Projects - New Zealand

Global Partnerships in Livestock Emissions Research

A restriction enzyme reduced representation sequencing approach for low-cost, high-throughput metagenome profiling

MK Hess, SJ Rowe, TC Van Stijn, HM Henry, SM Hickey, R Brauning, AF McCulloch, AS Hess, MR Kirk, S Kumar, C Pinares-Patiño, S Kittelmann, GR Wood, PH Janssen and JC McEwan

Microbial community profiles have been associated with a variety of traits, including methane emissions in livestock. These profiles can be difficult and expensive to obtain for thousands of samples (e.g. for accurate association of microbial profiles with traits), therefore the objective of this work was to develop a low-cost, high-throughput approach to capture the diversity of the rumen microbiome. Restriction enzyme reduced representation sequencing (RE-RRS) using ApeKI or PstI, and two bioinformatic pipelines (reference-based and reference-free) were compared to bacterial 16S rRNA gene sequencing using repeated samples collected two weeks apart from 118 sheep that were phenotypically extreme (60 high and 58 low) for methane emitted per kg dry matter intake (n = 236). DNA was extracted from freeze-dried rumen samples using a phenol chloroform and bead-beating protocol prior to RE-RRS. The resulting sequences were used to investigate the repeatability of the rumen microbial community profiles, the effect of laboratory and analytical method, and the relationship with methane production. The results suggested that the best method was PstI RE-RRS analyzed with the reference-free approach, which accounted for 53.3±5.9% of reads, and had repeatabilities of 0.49±0.07 and 0.50±0.07 for the first two principal components (PC1 and PC2), phenotypic correlations with methane yield of 0.43±0.06 and 0.46±0.06 for PC1 and PC2, and explained 41±8% of the variation in methane yield. These results were significantly better than for bacterial 16S rRNA gene sequencing of the same samples (p<0.05) except for the correlation between PC2 and methane yield. A Sensitivity study suggested approximately 2000 samples could be sequenced in a single lane on an Illumina HiSeq 2500, meaning the current work using 118 samples/lane and future proposed 384 samples/lane are well within that threshold. With minor adaptations, our approach could be used to obtain microbial profiles from other metagenomic samples.

DOI: https://doi.org/10.1371/journal.pone.0219882

Population analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by factors associated with the environment and genetics of the host

MK Hess, H Hodgkinson, AS Hess, L Zetouni, JCC Budel, H Henry, A Donaldson, TP Bilton, TC van Stijn, MR Kirk, KG Dodds, R Brauning, AF McCulloch, SM Hickey, PL Johnson, A Jonker, N Morton, S Hendy, VH Oddy, PH Janssen, JC McEwan and SJ Rowe

Sustainable methods for producing animal protein that reduce an animal’s impact on the environment, such as improved feed efficiency and lowered methane emissions, have gained interest in recent years. Genetic selection is one possible path to reduce the environmental impact of livestock production; however, these traits are difficult and expensive to measure on many animals. The rumen microbiome may serve as a proxy for these traits due to the role it plays in feed digestion. Restriction enzyme-reduced representation sequencing (RE-RRS) is a high-throughput and cost-effective approach to rumen metagenome profiling; however, the systematic and biological factors influencing the resulting reference based (RB) and reference free (RF) profiles need to be explored before widespread industry adoption is possible.

Metagenome profiles were generated by RE-RRS of 4,479 rumen samples collected from 1,708 sheep, and assigned to eight groups based on diet, age and time off feed at the time of sample collection. Systematic effects were found to have minimal influence on metagenome profiles. Diet was a major driver of differences between samples, followed by time off feed, then age of the sheep. The RF approach resulted in more reads being assigned per sample and afforded greater resolution when distinguishing between groups than the RB approach. Normalizing relative abundances within the sampling cohort abolished structures related to age, diet and time off feed, allowing a clear signal based on methane emissions to be elucidated. Genus-level abundances showed low-to-moderate heritability and repeatability estimates, with consistency between diets.

