Corso di Dottorato di Ricerca in Scienze e Biotecnologie Agrarie in convenzione con Università degli Studi di Udine
Dipartimento di Scienze AgroAlimentari, Ambientali e Animali Ciclo XXX Coordinatore: prof Giuseppe Firrao
TESI di DOTTORATO di RICERCA
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being.
Dottoranda Supervisore
Alice Colussi Bruno Stefanon
Anno Accademico 2017/2018
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
SUMMARY Chapter 1 OVERALL INTRODUCTION …………………………………………………………….. 3
1.1_AIMS OF THIS RESEARCH ...... 5
1.2_LITERATURE REVIEW ...... 6
1.2.1_SALIVA: A NON-INVASIVE, READILY AVAILABLE MATRIX FOR MANY BIOMARKERS ...... 6
1.2.1.1_Salivary Cortisol: a hormone used to evaluate HPA axis activity ...... 7
1.2.2_MICROBIOME: AN IMPORTANT MARKER FOR ANIMAL WELFARE ...... 11
1.2.2.1_Metagenomics as an evaluation tool of microbiome composition ...... 14
1.2.3_HAIR, A NON-INVASIVE MATRIX USEFUL FOR MEASURING SOME BIOMARKERS ...... 16
1.2.3.1_Hair Cortisol: a useful matrix for long term monitoring ...... 16
1.2.3.2_Heavy metals in hair as biomarkers of health state...... 18
1.3_OUTLINE OF THE THESIS ...... 19
1.4_REFERENCES …………………………………………………………………………………………………. 23
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
Part 1 SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI ...... 36
2.1_ABSTRACT ...... 37
2.2_THE PLEIOTROPIC ROLE OF CORTISOL ...... 37
2.3_ASSAYING CORTISOL IN BIOLOGICAL MATRICES ...... 40
2.3.1_Sampling ...... 40
2.3.2_Factors that influence salivary cortisol levels in the dog ...... 42
2.3.3_Potential applications of salivary cortisol assays ...... 46
2.3.3.1_Animal-assisted activities ...... 47
2.3.3.2_Genetics and breeding ...... 48
2.3.3.3_Diseases ...... 49
2.4_CONCLUSIONS ...... 50
2.5_REFERENCES …………………………………………………………………………………………………. 51
Chapter 3 SALIVARY CORTISOL CONCENTRATION IN HEALTHY DOGS IS AFFECTED BY SIZE, SEX, AND HOUSING CONTEXT ...... 57
3.1_ABSTRACT ...... 58
3.2_INTRODUCTION ...... 59
3.3_MATERIALS AND METHODS ...... 60
3.3.1_Animal selection ...... 60
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
3.3.2_Salivary sampling ...... 60
3.3.3_Statistical analysis ...... 61
3.4_RESULTS ...... 61
3.5_DISCUSSION ...... 66
3.6_CONCLUSIONS ...... 68
3.7_REFERENCES …………………………………………………………………………………………………. 69
Chapter 4 VARIATIONS OF SALIVARY CORTISOL IN DOGS EXPOSED TO DIFFERENT COGNITIVE AND PHYSICAL ACTIVITIES ………………………… 73
4.1_ABSTRACT ……………………………………………………………………………………………………… 74
4.2_INTRODUCTION …………………………………………………………………………………………….. 75
4.3_MATERIALS AND METHODS ………………………………………………………………………….. 76
4.3.1_Recruitment of dogs ……………………………………………………………………….. 76
4.3.1.1_Study 1: Baseline value of salivary cortisol ……………………….. 76
4.3.1.2_Study 2: Variation of salivary cortisol during activity ………… 76
4.4_RESULTS ………………………………………………………………………………………………………… 81
4.4.1_Study 1: Baseline value of salivary cortisol ……………………………………… 81
4.4.2_Study 2: Variation of salivary cortisol during activity ………………………. 81
4.5_DISCUSSION ...... 84
4.5.1_Study 1: Baseline value of salivary cortisol ……………………………………… 84
4.5.2_Study 2: Variation of salivary cortisol during activity ………………………. 84
4.6_CONCLUSIONS ………………………………………………………………………………………………. 86
4.7_REFERENCES …………………………………………………………………………………………………. 87
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
Part 2
NUTRIONAL EFFORTS, FAECAL MICROBIOME, HPA AXIS Chapter 5 PRELIMINARY STUDY: FAECAL MICROBIOME AS BIOINDICATOR OF DOG WELL-BEING AND POSSIBLE RELATION WITH HYPOTHALAMIC-PITUITARY-ADRENAL (HPA) AXIS ...... 91
5.1_ABSTRACT ……………………………………………………………………………………………………… 92
5.2_INTRODUCTION …………………………………………………………………………………………….. 93
5.3_MATERIALS AND METHODS ………………………………………………………………………….. 95
5.3.1_Animal selection ...... 95
5.3.2_Diet ...... 96
5.3.3_Experimental design ...... 97
5.3.4_Sample collection ...... 98
5.3.4.1_Faeces collection ...... 98
5.3.4.2 Salivary sampling ...... 98
5.3.5_Faeces analysis ...... 99
5.3.5.1_Faecal DNA extraction and sequencing ...... 99
5.3.5.2_Faecal score, pH, nitrogen and fatty acids analysis ...... 99
5.3.6_Saliva analysis ...... 101
5.3.7_Statistical Analysis ...... 101
5.4_RESULTS AND DISCUSSIONS ...... 103
5.4.1_Microbiome analysis ...... 103
5.4.2_Microbiome, SCFAs, lactate and nitrogen in faeces ...... 112
5.4.3_Faecal microbiome and salivary cortisol ...... 116
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
5.5_CONCLUSIONS ...... 117
5.6_REFERENCES ...... 119
Part 3
BIOMARKERS OF DOG WELL-BEING DETECTED IN HAIR Chapter 6 PRELIMINARY STUDY: HEAVY METALS AND CORTISOL IN HAIR, EVALUATED AS POSSIBLE BIOMARKERS OF DOG WELL-BEING ...... 126
6.1_ABSTRACT ……………………………………………………………………………………………………… 127
6.2_INTRODUCTION …………………………………………………………………………………………….. 128
6.3_MATERIALS AND METHODS ………………………………………………………………………….. 131
6.3.1_Animal selection ...... 131
6.3.2_Diet ...... 131
6.3.3_Experimental design ...... 131
6.3.4_Sample collection ...... 132
6.3.4.1_Hair sampling ...... 132
6.3.5_Samples analysis ...... 132
6.3.5.1_Cortisol analysis ...... 132
6.3.5.2_Heavy metal analysis ...... 133
6.3.6_Statistical analysis ...... 134
6.4_RESULTS AND DISCUSSION ……………………………………………………………………………. 135
6.4.1_Heavy metals in dog hair ...... 135
6.4.2_Cortisol in dog hair...... 138
6.4.3_Relationship between heavy metals and cortisol in dog hair ...... 139
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
INDEX OF CONTENT
6.5_CONCLUSIONS ………………………………………………………………………………………………. 141
6.6_REFERENCES …………………………………………………………………………………………………. 142
Chapter 7
CONCLUSION ...... 146
7.1_FUTURE RESEARCH ………………………………………………………………………………………. 147
7.1.1_Measurement of cortisol ...... 148
7.1.2_Evaluation of faecal microbiome ...... 151
7.1.3_Measurement of heavy metals ...... 151
7.2_FINAL NOTES …………………………………………………………………………………………………. 152
7.3_REFERENCES …………………………………………………………………………………………………. 154
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
SUMMARY
Development of biomarkers in non-invasive biological matrices in order to assess dog well-being SUMMARY
The domestic dog (Canis lupus familiaris) is the most phenotypically diverse mammal species known and has wide ranges in size between breeds (over two orders of magnitude from diminutive 1-kg Chihuahua to the 100-kg Mastiff). Furthermore breed conformation and also behavioural and physiological attributes are far more extreme in dogs (Wayne, 1986a,b; Coppinger and Coppinger, 2001; Wayne and vonHoldt, 2012). These are some of the reasons that make the research on physiological aspects of dogs more difficult. However it should be also considered the fact that the dog is the only large carnivore ever domesticated so far (Wayne and vonHoldt, 2012). As a consequence in the last decade the close relationship with human has caused an increased attention and an increased number of studies in dog well-being. The aim of numerous researches is to discover the largest number of not invasive biomarkers that could indicate the state of health of the animals. In these studies is important also to consider breed, gender, age of subjects and also environmental factor. The work of thesis is composed of three parts: The first part includes two already published articles and one in submission that identify cortisol as one of the biomarkers useful in evaluating adaptive response at different stimuli. In the second part, is reported a preliminary study about the evaluation of fecal microbiome in 8 healthy pet dogs after the probiotic addition in an extrused complete diet. For each faecal samples were also analysed short chain fatty acids (SCFA) and lactate, and nitrogen. For each subject were collected saliva and evaluated cortisol variations in correlation with faecal microbiome. In the third part, is reported a preliminary study that assess the content of heavy metals and cortisol in dogs hair. Hair samples were collected from the dorsal area of the same dogs that were considered in the trial of the second part of the thesis. In this third study, through the detection of heavy metal in hair, it was investigated a possible correlation between heavy metals in hair and
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Development of biomarkers in non-invasive biological matrices in order to assess dog well-being
SUMMARY probiotic addition to the diet. It was also assessed a possible correlation between cortisol and heavy metals.
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Chapter 1 OVERALL INTRODUCTION
Chapter 1
OVERALL INTRODUCTION
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Chapter 1 OVERALL INTRODUCTION
The domestic dog (Canis lupus familiaris) is the only large carnivore that have ever been domesticated by man (Wayne and vonHoldt, 2012). The current close relationship between humans and dogs is probably the result of a coevolution developed for cooperative work and that has been ongoing from 150.000 years (vonHoldt et al., 2010; Overall, 2011). Size, shape and behaviour variability, which characterise the current domestic dog breeds, are the result of human artificial selections performed in hundreds of years (Sutter et al., 2007; Overall, 2011). The majority of the physical variations are the consequence of an overt selection for specific behaviourals suites (e.g., coats adapted for hunting vs. retrieving behaviours, and the behavioural patterns that differ with tasks like herding vs. retrieving) (Overall, 2011). These selections are the reflection of both traditional classifications from various kennel clubs, but also from clustering analyses, which used genetic information from representative breeds (Parker et al., 2004; Parker and Ostrander, 2005). One of the consequence of the human-dog coevolution is that the domestic dog has become the most phenotypically diverse mammal species known. Ranges in size (over two orders of magnitude from diminutive 1-kg Chihuahua to the 100-kg Mastiff), breed conformation and also behavioural and physiological attributes are far more extreme in dogs (Wayne, 1986a,b; Coppinger and Coppinger, 2001; Wayne and vonHoldt, 2012). However, even if there is a marked variability in many canine physical and physiological aspects, the interest about physiological, behavioural and genetic characteristics of the domestic dog is constantly increasing. Furthermore, dogs seem to be an excellent model for the investigation of many aspects of human social behaviour, cognition and pathological conditions, including anxiety and brain aging (Overall, 2011). Saetre et al. (2004) suggest that, during domestication, the strong selection on dogs to fix the most suitable behaviour may have resulted in modifications of mRNA expression patterns in a few hypothalamic genes with multiple functions. In the research mentioned above, the researchers examined the rates of gene expression mutations in the regional brain tissue and, to date, the only species studied, which has comparable rates with those found in humans, seems to be the domestic dog.
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Chapter 1 OVERALL INTRODUCTION
1.1_ AIMS OF THIS RESEARCH
Researchers have been striving to develop objective ways to determine how stressor can influence the well-being of animals (Mason and Mendl, 1993; Protopopova, 2016). Definition of poor well- being of animals range from decrease of fitness, such as reduced life expectancy, impaired growth and reproduction (Barnett and Hemsworth, 1990; Broom, 1991; Protopopova, 2016), to a focus on the inability of the animal to cope with environmental changes (Broom, 1991). It is clear that the attention in understanding which can be the more suitable parameters to evaluate the state of health and well-being of the dog is an area of scientific interest concerning both companion and working dogs (Dreschel, 2010; Cobb et al., 2015). As a consequence of this increasing awareness, there are many scientists employed in investigations of several and reliable physiological and genetic markers of dog welfare. Until now, the measures used for well-being evaluation, include reproductive fitness, hypothalamic-pituitary-adrenal (HPA) axis activity, immunosuppression and abnormalities in behaviour (Protopopova, 2016). In canine research, salivary cortisol is one of the HPA axis biomarkers widely used as indicator of stress or well-being, but much remains unclear about the basic features of salivary cortisol in domestic dogs (Cobb et al., 2016). For these reasons, in this thesis was decided to deepen/investigate different factors that may affect the well-being of the animals. In the preliminary studies of this doctoral thesis, were used the least invasive and the most responsive matrices in relation to external and internal stimuli that can cause changes in animal welfare conditions. In order to evaluate the reliability of some biomarkers, it was developeded a spectrum of dog well-being state as complete as possible. Among the factors that influencing the animal well-being, was considered the HPA axis, as well as nutritional and environmental factors. Consequently were evaluated cortisol variations in different contexts and in two different matrices (saliva and hair). Moreover, as nutritional factors may play a structural or regulatory role in brain functions (Overall., 2011), it was tried to establish which biomarkers could be indicators of a possible correlation between nutritional aspects, gut microbiota and nervous system response. Environmental pollution and the toxicity of some dietary elements, have been measured in non-invasive matrix as the dog hair; in fact, as reported by Zaccaroni et al. (2014), the exposure to toxic elements could affect dog health, in terms of endocrine balance and/or immune system activity.
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Chapter 1 OVERALL INTRODUCTION
1.2_LITERATURE REVIEW
Although the interest for dog well-being is fairly recent, the continue increase in research on this topic had led to considerable progresses in the analysis of possible animal well-being biomarkers. The following overview of literature aims at showing the role of some matrices and biomarkers that have been taken into account for this study. In this thesis, these biomarkers were considered as possible indicators of dogs well-being.
1.2.1_SALIVA: A NON-INVASIVE, READILY AVAILABLE MATRIX FOR MANY
BIOMARKERS Saliva is a readily available matrix, which can be collected by non-invasive procedures. Normally, in carnivores the whole saliva is composed by water, electrolytes, proteins, hormones, antibodies and cellular components such as desquamated mucosal epithelium and microorganisms. It is known that a wide range of biomarkers can be measured in saliva, including heavy metals (e.g., lead), hormones (cortisol and dehydroxyepiandrosterone, DHEA), toxins and their metabolites (cotinine), enzymes (lysozyme, α-amylase), immunoglobulins (IgA), proteins (eosinophil cationic proteins) and DNA. Researchers are also studying the proteomic components of saliva in the perspective of identifying novel biomarkers of diseases (Koh and Koh, 2007). Saliva is also used in screening and diagnosis of acute mental stress, oral and systemic diseases as oral cancer, breast cancer (Devaraj, 2013; Naumova et al., 2014). For example, in humans the complex patterns of salivary responsiveness during mental stress are reflected in an increase of total salivary protein concentration (Bosch et al., 1996; Bosch et al., 2003; Naumova et al., 2014) and in cortisol levels fluctuations (Tornhage, 2009; Naumova et al., 2014). Saliva represents a good resource for the evaluation of some interesting biomarkers variations. Furthermore, it has to be considered that steroid hormones enter unbound (as ‘free’ molecules) in the saliva through passive diffusion. However, there could be some limitations due to the nature of this matrix. Potentially, everything which affects hormones protein-binding or binding proteins levels in the serum, can modify the concentration of these molecules, altering their measurements (Dreschel, 2007). Another possible limitation on salivary samplings depends on the ability to collect an adequate volume of this matrix. Although many of new assays require very little quantities, there are still some difficulties in obtaining sufficient amounts from dogs of small size (Dreschel, 2007). Furthermore, it is 6
Chapter 1 OVERALL INTRODUCTION important to recognise the exact moment when doing the sampling, as most of the salivary biomarkers are not secreted constantly but episodically. However saliva allows collecting the samples frequently, being, as mentioned above, a non-invasive matrix. In addition, its collection is also less likely to cause stress if compared to other procedures, such as phlebotomy. This is an important consideration when searching biomarkers of stress. Lastly, saliva samples can reflect real-time levels of biomarkers, unlike other biological fluids, such as urine, whose collection is possible only after a few hours of storage in the bladder (Koh and Koh, 2007). In humans, and in animals, analysis of salivary inflammatory biomarkers might offer an attractive opportunity for the diagnosis of different systemic disorders. In fact, saliva-based clinical tests can supply a potential diagnostic tool for the detection of certain diseases and syndromes by using biomarkers associated with enhanced systemic inflammation molecules (Rathnayake et al., 2013). For example, Rathanayake et al. (2013) observed that salivary Interleukin-8 (IL-8) concentration was twice as high in patients with experience of tumour diseases compared to subjects who had not suffered and that matrix metalloproteinase-8 (MMP-8) levels were elevated in patients after cardiac surgery or suffering from diabetes, and muscle and joint diseases. Additionally, it is important to pay attention to the methods of collecting saliva. In humans, when cotton-based saliva sampling methods are used, the salivary levels of steroid hormones (testosterone, progesterone, oestradiol and DHEA) may be artificially high, whereas salivary secretory IgA concentration may be artificially low. In contrast, salivary cortisol, DHEA sulphate and cotinine may not be affected by the cotton-based method. The reason for these differences is still uncertain. Awareness of such sampling issues is important in ensuring the measurement validity in any salivary biomarker assay. However, further researches on the individual salivary normal levels of the molecules mentioned above, is necessary before salivary biomarkers can be used in daily practice (Koh and Koh, 2007).
1.2.1.1_Salivary Cortisol: a hormone used to evaluate HPA axis activity
Cortisol is a glucocorticoid (GC) and is the end product of the hypothalamic-pituitary-adrenal (HPA) axis. The glucocorticoids are endogenous corticosteroids that are synthesised in the adrenal cortex from cholesterol. Glucocorticoids are produced in the middle zona fasciculate of adrenal gland and have effects on intermediary metabolism as on the metabolism of carbohydrates and, to a lesser extent, fats and proteins (Cuming et al., 2016). HPA axis is a collection of neural and
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Chapter 1 OVERALL INTRODUCTION endocrine structures that facilitate the adaptive response to stress (Gillespie et al., 2009). When a stressor is present in the environment, parvocellular neurons of the hypothalamic paraventricular nucleus (PVN) trigger a release of the corticotrophin-releasing hormone (CRH), oxytocin and arginine vasopressin. Then, CRH is transported to the anterior pituitary gland where it activates CRH receptors on pituitary corticotrophs, resulting in an increased secretion of the adrenocorticotrophic hormone (ACTH). ACTH released from the anterior pituitary into the systemic circulation stimulates the production and the release of corticosteroids (as cortisol, cortisone, corticosterone and aldosterone) from the adrenal cortex (Gillespie et al., 2009). After activation, caused by external or internal stimuli, HPA axis may spend 20-30 minutes to become fully stimulated and to observe peaks of glucocorticoid levels in the blood stream. The sympathetic nervous system (SNS) response is almost immediate. Cortisol released in the bloodstream, fluctuates depending from environmental stressors but also after physical efforts (Mason and Mendl, 1993; Protopopova, 2016). Besides, the high levels of cortisol inhibit further productions of the CRH and ACTH in a negative feedback loop (Figure 1.1). Actually, cortisol acts on mineralocorticoid and glucocorticoid receptors at the hippocampus, PVN and pituitary (de Kloet et al., 1991; Gillespie et al., 2009); it reduces stress-induced activation of the HPA axis and limits excessive secretion of glucocorticoids dampening effectively the stress response (Jacobson and Sapolsky, 1991; Gillespie et al., 2009; Protopopova, 2016).
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Chapter 1 OVERALL INTRODUCTION
Figure 1.1_Response of the HPA axis to a stressor (Protopopova, 2016).
Another important part of this mechanism, still to be explored in order to understand the different GCs mechanisms of action, is the structure/function of the glucocorticoid’s receptors (GR). A GR is built as a modular protein and the existence of different GR isoforms and combinations hereof with alternatively spliced variants is bound to contribute to the vast pleiotropicity of GR’s functionality in different tissues (Ratman et al., 2013). It is clear that different mechanisms of GR and consequently of GC, engrave on a large part of tissues. Furthermore, some studies in humans have led to the identification of FK506 binding protein 5, also known as FKBP5, key role in increasing HPA axis activity and GR sensitivity. FKBP5 is a co- chaperone component of the GR heterocomplex (Schiene-Fischer and Yu, 2001; Binder et al., 2008; Gillespie et al., 2009). An overexpression of FKBP5 reduces the hormone binding affinity and nuclear traslocation of GR (Denny et al., 2000; Scammell et al., 2001; Gillespie et al., 2009) (Figure 1.2). New world monkeys with naturally-occurring overexpression of FKBP5 experienced an increased GR resistance and hypercortisolemia (Denny et al., 2000; Scammell et al., 2001; Gillespie et al., 2009).
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Chapter 1 OVERALL INTRODUCTION
Figure 1.2_Schematic of FKBP5 cellular (modified from Gillespie et al., 2009).
Schematic diagram depicting the function of FKBP5 as a co-chaperone which regulates glucocorticoid receptor (GR) binding and translocation within the nucleus. When sufficient cortisol is present leading to GR dimerization and FKBP4 binding, FKBP5 is displaced allowing GR translocation and transcriptional activation. However, one of the gene targets of GR is the FKBP5 gene, which when increased in expression is thought to act as a negative intracellular feedback on the GR system within the cell (Gillespie et al., 2009).
As mentioned earlier, also immune system is involved in the response to a possible stressor stimulation. Prolonged exposures to stress may lead to immuno-suppression and dysregulation of HPA-axis. This dysregulation may manifest in an initial hypercorticolism, followed by hypocorticolism, where cortisol levels remain low even under stress situations (Dreschel, 2007; Protopopova, 2016). The neuro-endocrine and the immune networks work together in complex ways that affect each branches of all systems (Dreschel, 2007; Protopopova, 2016). In fact, there are many conflicting effects of glucocorticoids on immune system function and competence (Sapolsky et al., 2000). In the specific case of cortisol, its purpose is primarily to divert cellular processes from metabolic functions to functions that are necessary for immediate survival (“fight or flight” respose) (Protopopova, 2016). Salivary cortisol is extensively used as a measure of HPA activity in healthy dogs, as the unbound cortisol diffuses from blood into saliva passively (Vincent and Michell, 1992; Cobb et al., 2016). Likewise, patterns of secretion may be related to other behaviours such as exercise, sleep and eating. Ingestion of a high protein meal has shown to increase salivary cortisol levels in humans (Gibson et al., 1999). This response has not been tested in dogs to our knowledge; on the other hand, exercise has proved to rise plasma cortisol concentrations in dogs (Raekallio et al., 2005; Protopopova, 2016). 10
Chapter 1 OVERALL INTRODUCTION
In dogs, salivary cortisol has been revealed to be highly correlated with plasma cortisol with an approximately 20 minute temporal lag period during which hormone increments in plasma are reflected in saliva (Vincent and Michell, 1992). When compared to faecal or urinary cortisol, salivary one is temporally more closely related to the circulatory levels.
1.2.2_ MICROBIOME: AN IMPORTANT MARKER FOR ANIMAL WELFARE
In recent years, the research in the area of microbiome science has received a large amount of interest (O’Callaghan et al., 2016). The gastrointestinal tract of humans and animals is occupied by a dense and diverse population of microorganisms which can influence the health of the host (Panasevich et al., 2015). Gut microbiota is important in modulating human health, as it is in animals, such as cats, dogs and monkeys (McKenna et al., 2008; Suchodolski, 2011; Suchodolski et al., 2012; An et al., 2017). Thus, microbiome is thought to be a useful marker to determine the health state of both humans and animals, and the well-being of companion animals, just as their owners, depends also on the gut microbes functions (Daniels et al., 2014; Grześkowiak et al., 2015; An et al., 2017). Intestinal microbes play a crucial role in the preservation of host health by acting as a defending barrier against transient pathogens, supporting the host during the digestion and harvesting energy from diet; they can also stimulate the immune system and provide nutritional support for the enterocytes (Suchodolski, 2011; Suchodolski et al., 2012). Constant communication between gut and brain occurs mainly at a subconscious level and plays a critical role in the maintenance of an optimal health status. In humans the gastrointestinal tract, in addition of being the largest endocrine organ, is a nexus of communication among the immune cells (here present at the highest concentration in the body), 200-600 million neurons and the gut microbiome (trillions of bacteria, fungi and viruses) (Forsythe et al., 2016). Alterations of this complex ecosystem has been associate with numerous diseases in humans, dogs and cats (Suchodolski et al., 2010; Handl et al., 2011). Microbiome can be viewed as a metabolic ‘‘organ’’ tuned exquisitely to mammals physiology and that performs fuctions mammals have not had to evolve on their own. The definition of the host signalling pathways regulated by microbiome provides an opportunity to identify new therapeutic targets deputed in promoting health (Bäckhed et al., 2004). On the basis of this information, it seems reasonable to think that intestinal bacteria may influence the gut to brain communication, potentially leading to modulation of the central nervous
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Chapter 1 OVERALL INTRODUCTION system (CNS) functions. However, only in the last decade, with advances in sequencing technology, metabolomics and neurophysiology, it has been gained serious attention on the microbiota-gut-brain axis concept. To date, there is good evidence that the gut microbes exerts an important role in the normal CNS development, in particular, the enteric microorganisms colonization influences those systems associated with mammalian brain development and the subsequent adult behaviours, like stress response, anxiety and memory (Heijtz et al., 2011; Forsythe et al., 2016) (Figure 1.3).
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Chapter 1 OVERALL INTRODUCTION
Figure 1.3_Mechanisms of the microbiota-gut-brain axis (modified from Forsythe et al., 2016).
The intestinal microbiota has direct and indirect effects of on the intestinal epithelium, local mucosal immune system, enteric nervous system and spinal and vagal nerves. Signals from the Central Nervous System and neuroendocrine system, including cortisol, catecholamines and acetylcholine, can alter gut microbiota composition (Forsythe et al., 2016).
Furthermore, supplementing the diet of animals with probiotics seems to be an essential way to preserving and promoting the optimal gastrointestinal tract (GIT) health and the general well- being status of pets (Hill et al., 2014; Grześkowiak et al., 2015). In the FAO/WHO ‘Guidelines for the evaluation of probiotics in food’ (2002), a probiotic is defined as ‘live microorganism which, when administered in adequate amounts, confers a health benefit on the host’ (FAO/WHO, 2002). Hence, for the successful use as a probiotic, the bacterial species should have the same or a similar intestinal origin of the host, however the majority of the probiotics utilised for companion animals are not originally derived from the canine GIT microbiota. A correct use of probiotics in pets could lead to a variety of possible benefits such as: modulation of the immune system, support in stress response, protection from infections caused by enteropathogens, increment in growth and
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Chapter 1 OVERALL INTRODUCTION development, control of allergic disorders and, as recently reported by Grześkowiak et al. (2015), probiotic can contrast obesity. Therefore, properties, effects and characteristics of each individual strain should be well defined and demonstrated in a case by case manner (Gueimonde and Collado, 2012).
1.2.2.1_Metagenomics as an evaluation tool of microbiome composition
The intestinal ecology has been mostly investigated using culture-dependent techniques. The discovery of molecular methods, such as comparative 16S rRNA analysis, has revealed a much more diverse intestinal ecosystem than that recognised previously (Handl et al., 2011). In fact, until the last decade our knowledge on microbiota composition and development was largely based on the use of traditional culture-based methods. The culturing had provided interesting data, but also a very biased view of the gut microbiota composition. Traditional culture-based methods allow to identify only 1-3% of phyla of the microorganisms directly present in their environment (Handl et al., 2011; Gordon, 2012). The techniques based on extensive DNA sequencing, have increased enormously the awareness on microbiome composition and activity. The study of the entire microbial communities using genomic approaches has revealed a much greater diversity compared to what it was previously thought to exist and it has helped to determine the community structure of several ecosystems, previously unknown. Next generation sequencing technologies have recently been used to characterise the identity and functional capacity of a variety of microbial communities, including the gastrointestinal tracts of mammalian species (Swanson et al., 2011). Furthermore, another interesting and important aspect concerning the development of these techniques is the enormous contribution in exploring several aspects of the probiotics research (Gueimonde and Collado, 2012). Synthesis sequencing is the basis of these new technologies and it is expected that DNA fragments will be amplified into clusters, denatured and distributed in microarrays or microplates that are introduced into a flow cell where sequencing reactions take place. The various sequencing methods differ in the strategy used to amplify sequences, in the chemistry applied, and in the length of the reads. However, they have in common the possibility of sequencing up to several million DNA fragments in parallel (Delsenya et al., 2010).
