Research Article Pyrosequencing Reveals Fungal Communities ...downloads.hindawi.com/journals/bmri/2015/972481.pdf · Research Article Pyrosequencing Reveals Fungal Communities in

Research ArticlePyrosequencing Reveals Fungal Communities inthe Rhizosphere of Xinjiang Jujube

Peng Liu1 Xiao-Hui Wang2 Jian-Gui Li3 Wei Qin3 Cheng-Ze Xiao1 Xu Zhao1

Hong-Xia Jiang2 Jun-Kang Sui2 Rong-Bo Sa2 Wei-Yan Wang1 and Xun-Li Liu1

1College of Forest Shandong Agricultural University No 61 Daizong Street Taian Shandong 271018 China2College of Life Science Shandong Agricultural University Taian Shandong 271018 China3Research Institute of Forest in Xinjiang Agricultural University Urumqi Xinjiang 830052 China

Correspondence should be addressed to Xun-Li Liu xlliusdaueducn

Received 18 September 2014 Revised 26 November 2014 Accepted 29 November 2014

Academic Editor Ernesto Picardi

Copyright copy 2015 Peng Liu et alThis is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Fungi are important soil components as both decomposers and plant symbionts and play a major role in ecological andbiogeochemical processes However little is known about the richness and structure of fungal communities DNA sequencingtechnologies allow for the direct estimation of microbial community diversity avoiding culture-based biases We therefore used454 pyrosequencing to investigate the fungal communities in the rhizosphere of Xinjiang jujube We obtained no less than 40488internal transcribed spacer (ITS) rDNA reads the number of each sample was 6943 6647 6584 6550 6860 and 6904 and we usedbioinformatics and multivariate statistics to analyze the results The index of diversity showed greater richness in the rhizospherefungal community of a 3-year-old jujube than in that of an 8-year-old jujube Most operational taxonomic units belonged toAscomycota and taxonomic analyses identifiedHypocreales as the dominant fungal order Our results demonstrated that the fungalorders are present in different proportions in different sampling areas Redundancy analysis (RDA) revealed a significant correlationbetween soil properties and the abundance of fungal phyla Our results indicated lower fungal diversity in the rhizosphere ofXinjiang jujube than that reported in other studies and we hope our findings provide a reference for future research

1 Introduction

Fungi are important in soils as decomposers and plantsymbionts [1] it is thus important to understand the compo-sition of their complex communities [2] They play essentialroles in many aspects of ecosystem development functionand stability [3] Understanding fungal diversity communitystructure and spatial patterns is one of the central issues insoil microbial ecology [4] Microbial diversity is measuredby a variety of techniques including traditional plate culturephospholipid and fatty acid analysis phyloarrays fingerprint-ing techniques other DNA-based techniques such as restric-tion analysis and Sanger sequencing and denaturing gradientgel electrophoresis of microbial proteins [5 6] Plate cultureis the primary method used to study microbial communitiesbut because of the methodological limitations associatedwith it [7 8] only few studies have addressed the diversityof microbial communities Although the vast majority of

microbial soil species is unculturable the development ofculture-independent methods has yielded important insightsinto the phylogenetic diversity of soil microorganisms [9]High-throughput sequencing technologies now play a sig-nificant role in microbial ecology studies [10] and next-generation techniques such as 454 pyrosequencing facilitatethe detection of unculturable microbial communities [11 12]

Pyrosequencing is faster approximately 20ndash30 times lessexpensive than Sanger sequencing and does not requirecloning [13] The 454 pyrosequencing technique has recentlybeen used to characterize fungal diversity Buee et al [14]used this technique for forest soils to reveal unexpectedlyhigh fungal diversity Lim et al [13] assessed soil fungalcommunities by using pyrosequencing as did Jumpponenand Jones [15] to characterize the fungal communities inQuercus spp Ectomycorrhizas and revealed seasonal dynam-ics in urban and rural sitesThe 454 pyrosequencing platformis a new type of second-generation sequencing technology

Hindawi Publishing CorporationBioMed Research InternationalVolume 2015 Article ID 972481 8 pageshttpdxdoiorg1011552015972481

2 BioMed Research International

it produces 25 million base reads in a single run with anaccuracy of 99 [16] It is rapid flexible and inexpensiveand it does not require a cloning step [17] This technologypermits metagenomic analyses that exceed the capacity oftraditional Sanger sequencing-based approaches by severalorders of magnitude and offers the possibility of detectingvery rare phylotypes [18]

The Xinjiang Uygur Autonomous Region which is thelargest province in China is situated in the hinterland ofAsia and is characterized by very low precipitation andhigh evaporation [19] These unique climatic characteristicsof Xinjiang such as little rainfall with periodic droughtabundant sunshine and substantial differences between dayand night temperatures [20] create conditions in which thecomposition and distribution of soil fungal communitiesmayvary significantly For example Feng-gang et al [21] analyzedthe culturable fungal diversity in the rhizosphere of healthyand diseased cotton in southern Xinjiang Chinese jujube isa common traditional Chinese medicine and food that hasbeen used for thousands of years [22] Jujube is one of themain economic crops in Xinjiang as the quality of Xinjiangjujube is better than that in other regions because of theunique climatic conditions However the fungal communi-ties in the rhizosphere of Xinjiang jujube have never beenstudied previously

