Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.

/pdf/fast-tree-computing-large-minimum-evolution-trees-with-4grq0gwfpy.pdf

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Ruminant production is under increased public scrutiny in terms of the importance of cattle and other ruminants as major producers of the greenhouse gas methane. Methanogenesis is performed by methanogenic archaea, a specialised group of microbes present in several anaerobic environments including the rumen. In the rumen, methanogens utilise predominantly H2 and CO2 as substrates to produce methane, filling an important functional niche in the ecosystem. However, in addition to methanogens, other microbes also have an influence on methane production either because they are involved in hydrogen (H2) metabolism or because they affect the numbers of methanogens or other members of the microbiota. This study explores the relationship between some of these microbes and methanogenesis and highlights some functional groups that could play a role in decreasing methane emissions. Dihydrogen (‘H2’ from this point on) is the key element that drives methane production in the rumen. Among H2 producers, protozoa have a prominent position, which is strengthened by their close physical association with methanogens, which favours H2 transfer from one to the other. A strong positive interaction was found between protozoal numbers and methane emissions, and because this group is possibly not essential for rumen function, protozoa might be a target for methane mitigation. An important function that is associated with production of H2 is the degradation of fibrous plant material. However, not all members of the rumen fibrolytic community produce H2. Increasing the proportion of non-H2 producing fibrolytic microorganisms might decrease methane production without affecting forage degradability. Alternative pathways that use electron acceptors other than CO2 to oxidise H2 also exist in the rumen. Bacteria with this type of metabolism normally occupy a distinct ecological niche and are not dominant members of the microbiota; however, their numbers can increase if the right potential electron acceptor is present in the diet. Nitrate is an alternative electron sinks that can promote the growth of particular bacteria able to compete with methanogens. Because of the toxicity of the intermediate product, nitrite, the use of nitrate has not been fully explored, but in adapted animals, nitrite does not accumulate and nitrate supplementation may be an alternative under some dietary conditions that deserves to be further studied. In conclusion, methanogens in the rumen co-exist with other microbes, which have contrasting activities. A better understanding of these populations and the pathways that compete with methanogenesis may provide novel targets for emissions abatement in ruminant production.

Microbial ecosystem and methanogenesis in ruminants

Status of the phylogenetic diversity census of ruminal microbiomes

Methanogenic archaea reside primarily in the rumen and the lower segments of the intestines of ruminants, where they utilize the reducing equivalents derived from rumen fermentation to reduce carbon dioxide, formic acid, or methylamines to methane (CH4). Research on methanogens in the rumen has attracted great interest in the last decade because CH4 emission from ruminants contributes to global greenhouse gas emission and represents a loss of feed energy. Some DNA-based phylogenetic studies have depicted a diverse and dynamic community of methanogens in the rumen. In the past decade, researchers have focused on elucidating the underpinning that determines and affects the diversity, composition, structure, and dynamics of methanogen community of the rumen. Concurrently, many researchers have attempted to develop and evaluate interventions to mitigate enteric CH4 emission. Although much work has been done using plant secondary metabolites, other approaches such as using nitrate and 3-nitrooxy propanol have also yielded promising results. Most of these antimethanogenic compounds or substances often show inconsistent results among studies and also lead to adverse effects on feed intake and digestion and other aspects of rumen fermentation when fed at doses high enough to achieve effective mitigation. This review provides a brief overview of the rumen methanogens and then an appraisal of most of the antimethanogenic compounds and substances that have been evaluated both in vitro and in vivo. Knowledge gaps and future research needs are also discussed with a focus on methanogens and methane mitigation.

/pdf/rumen-methanogens-and-mitigation-of-methane-emission-by-anti-4r6c11m3qp.pdf

Rumen methanogens and mitigation of methane emission by anti-methanogenic compounds and substances

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security As the world population is predicted to reach approximately 97 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges

/pdf/addressing-global-ruminant-agricultural-challenges-through-3336513qsk.pdf

Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present and future

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.

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

Aims: To screen a pair of primers suitable for denaturing gradient gel electrophoretic (DGGE) analysis of ruminal methanogenic Archaea and to detect the archaeal communities in the rumen of goat.



Methods and Results:  Nine primer pairs for 16S rDNA of methanogenic Archaea, including six for directed polymerase chain reaction (PCR) and three for nested PCR were first evaluated by PCR amplification of the total DNA from rumen fluids and bacteria. The DGGE analysis of rumen fluids was then conducted with three primer sets (344fGC/915r, 1106fGC/1378r and 519f/915rGC) of the nine pairs tested. Good separation and quality of patterns were obtained in DGGE analysis with primer pairs 1106fGC/1378r and 519f/915rGC. A total of 40 DNA fragments were excised from the DGGE gels and their sequences were determined. All fragments belonged to methanogenic Archaea while primer pair 519f/915rGC had better amplification ranges than the other two primer pairs.



