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FINAL APPROVAL OF DISSERTATION Doctor of Philosophy in Biomedical Sciences

Genetic Dissection of Hypertension Related Renal Using the Dahl Salt-Sensitive Rat

Submitted by: Michael R. Garrett

In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Biomedical Sciences

Examination Committee

Major Advisor: Joseph Shapiro, M.D.

Academic Advisory Committee:

David Allison, M.D., Ph.D.

Richard J. Roman, Ph.D.

En-Bing Lin, Ph.D.

Deepak Malhotra, M.D., Ph.D.

Senior Associate Dean College of Graduate Studies Michael S. Bisesi, Ph.D.

Date of Defense: September 7, 2006

Genetic Dissection of Hypertension-Related Renal

Disease Using the Dahl Salt-Sensitive Rat

Michael R. Garrett

University of Toledo

2006

ii DEDICATION

I dedicate this thesis to my wife, Jean and my two sons, Noah and Parker. This work is not solely a personal achievement but a testament of their love, support, encouragement, and understanding. Thank you.

iii ACKNOWLEDGEMENTS

I wish to thank the many people who have helped me not only learn about research, but also more importantly what it means to be a scientist. In particular, I wish to

acknowledge John P. Rapp, D.V.M., Ph.D. for his guidance, support and encouragement.

I would like to thank my major advisor, Joseph Shapiro, M.D., for giving me the

opportunity to work under his supervision and my other graduate committee members,

David Allison, M.D., Ph.D., Deepak Malhotra, M.D., Ph.D., Richard Roman, Ph.D., and

En-Bing Lin, Ph.D. for their time and support. I appreciate the guidance and

encouragement provided by Sonia Najjar, Ph.D., Director of the Molecular Basis of

Disease (MBD) program.

I especially wish to thank Shane Yerga-Woolwine for his contributions to my

work. His involvement in taking blood pressure measurements and genotyping is

sincerely appreciated. I thank Tracy Radecki for her help in phenotyping congenic strains

and Kris Farms for her help in maintaining the rat colony. I thank Howard Dene, Ph.D.

for his involvement with blood pressure measurements and statistical calculations. The expertise of William Gunning, Ph.D. in light and electron microscopy is appreciated.

Finally, I wish to thank Bina Joe, Ph.D. and Yasser Saad, Ph.D. for their helpful discussions and interest in my work.

iv TABLE OF CONTENTS

Dedication………………………………………………………………………………ii

Acknowledgments……………………………………………………………………...iii

Table of Contents………………………………………………………………………iv

Introduction……………………………………………………………………………..1

Literature………………………………………………………………………………..4

Manuscript 1: Time-Course Genetic Analysis of Albuminuria in Dahl Salt-Sensitive

Rats on Low Salt Diet………………………………………………….56

Manuscript 2: Genetic Linkage of Urinary Albumin Excretion in Dahl Salt-Sensitive

Rats: Influence of Dietary Salt and Confirmation using

Congenic Strains……………………………………………………….96

Manuscript 3 Dissection of a Genetic Influencing Renal Function in the

Rat and its Concordance with Disease Loci on Human

Chromosome 1q21…………………………………………………….132

Summary………………………………………………………………………………193

Bibliography…………………………………………………………………………..198

Abstract………………………………………………………………………………..234

1 INTRODUCTION

Chronic kidney disease (CKD) is an important healthcare problem with increasing

incidence and prevalence worldwide (United States Renal Data System 2005). Chronic

kidney disease is characterized by a gradual decline in kidney function and can culminate

in end stage renal disease (ESRD) requiring expensive treatments of dialysis and renal

transplantation (USRDS 2005). Most CKD cases are not associated with primary renal

disease, but with systemic conditions like diabetes and hypertension. In fact, diabetes and

hypertension account for about two-thirds of patients that progress to ESRD.

Additionally, age, gender, race/ethnicity, and socioeconomic factors play a role in the

onset and progression of the disease (Agodoa et al. 2005; Norris and Agodoa 2005).

Analysis involving familial aggregation studies, comparison of incidence between

different racial and ethnic populations, and linkage analysis have provided strong

evidence that CKD is, in part, genetically determined (Bowden 2003). Genetic analysis

of congenital and familial forms of kidney disease has led to the identification of

required for proper functioning of the glomerular filtration barrier (Chow et al. 2005).

While these studies have provided insight into mechanisms of proteinuria and

glomerulosclerosis, they have not helped to explain common causes of kidney disease. A

number of genetic analyses have been performed to identify genes involved in diabetes

and hypertension related ESRD (Bowden et al. 2004; DeWan et al. 2001; Freedman et al.

2003, 2004). Hundreds of these studies have utilized the candidate approach, while only a handful have utilized the more systemic whole genome scan approach to identify genes involved in kidney disease (Bowden 2003). These genome scans have identified

2 many genomic regions linked to ESRD, but none have culminated in gene identification.

One reason is that the linkage analysis in humans suffer from several limitations that inherently make the process of gene identification difficult (Schork 1997). While some problems can be overcome with proper experimental design, an alternative approach is to utilize animal models of CKD (Jacob and Kwitek 2002).

The rat provides a particularly fertile model to study disease and there are many well-defined inbred rat strains currently being used to study the genetics of CKD

(Korstanje and DiPetrillo 2004). In particular, the Dahl salt-sensitive rat (S) was selectively bred as a model to study the genetics of salt-sensitive hypertension (Dahl et al.

1962; Rapp and Dene 1985). The S rat also has a unique early onset and marked propensity to develop proteinuria, glomerulosclerosis and progressive renal damage

(Hampton et al. 1989; Sterzel et al. 1988). In contrast, the spontaneously hypertensive rat

(SHR) is exactly opposite with regard to renal pathology. SHR have minimal proteinuria and a pronounced resistance to development of renal lesions in spite of their hypertension

(Feld et al. 1977; Karlsen et al. 1997). In order to understand the genetic causes of ESRD, the focus of this work is to define the genetic components responsible for the development of the rapidly progressing renal pathology in the S rat. The assumption (as with all models of human disease) is that knowledge gained using an animal model will foster understanding and treatment of human disease.

The aim of manuscript 1 was to conduct a genetic analysis of renal and cardiovascular traits using the S and SHR. Linkage analysis identified quantitative trait loci (QTL) on multiple (1, 2, 6, 8, 9, 10, 11, 13, and 19) for urinary

3 excretion (UPE) and/or urinary albumin excretion (UAE) with variable time-course

patterns.

Manuscript 2 sought to perform a second linkage analysis to determine if QTL for

UPE and/or UAE would be influenced by salt-loading either by altering the time-course pattern of known QTL or by identifying QTL that were not detected on low-salt. A second aim was to perform congenic strain analysis for several UPE and/or UAE QTL to confirm the linkage analysis (on rat chromosomes 2 6, 9, 11, and 13) and to demonstrate the magnitude of the effect of each QTL once isolated on the S background.

The objective of manuscript 3 was to characterize the 2 congenic strain [S.SHR(2)] by conducting a time-course analysis and establishing onset and progression of renal disease in comparison to both parental strains. A comprehensive approach was employed that examined several renal parameters, histology, electron microscopy, analysis, and gene pathway analysis to characterize the strain. A second aim was to employ recombinant progeny testing (RPT) to reduce the

QTL to a small genomic region to aid in gene identification.

4

LITERATURE

Physiology of Kidney Disease

Overview of Normal Kidney Physiology

The kidney is an important organ involved in eliminating waste products produced by metabolism, such as urea (from protein), uric acid (from nucleic acid), and creatinine (from muscle creatine). Just as important, the kidney plays a role in regulating water and electrolyte balance, acid-base balance (pH), and the secretion of hormones that participate in the regulation of systemic and renal hemodynamics, red blood production, and mineral metabolism (Berne 2004).

The kidney's ability to perform many of its functions depends on the three fundamental roles: (1) filtration, (2) reabsorption, and (3) secretion. Figure 1 shows a schematic drawing of the functional unit of the kidney, the nephron. Each nephron is composed of a renal corpuscle and a tubule extending from the renal corpuscle. The renal corpuscle contains a compact group of interconnected capillary loops, called the glomerulus and is surrounded by a hollow capsule known as the Bowman’s capsule. The glomerulus filters out large solutes from the blood (cells, , and other large molecules), delivering water and small solutes (glucose, salt, amino acids, and urea) to the renal tubule and ultimately producing urine. The renal tubule consists of different

5 segments, the proximal tubule, the loop of Henle, and the distal tubule, each of which

performs unique functions involved in reabsorption and secretion.

Figure 1. Nephron Structure

A simple diagram of a nephron illustrating its major structural components. The nephron is composed of a renal corpuscle, including the glomerulus and Bowman’s capsule and a tubule extending from the renal corpuscle. The glomerulus filters out large solutes from the blood entering from the afferent arterioles, and delivers water and small solutes to the renal tubule, ultimately producing urine. The renal tubule consists of different segments, the proximal tubule, the loop of Henle, and the distal tubule. Reproduced from (Eaton 2001) with permission.