In this study we used 454 pyrosequencing to analyze thefungal communities in the jujube rhizosphere in the threeareas of Xinjiang namely the prefectures of Hetian Kashiand Aksu in China The objectives of this study were tocharacterize fungal diversity and community compositionand to compare fungal communities from different areasand growth years We also isolated several fungal jujubepathogens Finally the relationship between fungal phyla andsoil conditions was tested by redundancy analysis (RDA)

2 Materials and Methods

21 Sample Collection andDNAExtraction Soil sampleswerecollected from the rhizosphere of jujube (variety Junzao)across the southern Xinjiang Uygur Autonomous Regionof China (Figure 1) Eighteen samples were collected fromthe prefectures of Kashi (Ka) Hetian (He) and Aksu (Ak)Samples were identified as He-3 He-8 Ka-3 Ka-8 Ak-3 andAk-8 where the numbers 3 and 8 represent the rhizosphereof 3- and 8-year-old jujube plantsThe three areas fromwhichsoil samples were collected were separated by distances ofabout 460ndash770 km Jujube trees aged 3 and 8 years wereselected at each site and three soil samples for each age werecollected by five-point sampling All soil samples were placedin separate sterile plastic bags that were immediately placedon ice and then transported to the laboratory where theywere stored at minus20∘C until DNA extraction The physico-chemical parameters of the soil were measured as followsSoil moisture content was estimated by the oven dry-weightmethod [23] and pH was determined using a glass electrodemeter in a suspension of 1 g of soil in 5mL of distilledwater Available phosphorous (P) was extracted using sodiumbicarbonate and then measured by the molybdenum bluemethod Available potassium (K) was determined by flame

photometry [24] Available nitrogen (N) was determined bypotassium persulfate oxidation [25] Organic matter contentwas determined as described byWalkey and Black [26] Totalgenomic DNA was extracted from soil samples (04 g) byusing the Soil DNA kit (OMEGA Bio-Tek USA) accordingto the manufacturerrsquos instructions The DNA extracted fromthree successive rounds of extraction from each sample waspooled prior to PCR

22 PCR Amplification and 454 Pyrosequencing PCR ampli-fication of the fungal internal transcribed spacer (ITS) rDNAgenes from genomic DNA was performed with barcodedprimersThe bacterial forward primer was ITS1F (CTTGGT-CATTTAGAGGAAGTAA) and the reverse primer was ITS2(GCTGCGTTCTTCATCGATGC) PCR was performed ina 20120583L reaction mixture containing 1 120583L of DNA template(10 ng120583L) 04 120583L of each primer (10 pmol) 015 120583L of ExTaq (TaKaRa) 2 120583L of 10x buffer 16 120583L of dNTPs (25mM)(TaKaRa) and 135 120583L of ddH

2O Cycling conditions were as

follows initial denaturation at 98∘C for 2min followed by35 cycles of 98∘C for 15 s 56∘C for 30 s and 72∘C for 40 swith a final extension at 72∘C for 10min PCR products werepurified using the TaKaRa Agarose Gel DNA Purificationkit and quantified with a NanoDrop A mixture of purifiedITS rDNA amplicons from each soil sample was subjectedto pyrosequencing on the 454 GS FLX titanium platform(Roche Basel Switzerland) at the National Human GenomeCentre of China (Shanghai)

23 Processing of Pyrosequencing Data Pyrosequencing datawere processed using Mothur (version 1251) following theSchloss standard operating procedure [27] Pyrosequencingreads with ambiguous nucleotides sequences lt200 bp inlength one or more primer mismatches or two or morebarcode mismatches were removed and excluded from fur-ther analysis The filtered sequences were then assigned tosoil samples according to the corresponding barcodes with aBespoke Java program Operational taxonomic units (OTUs)at the 3 dissimilarity level were determined by comparingthe sequences to those in the Silva database (httpwwwarb-silvade) using Mothur and the most abundant sequence ineach OTU was selected as the representative Representativesequences were taxonomically classified using a RibosomalDatabase Project naive Bayesian rRNA classifier 22 [28] witha confidence threshold of 08 The relative proportion of agiven phylogenetic group with respect to the entire fungalcommunitywas defined as the number of sequences affiliatedwith that group divided by the total number of sequences persample [29]

24 Bioinformatics and Statistical Analysis An OTU-basedanalysis was performed to calculate species richness diver-sity and coverage of each rhizosphere sample with a cutoffof 3 dissimilarity [29] The R software (version 2142 RDevelopment Core Team 2009) was used to calculate theShannon-Wiener index Simpsonrsquos Diversity index and Even-ness index of each sample [30] Rarefaction curves generatedby Mothur were used to compare relative levels of bacterialOTU diversity across all soil samples [29] The coefficient of

BioMed Research International 3

Hypocreales

Hypocreales

Hypocreales

Leotiomycetesorderincertae sedis

Tremellales

Tremellales

Unknown

Unknown

Others

Others

Others

Mortierellales

PleosporalesCapnodiales

SordarialesChina

Aksu

HetianKashi

45∘N

35∘N

40∘N

75∘E 80

∘E 85∘E

Glomerellales

Glomerellales

Glomerellales

Figure 1 Location of sampling sites of soil samples across the Xinjiang Uygur Autonomous Region of China and relative abundance of thedominant fungal order in 6 soil samples

community structure or distance coefficient was calculatedand used for UPGMA cluster analysis Venn diagrams weregenerated using custom Perl scripts [31] The relationshipbetween bacterial phyla and soil conditions was analyzed byredundancy analysis using R software All chemical data areexpressed as the mean value plusmn SE Analysis of variance wasperformed using SAS 80 to test for sample differences in 120572-diversity and soil properties

