The Root Endophytic Fungi Community Structure of Pennisetum Sinese from Four Representative Provinces in China

microorganisms

Article The Root Endophytic Fungi Community Structure of Pennisetum sinese from Four Representative Provinces in China

1, 1, 2 3 1 Zhen-Shan Deng †, Xiao-Dong Liu †, Bao-Cheng Zhang , Shuo Jiao , Xiang-Ying Qi , Zhi-Hong Sun 1, Xiao-Long He 1, Yu-Zhen Liu 1, Jing Li 1, Kai-Kai Chen 1, Zhan-Xi Lin 4 and Ying-Ying Jiang 1,* 1 College of Life Sciences, Yan’an University, Yan’an 716000, China 2 School of Biological and Agricultural Science and Technology, Zunyi Normal College, Zunyi 53602, China 3 State Key Laboratory of Crop Stress Biology in Arid Areas, College of Life Sciences, Northwest A&F University, Yangling 712100, Shaanxi, China 4 National Engineering Research Center of Juncao, Fuzhou 350002, China * Correspondence: [email protected]; Tel.: +86-0911-2332-030 These authors contributed equally to this work. † Received: 6 August 2019; Accepted: 7 September 2019; Published: 9 September 2019

Abstract: Pennisetum sinese is a good forage grass with high biomass production and crude proteins. However, little is known about the endophytic fungi diversity of P. sinese, which might play an important role in the plant’s growth and biomass production. Here, we used high throughput sequencing of the Internal Transcribed Spacer (ITS) sequences based on primers ITS5-1737 and ITS2-2043R to investigate the endophytic fungi diversity of P. sinese roots at the maturity stage, as collected from four provinces (Shaanxi province, SX; Fujian province, FJ; the Xinjiang Uyghur autonomous prefecture, XJ and Inner Mongolia, including sand (NS) and saline-alkali land (NY), China). The ITS sequences were processed using QIIME and R software. A total of 374,875 effective tags were obtained, and 708 operational taxonomic units (OTUs) were yielded with 97% identity in the five samples. Ascomycota and Basidiomycota were the two dominant phyla in the five samples, and the genera Khuskia and Heydenia were the most abundant in the FJ and XJ samples, respectively, while the most abundant tags in the other three samples could not be annotated at the genus level. In addition, our study revealed that the FJ sample possessed the highest OTU numbers (242) and the NS sample had the lowest (86). Moreover, only 22 OTUs were present in all samples simultaneously. The beta diversity analysis suggested a division of two endophytic fungi groups: the FJ sample from the south of China and the other four samples from north or northwest China. Correlation analysis between the environmental factors and endophytic fungi at the class level revealed that Sordariomycetes and Pucciniomycetes had extremely significant positive correlations with the total carbon, annual average precipitation, and annual average temperature, while Leotiomycetes showed an extremely significant negative correlation with quick acting potassium. The results revealed significant differences in the root endophytic fungi diversity of P. sinese in different provinces and might be useful for growth promotion and biomass production in the future.

Keywords: Pennisetum sinese; endophytic fungi diversity; diﬀerent ecoregion; forage grass

1. Introduction The Loess Plateau, which is located in the north of central China, is the largest loess accumulation plateau in the world and is one of the birthplaces of Chinese civilization. The Loess Plateau is one of the most severely eroded regions worldwide and has serious ecological problems. To solve these ecological

Microorganisms 2019, 7, 332; doi:10.3390/microorganisms7090332 www.mdpi.com/journal/microorganisms Microorganisms 2019, 7, 332 2 of 13 problems, the Chinese government introduced the Grain to Green Program (GTGP), which aims to convert farmland to forest and grassland [1]. The implementation of GTGP has greatly improved the local ecological environment and increased the absorption and fixation of greenhouse gas carbon dioxide (CO2), which is of great significance to the environment of China and the world [2]. However, the implementation of GTGP has also caused some problems for social and economic development, such as a shortage of forage grass. Pennisetum sinese, a good bioenergy source, has been introduced to the Loess Plateau to increase the production of forage grass and has achieved remarkable results. 1 1 The biomass production of P. sinese can reach up to 40 t ha− year− with a crude protein content of 15%–22% [3]. As an important forage grass, several studies have investigated the physiology and ecology of P. sinese, but little is known about the endophytic microbial community [3–5]. Endophytic microbes are considered to live inside a plant and do not cause host diseases [6,7]. Interactions between the endophytic microbes and the host plant can benefit both. The host can provide nutrients and shelter for endophytic microbes while the endophytic microbes can enhance the plant’s growth and stress resistance [7–10]. Thus, it is of great importance to investigate the endophytic microbes of P. sinese to promote its biomass production. Endophytic microbes include endophytic bacteria and endophytic fungi. Studies on endophytic bacteria of P. sinese from different provinces have shown great differences in different eco-regions of China, and soil properties and climate are the most important reasons behind these differences [11]. However, many studies on endophytes of P. sinese have evidenced a difference in the dominant bacteria [11,12]. Although some studies on the endophytic bacteria of P. sinese have been reported [11,12], there are still no reports on the root endophytic fungi community of P. sinese. Here, we ae the first to report the endophytic fungi diversity of P. sinese collected from four provinces of China (Shaanxi province, Fujian province, Xinjiang Uygur Autonomous Prefecture, and Inner Mongolia (including sandy and saline–alkali soil) using the high throughput sequencing method. The major objective of this study was to illuminate the endophytic fungi diversity and community of P. sinese from five representative provinces in China.

