1 23

Applied Microbiology andBiotechnology ISSN 0175-7598 Appl Microbiol BiotechnolDOI 10.1007/s00253-014-6153-1

Characterization of the rumen microbiomeof Indian Kankrej cattle (Bos indicus)adapted to different forage diet

Vilas Patel, Amrutlal K. Patel, NidhiR. Parmar, Anand B. Patel, BhaskarReddy & Chaitanya G. Joshi

1 23

Your article is protected by copyright and

all rights are held exclusively by Springer-

Verlag Berlin Heidelberg. This e-offprint is

for personal use only and shall not be self-

archived in electronic repositories. If you wish

to self-archive your article, please use the

accepted manuscript version for posting on

your own website. You may further deposit

the accepted manuscript version in any

repository, provided it is only made publicly

available 12 months after official publication

or later and provided acknowledgement is

given to the original source of publication

and a link is inserted to the published article

on Springer's website. The link must be

accompanied by the following text: "The final

publication is available at link.springer.com”.

GENOMICS, TRANSCRIPTOMICS, PROTEOMICS

Characterization of the rumen microbiome of Indian Kankrejcattle (Bos indicus) adapted to different forage diet

Vilas Patel & Amrutlal K. Patel & Nidhi R. Parmar &

Anand B. Patel & Bhaskar Reddy & Chaitanya G. Joshi

Received: 26 June 2014 /Revised: 6 October 2014 /Accepted: 12 October 2014# Springer-Verlag Berlin Heidelberg 2014

Abstract Present study described rumen microbiome ofIndian cattle (Kankrej breed) to better understand the micro-bial diversity and largely unknown functional capacity of therumenmicrobiome under different dietary treatments. Kankrejcattle were gradually adapted to a high-forage diet (fouranimals with dry forage and four with green forage) contain-ing 50 % (K1), 75 % (K2) to 100 % (K3) forage, andremaining concentrate diet, each for 6 weeks followed byanalysis of rumen fiber adherent and fiber-free metagenomiccommunity by shotgun sequencing using ion torrent PGMplatform and EBI-metagenomics annotation pipeline.Taxonomic analysis indicated that rumen microbiome wasdominated by Bacteroidetes followed by Firmicutes,Fibrobacter, Proteobacteria, and Tenericutes. Functionalanalysis based on gene ontology classified all reads in total157 categories based on their functional role in biological,molecular, and cellular component with abundance of genesassociated with hydrolase activity, membrane, transport, trans-ferase, and different metabolism (such as carbohydrate andprotein). Statistical analysis using STAMP revealed signifi-cant differences (P<0.05) between solid and liquid fraction ofrumen (in 65 categories), between all three treatments (in 56categories), and between green and dry roughage (17 catego-ries). Diet treatment also exerted significant difference inenvironmental gene tags (EGTs) involved in metabolic path-ways for production of volatile fatty acids. EGTs for butyrateproduction were abundant in K2, whereas EGTs for

propionate production was abundant during K1. Principalcomponent analysis also demonstrated that diet proportion,fraction of rumen, and type of forage affected rumenmicrobiome at taxonomic as well as functional level.

Keywords Rumenmicrobiome . Kankrej cattle . High foragediet . Shotgun sequencing . EBI-metagenomics

Introduction

Ruminant, specifically dairy cattle make up significant pro-portion of the domesticated animal species in developing aswell as in developed countries and considered as potentialsource of dairy products (Jami andMizrahi 2012; Krause et al.2003). Cattle are herbivores in nature and consume plantmaterials as food that is rich in cell wall polymers such ascellulose, xylan, and complex carbohydrate. The ability toabsorb and digest this insoluble form of plant fiber resides inthe ruminants’ foregut, which is known as rumen. The rumenis inhabited by a thousands of microorganisms, consisting ofbacteria, protozoa, archaea, and fungi, which serve as idealanaerobic environment for fiber fermentation or degradation(Flint 1997).

Among different ruminal microbes, bacteria are the mostdiverse and accounts for 1011 cells/ml of rumen fluid (Wrightand Klieve 2011). Most of these are obligate anaerobes andinvolved in the breakdown of lignocellulosic feeds throughdifferent enzymatic activities. Apart from lignocellulolyticbacteria, rumen also harbors proteolytic, amylolytic,acetogenic, lipolytic, ureolytic, and tanninolytic bacteria(Krause et al. 2003; Russell et al. 2009; Wright and Klieve2011). This capacity of rumen microbes is of enormous sig-nificance to human beings, as ruminants convert the energyfrom plant to digestible food products (Flint 1997). Ruminantherbivores obtain up to 80 % of total daily calories from

Electronic supplementary material The online version of this article(doi:10.1007/s00253-014-6153-1) contains supplementary material,which is available to authorized users.

V. Patel :A. K. Patel :N. R. Parmar :A. B. Patel :B. Reddy :C. G. Joshi (*)Department of Animal Biotechnology, College of Veterinary,Science and Animal Husbandry, Anand Agricultural University(AAU), Anand 388 001, Gujarat, Indiae-mail: cgjoshi@rediffmail.com

Appl Microbiol BiotechnolDOI 10.1007/s00253-014-6153-1

Author's personal copy

microbial fermentation with a mean forage retention time of57 h (Bergman et al. 1965; Uden et al. 1982). These bacteriaare known to colonize in the rumen soon after the birth and arehighly responsive to changes under different diet, age, antibi-otic treatment, geographical location, season, and feedingregime (Golder et al. 2014; Guan et al. 2008; Hook et al.2011). In addition to these, rumen microbes play an importantrole in the health status of ruminants, as well as in the produc-tion of the greenhouse methane gas. Therefore, analysis ofmicrobial populations from rumen microbiome under differ-ent diet treatment holds vast potential application for animalproduction and increasing the efficiency of animal feed.

Rumen bacterial community structure has been analyzedusing culture independent approach from many years, where-as, it provided an incomplete picture of the microbial commu-nity structure (Brulc et al. 2009; de Menezes et al. 2011; Hesset al. 2011; Jami and Mizrahi 2012; Kong et al. 2010; Pittaet al. 2010). However, with advent of next generation se-quencing, metagenomics approach provides a complete viewof community structure in depth (species richness and distri-bution), including fungi, archaeal and viral genomes, as wellas functional (metabolic) profile for degradation of complexcarbohydrate and lignocelluloses. This strategy will enhanceour understanding of host–microbe relationships with appli-cation to host metabolism and disease. Recent research hasfascinating glimpses into the metabolic activities of microbialenzymes for digestion of plant material in ruminants as well asin humans, rodents, cattle, and poultry (Brulc et al. 2009;Kurokawa et al. 2007; Qu et al. 2008; Turnbaugh et al.2008; Turnbaugh 2009; Turnbaugh et al. 2006).

India possesses 27 known indigenous breeds of cattleincluding Kankrej, Gir etc. and eleven breeds of buffaloes.Kankrej cattle (breed of Zebu cattle, Gujarat, India), is con-sidered as one of the heaviest breed of India and well knownfor milk and draught which makes it a very suitable livestockchoice in India. However, rumen microbiome from Kankrejcattle has not been characterized. The objective of this re-search was to study rumen microbiome using next gen-eration sequencing technology to compare the rumenbacterial population structure as well as their metabolicpotential in animals adapting to a high roughage (green as wellas dry roughage) diet.