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Chapter 1 OVERALL INTRODUCTION
Additionally, rRNA-based analysis remains the central method in microbiology, exploited not only to investigate microbial diversity, but also as a day-to-day method for bacterial identification (Wang et al., 2007). As it is well known, the ribosomal 16S rRNA is an essential component of the small ribosome unit containing a specific sequence for each bacterial species. For this reason, it is used in the analysis of the microbial community composition. The gene consists of 10 conserved regions and 9 hypervariable ones, it is subjected to a low evolutionary rate and conserved in all bacteria species. The choice of using the 16S rRNA as a phylogenetic marker to examine microbial diversity and to identify and classify microorganisms arises from the difficulty in cultivating most of the microorganisms present in natural environments. 16S rRNA gene is sequenced through NGS platforms and similar sequences are grouped into Operational Taxonomic Units (OTUs); these represent a system to distinguish species and classify nucleotide sequences at different taxonomic levels. The abundance of the different OTUs is then estimated from the number of corresponding sequences. The individual sequenced hypervariable regions are grouped into OTUs, computing the same conventional distance value as it is used for the full sequence of the 16S rRNA. In some recent studies, individual hypervariable regions have been compared to each other with the entire sequence of the gene in order to estimate relative abundances (Claesson et al., 2009). Hence, it is now proven that different hypervariable regions do not always yield the same results if applied to different biological samples. In each matrix, the most informative hypervariable regions must be identified according to the bacterial community. Specifically, faecal samples in dogs provide greater taxonomic information by studying the hypervariable V3-V4 region (Figure 1.4).
Figure 1.4_Variable and conserved regions.
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 bp V1 V2 V3 V4 V5 V6 V7 V8 V9
CONSERVED REGIONS: unspecific applications. VARIABLE REGIONS: grou or species-specific applications
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Chapter 1 OVERALL INTRODUCTION
1.2.3_HAIR, A NON-INVASIVE MATRIX USEFUL FOR MEASURING SOME BIOMARKERS
Hair has been successfully used to measure both internal and external exposure to a wide variety of organic and inorganic pollutants (Schramm, 1997; Covaci et al., 2002; Zhang et al., 2007; Smolder et al., 2009). As hair in humans grows about one centimetre per month, analysis of hair of different length may reflect cumulative exposure over several months. Taking advantage of this property, differences in exposure can be followed over several months or even years. Potential constraints on the use of hair as matrix include the difficulty in differentiating internal and external sources of contaminant (ATSDR, 2001; Schramm, 2008). Probably, the best-known usage of hair as a non-invasive matrix for metals is the biomonitoring of organic and inorganic mercury, as hair is by far the best integrator of past exposures (Gosselin et al., 2006; Choi and Grandjean, 2008), although also other metals have repeatedly been monitored using hair (Krause et al., 1996; ATSDR, 2007; McDowell et al., 2004). Many contaminants have been proven to reach hair and finger/toenails via two major routes: endogenous (xenobiotics reach the hair matrix through blood) and exogenous (atmospheric deposition) (Schramm, 1997; Esteban and Castano, 2009). Hence, it may be difficult to distinguish between contaminants absorbed and those related to external contamination.
1.2.3.1_Hair Cortisol: a useful matrix for long term monitoring
Cortisol has long been considered as a reliable physiological measure of the stress response in both humans (Russell et al., 2012) and other mammals (Park et al., 2016). Hair sampling is non-invasive, painless and particularly valuable in providing a marker, which is easy to store even for long periods. The hair matrix is a promising tool that allows to study prolonged changes in the HPA axis activity through the measurement of the cortisol incorporated in hair. It is a method that can be used as an instrument to evaluate the animal well-being (Koren et al., 2002; Accorsi et al., 2008; Bennett and Hayssen, 2010; Roth et al., 2016). Furthermore, the cortisol in hair can reflect the long term chronic stress response over a period of months without being influenced by circadian variation or the stress induced by the sample collection (Sauvé et al., 2007; Gow et al., 2010).
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Hair cortisol has been extensively studied during the last years and positively correlated with cortisol levels in both saliva and faeces of dogs (Accorsi et al., 2008; Bennett and Hayssen, 2010; Roth et al., 2016). It is known that hair growth is cyclic: anagen phase (new growth), it consists in the formation of all the hair structures as the papilla, the bulb, the sebaceous gland, the eruptive muscle and the follicle. In this phase there is also the melanin synthesis in the form of fusiform granules contained in the melanocytes present in the deep part of the bulb matrix, then deposited in the cortex cells; catagen phase (transition), period in which the formed hair moves toward the superficial layers of the epidermis and papilla, which decreases its size; telogen phase (quiescence) represents the sleeping phase of the hair. The papilla almost disappears and the hair prepares to fall; pellow phase characterised by hair loss, it is followed by a new anagen phase. However, the mechanism on how cortisol enters the hair is still unclear. On the other hand, a typical model of steroids incorporation in the hair starts with their assimilation by passive diffusion from the blood capillaries, which surrounds and supplies the growing hair cells (anagen phase), and ends with keratinization and dehydration of the hair cells (Cone, 1996; Meyer and Novak, 2012). Other proposed models include stereoids incorporation from shallow compartments of the skin during the formation of the hair shaft, or through the sebaceous gland attached to each hair follicle, otherwise from the nearby sweat glands that bathe the growing hair shaft for several days before it emerges from the skin. The substances can also be deposited from the outside environment. In addition, it seems that the hair follicles themselves could locally produce cortisol (Ito et al., 2005; Park et al., 2016). Regardless from the above mentioned mechanisms, cortisol concentrations in hair have been shown to reflect endocrine patterns (Park et al., 2016). A possible limitation about the detection of cortisol in dogs hair could be the hair colour. In fact, the biochemical control of the hair pigment is managed by the melanotropic hormone MSH, (melanocyte-stimulanting hormone) that derives from proopiomelanocortin (POMC), and by melanocortinic receptors. The biochemical control of cortisol employs the ACTH, which derives from the POMC too, and the melanocortinic receptors. Besides, the product of Agouti gene, one of the major genes implicated in coat colouring, has a competitive antagonist role against the melanocortin receptors of the MSH. Hence, coat colour control and cortisol concentrations may
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Chapter 1 OVERALL INTRODUCTION also be influenced by Agouti gene. It has been observed that black dogs exhibit a lower cortisol concentration than non-black dogs, as black hair contains a greater amount of pigment that reduces in receptors the 'available' space for glucocorticoids, whose concentrations, instead, are larger in the hairs of canine lighter coats (Bennett and Hayssen, 2010). Regarding the influence of temperature and photoperiod, it is currently unknown whether cortisol levels in the hair vary during the different seasons (Sauvé et al., 2007).
1.2.3.2_Heavy metals in hair as biomarkers of health state
Some metals and their compounds are essential to human health (i.e. iron (Fe), zinc (Zn), chromium (Cr)), although they are potentially harmful if consumed in large amounts. Other metals potentially harmful for health, arsenic (As), lead (Pb), cadmium (Cd) and mercury (Hg) do not have known beneficial biological functions, but long-term exposure to them may be toxic even at low doses (González-Muñoz et al., 2008). In recent years, human scalp hair has gained considerable attention for its use as a biomonitor of trace elements aimed at estimating environmental exposure levels and assessing the nutritional status, as well as for the utilisation as a mean of illness diagnosis. Scalp hair constitutes an optima matrix for its ability to accumulate greater trace elements than that of other biological matrices (Sanna et al., 2003; Dunicz-Sokolowska et al., 2006; Varrica et al., 2014). As reported by Beernaert et al. (2007), mammalian hair is predominantly composed by keratin, a protein rich in cystine sulfhydryl (thiol) groups with a binding affinity for various metals. Each hair shaft is continuously in contact with the bloodstream at the root, and thus may incorporate metals circulating in the blood during growth processes (McLean et al., 2009). Consequently, hair may reflect metal concentrations in the body and is a candidate as a non-invasive proxy for body metal burden. Sufficient evidences exist to suggest that mammalian hair is an appropriate accumulative indicator of metal bioavailability, with significant correlation between metals and metalloids within hair and other tissues. However, a few attempts have so far been made to study the content of elements in companion animals. Since dogs and humans share much of their environment, part of these investigations use companion animals (predominantly dogs) as valuable sentinels for the detection of metals exposures which may also concern their owners. However, scarce attempts have been performed
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Chapter 1 OVERALL INTRODUCTION in order to evaluate any potential correlation between the levels of metals in blood and hair (Zaccaroni et al., 2014). The mineral status of individuals has conventionally been determined by analysis of biological samples, basically blood ones. Nevertheless, the clinical utility of hair analysis is well accepted for some elements, as it is recognised that hair provides a better estimation of the total body intake of certain elements in comparison to blood or urine (Wilhelm et al., 1989). Anyway, this approach remains in the realm of clinical investigation predominantly (Druyan et al., 1998). Indeed, hair has become a well established metabolically inactive tissue, especially for investigating changes and levels of various trace elements accumulated in the body (Druyan et al., 1998). Furthermore, levels of these elements in the hair are less influenced by their immediate intake and for some of them may also prove to be reliable biological indicators in terms of nutritional status (Zaccaroni et al., 2014).
1.3_OUTLINE OF THE THESIS
It is known that, in canines, stressful situations can cause a wide variety of physiological and psychological health issues, such as an increased activity of the sympathetic nervous system, of catecholamine release, blood pressure augment and incremented permeability of the intestinal epithelial lining to microorganisms (Venable et al., 2016). Therefore, in order to prevent healthy issues, it seems to be important to have the necessary instruments to identify those biomarkers, which could provide an estimate of well-being in dogs. For these reasons, in this thesis, some physiological biomarkers were investigated in order to have a general spectrum of well-being state in dogs, as complete as possible. Cortisol was evaluated as one of the principal biomarkers of well-being and it was analysed in two different non-invasive matrices. In association with cortisol, other aspects of dog healthy status were taken into account, having some potential connections with the hormone or effects on theglucocrticoid. Moreover, in the present study, the possible variation of microbiome in association with probiotic addition to the diet and their connection with HPA axis were also explored. Additionally, heavy metals in hair were taken in consideration, especially in reference to their correlation with cortisol fluctuations. In the development of this thesis the following points are dealt: First point: the use of salivary cortisol as physiological biomarker in the evaluation of dog welfare status.
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Salivary cortisol is considered as one of the most useful biomarkers in evaluating adaptive responses to different stimuli and in pronouncing hypothalamic-pituitary-adrenal axis (HPA) activation. In particular, in the research field, cortisol is used as a tool for the evaluation of canine well-being (Cobb et al., 2016). In this part of the thesis, three studies, where saliva was used as a matrix for cortisol measurement, are described. Salivary cortisol reflects the blood changes of the hormone with a delay of 20 to 30 minutes (Vincent and Michell, 1992; Beerda et al., 1998; Oyama et al., 2013), moreover, saliva is one of the least invasive available matrices. The delay of the cortisol variation in saliva allows to avoid artefacts of this hormone caused by sampling manuality.Additionally, salivary cortisol reflects both the activation of the sympathetic nervous system (acute stress) as well as the activation of Hipothalamic Pituitary Adrenal (HPA) axis (Beerda et al., 1996; Beerda et al., 1998; Dreschel and Granger, 2009).
Second point: faecal microbiome as an indicator of the healthy state of the dogs and the effects of probiotic addition to the diet on dog well-being. In this second part of the thesis, the variation of faecal microbiome in relation to probiotic addition in an extruded complete diet was evaluated. Intestinal microbiome analyses were taken in consideration as a result of the important role of gut microbiome in host health both in humans and in domestic animals. Gut microbiota has a helpful role in the digestion, in the nutrition for the enterocytes, it plays an important role in the development of the immune system, acts as a barrier against pathogen invasion and it is also implicated in the regulation of the HPA axis (Neish, 2009; Dinan and Cryan, 2012). Nervethless, the evaluation of microbiome and the possible effects of probiotic addition to the diet were explored also to deepen their connection with HPA axis. In fact, the microbiota may influence the central nervous system (CNS) functions. On the other hand, also the brain can alter the microbiome through signalling molecules released into the gut lumen from cells in the lamina propria, since they are under CNS control thus resulting in changes in the gastrointestinal motility and secretion activity as well as intestinal permeability, altering in this way the enteric environment in which the bacteria reside. Furthermore, as regards probiotics, it is interesting to know that increased evidences suggest that animals treated with probiotics have a blunted HPA response, it seems also that probiotics influence the HPA axis and stress responsivity, including neurotransmission modulation. Recently, in human researches on probiotic administration, it has been observed that probiotics have a
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Chapter 1 OVERALL INTRODUCTION certain role for the microbiota in anxiety like behaviours, and it seems also that probiotics are involved in the decrement of the behavioural and endocrine components of stress (Dinan and Cryan, 2012). Even if it is not clear the connection of probiotic with cortisol, it seems possible to speculate that a reduction in cortisol levels is caused by a decrease in the release of pro- inflammatory cytokines, which activate the HPA, or alternatively by an alteration of neurotransmitter inputs such as 5-HT (5-hydroxy-tryptamine). As reported by Ait-Belgnaoui et al. (2012) in a research on rats, it seems that prevention of gut leakiness by intestinal microbiota modulation can lead to attenuated HPA axis response to an acute psychological stress (Dinan and Cryan, 2012).
Third point: evaluation of heavy metals and cortisol in hair and their possible correlation with dogs well-being. It is generally known that mammal tissues are good bioindicators of trace elements, including heavy metals. Metals circulate in the blood stream and they are stored in some body tissues, hair included (Blaurock-busch et al., 2012). In particular, animal hair appears to be a good bioindicator of heavy metal levels and is one of the matrices that gives a better estimate of certain elements than blood or urine does (Wilhelm et al., 1989; Filistowicz et al., 2012; Donnici et al., 2016; Han et al., 2016). For these reasons and for its non-invasive nature, hair matrix was chosen for this preliminary study. Heavy metals constitute a wide and special group of chemical agents that may exercise a definite influence on the control of biological functions (many of them are essential to the development of a variety of physiological functions), affecting hormone systems (some metals have been characterized as endocrine disruptors, and one of their targets is the hypothalamus- pituitary-gonadal and/or hypothalamus-pituitary-adrenal axis) and development of different body tissues (Park et al., 2005; Pérez-Cadahìa et al., 2008). In this preliminary research, cortisol in hair was taken in consideration as a biomarker of well- being. Cortisol is known to play a central role in maintaining internal homeostasis through gluco- and mineral corticoids function, and it is an end product of the hypothalamus-pituitary-adrenal axis in response to different stressors. Furthermore, hair cortisol concentrations show integrated cortisol secretion over a period of several months, and could be an indicative biomarker for stress- associated endocrine changes (Manenschijn et al., 2011; Corradini et al., 2013). Measurement of cortisol concentrations in canine hair is a validated method (Accorsi et al., 2008; Pérez-Cadahìa et
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Chapter 1 OVERALL INTRODUCTION al., 2008; Corradini et al., 2013). So, a possible correlation between cortisol and heavy metals was investigated in this thesis. An element that has shown a connection with cortisol is zinc (Zn). In the brain, zinc is stored in specific synaptic vesicles by glutamatergic neurons and can modulate brain excitability. It plays also a key role in synaptic plasticity and so in learning, in addition, Zn homeostasis plays a critical role in normal functioning of the brain and central nervous system (Osredkar and Sustar, 2011). In a human research, it was observed how cortisol seems to be the most sensitive to the effects of heavy metal exposure. Therefore, plasma levels of cortisol could represent a relevant biomarker when assessing the potentially damaging effects of exposure to heavy metals (Pérez-Cadahìa et al., 2008). Unfortunately, scarce data on this typology of relationship, regarding to genotoxicity and endocrine parameters are available in the literature (Pérez-Cadahìa et al., 2008). At the same time, despite the small amount of results, the confirmation in different studies of correlation between cortisol and zinc, make this type of research very interesting, in particular with regards to their use in evaluation of dog well-being.
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Roth LSV, Faresjö A, Theodorsson E, Jensen P. Hair cortisol varies with season and lifestyle and relates to human interactions in German Shepherd dogs. Sci Rep, 6:19631, 2016.
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Part 1 SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION
Part 1
SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION
Part 1 SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION
Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
A. Colussi, M. Sandri, B. Stefanon
Veterinaria, Vol 30, Issue 3, June 2016
Dipartimento di Scienze Agroalimentari, Ambientali e Animali. Università degli Studi di Udine
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
2.1_ABSTRACT
The increasing attention to animal well-being has stimulated the study of biomarkers of an organism’s adaptive response to the environment, with cortisol emerging as one of the most interesting. Saliva is a biological fluid that is easy to collect and has the advantage that its cortisol content parallels that in blood with a 20-30 minutes delay, thus “photographing” the adaptive response to a past stimulus without interference due to handling while taking the sample. Recent studies have shown that salivary cortisol can be used as a biomarker for some diseases and behavioural modifications, as well as in support of canine activities and sports. The new point-of- care devices for assaying salivary cortisol concentration in dogs provide practitioners with a useful method for evaluating the degree of activation of the hypothalamic-pituitary-adrenal axis in response to central or peripheral stimuli.
Keywords: Cortisol, Saliva, Pleiotropic role, Adaptive response, Environmental stimuli
Cortisol is a glucocorticoid (GC) that is often used to evaluate the activity of the hypothalamic- pituitary-adrenal (HPA) axis and the adaptive response of the body at a central level, in reaction to environmental stimuli to the nervous system, and peripherally, in reaction to metabolic variations, trauma or immune system responses of various tissues and organs. Besides being secreted by the HPA axis, in some cases, the secretion of GC can also be stimulated by higher brain centres, both in normal conditions (sleep-wake cycle in humans) and in unfavourable circumstances (fear, anxiety, pain, cold, etc.), thus promoting the recovery of homeostasis (Swenson and Reece, 2002; Poli, 2006).
2.2_THE PLEIOTROPIC ROLE OF CORTISOL
The multiple effects of cortisol on metabolism and the adaptive response require a complex mechanism of regulation which is not yet completely understood. Adrenocorticotropic hormone
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
(ACTH) stimulates the release of cortisol which, once in the circulation, has a half-life that varies between 70 and 120 minutes in humans (Sjaastad et al., 2003; Wiebke and Stewart, 2005). The main metabolic effects of cortisol are gluconeogenesis in the liver, deposition of fat, and regulation in the brain of the hypothalamic neuropeptides involved in appetite control. Furthermore, in these tissues, cortisol can be locally inactivated to cortisone through the action of isoforms 1 and 2 of 11β-hydroxysteroid dehydrogenase or regenerated starting from the same hormone, thereby bypassing secretion of the hormone by the adrenal glands (Harno and White, 2010). In circumstances of acute stress (“fight or flight”) high concentrations of cortisol increase the release of glucose into the blood and the feeling of hunger, thus facilitating the response to the stress itself. In adipose tissue, on the other hand, cortisol has an anabolic effect that can potentially promote the deposition of new tissue in metabolically disadvantaged areas. This feature has made cortisol a focus of attention in the strive for better understanding of the endocrine mechanisms related to obesity (Harno and White, 2010). The pleiotropic effects of cortisol are fundamental for the adaptive response of the body and strict regulation, through negative feedback on the HPA axis, avoids overexposure of target tissues, thus preventing the typical negative effects of cortisol, such as glucose intolerance, immunosuppression, increased blood pressure, osteoporosis, insulin resistance, altered growth and tissue repair and even Cushing’s syndrome (Harno and White, 2010). At a cellular level, the effects of cortisol are mediated by activation of GC receptors (GR), of which there are five different isoforms with specific functions, not yet completely understood, but probably responsible for the pleotropic activity of the hormone (Ratman et al., 2013). The complexity of the effects also derives from the ability of cortisol to regulate the transduction of signals in various tissues and systems through binding to both cytosolic receptors (non-genomic control) and nuclear receptors (genomic control) (Figure 2.1).
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
Figure 2.1_ Genomic and non-genomic mechanisms of GC signal transduction (modified from Ratman et al., 2013).
mGR: membrane glucocorticoid receptor; GR: glucocorticoid receptor; GC: glucocorticoid; GRE: glucocorticoid response element; TF: transcription factor.
As far as concerns non-genomic control, GC have an activating/deactivating role in the cytosol by interacting with the cell membranes in a GR-independent manner, acting directly on protein kinases (MAPKs) or by binding to membrane GR, which can lead to rapid activation of anti- inflammatory signals (Song and Buttgereit, 2006; Lowenberg et al., 2007). Furthermore, at a central level, GR, together with receptors for mineralocorticoids, play a critical role in coordinating a rapid adaptive response to stress, with the involvement of pre-receptors that are still under investigation (Strehl et al., 2011; Groeneweg et al., 2012). With regards to genomic control, on the other hand, the GR act directly on the nucleus. The GR are initially present in the cytosol in association with large, multiprotein complexes with chaperone functions. After binding to the ligand, the complex breaks down, exposing nuclear localisation sequences enabling translocation to the nucleus, where the GR can cause positive transcriptional modifications (transactivation) or negatives ones (transrepression) (Ratman et al., 2013).
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
2.3_ASSAYING CORTISOL IN BIOLOGICAL MATRICES
Cortisol is involved in various metabolic, immune and nervous system processes and variations in its levels therefore reflect the adaptive response to the environment in the broadest sense of the term, taking into account all the actions in which this hormone is involved. The cortisol assay is a method that, together with other diagnostic information, has the advantage of providing the most objective and complete evaluation of an organism’s biological response, thus enabling data collected in different contexts to be compared with reference values.
2.3.1_Sampling Cortisol can be assayed in various biological matrices including blood, saliva, urine and hair. The potential applications of cortisol assays have stimulated research into non-invasive methods of sampling that do not affect the secretion of the hormone, but that accurately reflect the activation of the HPA axis (Beerda et al., 1999b). Blood was one of the first reference substrates in which cortisol concentrations were evaluated to make a diagnosis of behavioural problems or diseases and, subsequently, to determine the efficacy of treatments. A blood sample provides a snapshot of cortisol levels. The secretion of cortisol is very sensitive to both internal and external stimuli, with these latter including handling during sample collection. Indeed, manipulation for more than 2 minutes can cause significant changes in blood cortisol concentrations with the risk of artefacts when evaluating activation of the HPA axis (Kobelt et al., 2003; Hiby et al., 2006; Accorsi et al., 2007; Bennet and Hayssen, 2010). An alternative to blood is hair, which does not provide information on acute stress, including that occurring during the sampling. So far, research into the mechanism by which steroids accumulate in growing hair indicates that this occurs through the blood vessels that supply the follicle and, to a lesser degree, also through the sebaceous sweat glands and surrounding eccrine glands which, once the hair has emerged from the scalp, coat it with sebum and sweat, respectively. However, it seems that the hair follicle also produces cortisol locally in response to more widespread systemic stress, to localised skin irritation or as part of its normal function. The relationship between cortisol and the hair fibre within the follicle is complex and probably involves both melanin and keratin (Bryan et al., 2012; Meyer and Novak, 2012). The amount of cortisol in the hair therefore reflects the endocrine secretion of the hormone over a period of months and is useful as a sensitive marker of chronic stress, without detectable changes related to short-lasting events. The
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
accumulation of cortisol in hair does, however, vary in relation to the characteristics of the animal’s coat, because, in mammals, there are physiological and biochemical similarities between the production of glucocorticoids and that of hair pigments. Indeed, pigment production is regulated by melanocyte-stimulating hormone (MSH), which is derived from proopiomelanocortin, which is also the precursor of ACTH, the hormone that stimulates the secretion of cortisol. Furthermore, the product of the Agouti genes, one of the main genes involved in coat colour, has a competitive antagonistic effect on the melanocortin receptors of MSH. Thus, genetic characteristics of the coat can also influence the accumulation of cortisol in the hair. Indeed, the concentration of cortisol is lower in black hairs than in non-black hairs, since the former contain more pigment that occupies the ‘space’ available for the GC, while the concentrations are higher in the hair of animals with lighter coloured coats (Bennet and Hayssen, 2010). The levels of cortisol in the saliva accurately reflect those in the blood, with the variations occurring about 20-30 minutes later (Vincent and Michell, 1992; Beerda et al., 1998; Oyoma et al., 2013). For this reason salivary cortisol levels are measured to assess the acute response to stress, being useful in dogs for evaluating the adaptive response, such as immediate reactions to threats and man-animal interactions. The slight delay in the variations avoids spurious values due to handling while taking the sample. In fact, salivary values simultaneously reflect the activity of the sympathetic nervous system (acute stress) and the HPA axis (Beerda et al., 1998). Furthermore, salivary sampling is a non-invasive method and repeating the assay over time enables evaluation of the adaptive response in the mid- and long-term (Beerda et al., 1996; Dreschel and Granger, 2009). Since the concentration of cortisol in the saliva is about 7-12% that in plasma the assay for the former must be more sensitive than that used for plasma, and care must be taken to ensure that materials which interact with the analyte are not present either in the sampling phase or during storage of the sample (Beerda et al., 1996; Wenger-Riggenbach et al., 2010). Contamination of the blood, haemolysis, pH, excessive residual food and material used to collect the sample are all factors that can influence the analysis of cortisol in the saliva, while plasma proteins, present only in trace amounts in the saliva, are not a problem (Beerda et al., 1996). It should be remembered that the secretion of saliva is under the control of the nervous system and that sympathetic nerve stimulation can cause vasoconstriction and a decrease in salivary flow, thus reducing the possibility of collecting a sample sufficient for analysis. For this reason, it is
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
always useful to stimulate the dog’s salivation, for example by giving it food to sniff for a few seconds, thus inducing secretion of saliva without causing stress (Bennet and Hayssen, 2010).
2.3.2_Factors that influence salivary cortisol levels in the dog In studies carried out in clinically healthy, adult dogs (Sandri et al., 2015; Stefanon et al., 2015) it was found that sexual status and gender have significant effects on salivary cortisol levels (Graph 2.1): sterilised animals have lower levels than sexually intact ones, while intact females and males do not have significantly different values Furthermore, there are relationships between salivary cortisol levels and dogs’ weight, size and breed (Sandri et al., 2015; Stefanon et al., 2015; Houpt, 2007; Spady, 2008) (Graph 2.2, Stefanon et al., 2015). These findings are consistent with those of another study (Vas et al., 2007) showing significantly greater activity and impulsiveness in small dogs than in large ones. Living environment also influences cortisol concentrations, with levels of the hormone being higher in animals living in shelters than in animals living in kennels or private homes (Graph 2.3, Sandri et al., 2015). In this regard, some researchers showed that admission of dogs into a shelter caused prolonged activation of the HPA axis (Hennessy et al., 1997). However, although this represented a condition of chronic stress, the animals gradually adapted to their new environment, as reflected by their cortisol concentrations. Furthermore, the dogs living in shelters showed changes of cortisol levels during the course of the day, with a decrease following interactions with humans (Graph 2.4, Stefanon et al.,2015). The samples of saliva taken before and after interaction with a human were both collected in the morning, about 30 minutes apart, to avoid any effects of circadian rhythm on the levels (Giannetto et al., 2014; Stefanon et al.,2015). The results appear to show that a positive interaction with a human improves social behaviour and physiological wellbeing of dogs living in shelters, giving the animals a greater possibility of adoption (Luescher and Meldlock, 2009). Other researchers also observed variations in cortisol concentrations in dogs in relation to the sex of the person with which the animals interacted, with there being lower concentrations of cortisol in the saliva following interactions with a female person (Hennessy et al., 1997).
