Unveiling microbial complexity within Astragalus propinquus and Glycyrrhiza uralensis roots

Unveiling microbial complexity within Astragalus propinquus and Glycyrrhiza uralensis roots | 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 Unveiling microbial complexity within Astragalus propinquus and Glycyrrhiza uralensis roots Zerrin KOZMA KIM, Young Sang Park, Tae-Jin Yang, Hyun Kim, Yong-Hwan Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5675838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 May, 2025 Read the published version in Annals of Microbiology → Version 1 posted 4 You are reading this latest preprint version Abstract Background Astragalus propinquus ( AP ) and Glycyrrhiza uralensis ( GU ), members of the Fabaceae family, are widely used for their therapeutic properties. However, the endophytic microbial communities in their roots remain largely unknown. Herein, we compared the structure and properties of root-associated bacterial and fungal communities of AP and GU , specifically excluding the microbial communities thriving in the rhizosphere, using both culture-dependent and -independent methods. Results A metabarcoding-based approach revealed a higher abundance of Proteobacteria in the root microbiome of GU than in that of AP . Fungal communities showed similar distinctions, with AP and GU predominantly harboring Ascomycota and Basidiomycota , respectively. The bacterial community in AP exhibited significantly higher diversity than in GU and included unique taxa, e.g., Steroidobacterales and Micromonosporales . However, the bacterial community in GU was relatively less diverse and dominated by Xanthomonadales . Differential abundance analysis revealed that the plant species significantly impacted 301 bacterial and 228 fungal amplicon sequence variants (ASVs) in AP and GU . Among these, B5_f_ Comamonadaceae was markedly more enriched in AP than in GU . A random forest model analyzing bacterial ASVs with significant differences in abundance indicated that most bacterial ASVs were enriched in AP . A pan-microbial community of 1,243 ASVs was identified, including 96 co-detected ASVs between AP and GU , with 3 core ASVs (B2_f_ Pseudomonas , B5_ Comamonadaceae , and B70_ Cutibacterium ). The fungal community comprised 435 ASVs, with 98 shared ASVs and 8 core ASVs (F5_ Paraphoma , F6_f_ Lysurus , F22_ Alternaria , F30_ Phaeosphaeria , F53_ Cladosporium , F36_ Moesziomyces , F55_f_ Neocucurbitaria , and F56_ Malassezia ). Hub nodes were identified to elucidate the roles of microorganisms within microbial networks. In AP , B152_o_ Burkholderiales , F14_ Exophiala , and F33_ Fusarium were the key hub nodes, whereas, in GU , B36_ Paenibacillus was the central hub node. The comparative analyses of in vitro culture data and molecular sequencing results showed overlapping patterns, with Pseudomonas dominant in AP and Bacillus in GU . Conclusions These findings highlight distinct microbial communities between AP and GU , with each species exhibiting unique bacterial and fungal orders and differences in microbial network complexity and diversity. These differences suggest the potential functional contributions, e.g., nutrient cycling and secondary metabolite production, of root-associated microbial communities, likely impacting the therapeutic properties of these plants. Endophytic fungi Endophytic bacteria Astragalus propinquus Root microbiome Glycyrrhiza uralensis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In nature, microbes and plants interact and evolve together (Lebeis, 2014 ; Müller et al., 2016 ). A growing number of studies have proven that microbes form symbiotic relationships with their hosts and that these intimate connections have developed and adapted to enable these tight interactions (Hassani et al., 2018 ; Uroz et al., 2019 ). Moreover, plants need microbiota for their growth and development (Chaparro et al., 2014 ; Chaparro et al., 2012 ; del Carmen Orozco-Mosqueda et al., 2018 ), stress resistance (Bakker & Mercado-Blanco, 2012; Marasco et al., 2012 ), and sustained economic growth (Kudjordjie et al., 2019 ; Turner et al., 2013 ). Endophytes are a unique class of plant microbiota (Hassani et al., 2018 ; Uroz et al., 2019 ) that inhabit and colonize internal plant niches. There is evidence that plant endophytes can boost therapeutic chemical production, support plant development, and protect host plants against pathogen invasion (Deng & Cao, 2017 ; Oono et al., 2015 ). Plant microbiome have been linked to the buildup of plant secondary metabolites (Li, Yang, et al., 2021 ; Zhang et al., 2020 ). These interactions highlight the critical role of microbiomes in shaping the phytochemical properties of plants. The functional variances revealed in rhizospheric microbiomes and endophytes demonstrate their potential resemblance to the therapeutic properties of conventional herbal remedies (Huang et al., 2018 ). The similarities between bioactive compounds derived from herbal remedies and the functional traits of rhizospheric microbiomes have sparked interest in elucidating the interactions between different therapeutic plant species and their microbiomes. Elucidating the composition and functional roles of endophytic bacteria and fungi within root niches is crucial for exploring their biotechnological applications, particularly in the context of the synthesis of secondary metabolites, which are considered a source of potential natural medicines and are crucial to the pharmacologic value of medicinal plants (Masand et al., 2015 ). Endophytic microbes, including fungi and bacteria, are known to contribute to the synthesis of pharmacologically relevant secondary metabolites in medicinal plants. Prior studies have provided the evidence of the involvement of these endophytes in partial and complete metabolic pathways, contributing to the synthesis of plant metabolites (Brader et al., 2014 ; Ludwig-Müller, 2015 ). For instance, Paraphoma species, commonly found in root tissues, are associated with the production of terpenoids, compounds with known anti-inflammatory properties. Similarly, in the plants of Swietenia mahagoni ( L .) Jacq , Cladosporium species have been reported to synthesize phenolic compounds, which exhibit antioxidant activity (Chandra et al., 2024 ). Moreover, huperzine, known for its potential therapeutic effects in Alzheimer’s disease and other memory disorders, is produced by the endophytic fungus Shiraia sp. Slf14, isolated from Huperzia serrata (Zhu et al., 2010 ). Moreover, complex interactions within the plant-associated microbiomes contribute to various plant traits, whereas individual microbial species play distinct roles in secondary metabolite production. These relationships highlight the multifaceted nature of plant–microbe interactions (Trivedi et al., 2020 ). Medicinal plants are widely recognized for their therapeutic bioactive compounds, which have been extensively studied for their health benefits. The accumulating evidence of the critical role of plant microbiomes in enhancing these therapeutic effects has made it essential to not only study these medicinal plants but also their interactions with their microbiomes. Astragalus propinquus [ AP , syn. Astragalus membranaceus (Fisch.) Bunge] and Glycyrrhiza uralensis ( GU ) represent a rich source of therapeutic compounds (Asl & Hosseinzadeh, 2008 ; Xu et al., 2006 ) and, both species are renowned for their medicinal properties (Chung et al., 2001 ; Liu & Lv, 2020 ; Stickel & Schuppan, 2007 ; Sun et al., 2012 ). The microbiome of AP , for instance, has been shown to harbor a diverse array of bacteria and fungi that contribute to the production of crucial secondary metabolites, such as polysaccharides and saponins (Sun et al., 2017 ). Similarly, the microbiome of GU has been reported to enhance the bioavailability and potency of the flavonoid and phenolic compounds produced in this species (Ji et al., 2016 ). Despite their common family lineage, these plants exhibit distinct phytochemical profiles believed to be partially impacted by their unique microbial communities (Bratkov et al., 2016 ; Liu et al., 2023 ). Both plants occupy a significant position in traditional Korean medicine and remain the subjects of considerable interest in modern pharmacological research. Currently, there is relatively less knowledge regarding microbial communities in the roots of AP and GU that have a potential to impact the phytochemical properties of medicinal plants. In this study, we aimed to conduct an in-depth examination of the diversity and composition of microbial communities in the roots of AP and GU plants. Specifically, beyond elucidating the complex nature of microbiomes, the aim is to determine the differences between the structure and associations of their bacterial and fungal communities using culture-independent and -dependent methods. The taxonomic composition of microbial communities in the roots of AP and GU may provide crucial insights into the biosynthesis of plant secondary metabolites, such as glycyrrhizin in GU and astragalosides in AP . Materials and Methods Experimental design and sample preparation The fresh whole roots of AP and GU plants were collected from the Care Farm in Iksan, South Korea (36°00′12.7′′N 127°03′51.8′′E), in March 2022, when AP and GU were in the vegetative growth stage. We acquired each of the two species of medicinal plants from a single site that most optimally represents the species to reduce the impact of environmental variations and highlight the role of genetic factors. The plants of AP and GU were morphologically identified by Prof. Tae-Jin Yang and Young Sang Park of the Department of Agriculture, Forestry and Bioresources, Seoul National University (Seoul, Korea). Three individual plant roots (at least 30 cm long) collected from each plant were kept in sterile plastic bags, placed into an ice box, and then transported to the laboratory within 24 h. All samples were stored at 4°C until DNA extraction. Root samples were washed under running tap water to remove bulk soil and then surface-sterilized sequentially for 1 min, 3 min, and 30 s with 75% ethanol, 5% NaOCl, and 75% ethanol, respectively. Approximately 1 cm long root segments were excised from the samples for subsequent analyses. The root segments were then transferred to lysing matrix S tubes included in the SPINeasy DNA Kit for Soil (MP Biomedicals, USA). Each tube contained three root segments to ensure sufficient DNA amount to be used for further analyses. To consider the variability in root microbiomes within each individual, eight analytic replicates (randomly selected two or three replicates for each individual) were prepared. All treated root samples were kept in 1X phosphate-buffered saline (PBS), pH 7.4, to avoid DNA denaturation. DNA extraction and molecular analyses The prepared samples were ground using a homogenizer (FastPrep-24™ 5G; MP Biomedicals, USA). DNA was extracted following the manufacturer’s instructions. All DNA samples were quality-checked and measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). The extracted DNA was subjected to polymerase chain reaction (PCR) utilizing the primer set 799F–1193R for the V5–V7 region of the bacterial 16S ribosomal RNA (rRNA) gene (Bulgarelli et al., 2012 ), and the ITS1 regions of the fungal rRNA genes were amplified by ITS1-F/R and ITS2-F/R PCR primers (Op De Beeck et al., 2014 ). The reaction mix consisted of 12.5 µL of 0.5 µM 2× Platinum SuperFi II Green PCR Master Mix (Thermo Fisher Scientific, USA), 1 µL each of 0.5 µM forward and reverse primers, and 0.8 µM of diluted DNA template. PCR was performed using the following program for 16S rRNA: initial denaturation at 98°C for 30 s, followed by 32 cycles of denaturation at 98°C for 10 s, primer annealing at 55°C for 10 s, and extension at 72°C for 40 s, final extension at 72°C for 5 min, and holding at 12°C. For the PCR amplification of fungal ITS regions, the same procedure was followed. The MEGAquick-spin Plus DNA Purification Kit (iNtRON Biotechnology, Korea) was used to pool and purify amplicon replicates obtained from the same DNA sample. The sequencing was performed at Seoul National University’s National Instrumentation Center for Environmental Management. Sequence processing and statistical analyses QIIME2 (v.2020.2) was used to process the sequencing reads (Bolyen et al., 2019 ). After demultiplexing, the sequences were combined and quality-filtered in the QIIME2 pipeline using the DADA2 plugin (Callahan et al., 2016 ). Amplicon sequence variants (ASVs) were then allocated to the processed reads. Non-chimeric ASV taxonomic classifications were assigned using the Nave Bayes technique, which is implemented in the q2-feature-classifier program (Bokulich et al., 2018 ). The taxonomies for the V5–V7 region of the 16S rRNA gene were based on the SILVA database (v.138) (Quast et al., 2012 ). The taxonomic designations for the ITS regions were performed using the UNITE database (UNITE_ver8_dynamic of May 2021) (Nilsson et al., 2019 ). Bacterial sequences with lengths ranging from 278 to 400 bp and fungal sequences ranging from 100 to 400 bp were included. The R package phyloseq was used to clean the ASV profiles (McMurdie & Holmes, 2013 ). These filtering techniques reduced the overall ASV count in bacteria to 1,339 and in fungi to 435. The ASVs designated as “kingdom Fungi” but unable to be recognized at the phylum level were subjected to a BLASTn search. The ensuing BLAST results were examined for plant sequences and those discovered were eliminated. In addition, chloroplasts and mitochondria were removed from the bacterial ASV profile. For all statistical analyses performed in R version 4.2.2 (R Core Team, 2013), the statistical significance threshold was set at p = 0.05. The cumNorm function in the R package metagenomeSeq (v. 3.8) was used to log-transform the ASV database and normalize it using cumulative-sum scaling (Paulson et al., 2013 ). The Chao1, Observed operational taxonomic unit (OTU), Shannon, Inverse Simpson, and Simpson indices were calculated using the alpha diversity function in the R package vegan (v2.5-3) (Oksanen, 2014). In addition, all Wilcoxon rank-sum tests were performed using the R software. The Bray–Curtis dissimilarity matrix was computed to generate primary coordinate analyses with and without restrictions. Constrained principal coordinate analyses (PCoAs) were performed using the phyloseq and vegan package functions. The adonis2 function from the vegan package (v2.5-3) (Oksanen, 2014) was used for the permutational multivariate analysis of variance (PERMANOVA). The permutest function of the vegan package was used to perform variance partitioning and significance analysis for experimental variables with 99,999 permutations. Using the FitZig function of the metagenomeSeq package, a zero-inflated Gaussian distribution mixture model was developed to examine the disparities in abundance between the bacterial and fungal ASVs. The makeContrasts and eBayes commands were developed using the R package limma (v.3.34.9) (Ritchie et al., 2015 ). The false discovery rate (FDR)-adjusted p -values were checked to establish the statistical significance of the differences in abundance, and those with values less than 0.01 were deemed significant. Volcano plots generated using ggplot2 were used to visualize the ASVs with various degrees of bacterial and fungal abundances. The parameters for the random classification model, which included the two species, i.e., AP and GU , were set based on the abundance of microbiota. The ROCR (v. 1.0.7) and