• DDGS;
  • DGGE;
  • pH;
  • real-time PCR;
  • rumen bacteria diversity;
  • Shannon–Weiner;
  • Simpson’s Index


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Aims:  To determine the effects of the removal of forage in high-concentrate diets on rumen fermentation conditions and rumen bacterial populations using culture-independent methods.

Methods and Results:  Detectable bacteria and fermentation parameters were measured in the solid and liquid fractions of digesta from cattle fed two dietary treatments, high concentrate (HC) and high concentrate without forage (HCNF). Comparison of rumen fermentation conditions showed that duration of time spent below pH 5·2 and rumen osmolality were higher in the HCNF treatment. Simpson’s index of 16S PCR-DGGE images showed a greater diversity of dominant species in the HCNF treatment. Real-time qPCR showed populations of Fibrobacter succinogenes (= 0·01) were lower in HCNF than HC diets. Ruminococcus spp., F. succinogenes and Selenomonas ruminantium were at higher ( 0·05) concentrations in the solid vs the liquid fraction of digesta regardless of diet.

Conclusions:  The detectable bacterial community structure in the rumen is highly diverse. Reducing diet complexity by removing forage increased bacterial diversity despite the associated reduction in ruminal pH being less conducive for fibrolytic bacterial populations. Quantitative PCR showed that removal of forage from the diet resulted in a decline in the density of some, but not all fibrolytic bacterial species examined.

Significance and Impact of the Study:  Molecular techniques such as DGGE and qPCR provide an increased understanding of the impacts of dietary changes on the nature of rumen bacterial populations, and conclusions derived using these techniques may not match those previously derived using traditional laboratory culturing techniques.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Ruminal bacteria are vital to the health and productivity of the host (Russell 2002; Welkie et al. 2010). Rumen microflora are known to be highly responsive to changes in diet, age and health of the host animal (Kocherginskaya et al. 2001; Li et al. 2009). Therefore, in healthy growing cattle, diet composition is the most important driver of hierarchical structural changes in bacterial populations (Welkie et al. 2010). By increasing the availability of fermentable carbohydrate, microbial growth is stimulated. This results in an increase in the rate of fermentation providing the animal with increased energy for growth (Nagaraja and Titgemeyer 2007). However, the proliferation of rumen cellulolytic organisms is directly correlated with the amount of fibre in the diet, and replacement of fibre with more readily fermentable carbohydrates impacts these organisms and alters the dynamics of the rumen ecosystem (Tajima et al. 2001; Klieve et al. 2003). Increased digestible carbohydrate intake has been associated with acidic rumen conditions (pH < 6·0) that have been shown to reduce the activity of fibrolytic bacteria and increase the activity of amylolytic and lactic acid-utilizing bacteria in the rumen (Russell 2002; Klieve et al. 2003; Nagaraja and Titgemeyer 2007). As rumen microbial ecologists have been attempting to discover ‘which bacteria are there’ (population), in what abundance (richness) and ‘what they are doing’ (community structure), it has been found that the rumen microbial community adapts at both the structural and population levels with diet change. However, the specifics of this adaptation are not well understood because of the extreme complexity of the rumen ecosystem (Li et al. 2009).

Culture-dependent studies have shown modest changes in the total culturable bacterial species in the rumen. However, changes in the entire bacterial community, including both culturable and unculturable species, are largely uncharacterized (Kobayashi 2006). Detection methods for studying rumen micro-organisms have recently moved from the limited capability of cultivation-based techniques to more sensitive molecular methods that allow the determination of community diversity and richness and can indirectly quantify populations without culturing (McSweeney et al. 2007). Understanding how dietary changes impact the rumen ecosystem will provide insight into why certain diets may impact animal health and productivity. The hypothesis of this experiment is that overall bacterial diversity as measured by denaturing gradient gel electrophoresis (DGGE) and fibrolytic bacteria as measured by qPCR will be reduced in animals fed a diet containing no forage as a result of decreased ruminal pH.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Experimental design

This trial involved a subset of data derived from an experiment by Li et al. (2011), which involved eight Angus heifers with ruminal fistulas (initial BW 455 ± 10·8 kg). The heifers were randomly assigned to a replicated 4 × 4 Latin square experiment, balanced for carry over effects. The objective of the study was to determine whether replacing barley grain with wheat dried distiller’s grains with solubles (DDGS), a nonstarch carbohydrate source, influences the animal’s susceptibility to sub-acute ruminal acidosis (SARA). The trial consisted of four 21-day periods, each consisting of 12 days of dietary adaptation and 9 days of data collection. The four experimental treatments consisted of barley silage, concentrate (barley grain + supplement) and wheat DDGS in ratios of 15 : 85 : 0, 10 : 65 : 25, 5 : 65 : 30 and 0 : 65 : 35 (DM basis), respectively. Comprehensive feed analysis, intake and digestibility, and rumen fermentation data have been published (Li et al. 2011).

