PART 1 Regulatory Relationship Between Quality Variation And Environment Of Cistanche Deserticola in Three Ecotypes Based On Soil Microbiome Analysis

The environment affects the composition and function of the soil microbiome, which indirectly influences the quality of plants. In this study, 16S amplicon sequencing was used to reveal the differences in soil microbial community composition of Cistanche deserticola in three ecotypes (saline-alkali land, grassland, and sandy land). through the correlation analysis of microbial community abundance, phenylethanoid glycoside contents, and ecological factors, the regulatory relationship between microbial community and the quality variation of C. deserticola was expounded. the metabolic function profile of the soil microbiome was predicted using Tax4Fun. Data showed that the soil microbial communities of the three ecotypes were significantly diferent (AMOVA, P < 0.001), and the alpha diversity of the grassland soil microbial community was the highest. core microbiome analysis demonstrated that the soil microbial communities of C. deserticola were mostly had a drought, salt tolerance, alkali resistance, and stress resistance, such as Micrococcales and Bacillales. The biomarkers, namely, oceanospirillales (saline-alkali land), Sphingomonadales (grassland), and propionibacteriales (sandy land), can distinguish three ecotype microbial communities, were excavated through LEfSe and random forest. Correlation analysis results demonstrated that 2′-acetylacteoside is positively correlated with oceanospirillales in saline-alkali land soil. The metabolic function profiles displayed highly enriched metabolism (carbohydrate and amino acid metabolisms) and environmental information processing (membrane transport and signal transduction) pathways. overall, the composition and function of soil microbiomes were found to be important factors to the quality variation of C. deserticola in diferent ecotypes. This work provided new insight into the regulatory relationship amongst the environment, soil microbial community, and plant quality variation.

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Cistanche deserticola is a non-photosynthetic parasitic plant that can grow in dry habitats, such as sandy land and dry river ditch, as well as under stressful conditions, such as saline-alkali land1. This plant parasitised on the roots of psammophyte Haloxylon ammodendron, which shows strong resistance to harsh environmental conditions2. Plants rely on the benefcial interaction between roots and microorganisms to obtain nutrients, promote growth and resist external stress3. Meanwhile, the soil microbiome governs the biogeochemical cycling of macronu- trients, micronutrients and other elements that are vital for plant growth4. However, the relationship between C. deserticola and rhizosphere soil microorganisms remains unclear. Consequently, presenting the rhizosphere soil microbiome of C. deserticola is necessary.

C. deserticola is often used in East Asia, Central Asia, North Africa and other countries as a kind of food and health care medicine to improve memory, enhance sexual function and protect the liver5,6. The main active ingredients of C. deserticola are phenylethanoid glycosides (PhGs), iridoids, lignans and polysaccharides. Amongst these components, the PhGs are the active components7. Previous study reported that the content of PhGs in diferent ecotypes of C. deserticola considerably difered, amongst which 2′-acetylacteoside could be used as a chemical marker to distinguish C. deserticola produced in Xinjiang and Inner Mongolia1. Studies on microbial communities have shown that microbial communities can regulate the metabolism of their hosts8,9. Therefore, a comparison of the relationship between microbial community and metabolites in the rhizosphere soil of C. deserticola in diferent ecotypes is urgently needed.16S amplicon sequencing of plant rhizosphere soil samples has been performed to explore the diversity of microbial communities, providing new insights into the relationship between plants and soil microbial communities10,11. For example, The association of the distribution and dynamics of endophytic fungi with C. songaricum and N. tangutorumwas investigated in microbiome studies using high-throughput sequencing11. In this study, we performed 16S rRNA amplicon sequencing to obtain the soil microbiomes of C. deserticola in different ecotypes. The differences of soil microbial community in various ecotypes were also compared, and the biomarkers that could distinguish the three ecotypes were excavated. Correlation analysis was then calculatedvia the key microbial community abundance, the content of PhGs and ecological factors, and the regulatory relationship was explored. Finally, the function of soil microbiomes of C. deserticola in diferent ecotypes was predicted.

