Essay: Dissertation: Studies on Porcine Diabetic Nephropathy

Abstract
Background: Diabetes Mellitus (DM) is a disease that is increasing in the entire world today. Diabetic Nephropathy (DN) is a common complication in human patients with DM, and is the leading cause of end-stage renal disease (ESRD).
Purpose: The aim of this study was to evaluate kidney function, gene expression and glomerular histopathology in a diabetic G??ttingen minipig model. The future objective is to be able to develop a minipig model of DM and DN in humans.
Study group: 9 intact female G??ttingen minipigs (Ellegaard G??ttingen Minipigs A/S), 16 months of age at euthanization. 4 of them were control animals and 5 of them were diabetic animals, both groups fed a standard minipig diet (SDS Mini-Pig Expanded). The five diabetic animals had diabetes induced medically by injecting 50 mg/kg streptozotocin on three consecutive days, to develop a diabetic condition lasting 7 months.
Methods: The kidney function was assessed through estimation of plasma inulin clearance to evaluate glomerular filtration rate (GFR), estimation of urinary albumin concentration with use of a pig albumin enzyme-linked immunosorbent assay (ELISA), and calculation of Urine Albumin-to-Creatinine Ratio (UACR). Validation of the ELISA kit was performed with use of intra- and inter-assay coefficient of variance (CV). A gene expression analysis of 10 genes were assessed with quantitative polymerase chain reaction (qPCR). An histopathological evaluation of glomeruli area and number of nuclei per glomeruli was performed.
Results: A noncompartmental analysis of inulin clearance per body weight (BW) and body surface area (BSA), showed a statistically significant elevated GFR compared to the control group (p-values=0.0357). No statistically significant difference in the urinary albumin concentration (p-values 0.7429 and 0.412) and Urine Albumin-to-Creatinine Ratio (UACR) (p-values= 0.0571 and 0.1111) between the two groups was foun. Two genes were statistically significant upregulated in the diabetic group compared to the control group (p-values=0.0159), the same two genes where also positively correlated to fructosamine concentration (p-values=0.014). A statistically significant difference was found in glomeruli area (p-value=0.0159), but no difference was found in nuclei per glomeruli (p-value=0.6825) when compared to the control group
Conclusion: The diabetic G??ttingen minipigs of this study had altered structural and functional changes in the kidney compared to the control animals. However, further studies are needed to be able to define a model of human DM and DN.
Abbreviations
ACTB: Beta actin
AUC: Area under the curve
BC: Before Christ
BSA: Body surface area
BW: Body weight
CCL2: chemokine ligand 2
CD4: Cluster of Differentiation 4
CD163: Cluster of Differentiation 163
COL1A1: Collagen type I alpha 1
COL3A1: Collagen type I alpha 3
CV: Coefficient of variance
DM: Diabetes Mellitus
DM type I: Diabetes Mellitus type I
DM type II: Diabetes Mellitus type II
DN: Diabetic Nephropathy
ECM: Extracellular matrix
ELISA: Albumin enzyme-linked immunosorbent assay
ESRD: End-stage renal disease
FITC-Inulin: Fluorescin isothiocyanate-inulin
FN1: Fibronectin 1
GBM: Glomerular basement membrane
GFR: Glomerular filtration rate
HAVCR1: hepatitis A virus cellular receptor 1
HE: Haematoxylin and Eosin Stain
HFHCHF: High-fat/ high-cholesterol/ high-fructose
HPRT1:
LCN2: Lipocalin 2
MMP2: Matrix Metalloproteinase-2
MRC1: Mannose receptor C type 1
PAS: Periodic acid-Achiff
qPCR: Quantitative polymerase chain reaction
RIN: RNA integrity number
SD: Standard deviation
UACR: Urine Albumin-to-Creatinine Ratio
UAE: Urinary albumin excretion
VCAM1: Vascular cell adhesion molecule 1
WHO: World Health Organisation
1. Introduction
1.1 Background
DM is a disease that has been known as far as back in the ancient Egyptians 1550 Before Christ (BC), where it was associated as the disease with ‘sweet urine’ (Eko?? et al. 2008).
Today DM is a disease of epidemic proportions and the number of people developing the disease is growing every year. The World Health Organisation (WHO) predicts DM to be the seventh leading cause of death in the entire world by year 2030. Also, total deaths from DM are said to rise by more than 50 % in the next 10 years (Zhang et al. 2010). One report by Zhang et al. (2010) estimated the global health expenditure on DM for the years 2010 and 2030. They estimated that 285 billion adults have DM and the total annual global health expenditure on DM was between 376 and 672 billion USD in 2010, this accounts for 12 % of the world’s total health expenditure. They estimated this number to be 30-34 % larger in 2030 compared to 2010 (Zhang et al. 2010).
These numbers and statistics reveals an increased need for DM diagnosis and treatment, but also for the recognition, screening, and early treatment of late complication diseases associated with DM (Ossman 2006); DN and cardiovascular disease, which are the two most expensive and devastating problems associated with DM (Ossman 2006).
DN affects around 20 % of adults, who have had DM for more than 20 years. Also, DN is the single most leading cause of ESRD in the Western World (Hohenadel & van der Woude 2004; Ossman 2006; Zhang et al. 2010; Wada et al. 2012).
The classical definition of DN is a urinary albumin excretion (UAE) of ‘300 mg/24h, and it can be coupled with hypertension, hyperglycemia, an eventual decline in glomerular filtration rate (GFR) and different histopathological changes (Dachs et al. 1963; Marshall 2004).
This study makes use of a streptozotocin-induced diabetic G??ttingen minipig model to investigate structural and functional changes linked with DN; enzyme-linked immunosorbent assay (ELISA) for measurement of urinary albumin concentration, qPCR analysis of gene expression on kidney tissue and histopathological evaluation of glomeruli on HE and PAS sections.
1.2 Purpose
The purpose of this study was to examine changes in the kidney of streptozotocin-induced diabetic and control G??ttingen minipigs fed on a standard minipig-diet through:
‘ GFR measurement through Plasma Inulin Clearance (FITC-Inulin) per BW and BSA
‘ Estimation of urinary albumin on ELISA, and calculation of UACR
‘ qPCR analysis on gene expression of 10 genes involved in a mice model of DN: collagen, type 1, alpha 1 (COL1A1), collagen, type 3, alpha 1 (COL3A1), Cluster of Differentiation 163 (CD163), Cluster of Differentiation 4 (CD4), fibronectin 1 (FN1), Matrix Metallopeptidase-2 (MMP2), vascular cell adhesion molecule 1 (VCAM1), lipocalin 2 (LCN2) also known as neutrophil gelatinase-associated lipocalin (NGAL), hepatitis A virus cellular receptor 1 (HAVCR1) and chemokine (C-C motif) ligand 2 (CCL2), and two reference genes: beta-actin (ACTB) and hypoxanthine phosphoribosyltransferase 1 (HPRT1)
‘ Renal histopathological evaluation of glomeruli area and number of nuclei per glomeruli
1.3 Hypothesis
In minipigs with streptozotocin-induced DM it is suggested that diabetic structural and functional changes in the kidney will occur as seen in humans:
The plasma inulin clearance will either show an increase in GFR as seen early in the DN course, or GFR will decrease due to later stages of DN compared to the control group.
DN will lead to an increase in the urinary albumin excretion (UAE) measured on ELISA. Also the UACR is expected to be increased compared to the control group.
The gene expression level of the 10 genes related to DN in mice are expected to be increased compared to the control group.
Glomeruli area and number of nuclei per glomeruli are expected to be increased compared to the control group.
1.4 Method
Prior research has been made on streptozotocin-induced diabetic minipigs all fed on a high fat, high fructose, high cholesterol diet as a part of a PhD project (Ludvigsen 2014) with three accompanying master theses (Fredholm 2014; Fuchs 2014; Uhre & Vinding 2014).
This project includes minipigs fed with a standard minipig-diet, hence they are lean and not obese. They have also been diabetic induced with streptozotocin.
