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Record W4200207372 · doi:10.1093/af/vfab057

Consequence of epigenetic processes on animal health and productivity: is additional level of regulation of relevance?

2021· article· en· W4200207372 on OpenAlexaff
Eveline M. Ibeagha‐Awemu, Ying Yu

Bibliographic record

VenueAnimal Frontiers · 2021
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRelevance (law)EpigeneticsProductivityBiologyComputational biologyGeneticsGenePolitical scienceEconomics

Abstract

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Epigenetic factors respond to environmental factors and underlying genotypes to influence livestock phenotypic expression (e.g., milk yield, wool quality, disease resistance, growth, and development, etc.). Epigenetic alterations are associated with livestock traits and with differences in livestock product yields. A window is opened for exploring the mechanisms of disease resistance in farm animals through epigenetic processes. Consideration of genetic mechanisms of Mendelian inheritance and epigenetic mechanisms of non-Mendelian inheritance may further increase genetic gain in livestock trait improvement. Epigenetic factors provide additional levels of livestock trait regulation and are relevant for livestock management and improvement. Over the years, the implementation of selective breeding and other improved management practices led to increased livestock productivity. In particular, improved practices over the last 80 years resulted in tremendous improvement in animal production; for example, the average milk produced per Holstein cow has more than doubled and feed efficiency in broiler chickens allow a 4-fold increase in body size with the same amount of feed input. In general, improved livestock productivity, for example, relies on increasing the rate of genetic gain (ΔG); and breeding programs will adopt improved practices (e.g., improved nutrition, improved management practices, improved health care, genetic selection, reproduction technologies, genomic selection, etc.) that increase the intensity of selection, selection accuracy, and genetic diversity while decreasing the generation interval (Figure 1). Moreover, the implementation of genomic breeding, which relies on the use of genomic information in the form of single nucleotide polymorphisms (SNP) resulted in rapid genetic improvement in lowly heritable traits (e.g., fertility, lifespan, and health traits, etc.), shortened generation interval, selection of animals at an early age, higher rate of genetic gain, increased reliability of predicting breeding value and higher intensity of selection (Wiggans et al., 2017; Van Doormaal, 2019). However, the human population is increasing and with it, increasing market demand for animal products. To meet this demand, current practices must be tailored to enhance productivity, which requires knowledge of further processes that drive phenotypic expression and animal productivity. While genomic breeding relies on using SNP data to calculate genomic breeding values, it is important to note that genetic variation between individuals only accounts for a portion of the genomic variation and does not sufficiently explain the phenotype diversity (Langevin and Kelsey, 2013; Ibeagha-Awemu and Khatib, 2017). Moreover, the DNA sequence cannot illustrate the functional and morphological diversity and differentiation between cell populations in multicellular organisms nor capture the effects of epigenomics factors that respond to specific environmental circumstances and growth stages to influence phenotypic expression. Farm management practices and technologies that enhance genetic gain in livestock breeding and improvement. Farm management practices and technologies that enhance genetic gain in livestock breeding and improvement. Looking back, traditional animal breeding practices generally considered that phenotypic value is equal to genotype value plus environmental effects (Figure 2, left panel). But in fact, the genotype and the environment values constitute a black box, because the extent of their interaction and actual effects on the phenotype is only beginning to emerge. With developments in the subject area of epigenetics and the discovery of epigenetic modifications, a “window” in this “black box” has been opened (Figure 2, right panel). Therefore, processes embedded in the epigenome provide additional avenues to investigate the secrets underlying phenotype diversity, and how they can be harnessed for improved productivity. The epigenome, which includes processes like DNA methylation, histone modification, chromatin remodeling, and non-coding RNA (ncRNA) regulation, regulate gene expression and therefore constitute significant players in modulating genome function and stability (Bird, 2002; Kouzarides, 2007; Morris and Mattick, 2014). Interestingly, these epigenetic factors have been shown to regulate livestock traits (Ibeagha-Awemu and Zhao, 2015; Do and Ibeagha-Awemu, 2017; Do et al., 2021; Wang and Ibeagha-Awemu, 2021) and could therefore be responsible for additional variation needed for continued improvements in livestock health and productivity. A window is opened for further exploration of the mechanisms of disease resistance in farm animals through epigenetic processes. A window is opened for further exploration of