For this study, two dietary treatments were selected to study the effects of replacing the forage component of a barley grain-based finishing diet with wheat DDGS on bacterial community structure. These included the 15 : 85 : 0 (high concentrate with forage; HC) and the 0 : 65 : 35 (high concentrate, no forage; HCNF) diets (Table 1). From the original Latin square design, all animals were sampled for this study when fed these two dietary treatments. One heifer was removed from the study because of illness. Animals were fed ad libitum, once daily at 1100 h. The experimental procedures used were approved by the Lethbridge Research Center Animal Care Committee and were in accordance with the guidelines of the Canadian Council of Animal Care (CCAC 1993).

Table 1.   Ingredient and chemical composition of the experimental diets*
  1. MGA, melengestrol acetate; DDGS, dried distiller’s grains with solubles.

  2. *Ingredient, chemical composition and analysis of feedstuffs have been previously reported as part of a larger study by Li et al. (2011).

  3. †Supplied per kilogram of dietary DM: 15 mg of Cu, 65 of mg Zn, 28 mg of Mn, 0·7 mg of I, 0·2 mg of Co, 0·3 mg of Se, 6000 IU of vitamin A, 600 IU of vitamin D, and 47 IU of vitamin E.

  4. ‡Values shown with standard error of means.

  5. §peNDF was determined by multiplying dietary NDF content by the proportion of the DM retained on the 19- and 8-mm sieves of a Penn State Particle Separator (Lammers et al., 1996).

Ingredient, % DM
 Barley silage15
 Barley grain, temper-rolled82·862·8
 Wheat DDGS35
 Canola meal0·500·50
 Calcium carbonate1·251·25
 LRC beef feedlot premix†0·050·05
 MGA 100 premix (220 mg kg−1)0·020·02
 Vitamin E (500 000 IU kg−1)0·0030·003
Chemical composition‡
 Dry Matter (DM), %71·5 ± 0·585·1 ± 0·1
 Crude Protein, % DM12·0 ± 0·222·9 ± 0·1
 Neutral Detergent Fibre, % DM24·4 ± 0·121·8 ± 0·1
 Acid Detergent Fibre, % DM11·1 ± 0·110·3 ± 0·2
 peNDF§, % DM2·81 ± 0·10·81 ± 0·1
 Ether Extract, % DM2·3 ± 0·23·1 ± 0·2
 Starch, % DM48·9 ± 0·434·9 ± 0·4

Rumen sampling

Daily feed intake (kg day−1) was calculated as the difference between feed offered and refused during the last 7 days of each period for each individual animal. The composition of experimental diets is given in Table 1. Rumen bacterial samples were collected 1 h before and 3 h after feeding on day 14 of each period. Rumen contents were sampled every 2 h over a 24-h period (16 days) for volatile fatty acids (VFA), osmolality and NH3-N analysis. In-dwelling pH data were collected over five consecutive days (12–17 days). Ruminal pH was continuously monitored every 30 s for 12 h from day 13 to 18 of each experimental period using the Lethbridge Research Center Ruminal pH Measurement System (LRCpH; Dascor, Escondido, CA, USA; Penner et al. 2006). The daily ruminal pH data were averaged for each minute and summarized as minimum pH, mean pH, maximum pH as well as duration and area under the curve below the benchmarks of pH 5·8, 5·5 and 5·2 (Nocek 1997; Penner et al. 2007; Beliveau and McKinnon 2009). Li et al. (2011) previously reported the collection and analysis of ruminal contents for fermentation measurements. A sub-set of these samples were used in this study to define the rumen fermentation conditions associated with the microbial populations examined.

