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Research Article| Volume 97, ISSUE 11, P7065-7075, November 2014

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Effects of Cordyceps militaris on the growth of rumen microorganisms and in vitro rumen fermentation with respect to methane emissions

Open ArchivePublished:September 06, 2014DOI:https://doi.org/10.3168/jds.2014-8064

      Abstract

      This experiment was designed to investigate the effects of different concentrations (0.00, 0.10, 0.15, 0.20, 0.25, and 0.30 g/L) of dried Cordyceps militaris mushroom on in vitro anaerobic ruminal microbe fermentation and methane production using soluble starch as a substrate. Ruminal fluids were collected from Korean native cattle, mixed with phosphate buffer (1:2), and incubated anaerobically at 38°C for 3, 6, 9, 12, 24, 36, 48, and 72 h. The addition of C. militaris significantly increased total volatile fatty acid and total gas production. The molar proportion of acetate was decreased and that of propionate was increased, with a corresponding decrease in the acetate:propionate ratio. As the concentration of C. militaris increased from 0.10 to 0.30 g/L, methane and hydrogen production decreased. The decrease in methane accumulation relative to the control was 14.1, 22.0, 24.9, 39.7, and 40.9% for the 0.10, 0.15, 0.20, 0.25, and 0.30 g/L treatments, respectively. Ammonia-N concentration and numbers of live protozoa decreased linearly with increasing concentrations of C. militaris. The pH of the medium significantly decreased at the highest level of C. militaris compared with the control. In conclusion, C. militaris stimulated mixed ruminal microorganism fermentation and inhibited methane production in vitro. Therefore, C. militaris could be developed as a novel compound for antimethanogenesis.

      Key words

      Introduction

      Methane (CH4) produced as a result of digestible structural carbohydrate fermentation in the rumen represents 7 to 10% feed energy loss to the host animal (
      • Takahashi J.
      • Chaudhry A.S.
      • Beneke R.G.
      • Suhubdy
      • Young B.A.
      Modification of methane emission in sheep by cysteine and a microbial preparation.
      ;
      • Takahashi J.
      Nutritional manipulation of methanogenesis in ruminants.
      ) and has received attention as a potential contributor to global warming (
      • Leng R.A.
      ;
      • Moss A.R.
      ). Because methane production is negatively correlated with energy utilization in ruminants (
      • Ørskov E.R.
      • Flatt W.P.
      • Moe P.W.
      Fermentation balance approach to estimated extent of fermentation and efficiency of volatile fatty acid formation in ruminants.
      ), many compounds have been tested in vitro and in vivo as methane inhibitors (
      • Czerkawski J.W.
      • Breckenridge G.
      Fermentation of various glycolytic intermediates and other compounds by rumen microorganisms, with particular reference to methane production.
      ;
      • Martin S.A.
      • Macy J.M.
      Effects of monensin, pyromellitic diimide and 2-bromoethanesulfonic acid on rumen fermentation in vitro.
      ). Methanogenic archaea were metabolically correlated with ciliate protozoa (
      • Stumm C.K.
      • Gijzen H.J.
      • Vogels G.D.
      Association of methanogenic bacteria with ovine rumen ciliates.
      ;
      • Newbold C.J.
      • Lassalas B.
      • Jouany J.P.
      The importance of methanogens associated with ciliate protozoa in ruminal methane production in vitro.
      ), and elimination of ciliate protozoa from the rumen reduced methane emissions by 30 to 45% (
      • Jouany J.P.
      • Zainab B.
      • Senaud J.
      • Groliere C.A.
      • Grain J.
      • Trivend P.
      Role of the rumen ciliate protozoa Polyplastron multivesiculatum, Entodinium spp. and Isotricha prostoma in the digestion of a mixed diet in sheep.
      ;
      • Itabashi H.
      • Kobayashi T.
      • Matsumoto M.
      The effects of rumen ciliate protozoa on energy metabolism and some constituents in rumen fluid and blood plasma of goats.
      ;
      • Ushida K.
      • Miyazaki A.
      • Kawashima R.
      Effect of defaunation on ruminal gas and VFA production in vitro..
      ). Several compounds have the potential to reduce methane production from ruminants although their long-term effects have not been well established. Some compounds are toxic or may not be economically feasible (
      • Hristov A.N.
      • Oh J.
      • Firkins J.L.
      • Dijkstra J.
      • Kebreab E.
      • Waghorn G.
      • Makkar H.P.S.
      • Adesogan A.T.
      • Yang W.
      • Lee C.
      • Gerber P.J.
      • Henderson B.
      • Tricarico J.M.
      Special topics: Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options.
      ), or an adaptive response may occur in some bioactive compounds after supplementation (
      • Wallace R.J.
      • McEwan N.R.
      • McIntosh F.M.
      • Teferedegne B.
      • Newbold C.J.
      Natural products as manipulators of rumen fermentation.
      ).
      Cordyceps militaris, a traditional Chinese medicinal mushroom, is an entomogenous fungus belonging to the Ascomycotina. The mushroom is traditionally called “DongChung HaCho” in Korea meaning “summer-plant and winter-worm.” During the past several decades, many kinds of bioactive constituents from Cordyceps spp. have been isolated and characterized. These include cordycepic acid (d-mannitol), cordycepin, ophicordin, polysaccharides, amino acids, galactosaminoglycan, nucleic acids, steroids, and l-tryptophan (
      • Tang W.
      • Eisenbrand G.
      ;
      • Namba T.
      The Encyclopedia of Wakan-Yaku (Traditional Sino-Japanese Medicines) with Color Pictures. Vol II.
      ;
      • Huang L.F.
      • Liang Y.Z.
      • Guo F.Q.
      • Zhou Z.F.
      • Cheng B.M.
      Simultaneous separation and determination of active components in Cordyceps sinensis and Cordyceps militaris by LC/ESIMS.
      ). In addition, C. militaris showed several therapeutic effects, including immunoregulative (
      • Zhu J.S.
      • Halpern G.M.
      • Johns K.
      The scientific rediscovery of an ancient Chinese herbal medicine: Cordyceps sinensis: part I.
      ,
      • Zhu J.S.
      • Halpern G.M.
      • Johns K.
      The scientific rediscovery of a precious ancient Chinese herbal regimen: Cordyceps sinensis: part II.
      ;
      • Ahn Y.J.
      • Park S.J.
      • Lee S.G.
      • Shin S.C.
      • Choi D.H.
      Cordycepin: selective growth inhibitor derived from liquid culture of Cordyceps militaris against Clostridium spp.
      ;
      • Zhou X.
      • Meyer C.U.
      • Schmidtke P.
      • Zepp F.
      Effect of cordycepin on interleukin-10M production peripheral blood mononuclear cells.
      ), anticancer (
      • Müller W.E.G.
      • Seibert G.
      • Beyer R.
      • Breter H.J.
      • Maidhof A.
      • Zahn R.K.
      Effect of cordycepin on nucleic acid metabolism in L5178Y cells and on nucleic acid-synthesizing enzyme systems.
      ), antibacterial (
      • Ahn Y.J.
      • Park S.J.
      • Lee S.G.
      • Shin S.C.
      • Choi D.H.
      Cordycepin: selective growth inhibitor derived from liquid culture of Cordyceps militaris against Clostridium spp.
      ), antifungal (
      • Sugar A.M.
      • McCaffrey R.P.
      Antifungal activity of 30-deoxyadenosine (cordycepin).
      ), larvicidal (
      • Kim J.R.
      • Yeon S.H.
      • Kim H.S.
      • Ahn Y.J.
      Larvicidal activity against Plutella xylostella of cordycepin from the fruiting body of Cordyceps militaris.
      ), and antioxidant (
      • Li S.P.
      • Li P.
      • Dong T.T.
      • Tsim K.W.
      Anti-oxidation activity of different types of natural Cordyceps sinensis and cultured Cordyceps mycelia.
      ;
      • Tsai C.H.
      • Stern A.
      • Chiou J.F.
      • Chern C.L.
      • Liu T.Z.
      Rapid and specific detection of hydroxyl radical using an ultraweak chemiluminescence analyzer and a low-level chemiluminescence emitter: Application to hydroxyl radical-scavenging ability of aqueous extracts of food constituents.
      ) effects. Cordyceps militaris mycelia have been shown to alter in vitro rumen microbial fermentation with increased production of gas and VFA, cellulose digestion, and cellulolytic enzyme activities (
      • Yeo J.M.
      • Lee S.J.
      • Lee S.M.
      • Shin S.H.
      • Lee S.H.
      • Ha J.K.
      • Kim W.Y.
      • Lee S.S.
      Effects of Cordyceps militaris mycelia on in vitro rumen microbial fermentation.
      ,
      • Yeo J.M.
      • Lee S.J.
      • Shin S.H.
      • Lee S.H.
      • Ha J.K.
      • Kim W.Y.
      • Lee S.S.
      Effects of Cordyceps militaris mycelia on fibrolytic enzyme activities and microbial populations in vitro.
      ). But no information exists with respect to C. militaris modulating methane production in the rumen. Therefore, the present study was conducted to observe the effects of C. militaris on ruminal microorganism fermentation with particular reference to methane production in vitro.

