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Article

Bacterial Community Structure and FEAST Source Tracking of Endophytes in Vernonia anthelmintica (L.) Willd. from Southern Xinjiang, China

by
Jiasen Zhao
1,†,
Fang Du
2,†,
Jinfang Zhu
1,3,*,
Geyu Liu
4,
Xiaobing Zhou
2,
Yuanming Zhang
2 and
Xiaoying Rong
2,*
1
College of Chemistry and Chemical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
3
College of Food Science and Pharmacy, Xinjiang Agricultural University, Urumqi 830052, China
4
Key Laboratory of Plant Resource Chemistry, Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microorganisms 2026, 14(2), 414; https://doi.org/10.3390/microorganisms14020414
Submission received: 5 January 2026 / Revised: 27 January 2026 / Accepted: 6 February 2026 / Published: 10 February 2026
(This article belongs to the Special Issue Microbial Dynamics in Desert Ecosystems)
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Abstract

Using 16S rRNA gene amplicon sequencing and FEAST microbial source tracking, this study characterized the bacterial communities in tissues (roots, stems, leaves, seeds) and associated soils (rhizosphere and bulk soil) of Vernonia anthelmintica, an important Uyghur medicinal plant endemic to arid southern Xinjiang. We found significantly higher bacterial diversity in soil than in plant tissues, with Pseudomonadota-dominated plant-associated communities and Actinobacteria co-dominating in soils. Bacterial community structures varied across plant compartments, with soil communities exhibiting greater stability and broader niche breadth. Critically, FEAST source tracking revealed that rhizosphere soil contributed 23.8%, 13.4%, 17.9%, and 10.8% of the bacterial communities in roots, stems, leaves, and seeds, respectively, identifying soil as the primary source of endophytic bacteria. These findings highlight the pivotal role of arid-region soil microbial diversity in shaping the unique endophytic microbiome of V. anthelmintica, providing a scientific basis for conserving soil microbial health to support its standardized cultivation and sustainable utilization in Uyghur medicine.

1. Introduction

The arid region of Northwest China (73°~123° E, 32°~50° N) is located on the Eurasian continent and characterized by dry climatic conditions, low precipitation, and high evaporation rates [1]. These unique climatic and geographical features have contributed to the prevalence of medicinal higher plants in this arid environment, particularly species from the families Ephedraceae, Apiaceae, Boraginaceae, Solanaceae, and Asteraceae. The Xinjiang Uygur Autonomous Region harbors over 3000 plant species, including 2014 medicinal plant species belonging to 158 families. Among these, the Uygur people commonly utilize 360 types of medicinal substances, the majority of which originate from arid environments, such as Fritillaria sinkiangensis, Dracocephalum moldavica, Matricaria chamomilla, Artemisia rupestris, and Vernonia anthelmintica [2]. Uygur medicine possesses a long history and rich resource diversity, employing numerous unique plant species, over 200 of which are either exclusive to Uygur medicine or rarely used in traditional Chinese medicine, yet demonstrate significant therapeutic efficacy. These distinctive medicinal resources hold considerable value for scientific research, pharmaceutical development, and clinical application [3]. Recent studies have demonstrated clear therapeutic benefits of Uygur medicine in treating rheumatoid arthritis, vitiligo, respiratory infections, and other diseases, underscoring its growing clinical relevance.
Endophytes are microbial communities that reside within plant tissues without inducing pathogenic effects. They play a crucial role in maintaining plant health by enhancing stress tolerance, promoting growth, facilitating nutrient uptake, synthesizing plant hormones, degrading toxic compounds, and improving resistance to abiotic stresses such as salinity, drought, and heavy metal exposure [4]. Medicinal plants harbor diverse populations of endophytic microbes, and it is exceedingly rare to find plants entirely devoid of these symbionts [5].
Endophytes primarily influence medicinal plants through several processes, including promoting growth, enhancing secondary metabolite production, and improving stress resistance. For example, the endophytic bacterium Serratia marcescens AL2-16 fixes atmospheric nitrogen in Achyranthes aspera [6], while microbial IAA production via the tryptophan-dependent pathway contributes to improved seed germination and reduced oxidative stress [7]. Endophytes can also suppress Paenibacillus ehimensis strains isolated from Lonicera japonica and enhance wheat seedling growth, and endophytes from sugarcane roots inhibit the fungal pathogens Bipolaris sacchari and Ceratocystis paradoxa [8].
Given that secondary metabolites constitute a primary source of bioactive compounds in medicinal plants, increasing their accumulation represents a key strategy for improving plant quality [9]. Evidence indicates that endophytic fungi can stimulate the accumulation of secondary metabolites in their host medicinal plants. Certain endophytes associated with medicinal plants, including species of Pseudomonas, Burkholderia, and Bacillus, have been shown to produce or induce the synthesis of immunosuppressive compounds [10]. For instance, endophytic fungi from Passiflora incarnata L. produced a butanol extract with DPPH radical-scavenging activity, and five isolates yielded substantial quantities of flavonoids and phenolic compounds [11]. The endophytic bacterium Fluorescent Pseudomonas ALEB7B significantly increased the accumulation of photosynthetic products in Atractylodes lancea (Thunb.) DC and triggered the biosynthesis of oxygenated sesquiterpenes [12].
In dynamic natural environments, plants are frequently exposed to various biotic and abiotic stresses. Over millennia of evolution, plants have developed sophisticated defense mechanisms, and endophytes are integral components of the plant immune system [13]. For example, 16 distinct halotolerant bacterial endophytes from Adhatoda vasica produced auxin, exhibited phosphate solubilization capacity, and four isolates displayed antagonistic activity against all tested fungal pathogens, including Fusarium oxysporum, F. moniliforme, and Rhizoctonia solani [14].
Microbial communities give rise to diverse microhabitats, and host plants exert strong influences on their microbiomes through immune responses, genetic regulatory networks, and root exudates [15]. Microbial source tracking (MST) technology identifies links between samples and their potential sources by comparing microbial community profiles, thereby enabling the determination of origins [16]. Xiong et al. [17] proposed the source model of plant microbiome (SMPM), which is based on the potential sources and assembly dynamics of microbial communities across different plant compartments. Their analysis revealed that crop-associated bacterial populations are primarily derived from non-rhizosphere soil and undergo progressive filtering as they colonize internal plant tissues.
Vernonia anthelmintica (L.) Willd., a member of the Asteraceae family, has emerged as an important medicinal plant among the Uyghur population in the arid regions of Xinjiang due to its tolerance to salinity and drought stress [18]. Its mature fruit, known in Uyghur medicine as ‘Kala Ziran’, is widely used to treat melanin-deficiency disorders such as vitiligo. The therapeutic properties of this plant are primarily attributed to bioactive compounds, including flavonoids, sesquiterpenes, and caffeoylquinic acids [19], which have demonstrated clinically relevant effects in promoting skin pigmentation and reducing edema through dampness-drying actions [20]. Current research on V. anthelmintica has predominantly focused on the extraction and pharmacological validation of these active constituents, while studies on its endophytic and root-associated microbial communities remain limited. In this study, we used 16S rRNA gene amplicon sequencing to analyze the bacterial composition and community structure in seeds, roots, stems, leaves, rhizosphere soil, and bulk soil of V. anthelmintica. By conducting microbial source tracking analysis, we aim to identify the likely origins of endophytic bacteria. The objective of this work is to clarify the ecological roles of the microbial community associated with the characteristics of locally distributed V. anthelmintica, gain insights into the mechanisms of microbe-host interactions, and provide scientific foundations for the exploration and application of endophytic microbial resources in Uyghur medicinal plants.