2. Materials and Methods

2.1. Sampling Sites and Sample Collection Root samples of P. sinese were collected during August to October 2018 at five sites in four eco-regions in China. The geographical characteristics of the sampling sites are shown in Figure1, and the soil physicochemical properties are shown in Table1. For sites in Bayannaoer, Inner Mongolia, sandy land and saline–alkali land were chosen for sample collection, because these two types of soil are both representative of their local environment. In each sampling site, five mature P. sinese plants were selected, and their healthy roots at different depths were cut down and stored in sterile tubes at 4 ◦C. For each plant root sample collection, a five-point sampling method was used. The root samples were then immediately transferred to the lab for further processing in less than 12 h. At the time of the root collection, soils around the roots were also collected to measure the soil’s physicochemical characteristics. To prevent the contamination of the soil and rhizosphere fungi, the root a surface sterilization procedure was performed, as previously described [11]. In order to verify the effect of surface sterilization, parts of the sterilized roots were rolled on potato dextrose agar (PDA) plates. Roots without fungi growth on the PDA plates were considered to be completely sterilized. Then, the root samples were stored at –80 ◦C until DNA extraction. Microorganisms 2019, 7, 332 3 of 13 Microorganisms 2019, 7, x FOR PEER REVIEW 3 of 13

Figure 1. Geographical characteristics and climate types of the ﬁve sampling sites. Geographical Figure 1. Geographical characteristics and climate types of the five sampling sites. Geographical characteristics of the ﬁve sampling sites are shown in the map (from the top to the bottom are latitude characteristics of the five sampling sites are shown in the map (from the top to the bottom are latitude and longitude, altitude (m), annual average temperature (◦C), and annual average precipitation and (mm)).longitude, The triangle altitude represents (m), annual Changji, average Xinjiang temperature Uygur Autonomous (°C), an Prefecture,d annual whichaverage has aprecipitation typical (mm)).continental The triangle arid climate.represents The Changji, circle represents Xinjiang Bayannaoer,Uygur Autonomous Inner Mongolia, Prefecture, which which hasa has typical a typical continentaltemperate arid continental climate. climate. The circle The pentagram represents represents Bayannaoer, Yan’an, ShannxiInner Mongolia, province, which which has has a typical a typical temperatewarm temperatecontinental semi-humid climate. The and drought-pronepentagram represents climate. TheYan’an, square Shannxi represents province, Fuzhou, which Fujian has a typicalprovince, warm which temperate has a typical semi-humid subtropical and monsoon drought- climate.prone climate. The square represents Fuzhou,

Fujian province, whichTable has 1. The a typical physicochemical subtropical characteristics monsoon climate. of the ﬁve soil samples.

The physicochemical SXcharacteristics FJ of the soil samples XJ were measured NS using NY the protocols Changji, Bayannaoer, Bayannaoer, Sampling Sites Yanan, Fuzhou, described by the USDA (1996) [13]. The altitudes andXinjiang geographical Uygur Inner coordinates Mongolia wereInner measured Mongolia using Shaanxi Province Fujian Province a GPS locator (JIEWEISEN WS-009, Guangdong,Autonomous China). Prefecture The soil(Sandy physicochemical Land) (Saline Alkali properties Land) are Total carbon (%) 2.47 4 1.44 0.77 0.23 shown in Table 1. Total nitrogen (%) 0.62 0.53 0.24 0.12 0.04 Total 1784 2065 2036 1465 1718 phosphorus (mg/kg)Table 1. The physicochemical characteristics of the five soil samples. Eﬀective 12.6 10 10.8 3 3.5 phosphorus (mg/kg) SX FJ XJ NS NY Quick acting 132.9 112.8 324.7 154.1 99.5Bayannaoer, potassium (mg/kg) Changji, Bayannaoer, Yanan, Fuzhou, Inner Sampling SitespH 8.35 6.98Xinjiang 8.27 Uygur 8.9Inner 9.38 Shaanxi Fujian Mongolia Autonomous Mongolia Province Province (Saline Alkali Prefecture (Sandy Land) Land) Total carbon (%) 2.47 4 1.44 0.77 0.23 Total nitrogen 0.62 0.53 0.24 0.12 0.04 (%) Total phosphorus 1784 2065 2036 1465 1718 (mg/kg)

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The physicochemical characteristics of the soil samples were measured using the protocols described by the USDA (1996) [13]. The altitudes and geographical coordinates were measured using a GPS locator (JIEWEISEN WS-009, Guangdong, China). The soil physicochemical properties are shown in Table1.