the mechanisms of disease resistance in farm animals through epigenetic processes. Epigenetics generally refers to mitotically stable states and molecular modifications related to gene activity without changing the DNA sequence. In other words, epigenetics describes the mechanisms underlying the phenotypic changes rather than DNA sequence alterations, thereby providing an alternative layer of information. Moreover, epigenetic mechanisms act as intermediates between environmental factors and host genotype thereby contributing to the regulation of various cellular processes and the expression of the phenotype (Figure 3). Furthermore, epigenetic alterations are specific to specific life stages or specific organs, and vary under various conditions and are therefore dynamic throughout a lifetime. Notably, embryogenesis is the peak time of epigenetic reprogramming, specially DNA methylation, but throughout growth and development, epigenetic factors respond to various environmental cues (the exposome) and the underlying genotype to continually modify cellular activity and phenotypic expression (Figure 3). During a production cycle, livestock goes through various stages of development, are subjected to various management practices and exposed to many factors (e.g., pathogens, nutrition, environmental stressors, etc.); therefore, understanding the role of epigenetics will open a window (Figure 2, right panel) to quantify a part of the missing causality and heritability of complex livestock health and production traits. Data from our work and other colleagues on the relevance of how these additional levels of regulators contribute to phenotypic expression of livestock traits, and how they can be exploited for improved livestock productivity and health management will be discussed in subsequent sections. In particular, postnatal epigenetic reprogramming will be discussed. Epigenetic factors respond to external and internal environment factors (the exposome) and interact with the underlying genotype to influence phenotypic outcomes. Epigenetic factors respond to external and internal environment factors (the exposome) and interact with the underlying genotype to influence phenotypic outcomes. Available data indicate that epigenetic processes respond to various stimuli including nutrition, pathogens, maternal and paternal behavior or stress, environmental stressors, management practices, chemicals, and pollutants to induce epigenetic alterations causing changes in phenotypic expression in livestock animals (Figure 3; Wang and Ibeagha-Awemu, 2021). In this section, some of the evidence of the effects of these epigenetic factors on livestock phenotypic expression will be examined. In farm animal production, nutrition is among the most import cost that determines farm profitability. Following conception, nutrition is the foremost environmental exposome that interacts with an individual’s genome to determine its growth, development, and phenotypic expression. Adequate nutrition and type of nutrients fed to animals are of vital importance, and they have been shown to influence the epigenome. Mounting evidence indicates that alterations of a permanent nature in the epigenome of embryos or germline could be transferred to offspring, a phenomenon referred to as intergeneration or transgenerational epigenetic inheritance (Miska and Ferguson-Smith, 2016; Perez and Lehner, 2019). In recent times, consistent nutritional stimulus has been shown to cause epigenetic modifications in somatic tissues and their effects on health and diseases of livestock could be transmitted between generations (Xue et al., 2016; Ideraabdullah and Zeisel, 2018). Moreover, excessive lack of nutrition or excessive availability of nutrients are also known to alter the epigenetic state of germ cells and transmission to subsequent generations (Guo et al., 2020). A plethora of investigations indicate that nutritional stimulus with different categories of nutrients induced epigenetic modifications resulting in altered gene and phenotypic expression (some of the evidence is summarized in Table 1). These pieces of evidence indicate that nutrition-induced epigenetic alterations can be heritable, but the underlying mechanisms are not clear. What is clear, is that DNA methylation modifications in response to nutritional stimulus certainly cause alteration in production performance and possibly disease susceptibility. Examples of nutritional stimulus impact on epigenetic alterations in some livestock species DMR, differentially methylated region; DMC, differentially methylated cytosine; H3K27me3, the tri-methylation at the 27th lysine residue of the histone H3 protein; H3K4me3, the tri-methylation at the 4th lysine residue of the histone H3 protein; H3K36me3, the tri-methylation at the 36th lysine residue of the histone H3 protein; H4K12ac, the acetylation at the 12th lysine residue of the histone H4 protein. Examples of nutritional stimulus impact on epigenetic alterations in some livestock species DMR, differentially methylated region; DMC, differentially methylated cytosine; H3K27me3, the tri-methylation at the 27th lysine residue of the histone H3 protein; H3K4me3, the tri-methylation at the 4th lysine residue of the histone H3 protein; H3K36me3, the tri-methylation at the 36th lysine residue of the histone H3 protein; H4K12ac, the acetylation