Livestock Science 188 (2016) 61–71

Contents lists available at ScienceDirect

Livestock Science

http://d1871-14

n CorrE-m

journal homepage: www.elsevier.com/locate/livsci

Analysis of the rumen bacteria and methanogenic archaea of yak (Bosgrunniens) steers grazing on the Qinghai-Tibetan Plateau

Dan Xue a,b,c, Huai Chen a,d,n, Fang Chen b, Yixin He a,c, Chuan Zhao a, Dan Zhu a,Lile Zeng a,b,c, Wei Li a,c

a Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory ofSichuan Province,Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, Chinab College of Life Sciences, Sichuan University, Chengdu 610064, Chinac University of Chinese Academy of Sciences, Beijing 100049, Chinad Zoige Peatland and Global Change Research Station, Chinese Academy of Sciences, Hongyuan 624400, China

a r t i c l e i n f o

Article history:Received 24 September 2015Received in revised form9 April 2016Accepted 12 April 2016

Keywords:YakProkaryotic communitiesHigh-throughput sequencingClone libraryRuminant

x.doi.org/10.1016/j.livsci.2016.04.00913/& 2016 Elsevier B.V. All rights reserved.

esponding author.ail address: chenhuai@cib.ac.cn (H. Chen).

a b s t r a c t

Yak is an important domesticated ruminant on the Qinghai-Tibet Plateau in China. The prokaryoticcommunity of yak remains largely uncharacterized when compared to that of other livestock species.In the present study, high-throughput sequencing of 16S rRNA genes (targeting bacterial and archaeal)and clone library of mcrA gene (targeting methanogenic archaea) were applied to investigate the rumenprokaryotic community structure. High-throughput sequencing results indicated that the rumenprokaryotic community consisted of 29 phyla, 40 classes, 63 orders, 77 families, and 79 genera. Bacter-oidetes (59.1%) was the most abundant phylum, followed by Firmicutes, Proteobacteria, Fibrobacteres andEuryarchaeota. Prevotella was the predominant genus, averaging 28.5% of all rumen prokaryotic genera.Archaea accounted for 2.26% of the total prokaryotic community, with their community dominated byMethanobacteriaceae (82%), followed by Methanomassiliicoccaceae, and Methanosarcinaceae. Comparedwith the clone library of mcrA gene, high-throughput sequencing of 16S rRNA genes yielded a greatercoverage of methanogenic archaea diversity. However, both molecular techniques showed that Metha-nobrevibacter is the predominant archaea of rumen microbiota in yaks grazing natural pastures. Ourresults should facilitate understanding of the complex rumen ecosystem and the main process of ruminalmethanogenesis, which may help to further mitigate CH4 emissions from ruminants.

& 2016 Elsevier B.V. All rights reserved.

1. Introduction

Yak (Bos grunniens) of the genus Bos may have diverged fromcattle at any point between one and five million years ago. It issuggested to be more closely related to bison than to othermembers of its designated genus (Guo et al., 2005). Yak is regardedas one of the world's most remarkable livestock because it adaptsto extreme harsh conditions (coldness, high altitude, strong UVradiation and poor forage resources on the alpine rangelands)while providing milk, meat, fuel and transportation for localherdsmen (Wiener et al., 2003). On the Qinghai-Tibet Plateau inChina of altitudes between 3000 and 5500 m, yak is the mostimportant domesticated ruminant (An et al., 2005). Yaks graze in afull-grazing style with coarse grasses as the only food. After a longperiod of evolution, yak has a series of special mechanisms to

adapt to the harsh living environment, including physiology, nu-trition metabolism and herding behavior.

The rumen as a complex microbial ecosystem consisting of avariety of anaerobic bacteria, methanogens, fungi and ciliate pro-tozoa, plays a critical role in ruminants including yaks. Ruminantsdepend on their rumen microorganisms to digest crude fiber(cellulose, hemicellulose, and lignin), and synthesize microbialprotein as an energy and protein supply for the animal. But thissystem has energy (losses of methane, CH4) and protein (losses ofammonia N) inefficiencies (Yáñez-Ruiz et al., 2010). In the naturalprocess of rumen fermentation, methanogenic archaea reduce CO2

or methylated compounds to CH4, resulting in the loss of 2–15% offeed gross energy and contributing to global greenhouse effects bya powerful greenhouse gas with 25 times global warming poten-tial to carbon dioxide (CO2) (Moss et al., 2000). Compared withagriculture that contributes about 50% of annual global anthro-pogenic CH4 emissions, the enteric CH4 from ruminants alone isestimated to account for 25% to 40% of the anthropogenic release

Dan Xue et al. / Livestock Science 188 (2016) 61–7162

of CH4 (Clark, 2013). Reducing enteric methane emissions is oneway of lowering global methane emissions. So we should find amethod to reduce methane emissions and increase production ornutritional efficiency in ruminants.

The nature of CH4 production in the rumen is a process of CO2

being reduced to CH4 by methanogenic acharea. Volatile fattyacids (VFA) are not commonly used as substrates for methano-genesis, since their conversion into CO2 and H2 is a lengthy pro-cess, which is inhibited by rumen turnover (Harfoot and Hazle-wood, 1997). CH4 emitted from ruminants is mainly generated inthe rumen by hydrogenotrophic methanogens that utilize H2 re-leased from bacteria, protozoa, and fungi, and through interspeciesH2 transfer to reduce CO2 to CH4 (Hegarty and Klieve, 1999; Wrightet al., 2007). The rumen cluster C clade of Thermoplasmata archaea(recently named Methanomassiliicoccales-the seventh order ofmethanogens) is a novel group of methylotrophic methanogens(Borrel et al., 2013). The Methanomassiliicoccales were found to bedecreased by rapeseed oil supplementation in ruminant feed, re-sulting in reduced CH4 emission. Therefore this order of metha-nogens should have a high potential as a target in future strategiesto mitigate methane emissions from ruminant livestock (Poulsenet al., 2013). A study measured the enteric methane emissions ofyaks in the Qinghai-Tibetan Plateau area and showed that theyproduce less methane (per unit of liveweight) compared to otherruminants (Ding et al., 2010). Another study with gnotobioticallyreared lambs revealed that increasing the growth of non-H2 cel-lulolytic microbial can both reduce ruminal methanogenesis andensure the efficiency of fiber digestion (Chaucheyras-Durand et al.,2010). To a better planning of mitigating CH4 emissions from ru-minants, clear understanding of the major components of rumenmicrobial ecosystems and their interactions is of great importance.

Various molecular techniques have been used to investigate themicrobial structure in rumen, including denaturing gradient gelelectrophoresis (DGGE) analysis, restriction fragment lengthpolymorphism (RFLP) and cloning library of 16S rRNA genes,quantitative PCR (qPCR), and high-throughput sequencing. Re-searchers usually used the 16S rRNA gene to reveal rumen bacteriaand methanogenic archaea diversity in yaks (Yang et al., 2010;Huang et al., 2012; Chen et al., 2015). In addition to the 16S rRNAgene, other marker genes such as amino acid sequences of themethyl coenzyme-M reductase α subunit (mcrA) gene have beenused to address the diversity of the methanogenic archaea in ru-men (Tatsuoka et al., 2004; Denman et al., 2007; Ozutsumi et al.,2012). Methyl coenzyme-M reductase is ubiquitous to methano-gens and is crucial to the terminal step of methanogenesis where itis involved in the reduction of the methyl group bound to coen-zyme-M (Denman et al., 2007). Though clone library analyses havesome limitations in completely revealing the microbial composi-tions, development of high-throughput sequencing technologiessuch as MiSeq Sequencing offers the opportunity to obtain moreaccurate compositions of a microbial community (Bao et al., 2011).