2.2. DNA Extraction and Illumina Sequencing The five root samples collected from the same site were mixed together and pooled as one sample. Approximately 300 g of the mixed root samples was used for DNA extraction. The genomic DNA was extracted using DNA quick plant system kit (Tiangen, Beijing, China), according to the manufacturer’s instruction, after gridding in liquid nitrogen. The genomic DNA’s concentration and purity were determined using Epoch (Bioteck, Winooski, VT, USA) and 1% agarose gel electrophoresis. The final concentration of each DNA sample was adjusted to 1.0 ng/µL using sterile distilled water. Primers ITS5-1737 (50-GGAAGTAAAAGTCGTAACAAGG-30) and ITS2-2043R (50-GCTGCGTTCTTCATCGATGC-30) were used to amplify the Internal Transcribed Spacer (ITS) sequences (about 300 bp). Novogene Co. Ltd. (Beijing, China) were entrusted to conduct the library construction and sequencing. Phusion* High-Fidelity PCR master mix with a GC buffer (New England Biolabs, Ipswich, UK) and an Eppendorf Gradient Thermocycler (Brinkman Instruments, Westbury, NY) were used for PCR amplification. PCR was carried out under the following conditions: initial denaturation at 95 ◦C for 180 s, 25 cycles of denaturation at 98 ◦C for 20 s, annealing at 55 ◦C for 15 s, extension at 72 ◦C for 15 s, and final extension at 72 ◦C for 1 min. The PCR amplicons were then tested using 2% agarose gel electrophoresis and pooled in equimolar ratios into a single tube. The target products were extracted using a Qiagen Gel Extraction Kit (Qiagen, Hilden, Germany), and, finally, the libraries were constructed using a TruSeq®DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA). The library was sequenced using an Illumina HiSeq 2500 platform and 2 250 bp paired-end reads were generated. × 2.3. Data Analysis The raw reads first underwent quality control and length trimming procedures and were then divided into five groups according to their barcodes. The barcode sequences were truncated for further analysis. The remaining reads were then assembled to generate raw tags [14]; the clean tags were obtained by trimming the low quality raw tags [15,16]. The effective tags were generated using the clean tags after chimera sequence detection and removal [17]. Uparse (Uparse v7.0.1001, http://drive5.com/uparse/)[18] was used to cluster the effective tags and generate an operational taxonomic unit (OTU) with an identity threshold of 97%. The OTUs with only one sequence were removed, and the rest OTUs were annotated by the SSUrRNA database using Mothur [19]. The alpha diversity (including the observed species, the Shannon index; the Simpson index; the abundance-based coverage estimator (ACE); good-coverage; rarefaction analysis; rank abundance analysis) and beta diversity (including principal coordinate analysis (PCoA); and the unweighted pair-group method with arithmetic means (UPGMA)) were calculated by QIIME and displayed using the R software [16,20]. The correlation between environmental factors and endophytic fungi was calculated and plotted using R. Microorganisms 2019, 7, 332 5 of 13

3. Results

3.1.Microorganisms Endophytic 2019 Fungal, 7, x FOR Community PEER REVIEW in Illumina Sequencing 5 of 13

chimeras,A total 374,875 of 385,438 effective raw tags tags were (ranging generated from from 52,308 the for Illumina XJ to 90,280 Miseq sequencingfor NS) were of obtained the five samples. for OTU Aftergeneration. quality The control, Q20 values a total for of the 375,609 five samples clean tags ranged remained. from 99.18 Then, to 99.38, after removalindicating of the the high chimeras, quality 374,875of the eIlluminaffective tagssequencing. (ranging An from overview 52,308 forof the XJ tosequencing 90,280 for data NS) wereis shown obtained in Table for OTU2. generation. The Q20 values for the five samples ranged from 99.18 to 99.38, indicating the high quality of the Illumina sequencing. An overviewTable of the2. Overview sequencing of the data sequencing is shown data. in Table 2.

