International Journal of Molecular Sciences

Article Composition and Genetic Diversity of the Nicotiana tabacum Microbiome in Different Topographic Areas and Growth Periods

Xiao-Long Yuan 1,†, Min Cao 2,†, Xin-Min Liu 1, Yong-Mei Du 1, Guo-Ming Shen 1, Zhong-Feng Zhang 1, Jin-Hai Li 3 and Peng Zhang 1,* 1 Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao 266101, China; [email protected] (X.-L.Y.); [email protected] (X.-M.L.); [email protected] (Y.-M.D.); [email protected] (G.-M.S.); [email protected] (Z.-F.Z.) 2 College of Marine Life Sciences, Ocean University of China, Qingdao 266100, China; [email protected] 3 National Tobacco Corporation in Hubei province, Wuhan 430000, China; [email protected] * Correspondence: [email protected]; Tel.: +86-532-6671-5079 † These authors contributed equally to this study.



Received: 9 August 2018; Accepted: 29 October 2018; Published: 31 October 2018


Abstract: Fungal endophytes are the most ubiquitous plant symbionts on earth and are phylogenetically diverse. Studies on the fungal endophytes in tobacco have shown that they are widely distributed in the leaves, stems, and roots, and play important roles in the composition of the microbial ecosystem of tobacco. Herein, we analyzed and quantified the endophytic fungi of healthy tobacco leaves at the seedling stage (SS), resettling growth stage (RGS), fast-growing stage (FGS), and maturing stage (MS) at three altitudes (600, 1000, and 1300 m). We sequenced the internal transcribed spacer (ITS) region of fungal samples to delimit operational taxonomic units (OTUs) and phylogenetically characterize the communities. The results showed that the numbers of clustering OTUs at SS, RGS, FGS, and MS were 516, 709, 469, and 428, respectively. At the phylum level, species in Ascomycota and Basidiomycota had absolute predominance, representing 97.8% and 2.0% of the total number of species, respectively. We also found the number of fungi at the RGS and FGS stages was higher than those at the other two stages. Additionally, OTU richness was determined by calculating the Observed Species, Shannon, Simpson, Chao1, abundance-based coverage estimator (ACE), Good’s coverage and phylogenetic distance (PD)_whole_tree indices based on the total number of species. Our results showed RGS samples had the highest diversity indices. Furthermore, we found that the diversity of fungal communities tended to decrease with increasing altitude. The results from this study indicated that tobacco harbors an abundant and diverse endophytic fungal community, which provides new opportunities for exploring their potential utilization.

Keywords: Nicotiana tabacum; topographic areas; growth periods; fungal community composition; genetic diversity; ITS region

1. Introduction Fungal endophytes are those microorganisms that live intercellularly or intracellularly in plants without causing apparent damage to host tissues [1]. There is a symbiotic relationship between endophytes and plant tissues, whereby plants can provide nutrition and protection for endophytes, and the endophytes in turn have significant influences on the plant, protecting their host from biotic and abiotic stresses [2]. Previously, endophytic fungi have been isolated from the healthy tissues of tobacco (Nicotiana tabacum)[3,4]. Studies on the fungal endophytes in tobacco have shown that they are widely distributed in the leaves, stems, and roots, and play important roles in the composition

Int. J. Mol. Sci. 2018, 19, 3421; doi:10.3390/ijms19113421 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2018, 19, 3421 2 of 14 of the microbial ecosystem of tobacco [5]. Furthermore, these endophytes have various biological functions. Endophytic fungi in tobacco can not only promote the growth of tobacco and enhance their capacity to resist diseases, pests, and/or abiotic stresses (e.g., resistance to heavy metals and drought stress) but they also play positive roles in decreasing the nitrosamine contents in tobacco and improving the quality of tobacco leaves. Therefore, it is desirable to determine the species distribution and characteristics of endophytic fungi in tobacco through a comprehensive investigation at different growth stages and altitudes. Traditionally, the taxonomic identification and classification of fungi have been performed based on their morphologies, growth conditions, and physiological and biochemical characteristics [6]. However, fungal taxa are complex, often lacking discriminatory morphological characters, and they are susceptible to environmental change. In earlier studies of the fungi associated with tobacco, researchers isolated fungi from tobacco leaves with diverse morphologies. Norse and Spurr found that Alternaria, Penicillium, Aspergillus, and Cladosporium spp. were frequent endophytes in Nicotiana spp. [3,5], whereas English and Mitchell found that Penicillium, Aspergillus, Trichoderma, and Circnella were the dominant populations [7,8]. Moreover, most species are not amenable to in vitro culturing [9]. Consequently, it is often difficult to identify species according to their morphologies. With the development of molecular biology techniques, DNA sequence analysis has been widely applied in the taxonomic identification of fungi. In this regard, the nuclear ribosomal internal transcribed spacer (ITS) region and 18s RDNA sequences are widely used markers [10–13]. The fungal ITS region includes the ITS1, ITS2, and 5.8S genes. Typically, the 5.8S gene is highly conserved compared with the ITS1 and ITS2 sequences. Thus, the ITS1 and ITS2 genes are generally used for resolution at the genus and species levels [14–16]. In 2011, Li et al. analyzed the community structure and diversity of fungi in tobacco at the mature stage of growth by amplifying the 18S and ITS genes [4]. Although these previous studies undoubtedly laid the foundations for our study on the fungal communities of tobacco, these studies were neither systematic nor comprehensive, and it is therefore highly probable that information on certain uncultured fungi has been overlooked. Nowadays, high-throughput sequencing technologies can facilitate the identification of low abundance species and can reduce costs and have thus become the first choice for fungal community diversity analysis. Therefore, based on the characteristics of the ITS region, we can construct pair-end libraries and determine sequences using technologies such as the Illumina HiSeq sequencing platform. Furthermore, we can analyze the abundance of species by clustering operational taxonomic units (OTUs) and determining the differences among samples by analyzing their alpha and beta diversities. In this study, we performed a metagenomic analysis of fungal communities at four growth stages (SS, RGS, FGS, and MS) of the healthy tobacco plants grown at three altitudes (600, 1000 and 1300 m) via high-throughput sequencing. Our main goal was to investigate the diversity and heritability of the Nicotiana tabacum microbiome. Specifically, we sought to compare the fungal communities at different growth stages, and examine the effects of altitudes on fungal community characteristics. The findings will not only provide a theoretical basis for the control of tobacco diseases, but also lay the foundations for future research on endophytic fungi in tobacco.