Conclusions:  The procedure of DGGE analysis with primer pair 519f/915rGC was more suitable for investigating methanogenic archaeal community in the rumen. The dominant methanogenic Archaea in the rumen of goat was Methanobrevibacter sp. and an unidentified methanogenic Archaea.



Significance and Impact of the Study:  One pair of primers suitable for DGGE analysis of ruminal methanogenic Archaea was obtained and the molecular diversity of ruminal methanogenic Archaea in goat was investigated by PCR-DGGE.

Molecular diversity analysis of rumen methanogenic Archaea from goat in eastern China by DGGE methods using different primer pairs.

The correlation between rumen chemical and bacterial changes was investigated during a four periodical stepwise adaptation to a high-concentrate diet (concentrate level at 0%, 30%, 50% and 70% for diet I to IV, respectively) in goats. The results showed that ruminal pH decreased from 6.7 to 5.5 after switching from diet I to II, and was maintained at about 5.5 on diet III. Denaturing gradient gel electrophoresis results showed that the rumen bacterial community was relatively stable during the initial three feeding periods, except for the appearance of three bands when diet changed from I to II, suggesting that an appropriate concentrate level can promote the proliferation of some bacteria. After 12 days of feeding diet III, total volatile fatty acid (VFA) concentration and butyrate proportion decreased. At days 2 and 3 of feeding diet IV, ruminal pH declined sharply to 5.3 and 4.7, respectively, and total VFA concentration decreased further while lactic acid concentration increased markedly, suggesting a relation between lactic acid accumulation and ruminal pH decline. At the same time, many bacteria disappeared, including most fibrolytic-related bacteria while Streptococcus bovis and Prevotella-like species dominated. Interestingly, Succinivibrio dextrinosolvens-like species maintained throughout the experiment, suggesting its tolerance to low pH. In conclusion, rumen bacterial community was relatively stable feeding 0% to 50% concentrate diets, and it was observed that appropriate concentrate levels in the diet could increase the diversity of rumen bacteria. However, concentrate-rich diets caused lactic acid accumulation and low ruminal pH that caused the disappearance of most fibrolytic-related bacteria sensitive to low pH while S. bovis and genus Prevotella persisted.

Rumen chemical and bacterial changes during stepwise adaptation to a high-concentrate diet in goats.

This study investigated the effect of saponins gypenoside (gynosaponins) on methane production and microbe numbers in a co-culture of a fungus, Piromyces sp. F1, and a methanogen, Methanobrevibacter sp.. Two co-culture systems were used: with methanogen (co-culture I) and without methanogen (co-culture II; methanogen growth inhibited by chloramphenicol). Each co-culture system was treated with 0, 50, 100 or 200 mg gynosaponins/l culture medium. Gas production, methane concentration and volatile fatty acid concentration (VFA) were measured for each treatment group. The numbers of anaerobic fungi and methanogen were quantified by real time PCR. The results showed that, compared with the control, gynosaponin significantly reduced the gas production, methane concentration, methane to TVFA ratio (total volatile fatty acid), TVFA concentration, number of fungi (except for 50 mg dose of gynosaponin in co-culture I) and number of methanogens. Methane was not detected in co-culture II. The individual VFAs proportion of TVFA were not affected by gynosaponins in either of the co-cultures. The pH was higher in both co-cultures than that of the control (P<0.01). These data suggest that gynosaponins has the potential for being used as feed additive to modulate the ruminal fermentation, inhibit the methanogen growth and reduce methane production.

http://www.jafs.com.pl/pdf-66184-5667?filename=Effect of the gynosaponin.pdf

Effect of the gynosaponin on methane production and microbe numbers in a fungus-methanogen co-culture

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 of Selenomonas ruminantium, a fumarate-reducing bacterium, was greater in the rumen of goats supplemented with DF. The abundance of fungi, protozoa, Ruminococus flavefaciens and Fibrobacter succinogenes were not affected by the addition of DF. Variable F : C ratios affected the abundance of methanogens, fungi and R. flavefaciens (P < 0.05), but did not affect methane emission. The result implied that DF had a beneficial effect on the in vivo rumen 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.

S.Y. Mao

Papers

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

Molecular diversity analysis of rumen methanogenic Archaea from goat in eastern China by DGGE methods using different primer pairs.

Rumen chemical and bacterial changes during stepwise adaptation to a high-concentrate diet in goats.

Effect of the gynosaponin on methane production and microbe numbers in a fungus-methanogen co-culture

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