design and in vitro evaluation of new rpob-dgge primers for ruminants

R E S E A R C H A R T I C L E

Designand invitro evaluationof newrpoB-DGGEprimers forruminantsSudeep Perumbakkam1 & A. Morrie Craig2

1Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR, USA; and 2College of Veterinary Medicine, Oregon State University,

Corvallis, OR, USA

Correspondence: Sudeep Perumbakkam,

Environmental and Molecular Toxicology,

Oregon State University, 139 Oak Creek

Building, Corvallis, OR 97331, USA.

Tel.: 11 541 737 6541; fax: 11 541 737

2730; e-mail: sudeep.perumbakkam@

oregonstate.edu

Received 26 March 2010; revised 17 November

2010; accepted 14 December 2010.

Final version published online 24 January 2011.

DOI:10.1111/j.1574-6941.2011.01042.x

Editor: Julian Marchesi

Keywords

rpoB; DGGE; phylogenetics; rumen.

Abstract

Two new primer sets based on the rpoB gene were designed and evaluated with

bovine and ovine rumen samples. The newly developed rpoB-DGGE primer set

was used along with the 16S rRNA gene-V3, and another (old) rpoB-DGGE-based

primer set from a previous study to in vitro compare the bovine and ovine rumen

ecosystems. The results indicate a significant (Po 0.001) difference in the

microbial population between the two ruminants irrespective of the primers used

in the analysis. Qualitative comparison of the data provides evidence for the

presence of similar phyla profiles between the 16S rRNA gene and the newly

developed rpoB primers. A comparison between the two rpoB-based primer sets

(old and new) showed that the old rpoB-based primers failed to amplify phylum

Bacteroidetes (a common phylum in the rumen) in both bovine and ovine rumen

samples. The old and new rpoB-DGGE-based primers amplified a large number of

clones belonging to phylum Proteobacteria, providing a useful insight into the

microbial structure of the rumen. ChaoI, ACE, Simpson, and Shannon–Weaver

index analysis estimated the bovine rumen to be more diverse than the ovine

rumen for all three primer sets. These results provide a new insight into the

community structure among ruminants using the newly developed primers in this

study.

Introduction

The gastrointestinal tract of the ruminant is comprised of a

complex ecosystem that plays an important role in provid-

ing necessary nutrition to the host and is involved in the

maintenance of animal health (Hungate, 1966). The micro-

bial population of the rumen is composed of numerous yet

undetermined numbers of protozoal, fungal, archaeal, and

bacterial species (Hungate, 1966; Russell & Rychlik, 2001;

Edwards et al., 2004). Like other microbial ecosystems, the

species present and their relative abundance can change with

time, age of the animal, seasons of the year, use of therapeu-

tic agents, growth-promoting antibiotics, and the diet of the

host animal (Dehority & Orpin, 1997).

The use of gene sequences as molecular markers for

determining evolution and phylogenetic relationships was

first proposed by Zuckerkandl and Pauling (1965). Woese

and colleagues (Fox et al., 1980; Woese, 1987) introduced a

novel classification system that utilizes the small subunit

rRNA gene as a universal marker for classifying microorgan-

isms. The 16S rRNA gene has provided microbiologists with

the necessary tool to identify and evaluate microbial com-

munities by direct amplification (Woese et al., 1975; Woese

et al., 1976). Most microbial ecologists, as well as microbial

pathologists, have utilized the 16S rRNA gene marker to

understand microbial community structure and function

(Deutschbauer et al., 2006; Green & Keller, 2006; Xu, 2006)

and in specific ecosystems such as cow rumen under

different feeding strategies (Zhou et al., 2009), human gut

flora (Ley et al., 2006; Ley et al., 2008a), and in enumerating

ocean bacteria (Rappe et al., 2002).

The 16S rRNA gene maker, however, has two limitations

when used to analyze complex microbial communities. The

first limitation is the heterogeneity in gene copy number

(1–13 copies) among bacterial species, making it inaccurate

when estimating population changes (Dahllof et al., 2000;

Case et al., 2007). The second limitation of this marker is its

inability to differentiate closely related species/strains, which

FEMS Microbiol Ecol 76 (2011) 156–169c� 2011 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

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makes it difficult to accurately analyze the lineages of

various medically important bacteria (Kim et al., 2003;

Khamis et al., 2004). These disadvantages have led research-

ers to look at alternative universally present genes that occur

as a single copy and can be used in conjunction with the 16S

rRNA gene marker.

Housekeeping genes such as dnaK (Griffiths & Gupta,

2001; Stepkowski et al., 2003; Eardly et al., 2005), gyrB

(Rodrigues & Tiedje, 2007; Volokhov et al., 2007), and rpoB

(Mollet et al., 1997; Bourne & Munn, 2005; Rigouts et al.,

2007) have become increasingly popular in understanding

bacterial systematics and evolutionary processes (Kupfer

et al., 2006). These markers are also ubiquitous in prokar-

yotes, occur in a single gene copy, and have less susceptibility

to lateral gene transfer (Case et al., 2007). In particular, the

RNA polymerase b subunit (rpoB) gene provides a better

phylogenetic resolution either as a nucleotide or as a protein

sequence (Case et al., 2007). Like the 16S rRNA gene marker,

the rpoB gene consists of both conserved and variable

regions. Studies have indicated that the hypervariable region

of the rpoB gene is located between positions 2300 and

3300 bp of the gene (Adekambi et al., 2009). Further, due to

the size of the gene (approximately 3500 bp), there is greater

flexibility in designing probes and primers for differentiat-

ing bacteria at the inter/intra species level.

The objectives of this study were to design, validate, and

compare in vitro new rpoB-based primers for denaturant

gradient gel electrophoresis (DGGE) and cloning (larger

fragment amplifying) to estimate bacterial populations in

the rumen. The robustness of the newly developed primer

sets was tested with pure culture isolates and in vitro rumen

samples obtained from the bovine (Bos taurus) and ovine

(Ovis aries). Further, the new rpoB-DGGE primers were

compared with a previously developed old rpoB-DGGE

primer set (Dahllof et al., 2000) for its efficiency to amplify

pure culture isolates and rumen communities. The new

rpoB-DGGE phylogenetic associations were compared with

in silico generated 16S rRNA gene trees to elucidate tree

topology differences. DGGE, cloning, and phylogenetic

analysis were performed to determine the species composi-

tion of the rumen microbial community present in these

ruminant samples.

Materials and methods

Bacterial cultures and media

A list of bacteria, media, and growth conditions used in this

study is described elsewhere (Supporting Information, Table

S1). Aerobic bacterial cultures of bacteria were grown either

on tryptic soy broth or on Luria–Bertani (LB) broth (Elbing

& Brent, 2002). Complex media (Supporting Information)

were used for the routine growth of anaerobic bacteria.

Bacterial cultures were transferred from a cryo-freezer and

grown at appropriate temperatures (Table S1) for 2 days,

before extraction of genomic DNA.

Cattle and sheep diets, experimental setup, andwhole rumen fluid (WRF) collection

WRF samples were collected from fistulated Holstein cows

(n = 2) and from fistulated wethers (n = 3). The Holstein

cattle were fed a diet of 63% grass hay, 30% concentrates,

and 7% crude protein. The sheep diet consisted of 100%

grass hay. Animals were housed in an independent semi-

enclosed barn enclosure. A 30 mL volume of rumen fluid

was collected from each animal (bovine or ovine) in a sterile

prewarmed CO2-flushed thermos. The samples were trans-

ported to the laboratory, respective samples were pooled,

homogenized, and DNA was isolated immediately.

Genomic and plasmid DNA isolation

Genomic DNA was extracted using the Gentra puregene kit

(Qiagen, Valencia, CA). Plasmid DNA was extracted as per

ABI’s (Applied Biosystems, Foster City, CA) modified alka-

line lysis protocol using PEG 8000 and chloroform.