2. Results

2.1. Sequencing Results Sequencing of the amplicon libraries representing four growth periods (SS, RGS, FGS, and MS stages) and three altitudes (600, 1000, and 1300 m) yielded a total of 2,888,484 raw reads before quality control (individual reads ranging from 70,606 to 89,979 bp). The average reads length was 313 bp before data processing. After quality control, the remaining high-quality reads in the dataset presented an average length of 314 bp (Table S1). We then assembled these high-quality reads into raw tags. Effective tags were finally obtained after qualification and removal of chimeras from raw tags. In order Int. J. Mol. Sci. 2018, 19, 3421 3 of 14 Int. J. Mol. Sci. 2018, 19, x 3 of 14 to studytags. theIn order species to diversitystudy the of specie the sample,s diversity the effective of the sample, tags of samplesthe effective with tags more of than samples 97% identitywith more werethan clustered 97% identity into OTUs. were clustered into OTUs.

2.2.2.2. Overview Overview of Fungal of Fungal Taxonomic Taxonomic Composition Composition ForFor the the annotation annotation of all of OTUs,all OTUs, we screenedwe screened the the identified identified species species according according to the to the classification classification resultsresults of eachof each sample. sample. At theAt the phylum phylum level, level, species species in Ascomycotain Ascomycota and and Basidiomycota Basidiomycota showed showed virtuallyvirtually absolute absolute predominance, predominance, representing representing 97.8% 97.8% and and 2.0% 2.0% of theof the total total number number of species,of species, respectively.respectively. The The top top species species at the at classthe class level level were we dominatedre dominated by Leotiomycetesby Leotiomycetes (90.74%), (90.74%), followed followed by Dothideomycetesby Dothideomycetes (6.01%) (6.01%) and and Agaricomycetes Agaricomycetes (0.85%). (0.85%). Erysiphales, Erysiphales, Pleosporales, Pleosporales, Capnodiales, Capnodiales, Polyporales,Polyporales, and and Agaricales, Agaricales, comprising comprising approximately approximately 90.71%, 90.71%, 4.97%, 4.97%, 0.58%, 0.58%, 0.27%, 0.27%, and and 0.30%, 0.30%, respectively,respectively, of theof the samples, samples, were were the the dominant dominant groups groups at theat the order order level. level. At At the the family family level, level, ErysiphaceaeErysiphaceae accounted accounted for 90.71%for 90.71% of the of the entire entire community, community, whereas whereas Pleosporaceae, Pleosporaceae, Davidiellaceae, Davidiellaceae, Didymosphaeriaceae,Didymosphaeriaceae, Meruliaceae, Meruliaceae, and and Leptosphaeriaceae Leptosphaeriaceae taken taken together together contributed contributed toto 5.19%5.19% of the thetotal communitycommunity inin allall samples. samples. In In terms terms of of genera, genera, the the abundances abundances of Microidiumof Microidium, Cladosporium, Cladosporium, , PseudopithomycesPseudopithomyces, Irpex, Irpex, and, andLysurus Lysuruswere were higher higher than than those those of other of other genera genera (Figure (Figure1 and 1 Tableand Table S2). S2).

FigureFigure 1. Taxonomic 1. Taxonomic tree tree of all of samples. all samples. Taxonomic Taxonomic tree tree of fungal of fungal communities communities of healthy of healthy tobacco tobacco plantsplants at four at four growth growth stages stages (seedling (seedling (SS), (SS), resettling resettlin growthg growth (RGS), (RGS), fast-growing fast-growi (FGS),ng (FGS), and and maturing maturing (MS)).(MS)).

Network analysis provides us with a new perspective for studying complex microbial community Network analysis provides us with a new perspective for studying complex microbial structurescommunity and functions.structures and Given func thattions. relationships Given that may relationsh vary underips may different vary environmental under different conditions,environmental the advantage conditions, of dominant the advantage species of and dominant the species species with and close the interactionsspecies with can close be interactions directly determinedcan be [directly17]. Through determined analyzing [17]. the Through correlation analyz indexing of allthe the correlation sample calculations, index of withall the a cutoff sample = 0.6calculations, to filter the with absolute a cutoff value = 0.6 of to the filter correlation the absolu coefficient,te value of coupled the correlation with species coefficient, abundances, coupled we with Udeniomyces constructedspecies abundances, a figure showing we constructed the network a figure of samples showing at the the genus network level. of samples In our study, at the genus level. In Rhodotorula andour study, Udeniomycesin the phylum and Basidiomycota Rhodotorula in were the the phylum genera Basidiomycota with the highest were abundances the genera and with were the Ascomycota Microidium, foundhighest to be abundances located in the and “hub” were position found to of be the located network. in the All “hub” members position of the of the network., All members of the Ascomycota, Microidium, Trimmatostroma, Ophiognomonia, Trichomerium, and Paraphaeosphaeria

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Trimmatostroma,were the top five Ophiognomonia, genera in terms Trichomerium, of richness.and Moreover,Paraphaeosphaeria we foundwere that the Udeniomyces top five genera had a in negative terms of relationshiprichness. Moreover, with Microidium we found (Figure that Udeniomyces 2). had a negative relationship with Microidium (Figure2).