Material and methods

Ethics statement

The experiment carried out was approved by the institutionalanimal ethics committee (IAEC) of College of VeterinaryScience and Animal Husbandry, Anand AgriculturalUniversity vide letter no. AAU/GVC/CPCSEA-IAEC/108/2013 dated 05/10/2013.

Cattle and diet

Eight healthy adult Kankrej cattle (Bos indicus) of 3–4 yearsold with an average body weight of 350–400 kg were used inthe present study. Animals were maintained on the diet as perNRC (National Royal Commission) standards (India) beforeexperiment. During experiment, two groups of four animalseach were maintained on locally available either dry roughage(D) or green roughage (G) at Livestock Research Station,Sardar Krushinagar Dantiwada Agricultural University(SDAU). The experimental diets were designed to have anincreasing concentration of roughage and a decreasing propor-tion of the concentrate mix. The diets (forage: concentrate)regimen were K1 (50:50), K2 (75:25), and K3 (100:0). Eachdiet regimen continued for 6 weeks and then switched to thenext. On the last day of each diet regimen feeding period,rumen samples were collected 3 h post feeding using gastriclavage. Composition of each diet has been given in Table S1.Each rumen sample was further separated to liquid (L) fractionsthrough gravitational filtering and solid (S) fraction by squeez-ing through a four-layered muslin cloth. Fresh rumen sampleswere immediately stored at −80 °C until DNA extraction.

DNA extraction

Genomic DNA was extracted and isolated from rumensamples using a Qiagen Stool kit (Qiagen, CA, USA).After extraction, DNA was quantified using a Nano-spectrophotometer (Thermo Fisher, USA).

Sequencing of rumen microbiome using ion torrent

All forty eight DNA samples were subjected to barcodedsequencing library preparation. Emulsion PCR was carriedout using the Ion OneTouch™ 400 Template Kit (LifeTechnologies) according to the manufacturer’s instructions.Quality and quantity of the enriched spheres were checked asdescribed in the appendix of the Ion Xpress Template kit userguide followed by sequencing of the libraries was carried outon the Ion Torrent Personal Genome Machine (PGM) systemusing the Ion Sequencing 400 kit (all Life Technologies).Metagenome sequence data were processed using fully auto-mated open source systems such as European BioinformaticsInstitute (EBI) (https://www.ebi.ac.uk/metagenomics/), andthe analysis included phylogenetic comparisons andfunctional annotations based on InterPro database and GeneOntology (Hunter et al. 2014). All analyses were performedwith an expected e-value cutoff of 1e−05.

Statistical approach

Principle component analyses (PCA) were performed usingthe Paleontological Statistics Software package version 2.17c

Appl Microbiol Biotechnol

Author's personal copy

(PAST) to determine effect of different treatment on rumenmicrobiome (Hammer et al. 2001). A linear correlation testwas used to investigate the correlation between different phylain rumen microbiome. Functional profiles of rumenmicrobiome were also calculated to determine statistical dif-ferences between all the samples based on the ANOVAs testalong with Tukey–Kramer Test with p values (P<0.05) usingthe software package STAMP v1.07 (Parks and Beiko 2010).

Nucleotide sequence accession numbers of rumenmicrobiome samples

All sequence data generated in this study have been submittedto EBI under accession numbers ERS428208 to ERS428161.

Results

Present study determined the effect of diet on rumenmicrobiome (solid as well as liquid fraction) of four healthyKankrej cattle. The sequencing data of all the samples obtain-ed after quality filtering were ranged from 116 to 722 Mb(Table S2).We had only considered those sequences with highquality parameters from all samples. The average read lengthranged from 165–217 bp from all samples. After qualityfiltering, these reads from all samples were processed throughthe EBI pipeline. From the total reads, 0.2–1.0 % (528–2,227reads) of the reads retrieved from all rumen microbiome wasmapped to 16S rRNA gene while remaining was classifiedinto different functional annotation.

Taxonomic assignment of 16S rRNA reads from all samples

The taxonomic classification of 48 samples was studied usingEBI pipeline andMEGAN. The observed number of OTUs (at99 % similarity) from bacterial 16S rRNA gene sequencesranged from 200 to 400 (28 to 37%) (Table S2). Domain-levelallocation based on OTUs distribution was primarily assignedto bacteria (99 % on average) and archaea (0.1 %). Based ontheir abundance in all samples, downstream analyses focusedon the bacteria at phylum and family level using MEGAN(Fig. 1a, c).

Effect of dry roughage on taxonomic distribution of bacterialphyla

In the rumen microbial community of liquid fraction of dryroughage (DL), Bacteroidetes [44.33%, 59%, 53.73 %] is themost abundant phylum followed by Firmicutes [10.31 %,8.9 %, 7.98 %], Verrucomicrobia [8.4 %, 7.75 %, 16.53 %],Fibrobacter [1.26 %, 4 %, 3.22 %], and Proteobacteria[2.84 %, 0.86 %, 0.56 %] in K1DL, K2DL, and K3DL,respectively. A closer look at the taxonomic distribution of

the numerically abundant bacterial family derived from therumen metagenome revealed that Prevotellaceae [19.64 %,33.5 %, 27.01 %] is most abundant family followed byFibrobacteraceae [1.26 %, 4.0 %, 3.2 %], Ruminococcaceae[3.37 %, 1.75%, 2.33%], and Porphyromonadaceae [3.10 %,1.33 %, 0.90 and other families %] in K1DL, K2DL, andK3DL, respectively (Fig. 1a, c).

In the rumen microbial community of solid fraction of dryroughage (DS), Bacteroidetes [42.19 %, 55.03 %, 30.47 %] isthe most abundant phylum followed by Firmicutes [28.30 %,22.00 %, 36.58 %], Fibrobacter [0.26 %, 1.51 %, 0.26 %],Verrucomicrobia [0.72 %, 1.41 %, 1.01 %], andProteobacteria [0.92 %, 0.59 %, 0.87 %] in K1DS, K2DS,and K3DS, respectively, and other phyla. At family level,Prevotellaceae [16.00%, 17.08 %, 10.62 %] is most abundantfamily followed by Ruminococcaceae [6.76 %, 3.73 %,6.23 %], Fibrobacteraceae [0.23 %, 1.5 %, 0.26 %], andPorphyromonadaceae [0.52 %, 0.27 %, 0 %)] in K1DS,K2DS, and K3DS, respectively and other families (Fig. 1a, c).

Effect of green roughage on taxonomic distributionof bacterial phyla

In the rumen microbial community of liquid fraction of greenroughage (GL), Bacteroidetes [55.95 %, 57.07 %, 57.08 %] isthe most abundant phylum followed by Firmicutes [7.8 %,10.56 %, 12.88 %], Verrucomicrobia [4.10 %, 7.91 %,7.51 %], Fibrobacter [1.16 %, 3.37 %, 1.89 %],Proteobacteria [4.65 %, 0.84 %, 0.52 %], and other phyla inK1GL, K2GL, and K3GL, respectively. At family level,Prevotellaceae [33.69 %, 33.96 %, 33.26 %] is most abundantfamily followed by Fibrobacteraceae [1.16 %, 3.37 %, 1.89 %]and Ruminococcaceae [2.46 %, 2.63 %, 2.46 %],Porphyromonadaceae [3.74 %, 2.35 %, 0.96 %], and otherfamilies in K1GL, K2GL, and K3GL, respectively (Fig. 1a, c).