randomForest packages (v. 4.6–14) were used for this task, and the final machine learning strategy utilizing the random forest (RF) model in R was used to investigate receiver operating characteristic curves (Liaw & Wiener, 2002 ). The average reduction in the Gini coefficient was used to evaluate the relevance of the ASVs in assessing how well the RF model predicted AP and GU and was performed using the significance function from the randomForest package. The top ASVs from the RF models of each kingdom were classified as AP _enriched and GU _enriched or non-differential ASVs based on the results of the differential abundance test. Taxa with a relative abundance greater than 0.5% were represented for taxonomic composition analysis using the R program ggplot2 (Wickham, 2009 ). The core ASVs for both medicinal plants were found. A prevalence criterion of 90 and 95% was specified for core bacterial and fungal ASVs, respectively. Microbial network analyses iNAP was mostly used in the selection process (Feng et al., 2022 ). The input data for the SparCC analysis was sourced from the combined bacterial and fungal ASV abundance tables (Friedman & Alm, 2012 ). The estimated correlations ( p = 0.05, two-sided) only included correlations for which the absolute coefficient values were ≥ 0.3 (Kurtz et al., 2015 ). The ForceAtlas2 layout was utilized for visualization using Gephi (v0.9.2) (Bastian et al., 2009 ). Degree, betweenness, closeness, and eigenvector centralities were calculated using R and Gephi (v0.9.2) (Bastian et al., 2009 ). The top 1% of degree and closeness centralities in each network were chosen as the hub nodes. Isolation of bacteria and fungi from root samples Surface-sterilized root samples were homogenized using a FastPrep-24TM 5G homogenizer (MP Biomedicals, USA), and the resulting root slurries were serially diluted 10-fold. The root samples were taken from the pooled and sterilized segments, and for each plant species, three replicates of 1 g of root tissue were homogenized in sterile PBS. The subsamples (0.1 µL) of each dilution were plated in triplicate onto ISP medium No. 5 (ISP5), ISP medium No. 2 (ISP2), starch agar, egg yolk agar (EYA), Luria–Bertani agar (LB), trypticase soy agar (TSA), and Reasoner’s 2A agar (R2A) agar for bacterial cultivation and yeast peptone dextrose adenine (YPDA), MEDION potato dextrose agar (MPDA), potato dextrose agar (PDA), rose bengal agar (RBA), and Sabouraud dextrose agar (SDA) for fungal cultivation. In addition, each medium used for bacterial or fungal cultivation contained 0.1 g L − 1 cycloheximide or chloramphenicol to inhibit fungal or bacterial growth, respectively. Plates were incubated in a growth incubator for 2–7 days at 25 and 28°C for bacterial and fungal cultivation, respectively, following which colonies were counted, and final counts were expressed as CFU g − 1 root. Microbial DNA was extracted from selected individual colonies to ensure a diverse and representative dataset. For bacterial isolation, seven different culture media (ISP5, ISP2, STARCH, EYA, LB, TSA, and R2A) were used, whereas five media (YPDA, MPDA, PDA, RBA, and SDA) were employed for fungal isolation. Colonies were selected based on their morphological diversity, with priority given to those displaying distinct morphotypes on the respective media. Additionally, colonies exhibiting robust and consistent growth were chosen to maximize diversity within the dataset. Identification of bacterial and fungal isolates The bacterial and fungal DNA samples were extracted and purified using the PURE gDNA Extraction Kit (Infusion Tech, Korea). A PCR mix of total volume 20 µL containing 1 µL DNA, 1 µL of each primer, 10 µL of 2X TOP Simple DyeMIX Taq master mix, and 7 µL of ddH 2 O was used for each PCR amplification. The PCR reactions were performed using the following protocol: initial denaturation at 95°C for 2 min, followed by 34 cycles of denaturation at 95°C for 30 s, primer annealing at 55°C for 60 s, and extension at 72°C for 100 s, final extension at 72°C for 5 min, and holding at 12°C. The amplified PCR products were verified with electrophoresis on a 1% agarose gel containing SafeView (Applied Biological Materials, Canada) to determine their sizes (∼500 bp) and approximate concentrations and purified using a MEGAquick-spin plus fragment DNA purification kit (iNtRON Biotechnology, Korea). The bacterial amplified PCR products were sequenced using the sequencing primers 515R (TTACCGCGGCTGCTGGCA), 926F (AAA CTCAAAGGAATTGACGG) (Lane, 1991 ), and 1055R (AGCTGACGACAGCCAT) (Lee et al., 1993 ) and assembled using the SeqMan Lasergene software version 7.1.0 (DNAStar, Madison, WI, USA). The similarity of the 16S rRNA gene sequence with those of known bacterial type strains was determined using the EzBioCloud server (Yoon et al., 2017 ). The pure cultures of the obtained microorganisms were established and stored at − 80°C for long-term storage as glycerol stocks. For the sequencing of fungal amplified PCR products, the ITS1 (TCCGTAGGTGAACCTGCGG) and ITS4 (TCCTCCGCTTATTGATATGC) primers were used (White, 1990). PCR was performed with initial denaturation at 95°C for 2 min, followed by 34 cycles of denaturation at 95°C for 30 s, primer annealing at 55°C for 30 s, and extension at 72°C for 90 s, final extension at 72°C for 5 min, and holding at 12°C. The process was the same for the PCR amplification of bacterial DNA. Results Microbial community composition and diversity varies in AP and GU We examined the makeup of bacterial and fungal communities at the level of the same family but different genera, i.e., AP and GU , by comparing the relative and the estimated absolute abundances of dominant bacterial and fungal phyla associated with the root. The examination of taxonomic affiliations revealed variations in the dominance of orders across both medicinal plants (Supplementary file: Figures S1 and S2). Specifically, in the bacterial communities of AP and GU , Proteobacteria emerged as the predominant phylum, with the order Pseudomonadales (46%) in AP and the order Xanthomonadales (41.6%) in GU leading in average relative abundance. Rhizobiales ranked as the second most dominant bacterial community in both plant species, with the abundances of 15 and 36.3%, respectively. Notably, AP housed seven bacterial orders not found in GU ( Steroidobacterales , Cytophagales , Micromonosporales , Nevskiales , Streptosporangiales , Glycomycetales , and Acidimicrobiales at 0.3, 0.2, 0.1, 0.1, 0.072, 0.066, and 0.062%, respectively), whereas GU exhibited two unique orders absent in AP ( Chlamydiales and Bacteroidales at 0.22 and 0.01%, respectively) (Fig. 1 a; Table S1 ). This compositional difference was also found in the fungal community, where Ascomycota ( Pleosporales , 26.75%) predominated in AP , and Basidiomycota ( Phallales , 52.1%) in GU . However, in both AP and GU , the order Boletales from the phylum Basidiomycota (32 and 68% in AP and GU , respectively) exhibited a significant presence, with 25 and 14.7% relative abundances, respectively (Fig. 1 b). In AP , three distinct fungal orders were identified that were not present in GU ( Thelebolales , Sordariales , and Helotiales at 0.6, 0.2, and 0.1%, respectively). In contrast, GU exhibited a unique fungal order ( Ustilaginales , 0.7%) absent in AP (Fig. 1 b; Table S1 ). AP and GU differed significantly in terms of their bacterial communities and dominant fungal communities. AP exhibited a diverse bacterial community and was mostly dominated by A scomycota in the fungal domain. In contrast, GU was characterized by the dominance of certain bacterial taxa, such as Xanthomonadales , and the dominance of Basidiomycota in the fungal kingdom. The observed composition patterns further supported the differences in community diversity. The alpha diversity of root microbial communities was assessed using the Shannon, Observed OTU, Inverse Simpson, Chao1, and Simpson indices (Fig. 1 c, d). For the bacterial community, AP showed significantly higher diversity than that in GU (Shannon, P = 0.01; Inverse Simpson, P = 0.005) (Fig. 1 c). In addition, richness (Observed OTU, P = 0.06; Chao1, P = 0.08) and evenness (Simpson, P = 0.06) were higher in AP than in GU ; however, the difference was not statistically significant. Regarding the fungal community, GU exhibited greater richness than that in AP (Observed OTU, P = 0.008; Chao1, P = 0.007) (Fig. 1 d). However, there were no significant differences in the diversity and evenness indices (Shannon, P = 0.4; Inverse Simpson, P = 0.8; Simpson, P = 0.4). The combined results of PCoA and PERMANOVA showed that the bacterial and fungal communities were significantly different between the two plant species (bacteria: R 2 = 0.25, P = 0.0001; fungi: R 2 = 0.22, P = 0.0001) (Fig. 1 e, f; Table S2 ). These results suggest that plant genetic or physiological factors may drive the differentiation of root endophytic bacterial and fungal communities between AP and GU under identical environmental conditions. Differential distribution of root-associated microbiotas in AP and GU As we found compositional differences among the medicinal plants of AP and GU , we aimed to seek distinct microbial taxa contributing to the observed composition patterns. The differential abundance analysis showed that, for AP and GU , 301 bacterial and 228 fungal ASVs, were significantly affected by the plant species (log 2 fold change > 2 or < − 2, FDR-adjusted P < 0.01). B5_f_ Comamonadaceae was notably more abundant in AP than in GU , whereas B1862_f_ Sphingomonadaceae exhibited a significantly higher abundance in GU that in AP . F1_ Fusarium , a fungus, was relatively more enriched in AP than in GU (Fig. 2 a, b; Table S3 ). As the differential abundance analysis could overrepresent the differences in abundance between the two plant species, we constructed two RF classification models for each domain to complement this limitation. These RF models were used to select the top 20 ASVs based on the similarity in their cross-validation error rates with those of the RF models (Table S4 ). Additionally, their importance in predicting the target variable was determined using the Gini uncertainty measure. In AP and GU , the top 20 bacterial ASVs (Fig. 2 c) consisted of Proteobacteria (15 ASVs), and Actinobacteria (5 ASVs). Among the bacterial ASVs showing significant differences in abundance distribution, most bacterial ASVs were enriched in AP . Among the fungal ASVs showing significant differences in abundance distribution, most fungal ASVs were “ AP -enriched,” except for the six “GU-enriched” ASVs (Fig. 2 d). Core ASVs of the root microbiome of AP and GU As AP and GU plants were grown under the same environmental conditions, we next aimed to examine the presence of common bacterial or fungal taxa which exist in both plant species. Therefore, we adopted the concept of core ASVs, which refer to species consistently found and plentiful in different samples or settings. To find the conserved fraction, we identified core ASVs with > 90% (bacteria)/95% (fungi) for AP and GU (Fig. 3 ). In the bacterial community, a pan-microbial community consisting of 1,243 ASVs were identified, including 96 co-detected ASVs between AP and GU , of which, only 3 ASVs were identified as core ASVs (B2_f_ Pseudomonas , B5_ Comamonadaceae , and B70_ Cutibacterium ) (Fig. 3 a). Within the fungal community, the eight major fungal taxa, i.e., F5_ Paraphoma , F6_f_ Lysurus , F22_ Alternaria , F30_ Phaeosphaeria , F53_ Cladosporium , F36_ Moesziomyces , F55_f_ Neocucurbitaria , and F56_ Malassezia , were identified as the prevalent core ASVs (Fig. 3 b). Although these bacterial and fungal taxa existed in both plant species, their abundance patterns differed across both species (Fig. 3 ). In particular, B2_f_ Pseudomonas , B5_ Comamonadaceae , F5_ Paraphoma , and F6_f_ Lysurus were relatively more abundantly distributed in AP . These results highlight that there may be common plant factors that help core ASVs colonize the root endosphere, and AP may provide relatively more appropriate niche environments to certain core taxa than offered by GU . Network analysis of endophytic microbial communities in the root microbiomes of AP and GU We constructed the microbial networks of both AP and GU to gain a comprehensive understanding of the complex and dynamic associations of root endophytic microbial communities. There were 80 bacterial and 42 fungal nodes and 565 connections (321 positive and 244 negative associations) for AP , whereas, for GU, there were 48 bacterial and 53 fungal nodes and 601 connections (374 positive and 227 negative associations) with a threshold set as the correlations of > 0.3 and < − 0.3 ( P < 0.05) (Fig. 4 a–d). In the AP network, fungi showed significantly higher connectivity than that exhibited by bacteria (degree, P = 0.04; closeness centrality, P = 0.12), whereas, in the GU network, bacteria exhibited higher connectivity than that exhibited by fungi (degree, P = 0; closeness centrality, P = 0) (Fig. 4 b–e). To obtain a clear impression of the roles of microorganisms in these microbial networks, hub nodes were identified using degree and closeness centrality measures, and nodes falling within the top 1 percentile in these measurements were considered the hub nodes. In AP , B152_o_ Burkholderiales , F14_ Exophiala , and F33_ Fusarium were identified as the hub nodes (Fig. 4 c). In GU , B36_ Paenibacillus was defined as the hub node (Fig. 4 f). Moreover, the possibility of common associations between the networks of AP and GU reveals a significant overlap between these networks. The high correlation values of the common associations in the two networks indicate that the majority of these associations are statistically significant (pseudo- P < 0.05) (Table S5 ). The predominance of positively correlated associations suggests that biological similarities and functional connections between these networks are strong. These results support that networks of AP and GU are shaped under similar environmental or biological processes. Comparison of ASVs and culture-dependent molecular identification methods To confirm the significance of the key microorganisms associated with AP and GU , as identified based on culture-independent studies, we employed a culture-dependent approach to isolate root microorganisms. The isolates acquired from the cultures were subjected to BLAST analysis to determine if they matched the bacterial and fungal species known to be positively associated with AP and GU . The stringent selection criteria applied during isolation aimed to ensure the purity and distinctiveness of the colonies, focusing on their unique morphotypes and strong growth on the selective media. Notably, during storage at − 80°C, some bacterial and fungal strains may be well preserved, whereas others may be adversely affected by the storage conditions, potentially contributing to the reduced number of successful isolates. Therefore, we analyzed 55 morphologically distinct bacterial and 13 fungal isolates from AP and GU root samples collected in March 2022 (Table S6 ). The culture-dependent approach revealed notable differences in the microbial composition between AP and GU . In AP , we identified 16 bacterial species from 7 different orders and 7 fungal species from 4 orders. In contrast, GU exhibited relatively less diversity, with 11 bacterial species predominantly from the orders Bacillales and Hyphomicrobiales , and only 2 fungal species from 2 different orders. These findings highlight that distinct culturable microbial communities are associated with each