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
Graph 2.1_Differences in salivary cortisol levels (ng/ml) in relation to sexual status.
Intact Castrated Intact Neutered Male Male Femal Female Male e a, b, c: the letters indicate statistically significant differences (P<0.05) between the means.
Graph 2.2_ Differences in salivary cortisol levels (ng/ml) in relation to size.
Giant Maxi Medium Small a, b, c: the letters indicate statistically significant differences (P<0.05) between the means.
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
Graph 2.3_Differences in salivary cortisol levels (ng/ml) in relation to living environment.
c b
a
a, b, c: the letters indicate statistically significant differences (P<0.05) between the means.
Graph 2.4_ Differences in salivary cortisol levels (ng/ml) in relation to environment and time of day.
a
b b b a a
b ab b
Owner Kennel Shelter
T0: sample taken in the morning during the first interaction with a human, just before a meal (6:00-8:00 a.m.). T1: sample taken 30 minutes after the first interaction with the human in the morning. T2: sample taken in the evening, 30 minutes after the last interaction with a human. a, b, c: the letters indicate statistically significant differences (P<0.05) between the means.
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
In another study, a significant increase in salivary cortisol concentration was found in dogs following their admission to hospital, with there being a statistically significant correlation between high concentrations and ‘anomalous’ behaviours such as panting and lip-licking (Hekman et al., 2012). In a previous study (Beerda et al., 1998), salivary cortisol concentrations increased from baseline values of between 2.16 and 4.68 ng/ml to a mean value of 6.01 ng/ml in dogs exposed to external stimuli. Similar results were obtained comparing dogs of the same breed subjected for 6 weeks to segregated housing in spaces more restricted than those to which they were accustomed. The salivary cortisol concentrations corresponded with the manifestation of abnormal behaviours, with subsequent development of depressed responsiveness of the HPA to acute stimuli (Beerda et al., 1999; Beerda et al., 1999b). In conclusion, the ease and non-invasiveness of salivary sampling make it possible for an owner to collect a sample at a particular time and in different circumstances from the canonical ones such as the veterinary clinic (Figures 2.2, 2.3, 2.4, 2.5). Furthermore, the delay of about 20-30 minutes in the increase of cortisol levels in the saliva compared to those in the blood means that the values in the saliva following an event of interest are not affected by handling the animal to collect the sample. In practice, in the case of a suspected behavioural problem related to a particular environmental situation, the sample of saliva can be collected 20-30 minutes after the dog has manifested the abnormal behaviour, in order to compare the concentration of cortisol with that found in a period of no stress.
Figure2.2_ Stimulation of salivation in the dog. Figure 2.3_ Sampling saliva in a dog.
Stimulating salivation in the dog by presenting Placing the swab in the dog’s mouth; to ensure that the the animal with food, but not allowing the dog swab becomes thoroughly imbibed with saliva, it should to eat it before having taken the saliva sample. be put in the mouth two or three times and left in place for 15-20 seconds.
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
Figure 2.4. Placing the imbibed swab Figure 2.5. Closing the test-tube in the test-tube. with its cap.
2.3.3_Potential applications of salivary cortisol assays The studies reported show the usefulness of assaying salivary cortisol levels in dogs in order to determine the state of activation of the HPA axis in relation to physiological conditions and various types of environmental stimuli. The interest in this information and its diagnostic utility are demonstrated by the growing number of scientific publications on salivary cortisol in the dog over the last 3-5 years. In the first place, cortisol assays are useful for supporting a diagnosis of behavioural disorders, but the pleiotropic role of GC, and cortisol in particular, lends them to numerous other uses, as already occurs in human biomedicine and animal livestock sciences.
Canine exercise and activities There has been considerable interest recently in the relationship between salivary cortisol and exercise in humans, both with regards to the response to intensity of the activity and the body’s recovery capacity, as well as to determine the efficacy of various functional supplements during physical activity (McGuigan et al. 2003; McNaughton et al., 2006; Gatti and De Paolo, 2011; Powell et al., 2015). Similarly, in canine activities, during which a dog is subjected to a variety of physical and psychological stimuli, variations in cortisol levels enable comparisons of the level of fitness between individuals during a working session and within the same animal during an exercise and training programme. The literature about changes in cortisol levels in dogs undergoing physical
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
activity is still very limited. One study evaluated the level of stress caused by agility competitions in purebred dogs (Border collie, Australian kelpie, Boxer, Belgian shepherd dog, German shepherd dog, Pit bull, Pinscher, Poodles) and mixed breed animals (Pastore et al., 2011). The results showed an increase in the concentrations of cortisol following the competition, as well as some stress-referable behaviours. With regards to the effects of different workloads on cortisol concentrations, some researchers have investigated the effects of low and high intensity exercise on dogs. The animals were made to exercise on a treadmill at different speeds for about 90 minutes (Radosevich et al., 1989). Blood cortisol concentrations increased during the exercise, at a rate reflecting the intensity of the exercise. The release of cortisol appeared to be related to both the duration and the intensity of the physical activity. In another preliminary study changes in salivary cortisol levels were measured during Pointing Hunting (English Setter), Blood Tracking (Bavarian mountain hound and Hannoverian scenthound) and Tracking for Ungulate Hunting (Istrian short-haired hound, Griffon nivernais, Italian short- haired hound). The duration and intensity of the mental and physical energy expended differed depending on the type of hunting in which the animals were engaged. There were significant increases in the concentrations of cortisol, which were particularly evident in the pointers, undergoing more intense physical effort (P<0.05) (Stefanon et al., 2015). The changes in salivary cortisol levels were, however, modest in the dogs employed in Blood Tracking and Tracking for Ungulate Hunting, confirming previous observations in German shepherd dogs involved in a track contest during an IPO competition, i.e., less activation of the HPA axis during activities in which greater mental concentration is required (Colussi, 2013).
2.3.3.1_Animal-assisted activities Mental exertion in suitably trained animals does not, therefore, seem to lead to stress phenomena such as to alter homeostasis substantially. This appears to confirm findings in dogs involved in Animal-Assisted Activities (AAA), in which salivary cortisol levels did not change significantly following interaction with the patients (Glenk et al., 2014). Similar results were obtained in a preliminary study in which salivary cortisol levels in dogs involved in AAA were significantly (P<0.05) lower 20 minutes after completion of the session (1.47±0.15 ng/ml) compared to the
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI levels before starting the activity (2.07±0.16 ng/ml). Furthermore, it should be noted that the AAA took place between 10:00 a.m. and 11:45 a.m., a period of the day in which an increase in salivary cortisol could be expected as a result of circadian rhythm (Stefanon et al., 2015; Giannetto et al., 2014). Measuring the concentration of cortisol in saliva could therefore offer a way of identifying animals less well adapted to AAA, through the detection of high levels of cortisol, and also a way of monitoring an animal’s acceptance of such activities over time and picking up any changes early (Stefanon et al., 2015).
2.3.3.2_Genetics and breeding The data available on changes in salivary cortisol levels in relation to AAA, training, educational programmes and psychomotor rehabilitation as well as activities to improve physical condition or coordination more generally, are still limited, but do offer a starting point for some important considerations not only from a physiological point of view, but also regarding ethology and breeding programmes. In humans, the CORNET (CORtisol NETwork) consortium has recently published a genome-wide association study showing that there is a significant genetic component in plasma cortisol concentrations, related to mutations in genes involving the binding of cortisol to corticosteroid-binding globulin and alpha1-antitrypsin (Bolton et al., 2014). Mutations in DNA associated with plasma cortisol concentrations have also been identified in pigs (Murani et al., 2010). According to CORNET, the hereditability of plasma cortisol in humans varies between 30 and 60%. As far as concerns the species Canis lupus familiaris, no association studies or estimates of hereditability are yet available, but analogies with other mammals suggest that there is a strong genetic basis to the adaptive response to the environment (Murani et al., 2010; Mormède et al., 2012). This could be used for breeding purposes, exploiting differences in temperament and aptitude between breeds and enabling the identification of blood lines better adapted to the typical activities of the breed. In this case, the level of cortisol, an accurate marker of an individual’s physiological condition, could be a phenotype for selective breeding, once the assay protocol has been standardised.
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
2.3.3.3_Diseases Analysis of cortisol in the saliva is also useful in all those situations of suspected hypercortisolism or disordered function of the adrenal glands, including secondary dysfunction (Wenger- Riggenbach et al., 2010). In human medicine, measurement of salivary cortisol is a diagnostic aid that is being increasingly used in a number of disorders, including non-alcoholic fatty liver disease, metabolic syndrome, obesity and cardiovascular diseases, as well as irritable bowel syndrome and inflammatory bowel disease (Vanuytsel et al., 2014; Woods et al., 2015; Baudrand et al., 2015). Table 2.1 reports some of the potential applications of salivary cortisol assays in the dog, on the basis of present knowledge and prospective uses, borrowing on current practice in humans (Chrousos and Gold, 1992; Charmandari et al., 2005).
Table 2.1 - Applications of salivary cortisol measurements in exercise and training, education, relational responses, pathophysiology and psychological disorders. Exercise and training Pathophysiology • Individual predisposition to canine activities • Cushing’s syndrome, hypercortisolism • Level of fitness during exercise and training • Addison’s disease, hypocortisolism • Monitoring stress during periods of intense canine • Diabetes mellitus activities • Metabolic syndrome and obesity and competitions • Cardiovascular diseases • Evaluation of efficacy of functional and ergogenic • Myocardial infarction compounds • Chronic inflammatory bowel disease and • Monitoring stress control in dogs working for voluntary irritable bowel syndrome civilian services, the military and police Psychopathology Education and Relational response • Anxiety • Monitoring stages of puppy socialisation • Obsessive-compulsive disorders/phobias • Animal assisted activities and therapies (AAA, AAT) • Depression • Quality of life in kennels and shelters • Hyperactivity/increased excitation and state of • Emotional response to education and re-education hypersensitivity-hyperactivity (HS-HA) programmes • Post-traumatic stress • Adaptation to adoption • Anorexia nervosa • Adaptation to new environments • Food-related compulsive behaviours
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Chapter 2 SALIVARY CORTISOL: A MARKER OF THE ADAPTIVE RESPONSE OF THE ORGANISM TO ENVIRONMENTAL STIMULI
2.4_CONCLUSIONS
The evaluation of cortisol concentrations in saliva can be considered a resource given the non- invasiveness of the sampling technique and the good parallelism between changes in this biological matrix and those in blood. Furthermore, the possibility of determining the cortisol concentration immediately, with the now available point-of-care kits is valuable for both veterinarians, who can gain a full picture of the state of an animal’s health, and for other professional figures such as dogtrainers and educators, providing them with a more complete assessment of the quality of psycho-educational and training programmes and the dog’s response to such activities. As far as concerns genetics, measuring salivary cortisol concentrations, in predetermined conditions and using a standardised protocol, could become a useful instrument for breeders to assess the character and aptitude of a breed, which could be of value for the purpose of selective breeding.
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2.5_REFERENCES
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Beerda B, Schilder MBH, Janssen NSCRM, Mol JA. The Use of Saliva cortisol, urinary cortisol, and catecholamine measurements for a noninvasive assessment of stress responses in dogs. Horm Behav, 30:272-279, 1996.
Bennett A, Hayssen V. Measuring cortisol in hair and saliva from dogs: coat color and pigment differences. Domest Anim Endocrinol, 39:171-180, 2010.
Bolton JL, Hayward C, Direk N, et al. Genome wide association identifies common variants at the serpina6/serpina1 locus influencing plasma cortisol and corticosteroid binding globulin. PLoS Genet, 10(7):e1004474, 2014.
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Bryan HM, Adams AG, Invik RM, Wynne-Edwards KE, Smits JE. Hair as a meaningful measure of baseline cortisol levels over time in dogs. J Am Assoc Lab Anim Sci, 52(2):189-196, 2013.
Charmandari E, Tsigos C, Chrousos G. Endocrinology of the stress response. Annual Review of Physiology, 67:259-284, 2005.
Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioural homeostasis. J Am Med Ass, 267(9):1244-1252, 1992.
Colussi A. Influenza delle componenti genetiche, fisiologiche ed ambientali sul cortisolo salivare del cane. Università degli studi di Udine. AA 2012/2013.
Dreschel NA, Granger DA. Methods of collection for salivary cortisol measurement in dogs. Horm Behav, 55(1):163-168, 2009.
Gatti R, De Palo EF. An update: salivary hormones and physical exercise. Scand J Med Sci Sports, 21:157-169, 2011.
Giannetto C, Fazio F, Assenza A, Alberghina D, Panzera M, Piccione G. Parallelism of circadian rhythmicity of salivary and serum cortisol concentration in normal dogs. J Appl Biomed, 12:229- 233, 2014.
Glenk LM, Kothgassner OD, Stetina BU, Palme R, Kepplinger B, Baran H. Salivary cortisol and behavior in therapy dogs during animal-assisted interventions: A pilot study. J Vet Behav, 9:98- 106, 2014.
Groeneweg FL, Karst H, de Kloet ER, Joels M. Mineralocorticoid and glucocorticoid receptors at the neuronal membrane, regulators of nongenomic corticosteroid signalling. Mol Cell Endocrinol, 350:299-309, 2012.
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Harno E, White A. Will treating diabetes with 11β-HSD1 inhibitors affect the HPA axis? Trends Endocrinol Metab, 2:619-627, 2010.
Hekman JP, Karas AZ, Dreschel NA. Salivary cortisol concentrations and behavior in a popolation of healthy dogs hospitalized for elective procedures. Appl Anim Behav Sci, 141:3-4, 2012.
Hennessy MB, Davis HN, Williams MT, Mellott C, Douglas CW. Plasma cortisol levels of dogs at a county animal shelter. Physiol Behav, 62(3):485-490, 1997.
Hiby EF, Rooney NJ, Bradshaw JW. Behavioural and physiological responses of dogs entering re- homing kennels. Physiol Behav, 89(3):385-391, 2006.
Houpt KA. Genetics of canine behavior. Acta Vet Brno, 76:431-444, 2007.
Kobelt AJ, Hemsworth PH, Barnett JL, Butler KL. Sources of sampling variation in saliva cortisol in dogs. Res Vet Sci, 75(2):157-161, 2003.
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Meyer JS, Novak MA. Minireview: hair cortisol: a novel biomarker of hypothalamic-pituitary- adrenocortical activity. Endocrinology, 153(9):4120-4127, 2012.
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Mormède P, Terenina E. Molecular genetics of the adrenocortical axis and breeding for robustness. Domest Anim Endocrin, 43:116-131, 2012.
Muráni E, Ponsuksili S, D’Eath RB, Turner SP, Kurt E, Evans G, Thölking L, Klont R, Foury A, Mormède P, Wimmers K. Association of HPA axis-related genetic variation with stress reactivity and aggressive behaviour in pigs. BMC Genet, 11:74, 2010.
Oyama D, Hyodo M, Doi H, Kurachi T, Takata M, Koyama S, Watanabe G. Saliva collection by using filter paper for measuring cortisol levels in dogs. Domest Anim Endocrinol, 46:20-25, 2013.
Pastore C, Pirrone F, Balzarotti F, Faustini M, Pierantoni L, Albertini M. Evaluation of physiological and behavioral stress-dependent parameters in agility dogs. J Vet Behav: Clin Appl Res, 6:188-194, 2011.
Poli A. Fisiologia degli animali. Regolazione-diversità-adattamento. Edizione Zanichelli, 2006.
Powell J, DiLeo T, Roberge R, Coca A, Kim J-H. Salivary and serum cortisol levels during recovery from intense exercise and prolonged, moderate exercise. Biol Sport, 32(2):91-95, 2015.
Radosevich PM, Nash JA, Lacy DB, O'Donovan C, Williams PE, Abumrad NN. Effects of low- and high-intensity exercise on plasma and cerebrospinal fluid levels or ir-beta-endorphin, ACTH, cortisol, norepinephrine and glucose in the conscious dog. Brain Res, 498(1):89-98, 1989.
Ratman D, Berghe WV, Dejager L, Libert C,Tavernier J, Beck I M, De Bosscher K. How glucocorticoid receptors modulate the activity of other transcription factors: A scope beyond tethering. Mol Cell Endocrinol, 380:41–54, 2013.
Sandri M, Colussi A, Perrotta MG, Stefanon B. Salivary cortisol concentration in healthy dogs is affected by size, sex, and housing context. J Vet Behav: Clin Appl Res, 10:302-306, 2015.
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Sjaastad ØV, Hove K, Sand O. Physiology of Domestic Animals. Scand Vet Press, 2003.
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Spady TC, Ostrander EA. Canine behavioral genetics: pointing out the phenotypes and herding up the genes. Am J Hum Biol, 82:10-18, 2008.
Stefanon B, Sandri M, Bastiani E. Misura del cortisolo salivare nel cane: sviluppo, validazione e applicazioni di un sistema Point of Care. Congresso Internazionale Scivac, Rimini, 2015.
Strehl C, Gaber T, Lowenberg M, Hommes DW, Verhaar AP, Schellmann S, Hahne M, Fangradt M, Wagegg M, Hoff P, Scheffold A, Spies CM, Burmester GR, Buttgereit F. Origin and functional activity of the membrane-bound glucocorticoid receptor. Arthritis Rheum, 63:3779-3788, 2011.
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Vanuytsel T, van Wanrooy S, Vanheel H. Psychological stress and corticotropin-releasing hormone increase intestinal permeability in humans by a mast cell-dependent mechanism. Gut, 63:1293- 1299, 2014.
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Vincent IC, Michell AR. Comparison of cortisol concentrations in saliva and plasma of dogs. Res Vet Sci, 53(3):342-345, 1992.
Wenger-Riggenbach B, Boretti FS, Quante S, Schellenberg S, Reusch CE, Sieber-Ruckstuhl NS. Salivary cortisol concentrations in healthy dogs and dogs with hypercortisolism. Vet Intern Med, 24(3):551-556, 2010.
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Wiebke A, Stewart PM. Adrenal corticosteroid biosynthesis, metabolism, and action. Endocrinol Metab Clin North Am, 34:293-313, 2005.
Woods CP, Hazlehurst JM, Tomlinson JW. Glucocorticoids and Non-Alcoholic Fatty Liver Disease. J Steroid Biochem Mol Biol, 154:94-103, 2015.
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Part 1 SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION
Chapter 3 SALIVARY CORTISOL CONCENTRATION IN HEALTHY DOGS IS AFFECTED BY SIZE, SEX, AND HOUSING CONTEXT
M Sandri a, A Colussi a, MG Perrotta b, B Stefanon a,*
Journal of Veterinary Behavior 10(2015):302-306
a Department of Agricultural and Environmental Science, University of Udine, Udine, Italy b EuroClone SpA Lab, “Leo Izzi” c/o AREA Science Park, Basovizza, Trieste, Italy
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3.1_ABSTRACT
The aim of the article was to investigate the effect of site of sampling, size, and sex on the variations of salivary cortisol of healthy dogs. Samples of saliva were collected from dogs of private owners (n = 13), kennels (n = 4), and shelters (n = 2). For each dog, samples were collected at the first interaction of the day with man (T0) before the morning meal (6:00-8:00 AM), 30 minutes after the meal (T1), and 30 minutes after the last interaction of the day with man (T2), when dogs were resting and apparently relaxed. A total of 92 dogs belonging to 17 different pure breeds or crossbred were eligible for the study, being 19 dogs privately owned, 47 recruited in kennels, and 26 hosted in shelters. Salivary cortisol concentrations of the dog population were not normally distributed, and data were transformed to natural logarithm (ln). The mean values ranged from - 0.70 to 3.40 ln ng/mL, with an average of 0.90±0.76 ln ng/mL, corresponding to 0.50, 30.00, and 3.48±4.05 ng/mL. Mean salivary cortisol was significantly higher for dogs hosted in shelters than those privately owned or in the kennels (P<0.05). Cortisol values from intact dogs did not differ between males and females, whereas for castrated males and spayed females, significantly lower values were found (P<0.01 intact vs. castrated males; P<0.05 intact vs. spayed females). Mean salivary cortisol concentration was significantly lower for giant and large-sized dogs than for small- sized dogs (P<0.01), whereas mean cortisol for medium-sized dogs was not significantly different from the other sizes. The interaction of site with time of sampling was significant (P<0.05), with the highest cortisol concentration at T2 for dogs privately owned and housed in the kennels and at T0 for dogs hosted in the shelters. This study, focused on healthy dogs, indicated that several factors can affect the concentration of salivary cortisol. Further studies also involving pathological conditions are required to identify critical values that can be used for clinical management settings.
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3.2_INTRODUCTION
The hypothalamic-pituitary-adrenal (HPA) axis is exquisitely sensitive in the transduction to biological responses of the exposure to cognitive and non cognitive stress. Cortisol is the main end product of the activation of HPA axis and is widely used and accepted to monitor the reactivity to stress, also in connection to disease (Gallagher and Ritsner, 2009; Hellhammer et al., 2009). Cortisol concentration can be measured in several matrixes, as blood, urines, feces, integument, milk, or saliva, each of them enabling the study of HPA axis from different perspectives (Bennet and Hayssen, 2010; Meyer and Novak, 2012). For chronic stress, hair has been often proposed because it accumulates a series of repeated and sometimes different stimuli in the period to which they refer, although color, climate, and other factors can affect at some extent the reliability of this matrix for long-term assessment (Bennet and Hayssen, 2010). In the case of an evaluation of the short-term response to stimuli, blood, tears, and saliva can be preferentially used, and the latter is the more accessible and easier to collect, minimizing animal restrain (Kobelt et al., 2003). Study on salivary cortisol concentrations to mark distress or undesirable outcomes in dogs is a topic that has received an increased attention in the last years (Bellaio et al., 2009; Bennet and Hayssen, 2010; Wenger-Riggenbach et al., 2010; Beetz et al., 2011; Pastore et al., 2011; Glenk et al., 2014). Saliva can be collected less invasively than blood or urine, and its cortisol concentration has shown to closely parallel plasma cortisol values (Beerda et al., 1996; Hellhammer et al., 2009). The vast majority of these researches used cortisol measurements to investigate stress-related response in dogs or to correlate HPA axis response to behavior, with the aim of using a biological marker to assess cognitive or non cognitive stress, also providing a practical tool for clinical management settings. However, these studies were more focused on salivary cortisol variations in relation to the specific experimental design than to assess physiological differences among genetic and environmental contexts as well as other animal-related factors, as size and sex. The aim of the article was to investigate the variations of salivary cortisol concentrations during an ordinary day of the life in healthy dogs. Salivary samples were collected from dogs at home, kennels, or shelters.
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3.3_MATERIALS AND METHODS
3.3.1_Animal selection Dogs were recruited from 13 private owners, 4 kennels, and 2 shelters, all located in the North East part of Italy. The aims of the study and the sampling procedures were disclosed to the owners, breeders, or shelter managers, and informed consents were obtained before any procedure. After showing instructions and training, owners, breeders, or shelter personnel were the only people who collected saliva samples, to avoid potential interference because of the presence of unknown people. The criteria for dogs to be eligible for the study were the following: (1) more than 12 months of age; (2) clinically healthy, free from pain, external and internal parasites, and immunized, as assessed by a veterinary practitioner; (3) without history of neurologic abnormalities; (4) without history of behavioral problems; (4) no recent history of corticosteroid administration; and (5) no drug therapy at sampling and from 1 month before. Clinical data were obtained from the medical records available from the owners or shelter and kennel veterinarians. For each dog, the following information was also collected: date of birth, breed, size, sex (male, castrated male, female, and spayed female), feeding schedule, and type of food. For each dog, 3 samples were collected during the same day. The T0 sample was collected in the morning at the first interaction with man immediately before the morning meal (6:00-8:00 AM) and the T1 sample 30 minutes after the meal. The last sample (T2) was collected 30 minutes after the last interaction of the day with man, when dogs were resting and apparently relaxed, according to the visual evaluation of the personnel in charge of the animal.
3.3.2_Salivary sampling To avoid contamination of samples and the interference with the enzyme immunoassay (Dreschel and Granger, 2009), dogs were to refrain from drinking and eating 20 minutes before sampling. Salivation was stimulated allowing the dogs only to sniff food treats (Bennet and Hayssen, 2010; Ligout et al., 2010). Saliva was collected with swabs (Salimetrics, State College, PA) gently placed into the cheek pouch of the dog by the owners, breeders, or shelter managers for approximately 90-120 seconds, a time considered adequate for the saturation with saliva. Samples were checked for visible contamination with food or blood. For ethical reasons, dogs were never restrained. After sampling, the swabs were introduced into tubes specifically designed to avoid cortisol 60
Chapter 3 SALIVARY CORTISOL CONCENTRATION IN HEALTHY DOGS IS AFFECTED BY SIZE, SEX, AND HOUSING CONTEXT sequestration (Salivette; no. 51.1534, Sarstedt, Nümbrecht, Germany), temporally stored in an iced box before the final storage at -20°C. Before analysis, performed within 15 days, swabs were thawed and centrifuged at room temperature at 1500xg for 15 minutes to obtain clear saliva, which was used for cortisol determination using an enzyme immunoassay kit (Salimetrics, State College, PA) (Hekman et al., 2012). Samples were assayed in duplicate, using 25 mL of sample per well. The kit’s lower limit of sensitivity was 0.03 ng/mL. Average intra- and interassay coefficients of variation were less than 12% and 8%, respectively.
3.3.3_Statistical analysis For the analysis, only dogs with all the 3 daily samples were considered, and a total of 92 dogs were available. Data were stored in a spreadsheet using Microsoft Office Excel (2010; Microsoft Corp, Redmond, WA), and the descriptive statistics and analyses were performed with the SPSS (1997) package (SPSS Inc, Chicago, IL). Normality of salivary cortisol was tested by the Kolmogorove Smirnov nonparametric test. Because data were not normally distributed, natural logarithm (ln) transformation was adopted. Dogs were classified according to the Federation Cynologique International (http://www.fci.be/en/) in small, medium, large, and giant sizes. For statistical analysis, a mixed model was used for the natural logarithm-transformed values, considering the fixed effects of site of sampling (owner, kennel, and shelter, from 1 to 3), sex (males, castrated males, females, and spayed females, from 1 to 4), size (small, medium, large, and giant, from 1 to 4), time of sampling (T0, T1, and T2, from 1 to 3), the random factor of subject repeated with time of sampling, and the covariate of age within size. To identify differences between means, the least significant differences test was applied.
3.4_RESULTS
A total of 92 dogs, belonging to 17 different pure breeds or crossbred, were eligible for the study. In particular, 19 dogs were privately owned, 47 were recruited in kennels, and 26 were hosted in shelters (Table 3.1). Most dogs were not neutered (26 males and 45 females), and the 7 castrated males came all from the shelters, whereas 8 of the 14 spayed females were from the shelters, 5 from private owners, and 1 from kennel. The most represented pure breeds within the population were dachshund, golden retriever, Irish wolfhound, and Jack Russell terrier (12, 11, 9, and 9 subjects, respectively). Seven pure breeds were represented by 1 dog each, and crossbred was the most numerous group (18 dogs). On average, dogs were 4.2±3.4 years old. 61
Chapter 3 SALIVARY CORTISOL CONCENTRATION IN HEALTHY DOGS IS AFFECTED BY SIZE, SEX, AND HOUSING CONTEXT
Table 3.1_ Numerosity, breed, and sex of the dogs recruited for the study, according to the site of sampling.
