Genetic (co-)variation of methane emissions, efficiency, and production traits in Danish Holstein cattle along and across lactations

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Selecting for Feed Efficient Cows Will Help to Reduce Methane Gas Emissions

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Community structure of the metabolically active rumen bacterial and archaeal communities of dairy cows over the transition period

Dairy cows experience dramatic changes in host physiology from gestation to lactation period and dietary switch from high-forage prepartum diet to high-concentrate postpartum diet over the transition period (parturition +/- three weeks). Understanding the community structure and activity of the rumen microbiota and its associative patterns over the transition period may provide insight for e.g. improving animal health and production. In the present study, rumen samples from ten primiparous Holstein dairy cows were collected over seven weeks spanning the transition period. Total RNA was extracted from the rumen samples and cDNA thereof was subsequently used for characterizing the metabolically active bacterial (16S rRNA transcript amplicon sequencing) and archaeal (qPCR, T-RFLP and mcrA and 16S rRNA transcript amplicon sequencing) communities. The metabolically active bacterial community was dominated by three phyla, showing significant changes in relative abundance range over the transition period: Firmicutes (from prepartum 57% to postpartum 35%), Bacteroidetes (from prepartum 22% to postpartum 18%) and Proteobacteria (from prepartum 7% to postpartum 32%). For the archaea, qPCR analysis of 16S rRNA transcript number, revealed a significant prepartum to postpartum increase in Methanobacteriales, in accordance with an observed increase (from prepartum 80% to postpartum 89%) in relative abundance of 16S rRNA transcript amplicons allocated to this order. On the other hand, a significant prepartum to postpartum decrease (from 15% to 2%) was observed in relative abundance of Methanomassiliicoccales 16S rRNA transcripts. In contrast to qPCR analysis of the 16S rRNA transcripts, quantification of mcrA transcripts revealed no change in total abundance of metabolically active methanogens over the transition period. According to T-RFLP analysis of the mcrA transcripts, two Methanobacteriales genera, Methanobrevibacter and Methanosphaera (represented by the T-RFs 39 and 267 bp), represented more than 70% of the metabolically active methanogens, showing no significant changes over the transition period; minor T-RFs, likely to represent members of the order Methanomassiliicoccales and with a relative abundance below 5% in total, decreased significantly over the transition period. In accordance with the T-RFLP analysis, the mcrA transcript amplicon sequencing revealed Methanobacteriales to cover 99% of the total reads, dominated by the genera Methanobrevibacter (75%) and Methanosphaera (24%), whereas the Methanomassiliicoccales order covered only 0.2% of the total reads. In conclusion, the present study showed that the structure of the metabolically active bacterial and archaeal rumen communities changed over the transition period, likely in response to the dramatic changes in physiology and nutritional factors like dry matter intake and feed composition. It should be noted however that for the methanogens, the observed community changes were influenced by the analyzed gene (mcrA or 16S rRNA)

Applications of hyperspectral imaging for documenting smoltification status and welfare in Atlantic salmon

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Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case Study

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue—A Salmon Case StudypublishedVersio

Genetic parameters of fillet fatty acids and fat deposition in gilthead seabream (Sparus aurata) using the novel 30 k Medfish SNP array

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Digital phenotyping of individual feed intake in Atlantic salmon (Salmo salar) with the X-ray method and image analysis

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Applying genetic technologies to combat infectious diseases in aquaculture

Disease and parasitism cause major welfare, environmental and economic concerns for global aquaculture. In this review, we examine the status and potential of technologies that exploit genetic variation in host resistance to tackle this problem. We argue that there is an urgent need to improve understanding of the genetic mechanisms involved, leading to the development of tools that can be applied to boost host resistance and reduce the disease burden. We draw on two pressing global disease problems as case studies—sea lice infestations in salmonids and white spot syndrome in shrimp. We review how the latest genetic technologies can be capitalised upon to determine the mechanisms underlying inter- and intra-species variation in pathogen/ parasite resistance, and how the derived knowledge could be applied to boost disease resistance using selective breeding, gene editing and/or with targeted feed treatments and vaccines. Gene editing brings novel opportunities, but also implementation and dissemination challenges, and necessitates new protocols to integrate the technology into aquaculture breeding programmes. There is also an ongoing need to minimise risks of disease agents evolving to overcome genetic improvements to host resistance, and insights from epidemiological and evolutionary models of pathogen infestation in wild and cultured host populations are explored. Ethical issues around the different approaches for achieving genetic resistance are discussed. Application of genetic technologies and approaches has potential to improve fundamental knowledge of mechanisms affecting genetic resistance and provide effective pathways for implementation that could lead to more resistant aquaculture stocks, transforming global aquaculture.publishedVersio

Review: Genetic and genomic selection as a methane mitigation strategy in dairy cattle

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Genetic control of methane emission, feed efficiency and metagenomics in dairy cattle