Therefore, in this study, clone library of mcrA gene, qPCR of 16SrRNA gene and MiSeq high-throughput sequencing methods wereused to analyze the composition, variation and the correlations ofbacteria and methanogenic archaea in the rumen of three naturalgrazing yaks. This was to the best of our knowledge the first in-vestigation on the diversity of rumen methanogens of yaks usingthe mcrA gene as the phylogenetic marker.

2. Materials and methods

2.1. Animals and sample collection

Rumen samples were collected in September 2014 from threedomesticated yaks (body weight: 26375 kg, Age: 570.6 years) in

Hong yuan County (3600 m a.s.l.; 32°48′3′N,102°33′10′E) of Si-chuan Province, China. The animals grazed on grassland with noconcentrate supplement in the alpine meadow pasture with Ko-bresia pygmaea and Kobresia setchwanensis as the dominant spe-cies. Three yak rumen samples were numbered as NG1, NG2 andNG3. Each rumen sample was collected from different positions ofthe rumen immediately after slaughtering and filtered throughfour layers of sterilized gauze. Approximately 200 ml of rumenfluid was collected from each animal. The ruminal fluid was se-parated into two aliquots; one (approximately 100 ml) was used toimmediately measure ruminal pH using a pH meter (pH-HJ 90;Aerospace Computer Company, Beijing, China), the pH were 7.31,6.96 and 7.08 respectively in NG1, NG2, NG3. While the other(approximately 100 ml) was immediately transferred into ster-ilized tubes and stored in liquid nitrogen for later DNA extractionand later analysis of ammonia nitrogen (NH3-N) which was mea-sured by the Nesslerization colorimetric method (Zhang et al.,2011), the ammonia were 13.89 mg/100 ml, 9.05 mg/100 ml and13.45 mg/100 ml respectively in NG1, NG2, NG3.

2.2. DNA extraction and quantification

Rumen samples were thoroughly homogenized before DNAextraction. The DNA was extracted using the Omega E. Z. N. A TMSoil DNA Kit (Omega Bio-tek, USA) according to the manufacturer'sinstructions. For each sample, 1 ml rumen fluid was centrifuged at12,000 g for 10 min at 4 °C; after discarding of the supernatantliquid, the sediment was added with 0.5 g glass beads, then sus-pended in 1 ml of TE SLX-Mlus buffer (Omega E. Z. N. A TM SoilDNA Kit) for later physical disruption using a bead beater (Mini-bead Beater, Bio Spec Products, Bartlesville, OK). The microbialDNA was extracted twice from each sample, with the duplicatesbeing combined and used as templates for qPCR, clone library andpyrosequencing analyses. The DNA concentration and quality werechecked using NanoDrop 2000 (Thermo Scientific, Wilmington,USA).

2.3. MiSeq sequencing and data processing

For 16S rRNA gene amplicon sequencing, the primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 909R (5′-CCCCGYCAATTCMTT-TRAGT-3′) (targeting bacterial and archaeal) with a 10-mer bar-code at the 5′ end of primer 515F were used to amplify V4-V5region of the 16S rRNA gene using Miseq sequencer (Caporasoet al., 2011; Caporaso et al., 2012). The PCR mixture (25 μl) con-tained 1x PCR buffer, 1.5 mM MgCl2, 0.4 μM of each deox-ynucleoside, 1.0 μM of each primer and 0.5 U of Ex Taq (TaKaRa,Dalian, China) and 10 ng genomic DNA. The PCR amplificationprogram included initial denaturation at 94 °C for 3 min, followedby 30 cycles of 94 °C for 40 s, 56 °C for 60 s, and 72 °C for 60 s, anda final extension at 72 °C for 10 min. For each sample two PCRreactions were conducted for combined results. The PCR productswere subjected to electrophoresis using 1.0% agarose gel. The bandwith a correct size was excised, purified with Omega Cycle-PureKit (Omega Bio-tek, USA) and DNA concentration was quantifiedwith Nanodrop. All samples were pooled together with equalmolar amount from each. The sequencing samples were preparedusing TruSeq DNA kit (Illumina,San Diego, USA) according to themanufacture's instruction. The purified library was diluted, dena-tured, re-diluted, mixed with PhiX (equal to 30% of final DNAamount) as described in the Illumina library preparation protocols,and then applied to an Illumina Miseq system for sequencing withthe Reagent Kit v2 2�250 bp (Illumina, San Diego, USA) as de-scribed in the manufacture manual.

The sequence data were analyzed using QIIME Pipeline–Version1.7.0 (http://qiime.org/). All sequence reads were trimmed and

Dan Xue et al. / Livestock Science 188 (2016) 61–71 63

assigned to each sample based on their barcodes. The sequenceswith high quality (length 4150 bp, without ambiguous base ‘N’,and average base quality score 430) were used for downstreamanalysis. Sequences were clustered into operational taxonomicunits (OTUs) at a 97% identity threshold. The aligned 16S rRNAgene sequences were used for chimera check using the Uchimealgorithm (Edgar et al., 2011). We calculated alpha-diversity(chao1 estimator of richness, observed species and Shannon's di-versity index) and beta-diversity (UniFrac) analyses (Caporasoet al., 2010), for which the rarefaction curves were generated fromthe observed species. Taxonomy was assigned using the RibosomalDatabase Project classifier (Wang et al., 2007).

2.4. McrA gene clone library and RFLP analysis

The mcrA gene was used as molecular marker to study me-thanogen communities in the rumen of yak. The primer pair ME1(5′-GCMATGCARATHGGWATGTC-3′) and ME2 (5′-TCATKGCR-TAGTTDGGRTAGT-3′) (Hales et al., 1996) were used in PCR ampli-fication of 750 bp of the mcrA gene. The PCR condition programwas 94 °C for 5 min, followed by 30 cycles consisting of 94 °C for45 s, 50 °C for 45 s,72 °C for 2 min, and a final extension period of72 °C for 7 min. PCR products were analyzed on 1% agarose gelswith Goldview staining. The PCR products from each rumensample were purified with Omega Cycle-Pure Kit (Omega Bio-tek,USA), and cloned into pGM-T vector plasmid according to themanufacturer's instructions (pGM-T Clone Kit, TIANGEN, Beijing,China). For each library, 140 white colonies were picked. Restric-tion enzymes TaqI and MspI/HpaII (ThermoScientific,Shanghai,China) were used to digest colony PCR products for RFLP. The co-lonies resulting in the same restriction map were considered to beidentical OTUs. At least two positive colonies from each OTU werechosen for sequence analysis. Then DNA sequences of multipleclones from each library were assigned to individual OTUs basedon a sequence similarity of at least 95% in homology tree analysisin DNAMAN (version 6.0.3.99, Lynnon Biosoft, San Ramon, USA).Phylogenetic analysis based on mcrA (amino acid) sequences wasperformed using MEGA (version 6.0), and a neighbor-joining treeof all the representative OTU sequences and reference sequencesobtained from GenBank was constructed using p-distance with1000 replicates to produce bootstrap values. At last, the mcrAnucleotide sequences will be submitted in Genbank by Bankit. Theaccession numbers were KT428115-KT428128.

Fig. 1. Relative proportion of the rumen prokaryotic community in the rumen ofthree yaks at phylum level.