Breed Owner Kennel Shelter Total No. of dogs Sex No. of dogs Sex No. of dogs Sex No. of dogs Sex M/CM/F/SF M/CM/F/SF M/CM/F/SF M/CM/F/SF American Stafforshire terrier 0 0/0/0/0 0 0/0/0/0 4 0/1/1/2 4 0/1/1/2 Bobtail 0 0/0/0/0 1 0/0/1/0 0 0/0/0/0 1 0/0/1/0 Border collie 4 1/0/3/0 0 0/0/0/0 0 0/0/0/0 4 1/0/3/0 Boxer 0 0/0/0/0 6 3/0/3/0 0 0/0/0/0 6 3/0/3/0 Czechoslovakian wolfdog 4 2/0/1/1 0 0/0/0/0 0 0/0/0/0 4 2/0/1/1 Crossbreed a 1 0/0/0/1 0 0/0/0/0 17 3/5/4/5 18 3/5/4/6 Dalmatian 0 0/0/0/0 0 0/0/0/0 1 1/0/0/0 1 1/0/0/0 Dachshund 4 1/0/1/2 8 3/0/5/0 0 0/0/0/0 12 4/0/6/2 German shepherd 0 0/0/0/0 1 0/0/1/0 2 1/0/1/0 3 1/0/2/0 Golden retriever 2 1/0/0/1 9 2/0/6/1 0 0/0/0/0 11 3/0/6/2 Irish wolfhound 0 0/0/0/0 9 2/0/7/0 0 0/0/0/0 9 2/0/7/0 Jack Russel terrier 0 0/0/0/0 9 3/0/6/0 0 0/0/0/0 9 3/0/6/0 Jagdterrier 0 0/0/0/0 0 0/0/0/0 1 0/1/0/0 1 0/1/0/0 Kurzhaar 1 1/0/0/0 4 1/0/3/0 0 0/0/0/0 5 2/0/3/0 Labrador retriever 1 0/0/1/0 0 0/0/0/0 0 0/0/0/0 1 0/0/1/0 Pointer 1 1/0/0/0 0 0/0/0/0 0 0/0/0/0 1 1/0/0/0 Rottwailer 0 0/0/0/0 0 0/0/0/0 1 0/0/0/1 1 0/0/0/1 Weimaraner 1 0/0/1/0 0 0/0/0/0 0 0/0/0/0 1 0/0/1/0 Total 19 7/0/7/5 47 14/0/32/1 26 5/7/6/8 92 26/7/45/14 M, male; CM, castrated male; F, female; SF, spayed female. a Size of the dogs: 1, mini; 10, medium; and 7, large. Size grouping mirrors the weight associated to size of the Federation Cynologique International (http://www.fci.be/en/).
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The values of salivary cortisol were not normally distributed in our dog population, and a natural logarithm transformation was applied. After transformation, data were normally distributed (Table 3.2), showing lower values for symmetry indexes, skewness, and kurtosis. The mean salivary cortisol ranged from -0.70 to 3.40 ln(ng/mL), with a mean value of 0.90±0.76 ln(ng/mL), corresponding to 0.50, 30.00, and 3.48±4.05 ng/mL (Table 2).
Table 3.2_ Descriptive statistics of salivary cortisol concentrations measured in a population of 92 dogs sampled 3 times during the same day (276 observations).
ng/mL ln (ng/mL)
Mean 3.48 0.90 Standard deviation 4.05 0.76 Median 2.32 0.84 Skewness 4.13 0.69 Standard error 0.15 0.15 Kurtosis 21.58 0.57 Standard error 0.29 0.29 Kolmogorov-Smirnov Z value 3.90 a 0.83 Minimum 0.50 -0.70 Maximum 30.00 3.40 Percentile 25 1.40 0.33 50 2.32 0.84 75 4.01 1.39 90 6.27 1.84 a KolmogoroveSmirnov Z value significantly deviated from normality for the untransformed cortisol data.
The effects of size, sex, site, and time of sampling on natural logarithm-transformed cortisol concentrations are reported in Table 3.3. Cortisol concentrations significantly differed between sites of sampling (P < 0.05), having the highest values in dogs hosted in shelters, followed by dogs privately owned and by dogs in kennels (0.99, 0.58, and 0.46 ln[ng/mL], respectively). Cortisol values did not differ between intact males and female dogs, whereas for castrated males and spayed females, significantly lower values were found (0.21 and 0.47 ln[ng/mL], respectively; P<0.01). Mean salivary cortisol concentrations were significantly higher for small-sized dogs in comparison to the other sizes (P<0.01). Giant and large-sized dogs had significantly lower mean cortisol concentration (P<0.01) than small-sized dogs but did not differ from medium-sized dogs. 63
Chapter 3 SALIVARY CORTISOL CONCENTRATION IN HEALTHY DOGS IS AFFECTED BY SIZE, SEX, AND HOUSING CONTEXT
Noteworthy, the interaction size x time of sampling was not significant, but small-sized and medium-sized dogs showed the lowest cortisol concentration in the last sample of the day (T2), whereas large and giant dogs showed the lowest mean value at the T1 sampling time (data not shown). Instead, the site x time of sampling interaction (Figure 3.1) was significant (P<0.05), showing the highest cortisol concentration at T2 for the dogs privately owned and housed in kennels and at T0 for those hosted in the shelters. The linear effect of age, nested within the size of the dogs, was significant for P<0.05.
Figure 3.1_ Effect of the site x time of sampling interaction on salivary cortisol concentration of the 92 dogs enrolled for the study.
a and b mean with different superscripts differ for a P<0.05. T0, sample collected at the first interaction of the day with man and immediately before the morning meal (6:00-8:00 AM); T1, sampled 30 minutes after the meal; T2, sample collected 30 minutes after the last interaction with man, when dogs were resting and apparently relaxed, according to the visual evaluation of the personnel in charge of the animal.
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Table 3.3_ Effects of site of sampling, size, sex, and time of sampling on salivary cortisol concentration measured in our dog population. Effects No. ng/mL Standard ln(ng/mL) Standard observation error error Site Owner 57 3.13 0.71 0.58 0.16 b Kennel 141 1.57 0.58 0.46 0.13 c Shelter 78 3.81 0.60 0.99 0.13 a Size a Small 66 4.09 0.67 1.15 0.15 A Medium 54 2.05 0.69 0.60 0.15 BC Large 126 3.37 0.46 0.75 0.11 B Giant 30 1.84 0.95 0.22 0.21 C Sex M 78 4.31 0.52 1.06 0.12 A CM 21 1.29 1.01 0.21 0.24 B F 135 3.91 0.46 0.99 0.10 A SF 42 1.84 0.69 0.47 0.16 B Time T0 92 3.48 0.67 0.73 0.11 NS T1 92 2.56 0.51 0.605 0.103 NS T2 92 2.47 0.39 0.703 0.094 NS
M, male; CM, castrated male; F, female; SF, spayed female; NS, not significant. In the model, the covariate for age within size was positive (P<0.05). For statistical analysis, log- transformed data were used, and a mixed model with the random factor of dog repeated within time of sampling was applied. Differences between means were assessed with the least significant differences test. a, b, and c means with different superscript differ for a P <0.05. A, B, and C means with different superscript differ for a P<0.01. T0, sample collected at the first interaction of the day with man and immediately before the morning meal (6:00-8:00 AM); T1, sampled 30 minutes after the meal; T2, sample collected 30 minutes after the last interaction with man, when dogs were resting and apparently relaxed, according to the visual evaluation of the personnel in charge of the animal. a Dogs were grouped in small, medium, large, and giant size according to the Federation Cynologique International. For crossbreeds, size grouping mirrors the weight associated to size of the Federation Cynologique International (http:// www.fci.be/en/).
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3.5_DISCUSSION
The HPA axis plays a pivotal role in the elaboration of animal response to environment, and the quantification of its activation is fundamental to investigate the extent of stimulation, which can affect health and well-being. Among the hormones secreted after activation of HPA axis, cortisol can be considered the most representative one or, at least, a reliable and widely used biomarker (Gallagher and Ritsner, 2009). In this study, we wanted to investigate sources of variability for the salivary cortisol in relation to size and breed, sex, and time of day, and also to propose a threshold that could identify a critical response of HPA axis to environmental stimuli. Indeed, we collected saliva samples from dogs in good clinical conditions, not suffering of behavioral disorders and acquainted to environment, management, and housing. According to Vincent and Michell (1992), salivary cortisol concentrations reflect the respective variations in plasma with a delay of 20-30 minutes, preventing, if sampling procedures are exploited in the due time, bias because of cortisol secretion in blood related to animal handling. As previously reported in the literature (Cooper et al., 2014), in our study, the descriptive statistic of saliva cortisol concentrations indicated a non-normal distribution, and a natural logarithm transformation before the mixed model analysis was applied (Table 3.2). For practical purposes, the identification of a threshold for the description of HPA axis activation in dogs requires a normal data distribution of salivary cortisol, which can be attained from the logarithm transformation of data. Small-sized and giant-sized dogs showed significant differences (P<0.01) of cortisol concentration in saliva, being the former higher and the latter lower, indicating that factors other than environment can affect saliva cortisol (Table 3.3). In our study, dachshund and Jack Russell were the most represented breeds for the small-sized dogs, crossbred for the medium sized, golden retriever for the large, and Irish wolfhound for the giant size. It is likely that the genetic background of breed other than liveweight is a factor affecting the responsiveness of the dogs to environmental stimulations (Houpt, 2007; Spady et al., 2008). From the best of our knowledge, no results are published for the effect of breed or size on cortisol secretion, but in other researches, the authors recognized the need to randomize dogs according to breed and size to avoid potential
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interferences (Bennet and Hayssen, 2010; Hekman et al., 2012). Furthermore, Vas et al. (2007) measured attention deficit hyperactivity disorder using a human questionnaire and found that small-sized dogs showed significantly differences in their activity/impulsive score in comparison to large-sized dogs. The genetic basis of behavior and the related neurobiochemical assessment are also the aim of the Canine Behavioral Genetics Project (Overall et al., 2006). It is known that larger breeds age earlier than smaller breeds (Greer et al., 2007), and the pure breed age earlier than crossbreed dogs (Patronek et al., 1997). The number of crossbreed dogs and older dogs was higher in shelters compared with the other site of sampling (Table 3.1) and in the shelters, the number of dogs with more than 10 years were 5, in comparison to 1 in the kennels and at home. Considering that, the covariate of age within sizewas used in the statistical model to account for the effect that age can have in different sizes. The significant positive relationship with the age (P<0.05) would support an increase of cortisol concentration in the saliva of older dogs. However, a limited number of studies on factors potentially affecting blood cortisol in dogs have been published, and the effect of aging is still controversial. Some authors reported an age-related increase in circulating cortisol (Rothuizen et al., 1993; Goy-Thollot et al., 2007), but other researches failed to find this relationship (Reimers et al., 1990; Hennessy et al., 1997; Mongillo et al., 2014). Site of sampling significantly affected mean cortisol concentration in saliva (Table 3.3), probably reflecting the different physical and emotional-related characteristics of these environments (Bergamasco et al., 2010; Wood et al., 2014). According to Beerda et al. (1998), the concentration of salivary cortisol increases from a mean basal value of 2.16-4.68 and up to 6.01 ng/mL in dogs exposed to different stimulations. In a study of Hekman et al. (2012) with dogs selected among a population of healthy patients hospitalized for an elective procedure, median and mean cortisol concentrations in saliva were 4.3 and 8.7 ng/mL, respectively. These values are above the 75 percentile of the present study (Table 2), confirming that saliva cortisol is a marker of HPA axis activation in response to the environment. Dogs hosted in shelters were individually housed in box and were all fed approximately at the same time from the petters and, not surprisingly, showed the highest cortisol concentration. It is well known that the rearrangement of groups in livestock causes a relevant HPA axis stimulation with an increase of cortisol in plasma (Sorrells, 2007) and milk (Fukasawa and Tsukada,
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2010) because of physical contacts among animals and the formation of new hierarchies. The introduction of new rescued dogs in the shelter or their adoption by new owners leads to a continuous turnover and the re-establishment of relationships, not necessarily based on physical interaction but on smells, pheromones, and vocalizations. According to Hennessy et al. (1997), the confinement in an animal shelter produces a prolonged activation of HPA axis, and also the interaction with human can modulate its response (Shiverdecker et al., 2013). Another reason for the higher mean salivary cortisol in shelter can be the competition of dogs for the care of petters in the morning, human contact, and expectation of food. The significant increase of salivary cortisol at T0 sample (Figure 3.1) would support this consideration. Why the concentration of cortisol increased in T2 samples in dogs at home and kennel is not simple to explain. The significant inverse trend observed for dogs at home or in the kennel can be considered the result of HPA activation because of the environmental effect. Probably, differences of breeds, daily routine, man to dog interaction, diet, and other confounding factors can interfere with HPA response, and these aspects deserve further investigation.
3.6_CONCLUSIONS
The study investigated the variation of cortisol concentrations in relation to environmental and physiological factors in a large population of healthy dogs. The results indicated that size of dogs, sex, and time of sampling in different environments have to be considered as factors that can influence basal cortisol values in the saliva. Further studies also involving pathological conditions are required to identify critical values that can be used for clinical management settings.
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3.7_REFERENCES
Beerda B, Schilder MBH, Janssen NSCRM, Mol JA. The use of saliva cortisol, urinary cortisol, and catecholamine measurements for a noninvasive assessment of stress responses in dogs. Horm Behav, 30:272-279, 1996.
Beerda B, Schilder MBH, Van Hooff JARAM, de Vries HW, Mol JA. Behavioural, saliva cortisol and heart rate responses to different types of stimuli in dogs. Appl Anim Behav Sci, 58:365-381, 1998.
Beetz A, Kotrschal K, Turner DC, Hediger K, Uvnäs-Moberg K, Julius H. The effect of a real dog, toy dog and friendly person on insecurely attached children during a stressful task: an exploratory study. Anthrozoos, 24:349-368, 2011.
Bellaio E, Normando S, Bono G. Stress assessment in rescue dogs during routine training sessions. J Vet Behav: Clin Appl Res, 4:83, 2009.
Bennet A, Hayssen V. Measuring cortisol in hair and saliva from dogs: coat color and pigment differences. Domest Anim Endocrinol, 39:71-180, 2010.
Bergamasco L, Osella MC, Savarino P, Larosa G, Ozella L, Manassero M, Badino P, Odore R, Barbero R, Re G. Heart rate variability and saliva cortisol assessment in shelter dog: humane animal interaction effects. Appl Anim Behav Sci, 125:56-68, 2010.
Cooper JJ, Cracknell N, Hardiman J, Wright H, Mills D. The welfare consequences and efficacy of training pet dogs with remote electronic training collars in comparison to reward based training. PLoS One 9:e102722, 2014.
Dreschel NA, Granger DA. Methods of collection for salivary cortisol measurement in dogs. Horm Behav, 55:63-68, 2009.
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Fukasawa M, Tsukada H. Relationship between milk cortisol concentration and the behavioral characteristics of postpartum cows introduced to a new group. Anim Sci J, 81:612-617, 2010.
Gallagher P, Ritsner MS. Can the cortisol to DHEA molar ratio be used as a peripheral biomarker for schizophrenia and mood disorders?. In: Ritsner MS, (Ed.). The Handbook of Neuropsychiatric Biomarkers, Endophenotypes and Genes, Vol. 3 Springer Science + Business Media, pp 27-46, 2009.
Glenk LM, Kothgassner OD, Stetina BU, Palme R, Kepplinger B, Baran H. Salivary cortisol and behavior in therapy dogs during animal-assisted interventions: a pilot study. J Vet Behav: Clin Appl Res, 9:98-106, 2014.
Goy-Thollot I, Decosne-Junot C, Bonnet JM. Influence of aging on adrenal responsiveness in a population of eleven healthy beagles. Res Vet Sci, 82:195-201, 2007.
Greer KA, Canterberry SC, Murphy KE. Statistical analysis regarding the effects of height and weight on life span of the domestic dog. Res Vet Sci, 82:208-214, 2007.
Hekman JP, Karasa AZ, Dreschelb NA. Salivary cortisol concentrations and behavior in a population of healthy dogs hospitalized for elective procedures. Appl Anim Behav Sci, 141(3-4):149-157, 2012.
Hellhammer DH, Wust S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinol, 34:163-171, 2009.
Hennessy MB, Davis HN, Williams MT, Mellott C, Douglas CW. Plasma cortisol levels of dogs at a county animal shelter. Physiol Behav, 62:485-490, 1997.
Houpt KA. Genetics of canine behavior. Acta Vet Brno, 76:431-444, 2007.
Kobelt AJ, Hemsworth PH, Barnett JL, Butler KL. Sources of sampling variation in saliva cortisol in dogs. Res Vet Sci, 75:157-161, 2003.
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Ligout S, Wright H, van Driel K, Gladweel F, Mills DS, Cooper JJ. Reliability of salivary cortisol measures in dogs in training context. J Vet Behav: Clin Appl Res, 5(1):49, 2010.
Meyer JS, Novak MA. Minireview: Hair cortisol: a novel biomarker of hypothalamic-pituitary- adrenocortical activity. Endocrinol, 153:4120-4127, 2012.
Mongillo P, Prana E, Gabai G, Bertotto D, Marinelli L. Effect of age and sex on plasma cortisol and dehydroepiandrosterone concentrations in the dog (Canis familiaris). Res Vet Sci, 96:33-38, 2014.
Patronek GJ, Waters DJ, Glickman LT. Comparative longevity of pet dogs and humans: implications for gerontology research. J Gerontol A Biol Sci Med Sci, 52:B171-B178, 1997.
Overall KL, Hamilton SP, Chang ML. Understanding the genetic basis of canine anxiety: phenotyping dogs for behavioral, neurochemical, and genetic assessment. J Vet Behav: Clin Appl Res, 1:124-141, 2006.
Pastore C, Pirrone F, Balzarotti F, Faustini M, Pierantoni L, Albertini M. Evaluation of physiological and behavioral stress-dependent parameters in agility dogs. J Vet Behav: Clin Appl Res, 6:188-194, 2011.
Reimers TJ, Lawler DF, Sutaria PM, Correa MT, Erb HN. Effects of age, sex, and body size on serum concentrations of thyroid and adrenocortical hormones in dogs. Am J Vet Res, 51:454-457, 1990.
Rothuizen J, Reul JM, Van Sluijs FJ, Mol JA, Rijnberk A, De Kloet ER. Increased neuroendocrine reactivity and decreased brain mineralocorticoid receptor-binding capacity in aged dogs. Endocrinol, 132:161-168, 1993.
Shiverdecker MD, Schiml PA, Hennessy MB. Human interaction moderates plasma cortisol and behavioral responses of dogs to shelter housing. Physiol Behav, 109:75-79, 2013.
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Sorrells AD, Eicher SD, Harris MJ, Pajor EA, Richert BT. Periparturient cortisol, acute phase cytokine, and acute phase protein profiles of gilts housed in groups or stalls during gestation. J Anim Sci, 85:1750-1757, 2007.
Spady TC, Ostrander EA. Canine behavioral genetics: pointing out the phenotypes and herding up the genes. Am J Hum Genet, 82:10-18, 2008.
SPSS Advanced Statistic 7.5. SPSS Base 7.5 for Windows User’s Guide. SPSS Inc, Chicago, IL, 1997.
Vas J, Topa J, Pech E, Miklosi A. Measuring attention deficit and activity in dogs: a new application and validation of a human ADHD questionnaire. Appl Anim Behav Sci, 103:105-117, 2007.
Vincent IC, Michell AR. Comparison of cortisol concentrations in saliva and plasma of dogs. Res Vet Sci, 53:342-345, 1992.
Wenger-Riggenbach B, Boretti FS, Quante S, Schellenberg S, Reusch CE, Sieber- Ruckstuhl NS. Salivary cortisol concentrations in healthy dogs and dogs with hypercortisolism. J Vet Intern Med, 24:551-556, 2010.
Wood PA, de Bie J, Clarke JA. Behavioural and physiological responses of domestic dogs (Canis familiaris) to agonistic growls from conspecifics. Appl Anim Behav Sci, 161:105-112, 2014.
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Part 1 SALIVARY CORTISOL: AN EFFICIENT BIOINDICATOR OF HYPOTHALAMIC–PITUITARY–ADRENAL AXIS ACTIVATION
Chapter 4 VARIATIONS OF SALIVARY CORTISOL IN DOGS EXPOSED TO DIFFERENT COGNITIVE AND PHYSICAL ACTIVITIES
A Colussi a*, B Stefanon a, C Adorini b M Sandri a
In submission in Italian Journal of Animal Science
a Department of AgriFood, Environmental and Animal Science, University of Udine, Udine, Italy b DVM, Veterinary Clinic “Chiara Adorini”, 33050 Zugliano di Pozzuolo del Friuli, Udine, Italy
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4.1_ABSTRACT
Working dogs are gaining popularity for their ability to learn and perform tasks entertaining their human companions. For this reason, dogs are often subjected to various stimuli due to inter- and intra-specific interactions, environmental variations and effort required by different activities. In the present study, salivary cortisol was measured to monitor physiological response to different conditions. The first study was performed to assess the variability of salivary cortisol in dogs in usual environmental conditions. For this salivary cortisol was measured in 10 dogs at home during 3 not consecutive days at 3 different times of the day and not significant variations between days and time of sampling were observed. In the second study, salivary cortisol was measured in dogs before and after Pointing Hunting (No. 5), Tracking for Ungulate Hunting (No. 6), Blood Tracking (No. 4), Agility Training (No. 6) and Animal Assisted Activities (AAA, No. 6). Salivary cortisol concentration significantly increased after the Pointing Hunting activity (P<0.05), while salivary cortisol significantly decreased at the end of AAA session (P<0.05). Not significant differences in cortisol variations were observed for Tracking for Ungulate Hunting, Blood Tracking and Agility Training, before and after the activities. The response of cortisol suggests that the extent of Hypothalamus–Pituitary–Adrenal axis activation varies between short high-intensity activities and endurance exercises. The measurement of salivary cortisol can support the trainers to evaluate the animal response to the stimulations.
Keywords: cortisol, saliva, stimuli, exercise, environment, dogs.
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4.2_INTRODUCTION
The growing popularity in canine competitions has led to an increase focusing on dogs health status, cognitive skills and level of fitness. Dogs are trained to perform different tasks to entertain its human companion during sporting and hunting activities or to assist people as a service therapy dog. In these contests, dog may experience stress associated to exercise, social interactions, environmental factors, loud noises, exposure to novelties or high expectations of the handler (Beerda et al., 1998; Pastore et al., 2011; Shiverdecker et al., 2013). In order to understand the physiological response of the animals to exercise and related activities, many studies have been carried out to monitor the associated neuroendocrine and biochemical changes (Arokoski et al., 1993; Angle et al., 2009; Wakshlag et al., 2010; Yazwinski et al., 2013; Tharwat et al., 2014). Among the biomarkers used to evaluate the physiological responses to exercise, salivary cortisol has been largely used in human and horse athletes (Schmidt et al., 2010; Pastore et al., 2011; Lippi et al., 2016; Vingren et al., 2016), but according to the recent review of Cobb et al. (2016) to a lesser extent in dogs. Cortisol is the main end product of the activation of Hypothalamic-Pituitary- Adrenal (HPA) axis and its secretion shows a sensitive response to environmental changes. However, cortisol concentration in blood can be affected by sampling procedure and sudden environmental changes. Thus alternative sites of sampling have been recently investigated and the measurement of cortisol in saliva has gained popularity to monitor variations of physiological states (Bergamasco et al., 2010; Cobb et al., 2016; Colussi et al., 2016). Concentrations of cortisol in blood and saliva are highly correlated and its transfer occurs with a mean delay of 20-30 minutes (Dreschel and Granger, 2009; Peeters et al., 2013). Furthermore, saliva is easy to sample and can be collected also by the owner or petter, without causing additional stress for the dog due to handling or changing environmental context (Hiby et al., 2006; Jones et al., 2014). In the present study, we measured salivary cortisol in relation to different dogs’ activities, like physical exercise and cognitive responses, to investigate their effects on the HPA axis stimulation. For this aim, salivary samples were collected from dogs performing Pointing Hunting, Tracking for Ungulate Hunting, Blood Tracking, Agility Training and Animal Assisted Activities (AAA).
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4.3_MATERIALS AND METHODS
4.3.1_Recruitment of dogs In the present study, all dogs were privately owned, more than 12 months old, clinically healthy, not in estrus or pregnant, vaccinated and free from external and internal parasites, as assessed by a veterinary practitioner. Moreover, dogs were not under corticosteroid administration or drug therapy for at least one month before the sampling procedure. For each dog, the following information was also collected: date of birth, breed, size, sex (male, castrated male, female, spayed female), feeding schedule and type of food. The aim of the study and the sampling procedures were disclosed to the owners and informed consents were obtained prior to any procedure. In order to avoid potential interference on cortisol response due to the presence of unknown people, saliva samples were collected by owners, trained on sample taking during a preliminary meeting. Each owner recorded the exact time of sample collection and signs of distress, if any. All procedures were performed in respect of the legislation on animal care (EU Directive 2010/63/EU) and the internal rules of University of Udine.
4.3.1.1_Study 1: Baseline value of salivary cortisol In the first study, 10 dogs were not involved in specific activities. Dog owners were asked to collect salivary samples for 3 not consecutive days (D1, D3 and D5): in the morning (MO), at the first interaction with human during the day (MD), and 30 minutes after the last interaction of the day with owner (EV), when dogs were rested and seemed relaxed. From each dog saliva was sampled at the same time. The times of MO samples ranged from 6:30 to 9:30, those of MD from 9:30 to 15:00 and those of EV from 20:00 to 23:00.
4.3.1.2_Study 2: Variation of salivary cortisol during activity In the second study, dogs were already trained for the following 5 activities: Pointing Hunting, Tracking for Ungulate Hunting, Blood Tracking, Agility Training and AAA (Table 4.1). In order to obtain individual references of salivary cortisol concentration, a baseline sample (T0) was collected the day before the activity at EV time period from all dogs included in the analysis. T0 samples were collected at home 30 minutes after the last interaction of the day with them. Dogs of Pointing Hunting, Tracking for Ungulate Hunting and Blood Tracking were housed in kennels and dogs of Agility Training and AAA were housed at home. Moreover from all dogs the second salivary
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sample (T1) was collected just before beginning of activity. This sample was collected to control the potential interference on the final results of the car transportation, from dogs home place to the place where performance was taken. Dogs involved in Pointing Hunting and Tracking for Ungulate Hunting conducted the activities with their owners in packs. Dogs involved in Blood Tracking and Agility Training conducted the activity individually, and those involved in AAA in couple, under owners control. In Pointing Hunting, Tracking for Ungulate Hunting and Blood Tracking, the samples of saliva from hunting dogs were collected during hunting sessions, which were fixed by hunters. Game animals were not specifically requested and involved for the study. Each activity occurred in one day and its briefly description, including the relative time schedule of salivary sampling, is described below. Pointing Hunting: all dogs were in one pack and the session started at 8:00 and finished at around 12:00 (4 hours). The pack carried out their ordinary hunting activities followed by their group handlers (the hunters). Dogs had to run quartering the hunting ground field appropriately and to point out typical upland game birds, maintaining steadiness until the bird is flushed, after the gun shot of the handler the game supposed to be successfully finished. These steps were repeated for the entire duration of the hunting session. A T2 salivary sample was collected roughly at 15 minutes from the end of the session, that took place in a morning of November (weather T 10.6- 11.9°C relative humidity Rh 56-66% and wind speed 18-22 km/h, 46°15'58.5"N 13°04'35.0"E geographical coordinates). Time elapsed between T1 and T2 sample was similar for all dogs, i.e. 258±3 minutes. Tracking for Ungulate Hunting: the dogs in packs reached the woodland hill field travelling in their owner’s car. All dogs arrived at the same time and T1 samples were collected 10 minutes after getting off the vehicle. Soon after, all dogs in packs were let free to smell the track of the deer, after around 30 minutes the ungulate was shot by handler/hunter. The wounded ungulate escaped to hide into the wood and dogs were hence put on the leash with his respective owner to track the blood scent of the escaping wounded deer for 30 to 50 minutes (mean 45±14 minutes), followed by the hunter until they found the deer. About 15 minutes (16±1 minutes) after the hunting is over a T2 sample was collected. Time elapsed between T2 and T1 was 91±14 minutes. The activity was carried out in the morning in February (weather T 7.1-8.4°C relative humidity Rh 92-94% and wind 3-8 km/h, 46°07'32.0"N 13°28'16.2"E geographical coordinates).
