Microbial community composition of Pulsatilla chinensis (Bunge) Regel root in different regions

Microbial community composition of Pulsatilla chinensis (Bunge) Regel root in different regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Microbial community composition of Pulsatilla chinensis (Bunge) Regel root in different regions Jun Shi, Gangjun Xi, Jiuling Ding, Hetong Yang, Chao Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4387229/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: This study was conducted to investigate the microbial community composition in the roots of Pulsatilla chinensis (Bunge) Regel samples from three different regions. Methods: 16S rRNA sequencing of P. chinensis (Bunge) Regel samples from three different regions, Anhui, Northeast China, and Hebei, was performed using Illumina high-throughput sequencing technology. Weighted gene co-expression network analysis (WGCNA) was employed to screen the highly collaborative bacterial modules, followed by hub microbiota identification. Finally, the correlation between the hub microbiota was evaluated. Results: The top 10 phyla in the three groups included Acidobacteriota , Actinobacteriota , and Bacteroidota ; and the top 10 genera in the three groups included Alphaproteobacteria , Dongia , and Gemmatimonadaceae . In addition, Gemmatimonadaceae , Gemmatimonadales , and Gemmatimonadetes were significantly overrepresented in the DB samples; Rhizobiales , Alphaproteobacteria , and Proteobacteria were all significantly overrepresented in the AH samples; and Rokubacteriales , Methylomirabilia , and Methylomirabilota were all significantly overrepresented in the HB samples. In addition, the brown and turquoise modules were closely related to regional groups (|r| > 0.5 and P < 0.05). Moreover, 14 hub microbiota were identified at the species level, including Gemmatimonadaceae , KD4-96 , MBNT15 , Micromonospora chersina , Rokubacteriales , Streptomyces , Reyranella , Acidobacteriota , uncultured Sinorhizobium , Proteobacteria , Verrucomicrobiotae , Litorilinea , Rhodoplanes , and bacteriap25 . Finally, the correlation between the hub microbiota was evaluated. Among which, Gemmatimonadaceae was positively correlated with Proteobacteria , whereas Acidobacteriota was negatively associated with bacteriap25 . Conclusion: The microbial community composition of P. chinensis (Bunge) Regel roots showed differences in the three different regions, and the 14 hub microbiota might be associated with the altitudinal, geographical, climatic, and cultivation practices of this species, which provides new insights into its cultivataion . Pulsatilla chinensis (Bunge) Regel microbial community root 16S rRNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights The microbial community composition on root of Pulsatilla chinensis (Bunge) Regel samples showed differences in three different regions. The 14 hub microbiota identified may be associated with the altitudinal, geographical, climatic, and cultivation practices of Pulsatilla chinensis (Bunge) Regel. This study provides new insights into the cultivation of Pulsatilla chinensis (Bunge) Regel. Background Pulsatilla chinensis (Bunge) Regel is a buttercup plant with a long medicinal history in China [1–3]. Its main chemical components are triterpenoid saponins, which are important for the treatment of intestinal diseases [4, 5]. It has also shown good pharmacological effects such as anti-inflammatory, antibacterial, antitumor, and immune regulation effects [6–8]. P. chinensis (Bunge) Regel is widely distributed and mainly produced in Heilongjiang, Jilin, Liaoning, Inner Mongolia, Henan, Shandong, Shaanxi, Jiangsu, and other provinces. A previous study discovered that the quantity of saponin B4 in P. chinensis (Bunge) Regel from 17 different production areas differed [9]. Therefore, it is of great significance to study P.a chinensis (Bunge) Regel from different regions. Plant microbiome is the collective term for microorganisms living inside and outside the plant [10, 11]. It is comprised of a variety of microorganisms, including bacteria, fungi, viruses, and protists [12]. The plant microbiome is an vital part of the interaction with the environment and has a significant impact on plant growth and environmental adaptation, such as helping plants absorb nutrients, controlling diseases, and improving plant resistance to stress [13, 14]. Therefore, owing to the importance of the quality and value of medicinal materials in P. chinensis (Bunge) Regel , the microbial community on the root of P. chinensis (Bunge) Regel should be thoroughly studied in different regions. 16S rRNA sequencing can be used to identify and classify microorganisms and provide information on the composition and diversity of microbial communities [15–17]. In this study, the microbial community composition in P. chinensis (Bunge) Regel roots from three different regions was studied using 16S rRNA sequencing, and the associations between hub microbiota were further explored. This study offers new perspectives on the cultivation of P. chinensis (Bunge) Regel. Materials and methods Sample collection P. chinensis (Bunge) Regel seeds were acquired from Anhui (AH), Northeast China (DB), and Hebei (HB) and planted in the greenhouses of Jiangsu Vocational College of Agricultural and Forestry. The plants were then taken out of the flowerpot, large soil aggregates were manually removed, and the soil firmly attached to the roots was acquired using a sterile brush and treated as the rhizosphere soil. To begin with, the rhizosphere soil was sampled and sieved to remove plant debris. Subsequently, a portion of the soil sample was placed in a sterile centrifuge tube, flash-frozen in liquid nitrogen, and stored at -80°C for subsequent analyses. DNA extraction and 16S rRNA sequencing DNA from the soil was collected using DNeasy Power soil kit (Qiagen, Valencia, CA, U.S.) and Qubit®dsDNA kit (Life Technologies, CA, U.S.) based on the manufacturer’s guidelines. DNA integrity and purity were verified using 1% agarose gel electrophoresis. DNA samples from the individual strains of each variety were used for 16S rRNA sequencing. 