plant. Specifically, Pseudomonas extremorientalis dominated the bacterial community in AP , comprising 16% of the relative abundance, whereas Fusarium pseudoanthophilum was the most prevalent fungal species, making up 30% of the fungal community. Conversely, in GU , Bacillus cereus represented 30.77% of the bacterial community, and Paradictyoarthrinium aquatica , belonging to the order Pleosporales , dominated the fungal community at 66.77%. All 68 isolates were subjected to BLAST identification with culture-independent data. The reliability of the evaluation results was determined to be 97%. When BLAST analysis was performed with amplicon data, Priestia megaterium , Pr. aryabhattai , B. cereus , B. paranthracis , and B. velezensis showed over 97% match with bacteria belonging to the genus Bacillus (Table S7 ). When comparing our culture-independent results with our culture-dependent findings, we observed the presence of several bacterial and fungal species across both approaches. Specifically, among the bacterial species, we identified Pseudomonas brassicacearum subsp. neoaurantiaca AP-B26 (100% similarity), Ps. frederiksbergensis AP-B12 (99.735% similarity), Ps. congelans AP-B14, Ps. caspiana AP-B15 (99.471% similarity), and Ps. extremorientalis AP-B1, AP-B2, and AP-B18 (99.206% similarity). Among the fungi, Paraphoma radicina AP-F2 (99.6% similarity) and Pa. radicina AP-F5 (98.776% similarity) were detected. Moreover, unique microbial isolates were identified in AP . Fusarium and Paraphoma species were exclusively associated with AP , with B2_ Pseudomonas , F786_ Fusarium , F5_ Paraphoma , and F28_ Paraphoma showing differential abundance in culture-independent analyses. These findings further confirm the presence of these bacterial strains in both AP and GU , with certain strains, such as B. cereus , demonstrating a higher prevalence in GU than in AP . Discussion It is known that helpful and nonbeneficial bacterial and fungal endophytes are commonly found in plant roots. The elements that decide whether endophytes will be advantageous for the host plant, as well as the extrinsic cues involved and the dynamics of the plant-endophyte connection, are not fully elucidated. Herein, the results obtained during the examination of microbial community composition in AP and GU plants provide valuable novel insights into the complex connections between medicinal plants and microbes. Our findings reveal distinct microbial compositions and diversities within these two medicinal plants, emphasizing their unique microbial compositions, diversities, and intricate relationships among microbes. The dominance of specific bacterial genera, including Bacillus , Pseudomonas , and Rhizobium , along with the fungal communities predominantly comprising Ascomycota in AP and Basidiomycota in GU , highlights the selective nature of root microbiomes and their fundamental compositional differences in host plants (Chen et al., 2022 ). The dominance of Proteobacteria in both AP and GU , although with different dominant orders ( Pseudomonadales in AP and Xanthomonadales in GU ), is consistent with the results of previous studies emphasizing the abundance of Proteobacteria in different plant-associated microbials. This prevalence can be attributed to the metabolic flexibility and adaptability of Proteobacteria (Compant et al., 2019 ; Franke-Whittle et al., 2015 ; Schlaeppi & Bulgarelli, 2015 ). The existence of distinct bacterial orders in both plant species, such as Steroidobacterales in AP and Chlamydiales in GU , indicates the presence of distinctive interactions between the host and microbes. The presence of these bacterial orders in AP and GU may impact the abundances and activities of Steroidobacterales and Chlamydiales , two groups of bacteria that play important roles in plant–microbe interactions. It has been reported that Steroidobacterales , a bacterial order present in AP , has the ability to produce antiproliferative and immunosuppressive compounds (Vurukonda et al., 2018 ). These interactions have the potential to affect the physiological characteristics and stress responses of plants (Berendsen et al., 2012 ; Brencic & Winans, 2005 ; Etesami & Beattie, 2017 ). The results of our study emphasize the difference in composition, with Ascomycota , specifically Pleosporales , being the most abundant in AP , whereas Basidiomycota , particularly Phallales , being more prevalent in GU . Nevertheless, the notable occurrence of the order Boletales from the phylum Basidiomycota in both locations indicates an intricate interaction among these fungal communities. This distinct fungal composition has the potential to improve the ability of the host plant to withstand various challenges, potentially providing diverse benefits to the diversity observed in AP and GU , likely implying a unique preference for fungi that can affect the ability of the host plant to withstand infections and environmental stressors (Kutos et al., 2022 ; Vadakattu et al., 2017 ). The notable differences in alpha diversity between the bacterial and fungal communities of AP and GU are of particular significance, particularly in light of the presence of Bacillus species within GU . The results of both culture-dependent and -independent analyses confirm the presence of Bacillus in GU , suggesting a potential key role for this genus in plant microbial. The bacterial community of AP , which exhibits greater alpha diversity than in GU , could enhance plant resilience and health by fostering a relatively more robust microbial community capable of combating diseases (Mendes et al., 2011 ), which is particularly relevant for Bacillus cereus G2, known to significantly improve the salt stress tolerance of licorice by enhancing its photosynthetic efficiency, carbohydrate metabolism, and the accumulation of medicinal compounds, such as glycyrrhizic acid and liquiritin (Zhang et al., 2022 ). The presence of Bacillus in GU may indicate a specialized adaptation, possibly contributing to the ability of the plant to thrive in specific environmental conditions. In contrast, the higher fungal alpha diversity in GU than in AP may suggest a varied fungal community that plays distinct ecological roles, potentially affecting nutrient cycling and soil health (Van Der Heijden et al., 2008 ). This diversity could reflect an intricate balance within the microbial, where bacteria like Bacillus work in tandem with fungi to support plant health. In our study, the proven presence of Bacillus in GU , particularly considering its crucial functions, raises possibilities regarding the interactions between bacterial and fungal communities and how these relationships could be leveraged to enhance plant resilience and productivity. This complex interplay between diverse microbial communities could be the key to understanding and optimizing plant–microbe interactions for improved agricultural outcomes. An important discovery of our research is the identification of key microorganisms within the root microbiomes of AP and GU . The core ASVs, which are consistently present in different samples, have crucial functions in the operation of the community and emphasize the stable elements of the microbiome associated with plants. In our study, the specific members of Proteobacteria and Actinobacteria , such as B70_ Cutibacterium , B5_f_ Comamonadaceae , and B2_f_ Pseudomonas , have been identified as the core ASVs of AP and GU . Studies have demonstrated that the microorganisms present in medicinal plants, such as Comamonadaceae and Pseudomonas , have a substantial impact on the production of bioactive substances, such as glycyrrhizin, which, in turn, enhances the therapeutic benefits of these plants (Abd Aziz et al., 2021 ). A key characteristic of Comamonadaceae is their adaptability to different environmental conditions. They have been isolated from both polluted and pristine environments, showcasing their ability to thrive in a variety of habitats. Furthermore, they have been found to interact symbiotically with plants, promoting growth and providing protection against pathogens (Sah et al., 2021 ). The widespread occurrence of Comamonadaceae can be attributed to their physiological and genetic diversity, which enables them to colonize and adapt to diverse ecological niches. They have been observed to form vibrant assemblages with other beneficial microbes, such as Pseudomonas spp., in the rhizosphere, creating a mutually beneficial relationship with the host plant (Sah et al., 2021 ). Exploring the potential overlap between the niches occupied by the Comamonadaceae species and the production of primary plant nutrients, such as carbohydrates, may provide valuable insights into the intricate interactions between these plants and their associated microbiomes (Andreote et al., 2014 ). Additionally, Actinobacteria has been found in enriched amounts in the rhizosphere, soil, and root of GU (Chen et al., 2022 ). Further research into specific microbial species within the core ASVs and their metabolic activities is suggested to harness these beneficial plant–microbe interactions (Chen et al., 2022 ; Qiao et al., 2018 ). These findings can facilitate the development of bioengineering techniques to improve the therapeutic characteristics of plants through favorable interactions with microbial communities (Andreote et al., 2014 ; Lareen et al., 2016 ; Niu et al., 2017 ; Trivedi et al., 2020 ). The results of the network analysis provide valuable insights into the intricate relationships within the endophytic microbial communities of AP and GU . Hubs that are highly connected and effective play a crucial role in shaping microbial assembly and enhancing biodiversity. The presence of these hub taxa is also impacted by the physicochemical parameters of the rhizospheric soil (Mora-Ruiz et al., 2016 ). In case of halophytes, such as Salicornia europaea , the endophytic bacterial and fungal communities are determined by the origin of salinity at the sites, with the bacterial community impacting the fungal community (Furtado et al., 2019 ). The finding of hub nodes in both plants highlights the central key within these microbial networks that can have a substantial impact on plant growth and health. In our study, B152_o_ Burkholderiales , F14_ Exophiala , and F33_ Fusarium were identified as key hub nodes in AP , whereas B36_ Paenibacillus emerged as an important key hub node in GU . Although there is limited specific research on these taxa in the context of AP and GU , studies in other plants suggest that Burkholderiales species are often involved in promoting plant growth and resistance against pathogens (Kang et al., 2012 ). Similarly, Exophiala spp. have been shown to be associated with endophytic traits that may enhance plant stress tolerance (Khan et al., 2011 ), and Fusarium is known for its dual role as a pathogen and a potential plant growth-promoting microorganism (Patel et al., 2022 ) under certain conditions. These findings support the hypothesis that the identified hub ASVs may also play similar roles in AP and GU and potentially contribute to plant health and resilience. Moreover, these findings identify important points in the microbial control of plants, offering a comprehensive view of the potential effects of these microbial networks. Gaining knowledge regarding these interactions can be a foundation for altering microbial communities to enhance plant resistance and productivity (Agler et al., 2016 ; Sturz & Nowak, 2000 ; Tao et al., 2018 ). The observation that AP and GU plants host distinct microbial communities within their root microbials is corroborated by both culture-dependent and -independent analyses. Notably, in AP , microorganisms, such as Ps. extremorientalis (16%) and Fu. pseudoanthophilum (30%) were found to be abundant when assessed using culture-dependent methods, suggesting that AP may have a relatively more diverse microbial community. Furthermore, culture-independent approaches revealed that Fusarium and Paraphoma spp. were specific to AP , suggesting that these microorganisms are relatively more prevalent in this plant and may form symbiotic relationships that enhance plant health. This unique microbial association could potentially confer improved resistance to environmental stresses or pathogenic attacks for AP . In contrast, the predominance of B. cereus and Par. aquatica in the GU microbial indicates a narrower microbial diversity, with a symbiotic community largely based on Bacillus spp. Antibiotics and enzymes produced by Bacillus , can promote improved growth by suppressing plant pathogens, thereby increasing the yield of bioactive compounds, such as polysaccharides and saponins (Lin et al., 2022; Sun et al., 2017 ). The microbial communities associated with AP and GU may play a significant role in shaping their phytochemical profiles. For instance, glycyrrhizin and astragalosides, key metabolites in GU and AP , could be impacted by the metabolic activities of associated microorganisms (Abd Aziz et al., 2021 ; Li, Liu, et al., 2021 ). Microbes, such as Pseudomonas and Cladosporium , identified in this study, are known producers of bioactive compounds that could impact plant metabolic pathways (Chandra et al., 2024 ). Pseudomonas spp. may be capable of producing phenazines and siderophores, which may enhance nutrient uptake or signaling processes in plants (Biessy & Filion, 2018 ). Similarly, Cladosporium spp. may be associated with the production of antioxidant phenolic compounds (Chandra et al., 2024 ). These findings suggest that both AP and GU selectively favor distinct microbial communities, each with a unique composition, which may have significant implications for their health and growth. Our research adds to the expanding knowledge base on plant-associated microbiomes, providing novel perspectives on the microbial communities and their organization in AP and GU medicinal plants. The unique microbial profiles discovered in these plants highlight the significance of specialized host–microbe interactions in impacting plant health, growth, and therapeutic properties. Future studies must focus on elucidating the practical consequences of these microbial relationships, which will help in devising strategies for effectively managing microorganisms in the cultivation and breeding of these medicinal plants. Abbreviations AP : Astragalus propinquus GU : Glycyrrhiza uralensis PBS: phosphate-buffered saline ISP5: ISP Medium No. 5 (glycerol–asparagine agar base) ISP2: ISP Agar 2 (yeast extract–malt extract agar) EYA: egg yolk agar LB: Luria–Bertani agar TSA: trypticase soy agar R2A: Reasoner’s 2A agar YPDA: yeast peptone dextrose adenine MPDA: MEDION potato dextrose agar PDA: potato dextrose agar RBA: rose bengal agar SDA: Sabouraud dextrose agar CFU: colony-forming unit PCR: polymerase chain reaction DNA: deoxyribose nucleic acid NCBI: National Center for Biotechnology Information Declarations Additional Information Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Files Supplementary file 1 Supplementary Table 1 Supplementary Table 2 Supplementary Table 3 Supplementary Table 4 Supplementary Table 5 Supplementary Table 6 Supplementary Table 7 Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All raw sequences derived from this experiment were submitted to the Sequence Read Archive of NCBI and can be found under the Bio Project accession numbers PRJNA1093218 ( AP ) and PRJNA1093232 ( GU ). Analysis codes are available from (https://github.com/papican). Competing interests The authors declare no conflict of interest. Funding This work was supported by the National Research Foundation of Korea grants funded by the Korean Government (MSIT) (2018R1A5A1023599, 2021M3H9A1096935, and RS-2023-00275965 to Y.