Figure 2. Network analysis at the genus level. Different nodes represent different genera, the node Figure 2. Network analysis at the genus level. Different nodes represent different genera, the node size represents the average relative abundance of the genus, and the nodes of the phylum have the size represents the average relative abundance of the genus, and the nodes of the phylum have the same colors. There is a positive relationship between the thickness of the line and absolute correlation same colors. There is a positive relationship between the thickness of the line and absolute correlation coefficient of species interaction. The blue line represents a negative correlation between the two coefficient of species interaction. The blue line represents a negative correlation between the two genera, and the red line is a positive correlation. genera, and the red line is a positive correlation. 2.3. Dissimilarity of Fungal Communities at Different Growth Stages 2.3. Dissimilarity of Fungal Communities at Different Growth Stages To understand the abundance of OTUs and the communities of fungi at different growth stages, we comparedTo understand the richness the abundance of OTUs andof OTUs species and compositions the communities at different of fungi growth at different stages. growth The number stages, of weclustering compared OTUs the for richness stages SS,of OTUs RGS, FGS,and species and MS comp wereositions 516, 709, at 469, different and 428, growth respectively stages. (FigureThe number3a,b). ofThe clustering results showed OTUs for that stages the numbers SS, RGS, of FGS, OTUs and were MS highest were 516, in the709, RGS 469, stages and 428, compared respectively with those(Figure in 3a,b).the other The three results stages. showed Conversely, that the numbers the number of OTUs of OTUs were in thehighest MS wasin the the RGS lowest. stages Statistical compared analysis with thoseshowed in the that other there three was stages. a significant Conversely, difference the numb (p < 0.05)er of inOTUs the OTUsin the numbersMS was the in lowest. these four Statistical stages. analysisFurthermore, showed we foundthat there that wa thes numbers a significant of unique difference OTUs (p in < the 0.05) RGS in andthe FGSOTUs (172 numbers and 85, in respectively) these four werestages. higher Furthermore, than those we in found the other that twothe numbers stages. Besides, of unique we OTUs also compared in the RGS species and FGS diversities (172 and from 85, respectively)different growth were stages. higher Inthan the those SS, Microidium in the other phyllanthi, two stages. Pseudopithomyces Besides, we also chartarum, compared Curvularia species diversitiessenegalensis, from Periconia different byssoides, growthand stages.Pseudozyma In the aphidisSS, Microidiumwere the phyllanthi, most abundant Pseudopithomyces species at 600 chartarum, m, with Curvulariapercentages senegalensis, of 90.43%, Periconia 1.92%, 1.22%, byssoides, 0.38%, and and Pseudozyma 0.24%, respectively. aphidis were Similarly, the mostMicroidium abundant phyllanthi,species at 600Pseudopithomyces m, with percentages chartarum, Pseudozymaof 90.43%, aphidis,1.92%, Candida1.22%, albicans0.38%, ,andand Penicillium0.24%, respectively. oxalicum were Similarly, the top Microidiumfive most abundant phyllanthi, species Pseudopithomyces at 1000 m, charta representingrum, Pseudozyma 94.80%, 0.36%,aphidis, 0.13%,Candida 0.07%, albicans and,and 0.07% Penicillium of the oxalicumtotal species, were respectively. the top five most At 1300 abundant m, Microidium species at phyllanthi 1000 m, representing(90.97%) was 94.80%, the most 0.36%, abundant 0.13%, species, 0.07%, andfollowed 0.07% by ofCladosporium the total species, funiculosum respectively.(1.39%), AtLysurus 1300 m, cruciatus Microidium(0.77%), phyllanthiCladosporium (90.97%) allicinum was the(0.03%), most abundantand Rhodotorula species, ingeniosa followed(0.03%). by InCladosporium the RGS, Microidium funiculosum phyllanthi (1.39%),(69.19%), LysurusCladosporium cruciatus allicinum(0.77%), (0.03%),CladosporiumPseudopithomyces allicinum (0.03%), chartarum and(0.29%), RhodotorulaIrpex ingeniosa lacteus (0.23%), (0.03%). and InCladosporium the RGS, Microidium funiculosum phyllanthi(0.08%) were(69.19%), the dominantCladosporium species allicinum at 600 (0.03%), m. At Pseudopithomyces 1000 m, Microidium chartarum phyllanthi (0.29%),, Pseudopithomyces Irpex lacteus (0.23%), chartarum and, CladosporiumPericonia byssoides funiculosum, Curvularia (0.08%) senegalensis were theand dominantWhalleya microplacaspecies atcomprised 600 m. At approximately 1000 m, Microidium 84.16%, phyllanthi0.02%, 0.02%,, Pseudopithomyces 0.02%, and 0.02%, chartarum respectively,, Periconia of thebyssoides total species., Curvularia In the senegalensis FGS and MS, andMicroidium Whalleya microplacaphyllanthi andcomprisedPseudopithomyces approximately chartarum 84.16%,were 0.02%, the dominant 0.02%, 0.02%, species and at 0.02%, all three respectively, altitudes, comprisingof the total species.more than In the 94% FGS of the and total MS, species Microidium (Figures phyllanthi S1–S12). and The Pseudopithomyces annotation results chartarum of OTUs were showed the dominant that the species at all three altitudes, comprising more than 94% of the total species (Figures S1–S12). The annotation results of OTUs showed that the species of fungi were most abundant in the RGS. Thus,

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among the four growth stages examined, we identified a higher abundance of OTUs and species species of fungi were most abundant in the RGS. Thus, among the four growth stages examined, we diversity at the RGS stage. identified a higher abundance of OTUs and species diversity at the RGS stage.

Figure 3. Comparison of fungal communities at different growth stages. (a) Venn diagram for samples Figure 3. Comparison of fungal communities at different growth stages. (a) Venn diagram for samples among different combinations. Each ellipse represents a sample (group), with the number in the among different combinations. Each ellipse represents a sample (group), with the number in the overlapping ellipses representing the number of operational taxonomic units (OTUs) shared between overlapping ellipses representing the number of operational taxonomic units (OTUs) shared between those samples (groups), and the number in the non-overlapping regions representing the number of those samples (groups), and the number in the non-overlapping regions representing the number of unique OTUs of that sample (group). (b) Statistics of the OTUs number in different growth stages. Total unique OTUs of that sample (group). (b) Statistics of the OTUs number in different growth stages. OTUs refers to all the OTUs in a certain growth stage. Unique OTUs are those specific to a particular Total OTUs refers to all the OTUs in a certain growth stage. Unique OTUs are those specific to a growth stage. particular growth stage. 2.4. Dissimilarity of Fungal Communities at Different Altitudes 2.4. Dissimilarity of Fungal Communities at Different Altitudes In order to explore the effects of different altitudes on the composition of tobacco endophytic fungi,In we order compared to explore the corresponding the effects of different number of alti OTUstudes and on fungalthe composition compositions of tobacco in different endophytic altitude locations.fungi, we Thecompared comparison the corresponding of the OTUs of number the different of OTUs samples and at fungal different compositions altitudes indicated in different that SSaltitude samples locations. shared The 98 common comparison OTUs, of andthe thatOTUs samples of the SSL,different SSM, andsamples SSH at had different 120, 52, altitudes and 140 uniqueindicated OTUs, that respectively.SS samples shared For the 98 RGS common samples, OTUs, 151 common and that OTUs samples were SSL, found SSM, at and different SSH altitudes.had 120, resettling52, and 140 growth unique stage OTUs, low respectively. (RGSL), resettling For the growth RGS stagesamples, middle 151 (RGSM),common andOTUs resettling were found growth at stagedifferent high altitudes. (RGSH) resettling samples containedgrowth stage 134, low 92, and(RGSL), 174 resettling unique OTUs, growth respectively. stage middle In addition,(RGSM), and 112 commonresettling OTUsgrowth were stage found high among(RGSH) the samples FGS samples. contained 114, 134, 82 92, and and 68 174 OTUs unique were OTUs, unique respectively. to the fast growthIn addition, stage 112 low common (FGSL), fast OTUs growth were stage found middle among (FGSM) the FGS and samples. fast growth 114, stage 82 and high 68 (FGSH) OTUs stages, were respectively.unique to the Further,fast growth 76 OTUs stage sharedlow (FGSL), in all fast the growth samples stage of MS middle stages, (FGSM) which and were fast annotated growth stage to 45 specieshigh (FGSH) (Figure stages,4a). The respectively. variation of Further, the OTUs 76 inOTUs SS and shared RGS in decreased all the samples between of 600 MS m stages, and 1000 which m, thenwere increasedannotated atto 130045 species m. However, (Figure 4a). the The tendency variation of of the the OTUs OTUs inin theSS and FGS RGS and decreased MS samples between was decreasing600 m and with1000 them, then increasing increased growth at 1300 altitudes m. However, (Figure4 b).the Thetendency fungal of communities the OTUs in showed the FGS a and similar MS tendency.samples was Therefore, decreasing we have with a the conclusion increasing that growth the diversity altitudes of fungal(Figure communities 4b). The fungal tends communities to decrease withshowed increasing a similar altitude tendency. based Therefore, on the OTU we have richness. a conclusion that the diversity of fungal communities tends to decrease with increasing altitude based on the OTU richness.

