Quantitation and diversity analysis of ruminal methanogenic populations in response to the antimethanogenic compound bromochloromethane

This study investigated the effects of disodium fumarate (DF) on methane emission, ruminal fermentation and microbial abundance in goats under different forage (F) : concentrate (C) ratios and fed according to maintenance requirements. Four ruminally fistulated, castrated male goats were used in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and the main factors being the F : C ratios (41 : 59 or 58 : 42) and DF supplementation (0 or 10 g/day). DF reduced methane production (P< 0.05) on average by 11.9%, irrespective of the F : C ratio. The concentrations of total volatile fatty acids, acetate and propionate were greater in the rumen of goats supplemented with DF (P< 0.05), whereas the abundance of methanogens was lower (P< 0.05). In high-forage diets, the abundance ofSelenomonas ruminantium, a fumarate-reducing bacterium, was greater in the rumen of goats supplemented with DF. The abundance of fungi, protozoa,Ruminococus flavefaciensandFibrobacter succinogeneswere not affected by the addition of DF. Variable F : C ratios affected the abundance of methanogens, fungi andR.flavefaciens(P< 0.05), but did not affect methane emission. The result implied that DF had a beneficial effect on thein vivorumen fermentation of the goats fed diets with different F : C ratios and that this effect were not a direct action on anaerobic fungi, protozoa and fibrolytic bacteria, the generally recognized fiber-degrading and hydrogen-producing microorganisms, but due to the stimulation of fumarate-reducing bacteria and the depression of methanogens.

Section: Methodsmentioning

confidence: 99%

Effect of disodium fumarate on microbial abundance, ruminal fermentation and methane emission in goats under different forage : concentrate ratios

Yang

Mao

Long

et al. 2012

“…Real-time PCR assay for quantification of total methanogens Real-time PCR was performed on an Applied Biosystems 7300 Real-Time PCR System (Applied Biosystems, California, USA) with the mcrA gene-specific primers, qmcrA-F (5 0 -TTC GGT GGA TCD CAR AGR GC-3 0 ) and qmcrA-R (5 0 -GBA RGT CGW AWC CGT AGA ATC C-3 0 ) (Denman et al, 2007). A reaction mixture (20 ml) consisted of 10 ml of IQ SYBR Green Supermix (Bio-Rad, California, USA), 0.2 mM of each primer set and 1 ml of the template DNA.…”

Section: Methodsmentioning

confidence: 99%

“…RPS4 (accession numbers of the 16S ribosomal RNA gene in the GenBank: EU544029) and Methanobacterium beijingense strain M4 (EU544027). The PCR was performed under the following cycle conditions: one cycle of 508C for 2 min and 958C for 2 min for initial denaturation, 40 cycles at 958C for 15 s and 608C for 1 min for primer annealing and product elongation (Denman et al, 2007). Fluorescence detection was performed at the end of each denaturation and extension step.…”

Section: Methodsmentioning

confidence: 99%

Diversity, abundance and novel 16S rRNA gene sequences of methanogens in rumen liquid, solid and epithelium fractions of Jinnan cattle