at the 12th lysine residue of the histone H4 protein. For specific examples, supplementation of the diets of cows with methionine throughout the periparturient period resulted in lowered global DNA methylation state of the liver, higher promoter methylation of PPARA (peroxisome proliferator-activated receptor alpha) gene (a nuclear receptor for long-chain fatty acids) and increased expression of PPARA gene in supplemented cows compared to non-supplemented cows (Osorio et al., 2016). Moreover, the expression of the gene targets of PPARA (ANGPTL4 [angiopoietin-like 4], FGF21 [fibroblast growth factor 21], and PCK1 [phosphoenolpyruvate carboxykinase 1]) were also increased, indicating that supplemental methionine activated the PPARA-regulated signaling pathway. Activation or upregulation of the hepatic PPARA pathway is known to been associated with improved lipid metabolism and immune function. Furthermore, maternal methionine supply during late pregnancy programmed hepatic metabolism of calves by maintaining methionine homeostasis, DNA methylation, energy metabolism thereby possibly contributing to better nutrient utilization efficiency, growth promotion, and enhanced development performance (Alharthi et al., 2019). In other studies, dietary restriction, feeding high- vs. low-concentrate diets, grain vs. grass, mineral/vitamin, and fatty acid supplementation of the diets of pigs, cows, or chickens altered the epigenetic state of various tissues or cells resulting in various phenotypes (Table 1). Environmental perturbations including heat stress, weaning stress, transportation stress, pathogens, dietary changes, and so on, limit livestock productivity through impacts on epigenetic factors, gene expression, metabolism, and immune response with direct effects on health, welfare, and productivity. For example, epigenetic mechanisms have been found to induce blood DNA methylation changes between heat-stressed and recovery periods in Nellore cattle (Del Corvo et al., 2021), or in various tissues (kidney, liver, muscle, lungs, heart, and brain) in response to hypoxic stress in Tibetan and Yorkshire pigs, Tibetan goat, Tibetan sheep, Chuanzhong goat, and small-tailed Han sheep (Wang et al., 2017b; Zhang et al., 2019). Moreover, stress due to transportation of Brahman cows induced epigenetic alterations in the blood of both cows and offspring (Littlejohn et al., 2018). Weaning, the process of separating young from their mothers, is a period of stress (e.g., dietary and social stress) linked to changes in the immune response and susceptibility to diseases in livestock production. Recently, weaning stress was found to induce DNA methylation and gene expression changes in weaned piglet peripheral blood mononuclear cells (Corbett et al., 2021). Livestock products of high economic importance like milk, beef, egg, and wool are under the regulation of epigenetic factors and other factors (e.g., genetics, nutrition, health, farm management, environmental factors, etc.). In recent years, research has shown that epigenetic modifications have important regulatory functions in livestock dairy animals with important effects on milk and dairy products (Wang and Ibeagha-Awemu, 2021). Following the pioneer report that abnormal DNA methylation at the STAT5-binding enhancer of the CSN1S1 (αS1-casein) promoter undesirably regulated αS1-casein synthesis in milk during lactation (Vanselow et al., 2006; Nguyen et al., 2014), subsequent studies have found effects of altered DNA methylation, chromatin modifications and ncRNA regulation on milk production in livestock. For example, differences in DNA methylation levels in blood were found between high and low milk yielding lactating dairy cows (Dechow and Liu, 2018); and of several (e.g., proliferator-activated receptor receptor and in the of lactating at and lactation periods et al., expression, found that regulate the lactation or specific stages of lactation et al., 2017). Moreover, increased methylation levels of some acid proliferator-activated receptor and regulatory factor 1]) and fatty acid synthesis of expression in have been (Wang et al., DNA methylation and regulation are important epigenetic mechanisms in the regulation of development and wool production. on the DNA methylation of from sheep found differentially methylated (e.g., and receptor A type and 1]) with in function et al., 2020). DNA methylation differences in several acid and were associated with changes in development and in cattle et al., 2019). In the DNA methylation of and with and are an that DNA methylation through regulation of the expression of (e.g., A and type in this process et al., 2020). the economic value of wool production in the recent knowledge indicates that epigenetic processes regulate wool production. For example, the genetic stability of wool traits between generations of is related to the state of DNA methylation et al., 2019). Furthermore, DNA methylation is in the regulation of wool development and of with (e.g., wool with or et al., et al., 2019). a recent expression in stages of development, associated altered expression with the development of in et al., 2020). Moreover, expression is different between the of high and during the period et al., 2020). specific of epigenetic on milk production and development, quality, fatty acid metabolism, production, growth, and other