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among the four growth stages examined, we identified a higher abundance of OTUs and species diversity at the RGS stage.

Figure 3. Comparison of fungal communities at different growth stages. (a) Venn diagram for samples among different combinations. Each ellipse represents a sample (group), with the number in the overlapping ellipses representing the number of operational taxonomic units (OTUs) shared between those samples (groups), and the number in the non-overlapping regions representing the number of unique OTUs of that sample (group). (b) Statistics of the OTUs number in different growth stages. Total OTUs refers to all the OTUs in a certain growth stage. Unique OTUs are those specific to a particular growth stage.

2.4. Dissimilarity of Fungal Communities at Different Altitudes In order to explore the effects of different altitudes on the composition of tobacco endophytic fungi, we compared the corresponding number of OTUs and fungal compositions in different altitude locations. The comparison of the OTUs of the different samples at different altitudes indicated that SS samples shared 98 common OTUs, and that samples SSL, SSM, and SSH had 120, 52, and 140 unique OTUs, respectively. For the RGS samples, 151 common OTUs were found at different altitudes. resettling growth stage low (RGSL), resettling growth stage middle (RGSM), and resettling growth stage high (RGSH) samples contained 134, 92, and 174 unique OTUs, respectively. In addition, 112 common OTUs were found among the FGS samples. 114, 82 and 68 OTUs were unique to the fast growth stage low (FGSL), fast growth stage middle (FGSM) and fast growth stage high (FGSH) stages, respectively. Further, 76 OTUs shared in all the samples of MS stages, which were annotated to 45 species (Figure 4a). The variation of the OTUs in SS and RGS decreased between 600 m and 1000 m, then increased at 1300 m. However, the tendency of the OTUs in the FGS and MS samples was decreasing with the increasing growth altitudes (Figure 4b). The fungal communities showed a similar tendency. Therefore, we have a conclusion that the diversity of fungal communities Int. J. Mol. Sci. 2018, 19, 3421 6 of 14 tends to decrease with increasing altitude based on the OTU richness.

Figure 4. Comparison of fungal communities at different altitudes. (a) Venn diagram for samples among different combinations. Each circle represents a sample (group), with the number in the overlapping circles representing the numbers of OTUs shared between those samples (groups), and the number in the non-overlapping regions representing the number of unique OTUs of that sample (group). (b) Statistics of the OTUs numbers at different growth stages. Total OTUs refers to all the OTUs in a certain growth stage. Unique OTUs are those specific to a particular growth stage.

2.5. Alpha Diversity QIIME was used to compare the alpha diversity of all the samples [18]. Rank abundance curves can reflect the richness and evenness of species in the samples (Figure5). Additionally, the OTU richness was determined by calculating the Observed Species, Shannon, Simpson, Chao1, abundance-based coverage estimator (ACE), Good’s coverage, and PD_whole_tree indices based on the total number of species. The alpha diversity indices are shown in Table1. The number of Observed Species was highest in the RGSH samples at 238.33 ± 61.61 but was lowest in the MSM samples at 88.67 ± 12.66. Our results showed that RGSL samples had the highest Shannon index values with an average value of 2.21 ± 1.14. In contrast, the lowest Shannon index values (0.90 ± 0.27) were obtained for the MSH samples. We found that RGSH (261.76 ± 59.95) and MSM (99.51 ± 17.63) samples had the highest and lowest Chao1 values, respectively. Similarly, values for the ACE and PD_whole_tree indices were highest for RGSH samples and lowest for MSM samples, with the values of 268.72 ± 60.71/105.90 ± 23.83 and 65.16 ± 8.26/31.27 ± 5.26, respectively. The Simpson indices of all the samples ranged from 0.25 to 0.52, whereas we found little variation in the values of Good’s coverage of all samples, all of which were nearly equal to one. Our goal was to compare fungal communities at different growth stages and examine the effects of altitude on fungal communities in tobacco leaves. Therefore, the variation in all the indices of alpha diversity was compared. The results showed that the indices were highest for the RGS samples, followed by the MS samples. For example, average values for observed species in SS, RGS, FGS, and MS were 143.89 ± 17.20, 212.89 ± 50.95, 141.49 ± 17.46, and 118.56 ± 24.50, respectively (Table S3). Statistical analysis of the alpha diversity indices showed that RGS samples had significantly different numbers of species compared with samples from other stages (p < 0.05) (Table S4). These trends were consistent with the change in abundance of the OTUs. At the same time, variation in the alpha diversity indices at different altitudes in SS, FGS, and MS samples was observed; nearly all the alpha diversity indices indicated that the diversity of fungal communities decreased with increasing altitude in these three growth stages (Table1). Int.Int. J. Mol.J. Mol. Sci. Sci.2018 2018, 19, 19, 3421, x 7 of7 of 14 14