Design of new rpoB primers

A total of 141 rpoB sequences, elucidated from whole

genome sequences, were obtained from the GenBank data-

base (Benson et al., 2010). Multiple alignments were per-

formed using CLUSTAL W v1.8 (Thompson et al., 1994). The

alignments were imported into a computer program called

GENEIOUS (Biomatters Ltd, Auckland) (Drummond et al.,

2010), and based on the percentage of nucleotide consensus,

regions of homology were chosen (Rozen & Skaletsky,

2000). Potential primer pairs were analyzed using the

nucleotide BLAST tool (Altschul et al., 1990). Primer sets were

designed to amplify a shorter DGGE fragment and a longer

cloning fragment. A GC clamp was added to the forward

DGGE primer set (Muyzer et al., 1993).

PCR setup

The reagents used were common to all PCR reactions. Two

separate PCR reactions were performed for amplifying the

DGGE fragment and the longer rpoB fragment. For the

DGGE fragment, PCR thermocycling was carried out using

recombinant AmpliTaqs Gold polymerase (Applied Biosys-

tems) in a PTC-200 thermocycler (MJ Research Inc., Water-

town, MA). PCR conditions were optimization with the

following parameters: amount of DNA, primer annealing

temperature, Mg1 concentration, primer concentration,

bovine serum albumin (BSA), and the type of polymerase.

Based on these optimization reactions, the final PCR proto-

col is given below. Each 50 mL PCR reaction contained 75 ng

FEMS Microbiol Ecol 76 (2011) 156–169 c� 2011 Federation of European Microbiological SocietiesPublished by Blackwell Publishing Ltd. All rights reserved

157rpoB-DGGE-based comparison of rumen ecosystem

of purified chromosomal DNA from WRF or o 10 ng of

pure culture DNA, 200 mmol of each dNTP, 5 mL of

10� PCR buffer, 5 mL of 25 mM MgCl, 20 ng of BSA, each

primer concentration at 25 pmol, 0.25 U polymerase, and

the remaining volume made up with sterile double-distilled

H2O. Amplification of the DGGE rpoB fragment was

achieved using a touchdown thermocycling program that

consisted of an initial 9-min denaturation cycle at 95 1C,

followed by subsequent denaturation at 95 1C for 20 s. The

initial PCR reaction was performed at 57 1C with a reduc-

tion of 0.5 1C at each cycle for 15 cycles. The annealing time

was 45 s for each cycle and the final touchdown temperature

was set at 49 1C for an additional 15 cycles. A final extension

was performed at 72 1C for 10 min. For the longer rpoB

fragment, the reaction mixture constituents were similar to

the DGGE setup described above. The PCR amplification

was performed under the following conditions: the initial

denaturation temperature was set at 95 1C for 9 min and the

subsequent denaturation at 95 1C for 20 s, annealing at 53 1C

for 40 s, and a primer extension at 72 1C for 1.5 min for 25

cycles with a final extension time of 10 min at 72 1C.

The hypervariable region, V3, of the 16S rRNA gene was

also used as a gene marker for amplifying the rumen

bacterial population of the two ruminants. The primers

and protocol for amplifying the 16S rRNA gene-V3 gene

marker have been described previously (Muyzer et al., 1993).

The old rpoB-based primer set was amplified as per pre-

viously published protocols (Dahllof et al., 2000). PCR

products were run on a 1.2% agarose gel and stained with

ethidium bromide.

Cloning, archiving of PCR products,and plasmid extraction

PCR products were visualized using a 1.2% agarose gel and

products purified using the QIAquick PCR purification kit

(Qiagen) according to the manufacturer’s recommenda-

tions. PCR products were quantified using a Nanodrop

ND-1000 spectrophotometer (Thermo Fisher, Waltham,

MA), cloned using the TOPOs TA Cloning Kit for sequen-

cing (Invitrogen Corporation, Carlsbad, CA) and trans-

formed into competent Escherichia coli cells as per

manufacturer’s recommendations. Transformants were pla-

ted on LB agar (EMD Chemicals Inc., Gibbstown, NJ)

supplemented with 50mg mL�1 kanamycin (EMD Chemi-

cals Inc.) and incubated overnight at 37 1C. Picked clones

were archived in 1.5 mL LB supplemented with 50 mg mL�1

kanamycin and 1�Hogness buffer (Sebat et al., 2003).

Colonies were stored at � 80 1C. The colonies were retrans-

ferred into LB broth supplemented with 50mg mL�1 kana-

mycin and grown overnight at 37 1C for plasmid isolation.

Plasmid DNA was extracted using the QIAprep spin kit

(Qiagen), quantified (Nanodrop), and stored at � 20 1C.

DGGE setup

The DCode system for DGGE (BioRad, Hercules, CA) was

used to analyze PCR fragments. The rpoB PCR products

were separated on an 8% polyacrylamide (37.5 : 1, acryl-

amide/bis-acrylamide) (BioRad) gel with a denaturing gra-

dient of 30–65%. The rpoB PCR products were mixed with

10 mL of 2� loading dye (BioRad) and brought to a final

volume of 20 mL with TE (pH 8.0) to yield a DNA concen-

tration of 10–15 ng for the pure culture samples. DNA

concentrations at 100 ng per well were used for the whole

rumen samples. Gels were run for 16 h at 60 V in

1�Tris–acetate–EDTA (TAE) at 60 1C. Gels were either

stained using SYBR Gold stain (Invitrogen Corporation) or

silver staining (BioRad) according to the manufacturer’s

instructions. Gels were visualized on a fluorescent light box

(Star X-ray, Amityville, NY) and gel images were captured

using a digital camera.

Sequencing and phylogenetics

Sequencing was performed using the BigDyes Terminator v.

3.1 Cycle Sequencing Kit (Applied Biosystems) using an ABI

Prisms 3730 Genetic Analyzer at the Center for Genomic

Research and Biocomputing (CGRB), Oregon State Univer-

sity. For sequencing, cloned DGGE products from the 16S

rRNA gene, old rpoB, and new rpoB clones were read

utilizing either the T3 or the T7 primer end. The longer

rpoB fragments were read from both directions of the

TOPOs cloning vector. The raw sequence files were ex-

tracted, processed, and compared with existing sequences

using the BLASTN algorithm (Altschul et al., 1990). Based on

E-value hits, sequences were included in the final alignment.

The sequences were aligned using the MUSCLE alignment

program (Edgar, 2004). The NEXUS alignment was modified

to an XML-formatted file using the BEAUTI program included

in the BEAST program suite (Drummond & Rambaut, 2007)

with the addition of the GTR model and generation time.

The nucleotide alignment was run twice for 10 million

generations and the resulting tree files from both runs were

combined using the LOGCOMBINER package provided with the

BEAST program. Of the 20 000 trees that were produced, 50%

of the trees were excluded from the final analysis. The final

consensus tree was viewed and edited using the software

program FIGTREE (http://tree.bio.ed.ac.uk/software/figtree/).

The RDPII website tools CLASSIFIER and LIBCOMPARE were

used for processing the 16S rRNA gene-V3 clone data. For

the rpoB-associated data, manual classification was under-

taken using the GenBank database (Benson et al., 2010). The

MOTHUR software package v 1.11.0 (Schloss et al., 2009) was

used for the analysis of collectors, rarefaction curves,

Libshuff analysis, and to calculate diversity indices such as

ACE, Simpson, and Shannon–Weaver Indices. All sequence

data were submitted to the GenBank database under


158 S. Perumbakkam & A.M. Craig

http://tree.bio.ed.ac.uk/software/figtree/

accession numbers (GU304662–GU305194, GU305195–GU

305532, and GU305533–GU305690).

In silico analysis of 16S rRNA gene fragments

From the GenBank database, 23 full-length 16S rRNA gene

sequences were obtained. These 16S rRNA gene sequences

were aligned using the MUSCLE program mentioned above. A

Bayesian phylogenetic tree was constructed using the para-

meters mentioned in the above section.