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Blood Tracking: activity was carried out on four tracks (one for each dog) of 1.5 km length, that were plotted on the ground by a third person with natural blood collected from the slaughter house. The blood was wrapped in a jute bag and left at the end of the tracks. Around the starting time of the activity (11:30 AM), each leashed dog was conducted on his own designated field and just before the beginning of activity the T1a sample was collected. This sampling correspond to the T1 sample of Tracking for Ungulate Hunting. Dogs were let free and a gunshot simulating the game wounding was used to initialize the trial; an extra sample was collected 10±1 minutes after the shot (T1b sample). The track of blood scent was followed by the dog on a leash for about 60 min (mean 55±8) and about 12 min (12±2) after the end of the tracking, when the dog found the blood bag, a T2 saliva sample was collected. Time elapsed between T2 and T1a was 77±9 minutes. The blood tracking trial was conducted in December (indicative weather T 7.8-9.9°C, humidity U 69- 76% and wind 6-10 km/h, 46°07'32.0"N 13°28'16.2"E geographical coordinates). Agility Training: samples were collected from dogs fit for competing at the professional level in the Large dogs category (65 cm jump obstacles for Border Collies), during a standard training session in a competition track. All dogs were familiar with the activity, arrived with the owner car to the filed on the same time (from 8:30 to 8:50) and stayed in the car until the training started. The T1 sample was collected just before the beginning of activity of each dog, which consisted of about 3 min of free walking/running and 3-4 bouts of a full course, complete of 15 jump obstacles, like “A” ramp, dog walk, tunnels, see saw, hoop and weaving poles. The T2 sample was collected 15 minutes after the end of the activity. The average time elapsed for the dogs from T2 to T1, including the warmup and 15 min after the end of exercise, was 40±3 min. The sampled training sessions took place in North East of Italy (45°42'28.839"N 13°44'0.839"E geographical coordinates) in early morning of February (weather T 8.0-10.5°C, humidity U 51-52% and Wind 39-41 km/h) and of September (weather T 24.3-24.9°C, relative humidity U 51-52% and Wind 39-41 km/h). Animal Assisted Activities (AAA): dogs trained to perform AAA were sampled during an indoor session in a kindergarten in North East part of Italy (46°4'15.85"N 13°14'4.485"E geographical coordinates) in May (T 21.3-21.9°C, relative humidity U 20% and Wind 18 km/h) and in June (T 23.7-24°C, relative humidity U 35-41% and Wind 7-12 km/h). Dogs arrived in the owner’s car and were sampled (T1) just before beginning of the activity about 30 minutes after getting off the vehicle. The activity started at 9:30 right after the sampling. The session included a simple dog- child interactions under the supervision of the owner, which was a certified AAA dog handler. In
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these sessions dogs were not on leash and groups were formed with 8-10 children, a couple of dogs, a teacher and the dog handlers. At the beginning of the AAA, children were seated on chairs and instructed by the dog handler how to interact with dogs. The human-animal interactions consisted of tactile and verbal contacts, gesturing with arms/hands, playing with dog’s toys, holding and pulling the lead to handle the dog and some basic obedience commands. The activity lasted about 1.5 hours and the T2 sample was taken 13±2 minutes after the end of the session. Time elapsed between T2 and T1 was 103±2 minutes.
Table 4.1_ Number, breed, sex and corresponding activity of dogs recruited for the study.
Activity Breed M/CM/F/SF Total Pointing Hunting English Setters 4/0/1/0 5
Tracking for Ungulate Hunting Istrian short-haired hound 1/0/2/0 3 Griffon nivernais 0/0/2/0 2 Italian Short-Haired Hound 0/0/0/1 1 Total 1/0/4/1 Blood Tracking Bavarian mountain hound 1/0/1/0 2 Hanoverian Scenthound 0/0/2/0 2 Total 1/0/3/0 Agility Training Border collie 3/0/1/0 4 Labrador retriever 0/0/1/0 1 Crossbreed 0/0/1/0 1 Total 3/0/3/0 Animal Assisted Activity Crossbred 0/0/0/3 3 Labrador retriever 1/0/0/0 1 Shitzu 0/0/1/0 1 Poodle 1/0/0/0 1 Total 2/0/1/3 Total dogs 11/0/12/4 27 M, male; CM, castrated male; F, female; SF, spayed female.
Salivary sampling procedure To avoid contamination of samples and interference with the enzyme immunoassay (Dreschel and Granger, 2009), dogs were refrain from drinking and eating 20 minutes before each sampling. Salivation was stimulated allowing dogs only to sniff food treats (Bennet and Hayssen, 2010; Ligout
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et al., 2010). Owners collected saliva by gently placing swabs (Salimetrics, State College, PA, USA) into cheek of the dog for approximately 90-120 seconds, a time adequate filling the swab with saliva. Samples were checked for visible contaminations with food or blood. For ethical reasons, dogs were never restrained. After sampling, the swabs were introduced into tubes specifically designed to avoid cortisol sequestration (Salivette®, 51.1534, Sarstedt, Germany), temporarily stored in an ice box before the final storage at -20°C until the analysis. Before analysis, swabs were thawed and centrifuged at room temperature at 1500 x g for 15 minutes to obtain clear saliva, which was used for cortisol determination using an EIA kit (Salimetrics, State College, PA, USA) (Hekman et al., 2012). The antibody of the EIA kit is highly specific for cortisol and, according to the manufacturer’s instructions, cross-reactivity with other steroids is lower than 0.57%. Samples were assayed in duplicate, using 25 μL of sample per well. The lower limit of sensitivity of the assay was 0.03 ng/ml. Average intra- and inter-assay coefficients of variation were less than 12% and 8%, respectively.
Statistical analysis The data of type of activity, sex, age, breed, time of sampling and cortisol concentrations were stored in a spreadsheet using Microsoft Office Excel (2010, Microsoft Corp., Redmond, WA). In the first study, 10 dogs were sampled and 85 samples out of 90 were available, since the amount of saliva in 5 samples was not enough for analysis. The normality of distribution was assessed with the Kolgomorv-Smirnov test. A mixed model design with the fixed effect for day (D1, D3 and D5), the time of the day (MO, MD and EV) and the interaction was applied (SPSS, 1997). A random factor of the subject repeated with day and time was included into the model. For the analysis in the second study, a total of 27 subjects were sampled and 93 samples out of 102 were available. Statistical analysis was performed using a mixed model procedure with the fixed effects of time of sampling (T0, T1, T2) and the random factor of subject repeated with time of sampling. Only for Blood Tracking, time of sampling was T0, T1a, T1b and T2. For both studies, adjusted means were calculated and significance between means were evaluated with the Fisher’s least significant difference (LSD) test.
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4.4_RESULTS
4.4.1_Study 1: Baseline value of salivary cortisol The mean concentration of salivary cortisol of the 10 dogs enrolled in the study 1 (Figure 4.1), did not differ between the 3 days of sampling and between the time of the day (MO, MD and EV). The values, although variable, were below 3.0 ng/ml, without clear circadian variations.
Figure 4.1_ Variation of mean salivary cortisol in 10 dogs for 3 non consecutive days (D1, D3, D5) in 3 different moments of the day (MO, MD, EV).
4.0
3.0
2.0
Salivary Salivary cortisol ng/ml 1.0
0.0 MO MD EV MO MD EV MO MD EV D1 D3D2 D5D3 Days and times of sampling
Data were analyzed with a mixed model with fixed effects for day and time of sampling and random effect of dog. No significant differences were observed. D1, sample collected on day 1; D3, sample collected after 2 days from day 1; D5, sample collected after 4 days from day 1; MO, sample collected at morning; MD, sample collected at midday; EV, sample collected at evening.
4.4.2_Study 2: Variation of salivary cortisol during activity The 27 sampled dogs belonged to 10 different pure breeds or crossbred (Table 4.1). Five dogs were sampled for the Pointing Hunting, 6 for the Tracking for Ungulate Hunting, 4 for the Blood Tracking, 6 dogs for the Agility Training and 6 for AAA. Among the not neutered dogs were 11 males and 12 females, and among the neutered dogs were 4 spayed females. Three spayed females practiced AAA and 1 spayed female practiced Tracking for Ungulate Hunting. Dogs were fed commercial complete dry food adequate to satisfy nutritional requirements. The effects of Pointing Hunting and Agility Trainer activities on cortisol concentrations are reported in the Table 2. Salivary cortisol concentration significantly differed between times of sampling in Pointing Hunting (P<0.05), increasing from T1 to T2 (4.61±1.38 ng/ml and 16.33±4.52 ng/ml respectively). 81
Chapter 4 VARIATIONS OF SALIVARY CORTISOL IN DOGS EXPOSED TO DIFFERENT COGNITIVE AND PHYSICAL ACTIVITIES
Instead, in Agility Training the variation of salivary cortisol was not significant, but the mean concentration of cortisol at the T2 showed the highest value (3.22±0.51 ng/ml). In Blood Tracking activity (Table 4.2), the highest salivary cortisol concentration mean value was measured at T1b (3.04±0.66 ng/ml) sampling time. The T2 sample, collected after the end of the activity, was not significantly different and numerically close to the baseline. Instead in Tracking for Ungulate Hunting the highest salivary cortisol concentration, although not significantly different between T0 and T1, was measured at the end of the session, at T2 sampling time. Salivary cortisol concentrations measured for the AAA section (Table 4.2) significantly differed between times of sampling (P<0.05), being higher at T1 (2.07±0.16 ng/ml) in comparison to T0 (1.44±0.10 ng/ml) and T2 (1.47±0.15 ng/ml).
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Table 4.2_ Variation of salivary concentrations of cortisol (ng/ml) measured in dogs and time elapsed between T2 and T1 for the different activities.
Activity Cortisol (ng/ml) Time of sampling Mean s.d Pointing Hunting T2 to T1 time elapsed, 258±3 min T0 – Evening 1.30 b 0.21 T1 - Before starting 4.61 b 1.38 T2 - After the end of hunting 16.33 a 4.52 Tracking for Ungulate Hunting T2 to T1 time elapsed, 91±14 min T0 – Evening 1.57 0.10 T1 - Before starting 2.60 1.76 T2 - After the end of tracking 3.34 2.54 Blood Tracking T2 to T1a time elapsed 77±9 min T0 - Evening 1.27 b 0.36 T1a - Before starting 2.71 ab 0.85 T1b - After gunshot 3.04 a 0.66 T2 - After the end of tracking 1.59 ab 0.19 Agility Training T2 to T1 time elapsed 40±3 min T0 – Evening 2.48 0.43 T1 - Before exercise 2.80 0.28 T2 - After the end of exercise 3.22 0.51 Animal Assisted Activity (AAA) T2 to T1 time elapsed, 103±2 min T0 - Evening 1.44 b 0.10 T1 - Before activity 2.07 a 0.16 T2 - After the end of activity 1.47 b 0.15 Values with superscript letters a, b, ab differ significantly, P < 0.05. T0 samples, for all activities were collected 30 minutes after the last interaction with the owner, when dogs were resting at home and apparently relaxed, according to the visual evaluation of the owner; Pointing Hunting: T1 samples were collected just before beginning of the activity and T2 samples were collected at 18±3 minutes from the end of the session; Tracking for Ungulate Hunting: T1 samples were collected just before the beginning of the activity and T2 samples were collected 16±1 minutes after the hunting was over; Blood Tracking: T1a samples were collected just before beginning of the activity, T1b samples were collected 10±1 minutes after the gunshot and T2 samples were collected 12±2 minutes after the end of the tracking; Agility Training: T1 samples were collected just before beginning of the activity and T2 samples were collected 15±0 minutes after the end of the activity; AAA: T1 samples were collected just before beginning of the activity and T2 samples were collected 13±2 min after the end of the session.
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4.5_DISCUSSION
4.5.1_Study 1: Baseline value of salivary cortisol The aim of the study 1 was to evaluate the extent of variation of salivary cortisol in dogs in a routine context and not stimulated for physical or psychological activities. For this reason, we asked the owners to collect the saliva on day 1 (D1) after day 2 (D3) and day 4 (D5) in 3 specific times of the day, in part following the sampling schedule adopted in a previous study (Sandri et al., 2015). Present results did not confirm the data obtained in the former study, which indicated a significant decrease of salivary cortisol in the evening sample. According to Giannetto et al. (2014) salivary cortisol shows a circadian rhythm, with peak around midday. This latter study was conducted in standardized conditions to evaluate the extent of circadian variations and sampling times were rigorously scheduled. Instead, in the present study, sampling times within the day were variable, to accomplish with owner availability to collect saliva during the day and, especially for the MD saliva swabs, a wide variation was obtained (from 9:30 to 15:00). The aim of our study was to understand if a single-shot sampling can be used as a baseline of salivary cortisol in a dog rather than studying circadian fluctuations or factor affecting cortisol variation. In a systematic review and meta-analysis of salivary cortisol in dog, Cobb et al. (2016) reported that salivary concentration measured early in the morning (from 6:00 AM to 8:00 AM) are significantly lower than those collected in the evening (from 6:00 PM to midnight) and during the day (from 8:00 AM to 6:00 PM). In our study, the mean concentrations measured were not significantly different and numerically very similar, being 1.90±1.59 ng/ml at MO, 2.20±1.84 ng/ml at MD and 1.72±1.88 ng/ml at EV. According to present and previous data (Giannetto et al., 2014; Glenk et al., 2014; Sandri et al., 2015), the late evening sample (EV) was considered appropriate as a baseline value. This sampling time was also the closest to the T1 sampling moment and this time was adopted in the study 2 as T0 value.
4.5.2_Study 2: Variation of salivary cortisol during activity
In human and horse, physical exertion activates HPA axis leading to an increase of circulating and salivary cortisol concentration which is related to the performances (Arokoski et al., 1993; Schmidt et al., 2010; Vingren et al., 2016; Lippi et al., 2016), but less information is available for dogs (Cobb
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et al.,2016). Table 4.2 reports the cortisol response to exercises in dogs undergoing a short- duration high-intensity agility training or a long-duration high-intensity hunting section. In the Agility Training, either physical and psychological response occurs and, according to Pastore et al. (2011) dogs can display several behaviors related to stress without a concomitant increase of salivary cortisol, probably due to the short duration of the exercise that activates only the sympathetic system. Instead, Pointing Hunting is an endurance exercise, which requires high amount of energy from body reserves. In this situation, the negative energy balance associated to strenuous physical activity can stimulate the secretion of cortisol (Royer et al., 2005). A significant increase of cortisol in blood has also been observed by Fergestad et al. (2016) after race in sled dogs and by Durocher et al. (2007) after a prolonged exercise in urine. Tracking for Ungulate Hunting and Blood Tracking (Table 4.2) engage lower physical activity and last shorter than Pointing Hunting (average of 91 minutes for Tracking for Ungulate Hunting Vs 77 minutes for Blood Tracking or around 4 hours for Pointing Hunting). In fact, in this study we did not observe significant variations of cortisol concentrations for both these activities and only a numerically increase from T0 to T1 and T2 was observed for Tracking for Ungulate Hunting. Interestingly, for Blood Tracking, the increase of cortisol after the gunshot (T1b) could indicates that the sudden sound or the starting of session might impact most on variation of cortisol concentration rather than physical effort. Beerda et al. (1998) have reported that a sound of blast triggers a significant increase of salivary cortisol, similar effect is obtained after a short electric shock, a falling bag, an opening umbrella and restraint. It is likely that the excitement or anxiety before some expected event could have caused the increase of cortisol, as already reported by Angle et al. (2009), but no signs of distress were observed in the dogs of the present study after the gunshot. Of note was that the olfactory activities associated to tracking did not stimulate the HPA axis at an extent able to show detectable cortisol response in saliva. Shin and Shin (2016) reported that exposure of dogs to owner voice or odor leads, after a separation anxiety test, to a significant reduction of salivary cortisol. Recent researches showed that the areas stimulated by olfactory system in conscious dogs do not include hypothalamus and pituitary axis (Jia et al., 2014). AAA does not require physical efforts, being a low-intensity exercise but it needs high psychological support. The significant increase of cortisol concentration before the activity (Table 4.2) can be also explain with the excitement or anxiety before the event, as already reported by Angle et al. (2009). However, care should be taken to understand if the dog could have been
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stressed by the travel before his arrival to the therapy site. The return to the baseline level (T0) at the end of the session (T2) would indicate that the dogs were well trained and did not undergo stressing conditions in performing the interaction activities with the children. According to the observations of their handlers, the AAA dogs did not show particularly sign of distress. If dogs are approached and petted by people with whom they are not familiar, some discomfort can occur (Serpell et al., 2010; Colussi et al., 2016), but to participate to AAA activities, dogs should to remain calm and relaxed in variable situations and also under stressful conditions. As reported by Coppola et al. (2006) and, although with limited results, by Conley et al. (2014), the positive contact of dogs with human could contribute to maintain a low cortisol concentration. Furhermore, a positive human contact in a limited duration of assisted session can contribute to decrease the salivary cortisol level in dogs. Glenk et al. (2014) reported that in dogs trained for AAA and being leash-free during the therapy session, significant increase of salivary cortisol was not observed.
4.6_CONCLUSIONS
In conclusion, the results of this study showed that the salivary concentration of cortisol in dogs varies in relation to the type of activity. Cortisol variations depend on the extent of the exercise and on the level of alertness requested for the performance. However, further studies with larger number of dogs are required to confirm the relationship between salivary cortisol concentration and factors as breed, predisposition to perform different activities and psychophysical preparation.
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4.7_REFERENCES
Angle CT, Wakshlag JJ, Gillette RL, Stokol T, Geske S, Adkins TO, Gregor C. Hematologic, serum biochemical, and cortisol changes associated with anticipation of exercise and short duration high- intensity exercise in sled dogs. Vet Clin Pathol, 38(3):370-374, 2009.
Arokoski J, Miettinen PV, Säämänen AM, Haapanen K, Parviainen M, Tammi M, Helminen HJ. Effects of aerobic long distance running training (up to 40 km.day-1) of 1-year duration on blood and endocrine parameters of female beagle dogs. Eur J Appl Physiol Occup Physiol, 67(4):321-329, 1993.
Beerda B, Schilder MBH, Van Hooff JARAM, de Vries HW, Mol JA. Behavioural, saliva cortisol and heart rate responses to different types of stimuli in dogs. Appl Anim Behav Sci, 58:365–381, 1998.
Bennet A, Hayssen V. Measuring cortisol in hair and saliva from dogs: coat color and pigment differences. Domest Anim Endocrinol, 39:171–180, 2010.
Bergamasco L, Osella MC, Savarino P, Larosa G, Ozella L, Manassero M, Badino P, Odore R, Barbero R, Re G. Heart rate variability and saliva cortisol assessment in shelter dog: human–animal interaction effects. Appl Anim Behav Sci, 125:56–68, 2010.
Cobb ML, Iskandarani K, Chinchilli VM, Dreschel NA. A systematic review and meta-analysis of salivary cortisol measurement in domestic canines. Domest Anim Endocrinol, 57:31-42, 2016.
Colussi A, Sandri M, Stefanon B. Salivary cortisol: a marker of the adaptive response of the organism to environmental stimuli. Veterinaria, 30:3, 2016.
Conley MJ, Fisher AD, Hemsworth PH. Effects of human contact and toys on the fear responses to humans of shelter-housed dogs. Appl Anim Behav Sci, 156:62–69, 2014.
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Coppola CL, Grandin T, Enns MR. Human interaction and cortisol: can human contact reduce stress for shelter dogs?. Physiol Behav, 87:537-541, 2006.
Dreschel NA, Granger DA. Methods of collection for salivary cortisol measurement in dogs. Horm Behav, 55:163-168, 2009.
Durocher LL, Hinchcliff KW, Williamson KK, McKenzie EC, Holbrook TC, Willard M, Royer CM, Davis MS. Effect of strenuous exercise on urine concentrations of homovanillic acid, cortisol, and vanillylmandelic acid in sled dogs. Am J Vet Res, 68(1):107-111, 2007.
Fergestad ME, Jahr TH, Krontveit RI, Skancke E. Serum concentration of gastrin, cortisol and C‑reactive protein in a group of Norwegian sled dogs during training and after endurance racing: a prospective cohort study. Acta Vet Scand, 58:24, 2016.
Giannetto C, Fazio F, Assenza A, Alberghina D, Panzera M, Piccione G. Parallelism of circadian rhythmicity of salivary and serum cortisol concentration in normal dogs. J Appl Biomed, 12:229- 233, 2014.
Glenk LM, Kothgassner OD, Stetina BU, Palme R, Kepplinger B, Baran H. Salivary cortisol and behavior in therapy dogs during animal-assisted interventions: A pilot study. J Vet Behav, 9:98- 106, 2014.
Hekman JP, Karas AZ, Dreschel NA. Salivary cortisol concentrations and behavior in a population of healthy dogs hospitalized for elective procedures. Appl Anim Behav Sci, 141(3-4):149-157, 2012.
Hiby EF, Rooney NJ, Bradshaw JWS. Behavioural and physiological responses of dogs entering re- homing kennels. Physiol Behav, 89:385-391, 2006.
Jia H, Pustovyy OM, Waggoner P, Beyers RJ, Schumacher J, Wildey C, Barrett J, Morrison E, Salibi N, Denney TS, Vodyanoy VJ, Deshpande G. Functional MRI of the olfactory system in conscious dogs. PLoS One, 9(1):e86362, 2014.
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Jones S, Dowling-Guyer S, Patronek GJ, Marder AR, D'Arpino SS, McCobb E. Use of accelerometers to measure stress levels in shelter dogs. J Appl Anim Welf Sci, 17(1):18-28, 2014.
Ligout S, Wright H, van Driel K, Gladweel F, Mills DS, Cooper JJ. Reliability of salivary cortisol measures in dogs in training context. J Vet Behav, 5(1):49, 2010.
Lippi G, Dipalo M, Buonocore R, Gnocchi C, Aloe R, Delsignore R. Analytical evaluation of free testosterone and cortisol immunoassays in saliva as a reliable alternative to serum in sports medicine. J Clin Lab Anal, 30(5):732–735, 2016.
Pastore C, Pirrone F, Balzarotti F, Faustini M, Pierantoni L, Albertini M. Evaluation of physiological and behavioral stress-dependent parameters in agility dogs. J Vet Behav, 6:188-194, 2011.
Peeters M, Closson C, Beckers JF, Vandenheede M. Rider and horse salivary cortisol levels during competition and impact on performance. J Equine Vet Sci, 33:155-160, 2013.
Royer CM, Willard M, Williamson K, Steiner JM, Williams DA, David M. Exercise stress, intestinal permeability and gastric ulceration in racing Alaskan sled dogs. Equine Comp Exerc Physiol, 2(1):53–59, 2005.
Sandri M, Colussi A, Perrotta MG, Stefanon B. Salivary cortisol concentration in healthy dogs is affected by size, sex, and housing context. J Vet Behav, 10:302-306, 2015.
Schmidt A, Aurich J, Möstl E, Müller J, Aurich C. Changes in cortisol release and heart rate variability during the initial training of 3-year-old sport horses. Horm Behav, 58:628–636, 2010.
Serpell JA, Coppinger R, Fine AH, Peralta JM. Handbook on Animal-Assisted Therapy: Theoretical Foundations and Guidelines for Practice. Third Edition. Academic Press: San Diego, USA. Chapter 23, Welfare considerations in therapy and assistance animals, pp 481-503, 2010.
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Shin YJ, Shin NS. Evaluation of effects of olfactory and auditory stimulation on separation anxiety by salivary cortisol measurement in dogs. J Vet Sci, 17(2):153-158, 2016.
Shiverdecker MD, Schiml PA, Hennessy MB. Human interaction moderates plasma cortisol and behavioural responses of dogs to shelter housing. Physiol Behav, 109:75-79, 2013.
Tharwat M, Al-Sobayil F, Buczinski S. Influence of racing on the serum concentrations of acute- phase proteins and bone metabolism biomarkers in racing greyhounds. Vet J, 202:372-377, 2014.
Vingren JL, Budnar RGJr, McKenzie AL, Duplanty AA, Luk HY, Levitt DE, Armstrong LE. The acute testosterone, growth hormone, cortisol and interleukin-6 response to 164-km road cycling in a hot environment. J Sports Sci, 34(8):694-699, 2016.
Wakshlag JJ, Stokol T, Geske SM, Greger CE, Angle CT, Gillette RL. Evaluation of exercise-induced changes in concentrations of C-reactive protein and serum biochemical values in sled dogs completing a long-distance endurance race. Am J Vet Res, 71(10):1207-1213, 2010.
Yazwinski M, Milizio JG, Wakshlag JJ. Assessment of serum myokines and markers of inflammation associated with exercise in endurance racing sled dogs. J Vet Intern Med, 27:371-376, 2013.
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Part 2
NUTRIONAL EFFORTS, FAECAL MICROBIOME, HPA AXIS
Part 2 NUTRIONAL EFFORTS, FAECAL MICROBIOME, HPA AXIS
Chapter 5 PRELIMINARY STUDY: FAECAL MICROBIOME AS BIOINDICATOR OF DOG WELL-BEING AND POSSIBLE RELATION WITH HYPOTHALAMIC-PITUITARY-ADRENAL (HPA) AXIS
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5.1_ABSTRACT
Probiotics are often used as supplementation in dogs food to improve the state of health and well- being of animals. Assess the variation of microbiota population seemed to be a good tool to understand the influence and effect of probiotics on gut microbiota equilibrium. The aim of this trial was to evaluate the relationship between a biomarker of central nervous system (cortisol) and the microbiome. In this trial 8 dogs were fed with an extruded complete diet. The day after the beginning of the test 4 dogs (PG: Probiotic Grup) were fed with a probiotic supplementation of a commercial Enterococcus faecium bacteria, at 3.5 x 1010 CFU/kg of body weight concentration. For the entire duration of the trial, the others 4 dogs (CG: Control Group) were fed without any addition to the extruded complete diet. The study lasted 30 days. For each dog salivary sample was collected, at time T29 and 43 days after T29 (T43). Faeces of each dog were collected after the morning meal, at the beginning of the study (T0), after 15 days (T15) and at the end of the experimental period (T29). For each sample, SCFAs, nitrogen were analysed and bacterial faecal DNA was extracted and sequenced. At genera taxa level the results showed a significant effect of time of sampling for Streptoccoccus (P<0.05) and for Prevotella (P<0.01). For Prevotella was also observed a significant interaction between treatment (with or without probiotic addition to the extruded complete diet) and time of sampling (P<0.01). Furthermore no marked differences emerged from the three different samplings in the two different groups. No increase of Enterococcus genera was observed even in dogs fed with the addition of the probiotic, even if probiotic administered to PG was Enterococcus faecium. Despite the limited amount of salivary cortisol in samples some interesting differences were emerged between dogs of CG, that presented a higher level of cortisol than the level of cortisol concentration in dogs of PG. However for future studies it seems to be necessary select probiotic composition with a major impact on the state of faecal microbiome and to deepen the relationship between gut microbiota and the central nervous system.
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Keywords: faecal microbiome, salivary cortisol, dog well-being, biomarkers.