16S rRNA sequencing was performed using the IonS5™XL sequencing technology platform with the universal primers 515F (5’–3’) and 806R (5’–3’) amplified the V4 hypervariable region of the 16S rRNA gene. High-quality clean reads were obtained by quality filtering of raw reads according to the Cutadapt (version 1.9.1) quality control process under specific filtering conditions. The UCHIME algorithm was used for comparison with the SILVA database, and the chimeric sequence was detected using the UCHIME algorithm, after which it was removed. Sequences with more than 97% similarity were allocated to a taxonomic unit (OTU). Phylogenetic classification was performed using the Ribosome Database Engineering (RDP) classifier in the bacterial SILVA database with a threshold of 80% confidence. Bioinformatics Diversity indices such as the Chao1, Shannon, and Simpson indices, were calculated. The similarity in microbial community structure among the three groups was examined using principal coordinate analysis (PCoA). LEfSe was used to confirm the significant differences in the presence of microbes. WGCNA was used to identify highly collaborative bacterial modules. First, the top 15% ASV with large inter-sample variation were selected and analyzed using R package “WGCNA” (Version 1.71) [18]. In order to satisfy the prerequisite of scale-free network distribution as much as possible, the power" value at which the square value of the correlation coefficient first reached 0.85 was selected. Parameters were set using clustering and dynamic pruning methods to aggregate bacteria with high correlations into modules. By calculating the correlation between modules and groups, modules closely related to the group (|r| > 0.5 and P < 0.05) were selected. In addition, the connectivity of the microbiota within key modules was calculated, and two criteria were utilized to screen the microbiota, including gene and key module significance > 0.2, module membership key module significance > 0.8. Cytoscape (version 3.6.1) was used to construct and visualize the interaction network of the microbiota between the key modules. The microbiota in the modules were compared and analyzed at the species level and the differences at the species level according to the annotation information to ASV, and the intersected microbiota were considered as the hub microbiota. Spearman’s rank correlation coefficient was utilized to calculate microbial abundance at the hub microbiota species level, and the correlation and P value between the hub microbiota were obtained. Results Change in bacterial diversity The rarefaction curve indicated the adequacy of all the samples (Fig. 1 A). Alpha diversity analysis showed that bacterial diversity in the DB group was greater than that in the AH group (P < 0.05; Fig. 1 B). PCA and PCoA showed different bacterial community compositions in the AH, DB, and HB groups (Figs. 1 C and D). Comparison of the taxonomic profile of the microbiome The relative abundances of the top 10 most abundant bacterial phyla and genera in the three groups are shown in Fig. 2 . The top 10 phylum in the three groups were Acidobacteriota (15.58% in DB, 14.52% in HB, and 14.92% in AH), Actinobacteriota (6.44% in DB, 6.61% in HB, and 9.74% in AH), Bacteroidota (3.40% in DB, 2.93% in HB, and 3.71% in AH), Chloroflexi (6.17% in DB, 7.85% in HB, and 6.68% in AH), Firmicutes (5.31% in DB, 4.19% in HB, and 4.88% in AH), Gemmatimonadota (8.52% in DB, 8.04% in HB, and 5.47% in AH), Myxococcota (3.43% in DB, 2.15% in HB, and 2.65% in AH), Planctomycetota (7.55% in DB, 6.25% in HB, and 8.06% in AH), Proteobacteria (27.05% in DB, 31.53% in HB, and 33.09% in AH), and Verrucomicrobiota (3.46% in DB, 4.89% in HB, and 3.94% in AH). The top 10 genera in the three groups were Alphaproteobacteria (1.41% in DB, 2.30% in HB, and 1.79% in AH), Dongia (1.42% in DB, 2.27% in HB, and 1.38% in AH), Gemmatimonadaceae (6.07% in DB, 5.04% in HB, and 2.79% in AH), KD4 − 96 (1.64% in DB, 2.49% in HB, and 1.47% in AH), Latescibacterota (1.99% in DB, 1.19% in HB, and 0.95% in AH), MND1 (2.92% in DB, 3.05% in HB, and 2.93% in AH), Rokubacteriales (1.80% in DB, 2.81% in HB, and 0.43% in AH), Subgroup_17 (1.90% in DB, 1.58% in HB, and 0.78% in AH), Vicinamibacteraceae (4.03% in DB, 2.56% in HB, and 4.38% in AH), and Vicinamibacterales (2.81% in DB, 2.97% in HB, and 3.88% in AH). Dominant bacterial taxa LEfSe was used to generate a cladogram for identifying specific bacteria (Fig. 3 ). Several bacteria, including Gemmatimonadaceae , Gemmatimonadales , and Gemmatimonadetes were significantly overrepresented in DB samples; Rhizobiales , Alphaproteobacteria , and Proteobacteria were all significantly overrepresented in AH samples; and Rokubacteriales , Methylomirabilia , and Methylomirabilota were all significantly overrepresented in HB samples. These results further support our diversity results, showing a large heterogeneity between the microbiota inside and outside the tumors. Identification of hub microbiota To maximize the fulfillment of the scale-free network distribution premise, the "power" value was selected as the first time the square value of the correlation coefficient reached 0.85, which is "power" = 12 (Fig. 4 A). Nine modules were integrated (Fig. 4 B). In addition, the brown and turquoise modules were closely related to the regional groups (|r| > 0.5 and P < 0.05) (Fig. 4 C). The key module networks were constructed. The brown module network contained 40 nodes and 90 edges (Fig. 5 A), and the turquoise module network included 22 nodes and 55 edges (Fig. 5 B). After taking the intersection of the brown and turquoise modules with DEspecies, respectively, 14 hub microbiota were identified at the species level (Fig. 5 C), including Gemmatimonadaceae , KD4-96 , MBNT15 , Micromonospora chersina , Rokubacteriales , Streptomyces , Reyranella , Acidobacteriota , uncultured Sinorhizobium , Proteobacteria , Verrucomicrobiotae , Litorilinea , Rhodoplanes , and bacteriap25 . Finally, the correlation between hub microbiota was evaluated (Fig. 5 D). Among which, Gemmatimonadaceae was positively correlated with Proteobacteria , whereas Acidobacteriota was negatively associated with bacteriap25 . Discussion The microbial community in the root of P. chinensis (Bunge) Regel should be studied in depth because of its importance for the quality and value of medicinal materials and because it is affected by various factors, such as geography, climate, and cultivation practices. Even in AH, DB, and HB, the microbial communities in the rhizosphere of several major regions of the Chinese white head were different, resulting in different qualities of medicinal materials in each region. In this study, the microbial community composition of roots was studied