Sample Raw CleanTable Effectiv 2. OverviewBase of the (nt) sequencing Average data. Q20 GC% Effective Name Tags Tags e Tags Length (nt) % Average SampleSX Name79,608 Raw Tags77,989 Clean Tags77,902 Eff ective20,915 Tags,412 Base (nt) 268 99.27Q20 56.08 GC% Effective%97.02 Length (nt) FJ 79,018 78,476 78,175 16,975,477 217 99.38 49.92 97.33 SX 79,608 77,989 77,902 20,915,412 268 99.27 56.08 97.02 XJFJ 52,987 79,018 52,439 78,476 52,308 78,17512,849,44316,975,477 246 217 99.1899.38 45.34 49.92 97.3397.65 NSXJ 94,083 52,987 90,362 52,439 90,280 52,30823,771,71412,849,443 263 246 99.3499.18 55.38 45.34 97.6595.27 NS 94,083 90,362 90,280 23,771,714 263 99.34 55.38 95.27 NYNY 79,742 79,742 76,343 76,343 76,210 76,21018,856,08818,856,088 247 247 99.3699.36 51.65 51.65 94.3794.37 The flat trend of the latter part of the rarefaction curve indicates that the sequencing data were sufficientThe flat for trend each of sample the latter to represent part of the the rarefaction fungal communities curve indicates (Figure that 2). theThe sequencing rarefaction datacurves were also sushowedfficient forthe eachdifferent sample abundance to represent of fungal the fungal species. communities FJ had the (Figure highest2). abundance The rarefaction of fungal curves species, also showedwhile NS the had diff theerent lowest. abundance The Good’s of fungal coverage species. valu FJes had for the highestfive samples abundance ranged of from fungal 99% species, to 100% while(Figure NS 2), had indicating the lowest. that The the Good’s sequencing coverage depth values was great for the enough five samples to capture ranged most from of the 99% endophytic to 100% (Figurefungi 2in), each indicating sample. that the sequencing depth was great enough to capture most of the endophytic fungi in each sample.

Figure 2. Rarefaction curves of the fungal community’s composition in five samples and Good’s coverageFigure 2. estimates Rarefaction (%). curves The rarefaction of the fungal curves community’s were assembled composition showing in the five number samples of operational and Good’s taxonomiccoverage unitsestimates (OTUs), (%). clustered The rarefaction at a 97% curves sequence we similarityre assembled cut-o showingff in mothur, the relativenumber to of the operational number oftaxonomic total sequences. units The(OTUs), Good’s clustered coverages at a of 97% the fivesequence samples similarity was calculated cut-off in mothur.mothur, relative to the number of total sequences. The Good’s coverages of the five samples was calculated in mothur.

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Ascomycota and Basidiomycota were the predominant phyla in all samples. The relative abundance of Ascomycota and Basidiomycota could comprise up to 99% in each sample (Figure3A). In SX, NS, and NY, the relative abundance of Basidiomycota was much higher than Ascomycota, while Microorganisms 2019, 7, x FOR PEER REVIEW 6 of 13 in FJ and XJ, the Ascomycota was higher than Basidiomycota. At the class level, Agaricomycetes were dominant in SX (95.9%), XJ (26.5%), NS (85.3%), and NY (52.4%), while the relative abundance of Incertae_sedis_AscomycotaAgaricomycetes in FJ was onlytook 2.9%. up to Sordariomycetes 39.5% in XJ, werewhile dominant the relative in FJ, withabundance a relative in abundance other samples was lowerof 91.2%, than while 2%. in Dothideomycetes other samples, they tookhad up a onlymuch 2% hi togher 3.4%. relative Incertae_sedis_Ascomycota abundance in XJ took (26.9%), up NS (11.9%),to 39.5%and NY in XJ, (27.2%) while thethan relative in SX abundance (0.3%) and in otherFJ (2.3%) samples (Figure was lower 3B). thanIn terms 2%. Dothideomycetes of genera, many tags that couldhad a not much be higherannotated relative under abundance known in genera XJ (26.9%), we NSre named (11.9%), according and NY (27.2%) to the than others in SX in (0.3%) Figure 3C. The relativeand FJ (2.3%)abundance (Figure of3B). the In other terms genera of genera, in manythe five tags samples that could of notSX, beFJ, annotated XJ, NS, and under NY known were 96.3%, 37.9%,genera 28.1%, were 87.5%, named and according 78.9%, to respectively. the others in Figure Khuskia3C. Thewas relative mainly abundance found in of FJ, the which other genera had a in relative the five samples of SX, FJ, XJ, NS, and NY were 96.3%, 37.9%, 28.1%, 87.5%, and 78.9%, respectively. abundance of 54.7%. Heydenia was mainly found in XJ, with a relative abundance of 39.5%. Alternaria Khuskia was mainly found in FJ, which had a relative abundance of 54.7%. Heydenia was mainly found had a inhigher XJ, with relative a relative abundance abundance in of 39.5%.XJ (26.7%)Alternaria and hada relative a higher lower relative abundance abundance inin XJ NS (26.7%) (4.1%) and and NY (2.8%).a Cladosporium relative lower abundance was mainly in NS found (4.1%) in and NS NY and (2.8%). NY,Cladosporium with a relativewas mainlyabundance found inof NS5.3% and and NY, 16.0%, respectively.with a relative Other abundancegenera with of 5.3%a relative and 16.0%, abundance respectively. lower Other than genera 10% within any a relative of the abundancefive samples are not presentedlower than specifically 10% in any ofhere. the five samples are not presented specifically here.

