Potentilla anserina L. developmental changes affect the rhizosphere prokaryotic community

Plant roots and soil prokaryotes primarily interact with each other in the rhizosphere. Changes in the rhizosphere prokaryotic structure are influenced by several factors. In this study, the community structure of the Potentilla anserina L. rhizosphere prokaryotes was identified and evaluated by high-throughput sequencing technology in different continuous cropping fields and developmental stages of the plant. In total, 2 archaeal (Euryarchaeota and Thaumarchaeota) and 26 bacterial phyla were identified in the P. anserina rhizosphere. The bacterial community was mainly composed of Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Planctomycetes, Proteobacteria, and Verrucomicrobia. Moreover, the prokaryotic community structure of the rhizosphere varied significantly during plant development. Our results provide new insights into the dynamics of the P. anserina rhizosphere prokaryotic community and may provide useful information for enhancing the growth and development of P. anserina through artificial control of the soil prokaryotes.

www.nature.com/scientificreports/ 68.33% (principle coordinate analysis PCoA 1 + PCoA 2) and 51.75% (PCA 1 + PCA 2) of the data variability, respectively. These results clearly demonstrate that the rhizosphere prokaryotic community had different structures throughout various plant developmental stages. Further analyses revealed significant differences in six phyla-GAL15, Latescibacteria, Nitrospirae, Omnitrophica, Planctomycetes, and WWE3-from rhizosphere samples collected from different continuous cropping years fields, whereas all of the other phyla did not change significantly ( Supplementary Fig. S1). Except for WWE3, the other five phyla showed the highest abundances in the rhizosphere soil of continuous cropping for 1 year while Latescibacteria, Nitrospirae, and Planctomycetes had the lowest abundances in the rhizosphere soil of continuous cropping for 8 years ( Supplementary  Fig. S1). These results reveal that while the soil prokaryotic community as a whole was maintained, soil-specific prokaryotic phyla were influenced by continuous cropping years. Moreover, significant differences in prokaryotic community composition were also observed between the different plant developmental stages, affecting 12 prokaryotic phyla (in particular: Euryarchaeota, Acidobacteria, Actinobacteria, Armatimonadetes, BRC1, Bacteroidetes, Chloroflexi, Fibrobacteres, Latescibacteria, Parcubacteria, Saccharibacteria, and Verrucomicrobia) ( Supplementary Fig. S2), while all other phyla did not change significantly. These data indicate that the rhizosphere prokaryotic community was influenced by plant development. Next, linear discriminant analysis effect size (LEfSe) analysis was performed to identify the taxonomical lineages that were significantly influenced by the plant developmental process (Fig. 4). The data showed that rhizosphere samples from flowering plants harbored more prokaryotes of the phyla Actinobacteria and Chloroflexi; classes Actinobacteria and Acidimicrobiia; orders Tepidisphaerales, Acidimicrobiales, and Propionibacteriales; and families Tepidisphaeraceae and Nocardiodaceae compared to the rhizosphere associated with other plant stages. The harvest stage was characterized by the presence of more prokaryotes of the phyla Bacteroidetes and    (Fig. 4). These results suggest that P. anserina can select the prokaryotes that populate the soil at different stages of its development, presumably for attaining specific benefits.
Rhizosphere prokaryotic α-diversity associated with P. anserina. The α-diversity analysis revealed that the prokaryotic community richness and diversity varied widely among the samples (Supplementary  Table S1). The Good's coverage values were > 0.95 in all samples, indicating that the sequencing depth was sufficient to investigate the various rhizosphere prokaryotic communities. We observed substantial variation in the prokaryotic diversity of taxa between different developmental stages (Fig. 5). The vegetative and harvest stages had the largest community richness (Chao1) and Good's coverage compared to the other stages, whereas the flowering stage had the lowest (p < 0.05). Although no statistically significant differences with respect to overall community characteristics were seen between the three different continuous cropping field soil samples, the prokaryotic community in continuous cropping for one year had the largest community diversity (Shannon) compared to the other continuous cropping years during the vegetative stage (p < 0.05). These findings suggest that both the overall structure of the rhizosphere prokaryotic community and specific prokaryotes changed throughout the plant developmental stages.