in P. chinensis (Bunge) Regel from three different regions, and associations between hub microbiota were explored. This study shows that the phyla Acidobacteriota, Firmicutes , and Gemmatimonadota were enriched, and Actinobacteriota, Chloroflexi , and Proteobacteria were depleted in the root microbiota of P. chinensis (Bunge) Regel in DB; whereas Actinobacteriota, Bacteroidota , and Proteobacteria were enriched, and Chloroflexi, Gemmatimonadota were depleted in the root microbiota of P. chinensis (Bunge) Regel in AH, which is in line with the results of previous studies investigating the rhizosphere communities of numerous plant species [19, 20]. Our method generated comparable microbiome profiles at the genus level. Overall, it was obvious that the microbial community composition of roots showed differences for the three different regions, resulting from the geography and environment P. chinensis (Bunge) Regel grew in. In addition, 14 hub microbiota were identified at the species level: Gemmatimonadaceae , KD4-96 , MBNT15 , Micromonospora chersina , Rokubacteriales , Streptomyces , Reyranella , Acidobacteriota , uncultured Sinorhizobium , Proteobacteria , Verrucomicrobiotae , Litorilinea , Rhodoplanes , and bacteriap25 . Wei et al. explored the distribution of the core root microbiota of Tibetan hulless barley along altitudinal and geographical gradients in the Tibetan Plateau and found that Gemmatimonadaceae was one of the core bacteria [21]. Peng et al. examined rhizosphere bacteria and soil functionality in response to different irrigation practices and found that Gemmatimonadaceae , which were the keystone taxa, were positively correlated with functional genes enriched in nutrient cycling [22]. Jambagi et al. revealed that the unclassified Chloroflexi group KD4-96 was the most abundant bacterial genus in the root-associated microbiota of red clover, which was determined by the geographic location and farming system [23]. Sun et al. suggested that wheat roots recruited specific Rokubacteriales under high levels of cadmium and arsenic, and their relative abundances were positively correlated with soil cadmium/arsenic bioavailability and resistance to cadmium and arsenic co-contamination [24]. Santos-Medellín et al. showed that the most abundant endosphere taxon during drought and weeks after re-watering was Streptomyces , and a corresponding isolate promoted root growth [25]. Duan et al. found that Reyranella is associated with smut resistance in sugarcane [26]. Chen et al. found that Reyranella , which is involved in denitrification, reduces soil nitrogen content and controls bacterial wilt [27]. Wang et al. explored the efficacy of fosthiazate against root-knot disease in Cucumis melo var. saccharinus and its underlying role on the rhizosphere microbiome, and the results indicated Acidobacteriota as the predominant phylum [28]. Grunert et al. evaluated the microbial and fungal community structures in four different tomato cultivation systems, including both soil-based and soilless culture systems. The results showed that Litorilinea was the most crucial factor in distinguishing between natural soils supplemented with animal and plant byproducts [29]. Sabri et al. explored the impact of soil temperature on lettuce growth and soil microbial diversity, which contributes to the maintenance of lettuce growth at low soil temperatures and found that Rhodoplanes were the predominant bacterial genera present in cooled soil [30]. Taken together, the results demonstrate that these 14 hub microbiota might be associated with the altitudinal, geographical, climatic, and cultivation practices of P. chinensis (Bunge) Regel , providing new insights into its cultivation. This study is the first to investigate the microbial community composition of P. chinensis (Bunge) Regel roots in three different regions. Nevertheless, this study had numerous deficiencies. First, the sample size was relatively limited. Therefore, a large-sample study should be conducted. Second, the microbial community composition of the roots of P. chinensis (Bunge) Regel samples from other regions should be explored. Finally, it is necessary to elucidate the molecular mechanisms underlying microbiota. Conclusion In conclusion, to date, information on the microbial community composition in the roots of P. chinensis (Bunge) Regel samples from three different regions is limited. This study provides basic information on the bacterial diversity and composition of P. chinensis (Bunge) Regel from three different regions. The 14 hub microbiota identified at the species level in this study may provide new insights into the cultivation of P. chinensis (Bunge) Regel. Declarations Declarations Ethics approval and consent to participate Pulsatilla chinensis (Bunge) Regel seeds used in this study were acquired from Anhui (AH), Northeast China (DB), and Hebei (HB) and planted in the greenhouses of Jiangsu Vocational College of Agricultural and Forestry.No permits were required for the collection of the samples. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This project was funded by Jiangsu Province Excellent Science and Technology Innovation Team Project(110751579), Yafu Technology Innovation and Service Project(2023KJ10), Science and Technology Project of Jiangsu Vocational College of Agricultural and Forestry(2021KJ50),The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contribution Conception and design of the research:JS;acquisition of data: GX and JS; analysis and interpretation of data: JD; statistical analysis: JD; drafting the manuscript: HY, JS and GX; revision of manuscript for important intellectual content: CX and JS. All authors read and approved the final manuscript. Data Availability Raw amplicon sequence data related to this study were deposited in the NCBI Sequence Read Archive (NCBI SRA) under Bioprojects PRJNA1104389. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4387229","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305552928,"identity":"160ee1bd-42fe-4278-bce9-9f305bcdc609","order_by":0,"name":"Jun Shi","email":"","orcid":"","institution":"Jiangsu Vocational College of Agricultural and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Shi","suffix":""},{"id":305552930,"identity":"e4ae6d41-1cfd-4e59-b1aa-3b296277cf28","order_by":1,"name":"Gangjun Xi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACfobDBx98/CPBzC//+OCDD8RokWw8lmw4s8GGXbIhLdlwBjFaDA6fUZPmbUjjNziQYybNQ5TLjp1hk+DdcVhasuHMN6AWOzndBgI6GHvOHraQPHPYmJ+xdxtQS7Kx2QECWpglziXeMGA7nCzZzAvSciBxGyEtbPJvDCQS2A7XbzjG8+x2DjFaeBjOGEkcbEtjNjjDw1ZtQ4wWCQZgIDecsWGWnMFmbDjDgAi/2B84fPDxnwpgVEowP3zxpcJOjqAWNGBAmvJRMApGwSgYBTgAAKD7SLpoz68NAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangsu Vocational College of Agricultural and Forestry","correspondingAuthor":true,"prefix":"","firstName":"Gangjun","middleName":"","lastName":"Xi","suffix":""},{"id":305552932,"identity":"4fe3eb62-207a-456b-9f53-775f20a0bfd9","order_by":2,"name":"Jiuling Ding","email":"","orcid":"","institution":"Jiangsu Vocational College of Agricultural and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Jiuling","middleName":"","lastName":"Ding","suffix":""},{"id":305552933,"identity":"30b742a1-b124-4bcf-bb7b-bd52ea54b9f2","order_by":3,"name":"Hetong Yang","email":"","orcid":"","institution":"Jiangsu Vocational College of Agricultural and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Hetong","middleName":"","lastName":"Yang","suffix":""},{"id":305552934,"identity":"8cf0af62-2414-4f3a-ba3d-1b3c46e67a7f","order_by":4,"name":"Chao Xu","email":"","orcid":"","institution":"Jiangsu Vocational College of Agricultural and Forestry","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-05-08 07:16:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4387229/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4387229/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57007461,"identity":"e2430e15-ede6-4c5a-bd7f-f94965379dcd","added_by":"auto","created_at":"2024-05-23 10:22:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1673675,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange in bacterial diversity. \u003c/strong\u003e(A) Rarefaction curves. (B) Alpha diversity analysis. (C) Principal components analysis (PCA). (D) Principal component analysis (PCoA). *, P \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/aca4bd09342660dd2cf9f4a1.jpg"},{"id":57007460,"identity":"f5af6d5f-5d74-40d2-9c9a-7f7141af3c3d","added_by":"auto","created_at":"2024-05-23 10:22:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":718079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relative abundance of bacteria at the phyla and genera level.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/012b8e3adc3dbedd448d2b25.jpg"},{"id":57007462,"identity":"f75b55e8-723c-4e59-b79b-911219667c7a","added_by":"auto","created_at":"2024-05-23 10:22:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2874364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferently abundant taxa identified using LEfSe analysis. \u003c/strong\u003e(A) Cladogram showing the differences in the relative abundance of taxa among the three groups. The plot was generated using the online LEfSe project. (B) Plot from the LEfSe analysis. The length of the bar column represents the LDA score. The figure shows the microbial taxa with significant differences among the three groups.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/9c027ac3d47d0e6c16d0e76a.jpg"},{"id":57007463,"identity":"39320b2c-0f9f-4e50-87a3-0c4f9513782a","added_by":"auto","created_at":"2024-05-23 10:22:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2514936,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWeighted gene co-expression network analysis.\u003c/strong\u003e (A) Hierarchical clustering of samples and selection of the weight parameter “power” of adjacency matrix and the mean connectivity. (B) Tree diagram of module division. (C) Global outline of the relationship between modules and regional groups.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/b917e5448e0ed53722dcbf46.jpg"},{"id":57007464,"identity":"8b70563a-1001-4244-9b65-7d55183becfc","added_by":"auto","created_at":"2024-05-23 10:22:50","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4601125,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of hub microbiota and correlation between the hub microbiota. \u003c/strong\u003e(A) Brown module network. (B) Turquoise module network. (C) Fourteen hub microbiota samples. (D) Correlation among hub microbiota. *, P \u0026lt; 0.05, **, P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/784d372ad619704f03201ed8.jpg"},{"id":57261352,"identity":"b1b60902-8b5c-4bb2-a4e4-67d54a71e890","added_by":"auto","created_at":"2024-05-28 09:46:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13695575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4387229/v1/72d96ae6-1125-44c0-b1d4-010f1df3c2b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbial community composition of Pulsatilla chinensis (Bunge) Regel root in different regions","fulltext":[{"header":"Highlights","content":"\u003col\u003e\n\u003cli\u003eThe microbial community composition on root of \u003cem\u003ePulsatilla chinensis (Bunge) Regel\u003c/em\u003e samples showed differences in three different regions.\u003c/li\u003e\n\u003cli\u003eThe 14 hub microbiota identified may be associated with the altitudinal, geographical, climatic, and cultivation practices of \u003cem\u003ePulsatilla chinensis (Bunge) Regel.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eThis study provides new insights into the cultivation of\u003cem\u003e Pulsatilla chinensis (Bunge) Regel.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Background","content":"\u003cp\u003e \u003cem\u003ePulsatilla chinensis (Bunge) Regel\u003c/em\u003e is a buttercup plant with a long medicinal history in China [1\u0026ndash;3]. Its main chemical components are triterpenoid saponins, which are important for the treatment of intestinal diseases [4, 5]. It has also shown good pharmacological effects such as anti-inflammatory, antibacterial, antitumor, and immune regulation effects [6\u0026ndash;8]. \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e is widely distributed and mainly produced in Heilongjiang, Jilin, Liaoning, Inner Mongolia, Henan, Shandong, Shaanxi, Jiangsu, and other provinces. A previous study discovered that the quantity of saponin B4 in \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e from 17 different production areas differed [9]. Therefore, it is of great significance to study \u003cem\u003eP.a chinensis (Bunge) Regel\u003c/em\u003e from different regions.