Project No. 18-025 Contract No. ON-00593 AWI Project Manager: Dr Carolina Diaz Contractor Name: The University of Queensland Prepared by: Dr Edward Narayan, Mr Gregory Sawyer, Mr Dylan Fox and Prof. Alan Tilbrook Publication date: June 2020
Sheep Shearing and Epigenetic Change
Published by Australian Wool Innovation Limited, Level 6, 68 Harrington Street, THE ROCKS, NSW, 2000
This publication should only be used as a general aid and is not a substitute for specific advice. To the extent permitted by law, we exclude all liability for loss or damage arising from the use of the information in this publication.
AWI invests in research, development, innovation and marketing activities along the global supply chain for Australian wool. AWI is grateful for its funding which is primarily provided by Australian woolgrowers through a wool levy and by the Australian Government which provides a matching contribution for eligible R&D activities © 2019 Australian Wool Innovation Limited. All rights reserved.
Contents
Executive Summary
1. Introduction 2. Literature Review 3. Project Objectives 4. Success in Achieving Objectives 5. Methodology 6. Results 7. Discussion 8. Impact of Wool Industry – Now & in 5 years’ time 9. Conclusions and Recommendations 10. Bibliography 11. List of abbreviations and/or glossary 12. Appendices a. Appendix 1 – AWI Communication Report Template (see attached) b. Appendix 2 – List of Milestones & Dates submitted c. Appendix 3 – Remaining Assets d. Appendix 4 – Any Project Intellectual Property e. Appendix 5 – Storage of Primary Research Data (Paper based and electronic) f. Appendix 6 – Animal Ethics Approval (if any)
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Executive Summary
The primary objective of this research was to test the influence of shearing frequency (twice shorn versus single shorn) on the epigenetic DNA methylation patterns in a Merino sheep flock over a period of 1 generation. To keep the trial commercially relevant the selected flock was genetically derived from a variety of bloodlines and of mixed aged Merino ewes. Within the results of this research, the frequency of shearing pattern did not provide an evidence of significant epigenetic changes within the ewes and lambs. This report also presents data and discussion on the trait variation in ewe sheep body condition, physiological (stress hormonal) parameters and wool quality indicators between once and twice shorn ewe groups.
It is concluded:
Smart tags sensor based technology provides a reliable way to track sheep activity.
Merino ewes and lambs (first generation) show diverse molecular epigenetic signatures that are not significantly influenced by shearing frequency.
This study provided foundation knowledge on the molecular epigenetic signatures in Merino sheep under exposure to natural environmental and management factors. The high quality molecular data obtained through this research project included DNA methylation profiles of over 30,000 genes that are directly associated with cellular and molecular processes that regulate whole-animal physiology, development and growth. Thus, the baseline molecular data can provide a useful resource for future research in many key areas such as animal welfare, diseases and climatic resilience that will benefit Merino sheep and wool production.
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1 Introduction
In the past decade, there has been an increase in scientific reporting into the effects of adversity in early life on the participants DNA profile within both human and animal studies (Bock et al., 2015; Lutz and Turecki, 2014). This emerging research area continues to be driven by scientists globally to better understand a wide range of effects caused by a variety of intrinsic and extrinsic influences on the DNA profile of ongoing generations within observed genotypes. From within the cell of the early stage developing embryo, transcriptional and epigenetic changes to the cell are occurring via remodelling and reprogramming within the cell nucleus. What is still unclear is how does the ram and ewe sheep interaction within their external and internal environments alter DNA, and at what phases of the embryo’s development and within its activation of the embryonic genome. This science is what is known as Epigenetics (Suzuki and Bird, 2008).
The term epigenetics since its inception in 1942 has evolved with increasingly varying terms of what is epigenetics. Generally, epigenetics represents the genome-wide study of the distribution of methylated and unmethylated nucleoside residues within the genome (Thompson et al., 2020). Whereas Daxinger and Whitelaw (2012) concluded that epigenetics refers to effects on phenotype (or on patterns of gene expression) that are passed from one generation to the next by molecules in the germ cells and that cannot be explained by Mendelian genetics (or by changes to the primary DNA sequence). More recent studies have further expanded on this description to now include that epigenetics includes heritable states of gene expression that are not dependent on alterations in the DNA sequence (Ibeagha-Awemu and Zhao, 2015).
Early biomedical studies into animals’ epigenetic behaviour have been focussed on mice due to their ability to reproduce quickly and for researchers to gain fast results with multiple offspring from the same female. Due to the nature of sheep growth and time to reach puberty, a predominant single offspring and the lack of funding for epigenetic research into the sheep there has been very limited research into this field (see Gonzalez-Recio et al., 2015; Goddard and WhiteLaw, 2014-MLA Report B.BSC.0114). However, due to foresight by early researchers there have been substantial advances in current genomic technologies to allow for development of genome analysis and sequencing in livestock (Ross and Sampaio, 2018).
This Australian Wool Innovation funded study (18-025) will continue to grow this body of evidence to assist further research into environmentally induced effects on the sheep DNA as caused via epigenetics. Similar to bovine embryo research, the sheep is also better suited mammalian model than mice to study human
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embryo development. A clear example of this interaction is the cloning of “Dolly” the sheep and her ability to create greater human scientific understanding of genetics and their interaction with the environment.
This explanation of epigenetics within the extensive studies of sheep globally has shown no known literature about the benefits of epigenetic and transgenerational epigenetic effect on the DNA caused by shearing patterns. This was until unpublished research outcomes into sheep physiology and wool quality within an artificial insemination and embryo transfer program were discovered within an earlier (2015 – 2017) research trial undertaken by Dr Edward Narayan and Gregory Sawyer (Narayan et al., 2018; Sawyer and Narayan, 2019a). This research discovered that the embryos from the same sire and dam that were placed into surrogates and raised under the same environmental and management regimes had significant varying wool quality trait outcomes (Fig. 1.) This data led researchers to conceptualize under the current project that epigenetic influences on embryos life-time productivity are influenced by nature and nurture of the offspring by the ewe, ewe’s exposure to various external stressors while gestating including but not limited to shearing pattern. This research selected shearing patterns as the external stressor to investigate epigenetic changes in the DNA profile of merino ewes and lambs of twice versus once shorn ewes.
Sire Dam name Dam body temp (oC) at AI Yearling Weight Yearling Micron
1 14 39.4 82 16.5
1 14 39.4 83 15.8
1 14 39.4 80 16.5
1 14 39.4 62.5 17.9
2 9 39.3 72 16.8
2 9 39.3 68 17.4
2 9 39.3 59.5 18.1
3 27 38.9 73.5 15.6
3 27 38.9 67 16.7
Figure 1.0 Production traits (Yearling Weight and Yearling Fibre Diameter-Microns) of merino lambs from donor ewes placed into same recipient ewe during AI/ET trial). Bold numbers for yearling weight and yearling microns represent variation across dam and lambs.
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2 Literature Review
The accepted common science application of evolution is based on Mendelian theories of which there are three in total. Of significance to this research is the first law, that being the Law of Dominance and Uniformity. The Law of Dominance and Uniformity express that within a cross between a homozygous dominant and a homozygous recessive organism yields a heterozygous organism whose phenotype displays only the dominant trait. The offspring in the first generation (F1) are equal to the examined characteristic in the genotype and phenotype showing the dominant trait. Varriale (2019) expanded on this law to conclude that accepted inheritance on the F1 generation does not depend on the exclusivity of the transmission of genetic material from both parents to the offspring – but is a combination of various responses to environmental stimuli (ES) that are delivered through variations in epigenetic marks on DNA sequence.
A vast array of ES (nutrition, pathogens, warming or cooling climate and other environmental factors) modify epigenetic marks in the DNA and leads to varying effects on production traits (phenotype) in livestock species (Ibeagha-Awemu and Zhao, 2015). The influence of ES on the phenotype of an animal is not only a function of its underlying DNA sequence but also of its current and past environment (Singh et al., 2010). It is therefore the science of epigenetics that challenges the previously accepted Mendelian theories with the divergence of traits that are not reliant on differences in the primary sequence of the DNA (Vogt, 2017). Many previous authors of epigenetics and transgenerational epigenetic change have further suggested that environmentally induced epigenetic change promotes DNA changes, which can be selected for and maintained as preserved environmentally induced traits in many generations (Varriale, 2019). This phenomena within science is known as transgenerational epigenetic change. Furthermore, Charles Darwin, “Original of Species” highlighted that the changing conditions throughout the lifetime of the sheep is of the highest importance in causing variability throughout the phenotype. Whilst this variability is governed by many unknown laws of nature, of greater interest to this research is how this variability is influenced by the ewes’ interaction within their in-situ and ex-situ environments.
The Genome
The epigenome of a cell is the complete collection of epigenetic marks, such as DNA methylation, histone tail modifications (acetylation, methylation, ubiquitylation, etc.), chromatin remodelling and other molecules that can transmit information through gene regulation such as non-coding ribonucleic acid (RNA) species (e.g., microRNAs and long non-coding RNAs), that exist in a cell at any given point in time (Rakyan et al., 2011). A number of genes have been shown to be epigenetically regulated at the level of DNA
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methylation and acetylation (Alikhani-Koopaei et al., 2004; De Filippis et al., 2013; Mueller and Bale, 2008; Newell-Price, 2003; Oberlander et al., 2008a; Oberlander et al., 2008b; Weaver et al., 2004).
Within an evolutionary time scale, the genome is plastic and is shaped via various combinations within the genome rearrangement and gene transfer all of which are influenced by the external environment to the host (Varriale., 2019). This provides acknowledgment of earlier research that shows the effects of in-vivo hormonal stimulation or female nutrition on oocyte/embryo quality and of in-vitro procedures such as artificial fertilization and embryo culture on the health of the newborn are already documented (Boerjan et al., 2000). Furthermore, it is effect of maternal stress at the early stages of pregnancy, which appears to result in greater implication of the epigenetic changes than that trigged at later stages of pregnancy (Oberlander et al., 2008, Sawyer and Narayan., 2019).
Pre-natal programming by maternal stress can propagate to the varying generations via impacts to the DNA sequence caused by epigenetic mechanisms (Cao-Lei et al., 2017). As the placenta acts as a connection between the mother and the developing foetus and stress activates maternal hypothalamo-pituitary-adrenal (HPA) axis functioning and triggers glucocorticoid (GC) or stress hormone secretion that reaches the foetus by trans placental passage (Glover, 2015). Epigenetic mechanisms can also influence rates of recombination and mutation, leading to genetic changes that are passed to offspring (Hauser et al., 2011) Maternal effects that influence traits such as resistance to heat stress and diseases (Jenkins and Hoffmann, 1994) are also likely to often reflect in a transgenerational epigenetic change into the offspring.
Normal cellular functions rely on the preservation of genetic and epigenomic homeostasis and a dynamic balance of stability and reversibility in gene expression patterns (Ibeagha-Awemu and Zhao, 2015). This is required to ensure cell identity, maintain growth and development, and enable cells to respond to stimuli. The placenta acts as a connection between the mother and the developing foetus and stress activates maternal HPA axis functioning and triggers glucocorticoid (GC) secretion that reaches the foetus by transplacental passage (Glover, 2015). Thus, prenatal stress and prenatal exposure to GCs have been shown to have long-term effects on the expression of numerous genes associated with HPA function, neurologic function and phenotype. Indeed, evidence is mounting that the long-term effects of prenatal stress and GC exposure on these genes are mediated through epigenetic mechanisms (Crudo et al., 2012; Mueller and Bale, 2008; Oberlander et al., 2008).