6 The capillaries of the glomerulus consist of three components involved in creating

the filtration barrier, the fenestrated , the basement membrane (BM), and

podocyte cells (Figure 2). The fenestrated endothelium primarily acts as a barrier to hold

back cells because of its large pore size. Podocytes have an unusual octopus-like

structure, known as “foot processes,” which are attached to the BM and encircle the outer

surface of the capillaries. Spaces between adjacent “foot processes” create gaps, forming

a filtration slit. Each filtration slit is bridged by a thin diaphragm, creating size selecting pores that can filter proteins and macromolecules that pass the fenestrated endothelium

and basement membrane. The presence of negatively charged proteins in the

endothelium, BM, and filtration slit provide additional selection for molecules. Therefore,

the glomerular filtration barrier restricts the filtration of molecules both on the basis of

size and electrical charge.

The volume of filtrate produced per unit time is known as the glomerular filtration

rate (GFR). GFR is an important measure of renal function. In normal adults, GFR is

~180 L/day or 125 ml/min, which means that an individual’s total plasma volume is

filtered, on average ~60 times a day (Berne 2004). This high rate of filtration gives the

kidney the ability to very precisely regulate and preserve the internal environment

necessary for cells and organ systems to function normally. In the clinical setting, creatinine clearance is often used to measure GFR and provides a measure to evaluate

proper kidney function in patients. Creatinine is a by-product of skeletal muscle creatine.

It is produced at a relatively constant rate and is freely filtered by the glomerulus.

However, small amounts are secreted by tubules, making creatinine clearance a close

approximation of GFR.

7 Figure 2. Filtration Barrier and Podocyte

Panel A depicts the glomerular filtration barrier. Blood enters the glomerulus through the afferent arteriole which branches into the glomerular capillaries where plasma is filtered and primary urine is formed. The filtration barrier is composed of a fenestrated endothelium, the glomerular basement membrane, and podocytes with extending “foot processes” which create the slit diaphragm. Panel B is a scanning electron micrograph of a normal rat glomerular capillary showing podocyte cells (P) and foot processes (FP). Figures are reproduced from (Tryggvason and Wartiovaara 2005) and (Pavenstadt et al. 2003) with permission from American Physiological Society via Copyright Clearance Center.

A consequence of a high GFR is that a significant amount of filtrate must be reabsorbed or an individual would quickly dehydrate. Renal tubules play an important role in kidney function mostly through reabsorption, although they are involved in

8 secretion. The proximal tubule reabsorbs about two-thirds of filtered salt, water, and all other organic solutes (primarily glucose and amino acids) (Eaton 2001). The loop of

Henle reabsorbs additional salt (20%) and water (10%) (Eaton 2001). Its primary role is to concentrate the salt in the tissue surrounding the loop thereby serving to make the filtrate more concentrated. The end of the loop contains specialized cells (macula densa) that are important in sensing sodium and chloride content. The distal tubule is similar to the proximal tubule in structure and function and reabsorbs additional salt and water.

Distal tubules from several nephrons join to form one tubule, the cortical collecting tubule. Cells in the cortical collecting tubule are regulated by hormones such as aldosterone and antidiuretic hormone (ADH). Aldosterone enhances sodium reabsorption and potassium secretion and ADH stimulates water reabsorption. Control of these hormones play a major role in regulating the amount of solutes and water present in the final urine. In summary, the nephron has the ability to expel unneeded and harmful substances by filtration and secretion, while reabsorbing substances useful to the body.

Kidney Disease

Kidney disease is classified as any disease or disorder that affects the function of the kidneys. Acute renal failure (ARF) is a sudden or rapid loss of kidney function that can occur as a result of , dehydration, trauma, and some medications (National

Kidney Foundation 2002). Acute renal failure is reversible, but it can lead to permanent loss of kidney function. More often than not, that affect the kidney are chronic problems and are classified as CKD. In general terms, CKD is defined as a loss of kidney

9 function that occurs gradually and is often silent, going undetected for months or even

years. Guidelines created by the National Kidney Foundation (NKF), Kidney Disease

Outcome Quality Initiative (K/DOQI) defines CKD as 1) kidney damage for ≥3 mo,

defined by either structural (pathologic) or functional abnormalities, such as the presence

of excess protein in the urine (proteinuria); and 2) GFR < 60 ml/min for ≥3 mo, with or

without kidney damage (NKF 2002). Additionally, the NKF established a system to

classify progression of CKD. Table I lists the five stages and prevalence of CKD at each

stage. The stages are based on GFR level, and the presence of kidney damage (usually

assessed by degree of protein/ albumin in urine), regardless of the clinical diagnosis of

kidney disease. Additional indicators of decreased kidney function and/or damage is high

blood urea nitrogen (BUN), dyslipidemia, hematuria, anemia, and edema (Snively and

Gutierrez 2004).

Table I. Stages and Prevalence of CKD

Stage Description GFR N (1000s) Prevalence (%)

1 Kidney damage with normal or ↑GFR ≥ 90 5900 3.3

2 Kidney damage with mild or ↓GFR 60-89 5300 3.0

3 Moderate ↓GFR 30-59 7600 4.3

4 Severe ↓GFR 15-29 400 0.2

5 Kidney failure <15 300 0.1

GFR, glomerular filtration rate. GFR estimated from using serum creatinine. Stage 1 and 2, kidney damage estimated by spot albumin-to-creatinine ratio >17mg/g in men or >25mg/g in women from two measurements. Reproduced with permission (NKF 2002).

The prevalence of CKD in the United States is 10.6 % considering stages 1-3

(Table I). Prevalence rates in other countries are similar, Austalia (11.2%) (Chadban et al.

10 2003), Japan (13.7%) (Iseki 2006) and South East Asia (6.8%) (Domrongkitchaiporn et

al. 2005). End stage renal disease occurs when the kidneys are no longer able to function

at a level that is necessary for day to day life and usually occurs when GFR <15 ml/min.

At this point, without dialysis or kidney transplantation, death will occur from

accumulation of fluids and waste products in the body. The prevalence of ESRD in the

United States is 1500 people per million (pmp) (USRDS 2005), which is similar to that

found in Japan (Bommer 2002). In Germany, and other middle European countries such

as Austria, Spain, Italy, and France, the prevalence is much lower, ranging from 670 to

764 pmp (Bommer 2002).

There are four general categories of risk factors associated with outcomes in CKD

(Table II). For example, individuals who are older, who belong to a certain racial or ethnic groups or have a family history are more susceptible to develop CKD. For example, the prevalence rate for African Americans is more than four times that seen among the white population (4700 pmp versus 1096 pmp) (USRDS 2005). Additionally,

American Indians, Asians, Native Hawaiians, and Hispanics all have a higher incidence and prevalence of ESRD compared to whites (Norris and Agodoa 2005; USRDS 2005).

Initiation factors, such as diabetes and hypertension account for a significant proportion of patients that progress to ESRD. Forty-five percent of ESRD can be attributed to diabetes and 27% to hypertension (USRDS 2005). The cause of diabetic nephropathy involves both hemodynamic and glucose dependent factor, including the accumulation of glycosylated products, endothelial dysfunction and loss of intraglomerular blood pressure regulation (Cooper et al. 1998). Systematic hypertension can cause direct damage to small blood vessels in the nephron resulting in the kidney’s

11 inability to autoregulate glomerular filtration and pressure. It may also lead to activation of the renin-angiotensin system (Adamczak et al. 2002). Additional causes can be attributed to glomerulonephritis (), glomerulosclerosis (scarring or hardening) and cystic kidney diseases which account for an additional 10.8% (USRDS

2005).

Tight control of blood glucose levels and blood pressure are important factors in slowing the progression to ESRD (Adamczak et al. 2002). Several clinical trials have demonstrated the benefit of strict blood pressure control using angiotensin-converting enzyme (ACE) inhibitors and/or angiotensin-II receptor antagonists (Kasiske et al. 1993;

MacKinnon et al. 2006; Peterson et al. 1995; Wright et al. 2002). These drugs work by lowering intraglomerular pressure and consequently serve to reduce proteinuria.

Ultimately, despite optimal treatment, kidney function may steadily decline and result in

ESRD, requiring dialysis or kidney transplantation.

Table II. Types of Risk Factors Influencing CKD

Type Definition Examples

Susceptibility increased susceptibility to kidney damage age, ethnicity, family history, factors low birth weight, low income

Initiation directly initiates kidney damage hypertension, diabetes, factors autoimmune diseases, urinary tract infection, drug toxicity

Progression cause worsening of kidney damage and poor control of blood sugar, factors faster decline in kidney function after smoking, higher level of initiation of kidney damage proteinuria or blood pressure

End-stage cause complications in patients with inadequate dialysis, anemia, factors kidney failure low serum albumin

Reproduced with permission (NKF 2002).

12 Genetic Analysis

Mendelian Genetics

Mendelian genetics refers to two fundamental principals regarding the

transmission of characteristics from parent to children and were established in a set of

classical experiments performed by Gregor Mendel in the 1860’s. Through selective cross-breeding of the pea plant, Mendel discovered that certain traits, such as flower color, stem length and seed shape, show up in offspring without any blending of parent characteristics (Griffiths 1996). He came to three important conclusions: (1) inheritance

of a trait is determined by “units” that are passed on to offspring unchanged, i.e., the

concept of the “gene;” (2) offspring inherit one such “gene” from each parent for each

trait; and (3) a trait may not show up in an individual but still can be passed on to

subsequent offspring.

