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

Universal sample preparation method for proteome analysis

Despite the fact that the ruminant diet is rich in polyunsaturated fatty acids (PUFA), ruminant products - meat, milk and dairy - contain mainly saturated fatty acids (SFA) because of bacterial lipolysis and subsequent biohydrogenation of ingested PUFA in the rumen. The link between SFA consumption by man and coronary heart disease is well established. In contrast, ruminant products also contain fatty acids that are known to be beneficial to human health, namely conjugated linoleic acids (CLAs). The aims of research in this field have been to understand the microbial ecology of lipolysis and biohydrogenation and to find ways of manipulating ruminal microbes to increase the flow of PUFA and CLA from the rumen into meat and milk. This review describes our present understanding of the microbial ecology of ruminal lipid metabolism, including some apparently anomalous and paradoxical observations, and the status of how the metabolism may be manipulated and the possible consequential effects on other aspects of ruminal digestion. Intuitively, it may appear that inhibiting the ruminal lipase would cause more dietary PUFA to reach the mammary gland. However, lipolysis releases the non-esterified fatty acids that form the substrates for biohydrogenation, but which can, if they accumulate, inhibit the whole process. Thus, increasing lipase activity could be beneficial if the increased release of non-esterified PUFA inhibited the metabolism of CLA. Rumen ciliate protozoa do not carry out biohydrogenation, yet protozoal lipids are much more highly enriched in CLA than bacterial lipids. How could this happen if protozoa do not metabolise PUFA? The answer seems to lie in the ingestion of plant organelles, particularly chloroplasts, and the partial metabolism of the fatty acids by contaminating bacteria. Bacteria related to Butyrivibrio fibrisolvens are by far the most active and numerous biohydrogenating bacteria isolated from the rumen. But do we misunderstand the role of different bacterial species in biohydrogenation because there are uncultivated species that we need to understand and include in the analysis? Manipulation methods include dietary vegetable and fish oils and plant-derived chemicals. Their usefulness, efficacy and possible effects on fatty acid metabolism and on ruminal microorganisms and other areas of their metabolism are described, and areas of opportunity identified.

https://biblio.ugent.be/publication/1083480/file/1083591.pdf

The role of microbes in rumen lipolysis and biohydrogenation and their manipulation

Methane represents 16 % of total anthropogenic greenhouse gas emissions. It has been estimated that ruminant livestock produce ca. 29 % of this methane. As individual animals produce consistently different quantities of methane, understanding the basis for these differences may lead to new opportunities for mitigating ruminal methane emissions. Metagenomics is a powerful new tool for understanding the composition and function of complex microbial communities. Here we have applied metagenomics to the rumen microbial community to identify differences in the microbiota and metagenome that lead to high- and low-methane-emitting cattle phenotypes. Four pairs of beef cattle were selected for extreme high and low methane emissions from 72 animals, matched for breed (Aberdeen-Angus or Limousin cross) and diet (high or medium concentrate). Community analysis was carried out by qPCR of 16S and 18S rRNA genes and by alignment of Illumina HiSeq reads to the GREENGENES database. Total genomic reads were aligned to the KEGG genes databasefor functional analysis. Deep sequencing produced on average 11.3 Gb per sample. 16S rRNA gene abundances indicated that archaea, predominantly Methanobrevibacter, were 2.5× more numerous (P = 0.026) in high emitters, whereas among bacteria Proteobacteria, predominantly Succinivibrionaceae, were 4-fold less abundant (2.7 vs. 11.2 %; P = 0.002). KEGG analysis revealed that archaeal genes leading directly or indirectly to methane production were 2.7-fold more abundant in high emitters. Genes less abundant in high emitters included acetate kinase, electron transport complex proteins RnfC and RnfD and glucose-6-phosphate isomerase. Sequence data were assembled de novo and over 1.5 million proteins were annotated on the subsequent metagenome scaffolds. Less than half of the predicted genes matched matched a domain within Pfam. Amongst 2774 identified proteins of the 20 KEGG orthologues that correlated with methane emissions, only 16 showed 100 % identity with a publicly available protein sequence. The abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes. Lower emissions were accompanied by higher Succinovibrionaceae abundance and changes in acetate and hydrogen production leading to less methanogenesis, as similarly postulated for Australian macropods. Large numbers of predicted protein sequences differed between high- and low-methane-emitting cattle. Ninety-nine percent were unknown, indicating a fertile area for future exploitation.

/pdf/the-rumen-microbial-metagenome-associated-with-high-methane-i7j0gwy1kq.pdf

The rumen microbial metagenome associated with high methane production in cattle

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

One clone encoding glycoside hydrolases was identified through functional screening of a rumen bacterial artificial chromosome (BAC) library. Of the 68 open reading frames (ORFs) predicted, one ORF encodes a novel endo-β-1,4-xylanase with two catalytic domains of family GH43 and two cellulose-binding modules (CBMs) of family IV. Partial characterization showed that this endo-xylanase has a greater specific activity than a number of other xylanases over a wide temperature range at neutral pH and could be useful in some industrial applications.

Novel glycoside hydrolases identified by screening a Chinese Holstein dairy cow rumen-derived metagenome library.