5.2_INTRODUCTION
The increased interest on the health and care of companion animals has led to an increasing attention on nutrition. The reasons for this trend are connected with a more profound relationship between owners and their pets, which is increased over the last decade. In fact, the preservation of dog health by offering wholesome nutritional diets is become an important component of a responsible pet ownership. Quality of life, measured in terms of reduced incidence of diseases and the ability to maintain an active life-style, would appear to be enhanced by an appropriate nutrition and nutraceutical supplementations (Bontempo, 2005). To feed properly a dog, it is important to keep in mind the animal health and life-style. Furthermore, it is important to remember that canine tooth structure and the intestinal tract have adapted to an opportunist carnivore habitus and dogs can meet their nutritional needs by eating a combination of vegetal and animal derived food. Well-balanced diets have also to include an appropriate amount of minerals, vitamins, essential amino acids and essential fatty acids. It is know indeed, that water, carbohydrates, proteins, fats, minerals, vitamins are the six indispensable nutrients that are required as part of a dog regular diet, being involved in all of the basic metabolic functions of its body. In particular, these components are required to construct tissues and conserve their integrity accomplishing several biological reactions (Yuill, 2011). The food amount and its shape, the feeding frequency and the diet composition are known to have important effects on gastro-intestinal (GI) functions. Both nutrients and non-nutritional dietary components influence the gut health in terms of intestinal microbiota composition (Hart et al., 2002; Hang et al., 2012). Furthermore, some researches showed as different diets can influence satiety, faecal consistency and quantity of Clostridium perfringens in faeces (Zoran et al., 2003; Zentek et al., 2004; Hang et al., 2012). As reported by Biagi et al. (2010) and Wakshlag et al. (2011), dietary variations in dog daily diet seem to cause fluctuations in faecal bacterial populations (Hang et al., 2012). This is one of the reasons why, in recent years, the intestinal microflora has attracted considerable interest, particularly about the ways in which the microbiota can be manipulated to improve the health state. As reported by Strompfova et al. (2006), a possible way to modulate the gut health is by using probiotics. Probiotics are defined as direct
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Chapter 5 PRELIMINARY STUDY: FAECAL MICROBIOME AS BIOINDICATOR OF DOG WELL-BEING AND POSSIBLE RELATION WITH HYPOTHALAMIC-PITUITARY-ADRENAL (HPA) AXIS feed microbes or microbial cell preparations with beneficial effects on the health and well-being of the host. The ability of probiotic bacteria to modulate the intestinal microbiome is one of the mechanisms by which they can also affect the immune system responsiveness (Nemcová, 1997; Strompfova et al., 2004; Sauter et al., 2006). The ideal probiotics are those microorganisms, which remain viable in a sufficient number by adhering to the intestinal epithelium in order to confer a significant health benefit (Kumari et al., 2011; Gosh, 2012). The most often used probiotic genera in humans and animals are Enterococcus, Lactobacillus and Bifidobacterium that are all normal inhabitants of the gut colonic flora (Sauter et al., 2006). As a matter of fact, a deeper understanding of the microbiome-modulating abilities by specific probiotic strains is needed. Implementing these data with studies on host gene response to changes microbiome composition and probiotic administration, will allow us to understand microbe–microbe and host–microbe interactions and, consequently, how the gut microbiota affects the well-being and physiology of hosts (Gueimonde and Collado, 2012). The hypothalamic-pituitary-adrenal (HPA) axis can also be influenced by gut microbes. The absence of microbes in the gut of mice has been associated with a significant increase in plasma of Aadrenocorticotropic Hormone (ACTH) and corticosterone levels in response to restrain stress (Sudo et al., 2004). In addition to these observations, Bercik et al. (2011) have demonstrated that gut microbes may influence brain chemistry and the behaviour regardless of the autonomic nervous system (Cani and Knauf, 2016). Based on these premises, in the present preliminary investigation, it was evaluated possible variations on dogs faecal microbiome composition due to daily administration of probiotic enriched diet. In addition, as the intestinal bacteria affect animal welfare, it was also investigated the interaction, if any, between microbes and cortisol, which is one of the most useful biomarker of animal well-being. Finally, through cortisol analysis, it was tried to figure out whether the addition of probiotics to the diet can affect the microbiome and, as a consequence, the HPA axis.
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5.3_MATERIALS AND METHODS
5.3.1_Animal selection
For this study, was recruited a sample composed by 8 (6 females and 2 males) adult dogs of normal weight in accordance with the belonging breed. Dogs were hosted in a private kennel in North East of Italy, and were of 3 different breeds (4 Cocker Spaniels, 3 Beagles and 1 Labrador Retriever). The average age of the dogs was 6±2.33 years, with a Body Condition Score (BCS) between 4.0 and 7.0. For this study, the BCS on a 9 points scale was applied, where dogs with a BCS of 4.0 and 5.0 are considered of ideal weight while 6.0 and 7.0 means overweight (Laflamme, 1997; Kealy et al., 2002; Kerr et al., 2013); scores were attributed by an operator of the kennel (Table 5.1). The study covered a period of 30 days during which the dogs were housed individually in box with an outdoor pen and a paved portion covered with a roof. After this period, dogs were housed in pairs. The inclusion criteria adopted to select dogs for this research were the following ones: (1) more than 12 months of age; (2) clinically healthy, free from pain, exogenous and endogenous parasites, and immunised, as assessed by a veterinary practitioner; (3) no recent history of corticosteroid administration; (4) no drug therapies while sampling and from 1 month before the beginning of the trial. Clinical data were obtained from the medical records available from the kennel veterinarians. For each dog, the following information was also collected: date of birth, breed, size, sex (male, castrated male, female, spayed female), feeding schedule, and typology of food.
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Table 5.1_Animals participating in the study.
Number Gender Breed Age (years) BCS Group
1 f Cocker Spaniel 8 4 Ctrl 2 f Cocker Spaniel 8 6 Ctrl 3 m Beagle 4 5 Ctrl 4 m Beagle 3 6 Ctrl 5 f Cocker Spaniel 8 5 Pro 6 f Cocker Spaniel 6 5 Pro 7 f Beagle 8 6 Pro 8 f Labrador retriever 3 7 Pro
Body condition score (BCS) was measured on a nine-point scale. f, female; m, male. Ctrl Group (CG), dogs fed with commercial extruded complete diets. Pro Group (PG), dogs fed with commercial extruded complete diets with a probiotic supplementation.
5.3.2_Diet For this investigation, we tested two commercial extruded complete diets (Table 5.2). Each dog was fed with an equal mixture of these two diets. Fresh water was available ad libitum; diet was provided once a day in the morning. Furthermore, in addition to the mixture of the extruded complete diets, it was administered a probiotic supplementation with a commercial Enterococcus faecium bacteria at a concentration of 3.5 x 1010 CFU/kg of body weight.
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Table 5.2_Composition of the two commercial extruded complete diets (Commercial Diet A; Commercial Diet B).
Commercial Commercial Diet A Diet B Analytical composition
Protein 24 % 24 % Raw oil and fats 10 % 10 % Raw fibres 2.5 % 3.5 % Raw ashes 7 % 9 % Calcium 1.7 % Phosphorus 1.1 % Nutritional additives
Vitamin A 15000 U.I. 10000 U.I. Vitamin D3 1500 U.I. 750 U.I. Vitamin E 80 mg 70 mg Iron (ferrous carbonate) 60 mg Iron (ferrous oxide) 550 mg Copper sulphate 10 mg 2.8 mg Zinc sulphate 80 mg 72 mg Manganese 6 mg Iodine 2 mg 1.3 mg Selenium 0.2 mg
5.3.3_Experimental design The 8 dogs were randomly split in two groups of 4 individuals. Before the beginning of this study (T0) all the 8 dogs were fed with the same commercial extruded completed diets and each dog was housed individually. One day after T0, one group (Control Group: CG) continued to be fed with the same diet, and the other group (Probiotic Group: PG) received the same diet of the CG supplemented with the probiotic. After 30 days (T29) the addition of probiotic to the diet was suspended and all the subjects were paired in boxes. The trial lasted 30 days.
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5.3.4_Sample collection Sample of faeces and saliva were collected from each dog.
5.3.4.1_Faeces collection Faeces were collected after the morning meal, at the beginning of the study (T0), after 15 days (T15) and at the end of the experimental period (T29). At each day of sampling, the first stool evacuated from each dog was immediately and entirely collected with sterile gloves in individual sterile plastic bags. When plastic bags arrived to the laboratory (Nutrition Laboratory, DI4A, University of Udine), they were immediately stored at -20°C for the analysis purposes. Frozen stools were carefully cleaned from external contaminants with a sterile blade. Then, two aliquots were obtained, placed in sterile plastic tubes and stored at -80°C for fatty acids and lactate or DNA analysis.
5.3.4.2 Salivary sampling For each dog, two salivary samples were collected. For the analysis of cortisol, the first sample, (T29) was taken at the end of the experiment period and the second sample(T43) was taken 43 days after T29 day. The samples were collected in the morning 1 hour after the meal, when dogs were resting and apparently relaxed. To avoid the samples contamination and subsequent interferences in the cortisol enzyme immunoassay (Dreschel and Granger, 2009), dogs were refrained from drinking and eating 20 minutes before sampling. Salivation was stimulated by letting dogs only to sniff food treats (Bennet and Hayssen, 2010; Ligout et al., 2010). Saliva was collected with swabs (Salimetrics, State College, PA, the USA) gently placed into the cheek pouch of the dog for approximately 90-120 seconds, a time interval considered adequate for the swab saturation with saliva. Samples were checked for visible contamination with food or blood. For ethical reasons, dogs were never restrained. After sampling, the swabs were introduced into plastic tubes specifically designed to avoid cortisol sequestration (Salivette; no. 51.1534, Sarstedt, Nümbrecht, Germany), temporally stored in ice before the final storage at -20°C.
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5.3.5_Faeces analysis
5.3.5.1_Faecal DNA extraction and sequecing As reported by Fortin et al. (2004), before DNA extraction, faecal samples (150 mg) were washed following a 3-step washing procedure. Microbial DNA was extracted using the bead beater method with a Faecal DNA MiniPrep kit (Zymo Research; Irvine, CA, the USA). Pre-amplification concentration of the DNA in the samples was measured with a Nanodrop 1000 (Thermo Scientific; Wilmington, DE, the USA). For each sample, two separate Real Time reactions were performed in order to test three bacterial primer pairs for 16S rDNA amplification. After checking the quality of the samples and the presence of bacterial DNA, samples were sequenced for bacterial identification. As reported by Sandri et al. (2017), DNA was fragmented and hyper variable V3 and V4 regions of the 16S rRNA and was amplified for library preparation. Two amplification steps in the library workflow are performed: an initial PCR amplification using locus specific 16S PCR primers and a subsequent amplification that integrates relevant Illumina flow-cell binding domains and unique indixes which was performed with a Nextera DNA Library Prep kit (Illumina; San Diego, CA, the USA). 16S Amplicon PCR Forward Primer=5' TCGTCGGCAG CGTCAGATGT GTATAAGAGA CAGCCTACGG GNGGCWGCAG 16S Amplicon PCR Reverse Primer=5' and GTCTCGTGGG CTCGGAGATG TGTATAAGAG ACAGGACTAC HVGGGTATCT AATCC were used (Klindworth et al., 2013). Around 460 bp amplicons were then sequenced with a MiSeq (Illumina; San Diego, CA, the USA) in paired end with 300 bp reads producing about 100.000 sequence per sample. Reads were de-multiplexed based on the Illumina indexing system. Where amplicon length was permissive with the respective sequencing length, 3’-ends of pairs were overlapped to generate consensus pseudo-reads.
5.3.5.2_Faecal score, pH, nitrogen and fatty acids analysis Faecal samples were assigned a quality score using a 5-points visual scale with 0.5 score interval ranging from 1 (hard and dry faeces) to 5 (liquid diarrhoea) (Moxham et al., 2001). Where Fecal Scores of 2.0 and 3.0 are considered ideal. Ideal faeces consist in firm but not dry stool, with little visible or no visibile segmentation and that leaves little residue on ground but holds the form when picked up.
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The measure of faecal pH was performed according to the following procedure: 2 g of faeces were mixed with 1:1 deionized water, then the pH was measured using a Mettler Toledo InLab® Expert Pro pH meter. Nitrogen in faeces was measured by the Kjeldahl method. For this type of analysis the faeces were weighed in a range from 2.0 to 4.5 g; after weighing, the standard procedure of Kjeldahl method was followed. This method consists of three steps: (A) digestion causing the nitrogen decomposition in samples, by boiling a homogeneous sample in concentrated sulfuric acid (Protein
+ H2SO4 → (NH4)2SO4(aq) + CO2(g) + SO2(g) + H2O(g). (B) Distillation adding excess base to the acid + digestion mixture to convert NH4 to NH3, followed by boiling and condensation of the NH3 gas in a receiving solution. (C) Titration to detect the ammonia (NH3) present in the obtained distillate by means of a colour change that permits to determine the calculation of unknown concentrations. As reported by Sandri et al. (2017) the analysis of short chain fatty acids (SCFA) (2:0, acetic; 3:0, propionic; 4:0, butyric; iso 4:0, isobutyric; 5:0, valeric; iso 5:0, isovaleric) and the lactic acid from the faecal samples was performed by High Performance Liquid Chromatography (HPLC). 3 g of faeces were diluted in 150 mL of 0.1 N H2SO4 aqueous solution and homogenised for 2 minutes by UltraTurrax (IKA®-Werke GmbH & Co. KG, Staufen, Germany). The mix was centrifuged (5,000 × g for 15 min at 4 °C) to separate the liquid phase from the solid residuals. Subsequently, the liquid phase was microfiltered (SLMV033RS, 0.45-μm Millex-HV, Merck-Millipore, Billerica, MA, the USA). The resulting sample was directly injected in the HPLC apparatus using an Aminex 85 HPX-87 H ion exclusion column (300 mm × 7.8 mm; 9-μm particle size; Bio-Rad, Milan, Italy) kept at 40 °C; the detection wavelength was 220 nm. The analyses were conducted by applying an isocratic elution
(flux 0.6 mL/min) with a 0.008 N H2SO4 solution as mobile phase; the injection loop was 20 μL. Individual SCFA and lactic acid were identified using a standard solution composed by lactic acid (4.50 mg/mL), acetic acid (5.40 mg/mL), propionic acid (5.76 mg/mL), butyric and isobutyric acid
(7.02 mg/mL) and valeric and isovaleric acid mixture (8.28 mg/mL) in 0.1 N H2SO4 (cod. Number 69775, 338826, 402907, B103500, 58360, 75054, 129542, respectively; Sigma- Aldrich, Milan, Italy). Quantification was executed using an external calibration curve based on the standards described above.
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5.3.6_Saliva analysis
Briefly, a 96-well microtitre plate (Optiplate, Perkin Elmer Life Science, IT) was coated with anti- rabbit ɣ-globulin serum raised in a goat, incubating overnight the antiserum diluted 1:1,000 in 0.15 mM sodium acetate buffer, pH 9, at 4°C. The plate was then washed twice with PBS 0,1% BSA, pH 7.4 (RIA buffer) and incubated overnight at 4°C with 200 μl of the anti-cortisol serum diluted 1:8,000. The antiserum (Centro Medico Diagnostico Emilia, Bologna, IT) was raised in the rabbit against cortisol-3 carboxymethyloxime-BSA and showed the following cross reactions: cortisol 100%, prednisolone 44.3%, 11-deoxycortisol 13.9%, cortisone 4.9%, corticosterone 3.5%, progesterone <0.01%. The plate was carefully washed with PBS buffer, and standards (3 to 200 pg/well), quality control, unknown extracts and tracer (1,2,6,7-3 H-cortisol, Perkin Elmer Life Sciences, 30 pg/well, specific activity: 3700 GBq/mmol) were added (final volume: 200 μl). The plate was incubated overnight at 4°C, the incubation mixture was decanted and wells washed with RIA buffer, added with 200 μl scintillation cocktail (Microscint 20, Perkin Elmer Life Sciences) and counted on the beta-counter (Top-Count, Perkin Elmer Life Sciences). All samples as received were assayed in duplicate. The intra- and inter-assay coefficients of variation (CV) were 3.6% and 10.0%. The sensitivity of the assay was defined as the dose of hormone at 90% binding (B/B0) and was 3.125 pg/well.
5.3.7_Statistical Analysis The preliminary evaluation of microbial composition was performed with descriptive statistical analysis. The relative abundance of OTUs was calculated at phylum, class, order, family and genus levels. To obtain an overview of the predominant bacterial phyla and genera present in allsamples, data were subjected to principal component analysis (PCA). PCA provides an exploratory data analysis based on a multivariate projection method that helps to visualise all the information contained in a data set. Faecal samples were characterised by microbial composition at time sampling (T0, T15 and T29), as described above. Further, considering the relative abundance of OTUs at genus level, ecological diversity measures were calculated. The Shannon biodiversity index was analysed to measure the degree of uncertainty associated with the random selection of an individual in the community.
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r Hpp iiln i1
Where pi is the frequency of each genus. Maximum diversity (H = ln r) is achieved when all species are equally present and minimum diversity is when there is only one species (H=0). Due to the high dimensionality, non-normality and phylogenetic structure of the data, it is difficult to test the association of microbiome composition directly with experimental conditions (diet) using OTUs or taxa abundances. Thus, multivariate analyses generally first need to select one distance measure method before conducting the analysis of the estimated distances, where these latter are defined as the distance between any of two microbiome samples (Xia and Sum, 2017). ANOSIM is one of most widely used multivariate methods in microbiome studies. In this study, ANOSIM was applied to compare within- and between-group similarity through a distance measure, to test the null hypothesis that the average rank similarity between samples within a treatment is the same as the average rank similarity between samples belonging to different treatments. Least Significant Difference statistics with Bonferroni multiple testing correction on estimated marginal means were used as significance test. The comparisons between diet (CG and PG) per genus were performed with ANOVA under the framework of a mixed linear model. Diet, time sampling, and their joint interaction were considered as fixed effects. Dog was considered a random effect. The Akaike information criterion (Akaike, 1974) and Bayesian information criterion (Schwarz, 1978) were used to determine the goodness of fit of the selected models. Means were separated using a LSD test. A P<0.05 was accepted as being statistically significant. The correlation between predominant genera and SCFA (Short Chain Fatty Acids) and between predominant genera and nitrogen in Control Group dogs and in Probiotic Group dogs was performed. The Pearson correlation coefficient was used to do a measure of the magnitude of the linear association between genera and SCFA or between genera and nitrogen. Lastly, InfoGen statistical program, version 2017 (National University of Córdoba, Argentina), was used in all the statistical analysis, except in ANOSIM and Mixed Linear Models, where the analyses were performed using the vegan and LMER packages of R software, version 3.4.1 (R Core Team, 2017).
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5.4_RESULTS AND DISCUSSIONS
The present data set pointed out some shortcomings, among which the small size of the data and the selection of dogs involved in this trial (as summarised in Table 5.1). As reported in the “Materials and methods” section, we collected faecal samples from 8 dogs (4=control diet and 4=probiotic diet) at 3 different times. Moreover, the surveyed samples were picked up by dogs of three different breeds (Cocker Spaniel, Beagle and Labrador Retriever), and this variety of breeds might have caused a further imprecision to the results of the trial. However, there is also a lack of knowledge regarding the influence of probiotic on canine gut microbiota and the possible correlation between microbiome and salivary cortisol variations. As previously mentioned, cortisol was considered as one of the HPA axis biomarkers. For these reasons, it has proven to be very interesting to start a preliminary study on the implication of probiotic on the gut microbiota health and to understand a possible involvement of these factors also in the HPA axis activation.
5.4.1_Microbiome analysis The average OTUs at each phylogenetic level (phylum, class, order, family and genus) were calculated and subsequently was evaluated the distribution among bacterial group (phylum: 5904 OTUs in 10 different bacterial groups; class: 5856 OTUs in 23 bacterial groups; order: 5784 OTUs in 39 bacterial groups; family: 5568 OTUs in 96 bacterial groups; genus: 5016 OTUs in 205: bacterial groups). To have a clearer image and an immediate indication about the more present phylogenetic levels in gut community and to provide a visual idea about the differences between treatments (CG and PG) and time of sampling (T0, T15, T29), a preliminary descriptive analysis of data was perfomed. However, as reported above, firstly, the data of faecal microbiome were transformed in relative abundance of the OTUs, then this unit of measure was used for all the rest of statistic analysis. The graphs (Figure 5.1; 5.2a; 5.2b; 5.2c) reported hereafter show the phyla trend in microbiome community during the 3 times of sampling and the differences of phyla presence for each dog. The 10 phyla that were present in all sampled dogs were Actinobacteria, Bacteroidetes, Deferribacteres, Euryarcaeota, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes, Thermi and Verrucomicrobia. Nevertheless, from the chart it emerged how Deferribacteres, Euryarcaeota,
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Spirochaetes, Thermi and Verrucomicrobia phyla in all dogs exhibited low values while, on the other hand, it seemed that the phylum with highest values in each sample was Firmicutes. A Principal Component Analysis (PCA) was used to deepen this observation with minimal losses of information, in order to find a new set of variables (components) not correlated, with the aim to explain the structure of variations in the ranks of the data table. Indeed, through the PCA, artificial axes (principal components) are constructed providing scatter plots of observations and/or variables with optimal properties for the interpretation of the underlying variability and covariates. The biplot allows to visualise observations and variables in the same space, so it is possible to identify associations between observations, among variables and between variables and observations. This analysis was applied for each phylogenetic level. In the case of phylum level at time T0, it was observed that the first two components explained the 98% of variability of data. The first component (CP1) explained a 94% of the variability of the data, while the second one had less impact explaining only 4% of total variability. Regarding the prevalent phyla, comparable results were reported by Handl et al. (2013), in fact, in their study Firmicutes bacteria were predominant in all dogs (>94%). In the PCA, the reported eigenvectors (e1 and e2) showed coefficients suggesting that each original variable was weighted to conform the CP1 and CP2. In the case of phyla in CP1 (e1) Firmicutes and Bacteroidetes had greater weights, whereas Proteobacteria, Fusobacteria, Deferribacteres, Actinobacteria and Spirochaetes had less weight. The bacterial group, which reported a weight of 0.00, was removed from the analysis, thus permitting to proceed with the construction of the biplot. At time T0 (Figure 5.3a), it was possible to observe a separation between the probiotic group (yellow point) and the control one (blue point). With the exception of only one “probiotic” subject that was closer to the control group than the probiotic one, however at this stage of the study, the administration of probiotic had not started yet, so considerable differences between the two groups (CG and PG) were not expected. At time T15 (Figure 5.3b) only one dog of the PG was close to Firmicutes phylum, instead the others were nearest to Bacteroidetes phylum. These observations showed that, despite the diet of PG dogs had been integrated with Enterococcus, only in one PG dog there was a prevalence of Firmicutes in the microbiome, a similar observation can be done at time T29 (Figure 5.3c). However, these details could be more evident in the biplot genus. Interestingly, a negative correlation between Bacteroidetes and Firmicutes was calculated (confirmed by the values of e1), while between Bacteroidetes and Fusobacteria the correlation
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was positive. Noteworthy is what had emerged from biplot, where Fusobacteria seemed to have an important role in the microbiome of dogs, even if it has to be specified that its component was heavier in the CP2, which, in these analysis, explained only a 2.4-11.2% of total variability. The remaining bacterial groups had a much smaller impact on the canine microbiome. These analyses displayed that there was not a clear separation due to the probiotic inclusion to the diet. From this examination bacterial phyla, which were present in the dog faecal microbiome, were in line with what reported by other researchers, since they mentioned Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria as the predominant phyla in the microbiome of healthy dogs (Bell et al., 2008; Handl et al., 2011; Guard and Suchodolsky, 2016). Instead the results of Beloshapka et al. (2013) indicated that the predominant phyla in their study were just Fusobacteria and Firmicutes. This lack of homogeneity about evidences for phyla prevalence can be a consequence of differences in the samples sequencing methodology. As reported by Handl et al. (2013), differences on the evaluation of microbial communities can be caused by certain methodology that leads to a better extraction, identification or quantification of some bacterial groups, changes in others, while less well-represented groups might be overlooked. Another consideration about these dissimilarities in the results was pointed out from the research of Garcia-Mazcorro et al. (2012). They considered that the microbial composition of faeces could suffer for variations day- by-day. Other possible critical points might be the difficulty to have big sized samples, belonging to the same breed and from which it is possible sampling for several consecutive days. Against this background, Isaiah et al. (2017) decided to apply a sub analysis in order to compare the microbial communities of German shepherds and dogs from other breeds. They evaluated the difference between faecal microbiome in dogs with exocrine pancreatic insufficiency (EPI) and in healthy ones. However, it did not emerge a significant difference in gut microbial population among breeds.
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Figure 5.1_ Overview of phyla trend in microbiome community of each dog, during the three times of sampling (T0, T15, T29).
OTU
Red bars: Control Group (CG) Blue bars: Probiotic Group (PG)
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Figure 5.2a_ T0 sampling: overview of each dog phyla trend in microbiome community.
OTU
Red bars: Control Group (CG) Blue bars: Probiotic Group (PG)
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Figure 5.2b_ T15 sampling: overview of each dog phyla trend in microbiome community.
OTU
Red bars: Control group (CG) Blue bars: Probiotic group (PG)
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Figure 5.2c_ T29 sampling: overview of each dog phyla trend in microbiome community.
OTU
Red bars: Control Group (CG) dogs Blue bars: Probiotic Group (PG) dogs
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Figure 5.3a_Principal Component Analysis plot of faecal bacterial phyla at time T0 in two canine groups fed with extruded commercial complete diets without (CG) or with (PG) the addition of probiotic (Enterococcus faecium).
Green dot: predominant phyla; Red dot: Control Group, CG; Blue dot: Probiotic Group, PG.
Figure 5.3b_Principal Component Analysis plot of faecal bacterial phyla at time T15 in two canine groups fed with extruded commercial complete diets without (CG) or with (PG) the addition of probiotic (Enterococcus faecium).
Green dot: predominant phyla; Red dot: Control Group, CG; Blue dot: Probiotic Group, PG.
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Figure 5.3c_Principal Component Analysis plot of faecal bacterial phyla at time T29 in two canine groups fed with extruded commercial complete diets without (CG) or with (PG) the addition of probiotic (Enterococcus faecium).
Green dot: predominant phyla; Red dot: Control Group, CG; Blue dot: Probiotic Group, PG.
The results obtained by PCA analyses at genus level, showed that the predominant bacterial groups were Allobaculum, Bifidobacterium, Clostridium, Lactobacillus and Prevotella. Despite in PG the diet was supplemented with probiotic bacteria (Enterococcus), from microbiome results we did not observe a predominant presence of this genus in the 4 surveyed dogs; also at time T29 we did not notice an increasing of enterococci in microbiome analysis. In Swanson et al. (2002) research emerged how the addition of a probiotic (Lactobacillus acidophilus: LAC) in the canine diet had different effects on the intestinal bacterial group composition. Indeed, the probiotic supplementation, in one of the trials of their study, did not affect the gut microbial population while in the other experiment bifidobacteria tended to show a greater concentration in dogs treated with the probiotic addition. Sometimes, as reported by Suchodolski et al. (2012), an increased abundance of Clostridium perfingens, Enterococcus faecalis and E. faecium is correlated to diarrheic episodes. Neverthless, in this doctoral investigation, diarrheic episodes were not detected.
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In this trial also Shannon biodiversity index (H) at genus level did not show significant differences between Control Group and Probiotic Group (results not shown). ANOSIM (Analysis of Similarity) was used for testing the significance of distance matrices at genus level between the Control Group dogs and the Probiotic Group ones. Therefore, there was no significance between the treatments (CG and PG) for each time of sampling. Analysis in this research at genere level has presented as predominant bacterial genera Lactobacillus, Clostridium, Blautia, Fusobacterium, Prevotella and Streptococcus (Table 5.3). The effect of time of sampling was significant for Streptoccoccus (P<0.05) and Prevotella (P<0.01). For Prevotella also the interaction between treatment (with or without probiotic addition) and time of sampling was significant (P<0.01). No significant effect was observed regarding treatment (Control Group and Probiotic Group) and no marked differences emerged from the three different samplings in the two different groups. No increase of Enterococcus genus was observed even in dogs fed with the probiotic addition. The absence of others genera abundance variation in the Probiotic Group microbiome could be caused by the origin of the probiotic, but also by the administrated dose of probiotic or by the duration of its administration. In fact, as observed by Marcinakova et al. (2006) the canine derived probiotic strain Enterococcus faecium EE3 was individually administrated to 11 healthy dogs at a dose of 109 CFU/mL for 1 week. They observed that this probiotic was able to survive in the gastrointestinal passage persisting in faeces for 3 months after cessation of its administration. In the same research, the strain might have provoked a decrease in Staphylococci and a significant diminution in Pseudomonas-like bacteria. However, in another study, the concentration of lactic acid bacteria increased while E. coli growth was not influenced (Grezskowiak et al., 2015).