2. Diabetes Mellitus
Diabetes is a group of many metabolic disorders characterized by hyperglycemia caused by defects in insulin secretion, insulin action or both (ADA 2014). The majority of diabetes fall into two categories. In the first category, type 1 diabetes, the cause is an absolute deficiency of insulin secretion. In the other and much more prevalent category, type 2 diabetes, the cause is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response (ADA 2014). TJEK FOR COPY COPY
2.1 Diabetes Mellitus Type I
This form accounts for 5-10 % of patients with DM (ADA 2014). It was previously known as insulin-dependent diabetes (Atkinson & Maclaren 1994), and results from a cellular-mediated autoimmune destruction of the ??-cells of the pancreas (Atkinson & Maclaren 1994; ADA 2014). Two markers of the immune destruction of the ??-cells have been useful in detecting DM type I years before the onset of hyperglycemia: islet-cell cytoplasmic autoantibodies and insulin autoantibodies (Atkinson & Maclaren 1994).
The rate of the ??-cell destruction is variable, being rapid in some, mainly infants and children, and slow in others, mainly adults (ADA 2014).
The disease is generally characterized by symptoms as weight loss, polyuria and polydipsia, sudden onset at young age but usually after puberty, immune-mediated loss of ??-cells by anti-islet cell antibodies, and a need for exogenous insulin therapy (Bellinger et al. 2006).
Islet cells secreting glucagon (??-cells), somatostatin (??-cells) or pancreatic polypeptide (pancreatic-polypeptide cells) are preserved (Epstein – mechanisms of disease).
Hyperglycemia is the hallmark of diabetes (Dokken 2008).
2.2 Diabetes Mellitus Type II
This type accounts for approximately 90-95 % of patients with DM. The etiology is multifactorial (Larsen et al. 2002). It is a combination of resistance to insulin action and an insufficient insulin secretion (Larsen et al. 2002; ADA 2014). Most patients with this type are obese, and obesity in itself causes some degree of insulin resistance. Insulin resistance may improve with weight reduction, but is almost never restored to normal. The risk of developing this type of DM increases with age, hypertension, obesity and the lack of physical activity (Larsen et al. 2002; ADA 2014).
DM Type II is characterized by insulin resistance (IR) and hyperglycemia and differs from DM Type I, in that patients often are overweight and asymptomatic in the early stages. It occurs most often in adults. Both IR and hyperglycemia mediates the pathophysiology of Type II DM independently. Chronic hyperglycemia has deleterious effects on both insulin secretion and insulin activity (glucotoxicosis) (Bellinger et al. 2006).
Type II DM is a metabolic disorder with multiple etiologies. Defects in important mechanisms of insulin secretion and/or insulin action are involved in the development of the disease. The specific reasons for development of this disorder is not yet clearly defined, although high-energy intake and obesity are known to be of major importance. Obese subjects have several abnormalities in their glucose homeostasis including increased hepatic glucose output and gluconeogenesis, insulin resistance, and abnormal insulin secretion (Larsen et al. 2002).
3. Diabetic Nephropathy
Diabetic Nephropathy (DN) is a common complication of both type I DM and type II DM in humans (ADA 2014), and the incidence of DN in the U.S. has been increasing the past years (Dronavalli et al. 2008). Around 20 ‘ 40 % of patients with either type I DM or type II DM develop clinical evidence of DN (Yee 2008). DN can eventually lead to ESRD (Ossman 2006; Dronavalli et al. 2008; ADA 2014), and is the primary leading cause of patients entering dialysis therapy or starting renal replacement therapy (Gross et al. 2005).
The clinical manifestations of DN are a progressive rise in UAE and an increase in GFR due to early stages of DN or a declining GFR due to later stages of DN (Marshall 2004).
Some of the structural manifestations of DN are enlargement of glomeruli and thickening of glomerular basement membrane (Dachs 1963; Wolf 2004), expansion of the mesangial area (Dachs 1963; Mauer et al. 1984), loss of podocytes (Vestra 2000; Marshall 2004; Wolf 2004) and changes in the tubulo-interstitium (Marshall 2004).
The earliest clinical evidence of DN in human patients is the appearance of low, but abnormally levels of albumin in the urine, also referred to as microalbuminuria, and a Urine Albumin-to-Creatinine Ratio (UACR) of 30-300 ??g/mg (Gross et al. 2005).
Factors associated with the development of DN are hyperglycemia, hypertension, genetic predisposition, obesity and elevated serum lipids (Marshall 2004; Gross et al. 2005).
The next sections given will focus on clinical parameters such as GFR, albuminuria, gene expression and glomerular histopathological changes.
3.1 Glomerular Filtration Rate and Plasma Inulin Clearance
GFR is the best parameter when evaluating the overall kidney function in human patients, therefore it is also evaluated in diabetic patients. GFR should be evaluated in all diabetic patients, since a subset of patients can have a rapid decline in GFR levels (Gross et al. 2005), therefor GFR is essential to estimate, to detect DN and follow its progression (Tall & Brenner 2012).
The GFR can be reduced by the compression of the glomerular capillaries as a result of glomerular mesangium expansion. Also, mesangial matrix formation correlates closely with declining GFR (Vestra et al. 2000).
Inulin is the gold standard as a plasma marker to evaluate GFR . Estimation of GFR can be based on plasma samples, because it has been found that plasma inulin correlates with urine inulin (Tall & Brenner 2012).
Inulin plasma clearance has been used for the evaluation of kidney function in studies using pigs as an experimental model (Frennby et al. 1996; van Westen et al. 2001).
3.2 Albuminuria and Urine Albumin-to-Creatinine Ratio
Microalbuminuria (MA) is considered to be one of the main risk factors for developing DN and progressive renal insufficiency in patients with DM (Feldt-Rasmussen et al. 1985; Tabaei et al. 2001; ADA 2004; Marshall 2004; Dronavalli et al. 2008). Therefore an evaluation of the urinary content of albumin is always performed in diabetic patients.
MA comes before clinical proteinuria in both type I and type II DM patients, however approximately 20 % of type II DM patients with MA progress to DN over a decade in contrast to over 80 % in type I DM patients (Fioretto et al. 1996).
It is important to detect MA in diabetic patients by sensitive, precise and accurate methods (Chiabrando et al. 1994). Microalbuminuria cannot be measured by urinalysis dipsticks which are used to measure the degree of proteinuria (Keen & Chlouverakis 1963; Fielding et al. 1983).
Therefor a range of other techniques are used to detect the degree of albuminuria (Fielding et al. 1983; Mohamed et al. 1984; Watts et al. 1986; Coppo et al. 1987). A specific measurement of urinary albumin can be made by using the Enzyme-Linked Immunosorbent Assay (ELISA) method. This is a more accurate measurement than urinalysis dipstick in assessing the degree of albuminuria (Keane & Eknoyan 1999).
DN has been classified into stages in human patients based on the urinary albumin excretion: appearance of low but abnormal levels of albumin in the urine (‘300 mg/24h or 20-199 ??g/min) also referred to as microalbuminuria, and the presence of high levels of albumin in the urine (‘300mg/24h or ‘200??g/min) also referred to as macroalbuminuria (ADA 2004; Gross et al. 2005).
UACR can be calculated from the urinary albumin and creatinine concentration. The UACR is unaffected by variation in urine concentration, hence it is a more precise estimation of albuminuria. The UACR is proposed to be more accurate than 24 hour urine protein collection, and albumin is said to be more sensitive marker than total protein. REFERENCE!
Under normal circumstances albumin does not cross the glomerular barrier due to a fixed electrostatic charge and pore size (Nakamura & Myers 1988). An increased urinary albumin excretion (UAE) may be due several hemodynamic changes occurring.
An increased permeability of the glomerular basement membrane will increase the UAE. UAE rates will depend not only upon the rate of entry of albumin into the glomerular filtrate but also upon the rate of its reabsorption in the renal tubules (Keen & Chlouverakis 1963; Marshall 2004). An increase in the intra-glomerular pressure, a loss of negatively charged glycosaminoglycans in the basement membrane and an increase in basement membrane pore size also contributes to the albuminuria (Marshall 2004).