3 Results

31 Estimators for Diversity and Species Richness of FungalCommunities For the six sequencing samples exclusion oflow-quality and short sequence reads yielded 40488 fungal

ITS pyrotag reads Each sample averaged 6748 reads thenumber of each sample was 6943 6647 6584 6550 6860and 6904 Using a 3 dissimilarity cut-off for clusteringthereads were grouped into different OTUs (Table 1) Thenumber of fungal OTUs was generally higher in soil samplescollected from the rhizosphere of 3-year-old jujube fromthe same area with the greatest number of OTUs (474) insample He-3and the least (189) in sample Ka-8 Rarefactioncurves for the fungal community at distance levels of 003 hadnot reached an asymptote (Figure 2) Thus the sequencingcapability did not fully represent the number of differentfungal communities but by combining the rarefaction curveswith the Shannon diversity index we found that with theincrease in sequencing number the Shannon diversity curves


Table 1 Diversity and richness index of fungal community from 6 soil samples

Sites Location Sample ID Cutoff OTUs ACE Chao Shannon Coverage

Hetian 37∘071015840 N79∘561015840 E

He-3 003 474 plusmn 14a 10649 plusmn 352a 7511 plusmn 353a 437 plusmn 001a 0969He-8 003 344 plusmn 33c 7786 plusmn 605c 5780 plusmn 465b 380 plusmn 003b 0976

Kashi 38∘111015840N77∘161015840 E

Ka-3 003 314 plusmn 16c 7889 plusmn 303c 5586 plusmn 361b 298 plusmn 004c 0976Ka-8 003 189 plusmn 24d 2874 plusmn 471d 2917 plusmn 688c 238 plusmn 005d 0988

Aksu 80∘111015840N40∘471015840 E

Ak-3 003 418 plusmn 28b 10409 plusmn 518a 7630 plusmn 726a 371 plusmn 002b 0970Ak-8 003 399 plusmn 30b 9274 plusmn 297b 7403 plusmn 349a 360 plusmn 005b 0972

Statistically significant differences (119875 lt 005) between 6 different soil samples from 3 areas Different letters (a b c and d) in column indicate significantdifference (119875 lt 005) between sampling sites according to Duncanrsquos multiple comparison

500

450

400

350

300

250

200

150

100

50

0

0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

OTU

num

ber

He-3He-8Ka-3

Ka-8Ak-3Ak-8

Figure 2 Rarefaction curves of fungal depicting the effect of 3dissimilarity on the number of OTUs identified in the 6 soil samples

(Figure 3) approached a plateauTherefore the data were suf-ficient to allow an analysis of fungal communitiesThe indicesof diversity and richness of fungi in soil samples are shownin Table 1 The ACE and Chao values which are indicatorsof species richness were greater for the rhizosphere fungalcommunity of the 3-year-old jujube than those for the 8-year-old jujube rhizosphere Shannon diversity index valuesshowed a similar trend in fungal diversity

32 Fungal Community Composition and Structure AnalysisAll fungal sequences were classified at the phylum level downto the genus level according to the Mothur program Of theclassifiable sequences four phyla were identified (Figure 4)with Ascomycota representing the most dominant phyla andaccounting for 732 717 989 978 976 and 971of the six soil samples The distribution of Basidiomycotavaried accounting for 203 in sampleHe-8 but only 0058in Ak-3 Some phyla existed in only specific soil samplesChytridiomycota was found only in samples He-3 and Ak-3 Glomeromycota was present only in He-3 and constituted1 OTU In addition the proportion of unclassified fungi wasrelatively large in He-3 (217) and He-8 (86)

0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

45

4

5

35

3

25

2

15

1

05

0

Shan

non

dive

rsity

He-3He-8Ka-3

Ka-8Ak-3Ak-8

Figure 3 Shannon curves of fungal depicting the effect of 3dissimilarity on the number of OTUs identified in the 6 soil samples

Down to the order level the most abundant fungal orderwas Hypocreales accounting for 563 919 and 851of the three sampling areas The diversity of fungal orderwas higher in Hetian than in other areas The Mortierellalesand Tremellales were the next most common orders at131 and 116 The Hetian area also included Glomerel-lales (46) Pleosporales (49) Capnodiales (20) andunknown (22) In the Kashi area Hypocreales accountedfor 919 Glomerellales was 66 and others were 15there were no other fungal orders making the diversityin this area the lowest of the tested regions Hypocreales(851) Tremellales (13) and Glomerellales (32) werefound in Aksu and Hetian Leotiomycetes (13) was foundonly in Aksu Mortierellales was absent from Aksu We alsoidentified jujube pathogens from the fungal sequence readsand found that the fewest pathogens were in the Xinjiangsoil samples (Aspergillus and Schizophyllum) Aspergillus wasfound inHetian andAksu only in 3-year-old jujube treesTheproportions of Aspergillus in He-3 and Ak-3 were 01 and04 respectively


He-3 He-8 Ka-3 Ka-8 Ak-3 Ak-8

100

90

80

70

60

50

40

30

20

10

0

()