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Furthermore, protein synthesis as well as DNA and RNA polymerization is broken under heat stress. Whereas protein and RNA synthesis recover rapidly after the heat exposure, DNA synthesis remains inhibited during a longer period (Hahn, 1982; Streffer, 1988). It was also shown that heat stress is not only responsible of protein denaturation, but also it induces their aggregation into the nuclear matrix. This aggregation increases the nuclear protein concentration (Hahn, 1982; Streffer, 1988). Therefore, many molecular functions are altered, such as DNA synthesis, replication and repair, cellular division and nuclear enzymes and DNA polymerases functions (Higashikubo et al., 1993). The consequences of an epigenetically inherited phenotype depend in large part on what the effect of that phenotype is on the overall fitness of the individual bearing it. Just like genetically inherited phenotypes, epigenetically-inherited phenotypes can be neutral, advantageous or disadvantageous.
Through previous work by Klironomos et al. (2013) they have provided a simple, but informative model of how increases in fitness in a population can be derived from either epigenetic or genetic changes in a population over tens of thousands of generations. However, the effect of epigenetic transgenerational change may not only be potentially wide-ranging and all-encompassing, but may also be felt immediately within a population exposed to the same environmental conditions.
When animal productivity is considered within Merino sheep – it is not known whether an external factor of shearing frequency that may affect the overall DNA profile of the offspring. The shearing of pregnant ewes increases cortisol concentrations (Corner et al., 2006, Sawyer and Narayan, 2019). Mid-pregnancy shearing in winter under outdoor pastoral grazing systems results in an increase in the birth weight of singleton lambs (Morris et al., 2000; Sherlock et al., 2003; Corner et al., 2006 and 2007b). Mid-pregnancy shearing also positively reduces the mortality of single and multiple-born lambs (Morris and McCutcheon, 1997; Smeaton et al., 2000; Kenyon et al., 2004; Corner et al., 2006 and 2007a). However, there has been no previous known studies conducted in sheep on the influence of shearing pattern (once a year verse twice a year) and the changes caused by this stressor on the offspring’s genome. Very early research from Texas, USA showed the twice shearing increased yield of wool however found no difference in body weight and lambing percentage, however lamb loss was also slightly lower in twice shorn ewes (Jones et al., 1937).
Minimally invasive glucocorticoid (stress hormone) monitoring tools (Möstl and Palme, 2002) are further advancing our knowledge of physiological stress responses of livestock to acute and chronic factors such as transportation, heat stress and diseases (Nejad et al., 2014; Combs et al., 2018; Narayan et al., 2018; Sawyer et al., 2019; Scherpenhuizen et al., 2020; Weaver et al., 2020; Narayan, 2020). Furthermore, the hypothalamo-pituitary adrenal (HPA) axis plays a critical role in the fetal programming of metabolic functions and development (Wu et al., 2006; Kapoor et al., 2008; Narayan and Parisella, 2017). Therefore, studying
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the stress hormone levels of the gestating ewes will shed light into the influence of environmental and management factors on maternal response to stress. While there are rapid advances that have been made in DNA sequencing technologies over the past 20 years (Zhao and Grant 2011; Couldrey et al., 2014), which have resulted in genome wide selection for production traits becoming a reality in agricultural animals (Goddard et al., 2010; Couldrey et al., 2014) there is very limited global farming animal research where it can be determined that production traits are influenced by the synergism of environmental factors and genetic factors (See Thompson et al., 2020).
Epigenetic Inheritance and the Current Evolution in Animals (Livestock)
Evolution within livestock is constantly revolving through the continual application of natural selection and biased human selection for specific phenotypical traits. The exposure of the host (cow/ bull, ewe/ram) to random mutation and genetic legacy are not entirely appropriate to explain individual and population fitness in response to changing environments (Varriale, 2019). Both environmental and human bias may either suppress or promote epigenetic variability within an individual or within a population.
Human intervention to the underlying DNA and genomic platform, has increased in the flock and herd with the development of Australian Sheep Breeding Value (ASBV) and Estimated Breeding Value (EBV). As a result, producers focussing on specific traits at ram selection time, results in a phenotypic switch more quickly and expansive. However, understanding how the new trait “washes in” and “washes out” to the original phenotype is important (Burggren., 2015).
Some researchers appear to consider an epigenetically inherited trait as digital: it is either present/on or absent/off. Thus, experiments are typically conducted where the P0 generation is subjected to some form of stressor, and the F1 generation are scored as to whether they have the trait or lack it (Burggren, 2016). Yet, evidence is accruing that epigenetically inherited traits in some organisms may take a few generations to actually “wash in”, even being completely absent in the initial F1 generation (Burggren, 2015). Similarly, traits may “wash out” over several generations (Burggren, 2015).
With a “wash in” and “wash out” effect occurring in transgenerational epigenetic change, questions and analysis of farmed animals’ production traits have very limited research globally (Thompson et al., 2020). There is a limited body of evidence in global sheep studies into the effects of adversity (stress) in early life on epigenetic change and subsequent transgenerational alterations. This is especially significant in the critical developmental window of intrauterine life, which has programming effects on health outcomes in
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postnatal life (Cao, 2017). Stressful conditions are reported to lead to epigenetic modifications that mobilize transposable elements, causing major genetic alterations and rearrangements (Zeh et al., 2009). In these cases, epigenetic mechanisms can lead to permanent changes in traits (Franks and Hoffmann, 2012.). Varriale (2019) proposed that the stimuli that promoted epigenetic change ceased, then the epigenetic change many become redundant or detrimental to future offspring.
Conclusion Whilst, normal cellular functions rely on the preservation of genetic and epigenomic homeostasis and a dynamic balance of stability and reversibility in gene expression patterns (Ibeagha-Awemu and Zhao, 2015). Plasticity is required to ensure cell identity, maintain growth and development, and enable cells to respond to the stimuli. Thus, the genome of a living organism is consistently stable within walls of the cell and from one generation to the next (Darmon and Leach, 2014). It is, however, the epigenome that is highly dynamic throughout life and is governed by a complex interplay of genetic and environmental factors (Bernstein et al., 2007). This concludes that within livestock production systems, the ability for the producer to understand the complex interaction of genetics, environment and the phenotype output over generations in varying changes to many stimuli is key to directly influencing subsequent generations of farm output and overall business productivity.
3 Project Objectives
The primary objective of this research was to test the influence of shearing frequency (twice shorn versus single shorn) on the epigenetic DNA methylation patterns within a Merino sheep flock over a period of 1 generation.
The following research questions were studied within this overarching project objective;
To evaluate the activity of Merino ewes measured using the Digibale Smart tags. To quantify genome-wide epigenetic changes in Merino ewes and lambs. To quantify changes in wool/faecal cortisol, body condition and wool quality indicators.
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4 Success in Achieving Objectives
These objectives have been fully achieved;
(1) Genome wide DNA methylation patterns were evaluated for parent ewe Merino sheep across the single and twice shorn groups. (2) DNA methylation patterns were linked between lambs and their mothers. (3) Shearing treatment did not provide statistically significant DNA methylation patterns. (4) Hormone analysis was completed using cortisol evaluation in wool and faeces in early, mid and late gestation period of Merino ewes. (5) Ewe activity assessed using Smart Tags to present the proportion of time spent standing, moving or grazing for each ewe sheep. (6) Body condition and wool quality (lambs) was collected and used for the comparative analysis.
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5 Methodology
5.1 Experimental Design
All Merino ewes were shorn prior in October 2018. In January 2019, a total of 100 merino ewes participated in natural joining. Once joined successfully, 48 mixed age (not maiden) Merino ewes were used for this trial (the trial ewes were run together with the rest of the ewes). Ewes were bought into the pens by the farmer and visually assessed and conditioned scored by the researcher. Upon confirmation that the ewe is sound (not sick, unwell or lame) she proceeded to inclusion in trial, if ewe was unsound she was not included in the research and removed from the trials at this point and given back to the farmer. There were 24 ewes per treatment group (once shorn or twice shorn). All ewes were run as a mob and shearing frequency was the only main factor which was different between the two groups. The sample size of the experiment was calculated using the formula for power calculator available from 3Rs reduction (http://www.3rs- reduction.co.uk/html/6__power_and_sample_size.html).
5.2 On Farm Assessment
Collection of Wool Fibre - Ewes were bought into the pens by the farmer and visually assessed and conditioned scored by the researcher. As part of the normal shearing regime on farm a sample of wool was collected from the fleece on the top knot (closest to the skin) as this is an area that can be accessed readily on the animal. All recording of date, individual tag number, and group name was written on the sample bag and the wool sample was placed into this bag. Each sample was then kept cold in a freezer prior to lab assessment
Collection of Faecal Matter - The ewe was held by the farmer in an upright position and fecal sample was manually collected from rectum of the ewe. Then, ewe was released by the farmer to stand freely in a pen.
TSU sampler – ear notch tissue was collected from the ewes and lambs using Allflex TSU sampler (Source: https://www.allflex.global/au/product/tsu-applicator/)
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5.3 Laboratory Methods
5.3.1 Hormone Analysis The wool cortisol concentration in each sample was determined by colourmetric analysis using polyclonal anticortisol antiserum (R4866 – supplier UC Davies California, USA) diluted in ELISA coating buffer
(Carbonate-Bicarbonate Buffer capsule (Sigma C-3041) and 100 mL Milli-Q water, pH 9.6), working dilution
1:15,000. This was followed by reactivity with Horseradish Peroxidase (HRP) conjugated cortisol label (CJM,
UC Davies) diluted 1:80,000, and cortisol standards diluted serially (1.56 – 400 pg/well). Nunc Maxi-Sorp™ plates (96 wells) were coated with 50 μL cortisol antibody solution and incubated for a minimum of 12 hours at 4 oC. Standards, including zeros and nsbs (non-specific binding wells), were prepared serially (2-fold) using
200 μL standard working stock and 200 μL assay buffer (39 mM NaH2PO4H2O, 61 mM NaHPO4, 15 mM NaCl).
For all assays, 50 μL of standard and (1:10) diluted 90% ethanol extracted wool samples were added to each well, followed by 50 μL of the cortisol HRP. Each plate was loaded in under 10 minutes. Plates were covered with acetate plate sealer and incubated at room temperature for 2 hours. After incubation, plates were washed 4 times using an automated plate washer (ELx50, BioTek™) with phosphate-buffered saline solution
(0.05% Tween 20) and then blotted on paper towel to remove any excess wash solution. Substrate buffer was prepared by combining 1 μL 30% H2O2, 75 μL 1% tetramethylbenzidine (TMB) and 7.425 μL 0.1 M acetate citrate acid buffer, pH 6.0 per plate. The TMB substrate was added to each well that contained a standard sample at 50 μL to generate colour change. The plates were covered with an acetate plate sealer and left to incubate at room temperature for 15 minutes. The reaction was stopped with 50 μL of Stop solution (0.5 M
H2SO4 and Milli-Q water) added to all wells in the Nunc Maxi-Sorp™ plates. To determine hormone concentration in each sample plates were read at 450nm (reference 630nm) on an ELx800 (BioTeck™) microplate reader. Cortisol concentrations were presented as ng/g.