Also, glomerular hyperperfusion and hyperfiltration resulting from a decreased resistance in the afferent and efferent arteriole of the glomerulus contributes to the albuminuria (Dronavalli et al. 2008).
Caramori et al. (2000) predicted albuminuria to be a marker rather than a predictor of DN and stated, that for an exact diagnosis of DN, a histopathological specimen must also be examined (Rasch & Mogensen 1980; Caramori et al 2000).
3.3 Gene expression changes
Only a subset of people with DM develop DN. This has for a long period been interpreted as an indication of, that there is a genetic susceptibility to the development of DN (Marshall 2004).
At the present time, no single gene with a large effect has been identified, and the opinion is divided as to whether there is one major gene effect or several smaller gene effects (Marshall 2004).
Today the gene expression profile from kidney tissue has been evaluated in some studies with animal models, but also in human patients (Baelde et al. 2004; Makino et al. 2006; Yang et al. 2011; Zhang et al. 2012; Wang 2014). Researchers at Novo Nordisk A/S (in house data ) has proposed a ‘top 10 gene list’ based on qPCR endpoints of the most upregulated genes in streptozotocin-treated mice compared with control mice. These genes include the given ones mentioned earlier, and function of the genes are given here in the table.
Gene name Function of the gene Regulation in humans Regulation in animal models
COL1A1 are a major component of the extracellular matrix (ECM), and forms in the spaces between the cells and provides structural support. These genes contributes to the formation of ECM
COL3A1
CD163 CD163, a protein encoded by the CD163 gene, is a receptor expressed on macrophages and monocytes, is found in a high number in inflamed tissue and is a mediator against systemic inflammation
CD4 CD4, encoded by the CD4 gene, is a glycoprotein on the surfaces of immune-cells such as T helper cells, macrophages and monocytes. It assists the T cell receptor in recognising and communicating with an antigen-presenting cell
FN1 FN1, is a gene that encodes a fibronectin present at the cell surfaces and in ECM. It is involved in adhesive and migratory process of cells and other ligands to ECM. Altered fibronectin expression has been associated with kidney fibrosis
MMP2 MMP2 is involved in the breakdown of ECM proteins in normal physiological processes and disease processes. A timed degradation of ECM is an important feature of development and tissue repair
VCAM1 VCAM1, a member of the immunoglobulin gene superfamily, mediates adhesion of lymphocytes, monocytes, eosinophils and basophils to the vascular endothelium (Marui et al. 1993), and is seen highly expressed in diabetic tubular epithelial cells (Yang et al. 2011). It may serve as a marker for progressive DN with a fold change of 2.5
CCL2 CCL2 encodes a cytokine that belongs to the chemokine family. It is one of the key chemokines that regulate migration and infiltration of monocytes and macrophages to sites of inflammation
LCN2/NGAL Two promising biomarkers for kidney injury exists today, LCN2/NGAL (Devarajan 2010; Lacquaniti et al. 2013) and HAVCR1/KIM-1 (Lim 2014; Luo 2014). It is to be noted that these are the most upregulated genes in acute kidney injury, hence they are not specific for nephropathy, but more a marker for general disease in the kidney
HAVCR1/KIM-1
The two collagens (COL1A1 and COL3A1) are a major component of the extracellular matrix (ECM), and forms in the spaces between the cells and provides structural support. These genes contributes to the formation of ECM (Zhang et al. 2012).
CD163, a protein encoded by the CD163 gene, is a receptor expressed on macrophages and monocytes, is found in a high number in inflamed tissue and is a mediator against systemic inflammation (Onofre et al. 2009). CD4, encoded by the CD4 gene, is a glycoprotein on the surfaces of immune-cells such as T helper cells, macrophages and monocytes. It assists the T cell receptor in recognising and communicating with an antigen-presenting cell (Onofre et al. 2009).
FN1, is a gene that encodes a fibronectin present at the cell surfaces and in ECM. It is involved in adhesive and migratory process of cells and other ligands to ECM. Altered fibronectin expression has been associated with fibrosis (Williams et al. 2009).
MMP2 is involved in the breakdown of ECM proteins in normal physiological processes and disease processes. A timed degradation of ECM is an important feature of development and tissue repair (Nagase et al. 2006).
VCAM1, a member of the immunoglobulin gene superfamily, mediates adhesion of lymphocytes, monocytes, eosinophils and basophils to the vascular endothelium (Marui et al. 1993), and is seen highly expressed in diabetic tubular epithelial cells (Yang et al. 2011). It may serve as a marker for progressive DN with a fold change of 2.5 (Yang et al. 2011).
CCL2 encodes a cytokine that belongs to the chemokine family. It is one of the key chemokines that regulate migration and infiltration of monocytes and macrophages to sites of inflammation (Deshmane et al. 2009).
Two promising biomarkers for kidney injury exists today, LCN2/NGAL (Devarajan 2010; Lacquaniti et al. 2013) and HAVCR1/KIM-1 (Lim 2014; Luo 2014). It is to be noted that these are the most upregulated genes in acute kidney injury, hence they are not specific for nephropathy, but more a marker for general disease in the kidney.
3.4 Histopathology
Histopathological specimens from patients with DN has been investigated for many years, with the first description of the lesions in diabetic glomerusclerosis made by Kimmelstiel and Wilson in 1936. These lesions described as nodules, were named Kimmelstiel-Wilson nodules (Wolf 2004) and are considered the hallmark of DN (Kimmelstiel & Wilson 1936). Nodular glomerusclerosis consists of areas of marked mesangial expansion forming large round fibrillar mesangial zones compressing the associated glomerular capillaries (Vestra et al. 2000).
Today many other changes are described, seen in various degrees often depending on the time of diabetes duration.
The earliest glomerular morphological changes of DN is expansion of the mesangial area, which is caused by an increase in the ECM and mesangial cell hypertrophy (Dachs et al. 1964; Mauer et al. 1984). Proteinuria and declining GFR, which are all manifestations of clinical DN, are related to the expansion of the mesangial area (Vestra et al. 2000).
Another early event is enlargement of the glomeruli and thickening of the glomerular basement membrane (GBM), this thickening is progressive over years (Dachs et al. 1964; Wolf 2004). Thickening of the GBM is caused by an increased ECM synthesis and a compromised removal of the extracellular matrix (Wolf 2004). There is a direct relationship between albumin excretion rate and GBM thickness (Vestra et al. 2000).
Recently, it has been demonstrated that the podocyte (a glomerular epithelial cell) may also have a role in increasing proteinuria through an altered glomerular permeability and developing glomerulosclerosis (Vestra et al. 2000; Marshall 2004; Wolf 2004). The podocytes offers support for the glomerular capillaries, buffers intraglomerular pressure and is the final layer in the barrier to protein passage across the glomerulus into the urinary space (Marshall 2004). Eventually there will be a loss of the podocytes, and podocytes cannot regenerate, so this loss is irreversible and creates the proteinuria (Marshall 2004).
Changes are also seen in the tubulo-interstitium, including thickening of the tubular basement membrane, tubular atrophy and interstitial fibrosis (Marshall 2004).
Tervaert et al. (2010) developed a classification scheme for human patients with DN consisting of four classes, all based on glomerular lesions. The sections evaluated should contain at least ten glomeruli. All sections should be stained by hematoxylin and eosin (HE), periodic acid-Schiff (PAS), Masson Trichrome and periodic acid methenmine silver for evaluation of alterations under the light microscope. Electron microscopy is performed to measure the average glomeruli area (Tervaert et al. 2010).
In this study an evaluation of the average glomeruli area and the number of nuclei per glomeruli area was performed on HE and PAS sections. Further visual inspection and evaluation of signs of pathological changes were not performed due to time limitations.
4. The Pig as an Experimental Model for Diabetes Mellitus
The pig has been used as a model for many human conditions, including type I and type II DM, because of its many similarities to humans which includes: cardiovascular, gastrointestinal, kidney and pancreas anatomy and function (Swindle et al. 2011). But also their overall metabolic status, lipoprotein profile, size, tendency and obesity (Bollen & Ellegaard 1997; Larsen & Rolin 2004; Rees & Alcolado 2005; Bellinger et al. 2006; Chen et al. 2009).