Impact of soil environmental factors on the rhizosphere prokaryotes. Spearman's rank correlation test was performed in order to clarify the relationship between environmental factors and prokaryotic diversity (Table 1). For the rhizosphere prokaryotic communities, the Chao1 index was negatively correlated with the available potassium (AK) content and temperature, but positively correlated with the accumulated precipitation (P) (p < 0.05). Similarly, the Shannon index was also negatively correlated with temperature (p < 0.05). Next, the relationships between prokaryotic composition and environmental factors were evaluated with a focus on taxa with a relative abundance at the phylum level (Table 2) and the top 15 at the genus level  Table S2). The Monte Carlo permutations results at the OTU level showed that the total nitrogen (TN), total phosphorous (TP), and available nitrogen (AN) soil contents had a highly significant influence on rhizosphere prokaryotic communities (p < 0.001), whereas the total potassium (TK) and available phosphorus (AP) contents had a significant influence (p < 0.05) ( Table 3). These findings suggest that precipitation, temperature, soil water and nitrogen, phosphorus, potassium content represent important contributing factors for regulation of the rhizosphere prokaryotes.  www.nature.com/scientificreports/

Discussion
Structure and potential function of the P. anserina rhizosphere prokaryotic community. A more detailed look at the assembled rhizosphere prokaryotic communities throughout plant development revealed that a core prokaryotic/bacterial microbiome was established, which comprised Actinobacteria, Bacteroidetes, Chloroflexi, Gemmatimonadetes, Planctomycetes, Proteobacteria, and Verrucomicrobia as previously observed in Arabidopsis 35,49,50 . In addition, the present study demonstrated that Acidobacteria were also consistently present throughout plant development (Fig. 2). Proteobacteria were the most abundant phylum within the rhizosphere of P. anserina. Some strains of Proteobacteria can promote plant growth by symbiotically fixing nitrogen 51,52 , such as Sphingomonas (its relative abundance was the second highest in the present study) and Dokdonella, which are very important genera for nitrogen and carbon cycling [53][54][55] . Studies have shown that www.nature.com/scientificreports/ Actinobacteria are involved in the soil phosphorous cycle 56,57 . Moreover, a previous study reported that bacteria, such as Proteobacteria and Actinobacteria, prefer nutrient-rich environments where they can grow rapidly 58,59 . Among the prokaryotes of the P. anserina soil rhizosphere, the abundances of Proteobacteria and Actinobacteria were first and third, respectively, which is in accordance with previous studies. Acidobacteria was the fourth most abundant taxon in the studied soils, and Acidobacteria Subgroup_6 was the most abundant genus in this phylum (fifth out of all prokaryotic genera), which may be a response to nitrogen availability 60 . Studies have indicated that the bacteria of the phylum Chloroflexi can participate in the carbon and nitrogen cycle via respiration of sugars, fermentation, carbon dioxide fixation, and nitrite oxidation [61][62][63] . Some Pseudomonas and Bacillus were also identified within the rhizosphere of P. anserina, and previous studies have shown that these genera can promote plant growth through nutrient acquisition, reducing abiotic or biotic stress, and phytohormone production 16,64 . Ultimately, the presence of so many prokaryotic taxa in the rhizosphere, most of which are unculturable, prevented us from understanding the role of individual prokaryotes in P. anserina growth 65-67 . Plant developmental changes affect the rhizosphere prokaryotic community. Bray-Curtis community dissimilarity analysis of the overall rhizosphere prokaryotic community throughout P. anserina development revealed that the prokaryotic community was significantly different at various developmental stages (Fig. 3). These results are in agreement with previous reports as the rhizosphere microbiome communities change according to a plant developmental gradient [35][36][37][38]68 . For example, Baudoin's 36 results argue in favor of a greater influence of the maize rhizosphere environment on bacterial metabolic potentialities, which were primarily based on the developmental state of the plant. In addition, the α-diversity of the prokaryotic community significantly changed with respect to the developmental stages (Fig. 5), and the prokaryotic community at the flowering stage was significantly different from the other developmental stages (Chao1, p = 0.001). Previous reports also showed that major modifications were recorded at the first reproductive stage (flowering) of Medicago truncatula for both bacterial and fungal communities 38 . For instance, during the flowering stage, genes involved in the synthesis of streptomycin were significantly induced 35 and a strengthening of defensive proteins secreted by the root system took place 69 , which effectively inhibited bacteria. On the contrary, during the vegetative stage, the significantly stronger rhizosphere effect toward bacteria over fungi could be ascribed to the expected higher release of rhizodeposits, primarily as soluble root exudates, which are more favorable to bacteria 38 . Studies on Arabidopsis thaliana have shown that the microbial community structure differs the most at the seedling stage 35 , which is inconsistent with our results, possibly because different species of plants secrete very different root exudates at various growth stages 29 . Phyla, such as Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, and Verrucomicrobia (Supplementary Fig. S2), and specific genera followed distinct patterns associated with plant development. The community dissimilarity analysis revealed that the structures of the prokaryotic communities changed significantly among the different plant developmental stages (Fig. 3); this was particularly noticeable for Actinobacteria, Acidobacteria, and Bacteroidetes (Fig. 4), which is in agreement with previous reports 35,49,50 . Additionally, previous research on the rhizosphere microbiome revealed that unique transcripts were significantly expressed at different stages of plant development 35 . Altogether, the plant secretes specific phytochemicals in the roots at distinct stages of development, thereby coordinating the structure of the rhizosphere microbial community and achieving specific results 35,37,38,49 . Soil environmental factors influence rhizosphere prokaryotic composition. Environmental conditions have been shown to significantly impact the microbial communities that populate the soil 70,71 . To fully investigate the impact of the environment on the rhizosphere prokaryotes of P. anserina, it is necessary to identify the key environmental factors that may be involved. The present study demonstrated that soil environmental factors were significantly correlated with the rhizosphere prokaryotic community structure associated with P. anserina (Tables 1, 2 and 3 and Table S2), which was in line with previous studies reporting on the significant roles of nitrogen and phosphorus in modulating the soil microbiome 26,[70][71][72][73] . Among the eight identified dominant phyla, Acidobacteria, Actinobacteria, Chloroflexi, Gemmatimonadetes, Planctomycetes, Proteobacteria, and Verrucomicrobia were significantly affected by nitrogen, phosphorus, potassium, moisture, temperature and accumulated precipitation (Table 2). Furthermore, 10 of the 15 genera with the highest relative abundances were significantly associated with multiple environmental factors (Supplementary Table S2). In summary, soil environmental factors have a significant influence on the structure of the rhizosphere prokaryotic community and a selective effect on rhizosphere prokaryotes, which is in agreement with previously reported data 26 . Interestingly, Bacteroidetes were not sensitive to environmental factors, but their relative abundance varied significantly at different plant growth stages (Supplementary Fig. S2). It is possible that the different root exudates throughout the four developmental stages can promote the conversion of microbial groups [44][45][46] .