Results

Design for the new rpoB-based DGGE and largefragment primers

Out of the total 141 primary sequences imported from

GenBank, 39 sequences from the phyla [Proteobacteria (16),

Firmicutes (11), Actinobacteria (8), Bacteroidetes (2), Aquificae

(1), and Deinococcus-Thermus (1)] (Table S2) were finally

included for primer development. Ten primer sets were

initially developed (data not shown) and only one set of rpoB-

DGGE primers (Table 1) could amplify all the test bacteria.

The new DGGE forward primer aligned to E. coli position

3205–3221 and the reverse primer aligned to E. coli position

3817–3834 of the rpoB gene. The forward primer amplified a

part of the rpoB variable region (approximately 70 bp)

(Adekambi et al., 2009). The rpoB-DGGE forward primer

had a degeneracy of R at the 13th nucleotide that represents

a base substitution for purine (A) or a pyrimidine (G) near

the 30 end of the forward primer (Table 1). Primer sets

designed without the degeneracy amplified fewer bacterial

strains (16 bacterial strains vs. 27 bacterial species with the

degeneracy) (data not shown). PCR amplification of E. coli

resulted in a 620-bp fragment (data not shown). There were

visible size differences in PCR products between Gram-

positive and Gram-negative bacterial strains (Fig. S4b). The

Gram-positive bacterial strains generated a PCR fragment

close to 450–500 bp, while Gram-negative bacterial strains

generated a 550–650-bp fragment. For the longer cloning

fragment, the forward primer aligned to E. coli position

2047–2066, while the reverse primer was the same as that

used in the DGGE primer design (Table 1) and aligned to

E. coli positions 3817–3834 bp. The product formed with

this amplification would amplify the entire hypervariable

region of the rpoB gene, which is situated from 2300 to

3300 bp of the gene (Adekambi et al., 2009).

The DGGE primer set was tested against 27 pure cultures

of bacteria (21 rumen-associated bacteria and six general

bacterial species) (Table 2) to evaluate the formation of

single bands (Figs S1–S3). The primers also elucidated visual

interspecies differences within the genus Ruminococcus sp.

(Fig. S1, lanes 5, 6, and 7 represent three species of

Ruminococcus sp. namely Ruminococcus albus 7, R. albus 8,

and Ruminococcus flavenflaciens C94).

For the longer fragment, 19 bacterial species were tested.

Eight bacterial species produced PCR bands with high

intensities and 11 bacterial strains produced PCR products

with light intensities (Table 2) (Fig. S5). Although band

intensities were light, there was enough PCR product for

downstream analysis, i.e. cloning and sequencing. Butyrivi-

brio fibrisolvens 49 (Table 2) (Fig. S5, lane 12) was the only

bacterial isolate that failed to amplify, although a related

strain B. fibrisolvens D1 (Fig. S5, lane 11) (Table 2) produced

PCR products. Differences in the length of the PCR products

were similar to the DGGE products. Gram-negative bacteria

generated longer fragments (E. coli fragment length 1800 bp)

than Gram-positive bacteria (approximately1300–1400 bp).

Comparison between old and new rpoB-DGGEprimer sets

The robustness of the newly developed primers was com-

pared with the old rpoB-DGGE primers from a published

study (Dahllof et al., 2000) (Table 2). Twenty-five strains of

rumen bacteria were tested with the new rpoB-DGGE

primers developed in this study. Twenty-four DNA samples

amplified with very good intensity, with the exception of B.

fibrisolvens nxy, (Table 2) (Fig. S4b, lane 14), which showed

no product formation. The primers amplified B. fibrisolvens

D1 (Fig. S4b, lane 15), a closely related species to B.

fibrisolvens nxy. The same set of 25 bacterial strains was also

used to amplify the old rpoB-DGGE primer set. The old

rpoB-DGGE primers amplified no more than seven bacteria

(Table 2) and resulted in light product formation in six

other test bacteria (Fig. S4a).

Analysis of new rpoB large fragment amplifyingprimers

A longer rpoB amplifying fragment was also tested using the

newly developed primers. Of the 19 clones that were

amplified with this primer set (Fig. S5), only five bacterial

species were sequenced. The sequenced representatives

Table 1. The rpoB-DGGE and cloning primers developed and used in

this study

Primer name Nucleotide sequence Reference

New rpoB-DGGE (F) 50-TCA CGG TAA CAA RGG-30� This study

New rpoB-DGGE (R) 50-AGT GCC CAT ACT TCC AT-30 This study

Long fragment (F) 50- GCG AAC ATG CAA CGT CAG

GC-30This study

Long fragment (R) 50-AGT GCC CAT ACT TCC AT-30 This study

Old rpoB-DGGE (F) 50-AAC ATC GGT TTG ATC AAC-30� Dahllof et al.

(2000)

Old rpoB-DGGE (R) 50-CGT TGC ATG TTG GTA CCC AT-30 Dahllof et al.

(2000)

�Represents GC clamp added to the 50 end of the primer.



also included PCR products from lower intensity bands. The

sequenced bacteria were B. fibrisolvens, Fibrobacter succino-

genes S85, Streptococcus caprinus 2.3, Selenomonas ruminan-

tium PC-18, and R. flavenflaciens C94. The percentage

identity of the BLAST hits occurred in the range of 78–97%

(Table 3). Three sequenced clones (B. fibrisolvens, S. capri-

nus, and R. flavenflaciens C94) showed close matches to

known rpoB references. The clone of F. succinogenes

S85 matched other rpoB sequences with lesser specificity.

Selenomonas ruminantium PC-18 had the least coverage at

94% and a maximum identity of 87%.

Phylogenetic classification of rumen fluid by the16S rRNA gene-DGGE (V3) marker

There were visual differences (not quantified) in the banding

pattern in the bacterial subpopulation of rumen fluid [Fig.

1, lanes D (Bovine) and A (Ovine)]. The PCR products were

cloned and sequence data were processed using the RDPII

Classifier (Wang et al., 2007) at a cutoff at 50% (Claesson

et al., 2009).

In the bovine rumen treatment, 168 sequences were used

to quantify the operational taxonomic units (OTUs). The

RDP Classifier placed the majority of sequences into two

main phyla. The largest OTU section was assigned to phylum

Firmicutes and followed by the phylum Bacteroidetes (Table

4). Minor components of the bovine rumen were composed

of the phyla TM7, Proteobacteria, and SR (Table 4). Nearly

8% of the sequences were unclassified bacteria.

At the genera level, of the115 clones that were classified as

phylum Firmicutes, the clones were distributed among 15

genera, with the three largest clone associations belonging

to unclassified Firmicutes, Butyrivibrio, and Fastidiosipila

(Table S3). The genera associated with the phylum

Table 2. Bacterial strains used to test the newly developed rpoB primers, the comparison of primers, and development of the large fragment amplifying

primers

Phylum Bacterial cultures (Genus species) Source DGGE�

DGGE primer comparisonw

Large fragmentzThis study Dalhoff

Actinobacteria

Bacteroidetes

Peptostreptococcus heliotrinreducens Rumen 1 1 � NT

Prevotella ruminicola GA33 Rumen 1 1 � P

Prevotella albensis M384 Rumen 1 1 P 1

Fibrobacteres

Firmicutes

Fibrobacter succinogenes S85 Rumen 1 1 P P

Streptococcus bovis IFO 12057 Rumen 1 1 1 1

Clostridium pasteurianum 5 Rumen 1 1 � 1

Lactobacillus vitulinus T-185 Rumen 1 1 � 1

Streptococcus caprinus 2.3 Rumen 1 1 P 1

Ruminococcus albus 7 Rumen 1 1 � NT

Ruminococcus albus 8 Rumen 1 1 � NT

Ruminococcus flavenflaciens C94 Rumen 1 1 � P

Streptococcus bovis JB1 Rumen 1 1 1 1

Staphylococcus epidermidis Human 1 1 P NT

Staphylococcus aureus Human 1 1 P 1

Megasphaera elsdenii T 81 Rumen 1 1 1 1

Butyrivibrio fibrisolvens D1 Rumen 1 1 1 1

Butyrivibrio fibrisolvens nxy Rumen NT � 1 �Selenomonas ruminantium PC-18 Rumen 1 1 � 1

Selenomonas ruminantium HD4 Rumen 1 1 � P

Succinogenes dextrisolvens Rumen 1 1 1 1

Anaerovibrio lipolytica Rumen 1 1 1 P

Bacillus subtilis Soil 1 DNS NT NT

Eubacterium ruminantium GA 195 Rumen 1 1 � 1

Proteobacteria Salmonella typhimurium Soil 1 1 P NT

Pseudomonas fluorescens Soil 1 DNS NT NT

Vibrio cholerae Soil 1 DNS NT NT

Klebsiella pneumoniae Soil 1 DNS NT NT

Escherichia coli Soil 1 1 1 1/DNS

�Amplification of pure cultures using the DGGE primers developed in this study.wComaprison of DGGE primers developed in this sudy against Dalhoff primers (Fig. S4).zFig. S5.