-H.L. and 2022R1C1C2002739 to H.K.). Author contributions ZKK and Y-HL conceived and planned the experiment. 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Genome Biol 14(6):1-10. https://doi.org/10.1186/gb-2013-14-6-209 Uroz S, Courty PE, Oger P (2019) Plant symbionts are engineers of the plant-associated microbiome. Trends Plant Sci 24(10):905-16. https://doi.org/10.1016/j.tplants.2019.06.008 Vadakattu G, Smith L, Scheikowski L, Hunter G, Greenfield P (2017) Region-based differences in the diversity and abundance of fungal community in cotton soils. In: Cotton Research Conference, Canberra, 5-7 September 2017. Van Der Heijden MG, Bardgett RD, Van Straalen NM (2008) The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11(3):296-310. https://doi.org/10.1111/j.1461-0248.2007.01139.x Vurukonda SSKP, Giovanardi D, Stefani E (2018) Plant growth promoting and biocontrol activity of Streptomyces spp. as endophytes. Int J Mol Sci 19(4):952. https://doi.org/10.3390/ijms19040952 White TJ, Bruns T, Lee SH, Taylor JW (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In: Innis MA, Gelfand DH, Sninsky JJ, White TJ. (eds) In: PCR protocols. London: Academic Press. p. 315-322. https://doi.org/10.1016/B978-0-12-372180-8.50042-1 Wickham H (2009) Elegant graphics for data analysis (ggplot2). New York: Springer-Verlag. Xu F, Zhang Y, Xiao S, Lu X, Yang D, Yang X et al. (2006) Absorption and metabolism of Astragali radix decoction: in silico, in vitro, and a case study in vivo. Drug Metab Dispos 34(6):913-24. https://doi.org/10.1124/dmd.105.008300 Yoon S-H, Ha S-M, Kwon S, Lim J, Kim Y, Seo H et al. (2017) Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol 67(5):1613-7. https://doi.org/10.1099/ijsem.0.001755 Zhang Y, Lang D, Zhang W, Zhang X (2022) Bacillus cereus enhanced medicinal ingredient biosynthesis in Glycyrrhiza uralensis Fisch. under different conditions based on the transcriptome and polymerase chain reaction analysis. Front Plant Sci 13:858000. https://doi.org/10.3389/fpls.2022.858000 Zhang Y, Zheng L, Zheng Y, Xue S, Zhang J, Huang P et al. (2020) Insight into the assembly of root-associated microbiome in the medicinal plant Polygonum cuspidatum . Ind Crops Prod 145:112163. https://doi.org/10.1016/j.indcrop.2020.112163 Zhu D, Wang J, Zeng Q, Zhang Z, Yan R (2010) A novel endophytic Huperzine A–producing fungus, Shiraia sp. Slf14, isolated from Huperzia serrata . J Appl Microbiol 109(4):1469-78. https://doi.org/10.1111/j.1365-2672.2010.04777.x Supplementary Files Supplementaryfile1.docx SupplementaryfileS1.tif SupplementaryfileS2.tif SupplementaryfileTableS1.xlsx SupplementaryfileTableS2.xlsx SupplementaryfileTableS3.xlsx SupplementaryfileTableS4.xlsx SupplementaryfileTableS5.xlsx SupplementaryfileTableS6.xlsx SupplementaryfileTableS7.xlsx Cite Share Download PDF Status: Published Journal Publication published 17 May, 2025 Read the published version in Annals of Microbiology → Version 1 posted Reviewers agreed at journal 15 Jan, 2025 Reviewers invited by journal 15 Jan, 2025 Editor assigned by journal 20 Dec, 2024 First submitted to journal 19 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5675838","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402513128,"identity":"b462b331-ffac-4f91-a24e-69f346557f85","order_by":0,"name":"Zerrin KOZMA KIM","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3LsQqCQBzH8b8cOJ21nhj2CidCEUXPogg2RlttQeAtPkIP0dhoHNhyD5C4tNTU0pYglAW5FFdtDfedfr/hA6BS/WGkXgi0fT2/JYj+SAD055ETk7HD6lIOxl1mpLNizaHJEuROJcTCSZjFOOwteSPMDcGBCA/5QkJs4qU7TDglCHdyLeIAO0CbuZT4UVbS64NMioq0PxGLBHqOveRBwKgIrYgvI2ac6nkrCe7EtYxohB3hLxwZIVt2zE7lkJKmcM5F1LftLeemjLyEAbSfgEqlUqnedAMF+kQ60Y90rgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9694-955X","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Zerrin","middleName":"KOZMA","lastName":"KIM","suffix":""},{"id":402513129,"identity":"f0db6ab6-a586-46d7-9276-ccdc2f1544d0","order_by":1,"name":"Young Sang Park","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Young","middleName":"Sang","lastName":"Park","suffix":""},{"id":402513130,"identity":"e39c515d-4ba0-486b-8517-ba7a5e2a37a7","order_by":2,"name":"Tae-Jin Yang","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Tae-Jin","middleName":"","lastName":"Yang","suffix":""},{"id":402513131,"identity":"d566fe2b-3f47-47fd-bc35-22643acdf829","order_by":3,"name":"Hyun Kim","email":"","orcid":"https://orcid.org/0000-0002-7322-713X","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"","lastName":"Kim","suffix":""},{"id":402513132,"identity":"d1c78548-2b16-495b-95d9-52a5642d20f7","order_by":4,"name":"Yong-Hwan Lee","email":"","orcid":"https://orcid.org/0000-0003-2462-1250","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yong-Hwan","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-12-19 10:17:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5675838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5675838/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13213-025-01802-0","type":"published","date":"2025-05-17T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74031109,"identity":"908f52e7-be7a-4773-b485-a5817fbace3c","added_by":"auto","created_at":"2025-01-17 06:54:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2195192,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance in the (a) bacterial and (b) fungal order, in the \u003cem\u003eAstragalus propinquus \u003c/em\u003e(\u003cem\u003eAP\u003c/em\u003e), and \u003cem\u003eGlycyrrhiza uralensis \u003c/em\u003e(\u003cem\u003eGU\u003c/em\u003e). Low abundance taxonomic groups with less than 5‰ (permille) of each sample are represented in gray. Unidentified taxonomic groups are indicated in black. The \u003cem\u003ep\u003c/em\u003e-values for alpha diversity were calculated using the Wilcoxon rank-sum test (c, d). Cumulative sum scaling/log transformed reads were used to calculate Bray–Curtis distances. Each point represents each sample replicate and was colored by plant species (purple for \u003cem\u003eAP\u003c/em\u003e; green for \u003cem\u003eGU\u003c/em\u003e) (e, f).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/bf51d582e61b5fe091de4f55.png"},{"id":74031099,"identity":"020a437b-f7c5-4ce9-8e23-36d52f5a2e52","added_by":"auto","created_at":"2025-01-17 06:54:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":521264,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential abundance test and random forest classification to identify the amplicon sequence variants (ASVs) responsible for differences between the microbial of \u003cem\u003eAstragalus propinquus\u003c/em\u003e (\u003cem\u003eAP\u003c/em\u003e) and \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e (\u003cem\u003eGU\u003c/em\u003e). Volcano plots were created to visualize the differentially abundant bacterial and fungal ASVs. To compare the microbial communities of the two plant species, we utilized a zero-inflated Gaussian distribution mixture model on ASV abundance tables normalized using cumulative sum scaling. The analysis included data from all eight replicates of each plant. The top 20 bacterial and fungal ASVs that most optimally discriminated between \u003cem\u003eAP \u003c/em\u003eand \u003cem\u003eGU\u003c/em\u003e were determined using a random forest classifier. The importance of each ASV in contributing to the accuracy of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e prediction in the random forest model was ranked by calculating the mean decrease in the Gini impurity coefficient.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/37d5ff0a1504860546dbd223.png"},{"id":74031091,"identity":"1f0abe59-3815-4daa-9f3c-9ba0608ec254","added_by":"auto","created_at":"2025-01-17 06:54:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299265,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representation of \u003cem\u003eAstragalus propinquus\u003c/em\u003e (\u003cem\u003eAP\u003c/em\u003e) and \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e (\u003cem\u003eGU\u003c/em\u003e) core amplicon sequence variants (ASVs) showing the frequency of each ASV. The prevalence threshold for core ASV was 90% (bacteria) and 95% (fungi). RA, relative abundance.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/8f5ab5e5d299937eb71a73f6.png"},{"id":74031120,"identity":"c2f94fca-e02b-453a-b8cb-4b82a8c0276e","added_by":"auto","created_at":"2025-01-17 06:54:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1403851,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence-based network of \u003cem\u003eAstragalus propinquus\u003c/em\u003e (\u003cem\u003eAP\u003c/em\u003e) and \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e (\u003cem\u003eGU\u003c/em\u003e) microbial amplicon sequence variants (ASVs) is detected. Each node corresponds to an ASV, and edges between nodes correspond to positive or negative correlations inferred from ASV abundance profiles using the SparCC method. ASVs belonging to bacteria are shown in light pink, fungi are shown in purple, and node size reflects their eigenvector centrality in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/5b4e08d7133a878146038933.png"},{"id":83068552,"identity":"ecfb9569-d438-4023-a32d-93691dc6d0c0","added_by":"auto","created_at":"2025-05-19 16:10:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5314439,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/f503a3b5-4120-4008-843a-e3785fe4de29.pdf"},{"id":74031131,"identity":"950764f3-fe33-445c-ae3c-e211dfa400ca","added_by":"auto","created_at":"2025-01-17 06:54:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1260472,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/d11d6150066b3cee67e1344c.docx"},{"id":74031049,"identity":"1ac99e10-0866-43dc-9d34-3c8428c632a5","added_by":"auto","created_at":"2025-01-17 06:54:07","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":43036652,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/56534c38d451f16de612bd3a.tif"},{"id":74031453,"identity":"733b8dbd-af1c-43e4-94b7-224c4d830fda","added_by":"auto","created_at":"2025-01-17 07:02:14","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":36233124,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/b4f9e23fc9820991d1befbaa.tif"},{"id":74031105,"identity":"c2b44125-7e5a-4686-a1bc-7a0011455d86","added_by":"auto","created_at":"2025-01-17 06:54:13","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":13694,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/36d769ec8195de16da1af546.xlsx"},{"id":74031110,"identity":"dd3bf228-3362-4973-8a1d-23adf5f34c06","added_by":"auto","created_at":"2025-01-17 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06:54:13","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9893,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/dc3ed13458cb7e2f789ed44e.xlsx"},{"id":74031447,"identity":"f1015fe3-6c0a-4032-9ce5-e2aa19302455","added_by":"auto","created_at":"2025-01-17 07:02:13","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":11184,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/19fff6811df9881f3c0f7f92.xlsx"},{"id":74031119,"identity":"53fe4998-2944-4922-91b3-42c597d6ce88","added_by":"auto","created_at":"2025-01-17 06:54:14","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13860,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/03551d71be0e03a9a84b87cb.xlsx"},{"id":74031048,"identity":"86399353-9198-4086-a29e-882e19e90e3c","added_by":"auto","created_at":"2025-01-17 06:54:06","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":28616,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileTableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5675838/v1/1831c843448f7161fec3a01f.xlsx"}],"financialInterests":"","formattedTitle":"Unveiling microbial complexity within Astragalus propinquus and Glycyrrhiza uralensis roots","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn nature, microbes and plants interact and evolve together (Lebeis, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A growing number of studies have proven that microbes form symbiotic relationships with their hosts and that these intimate connections have developed and adapted to enable these tight interactions (Hassani et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Uroz et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, plants need microbiota for their growth and development (Chaparro et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chaparro et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; del Carmen Orozco-Mosqueda et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), stress resistance (Bakker \u0026amp; Mercado-Blanco, 2012; Marasco et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and sustained economic growth (Kudjordjie et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Turner et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEndophytes are a unique class of plant microbiota (Hassani et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Uroz et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) that inhabit and colonize internal plant niches. There is evidence that plant endophytes can boost therapeutic chemical production, support plant development, and protect host plants against pathogen invasion (Deng \u0026amp; Cao, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oono et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Plant microbiome have been linked to the buildup of plant secondary metabolites (Li, Yang, et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These interactions highlight the critical role of microbiomes in shaping the phytochemical properties of plants. The functional variances revealed in rhizospheric microbiomes and endophytes demonstrate their potential resemblance to the therapeutic properties of conventional herbal remedies (Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The similarities between bioactive compounds derived from herbal remedies and the functional traits of rhizospheric microbiomes have sparked interest in elucidating the interactions between different therapeutic plant species and their microbiomes. Elucidating the composition and functional roles of endophytic bacteria and fungi within root niches is crucial for exploring their biotechnological applications, particularly in the context of the synthesis of secondary metabolites, which are considered a source of potential natural medicines and are crucial to the pharmacologic value of medicinal plants (Masand et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Endophytic microbes, including fungi and bacteria, are known to contribute to the synthesis of pharmacologically relevant secondary metabolites in medicinal plants. Prior studies have provided the evidence of the involvement of these endophytes in partial and complete metabolic pathways, contributing to the synthesis of plant metabolites (Brader et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ludwig-M\u0026uuml;ller, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, \u003cem\u003eParaphoma\u003c/em\u003e species, commonly found in root tissues, are associated with the production of terpenoids, compounds with known anti-inflammatory properties. Similarly, in the plants of \u003cem\u003eSwietenia mahagoni\u003c/em\u003e (\u003cem\u003eL\u003c/em\u003e.) \u003cem\u003eJacq\u003c/em\u003e, \u003cem\u003eCladosporium\u003c/em\u003e species have been reported to synthesize phenolic compounds, which exhibit antioxidant activity (Chandra