Bacterial extraction

Particulate and fluid samples from three rumen locations (top, bottom and middle of the rumen mat) were collected through a cannula, thoroughly mixed and separated into liquid and solid fractions as described by Kong et al. (2010). In brief, 100 ml of rumen contents were anaerobically transferred into a heavy-walled 250-ml beaker and squeezed with a Bodum coffee maker plunger (Bodum Inc., Triengen, Switzerland). The liquid fraction was decanted and sub-sampled into 2 ml Eppendorf tubes. The squeezed digesta was washed twice with 100 ml O2-free phosphate rinse buffer (K2HPO4, 30 mmol; KH2PO4, 20 mmol; NaHCO3, 35 mmol) by stirring gently with a spatula, followed by squeezing and disposing of the remaining liquid. After washing, 10 ml O2-free methyl cellulose release buffer containing phosphate rinse buffer with 0·2% methyl cellulose was blended with residual rumen contents using a Braun hand blender (MR4000; Braun GmbH, Kronberg, Germany) using three 2 s bursts with a 10 s pause in between. The blended digesta were then separated into liquid and particle fractions using a Bodum filter (Kong et al. 2010). Liquid (5 ml) obtained from the second decanting, containing the particle-associated bacterial fraction, was aliquoted into 15-ml falcon tubes. Liquid and solid bacterial fractions were centrifuged at 10 000 g for 10 min to pellet the bacteria. After the supernatant was discarded, 1·4 ml ASL stool lysis buffer (QIAamp DNA Stool Kit, Qiagen, Mississauga, ON, Canada) was added to each pellet and the pellet was resuspended. Samples were stored in 2 ml cryogenic tubes at −80°C until processed for DNA extraction.

Bacterial DNA extraction

Rumen samples (n = 64) were thawed at 95°C for 5 min and immediately centrifuged at 10 000 g for 5 min. Samples were extracted using the method described by Kong et al. (2010. In brief, all samples were treated with 0·4 mol l−1 potassium phosphate buffer, lysozyme (100 mg ml−1), mutanolysin (2·5 U μl−1) and proteinase K (20 mg ml−1) prior to bead beating. Glass beads (200 mg with 0·5 mm diameter and 300 mg with 1·0 mm diameter) were added to each tube, and the samples were processed in a bead-beating homogenizer (B. Braun, Melsungen, Hesse, Germany) for 3 min and then centrifuged. The supernatant obtained from the pellets after enzyme treatment/bead beating as well as prior to enzyme treatment and bead beating was processed using the DNA extraction protocol provided in the QIAamp DNA Stool Mini Kit (Qiagen). After extraction, DNA concentration and purity were assessed using a Synergy HT multi-detection microplate reader (Bio-Tek Instruments Inc., Winooksi, VT, USA) and gel electrophoresis, respectively. Each sample was divided into two sub-samples for PCR-DGGE and real-time PCR analysis.


Extracted undiluted bacterial DNA (3 μl) from each of the rumen samples was added as template to amplify the V3 region of the 16S rRNA gene for PCR-DGGE reactions in a 25 μl reaction. Amplification was performed using Qiagen HotStar Plus Master Mix Kit (Qiagen) and 500 nmol l−1 of forward and reverse primers (341f with GC-Clamp:CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGGCCTACGGGAGGCAGCAG and 534r:ATTACCGCGGCTGCTGG) developed by Muyzer et al. (1993). Polymerase chain reaction conditions were 95°C for 5 min, 94°C for 30 s, temperature gradient decreasing from 65 to 55°C by 0·5°C each cycle for 30 s, 72°C for 1 min for 20 cycles, followed by 94°C for 30 s, 56°C for 30 s, 72°C for 1 min for 10 cycles and 72°C for 10 min for final elongation. The quality of amplified DNA was verified using gel electrophoresis and quantified using flurospectrophotometry by measuring the A260/280(ND-3300 Nanodrop, Wilmington, DE, USA). All amplified DNA was then diluted to a concentration of 400 ng per lane and loaded on 8% acrylamide gels with a 45–60% denaturing gradient of urea and formamide. Electrophoresis was performed at 60°C and 40 V for 20 h. Three lanes on each gel were loaded with DDGE Marker II (Wako, Nippon Gene, Japan) to provide both an internal and external marker. Gels were stained with SybrGold Nucleic Acid Gel Stain (Invitrogen, Life Technologies Corp., Carlsbad, CA, USA) according to manufacturer’s instructions and photographed by UV transillumination.