AscomycotaBasidiomycotaChytridiomycota

Fungi phylum incertae sedisGlomeromycotaUnclassified fungi phylum

Figure 4 Relative abundance of the dominant fungal phyla in 6 soilsamples

33 Comparison of Fungal Communities between DifferentSampling Areas Fungal OTUs common to the three sam-pling areas were represented using a Venn diagram tocompare the relationships between the three communities(Figure 5) The number of fungal OTUs obtained for eachprefecture was as follows 656 for Hetian 449 for Kashi and653 for Aksu at the level of 3 dissimilarity Hetian and Kashishared 193 OTUs Hetian and Aksu shared 287 and Kashiand Aksu shared 275 All three sites shared 166 OTUs Twoprimary phyla were common to the three areas Ascomycotaand Basidiomycota Chytridiomycota was present in soilsamples from Hetian and Aksu Glomeromycota was presentonly in samples from Hetian Chao and Shannonrsquos indicesindicate that the diversity of fungal communities in Hetianwas higher than in Kashi and Aksu We also identifieddifferences between growth ages in the same sampling areaFor the soil samples from Hetian prefecture the dominantphyla were the same for samples He-3 and He-8 except forChytridiomycota and Glomeromycota which were identifiedonly in the He-3 sample However the dominant phyla hadan unbalanced distribution between the 3- and 8-year-oldsamples Ascomycota was the most abundant phylum in eachsample accounting for 732 in Ka-8 and 711 in He-3 Theproportion of Basidiomycota differed in He-3 (432) andHe-8 (203)

34 Relationships between Fungal Communities and Environ-mental Variables Redundancy analysis revealed a significantcorrelation between soil properties and the abundance offungal phyla (Figure 6) The abundance of phyla in He-3correlated with available K and N and the abundance ofphyla in He-8 correlated with pHThe fungal communities inother soil samples correlated negatively with organic matter

Hetian342

27

121

166

109

Kashi147

Aksu257

Figure 5 Venn diagram representing the number of fungal OTUsthat are unique and shared between the samples from 3 differentsampling areas

He-8

Ka-3Ka-8

Ak-3

Ak-8

Ascomycota

Basidiomycota

Chytridiomycota

Fungi phylum incertae sedis

GlomeromycotaUnclassified fungi phylum

pH

Organic matter

Available NAvailable K

Available P

minus10 15

minus06

10

Moisture

Figure 6 Redundancy analysis (RDA) of abundant fungal phyla andsoil properties for individual samples from 3 sampling areas

moisture and available P We found that soil organic mattermoisture and available P had negative effects on Ascomycotaabundance and positive effects on the fungi incertae sedisThe abundance of Chytridiomycotaand Glomeromycota cor-related with available K and N content Furthermore therelative abundance of Basidiomycota correlated with soilpH Redundancy analysis showed that the sampling siteshad less influence on the fungal communities than soilproperties supporting environmental variation as the majordeterminant of fungal community structure

4 Discussion

The rhizosphere is a critical interface that supports theexchange of resources between plants and their associated soilenvironment [32] Rhizosphere microorganisms can greatlyaffect plant growth by transforming nutrients contributing tosoil organicmatter formation and acting as root pathogens orantagonists of root pathogens In this study we used the 454


pyrosequencing technique to investigate rhizospherefungaldiversity and community structure at different growth stagesof Xinjiang jujube in three major jujube production areas inChina

At present the internal transcribed spacer (ITS) regionsin the 18S 58S 28S ribosomal RNA gene cluster of fungihave been validated as the best DNA barcode marker forfungal species identification and successfully inmetagenomicstudies [33] The primer pair (ITS1F ITS2) in our study isfungal-specific for the ITS1 region and amplifies a fragmentof c400 bp The sequencing yielded 40488 ITS rDNAeach sample averaged 6748 reads with only 189ndash474 OTUsidentified in each soil sampleThe number of reads andOTUswas lower than previous reports for example Buee et al [34]obtained 166350 reads and 600ndash1000 OTUs in each soil sam-ple In this study none of the rarefaction curves reached satu-ration indicating that further sampling would have revealedadditional diversity Our results are in concordance with pre-vious reports [27 35 36] however the Shannon index curvesbecame saturated suggesting that increasing the amount ofsequencing reads would not help identify fundamentally newspecies We thus concluded that our results are an accuratereflection of the fungal communities they represent

Sequence analyses revealed that the majority of recov-ered fungal sequences belonged to the Dikary (AscomycotaBasidiomycota) which were the most abundant phyla in allsamples accounting for 945 Ascomycota was the mostabundant phylum (897 of OTUs) whereasBasidiomycotaaccounted for a much smaller percentage (49) Theseresults were in agreement with those of Schadt et al [37]who also found a large proportion of Ascomycota in 125cloned fungal sequences from soils In contrast OrsquoBrien etal [38] used Sanger sequencing and found the proportionof Basidiomycota to be larger than that of AscomycotaThe Chytridiomycota and Glomeromycota were probablyunderestimated in our data Chytridiomycotawas found insamples He-3 (01) and Ak-3 (003) while Glomeromy-cotawas found in He-3 (005) and Ka-8 (011) In additionthere were large proportions of unclassified fungi in He-3 and He-8 A collection of curated sequences for fungalidentification is urgently needed or perhaps the databaseis inadequate Because there are fewer fungal phyla weanalyzed the fungal community structure down to the orderlevel Hypocreales was the most abundant fungal order incontrast to Taylor [39] who reported that Capnodiales wasthe most abundant fungal order and Hypocrealeswas presentin low proportion The large proportion of unknown fungifound in this and other soil surveys reveals the need fora collection of curated sequences for fungal identificationPathogenic fungi such asAspergillus and Schizophyllumwere-also detected The highest number of sequences (40 reads)matched Aspergillus a known pathogen that causes jujuberot From the RDA we know that the function of the fungalpopulations had links with environmental parameters ForexampleAspergillus belongs to Ascomycota which correlatednegatively with organic matter moisture and available Ptherefore we inferred that increasing the soil organic matteravailable P and irrigation can prevent the jujube disease