The pigs pancreas is related to the proximal duodenum, and is loosely attached to the blood supply of the duodenum, this enabling an easily dissection during a pancreatectomy. A portion of the pancreas is retroperitoneal as seen in humans. On a histologically level, the islet cells in pigs are functionally comparable to humans (Swindle et al. 2011).
The left kidney is placed more cranial than the right kidney in the pig, and the kidney is multirenculate and multipapillate of type with calices as seen in humans. Histologically, the tubuli appear dilated when the lumen diameter is compared to other experimental animals such as the dog or rodents, but this is a normal finding in pigs (Swindle et al. 2011).
The smaller pig, also referred to as the miniature pig, is a preferred model for longitudinal studies because of its smaller size and ease of handling (Bollen & Ellegaard 1997; Larsen & Rolin 2004). Today many different miniature pig breeds for experimental use exists: The Yucatan, Sinclair, G??ttingen, Ossabow, Yorkshire and many others (Bellinger et al. 2006; Swindle et al. 2011).
Of particular relevance to DM is the similarity between humans and pigs with regard to pharmacokinetics of compounds after subcutaneous administration (Larsen & Rolin 2004).
The rationale for choosing pigs for experimental purposes is based on several matters. First, the translational anatomy, but also the results of testing medicines in pigs have been regarded as having a high positive predictive value for the translation to humans (Bellinger et al. 2006). Also, they are easy to handle, have a small size even at full maturity and can be trained to allow performance of experiments in consciousness (Larsen & Rolin 2004).
Concerning the legal and ethical views, some believe that animals should not be used as experimental purposes (Rees & Alcolado 2005). On the other hand, the pig is an advantageous species, since it is generally accepted from the society as a production animal (Bollen & Ellegaard 1997) and is usually placed in the bottom of the zoo-sociological scale (Douglas 1971).
With introduction of the concept of the three R’s (refine, reduce and replace) focus has been on studies where the aim is to minimize the distress of the animals and have human endpoints in well-designed studies (Russell & Burch 1959). To study complications associated with DM, action should be placed to prevent or improve the adverse consequences of these complications. With suitable monitoring and veterinary care it is possible to reduce adverse effects and to produce new information about late complication diseases of DM, that will benefit experimental animals and humans (Bellinger et al. 2006).
Pigs do have some limitations when used as an experimental animal, which are mostly due to the expense (Bellinger et al 2006), but overall, pigs have great potential as a relevant animal model of DM to identify mechanisms that lead to the development of diabetic complications and to develop and test new therapeutic approaches (Bellinger et al. 2006).
8. Materials and Methods
8.1 Animals, diet and sampling
The experimental animals used in this study were nine intact female G??ttingen minipigs , 16 months of age at the time of euthanization. The animals were divided into two groups. The first group consisted of five streptozotocin-induced diabetic animals, and the second group consisted of four control animals.
They were housed at Novo Nordisk A/S site Ganl??se in individual pens, within sight and sound of one another. Straw and wood shavings were used as bedding. A 12:12 hour light/dark schedule was used. The animals were fed a standard minipig diet two times a day. They were also fed with an apple, some raisins and half a litre of yoghurt (3.5 % fat) with acidophilus bacteria in the morning. Water was provided ad libitum. The pigs had a central venous catheter placed in the jugular vein or the ear vein for blood sampling.
The diabetic group was chemically induced with diabetes with streptozotocin. 10 g of streptozotocin (50 mg/kg) was dissolved in 400 ml 0.1 M Na-citrate buffer with pH 4.5 (25 mg/ml) just before the injection. The injection (2 ml/kg) was given slowly intravenously over 2 minutes. After this, the pigs’ catheters were flushed with 20 ml 0.9 % NaCl and 0.4 ml Taurolock 500HEP.
The diabetic animals were given an insulin injection just before they were fed in the morning. The diabetic group had been diabetic for seven months and treated with different insulin analogues up to the present study.
The study was performed on two consecutive days. On the first day a combined intravenous glucose tolerance and inulin clearance test was carried out. On the second day, the animals were anaesthetized and euthanized by exsanguination, cystocentesis were performed on the bladder for albumin measurement and kidney samples were taken out for gene expression analysis and histopathology. All the samples collected during the two study days were stored in the freezer until six months later, today, where urinary albumin concentration was estimated on ELISA, gene expression level from kidney samples were measured on qPCR and an evaluation of alterations in glomeruli on histopathology was performed.
8.2 Combined intravenous glucose tolerance and inulin clearance test
The objective of this test, was to study the intravenous glucose tolerance and renal clearance in diabetic and control G??ttingen minipigs after seven months of diabetes duration.
Fluorescein isothiocyanate-inulin (FITC-Inulin) was used as a plasma marker to evaluate GFR, and sterile glucose was used to evaluate the intravenous glucose tolerance.
On the first day of the study this test was performed. Preparation of 2.5 % FITC-Inulin solution (25 mg/ml) was done in the morning. 2 g FITC-inulin was dissolved in 80 ml of sterile 0.9 % NaCl. The solution was heated on a heating plate up to 70 ??C with a magnetic stirrer. The concentration was then 25 mg/ml. Before usage, this solution was sterilized by filtration through a 45 ??m filter, and was kept at room temperature until dosing. The glucose used was in a sterile 500 g/L solution.
The animals were fasted approximately 18 hours before the experiment, but were allowed to drink water. Before dosage, a blood sample (pre-dose) was taken for analysis of various blood parameters. Then, the first thing given was the glucose (0.3 g/kg or 0.6 ml/kg) intravenously, followed by injection of 5 ml sterile saline. Then the inulin injection (5 mg/kg or 0.2 ml/kg) was given followed by 10 ml of sterile saline. The injections were given at the same speed in all animals, and all injections were finalized within one minute.
After the dosage at time = 0 min (where a clock timer was started), the following blood sampling schedule was conducted; 2 min, 5 min, 10 min, 15 min, 20 min, 30 min, 40 min, 50 min, 1 hr., 1.5 hr., 2 hr., 2.5 hr., 3 hr., 3.5 hr., 4 hr. and 5 hr. 0.8 ml blood was collected in tubes containing EDTA and trasylol. The tubes were gently tilted around 10 times to ensure sufficient mixing of blood and the anticoagulant. The blood samples were stored on ice, centrifuged (3000 rpm, 10 minutes, 4 ??C), and the plasma was pipetted into micronic tubes and frozen on dry ice. The tubes were kept in the freezer (- 20 ??C) for later analysis.
Only 3 control animals were included in this test, since one control pig did not have a catheter that worked that day.
8.2.1 FITC-inulin assay and calculation of Glomerular Filtration Rate
GFR was estimated through the FITC-Inulin assay by Novo Nordisk A/S. The measurements were performed in an EnVision?? 2102 Multiable Reader. A noncompartmental clearance model was employed for the calculation of the GFR. In the noncompartmental model, the inulin clearance was calculated by dividing the dose by the area under the curve (AUC). The noncompartmental clearance model was analysed per bodyweight (BW, ml/h/kg) and per body surface area (BSA, ml/h/m2). BSA was calculated by the formula 0.121*BW0.575 (Swindle et al. 2011).
8.3 Cystocentesis and tissue sampling
On the second day of the study, the aim was to collect urine samples and kidney tissue from the euthanized animals. The animals were also fasted for approximately 18 hr.
They were anaesthetized with a Zoletil mixture for minipigs that was prepared by mixing 1 vial of Zoletil without the solvent (125 mg Tiletamin + 125 mg Zolazepam) with 6.5 ml Rompun Vet. (1 mg/ml) and 1.25 ml Ketaminol Vet. (100 mg/ml) and 2.5 ml Torbugesic Vet. (10 mg/ml). The dose of Zoletil was 1 ml/10 kg and was given intramuscularly in the dorsal part of the neck or intravenously in the ear vein catheter or the jugular vein catheter. Anaesthesia was induced within 10 minutes. Euthanization was performed by cutting the large jugular veins.
