applications was conducted, which laid a certain foundation for the development of biological control applications in the next step and also for research to assess the existence of a relationship between biological control and geographical environment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13102500/s1. Supplementary Figure S1. Streptomyces strains were isolated and characterized from 13 provinces in southern China: Tibet, Guangxi, Guangdong, Hainan, Hunan, Jiangxi, Fujian, Jiangsu, Zhejiang, Shanghai, Yunnan, Guizhou and Sichuan. Supplementary Table S1. Streptomyces isolates were collected and characterized from 13 provinces in southern China. Samples were aggregated across three soil samples respectively to represent each region. Abbreviations: Lat., latitude; Long., longitude; PD, phylogenetic diversity; NTI, nearest taxon index; AAT, average annual temperature; AAP, average annual precipitation.

Author Contributions

L.Z., X.Z., J.C. and H.L. conceived and designed the experiments. F.P., C.Z., F.C., Y.H. and W.P. performed the experiments. F.P., L.Z., H.L. and X.Z. analyzed the data. L.Z. and F.P. discussed the results. F.P., L.Z. and X.Z. wrote the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Launching Funds for Talents of Zhejiang A & F University, grant numbers 2020FR036 and 2022LFR004, and the Zhejiang Province Key Research and Development Program, grant number 2023C04023.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Maximum likelihood tree based on 16S rDNA sequences of Streptomyces isolates. A total of 411 Streptomyces isolates were collected from 13 provinces in Southern China and classified into 96 operational taxonomic units (OTUs) on the basis of 16S rDNA sequence dissimilarity. The inner color ring makes the color block represent the 96 OTUs identified in this study. The outer color ring shows the separation position of each isolate with color. The sampling site of each isolate can be determined according to the isolate name described in Table S1.
Figure 1. Maximum likelihood tree based on 16S rDNA sequences of Streptomyces isolates. A total of 411 Streptomyces isolates were collected from 13 provinces in Southern China and classified into 96 operational taxonomic units (OTUs) on the basis of 16S rDNA sequence dissimilarity. The inner color ring makes the color block represent the 96 OTUs identified in this study. The outer color ring shows the separation position of each isolate with color. The sampling site of each isolate can be determined according to the isolate name described in Table S1.
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Figure 2. Principal coordinate analysis of 13 provinces. ■, the first group is the sample plots in Southwestern China: Yunnan, Guizhou, Sichuan, and XiZhang. ▲, the second group is the southeastern region of China: Fujian, Jiangxi, Hunan, Jiangsu, Shanghai, and Zhejiang. ●, the third group is the Tropic of Cancer and the regions to the south: Guangdong, Guangxi, and Hainan.
Figure 2. Principal coordinate analysis of 13 provinces. ■, the first group is the sample plots in Southwestern China: Yunnan, Guizhou, Sichuan, and XiZhang. ▲, the second group is the southeastern region of China: Fujian, Jiangxi, Hunan, Jiangsu, Shanghai, and Zhejiang. ●, the third group is the Tropic of Cancer and the regions to the south: Guangdong, Guangxi, and Hainan.
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Figure 3. Taxonomic (above) and phylogenetic (below) dissimilarities of Streptomyces vary with geographical distance between sampling sites. The Mantel coefficient is shown with the linear regression line.
Figure 3. Taxonomic (above) and phylogenetic (below) dissimilarities of Streptomyces vary with geographical distance between sampling sites. The Mantel coefficient is shown with the linear regression line.
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Figure 4. (A) Correlation coefficients for relationships between Streptomyces diversity and environmental characteristics across sampling sites. The bold value indicates a statistically significant correlation (*, p < 0.05; ***, p < 0.001). Abbreviations: phylogenetic diversity (PD), nearest taxon index (NTI), latitude (Lat.), longitude (Long.), average annual temperature (AAT), and average annual precipitation (AAP). (B) The annual average temperature is related to the phylogenetic diversity of Streptomyces, which is displayed by the latest taxon index. Pearson correlation coefficient is displayed together with linear regression line.