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5.3.2 Wool Laserscan Assessment
Snippets of raw wool are cut from samples of raw wool through a mini coring machine. The snippets are removed from the minicore and are washed in a solvent hexane for a period of thirty seconds to wash and blend the fibre snippets together. A shot of compressed air is used to dry the snippets. A random sample is removed via tweezers from the washed and dried snippets and placed in to a dilute suspension in a mixture of isopropanol (propan-2-ol) and water (8% by volume). The suspension of snippets is transported through a measuring cell which is positioned in a beam of laser light. The reduction in intensity of the laser beam as the individual snippets pass through the beam of light, approximately 500 micrometres in diameter, is sensed by a detector and transformed, using a calibration look-up table, into a diameter in micrometres. A computer is used to collect and summarise the individual measurements to give statistics such as mean and standard deviation of fibre diameter for the specimen. The Laserscan assessment was conducted using an approved Laserscan machine by an independent wool company (Chad Wool Dubbo, NSW).
5.3.3 Parentage testing
DNA based parentage determination was done by the Neogen® laboratory, Gatton, Queensland. It provides a very fast and efficient parentage testing service for evaluating the animal’s DNA for accurate parentage determination. Neogen’s lab uses Single Nucleotide Polymorphism (SNP) technology to genetically
“fingerprint” each animal at more than 100 chromosomal locations. The animal’s unique genetic fingerprint can be evaluated with the fingerprint of its expected parents by utilizing the knowledge that half of the animal’s genetic information was inherited from the sire (father) and the other half from the dam (mother).
Discrepancies in the inheritance pattern would suggest that an animal’s reputed parent would likely be incorrect. This SNP technology enables a higher level of accuracy in parentage testing for cattle, dog, sheep, and pigs (Source: https://genomics.neogen.com/en/parentage-testing-products).
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5.3.4 Molecular Epigenetic Analysis
Project Overview - Sheep DNA analysis and quality control was performed using Illumina NovaSeq RRBS
(Reduced Representative Bisulfite Sequencing) data of a barcoded 100 bp single end run. RRBS library was produced following the Nuggen’s Ovation® RRBS Methyl-Seq System in the Australian Genome Research
Facility (AGRF). The primary bioinformatics analysis involved quality control, trimming adapter, contamination and low-quality fragments, customized Nuggen’s adapter trimming, read mapping, customized Nuggen’s post-alignment processing, DMR (differential methylation region) analysis and annotating differentially methylated genes.
Bioinformatics Methods - Reads were assessed with FastQC v.0.11.8 (Andrews 2010) and trimmed with Trim
Galore v0.5.0 (F. Krueger 2015). Additional trimming was performed using Nugen’s diversity trimming script with default values. Mapping was carried out with Bismark v0.21.0 (Felix Krueger and Andrews 2011) to methylation-converted Oar3.1 genome. Alignments were performed with Bowtie2 v2.3.4 (Langmead and
Salzberg 2012) aligner with default parameters allowing 0 mismatch in a 20 bp seed. Then alignments were deduplicated using barcoded RRBS mode of Bismark. The primary bioinformatics analysis involved quality control, trimming adapter, contamination and low-quality fragments, customized Nuggen’s adapter trimming, read mapping, customized Nuggen’s post-alignment processing, DMR (differential methylation region) analysis and annotating differentially methylated genes.
Quality Control: All works carried out during the project have followed the strict requirements of ISO17025:
2005. AGRF Ltd is accredited in the field of Biological Testing (Scope: DNA Analysis) according to the
ISO17025: 2005 standard by the National Association of Testing Authorities (NATA). Staff follow Standard
Operating Procedures, which define their responsibilities and provide guidance on achieving standards; compliance is monitored at regular reviews and internal audits. The work was supervised by a person with
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relevant qualifications and was checked while in progress and upon completion to ensure that it met the necessary ISO17025: 2005 standards. The AGRF is an Illumina Certified Service Provider (CSPro) for the sequencing service. Illumina CSPro program is a collaborative service partnership ensuring Illumina's best practices are adhered to, ensuring that scientific methods and data quality are always optimized and maintained.
5.4 Statistical Analysis
5.4.1 Field and lab parameters (Hormone, Body Condition and Activity): Statistical analysis was done to test the hypothesis that (1) parameters (hormone, body condition and activity) will be significantly related to the stages of breeding in the merino ewes. Firstly, data checked for homogeneity of variances and were log- transformed prior to analysis. Statistical analysis was done using a repeated measures Analysis of Variance
(ANOVA) model using sample, date of sampling and sheep number as the factors and log-transformed data as the dependent variables. Level of significance for all statistical analysis was p < 0.05.
5.4.2 DNA Methylation: EdgeR (Chen et al. 2018) is a package used to detect and quantify differential methylation of digital RRBS data, that is, counts of reads support methylated and non-methylated cytosines for each locus of a given organism. The library sizes were corrected by the average of the total read counts for the methylated and un-methylated libraries in edgeR. A generalised linear model was then used to quantify the differential expression between the groups. Nearest transcriptional start site (TSS) was annotated to valid methylation loci using nearestTSS function in edgeR. DMR and all analyzed loci table
(Appendix 7) contain the following fields: Locus ID from the reference genome, Chromosome, Genome locus,
Entrez ID of the nearest gene, Symbol of the nearest gene, Strand of the nearest gene, Distance between the methylated locus and the TSS of the nearest gene, Width of the TSS region, logFC: log2-fold change of methylation between groups being compared, logCPM: average log count per million for the locus across all samples, LR: chisquare likelihood ratio statistic, PValue: p-value for test of methylation, FDR: false discovery
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rate / adjusted p-value for multiple hypothesis testing (BH-adjustment). Venn diagrams show the numbers of co-upregulated and co-downregulated gene sets between different comparisons (using DMR and their nearest genes DMG respectively).
5.4.3 Mapping: Raw sequencing reads was firstly processed with TrimGalore to remove adapter/primer, low quality fragments. Trimmed reads then went through a second trimming to remove Nugen’s RRBS specific primer. MultiQC report has been provided to show the statistics of clean reads. Note that the duplicate ratio here looks high because reads digested from the same locus of different molecules have the same sequence in RRBS. These reads were barcoded in library preparation in order to distinguish from true PCR replicates.
Clean reads were then mapped to reference genome (Oar3.1 build) and spike in Lambda DNA. PCR duplicates were removed from the alignments in the following de-duplication step and identical reads from different molecules with unique barcodes were retained. MultiQC report has been provided to summarize the mapping results.
Non-conversion rate: Non-methylated Lambda DNA was used as a spike in to estimate the non-conversion rate of bisulfite-treated genomic DNA. The non-conversion rate was computed by dividing the number of reads support methylated cytosineby the total reads for that cytosine on the non-methylated Lambda DNA, as follows:
Rnc (%) = Nmc / (Nmc + Nc) x 100
Where Rnc is non-conversion rate, Nmc is the number of reads supporting methylated cytosine and Nc is the number of reads supporting non-methylated cytosine. Appendix: Table 1. Alignment statistics of Lambda DNA and non-conversion rate
5.4.4 Differentially methylated regions: In this workflow, one of the most popular Bioconductor packages edgeR pipeline is used for assessing differential methylation regions (DMRs) in RRBS data. It is based on the negative binomial (NB) distribution and it models the variation between biological replicates through the NB dispersion parameter. The analysis was restricted to CpG sites that have enough coverage for the methylation level to be measurable in a meaningful way at that site. We require a CpG site to have a total count (both methylated and unmethylated) of at least 10 in every sample before it is considered in the
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analysis. The number of CpGs kept in the analysis is ensured large enough for our purposes after filtering. edgeR linear models are used to fit the total read count (methylated plus unmethylated) at each genomic locus. Differential methylation is assessed by likelihood ratio tests, so here we consider FDR value of less than 0.05 as significant DMR.
6 Results
6.1 All Field and Laboratory Data Summary
A total of 46 (single bearing) trial Merino ewe sheep (out of the 48 ewes) were successfully TSU sampled. Out of 74 lamb TSU samples collected, only 38 lambs were successfully matched to 34 ewes during the DNA parentage test (potentially because other lambs were from ewes that were not part of the trial). DNA parentage testing results, sheep body condition, wool micron profiling, hormonal and molecular raw data (retrieval) are provided in Appendix 1.
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TAG and DNA Details AGRF Lab ID Ewe Client Sample ID Actual Ewe Tag Number 2nd DNA Vile Number Shearing Preg Status Lamb Sample ID In Trial 9551-01 11 11 94257 Twice Single Late 25 Yes 9551-02 15 15 166173 Twice Single Late 19 Yes 9551-03 19 19 166161 Twice Single Late 6 Yes 9551-04 22 22 blank Twice Single Unknown Yes 9551-07 28 28 166150 Once Single Late 44 Yes 9551-08 29 27 166162 Lost tag Yes 9551-09 30 30 166147 Once Single Late 37 Yes 9551-10 31 31 blank Twice Single Late Yes 9551-11 33 33 blank Once Single Late Yes 9551-12 34 34 94269 Once Single Unknown Yes 9551-13 35 35 94304 Twice not known 16 Yes 9551-14 37 37 166189 Once Single Late Yes 9551-15 38 38 blank Twice Single Late Yes 9551-16 40 40 94313 Once Single Late 13 Yes 9551-17 41 41 94211 Once Single Late Yes 9551-18 42 39 94216 Twice Single Late 56 Yes 9551-19 45 45 94212 Twice single early 48 Yes 9551-20 46 46 166193 Once single early Yes 9551-21 47 47 94315 Twice single early 41 Yes 9551-22 48 48 94263 Twice Single Unknown Yes 9551-23 50 50 94355 Once Single Unknown Yes 9551-24 52 52 94318 Twice Single unknown 29 Yes 9551-26 65 65 166177 Once Single Early 45 Yes 9551-27 66 66 94320 Once Single Unknown Yes 9551-28 68 68 166190 Twice Single unknown 55 Yes 9551-29 70 70 blank Twice single early Yes 9551-30 76 76 94314 Twice Single Late 36 Yes 9551-31 77 77 94354 Once Single Unknown 33,53 Yes 9551-32 79 79 94352 Twice Single Late 57 Yes 9551-35 84 84 166185 Twice Single late 32 Yes 9551-36 85 85 94219 Twice Single Early 28 Yes 9551-37 87 87 94357 Once Single unknown 22,35 Yes 9551-40 96 96 166151 Once Single Late Yes 9551-41 100 100 94210 Once Single Late 4 Yes 9551-42 101 101 166145 Once Single Late Yes 9551-43 104 104 blank Twice Single Late Yes 9551-44 105 105 94319 Twice single early 60 Yes 9551-45 107 107 94356 Twice Single Late 70 Yes 9551-46 109 109 94264 Twice Single Late 14 Yes
Table 1. provides summary information on the sheep that was used in the pilot trial project. The information is matched according to the tag used for the Digibale SMART tag, TSU DNA vile and sample coded by AGRF for DNA epigenetic testing.
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6.2 Epigenetic DNA Methylation Results
Figure 2 shows log2-fold change of methylation between ewe groups being compared. The samples with similar expression profiles clustered together. FDR p-value were not significant between treatment and distinct clusters are not apparent by treatment (twice or single shorn). The preg scan information (early or late) is also shown for each group.
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Figure 3 shows log2-fold change of methylation between lamb groups being compared. The samples with similar expression profiles cluster together. FDR p-value were not significant and unique clusters are not apparent by treatment (twice or single shorn).
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Secondary Differential methylation analysis
Table 2. Over 20,000 genes were differentially methylated across the ewe and lamb sheep chromosome (1-23).