Immediately after euthanization, the animals were placed in a dorso-ventral position on a table, and a midline incision from just under the sternum to the pelvis was made. The bladder was removed by dissection, and cystocentesis was performed by puncturing the bladder with a 23G needle at a place with no blood vessels to avoid blood contamination. The urine was alliquoted into micronic tubes for albumin measurement and cobas cups for creatinine measurement, and kept at ‘ 80 ??C.
After removal of the bladder, the left kidney were taken out. Samples representing all compartments of the kidney were cut into 5 x 5 x 5 mm cubes, and placed in mRNA later tubes (1:9, tissue:liquid volume) for gene expression analysis. The tubes were immediately placed on ice, then in the fridge for 24 hours and then frozen at ‘ 20 ??C. For histopathology, samples representing all compartments of the kidney were sampled as well.
8.4 Enzyme-Linked Immunosorbent Assay
Measurement of urinary albumin was done at The University of Copenhagen using a Pig Albumin ELISA kit . This kit is based on a sandwich ELISA.
One laboratory technician performed the analyses of the urine, and the analyses were done after the protocol described in the test kit manual from the manufacturer .
The urine was analysed twice. Firstly, the urine was analysed in a thousand-fold dilution and concentrations given in the unit ng/ml. Each urine sample was measured in duplicates, both before and after a centrifugation process. Secondly, the urine was analysed in a five hundred-fold dilution. This was done, since two out of the five diabetic pigs’ albumin concentration were under the detection limit (<0.8845 ng/ml) in the thousand-fold dilution kit performed. This time the measurements were performed without a prior centrifugation process of the urine, and the concentration given in the unit ng/ml as well.
The absorbance on the ELISA kit was read using an ELX800 UV Universal Microplate Reader . The plate reader was set at 450 nm and the samples were read within five minutes after adding the stop solution. The amount of albumin in the urine samples was analysed using a standard curve, one for each kit. The standard curves were fitted into a four parameter curve, determined by the equation given in the kit protocol:
The program used to create the standard curves and to computerize the absorbance data was a KC4 statistical program6.
8.4.1 Evaluation of urinary albumin
Besides estimating the albumin concentration, an estimation of the imprecision, intra- and inter-kit variation, was performed as well. The intra- and inter-assay coefficient of variance was used (CV) and calculated using the standard deviation (SD) and the mean (CV=SD/mean). Absorbance value should be within 10% imprecision, therefore both inter- and intra-kit CV had to be ‘ 10%. The intra-kit variation was estimated with six different intern standards from both kits. The inter-kit variation was estimated with data from another ELISA kit from another masther thesis (Uhre & Vinding 2014).
8.5 Gene expression analyses on qPCR
Expression levels of the representative genes from the kidney tissue were verified by the qPCR method. Several steps were performed in generating the fold change from each animal.
Firstly, it was tested if the RNA in the kidney samples was of good quality and quantity to carry out the qPCR. Therefore, an assessment of the RNA integrity was made using Agilent 2100 Bioanalyzer and RNA LabChip?? kits . The kidney samples yielded RNA with a RNA integrity number (RIN) of 9.2 ?? 0.4. The concentration of the RNA in the tissue was estimated using a NanoDrop ND-1000 Spectrophotometer .
Secondly, before the qPCR was performed, the RNA was reverse transcribed into single-stranded cDNA in a process called Reverse Transcription. cDNA syntheses of the samples were performed in duplicate using 50 ng/??l of RNA. This was done using a High Capacity cDNA Reverse Transcription kit .
Thirdly, TaqMan?? Gene Expression Assays ordered for the given genes were performed in a qPCR application on a ViiA’ 7 Software12.
8.5.1 Generating fold change for gene expression level
In every well on the TaqMan probes the expression intensity of a given gene from a sample is measured. This measurement is expressed in Cycles to Treshold (Ct), this value represents the number of cycles at which the amount of cDNA reaches the threshold level. Determining differences in fold change between the diabetic and control group was done using the ‘?Ct method. The difference in Ct values (??Ct) between the target gene and the reference gene is calculated, and the ??Cts between the samples are compared. A high ??Ct signify low expression and vice versa. When calculating the fold change (-‘?Ct), it is possible to compare the normalised expression. If the fold change is positive, it means the gene is upregulated and the other way around. A fold change of ‘1.5 means the gene is upregulated.
8.6 Histopathology
The kidney samples representing all compartments of the kidney were stained with two different stains; Haematoxylin and Eosin Stain (HE) and Periodic acid-Schiff (PAS). Protocols of the staining process in Danish are in appendix.
Each left kidney removed from the animals was weighed and the kidney weight index (left kidney/body weight, g/kg) was calculated.
8.6.1 Evaluation of histopathology samples
For estimation of glomerular area and nuclei per glomerular area, scanned HE and PAS sections were used. Ten glomeruli with the hilus visible, spread over the whole section were randomly chosen from four sections per animal, so in total 40 glomeruli measurements per animal. For the measurements the program Visiomorph’ was used. The inside of the glomeruli were drawn by hand by the author and a test model was made to secure the most accurate calculations. The program was adjusted to calculate glomerular areal in ??m2 and total nuclei per glomeruli. These numbers were used to generate the average glomerular are and nuclei/glomeruli. For each section the average of the values were used for statistical analysis.
8.7 Statistical Analyses
Statistical analyses were performed on all collected data using the statistical software R and graphs were made in GraphPad Prism . Nonparametric tests were used for the data due to the limited sample size. A p-value ‘0.05 was considered statistically significant for all tests.
The inulin clearance was plotted in Phoenix WinNonlin Professional 6.3 and the statistics analysed in GraphPad Prism. The inulin clearance data was fitted into a noncompartmental analysis and GFR was estimated per BW (ml/h/kg) and per BSA (ml/h/m2). A nonparametric Mann Whitney test was performed on both to estimate difference between the groups.
A comparison of urinary albumin concentration when centrifuged and non-centrifuged in the thousand-fold kit was carried out by a paired t-test. To compare the urinary albumin concentration between the groups, in the thousand-fold and five hundred-fold dilution, a nonparametric Mann Whitney test was performed. The same was done for the UACR. To test whether the UACR’s calculated from the two ELISA kits were different, a Wilcoxon Signed Rank test was performed.
A Mann Whitney test was performed on the ??Ct values, and fold change data (-‘?Ct) on genes are plotted in graphs in appendix. A spearman’s rank correlation rho test was performed on fructosamine concentration and fold change from the ten genes.
9. Results
9.1 Inulin-FITC Clearance
The inulin clearance are presented in diagram 1. A statistically significance per BW (p-value=0.0357) and BSA (p-value=0.0357) was found.
Diagram 1. Illustration of Inulin Clearance. Noncompartmental analyses per bodyweight (BW) and body surface area (BSA).
9.2 Enzyme-Linked Immunosorbent Assay
The calculated intra-kit CV were under the desired 10 % variation, 2.68 % (thousand-fold dilution) and 3.42 % (five hundred-fold dilution). The inter-kit CV was also under the desired 10 % variation, 3.21 %.
There was no statistically significant difference in the urinary albumin concentration when centrifuged and non-centrifuged in the thousand-fold dilution kit (p-value=0.126).
No statistically significance in the urinary albumin concentration in the thousand-fold dilution (p-value=0.7429) or the five hundred-dilution (p-value=0.4127) was found between the two groups. The same was found for the UACR, thousand-fold dilution (p-value=0.0571) and the five hundred-fold dilution (p-value=0.1111). A Wilcoxon Signed Rank test performed on the UACR’s calculated from the two different dilutions showed a statistically significant difference (p-value=0.0156).
Diagram 2. Illustration of the urinary creatinine concentration (??mol/L) and the urinary albumin concentration (ng/ml) in the thousand-fold and five hundred-fold dilution.
Diagram 3. Illustration of the Urine Albumin-to-Creatinine Ratio (mg/g) calculated from the thousand-fold and five hundred-fold dilution.