      Materials and Methods

      Sample Preparation

      Because Cordyceps are very difficult to collect due to their very small size and restricted area of growth, mass production of these fungi has been established through artificial cultivation. Dried C. militaris was cultured on floral medium composed of gluten, soybean protein, beer yeast, and corn steep liquor (culturing method and medium composition were patented in Korea, patent registration No.1006442430000;

      Lee, H. G. 2006. Composition for cultivating Cordyceps and cultivating process using thereof. AJU International Law & Patent Group, assignee. Korea Pat. No. 1006442430000.

      ) obtained from EuGene Bio Farm (Hwaseong City, Gyeonggi Province, Korea). The manufacturer reported that C. militaris mycelia used in the present study contained about 2.3 times more cordycepin (1.6 mg/g of DM) than C. militaris traditionally cultured on faunal pupae (0.7 mg/g of DM). It contained 8.6% moisture, 76.2% CP, 12.2% crude fiber, 1.0% ether extract, 3.2% crude ash, and 7.4% nitrogen-free extract.

      In Vitro Batch Fermentation

      The anaerobic culture techniques of
      • Hungate R.E.
      The anaerobic mesophilic cellulolytic bacteria.
      with modifications (
      • Bryant M.P.
      • Burkey L.A.
      Cultural methods and some characteristics of some of the more numerous groups of bacteria in the bovine rumen.
      ;
      • Bryant M.P.
      Commentary on the Hungate techniques for culture of anaerobic bacteria.
      ) were carried out for all incubations and the experimental procedures were the same as those described in a previous study (
      • Yeo J.M.
      • Lee S.J.
      • Lee S.M.
      • Shin S.H.
      • Lee S.H.
      • Ha J.K.
      • Kim W.Y.
      • Lee S.S.
      Effects of Cordyceps militaris mycelia on in vitro rumen microbial fermentation.
      ) except that 200 mg of soluble potato starch (S2004; Sigma-Aldrich Korea, Yongin City, Gyeonggi-do, Korea) was used as a carbon source.
      Dried C. militaris was added gravimetrically to achieve final concentrations of 0.00, 0.10, 0.15, 0.20, 0.25, and 0.30 g/L. The bottles (3 replicates per treatment) were closed with butyl rubber stoppers under the Hungate anaerobic gassing system hooked to a source of oxygen-free gas, sealed with aluminum caps, and placed in an incubator at 38°C for 3, 6, 9, 12, 24, 36, 48, and 72 h without shaking. The experimental design was a complete randomized design with 3 replications per treatment.