Materials and Methods

Study site description and sampling. According to feld investigations, C. deserticola has three main ecotypes, including saline-alkali land, grassland and sandy land. In April, 2017, we collected soil samples representing the major ecotypes of C. deserticola in China. Soil samples of saline-alkali land were collected from Ebinur Lake (AB1, AB2, AB3) andBaicheng Beach (BJ1, BJ2, BJ3, BJ4, BJ5) in Xinjiang province. Grassland soil samples were taken fromTula Village (TL1, TL2, TL3, TL4, TL5) in Xinjiang province. Soil samples of Sandy land were collected from Alxa (AL1, AL2, AL3, AL4, AL5, AL6) in Inner Mongolia province and Minqin county (GS1, GS2), Changcheng county (GS3, GS4) in Gansu province. The longitude, latitude and altitude information of all sampling points are shown in Table 1 and Fig. 1. At the feld site, we used a stainless steel cylindrical drill with a diameter of 5 cm to collect the soil adjacent to the succulent stem of C. deserticola and its host, and immedi- ately stored it in a portable refrigerator at –20 °C. After transport to the laboratory, the soil samples were passed through a 2-mm sieve to remove plant tissues, roots, rocks, etc. and stored at –20 °C in a refrigerator before fur- ther experiments.

DNA Extraction and 16S rRNA Sequencing. Soil DNA was extracted using the PowerSoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA) following the manual12. The purity and quality of the genomic DNA were checked by 0.8% agarose gels electrophoresis and nanodrop.

The V3-V4 hypervariable region of bacterial 16S rRNA gene was amplified with the primers 338F (ACTCCTACGGGAGGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT)13. For each soil sample, a 10-digit barcode sequence was added to the 5′ end of the forward and reverse primers (Allwegene Company, Beijing)12. The PCR was performed on a Mastercycler Gradient (Eppendorf, Germany) using 25 μl reaction vol- umes containing 12.5 μl KAPA 2G Robust Hot Start Ready Mix, 1 µl forward primer (5 µM), 1 µl reverse primer (5 µM), 5 µl DNA (total template quantity is 30 ng) and 5.5 µl H2O. The cycling parameters are as follows: 95 °C for 5 min, followed by 28 cycles of 95 °C for 45 s, 55 °C for 50 s and 72 °C for 45 s, with a fnal extension at 72 °C for 10 min. Three PCR products per sample were pooled to mitigate reaction-level PCR biases. The PCR products were purifed using a QIAquick Gel Extraction Kit (QIAGEN, Germany) and then quantifed using real-time PCR14.

Deep sequencing was performed on a MISeq platform at the Allwegene Company (Beijing). After the run, image analysis, base calling and error estimation were performed using Illumina Analysis Pipeline Version 2.614.

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Data analysis. The raw data were frstly screened, and sequences were removed based on the following con- siderations: sequences shorter than 200 bp with low quality score (≤20) and contained ambiguous bases or did not match the primer sequences and barcode tags15. Qualifed reads were separated using the sample-specifc barcode sequences and trimmed with Illumina Analysis Pipeline Version 2.6. Then, the datasets were analysed using QIIME. The sequences were clustered into operational taxonomic units (OTUs) at a similarity level of 97%16 to generate rarefaction curves and calculate the richness and diversity indices. The Ribosomal Database Project Classifer tool was used to classify all sequences into diferent taxonomic groups15. Clustering analyses were per- formed based on the OTU information from each sample using R to examine the similarity between diferent samples17. The UniFracdistances matrix between microbial communities from each sample were calculated using the Tayc coefcient and represented as an unweighted pair-group method with arithmetic mean clustering tree, which describes the dissimilarity (1-similarity) amongst the multiple samples18. A Newick-formatted tree fle was also generated through this analysis. Alpha diversity was applied in the analysis of the complexity of species diversity for a sample using four indices, including Chao1, observed species, phylogenetic diversity (PD) whole tree and Shannon diversity index. These indices were calculated using the QIIME software (Boulder, CO, USA) in Python (v.1.8.0) (La Jalla, CA, USA)19. Beta diversity analysis was used to evaluate diferences of samples in terms of species complexity. Beta diversity was calculated using the principal coordinate analysis (PCoA) and cluster analysis in QIIME20. The analysis of molecular variance (AMOVA) was performed using mothur. EdgeR was used to calculate the OTU diference between groups. Heatmap.2 was used to draw the heat map, whilst Ggplot was used to draw the Manhattan map.

Determination of biomarker and core microbiome. The linear discriminant analysis (LDA) and ran- dom forest (RF) methods in the Microbiome Analyst website21 was used to determine the biomarker microbiome.