Figure 4. (A) Correlation coefficients for relationships between Streptomyces diversity and environmental characteristics across sampling sites. The bold value indicates a statistically significant correlation (*, p < 0.05; ***, p < 0.001). Abbreviations: phylogenetic diversity (PD), nearest taxon index (NTI), latitude (Lat.), longitude (Long.), average annual temperature (AAT), and average annual precipitation (AAP). (B) The annual average temperature is related to the phylogenetic diversity of Streptomyces, which is displayed by the latest taxon index. Pearson correlation coefficient is displayed together with linear regression line.
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Figure 5. Network analysis showed the sharing of operational taxonomic units (OTUs) among sampling sites, indicating that the clustering among sites is consistent with the geographical distribution. The sample points (circles) are colored by the distribution of southwest (green), southeast (yellow), or the Tropic of Cancer and the regions to the south (red). If 70% of the sequences are separated from the relevant regions, then the OTU (rectangle) is colored, and vice versa.
Figure 5. Network analysis showed the sharing of operational taxonomic units (OTUs) among sampling sites, indicating that the clustering among sites is consistent with the geographical distribution. The sample points (circles) are colored by the distribution of southwest (green), southeast (yellow), or the Tropic of Cancer and the regions to the south (red). If 70% of the sequences are separated from the relevant regions, then the OTU (rectangle) is colored, and vice versa.
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Figure 6. The diagram showed the preliminary biological control activity of 411 Streptomyces isolates. The yellow circle indicates isolates with broad-spectrum antimicrobial activity; the green circle indicates isolates with strong nematocidal activity; and the grey circle indicates the total number of bacteria.
Figure 6. The diagram showed the preliminary biological control activity of 411 Streptomyces isolates. The yellow circle indicates isolates with broad-spectrum antimicrobial activity; the green circle indicates isolates with strong nematocidal activity; and the grey circle indicates the total number of bacteria.
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Table 1. Relationships between environmental factors and phylogenetic (UniFrac distance) and taxonomic (Bray–Curtis dissimilarity) values of Streptomyces. The analyses were performed by either including all 16S rDNA sequences (weighted) or excluding duplicate sequences for each OTU (unweighted).
Table 1. Relationships between environmental factors and phylogenetic (UniFrac distance) and taxonomic (Bray–Curtis dissimilarity) values of Streptomyces. The analyses were performed by either including all 16S rDNA sequences (weighted) or excluding duplicate sequences for each OTU (unweighted).
Analysis TypeSequence
Inclusion		Correlation
Average Annual TemperatureLongitudeLatitudeAverage Annual
Precipitation
UniFracWeighted0.270.040.38 (p < 0.05)0.20
Unweighted−0.010.090.070.09
Bray–Curtis-−0.04−0.010.070.12