2. Materials and Methods

Samples of Vernoni anthelmintica (L.) Willd. were collected from the Uyghur Medicinal Plant Cultivation Base in Moyu, operated by the Innovation Center of Drug Research, Chinese Academy of Sciences (37°16′ N, 79°63′ E). This site is located in the southwestern part of the Xinjiang Uygur Autonomous Region, bordered to the south by the Kunlun Mountains and to the north by the Taklamakan Desert, and is characterized by a warm temperate arid desert climate. The average elevation is 1350 m above sea level. The mean annual temperature is 11.3 °C, with August averaging 23 °C and maximum temperatures reaching up to 36 °C. Annual precipitation averages approximately 36 mm. The soil type is saline-alkali sandy loam containing gravel of varying sizes. Soil moisture is generally low, and nitrogen and phosphorus deficiencies are widespread, whereas potassium levels are relatively high [21]. V. anthelmintica is an introduced species and is cultivated in designated plots within this ecological park. It is a tall annual herb with an erect and robust stem that can reach heights of 60 cm or more. Leaves are alternately arranged and exhibit a thin, membranous texture. Leaf morphology ranges from ovate to ovate-lanceolate or lanceolate, measuring 6–15 cm in length and 1.5–4.5 cm in width, with acute or acuminate apices. The leaf base gradually tapers into a petiole approximately 1 cm long and bears coarsely or sharply serrated margins. The fruits are either inversely conical or elongated cylindrical, 3.5–7.6 mm in length and 0.7–1.9 mm in diameter, featuring a flattened, thickened apex and a narrowed base bearing a persistent fruit stalk scar. They typically possess ten prominent longitudinal ridges and appear brownish-green or dark green in color. Under fluorescence microscopy, non-glandular trichomes are observed on the ridges, whereas glandular trichomes are located in the furrows between the ridges. The plant is characterized by a distinctive odor and an intensely bitter taste [22].
In August 2024, during the growth-to-mature stage of V. anthelmintica, five replicate plots (10 × 10 m each) were established within the cultivated field, with a 30 m interval between plots to minimize cross-contamination. From each plot, six individual plants were randomly selected, pooled, and treated as a single composite sample. The plants were carefully excavated from the soil, and loosely adhering soil was removed from the roots by gentle shaking. Using sterilized scissors, tissue samples of seeds, roots, stems and leaves were collected from each plant. For rhizosphere soil collection, a thin layer of soil tightly adhering to the root surface was gently brushed off using a sterile brush. Soil particles that detached naturally from the roots were collected separately and designated as bulk soil samples. Plant tissues were immediately flash-frozen in liquid nitrogen, while rhizosphere and bulk soil samples were stored in a car refrigerator for rapid transport to the laboratory. All samples were allocated for DNA extraction, physicochemical assay, and high-throughput DNA sequencing.

2.1. Tissue Pretreatment

Plant samples were processed in a laminar flow hood to maintain aseptic conditions. Each sample was initially rinsed with tap water to remove adhering soil particles, followed by gentle drying with sterile filter paper. Surface sterilization was carried out using the following sequential protocol: rinsing with sterile distilled water for 30 s; immersion in 70% sterile ethanol for 2 min; treatment in 2.5% sodium hypochlorite (NaClO) solution containing 0.1% Tween 80 for 5 min; and a subsequent rinse in 70% sterile ethanol for 30 s. Finally, the tissues were washed three times with sterile distilled water to ensure complete removal of residual sterilizing agents. Following surface sterilization, aliquots of the final rinse water were plated on nutrient agar and incubated at 37 °C for 48 h. The absence of microbial growth confirmed the efficacy of the sterilization process. The surface-sterilized plant tissues were immediately stored at −80 °C until further processing.

2.2. DNA Extraction and PCR Amplification

Amplicon sequencing was employed to assess bacterial diversity and community composition across samples. Microbial DNA was extracted from samples using the E.Z.N.A.® Soil DNA Kit (Omega BioTek, Norcross, GA, USA), following the manufacturer’s protocol. The quality and concentration of extracted nucleic acids were evaluated using a NanoDrop 2000 micro-spectrophotometer (Thermo Scientific, Waltham, MA, USA). DNA integrity was confirmed by electrophoresis on a 1% agarose gel. PCR amplification targeted the bacterial V4 region of the 16S rRNA gene using primers 799F (5′-AACMGGATTAGATACCCKG-3′) and 1193R (5′-ACGTCATCCCCACCTTCC-3′). Amplification reactions were performed with TransStart FastPfu DNA Polymerase (TransGen Biotech Co., Ltd., Beijing, China; Cat. No. AP221-02) on ®9700 Thermal Cycler (Applied Biosystems, Waltham, MA, USA). The thermal cycling conditions included an initial denaturation at 95 °C for 5 min, followed by 30 cycles of denaturation at 95 °C for 30 s, annealing at 58 °C for 30 s, extension at 72 °C for 45 s, and a final extension at 72 °C for 10 min. Purified PCR products were used to construct sequencing libraries with the NEXTFLEX Rapid DNA-Seq Kit. Paired-end sequencing was conducted on the Illumina MiSeq PE250 platform. All procedures, including PCR amplification, high-throughput sequencing, and preliminary processing, were carried out by Shanghai Ling’en Biotechnology Co., Ltd. (Shanghai, China). The complete sequences generated in this study are available in the NCBI SRA database under accession number PRJNA1398039.
Raw sequence reads were subjected to quality filtering using fastp (version 0.20.0) to remove low-quality bases and adapter sequences. Overlapping paired-end reads were merged into contiguous sequences using FLASH (version 1.2.7) with a minimum overlap of 10 bp and a maximum mismatch rate of 25%. Sequences shorter than 150 bp or of poor quality were trimmed using Cutadapt (version 1.9.1). After removal of barcodes and primer sequences, amplicon sequence variants (ASVs) were inferred using UPARSE (version 7.1) with a clustering threshold of 100% sequence identity to generate high-resolution bacterial profiles [23]. To enable comparative analysis of bacterial community structure and diversity, sequence counts were normalized to the lowest sequencing depth across all samples. The primers 799F–1115R employed in this study were specifically designed for the selective amplification of bacterial 16S rRNA genes in plant-associated microbiome research. This primer pair can effectively avoid the co-amplification of homologous 16S sequences from host mitochondria and chloroplasts. Additionally, during taxonomic annotation with the SILVA database, strict quality control was implemented for the annotation results to further eliminate interference from chloroplast and mitochondrial sequences.

2.3. Data Analysis

Statistical analyses were conducted systematically to ensure data reliability and validity. Data normality was assessed using the Shapiro–Wilk test (stats package in R, version 4.4.3) (https://cran.r-project.org/, accessed on 21 January 2026). For non-normally distributed data, log transformation was applied prior to parametric testing. Taxonomic assignment of ASVs was performed using the Bayesian classifier implemented in the dada2 package (https://library.qiime2.org/plugins/q2-dada2, accessed on 21 January 2026), with reference to the SILVA database (release 138). Alpha diversity indices (Chao1 and Shannon) were calculated using the phyloseq package (https://joey711.github.io/phyloseq, accessed on 21 January 2026), and intergroup differences were evaluated using Student’s t-tests within the vegan package. Beta diversity was estimated based on Bray–Curtis dissimilarity, and principal coordinate analysis (PCoA) was conducted using the ape package (version 5.8) in R software (version 4.4.3) for ordination visualization. Differences in microbial community composition among sample groups were statistically tested using analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA), both implemented in vegan. The Kruskal–Wallis test, a nonparametric method, was used to compare beta diversity across multiple groups when normality assumptions were not met. Shared and unique ASVs among plant compartments were visualized using UpSet plots generated with the UpSet R package (https://CRAN.R-project.org/package=UpSetR, accessed on 21 January 2026). Niche breadth was quantified using Levins’ index as implemented in the package, while community stability was assessed via the Average Variation Degree (AVD) index using custom R scripts. Microbial source tracking was performed using the FEAST package (https://github.com/cozygene/FEAST, accessed on 21 January 2026), which employs an expectation–maximization algorithm to estimate the proportional contributions of different sources (e.g., bulk soil, rhizosphere) to plant-associated bacterial communities. All statistical analyses were conducted in R (version 4.4.3) (https://www.r-project.org, accessed on 21 January 2026), and graphical visualizations were generated using ggplot2 (v3.4.2) (https://ggplot2.tidyverse.org, accessed on 21 January 2026).