Figure 3 illustrates how Mendel arrived at these three conclusions, using seed shape (round, R or wrinkled, w) as an example. It is important to point out that Mendel’s experiments were performed using “purebred” pea plants, which means each parent had two identical forms (or alleles) of the gene for seed shape, i.e., RR or ww. Mating these two parents, each homozygous (either RR or ww) for seed shape, produce offspring that are heterozygous, that is they have both an R and w allele. Heterozygous offspring exhibit only a round shape, demonstrating that the R allele is dominant, or masks the presence of the w allele. Once heterozygous Rw plants are bred to each other, offspring show a 3:1 ratio of round to wrinkled peas or 1:2:1 ratio of genotypes (Figure 3). However, it is

13 Figure 3. Mendel’s First Law of Segregation

The figure shows an example of Mendel’s first law of segregation using alleles for seed shape. The female is homozygous RR (round) and the male is homozygous ww (wrinkled). Each parent produces only one allelic variant in their gametes, resulting in a heterozygous Rw pea plant when the parental plants are crossed. Mating F1 progeny produces F2 with a genotype ratio of 1 RR: 2 Rw : 1 ww. Two classes are produced assuming the R allele is dominant to the w allele.

possible that some allelic variants of genes can show incomplete or partial

(although Mendel’s experiments didn’t demonstrate this phenomenon). In this case, the

phenotype (or observed trait) of the heterozygous pea would be intermediate (mode of

inheritance, additive or codominant) to that of the homozygous form of the gene.

Mendel's observations can be summarized in two principles: (1) the principal of

segregation which states that during gamete formation, each member of the allelic pair

14 separates from the other to form the genetic composition of the gamete; and (2) the

principle of independent assortment which states that different pairs of alleles (two or

more genes) are passed to offspring independently of each other. This is shown in Figure

4 and assumes that there is an adequate distance separating the two genes and that one

Figure 4. Mendel’s Second Law of Independent Assortment of Non-Alleles

The figure shows an example of Mendel’s second law of independent assortment using alleles for seed shape and color. The female is homozygous RRYY (round, yellow) and the male is homozygous wwgg (wrinkled, green). The segregation of each gene is independent of the other assuming the genes are not closely linked. A double heterozygous F1 (RwYg) plant produces gametes with equal ratio of all possible combinations of allelic variants for the two genes. Mating F1 progeny produces F2 with a phenotype ratio of 9: 3: 3: 1. The phenotype classes are produced assuming the R and Y alleles are dominant to the w and g allele.

15

allele of each gene displays dominance. Based on these genotypes, the F2 offspring are

grouped in one of four phenotypic classes (observed traits) in a 9:3:3:1 ratio. An obvious

conclusion to draw is that with each added trait that is examined and depending on the mode of inheritance (dominant, recessive, or codominant), the more complex the analysis becomes.

Linkage Maps

The segregation of alleles occurs as a consequence of the separation of

homologous chromosomes during meiosis (the process that transforms one diploid cell

into four haploid cells in ). In the case of the rat, each gamete will contain 20

autosomes and either an X or Y sex chromosome. The independent assortment of two

genes or loci is closely related to the chromosome distance separating the genes. The

distance between two genes determines the likelihood that there will be an exchange of

genetic material between chromosomes (crossover or recombination). Two genes that are

closely linked will exhibit only a few crossovers between them (Figure 5). In other

words, the farther apart two loci are from each other, the more likely that a crossover

event will happen between them (Figure 5). Thus, the frequency of recombination

provides an estimate of genetic distance between two loci on a chromosome. A

recombination frequency is calculated as the number of recombinant meiotic products

divided by the total number of meiotic products observed. Genetic distances between two

loci are measured in centiMorgans (cM), with 1cM defined as the distance between loci

16 Figure 5. Illustration of Chromosome Crossover and Genetic Distance

Panel A depicts the concept that the farther apart two loci are the more likely a recombination will occur between them. In example 1, two loci are located far apart from each other; example 2 the two loci are closely linked and no recombination takes place between the markers. Panel B illustrates how genetic distance between two loci can be determined based on recombination frequency.

with a recombinant frequency of 1% (Griffiths 1996). Thus, if 100 gametes were examined and 10 recombinants were observed between two loci, the distance between the two loci would be 10cM (Figure 5). By analyzing multiple markers throughout the genome on a given population (plant, animals, insects, etc.), a linkage map of each chromosome can be generated defining both the genetic makeup of each individual and determining the genetic distance between the markers.

17 In general, linkage maps are not constructed using genes, but highly abundant and polymorphic simple sequence repeats (SSR), also known as microsatellite markers (Rapp

2000). A microsatellite is a genomic element that consists of tandem repeats of one to

four nucleotides. The (CA)n repeat is the most common microsatellite in the

genome. Microsatellite analysis is performed by polymerase chain reaction (PCR) of

genomic DNA using pairs of primers complementary to the sequence flanking the

microsatellite repeat. The development of linkage maps is performed using two readily

available software programs, MAPMAKER/EXP (Lincoln et al. 1992) and/or

MapManager QTX (Manly et al. 2001). Map order and distance between markers are

calculated using the method of maximum likelihood analysis (Lander et al. 1987). The

maximum likelihood estimation (MLE) analysis calculates a logarithm of the odds (LOD)

score as the log10 of the likelihood that two loci are linked with a particular

recombination fraction (between 0 and 0.5), divided by the likelihood that the two loci

are unlinked and have a recombination fraction of 0.5 (Liu 1998). The higher the LOD

score the more likely two loci are linked. For example, a LOD score of 3.0 represents an

odds ratio of 1000:1 in favor of linkage.

Genome Wide Linkage Analysis

The object of linkage analysis is to look for a statistical association of genetic

markers [microsatellite, single nucleotide polymorphisms (SNP)] with a given phenotype

to identify genomic locations linked to a particular trait or disease process. In the case of

a single Mendelian trait, such as those described above, the genotype at a locus causing

the variation in the trait can be inferred from observing the phenotype. However, for

18 quantitative traits, such as renal function, alleles at multiple genetic loci influence the trait. Thus, knowing the phenotype of a given individual yields no unique information about the genotype at any given single locus because the trait is the net effect of many loci. The term quantitative trait locus (QTL) is used to describe a genomic location demonstrating linkage to a trait that is quantitative in nature which varies from low to high.

Linkage analysis of a complex trait, that is, a trait controlled by multiple genes, as well as being influenced by environmental factors requires four steps when performed using an animal model (such as the rat). Step 1, identify two inbred rat strains that differ widely in a particular trait (in general), such as CKD; step 2, develop a segregating

population and phenotype the animals; step 3, determine the genotype of each animal in

the population at multiple markers throughout the genome; and step 4, perform a

statistical analysis to identify an association of markers with the phenotype (i.e., QTL).

The use of inbred rats strains allow for the type of simple genetic analysis that

was demonstrated by Mendel’s pea plant experiments because the allele at any given

genomic location is fixed in the homozygous state. For example, the Dahl S is susceptible

to kidney disease and the SHR is resistant to kidney disease (step 1). In general, the

genotype at any given locus for the Dahl S is denoted as homozygous SS and similarly

homozygous SHRSHR for the SHR. The development of a segregating population (step

2) requires some knowledge of the mode of inheritance for a given trait [recessive,

dominant, or codominant (additive)]. This is achieved by crossing the parental S and

SHR to generate F1 offspring that then are evaluated using a marker of kidney disease, such as urinary protein excretion (UPE) or proteinuria. An F1 population with a

19 distribution of UPE values around the middle of the parental values indicates an additive mode of inheritance. Linkage analysis using an F2 population (F1 X F1) would be most

appropriate for this model. However, in the case of F1 (S X SHR) offspring, the

distribution is shifted to the right (Figure 6, panel A), demonstrating that SHR alleles are dominant to S alleles. Thus, a backcross population (F1 X S) would be the most

appropriate means to conduct a genetic analysis of proteinuria using the S and SHR.

Another important point is illustrated in Figure 6. Neither the parental strains nor

the F1 offspring exhibit a discrete value for UPE, but instead show some degree of

variation in these values. Since these animals are genetically identical, this variation can be attributed to environmental factors. The variance in the backcross population is larger than the F1 offspring primarily due to the segregation of alleles at loci influencing UPE

(environmental factors also play role). That is, each animal in the population is no longer

genetically identical (Figure 6, panel B). Animals that experience a higher degree of UPE

do so because they carry more susceptibility alleles than do animals that have lower UPE,

thereby creating the basis for linkage analysis.

A statistical analysis can next be done to establish if a given marker is linked to

UPE. The backcross population (F1 X S) can be subdivided into two genotypic classes at

each marker tested (i.e., animals can either be homozygous SS or heterozygous SSHR in a

1:1 ratio). In an F2 population (F1 X F1), three genotypic classes (SS, SSHR, and SHRSHR) would be observed in 1:2:1 ratio. A comparison of mean UPE for each genotype can be tested using a t-test, in the case of a backcross population or by one-way analysis of variance (ANOVA) for an F2. This analysis will give the probability that these means are

20 different from each other and establish if the marker is linked to UPE. However, since the

UPE effect observed at a given marker is influenced by both the distance between the

marker and the UPE QTL and the effect of the causative alleles at the QTL, this method

is not ideal and requires a more sophisticated approach.