et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, huperzine, known for its potential therapeutic effects in Alzheimer\u0026rsquo;s disease and other memory disorders, is produced by the endophytic fungus \u003cem\u003eShiraia\u003c/em\u003e sp. Slf14, isolated from \u003cem\u003eHuperzia serrata\u003c/em\u003e (Zhu et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, complex interactions within the plant-associated microbiomes contribute to various plant traits, whereas individual microbial species play distinct roles in secondary metabolite production. These relationships highlight the multifaceted nature of plant\u0026ndash;microbe interactions (Trivedi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMedicinal plants are widely recognized for their therapeutic bioactive compounds, which have been extensively studied for their health benefits. The accumulating evidence of the critical role of plant microbiomes in enhancing these therapeutic effects has made it essential to not only study these medicinal plants but also their interactions with their microbiomes. \u003cem\u003eAstragalus propinquus\u003c/em\u003e [\u003cem\u003eAP\u003c/em\u003e, syn. \u003cem\u003eAstragalus membranaceus\u003c/em\u003e (Fisch.) Bunge] and \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e (\u003cem\u003eGU\u003c/em\u003e) represent a rich source of therapeutic compounds (Asl \u0026amp; Hosseinzadeh, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and, both species are renowned for their medicinal properties (Chung et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Liu \u0026amp; Lv, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Stickel \u0026amp; Schuppan, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The microbiome of \u003cem\u003eAP\u003c/em\u003e, for instance, has been shown to harbor a diverse array of bacteria and fungi that contribute to the production of crucial secondary metabolites, such as polysaccharides and saponins (Sun et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, the microbiome of \u003cem\u003eGU\u003c/em\u003e has been reported to enhance the bioavailability and potency of the flavonoid and phenolic compounds produced in this species (Ji et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Despite their common family lineage, these plants exhibit distinct phytochemical profiles believed to be partially impacted by their unique microbial communities (Bratkov et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Both plants occupy a significant position in traditional Korean medicine and remain the subjects of considerable interest in modern pharmacological research. Currently, there is relatively less knowledge regarding microbial communities in the roots of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e that have a potential to impact the phytochemical properties of medicinal plants.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to conduct an in-depth examination of the diversity and composition of microbial communities in the roots of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e plants. Specifically, beyond elucidating the complex nature of microbiomes, the aim is to determine the differences between the structure and associations of their bacterial and fungal communities using culture-independent and -dependent methods. The taxonomic composition of microbial communities in the roots of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e may provide crucial insights into the biosynthesis of plant secondary metabolites, such as glycyrrhizin in \u003cem\u003eGU\u003c/em\u003e and astragalosides in \u003cem\u003eAP\u003c/em\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design and sample preparation\u003c/h2\u003e \u003cp\u003eThe fresh whole roots of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e plants were collected from the Care Farm in Iksan, South Korea (36\u0026deg;00\u0026prime;12.7\u0026prime;\u0026prime;N 127\u0026deg;03\u0026prime;51.8\u0026prime;\u0026prime;E), in March 2022, when \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e were in the vegetative growth stage. We acquired each of the two species of medicinal plants from a single site that most optimally represents the species to reduce the impact of environmental variations and highlight the role of genetic factors. The plants of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e were morphologically identified by Prof. Tae-Jin Yang and Young Sang Park of the Department of Agriculture, Forestry and Bioresources, Seoul National University (Seoul, Korea). Three individual plant roots (at least 30 cm long) collected from each plant were kept in sterile plastic bags, placed into an ice box, and then transported to the laboratory within 24 h. All samples were stored at 4\u0026deg;C until DNA extraction. Root samples were washed under running tap water to remove bulk soil and then surface-sterilized sequentially for 1 min, 3 min, and 30 s with 75% ethanol, 5% NaOCl, and 75% ethanol, respectively. Approximately 1 cm long root segments were excised from the samples for subsequent analyses. The root segments were then transferred to lysing matrix S tubes included in the SPINeasy DNA Kit for Soil (MP Biomedicals, USA). Each tube contained three root segments to ensure sufficient DNA amount to be used for further analyses. To consider the variability in root microbiomes within each individual, eight analytic replicates (randomly selected two or three replicates for each individual) were prepared. All treated root samples were kept in 1X phosphate-buffered saline (PBS), pH 7.4, to avoid DNA denaturation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDNA extraction and molecular analyses\u003c/h3\u003e\n\u003cp\u003eThe prepared samples were ground using a homogenizer (FastPrep-24\u0026trade; 5G; MP Biomedicals, USA). DNA was extracted following the manufacturer\u0026rsquo;s instructions. All DNA samples were quality-checked and measured using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). The extracted DNA was subjected to polymerase chain reaction (PCR) utilizing the primer set 799F\u0026ndash;1193R for the V5\u0026ndash;V7 region of the bacterial 16S ribosomal RNA (rRNA) gene (Bulgarelli et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the ITS1 regions of the fungal rRNA genes were amplified by ITS1-F/R and ITS2-F/R PCR primers (Op De Beeck et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The reaction mix consisted of 12.5 \u0026micro;L of 0.5 \u0026micro;M 2\u0026times; Platinum SuperFi II Green PCR Master Mix (Thermo Fisher Scientific, USA), 1 \u0026micro;L each of 0.5 \u0026micro;M forward and reverse primers, and 0.8 \u0026micro;M of diluted DNA template. PCR was performed using the following program for 16S rRNA: initial denaturation at 98\u0026deg;C for 30 s, followed by 32 cycles of denaturation at 98\u0026deg;C for 10 s, primer annealing at 55\u0026deg;C for 10 s, and extension at 72\u0026deg;C for 40 s, final extension at 72\u0026deg;C for 5 min, and holding at 12\u0026deg;C. For the PCR amplification of fungal ITS regions, the same procedure was followed. The MEGAquick-spin Plus DNA Purification Kit (iNtRON Biotechnology, Korea) was used to pool and purify amplicon replicates obtained from the same DNA sample. The sequencing was performed at Seoul National University\u0026rsquo;s National Instrumentation Center for Environmental Management.\u003c/p\u003e\n\u003ch3\u003eSequence processing and statistical analyses\u003c/h3\u003e\n\u003cp\u003eQIIME2 (v.2020.2) was used to process the sequencing reads (Bolyen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). After demultiplexing, the sequences were combined and quality-filtered in the QIIME2 pipeline using the DADA2 plugin (Callahan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Amplicon sequence variants (ASVs) were then allocated to the processed reads. Non-chimeric ASV taxonomic classifications were assigned using the Nave Bayes technique, which is implemented in the q2-feature-classifier program (Bokulich et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The taxonomies for the V5\u0026ndash;V7 region of the 16S rRNA gene were based on the SILVA database (v.138) (Quast et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The taxonomic designations for the ITS regions were performed using the UNITE database (UNITE_ver8_dynamic of May 2021) (Nilsson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Bacterial sequences with lengths ranging from 278 to 400 bp and fungal sequences ranging from 100 to 400 bp were included. The R package phyloseq was used to clean the ASV profiles (McMurdie \u0026amp; Holmes, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These filtering techniques reduced the overall ASV count in bacteria to 1,339 and in fungi to 435. The ASVs designated as \u0026ldquo;kingdom Fungi\u0026rdquo; but unable to be recognized at the phylum level were subjected to a BLASTn search. The ensuing BLAST results were examined for plant sequences and those discovered were eliminated. In addition, chloroplasts and mitochondria were removed from the bacterial ASV profile. For all statistical analyses performed in R version 4.2.2 (R Core Team, 2013), the statistical significance threshold was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05. The cumNorm function in the R package metagenomeSeq (v. 3.8) was used to log-transform the ASV database and normalize it using cumulative-sum scaling (Paulson et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The Chao1, Observed operational taxonomic unit (OTU), Shannon, Inverse Simpson, and Simpson indices were calculated using the alpha diversity function in the R package vegan (v2.5-3) (Oksanen, 2014). In addition, all Wilcoxon rank-sum tests were performed using the R software. The Bray\u0026ndash;Curtis dissimilarity matrix was computed to generate primary coordinate analyses with and without restrictions. Constrained principal coordinate analyses (PCoAs) were performed using the phyloseq and vegan package functions. The adonis2 function from the vegan package (v2.5-3) (Oksanen, 2014) was used for the permutational multivariate analysis of variance (PERMANOVA). The permutest function of the vegan package was used to perform variance partitioning and significance analysis for experimental variables with 99,999 permutations. Using the FitZig function of the metagenomeSeq package, a zero-inflated Gaussian distribution mixture model was developed to examine the disparities in abundance between the bacterial and fungal ASVs. The makeContrasts and eBayes commands were developed using the R package limma (v.3.34.9) (Ritchie et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The false discovery rate (FDR)-adjusted \u003cem\u003ep\u003c/em\u003e-values were checked to establish the statistical significance of the differences in abundance, and those with values less than 0.01 were deemed significant. Volcano plots generated using ggplot2 were used to visualize the ASVs with various degrees of bacterial and fungal abundances. The parameters for the random classification model, which included the two species, i.e., \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, were set based on the abundance of microbiota. The ROCR (v. 1.0.7) and randomForest packages (v. 4.6\u0026ndash;14) were used for this task, and the final machine learning strategy utilizing the random forest (RF) model in R was used to investigate receiver operating characteristic curves (Liaw \u0026amp; Wiener, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The average reduction in the Gini coefficient was used to evaluate the relevance of the ASVs in assessing how well the RF model predicted \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e and was performed using the significance function from the randomForest package. The top ASVs from the RF models of each kingdom were classified as \u003cem\u003eAP\u003c/em\u003e_enriched and \u003cem\u003eGU\u003c/em\u003e_enriched or non-differential ASVs based on the results of the differential abundance test. Taxa with a relative abundance greater than 0.5% were represented for taxonomic composition analysis using the R program ggplot2 (Wickham, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The core ASVs for both medicinal plants were found. A prevalence criterion of 90 and 95% was specified for core bacterial and fungal ASVs, respectively.\u003c/p\u003e\n\u003ch3\u003eMicrobial network analyses\u003c/h3\u003e\n\u003cp\u003eiNAP was mostly used in the selection process (Feng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The input data for the SparCC analysis was sourced from the combined bacterial and fungal ASV abundance tables (Friedman \u0026amp; Alm, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The estimated correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, two-sided) only included correlations for which the absolute coefficient values were \u0026ge; 0.3 (Kurtz et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The ForceAtlas2 layout was utilized for visualization using Gephi (v0.9.2) (Bastian et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Degree, betweenness, closeness, and eigenvector centralities were calculated using R and Gephi (v0.9.2) (Bastian et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The top 1% of degree and closeness centralities in each network were chosen as the hub nodes.\u003c/p\u003e\n\u003ch3\u003eIsolation of bacteria and fungi from root samples\u003c/h3\u003e\n\u003cp\u003eSurface-sterilized root samples were homogenized using a FastPrep-24TM 5G homogenizer (MP Biomedicals, USA), and the resulting root slurries were serially diluted 10-fold. The root samples were taken from the pooled and sterilized segments, and for each plant species, three replicates of 1 g of root tissue were homogenized in sterile PBS. The subsamples (0.1 \u0026micro;L) of each dilution were plated in triplicate onto ISP medium No. 5 (ISP5), ISP medium No. 2 (ISP2), starch agar, egg yolk agar (EYA), Luria\u0026ndash;Bertani agar (LB), trypticase soy agar (TSA), and Reasoner\u0026rsquo;s 2A agar (R2A) agar for bacterial cultivation and yeast peptone dextrose adenine (YPDA), MEDION potato dextrose agar (MPDA), potato dextrose agar (PDA), rose bengal agar (RBA), and Sabouraud dextrose agar (SDA) for fungal cultivation. In addition, each medium used for bacterial or fungal cultivation contained 0.1 g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e cycloheximide or chloramphenicol to inhibit fungal or bacterial growth, respectively. Plates were incubated in a growth incubator for 2\u0026ndash;7 days at 25 and 28\u0026deg;C for bacterial and fungal cultivation, respectively, following which colonies were counted, and final counts were expressed as CFU g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e root. Microbial DNA was extracted from selected individual colonies to ensure a diverse and representative dataset. For bacterial isolation, seven different culture media (ISP5, ISP2, STARCH, EYA, LB, TSA, and R2A) were used, whereas five media (YPDA, MPDA, PDA, RBA, and SDA) were employed for fungal isolation. Colonies were selected based on their morphological diversity, with priority given to those displaying distinct morphotypes on the respective media. Additionally, colonies exhibiting robust and consistent growth were chosen to maximize diversity within the dataset.