Isolation and biochemical characterization of two lipases from a metagenomic library of China Holstein cow rumen

Urea, a non-protein nitrogen for dairy cows, is rapidly hydrolyzed to ammonia by urease produced by ureolytic bacteria in the rumen, and the ammonia is used as nitrogen for rumen bacterial growth. However, there is limited knowledge with regard to the ureolytic bacteria community in the rumen. To explore the ruminal ureolytic bacterial community, urea or acetohydroxamic acid (AHA, an inhibitor of urea hydrolysis) were supplemented into the rumen simulation systems. The bacterial 16S rRNA genes were sequenced by Miseq high-throughput sequencing and used to reveal the ureoltyic bacteria by comparing different treatments. The results revealed that urea supplementation significantly increased the ammonia concentration, and AHA addition inhibited urea hydrolysis. Urea supplementation significantly increased the richness of bacterial community and the proportion of ureC genes. The composition of bacterial community following urea or AHA supplementation showed no significant difference compared to the groups without supplementation. The abundance of Bacillus and unclassified Succinivibrionaceae increased significantly following urea supplementation. Pseudomonas, Haemophilus, Neisseria, Streptococcus, and Actinomyces exhibited a positive response to urea supplementation and a negative response to AHA addition. Results retrieved from the NCBI protein database and publications confirmed that the representative bacteria in these genera mentioned above had urease genes or urease activities. Therefore, the rumen ureolytic bacteria were abundant in the genera of Pseudomonas, Haemophilus, Neisseria, Streptococcus, Actinomyces, Bacillus and unclassified Succinivibrionaceae. Insights into abundant rumen ureolytic bacteria provide the regulation targets to mitigate urea hydrolysis and increase efficiency of urea nitrogen utilization in ruminants.

/pdf/insights-into-abundant-rumen-ureolytic-bacterial-community-2paogfs9ed.pdf

Insights into Abundant Rumen Ureolytic Bacterial Community Using Rumen Simulation System

Ureolytic bacteria are key organisms in the rumen producing urease enzymes to catalyze the breakdown of urea to ammonia for the synthesis of microbial protein. However, little is known about the diversity and distribution of rumen ureolytic microorganisms. The urease gene (ureC) has been the target gene of choice for analysis of the urea-degrading microorganisms in various environments. In this study, we investigated the predominant ureC genes of the ureolytic bacteria in the rumen of dairy cows using high-throughput sequencing. Six dairy cows with rumen fistulas were assigned to a two-period cross-over trial. A control group (n = 3) were fed a total mixed ration without urea and the treatment group (n = 3) were fed rations plus 180 g urea per cow per day at three separate times. Rumen bacterial samples from liquid and solid digesta and rumen wall fractions were collected for ureC gene amplification and sequencing using Miseq. The wall-adherent bacteria (WAB) had a distinct ureolytic bacterial profile compared to the solid-adherent bacteria (SAB) and liquid-associated bacteria (LAB) but more than 55% of the ureC sequences did not affiliate with any known taxonomically assigned urease genes. Diversity analysis of the ureC genes showed that the Shannon and Chao1 indices for the rumen WAB was lower than those observed for the SAB and LAB (P < 0.01). The most abundant ureC genes were affiliated with Methylococcaceae, Clostridiaceae, Paenibacillaceae, Helicobacteraceae, and Methylophilaceae families. Compared with the rumen LAB and SAB, relative abundance of the OTUs affiliated with Methylophilus and Marinobacter genera were significantly higher (P < 0.05) in the WAB. Supplementation with urea did not alter the composition of the detected ureolytic bacteria. This study has identified significant populations of ureolytic WAB representing genera that have not been recognized or studied previously in the rumen. The taxonomic classification of rumen ureC genes in the dairy cow indicates that the majority of ureolytic bacteria are yet to be identified. This survey has expanded our knowledge of ureC gene information relating to the rumen ureolytic microbial community, and provides a basis for obtaining regulatory targets of ureolytic bacteria to moderate urea hydrolysis in the rumen.

/pdf/differences-in-ureolytic-bacterial-composition-between-the-427j6f2aqi.pdf

Differences in Ureolytic Bacterial Composition between the Rumen Digesta and Rumen Wall Based on ureC Gene Classification

Obligate anaerobic fungi of the phylum Neocallimastigomycota play a key role in digesting fibrous feeds in the gut of herbivores, but little is known about their global diversity. In this study, the collective diversity of gut anaerobic fungi was examined using all curated internal transcribed spacer 1 (ITS1) sequences of anaerobic gut fungi available in GenBank. The 262,770 quality-checked fungal ITS1 sequences downloaded from GenBank were assigned to 274 operational taxonomic units (OTUs) at the approximate species level. Of these approximate species-equivalent (Sp-eq) OTUs, 119 were represented by at least five ITS1 sequences, with 38 containing known species and 81 containing no known species. Based on a rarefaction analysis, the currently available ITS1 sequences represent nearly all the major species of gut anaerobic fungi, but much more sequencing effort is needed to assess the actual richness of minor OTUs. One dataset of ITS1 reference sequences (referred to as AF-RefSeq) and one comprehensive taxonomic framework are also presented, and they are shown to be suitable for taxonomic classification of most of the ITS1 sequences in GenBank. The results of the present study may help guide future studies involving taxonomic and phylogenetic analysis of ITS1 sequences of anaerobic fungi and targeted isolation and characterization of new anaerobic fungi.

Dengpan Bu

Papers

Novel glycoside hydrolases identified by screening a Chinese Holstein dairy cow rumen-derived metagenome library.

Isolation and biochemical characterization of two lipases from a metagenomic library of China Holstein cow rumen

Insights into Abundant Rumen Ureolytic Bacterial Community Using Rumen Simulation System

Differences in Ureolytic Bacterial Composition between the Rumen Digesta and Rumen Wall Based on ureC Gene Classification

A phylogenetic census of global diversity of gut anaerobic fungi and a new taxonomic framework