In the rumen microbial community of solid fraction ofgreen roughage (GS), Bacteroidetes [40.83 %, 53.13 %,31.48 %] is the most abundant phylum followed byFirmicutes [28.70 %, 24.49 %, 37.96 %], Fibrobacter[0.38 %, 2.00 %, 0.233 %], Proteobacteria [0.48 %, 0.87 %,0.46 %], and other phyla in K1GL, K2GL, and K3GL, re-spectively. At family level,Prevotellaceae [19.20%, 23.24%,11.97 %] is most abundant followed by Ruminococcaceae[5.46 %, 4.82 %, 7.1 %] Lachnospiraceae [6.53 %, 3.63 %,6.19 %], Fibrobacteraceae [0.38 %, 2.00 %, 0.23 %], andother families in K1GS, K2GS, and K3GS, respectively(Fig. 1a, b).

Bacteroidetes were found to be dominant in rumen duringK1 (considering both liquid and solid) followed by K2 andK3, whereas, Firmicutes were found to be dominant in K3followed by K2 and K1during dry as well as green roughagediet (Fig. S1). On the basis of the phylogenetic assignment, theratios of the phyla Firmicutes/Bacteroidetes were compared

Appl Microbiol Biotechnol

Author's personal copy

during three diet treatments (Fig. 1b). As shown in Fig. 1c,ratio was found to be decreasing from K1 to K3 treatment inliquid fraction of green forage treatment, whereas ratio in-creased from K1 to K3 in dry forage treatment. In solidfraction, ratio was found to be lower during K2 treatment,followed by K1 and K3.

Correlation between phyla in rumen microbiome

Little information is known regarding cross-phylum interac-tions among the different diet treatment in the rumen of cattle.To address this void, a comprehensive analysis of the patternsof abundance of phylum were determined across the 12 sam-ples of rumen (mean of four samples were considered). PASTwas used to calculate linear correlation coefficient and todetermine statistical significance of observed correlations.As shown in Tables 1 and 2, abundance of the Bacteroideteswas positively correlated with abundance of bacterial phylumVerrucomicrobiae, Proteobacteria, and Fibrobacter, whereasnegatively correlated with Firmicutes and Tennericutes during

green and dry roughage treatment. In addition to these, asshown in Table S3, S4, and S5, phyla interaction had beenvarying in each treatment.

Functional profiling of rumen microbiome

Functional annotation was assigned to protein domain basedon protein database and Gene Ontology depending on theirrole in different biological mechanism, cellular component,and molecular function.

Functional annotation based on interpro database

Based on InterPro database using EBI pipeline, genes wereidentified with glycoside hydrolase, NAD(P) binding domain,aldolase, Rossmann-like alpha/beta model and pyridoxalphosphate dependent transferase and others (Table S6). Themost prevalent functional groups to which the sequences werebinned included glycosyl hydrolase with predominance insecond treatment (K2).

0102030405060708090

100

K1G

LK

2GL

K3G

LK

1GS

K2G

SK

3GS

K1D

LK

2DL

K3D

LK

1DS

K2D

SK

3DS

Ab

un

dan

ce (

%)

Rumen Samples

Other phyla

Tenericutes

Proteobacteria

Fibrobacteres

Verrucomicrobia

Firmicutes

Bacteroidetes

0102030405060708090

100

Ab

un

dan

ce (

%)

Rumen Samples

Other family

Lentisphaerae

Ruminococcaceae

Clostridiaceae

Lachnospiraceae

Catabacteriaceae

Fibrobacteraceae

Prevotellaceae

Porphyromonadaceae

Bacteroidaece

0

0.2

0.4

0.6

0.8

1

1.2

1.4

K1 K2 K3

Fir

mic

ute

s/B

acte

roid

etes

Treatments

Dry roughage_Solid fractionDry roughage_Liquid fractionGreen roughage_Solid fractionGreen roughage_Liquid fraction

c

a b

Fig. 1 a The taxonomic distribution of bacterial phylum level based onOTUs distribution in each sample. b Firmicutes/Bacteroidetes ratio dur-ing three treatment. c The taxonomic distribution of bacterial family level

composition based on OTUs distribution in each sample (mean propor-tion of four animal maintained under same diet treatment were consideredin each samples)

Appl Microbiol Biotechnol

Author's personal copy

Functional annotation based on gene ontology

In biological function category, genes involved in nitrogencompound metabolic process were found to be dominantfollowed by biosynthetic process, nucleotide binding, smallmolecule metabolic process, carbohydrate metabolism, andprotein metabolic process. Within molecular function, genesinvolved in hydrolase activity, transport, and transferase ac-tivity were found to be dominant compared to other subcate-gories. In cellular component, genes assigned to subcategorysuch as membrane and cytoplasmwere dominant (Fig. S2). Toextend this analysis, we have applied statistical methods,which compare those subsystems that are more, or less, rep-resented in the different microbiomes. Figure 2 and Table S7,S8, and S9 show the subsystems that are present significantlyin different treatments, roughage diet, and fraction of rumensamples. Among all, 65 categories were found to be signifi-cantly different between solid and liquid fraction and 58categories were found to be significantly different betweendifferent treatments, whereas only 17 categories were found tobe significantly different between green and dry roughage(Fig. 2). Among 58 categories, genes associated with biosyn-thetic process, hydrolase activity, transport, transferase, car-bohydrate and protein metabolism, and other small moleculemetabolism were found to be significantly different betweenthree treatments, whereas genes associated with nitrogen com-pound metabolic process, transport, nucleotide binding, RNAmetabolic process oxidoreductase activity, etc. were found tobe significantly different between solid and liquid fractions.However, 17 categories, such as genes associated with mem-brane, transport, transporter activity, chemotaxis, cofactor

binding, aminoacyl-tRNA ligase activity and cell communi-cation were found to be significantly different between greenand dry roughage (Fig. 2d).

In order to access relatedness in bacterial diversity fromeach samples, principal component analysis (PCA) was car-ried out based on taxonomic and functional annotation usingPAST. Principal component analysis (PCA) of the 48 samplesaccording to the taxonomic or functional assignment of thereads gave similar clustering patterns as represented in Fig. S3and Fig. 3. Samples from each treatment of dry and greenroughage individuals, specifically of solid fraction, tended tocluster together, indicating that a similar set of functions wereencoded in their metagenome in each treatment. Genes asso-ciated with membrane, small molecule metabolic process,hydrolase activity, carbohydrate metabolism, nitrogen metab-olisms, etc. were found to be highly significant during threetreatment of solid particle adherent rumen microbiome.Enzymes involved in these different metabolic processes arerepresented in Table S10. PCA analysis revealed clear sepa-ration between solid and liquid fraction of rumen microbiomeafter each treatment. Additionally, we also observed that ani-mals fed with same diet were clustered together; however,there was no separation between types of forage (Fig. S3).