2.5. Real-time PCR

Quantification of the total methanogenic archaea were mea-sured by qPCR using Chromo 4 (Bio-Rad, America) and SYBR GreenReal Master Mix (Tiangen, China). The 16S rRNA gene-targetedprimer sets used in this study for methanogenic archaea were theprimer pair 1106F and 1378R (Watanabe et al., 2007). The reactionsolution (25 μl) contained 11.5 μl 1� SYBR Premix, 0.8 μl of eachprimer, 1 μl of DNA template and sterilize distilled water. The PCRprogram included a primary denaturation step of 10 min at 95 °C,followed by 40 cycles of 95 °C for 10 s, 57 °C for 10 s, 72 °C for 6 sFluorescence readings were taken after each extension step, and afinal melting analysis was obtained at a rate of 0.5 °C/0.5 s from 55to 95 °C. All standard reactions and samples were carried out intriplicate. The standard curve was generated using ten-fold dilu-tion series of the linearized plasmid containing the fragment of16 S rRNA (1106F and 1378R).The data were presented as theaverage copy number of the gene targeted per milliliter of rumenfluid.

2.6. Statistical analysis

The coverage of clone libraries (C) was calculated using equa-tion: C¼[1-(n/N)]�100, where n is the number of phylotypes in asample represented by one clone (singletons) and N is the totalnumber of clones examined (Good, 1953). The Shannon–Weaverdiversity index (H’) was calculated as: ′ = − ∑ ( )=H p plni

si i1 , where pi

is the proportion of clones belonging to each OTU, and s is the totalnumber of OTUs (Shannon and Weaver, 1963). Pearson's correla-tion analysis in SPSS 21.0 software was used to examine correla-tions among dominant bacteria and methanogens. All graphs weremade by Origin 8.1 software. Differences were considered sig-nificant when Po0.05.

3. Results

3.1. Sequencing results

A total of 20,676 high quality sequences were obtained (rangingfrom 3037 to 9071 per sample); all sequences were aligned andclustered to calculate OTUs using 97% sequence identity as a cutoff,resulting in 785–1121 OTUs at a sequencing depth of 3037 readsper sample (Table S1). The average number of OTUs was 1001, themicrobial average alpha diversity indices based on Chao1 richnesswas 1626.51, and Shannon's diversity was 8.79 (Table S1).

3.2. Prokaryotic community structure

The taxonomy-based analysis of three samples showed that therumen prokaryotic community of yak consisted of 29 phyla, 40classes, 63 orders, 77 families, and 79 genera. The most abundantphylum was Bacteroidetes (average relative abundance of 59.1%),followed by Firmicutes (25.81%), Proteobacteria (3.22%), Fi-brobacteres (2.28%), Euryarchaeota (2.27%), Lentisphaerae (1.24%)and Tenericutes (1.24%) (Fig. 1). The phyla Bacteroidetes and Fir-micutes dominated the prokaryotic community of yak rumen, ac-counting for 84.87% of the total reads.

For the two domains of prokaryotes (Sapp, 2005; Fuerst, 2010),we found a total of 77 families including 71 of bacteria and 6 ofarchaea. The most abundant bacterial families of the total pro-karyotic community were Prevotellaceae (30.67%), Planococcaceae(10.89%), Bacteroidaceae (5.08%), BS11 (4.48%), Ruminococcaceae(4.48%), and Paraprevotellaceae (3.28%) (Fig. 2A). Archaea ac-counted for only 2.27% of the total prokaryotic community, with82.5% of archaea community as Methanobacteriaceae, followed byMethanomassiliicoccaceae (13%), Methanosarcinaceae (3%), andMethanomicrobiales (1.5%) (Fig. 2B).

A total of 42 genera were shared by the three yaks, including 38

Fig. 2. Relative proportion of bacteria (A) and archaea (B) communities in the ru-men of three yaks at family level.

Fig. 3. Heat map analysis of 42 shared prokaryotic community genera of all yaks, as deSequencing Platform.

Dan Xue et al. / Livestock Science 188 (2016) 61–7164

bacterial and 5 archaeal. Most of them belonged to Bacteroidetes,Firmicutes and Proteobacteria. The Prevotella was the predominantcommon genus across all samples, accounting for an average 28.5%of all rumen prokaryotic genera, followed by Solibacillus (8.71%),BF311 (4.84%), Fibrobacter (2.28%), Ruminococcus (2.16%) and Lac-tobacillus (1.95%). These shared genera exhibited high variability inabundance across the individual samples (Fig. 3).

The data showed great variances among individual animals inprokaryotic community composition at the phylum, family andgenus levels.

3.3. Phylogenetic analysis of mcrA gene

A total of 355 positive clones containing the correct size ofmcrA gene fragment were obtained. All together 32 OTUs wererecovered through RFLP analysis, including 15, 20, and 13 OTUsfrom NG1, NG2, and NG3, respectively. After excluding chimerasand invalid sequences, we obtained 110 valid sequences. Thesesequences were grouped into 14 OTUs based on 95% threshold ofmcrA sequence identity (Tymensen et al., 2012). The 14 re-presentative OTUs, 36 reference sequences downloaded fromGenBank are included in phylogenetic tree (Fig. 4). Among the 14OTUs, 12 were relatively close to the mcrA gene of Methano-brevibacter, a group of methanogens that use H2/CO2 for growthand methanogenesis (Liu and Whitman, 2008). There was only1 OTU distribution in the order of Methanosarcinales and

termined by the relative abundance of each shared genera through MiSeq Illumina

Fig. 4. Phylogenetic analysis of representative mcrA gene inferred amino acid sequences from three natural grazing yaks using MEGA 6.0. GenBank accession number areindicated in parentheses and bootstrap values (450%) from 1000 replications are indicated on the tree. The scale bar corresponds to 5 changes per 100 positions.

Dan Xue et al. / Livestock Science 188 (2016) 61–71 65

Methanomicrobiales, respectively. With 96% identity, OTU12 wasthe most closely related to Methanomicrobium mobile (AF4140044)mrcA gene. Such result showed that Methanobrevibacter is thepredominant archaea of rumen microbiota in the natural grazingyaks. Examination of these 14 OTUs revealed 6 OTUs unique toNG2 and 2 OTUs to NG3 (Fig. S1); 3 OTUs (21.4%, OTU1, OTU2,OTU9) of Methanobacteriales were found to be common in all threeyaks. The Shannon-Weaver diversity index was 1.18, 1.84 and 1.63

in NG1, NG2, NG3, respectively.

3.4. Methanogenic archaea abundance

The copy numbers of methanogenic 16S rRNA genes in thethree rumen samples were determined using qPCR. The density ofrumen methanogens was 1.79�107, 1.43�107 and 2.25�107 co-pies per milliliter of rumen liquid in the NG1, NG2, NG3,

Fig. 5. Copy number of methanogenic 16S rRNA gene per milliliter of rumen liquidand the proportion of each OTU (Operational Taxonomic Unit) in three naturalgrazing yaks.

Dan Xue et al. / Livestock Science 188 (2016) 61–7166

respectively (Fig. 5). The 16S rRNA gene copies of each OTU wereobtained based on the proportion in clone library. For the 3 sharedOTUs (OTU1, OTU2 and OTU9), we found OTU9 the most abundant(average relative abundance of 46.32%) closely related to Metha-nobrevibacter ruminantium, followed by OTU1 (22.08%) closelyrelated to Methanobrevibacter sp. SM9, and OTU2 (14.71%) closelyrelated to Methanobrevibacter sp. D5.