Figure 6. Model of Genetic Inheritance of UPE for S and SHR

Panel A illustrates the mode of inheritance of UPE using S and SHR strains as an example. SHR animals show a distribution of low UPE because genetically they are resistance to develop kidney damage. On the other hand, S rats show a distribution of high UPE values because they carry alleles for susceptibility to kidney damage. Offspring of S and SHR can show three patterns of inheritance: (1) dominance, SHR alleles mask the effect of S allele resulting in F1 animals having UPE values close to the SHR parental; (2) additive (or codominant), F1 animals have UPE values midway between the parental; and (3) recessive, SHR alleles are masked by the effect of S alleles, resulting in F1 having UPE values close to the S parental. The best choice of which segregating population to construct depends on the mode of inheritance, in this case an F1 crossed to the S is used to construct a backcross population. Panel B illustrates the genetic make-up of the parental, F1, and backcross population.

21

Programs such as MAPMAKER/QTL or the Map Manager QTX program use an

algorithm to take these factors into consideration to perform interval mapping. These

programs utilize maximum likelihood estimation techniques to calculate LOD scores [in the case of Map Manager QTX, a likelihood ratio statistic (LRS) score is computed, which is similar to a LOD score] at multiple points between every two markers (Lander and Botstein 1989). The LOD scores are plotted out along each chromosome to give the most probable location of the QTL (Liu 1998). A LOD = 1.9 is considered evidence for suggestive significance for the presence of a QTL and LOD =3.3 is the significant threshold (Lander and Kruglyak 1995). The estimation of the QTL location is fairly rough and influenced by many factors, such as the size of the population, mode of inheritance (dominant, recessive, or additive) and the breeding paradigm (backcross or F2

) (Rapp 2000). At best, a QTL can only be localized to a 10cM region (Darvasi et al.

1993). A region this size can contain hundreds of genes, making it a difficult task to identify the actual gene involved in the trait.

Positional Cloning Using Congenic Strains

One way to better localize a QTL is through the construction of a congenic strain

(or consomic) and subsequently using congenic substrains. The construction of a congenic strain is a standard procedure in mammalian genetics (Rapp 2000; Yagil and

Yagil 2003). To develop a congenic strain, a chromosome region of interest from one inbred strain (recipient) is replaced by its counterpart from another inbred strain (donor) using a selective breeding strategy (Joe and Garrett 2004). Figure 7 illustrates the

22 Figure 7. Congenic Strain Breeding Scheme

In panel A the genetic background of the recipient (S) strain is represented by the white circle and that of the donor (SHR) strain by black circle. Under the traditional approach, at each generation, rats are screened at select markers, in this case, on chromosome 2 for the appropriate genotype (SSHR) denoted as the black dot in backcross (BC) 1. After each backcross, ~50% of the genome outside of the linked region is randomly replaced by the recipient genome demonstrated by the transition from black to white in each circle. For the speed congenic approach, the background genome is screened with additional

23 [Figure 7 Legend Continued] markers to find animals with the greatest amount of S genome. At BC8 heterozygous animals (SSHR) are intercrossed to produce offspring with 1(SS): 2(SSHR) :1(SHRSHR) ratio of genotypes for the congenic region. The homozygous SHRSHR animals are then propagated. The S.SHR(2) congenic is then maintained in the homozygous state. In panel B the schematic illustrates that the S.SHR(2) congenic is nearly identical to the S parental strain, except for the region on chromosome 2 donated by SHR (and some residual SHR alleles scattered throughout the genome).

breeding paradigm in constructing a congenic strain using the S (recipient) and SHR

(donor) on chromosome 2 as an example. At each backcross, 50% of the SHR genome, except for the region closely linked to the selected markers on chromosome 2 will be replaced by the S genome. After eight backcrosses, > 99.8% of the genetic background is from the S strain and thus is considered essentially identically to any other S rat, except for the region on chromosome 2 derived from the SHR (Figure 7). The strain is now denoted as S.SHR(2), i.e., recipient strain.donor strain (chromosome). A slight modification of this paradigm, named the speed congenic approach, seeks to eliminate the donor genome by selecting against donor alleles at markers throughout the genome, while at the same time selecting for markers within the QTL region (Markel et al. 1997; Weil et al. 1997). This approach shortens the time to develop a congenic strain to four or five backcrosses compared to the conventional approach of eight backcrosses.

Once a congenic strain is established, it is tested to confirm or disprove the presence of the QTL. This is done by phenotyping a group of S (n=10-20) and congenic animals (n=10-20) and comparing mean UPE (or other renal function measures) by performing a t-test. While a significant difference between the congenic and S may

prove the presence of the QTL, the size of the congenic interval is usually still too large

to identify the causative gene (Joe and Garrett 2004). Refinement of the QTL interval is

most often done through the construction of a series of congenic substrains, also called

24 substitution mapping. Congenic substrains are derived from the original congenic strain

and contain congenic segments of various sizes. Figure 8 presents an example of how

substitution mapping is used to reduce a QTL interval using the S.SHR(2) congenic. A

QTL is localized to a refined region on the basis of the presence or absence of a UPE

effect in relation to the location of the substituted chromosome segment. This type of

analysis is usually performed until the QTL is localized to within 1-2cM region allowing

for a detailed analysis of individual genes.

An alternative approach to fine mapping a QTL is recombinant progeny testing

(RPT). RPT has been suggested as an approach for fine mapping QTL with large and

dominant effects (Darvasi 1998). Although this approach has not been employed as

frequently as conventional substitution mapping, it has been successfully utilized to

substantially narrow QTL regions to small intervals (Christians et al. 2003; Christians

and Keightley 2004; Fehr et al. 2002; Liu et al. 2001; Oliver et al. 2005). Figure 8

demonstrates how RPT can be used to reduce a QTL interval using the S.SHR(2)

congenic. The basis of RPT is to find recombinants in an interval derived from a

congenic, to propagate these recombinants, then to measure the of their

progeny in order to determine the genotypic value of each. A population from S and

S.SHR(2) is developed to identify new recombinant animals within the congenic region.

Rats that carry a chromosome that is recombinant within the QTL region are crossed back

to one of the parental lines. Offspring are a mixture of recombinant animals (congenic-

like) and non-recombinant animals (S-like). The UPE effect of recombinant animals is compared to non-recombinant animals to determine whether or not the QTL is present.

25

Figure 8. Traditional Substitution Mapping and Recombinant Progeny Testing

In panel A the schematic shows how substitution mapping is used to refine the location of a QTL for UPE. Upon establishing that the S.SHR(2) congenic strain has a significant effect on UPE compared to the parental S, a series of substrains are developed containing congenic segments of various sizes, denoted by the black bars. Routinely a series of 4 to 8 substrains are developed and tested (first iteration). Some strains are found to have a significant effect on UPE (↓), whereas others do not (NC, no change). By comparing which strains still retain an effect on UPE, the QTL location can be refined. Typically 20 S and 20 congenic animals are tested for each experiment to achieve an appropriate level of statistical power. A second and perhaps a third iteration is needed to reduce the QTL to a manageable size (1-2 cM) to allow for a comprehensive evaluation of genes located

26 [Figure 8 Legend Continued] within the QTL interval. Each iteration takes ~1.5 years from initial substrain development to testing. Panel B illustrates an alternative approach to refining the location of a QTL by recombinant progeny testing (RPT). In this case, congenic families are used to test for the presence or absence of an effect on UPE. An animal that carries a recombinant chromosome is bred to a parental S strain. The offspring are then a mixture of animals that carry the recombinant (congenic-like) and those that do not (non-recombinant animals, S-like). Mean UPE for each group is compared. If there is a significant difference, then the QTL resides in the congenic segment. RPT animals are tested in the heterozygous state (blue bar) compared with traditional substitution mapping which is done on animals in the homozygous state (black bar). The main benefit of RPT is that it can expedite QTL localization because the animals take less time to develop and test.

The main benefit over conventional substitution mapping is the rapid development and testing of many recombinant animals. The scheme also has the advantage of minimizing environmental noise because the effects of the recombinant chromosomes are compared to non-recombinant littermates.

Gene Expression

The congenic approach, while being systematic, is quite laborious and time consuming (Joe and Garrett 2004). Gene expression studies (i.e., DNA microarray analysis) offer one way to expedite the path from QTL to causative gene. Gene expression profiling can be used to: (1) identify expression differences of positional candidates within the QTL region; (2) correlate gene expression changes over time between strains with disease progression; and (3) identify biochemical pathways potentially involved in the disease process. In recent years, several groups have successfully used this approach to expedite the identification of disease causing genes

(Aitman et al. 1999; Fehr et al. 2002; Karp et al. 2000; Rozzo et al. 2001), but the

27 successes are few. More often than not, gene expression studies are used as a tool to

prioritize candidate genes (within the QTL) for further study (Joe et al. 2005; McBride et al. 2003; Yagil et al. 2005). Most recently, two groups have utilized a large scale integrated approach using either a panel of recombinant inbred strains (Hubner et al.