Figure 5. Rank abundance plots of fungal community composition in Nicotiana tabacum. Figure 5. Rank abundance plots of fungal community composition in Nicotiana tabacum. Table 1. Median alpha diversity indices. Table 1. Median alpha diversity indices. Sample Observed Good’s PD_Whole Shannon Simpson Chao1 ACE Name Species Coverage Tree Sample Observed Good’s PD_Whole Shannon Simpson Chao1 ACE NameSSL 161.33Species± 11.59 1.24 ± 0.23 0.31 ± 0.06 188.74 ± 6.69 202.87 ± 18.15 0.99Coverage± 0 48.24 ± 2.25Tree SSM 108.67 ± 15.95 0.98 ± 0.36 0.26 ± 0.10 120.22 ± 22.99 126.28 ± 27.77 0.99 ± 0 39.10 ± 6.89 SSLSSH 161.33 161.67 ±± 11.5924.08 1.241.15 ± 0.230.54 0.29 0.31± ± 0.060.14 183.57 188.74± ±23.35 6.69 193.26 202.87± 18.65± 18.15 0.99 0.99± 0 ± 0 49.96 48.24± 9.17 ± 2.25 SSMRGSL 108.67 204.54 ±± 15.9569.00 0.982.21 ± 0.361.14 0.52 0.26± ± 0.100.28 232.33 120.22± ±65.59 22.99 246.19 126.28± 76.29± 27.77 0.99 0.99± 0 ± 0 56.10 ± 39.1011.99 ± 6.89 SSHRGSM 161.67 196.33 ±± 24.0836.56 1.151.75 ± 0.540.88 0.40 0.29± ± 0.140.17 231.17 183.57± ±54.97 23.35 247.21 193.26± 51.28± 18.65 0.99 0.99± 0 ± 0 52.26 49.96± 4.58 ± 9.17 RGSH 238.33 ± 61.61 1.28 ± 0.60 0.29 ± 0.12 261.76 ± 59.95 268.72 ± 60.71 0.99 ± 0 65.16 ± 8.26 RGSL 204.54 ± 69.00 2.21 ± 1.14 0.52 ± 0.28 232.33 ± 65.59 246.19 ± 76.29 0.99 ± 0 56.10 ± 11.99 FGSL 162.67 ± 14.57 1.29 ± 0.30 0.33 ± 0.09 196.60 ± 28.75 198.75 ± 18.52 0.99 ± 0 50.46 ± 5.90 RGSMFGSM 196.33 134.13 ±± 36.5618.52 1.751.08 ± 0.880.34 0.29 0.40± ± 0.170.09 159.54 231.17± ±29.82 54.97 169.47 247.21± 30.37± 51.28 0.99 0.99± 0 ± 0 51.84 52.26±± 3.10 4.58 RGSHFGSH 238.33 127.67 ±± 61.6119.30 1.281.05 ± 0.600.40 0.28 0.29± ± 0.120.09 136.94 261.76± ±21.49 59.95 141.81 268.72± 20.66± 60.71 1.00 0.99± 0 ± 0 48.14 65.16± 6.40 ± 8.26 ± ± ± ± ± ± ± FGSLMSL 162.67 164.67 ± 14.5744.77 1.291.20 ± 0.300.42 0.30 0.33 ± 0.090.12 188.69 196.60 ±60.70 28.75 196.37 198.75 57.17± 18.52 0.99 0.990 ± 0 54.48 50.467.73 ± 5.90 MSM 88.67 ± 12.66 0.94 ± 0.27 0.25 ± 0.06 99.51 ± 17.63 105.90 ± 23.83 1.00 ± 0 31.27 ± 5.26 FGSMMSH 134.13 102.33 ±± 18.5226.08 1.080.90 ± 0.340.27 0.25 0.29± ± 0.090.07 122.94 159.54± ±29.65 29.82 127.68 169.47± 34.26± 30.37 0.99 0.99± 0 ± 0 35.47 51.84± 6.91 ± 3.10 FGSH 127.67 ± 19.30 1.05 ± 0.40 0.28 ± 0.09 136.94 ± 21.49 141.81 ± 20.66 1.00 ± 0 48.14 ± 6.40 All data presented in text and tables are expressed as means ± standard deviation (SD). MSL 164.67 ± 44.77 1.20 ± 0.42 0.30 ± 0.12 188.69 ± 60.70 196.37 ± 57.17 0.99 ± 0 54.48 ± 7.73 MSM 88.67 ± 12.66 0.94 ± 0.27 0.25 ± 0.06 99.51 ± 17.63 105.90 ± 23.83 1.00 ± 0 31.27 ± 5.26 2.6. Beta Diversity MSH 102.33 ± 26.08 0.90 ± 0.27 0.25 ± 0.07 122.94 ± 29.65 127.68 ± 34.26 0.99 ± 0 35.47 ± 6.91 To determineAll data beta presented diversity, in text we and initially tables are obtained expressed information as means ± standard on species deviation abundance (SD). based on the annotation results of all the species and the abundance information of OTUs (Table S2). Meanwhile,2.6. Beta Diversity the other two important indices of beta diversity-Unifrac distance and Distance matrices were alsoTo calculateddetermine inbeta this diversity, study [19 we,20]. initially Finally, obta principalined information coordinates analysison species (PCoA) abundance and nonmetric based on multidimensionalthe annotation results scaling (NMDS)of all the were species used toand show the theabundance differences information between different of OTUs samples. (Table The S2). resultsMeanwhile, of the PCoAthe other based two on important unweighted indices Unifrac of beta distances diversity-Unifrac demonstrated distance that MSL, and MSM,Distance and matrices MSH sampleswere also tended calculated to cluster in together. this study A similar [19,20]. pattern Finally, was principal observed incoordinates the other three analysis stages (PCoA) (Figure 6and). Thenonmetric highest variationsmultidimensional in the microbiota scaling (NMDS) of different were used samples to show were the 12.34% differences (PC1) and between 8.98% different (PC2), representingsamples. The a strongresults separationof the PCoA based based on on the unweig differenthted stages Unifrac and distances altitudes (Figuredemonstrated6a). NMDS that isMSL, a rankingMSM, methodand MSH that samples is applicable tended to to ecology. cluster together. Nonlinear A NMDS similar models pattern can was overcome observed the in shortcomingsthe other three ofstages linear (Figure models 6). and The reflect highest the variations nonlinear in structure the microbiota of ecological of different data samples [21]. NMDS were 12.34% analysis (PC1) results and based8.98% on (PC2), OTU representing levels were shown a strong in separation Figure6b. Thesebased on results the different indicated stages that differentand altitudes growth (Figure stages 6a). playNMDS an important is a ranking role method in shaping that is fungalapplicable communities to ecology. in NonlinearN. tabacum NMDS. The models stress of can all overcome samples inthe ourshortcomings study was 0.194,of linear which models can accuratelyand reflect reflect the nonl theinear differences structure among of ecological the samples. data The[21]. results NMDS analysis results based on OTU levels were shown in Figure 6b. These results indicated that different