Real-Time PCR

Quantitative analysis was performed with the ABI PRISM 7700 Sequence Detection System (AB Applied Biosystems, Life Technologies Corp.), to quantify the relative abundance of 16S rRNA genes of seven bacterial species as a percentage of total eubacterial 16S rRNA, using the primers shown in Table 2. The quantification of DNA for each bacterial species in rumen contents was performed with Quantifast Kit (Qiagen) using SYBR green chemistry. Standards and samples were assayed in 25 μl reaction mixture containing 15 μl of Quantifast SYBR Green Master Mix, 8 μl of nuclease-free water and 2 μl of DNA template. Amplification programmes were performed under the following fast conditions: 95°C for 5 min, 95°C for 10 s, and a 30 s annealing/elongation (at the temperatures shown in Table 2 based on each primer pair) for 40 cycles. The melting curve of PCR products was monitored by slow heating with an increment of 0·1°C s−1 from 60 to 95°C with fluorescence collection at 0·1°C intervals to confirm specificity of amplification. A standard curve for each bacterial species was constructed by using plasmid DNA containing 16S rRNA inserts of DNA purified from a pure culture of the target species (Stevenson and Weimer 2007). Ruminococcus plasmid DNA was used as a standard template for the universal bacteria primers. Plasmid DNA was quantified and then subjected to seven sequential tenfold dilutions, each analysed in duplicate. A linear relationship was observed between the threshold cycle (Ct) and log of DNA concentration when each primer pair was tested against purified DNA from its target taxon (r2 = 0·97–0·99). Each sample was run in triplicate, and the PCR reaction cycle at which the reaction exceeded this was identified as the Ct. Copy numbers of total bacteria and each enumerated species, in 20 ng DNA, were determined by relating the Ct values to standard curves based on the following calculation:

Table 2.   Species- and genus-specific primers for the quantification of rumen bacteria using real-time PCR assay
Target taxon or strainPrimer sequence 5′–3′TmAmplicon lengthReferences
Selenomonas ruminantium DF:CAATAAGCATTCCGCCTGGG R:TTCACTCAATGTCAAGCCCTGG6182Stevenson and Weimer (2007), Li et al. (2009)
Streptococcus bovisF:CTAATACCGCATAACAGCAT R:AGAAACTTCCTATCTCTAGG57869Tajima et al. (2001)
Fibrobacter succinogenes S85F:GCGGGTAGCAAACAGGATTAGA R:CCCCCGGACACCCAGTAT5977Stevenson and Weimer (2007), Li et al. (2009)
Megasphaera elsdeniiF:AGATGGGGACAACAGCTGGA R:CGAAAGCTCCGAAGAGCCT5979Stevenson and Weimer (2007)
Prevotella GenusF:GGTTCTGAGAGGAAGGTCCCC R:TCCTGCACGCTACTTGGCTG61121Stevenson and Weimer (2007)
Ruminobacter amylophilusF:CAACCAGTCGCATTCAGA R:CACTACTCATGGCAACAT57642Tajima et al. (2001)
  • DNA (number of molecules) = (6·02 × 1023 (molecules/mol) × DNA amount (ng))/(DNA plasmid-insert length (bp) × 6·6 × 1011(ng mol−1 bp−1))

Amplification products were verified by horizontal gel electrophoresis of a 5 μl aliquot in a 1% agarose gel in Tris-Acetate-EDTA (40 mmol l−1 Tris acetate, 1 mmol l−1 EDTA; pH 8·5), followed by ethidium bromide staining and visualization under UV light. A 1-kb Ladder (Quickload, New England Biolabs Ltd, Pickering, ON, Canada.) was included on each gel to enable confirmation of the size of the amplified product.

Relative quantification

To minimize errors of absolute quantification of DNA from rumen samples, relative quantification methods were used. In relative quantification, amplification is expressed relative to the amplification of reference primers utilizing experimentally derived amplification efficiency (Stevenson and Weimer 2007). The proportion of each species was obtained by copy numbers of 16S rRNA gene of targeted species divided by the 16S rRNA genes amplified with a reference primer set (Khafipour et al. 2009; Li et al. 2009). A nondegenerate, domain-level primer set that amplified all eubacterial species was used as the reference primer set (Table 2).