1, Postive amplification; � , no amplification; NT, not tested; P, partial amplification (enough products for cloning); DNS, data not shown.



Bacteroidetes were Prevotella and Paraprevotella (Table S3).

The similarity of the RDPII hits for all clones ranged from

70% to 99% (data not shown).

In the ovine rumen sample, the RDPII Classifier placed

most sequences into the phyla Bacteroidetes and Firmicutes.

The minor components of the ovine rumen were composed

of the phyla TM7, Proteobacteria, Spirochetes, and Actino-

bacteria (Table 4). Seventeen percent of the sequences were

comprised of unclassified bacteria.

At the genera level, of the 90 clones that were classified as

phylum Bacteroidetes, the majority of clones belong to the genus

Prevotella (Table S4). The phylum Firmicutes was comprised of

10 genera, with the phyla Butyrivibrio forming the single largest

population. Phylum TM7 had seven clones that were associated

with the genus TM7 genera incertae sedis (Table S4).

A Libshuff analysis showed that the difference between

the microbial communities in the two ruminates was

significant (Po 0.0001). To deduce the phyla responsible

for this difference, a LIBCOMPARE analysis (Wang et al., 2007)

was performed showing significance in clone libraries asso-

ciated with the phyla Bacteroidetes (Po 0.0001) and Firmi-

cutes (Po 0.0001).

Phylogenetic classification of rumen fluid by anold rpoB-DGGE primer

There were visual differences (not quantified) in the banding

patterns in the bacterial populations of the two-rumen fluid

samples [Fig. 1, lanes E (Bovine) and B (Ovine)]. The

bovine rumen samples amplified with the old rpoB-DGGE-

Table 4. Taxonomic classification of 16S rRNA gene-V3 and the two rpoB–DGGE primer clone data

Phylum

Bovinew,# Ovinew,# Bovinez,# Ovinez,# Bovine‰,# Ovine‰,#

No. of

clones Total (%)

No. of

clones Total (%)

No. of

clones Total (%)

No. of

clones Total (%)

No. of

clones Total (%)

No. of

clones Total (%)

Acidobacteria 1 (1.32)

Actinobacteria 1 (0.63) 14 (18.42) 2 (2.47) 3 (3.26)

Bacteroidetes 31 (18.45) 90� (56.96) 33 (36.26) 26 (28.26)

Chlorobi 2 (2.63) 1 (1.10)

Cyanobacteria 1 (1.23) 5 (5.49)

Fibrobacteres 26 (32.10) (13.19) 2 (2.17)

Firmicutes 115� (68.45) 33 (20.89) 16 (21.05) 12 (14.81) 12 (16.48) 2 (2.17)

Proteobacteria 3 (1.79) 1 (0.63) 24 (31.53) 6 (7.14) 15 (23.08) 57 (61.96)

Spirochaetes 1 (0.63) 21 (2.20) 1 (1.09)

SR1 1 (0.60) 2

Tenericutes 2 (2.63) 1 (1.23)

TM7 4 (2.38) 5 (3.16)

Uncla. bacteriaz 14 (8.33) 27 (17.09) 10 (13.16) 7 (8.64) 1 (1.09)

Unclu. bacteriak 7 (9.21) 26 (32.10) 2 (2.20)

Total 168 100.00 158 100.00 76 100.00 81 100.00 91 100.00 92 100.00

Clones were sequenced, trimmed, and chimera was checked. The FASTA files of 16S rRNA gene sequence data were classified using the RDPII Classifier

tool. The rpoB sequences were manually classified using GenBank data.wDNA amplified with 16S rRNA gene-DGGE (V3) primers.zDNA amplified with old rpoB-DGGE primers.‰DNA amplified with new rpoB-DGGE primers developed in this study.zUnclassified bacteria.kUncultured bacteria.�Indicates LIBCOMPARE significance (Po 0.001).#Indicates Libshuff significance between primer pairs (Po 0.001).

Table 3. BLAST hits for the large amplifying rpoB fragment

Bacterial strains

(Genus species)

Closest BLAST hit

(Genus species)

Query

coverage (%)

Max

indent (%) E-value

Length of

sequence (bp)

Butyrivibrio fibrisolvens Butyrivibrio fibrisolvens 99 95 0.0 1455

Fibrobacter succinogenes S85 Streptococcus equinus 99 97 0.0 1306

Strepotoccus caprinus 2.3 Streptococcus equinus 99 93 0.0 1304

Selenomonas ruminantium PC-18 Veillonella detocariosa 94 87 5e�66 1480

Ruminococcus flavenflaciens C94 Ruminococcus sp. (Draft genome) 92 78 5e�86 1508



based primers grouped the majority of the sequences into

the phyla Proteobacteria, Firmicutes, and Actinobacteria. The

minor phyla included Chlorobi, Tenericutes, and Acidobac-

teria (Table 4). The Phylum Proteobacteria comprised 31%

of the total sequences. Thirteen percent of the clones did not

find any suitable match with the GenBank database (no

similarity) and 9% of the sequences were assigned to

uncultivable bacteria. The BLAST hits included sequences that

belonged to 23 different bacterial species (genus/species),

with Acidovorax sp. (12 clones) representing the single most

abundant species (Table S5). Phylum Fibrobacteres-asso-

ciated clones were absent in the bovine rumen samples.

The ovine rumen samples amplified with the old rpoB-

based primers placed 32% of the sequences in the phylum

Fibrobacteres, 32% in uncultured bacteria, 15% in the

phylum Firmicutes, and 7% in the phylum Proteobacteria.

The minor phyla included Cyanobacteria, Tenericutes, and

Acidobacteria (Table 3). Data suggested that there were 13

different genera associated with the clone data and F.

succinogenes made up the single largest represented species

(Table S6).

In order to examine differences in community composi-

tion, the Libshuff analysis was performed. The statistical

analysis showed a significant difference (Po 0.0001) be-

tween the two-rumen samples. Unfortunately, due to the

lack of a LIBCOMPARE-like analysis for the rpoB gene, the

precise phyla responsible for the significance could not be

determined statistically.

Phylogenetic classification of rumen fluid usinga new rpoB-DGGE primer

For the bovine sample, the manual classification using the

taxonomic browser at the phyla level placed 36% of

the sequences into the phylum Bacteroides, 23% into phy-

lum Proteobacteria, 16% into phylum Firmicutes, and 13%

into the phylum Fibrobacteres. The minor phyla included

Cyanobacteria, Spirochaetes, Chlorobi, and Acidobacteria

(Table 4). The clone data were made up of 31 unique

bacterial species, with Bacteroides vulgatus, Chitonophage

pinensis, and F. succinogenes comprising the largest clone

populations (Table S7).