Table 2. Broad-spectrum inhibition of 27 isolates of common plant pathogenic fungi. “-”: no obvious antimicrobial activity.
Table 2. Broad-spectrum inhibition of 27 isolates of common plant pathogenic fungi. “-”: no obvious antimicrobial activity.
Strain NumberCorrected Mortality (%)
Botryosphaeria dothideaLasiodiplodia theobromaePestalotiopsis
disseminate		Sclerotinia sclerotiorumRhizoctonia solaniFusarium graminearumPythium vexansNigrospora oryzaeF. oxysporumF. solani
hn0545.36 ± 1.52-67.36 ± 6.9119.55 ± 4.6815.46 ± 2.7459.26 ± 7.6038.22 ± 2.1747.16 ± 6.11-6.30 ± 2.70
hn1788.69 ± 6.4555.63 ± 5.99-12.36 ± 3.4339.25 ± 2.7119.36 ± 6.4625.87 ± 10.7846.25 ± 6.4714.23 ± 2.5539.14 ± 4.78
hn2363.25 ± 5.0716.98 ± 3.37-9.65 ± 4.8144.87 ± 4.5625.64 ± 0.7958.87 ± 5.36-80.05 ± 3.65-
hn27-52.36 ± 3.3254.68 ± 6.7730.05 ± 4.1362.53 ± 4.4275.22 ± 3.8036.55 ± 2.668.96 ± 0.5039.88 ± 4.81-
gx04-70.23 ± 3.5152.36 ± 10.0626.32 ± 4.50--16.23 ± 2.2546.25 ± 4.5138.35 ± 3.7419.85 ± 2.92
hn3958.97 ± 6.768.65 ± 2.8737.87 ± 6.77--44.52 ± 1.53-43.66 ± 2.8366.90 ± 2.5750.77 ± 5.63
gz0726.56 ± 2.4625.33 ± 5.276.05 ± 1.6858.25 ± 5.89-31.40 ± 2.2126.95 ± 1.8062.45 ± 2.8533.33 ± 2.49-
hn57--55.40 ± 11.3013.25 ± 2.5879.52 ± 4.99-71.27 ± 4.8259.80 ± 3.85--
gx1048.35 ± 5.3217.23 ± 4.70-78.55 ± 1.9732.00 ± 3.01-22.74 ± 4.0551.73 ± 9.8452.36 ± 4.3080.67 ± 8.34
sh0516.35 ± 5.1776.52 ± 6.35-7.52 ± 1.3454.35 ± 7.6736.66 ± 4.6348.01 ± 9.64--26.88 ± 2.98
xz2238.92 ± 4.4523.21 ± 9.5916.52 ± 6.2260.15 ± 3.70-59.33 ± 4.87-34.22 ± 8.5322.25 ± 2.5945.66 ± 2.92
xz29--25.88 ± 7.5441.04 ± 8.20-37.37 ± 7.25-36.52 ± 5.0257.36 ± 4.5167.23 ± 6.57
xz4756.35 ± 6.91---73.33 ± 4.6222.26 ± 3.2354.21 ± 3.75-25.52 ± 6.4339.48 ± 9.43
xz569.86 ± 4.06-16.50 ± 7.1252.22 ± 10.5076.28 ± 7.66-24.10 ± 3.91---
xz5841.20 ± 4.1468.32 ± 13.08-18.58 ± 5.54-36.11 ± 8.2119.58 ± 4.50-32.06 ± 4.946.55 ± 1.45
xz6670.21 ± 5.0422.10 ± 6.60---48.78 ± 3.6062.37 ± 3.72-15.83 ± 2.0038.77 ± 7.47
sh1965.25 ± 8.6533.35 ± 8.4262.32 ± 7.30---25.88 ± 10.2456.29 ± 7.45-18.55 ± 5.50
jx1756.25 ± 9.2616.25 ± 5.2023.01 ± 5.4031.22 ± 6.49--17.25 ± 4.5154.23 ± 6.1565.15 ± 9.23-
jx2336.25 ± 8.2256.35 ± 13.9058.69 ± 8.9340.01 ± 4.9323.16 ± 4.7122.54 ± 3.1978.52 ± 8.3912.25 ± 4.6061.85 ± 6.3335.28 ± 5.07
jx25-28.45 ± 13.0728.84 ± 7.7533.28 ± 3.9856.21 ± 3.8246.35 ± 7.22-61.52 ± 7.3835.55 ± 9.3458.20 ± 9.33
jx2779.14 ± 6.2315.36 ± 6.18-7.82 ± 1.6826.35 ± 6.6852.32 ± 10.3467.23 ± 10.3744.87 ± 4.3317.25 ± 5.5730.09 ± 7.90
jx3033.74 ± 10.47-21.22 ± 6.10-22.18 ± 4.5621.54 ± 2.9728.80 ± 9.8567.65 ± 9.76--
zj2556.35 ± 5.1025.32 ± 7.2072.78 ± 7.3646.25 ± 6.75-17.02 ± 4.1922.31 ± 6.5976.91 ± 5.1959.10 ± 4.0229.58 ± 8.48
zj2616.35 ± 2.5322.23 ± 3.81-21.54 ± 3.4568.56 ± 8.60-70.22 ± 2.41-32.05 ± 5.77-
jx4163.87 ± 4.60-49.53 ± 4.6442.33 ± 6.93-35.37 ± 4.2739.14 ± 5.3959.89 ± 5.9652.50 ± 2.4946.64 ± 9.59
xz7923.65 ± 2.9256.32 ± 6.6668.25 ± 3.8622.25 ± 9.5332.52 ± 6.32-12.86 ± 2.9819.85 ± 2.9135.28 ± 5.0728.95 ± 7.99
fj07--25.84 ± 1.9033.39 ± 4.1875.56 ± 9.2253.32 ± 5.5946.52 ± 3.2125.68 ± 4.2929.87 ± 1.7861.23 ± 6.19
Table 3. Results of the nematocidal activity of the fermentation broth. Isolates with strong nematocidal activity (mortality ≥ 80%) were listed. Isolates with more than 95% nematocidal activity were highlighted in bold.