Numerous studies have shown that environmental factorsshape community structure [40ndash42] In our study an RDAof the environmental variables indicated that pH availableN available K available P organic matter and moisture wereimportant for the phylogenetic diversity of fungi A differencein organic matter composition and functioning was reportedpreviously [43] It is essential to analyze identical numbersof sequences from all samples because diversity estimatesalways scale up with changes in sampling depth Our studyprovides an outline of the fungal diversity and community ofthe rhizosphere of Xinjiang jujube

5 Conclusions

Theaim of this research was to reveal the fungal communitiesliving in the rhizosphere of Xinjiang jujube 40488 fungal ITSpyrotag reads were yielded by pyrosequencing The resultsrevealed the low fungal diversity in this areas The mostabundant fungal phylum was Ascomycota and the mostabundant order was Hypocreales And the fungal commu-nities were different from other studies By comparison offungal communities between different sampling areas wefound that the diversity of fungal communities in Hetian washigher than in Kashi and Aksu Meanwhile we discussed therelationship between fungal communities and environmentalvariables Furthermore we hope that our results will providea reference for future research on the diversity of fungalcommunities which thrive in the rhizosphere

Conflict of Interests

The authors declare no conflict of interests

Acknowledgments

This work was financially supported by The National SpecialFunds for Nonprofit Research in Forestry Industry (Grantno 201304212 and Grant no 201304212) The authors thankWei Qin Yin-Peng Guo and Shou-Quan Yi from XinjiangAgricultural University for help during sample collection andthe measure of soil characterization The authors also thankZhi-WenWang Tientsin University for revising the paper

References

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[2] MW Taylor P Tsai N AnfangH A Ross andM R GoddardldquoPyrosequencing reveals regional differences in fruit-associatedfungal communitiesrdquoEnvironmentalMicrobiology vol 16 no 9pp 2848ndash2858 2014

[3] S J Morris and G P Robertson ldquoLinking function betweenscales of resolutionrdquo in The Fungal Community J Dighton JF White P Oudemans and J F White Eds pp 13ndash26 MarcelDekker New York NY USA 2005

[4] J L Green B J M Bohannan and R J Whitaker ldquoMicrobialbiogeography from taxonomy to traitsrdquo Science vol 320 no5879 pp 1039ndash1043 2008


[5] J Rousk E Baath P C Brookes C L Lauber C Lozuponeand J G Caporas ldquoPesticide effects on bacterial diversity inagricultural soils-a reviewrdquoMicrobial Ecology vol 33 pp 443ndash453 2001

[6] P Nannipieri J Ascher M T Ceccherini L Landi G Pietra-mellara andG Renella ldquoMicrobial diversity and soil functionsrdquoEuropean Journal of Soil Science vol 54 no 4 pp 655ndash6702003

[7] SMDGoldberg J JohnsonD Busam et al ldquoA sangerpyrose-quencing hybrid approach for the generation of high-qualitydraft assemblies of marine microbial genomesrdquo Proceedings ofthe National Academy of Sciences of the United States of Americavol 103 no 30 pp 11240ndash11245 2006

[8] R Ohtonen H Fritze T Pennanen A Jumpponen and JTrappe ldquoEcosystem properties andmicrobial community chan-ges in primary succession on a glacier forefrontrdquoOecologia vol119 no 2 pp 239ndash246 1999

[9] N Fierer D Nemergut R Knight and J M Craine ldquoChangesthrough time integrating microorganisms into the study ofsuccessionrdquo Research in Microbiology vol 161 no 8 pp 635ndash642 2010

[10] E R Mardis ldquoThe impact of next-generation sequencingtechnology on geneticsrdquo Trends in Genetics vol 24 no 3 pp133ndash141 2008

[11] R Blaalid T Carlsen S Kumar et al ldquoChanges in the root-associated fungal communities along a primary successiongradient analysed by 454 pyrosequencingrdquo Molecular Ecologyvol 21 no 8 pp 1897ndash1908 2012

[12] A Jumpponen andK L Jones ldquoMassively parallel 454 sequenc-ing indicates hyperdiverse fungal communities in temperateQuercus macrocarpa phyllosphererdquo New Phytologist vol 184no 2 pp 438ndash448 2009

[13] Y W Lim B K Kim C Kim et al ldquoAssessment of soil fungalcommunities using pyrosequencingrdquo The Journal of Microbiol-ogy vol 48 no 3 pp 284ndash289 2010

[14] M Buee M Reich C Murat et al ldquo454 pyrosequencinganalyses of forest soils reveal an unexpectedly high fungaldiversityrdquo New Phytologist vol 184 no 2 pp 449ndash456 2009