Statistical analysis

Experiment was analysed as a replicated 4 × 4 Latin square including fixed effect of treatment (diet) and random effects of animal and time. Data for rumen environmental parameters were analysed for effect of treatment, time and treatment × time interaction over the 12-h sampling period using the proc mixed procedure of sas (SAS Inst. Inc., Cary, NC, USA). Analysis of PCR-DGGE band patterns was accomplished using BioNumerics software (ver. 5.1, Applied Maths, Inc., Austin, TX, USA) and similarity matrices to identify community population differences between treatments, digesta fractions and individual animals. Using average Dice’s similarity coefficient (Dsc) index, with an optimization of 1% and with a tolerance of 1·5%, clustering was carried out using the unweighted pair group method with arithmetic means (UPGMA). Diversity indices were calculated applying the following equations using the band area as determined by BioNumerics software:

  • Relative Band Area = band area/Σ(all measured band areas in the sample)

  • Shannon–Weiner (H′) = Σ{−(Relative Band Area) [Log10(Relative Band Area)]}

  • Simpson’s Index (λ) = 1 − (Σ Relative Band Area in a Sample)2

Diversity index values were calculated for each sample and analysed using the one-way anova procedure of sas. Relative quantities of 16S rRNA as determined from real-time PCR were analysed using proc anom to determine significant differences in copy number between animals, treatments and pH profiles for each targeted bacterial species. Significance level α = 0·05; trends were declared for 0·10 ≥ α > 0·05.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Rumen fermentation characteristics

Cattle gained an average of 123 ± 24 kg over the course of this study. Dry matter intake was not altered by dietary treatment (Table 3). Duration (min day−1) below pH 5·2 was found to be longer (= 0·05) for animals fed a HCNF diet. The duration of time and area under the curve (pH × min) for pH < 5·5 also tended to be longer for HCNF (= 0·07 and = 0·06, respectively). Area of the curve below pH 5·2 showed a similar trend (= 0·08) with those animals fed the HCNF diet having a greater total area (28·24 pH × min) compared with those animals fed HC (5·55 pH × min). Lowest mean pH reached each day as indicated by mean nadir was 5·22 and 5·13 for HC and HCNF, respectively. Dietary treatment did not alter (P > 0·10) VFA or NH3-N concentrations (Table 3). Conversely, rumen osmolality was higher (= 0·05) in those animals fed HCNF as compared to HC.

Table 3.   Changes in dry matter intake, rumen pH, rumen VFA, NH3-N and osmolality in cattle fed a high concentrate ration and a high concentrate-to-no forage ration
Fermentation parametersDietary treatmentSEMP-value treatment
  1. HC, high concentrate; HCNF, high concentrate no forage; DMI, dry matter intake; VFA, volatile fatty acid.

DMI (kg day−1)9·508·770·390·21
Mean nadir5·225·130·060·38
Mean daily pH5·945·730·090·11
Rumen pH ≤ 5·8
 Duration (min day−1)580·80802·80120·000·22
 Area under (pH × min)180·35327·0562·030·12
Rumen pH ≤ 5·5
 Duration (min day−1)286·80541·8091·800·07
 Area under (pH × min)53·45142·4330·860·06
Rumen pH ≤ 5·2
 Duration (min day−1)65·40244·2057·600·05
 Area under (pH × min)5·5528·248·480·08
 Total, mmol l−1122·35119·185·470·69
 Acetate (A), %62·6755·134·130·22
 Propionate (P), %37·1339·003·660·72
 Butyrate, %18·4322·092·000·22
A:P (Acetate:Propionate)1·801·500·200·39
NH3-N, mmol l−19·0914·162·500·18
Osmolality, mOsm kg−1320·1340·76·800·05


Detectable bacterial PCR-DGGE profiles clustered similarly between the liquid and solid fractions of rumen contents and between the two dietary treatments (Fig. 1). The average Dsc of detectable bacterial profiles among digesta samples collected from the liquid and solid fractions in the rumen ranged from 77·7 to 97·4% (Table 4). Average Dsc among samples collected from the HC and HCNF diet ranged from 84·5 to 98·1% (Table 4). Simpson’s index showed that heifers fed the HCNF diet exhibited greater (= 0·05) diversity of predominant species (Table 5). The Shannon–Weiner index similarly showed that those animals fed the HCNF had a trend (P = 0·06) towards a greater number of unique species (HC = 1·26; HCNF = 1·35).


Figure 1.  Cluster analysis of DGGE–PCR fingerprint profiles similarity (%) for all animals, fed both HC and HCNF diets, for both the solid (S) and liquid (L) fractions of rumen digesta. Clustering was performed using Dice’s algorithm and UPGMA at an optimization of 1%, a tolerance of 1·5% and clusters were considered similar at 90% or higher.

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Table 4.   Average Dsc (%) in detectable bacterial diversity profiles (DGGE) among rumen digesta samples, across treatments based on the liquid vs solid fractions of digesta and dietary treatment. Similarity was measured at 90%
Animal IDDigesta fractionDietary treatment
  1. HC, high concentrate; HCNF, high concentrate no forage.