Diagram 4. Illustration of the difference in the calculated UACR’s from each animal between the two different dilutions.
9.3 Gene expression analysis
The expression profiles of 10 genes related to DN were analyzed as were 2 reference genes. A differentially expression of genes between the diabetic and control group are shown in diagrams below. Significance levels are given on the diagrams from each gene. Dots above the dotted line at 1.5 represents an animal with an upregulated expression of the given gene. A table showing the fold change value from the genes from each animal is given in appendix.
RIN values of a mean of 9.2 ?? 0.4 was confirmed.
A statistically significant difference was observed in the fold change in 2 out of the 10 tested genes, COL1A1 and COL3A1, the two collagens (p-values=0.0159). When corrected with the Bonferroni method, none of the genes were statistically significant between the groups. P-values corrected with Bonferroin are in appendix.
Fructosamine concentration seen in comparison to fold change showed statistically significance in two genes, COL1A1 and COL3A1A (p-value=0.014). Spearman’s rank correlation rho test performed on fructosamine concentration and fold change on the ten genes showed correlation in two out of ten genes, COL1A1(r=0.8) and COL3A1 (r=0.8).
Diagram 5. Illustrations of fold change values from the ten investigated genes.
Diagram 6. Illustrations of fructosamine concentration (??mol/L) and fold change in a correlation model,
p-values=; r=0.08.
9.4 Histopathology
There was no statistically significant difference in the left kidney weight index between the two groups (p-value=0.25), table given in appendix.
There was a statistically significant difference (p-value=0.0159) between the groups for the glomeruli area, but not for the average number of nuclei/glomeruli (p-value=0.6825).
Diagram 7. Illustration of the average glomeruli area (??m2) and average number of nuclei per glomeruli area (??m2).
10. Discussion
This study has investigated structural and functional changes in the kidney of intact female G??ttingen minipigs fed a standard diet, with and without streptozotocin-induced diabetes. Statistically significant differences between the groups were found in the estimation of inulin clearance, both per BW and per BSA. No statistically significant differences were found in the urinary albumin concentration or the UACR. A validation of the ELISA kit was performed, and the intra- and inter CV were under the desired 10 %.
There was found a statistically significant difference in the gene expression of two genes, and these were also positively correlated to fructosamine concentration. A statistically significant difference were found in the average glomeruli area between the groups, but no difference were found in the number of nuclei per glomeruli area.
Glomerular Filtration Rate
Enzyme-Linked Immunosorbent Assay
The principle of the ELISA assay is the detection and quantification of antibody and antigen interaction (Engvall & Perlmann 1972; Butler 2008). The method is found to be a suitable and valid method in estimating the urinary albumin concentration (Engvall & Perlmann 1972; Fielding et al. 1983; Mohamed et al. 1984; (Anon n.d.)Feldt-Rasmussen et al. 1985; Watts et al. 1986; Coppo et al. 1987; MacNeil et al. 1991; Chiabrando et al. 1994). The used kit was specific for the measurement of pig albumin. The within-run CV and the between-run CV has been estimated under the desired 10 % variation (Chiabrando et al. 1994).
An increased urinary albumin concentration has been found in human patients with type I and type II DM (Viberti et al. 1982; Fioretto et al. 1996), type I diabetic Munich Wistar rats (Russo et al. 2009) minipigs fed a high-fat/ high-sucrose/ high-cholesterol diet (Xi et al. 2004; Liu et al. 2007; Liu et al. 2011) and diabetic rhesus monkeys (Wang et al. 2014).
Although no difference in the urinary albumin concentration between the groups was found in this study, a tendency towards a greater UACR in the thousand-fold dilution was observed in the diabetic group.
The lack of significant urinary albumin increase in this studies could indicate that a short period of diabetes time is insufficient to induce signs of early nephropathy.
Gene expression analysis
The qPCR method has been proposed as an efficient method for quantification of mRNA levels, and provides measurement of gene expression from many different samples on a limited number of genes (Nygard et al. 2007). To compare gene expression level in different samples it is important to choose the right reference gene of high stability. The two reference genes chosen in this study, ACTB and HPRT1, has been proposed as having a high stability (Nygard et al. 2007).
Differentially expression of genes has been shown in human patients with DN (Baelde et al. 2004; Zheng et al. 2012). This finding is consistent with results from this study.
A difference in gene expression between a control group and an experimental group with DN has been shown in several species, including rats (Zhang et al. 2012), mice (Wilson et al. 2003; Makino et al. 2006; Yang et al. 2011; Brunskill & Potter 2012) and non-human primates (Wang et al. 2014).
To my knowledge no publication is available on gene expression on kidney tissue from pigs with DN.
The most significant upregulated gene in the DN group of rats were COL1A1 (average fold change 4.3, p-value=0.003) (Zhang et al. 2012). This gene is said to play a crucial role in accumulation of ECM proteins (Zhang et al. 2012).
In mice an upregulation of VCAM1 and CCL2 (4-fold) is seen in diabetic mice with a diabetes duration of 8 months, but no upregulation is seen at younger age, suggesting that diabetes duration and is an important factor. In this study VCAM1 also correlated with albuminuria (Yang et al. 2011).
More recently, whole genome scan data have become available. This method allows us to search in the entire genome for regions that are linked with a specific trait, for instance DN. Two studies identifying potential candidate genes have been done, but often linkage is only present in small ethnic subgroups and not in the majority of patients with DN (Imperatore et al. 1998; Bowden et al. 2004).
Finding out what genes are regulated, their function and what cellular og molecular pathway they are involved in, will provide information for further investigation of the mechanisms of DN development. This can also be important in developing mechanism-based and directed therapeutic strategies for DN (Zhang et al. 2012).
Histopathology
These data showed a difference in the glomeruli area between the groups, but no difference were found in the number of nuclei per glomeruli area or the left kidney weight index between the groups.
This study did not include an evaluation of the pathological changes on the renal specimens. If these changes were described and compared to the control group, they could have been categorized according to the classification by Tervaert et al. (2010).
Enlargement of the glomeruli and thickening of the glomerular basement membrane (GBM), is one of the earliest histopathological changes seen in human patients with DN (Dachs et al. 1964; Wolf 2004). This histopathological change is consistent with the finding in this study.
Research in non-human primates, rhesus monkeys, with a streptozotocin-induced diabetic condition, fed either a standard diet or a high-sodium/ high-fat diet, has shown comparable structural changes to that of humans, including a larger glomerular surface area, suggesting the pathogenesis seen in human patients with DN (Wang et al. 2014). But in general the alterations were more pronounced in the group fed a high-sodium/ high-fat diet, suggesting obesity is a contributing factor (Wang et al. 2014). But it is to be noted that the animals had a diabetes duration between 3-5 years, so the long diabetes duration must also have had a role.
An increased glomeruli area has also been found Chinese Bama minipigs fed a high-fat/ high-sucrose/ high-cholesterol diet (Liu et al. 2007), where the predominant form of glomerular alteration was hypertrophy.
An increased glomeruli area was also found in a streptozotocin-induced minipig model fed a high-fat/ high-cholesterol/ high-fructose (HFHCHF) diet (Fredholm 2014). These areas where much higher in the diabetic HFHCHF group ranging from 16863 ??m2 to 61851 ??m2 and on average 30972.06 ??m2, compared to an average glomeruli area of 17864 ??m2 in the diabetic group of this study. The strength in the average glomeruli area measurements in this study lies in collection of 40 glomeruli (4 samples with 10 chosen glomeruli) compared to 10 in Fredholm (2014).
The studies where the groups were fed a high-fat diet suggest obesity itself contribute to renal histopathological changes.
11. Conclusion
The aim of this study was to investigate structural and functional changes in the kidney, in a streptozotocin-induced diabetic G??ttingen minipig model comparable with human diabetic nephropathy.
The results showed a statistically significant difference in the following: FITC-Inulin clearance per BW and BSA, average glomeruli area, fold change of two genes and positively correlation between the same two genes and fructosamine concentration. This statistically significance was observed when comparing the diabetic and the control group. A tendency of increase in UACR was found. No statistically significance was found in the urinary albumin concentration, average nuclei/glomeruli or upregulation of the 8 genes.