[15] A Jumpponen and K L Jones ldquoSeasonally dynamic fungalcommunities in the Quercus macrocarpa phyllosphere differbetween urban and nonurban environmentsrdquo New Phytologistvol 186 no 2 pp 496ndash513 2010

[16] M Margulies M Egholm W E Altman et al ldquoGenomesequencing in microfabricated high-density picolitre reactorsrdquoNature vol 437 pp 376ndash380 2005

[17] Y W Lim B K Kim C Kim et al ldquoAssessment of soil fungalcommunities using pyrosequencingrdquo Journal of Microbiologyvol 48 no 3 pp 284ndash289 2010

[18] L Tedersoo R H Nilsson K Abarenkov et al ldquo454 pyrose-quencing and Sanger sequencing of tropical mycorrhizal fungiprovide similar results but reveal substantial methodologicalbiasesrdquo New Phytologist vol 188 no 1 pp 291ndash301 2010

[19] J Wu W Liu H Zeng L Ma and R Bai ldquoWater quantity andquality of six lakes in the Arid Xingjiang Region NW ChinardquoEnvironmental Processes vol 1 no 2 pp 115ndash125 2014

[20] W Zhang X Long X Huo Y Chen and K Lou ldquo16S rRNA-based PCR-DGGE analysis of actinomycete communities infields with continuous cotton cropping in Xinjiang ChinardquoMicrobial Ecology vol 66 no 2 pp 385ndash393 2013

[21] L Feng-gang L Wang L Yan-nan L Yang-yang Z Hai-yan and Z Li-li ldquoAnalysis of culturable fungal diversity in

ehizosphere soil of healthy and diseased cotton in SouthernXinjiangrdquo African Journal of Microbiology Research vol 6 pp7357ndash7364 2012

[22] J-W Li L-P Fan S-DDing andX-L Ding ldquoNutritional com-position of five cultivars of Chinese jujuberdquo FoodChemistry vol103 no 2 pp 454ndash460 2007

[23] C D Moodie H W Smith and R L Hausanbuiler LaboratoryManual for Soil Fertility Washington State University Depart-ment of Agronomy Pullman Washington DC USA 1963

[24] J Zhao R Zhang C Xue et al ldquoPyrosequencing revealscontrasting soil bacterial diversity and community structure oftwo main winter wheat cropping systems in Chinardquo MicrobialEcology vol 67 no 2 pp 443ndash453 2014

[25] M L Jackson Soil Chemical Analysis Prentice Hall EnglewoodCliffs NJ USA 1962

[26] A Walkey and I A Black ldquoAn examination of the Degtjareffmethod for determining soil organic matter and a proposedmodification of the chromic acid titrationmethodrdquo Soil Sciencevol 37 no 1 pp 29ndash38 1934

[27] O O Lee Y Wang J Yang F F Lafi A Al-Suwailem and P-Y Qian ldquoPyrosequencing reveals highly diverse and species-specific microbial communities in sponges from the Red SeardquoThe ISME Journal vol 5 no 4 pp 650ndash664 2011

[28] W Jie L Lite and D Yang ldquoThe correlation between freezingpoint and soluble solids of fruitsrdquo Journal of Food Engineeringvol 60 no 4 pp 481ndash484 2003


[30] M Hu X Wang X Wen and Y Xia ldquoMicrobial communitystructures in different wastewater treatment plants as revealedby 454-pyrosequencing analysisrdquo Bioresource Technology vol117 pp 72ndash79 2012

[31] Y-X Zeng M Yan Y Yu et al ldquoDiversity of bacteria insurface ice of Austre Lovenbreen glacier Svalbardrdquo Archives ofMicrobiology vol 195 no 5 pp 313ndash322 2013

[32] J A Peiffer A Spor O Koren et al ldquoDiversity and heritabilityof the maize rhizosphere microbiome under field conditionsrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 110 no 16 pp 6548ndash6553 2013

[33] K A Seifert ldquoIntegrating DNA barcoding into the mycologicalsciencesrdquo Persoonia vol 21 pp 162ndash166 2008


[35] L FW Roesch R R Fulthorpe A Riva et al ldquoPyrosequencingenumerates and contrasts soil microbial diversityrdquo The ISMEJournal vol 1 no 4 pp 283ndash290 2007

[36] L N Lemos R R Fulthorpe EW Triplett and L FW RoeschldquoRethinkingmicrobial diversity analysis in the high throughputsequencing erardquo Journal of Microbiological Methods vol 86 no1 pp 42ndash51 2011

[37] C W Schadt A P Martin D A Lipson and S K SchmidtldquoSeasonal dynamics of previously unknown fungal lineages intundra soilsrdquo Science vol 301 no 5638 pp 1359ndash1361 2003

[38] H E OrsquoBrien J L Parrent J A Jackson J-MMoncalvo and RVilgalys ldquoFungal community analysis by large-scale sequencingof environmental samplesrdquoApplied and EnvironmentalMicrobi-ology vol 71 no 9 pp 5544ndash5550 2005


[39] A F S Taylor ldquoFungal diversity in ectomycorrhizal communi-ties sampling effort and species detectionrdquo Plant and Soil vol244 no 1-2 pp 19ndash28 2002