Table 5.   Diversity and Dominance Indices calculated from DGGE incidence profiles of rumen fluid from two dietary treatments
Diversity measureDietary treatmentSEMP-value
  1. HC, high concentrate; HCNF, high concentrate no forage. Means followed by the different letters are significantly different (P < 0·05).

Number of bands27·429·51·110·17
Shannon–Weiner Index1·261·350·0320·06
Simpson’s Index0·93a0·94b0·0060·05

Real-time PCR

Seven bacterial species evaluated in this study were detected in all animals, in both diets and in both the solid and the liquid fractions of the rumen contents (Fig. 2). Quantities of bacteria expressed as a percentage of total enumerated bacteria ranged from 0·0001 to 70·2% between diets and from 0·0006 to 63·6% between the solid and the liquid fractions of rumen digesta (Table 6). There was a 57-fold decrease in the relative abundance of the Fibrobacter succinogenes population (= 0·01) in cattle fed the HCNF as compared to HC diet (Table 6). When treatments were compared, only F. succinogenes accounted for a (= 0·01) higher proportion of the total bacteria (1·14%) in the HC diet as compared to the HCNF diet (0·02%). Megasphaera elsdenii tended to be higher (= 0·09) in the HCNF diet (6·5-fold increase), whereas Ruminobacter amylophilus tended to be higher (= 0·08) in cattle fed the HC diet (12-fold increase; Table 6). Ruminococcus spp. (< 0·01) and Selenomonas ruminantium (P = 0·05) were both more than 1·4-fold higher in the solid fraction compared with the liquid fraction of rumen contents (Table 6). The amount of F. succinogenes associated with the solid fraction was substantially higher (P < 0·05) than with the liquid fraction. No significant treatment by digesta fraction interaction was found.


Figure 2.  Fold changes in relative quantification of populations of enumerated bacteria species between dietary treatments as determined by qPCR. Basal frequency represents total bacterial enumerated using the nondegenerate domain-level reference primer set. (inline image) S. ruminantium; (inline image) Ruminococcus Genus; (inline image) F. succinogenes; (inline image) Prevotella Genus; (inline image) R. amylophilus; (inline image) Melsdenii and (inline image) Strept. bovis.

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Table 6.   Effect of dietary treatment and digesta fraction on the percent of total enumerated eubacteria 16S rRNA genes of dominant rumen bacterial species using quantitative real-time PCR
BacteriaDietary treatment*SEMP-valueDigesta fraction*SEMP-value
  1. HC, high concentrate; HCNF, high concentrate no forage.

  2. *No significant treatment by digesta fraction interaction. Means followed by the different letters are significantly different (P < 0·05).

Ruminococcus spp.0·710·710·1530·970·40a1·01b0·1960·004
Fibrobacter succinogenes1·14a0·02b0·2190·010·20a0·96b0·2190·02
Prevotella spp.56·5770·226·9240·1863·2363·566·9240·97
Selenomonas ruminantium2·411·690·5340·260·87a1·73b7·6820·05
Megasphaera elsdenii0·120·780·2860·090·240·650·2860·28
Ruminobacter amylophilus0·0012a0·0001b>0·00010·080·082·341·4040·86
Streptococcus bovis0·51·981·4040·460·00070·00060·00040·89


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Using current molecular techniques, it was possible to study the impact of the removal of the forage component of the diet on the structure and stability of rumen bacterial populations. Composition of the diet, particle size and intake are critical factors in determining the impact of diet on rumen microbial activity, nutrient digestion and rumen function (Nagaraja and Titgemeyer 2007). The diets used in this study met or exceeded the energy and protein needs for maintenance and growth of the animals used in this study (NRC, 2000). Barley grain and wheat DDGS have been shown to have similar digestible energy values in ruminant diets (Beliveau and McKinnon 2008). Therefore, as a result of the replacement of silage with DDGS, the HCNF diet would be slightly higher in metabolizable energy content than the HC diet. The two diets also differed in the form that energy was supplied (i.e. starch vs. fermentable fibre, protein and fat).