Figure 3. The relative abundance of endophytic fungi in the five samples at different taxonomy levels. Figure 3. The relative abundance of endophytic fungi in the five samples at different taxonomy levels. (A): Relative abundance of fungi at the phylum level. (B): Relative abundance of fungi at the class level. (A): Relative(C): Relative abundance abundance of of fungi fungi at thethe genus phylum level level. with a ( relativeB): Relative abundance abundance of more than of fungi 1%. Others at the class level. (inC A,): Relative B, and C representabundance tags of without fungi annotations at the genus at specific level level.with a Other relative genera abundance in C represent of more tags that than 1%. Otherscan in annotateA, B, and genera C represent with a relative tags without abundance annotati of less thanons at 1%. specific level. Other genera in C represent tags that can annotate genera with a relative abundance of less than 1%. A total of 708 OTUs were obtained from the high-throughput sequencing, with 130, 242, 135, 86, and 115 for SX, FJ, XJ, NS, and NY, respectively. The OTU distribution in the five sample is shown in A total of 708 OTUs were obtained from the high-throughput sequencing, with 130, 242, 135, 86, the Venn diagram (Figure4). A total of 22 OTUs were shared by all five samples, and the unique OTUs and 115for for SX, SX, FJ, XJ,FJ, NS,XJ, andNS, NY and were NY, 9, respectively. 84, 12, 4, and 15, The respectively. OTU distribution in the five sample is shown in the Venn diagram (Figure 4). A total of 22 OTUs were shared by all five samples, and the unique OTUs for SX, FJ, XJ, NS, and NY were 9, 84, 12, 4, and 15, respectively.

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Figure 4. VennVenn diagram of the five ﬁve P. sinesesinese samples.samples. The numbers inside the diagram indicate the number of OTUs. A A total total of of 9, 9, 84, 84, 12, 12, 4, 4, and and 15 15 unique unique OTUs OTUs were were found found in in SX, SX, FJ, FJ, XJ, XJ, NS, NS, and and NY, NY, respectively, andand 2222 OTUsOTUs werewere sharedshared byby allall ﬁvefive samples.samples.

3.2. Alpha Alpha Diversity Diversity Analysis Analysis The alpha diversity indices of the five ﬁve samples are listed in Table 3 3.. AA wholewhole treetree ofof thethe highesthighest observed species, ACE and PD PD Whole Whole Tree, Tree, occurred in the FJ sample, sample, and and the the index index values values were were 223, 223, 246.86, and 57.80. In In the the contrast, contrast, a a whole whole tree tree of of the the lowest lowest observed observed species, ACE and PD, occurred inin the NS sample, and the values were 79, 89.13, and 15.89. The The NY NY sample sample had had the the highest highest Shannon index (2.43), while the lowest index was found in the SX sample at 0.56. The highest Simpson index occurred in the XJ sample at 0.71 0.71 while while the the lowest lowest Simpson Simpson index index was was found found in in the the SX SX sample sample at at 0.12. 0.12. The XJ sample possessed the highest Chao1 index at 238.50, while the NS NS sample sample possessed possessed the lowest lowest Chao1 index at 83.58.

Table 3. Alpha diversity indices of the ﬁve samples. Table 3. Alpha diversity indices of the five samples.

SampleSample Name Observed Observed_Species Shannon Simpson Chao1 ACE PD_Whole_TreePD_Whole Shannon Simpson Chao1 ACE NameSX _Species 109 0.56 0.12 130.37 136.71_Tree 29.92 SXFJ 109 2230.56 2.120.12 0.60130.37 237.88 136.71 246.86 29.92 57.80 XJ 135 2.205 0.71 238.50 179.16 32.84 NSFJ 223 792.12 1.190.60 0.30237.88 83.58 246.86 89.13 57.80 15.89 NYXJ 135 1062.205 2.430.71 0.69238.50 110.13 179.16 113.46 32.84 21.33 NS 79 1.19 0.30 83.58 89.13 15.89 3.3. BetaNY Diversity Analysis106 2.43 0.69 110.13 113.46 21.33

3.3. BetaThe Diversity dissimilarity Analysis coeffi cient between the five samples was calculated based the Weighted Unifrac and Unweighted Unifrac distance matrix (Figure5). The smaller the dissimilarity coe fficient, the smaller the diThefference dissimilarity between thecoefficient two samples. between Based the on five the Weightedsamples was Unifrac calculated distance based matrix, the the Weighted FJ sample Unifrachad the and highest Unweighted dissimilarity Unifrac with distance the SX matrix sample (Figure at 2.663, 5). The while smaller the the NS dissimilarity sample had coefficient, the lowest the smaller the difference between the two samples. Based on the Weighted Unifrac distance matrix, the FJ sample had the highest dissimilarity with the SX sample at 2.663, while the NS sample had the