2005) or chromosome substitution strains (Malek et al. 2006) with gene expression profiling.

Proteomics

Protein analysis on a genome wide scale (i.e., the proteome) is becoming a major focus of research because genetic effects are ultimately reflected at the level of protein function (Groenen and van den Heuvel 2006). Proteomic analysis can be viewed as a complementary approach to gene expression analysis because it provides information at the level of the protein which is subjected to numerous post-translational modifications

(glycosylation, phosphorylation, and alkylation) that directly affect protein function.

A common approach to proteomic analysis is through two-dimensional (2-D) electrophoresis followed by various mass spectrometric (MS) techniques for protein identification. Proteins are separated by isoelectric focusing according to their pI

(isoelectric point) in one dimension and then by their molecular weight in the second dimension, followed by MS. The approach has a number of limitation such as difficulty in solubilization membrane proteins, gel reproducibility, and relatively large amount of sample required (300- 1000 ug) (Groenen and van den Heuvel 2006). Reproducibility problems have been overcome by 2-D difference in gel electrophoresis (DIGE) whereby

28 each of two samples is labeled with a distinct fluorescent dye and the two samples are analyzed on a single gel (McBride et al. 2006). A more sophisticated approach aimed at generating large amounts of data involve complex protein samples being digested, fractionated, and analyzed by automated MS, i.e., shotgun approach (Domon and

Aebersold 2006). There have been a number of proteomic studies related to renal disease in the rat (Vidal et al. 2005). However, no study has been reported involving proteomic analysis using a rat derived congenic strain.

The Rat Genome and Comparative Genomics

The sequencing of the rat genome, completed in 2004 (Gibbs et al. 2004), along with the complete sequence of the human (Lander et al. 2001) and mouse (Waterston et al. 2002) genomes done earlier (2001 and 2002, respectively) represent a major leap- forward for positional cloning using the above paradigm. The ability of substitution mapping to fine map a QTL relies on how well recombination intervals can be defined, placing the limiting step on the availability of microsatellite markers (Rapp 2000). In the past, if there were no known polymorphic markers in a given region, new markers would have to be developed by screening large-insert libraries [yeast artificial chromosome

(YAC), and later P1-derived artificial chromosome (PAC) and bacterial artificial chromosome (BAC)] derived from rat genomic DNA (Cicila et al. 2001). The procedure was quite labor intensive. Currently, by searching several online databases

(www.ensembl.org and www.ncbi.nlm.nih.gov), new microsatellite markers can be obtained with great ease in almost any region of the rat genome. A second major impact

29 of sequencing the rat genome is the ability to quickly determine known and/or predicted genes within a QTL region. It is now possible to easily identify genes that map within a

QTL, enabling sequencing and/or functional analysis to be performed on these genes in tandem while conducting substitution mapping (McBride et al. 2006). Previously, once a

QTL was narrowed to a small genomic interval a number of time consuming and costly steps had to be undertaken including: (1) developing a physical map using PAC or BAC clones; (2) large scale sequencing of these clones; and (3) determining genes within the interval by alignment of known genes, expressed sequence tags (EST) or by using gene prediction software.

The big picture regarding the availability of genome sequences from multiple organisms it that a better understanding of biology and factors that cause disease can be gained. Each organism provides advantages that the other organisms do not. For example, the rat provides a wealth of physiological and pharmacological information, the mouse provides the ability to do genomic techniques not possible with the rat (e.g., gene knockout) and humans provide a diverse clinical spectrum for study (Jacob and Kwitek

2002). So by integrating physiological and genomic data from these organisms a clearer understanding of the pathogenesis of disease can be gained.

Complementation/Proof of the Causative Gene

The most conclusive evidence that a particular gene is causative to a QTL is through demonstrating that replacement of the nucleotide variants identified results in substituting one phenotype for another. This can be accomplished using several

30 techniques such as transgenesis, gene silencing using RNA interference (RNAi), knockout technology, and/or nuclear transfer (Smits and Cuppen 2006). The transgenic

approach provides a convenient method to test either loss of function (placement of

normal gene on genetic background of defective gene) or gain of function

(overexpression of gene on genetic background of normal rat) polymorphisms. This

procedure has been successfully used in the rat (Pravenec et al. 2001). A second

complementation approach is by in vivo knockdown of the gene using RNAi mediated gene silencing. This approach suffers for several technical problems, however; it has been successfully used in the rat (Bahi et al. 2005; Lai et al. 2004). Gene knockout by homologous recombination in embryonic stem (ES) cells is not yet available in the rat

because of the lack of pluripotent ES cells (Smits and Cuppen 2006). Another method

producing a similar result involves somatic cell nuclear transfer (Zhou et al. 2003). This

approach may allow for homologous recombination in somatic cells followed by nuclear

transfer into oocytes for the rat. A final option currently available involves target-selected

N-ethyl-N-nitrosoura (ENU) mutagenesis (Smits et al. 2004; Zan et al. 2003). This

technique involves the injection of ENU (mutagenic agent) into male animals which

induce into the rat germ-line. The rats are mated and offspring are screened

using various methods (yeast based systems, Cel-I heteroduplex cleavage assay, or direct sequencing) to identify mutations. A number of ENU knockout rats have been developed

using this technique are currently available for study (pga.mcw.edu).

31 Genetics of Human CKD

Mendelian Forms of Kidney Disease

The power of linkage analysis is best demonstrated in studies of familial forms of

kidney disease where there is a strong correlation between the observed phenotype and

genetic variants. Table III provides a summary of linkage analysis performed on familial

forms of kidney disease. Most of these diseases are directly related to the development of

glomerulosclerosis and are characterized by severe proteinuria. For several of these

familial nephropathies the gene and specific variants have been identified that cause the

disease (Table IV). These studies have provided valuable insight into the importance of

the podocyte in the filtration barrier (Padiyar and Sedor 2005).

Mutations in the nephrin (NPHS1) where identified as causing congenital

nephrotic syndrome of the Finish type (Kestila et al. 1998). It is rare autosomal recessive

disease that affect about 1:1000 births in Finland and is characterized by massive

proteinuria in utero and subsequent ESRD. The major is a 2 base deletion in

2 that results in a stop codon (Finmajor). Nephrin is expressed mainly in glomerular podocyte cells and localizes exclusively to the slit diaphragm between the foot processes

(Ruotsalainen et al. 1999). Nephrin knockout mice initially have normal appearing podocytes, despite abnormal appearing slit-diaphragms, suggesting that nephrin’s primary role is in the functioning of the slit diaphragm rather than podocyte development

(Donoviel et al. 2001).

A form of familial steroid-resistant nephrotic syndrome was found to be due to mutations in podocin (NPHS2). The syndrome is characterized by onset at 3-5 yr of

32

Table III. Linkage Analysis of Familial Forms of Human Nephropathy/ESRD

Nephropathy Type Race/Ethnicity/Location Chr Marker Position LOD Measure Reference

FSGS Oklahoma 19 D19S191 19q13 12.3 RB,SC,UPE,UAE (Mathis et al. 1998) FSGS New Zealand 11 D11S2002 11q21-22 9.9 RB,UPE (Winn et al. 1999) FSGS Pittsburgh 19 D19S425 19q13 2.41 RB, SC,UPE (Vats et al. 2000) FSGS Brazilian 1 D1S254 1q25-31 4 RB, UPE (Tsukaguchi et al. 2000) FSGS Finnish 19 D19S416 19q12-q13.1 2.9 RB, UPE (Kestila et al. 1994) FSGS/AS Indian 11 D11S4464 11q24 3.2 RB, H,SC,UPE (Prakash et al. 2003) FSGS/AS Palestinian 14 D14S77 14q24.2 3.5 RB,UPE,UAE (Ruf et al. 2003) FSGS/Hypertensive African America 9 D9S105 9q31-q32 3.9 RB, H,SC,UPE (Chung et al. 2003) FSGS/Steroid European, North African 1 D1S242 1q25-31 5.6 RB (Fuchshuber et al. 1995) Resistant IgA Italian, African American, 6 D6S1040 6q22-23 5.6 RB, H,UPE (Gharavi et al. 2000) Caucasian MPGN III Irish 1 GATA135F02 1q31-32 3.9 RB, H,UPE (Neary et al. 2002)

FSGS, focal segmental glomerulosclerosis; AS, Alport syndrome, MPGNIII, membranoproliferative glomerulonephritis type III; Chr, chromosome; LOD, logarithm of odds; RB, renal biopy; SC, serum creatinine; H, hematuria; UPE, urinary protein excretion; and UAE, urinary albumin excretion.

33

age with rapid progression to ESRD. The major mutation, accounting for approximately one-third of all patients, is a missense mutation resulting in an change

(R138Q). A variety of other mutations also were observed (Table IV). Podocin is

expressed almost exclusively in podocytes and appear to play a role in controlling

permeability of the filtration barrier (Boute et al. 2000). Podocin may participate in the

interaction between nephrin and the (Huber et al. 2001).

Mutations in α-actinin-4 (ACTN4) were found to cause another form of familial

focal segmental glomerulosclerosis (Kaplan et al. 2000). The disease is characterized by

adult onset, slowly progressing to ESRD in some individuals. ACTN4 appears to be

exclusively expressed in the podocyte and is an actin-filament cross linking protein.