Table 3. Results of the nematocidal activity of the fermentation broth. Isolates with strong nematocidal activity (mortality ≥ 80%) were listed. Isolates with more than 95% nematocidal activity were highlighted in bold.
Strain NumberCorrected Nematode Mortality (%)Strain NumberCorrected Nematode Mortality (%)Strain NumberCorrected Nematode Mortality (%)
fj0683.22 ± 2.07hn6698.30 ± 1.43sh0792.30 ± 5.93
fj0897.77 ± 3.16hn6791.14 ± 0.56sh1497.25 ± 3.89
fj0987.10 ± 0.70hn6897.38 ± 1.90sh1593.05 ± 5.23
fj1193.56 ± 5.15js0298.19 ± 1.52xz1094.53 ± 4.86
fj1583.57 ± 0.71js0795.85 ± 0.25xz1088.88 ± 0.56
gx1496.59 ± 4.83js0998.18 ± 2.58xz1193.39 ± 5.09
gx1798.45 ± 2.19js1297.16 ± 2.19xz1888.52 ± 9.37
gx2596.67 ± 0.92js1492.35 ± 1.89xz2093.02 ± 5.28
gx2788.90 ± 8.45js1991.35 ± 4.63xz2398.47 ± 2.17
gx3187.92 ± 4.28jx2587.53 ± 9.42xz2694.78 ± 4.27
gx3392.85 ± 10.11jx2695.37 ± 4.24xz6190.95 ± 6.71
gx4480.83 ± 14.66jx3084.18 ± 1.00xz8193.80 ± 4.87
gz0187.56 ± 0.56jx3597.25 ± 0.49yn0594.20 ± 4.73
gz1495.27 ± 1.25jx3784.61 ± 3.02zj0887.23 ± 5.92
gz1780.63 ± 0.93jx4288.01 ± 10.42zj1096.75 ± 4.59
gz1995.39 ± 3.38jx5385.71 ± 0.69zj1297.62 ± 3.37
hn1187.01 ± 0.33jx5483.97 ± 14.25zj1593.68 ± 4.58
hn1988.45 ± 6.34sc0886.03 ± 4.22zj2297.27 ± 2.23
hn2894.07 ± 5.39sc0997.60 ± 2.28zj2483.88 ± 1.02
hn3098.93 ± 0.82sc1098.31 ± 2.31zj2882.68 ± 14.14
hn3385.86 ± 0.64sc1297.95 ± 2.90zj3181.09 ± 8.65
hn3792.85 ± 6.79sc1789.41 ± 7.89zj3482.68 ± 4.49
hn3889.80 ± 7.86sc2690.47 ± 7.30zj4389.61 ± 0.46
hn6184.71 ± 2.69sh0493.82 ± 8.75
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Zhou, L.; Pei, F.; Pu, W.; Zhang, C.; Chen, F.; Hu, Y.; Chen, J.; Lin, H.; Zhou, X. Diversity Analysis and Biocontrol Potential of Cultivatable Terrestrial Bacterial Streptomyces in Southern China. Agronomy 2023, 13, 2500. https://doi.org/10.3390/agronomy13102500

AMA Style

Zhou L, Pei F, Pu W, Zhang C, Chen F, Hu Y, Chen J, Lin H, Zhou X. Diversity Analysis and Biocontrol Potential of Cultivatable Terrestrial Bacterial Streptomyces in Southern China. Agronomy. 2023; 13(10):2500. https://doi.org/10.3390/agronomy13102500

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Zhou, Lifeng, Fan Pei, Wangling Pu, Chuang Zhang, Fei Chen, Yuechen Hu, Jie Chen, Haiping Lin, and Xudong Zhou. 2023. "Diversity Analysis and Biocontrol Potential of Cultivatable Terrestrial Bacterial Streptomyces in Southern China" Agronomy 13, no. 10: 2500. https://doi.org/10.3390/agronomy13102500

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