\u003c/p\u003e \u003cp\u003ePlant microbiome is the collective term for microorganisms living inside and outside the plant [10, 11]. It is comprised of a variety of microorganisms, including bacteria, fungi, viruses, and protists [12]. The plant microbiome is an vital part of the interaction with the environment and has a significant impact on plant growth and environmental adaptation, such as helping plants absorb nutrients, controlling diseases, and improving plant resistance to stress [13, 14]. Therefore, owing to the importance of the quality and value of medicinal materials in \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e, the microbial community on the root of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e should be thoroughly studied in different regions.\u003c/p\u003e \u003cp\u003e16S rRNA sequencing can be used to identify and classify microorganisms and provide information on the composition and diversity of microbial communities [15\u0026ndash;17]. In this study, the microbial community composition in \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e roots from three different regions was studied using 16S rRNA sequencing, and the associations between hub microbiota were further explored. This study offers new perspectives on the cultivation of \u003cem\u003eP. chinensis (Bunge) Regel.\u003c/em\u003e\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003e \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e seeds were acquired from Anhui (AH), Northeast China (DB), and Hebei (HB) and planted in the greenhouses of Jiangsu Vocational College of Agricultural and Forestry. The plants were then taken out of the flowerpot, large soil aggregates were manually removed, and the soil firmly attached to the roots was acquired using a sterile brush and treated as the rhizosphere soil. To begin with, the rhizosphere soil was sampled and sieved to remove plant debris. Subsequently, a portion of the soil sample was placed in a sterile centrifuge tube, flash-frozen in liquid nitrogen, and stored at -80\u0026deg;C for subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and 16S rRNA sequencing\u003c/h2\u003e \u003cp\u003eDNA from the soil was collected using DNeasy Power soil kit (Qiagen, Valencia, CA, U.S.) and Qubit\u0026reg;dsDNA kit (Life Technologies, CA, U.S.) based on the manufacturer\u0026rsquo;s guidelines. DNA integrity and purity were verified using 1% agarose gel electrophoresis. DNA samples from the individual strains of each variety were used for 16S rRNA sequencing. 16S rRNA sequencing was performed using the IonS5\u0026trade;XL sequencing technology platform with the universal primers 515F (5\u0026rsquo;\u0026ndash;3\u0026rsquo;) and 806R (5\u0026rsquo;\u0026ndash;3\u0026rsquo;) amplified the V4 hypervariable region of the 16S rRNA gene. High-quality clean reads were obtained by quality filtering of raw reads according to the Cutadapt (version 1.9.1) quality control process under specific filtering conditions. The UCHIME algorithm was used for comparison with the SILVA database, and the chimeric sequence was detected using the UCHIME algorithm, after which it was removed. Sequences with more than 97% similarity were allocated to a taxonomic unit (OTU). Phylogenetic classification was performed using the Ribosome Database Engineering (RDP) classifier in the bacterial SILVA database with a threshold of 80% confidence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics\u003c/h2\u003e \u003cp\u003eDiversity indices such as the Chao1, Shannon, and Simpson indices, were calculated. The similarity in microbial community structure among the three groups was examined using principal coordinate analysis (PCoA). LEfSe was used to confirm the significant differences in the presence of microbes. WGCNA was used to identify highly collaborative bacterial modules. First, the top 15% ASV with large inter-sample variation were selected and analyzed using R package \u0026ldquo;WGCNA\u0026rdquo; (Version 1.71) [18]. In order to satisfy the prerequisite of scale-free network distribution as much as possible, the power\" value at which the square value of the correlation coefficient first reached 0.85 was selected. Parameters were set using clustering and dynamic pruning methods to aggregate bacteria with high correlations into modules. By calculating the correlation between modules and groups, modules closely related to the group (|r| \u0026gt; 0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were selected. In addition, the connectivity of the microbiota within key modules was calculated, and two criteria were utilized to screen the microbiota, including gene and key module significance\u0026thinsp;\u0026gt;\u0026thinsp;0.2, module membership key module significance\u0026thinsp;\u0026gt;\u0026thinsp;0.8. Cytoscape (version 3.6.1) was used to construct and visualize the interaction network of the microbiota between the key modules. The microbiota in the modules were compared and analyzed at the species level and the differences at the species level according to the annotation information to ASV, and the intersected microbiota were considered as the hub microbiota. Spearman\u0026rsquo;s rank correlation coefficient was utilized to calculate microbial abundance at the hub microbiota species level, and the correlation and P value between the hub microbiota were obtained.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eChange in bacterial diversity\u003c/h2\u003e \u003cp\u003eThe rarefaction curve indicated the adequacy of all the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Alpha diversity analysis showed that bacterial diversity in the DB group was greater than that in the AH group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). PCA and PCoA showed different bacterial community compositions in the AH, DB, and HB groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the taxonomic profile of the microbiome\u003c/h2\u003e \u003cp\u003eThe relative abundances of the top 10 most abundant bacterial phyla and genera in the three groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The top 10 phylum in the three groups were \u003cem\u003eAcidobacteriota\u003c/em\u003e (15.58% in DB, 14.52% in HB, and 14.92% in AH), \u003cem\u003eActinobacteriota\u003c/em\u003e (6.44% in DB, 6.61% in HB, and 9.74% in AH), \u003cem\u003eBacteroidota\u003c/em\u003e (3.40% in DB, 2.93% in HB, and 3.71% in AH), \u003cem\u003eChloroflexi\u003c/em\u003e (6.17% in DB, 7.85% in HB, and 6.68% in AH), \u003cem\u003eFirmicutes\u003c/em\u003e (5.31% in DB, 4.19% in HB, and 4.88% in AH), \u003cem\u003eGemmatimonadota\u003c/em\u003e (8.52% in DB, 8.04% in HB, and 5.47% in AH), \u003cem\u003eMyxococcota\u003c/em\u003e (3.43% in DB, 2.15% in HB, and 2.65% in AH), \u003cem\u003ePlanctomycetota\u003c/em\u003e (7.55% in DB, 6.25% in HB, and 8.06% in AH), \u003cem\u003eProteobacteria\u003c/em\u003e (27.05% in DB, 31.53% in HB, and 33.09% in AH), and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (3.46% in DB, 4.89% in HB, and 3.94% in AH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe top 10 genera in the three groups were \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (1.41% in DB, 2.30% in HB, and 1.79% in AH), \u003cem\u003eDongia\u003c/em\u003e (1.42% in DB, 2.27% in HB, and 1.38% in AH), \u003cem\u003eGemmatimonadaceae\u003c/em\u003e (6.07% in DB, 5.04% in HB, and 2.79% in AH), \u003cem\u003eKD4\u0026thinsp;\u0026minus;\u0026thinsp;96\u003c/em\u003e (1.64% in DB, 2.49% in HB, and 1.47% in AH), \u003cem\u003eLatescibacterota\u003c/em\u003e (1.99% in DB, 1.19% in HB, and 0.95% in AH), \u003cem\u003eMND1\u003c/em\u003e (2.92% in DB, 3.05% in HB, and 2.93% in AH), \u003cem\u003eRokubacteriales\u003c/em\u003e (1.80% in DB, 2.81% in HB, and 0.43% in AH), \u003cem\u003eSubgroup_17\u003c/em\u003e (1.90% in DB, 1.58% in HB, and 0.78% in AH), \u003cem\u003eVicinamibacteraceae\u003c/em\u003e (4.03% in DB, 2.56% in HB, and 4.38% in AH), and \u003cem\u003eVicinamibacterales\u003c/em\u003e (2.81% in DB, 2.97% in HB, and 3.88% in AH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDominant bacterial taxa\u003c/h2\u003e \u003cp\u003eLEfSe was used to generate a cladogram for identifying specific bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Several bacteria, including \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, \u003cem\u003eGemmatimonadales\u003c/em\u003e, and \u003cem\u003eGemmatimonadetes\u003c/em\u003e were significantly overrepresented in DB samples; \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e were all significantly overrepresented in AH samples; and \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eMethylomirabilia\u003c/em\u003e, and \u003cem\u003eMethylomirabilota\u003c/em\u003e were all significantly overrepresented in HB samples. These results further support our diversity results, showing a large heterogeneity between the microbiota inside and outside the tumors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eIdentification of hub microbiota\u003c/h2\u003e \u003cp\u003eTo maximize the fulfillment of the scale-free network distribution premise, the \"power\" value was selected as the first time the square value of the correlation coefficient reached 0.85, which is \"power\" = 12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Nine modules were integrated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In addition, the brown and turquoise modules were closely related to the regional groups (|r| \u0026gt; 0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The key module networks were constructed. The brown module network contained 40 nodes and 90 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and the turquoise module network included 22 nodes and 55 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). After taking the intersection of the brown and turquoise modules with DEspecies, respectively, 14 hub microbiota were identified at the species level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), including \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, \u003cem\u003eKD4-96\u003c/em\u003e, \u003cem\u003eMBNT15\u003c/em\u003e, \u003cem\u003eMicromonospora chersina\u003c/em\u003e, \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eReyranella\u003c/em\u003e, \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003euncultured Sinorhizobium\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobiotae\u003c/em\u003e, \u003cem\u003eLitorilinea\u003c/em\u003e, \u003cem\u003eRhodoplanes\u003c/em\u003e, and \u003cem\u003ebacteriap25\u003c/em\u003e. Finally, the correlation between hub microbiota was evaluated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Among which, \u003cem\u003eGemmatimonadaceae\u003c/em\u003e was positively correlated with \u003cem\u003eProteobacteria\u003c/em\u003e, whereas \u003cem\u003eAcidobacteriota\u003c/em\u003e was negatively associated with \u003cem\u003ebacteriap25\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe microbial community in the root of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e should be studied in depth because of its importance for the quality and value of medicinal materials and because it is affected by various factors, such as geography, climate, and cultivation practices. Even in AH, DB, and HB, the microbial communities in the rhizosphere of several major regions of the Chinese white head were different, resulting in different qualities of medicinal materials in each region. In this study, the microbial community composition of roots was studied in \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e from three different regions, and associations between hub microbiota were explored.\u003c/p\u003e \u003cp\u003eThis study shows that the phyla \u003cem\u003eAcidobacteriota, Firmicutes\u003c/em\u003e, and \u003cem\u003eGemmatimonadota\u003c/em\u003e were enriched, and \u003cem\u003eActinobacteriota, Chloroflexi\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e were depleted in the root microbiota of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e in DB; whereas \u003cem\u003eActinobacteriota, Bacteroidota\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e were enriched, and \u003cem\u003eChloroflexi, Gemmatimonadota\u003c/em\u003e were depleted in the root microbiota of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e in AH, which is in line with the results of previous studies investigating the rhizosphere communities of numerous plant species [19, 20]. Our method generated comparable microbiome profiles at the genus level. Overall, it was obvious that the microbial community composition of roots showed differences for the three different regions, resulting from the geography and environment \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e grew in.