3. Results

3.1. Bacterial Diversity in Vernonia anthelmintica Tissues and Associated Soils

Bacterial communities of V. anthelmintica were analyzed across six sample types: roots, stems, leaves, seeds, rhizosphere soil, and bulk soil. After clustering at 100% sequence similarity, a total of 4439 ASVs were identified. Rhizosphere soil harbored the highest number of ASVs (n = 2597), followed by bulk soil (2254), roots (985), seeds (929), stems (885), and leaves (760). UpSet analysis revealed that soil samples not only exhibited significantly higher bacterial abundance but also greater unique ASV diversity compared to plant tissues (Figure 1). Following taxonomic annotation of normalized ASVs, the bacterial communities were classified into 28 phyla, 87 classes, 193 orders, 376 families, and 542 genera at the group level. Consistent with abundance patterns, overall bacterial diversity was markedly higher in soil than in plant-associated compartments. At the phylum level, the dominant bacterial groups were Pseudomonadota, Actinobacteria, and Firmicutes, collectively accounting for 63.39% ± 0.82 in soil environments and up to 87.97% ± 3.66 in plant tissues. Pseudomonadota was particularly predominant in plant compartments, representing 90.4% of the bacterial community in leaves and 80.6% in seeds. In contrast, both Pseudomonadota and Actinobacteria co-dominated in soil habitats. At the genus level, Methylobacterium and Burkholderia-Caballeronia-Paraburkholderia were the most abundant genera within plant tissues, with Methylobacterium reaching 60.6% relative abundance in leaves. In soil, the dominant genera differed substantially and included TM7a, Aciditerrimonas, and Nocardioides (Supplementary Figures S1 and S2).
Alpha diversity indices revealed decreasing microbial richness and diversity from soil to plant tissues, with soil showing the highest diversity based on Shannon and Chao1 indices (Figure 2). Significant differences in bacterial community composition were observed among roots, stems, leaves, seeds, and soil (Figure 3). Beta diversity analysis showed distinct structural separation between soil and plant-associated microbial communities. PCoA demonstrated clear clustering patterns, with soil and plant samples forming distinct groups explaining 65.84% of total variance (p < 0.01; Figure 3a). Hierarchical clustering confirmed that endophytic bacterial communities in plant tissues are highly similar, while soil communities differ significantly from those in plants (Figure 3b).

3.2. Analysis of Bacterial Ecological Niches and Stability in Different Tissues of Vernonia anthelmintica

Bacterial niche breadth was widest in rhizosphere soil and decreased progressively in roots, stems, seeds, and leaves of Vernonia anthelmintica (Figure 4). Soil-associated bacterial communities exhibited significantly broader niche breadths than those in plant tissues, reflecting greater adaptability to diverse environmental conditions. Endophytic bacterial communities in V. anthelmintica displayed narrower niche breadths, indicating a trend toward functional specialization and increased dependence on the host’s internal environment. Community stability analysis revealed higher variability in plant-associated bacterial assemblages, suggesting reduced structural stability compared to soil communities. These findings highlight distinct ecological strategies: soil-dwelling bacteria function as generalists with high resilience, whereas endophytes represent specialists shaped by host-driven selection pressures.

3.3. Composition Varied Significantly Among Different Plant Parts of Vernonia anthelmintica

By comparing log2 fold change values between each plant tissue and its corresponding soil samples (Table 1, Table 2, Table 3, Table 4 and Table 5), we identified 198 amplicon sequence variants (ASVs) exhibiting significant differences in abundance. All pairwise comparison results between different compartments (including plant tissues, rhizosphere soil, and bulk soil) are provided in the Supplementary Materials. This study reveals distinct habitat selection patterns of microorganisms within the soil–plant continuum, based on comparisons of bacterial communities across different tissues of Vernonia anthelmintica and those in the rhizosphere and bulk soil. Bacterial genera significantly enriched in plant tissues—including roots, stems, leaves, and seeds—such as Aureimonas, Candidatus Alysiosphaera, Promicromonospora, and Rhizobium, are predominantly plant-beneficial taxa with potential functions in nitrogen fixation, growth promotion, and bioactive compound production, indicating functional selection by the host plant. In contrast, genera more abundant in soil environments, particularly bulk soil, such as Herpetosiphon and Kofleria, are primarily associated with organic matter decomposition, microbial predation, and secondary metabolic competition, representing typical functional groups involved in soil decomposition and ecological regulation. These findings suggest that the assembly of the bacterial community in V. anthelmintica involves selective recruitment of beneficial microbes from the soil microbial reservoir, coupled with the exclusion of competitive or predatory taxa.

3.4. Microbial Source Tracking via FEAST

To clarify the sources and migration paths of endophytic bacteria in different tissues of Vernonia anthelmintica, this study employed the FEAST source-tracking model and analyzed the “source–sink” relationships between rhizosphere soil, bulk soil, and various plant parts (roots, stems, leaves, and seeds) at the ASV level (Figure 5). The results indicated that rhizosphere soil was the main potential source of the endophytic bacterial community in the plant, but the contribution rate varied significantly among different tissues and was closely related to the spatial distribution and physiological functions of underground and aboveground tissues. There was a significant source–sink relationship within the soil system: up to 56.4% of the components in the rhizosphere microbial community originated from the rhizosphere soil itself, indicating that the rhizosphere bacteria mainly accumulated from the surrounding soil through the “rhizosphere selection effect”, forming a continuous microbial migration pathway. As the plant tissues extended from roots to stems and leaves, the contribution rate of rhizosphere soil gradually decreased (31.1%, 20.9%, and 21.9%, respectively), reflecting that the host plant exerted an increasingly progressive selective pressure during the upward migration and colonization of microorganisms, resulting in a continuous decrease in the proportion of microorganisms directly derived from the soil (p < 0.05). Additionally, the soil origin of the bacterial community in seeds was extremely low, suggesting that the assembly of the symbiotic community in seeds may follow a relatively independent path, relying on non-soil transmission routes.