      Total, Hydrogen, and Methane Gas Production

      At the end of each incubation time, a needle attached to a glass syringe was inserted through the butyl rubber stopper, and the volume of gas exceeding 1 atm was measured through displacement of the syringe plunger using the technique of
      • Fedorak P.M.
      • Hrwdey S.E.
      A simple apparatus for measuring gas production by methanogenic cultures in serum bottles.
      with modifications (
      • Callaway T.R.
      • Martin S.A.
      Effects of organic acid and monensin treatment on in vitro mixed ruminal microorganism fermentation of cracked corn.
      ). A 0.5-mL subsample of gas was analyzed for hydrogen and methane content by GC (model CP-3800, Varian Inc., Palo Alto, CA) using a molecular sieve 13×, 45- to 60-mesh column (2.0 mm × 3.2 mm × 2.0 mm, stainless steel) and a thermal conductivity detector (oven temperature = 60°C, injector and thermal conductivity detector temperature = 120°C, flame-ionization detector temperature = 200°C). The carrier gas (N2) flow rate was 50 mL/min.

      pH, NH3-N, and VFA

      After determination of gas production, the bottles were uncapped, and pH of the culture fluid was determined using a pH meter (MP 230, Mettler-Toledo, Greifensee, Switzerland). For analysis of ammonia-N and VFA, 1 mL of 25% meta-phosphoric acid was added to 5 mL of fermentation fluid and centrifuged (10,000 × g for 10 min at 4°C); supernatants were stored at −30°C until analysis. Volatile fatty acids were analyzed by GC (model GC-14B, Shimadzu Co. Ltd., Tokyo, Japan) using a Thermon-3000 5% Shincarbon A column (1.6m × 3.2 mm i.d., 60 to 80 mesh, Shinwakako, Kyoto, Japan) and flame-ionization detector (column temperature = 130°C, injector and detector temperature = 200°C). The carrier gas (N2) flow rate was 50 mL/min. The micro-diffusion method was used to determine NH3-N (
      • Conway E.J.
      ).

      Microbial Populations

      Total viable bacteria and fungi in the culture fluid were enumerated by the method of
      • Holdeman L.V.
      • Cato E.P.
      • Moore W.E.C.
      and
      • Joblin K.N.
      Bacterial and protozoal interactions with ruminal fungi.
      using anaerobic roll tubes. Samples were fixed in methylgreen-formalin-saline (MFS) solution consisting of 900 mL of distilled water, 100 mL of 35% formaldehyde solution, 0.6 g of methylgreen, and 8.0 g of NaCl before enumeration of rumen protozoa by the method of
      • Ogimoto K.
      • Imai S.
      . Protozoa fixed in MFS were diluted in the same solution and counted under a microscope with a plankton-counting glass (cat. no. 900, Hausser Scientific, Blue Bell, PA).

      Relative Quantification of Specific Ruminal Microbes

      Total nucleic acid was extracted from the incubated rumen samples using the modified bead-beating protocol (
      • Yu Z.
      • Morrison M.
      Improved extraction of PCR-quality community DNA from digesta and fecal samples.
      ) with the QIAamp DNA mini kit (Qiagen, Valencia, CA). This was accomplished by taking a 1.0-mL aliquot from the culture medium using a wide-bore pipette to ensure collection of a homogeneous sample. Nucleic acid concentrations were measured using a NanoDrop Spectrophotometer (Thermo Scientific, Wilmington, DE).
      Quantitative (q)PCR assays for enumeration of methanogenic archaea, ciliate protozoa, and cellulolytic bacterial species (Fibrobacter succinogenes, Ruminococcus flavefaciens, Ruminococcus albus) were performed according to the methods described by
      • Koike S.
      • Kobayashi Y.
      Development and use of competitive PCR assays for the rumen cellulolytic bacteria: Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens..
      ,
      • Denman S.E.
      • McSweeney C.S.
      Development of a real-time PCR assay for monitoring anaerobic fungal and cellulolytic bacterial populations within the rumen.
      , and
      • Denman S.E.
      • Tomkins N.W.
      • McSweeney C.S.
      Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane.
      on a real-time PCR machine (CFX96 Real-Time system, Bio-Rad, Hercules, CA) using the SYBR Green Supermix (QPK-201, Toyobo Co. Ltd., Tokyo, Japan). The PCR primer sets used are shown in Table 1. They included group-specific primers for total bacteria as reference genes and species-specific primers for F. succinogenes, R. flavefaciens, R. albus, methanogenic archaea, and ciliate protozoa. All microbial data were analyzed for calculating relative expressions to total bacteria (
      • Denman S.E.
      • McSweeney C.S.
      Quantitative (real-time) PCR.
      ). The values of cycle threshold (Ct) after real-time PCR were used to determine the fold change of different microbial populations relative to control (
      • Yuan J.S.
      • Reed A.
      • Chen F.
      • Stewart C.N.
      Statistical analysis of real-time PCR data.
      ). Abundance of these microbes was expressed by the equation relative quantification = 2−[ΔCt (Target) – ΔCt (Control)], where Ct represents threshold cycle. All qPCR reaction mixtures (final volume of 25 µL) contained forward and reverse primers (10 pmol each), the iQ SYBR Green Supermix (Toyobo Co. Ltd.), and DNA template ranging from 10 to 100 ng. A negative control without template DNA was used in every qPCR assay for each primer. The PCR amplification of the target DNA was conducted following the references in Table 1.
      Table 1Primers (F = forward; R = reverse) for real-time PCR assay
      Target speciesPrimer sequence (5′→3′)Size (bp)Reference
      Total bacteriaF: CGG CAA CGA GCG CAA CCC