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of bacterial and fungal isolates\u003c/h2\u003e \u003cp\u003eThe bacterial and fungal DNA samples were extracted and purified using the PURE gDNA Extraction Kit (Infusion Tech, Korea). A PCR mix of total volume 20 \u0026micro;L containing 1 \u0026micro;L DNA, 1 \u0026micro;L of each primer, 10 \u0026micro;L of 2X TOP Simple DyeMIX Taq master mix, and 7 \u0026micro;L of ddH\u003csub\u003e2\u003c/sub\u003eO was used for each PCR amplification. The PCR reactions were performed using the following protocol: initial denaturation at 95\u0026deg;C for 2 min, followed by 34 cycles of denaturation at 95\u0026deg;C for 30 s, primer annealing at 55\u0026deg;C for 60 s, and extension at 72\u0026deg;C for 100 s, final extension at 72\u0026deg;C for 5 min, and holding at 12\u0026deg;C. The amplified PCR products were verified with electrophoresis on a 1% agarose gel containing SafeView (Applied Biological Materials, Canada) to determine their sizes (\u0026sim;500 bp) and approximate concentrations and purified using a MEGAquick-spin plus fragment DNA purification kit (iNtRON Biotechnology, Korea). The bacterial amplified PCR products were sequenced using the sequencing primers 515R (TTACCGCGGCTGCTGGCA), 926F (AAA CTCAAAGGAATTGACGG) (Lane, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), and 1055R (AGCTGACGACAGCCAT) (Lee et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and assembled using the SeqMan Lasergene software version 7.1.0 (DNAStar, Madison, WI, USA). The similarity of the 16S rRNA gene sequence with those of known bacterial type strains was determined using the EzBioCloud server (Yoon et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The pure cultures of the obtained microorganisms were established and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for long-term storage as glycerol stocks. For the sequencing of fungal amplified PCR products, the ITS1 (TCCGTAGGTGAACCTGCGG) and ITS4 (TCCTCCGCTTATTGATATGC) primers were used (White, 1990). PCR was performed with initial denaturation at 95\u0026deg;C for 2 min, followed by 34 cycles of denaturation at 95\u0026deg;C for 30 s, primer annealing at 55\u0026deg;C for 30 s, and extension at 72\u0026deg;C for 90 s, final extension at 72\u0026deg;C for 5 min, and holding at 12\u0026deg;C. The process was the same for the PCR amplification of bacterial DNA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eMicrobial community composition and diversity varies in\u003c/b\u003e \u003cb\u003eAP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGU\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe examined the makeup of bacterial and fungal communities at the level of the same family but different genera, i.e., \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, by comparing the relative and the estimated absolute abundances of dominant bacterial and fungal phyla associated with the root. The examination of taxonomic affiliations revealed variations in the dominance of orders across both medicinal plants (Supplementary file: Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2). Specifically, in the bacterial communities of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e emerged as the predominant phylum, with the order \u003cem\u003ePseudomonadales\u003c/em\u003e (46%) in \u003cem\u003eAP\u003c/em\u003e and the order \u003cem\u003eXanthomonadales\u003c/em\u003e (41.6%) in \u003cem\u003eGU\u003c/em\u003e leading in average relative abundance. \u003cem\u003eRhizobiales\u003c/em\u003e ranked as the second most dominant bacterial community in both plant species, with the abundances of 15 and 36.3%, respectively. Notably, \u003cem\u003eAP\u003c/em\u003e housed seven bacterial orders not found in \u003cem\u003eGU\u003c/em\u003e (\u003cem\u003eSteroidobacterales\u003c/em\u003e, \u003cem\u003eCytophagales\u003c/em\u003e, \u003cem\u003eMicromonosporales\u003c/em\u003e, \u003cem\u003eNevskiales\u003c/em\u003e, \u003cem\u003eStreptosporangiales\u003c/em\u003e, \u003cem\u003eGlycomycetales\u003c/em\u003e, and \u003cem\u003eAcidimicrobiales\u003c/em\u003e at 0.3, 0.2, 0.1, 0.1, 0.072, 0.066, and 0.062%, respectively), whereas \u003cem\u003eGU\u003c/em\u003e exhibited two unique orders absent in \u003cem\u003eAP\u003c/em\u003e (\u003cem\u003eChlamydiales\u003c/em\u003e and \u003cem\u003eBacteroidales\u003c/em\u003e at 0.22 and 0.01%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This compositional difference was also found in the fungal community, where Ascomycota (\u003cem\u003ePleosporales\u003c/em\u003e, 26.75%) predominated in \u003cem\u003eAP\u003c/em\u003e, and Basidiomycota (\u003cem\u003ePhallales\u003c/em\u003e, 52.1%) in \u003cem\u003eGU\u003c/em\u003e. However, in both \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, the order \u003cem\u003eBoletales\u003c/em\u003e from the phylum \u003cem\u003eBasidiomycota\u003c/em\u003e (32 and 68% in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, respectively) exhibited a significant presence, with 25 and 14.7% relative abundances, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In \u003cem\u003eAP\u003c/em\u003e, three distinct fungal orders were identified that were not present in \u003cem\u003eGU\u003c/em\u003e (\u003cem\u003eThelebolales\u003c/em\u003e, \u003cem\u003eSordariales\u003c/em\u003e, and \u003cem\u003eHelotiales\u003c/em\u003e at 0.6, 0.2, and 0.1%, respectively). In contrast, \u003cem\u003eGU\u003c/em\u003e exhibited a unique fungal order (\u003cem\u003eUstilaginales\u003c/em\u003e, 0.7%) absent in \u003cem\u003eAP\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e differed significantly in terms of their bacterial communities and dominant fungal communities. \u003cem\u003eAP\u003c/em\u003e exhibited a diverse bacterial community and was mostly dominated by A\u003cem\u003escomycota\u003c/em\u003e in the fungal domain. In contrast, \u003cem\u003eGU\u003c/em\u003e was characterized by the dominance of certain bacterial taxa, such as \u003cem\u003eXanthomonadales\u003c/em\u003e, and the dominance of \u003cem\u003eBasidiomycota\u003c/em\u003e in the fungal kingdom.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe observed composition patterns further supported the differences in community diversity. The alpha diversity of root microbial communities was assessed using the Shannon, Observed OTU, Inverse Simpson, Chao1, and Simpson indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, d). For the bacterial community, \u003cem\u003eAP\u003c/em\u003e showed significantly higher diversity than that in \u003cem\u003eGU\u003c/em\u003e (Shannon, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; Inverse Simpson, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). In addition, richness (Observed OTU, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06; Chao1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08) and evenness (Simpson, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06) were higher in \u003cem\u003eAP\u003c/em\u003e than in \u003cem\u003eGU\u003c/em\u003e; however, the difference was not statistically significant. Regarding the fungal community, \u003cem\u003eGU\u003c/em\u003e exhibited greater richness than that in \u003cem\u003eAP\u003c/em\u003e (Observed OTU, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; Chao1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). However, there were no significant differences in the diversity and evenness indices (Shannon, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4; Inverse Simpson, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8; Simpson, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4). The combined results of PCoA and PERMANOVA showed that the bacterial and fungal communities were significantly different between the two plant species (bacteria: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001; fungi: \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, f; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These results suggest that plant genetic or physiological factors may drive the differentiation of root endophytic bacterial and fungal communities between \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e under identical environmental conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential distribution of root-associated microbiotas in\u003c/b\u003e \u003cb\u003eAP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGU\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs we found compositional differences among the medicinal plants of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, we aimed to seek distinct microbial taxa contributing to the observed composition patterns. The differential abundance analysis showed that, for \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, 301 bacterial and 228 fungal ASVs, were significantly affected by the plant species (log\u003csub\u003e2\u003c/sub\u003e fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2, FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). B5_f_\u003cem\u003eComamonadaceae\u003c/em\u003e was notably more abundant in \u003cem\u003eAP\u003c/em\u003e than in \u003cem\u003eGU\u003c/em\u003e, whereas B1862_f_\u003cem\u003eSphingomonadaceae\u003c/em\u003e exhibited a significantly higher abundance in \u003cem\u003eGU\u003c/em\u003e that in \u003cem\u003eAP\u003c/em\u003e. F1_\u003cem\u003eFusarium\u003c/em\u003e, a fungus, was relatively more enriched in \u003cem\u003eAP\u003c/em\u003e than in \u003cem\u003eGU\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). As the differential abundance analysis could overrepresent the differences in abundance between the two plant species, we constructed two RF classification models for each domain to complement this limitation. These RF models were used to select the top 20 ASVs based on the similarity in their cross-validation error rates with those of the RF models (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Additionally, their importance in predicting the target variable was determined using the Gini uncertainty measure. In \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, the top 20 bacterial ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) consisted of \u003cem\u003eProteobacteria\u003c/em\u003e (15 ASVs), and \u003cem\u003eActinobacteria\u003c/em\u003e (5 ASVs). Among the bacterial ASVs showing significant differences in abundance distribution, most bacterial ASVs were enriched in \u003cem\u003eAP\u003c/em\u003e. Among the fungal ASVs showing significant differences in abundance distribution, most fungal ASVs were \u0026ldquo;\u003cem\u003eAP\u003c/em\u003e-enriched,\u0026rdquo; except for the six \u0026ldquo;GU-enriched\u0026rdquo; ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCore ASVs of the root microbiome of\u003c/b\u003e \u003cb\u003eAP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGU\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e plants were grown under the same environmental conditions, we next aimed to examine the presence of common bacterial or fungal taxa which exist in both plant species. Therefore, we adopted the concept of core ASVs, which refer to species consistently found and plentiful in different samples or settings. To find the conserved fraction, we identified core ASVs with \u0026gt;\u0026thinsp;90% (bacteria)/95% (fungi) for \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the bacterial community, a pan-microbial community consisting of 1,243 ASVs were identified, including 96 co-detected ASVs between \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, of which, only 3 ASVs were identified as core ASVs (B2_f_\u003cem\u003ePseudomonas\u003c/em\u003e, B5_\u003cem\u003eComamonadaceae\u003c/em\u003e, and B70_\u003cem\u003eCutibacterium\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Within the fungal community, the eight major fungal taxa, i.e., F5_\u003cem\u003eParaphoma\u003c/em\u003e, F6_f_\u003cem\u003eLysurus\u003c/em\u003e, F22_\u003cem\u003eAlternaria\u003c/em\u003e, F30_\u003cem\u003ePhaeosphaeria\u003c/em\u003e, F53_\u003cem\u003eCladosporium\u003c/em\u003e, F36_\u003cem\u003eMoesziomyces\u003c/em\u003e, F55_f_\u003cem\u003eNeocucurbitaria\u003c/em\u003e, and F56_\u003cem\u003eMalassezia\u003c/em\u003e, were identified as the prevalent core ASVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Although these bacterial and fungal taxa existed in both plant species, their abundance patterns differed across both species (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In particular, B2_f_\u003cem\u003ePseudomonas\u003c/em\u003e, B5_\u003cem\u003eComamonadaceae\u003c/em\u003e, F5_\u003cem\u003eParaphoma\u003c/em\u003e, and F6_f_\u003cem\u003eLysurus\u003c/em\u003e were relatively more abundantly distributed in \u003cem\u003eAP\u003c/em\u003e. These results highlight that there may be common plant factors that help core ASVs colonize the root endosphere, and \u003cem\u003eAP\u003c/em\u003e may provide relatively more appropriate niche environments to certain core taxa than offered by \u003cem\u003eGU\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNetwork analysis of endophytic microbial communities in the root microbiomes of\u003c/b\u003e \u003cb\u003eAP\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eGU\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe constructed the microbial networks of both \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e to gain a comprehensive understanding of the complex and dynamic associations of root endophytic microbial communities. There were 80 bacterial and 42 fungal nodes and 565 connections (321 positive and 244 negative associations) for \u003cem\u003eAP\u003c/em\u003e, whereas, for GU, there were 48 bacterial and 53 fungal nodes and 601 connections (374 positive and 227 negative associations) with a threshold set as the correlations of \u0026gt;\u0026thinsp;0.3 and \u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;d). In the \u003cem\u003eAP\u003c/em\u003e network, fungi showed significantly higher connectivity than that exhibited by bacteria (degree, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; closeness centrality, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12), whereas, in the \u003cem\u003eGU\u003c/em\u003e network, bacteria exhibited higher connectivity than that exhibited by fungi (degree, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0; closeness centrality, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u0026ndash;e). To obtain a clear impression of the roles of microorganisms in these microbial networks, hub nodes were identified using degree and closeness centrality measures, and nodes falling within the top 1 percentile in these measurements were considered the hub nodes. In \u003cem\u003eAP\u003c/em\u003e, B152_o_\u003cem\u003eBurkholderiales\u003c/em\u003e, F14_\u003cem\u003eExophiala\u003c/em\u003e, and F33_\u003cem\u003eFusarium\u003c/em\u003e were identified as the hub nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In \u003cem\u003eGU\u003c/em\u003e, B36_\u003cem\u003ePaenibacillus\u003c/em\u003e was defined as the hub node (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Moreover, the possibility of common associations between the networks of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e reveals a significant overlap between these networks. The high correlation values of the common associations in the two networks indicate that the majority of these associations are statistically significant (pseudo-\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The predominance of positively correlated associations suggests that biological similarities and functional connections between these networks