4. Discussion

This study demonstrates that soil microbial communities in arid regions exhibit significantly higher diversity than the endophytic bacterial communities within V. anthelmintica tissues, with the rhizosphere serving as a primary reservoir for bacterial endophytes. Colonization of this medicinal plant follows a hierarchical filtration process, resulting in a progressive reduction in bacterial diversity from the rhizosphere to the root interior, driven by both environmental filtering and host-mediated selection. The functional contributions of key endophytic taxa—such as Promicromonospora and Rhizobium—in promoting plant growth and regulating secondary metabolism suggest their potential involvement in the biosynthesis and accumulation of medicinal compounds. These findings underscore the combined influence of abiotic environmental pressures and host-specific biological mechanisms in shaping the structure and functional dynamics of the V. anthelmintica microbiome.
Based on our data, the distribution pattern of endophytic diversity in Vernonia anthelmintica is largely consistent with that reported for other medicinal plants (e.g., Astragalus membranaceus, Glycyrrhiza uralensis), displaying a decreasing gradient from rhizosphere to root interiors and then to aboveground tissues. Nevertheless, the overall diversity of endophytes in V. anthelmintica remains relatively low, particularly in seeds and leaves. This reduced diversity may stem from the plant’s restricted geographic distribution—currently limited to southern Xinjiang, Pakistan, and India—and from the specific microenvironmental selection pressures associated with its ecological niche and medicinal properties. At the genus level, Bacillus and Pseudomonas were widely distributed across multiple plant tissues, suggesting potential functional roles in promoting growth and enhancing tolerance to abiotic stress. Furthermore, Sphingomonas, which was significantly enriched in roots, and Methylobacterium, which dominated leaf communities, may be involved in root immune modulation and phyllosphere methanol metabolism, respectively. These taxon-specific distribution patterns not only reflect the host’s long-term adaptive microbial selection in specialized habitats but also suggest a potential functional linkage between specific endophytic taxa and the biosynthesis of medicinal compounds. Thus, the distinct structure of the V. anthelmintica endobiome is shaped by the combined effects of environmental constraints from its narrow distribution range and its intrinsic physiological and biochemical traits as a medicinal plant.
The bacterial communities in both V. anthelmintica tissues and associated soil samples were dominated by Pseudomonadota, Actinobacteria, Bacteroidota, and Ascomycota. This microbial composition aligns with previous findings in other medicinal plants, such as Dendrobium candidum, Glycyrrhiza uralensis, and Gastrodia elata [24,25], indicating that V. anthelmintica harbors a relatively stable microbiome structure at the phylum level—a characteristic shared by many well-documented medicinal plant species. These core bacterial phyla may play essential roles in maintaining host metabolic homeostasis and ensuring the stability of the associated microbial community. For instance, members of Pseudomonadales exhibit strong environmental adaptability and colonization capacity, partly attributable to their outer membrane lipopolysaccharide structure [26]. Actinobacteria are widely recognized for enhancing plant resistance and promoting growth, while also producing diverse bioactive compounds of significant pharmaceutical value [27,28]. Bacteroidetes, as abundant components of the plant microbiome, may contribute to pathogen suppression and enhance phosphorus mobilization in the rhizosphere—a critical nutrient often limiting plant productivity in arid soils [29]. Other phyla, including Chloroflexota (formerly Chlorobactota), Patescibacteria, and Gemmatimonadota, are primarily associated with soil environments. Chlorobactota possess photoheterotrophic capabilities, enabling them to convert organic matter into energy using light [30], which may assist the host in responding to biotic and abiotic stresses, nutrient fluctuations, and immune challenges. Patescibacteria are linked to genes involved in heavy metal transport, indicating potential contributions to metal detoxification. Meanwhile, the high mobility of Pseudomonadota in soil may facilitate the translocation of heavy metals from bulk soil to the rhizosphere, increasing metal bioavailability and potentially enhancing phytoremediation efficiency [31]. Collectively, these bacterial communities may play integral roles in supporting the growth, development, and stress resilience of V. anthelmintica.
log2 fold change analysis revealed significant differences in genus-level bacterial communities between V. anthelmintica tissues and soil samples. Several genera were significantly enriched in plant tissues; for example, Methylobacterium and Burkholderia collectively accounted for up to 60.6% of the bacterial community in leaves, suggesting specialized functional roles in photosynthetic tissues, such as facilitating methanol metabolism and supplying phytohormones [32]. In addition to its rhizosphere colonization capacity, Burkholderia can produce antimicrobial substances and participate in nutrient cycling, thereby potentially contributing significantly to soil fertility and plant health [33]. In contrast, genera such as Sodalis, Acinetobacter, and Pseudomonas have been reported as predominant in sea buckthorn [34], whereas Halomonas and Pseudoalteromonas are dominant taxa in vanilla [35]. These observations indicate that endophytic microbial communities exhibit distinct host specificity, shaped by both host identity and habitat conditions [36].
Soil samples harbored a more diverse array of genera, including TM7a, Aciditerrimonas, and Nocardioides, reflecting the broader ecological niches and resource heterogeneity present in soil environments. Differential ASV analysis further revealed that certain taxa, such as ASV 2914: Candidatus Alysiosphaera, were highly enriched in soil but nearly absent from plant tissues, indicating adaptation to soil habitats. Conversely, specific Actinobacteria lineages (e.g., ASV 3649) were significantly enriched within plant tissues, suggesting potential endophytic colonization or specific symbiotic interactions with the host. These findings indicate that the structural differentiation of the V. anthelmintica microbial community is influenced not only by the host plant but also by the unique abiotic constraints of the arid southern Xinjiang environment.
We employed the average variation index (AVD) to evaluate the temporal stability of bacterial communities associated with V. anthelmintica tissues and surrounding soils. The results showed that bacterial communities in soil exhibited significantly lower AVD values than those within internal plant tissues, indicating greater structural stability in the rhizosphere. In contrast, higher AVD values in endophytic communities suggest increased compositional turnover, driven by host-mediated physiological fluctuations, immune surveillance, and microscale spatial heterogeneity. A distinct stability gradient was observed along the soil–V. anthelmintica continuum, with aboveground endophytic communities displaying elevated AVD values due to stronger regulatory pressures from host physiology and defense mechanisms. While the rhizosphere benefits from a continuous supply of root exudates—rich in carbohydrates, organic acids, amino acids, and signaling molecules—aerial compartments such as leaves and seeds are more isolated and subject to rapid changes in pH, oxygen availability, moisture levels, and antimicrobial compound production. For instance, reactive oxygen species (ROS) generated during pathogen challenge or the accumulation of phenolic antimicrobials during senescence can profoundly alter microbial survival and colonization dynamics [37]. Furthermore, leaf-associated microbiomes are exposed to extreme abiotic stresses, including ultraviolet radiation and desiccation, which further contribute to their compositional instability [38]. This dynamic microbial landscape implies that community assembly in internal tissues is influenced not only by dispersal limitation but also by priority effects and host developmental stage.
Soil functions as a microbial reservoir with substantial buffering capacity, where higher biodiversity supports enhanced community stability and ecosystem multifunctionality. The rhizosphere provides protective functions for root-associated microbial communities against disturbances such as antimicrobial metabolites, thereby maintaining a resilient assemblage capable of sustaining critical ecological processes like nitrogen cycling [39]. This buffering effect arises from both physical and biochemical mechanisms: the mucilaginous layer surrounding roots acts as a diffusion barrier that restricts the influx of toxic substances, while certain rhizobacteria possess enzymatic capabilities to degrade or neutralize host-derived antimicrobial compounds. Moreover, the high microbial density and metabolic redundancy in the rhizosphere enhance functional resilience—when specific taxa are suppressed, others with overlapping metabolic functions can compensate. For example, bacterial groups involved in ammonification and nitrification remain functionally active even under selective pressures, ensuring uninterrupted nutrient transformation processes (Table 1), particularly in nutrient-poor arid soils where V. anthelmintica naturally thrives. Beyond recruitment, root-derived signals from V. anthelmintica may regulate microbial gene expression, promote quorum sensing and resource utilization pathways, and enhance resistance to abiotic stresses such as drought and salinity, thereby improving overall ecological adaptability.
Microorganisms dynamically adjust their resource utilization strategies in response to fluctuations in resource availability and competitive interactions. Species with broader niche breadths generally exhibit wider geographic distributions and greater competitive advantages, especially in heterogeneous or disturbed environments. The differential stability patterns observed between endophytic and soil-associated bacterial communities of V. anthelmintica align with their respective niche width characteristics. Specifically, the rhizosphere and adjacent soils display broader resource utilization capacities, potentially enabling V. anthelmintica—native to arid regions—to recruit microbial taxa capable of metabolizing diverse carbon sources or tolerating variable redox conditions. Through the dynamic physicochemical gradients established by root exudates, V. anthelmintica selectively enriches a persistent and functionally robust microbial community that performs essential ecosystem services, including organic matter decomposition, nitrogen cycling, and pathogen suppression (Table 1). This functional redundancy is likely reinforced by horizontal gene transfer, genomic plasticity, and synergistic interactions among taxonomic groups. Collectively, these mechanisms support the potential establishment of a stable and adaptive microbiome, facilitating intimate symbiotic or mutualistic relationships between V. anthelmintica and its associated microorganisms, ultimately influencing host habitat adaptation and secondary metabolite production.
Microorganisms evolve rapidly through reproductive cycles, horizontal gene transfer, and frequent mutations, enabling swift adaptation to changing environmental conditions. This dynamic evolutionary capacity underscores the importance of identifying microbial origins to fully understand the complexity of plant–microbe interactions. Determining the sources of plant-associated microbial communities—whether from bulk soil, rhizosphere, phyllosphere, or seed endophytes—is essential for elucidating the mechanisms governing microbial community assembly, functional specialization, and host fitness outcomes. These insights are particularly valuable for the standardized cultivation of Uyghur medicinal plants, where manipulation of microbial inoculants and soil health management can enhance the consistency and quality of bioactive metabolite production. Our findings indicate that bacterial communities within V. anthelmintica tissues primarily originate from root-adjacent soils, undergoing progressive selection and enrichment as they migrate from the soil into various plant compartments. Previous studies have demonstrated that many endophytic bacteria initially derive from soil environments and subsequently colonize root tissues via natural entry points such as lateral root junctions, root tips, or fissures. For instance, members of the phyla Proteobacteria, Actinobacteria, and Bacteroidetes, commonly detected in plant endospheres, can be traced back to their high abundance in rhizosphere soils, highlighting a directional microbial flow along the soil–plant continuum.
As microorganisms transition from soil to distinct plant compartments, they are subjected to increasingly stringent ecological filtering and host-mediated selection pressures that shape community composition. The shift from bulk soil to the rhizosphere already imposes selective forces driven by resource availability and microbial competition. Upon entering the root system, additional barriers further refine community structure: physical constraints such as the Casparian strip, chemical defenses including antimicrobial phytoalexins, and immune recognition through pattern-triggered immunity (PTI) collectively exclude non-adapted or potentially pathogenic taxa. Only a subset of soil-derived microbes successfully establishes within root tissues, with an even smaller proportion reaching aerial organs such as stems, leaves, and seeds. This stepwise filtration process results in a progressive decline in microbial diversity from external to internal plant habitats, with host genotype playing a pivotal role in determining which microbial lineages are permitted to persist. In V. anthelmintica, the migration of endophytic bacteria from roots to aboveground tissues supports a model of structured microbial assembly in arid-adapted medicinal plants. These findings provide a scientific foundation for optimizing the standardized cultivation of medicinal plants in arid regions and inform evidence-based strategies for the development and management of medicinal plant cultivation bases.