The manual classification of the ovine samples placed the

majority of sequences into two phyla. The phylum Proteo-

bacteria comprised 62% of the total sequences, followed by

28% classified as the phylum Bacteroides. The minor phyla

included Actinobacteria, Firmicutes, Fibrobacteres, and Spir-

Fig. 1. DGGE analysis of 16S rRNA gene and the rpoB gene marker of

bovine and ovine rumen fluid samples. A 30–65% DGGE gel was cast. All

samples were loaded with equal amounts of DNA (100 ng per well) from

purified PCR products. The DGGE was run for 16 h at 60 V and silver

stained. Lane A (ovine) DNA amplified with the 16S rRNA gene-DGGE

(V3) primer set, lane B (ovine) DNA amplified with the old rpoB-DGGE

primer set, lane C (ovine) DNA amplified with the new rpoB-DGGE primer

set, lane D (bovine) DNA amplified with the 16S rRNA gene primer set,

lane E (bovine) DNA amplified with the old rpoB-DGGE primer set, and

Lane F (bovine) DNA amplified with the new rpoB-DGGE primer set.



ochaetes (Table 4). Twenty-three different genus/species

associations were made with the clone data and Desulfovi-

brio desulfuricans represented the single largest clone popu-

lation (Table S8).

The Libshuff analysis was conducted and showed a

difference (Po 0.0001) between the two-rumen samples.

Rarefaction analysis, ACE, Shannon--Weaver,Simpson index of rumen samples

A total of 664 sequences were used for the analysis of both

rumen samples by three gene markers (16S rRNA gene-V3

and two rpoB-based primers). Observed and estimated OTUs

were calculated at 97% sequence similarity or 0.03% distance

for the 16S rRNA gene-V3 primer and 100% or 0.00% for the

rpoB-DGGE primers based on single copy number (Table 5).

The results also show (Tables 5 and 6) the various cutoffs

(0.05% and 0.10%) for all three-gene markers and the

number of OTU assignment in all tested categories.

The observed OTU similarities between the two-rumen

systems were limited to 23 phylotypes with the 16S rRNA

gene primer, one with the old rpoB-DGGE based primer,

and 11 with the new rpoB-DGGE primer (Table 5). The

ChaoI analysis estimated the bovine rumen to consist of

more phylotypes than the ovine rumen irrespective of the

primer used at various cutoffs (Table 5). The rarefaction

curves showed no trend toward reaching a plateau, which

also indicates the need for more sampling in order to

observe all the diversity present in the rumen for all primer

sets (Fig. 2). Between the rpoB primers, the new primer set

estimated more phylotypes in the bovine sample compared

with the old rpoB-DGGE primer set at 100% and 97%

cutoff. At cutoffs set at 95% and 90%, the old rpoB-DGGE

primer set showed higher values than the new rpoB-DGGE

primer set. This trend was very similar in both observed and

estimated OTUs.

ACE, Simpson, and Shannon–Weaver indices were also

used to estimate diversity and species richness in the two

ruminant ecosystems (Table 6). The ACE analysis showed

that the bovine rumen had more phylotypes with the 16S

rRNA gene and new rpoB-DGGE primers. The old rpoB-

DGGE based primers switched these results by showing

greater diversity with the ovine than the bovine rumen

samples. Simpson and Shannon–Weaver index results were

consistent with bovine rumen having more diversity than

Table 5. Phylogenetic analyses of clone data of bovine and ovine sample to determine the observed and estimated OTUs

Observed OTUs Estimated OTUsw

Primers Cutoff (%)� Bovine Ovine Commonz Bovine Ovine Commonw

16S rRNA gene-V3 0.00 NA NA NA NA NA NA

0.03 113.00 86.00 23.00 305.36 173.35 52.92

0.05 96.00 72.00 28.00 243.40 139.57 56.19

0.10 57.00 47.00 22.00 88.50 84.50 50.41

Old rpoB-DGGE 0.00 48.00 42.00 1.00 92.00 79.80 1.00

0.03 43.00 33.00 2.00 80.80 59.25 2.00

0.05 42.00 33.00 2.00 71.54 59.25 2.00

0.10 42.00 30.00 3.00 71.54 49.12 3.00

New rpoB-DGGE 0.00 57.00 38.00 11.00 101.36 78.62 20.75

0.03 44.00 29.00 13.00 65.66 50.37 19.85

0.05 41.00 28.00 13.00 49.66 49.85 21.25

0.10 38.00 24.00 13.00 41.60 33.42 19.00

Sequence data were processed through the MOTHUR program and data represented as 0.03, 0.05, and 0.10% cutoffs for all primer sets.�Estimate at 100%, 97%, 95%, and 90% cutoffs.wChaoI analysis of bovine and ovine samples.zNumber of OTUs common to bovine and ovine treatment.

NA, data not analyzed.

Fig. 2. Rarefaction plot of 16S rRNA gene (V3) and two rpoB-based

OTUs. The estimates plotted represent a distance of 0.03 for the bovine

rumen samples (B) amplified with 16S rRNA gene primers (~), ovine

rumen samples (�) amplified with 16S rRNA gene primers ( ), bovine

samples amplified with the old rpoB-DGGE primers (DB) (�), bovine

rumen samples amplified with the new rpoB-DGGE primers (PB) (m),

ovine rumen samples amplified with the old rpoB-DGGE primers (DO)

(’), and ovine rumen samples amplified with the new rpoB-DGGE

primers (PO)(.).



the ovine rumen, except for old rpoB-DGGE primers. These

species richness scores are comparable to other complex

ecosystems (Borneman & Triplett, 1997).

Analysis of new rpoB and 16S rRNA gene treetopologies

The sequence data from new rpoB-DGGE clones and

reference GenBank BLAST hits were included to generate

a Bayesian tree as mentioned in Materials and methods

(Fig. 3). A similar 16S rRNA gene in silico tree was assembled

using sequences imported from the GenBank database

(Fig. 4)

It has been shown previously that there is a

good phylogenetic association between rpoB- and 16S rRNA

gene-based trees (Case et al., 2007). In general, most of

the bacteria sequenced belonged to the phylum Firmicutes

and tree topologies suggest good associations within this

group with both primers (Streptococcus sp. and Bacillus

sp. sequences). The Corynebacterium sp. (Corynebacterium

freneyi, Corynebacterium xerosis, and Corynebacterium

kutscheri) also formed similar associations with both phylo-

genetic markers. Few sequences did not show complete

congruence between the two primer sets. For example,

in the new rpoB-DGGE tree, phylum Bacteroides was

represented by three strains (Bacteroides vulgates, Prevotella

ruminicola, and Prevotella albensis). One of the

above-mentioned bacteria (P. ruminicola) associated itself

with the reference strain, while the other strain (P. albensis)

associated with Anaerovibrio lipolytica, a member of the

phylum Firmicutes. The P. albensis M384 and A. lipolytica

sequences closely associated with Corynebacterium sp. in the

new rpoB-based tree and showed a different topology in the

16S rRNA gene tree. Because there were no reference rpoB

sequences at the GenBank database, the two strains of

Ruminococcus and Butyrivibrio associated very closely with

each other and were similar to their 16S rRNA gene-based

topologies.

Fig. 3. Bayesian analysis of the rpoB-DGGE

fragments. Nucleotide data and reference BLAST

hit data were aligned using MUSCLE. Bayesian

analysis was performed to 10 million generations

using BEAST. Two runs produced 20 000 trees and

the final burnin set at 50%. GenBank reference

sequences are provided with accession numbers

where applicable and ATCC/DSZM strains are

provided for the test bacteria. Branch lengths

indicate the posterior probability.