[40] J Rousk E Baath P C Brookes et al ldquoSoil bacterial and fungalcommunities across a pH gradient in an arable soilrdquo ISMEJournal vol 4 no 10 pp 1340ndash1351 2010

[41] B F T Brockett C E Prescott and S J Grayston ldquoSoilmoistureis the major factor influencing microbial community structureand enzyme activities across seven biogeoclimatic zones inWestern Canadardquo Soil Biology and Biochemistry vol 44 no 1pp 9ndash20 2012

[42] C L Lauber M S Strickland M A Bradford and N FiererldquoThe influence of soil properties on the structure of bacterialand fungal communities across land-use typesrdquo Soil Biology ampBiochemistry vol 40 no 9 pp 2407ndash2415 2008

[43] J Moukoumi C Munier-Lamy J Berthelin and J RangerldquoEffect of tree species substitution on organicmatter biodegrad-ability and mineral nutrient availability in a temperate topsoilrdquoAnnals of Forest Science vol 63 no 7 pp 763ndash771 2006

Submit your manuscripts athttpwwwhindawicom

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it produces 25 million base reads in a single run with anaccuracy of 99 [16] It is rapid flexible and inexpensiveand it does not require a cloning step [17] This technologypermits metagenomic analyses that exceed the capacity oftraditional Sanger sequencing-based approaches by severalorders of magnitude and offers the possibility of detectingvery rare phylotypes [18]

The Xinjiang Uygur Autonomous Region which is thelargest province in China is situated in the hinterland ofAsia and is characterized by very low precipitation andhigh evaporation [19] These unique climatic characteristicsof Xinjiang such as little rainfall with periodic droughtabundant sunshine and substantial differences between dayand night temperatures [20] create conditions in which thecomposition and distribution of soil fungal communitiesmayvary significantly For example Feng-gang et al [21] analyzedthe culturable fungal diversity in the rhizosphere of healthyand diseased cotton in southern Xinjiang Chinese jujube isa common traditional Chinese medicine and food that hasbeen used for thousands of years [22] Jujube is one of themain economic crops in Xinjiang as the quality of Xinjiangjujube is better than that in other regions because of theunique climatic conditions However the fungal communi-ties in the rhizosphere of Xinjiang jujube have never beenstudied previously

In this study we used 454 pyrosequencing to analyze thefungal communities in the jujube rhizosphere in the threeareas of Xinjiang namely the prefectures of Hetian Kashiand Aksu in China The objectives of this study were tocharacterize fungal diversity and community compositionand to compare fungal communities from different areasand growth years We also isolated several fungal jujubepathogens Finally the relationship between fungal phyla andsoil conditions was tested by redundancy analysis (RDA)

2 Materials and Methods

21 Sample Collection andDNAExtraction Soil sampleswerecollected from the rhizosphere of jujube (variety Junzao)across the southern Xinjiang Uygur Autonomous Regionof China (Figure 1) Eighteen samples were collected fromthe prefectures of Kashi (Ka) Hetian (He) and Aksu (Ak)Samples were identified as He-3 He-8 Ka-3 Ka-8 Ak-3 andAk-8 where the numbers 3 and 8 represent the rhizosphereof 3- and 8-year-old jujube plantsThe three areas fromwhichsoil samples were collected were separated by distances ofabout 460ndash770 km Jujube trees aged 3 and 8 years wereselected at each site and three soil samples for each age werecollected by five-point sampling All soil samples were placedin separate sterile plastic bags that were immediately placedon ice and then transported to the laboratory where theywere stored at minus20∘C until DNA extraction The physico-chemical parameters of the soil were measured as followsSoil moisture content was estimated by the oven dry-weightmethod [23] and pH was determined using a glass electrodemeter in a suspension of 1 g of soil in 5mL of distilledwater Available phosphorous (P) was extracted using sodiumbicarbonate and then measured by the molybdenum bluemethod Available potassium (K) was determined by flame

photometry [24] Available nitrogen (N) was determined bypotassium persulfate oxidation [25] Organic matter contentwas determined as described byWalkey and Black [26] Totalgenomic DNA was extracted from soil samples (04 g) byusing the Soil DNA kit (OMEGA Bio-Tek USA) accordingto the manufacturerrsquos instructions The DNA extracted fromthree successive rounds of extraction from each sample waspooled prior to PCR

22 PCR Amplification and 454 Pyrosequencing PCR ampli-fication of the fungal internal transcribed spacer (ITS) rDNAgenes from genomic DNA was performed with barcodedprimersThe bacterial forward primer was ITS1F (CTTGGT-CATTTAGAGGAAGTAA) and the reverse primer was ITS2(GCTGCGTTCTTCATCGATGC) PCR was performed ina 20120583L reaction mixture containing 1 120583L of DNA template(10 ng120583L) 04 120583L of each primer (10 pmol) 015 120583L of ExTaq (TaKaRa) 2 120583L of 10x buffer 16 120583L of dNTPs (25mM)(TaKaRa) and 135 120583L of ddH

2O Cycling conditions were as

follows initial denaturation at 98∘C for 2min followed by35 cycles of 98∘C for 15 s 56∘C for 30 s and 72∘C for 40 swith a final extension at 72∘C for 10min PCR products werepurified using the TaKaRa Agarose Gel DNA Purificationkit and quantified with a NanoDrop A mixture of purifiedITS rDNA amplicons from each soil sample was subjectedto pyrosequencing on the 454 GS FLX titanium platform(Roche Basel Switzerland) at the National Human GenomeCentre of China (Shanghai)