5. Conclusions

Our study demonstrates that the bacterial community diversity in Vernonia anthelmintica adheres to the “diversity-stability” paradigm, with significantly higher species richness and community diversity observed in soil environments compared to plant tissues. There are significant differences in the bacterial community composition among different tissues of V. anthelmintica. This is mainly driven by ecological differentiation and functional specialization—such as nitrogen fixation by Rhizobium in roots, drought tolerance conferred by endophytes in stems and seeds, and disease resistance mediated by leaf-associated bacteria. Microbial source tracking using the FEAST model indicates that rhizosphere soil serves as the primary reservoir for endophytic bacteria, contributing 16.6–56.4% of the bacterial communities across various plant compartments (roots > stems > leaves > seeds), underscoring the critical role of soil microbial pools in supporting host adaptability and enhancing medicinal properties. A clear functional partition exists between plant-enriched beneficial taxa, which promote host growth and metabolic activity, and soil-enriched decomposers, which facilitate organic matter degradation and nutrient cycling, collectively forming a synergistic microbiome system that supports plant survival in arid, nutrient-limited conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020414/s1, Figure S1: Network diagram of phylum-level taxonomic unit associations of endophytic bacterial communities across different habitats. Figure S2: Network diagram of genus-level taxonomic unit associations of endophytic bacterial communities across different habitats. Table S1: Log2 fold change values and statistical information of differentially abundant ASVs between bulk soil and leaf bacterial communities. Table S2: Log2 fold change values and statistical information of differentially abundant ASVs between bulk soil and rhizosphere soil bacterial communities. Table S3: Log2 fold change values and statistical information of differentially abundant ASVs between bulk soil and root bacterial communities. Table S4: Log2 fold change values and statistical information of differentially abundant ASVs between bulk soil and seed bacterial communities. Table S5: Log2 fold change values and statistical information of differentially abundant ASVs between bulk soil and stem bacterial communities. Table S6: Log2 fold change values and statistical information of differentially abundant ASVs between leaf and rhizosphere soil bacterial communities. Table S7: Log2 fold change values and statistical information of differentially abundant ASVs between bulk leaf and root bacterial communities. Table S8: Log2 fold change values and statistical information of differentially abundant ASVs between leaf and seed bacterial communities. Table S9: Log2 fold change values and statistical information of differentially abundant ASVs between leaf and stem bacterial communities. Table S10: Log2 fold change values and statistical information of differentially abundant ASVs between rhizosphere soil and seed bacterial communities. Table S11: Log2 fold change values and statistical information of differentially abundant ASVs between rhizosphere soil and stem bacterial communities. Table S12: Log2 fold change values and statistical information of differentially abundant ASVs between root and rhizosphere soil bacterial communities. Table S13: Log2 fold change values and statistical information of differentially abundant ASVs between root and seed bacterial communities. Table S14: Log2 fold change values and statistical information of differentially abundant ASVs between root and stem bacterial communities. Table S15: Log2 fold change values and statistical information of differentially abundant ASVs between seed and stem bacterial communities.

Author Contributions

Conceptualization, J.Z. (Jiasen Zhao); Formal analysis, J.Z. (Jiasen Zhao) and F.D.; Supervision and Writing—review and editing, J.Z. (Jinfang Zhu) and X.R.; Investigation, G.L.; Resources and Technical guidance, X.Z.; Project administration, Funding acquisition, and Overall supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Project of the Natural Science Foundation of Xinjiang Uygur Autonomous Region (Grant No. 2022D01D083), the National Natural Science Foundation of China (Grant No. 42471072), the Tianshan Youth Talent Top-notch Project of Xinjiang (Grant No. 2022TSYCCX0007), and the Leading Talent Project in Technological Innovation under the “Tianshan Talent” Training Program of the Xinjiang Uygur Autonomous Region (Grant No. 2022TSYCLJ0058).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data of this study are openly available on NCBI. Further inquiries can be directed to the corresponding authors.

Acknowledgments

I would like to thank Haibaier Huojiaaihemaiti for his continuous support during the sampling work of this project. The funding support (detailed in the Funding Section) is gratefully acknowledged.

Conflicts of Interest

The author declares no conflicts of interest.