Top 30 unique genes are presented in the table below. Functional significance of the highlighted genes are provided (http://gepia.cancer-pku.cn/detail.php?gene=Mar2).
External % # of Function Gene occurrence occurrences Name of of DMRs methylation across whole genome 1st NA 28.0648% 294281 This locus controls the synthesis of the Kell blood group 'precursor substance' (Kx). Mutations in this gene have been associated with McLeod syndrome, an X-linked, recessive disorder characterized by abnormalities in the neuromuscular and hematopoietic systems. The encoded protein has structural characteristics of prokaryotic and eukaryotic membrane transport proteins. [provided by RefSeq, Jul 2008] 2nd U6 1.4923% 15648 U6 snRNA is the non-coding small nuclear RNA component of U6 snRNP, an RNA-protein complex that combines with other snRNPs, unmodified pre- mRNA, and various other proteins to assemble a spliceosome, a large RNA-protein molecular complex that catalyzes the excision of introns from pre-mRNA. 3rd 5S_rRNA 0.7025% 7366 The 5S ribosomal RNA is an approximately 120 nucleotide-long ribosomal RNA molecule with a mass of 40 kDa. It is a structural and functional component of the large subunit of the ribosome in all domains of life, with the exception of mitochondrial ribosomes of fungi and animals. 4th 7SK 0.2855% 2994 small nuclear RNA, a multifunctional transcriptional regulatory RNA with gene-specific features 5th SNORA70 0.1655% 1735 Small nucleolar RNA SNORA70 is a non-coding RNA molecule which functions in the biogenesis of other small nuclear RNAs. This type of modifying RNA is located in the nucleolus of the eukaryotic cell which is a major site of snRNA biogenesis. 6th U1 0.1600% 1678 Plays role in pre-mRNA splicing 7th U2 0.1037% 1087 Plays role in pre-mRNA splicing 8th U4 0.0968% 1015 Plays role in pre-mRNA splicing
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9th PAX6 0.0813% 853 This gene encodes paired box gene 6, one of many human homologs of the Drosophila melanogaster gene prd. In addition to the hallmark feature of this gene family, a conserved paired box domain, the encoded protein also contains a homeo box domain. Both domains are known to bind DNA and function as regulators of gene transcription. This gene is expressed in the developing nervous system, and in developing eyes. Mutations in this gene are known to cause ocular disorders such as aniridia and Peter's anomaly. Alternatively spliced transcript variants encoding multiple isoforms have been observed for this gene. [provided by RefSeq, May 2012] 10th TUBGCP3 0.0766% 803 Protein Coding gene 11th FOXF1 0.0744% 780 FOXF1 (Forkhead Box F1) is a Protein Coding gene. Diseases associated with FOXF1 include Alveolar Capillary Dysplasia With Misalignment Of Pulmonary Veins and Persistent Fetal Circulation Syndrome. Among its related pathways are FOXA2 and FOXA3 transcription factor networks and Embryonic and Induced Pluripotent Stem Cell Differentiation Pathways and Lineage-specific Markers. Gene Ontology (GO) annotations related to this gene include DNA-binding transcription factor activity and transcription regulatory region DNA binding. An important paralog of this gene is FOXC1. 12th B3GAT1 0.0714% 749 B3GAT1 (Beta-1,3-Glucuronyltransferase 1) is a Protein Coding gene. Diseases associated with B3GAT1 include Renal Adenoma and Chronic Nk- Cell Lymphocytosis. Among its related pathways are Glycosaminoglycan metabolism and Metabolism. Gene Ontology (GO) annotations related to this gene include UDP-galactose:beta-N- acetylglucosamine beta-1,3-galactosyltransferase activity and galactosylgalactosylxylosylprotein 3- beta-glucuronosyltransferase activity. An important paralog of this gene is B3GAT2. 13th RTN4R 0.0654% 686 RTN4R (Reticulon 4 Receptor) is a Protein Coding gene. Diseases associated with RTN4R include Schizophrenia and Leukemia, Acute Lymphoblastic. Among its related pathways are Spinal Cord Injury and NgR-p75(NTR)-Mediated Signaling. An important paralog of this gene is RTN4RL1. 14th CELF4 0.0646% 677 CELF4 (CUGBP Elav-Like Family Member 4) is a Protein Coding gene. Diseases associated with CELF4 include Speech And Communication Disorders and Specific Language Impairment. Among its related pathways are mRNA Splicing - Major Pathway. Gene Ontology (GO) annotations
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related to this gene include nucleic acid binding and mRNA binding. An important paralog of this gene is CELF6. 15th DAB2IP 0.0603% 632 DAB2IP is a Ras (MIM 190020) GTPase-activating protein (GAP) that acts as a tumor suppressor. The DAB2IP gene is inactivated by methylation in prostate and breast cancers (Yano et al., 2005 [PubMed 15386433]).[supplied by OMIM, May 2010] 16th CASZ1 0.0570% 598 17th KIRREL3 0.0559% 586 18th ZNF536 0.0553% 580 19th PITX2 0.0545% 571 20th PAX7 0.0533% 559 21st WNT7B 0.0525% 550 22nd FOXA2 0.0521% 546 23rd LHX5 0.0511% 536 24th SOX14 0.0509% 534 25th SIM2 0.0507% 532 26th NFATC1 0.0503% 527 27th ONECUT1 0.0499% 523 28th PAX2 0.0498% 522 29th ZIC4 0.0493% 517 30th NKX2-1 0.0490% 514 31st DMRTA2 0.0470% 493 32nd GATA6 0.0470% 493 33rd HAND2 0.0470% 493 34th LHX1 0.0470% 493 35th PAX5 0.0468% 491 36th PTMA 0.0466% 489 37th NFIX 0.0455% 477 38th TWIST2 0.0454% 476 39th PITX1 0.0453% 475 40th GATA3 0.0440% 461 41st BCAR1 0.0439% 460 42nd TBX5 0.0436% 457 43rd SIM1 0.0433% 454 44th Vault 0.0433% 454 45th SOX17 0.0428% 449 46th EVX2 0.0427% 448 47th HAND1 0.0427% 448 48th TNFRSF25 0.0424% 445 49th RGMA 0.0423% 444 50th RAB28 0.0421% 441 Please view the following links to access function of any of the above genes; https://www.genecards.org/cgi-bin/carddisp.pl?gene=DAB2IP http://gepia.cancer-pku.cn/index.html
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External Gene # of Name % occurrences Expression on sheep tissues 1st NA 28.0648% 294281 adrenal gland, pituitary gland, brain stem, hypothalamus, duodenum 2nd U6 1.4923% 15648 everything 3rd 5S_rRNA 0.7025% 7366 protein coding, placenta membranes, ribosomal protein 4th 7SK 0.2855% 2994 testes 5th SNORA70 0.1655% 1735 guides modification RNA Pseudouridine 6th U1 0.1600% 1678 everything 7th U2 0.1037% 1087 everything 8th U4 0.0968% 1015 everything 9th PAX6 0.0813% 853 nervous system, cerebellum, brain stem, pituitary, hypothalamus 10th TUBGCP3 0.0766% 803 testes, placenta, membranes 11th FOXF1 0.0744% 780 lung, stomach, bowels, protein 12th B3GAT1 0.0714% 749 brain, hypothalmus, cerebrum 13th RTN4R 0.0654% 686 plasticity, adult, central nervous system 14th CELF4 0.0646% 677 Brain development 15th DAB2IP 0.0603% 632 uterus, placental membranes, cervix, cerebellum 16th CASZ1 0.0570% 598 predominately skin/ wool 17th KIRREL3 0.0559% 586 cerebellum 18th ZNF536 0.0553% 580 cerebellum, pituitary 19th PITX2 0.0545% 571 animal growth through the HPA axis 20th PAX7 0.0533% 559 skeletal muscle development 21st WNT7B 0.0525% 550 kidney and skin 22nd FOXA2 0.0521% 546 abomasum, rectum, colon, lungs 23rd LHX5 0.0511% 536 Testes 24th SOX14 0.0509% 534 protein coding, DNA binding protein 25th SIM2 0.0507% 532 tonsils, kidney
Please view the following links to access function of any of the above genes;
https://www.genecards.org/cgi-bin/carddisp.pl?gene=DAB2IP
http://gepia.cancer-pku.cn/index.html
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6.3 Ewe Body Condition Changes
Ewe Body Condition Score Descriptive Statistics Shorn once Shorn twice Pregnancy n Median Mean Standard n Median Mean Standard status Error Error
Pre-joining 40 2.5 2.59 0.0692 25 2.75 2.64 0.0708
Mid 38 2.6 2.61 0.0781 25 2.75 2.77 0.0721
Late 38 2.5 2.54 0.1009 23 3 2.77 0.1369
Post-lambing 28 1.5 1.5 0.0630 20 1.5 1.48 0.0923
Table 3. Count, mean and standard error values for January, April and May condition score across shorn once and shorn twice treatments. Means and standard errors rounded to two decimal places where necessary.
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P A 12 10
8
6
4
frequency Score 2 0 1 2 3 4 5 1.5 2.5 3.5 4.5
1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 Condition score
B 12
10
8 6 4
Score frequency Score 2
0 1 2 3 4 5 1.5 2.5 3.5 4.5 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 Condition score
C 12
10
8 6 4
Score frequency frequency Score 2
0 1 2 3 4 5 1.5 2.5 3.5 4.5 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 Condition score
Figure 4 shows the change in body condition score of ewe sheep (A-pre joining; B-mid pregnancy; C-late pregnancy) in the once shorn group. Red dashed box depicts the normal distribution of body condition.
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12 A 10
8 6 4
frequency Score 2 0 1 2 3 4 5 1.5 2.5 3.5 4.5
1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 Condition score
12 B 10
8
6 4
Score frequency Score 2
0 1 2 3 4 5 1.5 2.5 3.5 4.5 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 follows: Condition score
C 12 10 8 6 4
frequency Score 2
0 1 2 3 4 5
1.5 2.5 3.5 4.5 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 Condition Score
Figure 5 shows the change in body condition score of ewe sheep (A-pre joining; B-mid pregnancy; C-late pregnancy) in the twice shorn group. The red dashed box represents the average range of condition scores (2.5-3.5) typical of healthy dry ewes. Outside the box, sheep with a low condition score are generally thin with very little fat covering, whilst sheep with a high condition score have a thick loin muscle and fat covering. 76, 92 and 68 percent of ewes shorn twice are within the ideal body condition range across pre- joining, mid- and late pregnancy respectively. Condition score recorded at 0.25 intervals between 1-5.
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6.4 Hormone Profiles
60
50
40
30 (ng/g) 20
Wool Cortisol Metabolites Cortisol Wool 10
0 Pre-joining Mid Late Pregnancy status
Shorn Once Shorn Twice
Figure 6. Mean Wool Cortisol Metabolites (WCM) of Super Fine Merino Ewes Shorn Once and Twice. WCM increased between April (mid gestation) and May (late gestation) across both treatments with shorn once ewes recording the highest results. Shorn once means determined from January (n= 34), April (n= 33) and May (n= 36). Shorn twice means determined from January (n= 23), April (n= 23) and May (n= 24). Error bars represent standard error of individual data set.
Shorn once Shorn twice Pregnancy
status n Mean WCM Standard n Mean WCM Standard Error Error
Pre-joining 34 26.36 4.1969 23 31.65 5.9151
Mid 33 11.36 1.6605 23 8.16 1.4468
Late 36 41.03 6.5834 24 24.36 4.9945
Table 4. Count, mean and standard error values of wool cortisol metabolites analysed from samples collected throughout pregnancy. Mean values rounded to two and standard error values to four decimal places where necessary.