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lowest dissimilarity with the SX sample at 0.306. However, there was little difference in the dissimilaritydissimilarity withcoefficient the SX calculated sample at based 0.306. on However, the Unweighted there was Unifrac little di ffdistance.erence in The the dissimilaritydissimilarity coecoefficientfficient calculatedbetween the based FJ onand the NS Unweighted samples was Unifrac the highest distance. at The0.812 dissimilarity and was the coe lowest,fficient betweenat 0.398, thebetween FJ and NY NS and samples NS. was the highest at 0.812 and was the lowest, at 0.398, between NY and NS.

FigureFigure 5.5.Heatmap Heatmap of of the the dissimilarity dissimilarity coe coefficientfficient between between the fivethe P.five sinese P. samples.sinese samples. In the sameIn the square, same thesquare, upper the and upper lower and values lower represent values the represent coefficients the calculatedcoefficients based calculated on the Weightedbased on Unifracthe Weighted matrix andUnifrac Unweighted matrix and Unifrac Unweighted distance Unifrac matrix. distance matrix.

AA principalprincipal coordinatecoordinate analysisanalysis (PCoA)(PCoA) basedbased onon thethe weightedweighted UnifracUnifrac distancedistance matrixmatrix waswas conductedconducted toto showshow thethe relationshiprelationship betweenbetween the didifferentfferent P.P. sinesesinese samplessamples (Figure(Figure6 ).6). In In the the PCoA PCoA result,result, thethe firstfirst axisaxis explainedexplained 67.47%67.47% ofof thethe data’sdata’s variabilityvariability and the second axis explained 23.63%. TheThe distancedistance between between the the NS NS and and SX wasSX was the smallest,the smal andlest, the and FJ samplethe FJ sample possessed possessed the largest the distance largest fromdistance other from samples. other Thesamples. Unweighted The Unweighted Pair-group Pair-group Method with Method Arithmetic with MeanArithmetic (UPGMA) Mean method (UPGMA) was usedmethod to clusterwas used the to five cluster samples the basedfive samples on the weightedbased on Unifracthe weighted distance Unifrac matrix distance (Figure matrix7). The (Figure result revealed7). The result that SX revealed and NS that were SX clustered and NS together were clus whiletered FJ together had the largestwhile FJ di ffhaderence the comparedlargest difference to other samplescompared in to its other fungi diversity,samples in which its fungi is similar diversity, to the which results is of similar the PCoA. to the Moreover, results of a correlationthe PCoA. betweenMoreover, the a environmentalcorrelation between factors the and environmental the endophytic fa fungictors and at the the class endophytic level was fungi conducted at the class (Figure level8). Sordariomyceteswas conducted (Figure and Pucciniomycetes 8). Sordariomycetes possessed and Pucciniomycetes an extremely significant possessed positive an extremely correlation significant with thepositive total carboncorrelation (TN), with annual the averagetotal carbon precipitation (TN), annual (AAP), average and annual precipitation average (AAP), temperature and annual (AAT) whileaverage Monoblepharidomycetes temperature (AAT) while and ChytridiomycetesMonoblepharidomycetes possessed and a significantlyChytridiomycetes positive possessed correlation a withsignificantly TN, AAP, andpositive AAT. Sordariomycetescorrelation with and Incertae_sedis_ZygomycotaTN, AAP, and AAT. possessedSordariomycetes a significantly and positiveIncertae_sedis_Zygomycota correlation with the total possessed nitrogen a significantly and total phosphorus, positive correlation respectively. with Leotiomycetes the total nitrogen possessed and antotal extremely phosphorus, significant respectively. negative correlation Leotiomycetes with quickpossessed acting potassiuman extremely (AP). Sordariomycetessignificant negative and Microbotryomycetescorrelation with quick possessed acting potassium a significantly (AP). negativeSordariomycetes correlation and with Microb pH whileotryomycetes Dothideomycetes possessed anda significantly Tremellomycetes negative possessed correlation a significantly with pH wh negativeile Dothideomycetes correlation with and TN.Tremellomycetes possessed a significantly negative correlation with TN.

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Figure 6. Principal coordinate analysis (PCoA) plot based on the weighted Unifrac distance matrix for FiguretheFigure five 6. 6.P. Principal Principalsinese samples. coordinatecoordinate analysis analysis (PCoA) (PCoA) plot basedplot ba onsed the on weighted the weighted Unifrac distanceUnifrac matrix distance for matrix forthe the ﬁve fiveP. sinese P. sinesesamples. samples.