Pei

Mao

Cheng

et al. 2010

Three methanogen 16S rRNA gene clone libraries were constructed from liquid (LM), solid (SM) and epithelium (EM) fractions taken from the rumen of Jinnan cattle in China. After the amplification by PCR using methanogen-specific primers Met86F and Met1340R, equal quantities of PCR products from the same fractions from each of the four cattle were mixed together and used to construct the three libraries. Sequence analysis showed that the 268 LM clones were divided into 35 phylotypes with 18 sequences of phylotypes affiliated with the genus Methanobrevibacter (84.3% of clones). The 135 SM clones were divided into 19 phylotypes with 11 phylotypes affiliated with the genus Methanobrevibacter (77.8%). The 267 EM clones were divided into 33 phylotypes with 15 phylotypes affiliated with the genus Methanobrevibacter (77.2%). Clones closely related to Methanomicrobium mobile and Methanobrevibacter wolinii were only found in the LM library, and those to Methanobrevibacter ruminantium and Methanobrevibacter gottschalkii only in the SM library. LM library comprised 12.4% unidentified euryarchaeal clones, SM library 23.7% and EM library 25.5%, respectively. Five phylotypes (accession number: EF055528 and EF055531-EF055534) did not belong to the Euryarchaeota sequences we had known. One possible new genus (represented by phylotype E17, accession number EF055528) belonging to Methanobacteriaceae was identified from EM library. Quantitative real-time PCR for the first time revealed that epithelium fraction had significantly higher density of methanogens, with methanogenic mcrA gene copies (9.95 log 10 (copies per gram of wet weight)) than solid (9.26, P , 0.01) and the liquid (8.44, P , 0.001). The three clone libraries also appeared different in Shannon index (EM library 2.12, LM library 2.05 and SM library 1.73). Our results showed that there were apparent differences in the methanogenic diversity and abundance in the three different fractions within the rumen of Jinnan cattle, with Methanobrevibacter species predominant in all the three libraries and with epithelium fraction having more unknown species and higher density of methanogens.Keywords: rumen methanogen, molecular diversity, different fractions ImplicationsInterest in methanogens from ruminants has resulted from the role of methane in global warming and from the fact that cattle typically lose 2% to 12% of ingested energy as methane (Johnson and Johnson, 1995). However, little is known about their distribution in the rumen of Chinese ruminants. To understand their diversity distribution and their abundance, three methanogenic 16S rRNA gene libraries were constructed from different fractions of rumen of Jinnan cattle and their density determined by quantitative real-time PCR. Our results showed that Methanobrevibacter species were predominant in all three libraries, and that there were differences in the methanogenic diversity and abundance in the three different fractions within the rumen. The study for the first time demonstrated that many unidentified sequences wer...

“…The smallsubunit ribosomal RNA gene (rrs) is the most commonly used target for characterising this diversity. For methanogens, the methyl coenzyme-M reductase (mcrA) gene of the methanogenesis pathway is also a phylogenetic marker (Luton et al, 2002) that has been used in rumen studies (Denman et al, 2007). Recently, the diversity of rumen methanogens was investigated using the gene encoding type II chaperonins (Chaban and Hill, 2012).…”

Section: Rumen Microbial Diversitymentioning

confidence: 99%

Rumen microbial (meta)genomics and its application to ruminant production

Morgavi

Kelly

Janssen

et al. 2013

194

159

Meat and milk produced by ruminants are important agricultural products and are major sources of protein for humans. Ruminant production is of considerable economic value and underpins food security in many regions of the world. However, the sector faces major challenges because of diminishing natural resources and ensuing increases in production costs, and also because of the increased awareness of the environmental impact of farming ruminants. The digestion of feed and the production of enteric methane are key functions that could be manipulated by having a thorough understanding of the rumen microbiome. Advances in DNA sequencing technologies and bioinformatics are transforming our understanding of complex microbial ecosystems, including the gastrointestinal tract of mammals. The application of these techniques to the rumen ecosystem has allowed the study of the microbial diversity under different dietary and production conditions. Furthermore, the sequencing of genomes from several cultured rumen bacterial and archaeal species is providing detailed information about their physiology. More recently, metagenomics, mainly aimed at understanding the enzymatic machinery involved in the degradation of plant structural polysaccharides, is starting to produce new insights by allowing access to the total community and sidestepping the limitations imposed by cultivation. These advances highlight the promise of these approaches for characterising the rumen microbial community structure and linking this with the functions of the rumen microbiota. Initial results using high-throughput culture-independent technologies have also shown that the rumen microbiome is far more complex and diverse than the human caecum. Therefore, cataloguing its genes will require a considerable sequencing and bioinformatic effort. Nevertheless, the construction of a rumen microbial gene catalogue through metagenomics and genomic sequencing of key populations is an attainable goal. A rumen microbial gene catalogue is necessary to understand the function of the microbiome and its interaction with the host animal and feeds, and it will provide a basis for integrative microbiome–host models and inform strategies promoting less-polluting, more robust and efficient ruminants.