traits, and so on, are in Table these data indicate important regulatory of DNA methylation alterations in milk and egg, and wool traits. livestock product phenotypes have altered epigenetic DMC, differentially methylated DMR, differentially methylated region; acetylation at the 27th lysine residue of histone H3 protein; H3K36me3, tri-methylation at the 36th lysine residue of histone H3 protein. livestock product phenotypes have altered epigenetic DMC, differentially methylated DMR, differentially methylated region; acetylation at the 27th lysine residue of histone H3 protein; H3K36me3, tri-methylation at the 36th lysine residue of histone H3 protein. resistance can be the and The refers to disease resistance and disease as as the and to and 2020). In a disease resistance refers to the of the animal to the rate of by their which is not only related to the resistance to a but also related to the et al., 2020). of the traits, as or specific are lowly heritable traits. In to genetic factors, environment pathogens, management practices, etc.) and epigenetic modifications are factors of animal and traits (Figure 3). In recent years, more and more has been to the regulatory mechanisms and function of host epigenetic modifications on animal health and disease resistance traits. is among the most dynamic and in of both animal and the is related to health milk and dairy products have an of nutrition over the and production are by many factors, including of the is of the most important diseases that the development of the dairy The cause of is that the through the or to can be and of dairy cows not only milk and quality, but also and of cows, to economic to the dairy most complex resistance is by many factors, including host genetic pathogens, epigenetic factors, and the In the and of cow is to induce resistance, are through disease resistance The low heritability of resistance breeding for this trait genetic with epigenetic information could provide an for further improvement of resistance (Figure In recent times, evidence indicates that epigenetic an important role in the and development of et al., of epigenetic regulation on diseases have to and research for the of regulatory mechanisms in In response to the host or to DNA methylation a role in the regulation of gene expression. In dairy studies that DNA methylation changes are related to the and development of and (Vanselow et al., 2006; Wang et al., 2013; Wang and Ibeagha-Awemu, 2021). The promoter of the gene and of the expression of the gene has been shown in of dairy cows with induced by et al., Moreover, of the promoter of gene in peripheral blood cells may the expression of the gene in Holstein cows (Wang et al., In recent years, more studies have been to the exploration of and epigenetic regulatory mechanisms of in dairy to for resistance selection and molecular is of the of with other the of are for and Moreover, can in the cattle during which the and of more et DNA methylation differences in peripheral blood between Holstein cows with and The further the DNA methylation levels of and were with the of epigenetic that can be to the resistance of cows to DNA methylation in the form of A or were and in of Holstein cows induced with (Wang et al., 2020). The further that DNA methylation is of the mechanisms gene expression in processes during Moreover, a of differentially methylated in cell and receptor may be for (Wang et al., 2020). of DNA methylation changes related to are shown in Table DNA methylation changes due to various causing various livestock as and so on, are summarized in Table Examples of impact of epigenetic changes due to various on livestock health DMR, differentially methylated region; DMC, differentially methylated cytosine; H3K4me3, tri-methylation at the 4th lysine residue of histone H3 protein; H3K27me3, tri-methylation at the 27th lysine residue of histone H3 protein; disease acetylation at the 27th lysine residue of histone H3 protein; the at the 4th lysine residue of the histone H3 protein. Examples of impact of epigenetic changes due to various on livestock health DMR, differentially methylated region; DMC, differentially methylated cytosine; H3K4me3, tri-methylation at the 4th lysine residue of histone H3 protein; H3K27me3, tri-methylation at the 27th lysine residue of histone H3 protein; disease acetylation at the 27th lysine residue of histone H3 protein; the at the 4th lysine residue of the histone H3 protein. from DNA methylation, is important epigenetic that regulate the expression of expression of and in cells at with high or low of et found compared to the significant upregulation of and were in the of cows with a high of that the role of these in host is through of and receptor et al., 2016). Moreover, also found differentially and in cells et al., 2014). associated with and other livestock diseases have been in in Do et These how the of some DNA methylation and vital influence important to phenotypic changes, and also that the response of the and other tissues to is regulated by a complex of epigenetic is important to how a process is by a but most is how to use that factor to influence that is knowledge as discussed that epigenetic processes have effects on livestock production and importance for the of livestock productivity is how to these effects for improved livestock productivity and health discussed