      R: CCA TTG TAG CAC GTG TGT AGC C
      130
      • Denman S.E.
      • McSweeney C.S.
      Development of a real-time PCR assay for monitoring anaerobic fungal and cellulolytic bacterial populations within the rumen.
      Ruminococcus albusF: CCC TAA AAG CAG TCT TAG TTC G

      R: CCT CCT TGC GGT TAG AAC A
      175
      • Koike S.
      • Kobayashi Y.
      Development and use of competitive PCR assays for the rumen cellulolytic bacteria: Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens..
      Ruminococcus flavefaciensF: CGA ACG GAG ATA ATT TGA GTT TAC TTA GG

      R: CGG TCT CTG TAT GTT ATG AGG TAT TAC C
      132
      • Denman S.E.
      • McSweeney C.S.
      Development of a real-time PCR assay for monitoring anaerobic fungal and cellulolytic bacterial populations within the rumen.
      Fibrobacter succinogenesF: GTT CGG AAT TAC TGG GCG TAA A

      R: CGC CTG CCC CTG AAC TAT C
      121
      • Denman S.E.
      • McSweeney C.S.
      Development of a real-time PCR assay for monitoring anaerobic fungal and cellulolytic bacterial populations within the rumen.
      Methanogenic archaeaF: TTC GGT GGA TCD CAR AGR GC

      R: GBA RGT CGW AWC CGT AGA ATC C
      140
      • Denman S.E.
      • Tomkins N.W.
      • McSweeney C.S.
      Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane.
      Ciliate protozoaF: GAG CTA ATA CAT GCT AAG GC

      R: CCC TCA CTA CAA TCG AGA TTT AAG G
      180
      • Skillman L.C.
      • Evans P.N.
      • Strömpl C.
      • Joblin K.N.
      16S rDNA directed PCR primers and detection of methanogens in the bovine rumen.

      Computation of Data and Statistical Analysis

      To give a more precise estimate of gas production throughout fermentation, the following calculation was used to analyze the kinetic data, as described by
      • Ørskov E.R.
      • McDonald I.
      The estimation of protein degradability in the rumen from incubation measurements weighted according to rate of passage.
      :
      GP=a+b1-exp-c×time,


      where GP is gas production (mL/0.1 g DM of substrate) at time t; a, b, and c are the scaling factor for Y-axis intercept (mL/0.1 g of DM), potential gas production (mL/0.1 g of DM), and the rate constant for gas production per hour (h−1), respectively. Gas production rate was fitted to the model by using the nonlinear (NLIN) procedure (
      SAS Institute
      ) using Marquardt’s algorithm while varying a, b, and c. Effective gas production (EGP: substrate availability) from the culture was estimated as EGP = a + b[cd/(cd + cp)], where cd is a gas production rate constant, and cp is a passage rate constant assumed to be 0.05/h (
      NRC
      ).
      Data obtained from the experiment were analyzed using the SAS/OR (
      SAS Institute
      ) software package and differences were tested by Duncan’s multiple range test. Significance was declared at P < 0.05.

      Results

      Table 2 shows the effects of C. militaris on cumulative gas production and its parameters at different incubation times. Gas production was linearly increased by the addition of C. militaris at all incubation times. The potential gas production (a + b) was significantly higher for C. militaris treatments than for the control treatment. In all treatments, cumulative gas production by mixed rumen microorganisms rapidly increased from 3 to 12 h of incubation. The addition of C. militaris significantly increased (P < 0.05) total gas production compared with the control except at 6, 9, and 24 h of incubation for 0.10 and 0.15 g/L treatments. The highest total gas production was seen (P < 0.05) in the 0.25 g/L treatment from 24 to 72 h of incubation.
      Table 2Effects of different doses of Cordyceps militaris on in vitro cumulative gas production (at 3 to 72 h of incubation) by mixed rumen anaerobic microbial fermentation
      Incubation time (h)Dose (g/L)SEMContrast
      0.000.100.150.200.250.30LinearQuadraticCubic
      Gas production