Mutant forms of α-actinin-4 was found to bind F-actin more strongly than the wild-type

of α-actinin-4 implying that changes in the actin cytoskeleton of podocytes alter the

filtration characteristics of the glomerulus (Kaplan et al. 2000).

Alport syndrome is characterized by hearing loss, ocular lesions, hematuria, and

structural abnormalities in the glomerular basement membrane and progression to renal

failure. The X-linked form was shown to be due to mutations in the COL4A5 gene

encoding for α5(IV) collagen which is a component of the glomerular basement

membrane along with several other structural components (Barker et al. 1990). An

autosomal form of Alport syndrome has been shown to be due to numerous mutation in

the genes for α3(IV) and α4(IV) collagen (COL4A3 and COL4A4, respectively)

(Mochizuki et al. 1994; Rana et al. 2005). Most recently, mutations in transient receptor

potential cation channel 6 (TRPC6) were identified using a large New Zealand family

with FSGS (Winn et al. 2005). It is believed that mutations in TRPC6 cause exaggerated

34

Table IV. Genetic Variants Linked to Familial Forms of Human Nephropathy/ESRD

Nephropathy Type of Nucleotide/ Amino Type Race/Ethnicity Chr Gene Mutation cDNA Acid/Protein Reference FSGS/Proteinuria Finnish 19 NPHS1 deletion 121(del2)1 truncation (Kestila et al. 1998) nonsense 3325(C→T)2 R1109X FSGS/Proteinuria European, North African 1 NPHS2 nonsense 412(C→T) R138X (Boute et al. 2000) frameshift 104(105insG) truncation frameshift 419(delG) truncation frameshift 855(856delAA) truncation missense 59(C→T) P20L missense 274(G→T) G92C missense 413(G→A)1 R138Q3 missense 479(A→G) D160G missense 538(G→A) V180M missense 871(C→T) R291W FSGS/Proteinuria Oklahoma, California, Canary Islands 19 ACTN4 missense 682(A→G) K228E (Kaplan et al. 2000) missense 695(C→T) T232I missense 703C(T→C) S235P FSGS/Proteinuria New Zealand 11 TRPC6 missense 335(C→A) P112Q (Winn et al. 2005) AS X Col4A5 various4 >138 (Lemmink et al. 1997) 2 Col4A3 various4 >20 (Rana et al. 2005) 2 Col4A4 various4 >20 ADPKD Type 1 16 PKD1 various4 >200 truncation5 (Ong and Harris 2005) ADPKD Type 2 4 PKD2 various4 >50 truncation5 (Ong and Harris 2005)

Chr, chromosome; FSGS, focal segmental glomerular sclerosis; AS, Alport syndrome; ADPKD, autosomal dominant polycystic disease; NPHS1, Nephrin, of Ig superfamily;NPHS2, podocin, familiy member of stomatin; TRPC6, transient receptor potential cation channel 6; ACTN4, α-actinin- 4, an actin filament crosslinking protein; PKD1, polycystin-1, a large receptor-like integral membrane protein; PKD2, polycystin-2, with 1 2 3 4 voltage-activated ; denoted as Finmajor, ; denoted as Finminor; R138Q mutation found in approximately one-third of patients; majority of mutations are unique and family dependent; mutations identified include: missense, nonsense, and insertion/deletions; and 5 most mutations are predicted to truncate the protein.

35 calcium signaling, disrupting glomerular cell function and/or causing apoptosis (Winn et

al. 2005). The importance of this finding is that there are alternative mechanisms for the

pathogenesis of glomerular disease that appear independent of defects in cytoskeletal and

structural components of the podocyte.

Lastly, mutations in two genes were identified that cause autosomal dominant

forms of polycystic kidney disease (PKD). These genes don’t play a specific role in

filtration and podocyte function as do the others described; however, the work does

implicate additional genes involved in renal function. Polycystic kidney disease is a

characterized by the growth of numerous cysts, that can slowly replace

much of the mass of the kidneys, reducing kidney function and leading to kidney failure

(Al-Bhalal and Akhtar 2005). Mutations in polycystin-1 (PKD1) explain the majority of

cases (80-85%). Mutations in polycystin-2 (PKD2) account for the remaining 10-15%.

Both polycystins function together and co-localize to the primary cilia of renal epithelial

cell. Consequently, loss of function of either PKD1 or PKD2 result in tubular cells

reverting to a less differentiated state, which are more prone to proliferation (Al-Bhalal and Akhtar 2005).

Other Genes Controlling Glomerular Structure/Function

The glomerular basement membrane of immature mice is rich in laminin β1, and

is replaced during development with laminin β2. Consequently, laminin β2 is a major

component of the renal glomerular basement membrane in adult mice. The gene

(LamB2) coding laminin β2 has been knocked out in mice (Noakes et al. 1995). The

glomerular basement membranes of these mice are histologically normal. The mice,

36 however, show fusion of podocyte foot processes due presumably to the inability of foot processes to properly attach to the basement membrane, and subsequently develop massive proteinuria by 1 wk of age (Noakes et al. 1995).

CD2-associated protein (Cd2ap) functions in stabilizing contacts between T cells and antigen presenting cells. Cd2ap knockout mice, besides having compromised immune functions, showed fusion of podocyte foot processes, proteinuria, and the mice die at 6-7 wk of age from renal failure (Shih et al. 1999). Cd2ap was found to associate

with nephrin and presumably acts to connect nephrin to the podocyte cytoskeleton (Huber

et al. 2003). Another protein, glomerular epithelial protein 1 (Glepp1) was cloned in a

search for podocyte specific proteins. It is expressed on the apical surface of podocyte foot processes. Knockout of GLEPP1 results in increased width of foot processes, but no proteinuria (Wharram et al. 2000).

Two transcription factors have been described that are involved in podocyte differentiation. They are LIM-homeodomain (Lmx1b) and kreisler

(Krml1). Mutations in Lmx1b cause nail-patella syndrome, a disease characterized by skeletal abnormalities, nail hypoplasia, and nephropathy (Miner et al. 2002; Rohr et al.

2002). Knockout mice exhibit a reduced number of podocyte foot processes and lack typical slit diaphragms (Miner et al. 2002). Mice homozygous for the kreisler (kr) mutation (Krml1) develop proteinuria, and fusion and effacement of podocyte foot processes (Sadl et al. 2002). Additionally, the Wilms' tumor gene WT1 is a transcription cofactor or post transcriptional regulator that functions in embryogenesis and is present in

adult podocytes (Guo et al. 2002). Using either WT1-knockout or inducible YAC transgenic mice it was shown that reduced expression levels of WT1 results in either

37 crescentic glomerulonephritis or mesangial sclerosis depending on the gene dosage (Guo

et al. 2002).

In summary, the glomerular podocyte plays a central role in controlling glomerular filtration. Proteins that are critical to podocyte structure and function can be placed into three categories: (1) proteins which make up the slit diaphragm between podocyte foot processes, and proteins that connect the slit diaphragm apparatus to the podocyte cytoskeleton; (2) the podocyte cytoskeleton itself; and (3) proteins in the podocyte and/or the glomerular basement membrane which attach foot processes to the basement membrane. Regardless, it is clear that disruption of any of these components results in various forms and degrees of glomerular disease and proteinuria.

Whole Genome Scans for Common Causes of CKD

Hundreds of studies have utilized the candidate gene approach to identify genes involved in kidney disease (Bowden 2003). More recently, whole genome scans have been performed to identify regions in humans linked to common causes of kidney failure

(Bowden et al. 2004; DeWan et al. 2001; Freedman et al. 2003, 2004). A summary of these studies is presented in Table V. The majority of these studies involved populations enriched for either diabetes or hypertension (Table V). However, there were a few genome scans performed on populations designated as “all cause” because no attempt was made to enrich for any specific type of nephropathy. In total, almost every chromosome has been linked to at least one type of nephropathy, excluding chromosomes

14, 15, 16, and 17. Fifteen genomic regions have been linked to diabetes related kidney

38

Table V. Genome Wide Linkage Analysis for Human Nephropathy/ESRD

Nephropathy Type Race/Ethnicity/Location Chr Marker Position LOD Measure Reference

All Cause African American 1 D1S1589 1q25.1 1.1 SC (Freedman et al. 2005) 4 D4S2639 4p15.32 1.0 9 D9S1826 9q34.3 1.2 13 D13S796 13q33.3 1.7 Framingham Study 3 D3S2427 3q26.31 1.9 SC, GFR,CC (Fox et al. 2004) 4 D4S2368 4q32.3 2.28 11 D11S1392 11p13 2.2 Framingham Study1 8 D8S1179 8q 2.1 ACR (Fox et al. 2005) Caucasian/Utah 10 D10S677 10q 3.4 CC (Hunt et al. 2002) Caucasian/Utah 2 D2S1334 2q21 3.2 SC, GFR (Hunt et al. 2004)

Type 1 Diabetes Russian 3 D3S2326 3q23-24 UAE (Chistiakov et al. 2004)