References

  1. Zhou, L.M.; Kurawan, Y.M.R.; Rusuli, Y.S.F.J.; Wu, H.Z.; Nijiat, N.Z.K.T. Spatiotemporal Differences and Influencing Factors of Lake Ice Phenology in Large Lakes in the Arid Region of Northwest China. Arid Land Geogr, 2025; in press. (In Chinese) [Google Scholar]
  2. Bai, C.C.; Han, L.; Huang, M.; Zhao, Y.S. Investigation Report on the Distribution and Development Status of Medicinal Plant Resources in Arid Areas of Northwest China. Asia-Pac. Tradit. Med. 2011, 7, 3–4. (In Chinese) [Google Scholar]
  3. Li, L.Y.; Zhang, F.; Wan, D.R. Investigation and Achievement Overview of Uyghur Medicinal Resources in China. Asia-Pac. Tradit. Med. 2020, 16, 31–33. (In Chinese) [Google Scholar]
  4. Upadhyay, A.; Khandelwal, V. Endophytes in Medicinal Plants: A Sustainable Solution for Coping with Environmental Stresses. Curr. Microbiol. 2025, 82, 529. [Google Scholar] [CrossRef] [PubMed]
  5. Cheng, L.Y.; Zhang, G.H.; Sun, Y.; Zhao, J.L.; Ni, C.Y.; Sun, Y.; Xu, L.C. Research Progress on the Interaction Among Medicinal Plants, Endophytes and Rhizosphere Microorganisms. Chin. Tradit. Herb. Drugs 2024, 55, 5264–5273. (In Chinese) [Google Scholar]
  6. Wu, W.; Chen, W.H.; Liu, S.Y.; Wu, J.J.; Zhu, Y.T.; Qin, L.P.; Zhu, B. Beneficial Relationships Between Endophytic Bacteria and Medicinal Plants. Front. Plant Sci. 2021, 12, 646146. [Google Scholar] [CrossRef] [PubMed]
  7. Latif, A.K.; Abdullah, S.G.; Muhammad, W.; Waqas, M.; Al-Hosni, K.; Al-Khiziri, S.; Kim, Y.-H.; Ali, L.; Kang, S.-M.; Asaf, S.; et al. Endophytes from Medicinal Plants and Their Potential for Producing Indole Acetic Acid, Improving Seed Germination and Mitigating Oxidative Stress. J. Zhejiang Univ. Sci. B 2017, 18, 125–137. [Google Scholar]
  8. Narayanan, Z.; Glick, B.R. Secondary Metabolites Produced by Plant Growth-Promoting Bacterial Endophytes. Microorganisms 2022, 10, 2008. [Google Scholar] [CrossRef] [PubMed]
  9. Ouyang, Y.; Cheng, Q.; Cheng, C.; Tang, Z.; Huang, Y.; Tan, E.; Ma, S.; Lin, X.; Xie, Y.; Zhou, H. Effects of Plant-Associated Microbiota on Cultivation and Quality of Chinese Herbal Medicines. Chin. Herb. Med. 2024, 16, 190–203. [Google Scholar] [CrossRef]
  10. Linh, D.N.; Thin, B.V.; Ni, T.T.A.; Duy, N.T.; Van Minh, N.; Dong, T.C. Studies on Antimicrobial Activities of Endophytic Bacteria Isolated from Neem Tree (Azadirachta indica). Eng. Technol. 2020, 10, 72–80. [Google Scholar] [CrossRef]
  11. da Silva, M.H.R.; Cueva-Yesquén, L.G.; Júnior, S.B.; Garcia, V.L.; Sartoratto, A.; Angelis, D.d.F.d.; de Angelis, D.A. Endophytic Fungi from Passiflora incarnata: An Antioxidant Compound Source. Arch. Microbiol. 2020, 202, 2779–2789. [Google Scholar] [CrossRef]
  12. Zhou, J.-Y.; Kai, S.; Fei, C.; Yuan, J.; Li, X.; Dai, C.C. Endophytic Pseudomonas Induces Metabolic Flux Changes that Enhance Medicinal Sesquiterpenoid Accumulation in Atractylodes lancea. Plant Physiol. Biochem. 2018, 130, 473–481. [Google Scholar] [CrossRef] [PubMed]
  13. Li, C.; Wang, L.; Tong, C.W.; Li, H.B.; Qin, Z.; Zeng, X.P.; Chang, Y.Y.; Li, M.J.; Yang, Q.X. Molecular Insights into the Defense of Dioscorea opposita Cultivar Tiegun Callus Against Pathogenic and Endophytic Fungal Infection Through Transcriptome Analysis. Phytopathology 2024, 114, 1893–1903. [Google Scholar] [CrossRef]
  14. Vyas, P.; Kaur, R. Culturable Stress-Tolerant Plant Growth-Promoting Bacterial Endophytes Associated with Adhatoda vasica. J. Soil Sci. Plant Nutr. 2019, 19, 290–298. [Google Scholar] [CrossRef]
  15. Zhang, J.; Liu, W.; Bu, J.; Lin, Y.; Bai, Y. Host Genetics Regulate the Plant Microbiome. Curr. Opin. Microbiol. 2023, 72, 102268. [Google Scholar] [CrossRef] [PubMed]
  16. Xu, X.L.; Luo, L.X.; Feng, J.J.; Zhang, G.M. Research Progress on Microbial Traceability Technology. In Proceedings of the 2019 Annual Meeting of the Chinese Society for Plant Pathology; College of Plant Protection, China Agricultural University, Shenzhen Customs Inspection and Quarantine Technology Center for Animals and Plants, Shenzhen Key Laboratory of Detection Technology R&D for Invasive Pests, Eds.; China Agricultural Science and Technology Press: Beijing, China, 2019; pp. 597–602. (In Chinese) [Google Scholar]
  17. Xiong, C.; Zhu, Y.-G.; Wang, J.-T.; Singh, B.; Han, L.-L.; Shen, J.-P.; Li, P.-P.; Wang, G.-B.; Wu, C.-F.; Ge, A.-H.; et al. Host Selection Shapes Crop Microbiome Assembly and Network Complexity. New Phytol. 2021, 229, 1091–1104. [Google Scholar] [CrossRef] [PubMed]
  18. Zeng, W.H.; Qu, B.P.; Zhang, X.P.; Wang, Y.F.; Li, T.M.; Chen, P. Research Progress on Vernonia anthelmintica in the Treatment of Vitiligo. Acta Chin. Med. Pharmacol. 2016, 44, 96–101. (In Chinese) [Google Scholar]
  19. Wang, J.L.; Pan, J.H.; Yuan, J.; Zhao, W.T.; Yin, Z.H. Chemical Constituents and Tyrosinase Inhibitory Activity of Vernonia anthelmintica. J. Chin. Med. Mater. 2017, 40, 1833–1837. (In Chinese) [Google Scholar]
  20. Miereguli, K.M.; Aihemaiti, M.M.; Sailiman, K.D. Research Progress on Vernonia anthelmintica. Chin. J. Inf. Tradit. Chin. Med. 2007, 14, 92–94. (In Chinese) [Google Scholar]
  21. Ji, Y.H.; Ning, H.S.; Wang, R.H.; Zhang, Y.; Yin, H.X.; Wang, D.D. Relationship Between Soil Physicochemical Properties and Artificial Vegetation Traits in Arid Area: A Case Study of Moyu Ecological Industry Zone in Hotan, Xinjiang. Res. Soil Water Conserv. 2010, 17, 20–25+30. (In Chinese) [Google Scholar]
  22. Kang, S.; Liu, J.; Fang, W.L.; Liu, X.G.; Zhao, Y.; Gao, H.; Ni, H. Identification Study on Vernonia anthelmintica, a Commonly Used Medicinal Material of Uyghur Nationality. Chin. J. Pharm. Anal. 2021, 41, 1335–1342. (In Chinese) [Google Scholar]
  23. Bukin, Y.S.; Mikhailov, I.S.; Petrova, D.P.; Galachyants, Y.P.; Zakharova, Y.R.; Likhoshway, Y.V. The effect of metabarcoding 18S rRNA region choice on diversity of microeukaryotes including phytoplankton. World J. Microbiol. Biotechnol. 2023, 39, 229. [Google Scholar] [CrossRef]
  24. Zhao, R.; Zheng, S.; Hu, Y.; Li, H.; Chen, Y.; Chun, Z. Endophytic Bacterial Diversity of Medicinal Orchid Dendrobium nobile. S. Afr. J. Bot. 2023, 158, 90–97. [Google Scholar] [CrossRef]
  25. Zhang, Z.; Ma, A.; Zhang, T.; Zhuang, L.; Dang, H. Analysis of Composition, Structure, and Driving Factors of Root-Associated Endophytic Bacterial Communities of the Chinese Medicinal Herb Glycyrrhiza. Biology 2025, 14, 856. [Google Scholar] [CrossRef] [PubMed]
  26. Negi, R.; Sharma, B.; Kumar, S.; Chaubey, K.K.; Kaur, T.; Devi, R.; Yadav, A.; Kour, D.; Yadav, A.N. Plant Endophytes: Unveiling Hidden Applications Toward Agro-Environmental Sustainability. Folia Microbiol. 2023, 69, 181–206. [Google Scholar] [CrossRef]
  27. Rana, K.L.; Kour, D.; Kaur, T.; Devi, R.; Yadav, A.N.; Yadav, N.; Dhaliwal, H.S.; Saxena, A.K. Endophytic Microbes: Biodiversity, Plant Growth-Promoting Mechanisms and Potential Applications for Agricultural Sustainability. Antonie Van Leeuwenhoek 2020, 113, 1075–1107. [Google Scholar] [CrossRef]
  28. Salama, S.; Mostafa, H.S.; Husseiny, S.; Sebak, M. Actinobacteria as Microbial Cell Factories and Biocatalysts in the Synthesis of Chiral Intermediates and Bioactive Molecules: Insights and Applications. Chem. Biodivers. 2023, 21, e202301205. [Google Scholar] [CrossRef]
  29. Lidbury, I.D.E.A.; Borsetto, C.; Martin, J.R.A.; Bottrill, A.; Jones, A.M.E.; Bending, G.D.; Hammond, J.P.; Chen, Y.; Wellington, E.M.H.; Scanlan, D.J. Niche Adaptation in Plant-Associated Bacteroidetes Favors Specialization in Organic Phosphorus Mineralization. ISME J. 2020, 15, 1064–1077. [Google Scholar]
  30. Scott, M.P.; Weber, M.L.; Ferry, W.W. Evolution of the 3-Hydroxypropionate Bicycle and Recent Transfer of Anoxygenic Photosynthesis into the Chloroflexi. Proc. Natl. Acad. Sci. USA 2017, 114, 10749–10754. [Google Scholar]
  31. Tian, Z.J.; Li, G.W.; Tian, W.Z.; Zhu, Q.; Li, X.; Du, C.; Li, C.; Li, J.; Zhao, C.; Zhang, L. Role of Sedum alfredii and Soil Microbes in the Remediation of Ultra-High Content Heavy Metals Contaminated Soil. Agric. Ecosyst. Environ. 2022, 339, 108090. [Google Scholar] [CrossRef]