Rumen fermentation characteristics

Reducing the starch content of the diet by substituting wheat DDGS for grain and silage did not influence dry matter intake. A similar response has been observed in other studies where wheat DDGS replaced barley grain at similar levels (Beliveau and McKinnon 2008; Gibb et al. 2008). There was an indication that substitution of DDGS for part of the barley and all of the forage increased the daily duration that ruminal micro-organisms were exposed to lower pH. This substitution reduced the mean particle size in the diet, an outcome that increases the total surface area available for microbial colonization and reduces rumination (Allen and Mertens 1988). These responses are also reflected in the 3·5-fold decrease in physically effective fibre (peNDF) in the HCNF diet (Table 1). Reduced peNDF would also result in reduced saliva secretion and associated buffering within the rumen. Under this scenario, ruminal pH can decline (Ørskov 1999) even if VFA concentrations are similar among diets as was the case in our study. Furthermore, DDGS while high in neutral detergent fibre (NDF) are low in lignin and high in digestible fibre (i.e. 62 to >71%), further promoting their fermentation in the rumen (Birkelo et al. 2004; Klopfenstein et al. 2008; Vander Pol et al. 2009). The highly digestible nature of the HCNF diet could also have increased the daily duration that microbial populations were exposed to lower pH. The negative effect of feeding wheat DDGS on rumen pH, particularly on the duration of pH under 5·2 and area under the curve for pH 5·2, has been reported when DDGS have been used to replace barley grain (Beliveau and McKinnon 2009) and barley silage (Wierenga et al. 2010).


Detectable bacterial PCR-DGGE profiles between treatments were statistically similar, however, some liquid and solid fractions from the same diet clustered closely, whereas others showed no similarity (Fig. 1). Failure to see dietary differences in clustering may be a result of the fact that both diets were high in concentrate and low in effective fibre. As a result, individual animal variation may have masked any treatment effects on bacterial populations. This would explain why our results differ from Kocherginskaya et al. (2001) who showed a significant clustering effect between forage (100% hay) and concentrate (72% grain) fed animals. Supporting this conclusion, Li et al. (2009) using PCR-DGGE methods showed high individual animal variation as well as animal-specific clustering in cattle fed a 55% concentrate and 45% forage ration. Animal variation within this experiment was seen not only in DGGE–PCR similarity coefficients (Table 4) but also in the high variation in pH measurements. The high variation in pH reflects the different abilities of individual animals to cope with dietary change and imbalances in the production and absorption of VFA’s (Brown et al. 2000; Bevans et al. 2005; Penner et al. 2009). Thus, even when treatment means are similar, irregular variance from the mean reflects the differential extent to which animals are able to compensate or tolerate the change in rumen fermentation conditions associated with a change in dietary substrate (Bevans et al. 2005).

According to DGGE, band profiles did not differ significantly between the two diets, although animals fed HCNF had numerically more bands than those fed HC (Table 5). Multiple methods of diversity analysis were undertaken to increase our confidence in differentiating bacterial diversity among the diets. Shannon–Weiner diversity index and Simpson’s index both showed a trend towards greater microbial diversity and a greater diversity in predominant species in animals fed the HCNF diet, respectively. These results contradict our hypothesis that the removal of forage would reduce microbial diversity. However, it should also be noted that removal of the silage did not completely remove dietary fibre. The HCNF diet still contained significant structural carbohydrate fraction, although it was derived from a different source (i.e. wheat DDGS) and had a considerably higher protein content. As a result, fibrolytic bacteria may still have remained active against fermentable fibre, and an increase in other bacteria because of increased fermentable substrates may have contributed to the greater diversity observed. While this methodology provides a rapid and repeatable characterization of the system, it is important to note that the level of resolution offered by DGGE is low because of co-migration of DNA along the gel resulting in bands that may contain multiple bacterial species. Furthermore, the use of touchdown PCR in DGGE selectively amplifies the most abundant phylotypes, and therefore, resolution of diversity is decreased (Kocherginskaya et al. 2001).