      (mL/0.1 g DM of substrate)
       3 h4.57
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      8.77
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      8.60
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      10.37
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      9.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      9.73
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.550.0020.0170.200
       6 h5.57
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      9.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      11.10
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      12.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      13.23
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      15.80
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.000.0010.5500.480
       9 h10.77
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      14.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      15.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      17.47
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      20.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.27
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.98<0.0010.6500.960
       12 h13.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      16.90
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      19.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      18.23
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      24.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      28.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.20<0.0010.0520.043
       24 h17.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      19.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.10
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      20.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      25.63
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      19.77
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.68<0.0010.0020.014
       36 h17.00
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      22.47
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.17
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      26.97
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.17
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.77<0.001<0.0010.058
       48 h18.27
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      22.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      28.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      20.67
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.80<0.001<0.0010.005
       72 h19.40
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.60
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.57
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.80
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      29.40
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      22.53
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.77<0.001<0.001<0.001
      Gas production parameters
      Gas production parameters, a, b, and c, for the negative exponential equation GP=a + b(1 – exp−c×time), where GP is gas production (ml/0.1g DM of substrate) of time t; a=gas production from the immediately soluble fraction; b=gas production from the insoluble fraction; c=the fractional rate of gas production per hour; a + b=potential extent of gas production; EGP=effective gas production rate from the cultures, calculated as EGP=a + b[kd/(kd + kp)], where kd (k) is a gas production rate constant, and kp is a passage rate constant assumed to be 0.05h−1.
      a (mL/0.1 g DM of substrate)−1.38
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      3.12
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.81
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.06
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      −2.34
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      −3.62
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.250.0560.0520.280
      b (mL/0.1 g DM of substrate)20.05
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      18.57
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.53
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      18.14
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      30.49
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      27.96
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.050.0310.0790.350
      a + b (mL/0.1 g DM of substrate)18.68
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.69
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.34
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      23.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      28.16
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      22.35
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.55<0.001<0.0010.004
      k (GP·h−1)0.0983
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.0977
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.1123
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.1050
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.1453
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.2730
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.080.0080.0270.150
       EGP (%)11.90
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      15.31
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      16.66
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      17.25
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      20.27
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      18.46
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.53<0.001<0.0010.061
      a–d Means (n = 3) with different superscripts in the same rows are different (P < 0.05).
      1 Gas production parameters, a, b, and c, for the negative exponential equation GP = a + b(1 – expc×time), where GP is gas production (ml/0.1 g DM of substrate) of time t; a = gas production from the immediately soluble fraction; b = gas production from the insoluble fraction; c = the fractional rate of gas production per hour; a + b = potential extent of gas production; EGP = effective gas production rate from the cultures, calculated as EGP = a + b[kd/(kd + kp)], where kd (k) is a gas production rate constant, and kp is a passage rate constant assumed to be 0.05 h−1.
      Table 3 shows the effects of C. militaris on methane and hydrogen gas production. The addition of C. militaris reduced methane production linearly (P < 0.05) from 24 to 72 h, but a linear reduction of hydrogen gas production was seen only at 24 h of incubation. The largest reduction of methane production relative to the control was seen at 24 h of incubation, showing reductions of 14.1, 22.0, 24.9, 39.7, and 40.9% for 0.10, 0.15, 0.20, 0.25, and 0.30 g/L treatments, respectively.
      Table 3Effects of different doses of Cordyceps militaris on methane (CH4) and hydrogen (H2) gas production (at 3 to 72 h of incubation) in supernatant of growing mixed rumen anaerobic microorganisms
      ItemDose (g/L)SEMContrast
      0.000.100.150.200.250.30LinearQuadraticCubic
      Methane production (mM)
       3 h1.37
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.95
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      2.04
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      2.55
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      2.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      2.31
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.210.0090.0600.444
       6 h1.642.012.552.122.082.500.270.1000.4160.153
       9 h4.593.473.883.673.394.180.550.5950.2030.930
       12 h6.315.534.885.095.025.310.420.0970.0720.693
       24 h15.63
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      13.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      12.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      11.73
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      9.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      9.23
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.45<0.00010.1890.730
       36 h23.33
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      19.22
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      17.52
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      18.18
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      15.35
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      14.49
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.90<0.00010.1820.129
       48 h29.90
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      20.75
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      24.10
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      26.83
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      22.00
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      21.30
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.950.0010.292<.0001
       72 h39.64
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      37.45
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      35.13
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      33.21
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      34.67
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      32.35
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.250.0010.2320.590
      Hydrogen production (mM)
       3 h0.01
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.000.0020.0680.088
       6 h0.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.03
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.000.0930.9820.064
       9 h0.050.050.040.050.040.050.010.6990.4800.424
       12 h0.07
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.07
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.06
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.07
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.05
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.07
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.000.5260.1270.172
       24 h0.22
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.19
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.17
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.14
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.12
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.12
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.01<0.00010.0600.656
       36 h0.25
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.25
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.24
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.23
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.22
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.010.1710.5440.407