4. Discussion

4.1. Dominant bacteria and methanogens in different rumenecosystems

The summary of research results of the diversity of bacteria andmethanogens in different rumen ecosystems (Table, 1) shows thatthe dominant bacteria and methanogens were different for rumenof the same kind of animal in different areas of China or underdifferent feeding regimes. This study found that Methanobacter-iales was the predominant archaea of rumen microbiota in thenatural grazing yaks in Sichuan; Huang et al. (2012) found thatMethanomassiliicoccales was the dominant methanogens in do-mesticated yaks in Gansu. Lin et al. (2015) found no obvious cor-relation between archaeal and bacterial community profiles andBuffalo diet. But Zhou et al. (2009) indicated that methanogencommunities differed between cattle herds of different feed effi-ciency, which may be an important factor causing their differencein CH4 production. Henderson et al. (2015) also found that feed isone of the most important factors determining the rumen micro-bial composition. Feed quality and the feed efficiency of cattle,very reasonably, differ with the geographic environment of thecattle. The difference in dominant methanogens of yak rumenfrom different areas indicates the necessity to research the pro-karyotic communities in the rumen of yaks in different areas bythe same molecular technique and primers.

4.2. Prokaryotic community of yak rumen

In the present study, the microbial community of the yak ru-men was predominated by phyla Bacteroidetes (59.06%) and Fir-micutes (25.81%); such microbial distribution of the major phylawas similar to the rumen bacterial community structure of yaks inSichuan (Chen et al., 2015) and other ruminants from the genusBos (Jami and Mizrahi, 2012; Lee et al., 2012; Wu et al., 2012;Zened et al., 2013; Kim et al., 2014; Lin et al., 2015). However, someother studies had different reports. For instance, Guo et al. (2015)found Firmicutes and Bacteroidetes accounted for 45.90% and39.68% respectively of total OTUs in the rumen of yaks. Obviously,the percentage of Bacteroidetes was less than Firmicutes. Jami et al.(2013) found Bacteroidetes more abundant than Firmicutes in older

bovines with diet composed mainly of plant fiber, but less abun-dant in newborns, and that the phylum Bacteroidetes was mainlycomposed of the genus Bacteroides. We speculated that the dom-inance of Bacteroidetes or Firmicutes could be attributable to thevariations in diet, species, seasonal and geographical environment(Armougom and Raoult, 2008; Mariat et al., 2009; Amato Ka-therine, 2013). Several studies suggested that obesity was relatedto an increase in the ratio of Firmicutes to Bacteroidetes in humansand other mammals (Ley et al., 2006; Turnbaugh et al., 2008;Pedersen et al., 2013). The sequencing results of our research re-vealed the existence of a number of uncultured or unclassifiedspecies at the family level in the yak rumen. We identified theunique families of BS11, RF16 and S24-7 belonging to the phylumBacteroidetes. Chen et al. (2015) speculated these bacteria maypossess important and yet unrecognized ecological functions andoccupy a special ecological niche in the rumen. However, themetabolic mechanism of these unclassified species is not yet clear.

All together 42 highly diverse genera were found to be sharedby all yaks, accounting for as low as 0.03% for some of the generaand up to 28.5% for others (Fig. 5). As we all know, the degradationof cellulose and starch is an important process of rumen fermen-tation, in which bacteria community plays a key role. This researchfound in natural grazing yaks a very large number of cellulolyticbacteria including Ruminococcus, Fibrobacter, Clostridium, Butyr-ivibrio, and Treponema (Yang et al., 2010; Leng et al., 2011). Thefinal metabolites by cellulolytic bacteria are mainly hydrogen,acetic acid and formic acid, as well as a small amount of ethanoland lactic acid (Krumholz and Bryant, 1986). Similar to the result ofGrilli et al. (2013) that Pseudobutyrivibrioruminis and Pseudobu-tyrivibrio xylanivorans were the main plant cellulolytic bacteriaspecies in rumen of Creole goats fed native forage diet, this studyalso found Pseudobutyrivibrio in the rumen of natural grazing yaks,suggesting that Pseudobutyrivibrio may be an important cellulo-lytic bacteria in the yak rumen ecology system. We also foundPrevotella the most abundant bacterial genus, accounting for anaverage of 28.5% in the rumen of yaks, less than the average 50% ofall reads in the rumen of cows (Jami and Mizrahi, 2012). The genusPrevotella includes a group of bacteria with great genetic diver-gence and functional versatility (Ramšak et al., 2000), which mayplay a major role in initial dietary protein breakdown and efficientutilization of hemicelluloses (Osborne and Dehority, 1989).Therefore the difference in abundance of Prevotella between yakand cow was likely a result of difference in their diets.

In this study, we found Lactobacillus quantity negatively cor-related with not only Methanobrevibacter, Methanocorpusculum,but also Butyrivibrio. However, the number of Butyrivibrio waspositively associated with the number of Methanobrevibacter (Ta-ble S2). Butyrate is generated through butyrivibrio degradation ofcellulose, and then it ferments to acetate and hydrogen. But de-gradation of butyrate is feasible only at a low H2 partial pressure,which can be maintained by hydrogenotrophic methanogens(Mcinerney et al., 1981; Schink, 1997). At the same time, we alsofound Syntrophomona existing in the rumen procaryote micro-biology system. The pathway of butyrate oxidation in syntrophicbutyrate-oxidizing bacteria is tentatively elucidated with Syn-trophomonas wolfei (Sobieraj and Boone, 2006). Therefore, ourresult could indirectly prove that hydrogenotrophic methanogenswere the major metabolic pathways of methane emissions in theyak rumen. There was a significant positive correlation betweenthe hydrogen producing Thermodesulfovibrio and Methanosarcina(Table S2), indicating that Methanosarcina used the hydrogen re-duction of carbon dioxide to generate methane, which indirectlyproved that hydrogen is the major substrate for methanogenesis(Hook et al., 2010).

4.3. Comparison of results of the diversity of rumen methano-gens by MiSeq sequencing of 16S rRNA and RFLP clone library of

Table 1Dominant bacteria and methanogens in different rumen ecosystems.

Animals Feeding Classification Location Markergenes

Primer sequences (5′to 3′) Moleculartechniques

OTUs Dominant species/genera/order/phylum References

Yak Grazing: alpinemeadow pasturewith Kobresiapygmaea and Ko-bresia setchwa-nensis as thedominant species.

Methanogens Qinghai-Tibetanplateau(Sichuan)

16SrRNA

515 F (GTGCCAGCMGCCGCGGTAA) 909 R(CCCCGYCAATTCMTTTRAGT)

MiSeqsequencing

32 Methanobacteriales (82.5%), Methano-massiliicoccales (13%), Methanosarcina-ceae (3%), Methanomicrobiales (1.5%).

This study

mcrA ME1 (GCMATGCARATHGGWATGTC) ME2(TCATKGCRTAGTTDGGRTAGT)

Clone library 14 Methanobacteriales (98.4%), Methano-sarcinales (0.8%), Methanomicrobiales(0.8%).

This study

Yak Grazing: Kobresiapasture

Qinghai-Tibetanplateau(Gansu)

16SrRNA

Met86F (GCTCAGTAACACGTGG) Met1340R(CGGTGTGTGCAAGGAG)

Clone library 61 Methanomassiliicoccales (80.9%), Me-thanobacteriales (12.4%), Methanomi-crobiales (0.96%).

(Huanget al., 2012)

Cattle 49 Methanomassiliicoccales (62.9%), Me-thanobacteriales (21.5%), Methanomi-crobiales (9.8%).

Cattle (Control ) Grazing on longchopped Rhodesgrass, 100 g cottonseed meal was ad-ded twice daily,

Australia mcrA M13F (GGTGGTGTMGGATTCACACARTAYGCWA-CAGC) M13R (TTCATTGCRTAGTTWGGRTAGTT)

Clone library 24 Methanobacteriales (74%), Methano-coccales (26%).