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60
50
40
30 (ng/g)
20
Faecal Cortisol Metabolites Cortisol Faecal 10
0 Pre-joining Mid Late Pregnancy status
Once Twice
Figure 7. Mean Fecal Cortisol Metabolites (FCM) of Super Fine Merino Ewes Shorn Once and Twice. FCM increased between April (mid gestation) and May (late gestation) across both treatments. Shorn once means determined from n= 21 ewe sample per bar graph. Shorn twice means determined from n= 14 ewe sample per bar graph. Error bars represent standard error of individual data set.
Pregnancy Shorn Once Shorn Twice status n Mean FCM SE n Mean FCM SE
Pre-joining 31 7.93 0.2673 18 7.22 0.5591
Mid 28 20.54 2.8296 20 21.82 3.0940
Late 34 42.90 1.8875 23 44.90 3.3434
Table 5. Count, mean and standard error values of faecal cortisol metabolites analysed from samples collected throughout pregnancy. Mean values rounded to two and standard error to four decimal places where necessary.
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6.5 Comparison of ewe body condition and cortisol levels across shearing groups
20
18
16
14
12
10
8
Number of Sheep Sheep of Number 6
4
2
0 Positive Negative Neutral Body Condition Response to Stressors
Once Twice
Figure 8. Summed (n= 59) body condition response to shearing and gestational stress of merino ewes shorn once and shorn twice. Shorn once sheep responded mostly negatively to additional stress, whilst a minute difference was observed for shorn twice sheep with 12 and 11 sheep responding positively and negatively, respectively.
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50 5 4.75 45 4.5 4.25 40 4 3.75 35 3.5 3.25 30 3 2.75 25 2.5 2.25 20 2
1.75 Score Condition 15 1.5 1.25 10 1
Wool Cortisol Metabolites (ng/g) Metabolites Cortisol Wool 0.75 5 0.5 0.25 0 0 Pre-joining Mid-pregnancy Late-pregnancy WCM Condition Score
50 5 4.75 45 4.5 4.25 40 4 3.75 35 3.5 3.25 30 3 2.75 25 2.5 2.25 20 2 1.75 15 1.5 Score Condition 1.25 10 1 0.75 Wool Cortisol Metabolites (ng/g) Metabolites Cortisol Wool 5 0.5 0.25 0 0 Pre-joiningPre-joining mid-pregnancy Mid late-pregnancy Late
WCM Condition Score
Figure 9. Condition score and wool cortisol metabolite fluctuation during pregnancy for ewes shorn once (top panel) and twice (bottom panel). Once (n= 28-33). Twice (n= 23-24). Mean condition score decreased continuously throughout the pregnancy of ewes shorn once, whilst ewes shorn twice recorded an increase between pre-joining and mid-pregnancy, followed by a slight decrease between mid- and late pregnancy. Across treatments cortisol decreased after joining -59 and 74 percent respectively - and increased significantly between mid- and late pregnancy, producing a 244 and 199 percent increase respectively. All values are statistically significant (P<0.05).
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Mid-gestation Late-gestation
ewesAll
(a) (b)
Shorn once
(c)
Shorn Twice
(f) (e)
Figure 10. Relationship between wool cortisol metabolites and body condition score of maiden merino ewes during mid- to late pregnancy shorn once and twice. Graphs represent (a) April results for all trial sheep (both those shorn once and twice); (b) May results for all trial sheep (both those sheep shorn once and twice); (c) April values for sheep shorn once; (d) May values for sheep shorn once; (e) April values for sheep shorn twice; and (f) May values for sheep shorn twice. All results were statistically significant (P<0.05) with very weak to weak Spearman’s correlation values, as displayed, ranging from 0.03 to 0.23. Page | 33
6.6 Smart Tag Profiling
Data was accessed from SmartTag (https://smarttag.digibale.com/mob-9/)
Smart tag data was analysed between shearing (14 April, 2019) and 24 May, 2019 (late-gestation) to determine where shearing once (n = 9 sheep with data) or twice (n = 11 sheep with data) led to a significance change in behaviour between the treatments.
70
60
50
40
30 % of time of % 20
10
0 Grazing Standing Walking Activity
Figure 11. Mean (n= 9) activity budget of Merino ewes shorn once.
70
60
50
40
30 % of timeof % 20
10
0 Grazing Standing Walking Activity
Figure 12. Mean (n= 11) activity budget of Merino ewes shorn twice.
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70
60
50
40
30 % of timeof % 20
10
0 Grazing Standing Walking Activity
Figure 13. Mean (n= 20) activity budget of Merino ewes independent of shearing treatment.
0.13%
34.88% Grazing Standing Walking 64.99%
Figure 14. Mean (n= 9) activity budget of Merino ewes shorn once.
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1.47%
Grazing 45.65% Standing 52.89% Walking
Figure 15. Mean (n= 11) activity budget of Merino ewes shorn twice.
0.87%
40.80% Grazing Standing 58.33% Walking
Figure 16. Mean (n= 20) activity budget of Merino ewes independent of shearing treatment.
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Anova: Single Factor comparison of grazing proportion between ewes
SUMMARY Groups Count Sum Average Variance Once shorn 9 313.8992 34.87769 60.16589 Twice shorn 11 502.1159 45.6469 98.76421
ANOVA Source of Variation SS df MS F P-value F crit Between Groups 574.0809 1 574.0809 7.034495 0.016211 4.413873 Within Groups 1468.969 18 81.6094
Total 2043.05 19
Statistical analysis showed a significant difference in grazing frequency between once and twice shorn ewe sheep (p < 0.05). Twice shorn sheep were grazing proportionally more than once shorn sheep by >10% during mid-late gestation.
Anova: Single Factor comparison of standing proportion between ewes
SUMMARY Groups Count Sum Average Variance standing1 9 584.9162 64.99069 60.63252 standing2 11 581.749 52.88627 91.47329
ANOVA Source of Variation SS df MS F P-value F crit Between Groups 725.2591 1 725.2591 9.326138 0.006834 4.413873 Within Groups 1399.793 18 77.76628
Total 2125.052 19
Statistical analysis shows a significant difference in standing frequency between once and twice shorn ewe sheep (p <0.05). Once shorn sheep were standing proportionally more than twice shorn sheep by >10% during mid-late gestation.
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Anova: Single Factor comparison of walking proportion between ewes
SUMMARY Groups Count Sum Average Variance Walking 9 1.184565 0.131618 0.001701 Walking 11 16.1346 1.466782 5.27642
ANOVA Source of Variation SS df MS F P-value F crit Between Groups 8.824171 1 8.824171 3.009505 0.099868 4.413873 Within Groups 52.77781 18 2.9321
Total 61.60198 19
Statistical analysis showed no significant difference in walking frequency between once and twice shorn ewe sheep (p > 0.05) during mid-late gestation.
Wool microns of lambs between once and twice shorn treatment groups
MICRONS OF ALL LAMBS Mean 16.66 Standard Error 0.15 Median 16.6 Mode 16.8 Standard Deviation 1.23 Sample Variance 1.53 Kurtosis 0.014 Skewness 0.54 Range 5.7 Minimum 14.5 Maximum 20.2 Sum 1100.1 Count 66 Anova: Single Factor
SUMMARY Groups Count Sum Average Variance Once 8 134 16.75 1.882857 Twice 16 263.7 16.48125 1.249625
ANOVA
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Source of Variation SS df MS F P-value F crit Between Shearing Groups 0.385208 1 0.385208 0.265458 0.611533 4.30095 Within Shearing Groups 31.92438 22 1.451108
Total 32.30958 23
Statistical analysis showed no significant difference in lamb wool microns between once and twice shorn ewe sheep (p > 0.05).
6.7 Wool microns of lambs across ewe pregnancy status and shearing frequency Raw wool characteristics of lambs are provided in Appendix Table3A. The results in the table below show wool characteristics of the lambs that were matched to their ewes using the DNA parentage test. The results discussed using table 6 have a caveat because the sire DNA epigenetics were not evaluated at the time of the trial. Please note that sire wool or molecular trait evaluation was not an objective of this trial.
Ave Ave Ave Spin Ave Group Number Mic Min Mic Max Mic Ave STD COMFF Fineness CURV All 31 16.6 14.5 18.6 3.3 99.58 16 78 Ewes Scanned Single Early 5 16.5 15.1 17.8 3.1 99.76 15.7 75.3 Ewes Scanned Single Late 16 16.4 14.5 18.6 3.2 99.7 15.7 78.2 Ewes Scanned Single Unknown 6 16.8 14.8 17.7 2.1 99.38 16.3 79.7 Ewes Scanned with twins 3 17.4 16.9 17.7 3.9 99.06 17.2 73.5 Ewes Shearing Once 18 16.9 14.5 18.6 3.4 99.41 16.4 75.7 Ewes Shearing Twice 13 16.3 15 18.5 3 99.81 15.5 81.2
Table 6. Average raw wool micron, CC%, comfort factor average spinning fineness or average curvature of lambs grouped to ewes (Group column). The lambs from the shorn twice cohort of ewes had a visually finer wool (Average micron column), higher average comfort factor of 0.4 percent (Ave COMFF column) and spinning fineness difference between shearing frequency groups was 0.9 microns (Ave Spin Fineness Column).
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7 Discussion
Animal body size such as height, weight or body mass, is a complex trait determined by environment factors and genetic factors (Berenos et al., 2015: Cao, 2015). In 2015 research by Cao et al. (2015) into DNA methylation landscape of body size variation in Mongolian sheep, showed that body size is usually considered to have been influenced by hundreds of genes. They identified in DNA methylation maps for different breeds of Mongolian sheep which identified DMRs located in human orthologs which associated with body size and established a list of DMRs potentially associated with body size variation. Cao et al. (2015) further examined seven regions in five genes to study the relationship between DNA methylation and MBMI (MBMI = body weight/body length) and found that two genes (AKT1 and TSC1) showed strong correlation between DNA methylation and expression.
In early research outcomes of this trial shows that the gene responsible for skin / wool production is known as V15, this did not have any tested methylation observed. However, other genes that have lower influence on the skin / wool production were methylated specifically gene, U6, CASZ1, WNT7B, U1, U2, U4 (See table 2). This provides a pattern that epigenetic alterations within a genome for skin / wool in the trial sheep were changed somewhat and this was evident with the different methylated levels observed within the breakdown of the various genes – but not all the gene for skin /wool was changed.