Type 2 Diabetes African American 3 D3S2460 3q13.32 4.6 SC, UPE, (Bowden et al. 2004) PCR 7 D7S1802 7p 3.6 18 D18S1364 18p 3.7 Turkish 18 D18S469 18q22.3-23 6.1 UAE, ACR (Vardarli et al. 2002) Caucasian 10 D10S1432 10p 3.0 SC, UPE (Iyengar et al. 2003) Pima Indians 3 D3S3053 3q 1.5 PCR, ACR (Imperatore et al. 1998) 7 D7S500 7q 2.7 9 D9S910 9q 1.2 20 D20S115 20p 1.8 Pima Indians 10 D10S674 10p13 1.2 PCR, ACR (Imperatore et al. 2001) 18 D18S535 18q12.3 1.5 African American, Caucasian 5 D5S2500 5q 3.4 ACR (Krolewski et al. 2006) 7 D7S3058 7q 3.1 21 D21S1437 21 2.1 22 D22S683 22q 3.7

39

Table V. Genome Wide Linkage Analysis for Human Nephropathy/ESRD [CON’T]

Nephropathy Type Race/Ethnicity/Location Chr Marker Position LOD Measure Reference

Type 2 Diabetes African American 1 D1S1589 1q25.1 1.6 UPE (Freedman et al. 2004) [CON’T] 2 D2S1391 2q32.1 3.9 9 D9S1121 9p21.3 2 13 D13S1493 13q13.1 3.9 13 D13S796 13q33.3 1

Hypertension Caucasian 1 D1S534 1p12 1.9 CC (DeWan et al. 2001) 3 D3S4529 3.4 6 D6S1056 6q16.1 2.4 6 D6S305 6q26 1.9 African American 3 D3S2398 3q27 3.6 African American 3 3p 4.7 CC (DeWan et al. 2002) African American, Caucasian 12 PAH2 12p 1.8 ACR (Freedman et al. 2003) 19 D19S591 19q 2

SLE African American, Caucasian 2 D2S2972 2q34-35 2.2 ACR (Quintero-Del-Rio et al. 2002) 10 D10S2470 10q22.3 3.2 11 D11S1392 11p15.6 2.6

IgA Japanese 1 PIGR3 1q31-41 RB,SC,UPE (Obara et al. 2003)

Chr, chromosome; LOD, logarithm of odds. All cause, data collected from the general population and not enriched for patients with hypertension or diabetes; SLE, systematic lupus erythematosus. 1 both overall population and enriched for hypertension, 2 phenylalanine-4-hydroxylase, 3 polymeric immunoglobulin receptor. SC, serum creatinine; GFR, glomerular filtration rate, CC, creatinine clearance; ACR, albumin-creatinine clearance; PCR, protein-creatinine clearance; UPE, urinary protein excretion; UAE, urinary albumin excretion; and RB, renal biopsy.

40 disease, 10 regions for “all cause” and 6 regions for hypertension related kidney disease.

A region on human chromosome 3q appears to play an important role in regulating kidney function because the region consistently is linked to disease regardless of the type of population studied.

The main point demonstrated by these multiple linkage analyses is that common forms of CKD are diverse and the underlying genetic causes are many. Genetic analysis, especially in humans is confounded by several factors which make identifying gene involved in complex traits difficult (Schork 1997). These include: (1) genetic heterogeneity, individuals with similar clinical features possess different defective genes;

(2) epistasis or gene interaction, the degree to which one gene confers susceptibility to disease is dependent on another; (3) environmental vulnerability, gene products and subsequent phenotypes they effect are influenced by environment; (4) gene X environment interactions, gene(s) contributes to susceptibility under a particular environment condition; and (5) developmental or time dependent expression of genes, at what point does the gene(s) demonstrate the most pronounced effect. Although human studies can be designed to address some of these problems, an alternative approach is through the use of animals models.

41 Rat Models of CKD

Inbred Rat Models of CKD

Clearly, genetic analysis is done using human populations and can result in the identification of genes involved in Mendelian forms kidney disease. However, rodent models offer several advantages for genetic analysis including: (1) the ability to perform genetic manipulation (congenic strains, induce mutations, or gene knockout); (2) the production of large segregating populations for linkage analysis; (3) minimization of environmental interference by conducting experiments under the same conditions; and (4) rapid breeding and modest maintenance costs. These attributes apply equally well to both mice and rats. However, the rat provides an added advantage over the mouse that makes it especially useful to study disease. There are a large number of well characterized selectively bred inbred strains with susceptibility or resistance to many complex traits

(hypertension, diabetes, or arthritis) that are just not available for the mouse (Jacob and

Kwitek 2002). In particular, there are a number of rat strains (Table VI) that are susceptible to develop to kidney disease and, therefore, provide the basis for genetic analysis.

The Dahl S rat is one of the most widely studied models of salt-sensitive hypertension (Rapp 2000). The S rat was selectively bred from a Sprague-Dawley (SD) stock for sensitivity (S) to the hypertensive effect of high salt (8% NaCl) (Dahl et al.

1962). A contrasting strain was also produced that was resistance [Dahl salt-resistance

(R) rat] to salt-induced high blood pressure. Inbred S rats develop severe hypertension

42 and concomitant severe renal vascular and glomerular lesions on high-salt diet (Hampton

et al. 1989; Rapp and Dene 1985; Sterzel et al. 1988). Massive proteinuria also is

observed (Sterzel et al. 1988; Sustarsic et al. 1981). In a very careful longitudinal study,

5-6 wk old S rats were found to develop proteinuria that associated with segmental

retraction of the podocyte foot processes (Sterzel et al. 1988). The early phase of

hypertension was not associated with an overall loss of renal function, lower number of glomeruli or glomerular hypertension. Of course as hypertension progressed in the S rat, vascular and glomerular lesions and loss of renal function became marked (Sterzel et al.

1988). Abnormal pressure diuresis and natriuresis also has been observed in the S rat

(Roman 1986). Additionally, proteinuria and associated renal damage also is observed in the S rat raised on low-salt (Garrett et al. 2003; Poyan Mehr et al. 2003).

Table VI. Inbred Rat Strains with Susceptibility to Kidney Damage

Name Symbol Characteristics

Dahl Salt-Sensitive S S, SI, H,HP, F Fawn Hooded Hypertensive FHH S,SI, M,HP, F, B Munich Wistar Fromter MWF S, M, HP, F Sabra Hypertensive Prone SBH SI, M,MP Lyon Hypertensive LH S, M Spontaneously Hypertensive Rat- Stroke Prone SHRSP S, SI, H, K Buffalo BUF N, T, MP

S, spontaneous hypertension; SI, salt-induced/exacerbated hypertension; M, modest hypertension (systolic >150mm Hg); H, severe hypertension (systolic >200 mm Hg); MP, modest proteinuria (>50 mg/24 hours); HP, high proteinuria (100 mg/24 hours), F; focal segmental glomerulosclerosis; B, bleeding disorder; K, stroke prone

43 The fawn-hooded hypertensive (FHH) rat develops moderate hypertension,

proteinuria and focal and segmental glomerulosclerosis (FSGS) (Kuijpers and Gruys

1984; Provoost 1994). GFR, renal blood flow (RBF), and glomerular capillary pressure

(PGC) are elevated also in FHH rat suggesting that the control of renal vascular resistance may play a role in the cause and progression of renal disease (Simons et al.

1993).

The Munich Wistar Fromter (MWF) rat was originally selected for large numbers

of superficial glomeruli for the purpose of renal micropuncture studies (Fassi et al. 1998).

In comparison to Wistar control rats, MWF have markedly reduced number of glomeruli,

develop moderate hypertension, proteinuria and glomerulosclerosis (Fassi et al. 1998;

Schulz et al. 2002). Interesting, males develop spontaneous hypertension and renal

damage, whereas females experience less hypertension and little to no proteinuria or

glomerulosclerosis (Remuzzi et al. 1992). The current data suggests that the molecular

mechanism of abnormal glomerular filtration in the MWF is a result of functional impairment of the glomerular wall as opposed to structural impairment (Schulz et al.

2002).

The Sabra hypertensive prone (SBH) rat was bred on the basis of blood pressure response to unilateral nephrectomy (UNIX), treatment with deoxycorticosterone acetate

(DOCA), and 1% NaCl (Ben-Ishay et al. 1972). SBH develop severe proteinuria and

FSGS only under conditions of UNIX and salt-loading (Yagil et al. 1998). Otherwise,

mild proteinuria will result only after a prolonged period > 9 mo. The Buffalo (BUF) and

spontaneously hypertensive rat- stroke prone (SHRSP) are additional strains that exhibit

renal damage. BUF rats develop FSGS and proteinuria (Howie et al. 1989; Nakamura et

44 al. 1986). Proteinuria starts to appear at 15 wk of age and reaches a plateau at 20 wk of

age. The SHRSP develops hypertension and shows a high incidence of renal vascular and

parenchymal damage when exposed to Japanese style diet (altered sodium/potassium

ratio, low protein, and 1% NaCl) (Gigante et al. 2003).

