Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies.

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

Meta-proteomics of rumen microbiota indicates niche compartmentalisation and functional dominance in a limited number of metabolic pathways between abundant bacteria.

The rumen is a complex ecosystem. It is the primary site for microbial fermentation of ingested feed allowing conversion of a low nutritional feed source into high quality meat and milk products. However, digestive inefficiencies lead to production of high amounts of environmental pollutants; methane and nitrogenous waste. These inefficiencies could be overcome by development of forages which better match the requirements of the rumen microbial population. Although challenging, the application of meta-proteomics has potential for a more complete understanding of the rumen ecosystem than sequencing approaches alone. Here, we have implemented a meta-proteomic approach to determine the association between taxonomies of microbial sources of the most abundant proteins in the rumens of forage-fed dairy cows, with taxonomic abundances typical of those previously described by metagenomics. Reproducible proteome profiles were generated from rumen samples. The most highly abundant taxonomic phyla in the proteome were Bacteriodetes, Firmicutes and Proteobacteria, which corresponded with the most abundant taxonomic phyla determined from 16S rRNA studies. Meta-proteome data indicated differentiation between metabolic pathways of the most abundant phyla, which is in agreement with the concept of diversified niches within the rumen microbiota.

Fertility and genomics: comparison of gene expression in contrasting reproductive tissues of female cattle.

To compare gene expression among bovine tissues, large bovine RNA-seq datasets were used, comprising 280 samples from 10 different bovine tissues (uterine endometrium, granulosa cells, theca cells, cervix, embryos, leucocytes, liver, hypothalamus, pituitary, muscle) and generating 260 Gbases of data. Twin approaches were used: an information-theoretic analysis of the existing annotated transcriptome to identify the most tissue-specific genes and a de-novo transcriptome annotation to evaluate general features of the transcription landscape. Expression was detected for 97% of the Ensembl transcriptome with at least one read in one sample and between 28% and 66% at a level of 10 tags per million (TPM) or greater in individual tissues. Over 95% of genes exhibited some level of tissue-specific gene expression. This was mostly due to different levels of expression in different tissues rather than exclusive expression in a single tissue. Less than 1% of annotated genes exhibited a highly restricted tissue-specific expression profile and approximately 2% exhibited classic housekeeping profiles. In conclusion, it is the combined effects of the variable expression of large numbers of genes (73%-93% of the genome) and the specific expression of a small number of genes (<1% of the transcriptome) that contribute to determining the outcome of the function of individual tissues.

The integration of 'omic' disciplines and systems biology in cattle breeding.

Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists - and they will also be discussed.

Duplicate retention in signalling proteins and constraints from network dynamics.

Duplications are a major driving force behind evolution. Most duplicates are believed to fix through genetic drift, but it is not clear whether this process affects all duplications equally or whether there are certain gene families that are expected to show neutral expansions under certain circumstances. Here, we analyse the neutrality of duplications in different functional classes of signalling proteins based on their effects on response dynamics. We find that duplications involving intermediary proteins in a signalling network are neutral more often than those involving receptors. Although the fraction of neutral duplications in all functional classes increase with decreasing population size and selective pressure on dynamics, this effect is most pronounced for receptors, indicating a possible expansion of receptors in species with small population size. In line with such an expectation, we found a statistically significant increase in the number of receptors as a fraction of genome size in eukaryotes compared with prokaryotes. Although not confirmative, these results indicate that neutral processes can be a significant factor in shaping signalling networks and affect proteins from different functional classes differently.

Clann: investigating phylogenetic information through supertree analyses.

UNLABELLED: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees. AVAILABILITY: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Clann has been written in the C programming language. Source code is available on request.

CRANN: detecting adaptive evolution in protein-coding DNA sequences.

A software program CRANN has been developed in order to detect adaptive evolution in protein-coding DNA sequences.