  32. Lai, K.; Nguyen, N.T.; Miwa, H.; Yasuda, M.; Nguyen, H.H.; Okazaki, S. Diversity of Methylobacterium spp. in the Rice of the Vietnamese Mekong Delta. Microbes Environ. 2020, 35, ME19111. [Google Scholar] [CrossRef]
  33. Ramírez, M.D.; España, M.; Aguirre, C.; Kojima, K.; Ohkama-Ohtsu, N.; Sekimoto, H.; Yokoyama, T. Burkholderia and Paraburkholderia are Predominant Soybean Rhizobial Genera in Venezuelan Soils in Different Climatic and Topographical Regions. Microbes Environ. 2019, 34, 43–58. [Google Scholar] [CrossRef]
  34. Li, B.; Gao, X.M.; Wang, N.; Yan, A.Q.; Liao, H.T.; Meng, L.Z.; Cheng, F. Analysis of Endophytic Microbial Community Composition and Diversity in Sea Buckthorn Seeds from Different Origins. North. Hortic. 2024, 94–101. (In Chinese) [Google Scholar]
  35. Mahadeo, K.; Taïbi, A.; Meile, J.-C.; Côme, B.; Gauvin-Bialecki, A.; Boubakri, H.; Herrera-Belaroussi, A.; Kodja, H. Exploring Endophytic Bacterial Communities of Vanilla planifolia. BMC Microbiol. 2024, 24, 218. [Google Scholar] [CrossRef]
  36. Yao, H.S.; Zhou, C.; Li, J.S.; Li, Y.H.; Shen, C.W.; Tao, Y.; Li, X. Microbial Diversity Across Tea Varieties and Ecological Niches: Correlating Tea Polyphenol Contents with Stress Resistance. Front. Microbiol. 2024, 15, 1439630. [Google Scholar] [CrossRef] [PubMed]
  37. Camejo, D.; Guzmán-Cedeño, Á.; Moreno, A. Reactive Oxygen Species, Essential Molecules, During Plant–Pathogen Interactions. Plant Physiol. Biochem. 2016, 103, 10–23. [Google Scholar] [CrossRef] [PubMed]
  38. Vanhaelewyn, L.; Van Der Straeten, D.; De Coninck, B.; Vandenbussche, F. Ultraviolet Radiation From a Plant Perspective: The Plant-Microorganism Context. Front. Plant Sci. 2020, 11, 597642. [Google Scholar] [CrossRef] [PubMed]
  39. Song, R.; Lv, B.; He, Z.; Li, H.; Wang, H. Rhizosphere Metabolite Dynamics in Continuous Cropping of Vineyards: Impact on Microflora Diversity and Co-Occurrence Networks. Microbiol. Res. 2025, 296, 128134. [Google Scholar] [CrossRef] [PubMed]
Microorganisms 14 00414 g001
Figure 1. UpSet Venn plots reveal the distribution and overlap of bacterial ASVs across compartments: leaf, stem, seed, root, rhizosphere soil, and bulk soil. The UpSet plot (a) displays the number of shared ASVs through vertical blue bars (representing ASV counts for specific compartment intersections) and connected dots (indicating which compartments form the intersection); orange vertical bars highlight ASVs common to all six compartments (relatively rare). Horizontal bars (left) represent the total number of ASVs in each individual compartment, reflecting microbial abundance per habitat. Venn plot (b) provides a complementary view of overlapping ASVs among subsets of compartments: blue circles/bars denote ASVs unique to or shared by subsets of compartments, while orange circles/bars indicate ASVs common to all six compartments.
Figure 1. UpSet Venn plots reveal the distribution and overlap of bacterial ASVs across compartments: leaf, stem, seed, root, rhizosphere soil, and bulk soil. The UpSet plot (a) displays the number of shared ASVs through vertical blue bars (representing ASV counts for specific compartment intersections) and connected dots (indicating which compartments form the intersection); orange vertical bars highlight ASVs common to all six compartments (relatively rare). Horizontal bars (left) represent the total number of ASVs in each individual compartment, reflecting microbial abundance per habitat. Venn plot (b) provides a complementary view of overlapping ASVs among subsets of compartments: blue circles/bars denote ASVs unique to or shared by subsets of compartments, while orange circles/bars indicate ASVs common to all six compartments.
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Figure 2. Alpha diversity estimates of endophytic microbial communities. (a) Chao1 index at the ASV level for leaf, seed, stem, root, rhizosphere soil, and bulk soil. (b) Shannon index at the ASV level for leaf, seed, stem, root, rhizosphere soil, and bulk soil. Different letters indicate significant differences among different treatments (p < 0.001).
Figure 2. Alpha diversity estimates of endophytic microbial communities. (a) Chao1 index at the ASV level for leaf, seed, stem, root, rhizosphere soil, and bulk soil. (b) Shannon index at the ASV level for leaf, seed, stem, root, rhizosphere soil, and bulk soil. Different letters indicate significant differences among different treatments (p < 0.001).
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Figure 3. Unconstrained PCoA (for principal coordinates PCoA1 and PCoA2) (a) and hierarchical cluster tree (b) based on Bray–Curtis dissimilarity, showing distinct clustering of bacterial communities from leaf, seed, stem, root, rhizosphere soil, and bulk soil in V. anthelmintica. In panel (b), different letters (e.g., a, b) adjacent to clusters indicate significant differences between the corresponding bacterial community groups (post-hoc test, p < 0.05). Significant differences were observed among samples (p < 0.001), as determined by Permutational Multivariate Analysis of Variance (PERMANOVA; Adonis).
Figure 3. Unconstrained PCoA (for principal coordinates PCoA1 and PCoA2) (a) and hierarchical cluster tree (b) based on Bray–Curtis dissimilarity, showing distinct clustering of bacterial communities from leaf, seed, stem, root, rhizosphere soil, and bulk soil in V. anthelmintica. In panel (b), different letters (e.g., a, b) adjacent to clusters indicate significant differences between the corresponding bacterial community groups (post-hoc test, p < 0.05). Significant differences were observed among samples (p < 0.001), as determined by Permutational Multivariate Analysis of Variance (PERMANOVA; Adonis).
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Figure 4. Box plots showing the distribution of niche breadth indices across different plant-associated groups, reflecting the extent of biological resource utilization and ecological niche occupation (a). AVD values represent a measure of bacterial community stability (b). In both panels (a,b), different lowercase letters (e.g., a, b) above the boxes indicate significant differences between groups (p < 0.05, Wilcoxon rank-sum test). Significant differences among groups were detected (p < 0.001, Wilcoxon rank-sum test).
Figure 4. Box plots showing the distribution of niche breadth indices across different plant-associated groups, reflecting the extent of biological resource utilization and ecological niche occupation (a). AVD values represent a measure of bacterial community stability (b). In both panels (a,b), different lowercase letters (e.g., a, b) above the boxes indicate significant differences between groups (p < 0.05, Wilcoxon rank-sum test). Significant differences among groups were detected (p < 0.001, Wilcoxon rank-sum test).
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Figure 5. Fast expectation–maximization microbial source tracking analysis was conducted at the ASV level. Arrow directions represent source-to-sink bacterial transfer patterns, and the relative contribution percentages of each source are shown accordingly. To assess significant differences in ASV abundance between plant compartments, pairwise Wilcoxon rank-sum tests were performed in R (p < 0.05).
Figure 5. Fast expectation–maximization microbial source tracking analysis was conducted at the ASV level. Arrow directions represent source-to-sink bacterial transfer patterns, and the relative contribution percentages of each source are shown accordingly. To assess significant differences in ASV abundance between plant compartments, pairwise Wilcoxon rank-sum tests were performed in R (p < 0.05).
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Table 1. Logarithmic-fold changes in relative abundance estimates in different habitats (bulk soil vs. other habitats).
Table 1. Logarithmic-fold changes in relative abundance estimates in different habitats (bulk soil vs. other habitats).
GroupBaseMeanlog2 FCASV IDClassification Annotation (Phylum, Family, and Genus)