Effect of diet onmetabolic pathways for production of volatilefatty acids

In addition to these, EGTs involved in VFAs production suchas propionate and butyrate production were also studied dur-ing three different diets (Figs. 4 and 5). As shown in Fig. 4, thefirst step in propionate production is the conversion of

Table 1 Cross-phylum interaction using paleontological statistical analysis under dry roughage treatment

0.000 Bacteroidetes Firmicutes Verrucomicrobia Proteobacteria Tenericutes Fibrobacter

Bacteroidetes 0.000 0.138 0.466 0.563 0.028 0.088

Firmicutes −0.679 0.000 0.018 0.594 0.479 0.035

Verrucomicrobia 0.374 −0.890 0.000 0.653 0.732 0.049

Proteobacteria −0.300 −0.278 0.236 0.000 0.137 0.645

Tenericutes −0.860 0.363 −0.181 0.681 0.000 0.148

Fibrobacter 0.747 −0.843 0.814 −0.242 −0.667 0.000

Table 2 Cross-phylum interaction using paleontological statistical analysis under green roughage treatment

0.000 Bacteroidetes Firmicutes Verrucomicrobia Proteobacteria Tenericutes Fibrobacter

Bacteroidetes 0.000 0.011 0.146 0.635 0.112 0.023

Firmicutes −0.912 0.000 0.083 0.377 0.408 0.138

Verrucomicrobia 0.670 −0.754 0.000 0.805 0.317 0.113

Proteobacteria 0.249 −0.444 −0.131 0.000 0.603 0.686

Tenericutes −0.712 0.420 −0.496 0.271 0.000 0.024

Fibrobacter 0.875 −0.679 0.711 −0.213 −0.871 0.000

Appl Microbiol Biotechnol

Author's personal copy

succinate to succinyl Co-A via succinate CoA-synthetase.Further, succinyl CoA is converted to 2-methyl-malonyl-CoA via methyl malonyl-CoA mutase and methyl malonyl-CoA epimerase fol lowed by decarboxylat ion ofmethylmalonyl-CoA to propionyl-CoA via methyl malonyl-CoA decarboxylase. After the formation of propionyl-CoA,propionate production can proceed either by acetate—CoAligase or via the enzymes formate acetyltransferase and acetatekinase (Fig. 4). Third treatment (K3) showed consistentlyhigher abundance of genes required for propionate productionfollowed by first (K1) and second treatment. Phylogeneticassignment of reads containing EGTs for VFA productionwas also studied. As shown in Fig. 4b, majority of the readsfor propionate production were affiliated to the genera ofBacteroidetes and Prevotella in all samples with varied pro-port ion. When three treatments were compared,Bacteroidetes, Clostridium, and Arcanobacterium were foundto be dominant in K1 compared to K3, whereas,Porphyromonas observed as dominant genus for propionatein K3 compared to K1 treatment. In addition to these, many ofthe genera were not accounted in K3 treatments such asRuminococcus, Fibrobacter, Prevotella, and Parabacteroides.

Similarly, butyrate production was also studied during allthree treatments (Fig. 5). The first step in butyrate productionis via acetoacetate succinyl-CoA transferase which convertacetoacetate to acetoacyl CoA, followed by conversion tocrotonyl Co-A via two enzymes 3-hydroxyacyl-CoAdehydrogenase and enoyl-CoA hydratase. Further, croto-nyl Co-A is converted to butyryl-CoA via trans-2-enoyl-CoAreductase. After the formation of butyryl-CoA, butyrate fer-mentation can proceed either by acetate CoA-transferase orvia the enzymes phosphotransbutyrylase and butyrate kinase(Fig. 5). Butyrate formation from butyryl-CoA viaphosphotransbutyrylase and butyrate kinase is well represent-ed in rumen specifically during second treatment. As shown inFig. 5, enzymes involved in butyrate production were found tobe abundant consistently during second treatment followed byfirst treatment and third treatment. Phylogenetic assignment ofreads containing EGTs for VFA production was also studied.As shown in Fig. 5b, majority of the reads for butyrateproduction were affiliated to the genera of Bacteroidetes andPrevotella in all samples with varied proportion. When threetreatments were compared, Bacteroidetes, Prevotella, andParabacteroides were found to be dominant in K1 comparedto K3, whereas Clostridium, Acidaminococcus, andButyrivibrio were observed as dominant genera for VFAsynthesis in K3 compared to K1 treatment.

Discussion

Nowadays, the effect of diet on rumen microbiome is of greatinterest as it increases energy density within rumen and helps

to improve feed efficiency. However, most of the stud-ies have used traditional approach to study bacterialcommunity structure, whereas the present study describedrumen microbiome from Kankrej cattle using next generationsequencing present broader picture of rumen microbial popu-lation during adaptation to high forage diet along with differ-ent type of forage.

Though microorganisms play a critical role in the level ofefficiency and productivity of ruminant animals, its composi-tion or population structure in the Kankrej cattle remainslargely uncharacterized. The focus of present investigationwas to study the effect of different forage diet at differentproportion on rumen microbiome (liquid and solid). Deepsequencing of the rumen microbiome samples collected fromthe Kankrej cows fed with the two different diets at threedifferent concentrations (50, 75, and 100 %) provided a de-tailed view of the cattle rumen microbiome. In present study,we have selected four replicates, or animals were to determinethe effect of different proportion of forage and type of foragediet, due to substantial animal-to-animal variation in the ru-men bacterial community; therefore, replicates should be re-quired to observe the effect of diet on rumen microbiome(Lawrence et al. 2011).

Taxonomic profiling

Impact of diet concentration on taxonomic distribution

Bacteroidetes phylum was dominant in all rumen samplesbased on OTUs distribution. Within Bacteroidetes phylum,Prevotellaceae family appeared most abundant in all sampleswith predominance in K2 (Fig. 1). This was dominant phylumin majority of gut-associated phylotypes in a variety of differ-ent mammalian species including the bovine gut (Bergmanet al. 1965; Pitta et al. 2010; Stevenson and Weimer 2007;Whitford et al. 1998). Many previous studies have reportedsignificant role of Prevotellaceae in metabolism of starch,hemicellulose, pectin, and peptide or protein catabolismwhich further implicated in energy production (Cotta 1992;de Menezes et al. 2011; Dehority 1991; Matsui et al. 2000). Inaddition to this, results also suggested that as the concentrationof forage was increased from K1 to K2, diversity ofPrevotellaceae also increased, whereas further increase inforage concentration during K3 resulted in decreased diversityof Prevotellaceae. The increased concentration of roughage inK2 treatment resulted in increased fiber-degrading bacteria.This result corroborate with earlier studies (De Filippo et al.2010; Jami et al. 2013). Recently McCann et al. (2014) alsoobserved greater diversity in rumen during high-fiber dietcompared to higher grain diet. However, Bacteroidetes popu-lation declined during K2 and K3 diet treatment. These resultssuggest that Bacteroidetes play significant role in degradationof starch, hemicellulose, pectin, and peptide or protein

Appl Microbiol Biotechnol

Author's personal copy

catabolism (de Menezes et al. 2011; Dehority 1991).Inversely, Firmicutes population was found abundant in K3followed by K2 and K1. Similarly, Fernando et al. (2010) alsostated that as animal was shifted from fiber-rich diet to con-centrate diet, Firmicutes/Bacteroidetes ratio was decreaseddue to decreased Bacteroidetes population.

