Exploring the Prokaryotic Community Associated With the Rumen Ciliate Protozoa Population

Ciliate protozoa are an integral part of the rumen microbiome and were found to exert a large effect on the rumen ecosystem itself as well as their host animal physiology. Part of these effects have been attributed to their ability to harbor a diverse ecto- and endo-symbiotic community of prokaryotic cells. Studies on the relationship between the protozoa population and their associated prokaryotic community in the rumen mainly focused on the methanogens, revealing that protozoa play a major role in enhancing methanogenesis potential. In contrast, little is known about the composition and function of the bacteria associated with rumen protozoa and the extent of this association. In this study, we characterize the prokaryotic communities associated with different protozoa populations and compare their structure to the free-living prokaryotic population residing in the cow rumen. We show that the overall protozoa associated prokaryotic community structure differs significantly compared to the free-living community in terms of richness and composition. The methanogens proportion was significantly higher in all protozoa populations compared to the free-living fraction, while the Lachnospiraceae was the most prevalent bacterial family in the protozoa associated bacterial communities. Several taxa not detected or detected in extremely low abundance in the free-living community were enriched in the protozoa associated bacterial community. These include members of the Endomicrobia class, previously identified as protozoa symbionts in the termite gut. Our results show that rumen protozoa harbor prokaryotic communities that are compositionally different from their surroundings, which may be the result of specific tropism between the prokaryotic community and protozoa

Currency, Exchange, and Inheritance in the Evolution of Symbiosis

Highlights: Inspired by the evolution of eukaryotic organelles, we propose a conceptual framework to study the evolutionary and ecological drivers of symbiosis, including three main elements: a currency, mechanisms of currency exchange, and inheritance. Currency in symbiosis is the type resources that species in a beneficial symbiosis gain from their partner. Currency exchange is a complex process that requires molecular adaptations in one or both partners. We identify two distinct but not mutually exclusive initial evolutionary imperatives for the establishment of symbiosis, termed currency first, in which the initial interaction stems from a common currency exchange between the interacting partners to complement their environmental requirements, and transmission first, in which stable transgenerational transmission precedes the evolution of currency exchange. Symbiotic interactions between eukaryotes and prokaryotes are widespread in nature. Here we offer a conceptual framework to study the evolutionary origins and ecological circumstances of species in beneficial symbiosis. We posit that mutual symbiotic interactions are well described by three elements: a currency, the mechanism of currency exchange, and mechanisms of symbiont inheritance. Each of these elements may be at the origin of symbiosis, with the other elements developing with time. The identity of currency in symbiosis depends on the ecological context of the symbiosis, while the specificity of the exchange mechanism underlies molecular adaptations for the symbiosis. The inheritance regime determines the degree of partner dependency and the symbiosis evolutionary trajectory. Focusing on these three elements, we review examples and open questions in the research on symbiosis

Composition and Similarity of Bovine Rumen Microbiota across Individual Animals

The bovine rumen houses a complex microbiota which is responsible for cattle's remarkable ability to convert indigestible plant mass into food products. Despite this ecosystem's enormous significance for humans, the composition and similarity of bacterial communities across different animals and the possible presence of some bacterial taxa in all animals' rumens have yet to be determined. We characterized the rumen bacterial populations of 16 individual lactating cows using tag amplicon pyrosequencing. Our data showed 51% similarity in bacterial taxa across samples when abundance and occurrence were analyzed using the Bray-Curtis metric. By adding taxon phylogeny to the analysis using a weighted UniFrac metric, the similarity increased to 82%. We also counted 32 genera that are shared by all samples, exhibiting high variability in abundance across samples. Taken together, our results suggest a core microbiome in the bovine rumen. Furthermore, although the bacterial taxa may vary considerably between cow rumens, they appear to be phylogenetically related. This suggests that the functional requirement imposed by the rumen ecological niche selects taxa that potentially share similar genetic features

Determining crystal structures through crowdsourcing and coursework

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality

Evaluation of Automated Ribosomal Intergenic Spacer Analysis for Bacterial Fingerprinting of Rumen Microbiome Compared to Pyrosequencing Technology