Int. J. Mol. Sci. 2018, 19, x 8 of 14 Int. J. Mol. Sci. 2018, 19, x 8 of 14 growth stages play an important role in shaping fungal communities in N. tabacum. The stress of all Int.growth J. Mol. stages Sci. 2018 play, 19, 3421 an important role in shaping fungal communities in N. tabacum. The stress 8of of all 14 samples in our study was 0.194, which can accurately reflect the differences among the samples. The samples in our study was 0.194, which can accurately reflect the differences among the samples. The results showed that there were high similarities among the samples collected at different growth results showed that there were high similarities among the samples collected at different growth showedstages. On that the there basis were of high the similaritiessimilarities amongof fung theal communities, samples collected samples at different were divided growth stages.into three On stages. On the basis of the similarities of fungal communities, samples were divided into three thegroups, basis namely, of the similarities samples RGSL, of fungal RGSM, communities, and RGSH; samplesand the remaining were divided samples into threeformed groups, a community. namely, groups, namely, samples RGSL, RGSM, and RGSH; and the remaining samples formed a community. samplesThese clusters RGSL, confirmed RGSM, and that RGSH; growth and stage the remainingis the most samples important formed factor a influencing community. the These elevation clusters in These clusters confirmed that growth stage is the most important factor influencing the elevation in confirmedbeta diversity that among growth the stage fungal is the communities most important of the factor multiple influencing samples. the Both elevation the results in beta of PCoA diversity and beta diversity among the fungal communities of the multiple samples. Both the results of PCoA and amongNMDS theshowed fungal that communities the higher of the the elevations multiple samples. are, the Bothsmaller the the results differences of PCoA between and NMDS samples. showed In NMDS showed that the higher the elevations are, the smaller the differences between samples. In thatorder the to higher clearly the compare elevations the are,beta the diversity smaller amon the differencesg samples, betweenwe generated samples. boxplots In order to toshow clearly the order to clearly compare the beta diversity among samples, we generated boxplots to show the comparevariation theamong beta all diversity samples. among The weighted samples, UniFrac we generated distance boxplots indicated to showthat the the RGSL variation sample among had the all variation among all samples. The weighted UniFrac distance indicated that the RGSL sample had the samples.highest variation The weighted (Figure UniFrac 7). Furthermore, distance indicatedwe identifi thated a the trend RGSL indicating sample that had the the diversity highest variationof fungal highest variation (Figure 7). Furthermore, we identified a trend indicating that the diversity of fungal (Figurecommunities7). Furthermore, tended to decrease we identified with aincreasing trend indicating tobacco thatgrowth the diversityaltitude. Interestingly, of fungal communities the fungal communities tended to decrease with increasing tobacco growth altitude. Interestingly, the fungal tendedcommunities to decrease in the with samples increasing were highly tobacco similar, growth apart altitude. from Interestingly, samples FGSH the fungaland RGSL communities (p = 0.0053), in communities in the samples were highly similar, apart from samples FGSH and RGSL (p = 0.0053), theFGSM samples and RGSL were highly(p = 0.0151), similar, MSH apart and from RGSL samples (p = 0.0285), FGSH andMSM RGSL and (RGSLp = 0.0053 (p =), 0.0218), FGSM andand RGSLRGSL FGSM and RGSL (p = 0.0151), MSH and RGSL (p = 0.0285), MSM and RGSL (p = 0.0218), and RGSL (andp = 0.0151SSM (),p MSH= 0.0239) and RGSL(Table ( pS5),= 0.0285), which MSMsuggests and that RGSL the (p fungal= 0.0218), communities and RGSL in and these SSM samples (p = 0.0239) are and SSM (p = 0.0239) (Table S5), which suggests that the fungal communities in these samples are (Tabledifferentiated S5), which from suggests each other. that the Overall, fungal there communities was a higher in these abundance samples of are fungal differentiated species in from the eachRGS differentiated from each other. Overall, there was a higher abundance of fungal species in the RGS other.samples Overall, and a thereclear wastrend a higherof decreasing abundance fungal of fungalcommu speciesnity diversity in the RGS with samples increasing and aaltitude, clear trend based of samples and a clear trend of decreasing fungal community diversity with increasing altitude, based decreasingon the Unifrac fungal distances. community diversity with increasing altitude, based on the Unifrac distances. on the Unifrac distances.

Figure 6. The beta( diversitya)( indices of different samples of Nicotiana tabacum (a)b Principal) coordinate Figure 6. The beta diversity indices of different samples of Nicotiana tabacum (a) Principal coordinate Figureanalysis 6. (PCoA)The beta based diversity on the indices weighted of different and unweighted samples ofUnifracNicotiana distances. tabacum (b()a NMDS) Principal analysis coordinate results analysis (PCoA) based on the weighted and unweighted Unifrac distances. (b) NMDS analysis results analysisbased on (PCoA) operational based taxonomic on the weighted unit (OTU) and unweighted levels. Unifrac distances. (b) NMDS analysis results based on operational taxonomic unit (OTU) levels. based on operational taxonomic unit (OTU) levels.

Figure 7. Boxplots of beta diversity based on the weighted Unifrac distances.