are strong. These results support that networks of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e are shaped under similar environmental or biological processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of ASVs and culture-dependent molecular identification methods\u003c/h3\u003e\n\u003cp\u003eTo confirm the significance of the key microorganisms associated with \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, as identified based on culture-independent studies, we employed a culture-dependent approach to isolate root microorganisms. The isolates acquired from the cultures were subjected to BLAST analysis to determine if they matched the bacterial and fungal species known to be positively associated with \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. The stringent selection criteria applied during isolation aimed to ensure the purity and distinctiveness of the colonies, focusing on their unique morphotypes and strong growth on the selective media. Notably, during storage at \u0026minus;\u0026thinsp;80\u0026deg;C, some bacterial and fungal strains may be well preserved, whereas others may be adversely affected by the storage conditions, potentially contributing to the reduced number of successful isolates. Therefore, we analyzed 55 morphologically distinct bacterial and 13 fungal isolates from \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e root samples collected in March 2022 (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). The culture-dependent approach revealed notable differences in the microbial composition between \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. In \u003cem\u003eAP\u003c/em\u003e, we identified 16 bacterial species from 7 different orders and 7 fungal species from 4 orders. In contrast, \u003cem\u003eGU\u003c/em\u003e exhibited relatively less diversity, with 11 bacterial species predominantly from the orders \u003cem\u003eBacillales\u003c/em\u003e and \u003cem\u003eHyphomicrobiales\u003c/em\u003e, and only 2 fungal species from 2 different orders. These findings highlight that distinct culturable microbial communities are associated with each plant. Specifically, \u003cem\u003ePseudomonas extremorientalis\u003c/em\u003e dominated the bacterial community in \u003cem\u003eAP\u003c/em\u003e, comprising 16% of the relative abundance, whereas \u003cem\u003eFusarium pseudoanthophilum\u003c/em\u003e was the most prevalent fungal species, making up 30% of the fungal community. Conversely, in \u003cem\u003eGU\u003c/em\u003e, \u003cem\u003eBacillus cereus\u003c/em\u003e represented 30.77% of the bacterial community, and \u003cem\u003eParadictyoarthrinium aquatica\u003c/em\u003e, belonging to the order \u003cem\u003ePleosporales\u003c/em\u003e, dominated the fungal community at 66.77%.\u003c/p\u003e \u003cp\u003eAll 68 isolates were subjected to BLAST identification with culture-independent data. The reliability of the evaluation results was determined to be 97%. When BLAST analysis was performed with amplicon data, \u003cem\u003ePriestia megaterium\u003c/em\u003e, \u003cem\u003ePr. aryabhattai\u003c/em\u003e, \u003cem\u003eB. cereus\u003c/em\u003e, \u003cem\u003eB. paranthracis\u003c/em\u003e, and \u003cem\u003eB. velezensis\u003c/em\u003e showed over 97% match with bacteria belonging to the genus \u003cem\u003eBacillus\u003c/em\u003e (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). When comparing our culture-independent results with our culture-dependent findings, we observed the presence of several bacterial and fungal species across both approaches. Specifically, among the bacterial species, we identified \u003cem\u003ePseudomonas brassicacearum subsp. neoaurantiaca\u003c/em\u003e AP-B26 (100% similarity), \u003cem\u003ePs. frederiksbergensis\u003c/em\u003e AP-B12 (99.735% similarity), \u003cem\u003ePs. congelans\u003c/em\u003e AP-B14, \u003cem\u003ePs. caspiana\u003c/em\u003e AP-B15 (99.471% similarity), and \u003cem\u003ePs. extremorientalis\u003c/em\u003e AP-B1, AP-B2, and AP-B18 (99.206% similarity). Among the fungi, \u003cem\u003eParaphoma radicina\u003c/em\u003e AP-F2 (99.6% similarity) and \u003cem\u003ePa. radicina\u003c/em\u003e AP-F5 (98.776% similarity) were detected. Moreover, unique microbial isolates were identified in \u003cem\u003eAP\u003c/em\u003e. \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003eParaphoma\u003c/em\u003e species were exclusively associated with \u003cem\u003eAP\u003c/em\u003e, with B2_\u003cem\u003ePseudomonas\u003c/em\u003e, F786_\u003cem\u003eFusarium\u003c/em\u003e, F5_\u003cem\u003eParaphoma\u003c/em\u003e, and F28_\u003cem\u003eParaphoma\u003c/em\u003e showing differential abundance in culture-independent analyses. These findings further confirm the presence of these bacterial strains in both \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, with certain strains, such as \u003cem\u003eB. cereus\u003c/em\u003e, demonstrating a higher prevalence in \u003cem\u003eGU\u003c/em\u003e than in \u003cem\u003eAP\u003c/em\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt is known that helpful and nonbeneficial bacterial and fungal endophytes are commonly found in plant roots. The elements that decide whether endophytes will be advantageous for the host plant, as well as the extrinsic cues involved and the dynamics of the plant-endophyte connection, are not fully elucidated. Herein, the results obtained during the examination of microbial community composition in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e plants provide valuable novel insights into the complex connections between medicinal plants and microbes. Our findings reveal distinct microbial compositions and diversities within these two medicinal plants, emphasizing their unique microbial compositions, diversities, and intricate relationships among microbes. The dominance of specific bacterial genera, including \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e, along with the fungal communities predominantly comprising \u003cem\u003eAscomycota\u003c/em\u003e in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e, highlights the selective nature of root microbiomes and their fundamental compositional differences in host plants (Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dominance of \u003cem\u003eProteobacteria\u003c/em\u003e in both \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, although with different dominant orders (\u003cem\u003ePseudomonadales\u003c/em\u003e in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eXanthomonadales\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e), is consistent with the results of previous studies emphasizing the abundance of \u003cem\u003eProteobacteria\u003c/em\u003e in different plant-associated microbials. This prevalence can be attributed to the metabolic flexibility and adaptability of \u003cem\u003eProteobacteria\u003c/em\u003e (Compant et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Franke-Whittle et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schlaeppi \u0026amp; Bulgarelli, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The existence of distinct bacterial orders in both plant species, such as \u003cem\u003eSteroidobacterales\u003c/em\u003e in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eChlamydiales\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e, indicates the presence of distinctive interactions between the host and microbes. The presence of these bacterial orders in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e may impact the abundances and activities of \u003cem\u003eSteroidobacterales\u003c/em\u003e and \u003cem\u003eChlamydiales\u003c/em\u003e, two groups of bacteria that play important roles in plant\u0026ndash;microbe interactions. It has been reported that \u003cem\u003eSteroidobacterales\u003c/em\u003e, a bacterial order present in \u003cem\u003eAP\u003c/em\u003e, has the ability to produce antiproliferative and immunosuppressive compounds (Vurukonda et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These interactions have the potential to affect the physiological characteristics and stress responses of plants (Berendsen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brencic \u0026amp; Winans, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Etesami \u0026amp; Beattie, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results of our study emphasize the difference in composition, with \u003cem\u003eAscomycota\u003c/em\u003e, specifically \u003cem\u003ePleosporales\u003c/em\u003e, being the most abundant in \u003cem\u003eAP\u003c/em\u003e, whereas \u003cem\u003eBasidiomycota\u003c/em\u003e, particularly \u003cem\u003ePhallales\u003c/em\u003e, being more prevalent in \u003cem\u003eGU\u003c/em\u003e. Nevertheless, the notable occurrence of the order \u003cem\u003eBoletales\u003c/em\u003e from the phylum \u003cem\u003eBasidiomycota\u003c/em\u003e in both locations indicates an intricate interaction among these fungal communities. This distinct fungal composition has the potential to improve the ability of the host plant to withstand various challenges, potentially providing diverse benefits to the diversity observed in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, likely implying a unique preference for fungi that can affect the ability of the host plant to withstand infections and environmental stressors (Kutos et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vadakattu et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe notable differences in alpha diversity between the bacterial and fungal communities of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e are of particular significance, particularly in light of the presence of \u003cem\u003eBacillus\u003c/em\u003e species within \u003cem\u003eGU\u003c/em\u003e. The results of both culture-dependent and -independent analyses confirm the presence of \u003cem\u003eBacillus\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e, suggesting a potential key role for this genus in plant microbial. The bacterial community of \u003cem\u003eAP\u003c/em\u003e, which exhibits greater alpha diversity than in \u003cem\u003eGU\u003c/em\u003e, could enhance plant resilience and health by fostering a relatively more robust microbial community capable of combating diseases (Mendes et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is particularly relevant for \u003cem\u003eBacillus cereus\u003c/em\u003e G2, known to significantly improve the salt stress tolerance of licorice by enhancing its photosynthetic efficiency, carbohydrate metabolism, and the accumulation of medicinal compounds, such as glycyrrhizic acid and liquiritin (Zhang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The presence of \u003cem\u003eBacillus\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e may indicate a specialized adaptation, possibly contributing to the ability of the plant to thrive in specific environmental conditions. In contrast, the higher fungal alpha diversity in \u003cem\u003eGU\u003c/em\u003e than in \u003cem\u003eAP\u003c/em\u003e may suggest a varied fungal community that plays distinct ecological roles, potentially affecting nutrient cycling and soil health (Van Der Heijden et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This diversity could reflect an intricate balance within the microbial, where bacteria like \u003cem\u003eBacillus\u003c/em\u003e work in tandem with fungi to support plant health. In our study, the proven presence of \u003cem\u003eBacillus\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e, particularly considering its crucial functions, raises possibilities regarding the interactions between bacterial and fungal communities and how these relationships could be leveraged to enhance plant resilience and productivity. This complex interplay between diverse microbial communities could be the key to understanding and optimizing plant\u0026ndash;microbe interactions for improved agricultural outcomes.\u003c/p\u003e \u003cp\u003eAn important discovery of our research is the identification of key microorganisms within the root microbiomes of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. The core ASVs, which are consistently present in different samples, have crucial functions in the operation of the community and emphasize the stable elements of the microbiome associated with plants. In our study, the specific members of \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e, such as B70_\u003cem\u003eCutibacterium\u003c/em\u003e, B5_f_\u003cem\u003eComamonadaceae\u003c/em\u003e, and B2_f_\u003cem\u003ePseudomonas\u003c/em\u003e, have been identified as the core ASVs of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. Studies have demonstrated that the microorganisms present in medicinal plants, such as \u003cem\u003eComamonadaceae\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e, have a substantial impact on the production of bioactive substances, such as glycyrrhizin, which, in turn, enhances the therapeutic benefits of these plants (Abd Aziz et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A key characteristic of \u003cem\u003eComamonadaceae\u003c/em\u003e is their adaptability to different environmental conditions. They have been isolated from both polluted and pristine environments, showcasing their ability to thrive in a variety of habitats. Furthermore, they have been found to interact symbiotically with plants, promoting growth and providing protection against pathogens (Sah et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The widespread occurrence of \u003cem\u003eComamonadaceae\u003c/em\u003e can be attributed to their physiological and genetic diversity, which enables them to colonize and adapt to diverse ecological niches. They have been observed to form vibrant assemblages with other beneficial microbes, such as \u003cem\u003ePseudomonas\u003c/em\u003e spp., in the rhizosphere, creating a mutually beneficial relationship with the host plant (Sah et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Exploring the potential overlap between the niches occupied by the \u003cem\u003eComamonadaceae\u003c/em\u003e species and the production of primary plant nutrients, such as carbohydrates, may provide valuable insights into the intricate interactions between these plants and their associated microbiomes (Andreote et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Additionally, \u003cem\u003eActinobacteria\u003c/em\u003e has been found in enriched amounts in the rhizosphere, soil, and root of \u003cem\u003eGU\u003c/em\u003e (Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further research into specific microbial species within the core ASVs and their metabolic activities is suggested to harness these beneficial plant\u0026ndash;microbe interactions (Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qiao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings can facilitate the development of bioengineering techniques to improve the therapeutic characteristics of plants through favorable interactions with microbial communities (Andreote et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lareen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Trivedi et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the network analysis provide valuable insights into the intricate relationships within the endophytic microbial communities of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. Hubs that are highly connected and effective play a crucial role in shaping microbial assembly and enhancing biodiversity. The