(Denmanet al., 2007)

Cattle (Bromochloromethane) 29 Methanobacteriales (56%), Methano-microbiales, Methanosacinales,Methanococcales.

Cattle (faunated) Diet: 66% (drymatter) choppedSudangrass hayand 34% con-centrate mixture,twice daily.

Tokyo,Japan

mcrA ME1 (GCMATGCARATHGGWATGTC) ME2(TCATKGCRTAGTTDGGRTAGT)

Clone library 18 Methanobacteriales (100%). (Ozutsumiet al., 2012)Cattle (unfaunated) 13 Methanobacteriales (100%).

Cattle (faunated) 16SrRNA

ME855F (TTAAAGGAATTGGCGGGGGA) ME1354R(TGACGGGCGGTGTGTGCAAG)

4 Methanobacteriales (100%).Cattle (unfaunated) 7 Methanobacteriales (86%), Methano-

massiliicoccales (14%).

Cattle (L-RFI) Diet: 74% oats, 20%hay, and 6% feedlotsupplement (32%crude protein beefsupplement con-taining Rumensin[400 mg/kg of bw]and 1.5% canolaoil)

Canada 16SrRNA

Met86f (GCTCAGTAACACGTGG) Met915r(GTGCTCCCCCGCCAATTCCT)

Clone library 22 M. ruminantium (89.2%), M. thaueri, M.smithii (1.0%), M. wolinii, and Methano-sphaera stadtmanae (1.7%).

(Zhou et al.,2009)

Cattle (H-RFI) 27 M. ruminantium (73.0%), M. thaueri, M.smithii (10.8%), M. wolinii, and Me-thanosphaera stadtmanae (5.7%).

Cow Diet: dry mattercontent was 35%corn silage, 33%second-cut hay-lage, 72% hay,13.2% canola meal,and 19.8% soybeanmeal.

USA 16SrRNA

Met86F (GCTCAGTAACACGTGG) Met1340R(CGGTGTGTGCAAGGAG)

Clone library 55 Methanobacteriales (94%), Methano-massiliicoccales (3%).

(King et al.,2011)

Cow Diet:a mixture(2:1) of hay andconcentrate twicea day.

Tokyo,Japan

mcrA ME1 (GCMATGCARATHGGWATGTC) ME2(TCATKGCRTAGTTDGGRTAGT)

Clone library 25 Methanobrevibacter ruminantium(100%).

(Tatsuokaet al., 2004)

Buffalo Diet: 45% corn si-lage and 55%concentrate.

Brazil 16SrRNA

Met86F (GCTCAGTAACACGTGG) Met1340R(CGGTGTGTGCAAGGAG)

Clone library 19 Methanobrevibacter (91.4%),Methanomassiliicoccales.

(Franzolinet al.,2012)

Dan

Xue

etal./

LivestockScience

188(2016)

61–71

67

Table 1 (continued )

Animals Feeding Classification Location Markergenes

Primer sequences (5′to 3′) Moleculartechniques

OTUs Dominant species/genera/order/phylum References

Buffalo (HC) 5 kg dry matter(DM)/head/day

Guangxi,China

16SrRNA

Ar915aF (AGGAATTGGCGGGGGAGCAC) Ar1386R(GCGGTGTGTGCAAGGAGC)

454 pyr-osequencing

ND Methanobrevibacter gottschalkii (70.2%),Methanomassiliicoccales (15.6%), Metha-nobrevibacter ruminantium (6.61%), Me-thanosphaera spp. (2.31%).

(Lin et al.,2015)

Buffalo (LC) 3 kg dry matter(DM)/head/day

16SrRNA

Methanobrevibacter gottschalkii(61.2%), Methanomassiliicoccales(27.1%), Methano-brevibacter3ruminantium (8.43%), Me-thanosphaera spp. (2.08%).

Buffalo (Murrah) diet: Concentrate:roughage ¼50: 50,twice daily.

India mcrA M13F (GGTGGTGTMGGATTCACACARTAYGCWA-CAGC) M13R (TTCATTGCRTAGTTWGGRTAGTT)

Clone library 26 Uncultured group of methanogens(61.5%), Methanomicrobium mobile(26.9%), Methanobrevibacter gottschalkii(11.5%).

(Chaudharyet al., 2011)

Yak Grazing: alpinemeadow pasture.

Bacteria Qinghai-Tibetanplateau(Sichuan)

16SrRNA

515 F (GTGCCAGCMGCCGCGGTAA) 909 R(CCCCGYCAATTCMTTTRAGT)

MiSeqsequencing

1811 Prevotella (28.5%), Solibacillus (8.71%),BF311 (4.84%), Fibrobacter (2.28%), Ru-minococcus (2.16%) and Lactobacillus(1.95%).

This study

Yak ND 16SrRNA

338 F (ACTCCTACGGGAGGCAGCA) 806 R(GGACTACHVGGGTWTCTAAT)

MiSeqsequencing

817 Prevotella (15%), Butyrivibrio, Fibrobacter(2.5%), RC9 (13.12%), BS11 (10.10%), Ru-minococcus (0.4%).

(Chen et al.,2015)

Yak Diet: pelleted lu-cerne (Medicagosativum)

Yunnan,China

16SrRNA

F27 (AGAGTTTGATCMTGGCTCAG) R1492(TAGGYTACCTTGTTACGACT)

Clone library 74 Low GþC (63.8%), Cytophaga-Flex-ibacter-Bacteroides phylum (35.4%) andProteobacteria (0.8%).

(Yang et al.,2010)

Buffalo (HC) 5 kg dry matter(DM)/head/day

Guangxi,China

16SrRNA

Ba9F (GAGTTTGATCMTGGCTCAG) Ba515Rmod(CCGCGGCKGCTGGCAC)

454 pyr-osequencing

ND Prevotella (36.9%), Plaudibacter (4.84%),Fibrobacter (6.92%), Ruminococcus(2.2%).

(Lin et al.,2015)

Buffalo (LC) 3 kg dry matter(DM)/head/day

Prevotella (50.8%), Plaudibacter(6.46%), Fibrobacter (4.9%), Rumino-coccus (1.5%).

HC, high concentrate diet; LC, lowconcentrate diet; L-RFI, low residual feed intake; H-RFI, high residual feed intake; ND, no data available.

Dan

Xue

etal./

LivestockScience

188(2016)

61–71

68

Dan Xue et al. / Livestock Science 188 (2016) 61–71 69

mcrA.The average Shannon-Weaver Index of bacteria and archaea by

16S rRNA was 5.98 and 2.82 respectively, and the Index of archaeafrom mcrA gene was 1.55. The diversity of rumen methanogensfrom high-throughput sequencing of 16S rRNA was higher thanRFLP clone library of mcrA. Snelling et al. (2014) found the averageShannon index of methanogenic archaeal from Scottish UplandSheep as 2.4 by Illumina Metagenome 16S rRNA and as 2.0 bymcrA. The average Shannon index of methanogens in the rumen ofcattle was found by 16S rRNA gene clone library as 1.45 (Ozutsumiet al., 2012). Chen et al. (2015) found by the pyrosequencing of 16SrRNA an average Shannon index of yak rumen bacteria commu-nities as 5.37. The comparison showed that our study presented ahigher diversity of both methanogens and bacteria than results ofother researches.