The website firstly performs non-parametric factorial Kruskal–Wallis sum–rank test to identify features with signifcant diferential abundance considering the experimental factor or class of interest, followed by LDA to calculate the efect size of each diferentially abundant features21. The features are considered signifcant based on their adjusted p-value. The default adj.p-value cutof = 0.05. RF analysis is performed using the randomForest package5. RF is a supervised learning algorithm that is suitable for high-dimensional data analysis. This method utilises an ensemble of classifcation trees, each of which is grown via random feature selection from a bootstrap sample at each branch22. Core microbiome analysis was adopted from the core function in the R package micro- biome by Microbiome Analyst

correlation analysis of key microbial communities, PhGs content and ecological factors. The contents of seven phenylethanoid glycosides (PhGs) of the three ecotypes of C. deserticola, namely, echinacoside, cistanoside A, acteoside, isoacteoside, 2′-acetylacteosid, tubuloside A and cistanoside F, were determined through HPLC. The chromatographic conditions involve a Waters C18 column (150 mm × 3.9 mm, 4.6 μm), and the mobile phase comprises acetonitrile and 0.2% formic acid. The chromatographic settings were as follows: 0–10 min, 10%→15% A; 10–30 min, 15%→40% A. The fow rate was 1 mL/min. The absorption wavelength, injection volume and column temperature were 330 nm, 10 μL and 27 °C, respectively. The methodology study refers to the preliminary experimental work of the reference group1.

This study collected 16 meteorological stations near the three ecological types of C. deserticola Xinjiang Bole 51238, Tacheng 51133, Tori 51241, Karamay 51243, Buxail 51156, Yumin 51137, Emin

51145, Gansu Minqin 52681, Yongchang 52674, Wuwei 52679, Gulang 52784, Inner Mongolia Suikou 53419, Hangjinhouqi 53420 and Wuhai 53512. Data of seven climatic factors from 1981–2010 served as the climatolog- ical factor data for subsequent correlation analysis.

Redundancy analysis of diferential metabolites and bioclimatic factors was performed by using Canoco 5 software23. Pearson correlation coefcient was calculated for biomarker microbiome abundance, compound con- tent and ecological factor data integration. In this study, the log2 data conversion was uniformly performed before the analysis. SPSS was used to calculate the Pearson correlation coefcient of the six biomarkers, six core micro- biomes, seven main active components of C. deserticola and the ecological factors in the three habitats, and the screening standard was as follows: pearson correlation coefcient (r) >0.5 and p value < 0.05. The relationships amongst the above factors were visualised using Cytoscape24 (The Cytoscape Consortium, San Diego, CA, USA, version 3.7.0) and pheatmap (R package).

Prediction of the microbial functional profles of the microbiome. Tax4Fun25 (R package, http:// tax4fun.gobics.de/) was used to predict the microbial functional profles of microbiomes in the soil samples. The OTU Biom table of soil microbiome was used as an input fle for the metagenome imputation of C. deserticola soil samples. Then, the predicted gene class abundances were analysed at the KEGG Orthology (KO) group level 3. The results from Tax4Fun were analysed in doBy (R package)26.

Results

Microbial community composition. The 16S rRNA sequencing resulted in 834,323 raw reads, amongst which 522,929 passed the quality and length fltering. The data set comprised 20,511–48,337 (mean: 33,994) sequences per sample. The high-quality reads were clustered using >97% sequence identity into 3541 microbial OTUs (Table 1).

The microbial community was generally classifed into 19 phyla, 46 classes and 87 orders (Figs. 2a,b and S1–S3). At the phylum level, Actinobacteria (35%), Proteobacteria (31%) and Firmicutes (15%) were dominant in saline-alkali land. The grassland was dominated by Actinobacteria (37%), Proteobacteria (29%) and Bacteroidetes (9%). In sandy land, Actinobacteria (42%), Proteobacteria (33%) and Firmicutes (6%) were dominant. At class level, Actinobacteria (25%), Gammaproteobacteria (21%) and Bacilli (15%) were dominant in saline-alkali land, whilst Actinobacteria (22%), Alphaproteobacteria (16%) and Bacilli (8%) were dominant in grassland. In sandy land, Actinobacteria(23%), Alphaproteobacteria (20%) and Gammaproteobacteria (9%) dominated. At the order level (Fig. 2c–e), Micrococcales (22%), Oceanospirillales (15%) and Bacillales (15%) were dominant in saline-alkali land. Micrococcales (11%) and Bacillales (8%) dominated in grassland. Thus, the most dominant bacterial orders were Micrococcales (10%) and Acidimicrobiales (7%).