presence of these hub taxa is also impacted by the physicochemical parameters of the rhizospheric soil (Mora-Ruiz et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In case of halophytes, such as \u003cem\u003eSalicornia europaea\u003c/em\u003e, the endophytic bacterial and fungal communities are determined by the origin of salinity at the sites, with the bacterial community impacting the fungal community (Furtado et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The finding of hub nodes in both plants highlights the central key within these microbial networks that can have a substantial impact on plant growth and health. In our study, B152_o_\u003cem\u003eBurkholderiales\u003c/em\u003e, F14_\u003cem\u003eExophiala\u003c/em\u003e, and F33_\u003cem\u003eFusarium\u003c/em\u003e were identified as key hub nodes in \u003cem\u003eAP\u003c/em\u003e, whereas B36_\u003cem\u003ePaenibacillus\u003c/em\u003e emerged as an important key hub node in \u003cem\u003eGU\u003c/em\u003e. Although there is limited specific research on these taxa in the context of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, studies in other plants suggest that \u003cem\u003eBurkholderiales\u003c/em\u003e species are often involved in promoting plant growth and resistance against pathogens (Kang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similarly, \u003cem\u003eExophiala\u003c/em\u003e spp. have been shown to be associated with endophytic traits that may enhance plant stress tolerance (Khan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and \u003cem\u003eFusarium\u003c/em\u003e is known for its dual role as a pathogen and a potential plant growth-promoting microorganism (Patel et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) under certain conditions. These findings support the hypothesis that the identified hub ASVs may also play similar roles in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e and potentially contribute to plant health and resilience. Moreover, these findings identify important points in the microbial control of plants, offering a comprehensive view of the potential effects of these microbial networks. Gaining knowledge regarding these interactions can be a foundation for altering microbial communities to enhance plant resistance and productivity (Agler et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sturz \u0026amp; Nowak, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Tao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observation that \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e plants host distinct microbial communities within their root microbials is corroborated by both culture-dependent and -independent analyses. Notably, in \u003cem\u003eAP\u003c/em\u003e, microorganisms, such as \u003cem\u003ePs. extremorientalis\u003c/em\u003e (16%) and \u003cem\u003eFu. pseudoanthophilum\u003c/em\u003e (30%) were found to be abundant when assessed using culture-dependent methods, suggesting that \u003cem\u003eAP\u003c/em\u003e may have a relatively more diverse microbial community. Furthermore, culture-independent approaches revealed that \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003eParaphoma\u003c/em\u003e spp. were specific to \u003cem\u003eAP\u003c/em\u003e, suggesting that these microorganisms are relatively more prevalent in this plant and may form symbiotic relationships that enhance plant health. This unique microbial association could potentially confer improved resistance to environmental stresses or pathogenic attacks for \u003cem\u003eAP\u003c/em\u003e. In contrast, the predominance of \u003cem\u003eB. cereus\u003c/em\u003e and \u003cem\u003ePar. aquatica\u003c/em\u003e in the \u003cem\u003eGU\u003c/em\u003e microbial indicates a narrower microbial diversity, with a symbiotic community largely based on \u003cem\u003eBacillus\u003c/em\u003e spp. Antibiotics and enzymes produced by \u003cem\u003eBacillus\u003c/em\u003e, can promote improved growth by suppressing plant pathogens, thereby increasing the yield of bioactive compounds, such as polysaccharides and saponins (Lin et al., 2022; Sun et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The microbial communities associated with \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e may play a significant role in shaping their phytochemical profiles. For instance, glycyrrhizin and astragalosides, key metabolites in \u003cem\u003eGU\u003c/em\u003e and \u003cem\u003eAP\u003c/em\u003e, could be impacted by the metabolic activities of associated microorganisms (Abd Aziz et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li, Liu, et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Microbes, such as \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eCladosporium\u003c/em\u003e, identified in this study, are known producers of bioactive compounds that could impact plant metabolic pathways (Chandra et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003ePseudomonas\u003c/em\u003e spp. may be capable of producing phenazines and siderophores, which may enhance nutrient uptake or signaling processes in plants (Biessy \u0026amp; Filion, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, \u003cem\u003eCladosporium\u003c/em\u003e spp. may be associated with the production of antioxidant phenolic compounds (Chandra et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings suggest that both \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e selectively favor distinct microbial communities, each with a unique composition, which may have significant implications for their health and growth.\u003c/p\u003e \u003cp\u003eOur research adds to the expanding knowledge base on plant-associated microbiomes, providing novel perspectives on the microbial communities and their organization in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e medicinal plants. The unique microbial profiles discovered in these plants highlight the significance of specialized host\u0026ndash;microbe interactions in impacting plant health, growth, and therapeutic properties. Future studies must focus on elucidating the practical consequences of these microbial relationships, which will help in devising strategies for effectively managing microorganisms in the cultivation and breeding of these medicinal plants.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eAP\u003c/em\u003e: \u003cem\u003eAstragalus propinquus\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGU\u003c/em\u003e: \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePBS: phosphate-buffered saline\u003c/p\u003e\n\u003cp\u003eISP5: ISP Medium No. 5 (glycerol\u0026ndash;asparagine agar base)\u003c/p\u003e\n\u003cp\u003eISP2: ISP Agar 2 (yeast extract\u0026ndash;malt extract agar)\u003c/p\u003e\n\u003cp\u003eEYA: egg yolk agar\u003c/p\u003e\n\u003cp\u003eLB: Luria\u0026ndash;Bertani agar\u003c/p\u003e\n\u003cp\u003eTSA: trypticase soy agar\u003c/p\u003e\n\u003cp\u003eR2A: Reasoner\u0026rsquo;s 2A agar\u003c/p\u003e\n\u003cp\u003eYPDA: yeast peptone dextrose adenine\u003c/p\u003e\n\u003cp\u003eMPDA: MEDION potato dextrose agar\u003c/p\u003e\n\u003cp\u003ePDA: potato dextrose agar\u003c/p\u003e\n\u003cp\u003eRBA: rose bengal agar\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSDA: Sabouraud dextrose agar\u003c/p\u003e\n\u003cp\u003eCFU: colony-forming unit\u003c/p\u003e\n\u003cp\u003ePCR: polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eDNA: deoxyribose nucleic acid\u003c/p\u003e\n\u003cp\u003eNCBI: National Center for Biotechnology Information\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s Note\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary file 1\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1\u003c/p\u003e\n\u003cp\u003eSupplementary Table 2\u003c/p\u003e\n\u003cp\u003eSupplementary Table 3\u003c/p\u003e\n\u003cp\u003eSupplementary Table 4\u003c/p\u003e\n\u003cp\u003eSupplementary Table 5\u003c/p\u003e\n\u003cp\u003eSupplementary Table 6\u003c/p\u003e\n\u003cp\u003eSupplementary Table 7\u003c/p\u003e\n\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequences derived from this experiment were submitted to the Sequence Read Archive of NCBI and can be found under the Bio Project accession numbers PRJNA1093218 (\u003cem\u003eAP\u003c/em\u003e) and PRJNA1093232 (\u003cem\u003eGU\u003c/em\u003e). Analysis codes are available from (https://github.com/papican).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Research Foundation of Korea grants funded by the Korean Government (MSIT) (2018R1A5A1023599, 2021M3H9A1096935, and RS-2023-00275965 to Y.-H.L. and 2022R1C1C2002739 to H.K.). \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZKK and Y-HL conceived and planned the experiment. ZKK performed public data analyses and wrote the original draft. YSP and T-JY collected plant samples. ZKK, and HK contributed to the preparation of the manuscript. ZKK, HK, and Y-HL contributed to manuscript editing and finalization. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbd Aziz NA, Hasham R, Sarmidi MR, Suhaimi SH, Idris MKH (2021) A review on extraction techniques and therapeutic value of polar bioactives from Asian medicinal herbs: Case study on \u003cem\u003eOrthosiphon aristatus\u003c/em\u003e, \u003cem\u003eEurycoma longifolia\u003c/em\u003e and \u003cem\u003eAndrographis paniculata\u003c/em\u003e. Saudi Pharm J 29(2):143-65. https://doi.org/10.1016/j.jsps.2020.12.016\u003c/li\u003e\n\u003cli\u003eAgler MT, Ruhe J, Kroll S, Morhenn C, Kim S-T, Weigel D et al. (2016) Microbial hub taxa link host and abiotic factors to plant microbiome variation. 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J Appl Microbiol 109(4):1469-78. https://doi.org/10.1111/j.1365-2672.2010.04777.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Endophytic fungi, Endophytic bacteria, Astragalus propinquus, Root microbiome, Glycyrrhiza uralensis","lastPublishedDoi":"10.21203/rs.3.rs-5675838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5675838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAstragalus propinquus\u003c/em\u003e (\u003cem\u003eAP\u003c/em\u003e) and \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e (\u003cem\u003eGU\u003c/em\u003e), members of the Fabaceae family, are widely used for their therapeutic properties. However, the endophytic microbial communities in their roots remain largely unknown. Herein, we compared the structure and properties of root-associated bacterial and fungal communities of \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, specifically excluding the microbial communities thriving in the rhizosphere, using both culture-dependent and -independent methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA metabarcoding-based approach revealed a higher abundance of \u003cem\u003eProteobacteria\u003c/em\u003e in the root microbiome of \u003cem\u003eGU\u003c/em\u003e than in that of \u003cem\u003eAP\u003c/em\u003e. Fungal communities showed similar distinctions, with \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e predominantly harboring \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e, respectively. The bacterial community in \u003cem\u003eAP\u003c/em\u003e exhibited significantly higher diversity than in \u003cem\u003eGU\u003c/em\u003e and included unique taxa, e.g., \u003cem\u003eSteroidobacterales\u003c/em\u003e and \u003cem\u003eMicromonosporales\u003c/em\u003e. However, the bacterial community in \u003cem\u003eGU\u003c/em\u003e was relatively less diverse and dominated by \u003cem\u003eXanthomonadales\u003c/em\u003e. Differential abundance analysis revealed that the plant species significantly impacted 301 bacterial and 228 fungal amplicon sequence variants (ASVs) in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e. Among these, B5_f_\u003cem\u003eComamonadaceae\u003c/em\u003e was markedly more enriched in \u003cem\u003eAP\u003c/em\u003e than in \u003cem\u003eGU\u003c/em\u003e. A random forest model analyzing bacterial ASVs with significant differences in abundance indicated that most bacterial ASVs were enriched in \u003cem\u003eAP\u003c/em\u003e. A pan-microbial community of 1,243 ASVs was identified, including 96 co-detected ASVs between \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, with 3 core ASVs (B2_f_\u003cem\u003ePseudomonas\u003c/em\u003e, B5_\u003cem\u003eComamonadaceae\u003c/em\u003e, and B70_\u003cem\u003eCutibacterium\u003c/em\u003e). The fungal community comprised 435 ASVs, with 98 shared ASVs and 8 core ASVs (F5_\u003cem\u003eParaphoma\u003c/em\u003e, F6_f_\u003cem\u003eLysurus\u003c/em\u003e, F22_\u003cem\u003eAlternaria\u003c/em\u003e, F30_\u003cem\u003ePhaeosphaeria\u003c/em\u003e, F53_\u003cem\u003eCladosporium\u003c/em\u003e, F36_\u003cem\u003eMoesziomyces\u003c/em\u003e, F55_f_\u003cem\u003eNeocucurbitaria\u003c/em\u003e, and F56_\u003cem\u003eMalassezia\u003c/em\u003e). Hub nodes were identified to elucidate the roles of microorganisms within microbial networks. In \u003cem\u003eAP\u003c/em\u003e, B152_o_\u003cem\u003eBurkholderiales\u003c/em\u003e, F14_\u003cem\u003eExophiala\u003c/em\u003e, and F33_\u003cem\u003eFusarium\u003c/em\u003e were the key hub nodes, whereas, in \u003cem\u003eGU\u003c/em\u003e, B36_\u003cem\u003ePaenibacillus\u003c/em\u003e was the central hub node. The comparative analyses of \u003cem\u003ein vitro\u003c/em\u003e culture data and molecular sequencing results showed overlapping patterns, with \u003cem\u003ePseudomonas\u003c/em\u003e dominant in \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e in \u003cem\u003eGU\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings highlight distinct microbial communities between \u003cem\u003eAP\u003c/em\u003e and \u003cem\u003eGU\u003c/em\u003e, with each species exhibiting unique bacterial and fungal orders and differences in microbial network complexity and diversity. These differences suggest the potential functional contributions, e.g., nutrient cycling and secondary metabolite production, of root-associated microbial communities, likely impacting the therapeutic properties of these plants.\u003c/p\u003e","manuscriptTitle":"Unveiling microbial complexity within Astragalus propinquus and Glycyrrhiza uralensis roots","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 06:53:33","doi":"10.21203/rs.3.rs-5675838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-01-15T15:07:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-15T13:30:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-20T05:56:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Microbiology","date":"2024-12-19T05:16:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amoa","sideBox":"Learn more about [Annals of Microbiology](https://www.springer.com/journal/13213)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/amoa/default.aspx","title":"Annals of Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4c0c4d67-5371-46d9-8c33-26e879156480","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:09:01+00:00","versionOfRecord":{"articleIdentity":"rs-5675838","link":"https://doi.org/10.1186/s13213-025-01802-0","journal":{"identity":"annals-of-microbiology","isVorOnly":false,"title":"Annals of Microbiology"},"publishedOn":"2025-05-17 15:58:04","publishedOnDateReadable":"May 17th, 2025"},"versionCreatedAt":"2025-01-17 06:53:33","video":"","vorDoi":"10.1186/s13213-025-01802-0","vorDoiUrl":"https://doi.org/10.1186/s13213-025-01802-0","workflowStages":[]},"version":"v1","identity":"rs-5675838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5675838","identity":"rs-5675838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}