Int. J. Mol. Sci. 2018, 19, 3421 9 of 14

3. Discussion Currently, it has been demonstrated that endophytic fungi play important roles in providing nutrients to their hosts, adapting hosts to their environments, defending hosts from biotic and abiotic stresses, and promoting plant community biodiversity [22], but with limited research focusing on the diverse communities and indispensable functions in tobacco, even though some studies demonstrated that metabolites of tobacco endophytic fungi may play an important role in the growth and biological prevention and control [23]. However, there still lacks a comprehensive study on the fungal communities in tobacco. Therefore, we performed a comprehensive comparison of the fungal communities of tobacco at four growth stages and three altitudes. For the study on the endophytic fungi in tobacco, Pei et al. isolated and analyzed the dynamic population of endophytic fungi in different organs of tobacco and found that the abundance of endophytic fungi in tobacco leaves were the highest and Alternaria and Chaetomium were the dominant genera [24]. In contrast, our study showed that Microidium, Cladosporium, Pseudopithomyces, Irpex, and Lysurus were the top five genera according to their abundances using a high-throughput sequencing technique. This difference may be a result of different methods having different detection capabilities for endophytic fungi. The results of our study imply that these top genera may play critical roles in maintaining the structure and function of fungal communities in tobacco. So, what roles do these dominant flora play in tobacco? Within these genera, Irpex lacteus is a robust fungus that is efficient at degrading various toxic compounds, colonizing soil, inhibiting soil bacteria, etc. These characteristics make it suitable for water and soil bioremediation for organisms [25,26]. Combined with the existing functions, we speculated that I. lacteus may play a similar role in the leaves of tobacco, such as removing the effects of polycyclic aromatic hydrocarbons on the leaves by exerting the activity of manganese peroxidase. Unfortunately, the functions of the other four genera are still unknown and need further exploration. In addition, we also found some endophytic fungi that possess potential ability in resisting abiotic and abiotic stress in tobacco. Previous studies demonstrated that the fermentation products of the endophytic fungus Aspergillus can inhibit the tobacco mosaic virus [27]. Besides, it has been demonstrated that the contents of aldehydes and ketones in the tobacco were doubled by the solid fermentation product from Aspergillus [28]. A total of 10 species belonging to Aspergillus were found in our study. We speculated that their secondary metabolites may play a similar role in tobacco. Moreover, three species of Fusarium were detected, which has been shown to reduce the heavy metal content of plants [29]. A large number of endophytic fungi have been identified in tobacco, but further research and clarification are needed to elucidate their roles and functions in tobacco. Moreover, our results showed that the RGS stage possessed the highest abundance of fungal species. The high richness at the RGS stage may be due to the fact that fungal growth rate will be accelerated after tobacco is transplanted into fields, at which time plant vegetative growth predominates, and there is a high requirement for water and nutrients. Therefore, a diverse group of fungal species can readily form at the RGS stage compared with other stages. Besides, after the seedlings have been transplanted and the fungicidal treatment discontinued, endophytic fungi can colonize in the leaves [3]. In contrast, the lowest abundance of endophytes was detected at the MS stage. In addition, we found higher numbers of unique fungi at the RGS and FGS stages compared with the other two stages. Given that these two stages coincide with a period of rapid plant growth, endophytic fungi may thus be associated with the promotion of growth in tobacco, which would be consistent with the findings of a previous study [23]. The general trend observed in the variation of endophytes in tobacco is that the young tissues or fast growth stages tend to have higher fungal diversity than the mature tissues. By the time of MS, the whole plant has changed from vegetative growth to reproductive growth, and the overall growth trend also stabilized. Correspondingly, the endophytic flora closely related to it also tends to be stable. This phenomenon is further supported by the findings of Houlden et al., who detected a decrease in the diversity of symbiotic fungal species with plant maturity, which has also been found in mycorrhizal associations as well as rhizosphere communities [30]. Our results also showed clear differences in the relative abundances of certain species among the four growth Int. J. Mol. Sci. 2018, 19, 3421 10 of 14 stages examined, suggesting that some endophyte species may preferentially proliferate at a certain stage and play ecological roles that are distinct from those of other endophytes. At the SS and RGS, the diversity of endophytic fungi showed an initial decrease and then increased with an increase in altitude. In contrast, at the FGS and MS stages, the diversity of endophytic fungi in tobacco showed a decreasing trend with increasing altitude, which was paralleled by a decrease trend in alpha diversity. Overall, we identified a trend whereby the diversity of fungal communities tended to decrease with an increase in the altitude at which tobacco was grown. This observation is consistent with the findings of previous studies that altitude can influence the ecosystem by regulating the living environment through climatic factors and soil formation [31,32]. Similarly, previous studies have demonstrated that the physical and chemical properties of soil change with an increase of altitude, and that this is associated with a decrease in microbial community abundance and diversity [33,34]. Our results showed that there were significant differences in the functional diversity of fungal communities at different elevations.

4. Materials and Methods

4.1. Material Collection A total of 36 tobacco leaves were collected from April to August in 2016 from the Modern Tobacco Agricultural Science and Technology Demonstration Garden on Wangcheng Slope, Enshi, Hubei (108◦23012”–110◦38008” E, 29◦07010”–31◦24013” N). These 36 samples were collected from four growth stages, the seedling stage (SS), resettling growth stage (RGS), fast-growing stage (FGS), and maturing stage (MS), grown at three altitudes (600, 1000, and 1300 m). To ensure that the experiment was representative, we randomly selected three plants at each geographic location for each growth phase. For example, we firstly selected three strains randomly in this position when we collected the tobacco leaves of the seedling stages at 600 m. Then, we collected upper, middle, and lower leaves equivalent from one strain and then mixed them for a total weight of 100 mg for the following experiments. The materials from the other growth stages and altitudes were also collected as described above. Meanwhile, in order to remove other microbial interference on the surface of tobacco leaves (epiphytic fungi), the surface sterilization procedure with 70% EtOH for 15 s was conducted. After this, the tobacco leaves were washed with filtered water three times and then wiped off with filter paper before DNA extraction. All samples were immediately put on ice and then stored at −80 ◦C as soon as possible until total DNA extraction. All samples representing different growth stages and altitudes were collected in three replicates. The samples from the seedling stage at 600 m (L), 1000 m (M), and 1300 m (H) were designated as SSL1, SSL2, SSL3, SSM1, SSM2, SSM3, SSH1, SSH2, SSH3. Similarly, samples collected from the other three growth stages were designated as RGSL1, RGSL2, RGSL3, RGSM1, RGSM2, RGSM3, RGSH1, RGSH2, and RGSH3; FGSL1, FGSL2, FGSL3, FGSM1, FGSM2, FGSM3, FGSH1, FGSH2, and FGSH3; and MSL1, MSL2, MSL3, MSM1, MSM2, MSM3, MSH1, MSH2, and MSH3.

4.2. DNA Extraction and PCR Amplification Approximately 100 mg of tobacco leaves were used for each individual DNA extraction. DNA was extracted using the cetyltrimethylammonium bromide (CTAB)method [35]. After extraction, the integrities of DNA were determined using 1% agarose gel electrophoresis. The purity and concentration of DNA were determined using a NanoDrop 2000c spectrophotometer. Each DNA sample was diluted to a final concentRAtion of 1 ng/µL with sterile distilled water and then was used as a DNA template. ITS1 regions of endophytic fungi sequencing were generated by the PCR primers pair CTTGGTCATTTAGAGGAAGTAA and GCTGCGTTCTTCATCGATGC. This amplification was conducted using these primers without the sequencing adapters and sample-specific barcodes. The PCR reactions were carRIed out in 20 µL reaction mixtures, COntaining 5.8 µL sterile distillED water, 10.0 µL 5× Phusion® High-Fidelity PCR Master Mix with GC Buffer (New England Biolabs, Ipswich, Int. J. Mol. Sci. 2018, 19, 3421 11 of 14

Ma, USA), 1.0 µL forwARd primeR (10 µM), 1.0 µL reveRSe primeR (10 µM), 0.2 µL Taq DNA PolymERase, and 2.0 µL template DNA. The PCR amplification conditions were as follows: initial denaturation at 95 ◦C for 2 min, 30 cycles of denaturation at 95 ◦C for 30 s, annealing at 50 ◦C for 20 s, elongation at 72 ◦C for 60 s, elongation at 72 ◦C for 10 min, and then held at 4 ◦C.