Diversity of soil bacteria in the three ecotypes of C. deserticola. The measures of within-sample diversity (α-diversity) revealed a diversity gradient amongst the three habitats (Fig. 2f and Table S1). The alpha diversity of the soil bacterial microbial community of each sample was estimated using the community richness (Chao 1, which was expressed as the projected total number of OTU in each sample), observed species, PD whole tree and Shannon diversity index. The observed species, Chao 1 and PD whole tree index suggested that grassland soil communities had the highest α-diversity, whilst the α-diversity of saline-alkali and sandy land soils is similar. The results of therarefaction curves (Fig. S1) are similar to the above results.

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The AMOVA results (Table S2) showed that p < 0.001, which indicates a signifcant diference amongst the three habitats. The bray distance diversity tree clustering results (Fig. S2) of the three habitat soil samples demon- strated that the saline-alkali soil samples were closely clustered, and the distance between the grassland and sandy soil samples was close. The results of the unconstrained PCoAs of unweighted UniFrac distance 2D plots (Fig. 2g) showed that the diferent soil samples of bacterial microbial community from diferent habitats were well-clustered.

comparison of soil bacterial communities of the three ecotypes of c. deserticola. Diferent OTU abundance heatmapsand volcano map(Fig. 3b,e,d,h and Table S3) indicated that compared with sandy land samples, 24 and 17 OTUs were respectively enriched and depleted in saline-alkali land at order level. In addition, compared with sandy land samples, 8 and 11 OTUs were respectively enriched and depleted in the grassland. The Venn diagram (Fig. 3c,f) illustrated that 216 OTUs were depleted in sandy land, 8 were depleted in saline-alkali

land and 1 OTU overlapped between saline-alkali and sandy lands. Furthermore, 3 OTUs were enriched in grass- land, whilst 161 were enriched in sandy land, including 6 OTUs that overlapped between grassland and sandy land. The Manhattan plots (Fig. 3a) demonstrated that Xanthomonadales were more enriched and depleted in saline-alkali land than those in sandy land, whilst Xanthomonadaceae bacterium WWH73, WD2101 soil group, Vibrionales and Verrucomicrobiales were signifcantly depleted at order level. Comparing grassland with sandy land, Xanthomonadales, WD2101 soil group, Vibrionales and Verrucomicrobiales were signifcantly depleted.

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Determination of biomarker and core microbiome in the three ecotypes of C. deserticola. The LEfSe and RF (Fig. 4a,b,d) methods were used to identify features with signifcant diferential abundance across the soil samples, calculate the efect size of each diferentially abundant features and determine the biomarker bacterial microbiome in the three habitats at order level. The results presented in Table S4 revealed that the LDA score of Oceanospirillales, Bacillales and Flavobacteriales was the highest amongst those in the saline-alkali land, whilst that of Sphingomonadales, Gaiellales, Rubrobacterales, Burkholderiales and Sphingobacteriales was the highest amongst those in grassland. The LDA score of Propionibacteriales, Rhodospirillales, Solirubrobacterales, Rhizobiales, Xanthomonadales and Pseudonocardiales were dominant in sandy land. With the exclusion of unde- fned and duplicated orders, these OTUs were classifed into six orders, and their abundances were drawn in a heatmap (Fig. 4c).

The persistence method was adopted from the core function in the R package microbiome to identify the core microbiome in the three ecotypes of C. deserticola. This core bacterial microbiome contained six orders, includ- ing Micrococcales, Bacillales, Rhizobiales, Acidimicrobiales, Streptomycetales andSphingomonadales. With the exclusion of undefned and duplicated orders, these OTUs were classifed into six orders, and their abundances were drawn in a heatmap (Fig. 2h).

The persistence method was adopted from the core function in the R package microbiome to identify the core microbiome in the three ecotypes of C. deserticola. This core bacterial microbiome contained six orders, includ- ing Micrococcales, Bacillales, Rhizobiales, Acidimicrobiales, Streptomycetales andSphingomonadales. With the exclusion of undefned and duplicated orders, these OTUs were classifed into six orders, and their abundances were drawn in a heatmap (Fig. 2h).