Acute phase proteins and biomarkers for health in chickens

Acute phase proteins (APPs) are proteins synthesised predominantly in the liver, whose plasma concentrations increase (positive APP) or decrease (negative APP) as a result of infection, inflammation, trauma and tissue injury. They also change as a result of the introduction of immunogens such as bacterial lipopolysaccharide (LPS), turpentine and vaccination. While publications on APPs in chickens are numerous, the limited availability of anti-sera and commercial ELISAs has resulted in a lot of information on only a few APPs. Disease is a threat to the poultry industry, as pathogens have the potential to evolve, spread and cause rapid onset of disease that is detrimental to the welfare of birds. Low level, sub-acute disease with non-specific, often undiagnosed causes can greatly affect bird health and growth and impact greatly on productivity and profitability. Developing and validating methods to measure and characterise APPs in chickens will allow these proteins to be used diagnostically for monitoring flock health. Using immune parameters such as APPs that correlate with disease resistance or improvements in production and welfare will allow the use of APPs as selection parameters for breeding to be evaluated. For APPs to be useful parameters on which to evaluate chicken health, information on normal APP concentrations is required. Ceruloplasmin (Cp) and PIT54 concentrations were found to be much lower in healthy birds form commercial production farms than the reported normal values obtained from the literature. These APPs were found to be significantly higher in culled birds from a commercial farm and Cp, PIT54 and ovotransferrin (Ovt) were significantly higher in birds classified as having obvious gait defects. Using quantitative shotgun proteomics to identify the differentially abundant proteins between three pools: highly acute phase (HAP), acute phase (AP) and non-acute phase (NAP), generated data from which a selection of proteins, based on the fold difference between the three pools was made. These proteins were targeted on a individual samples alongside proteins known to be APPs in chickens or other species: serum amyloid A (SAA), C-reactive protein (CRP), Ovt, apolipoprotein A-I (apo-AI), transthyretin (Ttn), haemopexin (Hpx) and PIT54. Together with immunoassay data for SAA, Ovt, alpha-1-acid glycoprotein (AGP) and Cp the results of this research reveal that SAA is the only major APP in chickens. Ovotransferrin and AGP behave as moderate APPs while PIT54 and Cp are minor APPs. Haemopexin was not significantly different between the three acute phase groups. Apolipoprotein AI and Ttn were significantly lower in the HAP and AP groups and as such can be classed as negative APPs. In an effort to identify CRP, multiple anti-sera cross reacting with CRP from other species were used and a phosphorylcholine column known to affinity purify CRP were used. Enriched fractions containing low molecular weight proteins, elutions from the affinity column together with HAP, AP and NAP pooled samples were applied to a Q-Exactive Hybrid Quadrupole–Orbitrap mass spectrometer (Thermo Scientific) for Shotgun analysis and CRP was not identified. It would appear that CRP is not present as a plasma protein constitutively or during an APR in chickens and as such is not an APP in this species. Of the proteins targeted as possible novel biomarkers of the APR in chickens mannan binding lectin associated serine protease-2, α-2-HS-glycoprotein (fetuin) and major facilitator superfamily domain-containing protein 10 were reduced in abundance in the HAP group, behaving as negative biomarkers. Myeloid protein and putative ISG(12)2 were positively associated with the acute phase being significantly higher in the HAP and AP groups. The protein cathepsin D was significantly higher in both HAP and AP compared to the NAP indicating that of all the proteins targeted, this appears to have the most potential as a biomarker of the acute phase, as it was significantly increased in the AP as well as the HAP group. To evaluate APPs and investigate biomarkers of intestinal health, a study using re-used poultry litter was undertaken. The introduction of litter at 12 days of age did not significantly increase any APPs measured using immunoassays and quantitative proteomics at 3, 6 and 10 days post introduction. While no APP was found to be significantly different between the challenged and control groups at anytime point, the APPs AGP, SAA and Hpx did increase over time in all birds. The protein apolipoprotein AIV (apo-AIV) was targeted as a possible APP and because of its reported role in controlling satiety. An ELISA was developed, successfully validated and used to measure apo-AIV in this study. While no significant differences in apo-AIV plasma concentrations between challenged and control groups were identified apo-AIV plasma concentrations did change significantly between certain time points in challenged and control groups. Apoliporotein AIV does not appear to behave as an APP in chickens, as it was not significantly different between acute phase groups. The actin associated proteins villin and gelsolin were investigated as possible biomarkers of intestinal health. Villin was found not to be present in the plasma of chickens and as such not a biomarker target. Gelsolin was found not to be differentially expressed during the acute phase or as a result of intestinal challenge. Finally a proteomic approach was undertaken to investigate gastrocnemius tendon (GT) rupture in broiler chickens with a view of elucidating to and identify proteins associated with risk of rupture. A number of proteins were found to be differentially expressed between tendon pools and further work would enable further detailing of these findings. In conclusion this work has made a number of novel findings and addressed a number of data poor areas. The area of chicken APPs research has stagnated over the last 15 years with publications becoming repetitive and reliant on a small number of immunoassays. This work has sought to characterise the classic APPs in chickens, and use a quantitative proteomic approach to measure and categorise them. This method was also used to take a fresh approach to biomarker identification for both the APR and intestinal health. The development and validation of assays for Ovt and apo-AIV and the shotgun data mean that these proteins can be further characterised in chickens with a view of applying their measurement to diagnostics and selective breeding programs.