       48 h0.32
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.27
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.26
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.35
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.24
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.29
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.010.2430.3980.130
       72 h0.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.50
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.40
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.44
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.39
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.44
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.010.0340.4750.002
      a–d Means (n = 3) with different superscripts in the same rows are different (P < 0.05).
      A linear reduction of the concentration of ammonia-N by the addition of C. militaris was seen at 12 and 24 h of incubation (Table 4). Total VFA concentration was linearly increased (P < 0.05) by the addition of C. militaris from 24 to 72 h (Table 4), and corresponding decreases of pH were seen. At all levels of C. militaris addition at 24 h of incubation (Figure 1), the molar proportion of acetate was decreased (P < 0.05) compared with the control and that of propionate was increased (P < 0.05) in the 0.20 to 0.30 g/L treatments. This led to corresponding decreases in acetate:propionate ratio as the addition of C. militaris increased.
      Table 4Effects of different doses of Cordyceps militaris on pH value, ammonia-N, and total VFA production (at 3 to 72 h of incubation) in supernatant of growing mixed rumen anaerobic microorganisms
      ItemDose (g/L)SEMContrast
      0.000.100.150.200.250.30LinearQuadraticCubic
      pH
       3 h6.47
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.54
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.45
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.46
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.46
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.47
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.210.0090.0600.444
       6 h6.346.236.246.266.446.260.270.1000.4160.153
       9 h6.256.436.106.366.286.390.550.5950.2030.930
       12 h6.206.176.096.136.226.010.420.0970.0720.693
       24 h6.17
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.05
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.14
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.95
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.78
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.45<0.00010.1890.730
       36 h6.00
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.95
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.89
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.80
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.65
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.02
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.90<0.00010.1820.129
       48 h5.66
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.53
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.36
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.37
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.21
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.37
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.950.0010.292<0.0001
       72 h5.48
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.45
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.14
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.16
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.14
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.24
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      1.250.0010.2320.590
      Ammonia-N (mg/dL)
       3 h3.873.834.304.404.034.180.480.58420.58640.9696
       6 h4.434.034.274.804.404.260.360.80440.71650.2739
       9 h5.505.905.576.506.304.620.560.64170.08060.1742
       12 h5.83
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      6.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.20
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.43
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      4.93
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      4.73
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.340.00770.78580.6087
       24 h6.47
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.90
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      5.30
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      4.93
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      4.30
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      3.57
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.25<0.00010.68380.5987
       36 h10.609.609.8310.109.739.000.800.29210.88610.3672
       48 h17.3715.3316.5016.8020.8716.402.900.62770.98330.272
       72 h19.6018.5021.3020.0722.3322.531.880.13830.80390.7788
      Total VFA production (mM)
       3 h30.4531.8430.6831.5232.0631.140.750.4480.4990.890
       6 h31.8231.1030.4831.5033.0333.160.850.0810.1230.357
       9 h36.3036.6135.7537.1636.7238.591.000.1430.3230.716
       12 h39.1240.0643.1340.5840.3344.352.850.3270.9370.385
       24 h43.56
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      44.51
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      45.72
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      47.92
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      48.18
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      48.89
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      0.80<0.00010.5070.478
       36 h46.98
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      54.55
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      59.48
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      56.19
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      65.28
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      65.52
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      3.710.0020.5660.548
       48 h49.93
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      59.76
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      62.41
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      58.23
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      67.56
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      66.16
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      3.600.0060.3900.388
       72 h59.39
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      61.64
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      59.88
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      69.55
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      66.97
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      70.67
      Means (n=3) with different superscripts in the same rows are different (P<0.05).
      2.440.0020.8610.561
      a–e Means (n = 3) with different superscripts in the same rows are different (P < 0.05).
      Figure thumbnail gr1
      Figure 1Influence of different doses of Cordyceps militaris on total VFA (○), and the molar proportion (%) of acetate (shaded bars), propionate (open bars), and acetate:propionate ratio (A:P ratio, ●) in supernatant of growing mixed rumen anaerobic microorganisms after a 24-h incubation. Lowercase letters indicate statistical significance; means (n = 3) with different letters are significantly different (P < 0.05). Color version available in the online PDF.
      Figure 2 shows the effects of C. militaris on microbial populations in culture fluid after 24 h of incubation. The numbers of total and cellulolytic bacteria in the supernatant significantly increased (P < 0.05) at the highest dose level of C. militaris compared with the control. Significant decreases (P < 0.05) in the number of live protozoa and anaerobic fungi were seen in the 0.25 and 0.30 g/L treatments compared with the control, whereas numbers of dead protozoa remained similar between the treatments.
      Figure thumbnail gr2
      Figure 2Influence of different doses of Cordyceps militaris on the populations of total bacteria (×109 cfu/mL, shaded bars), cellulolytic bacteria (×106 cfu/mL, solid bars), anaerobic fungi (×103 cfu/mL, open bars), live protozoa (×102 cfu/mL, ○), and dead protozoa (×103 cfu/mL, ●) in supernatant of growing mixed rumen anaerobic microorganisms after a 24-h incubation. Lowercase letters indicate statistical significance; means (n = 3) with different letters are significantly different (P < 0.05). Color version available in the online PDF.
      Real-time PCR analysis indicated that C. militaris significantly affected abundance of cellulolytic bacteria (R. albus, R. flavefaciens, and F. succinogenes), ciliate protozoa, and methanogenic archaea (Figure 3). The addition of C. militaris significantly decreased (P < 0.05) the abundance of R. albus in the 0.25 and 0.30 g/L treatments at 24 and 48 h of incubation, and for the 0.10, 0.20, and 0.30 g/L treatments at 12 h of incubation. Supplementation with C. militaris also decreased the abundance of F. succinogenes at 24 h except in the 0.15 and 0.20 g/L treatments but decreased responses were not shown at 12 and 48 h of incubation. On the other hand, R. flavefaciens in the 0.15, 0.25, and 0.30 g/L treatments was significantly increased (P < 0.05) at 24 h of incubation, and increased responses were shown for the 0.10 and 0.15 g/L treatments only at 12 and 48 h of incubation, respectively. A significant decrease (P < 0.05) in the abundance of ciliate protozoa was evident at 24 h of incubation when C. militaris was added at a level greater than 0.15 g/L. At 48 h of incubation, reductions in the abundance of ciliate protozoa were seen only in the 0.25 and 0.30 g/L treatments. The effects of C. militaris addition on the abundance of methanogenic archaea were inconsistent. The 0.10 and 0.30 g/L treatments at 12 h and the 0.25 g/L treatment at 24 h of incubation decreased the abundance of methanogenic archaea; however, at 48 h of incubation, addition of C. militaris, except at the 0.25 g/L level, increased the abundance of methanogenic archaea.
      Figure thumbnail gr3
      Figure 3Influence of different doses of Cordyceps militaris on the relative quantification analysis of methanogenic archaea (●), ciliate protozoa (○), and cellulolytic bacteria: Ruminococcus albus (shaded bars), Ruminococcus flavefaciens (solid bars), and Fibrobacter succinogenes (open bars) in supernatant of growing mixed rumen anaerobic microorganisms after (A) 12-h, (B) 24-h, and (C) 48-h incubations. Lowercase letters indicate statistical significance; means (n = 3) with different letters are significantly different (P < 0.05). Color version available in the online PDF.