23 Processing of Pyrosequencing Data Pyrosequencing datawere processed using Mothur (version 1251) following theSchloss standard operating procedure [27] Pyrosequencingreads with ambiguous nucleotides sequences lt200 bp inlength one or more primer mismatches or two or morebarcode mismatches were removed and excluded from fur-ther analysis The filtered sequences were then assigned tosoil samples according to the corresponding barcodes with aBespoke Java program Operational taxonomic units (OTUs)at the 3 dissimilarity level were determined by comparingthe sequences to those in the Silva database (httpwwwarb-silvade) using Mothur and the most abundant sequence ineach OTU was selected as the representative Representativesequences were taxonomically classified using a RibosomalDatabase Project naive Bayesian rRNA classifier 22 [28] witha confidence threshold of 08 The relative proportion of agiven phylogenetic group with respect to the entire fungalcommunitywas defined as the number of sequences affiliatedwith that group divided by the total number of sequences persample [29]

24 Bioinformatics and Statistical Analysis An OTU-basedanalysis was performed to calculate species richness diver-sity and coverage of each rhizosphere sample with a cutoffof 3 dissimilarity [29] The R software (version 2142 RDevelopment Core Team 2009) was used to calculate theShannon-Wiener index Simpsonrsquos Diversity index and Even-ness index of each sample [30] Rarefaction curves generatedby Mothur were used to compare relative levels of bacterialOTU diversity across all soil samples [29] The coefficient of


Hypocreales

Hypocreales

Hypocreales


Tremellales

Tremellales

Unknown

Unknown

Others

Others

Others

Mortierellales


SordarialesChina

Aksu

HetianKashi

45∘N

35∘N

40∘N

75∘E 80

∘E 85∘E

Glomerellales

Glomerellales

Glomerellales



3 Results






Hetian 37∘071015840 N79∘561015840 E


Kashi 38∘111015840N77∘161015840 E


Aksu 80∘111015840N40∘471015840 E



500

450

400

350

300

250

200

150

100

50

0

0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

OTU

num

ber

He-3He-8Ka-3

Ka-8Ak-3Ak-8




0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

45

4

5

35

3

25

2

15

1

05

0

Shan

non

dive

rsity

He-3He-8Ka-3

Ka-8Ak-3Ak-8





100

90

80

70

60

50

40

30

20

10

0

()






Hetian342

27

121

166

109

Kashi147

Aksu257


He-8

Ka-3Ka-8

Ak-3

Ak-8

Ascomycota

Basidiomycota

Chytridiomycota



pH

Organic matter


Available P

minus10 15

minus06

10

Moisture



4 Discussion







5 Conclusions




Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Hypocreales

Hypocreales

Hypocreales


Tremellales

Tremellales

Unknown

Unknown

Others

Others

Others

Mortierellales


SordarialesChina

Aksu

HetianKashi

45∘N

35∘N

40∘N

75∘E 80

∘E 85∘E

Glomerellales

Glomerellales

Glomerellales



3 Results






Hetian 37∘071015840 N79∘561015840 E


Kashi 38∘111015840N77∘161015840 E


Aksu 80∘111015840N40∘471015840 E



500

450

400

350

300

250

200

150

100

50

0

0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

OTU

num

ber

He-3He-8Ka-3

Ka-8Ak-3Ak-8




0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

45

4

5

35

3

25

2

15

1

05

0

Shan

non

dive

rsity

He-3He-8Ka-3

Ka-8Ak-3Ak-8





100

90

80

70

60

50

40

30

20

10

0

()






Hetian342

27

121

166

109

Kashi147

Aksu257


He-8

Ka-3Ka-8

Ak-3

Ak-8

Ascomycota

Basidiomycota

Chytridiomycota



pH

Organic matter


Available P

minus10 15

minus06

10

Moisture



4 Discussion







5 Conclusions




Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology




Hetian 37∘071015840 N79∘561015840 E


Kashi 38∘111015840N77∘161015840 E


Aksu 80∘111015840N40∘471015840 E



500

450

400

350

300

250

200

150

100

50

0

0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

OTU

num

ber

He-3He-8Ka-3

Ka-8Ak-3Ak-8




0 1000 2000 3000 4000 5000 6000 7000 8000

Sequence number

45

4

5

35

3

25

2

15

1

05

0

Shan

non

dive

rsity

He-3He-8Ka-3

Ka-8Ak-3Ak-8





100

90

80

70

60

50

40

30

20

10

0

()






Hetian342

27

121

166

109

Kashi147

Aksu257


He-8

Ka-3Ka-8

Ak-3

Ak-8

Ascomycota

Basidiomycota

Chytridiomycota



pH

Organic matter


Available P

minus10 15

minus06

10

Moisture



4 Discussion







5 Conclusions




Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



100

90

80

70

60

50

40

30

20

10

0

()






Hetian342

27

121

166

109

Kashi147

Aksu257


He-8

Ka-3Ka-8

Ak-3

Ak-8

Ascomycota

Basidiomycota

Chytridiomycota



pH

Organic matter


Available P

minus10 15

minus06

10

Moisture



4 Discussion







5 Conclusions




Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






5 Conclusions




Acknowledgments


References






















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology














Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

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