in the epigenetic factors respond to different environmental (Figure and interact with the underlying genetic during an individual’s and therefore the of phenotype at specific time or growth the between livestock traits and epigenetic can to the development of epigenetic for livestock improved management, as as further enhance genetic gain in the resulting in livestock improvement. For example, the effects of environmental factors as farm feed and and so on, through epigenetic studies can be and in livestock management of epigenetic in livestock management were (Wang and Ibeagha-Awemu, Epigenetic can be to Epigenetic factors shown to with livestock diseases in Table can be for use in or of livestock For example, epigenetics could important in the management of and livestock diseases with as and For human health management, as and have been for use as epigenetic by the and and they have high efficiency in the of et al., 2020). For disease a DNA methylation on DNA methylation alteration of to and of to to and 2017). Epigenetic can be to livestock growth, and Epigenetic associated with livestock productivity, and health to provide their interaction with other processes (e.g., and immune to phenotype and can be exploited for livestock shown in and 2, specific epigenetic factors are associated with growth and development and product phenotypes in response to paternal nutrition, environmental disease pathogens, and so For epigenetic in can and selection of breeding Moreover, and as as blood can as for phenotypes of in of changes of epigenetic due to nutrition, environmental and so on, during may to to of environmental and implementation of Epigenetic can be for livestock trait it is knowledge that genetic data not explain the phenotypic in livestock traits, the black by epigenetics and other factors avenues to further the genetic gain in evidence of transgenerational epigenetic inheritance has been for livestock traits (Miska and Ferguson-Smith, 2016; et al., 2016; Perez and Lehner, 2019). somatic cell as a for health, et the lactation of from or from and found that to lactation cows with in The that a underlying mechanisms including to and changes in the epigenome a role in milk synthesis in the that maternal stress due to disease resulting in alterations in the epigenome was responsible for the in the and therefore an on must be epigenetic information for livestock improvement (Ibeagha-Awemu and Khatib, the nature of the epigenetic or or of the epigenetic on DNA and the or of the epigenetic Therefore, the nature and of the epigenetic variation must be and While the epigenetic effects can be exploited for management, transgenerational effects can be the of breeding is in with the that genetic effects and inheritance effects be and in the of trait heritability et al., The improvement of animal health can be from the of disease and from the of and of nutritional and improvement of environmental from the of nutrition and animal breeding for disease resistance from the of genetic and epigenetic data in breeding programs for disease resistance (Figure for genetic and epigenetic data for breeding for disease resistance in livestock. for genetic and epigenetic data for breeding for disease resistance in livestock. these the influence of breeding for disease resistance is for disease resistance includes is from the of genetics, which the genetic variation in genetic is to the health of the population can be improved individuals with disease resistance from a generation are to form the the same the epigenetic regulatory mechanisms due to non-Mendelian inheritance be exploited for the improvement of disease of the as as and transgenerational inheritance of epigenetics and Lehner, the health of both and generations can be improved by using the and mechanisms of Therefore, the genetic mechanisms of Mendelian inheritance and the epigenetic mechanisms of non-Mendelian can the of animal health in a and from the generation and the In these the disease resistance of farm animals can be and In livestock are exposed to many factors during a production and these factors interact with the epigenome and underlying genetic to determine phenotypic expression. Epigenetic factors are in the of livestock growth, productivity, and The altered epigenetic factors associated to various livestock traits are additional relevant of information that can be exploited for livestock management and improvement. Ibeagha-Awemu is a research with and includes a in and of a in and studies in has and for research in animal and research on and technologies to the molecular mechanisms of genetic and epigenetic of production and health traits, the of epigenetic factors to phenotypic the molecular mechanisms underlying and and for health is with in and to an to livestock genetic improvement in has in and and including as of the of is a and of the of and at of and is a of the Farm a in and from a at of has as the of on Epigenetic and and and of the on and of research on genetic and epigenetic regulatory mechanisms of animal health and of diseases in farm mechanisms of epigenetic inheritance and its in breeding animals for disease for this was by and of The that is of

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.310
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2021
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