This pilot research project was aiming to study the production quality of merino ewe sheep using a combination of physiological, animal tracking, molecular epigenetics and farm husbandry techniques. The original question answered using these methods was: Does twice or once shearing of ewes during gestation influence stress hormone, activity budget and molecular epigenetic profiling, body condition and overall wool quality of first generation lambs? Based on the results from the trial, it was determined that shearing frequency did not result in significant molecular epigenetic changes in ewes and lambs. However, it was interesting that twice shorn ewes showed on average higher activity, better body condition, and lower stress levels than once shorn ewes. Furthermore, first generation lambs matched to twice shorn ewes expressed visually finer wool with better comfort scores than those F1 lambs that were matched to once shorn ewes. It will be worthwhile to further investigate the potential wash-in/wash-out effects of molecular epigenetic changes of shearing frequency by repeating the trial using second-generation ewe lambs to confirm whether the epigenetic effects of shearing frequency are transgenerational. Overall, the research outcomes contribute significant new knowledge to the Australian sheep production industry, and it will be valuable tools for sheep health and welfare assessments in future
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8 Impact of Wool Industry – Now & in 5 years’ time
Sheep health and welfare are important components of sheep production in Australia and globally. Technology optimisation through new and robust testing of field and laboratory based methods will hugely boost these areas of research and create new opportunities for productivity gains across the sheep and wool industry in 5 years’ time. The epigenetic molecular data provides substantial resource bank for further evaluation of blue sky as well as specific applied research questions such as the influence of environment of maternal genome and the trans-generational transfer of genetic resilience that can improve productivity indicators such as wool microns. Furthermore, the Smart tag technology provides a user-friendly way to assess the activity budget of sheep mobs and this will become a vital resource for assessments of sheeps’ access to quality feed and nutrition especially in times of climatic variation such as prolonged droughts. Thus the applied knowledge and technology explored through this pilot project will positively impact the wool industry through practical knowledge advancement and improvements in tools that can make possible robust assessments of sheep welfare and productivity. 9 Conclusions and Recommendations
From the various key outcomes of this pilot research project it can be concluded that alteration of shearing frequency may create changes in the activity levels of gestating merino ewes which may impact on their nutritional gain and down-stream effects on lambing success and wool quality. There may be other important indicators of maternal resilience such as sire influence, environmental influence through climatic, factors, disease and social interactions between sheep. The availability of robust field sampling techniques and Smart tag technology makes it easier to assess the physiology and behaviour of ewe sheep during critical reproductive period.
Farmers may consider twice shearing of their ewe sheep following stringent farm welfare and management practices, and use the Smart tag and wool stress hormone monitoring technologies to monitor activity (walking, standing, grazing) and longitudinal changes in stress levels of ewes, which may assist producers and managers prepare interventions that may reduce risks of lamb losses.
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10 References
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Cao-Lei, L., De Rooij, S.R., King, S., Matthews, S.G., Metz, G.A.S., Roseboom, T.J. and Szyf, M., 2017. Prenatal stress and epigenetics. Neuroscience & Biobehavioral Reviews.
Combs, M.D., Edwards, S.H., Scherpenhuizen, J.M., Narayan, E.J., Kessell, A.E., Piltz, J., Raidal, S.R., Ramsay, J. and Quinn, J.C., 2018. Development of a model for investigation of perennial ryegrass toxicosis in sheep. New Zealand veterinary journal, 66(6), pp.281-289.
Couldrey, C., Brauning, R., Bracegirdle, J., Maclean, P., Henderson, H.V. and McEwan, J.C., 2014. Genome- wide DNA methylation patterns and transcription analysis in sheep muscle. PloS one, 9(7).
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11 List of abbreviations and/or glossary
ACTH: Adrenocorticotrophic Hormone AI: Artificial Insemination BCS: Body Condition Score BW: Body Weight CORT: Cortisol DHEAS: Dehydroepiandrostendione Sulphate DNA: Deoxyribonucleic Acid ET: Embryo Transfer FSH: Follicle Stimulating Hormone GC: Glucocorticoid GnRH: Gonadotrophin Releasing Hormone GPS: Global Positioning System HPA: Hypothalamic Pituitary Adrenal Axis HPG: Hypothalamic Pituitary Gonadal Axis HSP: Heat Shock Proteins HWCC: Hair / Wool Cortisol Concentrations LH: Luteinising Hormone LTHA: Long Term Heat Acclimation MOET: Multiple Ovulation and Embryo Transfer NEBAL: Negative Energy Balance NIRTM: Non Invasive Research Techniques and Methodology PES: Physical Environmental Stressors PG: Progesterone RNA: Ribonucleic Acid RW: Reproductive Wastage STHA: Short Term Heat Acclimation THI: Temperature Humidity Index THS: Thermal Heat Stress TNZ: Thermal Neutral Zone WCM: Wool Cortisol Metabolites
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Appendix 1 - AWI Communication Report
Name of project Shearing and Epigenetic Change Name of research body Australian Wool Innovation Limited Name(s) of any other project co-funding bodies and funding split N/A Name(s) of any organisations involved (and specify how they are involved) The University of Queensland – Research Provider Sweven, Cattai NSW - field site. Project start date January 2019 Project end date 1 May 2020 Main objectives of the project (approx. 150 words) The primary objectives of this project are to quantify physiological and behavioural responses in Merino ewes during natural joining and varying shearing frequencies in order to determine the following key aims; Genome-wide epigenetic change in ewes and lambs. Activity of ewes measured using the Digibale tags. Quantify changes in stress hormones of ewes during gestation. Quantify wool quality of lambs. Project description (approx. 250 words) The project aims to test the hypothesis of how does shearing patterns (annual v more frequently) influence productivity of the off spring via epigenetic change. To investigate this hypothesis each naturally joined merino ewe will be fitted with AWI’s Smart Tag technology (‘Smart Tags’). The use of the ‘Smart Tags’ will show variation in movement/feedbase interaction. Hence, through ‘Smart Tag’ application the research outcomes will demonstrate greater new knowledge on the life-time production traits in merinos influenced by their shearing patterns. Project (and key milestones) outcomes and outputs (approx. 250 words) Measurable knowledge – completed training of Agriculture scholars through Masters and/or PhD research in the advancement of technology on farm for animal health. New scientific publications in highly reputed journals (e.g. PLoS One, CSIRO Journals). Small Impact at farm level of this research: Integrating epigenetic modelling with grazier knowledge within scientific programs would be a small positive impact over time through extensions adoption from the finding of this research. This is mostly to the complex nature of epigenetics and the application of cutting edge technology in an aging workforce. Moderate Impact at farm level of this research: With ever increased GPS technology being developed for sheep producers, the opportunity to individually manage flocks over small/large acreage would provide moderate impact due to the uptake of the technology by producers that are wanting to enhance their business model. Significant Impact at farm level of this research: in order to maintain profitable operations that graziers/pastoralists it is deemed (through previous on farm and extension activities by researchers) that producers want to understand the ecological systems better within their landscapes. They want to evaluate the point at which they function optimally, comprehend responses of systems to different environmental drivers (e.g. rainfall, season, and stocking rates), and modify their management regimes when necessary.
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A further significant impact is the ability of this research is to uplift productivity through reducing reproductive wastage and grower retaining genetics suitable for their environment. This would be achieved through greater understanding of the changes that occur in vino on the Australian Merino Lamb under Australian climatic conditions. Project / key milestone outcomes, outputs and impacts segmented by state N/A Benefits for woolgrowers and wool industry (approx. 150 words) Benefits: The new data obtained will give us an elaborate picture on the influence of management practice on the reproductive potential of sheep by pin pointing to biomolecular and epigenetic changes in crucial physiological systems that translate to impact on sheep on- farm reproductive productivity and whole farm profitability.
New technology for farmers: Through our current research discussions with a variety of sheep producers (>80 producers) form the rangelands to the tablelands we have determined that the application of cutting edge scientific techniques which tests the biomarkers of sheep epigenetic is crucial to the sheep industry. There is a want by producers to have greater access to this scientific data to be included in adoptable technology (e.g. mobile app) making it possible for producers to assess the reproductive potential of their stock in real-time. Through scientific assessment from various commercial sheep breeding operations our hypothesis can provide new biomarkers that will assist to boost sheep welfare and reproduction programs.