5.4.2_Microbiome, SCFAs, lactate and nitrogen in faeces In this study, faecal score, pH, nitrogen, the SCFAs and lactate in faeces did not show significant variations between the two groups (CG and PG) and between the three times of sampling (Figure 5.4a, 5.4b). Faecal score both for CG and PG dogs showed mean value around 3.0 in each time of sampling (T0, T15 and T29). Also pH values were stable during the three time of sampling and the mean value for each time of sampling (T0, T15 and T29) was for both groups (CG and PG) around 6.60.
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SCFAs did not show significant differences between the two groups of dogs (Figure 5.4a). However SCFAs profile resulted in this study was in line with results observed in previous research (Xu et al., 2016). In figure 5.4b looking at the three time of sampling (T0, T15 and T29) it was found a constant increase of isobutyrate and a constant decrease of lactate in faeces of PG dogs. Furthermore, correlation analysis between microbiome and SCFAs in dogs of the both groups (CG and PG) reported only a few significant effects (Table 5.4). Lactate was positively correlated with the genus Bifidobacterium (P<0.05). Valerate had a positive correlation with Phascolarctobacterium (P<0.01) and Prevotella (P<0.01), however it was negatively correlated with Blautia (P<0.05). Propionate and isovalerate were positively correlated with Peptostreptoccocus (P<0.01 for both). An interesting aim for future studies can be the evaluation of proponic acid levels variations. Because, propionate is a metabolite formed by gut microbiota from complex dietary carbohydrate and has shown to reduce food intake. But also it has shown to protect against diet-induced obesity and insulin resistence and to regulate gut hormone release (Lin et al., 2012). In addition, as reported by Forsythe et al. (2016), high levels of microbial propionic acid production, seems to have detrimental effects on brain development. These detrimental effects on human brain can induce behavioural changes, correlate with autism (Macfabe et al., 2007). Furthermore, in this research was observed a negative correlation between acetate and Anaerobiospirillum in Probiotic Group. However, as reported in Handl et al. (2013) from these analysis was predictable a correlation between members of Clostridiaceae and Prevotellaceae with SCFAs, since it is known that these two groups of bacteria possess enzymes that degrade complex indigestible carbohydrates and produce SCFAs, which can be used as an energy source by the host (Handl et al., 2013). The specific role of acetate, isobutyrate and valerate is not very clear in mammals. Even if Lin et al. (2012) in their research reported that acetate protects against diet- induced obesity without causing hypophagia, however in their trial acetate has proven to be less effective than butyrate. The confliting results obtained by this doctoral research demand further researches to clarify physiological role of acetate, isobutyrate and valerate in dogs and to deepen the important role of propionate. The Pearson correlation coefficient was also calculated to evaluate the relationship between nitrogen in faeces to the two different groups, but no significant results were obtained.
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Table. 5.3_Prominent bacterial genera (expressed as relative abundance, %) in the faeces of dogs fed with the control diet, without and with the addition of the probiotic (Enterococcus faecium). T0 T15 T29 P-value Item Time Treatment CG PG CG PG CG PG Treatment sampling *Time Allobaculum 0.77 3.84 1.42 3.28 2.15 2.72 0.33 0.97 0.12 Anaerobiospirillum 0.47 0.75 0.35 0.47 0.23 0.47 0.64 0.47 0.92 Bacteroides 6.59 1.18 3.15 3.73 2.28 4.25 0.69 0.98 0.50 Bifidobacterium 1.74 2.06 0.35 1.52 0.52 1.70 0.60 0.07 0.49 Blautia 11.39 16.03 15.11 12.59 16.48 15.24 0.90 0.51 0.21 Catenibacterium 2.77 1.66 3.54 2.02 5.79 2.75 0.33 0.41 0.80 Cetobacterium 2.05 0.49 1.50 1.32 0.69 1.67 0.59 0.91 0.10 Clostridium 18.27 12.09 14.65 12.17 22.82 10.11 0.07 0.45 0.13 Collinsella 2.16 1.91 2.72 1.26 3.90 1.26 0.18 0.48 0.12 Enterococcus 0.02 0.06 0.02 1.53 0.09 0.23 0.21 0.21 0.42 Eubacterium 0.05 0.05 0.02 0.10 0.02 0.05 0.34 0.69 0.74 Faecalibacterium 0.85 0.77 0.92 0.92 1.00 1.20 0.94 0.20 0.64 Fusobacterium 10.30 3.97 4.65 6.07 4.52 4.55 0.24 0.26 0.06 Lactobacillus 15.02 22.15 14.47 15.58 9.33 15.54 0.58 0.58 0.86 Megamonas 2.99 0.79 1.38 0.88 1.43 1.24 0.39 0.30 0.35 Megasphaera 0.00 0.41 0.00 0.12 0.00 0.69 0.36 0.40 0.40 Peptococcus 0.82 0.54 0.44 0.58 0.63 0.59 0.81 0.47 0.33 Peptostreptococcus 0.02 0.88 0.45 1.40 0.06 0.62 0.43 0.20 0.82 Phascolarctobacterium 0.57 0.55 0.64 1.10 0.15 0.54 0.59 0.45 0.82 Prevotella 5.81 4.13 7.31 2.83 5.21 5.55 0.61 <0.0001** <0.0001** Ruminococcus 0.43 0.69 0.42 1.66 0.51 0.61 0.39 0.46 0.39 Streptococcus 1.91 3.06 8.05 5.77 6.21 7.06 0.98 0.04* 0.57 Turicibacter 1.26 1.49 0.66 1.87 1.13 2.96 0.18 0.73 0.64 CG, Control Group, dogs fed with extruded diet; PG, Probiotic Group, dogs fed with extruded diet and probiotic (Enterococcus) added. *Significant for P<0.05 **Significant for P<0.01
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Figure.5.4a_ Mean (±s.e.) of lactate and SCFA (% Dry Matter) contents in the faeces of dogs fed with the control diet without (CG) and with (PG) the addition of the probiotic (Enterococcus faecium).
Red bars:Control Group; Blue bars: Probiotic Group.
Figure.5.4b_Mean (±s.e.) of lactate and SCFA (% Dry Matter) contents in the faeces of PG dogs, measured in the three times of sampling (T0, T15, T29).
T0, the beginning of the study; T15, 15 days after the beginning of the study; T29, last day of the experimental period.
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Table. 5.4_ Significant correlations between bacterial genera and molar proportion of short chain fatty acid and lactate in Control and Probiotic Group.
acetate% butyrate% isobutyrate% isovalerate% lactate% propionate% valerate% Control Group Bifidobacterium 0.58* Blautia -0.60* Peptostreptococcus 0.70** 0.97** Phascolarctobacterium 0.76** Prevotella 0.72** Probiotic Group Anaerobiospirillum -0.63*
*significant for P < 0.05 **significant for P < 0.01 Proportion is calculated as % of each acid on the sum of the SCFAs
5.4.3_Faecal microbiome and salivary cortisol Another aspect that was explored in this trial was the relationship between faecal microbiome and cortisol as biomarker of autonomic nervous system. Indeed, as mentioned before, in humans there is a good evidence that gut microbiota plays an important role in the normal CNS development, and, in particular, it can influence those systems associated with stress response, anxiety and memory (Heijtz et al., 2011; Neufeld et al., 2011; Forsythe et al., 2016). Samples of saliva were collected from 8 dogs at two times, however 3 saliva samples were not analysed due to the low amount collected. Furthermore, due to the small size of samples statistical analysis were not perfomed. Nevertheless, it is possible to make some considerations. At both times of saliva collection, the Probiotic Group had lower levels of cortisol, on the other hand, at the first time of sampling, in the Control Group high levels of the hormone in two dogs (one subject had a concentration of >30.0 ng/ml salivary cortisol, the other one had a cortisol concentration of 14.58 ng/ml) were detected . At the second time (T43) of saliva collection, salivary cortisol decreased for the two dogs of the CG with high levels of cortisol at time T29, and cortisol concentration decreased further for PG dogs. This variation of cortisol concentration could depend also on the different housing system of subjects. In fact, at T29 sampling dogs were kept in individual kennels while at T43 they were coupled. Moreover, individual variability should be considered. Anyway, it could be an interesting starting point for future researches to take into consideration cortisol in saliva samples as biomarker of HPA axis and to evaluate its synergy with microbiome. Accordingly, these further researches can help to deepen the role of the microbiota-gut-brain axis in dogs. In
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Chapter 5 PRELIMINARY STUDY: FAECAL MICROBIOME AS BIOINDICATOR OF DOG WELL-BEING AND POSSIBLE RELATION WITH HYPOTHALAMIC-PITUITARY-ADRENAL (HPA) AXIS fact, in a research of Messaoudi et al. (2011) after a daily administration of L. Helveticus and Bifidobacterium longum to women and men, the 24 h urinary cortisol levels were similarly reduce. Furthermore, they observed also a improvement in scores releted to perceived stress, anxiety and depression (Forsythe et al., 2016). In this doctoral study, it was interesting to notice that the Probiotic Group maintained low cortisol levels in both analyses. Therefore, it would be a very stimulating point discovering if there might be a real positive correlation between probiotics assumption and a consequent reduction of HPA axis activation. Indeed, as reported Forsythe et al. (2016), the communication between gut and brain is bi-directional, and there is a strong evidence that stress has a significant effect on the composition and functions of the gut microbiota. Furthermore, it is known that Bacteria can synthesize and respond to hormones and neurotransmitters. In particular as reported by Galland (2014) Enterococcus produce serotonin. Serotonin is a monoamine neurotransmitter, biochemically derived from tryptophan and in animals is primary found in GI tract, blood platelets and the CNS. Moreover, serotonin has been implicated in vast range of behaviors, such as appetitive, emotional, motor, cognitive and participate in the hypothalamic control of pituitary secretion, particularly in the regulation of adrenocorticotropin (ACTH) (Frazer and Hensler, 1999). Furthermore serotonin is frequently associated with well-being feelings. These aspects can add others important informations to evaluate the role of some bacterial populations on well-being of animals.
5.5_CONCLUSIONS
The gut microbes contribute to maintain and improve the health and well-being of companion animals. The increasing researches on novel tools to protect gut microbiota led to study the influence of probiotic on microbial population and on well-being of dogs. However in this preliminary trial, significant variation in microbiome population between dogs fed with a control diet and dogs fed with the addition of probiotic on control diet was not observed. A possible constraint in the choice of correct probiotic is the source. Indeed, as reported by Grzeskowiak et al. (2015) the host-derived microorganisms might be the most appropriate probiotic source. Furthermore seems to be necessary do others controlled trials to find the probiotics that improve the impact on health and well-being of dogs. In this study were not
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observed probiotic influences on SCFAs and nitrogens in dog faeces. This result might be also an index of strong balance of the intestinal microbial population. In addition in this study was investigated a possible correlation between faecal microbiome and salivary cortisol. Unfortunately samples were not enough to do a statistical analysis, but concentrations of cortisol in dogs fed with the addition of probiotic on diet showed lower cortisol levels than dogs fed with control diet. Indeed, as reported by Forsythe et al. (2016), the result of this research seems to be a good evidence for the role played by gut microbiota in normal central nervous system development and in particular in systems associated with stress response. This trial included small amount of animals, however, it is very difficult to find studies with accurate data and a big data set about this type of observations. Furthermore, variables involved in these researches are numerous, among which there is the impossibility to have a large sample of dogs of the same breed and housed in the same environment. Consequently, the results that were obtained can be considered as useful bases for the development of further surveys.
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Part 3
BIOMARKERS OF DOG WELL-BEING DETECTED IN HAIR
Part 3 BIOMARKERS OF DOG WELL-BEING DETECTED IN HAIR
Chapter 6 PRELIMINARY STUDY: HEAVY METALS AND CORTISOL IN HAIR, EVALUATED AS POSSIBLE BIOMARKERS OF DOG WELL-BEING
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6.1_ABSTRACT
Detection of heavy metals in hair is an interesting measure for different aspects: hair is used as a matrix to assess environmental exposure to metals, but also to evaluate dietary intakes in particular of toxic and essential mineral elements. Heavy metals in hair might be used as possible biomarkers of human well-being. Often dogs, for their close relationship with human, assumed the role of sentinels and their hair is used to evaluate the presence of heavy metals in environment and consequently, the effects on human. Hair matrix is frequently used as a non-invasive indicator of these elements exposure. Furthermore, hair is considered as the best matrix for cortisol evaluation in chronic stress. For this study, were enrolled 8 adult dogs. In the first part of the study, that covered a period of 30 days, dogs were housed individually in kennels. At the beginning of this trial (T0) all the dogs were fed with the same mixture of commercial extruded complete diets and each dog was housed individually. One day after T0, one group (Control Group: CG) continued to be fed with the same alimentation, while the second group (Probiotic Group: PG) was fed with the same diet of CG with the supplement of the probiotic bacteria Enterococcus faecium. After 30 days (T29), the addition to the diet of probiotic was suspended and all subjects returned in boxes in pairs. Hair samples were collected at time T29, and at time T43, 43 days after the suspension of probiotic addition. Heavy metals and cortisol were measured in dog hair samples. The results of this study showed a significant correlation (P<0.05) between zinc element and cortisol in dogs fed with control diet (CG, control group) in both times of sampling. A significant correlation was measured also between cortisol and strontium in dogs that for 30 days (until T29 sampling) were fed with probiotic diet (PG) but only at time of sampling T29. In conclusion, in this study did not emerge a strong correlation between heavy metals in hair and dietary intake, but emerged an interesting correlation between a heavy metal (Zc) and cortisol.
Keywords: heavy metals, cortisol, dog, hair, probiotic, biomarkers
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6.2_INTRODUCTION
Heavy metals are essential for biological systems, and in some cases, provide beneficial effects to animals. On the other hand, their excess and the deficiency of some essential metals (such as calcium, copper, zinc and magnesium) are usually associated with harmful effects in humans and in animals. Some of the documented harmful effects are: the decline in the immunological processes and the consequent appearance of various diseases (Lech, 2002; Park et al., 2005). For example, Lech (2002), in his research, showed as children with reduced mean levels of magnesium in the hair suffered from selected neurological diseases. Furthermore, Schell (1991) reported that the toxic effects of heavy metals are particularly damaging for growing subjects, moreover Teresa et al. (1997) mentioned that these compounds may exert a definite influence on the control of biological functions, affecting the endocrine system and the development of various body tissues (Park et al., 2005). Additionally, in order to have a general spectrum of the situation, it is important also to remember that toxic metal accumulation is influenced by diet, sex and age (Lopez-Alonso et al., 2007). For example, in humans, lead (Pb) concentrations in the bone tissue (e.g. femur) augment with age increasing (Komarnicki, 2000). However, Pb is the most toxic heavy metal and dogs and children seem to show very similar susceptibility to it, since they share analogous behaviours (e.g. playing with the ground) or the same environment. Pb is toxic to many living organisms and one of the way of exposure is through the food. About this aspect, some studies reported that several heavy metals are added to animal diets for various purposes, resulting in a high concentration of these compounds in animal wastes. For these reasons, canines could be used as ‘bioindicators’ of environmental quality in terms of heavy metal concentrations (Ko et al., 2004; Park et al., 2005). In fact, pets may be even more exposed than their owners to some contaminants sources, such as soil or house dust (Park et al., 2005). Moreover, lead can influence numerous enzymatic activities, it can exert a competitive action with calcium in different tissues and interact with DNA and RNA. Pb is implicated in many metabolic pathways involving the amino acid cystein (for example in the gluconeogenesis or in the fatty acid synthesis) and it is also capable in inhibiting the development of long and flat bones (Gerber et al., 1980). Nonetheless, also arsenic (As) can negatively affect the health of animals and humans, since As introduction into the body is principally conveyed by inhaling its powders and vapours or via the 128
Chapter 6 PRELIMINARY STUDY: HEAVY METALS AND CORTISOL IN HAIR, EVALUATED AS POSSIBLE BIOMARKERS OF DOG WELL-BEING food and water intake. In fact, animals are exposed to this metal through contaminated water, feedstuff, grasses, vegetables and different leaves. Arsenic has been reported as the most common cause of inorganic chemical poisoning in farmed animals (Mandal, 2017). Most of the ingested organic arsenic is absorbed by the gastrointestinal tract in a few hours. Once absorbed, As passes rapidly into the bloodstream (where it is partially fixed to the plasma proteins), then it distributes in various organs especially in the liver, kidneys, bones and skin (in particular, it tends to accumulate in nails and hair). Arsenic can also provoke an irritating action on the mucosal membranes and can interest the nervous system and the digestive tract. In this preliminary study, heavy metal concentrations were measured in canine hair and it was assessed whether occurs a correlation between heavy metals and well-being of dog. For this reason and with the purpose to obtain a bioindicator of canine well-being, it was analysed also cortisol levels from the same matrix, because, as reported by Accorsi et al. (2008), the analysis of hair steroidal hormones could be useful to study the chronic stress and welfare that require observations of adrenal functions for extended periods. Hair matrix was also selected because it is a biological sample easy to collect, with a non-invasive method, sampling costs are minimal and its transportation to the laboratory is simple. In the case of metals, hair matrix is well established, especially for investigating levels and changes in many trace elements that could be accumulated in the body. Furthermore, hair is less sensitive to immediate intake and could therefore also be a good biological indicator of the nutritional status of certain elements. These features make hair an attractive bio-monitoring substrate. However, a broad debate on hair limitations as a metal exposure biomarker is still open (ATSDR, 2001; Morton et al., 2002), from the moment it could be also contaminated by dust and sweat. In contrast with these statements, Varrica et al. (2014) reported that human scalp hair constitutes an optimal matrix in this type of survey for its ability to accumulate trace elements, superiorly to that of other biological matrices. Furthermore, variations of metals concentrations could be also influenced by age, sex, hair colour and area of residence (Wolfsperger et al., 1994; Druyan et al., 1998; Khalique et al., 2005; Dunicz-Sokolowska et al., 2006; González-Muñoz et al., 2008). The aim of this preliminary study was to understand if the surveyed heavy metals in hair can be adopted as biomarker of dog well- being. It was evaluated also a possible association between some heavy metals and cortisol levels in hair. In the present doctoral investigation, the sampled subjects belonged to the same kennel,
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they were housed in the same environment and fed with the same diet. However, for future researches it is necessary to expand the sample size and to identify the most suitable classes of subjects to sample. In order to reduce factors as age, sex, colour of hair, which seem to affect variations of elements concentration.
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6.3_MATERIALS AND METHODS
6.3.1_Animal selection For this study, was recruited a sample composed by 8 (6 females and 2 males) adult dogs of normal weight in accordance with the belonging breed. Dogs were hosted in a private kennel in North East of Italy, and were of 3 different breeds (4 Cocker Spaniels, 3 Beagles and 1 Labrador Retriever). The average age of the dogs was 6±2.33 years, with a Body Condition Score (BCS) between 4.0 and 7.0. For this study, the BCS on a 9 points scale was applied, where dogs with a BCS of 4 and 5 are considered of ideal weight while 6 and 7 means overweight (Laflamme, 1997; Kealy et al., 2002; Kerr et al., 2013); scores were attributed by an operator of the kennel (Table 5.1, Chapter 5). The study covered a period of 30 days during which the dogs were housed individually in kennels with an outdoor pen and a paved portion covered with a roof. After this period, dogs were housed in pairs. The inclusion criteria adopted to select dogs for this research were the following ones: (1) more than 12 months of age; (2) clinically healthy, free from pain, exogenous and endogenous parasites, and immunised, as assessed by a veterinary practitioner; (3) no recent history of corticosteroid administration; (4) no drug therapies while sampling and from 1 month before the beginning of the trial. Clinical data were obtained from the medical records available from the kennel veterinarians. For each dog, the following information was also collected: date of birth, breed, size, sex (male, castrated male, female, spayed female), feeding schedule, and typology of food.
6.3.2_Diet Dogs were fed with two equally mixed commercial extruded complete diets (Table 5.2, Chapter 5). Fresh water was available ad libitum. The diet was administered once a day in the morning. Furthermore, in addition to the two mixed extruded complete diets, it was provided a supplementation of the probiotic Enterococcus faecium at the concentration of 3.5 x 1010 CFU/kg of body weight.
6.3.3_Experimental design The 8 dogs were randomly split into two groups of 4 individuals.
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At the beginning of this preliminary study (T0) all the dogs were fed with the same mixture of the two commercial extruded complete diets and each dog was housed individually. One day after T0, one group (Control Group: CG) continued to be fed with the same alimentation, while the second group (Probiotic Group: PG) was fed with the same diet of CG with the supplement of the probiotic bacteria (Enterococcus faecium). After 30 days (T29), the addition of probiotic to the diet was suspended and all subjects returned in boxes in pairs.
6.3.4_Sample collection Samples of hair were collected from each dog.
6.3.4.1_Hair sampling For each dog, two hair samples were collected. The first sample, (T29) was taken at the end of the experiment period (see Chapter 5), the second one (T43) 43 days after T29 sample. Hair samples collected at the time T43, were cut in the same cervical region point where were cut T29 hair samples. The samples were collected in the morning 1 hour after the meal, when dogs were resting and apparently relaxed. Hair samples were cut from the cervical region near to the skin (proximal) by means of stainless- steel surgical scissors. Hair samples were identified, labelled and stored at room temperature until analysis.
6.3.5_Samples analysis
6.3.5.1_Cortisol analysis The extraction of the cortisol from the hair was performed as described by Accorsi et al. (2008). Hair was first washed three times with isopropanol and dried for at least 2- days under a stream hood to ensure complete isopropanol evaporation. Then, hair was minced into 1-3 mm length fragments and 30 mg of trimmed hair were placed in a glass vial. Methanol (concentration ≥99.9%) was added and vials were incubated in warmed water bath at 50°C for 18-24 h. The contents of the vials were then centrifuged for 15 min at 3000 rpm. The supernatant was transferred to clean the 2 ml microcentrifuge tube and the methanol was dried down using a Vacuum Concentrator (Centrifugal System Jouan RC 10.10). Following removal of the methanol, the cortisol extracted was reconstituted with 0.6 ml of phosphate-buffered saline (PBS) 0.01 M with 0.1% BSA. Hence, samples were frozen at -20°C until analysis.
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Cortisol concentrations were determined by a Radio Immuno Assay (RIA) based on H-steroid by competitive adsorption. Briefly, a 96-well microtitre plate (Optiplate, Perkin Elmer Life Science) was coated with anti-rabbit ɣ-globulin serum raised in goat, incubated overnight with the antiserum diluted 1:1,000 in 0.15 mM sodium acetate buffer, pH 9, at 4°C. The plate was then washed twice with PBS 0.1% BSA, pH 7.4 (RIA buffer) and incubated overnight at 4°C with 200 μl/well of the anti-cortisol serum diluted 1:8,000. The antiserum (Centro Medico Diagnostico Emilia, Bologna, IT) was raised in rabbit against cortisol-3 carboxymethyloxime-BSA and showed the following cross reactions: cortisol 100%, prednisolone 44.3%, 11-deoxycortisol 13.9%, cortisone 4.9%, corticosterone 3.5%, progesterone <0.01%. The plate was carefully washed with PBS buffer, and standards (3 to 200 pg/well), quality control, unknown extracts and tracer (1,2,6,7- 3 H-cortisol, Perkin Elmer Life Sciences, 30 pg/well, specific activity: 3700 GB q/mmol) were added (final volume: 200 μl/well). The plate was incubated overnight at 4°C, the incubation mixture was decanted and wells washed with PBS buffer, added with 200 μl scintillation cocktail (Microscint 20, Perkin Elmer Life Sciences) and counted by means of the beta-counter (Top-Count, Perkin Elmer Life Sciences). All samples were assayed in duplicate. The intra- and inter-assay coefficients of variation (CV) were 3.6% and 10.0%. The sensitivity of the assay was defined as the dose of hormone at 90% binding (B/B0) that was 3.125 pg/well. The concentrations were expressed in pg/mg of hair.
6.3.5.2_Heavy metal analysis First, hair samples were weighed then washed by soaking them in a 2:1 mixture solution of methanol and chloroform for about 2 hours. Hence, they were rinsed twice in distilled water (O’Connel and Hedges, 1999). Samples were then dried under a stream hood. Element analysis was performed as described by McLeon et al. (2009) and Zaccaroni et al. (2014) with some modifications, using an Inductively Coupled Plasma-Optic Emission Spectrometry (ICP- OES) technique after hair digestion. Samples (0.2 g of hair) were microwave digested by means of the Milestone ETHOS ONE oven using nitric acid (9 mL) and hydrogen peroxide (1 mL). Subsequently, samples were filtered with a 45 µm filter and 1 mL of the filtered sample was put in vials with 8.80 mL of Milli-Q water and 200 µl of yttrium (Y) (2 ml of Y 50 ppm in 98 ml of nitric acid
3.25%). Analyte detection was calibrated using the external calibration method employing ten
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standards. Three blanks were run during each set of analysis to check for chemicals purity. Elements were quantified by the ICP-OES technique.
6.3.6_Statistical analysis The mean and the relative standard deviation (s.d.) was calculated for both groups (CG and PG) and for each time of sampling (T29 and T43). To obtain an overview of the elements concentration in hair and to try to select some of them, the data were subjected to principal component analysis (PCA). PCA provides an exploratory data analysis based on a multivariate projection method that helps to visualise all the information contained in a data set. The correlation between predominant heavy metals and cortisol in hair of Control Group dogs and of Probiotic Group ones was applied to provide a technical description between data obtained by different treatment administration (CG and PG) and different time of sampling (T29 and T43).
InfoGen statistical program, version 2017 (National University of Córdoba, Argentina), was used in all the statistical analysis.
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6.4_RESULTS AND DISCUSSION
6.4.1_Heavy metals in dog hair Regarding the evaluation of elements variations in dog hair, there are few bibliographic informations. For this reason, it is very difficult to compare the results achieved. However the method (ICP-OES) used in this preliminary study for the analyses has explored the following elements: Ag (Silver), Al (Aluminum), B (Boron), Ba (Barium), Bi (Bismuth), Cd (Cadmium), Cr (Chromium), Cu (Copper), Fe (Iron), Ga (Gallium), In (Indium), K (Potassium), Li (Lithium) , Mg (Magnesium), Mn (Manganese), Ni (Nickel), Pb (Lead), S (Sulphur), Sr (Strontium), TI (Thallium), Zn (Zinc). The mean values ± s.d. (standard deviation) of all elements detected in the hair of dogs from the two different treatments and from the two time of sampling is reported in Table 6.1. Ga, In, Tl, S were excluded from mean±s.d. evaluation because these elements were absent in samples of this study. From the Table 6.1 it is possible to observe a great standard deviation in Ag, Al, B, Ba, Li, Mn, Ni and Pb. This value might depend on the small size of the sample and consequently to the high variability between samples belonging to the same group. Furthermore, from the Table 6.1 emerge high levels of Ag in both groups (CG and PG) and times of sampling (T29 and T43). It is possible also to observe in the two canine groups an increase of Al and K at T43, and a mean of Mg and Sr in CG higher than in PG. Additionally, B seems to be present only in CG at time T29, however, as mentioned before, this element exhibited also a high value of standard deviation. Principal Component Analysis was applied to better understand the distribution of these elements and which of them had a greater weight. With PCA it was investigated which heavy metal affected more the elements composition of the hair. However, the greater interest was focused principally in examining possible differences between dogs fed with (PG) or without (CG) the probiotic addition to the commercial extruded complete diets. Furthermore, this trial aimed to observe possible variations of heavy metals concentrations between the two different times of hair collection (T29 vs T43). As reported before, the subjects of this research were housed in the same place, so in the results were not expected variations correlated with the environment. In addition, it was not taken into consideration the possible influence of gender and age because the size of sample was limited.
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Table 6.1 Mean values ± s.d. of the elements detected in hair of dogs fed without (CG) and with (PG) probiotic addition.
T29 T43 CG (mg/kg) PG (mg/kg) CG (mg/kg) PG (mg/kg)
Mean ± s.d. Mean ± s.d. Mean ± s. d. Mean ± s.d.