Methanomassiliicoccales is a novel group of archaea distantlyrelated to Thermoplasmatales (Horz et al., 2012). They are found tobe abundantly present in ruminants by studies based on 16S rRNAgenes and mcrA gene (Wright et al., 2004; Janssen and Kirs, 2008;Poulsen et al., 2013; Jin et al., 2014). The mcrA gene codes a-sub-unit of methyl-coenzyme M reductase (MCR) that plays an im-portant part in methanogenesis (Thauer and Shima, 2006). Thepresent study used both MiSeq sequencing of 16S rRNA and clonelibrary of mcrA to analyze the diversity of methanogens. Accordingto our high-throughput sequencing analyses of archaeal commu-nities, Methanobacteriales (82.13%) was the dominant order amongthe archaea, followed by Methanomassiliicoccales (13.04%), Me-thanosarcinales (2.9%), and Methanomicrobiales (1.45%). Suchcommunity composition was similar to that summarized by thereview of Janssen and Kirs (2008), although findings of individualstudies can largely vary. However, our results of mcrA gene libraryanalysis showed that 12 OTU of 14 OTUs were grouped within theorder Methanobacteriales, with only 1 OTU for each of Methano-sarcinales and Methanomicrobiales. Such result was similar to thestudy by Denman et al. (2007), in which methyl coenzyme-M re-ductase A (mcrA) clone libraries were generated from microbialDNA extracted from the rumen of cattle fed a roughage diet withand without supplementation of the antimethanogenic compoundbromochloromethane. The bromochloromethane treatment re-sulted in a 33% reduction of methane emission. Compared with thecontrol library, the bromochloromethane library showed morediverse methanogenic population with representatives from Me-thanococcales, Methanomicrobiales and Methanosacinales (Table 1).We speculated that the decrease in methane emission was causedby the increase in methanogen diversity. In our study, we foundthe diversity of methanogens in yak higher than that in otherruminants (Table 1) (King et al., 2011; Franzolin et al., 2012;Ozutsumi et al., 2012). This may also explain why yak should be alow-methane emitter compared to other ruminants. Previous re-search demonstrated that yak seems to have digestion and me-tabolism different from cattle, probably because it develops spe-cific mechanisms to better cope with the harsh environment of theQinghai-Tibetan Plateau (e.g., low quality forage, high altitude).Such specific mechanism could also include energy saving meta-bolic functions, which probably leads to less GHG emitted fromnatural grazing yaks than from cattle (Ding et al., 2010). Denmanet al. (2007) using the primers M13F and M13R described by Lutonet al. (2002) also detected a group of mcrA sequences belonging toan uncultured group of archaea. In light of these results, wespeculated that the group of uncultured rumen archaea couldbelong to Methanomassiliicoccales, though like others, our studydid not detect the presence of Methanomassiliicoccales (Table, 1)(Tatsuoka et al., 2004; Franzolin et al., 2012; Ozutsumi et al., 2012).We considered it could be caused by the primer set ME1 and ME2,which is shown to reduce the diversity coverage of the metha-nogenic lineages (Lueders et al., 2001).

5. Conclusions and limitations

The enteric CH4 is a result of the degradation of plant materialby the microbial community in the rumen. High-throughput se-quencing data indicated that the yak rumen has highly diversemicrobial communities of uncultured or unclassified microorgan-isms. This research found that the phyla Bacteroidetes, Firmicutes,Proteobacteria, Fibrobacteres and Euryarchaeota constituted themajority of the prokaryotic community in samples from three yaksfrom the Qinghai-Tibetan Plateau of China. Though sequencing of16S rRNA genes yielded a greater coverage of methanogenic ar-chaea diversity than clone library of mcrA gene and allowed theidentification of low abundant populations, both molecular tech-niques showed that Methanobrevibacter is the predominant ar-chaea of rumen microbiota in the yaks grazing natural pastures.These observations are helpful for understanding of the complexrumen ecosystem of yaks, and therefore for further mitigating CH4

emissions from the ruminant.There were some limitations in this study. The most prominent

one was that we only calculated the correlation between bacteriaand methanogens, but did not analyze fungi or ciliate protozoa.Anaerobic fungi play an important role in fiber digestion andprotozoa establish close endo- and ectosymbiotic associationswith methanogens. In future research more attention should bepaid to the correlations of rumen microorganisms and the di-versity of fungi and protozoa.

Conflict of interest statement

No conflict of interest exits in the submission of this manu-script, and all the authors listed have read and approved themanuscript that is enclosed.

Acknowledgments

This study was supported by 100 Talents Program of The Chi-nese Academy of Sciences, 1000 Talents Program of Sichuan Pro-vince and the National Natural Science Foundation of China(No. 41201205, 31300417), Special Project for Youth Sci-tech In-novation Research Team of Sichuan Province (No. 2015TD0026).The authors give special thanks to Ms. Wan Xiong for her editingand valuable comments on the manuscript.

Appendix A. Supplementary material

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.livsci.2016.04.009.

References

Amato, K.R., 2013. Co-evolution in context: the importance of studying gut mi-crobiomes in wild animals. Microbiol. Sci. Med. 1, 2084–7653.

An, D., Dong, X., Dong, Z., 2005. Prokaryote diversity in the rumen of yak (Bosgrunniens) and Jinnan cattle (Bos taurus) estimated by 16S rDNA homologyanalyses. Anaerobe 11, 207–215.

Armougom, F., Raoult, D., 2008. Use of pyrosequencing and DNA barcodes tomonitor variations in Firmicutes and Bacteroidetes communities in the gutmicrobiota of obese humans. BMC Genom. 9, 576.

Bao, S., Jiang, R., Kwan, W., Wang, B., Ma, X., Song, Y.Q., 2011. Evaluation of next-generation sequencing software in mapping and assembly. J. Hum. Genet. 56,406–414.

Borrel, G., O’Toole, P.W., Harris, H.M., Peyret, P., Brugere, J.F., Gribaldo, S., 2013.Phylogenomic data support a seventh order of Methylotrophic methanogensand provide insights into the evolution of Methanogenesis. Genome Biol. Evol.5, 1769–1780.

Dan Xue et al. / Livestock Science 188 (2016) 61–7170

Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K.,Fierer, N., Pena, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T.,Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D.,Pirrung, M., Reeder, J., Sevinsky, J.R., Tumbaugh, P.J., Walters, W.A., Widmann, J.,Yatsunenko, T., Zaneveld, J., Knight, R., 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336.

Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Huntley, J., Fierer, N.,Owens, S.M., Betley, J., Fraser, L., Bauer, M., Gormley, N., Gilbert, J.A., Smith, G.,Knight, R., 2012. Ultra-high-throughput microbial community analysis on theIllumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624.

Caporaso, J.G., Lauber, C.L., Walters, W.A., Berg-Lyons, D., Lozupone, C.A., Turn-baugh, P.J., Fierer, N., Knight, R., 2011. Global patterns of 16S rRNA diversity at adepth of millions of sequences per sample. Proc. Npatl. Acad. Sci. U.S.A. 108,4516–4522.

Chaudhary, P.P., Sirohi, S.K., Singh, D., Saxena, J., 2011. Methyl coenzyme M re-ductase (mcrA) gene based phylogenetic analysis of methanogens population inMurrah buffaloes (Bubalus bubalis). J. Microbiol. 49, 558–561.

Chaucheyras-Durand, F., Masseglia, S., Fonty, G., Forano, E., 2010. Influence of theComposition of the Cellulolytic Flora on the Development of HydrogenotrophicMicroorganisms, Hydrogen Utilization, and Methane Production in the Rumensof Gnotobiotically Reared Lambs. Appl. Environ. Microbiol. 76, 7931–7937.