\u003c/p\u003e \u003cp\u003eIn addition, 14 hub microbiota were identified at the species level: \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, \u003cem\u003eKD4-96\u003c/em\u003e, \u003cem\u003eMBNT15\u003c/em\u003e, \u003cem\u003eMicromonospora chersina\u003c/em\u003e, \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eReyranella\u003c/em\u003e, \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003euncultured Sinorhizobium\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobiotae\u003c/em\u003e, \u003cem\u003eLitorilinea\u003c/em\u003e, \u003cem\u003eRhodoplanes\u003c/em\u003e, and \u003cem\u003ebacteriap25\u003c/em\u003e. Wei et al. explored the distribution of the core root microbiota of Tibetan hulless barley along altitudinal and geographical gradients in the Tibetan Plateau and found that \u003cem\u003eGemmatimonadaceae\u003c/em\u003e was one of the core bacteria [21]. Peng et al. examined rhizosphere bacteria and soil functionality in response to different irrigation practices and found that \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, which were the keystone taxa, were positively correlated with functional genes enriched in nutrient cycling [22]. Jambagi et al. revealed that the unclassified Chloroflexi group \u003cem\u003eKD4-96\u003c/em\u003e was the most abundant bacterial genus in the root-associated microbiota of red clover, which was determined by the geographic location and farming system [23]. Sun et al. suggested that wheat roots recruited specific \u003cem\u003eRokubacteriales\u003c/em\u003e under high levels of cadmium and arsenic, and their relative abundances were positively correlated with soil cadmium/arsenic bioavailability and resistance to cadmium and arsenic co-contamination [24]. Santos-Medell\u0026iacute;n et al. showed that the most abundant endosphere taxon during drought and weeks after re-watering was \u003cem\u003eStreptomyces\u003c/em\u003e, and a corresponding isolate promoted root growth [25]. Duan et al. found that \u003cem\u003eReyranella\u003c/em\u003e is associated with smut resistance in sugarcane [26]. Chen et al. found that \u003cem\u003eReyranella\u003c/em\u003e, which is involved in denitrification, reduces soil nitrogen content and controls bacterial wilt [27]. Wang et al. explored the efficacy of fosthiazate against root-knot disease in \u003cem\u003eCucumis melo\u003c/em\u003e var. \u003cem\u003esaccharinus\u003c/em\u003e and its underlying role on the rhizosphere microbiome, and the results indicated \u003cem\u003eAcidobacteriota\u003c/em\u003e as the predominant phylum [28]. Grunert et al. evaluated the microbial and fungal community structures in four different tomato cultivation systems, including both soil-based and soilless culture systems. The results showed that \u003cem\u003eLitorilinea\u003c/em\u003e was the most crucial factor in distinguishing between natural soils supplemented with animal and plant byproducts [29]. Sabri et al. explored the impact of soil temperature on lettuce growth and soil microbial diversity, which contributes to the maintenance of lettuce growth at low soil temperatures and found that \u003cem\u003eRhodoplanes\u003c/em\u003e were the predominant bacterial genera present in cooled soil [30]. Taken together, the results demonstrate that these 14 hub microbiota might be associated with the altitudinal, geographical, climatic, and cultivation practices of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e, providing new insights into its cultivation.\u003c/p\u003e \u003cp\u003eThis study is the first to investigate the microbial community composition of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e roots in three different regions. Nevertheless, this study had numerous deficiencies. First, the sample size was relatively limited. Therefore, a large-sample study should be conducted. Second, the microbial community composition of the roots of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e samples from other regions should be explored. Finally, it is necessary to elucidate the molecular mechanisms underlying microbiota.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, to date, information on the microbial community composition in the roots of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e samples from three different regions is limited. This study provides basic information on the bacterial diversity and composition of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e from three different regions. The 14 hub microbiota identified at the species level in this study may provide new insights into the cultivation of \u003cem\u003eP. chinensis (Bunge) Regel.\u003c/em\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003ePulsatilla chinensis (Bunge) Regel seeds used in this study were acquired from Anhui (AH), Northeast China (DB), and Hebei (HB) and planted in the greenhouses of Jiangsu Vocational College of Agricultural and Forestry.No permits were required for the collection of the samples.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was funded by Jiangsu Province Excellent Science and Technology Innovation Team Project(110751579), Yafu Technology Innovation and Service Project(2023KJ10), Science and Technology Project of Jiangsu Vocational College of Agricultural and Forestry(2021KJ50),The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design of the research:JS;acquisition of data: GX and JS; analysis and interpretation of data: JD; statistical analysis: JD; drafting the manuscript: HY, JS and GX; revision of manuscript for important intellectual content: CX and JS. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw amplicon sequence data related to this study were deposited in the NCBI Sequence Read Archive (NCBI SRA) under Bioprojects PRJNA1104389.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang WH, Yang J, Peng HS, Qian JP: \u003cstrong\u003eStudy on morphological characteristics and microscopic structure of medicinal organs of Pulsatilla chinensis (Bunge) Regel\u003c/strong\u003e. \u003cem\u003eMicroscopy research and technique \u003c/em\u003e2017, \u003cstrong\u003e80\u003c/strong\u003e(8):950-958.\u003c/li\u003e\n\u003cli\u003eLing Y, Lin Z, Zha W, Lian T, You S \u003cstrong\u003eRapid Detection and Characterisation of Triterpene Saponins from the Root of Pulsatilla chinensis (Bunge) Regel by HPLC-ESI-QTOF-MS/MS\u003c/strong\u003e. \u003cem\u003ePhytochemical analysis : PCA \u003c/em\u003e2016; \u003cstrong\u003e27\u003c/strong\u003e(3-4):174-183.\u003c/li\u003e\n\u003cli\u003eCheng L, Zhang M, Zhang P, Song Z, Ma Z, Qu H: \u003cstrong\u003eSilver complexation and tandem mass spectrometry for differentiation of triterpenoid saponins from the roots of Pulsatilla chinensis (Bunge) Regel\u003c/strong\u003e. \u003cem\u003eRapid communications in mass spectrometry : RCM \u003c/em\u003e2008, \u003cstrong\u003e22\u003c/strong\u003e(23):3783-3790.