The dairy industry faces the challenges of increasing production, remaining economically viable whilst simultaneously minimising impacts on the environment. The cost of feed is the highest variable cost of milk production, thus, improved feed efficiency is a strong wish. However, CH4 is a potent greenhouse gas with an energy value estimated as 2 -12% of the gross feed energy intake and thus represents a loss. There is, therefore, a need to identify the phenotypic and genetic relationships between efficiency of feed utilisation and CH4 production to ensure optimal breeding methods of increasing profitability and limiting environmental impact of dairy production. Feed is degraded and CH4 is produced by rumen microbes and not by the cow. The mechanisms which influence the composition of the rumen microbial community and how they, in turn, influence the feed efficiency and CH4 production of the host, are not well understood. Among the possible strategies, selective breeding has the benefit over others by being cumulative and persistent over generations. Genetic improvement through selection requires that phenotypes are recorded on large numbers of animals. Moreover, phenotypes must show variation, a portion of which must be genetic, and must have economic or societal value. Understanding the genetic co-variation behind and between these measures is crucial to simultaneous breeding for a more profitable and climate friendly dairy industry. However, the measurement of CH4 emissions, feed efficiency and the rumen microbiome under commercial conditions on a large scale is not a trivial task. The aim of this PhD project was to develop and integrate phenotyping measures for CH4 emission, feed efficiency and the rumen microbiome and to investigate their genetic potential for selective breeding. Firstly, in Chapter 2, improvements where made to the sniffer method of CH4 breath concentration recording in dairy cattle during automatic milking. An algorithm was developed to efficiently detect and correct for variable and random drift in time series between instruments and to detect when the cow’s head is out of the feed bin. Using linear mixed model methodology, repeated measures per cow were used to improve precision and control sources of inaccuracy such as sensor drift, background gas concentrations and diurnal variation, that were subsequently removed. Resultantly, highly repeatable phenotypes where obtained which demonstrated adequate agreement for the interchangeable use of two instruments. In Chapter 3, the ranking of cows under commercial conditions using the sniffer method was compared with the “gold standard” respiration chambers. Individual level correlations estimated as proxies for genetic correlations revealed a high correlation between sniffer-predicted CH4 production and CH4 production in the RC. These findings offer a proof of concept that sniffer CH4 phenotypes recorded over a week of lactation show substantial promise as large scale indicator traits for CH4 production using RC. In Chapter 4, genetic parameters were estimated between feed intake, milk production and CH4 breath concentration from sniffers over the course of the first lactation in Holstein cows in Denmark and The Netherlands. Through combining data between countries, genetic residual feed intake and breath gas concentrations were found to be significantly heritable, demonstrating that genetic improvement of feed efficiency and CH4 breath gas concentration is feasible in dairy cattle. The estimated genetic correlations from the largest dataset indicated that improved feed efficiency will also result in decreased gas emissions. Furthermore, including the breath gas concentrations in a multitrait genetic evaluation increased the accuracy of bull breeding values for gRFI, demonstrating an indirect economic value of CH4 and CO2 breath concentration phenotypes. In Chapter 5, we estimated the relative abundance of rumen bacteria and archaea and found a portion of these to be heritable in dairy cattle. The results demonstrate that host additive genetics has an influence on the abundance of some rumen bacteria and archaea. We detected significant associations between certain bacterial genera and differences in CH4 production of the host cow, further contributing to knowledge of the underlying biological mechanisms driving CH4 production of the host. We further extended quantitative genetic methods to estimate rumen microbial kinships between cows in place of additive genetic relationships. This enabled the quantification of variation in host CH4 production explained by the rumen microbial composition, expressed in the new term ‘microbiability’, as the relative proportion of host variation explained by associated microbes. Crucially the microbiability and the heritability of dairy cattle CH4 production were largely independent. Thus, selective breeding for reduced CH4 production can be extended by methods perturbing the rumen microbiota towards reduced CH4 production. In Chapter 6 (the general discussion), the value of method comparisons for phenotype development by comparatively quantifying sources of error between cheaper alternative methods and intensive gold standard methods was discussed. The primary constraint to breeding for improved feed efficiency and CH4 production remains the recording of feed intake on a large scale under commercial conditions and recording of “true” CH4 production. It was proposed that the accuracy of bull breeding values for both feed efficiency and CH4 production can be increased through the use of sniffer phenotypes in robot milking herds, using individual level correlations but a genetic correlation between sniffer phenotypes and RC CH4 production are still needed. The records required for estimating genetic correlations with meaningful standard errors can only be achieved through substantial financial investments, development of cheaper alternative methods of phenotype recording or international collaborations. Further to the general discussion, a portion of host phenotypic variation in CH4 production was found to be associated with the rumen bacterial and archaeal composition. However, research is needed to determine if microbial associations are causative and methods to direct desired changes in the rumen microbial composition are still needed to unlock the potential of this under-exploited resource. The methods developed for quantifying the microbial contribution to host phenotypic variation will be of value to inform research into complex microbial-associated phenotypes, such as diseases and digestion in dairy cattle, other livestock species and humans. This thesis therefore contributes to the understanding of the genetic variation in feed efficiency, methane emissions and rumen metagenome of dairy cows.</p