      Discussion

      In general, in vitro ruminal anaerobic microbial fermentation was strongly affected by the addition of dried C. militaris. The addition of C. militaris increased cumulative and potential gas production, but reduced production of methane and hydrogen gas. Supplementation with C. militaris appeared to accelerate the fermentation process, especially in the early stages of incubation, as shown by accelerated rates of cumulative gas production (Table 2). Total gas production was closely related to the digestion of fermentation substrates, VFA production, and microbial activity and growth (
      • Getachew G.
      • Robinson P.H.
      • DePeters E.J.
      • Taylor S.J.
      Relationships between chemical compositions, dry matter degradation and in vitro gas production of several ruminant feeds.
      ). In the present study, although we observed a lag time between gas production and the responses of pH and total VFA production, a positive correlation between gas production and total VFA production was found (R2 = 0.63, P < 0.001). The increases in total gas production in response to the addition of C. militaris, which is highly nutritious, might be due to the increased activity of related microbes. It is true that the control vials contained less nutrients (N and carbohydrates) than did the treatment vials, but the supply of N in the control vials would seem unlikely to be limited during the early incubation periods, as shown by the similar ammonia-N concentrations between treatments (Table 4). Furthermore, ammonia-N concentration at 12 and 24 h was lower for the treatments than for the control. The difference in supply of carbohydrates would likely be minimal because of the high level of CP (76%) in C. militaris. This suggests that stimulatory responses to C. militaris might have been from an adverse effect on protozoan population and a positive effect on bacterial population rather than from differences in the supply of major nutrients. The numbers of total and cellulolytic bacteria were increased by the addition of C. militaris (Figure 2).
      Methane produced by enteric fermentation in ruminants not only represents a severe loss of feed energy for the animals but also has an ecological impact. Therefore, reducing methane production could have significant economic and environmental benefits. In the present study, addition of C. militaris decreased methane production linearly from 24 to 72 h of incubation with a maximum reduction of 40.9% observed for the highest level of C. militaris at 24 h of incubation (Table 3).
      • Kamra D.N.
      • Patra A.K.
      • Chatterjee P.N.
      • Kumar R.
      • Agarwal N.
      • Chaudhary L.C.
      Effect of plant extracts on methanogenesis and microbial profile of the rumen of buffalo: A brief overview.
      screened 93 plant extracts for their potential to inhibit in vitro methanogenesis and ciliate protozoa using buffalo rumen liquor, and reported that 20 extracts abated methane production by more than 25%, accompanied by a sharp decline in methanogen numbers. Some plant species showing a more pronounced effect are rich in saponins (Sapindus mukorossi), tannins (Terminalia chebula, Populus deltoids, Mangifera indica, and Psidium guajava), or essential oils (Syzygium aromaticum and Allium sativum). In the RUMEN-UP project (Rumen Metabolism Enhanced Naturally Using Plants; http://www.rowett.ac.uk/rumen_up/index.html), potential candidates were selected from 500 different plant species based on their ability to inhibit methane production by 15 to 27% without a detrimental effect on total VFA production or feed digestibility. The plant species selected were the Italian plumeless thistle (Carduus pycnocephalus, 30% inhibition), the Chinese peony (Paeonia lactiflora, 8–53%), the European aspen (Populus tremula, 25%), the sweet cherry (Prunus avium, 20%), goat willow (Salix caprea, 30%), English oak (Quercus pedunculata, 25%), and Sikkim rhubarb (Rheum nobile, 25%). The application of these candidate species to ruminant livestock is still in the early stage and many points still need to be clarified (
      • Kobayashi Y.
      Abatement of methane production from ruminants: Trends in the manipulation of rumen fermentation.
      ).
      Our findings cannot be directly compared with numerous methane-suppressing agents reported in the literature because this is the first study to show that C. militaris can suppress methane emission. However, the modes of action of C. militaris appear similar to those of monensin and secondary plant metabolites (saponins) because the reduction of methane production in response to the addition of C. militaris was accompanied by a decrease in live protozoan population (Figure 2) and abundance of ciliate protozoa (Figure 3). It has been reported that monensin and saponin affect methanogens indirectly by suppressing ciliate protozoa (
      • Hino T.
      • Takeshi K.
      • Kanda M.
      • Kumazawa S.
      Effects of aibellin, a novel peptide antibiotic on rumen fermentation in vitro.
      ;
      • Lila Z.A.
      • Mohammed N.
      • Kanda S.
      • Kamada T.
      • Itabashi H.
      Effect of sarsaponin on ruminal fermentation with particular reference to methane production in vitro.
      ). Rumen ciliate are known to provide hydrogen as a substrate for methanogens (
      • Stumm C.K.
      • Zwart K.B.
      Symbiosis of protozoa with hydrogen utilizing methanogens.
      ;
      • Ushida K.
      • Tokura M.
      • Takenaka A.
      • Itabashi H.
      Ciliate protozoa and ruminal methanogenesis.
      ), and methanogenic archaea are metabolically correlated with ciliate protozoa (
      • Stumm C.K.
      • Gijzen H.J.
      • Vogels G.D.
      Association of methanogenic bacteria with ovine rumen ciliates.
      ;
      • Newbold C.J.
      • Lassalas B.
      • Jouany J.P.
      The importance of methanogens associated with ciliate protozoa in ruminal methane production in vitro.
      ). Therefore, elimination of ciliate protozoa from the rumen leads to reduced methane emission (
      • Jouany J.P.
      • Zainab B.
      • Senaud J.
      • Groliere C.A.
      • Grain J.
      • Trivend P.
      Role of the rumen ciliate protozoa Polyplastron multivesiculatum, Entodinium spp. and Isotricha prostoma in the digestion of a mixed diet in sheep.
      ;