In addition to the Bacteroidetes, Firmicutes were alsofound as significant phyla in rumen microbiome (Fig. 1).Most of the Firmicutes sequences belonged to the orderClostridiales, being Lachnospiraceae, Clostridiaceae andRuminococcaceae as the most represented families. Thesefamilies are known pectin and cellulose degraders important

in rumen fermentation of dietary fibers (Kong et al. 2010).Other phyla such as Fibrobacter gradually increased as ani-mals were adapted to K2 diet. As expected, Fibrobacter is afibrolytic bacterium that digests fiber, therefore predominantlypresent in diets which are high in fiber (Tajima et al. 2001).This result is consistence with earlier observation by Fernandoet al. (2010) that Fibrobacter decreased by 40 fold as animalwas shifted to high grain diet. Similarly, Jami and Mizrahi(2012) studied effect of diet on microbial population andobserved that increased roughage diet concentration from 30to 50 % led to increase in 24-fold Fibrobacter population inrumen microbiome. The low abundance of microaerobic

mem

bran

e

trans

port

trans

porte

r activi

ty

cofactor

binding

aminoa

cyl-t

RNA ligas

e activi

ty

rece

ptor

activ

ity

cofactor

metab

olic

proc

ess

cell c

ommun

icatio

n

signa

l tra

nsdu

ction

0

1

2

3

4Dry RoughesGreen Roughes

Significant functional categories based on gene ontology

mea

n re

l. fr

eq. (

%)

hydr

olas

e activi

ty

nucle

otide b

inding

carb

ohyd

rate m

etab

olic

proc

ess

trans

ferase

activ

ity

nucle

ic ac

id binding

mem

bran

e

RNA

metab

olic

proc

ess

cytoplas

m

ligas

e activi

ty

DNA

metab

olic

proc

ess

0

1

2

3

4

5

6

7Green LiquidGreen Solid

Significant difference betwenn solid and liquid fraction of green roughes

mea

n re

l. fr

eq. (

%)

bios

ynthetic process

hydrolase activ

ity

small m

olecule metab

olic process

nucleic acid binding

carboh

ydrate m

etab

olic process

trans

ferase activity

mem

bran

e

protein metab

olic process

trans

port

oxidored

uctase activity

cytoplasm

0

2

4

6

8K1K2K3

Significant functional categories based on gene ontology

mea

n re

l. fr

eq. (

%)

nitro

gen co

mpo

und metab

olic

proc

ess

nucle

otide b

inding

nucle

ic ac

id binding

RNA metab

olic

proc

ess

trans

port

oxidor

educ

tase

activ

ity

trans

porte

r activi

ty

oxidation-

redu

ction pr

oces

s

cofactor

binding

DNA metab

olic

proc

ess

trans

criptio

n0

2

4

6

8 Dry LiquidDry Solid

Significant functional categories based on gene ontology

mea

n re

l. fr

eq. (

%)

(b)(a)

(d)(c)

Fig. 2 Statistical analysis based on functional analysis using STAMP a between liquid and solid fraction of dry roughage, b between liquid and solidfraction of green roughage, c between all three treatment, and d between dry and green roughage

Appl Microbiol Biotechnol

Author's personal copy

Proteobacteria sequences can be explained by anaerobic en-vironment of the rumen.

Impact of Fraction on taxonomic distribution

As shown in Fig. 3, principal component analysis revealedsignificant difference at taxonomic level between solid andliquid fraction of both diet (dry and green). As shown inFig. 1c, Prevotellaceae and Fibrobactereaceae were moreabundant in liquid fraction, whereas family of Firmicutes suchasRuminococcaceae andClostridiaceaeweremore abundant insolid samples. These results are consistent with previous obser-vations that Prevotella genera of Bacteroidetes are more prev-alent in the liquid fraction of pasture-fed cows(de Menezes et al. 2011) and Bermuda grass hay- or wheat-fed steers (Pitta et al. 2010). Similarly, Ruminococcus a memberof Firmicutes phyla was also shown to be more abundant in thesolid fraction of cows fed with different diet (de Menezes et al.2011) and Bermuda grass hay- or wheat-fed steers (Pitta et al.2010). In previous study, Kong et al. (2010) also observed thatRuminococcus accounted for 12.2 % in solid fraction, whereasin liquid fraction, it was 3.3 %. In addition to these, they alsoused microscopy as well as group specific FISH and suggestedthat the primary colonizers of insoluble substrates found in thegut are restricted to certain specialized groups of bacteria.Bacteroidetes group generally depends to a large extent onutilization of solubilized polysaccharides, generally found inliquid phase of rumen that are released by the activities ofother bacteria (Dehority 1991). In addition to these, resultsalso indicated that the abundance of Verrucomicrobia wasfound to be higher in liquid fraction of rumen microbiotacompared to solid adherent rumen microbiota.

Bacteroidetes was positively correlated with abundance ofbacterial phylum Verrucomicrobiae, Proteobacteria, andFibrobacter, whereas negatively correlated with Firmicutesand Tennericutes during green and dry roughage treatment.Recently, Jami et al. (2013) also reported that the Prevotella

belonging to Bacteroidetes phyla negatively correlated withFirmicutes as well as milk fat yield, whereas positively cor-related with feed conversion ratio. Future studies are needed toverify these cross-phylum correlations and to provide a bio-logical explanation to these (e.g., which community membersare potentially metabolically interchangeable).

Functional profiling of rumen microbiome

Based on EBI pipeline, genes were functionally assigned to157 category based on their role in different biological mech-anism, cellular component and molecular function. Among allcategories, 65 categories were found to be significantly dif-ferent between solid and liquid fraction and 58 categorieswere found to be significantly different between differenttreatments, whereas only 17 categories were found to besignificantly different between green and dry roughage diets.Present results clearly stated that roughage proportion in dietexerted more effect on rumen microbiome than type of rough-age (green as well dry).