Chen, Y.B., Lan, D.L., Tang, C., Yang, X.N., Li, J., 2015. Effect of DNA extractionmethods on the apparent structure of yak rumen microbial communities asrevealed by 16S rDNA sequencing. Pol. J. Microbiol. 64, 29–36.

Clark, H., 2013. Nutritional and host effects on methanogenesis in the grazing ru-minant. Animal 7, 41–48.

Denman, S.E., Tomkins, N.W., McSweeney, C.S., 2007. Quantitation and diversityanalysis of ruminal methanogenic populations in response to the anti-methanogenic compound bromochloromethane. FEMS Microbiol. Ecol. 62,313–322.

Ding, X.Z., Long, R.J., Kreuzer, M., Mi, J.D., Yang, B., 2010. Methane emissions fromyak (Bos grunniens) steers grazing or kept indoors and fed diets with varyingforage concentrate ratio during the cold season on the Qinghai-Tibetan Plateau.Anim. Feed Sci. Technol. 162, 91–98.

Edgar, R.C., Haas, B.J., Clemente, J.C., Quince, C., Knight, R., 2011. UCHIME improvessensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200.

Franzolin, R., St-Pierre, B., Northwood, K., Wright, A.D.G., 2012. Analysis of rumenmethanogen diversity in water buffaloes (bubalus bubalis) under three differ-ent diets. Microb. Ecol. 64, 131–139.

Fuerst, J.A., 2010. Beyond prokaryotes and eukaryotes: planctomycetes and cellorganization. Nat. Educ. 3 (9), 44.

Good, I.J., 1953. The population frequencies of species and the estimation of po-pulation parameters. Biometrika 40, 237–264.

Grilli, D.J., Ceron, M.E., Paez, S., Egea, V., Schnittger, L., Cravero, S., Escudero, M.S.,Allegretti, L., Arenas, G.N., 2013. Isolation of Pseudobutyrivibrio ruminis andPseudobutyrivibrio xylanivorans from rumen of Creole goats fed native foragediet. Folia Microbiol. 58, 367–373.

Guo, S., Liu, J., Qi, D., Yang, J., Zhao, X., 2005. Taxonomic placement and origin ofyaks: implications from analyses of mtDNA D-loop fragment sequences. ActaTheriol. Sin. 26, 325–330.

Guo, W., Li, Y., Wang, L., Wang, J., Xu, Q., Yan, T., Xue, B., 2015. Evaluation of com-position and individual variability of rumen microbiota in yaks by 16S rRNAhigh-throughput sequencing technology. Anaerobe 34, 74–79.

Hales, B.A., Edwards, C., Ritchie, D.A., Hall, G., Pickup, R.W., Saunders, J.R., 1996.Isolation and identification of methanogen-specific DNA from blanket bog featby PCR amplification and sequence analysis. Appl. Environ. Microb. 62,668–675.

Harfoot, C.G., Hazlewood, G.P., 1997. Lipid metabolism in the rumen. In: Hobson, P.N., Stewart, C.S. (Eds.), The Rumen Microbial Ecosystem. Springer, Netherlands,London, pp. 382–426.

Hegarty, R.S., Klieve, A.V., 1999. Opportunities for biological control of ruminalmethanogenesis. Aust. J. Agr. Res. 50, 1315–1320.

Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Janssen, P.H., 2015. Rumenmicrobial community composition varies with diet and host, but a core mi-crobiome is found across a wide geographical range. Sci. Rep. 5, 14567.

Hook, S.E., Wright, A.D., Mcbride, B.W., 2010. Methanogens: methane producers ofthe rumen and mitigation strategies. Archaea 2010, 11.

Horz, H.-P., Seyfarth, I., Conrads, G., 2012. McrA and 16S rRNA gene analysis sug-gests a novel lineage of Archaea phylogenetically affiliated with Thermo-plasmatales in human subgingival plaque. Anaerobe 18, 373–377.

Huang, X.D., Tan, H.Y., Long, R.J., Liang, J.B., Wright, A.D.G., 2012. Comparison ofmethanogen diversity of yak (Bos grunniens) and cattle (Bos taurus) from theQinghai-Tibetan plateau, China. BMC Microbiol. 12, 237.

Jami, E., Israel, A., Kotser, A., Mizrahi, I., 2013. Exploring the bovine rumen bacterialcommunity from birth to adulthood. ISME J. 7, 1069–1079.

Jami, E., Mizrahi, I., 2012. Composition and similarity of bovine rumen microbiotaacross individual animals. Plos One 7, e33306.

Janssen, P.H., Kirs, M., 2008. Structure of the archaeal Community of the Rumen.Appl. Environ. Microbiol. 74, 3619–3625.

Jin, W., Cheng, Y.F., Mao, S.Y., Zhu, W.Y., 2014. Discovery of a novel rumen metha-nogen in the anaerobic fungal culture and its distribution in the rumen as re-vealed by real-time PCR. BMC Microbiol. 14, 104.

Kim, M., Wang, L., Morrison, M., Yu, Z., 2014. Development of a phylogenetic mi-croarray for comprehensive analysis of ruminal bacterial communities. J. Appl.Microbiol. 117, 949–960.

King, E.E., Smith, R.P., St-Pierre, B., Wright, A.-D.G., 2011. Differences in the rumen

methanogen populations of lactating jersey and holstein dairy cows under thesame diet regimen. Appl. Environ. Microbiol. 77, 5682–5687.

Krumholz, L.R., Bryant, M.P., 1986. Eubacterium oxidoreducens sp. nov. requiring H2or formate to degrade gallate, pyrogallol, phloroglucinol and quercetin. Arch.Microbiol. 144, 8–14.

Lee, H.J., Jung, J.Y., Oh, Y.K., Lee, S.S., Madsen, E.L., Jeon, C.O., 2012. Comparativesurvey of rumen microbial communities and metabolites across one caprineand three bovine groups, using bar-coded pyrosequencing and H-1 nuclearmagnetic resonance spectroscopy. Appl. Environ. Microbiol. 78, 5983–5993.

Leng, J., Xie, L., Zhu, R., Yang, S., Gou, X., Li, S., Mao, H., 2011. Dominant bacterialcommunities in the rumen of Gayals (Bos frontalis), Yaks (Bos grunniens) andYunnan Yellow Cattle (Bos taurs) revealed by denaturing gradient gel electro-phoresis. Mol. Biol. Rep. 38, 4863–4872.

Ley, R.E., Turnbaugh, P.J., Klein, S., Gordon, J.I., 2006. Microbial ecology: human gutmicrobes associated with obesity. Nature 444, 1022–1023.

Lin, B., Henderson, G., Zou, C., Cox, F., Liang, X., Janssen, P.H., Attwood, G.T., 2015.Characterization of the rumen microbial community composition of buffalobreeds consuming diets typical of dairy production systems in Southern China.Anim. Feed Sci. Technol. 207, 75–84.

Liu, Y., Whitman, W.B., 2008. Metabolic, Phylogenetic, and Ecological Diversity ofthe Methanogenic Archaea. Ann. N. Y. Acad. Sci. 1125, 171–189.

Lueders, T., Chin, K.J., Conrad, R., Friedrich, M., 2001. Molecular analyses of methyl-coenzyme M reductase alpha-subunit (mcrA) genes in rice field soil and en-richment cultures reveal the methanogenic phenotype of a novel archaeallineage. Environ. Microbiol. 3, 194–204.

Luton, P.E., Wayne, J.M., Sharp, R.J., Riley, P.W., 2002. The mcrA gene as an alter-native to 16S rRNA in the phylogenetic analysis of methanogen populations inlandfill. Microbiology 148, 3521–3530.