      • Itabashi H.
      • Kobayashi T.
      • Matsumoto M.
      The effects of rumen ciliate protozoa on energy metabolism and some constituents in rumen fluid and blood plasma of goats.
      ;
      • Ushida K.
      • Miyazaki A.
      • Kawashima R.
      Effect of defaunation on ruminal gas and VFA production in vitro..
      ). But it has been reported that the response of methane production to saponin in in vitro could be affected by saponin source and dose level (
      • Hess H.D.
      • Kreuzer M.
      • Díaz T.E.
      • Lascano C.E.
      • Carulla J.E.
      • Soliva C.R.
      • Machmüller A.
      Saponin rich tropical fruits affect fermentation and methanogenesis in faunated and defaunated rumen fluid.
      ;
      • Beauchemin K.A.
      • Kreuzer M.
      • O’Mara F.
      • McAllister T.A.
      Nutritional management for enteric methane abatement: A review.
      ), and also by the potential adaptation of the rumen microflora and ruminal fiber digestion in vivo (
      • Lu C.D.
      • Jorgensen N.A.
      Alfalfa saponins affect site and extent of nutrient digestion in ruminants.
      ;
      • Holtshausen L.
      • Chaves A.V.
      • Beauchemin K.A.
      • McGinn S.M.
      • McAllister T.A.
      • Odongo N.E.
      • Cheeke P.R.
      • Benchaar C.
      Feeding saponin-containing Yucca Schidigera and Quillaja saponaria to decrease enteric methane production in dairy cows.
      ).
      In the present study, a substantial reduction in methane production did not result in a corresponding decrease in the abundance of methanogenic archaea (Figure 3), as was observed in the study of
      • Hess H.D.
      • Kreuzer M.
      • Díaz T.E.
      • Lascano C.E.
      • Carulla J.E.
      • Soliva C.R.
      • Machmüller A.
      Saponin rich tropical fruits affect fermentation and methanogenesis in faunated and defaunated rumen fluid.
      . It has been reported that decreases in methanogen populations may not necessarily lead to a reduction in methane production, at least within a short period of time (
      • Zhou Z.
      • Meng Q.
      • Yu Z.
      Effects of methanogenic inhibitors on methane production and abundances of methanogens and cellulolytic bacteria in in vitro ruminal cultures.
      ). The discrepancy between the production of methane and the dynamics of the methanogen population might be partly attributable to the insensitivity of some ruminal methanogens to C. militaris.
      In the present study, although the adverse effects of C. militaris on protozoa were similar to those of monensin and saponin, the increase in numbers of cellulolytic bacteria resulting from addition of C. militaris (Figure 2) was different from increases due to additions of monensin and sarsaponin, in which cellulolytic bacteria numbers were reduced. It has been suggested that the main reason for the methane-suppressing effects of sarsaponin might be the inhibition of H2-producing bacteria such as cellulolytic bacteria (
      • Wang Y.
      • McAllister T.A.
      • Yanke L.J.
      • Cheeke P.R.
      Effect of steroidal saponin from Yucca schidigera extract on ruminal microbes.
      ). In the present study, C. militaris increased R. flavefaciens, whereas F. succinogenes and R. albus were decreased (Figure 3). The reasons for the different responses within cellulolytic bacteria populations to the addition of C. militaris are not clear.
      It is also interesting to note that total VFA increased but ammonia-N concentration decreased as the supplementation level of C. militaris increased (Table 4). Volatile fatty acids are the end products of rumen microbial fermentation and represent the main supply of metabolizable energy for ruminants. Therefore, an increase in VFA production would be nutritionally favorable for the animal. In the present study, the addition of C. militaris increased total VFA in the culture fluid. Molar proportion of acetate and acetate:propionate ratio decreased (P < 0.05) and propionate increased as C. militaris increased (Figure 1). Similar results were obtained for monensin (
      • Chen M.
      • Wolin M.J.
      Effect of monensin and lasalocid on the growth of methanogenic and rumen saccharolytic bacteria.
      ;
      • Newbold C.J.
      • Lassalas B.
      • Jouany J.P.
      The importance of methanogens associated with ciliate protozoa in ruminal methane production in vitro.
      ) and sarsaponin (
      • Lila Z.A.
      • Mohammed N.
      • Kanda S.
      • Kamada T.
      • Itabashi H.
      Effect of sarsaponin on ruminal fermentation with particular reference to methane production in vitro.
      ), both of which shifted the proportions of VFA toward higher propionate and decreased acetate. The decreased acetate:propionate ratio reflects both the reduced production of methane and the redirection of hydrogen from methane to propionate (
      • Demeyer D.I.
      • Van Nevel C.J.
      Methanogenesis, an integrated part of carbohydrate fermentation and its control.
      ).
      Reduced ammonia-N concentrations in the rumen are typical when protozoa are inhibited (
      • Williams A.G.
      • Coleman G.S.
      ). It has been reported that the ionophores that inhibit gram-positive bacteria and protozoa also decrease deamination (
      • Van Nevel C.J.
      • Demeyer D.I.
      Effect of monensin on rumen metabolism in vitro..
      ). Similar to this, in the present study, a linear reduction of the concentration of ammonia-N, coupled with a decreased protozoan population, was seen following addition of C. militaris.

      Conclusions

      Dried C. militaris has the ability to partly inhibit methane production in in vitro microbial fermentations. This compound stimulated mixed ruminal microorganism fermentation and a change in fermentation products, and it decreased methane and hydrogen gas production. Further research is necessary to establish the long-term efficacy of C. militaris to inhibit methanogenesis and improve animal performance.

      Acknowledgements

      This research was supported by Bio-industry Technology Development Program of Food & Rural Affairs in Ministry of Agriculture (Sejong, Korea), and Cooperative Research Program for Agriculture Science & Technology Development of Rural Development Administration (Jeonju, Korea).

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