Variation in rumen metagenomic profiles is influenced by diet, age, time off feed and genetics. Not accounting for environmental factors may encumber the ability to associate the profile with traits of interest. However, these differences can be accounted for by adjusting for cohort effects, resulting in robust biological signals being revealed. The abundances of some genera were consistently heritable and repeatable across different environments, suggesting that metagenomic profiles could be used to predict an individual’s future performance, or performance of its offspring, in a range of environments. These results highlight the potential of using rumen metagenomic profiles for selection purposes in a practical, agricultural setting.



Combining Host and Rumen Metagenome Profiling for Selection in Sheep: Prediction of Production, Health and Environmentally Important Traits

MK Hess, AS Hess, KG Dodds, HM Henry, R Brauning, AF McCulloch, SM Hickey, PL Johnson, A Jonker, PH Janssen, JC McEwan and Suzanne J. Rowe

The rumen metagenome breaks down complex carbohydrates into energy sources for the host and is increasingly shown to be a key aspect of animal performance. Host genotypes can be combined with microbial sequencing to predict performance traits or traits related to environmental impact, such as enteric methane emissions. Metagenome profiles were generated from 3,139 rumen samples, collected on 1,200 dual purpose ewes, using Restriction Enzyme-Reduced Representation Sequencing (RE-RRS). Phenotypes were available for methane (CH4) and carbon dioxide (CO2) emissions, methane yield (CH4YLD), feed efficiency (RFI), liveweight (LWT), hogget fleece weight (HFW) and parasite resistance measured by Faecal egg count (FEC). We estimated the phenotypic variance explained by host genetics and the rumen microbiome, as well as prediction accuracies for each of these traits.

Incorporating metagenome profiles increased the variance explained and prediction accuracy compared to fitting only genomics for all traits except for CO2 emissions in grass. Using a metagenome profile from lambs combined with host genotype explained more than 70% of the variation in methane emissions and residual feed intake. Predictions were generally more accurate when incorporating metagenome profiles compared to genetics alone, even when considering profiles collected at different ages (lamb vs adult), or on different feeds (grass vs lucerne pellet). A reference-free approach to metagenome profiling performed better than metagenome profiles that were restricted to capturing genera from a reference database. We hypothesise that our reference-free approach is likely to outperform other reference-based approaches such as 16S rRNA gene sequencing for use in prediction of individual animal performance.

This paper shows the potential of using RE-RRS as a low-cost, high-throughput approach for generating metagenome profiles on thousands of animals for improved prediction of economically and environmentally important traits. A reference-free approach using a cohort-adjusted microbial relationship matrix is recommended for future predictions using RE-RRS metagenome profiles.

Enteric fermentation flagship project

The Enteric Fermentation Flagship (EFF) project is a Global Research Alliance (GRA) project, funded by the New Zealand Government to support the Global Research Alliance on Agricultural Greenhouse Gases, that was initiated in 2018 to develop methods to profile rumen microbiomes for predicting methane and associated traits in bovine animals. A key aspect of the project was to ensure any methods developed could be applied across the global so that they could be utilized in developing countries with limited funds and resources. This required developing a low-cost and high-throughput protocol for profiling bovine rumen microbiomes. In the project, the restriction enzyme reduced representational sequencing (RE-RRS) method developed by Hess et al. (2020) (https://doi.org/10.1371/journal.pone.0219882) was used for metagenomic sequencing, while new solutions for preserving rumen samples that reduces cost and improves throughput were developed and successfully trialled on Sheep (Budel et al., 2022; https://doi.org/10.1186/s42523-022-00190-z).

A major goal of the EFF project was to profile over 1,000 bovine rumen microbiomes from across the global and at the end of the development phase (completed in mid-2022), approximately 1,100 bovine rumen samples have been collected and successfully sequenced using the RE-RRS methodology. These samples were collected from across 5 countries (Brazil, Ireland, New Zealand, Uruguay & Peru) that span 3 continents and represented a diverse range of environments, rearing systems and genetics (e.g., breeds). A linear mixed model and a machine learning method were employed to investigate the predictive ability of using the rumen microbiome profiles generated in the project for predicting methane and feed intake traits associated with the samples. Results indicate that rumen microbiome profiles can be utilized for trait prediction using the methods employed within a country with moderate to high prediction accuracies obtained.