Interaction between plants and microbes plays an important role in agricultural systems 11 . With this in mind, our future investigations will focus on the functions of rhizosphere prokaryotes. In particular, we aim to improve our understanding of the beneficial and harmful impacts of specific plant prokaryotic communities in order to pave the way for improved agricultural production. Such findings will enhance our understanding of these interactions and, in the future, provide evidence for the sustainable use of farmland to meet the needs of more efficient and productive agriculture by selectively enhancing the development of prokaryotic strains with beneficial functions. In the present study, the classical definition of the rhizosphere described by Chaparro et al. 35 was used. Soil samples were collected from the surface layer (0-30 cm) of a field attached to the roots. We sampled three quadrats (3 × 3 m) in each field, and the distance between each quadrat was > 10 m. From each quadrat, the rhizosphere soil of five plants with the same growth potential was collected, and the five sub-samples were mixed to create one sample. The sampled plants were labeled for identification at each sampling time. Three biological replicates were selected from each of the 3 sample quadrats in each field across the 4 developmental stages (36 samples). The samples were air dried, cleaned of plant debris, thoroughly homogenized, and stored at − 80 ℃ for future use.  75 . The purified product underwent a new PCR amplification using the same primer as before, which had been attached an eight-base sequence unique to each sample. The PCR reactions were performed in a 25 μL mixture containing 5 μL of (5×) GC Buffer, 0.75 μL of KAPA dNTP Mix, 0.5 μL of KAPA HiFi HotStart DNA Polymerase, 1.5 μL of each primer (10 pM), and 5 μL of the purified product. The PCR reaction cycling protocol was as follows: 95 °C for 3 min followed by 8 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 5 min 75 . The amplicons were subsequently purified using AMPure XP beads to clean up the final library before quantification. Lastly, the purified amplicons were pooled in equimolar concentrations and paired-end sequenced (2 × 250) on an Illumina HiSeq platform (Illumina, San Diego, CA, USA) according to the standard protocols. The raw reads were stored in the National Center for Biotechnology Information Sequence Read Archive database. Bioinformatics analysis. The Fast Length Adjustment of SHort reads software tool was used to merge paired-end reads from the next-generation sequencing analysis 76 . Low quality reads were filtered using the fastq_quality_filter algorithm (-p 90 -q 25 -Q33) of the FASTX Toolkit (v0.0.14, http://hanno nlab.cshl.edu/ fastx _toolk it/index .html), and chimera reads were removed by USEARCH (64 bit, v8.0.1517, https ://www.drive 5.com/usear ch/). The number of reads for each sample was normalized based on the smallest size sample by random subtraction. OTUs were aligned using the UCLUST algorithm with 97% identity and taxonomically classified using the SILVA 16S rRNA database v128 77 (https ://www.arb-silva .de/docum entat ion/relea se-128/).

Statistical analysis.
We used custom R scripts in R software (v2.13.2) to calculate the percentage of classifiable reads. Differences in prokaryotic community composition within the sampled locations were analyzed by one-way analysis of variance and Tukey's post hoc test, and correlations between prokaryotic diversity, prokaryotic community structure, and environmental variables were determined using Spearman's rank correlation test. These statistical analyses were performed using IBM SPSS Statistics 20 software (IBM SPSS, Armonk, NY, USA). The α-and β-diversities were generated via the Quantitative Insights Into Microbial Ecology (http://qiime .org) pipeline and calculated based on the Bray-Curtis index 78 . Centroids of distance matrices were tested using ANOSIM to assess the multivariate homogeneity of groups. We used the LEfSe method to identify species that showed statistically significant differential abundances between groups 79 . To further investigate the effects of www.nature.com/scientificreports/ environmental factors on the prokaryotes and identify the key factors, a significance analysis was performed using Monte Carlo permutations. A p value < 0.05 was considered to be statistically significant. www.nature.com/scientificreports/