Nonparametric statistics was employed for all data due to the limited number of animals in the groups. Two factors are thought to play a role in the results not reaching statistical significance for all measurable parameters: the low number of animals per group (5 and 4) shorter diabetes duration.
An examination of histopathological changes as seen in other studies including enlarged glomeruli, matrix expansion, increased glomeruli basement membrane thickness, adherence of glomeruli to Bowmans’ capsule etc., was not performed in this study due to time limitations, but these findings could have revealed more knowledge about the overall diabetic nephropathy status of the animals.
The fact that it was possible to run the qPCR on pig kidney tissue states that it is possible to make further analysis regarding gene expression studies. The very high RIN values shows that the quality of the RNA was good and therefor valid results were accomplished.
To summarize, the conclusion of this study is that the tendencies observed in this diabetic lean G??ttingen minipig model is comparable to those found in human patients with DN. Structural and functional changes are seen, despite having a relatively stable glycemic control with insulin. The G??ttingen minipig serve a great potential as a large animal model for human diabetic nephropathy.
12. Perspectives
For an evaluation of the functional and structural changes in development of a diabetic minipig model for human DN, further studies are needed.
The tendencies seen in this study, including the small number of animals and short period of diabetes duration, underlines the potential of this large animal model.
NOT FINISHED
13. Footnotes
NOT FINISHED
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15. Appendices
Appendix I: Feed diet composition
Appendix II: Inulin Clearance
Pig id Group NCA Inulin clearance, BW (ml/hr/kg) NCA Inulin clearance, BSA (ml/hr/m2)
313-568 Diabetic 82.03 2772.705
313-355 Diabetic 44.65 1532.305
313-826 Diabetic 56.08 1924.561
313-739 Diabetic 52.49 1866.895
313-727 Diabetic 30.5 2023.229
313-896 Normal 32.6 1009.766
313-621 Normal 28.19 1032.27
216-428 Normal 32.71 1203.897
217-590 Normal Excluded in this test Excluded in this test
Appendix III: ELISA
Thousand-fold dilution:
Well ID Conc.Dil. Well Concentrations Concentrations x Dil. Nb Mean Std Dev CV (%)
SPL1 1000 C5 27.099 27099.4 2 26613.9 686,615 2,58
C6 26.128 26128.3
SPL2 1000 D5 27.027 27027.4 2 26362.3 940,499 3,568
D6 25.697 25697.3
SPL3 1000 E5 7.258 7258.03 2 7035.86 314,192 4,466
E6 6.814 6813.69
SPL4 1000 F5 6.543 6543.05 2 6474.78 96,556 1,491
F6 6.407 6406.5
SPL5 1000 G5 11.112 11111.8 2 11210.3 139,375 1,243
G6 11.309 11308.9
SPL6 1000 H5 9.993 9992.58 2 10073.4 114,283 1,135
H6 10.154 10154.2
SPL7 1000 A7 <0.884 <884.480
A8 <0.884 <884.480
SPL8 1000 B7 <0.884 <884.480
B8 <0.884 <884.480
SPL9 1000 C7 <0.884 <884.480
C8 <0.884 <884.480
SPL10 1000 D7 <0.884 <884.480
D8 <0.884 <884.480
SPL11 1000 E7 9.952 9952.08 2 10113.5 228,351 2,258
E8 10.275 10275
SPL12 1000 F7 10.073 10073.5 2 10113.8 57,087 0,564
F8 10.154 10154.2
SPL13 1000 G7 15.83 15830.4 2 15663.7 235,77 1,505
G8 15.497 15497
SPL14 1000 H7 15.978 15978.3 2 15626.2 497,924 3,186
H8 15.274 15274.1
SPL15 1000 A9 11.857 11856.9 2 12243.8 547,175 4,469
A10 12.631 12630.7
SPL16 1000 B9 7.824 7824.46 2 8038.52 302,723 3,766
B10 8.253 8252.58
SPL17 1000 C9 21.14 21139.7 2 21606.8 660,571 3,057
C10 22.074 22073.8
SPL18 1000 D9 20.78 20779.9 2 20869.8 127,231 0,61
D10 20.96 20959.8
SPLC1 1000 A3 44.741 44740.8 2 43988.3 1064,11 2,419
A4 43.236 43235.9
SPLC2 1000 B3 15.645 15645.3 2 16874.5 1738,28 10,301
B4 18.104 18103.6
SPLC3 1000 C3 514.117 514117 2 516900 3936,52 0,762
C4 519.684 519684
SPLC4 1000 D3 20.6 20599.9 2 20599.9 0 0
D4 20.6 20599.9
SPLC5 1000 E3 149.332 149332 2 149217 162,438 0,109
E4 149.102 149102
SPLC6 1000 F3 34.316 34316.2 2 34929.7 867,572 2,484
F4 35.543 35543.2
SPLC7 1000 G3 61.611 61611.1 2 61009.1 851,368 1,395
G4 60.407 60407.1
SPLC8 1000 H3 32.914 32913.5 2 33060.7 208,215 0,63
H4 33.208 33208
SPLC9 1000 A5 6.858 6858.49 2 6655.3 287,354 4,318
A6 6.452 6452.11
SPLC10 1000 B5 8.039 8039.29 2 7888.69 212,991 2,7
B6 7.738 7738.08
CTL1 A1 <0.884
A2 <0.884
STD1 1,23 B1 <0.884
B2 <0.884
STD2 3,7 C1 4.434 2 3,9 0,755 19,361
C2 3.366
STD3 11,1 D1 13.547 2 13,127 0,594 4,523
D2 12.708
STD4 33,3 E1 33.798 2 34,039 0,34 0,999
E2 34.279
STD5 100 F1 94.472 2 95,809 1,89 1,973
F2 97.146
STD6 300 G1 318.295 2 334,498 22,914 6,85
G2 350.701
STD7 900 H1 806.903 2 764,112 60,515 7,92
H2 721.321
Five-hundred fold dilution:
Well ID Conc.Dil. Well Concentrations Concentrations x Dil. Nb Mean Std Dev CV (%)
SPL1 500 G9 40.342 20171.2 2 19949 314.297 1.576
G10 39.454 19726.8
SPL2 500 H9 9.001 4500.46 2 4500.46 0 0
H10 9.001 4500.46
SPL3 500 A11 11.913 5956.58 2 6238.26 398.344 6.386
A12 13.04 6519.93
SPL4 500 B11 1.912 956.099 2 956.099 0 0
B12 1.912 956.099
SPL5 500 C11 1.612 805.854 2 866.027 85.097 9.826
C12 1.852 926.2
SPL6 500 D11 11.464 5731.87 2 5759.94 39.703 0.689
D12 11.576 5788.02
SPL7 500 E11 21.584 10792.2 2 11000.2 294.248 2.675
E12 22.417 11208.3
SPL8 500 F11 12.307 6153.49 2 6040.98 159.107 2.634
F12 11.857 5928.48
SPL9 500 G11 32.053 16026.4 2 16397.4 524.609 3.199
G12 33.537 16768.3
SPLC1 500 A3 72.635 36317.4 4 42778.1 6860.38 16.037
A4 74.814 37407.1
1000 B3 49.246 49246.2
B4 48.142 48141.8
SPLC2 500 C3 28.886 14443.2 4 15012.7 673.289 4.485
C4 28.949 14474.4
1000 D3 15.824 15823.7
D4 15.31 15309.5
SPLC3 500 E3 365.22 182610 4 249210 69765 27.994