Table 6. ACE, Simpson, and Shannon–Weaver index analysis

Primers

Cutoff

(%)�

ACE

Simpson

index

Shannon–Weaver

index

Bovine Ovine Bovine Ovine Bovine Ovine

16S rRNA

gene-V3

0.00 ND ND ND ND ND ND

0.03 368.91 298.53 0.01 0.01 4.63 4.18

0.05 376.11 204.72 0.01 0.02 4.42 3.88

0.10 119.10 130.67 0.03 0.04 3.74 3.30

Old rpoB-

DGGE

0.00 100.19 186.06 0.02 0.05 3.62 3.31

0.03 88.57 67.24 0.02 0.09 3.46 2.85

0.05 78.75 67.24 0.03 0.09 3.44 2.85

0.10 78.75 55.52 0.03 0.12 3.44 2.69

New rpoB-

DGGE

0.00 111.36 78.62 0.02 0.03 3.76 3.19

0.03 75.66 50.37 0.02 0.10 3.59 2.54

0.05 59.66 49.85 0.03 0.26 3.21 2.52

0.10 51.60 33.42 0.03 0.27 3.05 2.43

Sequence data were processed through the MOTHUR program and data

represented as 0.03, 0.05, and 0.10% cutoffs for all primer sets.�Percentage of homology at 100%, 97%, 95%, and 90%.

ND, not determined.



Discussion

The goals of this study were to develop and test new rpoB-

based primer sets (DGGE and a longer fragment amplifying)

specifically for evaluating microbial communities in the

rumen ecosystems. The 16S rRNA gene marker has been

used as a phylogenetic anchor for classifying microorgan-

isms in numerous ecosystems. One important feature for a

gene marker is the presence of both variable and conserved

regions for phylogenetic quantification. Both markers (16S

and rpoB) have variable and conserved regions (Adekambi

et al., 2009), giving them flexibility to be used in microbial

community analysis.

The rpoB gene averages (approximately) 3500 bp and a

single region of homology that encompassed all 141 se-

quences was not found in the development of either a

forward or a reverse primer. Subsequently, the primers were

designed based on the latter part of the rpoB gene using a

reductionist approach. Finally, 39 reference sequences were

selected for developing the new rpoB-DGGE and larger

fragment amplifying primer sets (Table S2). Because very

limited number of rumen bacterial genomes has been

sequenced at the time of primer design, 39 reference

sequences were selected based on the prevalence of certain

phyla in the rumen and the ability to find a region of

consensus during the alignment process. A few phyla were

represented in higher numbers (Firmicutes vs. Bacteroides)

in the final composition of the reference sequences. There

were also differences in the size of the rpoB gene when

compared between Gram-positive (Staphylococcus aureus,

3552 bp) and Gram-negative bacteria (E. coli, 4029 bp). The

occurrence of such significant differences in gene size in

other household gene markers is unclear. The DGGE and

longer fragment amplifying primer sets designed in this

study make use of this region for a simple visual distinction.

This difference was consistent with all the strains analyzed in

this study (Fig. S4b). The primers also amplified a very small

variable region of the rpoB gene (approximately 70 bp) with

the DGGE primer and a complete variable region with the

large fragment amplifying primer set, making them suitable

for distinguishing closely related bacterial species.

In this study, the use of degeneracy at the 30 end of the

forward primer was successful due to the presence of either

an A or a G nucleotide at the specific nucleotide position in

most of the reference sequences. Primer sets designed with-

out degeneracy were selective in amplifying fewer bacterial

strains and failed to outperform the degeneracy-based

primer by 40% in bacteria amplified. These results support

the use of primer sets with a single degeneracy as long as

sequences have a limited number of nucleotide transver-

sions. The new rpoB-DGGE primer set was more robust

than the previously developed old rpoB primer set (Dahllof

et al., 2000) both in pure culture and in vitro testing. The

robustness of the new rpoB-DGGE primer set is due to the

cumulative effect of utilizing more reference sequences in

the design/alignment process (four sequences vs. 39 se-

quences), using a specific degeneracy for the forward primer

and optimization of PCR using a touchdown protocol.

Fig. 4. Bayesian analysis of the 16S rRNA gene

sequences. Nucleotide sequences were aligned

using MUSCLE. Bayesian analysis was performed to

10 million generations using BEAST. Two runs

produced 20 000 trees and the final burnin set at

50%. GenBank reference sequences are

provided with accession numbers. Branch

lengths indicate the posterior probability.



With an exponential increase in sequenced bacterial

genomes, an in silico analysis was performed to test the

amplification potential of the new rpoB-DGGE primers. The

primers matched 240 rpoB sequences derived from bacterial

genomes, with few genera represented by single isolates. The

significantly represented genera were Acinetobacter, Bacillus,

Desulfovibrio, Enterobacter, Escherichia, Eubacterium, Geoba-

cillus, Geobacter, Haemophilus, Klebsiella, Paenibacillus, Sta-

phylococcus, Streptococcus, Vibrio, and Yersinia (data not

shown). Although in silico analysis with sequenced genomes

estimated the potential amplification range, both the new

rpoB primers (DGGE and longer amplifying) would need

further evaluation to be used in other ecosystems.

The 16S rRNA gene marker was used as a reference

classification for the two-rumen samples. In the present

study, using the 16S rRNA gene marker, it was determined

that the two phyla predominantly present in the bovine and

ovine rumen are Firmicutes and Bacteroidetes. These results

correlate well with other published research that has char-

acterized the rumen using the 16S-based gene marker

(Whitford et al., 1998; Tajima et al., 2001; Nelson et al.,

2003; Edwards et al., 2004; An et al., 2005). Prevotella sp. has

been reported to be the most dominant genus and account

for 42–60% of the bacterial 16S rRNA gene sequences

(Stevenson & Weimer, 2007). In the present study, the genus

Prevotella accounted for 13% of the sequences in the bovine

samples and 32% of sequences for the ovine samples.

Although, in this study, the rumen was not sampled in its

entirety and similar numbers of clones were not used for

comparison, this study’s data qualitatively represent the

major phyla present in the rumen. The16S rRNA gene-

DGGE primers and the newly designed rpoB-DGGE-based

primers showed similar phyla-level-based associations

(Table 4). The old rpoB-DGGE primers failed to amplify the

phylum Bacteroidetes in both the pure culture as well as the in

vitro analysis. It has been shown previously that bacteria

associated with the phylum Bacteroidetes are a principal

component of the rumen flora and are mainly associated

with fiber degradation (Koike et al., 2003).

Both the rpoB primer sets (old and new rpoB-DGGE

primer sets) provided a unique perspective on the rumen

community structure by reporting a large percentage of

clones associated with the phylum Proteobacteria. Percen-

tages of clones associated with phylum Proteobacteria were

more predominant in the bovine rumen sample amplified

with both rpoB-DGGE gene markers (23% clones with the

new rpoB-DGGE primer set and 24% with the old rpoB-

DGGE primers) when compared with the 16S rRNA gene-

V3 gene marker (3% of clones). Because members of the

phylum Proteobacteria are generally associated with soil,

these microorganisms could be a transient population

entering the rumen due to grazing. Other possible introduc-

tions could be due to the variety of grass fed or a function of

animal enrichment/physiology. A useful approach for

determining the truly dominant components of the gastro-

intestinal ecosystem would involve next-generation sequen-

cing combined with direct in situ hybridization with

genus/species-specific oligonucleotide probes (Amann

et al., 1995).

The phylogenetic analysis of the rumen samples consis-

tently showed that bovine rumen was much more diverse

than the ovine rumen. Comparison of the primers showed a

higher number of OTUs associated with the 16S rRNA gene

marker than the two-rpoB gene markers used in this study.

However, the abundance values must be compared with

caution because the number of sequences used in the

estimation was not equal and the rarefaction curves were

not asymptotic (Fig. 2). The cutoffs for assigning OTUs were

chosen at 97% for the 16S rRNA gene and 100% for the rpoB

gene due to the single copy number of the rpoB gene.