4.3. Library Construction and Sequencing Following the above amplification, the PCR amplicons were detected using 2% agarose gel electrophoresis. Subsequently, adapters including sample-specific barcodes were added to these amplicons by PCR amplification. PCR cycling conditions were the same as described above, except that the number of PCR cycles was changed to 20. After that, all the amplicons were then pooled in equimolar ratios into a single tube. The mixed amplicons were further detected using 2% agarose gel electrophoresis and the target sequences were extracted using a QIAquick Gel Extraction Kit (Qiagen, 28704, Munich, Germany). The libraries were constructed using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, 20015963, San Diego, CA, USA). The libraries were quantified using a Qubit® 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA) and qPCR, and then sequenced using the Illumina HiSeq 2500 platform. All sequence data have been submitted to the Sequence Read Archive (SRA: SRP145216) and are freely available at the NCBI (BioProject: PRJNA464258).

4.4. Data Processing To perform an accurate taxonomic assignment for each sequence, quality control and length trimming for the raw reads were needed. Briefly, the qualities of raw reads were trimmed by removing reads shorter than 200 bases and reads containing ambiguous bases. Meanwhile, primers and barcodes were removed. Sequences that passed the pre-processing were used for the following analyses. The remaining reads of each sample were then assembled using FLASH (V1.2.7) (http://ccb.jhu. edu/software/FLASH/)[36] to generate raw tags. QIIME (V1.7.0) (http://qiime.org/scripts/split_ libraries_fastq.html)[19] was used with the following parameters for quality filtering of the tags: (a) tag truncating: raw tags from the continuous low-quality value (the default quality value ≤ 19) with the setting length (the default value is 3) were truncated at the first low-quality base site; (b) tag length filtering: removing the length <75% tags. Chimeras were checked against the Unite database (https://unite.ut.ee/)[37] using the UCHIME Algorithm (http://www.drive5.com/usearch/manual/ uchime_algo.html)[38] and then removed. Finally, the effective tags were obtained.

4.5. OTU Clustering and Annotation Uparse (Uparse v7.0.1001, http://drive5.com/uparse/)[39] was used to cluster all the effective tags. The effective tags with 97% identity were clustered into the same OTUs. The OTUs with the highest frequency were selected as the representative of OTU sequences. We removed OTUs with only one sequence from the dataset since these unique OTUs could result from sequencing errors. For a further determination of their function, these OTUs were annotated using the blast method with QIIME (Version 1.7.0) [19–21] based on the Unite database (https://unite.ut.ee/)[40]. Thereafter, the fungal communities at different taxonomic levels (kingdom, phylum, class, order, family, genus, and species) were counted. The phylogenetic relationships among all the OTUs were initially blasted using MUSCLE (Version 3.8.31, http://www.drive5.com/muscle/)[41], and then we analyzed their phylogenetic relationships using MrBayes 3 [42]. For the comparison of the OTUs and fungal communities at different altitudes and growth stages, we used Venn diagrams (http: //bioinformatics.psb.ugent.be/webtools/Venn/).

4.6. Alpha Diversity The Observed Species, Chao-1, Shannon, Simpson, ACE, Good’s -coverage, and PD_whole_tree diversity indices were determined using QIIME (Version 1.7.0) [19]. Chao and ACE indices were used to determine community richness, and Shannon and Simpson indices were used to determine community Int. J. Mol. Sci. 2018, 19, 3421 12 of 14 diversity. These metrics are useful for estimating microbial diversity and richness. Coverage represents the sequencing depth. The PD_whole_tree index was used to compute Faith’s phylogenetic diversity metric. R software (Version 2.15.3, R Foundation for Statistical Computing, Vienna, Austria) was used to draw the dilution, rank abundance, and species accumulation curves, and to analyze the difference in alpha diversity indices among all groups.

4.7. Beta Diversity Beta diversity is a comparative analysis of microbial community constitutions for all different samples. To determine beta diversity, we initially obtained information on species abundance based on the annotation results of all the species and the abundant information of OTUs. Unifrac distance refers to the relative relatedness of OTUs in a community and was calculated from the sample sequences. Unifrac distance metrics were used in an Unweighted Pair Group Method with Arithmetic Mean (UPGMA) using QIIME software (Version 1.7.0) [19]. PCoA and NMDS were used to show the differences between different samples. Subsequently, the ade4 and ggplot2 packages, WGCNA, stats and ggplot2 packages, and vegan packages of R software (Version 2.15.3) were used to draw principal coordinate analysis (PCoA) and NMDS diagrams, respectively. In order to obtain a better insight into the clustering of fungal communities, both weighted (takes into account changes in relative taxon abundance) and unweighted UniFrac metrics were used for beta diversity [20]. Metastats analysis was performed on different taxonomic levels (phylum, class, order, family, genus, and species), using the permutation test between groups. Statistical analyses were performed in R 2.15.1 (The R Foundation for Statistical Computing, Vienna, Austria).

5. Conclusions In this study, we analyzed and quantified the endophytic fungi of healthy tobacco leaves from SS, RGS, FGS, and MS at three altitudes (600, 1000, and 1300 m) using a high-throughput sequencing technique. Our results indicated that tobacco is host to an abundant community of endophytic fungi, which provides new opportunities for exploring their potential utilization. In future studies, more tissues and environmental factors should be integrated to better characterize the distributions and functions of the endophytic fungal community in tobacco.

Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/19/11/ 3421/s1. Author Contributions: P.Z. and Z.-F.Z. designed experiments; M.C. revised the methodology; X.-L.Y. and J.-H.L. carried out experiments. X.-M.L., M.C., Y.-M.D. and G.-M.S. analyzed experimental results; X.-L.Y. wrote the manuscript. Funding: This work was supported by the Natural Science Foundation of China (grant number 31700295) and from the Science Foundation for Young Scholars of Institute of Tobacco Research of Chinese Academy of Agricultural Sciences (grant number 2016A02). Conflicts of Interest: The authors declare no conflict of interest.

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