The mammalian gut houses a complex microbial community which is believed to play a significant role in host physiology. In recent years, several microbial community analysis methods have been implemented to study the whole gut microbial environment, in contrast to classical microbiological methods focusing on bacteria which can be cultivated. One of these is automated ribosomal intergenic spacer analysis (ARISA), an inexpensive and popular way of analyzing bacterial diversity and community fingerprinting in ecological samples. ARISA uses the natural variability in length of the DNA fragment found between the 16S and 23S genes in different bacterial lineages to infer diversity. This method is now being supplanted by affordable next-generation sequencing technologies that can also simultaneously annotate operational taxonomic units for taxonomic identification. We compared ARISA and pyrosequencing of samples from the rumen microbiome of cows, previously sampled at different stages of development and varying in microbial complexity using several ecological parameters. We revealed close agreement between ARISA and pyrosequencing outputs, especially in their ability to discriminate samples from different ecological niches. In contrast, the ARISA method seemed to underestimate sample richness. The good performance of the relatively inexpensive ARISA makes it relevant for straightforward use in bacterial fingerprinting analysis as well as for quick cross-validation of pyrosequencing data

Evaluation of Automated Ribosomal Intergenic Spacer Analysis for Bacterial Fingerprinting of Rumen Microbiome Compared to Pyrosequencing Technology

The mammalian gut houses a complex microbial community which is believed to play a significant role in host physiology. In recent years, several microbial community analysis methods have been implemented to study the whole gut microbial environment, in contrast to classical microbiological methods focusing on bacteria which can be cultivated. One of these is automated ribosomal intergenic spacer analysis (ARISA), an inexpensive and popular way of analyzing bacterial diversity and community fingerprinting in ecological samples. ARISA uses the natural variability in length of the DNA fragment found between the 16S and 23S genes in different bacterial lineages to infer diversity. This method is now being supplanted by affordable next-generation sequencing technologies that can also simultaneously annotate operational taxonomic units for taxonomic identification. We compared ARISA and pyrosequencing of samples from the rumen microbiome of cows, previously sampled at different stages of development and varying in microbial complexity using several ecological parameters. We revealed close agreement between ARISA and pyrosequencing outputs, especially in their ability to discriminate samples from different ecological niches. In contrast, the ARISA method seemed to underestimate sample richness. The good performance of the relatively inexpensive ARISA makes it relevant for straightforward use in bacterial fingerprinting analysis as well as for quick cross-validation of pyrosequencing data

Rarefaction analysis for the assessment of OTU coverage.

<p>(<b>A</b>) Sample-based rarefaction curve showing the increase in OTU numbers as a function of the number of individuals sampled. Each added sample adds OTUs to the plot which has not yet been seen in previous samples. The curve becomes asymptotic as the OTU number saturates, and each sample adds an increasingly smaller number of new OTUs, indicating adequate coverage for the environment being tested. (<b>B</b>) Individual rarefaction curves for each rumen sample taken.</p

Shared genera and abundance across samples.

<p>Box plot showing the relative abundance of the bacterial genera shared by all samples, represented as log percentage on the X-axis. The boxes represent the interquartile range (IQR) between the first and third quartiles (25^th and 75^th percentiles, respectively) and the vertical line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Samples with a relative abundance of a given taxon exceeding those values are represented as points beside the boxes. The box color denotes the phylum of the genera: Bacteroidetes (blue), Firmicutes (red), Proteobacteria (green), Tenericutes (light blue), Cyanobacteria (orange), TM7 (gray), Actinobacteria (purple). Taxa not indentified at the genus level are identified by an asterisk and their highest taxonomic identification.</p

Composition and abundance of bacterial taxa, as determined by pyrosequencing of the 16 S rDNA gene.

<p>(<b>A</b>) Pie chart showing the average distribution of the phyla across all ruminal samples. (<b>B</b>) Box plot showing the relative abundance of each phylum, represented as percentage on the Y-axis. The boxes represent the interquartile range (IQR) between the first and third quartiles (25^th and 75^th percentiles, respectively) and the vertical line inside the box defines the median. Whiskers represent the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Samples with a relative abundance of a given phylum exceeding those values are represented as points beside the boxes (color-coded).</p