Between the two rpoB-DGGE primer sets, the bovine

samples amplified with the new rpoB-DGGE primers

showed higher observed and estimated OTU numbers with

cutoffs set at 100% and 97%. This trend was not consistent

and there was a reduction in OTU estimation with the new

rpoB-DGGE primer set at 95% and 90% compared with the

old rpoB-DGGE primer set. Such a reduction could be due

to the region of the rpoB gene or the clustering algorithm

used in the analysis. Further analysis pertaining to influence

by region of amplification (conserved vs. variable regions)

between the two rpoB-DGGE primer sets has to be carried

out to arrive at a clear conclusion.

The ovine rumen samples amplified with the new rpoB-

DGGE primer set showed lesser diversity in all phylogenetic

analyses compared with the old rpoB-DGGE primer coun-

terpart due to the bias induced by the presence of 48 clones

of D. desulfurican belonging to the phylum Proteobacteria.

This over-representation of one sequence type undervalued

downstream analysis such as ChaoI estimate, ACE, Simpson,

and Shannon–Weaver indices. An explanation for such

biases could be the various factors such as the time of

sampling the rumen, animal characteristic, primers, PCR,

and cloning biases. Primer-based biases have been evaluated

in the 16S rRNA gene marker in ecosystems (Forney et al.,

2004) and have been corrected or modified to amplify a

wider range of microorganisms (Baker et al., 2003).

This study shows a difference in the species composition

of the bovine and ovine rumen irrespective of the gene

marker used. This difference in the microbial diversity in the

two ruminants is likely to diverge with more sampling and

utilization of improved sequencing technology such as next-

generation sequencing. Anthropogenic management strate-

gies, size of rumen, diet, co-evolution, immunity (Dethlef-

sen et al., 2007), and selection-based immunity of the host

animal could also be responsible for differences between the

two ruminants (Ley et al., 2008b).



Because of the differences in the evolution of 16S rRNA

gene and the rpoB gene, it is recommended not to compare

these two phylogenetic markers, but to use them in con-

junction with each other to analyze communities. There are

two limitations in using the rpoB-based gene marker. The

primary limitation in using housekeeping genes for primer

development is codon usage and nucleotide substitution

(Case et al., 2007). The second limitation is implementation

of tools such as the RDPII database, CLASSIFIER, and LIBCOM-

PARE. With increased bacterial genomes sequenced, such

databases and tools would be easy to duplicate for the rpoB

gene.

In conclusion, the 16S rRNA gene and new rpoB-DGGE

primers designed in this study have qualitatively similar

diversity profiles when tested in ruminants. The significant

number of clones associated with phylum Proteobacteria

when the bovine rumen community was analyzed by

independently developed rpoB-DGGE primer sets leads to

an important question about the true rumen diversity.

Moreover, it is suggested, based on the data presented here,

that true phylogenetic analysis of a ecosystem would require

analysis with more than one gene marker to completely

estimate diversity in a ecosystem. A computational ap-

proach could be used to add a weight matrix to each gene

analysis and compute to a ‘true’ phylogenetic tree.

Acknowledgements

This material is based on research supported and jointly

funded by the Oregon Agricultural Experiment Station

project ORE00871 and by the U.S. Department of Agricul-

ture, under project number 6227-21310-007-00D agreement

numbers 58-6227-8-044 and 58-1265-6-076. Any opinions,

findings, conclusion, or recommendations expressed in this

publication are those of the author(s) and do not necessarily

reflect the view of the U.S. Department of Agriculture. The

authors would like to thank Mr Rogan Rattary for assistance

during this project, Ms. Kelsey Hughson for help with the

DGGE, and Ms. Zelda Zimmerman for editorial assistance.

The authors also thank the reviewers for help with the

discussion and presentation of this manuscript.

References

Adekambi T, Drancourt M & Raoult D (2009) The rpoB gene as a

tool for clinical microbiologists. Trends Microbiol 17: 37–45.

Altschul SF, Gish W, Miller W, Myers EW & Lipman DJ (1990)

Basic local alignment search tool. J Mol Biol 215: 403–410.

Amann RI, Ludwig W & Schleifer KH (1995) Phylogenetic

identification and in situ detection of individual microbial

cells without cultivation. Microbiol Rev 59: 143–169.

An D, Dong X & Dong Z (2005) Prokaryote diversity in the

rumen of yak (Bos grunniens) and Jinnan cattle (Bos taurus)

estimated by 16S rDNA homology analyses. Anaerobe 11:

207–215.

Baker GC, Smith JJ & Cowan DA (2003) Review and re-analysis

of domain-specific 16S primers. J Microbiol Meth 55: 541–555.

Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J & Sayers EW

(2008) GenBank. Nucleic Acids Res 38: D46–51.

Borneman J & Triplett EW (1997) Molecular microbial diversity

in soils from eastern Amazonia: Evidence for unusual

microorganisms and microbial population shifts associated

with deforestation. Appl Environ Microbiol 63: 2647–2653.

Bourne DG. & Munn CB (2005) Diversity of bacteria associated

with the coral Pocillopora damicornis from the Great Barrier

Reef. Environ Microbiol 7: 1162–1174.

Case RJ, Boucher Y, Dahllof I, Holmstrom C, Doolittle WF &

Kjelleberg S (2007) Use of 16S rRNA and rpoB genes as

molecular markers for microbial ecology studies. Appl Environ

Microb 73: 278–288.

Claesson MJ, O’Sullivan O, Wang Q, Nikkila J, Marchesi JR,

Smidt H, de Vos WM, Ross RP & O’Toole PW (2009)

Comparative analysis of pyrosequencing and a phylogenetic

microarray for exploring microbial community structures in

the human distal intestine. PLoS One 4: e6669.

Dahllof I, Baillie H & Kjelleberg S (2000) rpoB-based microbial

community analysis avoids limitations inherent in 16S rRNA

gene intraspecies heterogeneity. Appl Environ Microb 66:

3376–3380.

Dehority BA & Orpin CG (1997) The Rumen Microbial Ecosystem.

Chapman and Hall, London, UK. pp. 196–245.

Dethlefsen L, McFall-Ngai M & Relman DA (2007) An ecological

and evolutionary perspective on human–microbe mutualism

and disease. Nature 449: 811–818.

Deutschbauer AM, Chivian D & Arkin AP (2006) Genomics for

environmental microbiology. Curr Opin Biotech 17: 229–235.

Drummond AJ & Rambaut A (2007) BEAST: Bayesian

evolutionary analysis by sampling trees. BMC Evol Biol 7: 214.

Drummond AJ, Ashton B, Cheung M, Heled J, Kearse M, Moir R,

Stones-Havas S, Thierer T & Wilson A (2010) Geneious v5.1.

Available from http://www.geneious.com

Eardly BD, Nour SM, van Berkum P & Selander RK (2005)

Rhizobial 16S rRNA and dnaK genes: mosaicism and the

uncertain phylogenetic placement of Rhizobium galegae. Appl

Environ Microb 71: 1328–1335.

Edgar RC (2004) MUSCLE: multiple sequence alignment with

high accuracy and high throughput. Nucleic Acids Res 32:

1792–1797.

Edwards JE, McEwan NR, Travis AJ & John R (2004) 16S rDNA

library-based analysis of ruminal bacterial diversity. Antonie

van Leeuwenhoek 86: 263–281.

Elbing K & Brent R (2002) Media preparation and bacteriological

tools. Curr Protoc Mol Biol 59: 1.1.1–1.1.7.

Forney LJ, Zhou X & Brown CJ (2004) Molecular microbial

ecology: land of the one-eyed king. Curr Opin Microbiol 7:

210–220.

Fox GE, Stackebrandt E, Hespell RB et al. (1980) The phylogeny

of prokaryotes. Science 209: 457–463.



Green BD & Keller M (2006) Capturing the uncultivated

majority. Curr Opin Biotech 17: 236–240.

Griffiths E & Gupta RS (2001) The use of signature sequences in

different proteins to determine the relative branching order of

bacterial divisions: evidence that Fibrobacter diverged at a

similar time to Chlamydia and the Cytophaga–Flavobacterium

–Bacteroides division. Microbiology 147: 2611–2622.