\u003c/li\u003e\n\u003cli\u003eWang T, Song Y, Ai Z, Liu Y, Li H, Xu W, Chen L, Zhu G, Yang M, Su D: \u003cstrong\u003ePulsatilla chinensis saponins ameliorated murine depression by inhibiting intestinal inflammation mediated IDO1 overexpression and rebalancing tryptophan metabolism\u003c/strong\u003e. \u003cem\u003ePhytomedicine : international journal of phytotherapy and phytopharmacology \u003c/em\u003e2023, \u003cstrong\u003e116\u003c/strong\u003e:154852.\u003c/li\u003e\n\u003cli\u003eLi Z, Song Y, Xu W, Chen J, Zhou R, Yang M, Zhu G, Luo X, Ai Z, Liu Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePulsatilla chinensis saponins improve SCFAs regulating GPR43-NLRP3 signaling pathway in the treatment of ulcerative colitis\u003c/strong\u003e. \u003cem\u003eJournal of ethnopharmacology \u003c/em\u003e2023, \u003cstrong\u003e308\u003c/strong\u003e:116215.\u003c/li\u003e\n\u003cli\u003eZheng Y, Zhou F, Wu X, Wen X, Li Y, Yan B, Zhang J, Hao G, Ye W, Wang G \u003cstrong\u003e23-Hydroxybetulinic acid from Pulsatilla chinensis (Bunge) Regel synergizes the antitumor activities of doxorubicin in vitro and in vivo\u003c/strong\u003e. \u003cem\u003eJournal of ethnopharmacology \u003c/em\u003e2010, \u003cstrong\u003e128\u003c/strong\u003e(3):615-622.\u003c/li\u003e\n\u003cli\u003eLiu T, Ye L, Guan X, Liang X, Li C, Sun Q, Liu Y, Chen S, Bang F, Liu B: \u003cstrong\u003eImmunopontentiating and antitumor activities of a polysaccharide from Pulsatilla chinensis (Bunge) Regel\u003c/strong\u003e. \u003cem\u003eInternational journal of biological macromolecules \u003c/em\u003e2013, \u003cstrong\u003e54\u003c/strong\u003e:225-229.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhou M, Yang M, Jin C, Song Y, Chen J, Gao M, Ai Z, Su D: \u003cstrong\u003ePulsatilla chinensis Saponins Ameliorate Inflammation and DSS-Induced Ulcerative Colitis in Rats by Regulating the Composition and Diversity of Intestinal Flora\u003c/strong\u003e. \u003cem\u003eFrontiers in cellular and infection microbiology \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e:728929.\u003c/li\u003e\n\u003cli\u003eLi-Shun LI, Zi-Xue Z, Wei-Jing S, Dong-Mei YJCT. 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\u003cstrong\u003e33\u003c/strong\u003e(2):144-150.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pulsatilla chinensis (Bunge) Regel, microbial community, root, 16S rRNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-4387229/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4387229/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eThis study was conducted to investigate the microbial community composition in the roots of \u003cem\u003ePulsatilla chinensis (Bunge) Regel\u003c/em\u003e samples from three different regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 16S rRNA sequencing of \u003cem\u003eP. chinensis (Bunge) Regel\u003c/em\u003e samples from three different regions, Anhui, Northeast China, and Hebei, was performed using Illumina high-throughput sequencing technology. Weighted gene co-expression network analysis (WGCNA) was employed to screen the highly collaborative bacterial modules, followed by hub microbiota identification. Finally, the correlation between the hub microbiota was evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The top 10 phyla in the three groups included \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e, and \u003cem\u003eBacteroidota\u003c/em\u003e; and the top 10 genera in the three groups included \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eDongia\u003c/em\u003e, and \u003cem\u003eGemmatimonadaceae\u003c/em\u003e. In addition,\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eGemmatimonadaceae\u003c/em\u003e, \u003cem\u003eGemmatimonadales\u003c/em\u003e, and \u003cem\u003eGemmatimonadetes\u003c/em\u003e were significantly overrepresented in the DB samples; \u003cem\u003eRhizobiales\u003c/em\u003e, \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e were all significantly overrepresented in the AH samples; and \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eMethylomirabilia\u003c/em\u003e, and \u003cem\u003eMethylomirabilota\u003c/em\u003ewere all significantly overrepresented in the HB samples. In addition, the brown and turquoise modules were closely related to regional groups (|r| \u0026gt; 0.5 and P \u0026lt; 0.05). Moreover, 14 hub microbiota were identified at the species level, including \u003cem\u003eGemmatimonadaceae\u003c/em\u003e, \u003cem\u003eKD4-96\u003c/em\u003e, \u003cem\u003eMBNT15\u003c/em\u003e, \u003cem\u003eMicromonospora chersina\u003c/em\u003e, \u003cem\u003eRokubacteriales\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eReyranella\u003c/em\u003e, \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003euncultured Sinorhizobium\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobiotae\u003c/em\u003e, \u003cem\u003eLitorilinea\u003c/em\u003e, \u003cem\u003eRhodoplanes\u003c/em\u003e, and \u003cem\u003ebacteriap25\u003c/em\u003e. Finally, the correlation between the hub microbiota was evaluated. Among which, \u003cem\u003eGemmatimonadaceae\u003c/em\u003e was positively correlated with \u003cem\u003eProteobacteria\u003c/em\u003e, whereas \u003cem\u003eAcidobacteriota\u003c/em\u003e was negatively associated with \u003cem\u003ebacteriap25\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The microbial community composition of \u003cem\u003eP. chinensis (Bunge) Regel \u003c/em\u003eroots showed differences in the three different regions,\u003cstrong\u003e \u003c/strong\u003eand the 14 hub microbiota might be associated with the altitudinal, geographical, climatic, and cultivation practices of this species, which provides new insights into its cultivataion\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Microbial community composition of Pulsatilla chinensis (Bunge) Regel root in different regions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 10:22:45","doi":"10.21203/rs.3.rs-4387229/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7706ba1-6615-43b4-be4b-2a58aefdd2c6","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T09:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 10:22:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4387229","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4387229","identity":"rs-4387229","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}