Developing an index of dietary estimation of fermentable carbohydrate to allow dietary analysis for epidemiological studies

It has been proposed that long-term consumption of diets rich in non-digestible carbohydrates (NDCs), such as cereals, fruit and vegetables might protect against several chronic diseases, however, it has been difficult to fully establish their impact on health in epidemiology studies. The wide range properties of the different NDCs may dilution their impact when they are combined in one category for statistical comparisons in correlations or multivariate analysis. Several mechanisms have been suggested to explain the protective effects of NDCs, including increased stool bulk, dilution of carcinogens in the colonic lumen, reduced transit time, lowering pH, and bacterial fermentation to short chain fatty acids (SCFA) in the colon. However, it is very difficult to measure SCFA in humans in vivo with any accuracy, so epidemiological studies on the impact of SCFA are not feasible. Most studies use dietary fibre (DF) or Non-Starch Polysaccharides (NSP) intake to estimate the levels, but not all fibres or NSP are equally fermentable. It has been proposed that long-term consumption of diets rich in non-digestible carbohydrates (NDCs), such as cereals, fruit and vegetables might protect against several chronic diseases, however, it has been difficult to fully establish their impact on health in epidemiology studies. The wide range properties of the different NDCs may dilution their impact when they are combined in one category for statistical comparisons in correlations or multivariate analysis. Several mechanisms have been suggested to explain the protective effects of NDCs, including increased stool bulk, dilution of carcinogens in the colonic lumen, reduced transit time, lowering pH, and bacterial fermentation to short chain fatty acids (SCFA) in the colon. However, it is very difficult to measure SCFA in humans in vivo with any accuracy, so epidemiological studies on the impact of SCFA are not feasible. Most studies use dietary fibre (DF) or Non-Starch Polysaccharides (NSP) intake to estimate the levels, but not all fibres or NSP are equally fermentable. The first aim of this thesis was the development of the equations used to estimate the amount of FC that reaches the human colon and is fermented fully to SCFA by the colonic bacteria. Therefore, several studies were examined for evidence to determine the different percentages of each type of NDCs that should be included in the final model, based on how much NDCs entered the colon intact and also to what extent they were fermented to SCFA in vivo. Our model equations are FC-DF or NSP$ 1: 100 % Soluble + 10 % insoluble + 100 % NDOs¥ + 5 % TS** FC-DF or NSP 2: 100 % Soluble + 50 % insoluble + 100 % NDOs + 5 % TS FC-DF* or NSP 3: 100 % Soluble + 10 % insoluble + 100 % NDOs + 10 % TS FC-DF or NSP 4: 100 % Soluble + 50 % insoluble + 100 % NDOs + 10 % TS *DF: Dietary fibre; **TS: Total starch; $NSP: non-starch polysaccharide; ¥NDOs: non-digestible oligosaccharide The second study of this thesis aimed to examine all four predicted FC-DF and FC-NSP equations developed, to estimate FC from dietary records against urinary colonic NDCs fermentation biomarkers. The main finding of a cross-sectional comparison of habitual diet with urinary excretion of SCFA products, showed weak but significant correlation between the 24 h urinary excretion of SCFA and acetate with the estimated FC-DF 4 and FC-NSP 4 when considering all of the study participants (n = 122). Similar correlations were observed with the data for valid participants (n = 78). It was also observed that FC-DF and FC-NSP had positive correlations with 24 h urinary acetate and SCFA compared with DF and NSP alone. Hence, it could be hypothesised that using the developed index to estimate FC in the diet form dietary records, might predict SCFA production in the colon in vivo in humans. The next study in this thesis aimed to validate the FC equations developed using in vitro models of small intestinal digestion and human colon fermentation. The main findings in these in vitro studies were that there were several strong agreements between the amounts of SCFA produced after actual in vitro fermentation of single fibre and different mixtures of NDCs, and those predicted by the estimated FC from our developed equation FC-DF 4. These results which demonstrated a strong relationship between SCFA production in vitro from a range of fermentations of single fibres and mixtures of NDCs and that from the predicted FC equation, support the use of the FC equation for estimation of FC from dietary records. Therefore, we can conclude that the newly developed predicted equations have been deemed a valid and practical tool to assess SCFA productions for in vitro fermentation.

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.