Hungate RE (1966) The Rumen and Its Microbes. Academic Press,

New York.

Khamis A, Raoult D & La Scola B (2004) rpoB gene sequencing

for identification of Corynebacterium species. J Clin Microbiol

42: 3925–3931.

Kim KS, Ko KS, Chang MW, Hahn TW, Hong SK & Kook YH

(2003) Use of rpoB sequences for phylogenetic study of

Mycoplasma species. FEMS Microbiol Lett 226: 299–305.

Koike S, Pan J, Kobayashi Y & Tanaka K (2003) Kinetics of in

sacco fiber-attachment of representative ruminal cellulolytic

bacteria monitored by competitive PCR. J Dairy Sci 86:

1429–1435.

Kupfer M, Kuhnert P, Korczak BM, Peduzzi R & Demarta A

(2006) Genetic relationships of Aeromonas strains inferred

from 16S rRNA, gyrB and rpoB gene sequences. Int J Syst Evol

Micr 56: 2743–2751.

Ley RE, Turnbaugh PJ, Klein S & Gordon JI (2006) Microbial

ecology: human gut microbes associated with obesity. Nature

444: 1022–1023.

Ley RE, Hamady M, Lozupone C et al. (2008a) Evolution of

mammals and their gut microbes. Science 320: 1647–1651.

Ley RE, Lozupone CA, Hamady M, Knight R & Gordon JI

(2008b) Worlds within worlds: evolution of the vertebrate gut

microbiota. Nat Rev Microbiol 6: 776–788.

Mollet C, Drancourt M & Raoult D (1997) rpoB sequence analysis

as a novel basis for bacterial identification. Mol Microbiol 26:

1005–1011.

Muyzer G, de Waal EC & Uitterlinden AG (1993) Profiling of

complex microbial populations by denaturing gradient gel

electrophoresis analysis of polymerase chain reaction-

amplified genes coding for 16S rRNA. Appl Environ Microb 59:

695–700.

Nelson KE, Zinder SH, Hance I, Burr P, Odongo D, Wasawo D,

Odenyo A & Bishop R (2003) Phylogenetic analysis of the

microbial populations in the wild herbivore gastrointestinal

tract: insights into an unexplored niche. Environ Microbiol 5:

1212–1220.

Rappe MS, Connon SA, Vergin KL & Giovannoni SJ (2002)

Cultivation of the ubiquitous SAR11 marine bacterioplankton

clade. Nature 418: 630–633.

Rigouts L, Nolasco O, de Rijk P, Nduwamahoro E, Van Deun A,

Ramsay A, Arevalo J & Portaels F (2007) Newly developed

primers for comprehensive amplification of the rpoB gene and

detection of rifampin resistance in Mycobacterium tuberculosis.

J Clin Microbiol 45: 252–254.

Rodrigues DF & Tiedje JM (2007) Multi-locus real-time PCR for

quantitation of bacteria in the environment reveals

Exiguobacterium to be prevalent in permafrost. FEMS

Microbiol Ecol 59: 489–499.

Rozen S & Skaletsky HJ (2000) Bioinformatics Methods and

Protocols: Methods in Molecular Biology. Humana Press,

Totowa, NJ, pp. 365–386.

Russell JB & Rychlik JL (2001) Factors that alter rumen microbial

ecology. Science 292: 1119–1122.

Schloss PD, Westcott SL, Ryabin T et al. (2009) Introducing

Mothur: open source, platform-independent, community-

supported software for describing and comparing

microbial communities. Appl Environ Microb 75:

7537–7541.

Sebat JL, Colwell FS & Crawford RL (2003) Metagenomic

profiling: microarray analysis of an environmental genomic

library. Appl Environ Microb 69: 4927–4934.

Stepkowski T, Czaplinska M, Miedzinska K & Moulin L (2003)

The variable part of the dnaK gene as an alternative marker for

phylogenetic studies of rhizobia and related alpha

Proteobacteria. Syst Appl Microbiol 26: 483–494.

Stevenson DM & Weimer PJ (2007) Dominance of Prevotella and

low abundance of classical ruminal bacterial species in the

bovine rumen revealed by relative quantification real-time

PCR. Appl Microbiol Biot 75: 165–174.

Tajima K, Nagamine T, Matsui H, Nakamura M & Aminov RI

(2001) Phylogenetic analysis of archaeal 16S rRNA libraries

from the rumen suggests the existence of a novel group of

archaea not associated with known methanogens. FEMS

Microbiol Lett 200: 67–72.

Thompson JD, Higgins DG & Gibson TJ (1994) CLUSTAL W:

improving the sensitivity of progressive multiple sequence

alignment through sequence weighting, position-specific gap

penalties and weight matrix choice. Nucleic Acids Res 22:

4673–4680.

Volokhov DV, Neverov AA, George J, Kong H, Liu SX, Anderson

C, Davidson MK & Chizhikov V (2007) Genetic analysis of

housekeeping genes of members of the genus Acholeplasma:

phylogeny and complementary molecular markers to the 16S

rRNA gene. Mol Phylogenet Evol 44: 699–710.

Wang Q, Garrity GM, Tiedje JM & Cole JR (2007) Naive

Bayesian classifier for rapid assignment of rRNA sequences

into the new bacterial taxonomy. Appl Environ Microb 73:

5261–5267.

Whitford MF, Forster RJ, Beard CE, Gong J & Teather RM (1998)

Phylogenetic analysis of rumen bacteria by comparative

sequence analysis of cloned 16S rRNA genes. Anaerobe 4:

153–163.

Woese C, Sogin M, Stahl D, Lewis BJ & Bonen L (1976) A

comparison of the 16S ribosomal RNAs from mesophilic and

thermophilic bacilli: some modifications in the Sanger method

for RNA sequencing. J Mol Evol 7: 197–213.

Woese CR (1987) Bacterial evolution. Microbiol Rev 51: 221–271.

Woese CR, Sogin ML, Bonen L & Stahl D (1975) Sequence studies

on 16S ribosomal RNA from a blue-green alga. J Mol Evol 4:

307–315.



Xu J (2006) Microbial ecology in the age of genomics and

metagenomics: concepts, tools, and recent advances. Mol Ecol

15: 1713–1731.

Zuckerkandl E & Pauling L (1965) Molecules as documents of

evolutionary history. J Theor Biol 8: 357–366.

Zhou M, Hernandez-Sanabria E & Guan le L (2009) Assessment

of the microbial ecology of ruminal methanogens in cattle with

different feed efficiencies. Appl Environ Microb 75: 6524–6533.

Supporting Information

Additional Supporting Information may be found in the

online version of this article:

Appendix S1. Materials and methods.

Fig. S1. DGGE analysis of pure culture isolates, lanes 1–13.

Fig. S2. DGGE analysis of pure culture isolates, lanes 14–25.

Fig. S3. DGGE analysis of pure culture isolates, lanes 26 and

27.

Fig. S4. PCR products of the new rpoB-DGGE primers

amplified and run on a 1.2% agarose gel with 1X TAE buffer.

Fig. S5. PCR products of the large amplifying new rpoB

primers amplified and run on a 1.2% agarose gel with 1X

TAE buffer.

Table S1. Bacterial pure cultures used in this study to test

newly developed rpoB primers.

Table S2. Reference bacterial strains used for the develop-

ment of rpoB primers in this study.

Table S3. 16S rRNA gene-V3 (Bovine).

Table S4. 16S rRNA gene-V3 (Ovine).

Table S5. rpoB-Dahllof (Bovine).

Table S6. rpoB-Dahllof (Ovine).

Table S7. rpoB – this study (Bovine).

Table S8. rpoB – this study (Ovine).

Please note: Wiley-Blackwell is not responsible for the

content or functionality of any supporting materials sup-

plied by the authors. Any queries (other than missing

material) should be directed to the corresponding author

for the article.



design and in vitro evaluation of new rpob-dgge primers for ruminants

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