FigureFigure 7. Unweighted 7. Unweighted Pair-group Pair-group Method Method with with Arithmet Arithmeticic MeanMean (UPGMA) (UPGMA) clustering clustering tree tree based based on on the Figureweightedthe weighted 7. Unweighted Unifrac Unifrac distance distance Pair-group matrix. matrix. Method The The relative relative with abundances abundancesArithmetic of Meanof the the top (UPGMA)top ten ten phyla phyla clustering in allin samplesall samples tree are based are on indicated, and the rest of the phyla are indicated as “Others”. indicated,the weighted and the Unifrac rest of distancethe phyla matrix. are indicated The relative as “Others”. abundances of the top ten phyla in all samples are indicated, and the rest of the phyla are indicated as “Others”.

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Figure 8.Figure Heatmap 8. Heatmap of correlation of correlation between between the theenvironmen environmentaltal factors factors and and the the endophytic fungi fungi at at the the class level. * and ** represent the p < 0.05 and p < 0.01, respectively. TN, EP, TP, TC, AAP, AAT, class level. * and ** represent the p < 0.05 and p < 0.01, respectively. TN, EP, TP, TC, AAP, AAT, and and AP represent total nitrogen, effective phosphorus, total phosphorus, total carbon, annual average AP representprecipitation, total annualnitrogen, average effe temperature,ctive phosphorus, and quick total acting phosphorus potassium,, respectively. total carbon, annual average precipitation, annual average temperature, and quick acting potassium, respectively. 4. Discussion 4. DiscussionBecause P. sinese is an important feedstock in graziery and agroforestry in many places in China, it is important to study its endophytic microbial community. Many reports have suggested that Because P. sinese is an important feedstock in graziery and agroforestry in many places in China, endophytic fungi could promote host growth, tolerance to biotic and abiotic factors, and nutrient it is importantabsorption to [ 21study–24]. Ourits endophytic study focused microb on theial root community. endophytic fungi Many diversity report ofs P.have sinese suggestedcollected that endophyticfrom fivefungi provinces could promote in China. Investigationhost growth, of tolera the endophyticnce to biotic fungi and of P. abiotic sinese roots factors, does notand only nutrient absorptionprovide [21–24]. information Our study needed focused for further on study the butroot also endophytic provides an fungi understanding diversity of of the P. di ffsineseerences collected in from fivethe provinces endophytic in fungal China. diversity Investigation in different of environments. the endophytic Meanwhile, fungi aof better P. sinese understanding roots does of rootnot only provideendophytic information fungi needed could help for usfurther better study understand but al theso interactions provides betweenan understanding the fungi and ofP. the sinese differencesand in the endophyticpromote the productionfungal diversity and quality in different of forage environments. grass. Meanwhile, a better understanding of To investigate the root endophytic fungi diversity of P. sinese, a high throughput method was root endophytic fungi could help us better understand the interactions between the fungi and P. sinese used identify the ITS1 sequence of P. sinese root samples, and a total of 798 OTUs, ranging from 79 and promotein the NSthe sample production to 223 inand the quality FJ sample, of forage were identified grass. (Table1). Ascomycota and Basidiomycota Towere investigate the two dominantthe root phylaendophytic in the five fungi samples. diversity This phenomenonof P. sinese, a has high also throughput been shown bymethod other was used identifyresearchers the ITS1 [25–28 sequence]. In the of root P. ofsineseP. sinese rootcollected samples, from and Yanan, a total Shannxi of 798 OTUs, province, ranging most of from the 79 in the NS samplesequencing to 223 tags in (94%) the FJ belonged sample, to were phylum identified Basidiomycota (Table and 1). Ascomycota order Russulales, and but Basidiomycota the tags could were the twonot dominant be annotated phyla to the in family the five level. samples. The same situationThis phenomenon also occurred has for thealso root been samples shown collected by other from Bayannaoer, Inner Mongolia, in both sandy land and saline–alkali land. The dominant unknown researchers [25–28]. In the root of P. sinese collected from Yanan, Shannxi province, most of the fungi sequenced SX, NS, and NY samples indicate the need for further study of root endophytic fungi. sequencingThe high tags abundance (94%) belonged of unknown to phylum fungi may Basidi play anomycota important and role order in the growthRussulal of P.es, sinese but inthe Shannxi tags could not be annotatedprovince and to Inner the family Mongolia. level. The The most same dominant situation OTU inalso the occurred FJ sample belongedfor the root to species samplesKhuskia collected from Bayannaoer,oryzae. Some Inner species Mo ofngolia, genus Khuskia in bothare sandy plant land pathogens and sali [29ne–alkali]. However, land. studies The on dominantKhuskia oryzae unknown fungi sequencedare still rare. SX, The NS, high and abundance NY samples of K. indicate oryzae in the the need root offorP. further sinese collected study of from root Fuzhou, endophytic Fujian fungi. The highprovince, abundance indicates of theunknown important, fungi yet unknown, may play roles an that importantK. oryzae playrole inin the the growth growth of P. of sinese P. .sinese in Shannxi provinceThe results and of Inner the alpha Mongolia. diversity The analysis most revealeddominant a higher OTU diversity in the FJ of sample endophytic belonged fungi in to the species FJ sample than in the other four samples (Table3). In addition, the PCoA analysis showed an obvious Khuskia oryzae. Some species of genus Khuskia are plant pathogens [29]. However, studies on Khuskia division, in the first axis, between sample group of SX, XJ, NS and the FJ sample (Figure6). The higher oryzae are still rare. The high abundance of K. oryzae in the root of P. sinese collected from Fuzhou, Fujian province, indicates the important, yet unknown, roles that K. oryzae play in the growth of P. sinese. The results of the alpha diversity analysis revealed a higher diversity of endophytic fungi in the FJ sample than in the other four samples (Table 3). In addition, the PCoA analysis showed an obvious division, in the first axis, between sample group of SX, XJ, NS and the FJ sample (Figure 6). The higher rainfall and temperature might be the main cause of this difference. Meanwhile, the correlation analysis (using the Spearman method) between the endophytic fungi at the class level and environmental factors also revealed an extremely significant positive relationship between the AAP