Mariat, D., Firmesse, O., Levenez, F., Guimaraes, V., Sokol, H., Dore, J., Corthier, G.,Furet, J.-P., 2009. The Firmicutes/Bacteroidetes ratio of the human microbiotachanges with age. BMC Microbiol. 9, 123.

Mcinerney, M.J., Bryant, M.P., Hespell, R.B., Costerton, J.W., 1981. Syntrophomonaswolfei gen. nov. sp. nov., an anaerobic, syntrophic, fatty acid-oxidizing bacter-ium. Appl. Environ. Microbiol. 41, 1029–1039.

Moss, A.R., Jouany, J.-P., Newbold, J., 2000. Methane production by ruminants: itscontribution to global warming. Ann. Zootech. 49, 231–253.

Osborne, J.M., Dehority, B., 1989. Synergism in degradation and utilization of intactforage cellulose, hemicellulose, and pectin by three pure cultures of ruminalbacteria. Appl. Environ. Microbiol. 55, 2247–2250.

Ozutsumi, Y., Tajima, K., Takenaka, A., Itabashi, H., 2012. The mcrA gene and 16SrRNA gene in the phylogenetic analysis of methanogens in the rumen of fau-nated and unfaunated cattle. Anim. Sci. J. 83, 727–734.

Pedersen, R., Andersen, A.D., Molbak, L., Stagsted, J., Boye, M., 2013. Changes in thegut microbiota of cloned and non-cloned control pigs during development ofobesity: Gut microbiota during development of obesity in cloned pigs. BMCMicrobiol. 13, 30.

Poulsen, M., Schwab, C., Jensen, B.B., Engberg, R.M., Spang, A., Canibe, N., Hojberg,O., Milinovich, G., Fragner, L., Schleper, C., Weckwerth, W., Lund, P., Schramm,A., Urich, T., 2013. Methylotrophic methanogenic Thermoplasmata implicatedin reduced methane emissions from bovine rumen. Nat. Commun. 4, 1428.

Ramšak, A., Peterka, M., Tajima, K., Martin, J.C., Wood, J., Johnston, M.E.A., Aminov,R.I., Flint, H.J., Avguštin, G., 2000. Unravelling the genetic diversity of ruminalbacteria belonging to the CFB phylum. FEMS Microbiol. Ecol. 33, 69–79.

Sapp, J., 2005. The prokaryote-eukaryote dichotomy: Meanings and mythology.Microbiol. Mol. Biol. R. 69, 292–305.

Schink, B., 1997. Energetics of syntrophic cooperation in methanogenic degrada-tion. Microbiol. Mol. Biol. R. 61, 262–280.

Shannon, C.E., Weaver, W., 1963. The Mathematical Theory of Communication.University of Illinois Press,, Champaign.

Snelling, T.J., Genç, B., McKain, N., Watson, M., Waters, S.M., Creevey, C.J., Wallace, R.J., 2014. Diversity and community composition of methanogenic archaea in therumen of scottish upland sheep assessed by different methods. Plos One 9,e106491.

Sobieraj, M., Boone, D.R., 2006. Syntrophomonadaceae. In: Dworkin, M., Falkow, S.,Rosenberg, E., Schleifer, K.-H., Stackebrandt, E. (Eds.), The Prokaryotes: Volume4: Bacteria: Firmicutes, Cyanobacteria. Springer, US, New York, NY,pp. 1041–1049.

Tatsuoka, N., Mohammed, N., Mitsumori, M., Hara, K., Kurihara, M., Itabashi, H.,2004. Phylogenetic analysis of methyl coenzyme-M reductase detected fromthe bovine rumen. Lett. Appl. Microbiol. 39, 257–260.

Thauer, R.K., Shima, S., 2006. Methyl-Coenzyme M Reductase in Methanogens andMethanotrophs, in Archaea: Evolution. In: Garrett, R.A., Klenk, H.-P. (Eds.),Physiology, and Molecular Biology. Blackwell Publishing Ltd, Malden, MA, USA.

Turnbaugh, P.J., Baeckhed, F., Fulton, L., Gordon, J.I., 2008. Diet-induced obesity islinked to marked but reversible alterations in the mouse distal gut microbiome.Cell Host Microbe 3, 213–223.

Tymensen, L.D., Beauchemin, K.A., McAllister, T.A., 2012. Structures of free-livingand protozoa-associated methanogen communities in the bovine rumen differaccording to comparative analysis of 16S rRNA and mcrA genes. Microbiology158, 1808–1817.

Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naive Bayesian classifier forrapid assignment of rRNA sequences into the new bacterial taxonomy. Appl.Environ. Microbiol. 73, 5261–5267.

Watanabe, T., Kimura, M., Asakawa, S., 2007. Dynamics of methanogenic archaealcommunities based on rRNA analysis and their relation to methanogenic ac-tivity in Japanese paddy field soils. Soil. Biol. Biochem. 39, 2877–2887.

Dan Xue et al. / Livestock Science 188 (2016) 61–71 71

Wiener, G., Han, J., Long, R., 2003. Origins, domestication and distribution of yak,and production characteristics of yak. In: Wiener, G.H.J.L.R. (Eds.), The Yak, 2ndedn. Regional Office for Asia and the Pacific of the Food and Agriculture Or-ganization of the United Nations, Bangkok, pp. 136–137.

Wright, A.D., Williams, A.J., Winder, B., Christophersen, C.T., Rodgers, S.L., Smith, K.D., 2004. Molecular diversity of rumen methanogens from sheep in WesternAustralia. Appl. Environ. Microbiol. 70, 1263–1270.

Wright, A.D.G., Auckland, C.H., Lynn, D.H., 2007. Molecular diversity of methano-gens in feedlot cattle from Ontario and Prince Edward Island, Canada. Appl.Environ. Microbiol. 73, 4206–4210.

Wu, S., Ransom, L., Baldwin 6th, Li, W., Li, C., Connor, E.E., Li, R.W., 2012. The Bac-terial community composition of the bovine rumen detected using pyr-osequencing of 16S rRNA genes. Metagenomics 1, 1–11.

Yáñez-Ruiz, D.R., Macías, B., Pinloche, E., Newbold, C.J., 2010. The persistence ofbacterial and methanogenic archaeal communities residing in the rumen of

young lambs. FEMS Microbiol. Ecol. 72, 272–278.Yang, L.Y., Chen, J., Cheng, X.L., Xi, D.M., Yang, S.L., Deng, W.D., Mao, H.M., 2010.

Phylogenetic analysis of 16S rRNA gene sequences reveals rumen bacterial di-versity in Yaks (Bos grunniens). Mol. Biol. Rep. 37, 553–562.

Zened, A., Combes, S., Cauquil, L., Mariette, J., Klopp, C., Bouchez, O., Troegeler-Meynadier, A., Enjalbert, F., 2013. Microbial ecology of the rumen evaluated by454 GS FLX pyrosequencing is affected by starch and oil supplementation ofdiets. FEMS Microbiol. Ecol. 83, 504–514.

Zhang, J., Li, D.P., Gao, P., Tao, Y., Wang, X.M., He, X.H., 2011. Nitrification and ni-trifying bacteria in the Chengdu section of middle Min River (China). Afr. J.Biotechnol. 10, 5635–5647.

Zhou, M., Hernandez-Sanabria, E., Guan, L.L., 2009. Assessment of the microbialecology of ruminal methanogens in cattle with different feed efficiencies. Appl.Environ. Microbiol. 75, 6524–6533.