E4 390.831 195415
1000 F3 305.901 305901
F4 312.914 312914
SPLC4 500 G3 41.376 20688.2 4 20701.1 928.601 4.486
G4 38.842 19421
1000 H3 21.584 21584.3
H4 21.111 21110.9
SPLC5 500 A5 253.039 126520 4 146088 17254.1 11.811
A6 274.931 137466
1000 B5 164.571 164571
B6 155.794 155794
SPLC6 500 C5 57.438 28719 4 29149.1 1328.34 4.557
C6 54.928 27464.1
1000 D5 30.08 30080.3
D6 30.333 30333.1
SPLC7 500 E5 108.12 54060 14 53188.9 4019.01 7.556
E6 104.475 52237.5
F5 106.579 53289.6
F6 105.874 52937
G5 104.94 52469.9
G6 101.721 50860.6
H5 105.874 52937
H6 106.579 53289.6
A7 99.47 49735
A8 94.545 47272.3
B7 103.436 51717.8
B8 100.816 50408
1000 C7 61.715 61715.3
C8 61.715 61715.3
SPLC8 500 D7 60.245 30122.6 4 29556.8 430.751 1.457
D8 59.034 29517
1000 E7 29.074 29074.1
E8 29.513 29513.3
SPLC9 500 F7 16.856 8428.17 14 8223.94 371.178 4.513
F8 17.029 8514.51
G7 16.053 8026.34
G8 16.511 8255.74
H7 16.569 8284.45
H8 15.709 7854.67
A9 17.144 8572.12
A10 14.285 7142.61
B9 16.225 8112.3
B10 16.741 8370.65
C9 16.799 8399.4
C10 16.914 8456.94
1000 D9 8.219 8219.01
D10 8.498 8498.25
SPLC10 500 E9 17.779 8889.66 4 8791.69 132.2 1.504
E10 17.779 8889.66
1000 F9 8.777 8777.49
F10 8.61 8609.94
CTL1 A1 <0.884
A2 <0.884
STD1 1,23 B1 <0.884
B2 1.307
STD2 3,7 C1 3.604
C2 3.432
STD3 11,1 D1 11.576
D2 11.183
STD4 33,3 E1 34.318
E2 33.342
STD5 100 F1 101.268
F2 95.84
STD6 300 G1 303.613
G2 304.373
STD7 900 H1 962.325
H2 828.569
Intrakit CV ‘ 1000-fold dilution kit:
Well ID Name Conc.Dil. Concentrations Nb Mean Std Dev CV (%)
SPLC1 Int 1 1000 44740.8 2 43988,3 1064,11 2,419
43235.9
SPLC2 Int 2 1000 15645.5 2 16874,5 1738,28 10,301
18103.6
SPLC3 Int 3 1000 514117 2 516900 3936,52 0,762
519684
SPLC4 Int 4 1000 20599.9 2 20599,9 0 0
20599.9
SPLC5 Int 5 1000 149332 2 149217 162,438 0,109
149102
SPLC6 Int 6 1000 34316.2 2 34929,7 867,572 2,484
35543.2
Intra assay CV = 2.679 %
Intrakit CV ‘ 500-fold dilution kit:
Well ID Name Conc.Dil. Concentrations Nb Mean Std Dev CV (%)
SPLC1 Int 1 500 36317.369 2 36862.23 770.5505 2.090352
37407.092
SPLC2 Int 2 500 14443.162 2 14458.8 22.11971 0.152984
14474.444
SPLC3 Int 3 500 182609.96 2 189012.7 9054.822 4.79059
195415.412
SPLC4 Int 4 500 20688.229 2 20054.59 896.1016 4.468312
19420.95
SPLC5 Int 5 500 126519.663 2 131992.7 7740.025 5.86398
137465.711
SPLC6 Int 6 500 28718.967 2 28091.51 887.3589 3.158815
27464.052
Intra assay CV = 3.420839 %
Interkit CV:
Name Nb Mean concentration Std Dev CV (%) Plate kit
STD1 2 <0.884 My data
2 <0.884 Uhre & Vinding 2014
STD2 2 3,9 0.308228 8.371093 My data
2 3,4641 Uhre & Vinding 2014
STD3 2 13,127 0.51336 4.021933 My data
2 12,401 Uhre & Vinding 2014
STD4 2 34,039 0.045962 0.134898 My data
2 34,104 Uhre & Vinding 2014
STD5 2 95,809 0.093338 0.097354 My data
2 95,941 Uhre & Vinding 2014
STD6 2 334,498 6.157486 1.865091 My data
2 325,79 Uhre & Vinding 2014
STD7 2 764,112
817,5 37,75102 4.773739 My data
Uhre & Vinding 2014
Inter assay CV = 3,210685 %
Pig id Group Urinary creatinine concentration (??mol/L) Urinary albumin concentration (non-centrifuged), 1000-fold dilution (mg/L) UACR, 1000-fold dilution (mg/g) Urinary albumin concentration, 500-fold dilution (mg/L) UACR, 500-fold dilution (mg/g)
313-568 Diabetic 6.696 26.362 34.80354756 39.898 52.6739982
313-355 Diabetic 2.289 6.4748 25.00582393 9.001 34.76206542
313-826 Diabetic 5.118 10.0733 17.39931257 12.4765 21.55028872
313-739 Diabetic 1.305 <0.884 1.912 12.95203412
313-727 Diabetic 0.571 <0.884 1.732 26.81466545
313-896 Normal 20.116 10.1135 4.444474875 11.52 5.062574831
313-621 Normal 21.746 15.626 6.352271324 22.0005 8.943628905
216-428 Normal 20.192 8.03855 3.51932183 12.082 5.289566694
217-590 Normal 9.392 2.087 1.96437758 32.795 30.86811822
Appendix IV: RIN-values and fold change values
Pig id. 217-428
Pig id. 313-355
Pig id. 313-826
Pig id. 313-896
Pig id. 313-621
Pig id. 313-727
Pig id. 313-568
Pig id. 217-590
Pig id. 313-739
Fold change values:
Pig id + group MMP2 VCAM1 COL3A1 LCN2 HAVCR1 CCL2 FN1 COL1A1 CD163 CD4
313-568
Diabetic 0.751 0.967 0.652 0.813 0.807 0.728 0.596 0.604 0.741 0.491
313-355
Diabetic 1.01 1.112 1.204 1.194 0.92 1.023 1.016 1.117 1.288 1.112
313-826
Diabetic 2.46 1.466 5.589 0.895 1.001 3.341 9.588 9.362 1.444 1.674
313-739
Diabetic 0.794 0.712 1.472 0.79 0.444 0.505 0.879 1.386 0.918 0.633
313-727
Diabetic 3.734 2.038 5.173 4.584 0.568 4.13 6.365 7.08 4.688 2.348
313-896
Normal 1.495 0.593 1.329 0.532 0.664 0.79 0.973 1.686 1.036 0.808
313-621
Normal 1.592 1.083 1.527 0.923 0.931 0.86 1.065 1.578 1.184 0.73
216-428
Normal 1.421 1.108 1.234 1.082 1.597 1.358 1.638 1.311 1.395 1.139
217-590
Normal 0.819 0.813 0.909 0.91 0.676 0.89 0.75 0.967 0.576 1.259
Appendix V: Histopathology
Pig ID Group Kidney weight (g) Body weight (kg) Kidney weight/ Body weight (g/kg)
313-896 n 50.74 32.6 1.556441718
313-621 n 59.9 33.2 1.804216867
217-428 n 60.4 33.6 1.797619048
313-568 d 111.3 27.5 4.047272727
313-355 d 76.71 28.5 2.691578947
313-826 d 94.08 28.5 3.301052632
313-739 d 92.2 31 2.974193548
313-727 d 46.44 30.5 1.522622951
Measurement Animal Group Average area (??m2) Average nuclei/ glomeruli area (n)
3544_-_2014-10-21_17_53_57 313-568 D 25160 0.00939
3570_-_2014-10-21_18_24_08 313-355 D 17305 0.01088
3596_-_2014-10-22_08_15_15 313-826 D 19118 0.01115
3622_-_2014-10-21_19_16_04 313-739 D 15870 0.01020
3648_-_2014-10-21_19_38_07 313-727 D 17864 0.01031
3674_-_2014-10-21_20_07_11 313-896 N 12326 0.01075
3700_-_2014-10-21_20_27_30 313-621 N 12001 0.00971
3726_-_2014-10-21_20_48_40 217-428 N 12703 0.00989
3752_-_2014-10-21_21_12_18 217-590 N 14512 0.01032