Biological function

Gene associated with biological process were found to bedominant in all the samples. However, certain subcategoriesof biological process were changed under different treatmentsas well in different fractions. Nitrogen compound metabolicprocess subcategory was found dominant in all the samples.As the rumen diet is rich in nitrogen-containing compound,the enzymes involved in nitrogen containing compound couldbe high. Significant difference in nitrogen compound meta-bolic process subcategory was found between dry solid andliquid fraction (P<0.000013), whereas there was no signifi-cant difference between different treatment and between greenand dry roughage. This suggested that microbial activitiesbetween different rumen fractions (solid and liquid) weredifferent. In addition to these, other categories involved in

K1GLK2GL

K3GLK1GS

K2GS

K3GS

K1DL

K2DL

K3DL

K1DS

K2DS

K3DS

-48 -40 -32 -24 -16 -8 0 8 16 24Component 1

-20

-16

-12

-8

-4

0

4

8

12

16

Com

pone

nt 2

K1DL

K1DSK1GL

K1GS

K2DL

K2DS

K2GL

K2GSK3DL

K3DS

K3GL

K3GS

-1.6 -0.8 0.0 0.8 1.6Component 1

-1.6

-0.8

0.0

0.8

1.6

Com

pone

nt 2

(a) (b)Fig. 3 a Principal componentanalysis based on taxonomicdistribution of bacterial phylumlevel distribution in each sampleusing PAST. b Principalcomponent analysis based onfunctional analysis from eachsamples in each sample usingPAST (mean proportion of fouranimals maintained under samediet treatment were considered ineach samples)

Appl Microbiol Biotechnol

Author's personal copy

biological function such as carbohydrate and protein metabo-lism were also found to be in abundance in all the samples aswell as significant difference were found between differentfraction (P<0.002) and between treatment (P<0.0003). Therumen microbiome contains more representative enzymes andprotein that are involved in degradation of lignocellulosessuch as cellulose, xylose, and other complex plant material.Recently, (Brulc et al. 2009) showed that most of the

annotated transcripts of rumen microbiome samples includedglycosyl hydrolases. The high abundance and significant dif-ference between treatments found in this rumen microbiomemay be a result of the increased concentration of complexpolysaccharides from K1 to K3 treatment which corroboratewith Singh et al. (2012). Other genes related to carbohydratemetabolism, such as those related to biosynthetic process andsmall molecule metabolic process (involve in chemical

0

10

20

30

40

50

60

k1 k2 k3

Abu

ndan

ce (%

)

Treatments

RuminococcusPrevotellaPorphyromonasParabacteroidesFibrobacterClostridiumButyrivibrioBacteroidesArcanobacterium

(a)

(b)

Fig. 4 a Proposed pathways forpropionate production in therumen microbiome during threedifferent treatments (abundanceof enzymes during three differenttreatment has been shown inparentheses). b Phylogeneticassignment of reads containingEGTs for propionate productionduring each treatment

Appl Microbiol Biotechnol

Author's personal copy

process to form substance in metabolism) were also found tobe abundant in all samples (with predominant) and differsignificantly between different treatment (P<3.55E-06,P<1.30E-08, respectively), whereas type of roughage andfraction of rumen did not reflect major differences in bothmetabolism. This result may be due to the fact that as theanimal fed with the high roughage concentration diet likelyhad more available substrate for bacterial fermentation whichresulted in increased protein biosynthesis as well ascarbohydrate metabolisms during this diet treatment. Wanget al. (2003) and Firkins et al. (2006) also stated that rumenmicrobiome can be affected by diet as well as by host genetics.

Molecular function

Genes related to hydrolase activities which are involved incatalysis of the hydrolysis of various bonds (amylase, peptidase,

cellulase, xylanase, etc.) were found to be in abundance in allsamples with most abundant in K2. As shown in Fig. 2, signif-icant difference was also observed between treatments(P<0.008), and between solid and liquid fractions (P<0.01),whereas we could not observe any significant difference be-tween dry and green roughage which clearly suggested that themicrobial activities are being changed as exposed to differentroughage concentrations diet. The transferase activities involvedin catalytic activities were found to differ significantly betweendifferent treatments (P<0.004) and between solid and liquidfractions (P<2.04E-11). This has been reported previously indifferent surveys of the gut microbiota (de Menezes et al. 2011;Kong et al. 2010). In addition to these, gene associated withtransport, involved in the transport of substance between allthree treatments (P<0.01), between solid and liquid fractions(P<0.004) and between type of roughages (P<0.04), withhigher abundance were found in the second treatment.

0

10

20

30

40

50

60

K1 K2 K3

Ab

un

dan

ce (

%)

Tretament

Acidaminococcus

Prevotella

Parabacteroides

Clostridium

Butyrivibrio

Bacteroides

b

aFig. 5 Proposed pathways forbutyrate production in the rumenmicrobiome during three differenttreatments (abundance ofenzymes during three differenttreatment has been shown inparentheses). b Phylogeneticassignment of reads containingEGTs for butyrate productionduring each treatment

Appl Microbiol Biotechnol

Author's personal copy

Cellular component

Genes associated with membrane and cytoplasm was found tobe abundant in all the samples with predominance in K2. Qinet al. (Qin et al. 2010) also found that synthesis of cellularcomponents were abundant in gut using DNA-basedmetagenomic approach, whereas Gosalbes et al. (Gosalbeset al. 2011) also suggested that genes coding for cellularcomponent were found to be abundant in gut usingmetatranscriptomics approaches. Similar findings were alsoobserved by Verberkmoes et al. (Verberkmoes et al. 2009)using proteomics approach. Results also stated that genesassociated with membrane were significantly different be-tween roughages type (P<0.002), different diet treatments(P<0.013), and between solid and liquid fractions(P<0.00019) of rumen microbiome. Additionally, we alsoobserved that the genes associated with cell wall, chemotaxis,cell communication, etc. were found to differ significantlybetween dry and green roughage. These results could be dueto differences in variation of polysaccharides/substrates foundin the both type of roughages.

Results also stated that diet had more significanteffect on solid adherent rumen microbiota compared tomicrobiota in rumen liquid at taxonomic/functional level. Thisis due to the fact that solid adherent bacteria are likely to play akey/preliminary role in releasing energy from insoluble sub-strates as well as degradation of lignocellulosic material(Walker et al. 2008).

Effect of diet onmetabolic pathways for production of volatilefatty acids

Volatile fatty acids such as propionic acid, butyric acid, andacetate are produced in the rumen by microbial fermentation,which are subsequently used for milk as well as maintainhealth of animals. It contributes approximately 70 % of thedaily energy requirement of ruminants (Bergman 1990).There are three pathways for propionate production, succinatepathway, acrylate pathway, and propanediol pathway(Reichardt et al. 2014). The succinate pathway is by far themost commonly used by bacteria for propionate production inhuman gut as well as in gut microbiome of other mammals(Reichardt et al. 2014; Sergeant et al. 2014). Propionate pro-duction in rumen was found using succinate pathway. This isdue to the fact that Bacteroidetes are found to be majordominant phyla in rumen as well as in human gut.Bacteroidetes ut i l ize the succinate pathway viamethylmalonyl-CoA (Macy and Probst 1979). Succinate path-way for propionate production was found to be well repre-sented in rumen microbiome during all three treatments spe-cifically during first treatment. Similarly, (Lettat et al. 2013)also observed that cow fed with forage (60 %):concentrate(40 %) resulted in abundance of Prevotella sp. as well as

increased propionate production in rumen at the expense ofacetate and butyrate.