Real-time PCR

The prevalence of F. succinogenes according to relative quantification was decreased 57-fold in cattle fed the HCNF as compared to the HC diet. Similar results were reported in other studies where cattle were switched from a forage-based to a high concentrate-based diet (Tajima et al. 2001; Fernando et al. 2010). Fibrobacter succinogenes is one of the most active cellulolytic bacteria that adhere to the fibrous components of the diet (Halliwell and Bryant 1963; Koike and Kobayashi 2009). It is therefore not surprising to see the drop in numbers of this species in a diet that contained no forage. Supporting this conclusion is the fact that F. succinogenes was 4·8-fold higher in the solid vs the liquid fraction of the digesta. However, F. succinogenes and other cellulolytic species such as Ruminococcus spp. did not completely disappear from the diets containing no forage. Therefore, the significant decrease in F. succinogenes may be a reflection of the highly digestible nature of fibre in DDGS more than the removal of silage as a forage source from the diet. Ruminobacter amylophilus also showed a trend towards decreased levels in the HCNF diet. Ruminobacter amylophilus is an obligate anaerobe that uses only α-linked glucose molecules like maltose, maltodextrins and starch as a source of energy (Anderson 1995). Therefore, the trend towards lower levels of R. amylophilus with the replacement of high-starch barley with low-starch DDGS is expected. Application of qPCR was unable to detect a dietary treatment effect for other targets (Ruminococcus spp. S. ruminantium, M. elsdenii, Streptococcus bovis and Prevotella; Table 6). For Ruminococcus spp., the similarities between diets may reflect the fact that some of the species in this genus are capable of using starch as a substrate. However, the complete lack of a dietary effect was unexpected, because like F. succinogenes, they are known to be primarily a fibrolytic genus (Koike and Kobayashi 2001). However, similar to F. succinogenes, Ruminococcus spp. were noted to be 2-fold higher in the solid than the liquid fraction of the digesta, likely due to their pivotal role in the initial establishment of rumen biofilms on cellulosic feedstuffs (McAllister et al. 1994). The lack of a dietary effect may reflect the fact that genus level primers were unable to detect important species level changes in Ruminococcus.

Similar to Ruminococcus spp., there was a (1·9-fold) higher count of S. ruminantium in the solid fraction of digesta compared with the liquid fraction. This may be explained by the secondary fermentative action of S. ruminantium and their dependence on primary colonizing populations of fibrolytic bacteria for nutrients (McAllister et al. 1994). Selenomonas ruminantium has been found to synthesize propionate, malate, and lactate from primary fermentation products such as pyruvate and succinate (Hungate 1966; Evans and Martin 1997). Unlike M. elsdenii, which shows no catabolite repression by carbohydrates such as glucose and maltose, S. ruminantium first ferments glucose, sucrose and xylose prior to fermenting dl-lactate, substrates that rapidly become limiting in the liquid because of the fact they can be used by a variety of bacterial species (Counotte et al. 1981).

No effect of treatment or distribution between liquid and solid fraction of rumen contents was found for Prevotella. Prevotella was the most dominant group within the rumen microbial community structure, accounting for as much as 56% of the total bacteria DNA enumerated in the HC diet and up to 70% in the HCNF. Consistent with our results, Stevenson and Weimer (2007) reported Prevotella spp. levels up to 60% of the total enumerated bacteria when using primers specific to the genus level. Strep. bovis and M. elsdenii were also not affected by diet or digesta sample fraction. Streptococcus bovis counts exhibited a large standard error, likely due to the primer used to amplify the 16S rRNA region of Strep. bovis (Tajima et al. 2001). This primer was designed to amplify an 800-bp fragment, more than four times larger than what is recommended for efficient real-time PCR quantification. When results were adjusted for PCR efficiency, the standard error was amplified. However, unless they are clinically acidotic, it is commonly reported that there is no significant difference in Strep. bovis in ruminants regardless of diet (Goad et al. 1998; Al Jassim et al. 2003; Klieve et al. 2003; Fernando et al. 2010).

Differences in dietary protein content between HC and HCNF may have also altered the abundance of proteolytic bacterial populations in the rumen. Of the bacteria examined, Prevotella spp., Strep. bovis and S. ruminantium all are proteolytic (Russell and Hespell 1981), but as outlined earlier, the magnitude of change in the relative abundance of these populations was minor compared with the differences in protein levels between diets as a result of the addition of DDGS.

Using, DGGE the current study showed that bacterial diversity was not reduced when DDGS replaced a portion of the barley and all of the silage in a finishing diet, despite increasing the duration that the microbial community was exposed to a lower ruminal pH. In fact, both ecological diversity indices suggested an increase in diversity in diets containing no forage. Quantitative real-time PCR analysis clearly showed that relative quantities of key cellulolytic species decreased when forage was removed from the diet. Owing to the structural complexity of feeds, digestive bacterial populations may remain diverse and complex even when the structural carbohydrate content of the diet is low.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Appreciation is expressed to the staff of the Lethbridge Research Station for the care of the cattle used in these studies, to the collaborators on this project Alastair Furtado and those technicians who provided assistance in the laboratory including Shaun Cook, Wendi Smart, Pamela Lussier and Lyn Paterson.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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