Bulk Soil vs. Leaf295.727.79ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
234.9227.64ASV 4213Actinomycetota; Promicromonosoraceae; Promicromonospora
103.9727.46ASV 3955Bacteroidota; Sphingobacteriaceae; Mucilaginibacter
39.33−18.32ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
Bulk Soil vs. Root295.727.79ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
175.3427.42ASV 2716Pseudomonadota; Rhizobiaceae; Rhizobium
133.9727.18ASV 474Verrucomicrobiota; Pedosphaeraceae; ADurb.Bin063-1
17.96−17.33ASV 4434Myxococcota; Myxococcaceae; P3OB-42
39.33−17.84ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
29.12−23.3ASV 1204Bacteroidota; Bacteroidia; Asinibacterium
Bulk Soil vs. Stem234.9227.47ASV 4213Actinomycetota; Promicromonosoraceae; Promicromonospora
103.9727.19ASV 3255Bacteroidota; Sphingobacteriaceae; Mucilaginibacter
118.3127.06ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
−17.33−17.33ASV 4434Myxococcota; Myxococcaceae; P3OB-42
−20.78−20.78ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
Bulk Soil vs. Seed295.728.1ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
175.3427.86ASV 2716Pseudomonadota; Rhizobiaceae; Rhizobium
103.9727.79ASV 3255Bacteroidota; Sphingobacteriaceae; Mucilaginibacter
39.33−19.37ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
40−21.85ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
Bulk Soil vs. Rhizosphere Soil85.3324.29ASV 3518Pseudomonadota; Alphaproteobacteria; Candidatus Alysiosphaera
75.7724.22ASV 1546Pseudomonadota; Hyphomicrobiales Incertae Sedis; Bauldia
13.8523.95ASV 519Bdellovibrionota; Oligoflexaceae; Oligoflexus
40−21.85ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
30.97−23.51ASV 2351Chloroflexota
47.53−23.59ASV 2259Thermodesulfobacteriota; Desulfuromonadia
Table 2. Logarithmic-fold changes in relative abundance estimates in different habitats (rhizosphere soil vs. other habitats).
Table 2. Logarithmic-fold changes in relative abundance estimates in different habitats (rhizosphere soil vs. other habitats).
GroupBaseMeanlog2 FCASV IDClassification Annotation (Phylum, Family, and Genus)
Rhizosphere Soil vs. Seed47.5329.78ASV 2259Thermodesulfobacteriota; Desulfuromonadia
30.9728.23ASV 2351Chloroflexota
295.727.75ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
124.5−21.48ASV 3083Myxococcota; Haliangiaceae; Kofleria
39.33−36.29ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
Rhizosphere Soil vs. Stem4035.34ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
47.5329.18ASV 2259Thermodesulfobacteriota; Desulfuromonadia
30.9728.05ASV 2351Chloroflexota
124.5−24.18ASV 3083Myxococcota; Haliangiaceae; Kofleria
39.33−37.7ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
Rhizosphere Soil vs. Leaf39.3335.24ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
124.521.65ASV 3083Myxococcota; Haliangiaceae; Kofleria
26.0419.15ASV 3221Gemmatimonadota; Gemmatimonadaceae; Gemmatirosa
30.97−28.08ASV 2351Chloroflexota
47.53−29.39ASV 2259Thermodesulfobacteriota; Desulfuromonadia
40−35.64ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
Rhizosphere Soil vs. Root39.3334.76ASV 3649Actinomycetota; Microbacteriaceae; Agromyces
29.1326.45ASV 1204Bacteroidota; Bacteroidia; Asinibacterium
124.521.89ASV 3683Myxococcota; Haliangiaceae; Kofleria
105.58−27.49ASV 1939Pseudomonadota; Nitrosomonadaceae; MND1
30.97−28.12ASV 2351Chloroflexota
47.53−29.34ASV 2259Thermodesulfobacteriota; Desulfuromonadia
Table 3. Logarithmic-fold changes in relative abundance estimates in different habitats (leaf vs. other habitats).
Table 3. Logarithmic-fold changes in relative abundance estimates in different habitats (leaf vs. other habitats).
GroupBaseMeanlog2 FCASV IDClassification Annotation (Phylum, Family, and Genus)
Leaf vs. Stem70.3621.99ASV 4203Pseudomonadota; Rhizobiaceae; Aureimonas
26.0421.96ASV 3221Gemmatimonadota; Gemmatimonadaceae; Gemmatirosa
32.7521.32ASV 4342Pseudomonadota; Nitrosomonadaceae; MND1
28.59−20.94ASV 1620Ignavibacteriota; Ignavibacteriaceae; Ignavibacterium
295.7−22.61ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
19.84−24.81ASV 3936Acidobacteriota; Acidobacteriaceae; Candidatus Koribacter
Leaf vs. Seed17.9630.76ASV 4434Myxococcota; Myxococcaceae; P3OB-42
26.0422.39ASV 3221Gemmatimonadota; Gemmatimonadaceae; Gemmatirosa
32.7522.38ASV 4342Pseudomonadota; Nitrosomonadaceae; MND1
38.47−20.74ASV 2469Pseudomonadota; Sphingomonadaceae; Sphingomonas
105.58−21.44ASV 1939Pseudomonadota; Nitrosomonadaceae; MND1
40−29.56ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
Leaf vs. Root70.3622.34ASV 4203Pseudomonadota; Rhizobiaceae; Aureimonas
26.0422.22ASV 3221Gemmatimonadota; Gemmatimonadacea; Gemmatirosa
32.7522.04ASV 4342Pseudomonadota; Nitrosomonadaceae; MND1
234.92−22.78ASV 4213Actinomycetota; Promicromonosoraceae; Promicromonospora
29.13−29ASV 1204Bacteroidota; Chitinophagaceae; Asinibacterium
40−29.33ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
Table 4. Logarithmic-fold changes in relative abundance estimates in different habitats (stem vs. other habitats).
Table 4. Logarithmic-fold changes in relative abundance estimates in different habitats (stem vs. other habitats).
GroupBaseMeanlog2 FCASV IDClassification Annotation (Phylum, Family, and Genus)
Stem vs. Seed4029.26ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
105.5820.95ASV 1939Pseudomonadota; Nitrosomonadaceae; MND1
234.9320.46ASV 4213Actinomycetota; Promicromonosoraceae; Promicromonospora
28.59−21.33ASV 1620Ignavibacteriota; Ignavibacteriaceae; Ignavibacterium
295.7−22.92ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
17.96−36.88ASV 4434Myxococcota; Myxococcaceae; P3OB-42
Stem vs. Root4029.02ASV 611Chloroflexota; Herpetosiphonaceae; Herpetosiphon
29.1328.77ASV 1204Bacteroidota; Bacteroidia; Asinibacterium
234.9222.61ASV 4213Actinomycetota; Promicromonosoraceae; Promicromonospora
175.34−18.6ASV 2716Pseudomonadota; Rhizobiaceae; Rhizobium
105.58−21.75ASV 1939Pseudomonadota; Nitrosomonadaceae; MND1
295.7−22.61ASV 2914Pseudomonadota; Geminicoccaceae; Candidatus Alysiosphaera
Table 5. Logarithmic-fold changes in relative abundance estimates in different habitats (root vs. other habitats).
Table 5. Logarithmic-fold changes in relative abundance estimates in different habitats (root vs. other habitats).
GroupBaseMeanlog2 FCASV IDClassification Annotation (Phylum, Family, and Genus)
Root vs. Seed17.9633.7ASV 4434Myxococcota; Myxococcaceae; P3OB-42
29.1229.33ASV 1204Bacteroidota; Chitinophagaceae; Asinibacterium
28.5921.62ASV 1620Ignavibacteriota; Ignavibacteriaceae; Ignavibacterium
17.11−20.68ASV 1212Hydrogenedentes; Hydrogenedensaceae; YC-ZSS-LKJ63
38.47−21.1ASV 2469Pseudomonadota; Sphingomonadaceae; Sphingomonas
105.58−21.75ASV 1939Pseudomonadota; Nitrosomonadaceae; MND1
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Zhao, J.; Du, F.; Zhu, J.; Liu, G.; Zhou, X.; Zhang, Y.; Rong, X. Bacterial Community Structure and FEAST Source Tracking of Endophytes in Vernonia anthelmintica (L.) Willd. from Southern Xinjiang, China. Microorganisms 2026, 14, 414. https://doi.org/10.3390/microorganisms14020414

AMA Style

Zhao J, Du F, Zhu J, Liu G, Zhou X, Zhang Y, Rong X. Bacterial Community Structure and FEAST Source Tracking of Endophytes in Vernonia anthelmintica (L.) Willd. from Southern Xinjiang, China. Microorganisms. 2026; 14(2):414. https://doi.org/10.3390/microorganisms14020414

Chicago/Turabian Style

Zhao, Jiasen, Fang Du, Jinfang Zhu, Geyu Liu, Xiaobing Zhou, Yuanming Zhang, and Xiaoying Rong. 2026. "Bacterial Community Structure and FEAST Source Tracking of Endophytes in Vernonia anthelmintica (L.) Willd. from Southern Xinjiang, China" Microorganisms 14, no. 2: 414. https://doi.org/10.3390/microorganisms14020414

APA Style

Zhao, J., Du, F., Zhu, J., Liu, G., Zhou, X., Zhang, Y., & Rong, X. (2026). Bacterial Community Structure and FEAST Source Tracking of Endophytes in Vernonia anthelmintica (L.) Willd. from Southern Xinjiang, China. Microorganisms, 14(2), 414. https://doi.org/10.3390/microorganisms14020414

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