Alternatively, butyrate production was found to be wellrepresented during second treatment followed by first andthird treatment which implied that 75 % roughage and 25 %concentrate was found to be better diet compared to roughage-rich diet (K3) and concentrate-rich diet (K1) for animal healthas butyrate synthesis is directly related animal health. Thisruminal fermentation pattern was commonly observed in ru-men during different diet treatment. Although acetate andpropionate hold a prominent position in providing energy toruminant metabolism, butyrate, seems to be involved in me-tabolism beyond its role as a nutrient. Butyrate is an importantregulator of host physiology and also acts as a signalingmolecule in epithelial cells which modulates cell differentia-tion, proliferation, induce cell cycle arrest and apoptosis;therefore, butyrate plays important role in animal health(Li and Li 2008; Li and Li 2006)

To the best of our knowledge, this is the first comparison ofthe rumen microbiome under different diet treatments by deepsequencing in Kankrej cattle with four replicates. This studydepicts that individual genotype did not significantly affectbacterial community structure. According to our findings, thediets exert significant effect on rumen microorganisms attaxonomic as well as at functional levels, and it leads tochange in VFA production. Current study also statedthat second treatment was found to better compared tofirst and second on the basis of metabolic profile forVFA synthesis and other metabolism. This comprehen-sive study will enhance the overall understanding of themicrobial ecology of the rumen in cattle at taxonomic andfunctional level affected by diet.

Acknowledgments This work was supported by the Niche areaof excellence program on Metagenomic analysis of ruminal mi-crobes funded by Indian Council of Agriculture Research (ICAR),New Delhi, India

References

Bergman EN (1990) Energy contributions of volatile fatty acids from thegastrointestinal tract in various species. Physiol Rev 70:567–590

Bergman EN, Reid RS,MurrayMG, Brockway JM,Whitelaw FG (1965)Interconversions and production of volatile fatty acids in the sheeprumen. Biochem J 97:53–58

Brulc JM, Antonopoulos DA, Miller ME, Wilson MK, Yannarell AC,Dinsdale EA, Edwards RE, Frank ED, Emerson JB, WacklinP, Coutinhoj PM, Henrissatj B, Nelsoni KE, Whitea BA(2009) Gene-centric metagenomics of the fiber-adherent bo-vine rumen microbiome reveals forage specific glycoside hydro-lases. Proc Natl Acad Sci U S A 106:1948–1953. doi:10.1073/pnas.0806191105

Cotta MA (1992) Interaction of ruminal bacteria in the production andutilization of maltooligosaccharides from starch. Appl EnvironMicrobiol 58:48–54

Appl Microbiol Biotechnol

Author's personal copy

De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB,Massart S, Collini S, Pieraccini G, Lionetti P (2010) Impact of dietin shaping gut microbiota revealed by a comparative study inchildren from Europe and rural Africa. Proc Natl Acad Sci U S A107:14691–14696. doi:10.1073/pnas.1005963107

de Menezes AB, Lewis E, O'Donovan M, O'Neill BF, Clipson N, DoyleEM (2011) Microbiome analysis of dairy cows fed pasture or totalmixed ration diets. FEMSMicrobiol Ecol 78:256–265. doi:10.1111/j.1574-6941.2011.01151.x

Dehority BA (1991) Effects of microbial synergism on fibre digestion inthe rumen. Proc Nutr Soc 50:149–159

Fernando SC, Purvis HT 2nd, Najar FZ, Sukharnikov LO, Krehbiel CR,Nagaraja TG, Roe BA, Desilva U (2010) Rumen microbial popula-tion dynamics during adaptation to a high-grain diet. Appl EnvironMicrobiol 76:7482–7490. doi:10.1128/AEM. 00388-10

Firkins JL, Hristov AN, Hall MB, Varga GA, St-Pierre NR (2006)Integration of ruminal metabolism in dairy cattle. J Dairy Sci89(Suppl 1):E31–E51. doi:10.3168/jds.S0022-0302(06)72362-1

Flint HJ (1997) The rumen microbial ecosystem—some recent develop-ments. Trends Microbiol 5:483–488. doi:10.1016/S0966-842X(97)01159-1

Golder HM, Denman SE, McSweeney C, Celi P, Lean IJ (2014) Ruminalbacterial community shifts in grain-, sugar-, and histidine-challenged dairy heifers. J Dairy Sci 97:5131–5150. doi:10.3168/jds. 2014-8003

GosalbesMJ, Durban A, Pignatelli M, Abellan JJ, Jimenez-Hernandez N,Perez-Cobas AE, Latorre A, Moya A (2011) Metatranscriptomicapproach to analyze the functional human gut microbiota. PLoSOne6:e17447. doi:10.1371/journal.pone.0017447

Guan LL, Nkrumah JD, Basarab JA, Moore SS (2008) Linkage ofmicrobial ecology to phenotype: correlation of rumen microbialecology to cattle's feed efficiency. FEMS Microbiol Lett 288:85–91. doi:10.1111/j.1574-6968.2008.01343.x

Hammer O, Harper DAT, Ryan PD (2001) PAST: paleontological statis-tics software package for education and data analysis. PalaeontolElectron 4:1–9

HessM, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, Luo S,Clark DS, Chen F, Zhang T, Mackie RI, Pennacchio LA, Tringe SG,Visel A, Woyke T, Wang Z, Rubin EM (2011) Metagenomic dis-covery of biomass-degrading genes and genomes from cow rumen.Science 331:463–467. doi:10.1126/science.1200387

Hook SE, Steele MA, Northwood KS, Wright AD, McBride BW (2011)Impact of high-concentrate feeding and low ruminal pH onmethanogens and protozoa in the rumen of dairy cows. MicrobEcol 62:94–105. doi:10.1007/s00248-011-9881-0

Hunter S, CorbettM, Denise H, FraserM, Gonzalez-Beltran A, Hunter C,Jones P, Leinonen R,McAnulla C,Maguire E,Maslen J, Mitchell A,Nuka G, Oisel A, Pesseat S, Radhakrishnan R, Rocca-Serra P,Scheremetjew M, Sterk P, Vaughan D, Cochrane G, Field D,Sansone S (2014) EBI metagenomics—a new resource for theanalysis and archiving of metagenomic data. Nucleic Acids Res42:D600–D606. doi:10.1093/nar/gkt961

Jami E, Mizrahi I (2012) Composition and similarity of bovine rumenmicrobiota across individual animals. PLoS One 7:e33306. doi:10.1371/journal.pone.0033306

Jami E, Israel A, Kotser A, Mizrahi I (2013) Exploring the bovine rumenbacterial community from birth to adulthood. ISME J 7:1069–1079.doi:10.1038/ismej.2013.2

Kong Y, Teather R, Forster R (2010) Composition, spatial distribution,and diversity of the bacterial communities in the rumen of cows feddifferent forages. FEMSMicrobiol Ecol 74:612–622. doi:10.1111/j.1574-6941.2010.00977.x

Krause DO, Denman SE, Mackie RI, MorrisonM, Rae AL, Attwood GT,McSweeney CS (2003) Opportunities to improve fiber degradationin the rumen: microbiology, ecology, and genomics. FEMSMicrobiol Rev 27:663–693

Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H, Toyoda A, TakamiH, Morita H, Sharma VK, Srivastava TP, Taylor TD, Noguchi H,Mori H, Ogura Y, Ehrlich DS, Itoh K, Takagi T, Sakaki Y, HayashiT, Hattori M (2007) Comparative metagenomics revealed common-ly enriched gene sets in human gut microbiomes. DNARes 14:169–181. doi:10.1093/dnares/dsm018