Microorganisms 2019, 7, 332 11 of 13 rainfall and temperature might be the main cause of this difference. Meanwhile, the correlation analysis (using the Spearman method) between the endophytic fungi at the class level and environmental factors also revealed an extremely significant positive relationship between the AAP and AAT and Sordariomycetes and Pucciniomycetes, which occupied a much higher proportion in the FJ sample than in the four other samples (Figure8). The UPGMA analysis also suggested that the FJ sample is significantly different from the other four samples. Our results also revealed that the endophytic fungi diversity in the south of China (the FJ sample) was different from the diversity in north and northwest China (the NS, NY, SX, and XJ samples). Previous studies have already demonstrated that both the climatic conditions and soil physicochemical properties might affect the diversity and composition of the endophytic microbes [30–32]. The correlation analysis results also revealed that both the climatic conditions and soil physicochemical properties had significant influence on certain fungi at the class level (Figure8). Although the climate was the same for the NS and NY samples, there were still obvious differences in their endophytic fungi diversity (Figures3B and8). In addition, the endophytic fungi composition of the NS sample showed a closer relationship to the SX sample than to the NY sample (Figure6), which suggests the influence of soil’s physicochemical properties on fungi diversity. Our results, then, are consistent with the previous reports and demonstrate that both soil characteristics and climate factors play an important role in determining endophytic fungi diversity and community composition. In conclusion, this article is the first to report the diversity of endophytic fungi of P. sinese from four ecoregions in China. Alpha diversity and beta diversity analyses revealed that the endophytic fungi structure and composition was different in the roots of P. sinese collected from different ecoregions, and the soil’s physicochemical properties and climate factors had important impacts on endophytic fungi diversity and the abundance of P. sinese in the different samples. Isolation and function studies of these endophytic fungi are required to understand the role the fungi play in plant growth. This study provides the basic information we need to understand the endophytic fungi diversity of P. sinese and might help improve the growth, production, and quality of P. sinese.

Author Contributions: Conceptualization, Z.-S.D., X.-L.H., and Z.-X.L.; Investigation, Y.-Z.L., J.L., and K.-K.C.; Methodology, X.-D.L., B.-C.Z., X.-Y.Q., and Y.-Y.J.; Software, S.J., X.-Y.Q., and Z.-H.S.; Writing—original draft, X.-D.L.; Writing—review and editing, Z.-H.S. Funding: This study was funded by the Special Project serving the Local Education Department of Shaanxi Province of China (grant number16JF029), the Innovation Program of Shaanxi Province of China (2012CGX7; 2012KTZB03-02-03), and the Research Fund for the County Key Technology Project of Shaanxi Province of China (2018XY-14), as well as the National Natural Science Foundation Project (No. 31660106); the Guizhou Science and Technology Department Cooperation Project (LH(2016)7012, LH(2017)7063); and the Guizhou Provincial Department of Education (Qianjiaohe KY(2018)064). Acknowledgments: This work was supported by the service for the local area Foundation of Education department of Shaanxi Province of China (16JF029), the Innovation Program of Shaanxi Province of China (2012CGX7; 2012KTZB03-02-03; 2016TTC-N-3-1), the National Natural Science Foundation of China (U1204301), and the Research Fund for the County Key Technology Project of Shanxi Province of China (2018XY-14), as well as the National Natural Science Foundation Project (No. 31660106); the Guizhou Science and Technology Department Cooperation Project (LH(2016)7012, LH(2017)7063); and the Guizhou Provincial Department of Education (Qianjiaohe KY(2018)064). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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