Is the project related to other AWI-funded or other past/present research? No Potential/real next steps in the research/project Carry out further replication studies using the F1 generation and adoption phase (2020) Names(s)/roles(s)/contact details of the potential spokesperson/people Dr Edward Narayan | Senior Lecturer, School of Agriculture and Food Sciences Affiliate Senior Research Fellow QAAFI BSc, PhD, Grad Cert in University Learning and Teaching School of Agriculture and Food Sciences | Faculty of Science The University of Queensland Phone : +61 07 5460 1693 Office Building 8117A, Room 228 Email : [email protected] W: https://agriculture.uq.edu.au/profile/6902/edward-narayan Cricos Code 00025B UQ Expert Profile: https://researchers.uq.edu.au/researcher/25187 Names(s)/roles(s)/contact details of the key personnel in the project that can be contacted for information for communication purposes (if different from above) As per above Current images/video assets and potential opportunities TBA
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Appendix 2 – List of Milestones & Dates submitted 4005340-0010 Deed execution (04.1.2019) 4005240-0020 Application of Digibale Sheep Tags (18.01.19) 400520-0030 Progress report on laboratory analysis (01.06.19) 400520-0040 Progress on epigenetic analysis (02.12.19) 4005240-0050 Progress on statistical analysis (02.03.20) 4005240-0060 Final Report (01.05.20)
Appendix 3 – Remaining Assets 15 Ewe lambs are currently available within this trial project for potential F2 generation research Appendix 4 – Any Project Intellectual Property N/A
Appendix 5 – Storage of Primary Research Data (Paper based and electronic)
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Chr Locus feature external_gene_namefeature_stranddistancetoFeatureinsideFeaturelogFC logCPM LR PValue FDR 20-50404014 20 50404014 ENSOARG00000002778NA - 5478 downstream-0.79126 -1.66351 27.68453 1.43E-07 0.413022 5-48058576 5 48058576 ENSOARG00000017094MZB1 - -5565 upstream -2.22088 -2.33118 26.29895 2.92E-07 0.422925 19-59541209 19 59541209 ENSOARG00000005578MGLL + 6817 inside -1.66403 -2.76311 24.4472 7.64E-07 0.593841 10-35225419 10 35225419 ENSOARG00000026298NA - -108351 upstream -1.17466 -2.39809 22.68399 1.91E-06 0.593841 20-50404053 20 50404053 ENSOARG00000002778NA - 5439 downstream-0.70585 -1.65595 22.49857 2.10E-06 0.593841 20-50404024 20 50404024 ENSOARG00000002778NA - 5468 downstream-0.7414 -1.6582 22.09415 2.60E-06 0.593841 3-216920269 3 2.17E+08 ENSOARG00000021985MIR33A + 2879 downstream0.895004 -2.14284 21.60418 3.35E-06 0.593841 1-10778174 1 10778174 ENSOARG00000019635NA - 2261 inside -0.53701 5.824474 21.33791 3.85E-06 0.593841 13-3401535 13 3401535 ENSOARG00000010523JAG1 - 49976 downstream-0.99276 -2.27444 20.96626 4.67E-06 0.593841 13-3401531 13 3401531 ENSOARG00000010523JAG1 - 49980 downstream-0.98492 -2.27444 20.46943 6.06E-06 0.593841 17-70485734 17 70485734 ENSOARG00000012898NA + 168 inside -1.01531 -2.68694 20.24839 6.80E-06 0.593841 7-47256073 7 47256073 ENSOARG00000000893FOXB1 - -570 upstream -2.60696 -2.13327 19.69994 9.06E-06 0.593841 21-12389219 21 12389219 ENSOARG00000005810PRCP + 162144 downstream2.438263 -2.23846 19.49778 1.01E-05 0.593841 9-14738 9 14738 ENSOARG00000010766NA - 2433 inside -2.73972 0.602739 19.40767 1.06E-05 0.593841 14-53309855 14 53309855 ENSOARG00000010972CCDC9 + -17489 upstream 1.784896 -2.2445 19.09102 1.25E-05 0.593841 10-35225504 10 35225504 ENSOARG00000021658NA + -108301 upstream -1.01219 -2.40565 19.0416 1.28E-05 0.593841 25-34664914 25 34664914 ENSOARG00000009725NA + -47727 upstream -1.08956 -2.58786 19.03891 1.28E-05 0.593841 20-50309446 20 50309446 ENSOARG00000002778NA - 100046 downstream-1.09065 -2.55861 18.89121 1.38E-05 0.593841 12-2848622 12 2848622 ENSOARG00000004441SLC41A1 - 12096 inside -1.63478 -2.79408 18.76062 1.48E-05 0.593841 11-61867677 11 61867677 ENSOARG00000015744CACNG5 + -10448upstream -1.6349 -2.4883 18.50843 1.69E-05 0.593841 24-7484874 24 7484874 ENSOARG00000005140TMEM114 - 58 inside 2.54156 -2.65977 18.21854 1.97E-05 0.593841 5-44624022 5 44624022 ENSOARG00000014806SLC25A48 + 31 inside -2.06451 -2.29873 18.16209 2.03E-05 0.593841 19-54964117 19 54964117 ENSOARG00000012516SLC6A1 + 14486 downstream-1.59997 -2.70691 18.09529 2.10E-05 0.593841 14-11685843 14 11685843 ENSOARG00000011547FOXF1 + -142227 upstream -0.84359 -2.54522 18.04688 2.16E-05 0.593841 24-34938012 24 34938012 ENSOARG00000014629CUX1 - 12950 inside -1.00009 -2.72059 17.89422 2.34E-05 0.593841 11-36887903 11 36887903 ENSOARG00000006175PHOSPHO1+ 4040 inside -1.46586 -2.24928 17.8463 2.39E-05 0.593841 2-3574237 2 3574237 ENSOARG00000024695NA + -275525 upstream 1.015503 -2.84922 17.81757 2.43E-05 0.593841 18-75415 18 75415 ENSOARG00000009028MKRN3 + -199244 upstream -2.49673 -0.14052 17.72464 2.55E-05 0.593841 3-134125461 3 1.34E+08 ENSOARG00000017214NR4A1 - -8955 upstream -1.20251 -2.12966 17.66052 2.64E-05 0.593841 1-267019253 1 2.67E+08 ENSOARG00000014072SIM2 + 1118 inside -0.86493 -2.38124 17.58769 2.74E-05 0.593841 17-69656862 17 69656862 ENSOARG00000010610EIF4ENIF1 - 20177 inside -0.91467 -2.44435 17.57773 2.76E-05 0.593841 11-44499879 11 44499879 ENSOARG00000022843U6 - -4047 upstream -1.10729 -2.66897 17.5736 2.76E-05 0.593841 2-237545338 2 2.38E+08 ENSOARG00000002668OPRD1 - 4959 inside 3.514547 -2.3103 17.50751 2.86E-05 0.593841 23-2195205 23 2195205 ENSOARG00000004374NA + -60646 upstream -0.74696 -2.30692 17.4834 2.90E-05 0.593841 3-217124011 3 2.17E+08 ENSOARG00000024309NA - 4169 downstream-1.29629 -2.53399 17.19746 3.37E-05 0.593841 12-45020143 12 45020143 ENSOARG00000012170TNFRSF25 + 7967 downstream-2.51471 -2.33848 17.16454 3.43E-05 0.593841 14-60617876 14 60617876 ENSOARG00000003308ZNF667 + -7331 upstream -1.18402 0.572162 17.1102 3.53E-05 0.593841 6-112395962 6 1.12E+08 ENSOARG00000010677NA + 29107 downstream1.067122 -1.66704 16.97439 3.79E-05 0.593841 2-46532452 2 46532452 ENSOARG000000248267SK - 105512 downstream1.277247 -2.19205 16.93457 3.87E-05 0.593841 11-7956923 11 7956923 ENSOARG00000007910NA + -40247 upstream -0.99848 -2.67612 16.90823 3.92E-05 0.593841 11-61867694 11 61867694 ENSOARG00000015744CACNG5 + -10431 upstream -2.19222 -2.48954 16.85803 4.03E-05 0.593841 6-91390885 6 91390885 ENSOARG00000017421SOWAHB - 2015 inside 0.758277 -2.2919 16.76051 4.24E-05 0.593841 2-68725310 2 68725310 ENSOARG00000013235DMRT3 + 3817 inside 1.076111 -2.31724 16.74869 4.27E-05 0.593841 13-3401564 13 3401564 ENSOARG00000010523JAG1 - 49947 downstream-0.8559 -2.27444 16.72961 4.31E-05 0.593841 4-68798051 4 68798051 ENSOARG00000009439EVX1 - -6834 upstream 2.096749 -2.39909 16.6932 4.39E-05 0.593841 10-85016338 10 85016338 ENSOARG00000026338NA - -25511 upstream -1.0784 -2.47662 16.64874 4.50E-05 0.593841 2-244881250 2 2.45E+08 ENSOARG00000009126PINK1 - 9795 inside -0.8211 -2.45976 16.63192 4.54E-05 0.593841 11-44499891 11 44499891 ENSOARG00000022843U6 - -4059 upstream -0.96367 -2.66897 16.61876 4.57E-05 0.593841 11-7665069 11 7665069 ENSOARG00000007848NA + 23654 inside -0.82316 -2.60968 16.61717 4.57E-05 0.593841 3-81528585 3 81528585 ENSOARG00000007454KCNG3 + 171 inside 1.826573 -2.20821 16.57097 4.69E-05 0.593841 Table 1A Molecular Epigenetic Data
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Selected graphs of % methylation of individual sheep
Ewe ID 28 (Shorn once)
Lamb no. 6 (twice shorn)
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Lamb no. 44 (once shorn)
DATA RETRIEVEL
Molecular Data The following files have been sent to you on your account directory. AGRF_CAGRF21626_Custom • • 01_CleanReadsQC: quality control multiQC reports of trimmed clean reads • • 02_Mapped: BAM format aligned read files • • 03_Deduplication: deduplicated bam files • 04_MethylationCounts: count files of methylation extraction o Extraction_report: a report summary of methylation extraction • 05_DMR: differential methylation analysis results and nearest gene annotation • 06_BigWig: BigWig format file for methylation visualization
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Ewe Body Condition
Tag Number January April May August 11 2.75 3 3.75 1.5 15 3 3.5 3.5 1 19 2.5 3 3.5 2 22 3 2.5 3.5 27 3 2.75 3 1.5 28 2.25 2 1.5 30 2.75 2.5 2.75 31 2.75 3 2.5 1 33 2.75 3 2.25 1.5 34 3 3.5 2.75 2 35 2.25 3 2.25 1.5 37 3 2.75 3 38 2.5 2.5 3 1.5 40 2 1.5 3 1.5 41 3 2 2.75 1.5 39 2.5 2.5 2.5 1.5 45 3.25 2.5 2 1 46 2.75 3 3 2 47 3 2.5 2.75 1.5 48 2.75 3.5 3 2.5 50 3.5 2.5 2.5 2 52 2.5 2.5 2 1.5 65 2 3 2.5 1 66 2.75 2.5 2.25 2 68 2.25 3 2.75 70 2 2.5 2.5 76 3 2.75 3.5 2 77 3.5 2.75 3 1.5 79 2.75 2 1.5 1 84 2 2.5 4 1.5 85 2.75 2.75 1.75 87 3 3 2.5 1.5 96 2.75 3 2.5 100 2 2 2.75 1.5 101 2.5 2 1.5 1.5 104 2.25 3 3.5 1.5 105 2.5 2.5 2.5 1 107 2.75 3 3 1.5 109 3.25 3 3.5 2
Table 2A. shows the body condition of all trial ewes present in the paddock during the trial in 2019.
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Lamb Wool Quality
REF MIC STDEV COVAR COMFF CURV SPIN 01 16.8 3.9 23.0 98.8 76.0 16.6 02 16.1 4.4 27.2 99 69.2 16.6 03 17.2 2.6 14.9 99.5 69.5 16.0 04 18.6 3.8 20.6 99.5 65.6 18.1 06 16.8 3.3 19.9 99.5 72.3 16.2 07 15.3 2.5 16.5 99.8 78.3 14.4 08 16.8 2.9 17.6 99.8 79.8 15.9 09 19.1 3.4 18.0 100 75.4 18.1 10 15.1 2.9 19.2 99.8 83.9 14.5 11 15.2 2.3 15.1 100 60.8 14.1 12 16.5 3.0 18.3 99.5 92.9 15.8 13 14.5 2.8 19.2 100 81.1 13.9 14 15.4 3.0 19.7 99.8 79.2 14.9 15 15.1 3.4 22.6 99.5 80.4 14.9 16 18.5 3.9 21.2 99.5 92.8 18.0 18 15.9 2.6 16.6 99.5 69.6 15.0 19 17.8 3.1 17.4 99.8 66.5 16.9 20 17.2 3.3 19.4 99.5 77.4 16.5 21 16.8 4.4 26.4 99.5 77.0 17.1 23 16.1 2.6 15.9 99.5 83.7 15.0 24 15.4 2.0 13.0 100 79.5 14.1 25 15.6 3.1 19.6 100 83.9 15.0 26 19.4 3.9 19.9 99.2 79.7 18.7 28 16.2 3.0 18.2 100 90.1 15.4 29 14.8 2.1 14.2 100 71.5 13.7 30 16.6 2.8 16.7 100 85.4 15.6 31 17.3 2.9 17.1 100 73.4 16.3 32 15.5 2.9 18.7 99.8 85.9 14.8 33 16.6 2.8 16.9 99.8 86.9 15.7 34 15.0 2.8 18.8 99.8 80.5 14.4 35 17.6 2.7 15.5 99.8 88.0 16.4 36 16.0 2.8 17.3 100 81.8 15.1 37 15.1 2.7 17.7 99.8 79.7 14.3 38 17.1 3.2 18.6 99.8 74.2 16.3 39 16.9 3.7 22.0 99.5 71.7 16.6 40 16.0 3.8 23.4 99.5 72.4 15.9 41 17.8 3.4 19.0 100 78.4 17.0 42 18.4 3.2 17.3 99.5 74.3 17.3 43 17.1 2.7 15.6 100 80.0 15.9 44 16.6 4.6 27.5 98.5 68.4 17.2 45 17.3 3.2 18.4 99.2 74.4 16.5 46 17.6 4.4 24.7 98.5 66.3 17.7 47 17.6 3.3 18.9 100 76.4 16.8 48 15.1 2.9 19.3 99.8 64.8 14.5 49 15.2 2.6 17.1 100 71.2 14.4 50 17.1 4.8 28.3 98.5 77.6 17.9 51 17.7 3.7 20.7 99.2 82.5 17.2 52 16.2 4.0 24.5 99 65.7 16.3 53 17.7 4.5 25.5 98.2 77.8 17.9 54 16.3 2.7 16.4 99.8 60.5 15.3 55 17.0 3.1 18.4 100 76.6 16.2 56 17.0 3.1 18.1 99.8 80.5 16.1 57 16.1 2.9 17.7 99.8 86.2 15.3 58 18.3 3.7 20.2 99 75.6 17.7 59 15.3 2.8 18.1 99.8 82.5 14.5 60 16.1 2.8 17.5 99.8 69.0 15.2 66 16.6 3.0 18.0 99.8 80.0 15.8 67 16.3 2.7 16.4 100 71.9 15.3 68 17.2 2.7 15.9 99.8 73.2 16.1 69 15.7 3.2 20.5 100 87.3 15.2 70 18.0 3.5 19.4 99.5 85.3 17.2 71 19.2 4.6 23.7 98.5 71.7 19.1 72 20.2 4.1 20.4 98.8 81.6 19.6 Z1XL 17.0 4.1 24.2 99.2 78.7 17.1 Z2D7 15.2 3.0 19.5 100 87.3 14.6 Z2F3 15.3 2.9 19.1 100 69.0 14.7 Table 3A. shows the wool quality data for all lambs born in the paddock at Sweven in 2019.