Ag 163.93 251.43 180.09 84.96 36.27 5.66 23.92 14.93 Al 2.21 19.23 48.57 71.33 159.68 197.11 193.37 217.79
B 32.24 40.00 0.00 0.00 0.00 0.00 0.00 0.00 Ba 0.81 1.17 0.09 0.13 2.35 2.52 0.85 0.83
6.98 7.44 2.68 4.93 2.66 2.23 0.65 1.22 Bi Cd 0.12 0.11 0.20 0.17 0.22 0.06 0.19 0.03
Cr 0.01 0.06 0.00 0.00 0.00 0.00 0.05 0.09
Cu 5.73 5.38 11.42 4.22 9.86 2.53 10.30 1.81 Fe 62.77 27.13 64.89 69.50 141.15 115.78 135.02 131.82
K 76.89 23.89 76.68 69.30 255.58 172.29 142.04 97.90 Li 0.09 0.10 0.05 0.07 0.15 0.22 0.20 0.22
Mg 219.22 179.46 169.72 184.68 424.23 285.23 204.59 145.79 Mn 3.76 3.55 1.36 1.55 7.15 6.78 3.56 3.12
Ni 1.20 1.10 0.36 0.61 0.76 0.84 0.37 0.37 Pb 4.57 5.37 0.81 0.64 0.00 0.00 0.31 0.52
Sr 3.44 3.16 1.41 1.02 4.73 3.69 2.54 1.58 Zn 186.29 35.36 205.40 20.29 215.73 43.02 217.41 27.03
CG, Control Group: dogs fed with mixture of two commercial extruded complete diets PG, Probiotic Group: fed with the same diet of CG with the supplement of the probiotic bacteria (Enterococcus faecium) T29: at the end of the experiment period T43: 43 days after T29 day
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Figure. 6.1a_Principal Component Analysis plot of heavy metals presented at time T29 in the two canine groups. Dogs were fed with extruded commercial complete diets without (CG) or with (PG) the addition of probiotic (Enterococcus faecium).
Green dot: heavy metals; Red dot: Control Group, CG; Blue dot: Probiotic Group, PG.
Figure. 6.1b_Principal Component Analysis plot of heavy metals presented at time T43 in the two canine groups. Dogs were fed with extruded commercial complete diets without (CG) or with (PG) the addition of probiotic (Enterococcus faecium).
Green dot: heavy metals; Red dot: Control Group, CG; Blue dot: Probiotic Group, PG.
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Principal Component Analysis plot (Fig. 6.1a and 6.1b) showed that in samples of this trial there was not a predominant element, in fact from the plot emerged as most of elements had the same distance and, consequently, each heavy metal has displayed almost the same weight. Another important observation that can be extrapolated from the plot was the distribution of the different subjects. No diversification between the two treatments (CG and PG) was noticed. However, one subject belonging to the probiotic sample (blue dot) at time T43 presented a close relationship with Cr. On the other hand, it is important to underline that Cr element at T43 sampling time, was more important in the second Principal Component, so its weight was smaller than the other elements. The results of this study did not reveal a relation between probiotic administration and heavy metals. In keeping with these observations, Nowak and Chmielnicka (2000) noticed that the hair mineral content did not reflect the intake of the same elements through the food. However, some researches in human revealed that gender but also the age might be variables that have to be considered when heavy metals are analysed in hairs. In fact, as reported by Khalique et al. (2005) hair from women presented higher levels than hair from men for all the metals, with the exception of Fe and Co, and that for males most of the hair metal concentration decreased with age. However, in females appeared that the age increasing might be accompanied by a rising of hair metal concentration (Gonzàles-Munoz et al., 2008). In the case of this trial, due to the limited size of samples, it was very difficult to study the differences of elements in hair among different genders and ages. However, this interesting point has to be considered for future analysis.
6.4.2_ Cortisol in dog hair As it can be observed at Figure 6.2, hair cortisol concentration of CG at time T29 presented higher values than PG dogs and at time T43 cortisol levels in CG dogs hairs have dropped. PG group instead has maintained the same levels of cortisol concentration in both time of sampling. At T43 all dogs of this study were placed in box in pairs, this is an important factor that might influence the decrease of cortisol in CG dogs. However in both groups of dogs the levels of cortisol were high respect the levels of cortisol that were measured in others researches (Mesarcova et al., 2017). These results might be connect with differents factors, like colour of coat (as mentioned before), methology of exctraction. However these high levels of cortisol might indicate also a
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prolonged overproduction of glucocorticoids resulting in possible problems in welfare state of the animal.
Figure 6.2_Mean (± s.e.) of hair cortisol (pg/mg) concentrations in dogs fed without (CG) and with (PG) the addition of probiotic (Enterococcus Faecium) to the commercial extruded complete diet.
CG, Control Group: dogs fed with mixture of two commercial extruded complete diets. PG, Probiotic Group: fed with the same diet of CG with the supplement of the probiotic bacteria (Enterococcus faecium). T29: at the end of the experiment period T43: 43 days after T29 day.
6.4.3_Relationship between heavy metals and cortisol in dog hair During this research, it was also interesting to understand if it can be a relation between heavy metals and cortisol in hair. It was set up a system of analysis to investigate the possible correlation between these elemens and cortisol. In the analysis of the correlation between cortisol and heavy metals were taken also into account the different treatments (without (CG) or with (PG) probiotic addition to the extruded diet) and the different time of sampling. The results that were obtained suggested a significant correlation (P<0.05) between Zn element and cortisol in Control Group dogs both at T29 and T43 time of sampling, and also a significant correlation (P<0.05) between Sr element and cortisol concentration in PG dogs at T29 (data not shown). In humans, as reported by Bertazzo et al. (1998) the gender do not influence Zn presence in hair, so this element could be influenced by other different exogenous and endogenous factors. A 139
Chapter 6 PRELIMINARY STUDY: HEAVY METALS AND CORTISOL IN HAIR, EVALUATED AS POSSIBLE BIOMARKERS OF DOG WELL-BEING positive and significant correlation between cortisol and Zn in hair was found also by Vaghri et al. (2013) by exploring the relation between the levels of the hormone in sampled children’s hair and the socioeconomic status of their family. The authors considered zinc as a biological marker of its adequate long-term intake. Nevertheless, in their research a significant inverse association among zinc and cortisol in hair reflected some levels of chronic stress in children. These researches seem to confirm a potential association between zinc and cortisol; moreover, as reported by Vaghri et al. (2013) a nutritional deficiency of this element can result in physiological stress in the human body. For these reasons, it might be very interesting to deepen this relation and to understand the mechanisms that connect zinc with the body’s ability to respond to stress. Furthermore, another role of Zn is that it is used to limit the accumulation of potentially toxic cadmium and lead in horses and rats (Jihen et al., 2008; Topczewska and Krupa, 2013). About the relationship between strontium and cortisol in hair there is not information in literature. However, studies on animals reported that high doses of strontium induced alterations in the mineralization of bones, while in rats it provokes chronic renal problems (Cohen-Solal, 2002). In contrast, Sr supplementation can also induce a positive effect on bone density, volume and architecture (Shahnazari et al., 2006).
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6.5_CONCLUSIONS
Despite this preliminary trial presented a small amount of samples, from these analysis emerged some evidence that might be a good starting point for future study. The choice to collect hair samples from dogs located in the same kennel have reduced the variability caused by environment. However, it is necessary to point out that this variable is often taken in consideration from many studies, because detection of heavy metals in dogs hair is often used as bioindicator of environmental pollution (Backer et al., 2001; Dunlap et al., 2007). Neverthless, this study aimed to understand if heavy metals concentration in hair can be influence by diet and also if there might be a relationship between some elements in hair and cortisol. For this reason, limited the environmental variable seemed to be the best choice. The results obtained in this study showed no correlation between diet and elements in hair, the probiotic addition did not seem to influence the variation of heavy metals concentration. However, the correlation between zinc and cortisol in hair obtained, resulted to be quit interesting. In fact, these observations led to consider zinc as a possible biomarker also for behaviour status of animals. Also in the study of Vaghri et al. (2013) conducted on samples of children’s hair emerged this correlation between these two biological indicators. Unfortunately, in literature are not indications about the interaction between strontium element and cortisol, so it should be necessary proceed with others researchers in this field. For future studies it might be also important to individuate and standardize reference concentration ranges of these elements in the hair matrix. These references might be very useful for a comparison results from various researches.
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6.6_REFERENCES
Accorsi PA, Carloni E, Valsecchi P, Viggiani R, Gamberoni M, Tamanini C, Seren E. Cortisol determination in hair and faeces from domestic cats and dogs. Gen Comp Endocrinol, 155(2):398– 402, 2008.
ATSDR. Hair analysis panel discussion: exploring the state of the science. Atlanta, GA: Agency for Toxic Substances and Disease Registry, 2001.
Backer LC, Grindem CB, Corbett WT, Cullins L, Hunter JL. Pet dogs as sentinels for environmental contamination. Sci Total Environ, 274:161-169, 2001.
Bertazzo A, Costa C, Biasiolo M, Allegri G, Cirrincione G, Presti G. Determination of copper and zinc levels in human hair: influence of sex, age, and hair pigmentation. Biol Trace Elem Res, 52:37–53, 1996.
Cohen-Solal M. Strontium overload and toxicity: impact on renal osteodystrophy. Nephrol Dial Transplant, 17(Suppl 2):30-34, 2002.
Druyan ME, Bass D, Puchyr R. Determination of reference ranges for elements in human scalp hair. Biol Trace Elem Res, 62:183–187, 1998.
Dunicz-Sokolowska A, Radomska K, Dlugaszek M, Graczyk A. Contents of bioelements and toxic metals in the Polish population determined by hair analysis. Part 1. Children aged 1–10 years. Magnes Res, 19:35–45, 2006a.
Dunlap KL, Reynolds AJ, Bowers PM, Duffy LK. Hair analysis in sled dogs (Canis lupus familiaris) illustrates a linkage of mercury exposure along the Yukon River with human subsistence food systems. Sci Total Environ, 385:80–85, 2007.
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Gerber GB, Leonard A, Jacquet P. Toxicity, mutagenicity and teratogenicity of lead. Mutat Res, 76(2):115-141, 1980.
González-Muñoz MJ, Peña A, Meseguer I. Monitoring heavy metal contents in food and hair in a sample of young Spanish subjects. Food Chem Toxicol, 46:3048–3052, 2008.
Jihen EH, Imed M, Fatima H, Abdelhamid K. Protective effects of selenium (Se) and zinc (Zn) on cadmium (Cd) toxicity in the liver and kidney of the rat: Histology and Cd accumulation. Food Chem Toxicol, 46:3522–3527, 2008.
Kealy RD, Lawler DF, Ballam JM, Mantz SL, Biery DN, Greeley EH, Lust G, Segre M, Smith GK, Stowe HD. Effects of diet restriction on life span and age-related changes in dogs. JAVMA, 220:1315- 1320, 2002.
Kerr KR, Forster G, Dowd SE, Ryan EP, Swanson KS. Effects of dietary cooked navy bean on the fecal microbiome of healthy companion dogs. PLoS One, 8(9):e74998, 2013.
Khalique A, Ahmad S, Anjum T, Jaffar M, Shah MH, Shaheen N, Tariq SR, Manzoor S. A comparative study based on gender and age dependence of selected metals in scalp hair. Environ Monit Assess, 104:45–57, 2005.
Ko HJ, Choi HL, Park HS, Lee HW. Prediction of heavy metal content in compost using near infrared reflectance spectroscopy. Asian Aust J Anim Sci, 17:1736-1740, 2004.
Komarnicki GJK. Tissue, sex and age specific accumulation of heavy metals (Zn, Cu, Pb, Cd) by populations of the mole (Talpa europaea L.) in a central urban area. Chemosphere, 41:1593-1602, 2000.
Laflamme D. Development and validation of a body condition score system for dogs. Canine Pract, 22:10215, 1997.
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Lech T. Lead, copper, zinc, and magnesium in hair of children and young people with some neurological disease. Biol Trace Elem Res, 85:111-126, 2002.
López-Alonso M, Miranda M, García-Partida P, Cantero F, Hernández J, Benedito JL. Use of dogs as indicators of metal exposure in rural and urban habitats in NW Spain. Sci Total Environ, 372:668– 675, 2007.
Mandal P. An insight of environmental contamination of arsenic on animal health. Emerg Contam, 3:17-22, 2017.
McLean CM, Koller CE, Rodger JC, MacFarlane GR. Mammalian hair as an accumulative bioindicator of metal bioavailability in Australian terrestrial environments. Sci Total Environ, 407:3588–3596, 2009.
Mesarcova L, Kottferova J, Skurkova L, Leskova L, Kmecova N. Analysis of cortisol in dog hair-a potential biomarker of chronic stress: a review. Vet Med, 62:263-376, 2017.
Morton J, Carolan VA, Gardiner PHE. Removal of exogenously bound elements from human hair by various washing procedures and determination by inductively coupled plasma mass spectrometry. Anal Chim Acta, 455:23–34, 2002.
Nowak B, Chmielnicka J. Relationship of lead and cadmium to essential elements in hair, teeth, and nails of environmentally exposed people. Ecotoxicol Environ Saf, 46:265–274, 2000.
O’Connel TC, Hedges REM. Investigations into the effect of diet on modern human hair isotopic values. Am Jou Phys Anthro, 108:409-425, 1999.
Park SH, Lee MH, Kim SK. Studies on Cd, Pb, Hg and Cr values in dog hairs from urban Korea. Asian Aust J Anim Sci, 18(8):1135-1140, 2005.
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Prasad A. Zinc in growth and development and spectrum of human zinc deficiency. J Am Coll Nutr, 7:377–384, 1988.
Shahnazari M, Sharkey NA, Fosmire GJ, Leach RM. Effects of strontium on bone strength, density, volume, and microarchitecture in laying hens. J Bone Miner Res, 21(11):1696-1703, 2006.
Shell LM. Effect of pollutants on human prenatal and postnatal growth: noise, lead, polychlorobiphenyl compounds, and toxic wastes. Yearb Phys Anthropol, 34:157-188, 1991.
Teresa M, Vasconcelos SD, Tavares HM. Trace element concentrations in blood and hair of young app rentices of a technical-professional school. Sci Total Environ, 205:189-199, 1997.
Topczewska J, Krupa W. Influence of horse breed and housing system on the level of selected elements in horse’s hair. J Elem, 18(2):287–295, 2013.
Vaghri Z, Guhn M, Weinberg j, Grunau RE, Yu W, Hertzman C. Hair cortisol reflects socio-economic factors and hair zinc in preschoolers. Psychoneuroendocrinol, 8(3):331–340, 2013.
Varrica D, Tamburo E, Milia N, Vallascas E, Cortimiglia V, DeGiudici G, Dongarrà G, Sanna E, Monna F, Losno R. Metals and metalloids in hair samples of children living near the abandoned mine sites of Sulcis-Inglesiente (Sardinia, Italy). Environ Res, 134:366–374, 2014.
Wolfsperger M, Hauser G, Gossler W, Schlagenhaufen C. Heavy metals in human hair samples from Austria and Italy—influence of sex and smoking-habits. Sci Total Environ, 156:235–242, 1994.
Zaccaroni A, Corteggio A, Altamura G, Silvi M, Di Vaia R, Formigaro C, Borzacchiello G. Elements levels in dogs from ‘‘triangle of death’’ and different areas of Campania region (Italy). Chemosphere, 108:62–69, 2014.
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Chapter 7
CONCLUSION
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Domestic dogs are highly social animals and the quality of the relationship between conspecifics is an integral part of dogs’ social environments (Dreschel and Granger, 2005). Furthermore, it is known that between humans and dogs there is a strong bond and this closely relationship has led to a particular interest in dog well-being. Not surprisingly, the current research on canine welfare is largely directed towards the physical and social contexts including the role of human contact, inter-dog interaction, environmental enrichment and housing. For these reasons a number of studies has used physiological measures such as the cortisol hormone to index the effects of stressful situations on domestic canines (Dreschel and Granger, 2005). In particular, the researches have focused on the evaluation of what biomarkers could be the most indicative for the evaluation of dog well-being status and of which matrices could be the least invasive. In this dissertation, different non-invasive matrices were considered in order to measure some indicators that can occur to evaluate dog well-being. For this thesis, saliva, faeces and hair were selected as non-invasive matrices. Moreover, the matrices, chosen for these preliminary studies, are easy to be sampled and their collection does not influence the normal physiological state of the animal.
7.1_FUTURE RESEARCH
The field of applied animal well-being is growing rapidly, moreover, dogs provide unique opportunities in term of research models into specific disease processes. Dogs are social animals and the effects of stimuli originating from different environmental factors (such as the presence of other dogs or nutritional changes), genetic factors involved in the behavioural and mental development have been currently studied. Genetic factors are correlated also with breed selection, which has led to the development of breed specific behaviours. Cortisol is a biomarker that increases in response to different stimuli, in particular stressful ones, but intervenes also in long-term stressful stimuli and/or environmental changes. However, a number of variables do have the potential to interfere with cortisol levels and these variables must be taken into account and reduced when designing studies. In this dissertation further biomarkers were examined in non-invasive matrices to furnish a more complete spectrum of the state of dog well-being. This is the reason why it turned out to be necessary to take in consideration other aspects involved in dog well-being, such as faecal microbiome (in association with probiotic addition to the diet) and the levels of some heavy metals in hair. Despite studies showed in this
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thesis were conducted on a small sample of dogs, the results achieved have highlighted some interesting points that should be investigated in future. In particular, future investigations might be finalised in analysing potential and significant differences between microbiome of dogs housed in different environment and if these differences affect their health status. In addition, another aspect that could be studied is the possible influence of nutritional changes (such as pro- or prebiotic addition) on gut microbiome with the consequent repercussion on dog well-being.
7.1.1_Measurement of cortisol It is known that cortisol is a glucocorticoid (GC), which has multiple effects on different tissues. In fact, GCs influence the cardiovascular tone, immunity and inflammatory system (by inhibiting the synthesis, release and/or efficacy of cytokines and other mediators that promote immune and inflammatory reactions), metabolism (through the increase of circulating glucose concentrations, mobilisation of lipids by the lipolysis in adipose cells, inhibition of protein synthesis with stimulation of proteolysis in various muscle types), neural function, behaviour and reproduction. In particular, cortisol can be defined as the center of an interconnected web of physiological, behavioural and developmental functions. Indeed, this hormone has catabolic, proteolytic and lipolytic activities in peripheral tissues and anabolic activity in the liver, including gluconeogenesis and protein synthesis. In addition, cortisol reduces the entrance of glucose into the cells, it increases blood glucose and insulin secretion (Mormède et al., 2007). Furthermore, cortisol promotes food intake by an action on the brain with the consequent increment of energy availability. This is a coordinated process via peripheral and central mechanisms (Tempel and Leibowitz, 1994). The combination of increased cortisol and insulin leads to the storage of energy in the form of fat in adipose tissue, when it is not used in the stress response, for istance, for behavioural adjustments. The net effect is an increment of fat depots at the expense of tissue proteins (Sapolsky et al., 2000; Mormède et al., 2008). Cortisol concentrations in this thesis were assessed in salivary and hair matrices. Salivary matrix provides instantaneous views of cortisol concentrations, since salivary cortisol is well correlated with plasma values of the hormone; instead, hair was used to measure both basal and chronically elevated hormone concentrations (Bennet and Hayssen, 2010). Behavioural and physiological parameters have both been used to measure the effects of stressful situations on domestic dogs (Beerda et al., 1998; Dreschel and Granger, 2005; Blackwell et al., 2010; Siniscalchi et al., 2013). However, despite the fact that modifications in cortisol levels are well known to be a major 148
Chapter 7 CONCLUSION physiological response to stressors stimuli (Coppola et al., 2006), the relationship between cortisol levels and stress-induced behaviour remains unclear. The variations in cortisol concentration may differ between canines in function of the different types of presented stimuli and dissimilarities in temperament between dogs (Rooney et al., 2007; Siniscalchi et al., 2013). Furthermore, salivary cortisol is extensively used as a measure of a pronounced hypothalamic-pituitary-adrenal axis (HPA) activation and, in canine research, has shown to be a useful tool for the evaluation of well- being status and behavioural modifications, as well as in support of canine activities and sports (Cobb et al., 2016). For these reasons, salivary cortisol was taken into consideration as an interesting biomarker to evaluate dog well-being. Inter alia, in the first part of the thesis (Chapter 3) the concentration of salivary cortisol was analysed in relation to environmental and physiological factors (site of sampling, size and sex) in a large population of healthy dogs. The objective of this this study was to understand the influence of some factors on the basal levels of the hormone aimed at obtaining further reference points to develop more specific studies for the cortisol evaluation in dogs in different situations. The results of the first study have shown that the size and sex of dogs and the time of sampling in different environments have to be considered as factors that can influence basal cortisol values in the saliva. Consequently in the second study, as reported in the Chapter 4, salivary cortisol was measured in dogs to monitor physiological response to different conditions. Dogs were subjected to various stimuli due to inter and intra- specific interactions, environmental variations and efforts required by different activities. The response of cortisol concentrations has showed that the salivary concentration of cortisol in dogs varies in relation to the type of activity. In fact, it seems that the extent of HPA axis activation differs between short high-intensity activities and endurance exercises and also on the level of alertness required for the performance. However, further studies with a larger number of dogs are necessary to confirm the relationship between salivary cortisol concentration and some factors such as breed, predisposition in performing different activities and psychophysical preparation. Furthermore, considering the dog genetic, the measurement of salivary cortisol concentrations in predetermined conditions and the observation of a standardised protocol, could become a useful instrument for breeders to assess the character and aptitude of a certain breed. This could be noteworthy in the perspective of selective breeding, but also to have clear parameters to evaluate the state of well-being of the subjects.
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Despite salivary cortisol provides an instant image of the dog response to different stimuli, to have a more complete switchboard of dog well-being it might be necessary take in consideration a combination of multiple measurements (Csoltova et al., 2017). For this reason, in the present dissertation, other indicators and matrices were taken into account. A matrix surveyed in this research was hair, which may function as a storage site for cortisol. Little is known about how cortisol is stored or circulates within the hair shaft and/or about the possible differences between proximal and distal hair segments. However, mammals have developmental, physiological and biochemical similarities with respect to glucocorticoid production (Bennet and Hayssen, 2010). Hair is now recognized as a valuable matrix for measuring cortisol in humans and other mammals. Hair integrates steroids over the entire period of its growth (from months to years) and this characteristic may be particularly valuable in tracking gradual changes related to disease progressions, as well as responses to social or environmental stressors (Bryan et al., 2013). The possibility to have a long-term photography of the state of dog well-being, has led to consider hair cortisol as a useful biomarker to comprehend the physiological response of canines to the probiotic addition to their diet and to the social-environmental changes. In the preliminary study reported in the third part (Chapter 6) of this dissertation, despite the small amount of samples, some interesting aspects can be highlighted. In particular, levels of cortisol were high for all dogs in both social-environmental conditions. High levels of cortisol in hair might indicate a prolonged overproduction of glucocorticoids resulting in possible problems in the well-being state of the animals. Another interesting result was that dogs with probiotic addition to the diet maintened the same levels of cortisol concentration during the whole period of the investigation, while the animals belonging to the group fed without the probiotic supplementation showed a decrement of the hormone levels after the social-environmental change. In fact, cortisol decrease has occurred after dog placement for 43 days in pairs box. Even though these findings are only preliminary results, the probiotic addition and a social-environmental change seem to show a positive influence on the decrease of hair cortisol levels. However, these suppositions need further investigations, since it is known that various factors (such as breed differences, the number of dogs and the place of sampling as well as the method used to determine the health status of the dog and what the animal is used for) could have a potential impact on hair cortisol concentration in canines (Mesarcova et al., 2017).
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7.1.2_Evaluation of faecal microbiome To evaluate dogs’ well-being also the faecal microbiome could be considered as a useful and interesting tool also as a support to cortisol measurements. The microbiota-gut-brain axis is a dynamic matrix of tissues and organs that communicate in a complex multidirectional manner to maintain homeostasis; alterations in this ‘environment’ can lead to a broad spectrum of physiological and behavioural effects including hypothalamic-pituitary-adrenal (HPA) axis activation and altered activity of neurotransmitter systems and immune function. Additionally, the study of gastrointestinal microbiota has recently highlighted the role of the microbiota as a key facilitator of stress adaptation and immune response. It seems that resilience to stress- and immune-related disorders and the dysfunction of stress- and immune-systems may be dependent on the diversity and complexity of gastrointestinal microbiota (Rea et al., 2016). As reported in this thesis (Chapter 5), probiotic addition on the control diet seems not to have caused significant variations in microbiome populations of dogs. Despite the small amount of animals recruited for this preliminary study, the results obtained about the salivary cortisol variations are interesting and seem to confirm what reported before about the correlation between microbiome and HPA axis. In fact, dogs fed with the probiotic addition presented lower levels of cortisol than canines fed without the probiotic supplementation. Indeed probiotic seems to influence both the microbial population and the animal well-being. At this moment, it is very difficult to find in the literature studies with accurate data and a big data set, however it could be very interesting to examine the faecal microbiome in dogs placed in different environments and evaluate its correlation with cortisol levels in order to assess factors that may result in variation of the microbiota-gut-brain axis equilibrium.
7.1.3_Measurement of heavy metals The impact of metals on health is currently an area of intense interest, in particular some metals have been characterized as endocrine disruptors, and one of their targets is the hypothalamus- pituitary-gonadal and/or hypothalamus-pituitary-adrenal axis (Pérez-Cadahìa et al., 2008). The relationship between HPA axis and the measurement of these elements may provide a further contribution for the evaluation of dog well-being, and it might be an interesting marker in association with cortisol. Despite the current small amount of reference contributions, some researches have showed interesting evidences. The results obtained in the preliminary study reported in this dissertation (Chapter 6) have showed no correlation between diet and elements in 151
Chapter 7 CONCLUSION hair. However, the correlation between zinc (Zn) and cortisol in hair obtained in this study, resulted to be reasonably interesting. In human researches concerning different elements, emerged that Zn stands out for having some opposite effects to the other metals (such as protective effects of zinc against cadmium (Cd) and lead (Pb) toxicity): it is co-released with neurotransmitters by neurons, and zinc supplements may delay the progression of Alzheimer’s disease. Furthermore, cortisol seems to be the most sensitive to the effects of heavy metal exposure and could represent a relevant biomarker when assessing the potentially damaging effects of heavy metals exposure (Baos et al., 2006; Blaurock-Busch et al., 2012; Vaghri et al., 2013). As reported by Blaurock-Busch et al. (2012) hair zinc provided an index of adequacy of the element nutritional intake and it is useful to explore the possible relationships between zinc and cortisol in the hair. Even though the results are fragmented, zinc seems to be an interesting biomarker to evaluate different aspects of dog well-being.
7.2_FINAL NOTES
Central to this thesis has been the identification of some biomarkers in non-invasive matrices with the aim to evaluate the state of dog well-being. In this dissertation the welfare of canines placed at home, in kennels and in shelters, but also of dogs employed in different activities were examined. Furthermore, it was taken in consideration different aspects of dog well-being, which can influence the activation of HPA axis or that showed a connection with it. There is an increasing public demand to evaluate what are the most reliable biomarkers to indicate the dog well-being. Cortisol evaluation in different matrices provides various information about the state of canine well-being, however this biomarker is not sufficient and needs other biological indicators, possibly easy to be collected and present in non-invasive matrices, to provide information on the animal health status. For example, the analysis of the microbiome could contribute in providing more precise data by evaluating the effects of environmental and nutritional changes and to estimating the association with cortisol. Also heavy metals measurements might give further information about the environmental and nutritional influence on animal welfare. In fact, some heavy metals have a strong correlation with diseases or consequences on mental disorders. If these biological indicators have been considered together, they could give an ever more complete framework of well-being of examined dogs, since this aspect allows to take in consideration different factors
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influencing the activation of HPA axis, which, as mentioned above, is involved to adaptive responses to both physical and psychological stressor.
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