Lawrence P, Kenny DA, Earley B, Crews DH Jr, McGee M (2011) Grasssilage intake, rumen and blood variables, ultrasonic and body mea-surements, feeding behavior, and activity in pregnant beef heifersdiffering in phenotypic residual feed intake. J Anim Sci 89:3248–3261. doi:10.2527/jas. 2010-3774

Lettat A, Hassanat F, Benchaar C (2013) Corn silage in dairy cow diets toreduce ruminal methanogenesis: effects on the rumen metabolicallyactive microbial communities. J Dairy Sci 96:5237–5248. doi:10.3168/jds. 2012-6481

Li RW, Li C (2006) Butyrate induces profound changes in gene expres-sion related to multiple signal pathways in bovine kidney epithelialcells. BMC Genomics 7:234. doi:10.1186/1471-2164-7-234

Li CJ, Li RW (2008) Butyrate induced cell cycle arrest in bovine cellsthrough targeting gene expression relevant to DNA replicationapparatus. Gene Regul Syst Biol 2:113–123

Macy JM, Probst I (1979) The biology of gastrointestinal bacteroides.Annu Rev Microbiol 33:561–594. doi:10.1146/annurev.mi.33.100179.003021

McCann JC, Wiley LM, Forbes TD, Rouquette FM, Tedeschi LO(2014) Relationship between the rumen microbiome and re-sidual feed intake-efficiency of Brahman bulls stocked onbermudagrass pastures. PloS One 9:e91864 doi:10.1371/journal.pone.0091864

Matsui H, Ogata K, Tajima K, Nakamura M, Nagamine T, Aminov RI,Benno Y (2000) Phenotypic characterization of polysaccharidasesproduced by four Prevotella type strains. Curr Microbiol 41:45–49

Parks DH, Beiko RG (2010) Identifying biologically relevant differencesbetween metagenomic communities. Bioinformatics 26:715–721.doi:10.1093/bioinformatics/btq041

Pitta DW, Pinchak E, Dowd SE, Osterstock J, Gontcharova V, Youn E,Dorton K, Yoon I, Min BR, Fulford JD, Wickersham TA,Malinowski DP (2010) Rumen bacterial diversity dynamics associ-ated with changing from bermudagrass hay to grazed winter wheatdiets. Microb Ecol 59:511–522. doi:10.1007/s00248-009-9609-6

Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, NielsenT, Pons N, Levenez F, Yamada T, Daniel R,Mende LJ, Xu J, Li S, LiD, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P,Bertalan M, Batto J, Hansen T, Le Paslier D, Linneberg A, NielsenHB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, YuC, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, WangJ, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, ParkhillJ, Weissenbach J, Bork P, Ehrlich SD, Wang J (2010) A human gutmicrobial gene catalogue established by metagenomic sequencing.Nature 464:59–65. doi:10.1038/nature08821

Qu A, Brulc JM, Wilson MK, Law BF, Theoret JR, Joens LA, KonkelME, Angly F, Dinsdale EA, Edwards RA, Nelson KE, White BA(2008) Comparative metagenomics reveals host specificmetavirulomes and horizontal gene transfer elements in the chickencecum microbiome. PLoS One 3:e2945. doi:10.1371/journal.pone.0002945

Reichardt N, Duncan SH, Young P, Belenguer A, McWilliam Leitch C,Scott KP, Flint HJ, Louis P (2014) Phylogenetic distribution of threepathways for propionate production within the human gut microbi-ota. ISME J 8:1323–1335. doi:10.1038/ismej.2014.14

Russell JB, Muck RE, Weimer PJ (2009) Quantitative analysis ofcellulose degradation and growth of cellulolytic bacteria inthe rumen. FEMS Microbiol Ecol 67:183–197. doi:10.1111/j.1574-6941.2008.00633.x

Sergeant MJ, Constantinidou C, Cogan TA, Bedford MR, Penn CW,Pallen MJ (2014) Extensive microbial and functional diversity

Appl Microbiol Biotechnol

Author's personal copy

within the chicken cecal microbiome. PLoS One 9:e91941. doi:10.1371/journal.pone.0091941

Singh KM, Ahir VB, Tripathi AK, Ramani UV, Sajnani M, Koringa PG,Jakhesara S, Pandya PR, Rank DN, Murty DS, Kothari RK, JoshiCG (2012) Metagenomic analysis of Surti buffalo (Bubalus bubalis)rumen: a preliminary study. Mol Biol Rep 39:4841–4848. doi:10.1007/s11033-011-1278-0

Stevenson DM, Weimer PJ (2007) Dominance of Prevotella and lowabundance of classical ruminal bacterial species in the bovine rumenrevealed by relative quantification real-time PCR. Appl MicrobiolBiotechnol 75:165–174. doi:10.1007/s00253-006-0802-y

Tajima K, Aminov RI, Nagamine T, Matsui H, Nakamura M, Benno Y(2001) Diet-dependent shifts in the bacterial population of the rumenrevealed with real-time PCR. Appl Environ Microbiol 67:2766–2774. doi:10.1128/AEM. 67.6.2766-2774.2001

Turnbaugh PJ (2009) A core gut microbiome in obese and lean twins.Nature 457:480–484. doi:10.1038/nature07540

Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, GordonJI (2006) An obesity-associated gut microbiome with increasedcapacity for energy harvest. Nature 444:1027–1031. doi:10.1038/nature05414

Turnbaugh PJ, Backhed F, Fulton L, Gordon JI (2008) Diet-inducedobesity is linked to marked but reversible alterations in the mousedistal gut microbiome. Cell HostMicrobe 3:213–223. doi:10.1016/j.chom.2008.02.015

Uden P, Rounsaville TR, Wiggans GR, Van Soest PJ (1982) The mea-surement of liquid and solid digesta retention in ruminants, equinesand rabbits given timothy (Phleum pratense) hay. Br J Nutr 48:329–339

Verberkmoes NC, Russell AL, Shah M, Godzik A, Rosenquist M,Halfvarson J, Lefsrud MG, Apajalahti J, Tysk C, Hettich RL,Jansson JK (2009) Shotgun metaproteomics of the humandistal gut microbiota. ISME J 3:179–189. doi:10.1038/ismej.2008.108

Walker AW, Duncan SH, Harmsen HJ, Holtrop G, Welling GW, Flint HJ(2008) The species composition of the human intestinal microbiotadiffers between particle-associated and liquid phase communities.Environ Microbiol 10:3275–3283. doi:10.1111/j.1462-2920.2008.01717.x

Wang X, Heazlewood SP, Krause DO, Florin TH (2003) Molecularcharacterization of the microbial species that colonize human ilealand colonic mucosa by using 16S rDNA sequence analysis. J ApplMicrobiol 95:508–520

Whitford MF, Forster RJ, Beard CE, Gong J, Teather RM (1998)Phylogenetic analysis of rumen bacteria by comparative sequenceanalysis of cloned 16S rRNA genes. Anaerobe 4:153–163. doi:10.1006/anae.1998.0155

Wright AD, Klieve AV (2011) Does the complexity of the rumen micro-bial ecology preclude methane mitigation? Anim Feed Sci Technol166–167:248–253

Appl Microbiol Biotechnol

Author's personal copy