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Hormone Data
January April May Sheep# Fecal cortisol Sheep# Fecal cortisol Sheep# Fecal cortisol metabolites (ng/g) metabolites (ng/g) metabolites (ng/g) 15 8.88 11 25.75 15 45.44 19 8.88 15 34.79 19 39.75 28 8.52 19 27.68 22 28.46 30 6.83 22 22.34 28 35.21 33 9.66 27 20.23 30 302.54 34 6.39 28 20.53 31 39.75 35 6.39 30 23.40 33 39.75 37 8.88 31 40.02 34 75.92 40 9.26 33 24.66 35 62.48 41 7.89 34 16.36 37 39.75 45 4.09 35 34.13 38 39.75 46 6.19 37 42.66 40 62.48 48 9.26 38 29.15 41 62.48 50 8.20 40 47.34 45 45.44 52 0.10 41 19.75 45 35.21 65 10.58 42 29.84 46 39.75 68 5.82 45 64.69 47 75.92 70 7.89 46 77.26 48 75.92 77 5.17 47 17.10 50 25.89 79 7.33 48 15.39 52 31.52 84 9.26 50 7.86 65 39.75 85 7.07 52 10.00 66 45.44 87 10.10 65 9.07 68 31.52 96 9.66 66 14.38 70 31.52 104 12.93 68 11.54 76 35.21 104 6.83 70 9.64 77 52.76 105 6.61 76 13.30 79 35.21 107 9.66 77 9.68 81 45.44
79 12.47 82 52.76
84 7.56 84 28.46
85 9.47 85 75.92
87 8.85 87 35.21
89 9.27 89 62.48
94 31.52
96 45.44
100 39.75
101 45.44
104 39.75
105 45.44
107 28.46
Table 4A. shows faecal cortisol data for all trial ewes (those with matched DNA highlighted in bold)
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January April May Sheep ID Wool cortisol metabolites (ng/g) Sheep ID WCM (ng/g) Sheep ID Wool cortisol metabolites (ng/g) 11 9.56 11 6.03 11 0.72 13 38.39 13 11.51 13 10.02 14 5.49 15 58.74 15 5.02 15 40.41 17 16.90 17 11.01 17 15.00 18 31.61 18 7.03 18 158.49 19 66.39 19 14.61 19 12.98 20 4.19 20 69.19 21 9.54 21 3.18 21 100.02 22 68.45 22 0.03 22 23.42 25 49.40 26 53.45 26 11.51 26 20.04 27 37.92 27 46.35 28 3.35 28 3.69 28 25.46 30 47.56 30 3.05 30 9.05 31 46.89 31 11.33 31 14.80 33 48.76 33 16.65 33 15.09 34 12.34 34 0.62 34 17.33 35 7.36 35 4.83 35 17.45 37 4.27 38 1.22 38 8.63 38 25.79 39 6.97 39 1.45 39 40.62 40 29.13 40 1.90 40 25.97 41 19.40 41 3.42 41 0.91 42 23.31 42 0.56 44 16.19 44 8.03 44 57.58 45 37.29 45 7.05 46 21.68 46 5.58 46 7.31 47 0.63 47 2.82 47 116.90 48 12.62 48 2.95 48 1.50 50 12.69 50 13.62 50 39.70 52 35.11 52 7.21 52 30.42 55 7.07 55 17.22 56 56.86 58 10.17 58 20.82 58 126.92 59 49.82 59 9.85 59 22.64 63 21.79 63 3.68 65 27.44 65 6.06 65 25.46 66 42.28 66 0.12 66 30.23 67 26.27 68 17.10 68 2.05 70 5.92 70 1.34 70 12.02 71 110.69 74 3.55 74 26.99 74 60.00 76 48.67 76 12.84 76 33.24 77 20.30 77 42.35 78 19.47 78 14.95 78 76.86 79 2.93 79 8.78 79 22.30 81 2.72 81 3.38 81 9.91 82 2.00 82 11.19 82 57.11 83 15.33 83 9.28 83 35.32 84 1.34 85 5.45 85 10.92 85 10.03 86 31.06 86 0.74 87 26.88 87 7.21 87 43.23 89 13.14 89 22.81 89 5.46 91 37.60 91 30.70 91 93.76 93 114.59 93 134.74 94 19.70 94 18.40 96 7.04 96 10.61 97 54.78 97 11.03 100 7.40 100 22.02 100 7.89 101 12.97 101 28.74 102 96.46 102 23.12 102 12.00 104 43.64 104 29.37 104 53.34 105 13.32 105 13.26 106 8.77 106 41.52 107 40.19 107 0.14 107 7.10 109 106.90 109 19.25 109 47.44
Table 5A. shows wool cortisol data for all trial ewes (those with matched DNA highlighted in bold) Page | 55
Behaviour Data
Name Tag NumberTime Behavior History Duration Latitude Longitude E775 28 (c366283b9279) Z1AN 19-04-14 00:00:03.397Standing 00:00:04.350 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:07.748Standing 00:00:05.213 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:12.962Standing 00:00:06.411 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:19.374Standing 00:00:10.663 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:30.038Standing 00:00:15.982 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:46.020Standing 00:00:03.172 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:49.193Standing 00:00:08.051 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:57.245Standing 00:00:00.507 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:00:57.752Standing 00:00:06.444 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:04.197Standing 00:00:03.178 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:07.375Standing 00:00:02.332 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:09.708Standing 00:00:00.845 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:10.554Standing 00:00:02.237 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:12.792Standing 00:00:09.600 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:22.392Standing x 00:00:13.727 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:36.120Standing 00:00:10.667 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:46.788Standing 00:00:09.059 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:55.848Standing 00:00:02.891 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:01:58.739Standing 00:00:09.500 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:08.240Standing 00:00:03.147 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:11.388Standing 00:00:12.785 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:24.174Standing 00:00:09.600 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:33.774Standing x 00:00:09.600 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:43.374Standing x 00:00:10.767 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:54.141Standing 00:00:00.066 -33.5173 150.912 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:54.207Standing 00:00:00.166 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:02:54.373Standing 00:00:07.174 -33.5173 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:01.548Standing 00:00:07.460 -33.5173 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:09.008Standing 00:00:11.765 -33.5173 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:20.774Standing 00:00:11.669 -33.5173 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:32.443Standing 00:00:14.924 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:47.367Standing 00:00:10.819 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:03:58.187Standing 00:00:10.861 -33.5173 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:04:09.049Standing 00:00:11.380 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:04:20.429Standing 00:00:09.600 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:04:30.029Standing x 00:00:09.600 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:04:39.629Standing x 00:00:09.600 -33.5174 150.9121 E775 28 (c366283b9279) Z1AN 19-04-14 00:04:49.229Standing x 00:00:09.600 -33.5174 150.9121
Table 6A shows sample behaviour data obtained using the Digibale SMART tag for trial ewe no.28
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PROJECT FINAL REPORT
Behaviour Data
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DNA Parentage Test Results Group Anim ID Dam Dam 11 Progeny: 1 Dam 14 Progeny: 1 Dam 15 Progeny: 1 Dam 19 Progeny: 1 Dam 20 Progeny: 0 Dam 21 Progeny: 0 Dam 27 Progeny: 0 Dam 28 Progeny: 1 Dam 30 Progeny: 1 Dam 34 Progeny: 0 Dam 35 Progeny: 1 Dam 37 Progeny: 0 Dam 39 Progeny: 1 Dam 40 Progeny: 1 Dam 42 Progeny: 1 Dam 45 Progeny: 1 Dam 47 Progeny: 1 Dam 48 Progeny: 0 Dam 50 Progeny: 0 Dam 52 Progeny: 1 Dam 65 Progeny: 1 Dam 66 Progeny: 0 Dam 68 Progeny: 1 Dam 76 Progeny: 1 Dam 77 Progeny: 2 Dam 78 Progeny: 1 Dam 79 Progeny: 1 Dam 84 Progeny: 1 Dam 85 Progeny: 1 Dam 86 Progeny: 0 Dam 87 Progeny: 2 Dam 91 Progeny: 1 Dam 93 Progeny: 1 Dam 96 Progeny: 0 Dam 97 Progeny: 2 Dam 100 Progeny: 1 Dam 101 Progeny: 0 Dam 102 Progeny: 0 Dam 105 Progeny: 1 Dam 107 Progeny: 1 Dam 109 Progeny: 1 Dam Z1A3 Progeny: 0 Dam Z1GC Progeny: 2 Dam Z1GH Progeny: 0 Dam Z1RK Progeny: 1 Dam Z1UX Progeny: 1 Dam Z26X Progeny: 1 Dam Z2CN Progeny: 0 Dam Z2F6 Progeny: 1 Progeny 1 NOT PRESENT Progeny 2 NOT PRESENT
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PROJECT FINAL REPORT
Progeny 3 NOT PRESENT Progeny 4 100 Progeny 6 19 Progeny 7 NOT PRESENT Progeny 8 Z1GC Progeny 9 NOT PRESENT Progeny 10 NOT PRESENT Progeny 11 NOT PRESENT Progeny 12 NOT PRESENT Progeny 13 40 Progeny 14 109 Progeny 15 Z2F6 Progeny 16 35 Progeny 17 Z1GC Progeny 18 NOT PRESENT Progeny 19 15 Progeny 20 NOT PRESENT Progeny 21 NOT PRESENT Progeny 22 87 Progeny 23 NOT PRESENT Progeny 24 NOT PRESENT Progeny 25 11 Progeny 26 NOT PRESENT Progeny 28 85 Progeny 29 52 Progeny 30 NOT PRESENT Progeny 31 NOT PRESENT Progeny 32 84 Progeny 33 77 Progeny 33 NOT PRESENT Progeny 34 39 Progeny 35 87 Progeny 36 76 Progeny 37 30 Progeny 38 14 Progeny 39 97 Progeny 40 Z1UX Progeny 41 NOT PRESENT Progeny 41 47 Progeny 42 NOT PRESENT Progeny 43 NOT PRESENT Progeny 44 28 Progeny 45 65 Progeny 46 97 Progeny 46 NOT PRESENT Progeny 47 NOT PRESENT Progeny 48 45 Progeny 49 NOT PRESENT Progeny 50 78 Progeny 51 93 Progeny 52 NOT PRESENT Progeny 53 77 Progeny 54 NOT PRESENT
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Progeny 55 68 Progeny 56 42 Progeny 57 79 Progeny 58 NOT PRESENT Progeny 59 NOT PRESENT Progeny 60 105 Progeny 66 Z26X Progeny 67 NOT PRESENT Progeny 68 Z1RK Progeny 69 NOT PRESENT Progeny 70 107 Progeny 71 NOT PRESENT Progeny 72 NOT PRESENT Progeny AP7166163 NOT PRESENT Progeny AP7166171 NOT PRESENT Progeny AP7166182 NOT PRESENT Progeny AQ7094213 NOT PRESENT Progeny Z1XL NOT PRESENT Progeny Z2D7 91
Appendix 6 – Animal Ethics Approval
The University of Queensland ACEC Approval Protocol (SAFS/544/19)
Western Sydney University ACEC Protocol (A12610)
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