Summary of Linkage Analysis Using Rat Models of CKD

To date, complete genome scan have been done using 11 experimental crosses

and have identified > 100 QTL linked to renal function and/or renal damage related traits

(Table VII). Most of the analyses were conducted using male rats, on populations

numbering between 75 and 539 rats, and exposure to various sodium chloride containing

diets. The average LOD = 4.3 and ranged from 3 to 18. The earliest linkage analysis was done using a backcross population [F1 (FHH x ACI) x FHH] derived from the FHH and

the August-Copenhagen-Irish (ACI) as a contrasting strain resistant to kidney damage

(Brown et al. 1996). This analysis was followed using an F2 (FHHxACI) population that

had undergone UNIX (Shiozawa et al. 2000). Both populations yielded strong evidence

for two renal function QTL on , which were named Rf-1 and Rf-2 (renal failure 1 and 2). Rf-2 was linked to BP, where as Rf-1 was not. Additional renal function

QTL were observed in the F2 population on chromosomes 3, 14, and 17 (Rf-3,-4,-5). The

next linkage analysis performed for kidney related traits was derived from the BUF and

Wistar-Kyoto (WKY) rat. An F1 (BUF x WKY) x BUF population identified a single locus on chromosome 13 linked to UPE with a LOD =18 (Murayama et al. 1998). This

45 Table VII. Summary of Linkage Analysis for CKD/ Kidney Damage Using the Rat

Chr Cross Sex Pop Age Diet/ QTL QTL Peak LOD Postion Reference Size Condition Type Symbol Marker (Mb)

1 F1 (MWF x LEW) x LEW M 213 24 LS GN Glom2 D1Rat151 3 237-267 (Schulz et al. 2002) 1 F1(FHH x ACI) x FHH M 134 24-36 UNIX UPE Rf1 D1Mgh12 17 217 (Brown et al. 1996) 1 F1(FHH x ACI) x FHH M 134 24-36 UNIX UPE Rf2 D1Mit2 4 135-147 (Brown et al. 1996) 1 F2 (S X BN) F 99 12-13 LS,HS RBF Rf6 D1Rat167 3 4-12 (Moreno et al. 2003) 1 F2 (S X BN) F 99 12-13 LS,HS CC Rf7 D1Rat295 3 190-227 (Moreno et al. 2003) 1 F2 (S X BN) F 99 12-13 LS,HS CC Rf8 D1Rat90 4 235-267 (Moreno et al. 2003) 1 F2 (S X BN) F 99 12-13 LS,HS CC Rf27 D1Mgh3 5 30-78 (Moreno et al. 2003) 1 F2 (S X BN) F 99 12-13 LS,HS UKE Rf28 D1Mit10 5 78 (Moreno et al. 2003) 1 F2 (S X BN) F 99 12-13 LS,HS UKE Rf29 D1Mit10 5 57 (Moreno et al. 2003) 1 F2 (LH X LN) 327 29-31 LS SC Rf46 D1Rat43 4 103 (Bilusic et al. 2004) 1 F2 (S X BN) F 99 12-13 LS,HS RBF Rf51 D1Mit7 5 225-264 (Moreno et al. 2003) 1 F1 (MWF x LEW) x LEW M 213 8-24 LS UAE Uae1 D1Rat38 4 90-186 (Schulz et al. 2002) 1 F1 (S x SHR) x S M 276 8-16 LS,HS UAE Uae5 D1Uia5 4 154-260 (Garrett et al. 2003; Garrett et al. 2006) 1 F1(MWF x SHR) x MWF M 215 8-24 LS UAE Uae20 D1Rat75 4 227-267 (Schulz et al. 2003) 1 F2 (SHR x SHRSP) B 154 10 MS KLG D1Rat238 4 (Gigante et al. 2003)

2 F1(MWF x SHR) x MWF M 215 24 LS GN Glom4 D2Rat14 2 43 (Schulz et al. 2003) 2 F2 (S X BN) F 99 12-13 LS,HS GD Rf9 D2Rat40 3 139-211 (Moreno et al. 2003) 2 F2 (LH X LN) 327 29-31 LS SC Rf48 D2Rat221 3 76 (Bilusic et al. 2004) 2 F2 (LH X LN) 327 29-31 LS UV Rf50 D2Mit35 4 54 (Bilusic et al. 2004) 2 F1 (S x SHR) x S M 276 8-16 LS UAE Uae6 D2Rat179 10 80-227 (Garrett et al. 2003; Garrett et al. 2006) 2 F1 (S x SHR) x S M 276 16 LS KLG D2Rat179 6 80-227 (Garrett et al. 2003) 2 F2 (S x SHR) M 539 14 LS UAE Uae13 D2Rat126 4 0-29 (Poyan Mehr et al. 2003) 2 F2 (SBH X SBN) M 75 8-40 MS, UNIX UPE D2Rat247 4-13 121-146 (Yagil et al. 2006)

3 F2 (S x SHR) M 230 14 HS UPE D3Rat53 4 4-25 (Siegel et al. 2004) 3 F2(FHH x ACI) M 337 14 UNX UPE Rf3 D3Mit4 7 116-146 (Shiozawa et al. 2000)

46 Chr Cross Sex Pop Age Diet/ QTL QTL Peak LOD Postion Reference Size Condition Type Symbol Marker (Mb)

3 F2 (S X BN) F 99 12-13 LS,HS RBF Rf10 D3Rat11 4 163-167 (Moreno et al. 2003) 3 F2 (S X BN) F 99 12-13 LS,HS RBF Rf11 D3Rat11 3 121-148 (Moreno et al. 2003) 3 F2 (S X BN) F 99 12-13 LS,HS RBF Rf12 D3Rat169 4 59-122 (Moreno et al. 2003) 3 F2 (S X BN) F 99 12-13 LS,HS UKE Rf30 D3Mgh6 4 36 (Moreno et al. 2003) 3 F2 (SBH X SBN) M 75 8-40 MS, UNIX UPE D3Mgh14 3-18 77-149 (Yagil et al. 2006)

4 F2 (S X BN) F 99 12-13 LS,HS RBF Rf13 D4Rat204 4 161-185 (Moreno et al. 2003) 4 F2 (S X BN) F 99 12-13 LS,HS UKE Rf31 D4Mgh2 3 37 (Moreno et al. 2003) 4 F1(MWF x SHR) x MWF M 215 8-24 LS UAE Uae21 D4Rat95 2 116-146 (Schulz et al. 2003)

5 F2 (S X BN) F 99 12-13 LS,HS RVR Rf32 D5Mgh8 3 136 (Moreno et al. 2003) 5 F2 (S X BN) F 99 12-13 LS,HS RVR Rf33 D5Mgh23 4 45 (Moreno et al. 2003) 5 F2 (S X BN) F 99 12-13 LS,HS RBF Rf34 D5Mgh5 4 49 (Moreno et al. 2003) 5 F2 (S X BN) F 99 12-13 LS,HS RBF Rf35 D5Mgh5 4 45 (Moreno et al. 2003) 5 F2 (S X BN) F 99 12-13 LS,HS RBF Rf36 D5Mit4 3 35 (Moreno et al. 2003)

6 F1(MWF x SHR) x MWF M 215 24 LS GN Glom7 D6Mit8 6 82-112 (Schulz et al. 2003) 6 F1(MWF x SHR) x MWF M 215 24 LS GN Glom8 D6Rat12 7 99-129 (Schulz et al. 2003) 6 F1(MWF x SHR) x MWF M 215 24 LS RIF D6Mit8 2 82-112 (Schulz et al. 2003) 6 F2 (S X BN) F 99 12-13 LS,HS RBF Rf14 D6Rat26 3 35-80 (Moreno et al. 2003) 6 F2 (S X BN) F 99 12-13 LS,HS RBF Rf15 D6Rat42 4 13-57 (Moreno et al. 2003) 6 F2 (S X BN) F 99 12-13 LS,HS RBF Rf16 D6Rat62 4 13-35 (Moreno et al. 2003) 6 F2 (S X BN) F 99 12-13 LS,HS RVR Rf37 D6Mit8 3 75 (Moreno et al. 2003) 6 F2 (S X BN) F 99 12-13 LS,HS RVR Rf38 D6Mgh11 3 45 (Moreno et al. 2003) 6 F1 (MWF x LEW) x LEW M 213 8-24 LS UAE Uae2 D6Mgh5 3 76-114 (Schulz et al. 2002) 6 F1 (S x SHR) x S M 276 8-16 LS,HS UAE Uae7 D6Uia5 4 75-137 (Garrett et al. 2003; Garrett et al. 2006) 6 F1 (S x SHR) x S M 276 16 LS KLG D6Uia5 2 75-137 (Garrett et al. 2003; Garrett et al. 2006) 6 F2 (S x SHR) M 539 14 LS,HS UAE Uae14 D6Rat12 7 0-89 (Poyan Mehr et al. 2003; Siegel et al. 2004)

47 Chr Cross Sex Pop Age Diet/ QTL QTL Peak LOD Postion Reference Size Condition Type Symbol Marker (Mb)

6 F1(MWF x SHR) x MWF M 215 8-24 LS UAE Uae22 D6Rat12 10 68 (Schulz et al. 2003)

7 F1 (MWF x SHR) x MWF M 215 24 LS GN Glom5 D7Rat7 3 111-141 (Schulz et al. 2003) 7 F1 (MWF x SHR) x MWF M 215 8-24 LS UAE Uae23 D7Rat4 2 120-143 (Schulz et al. 2003)

8 F2 (S X BN) F 99 12-13 LS,HS RBF Rf17 D8Rat49 3 30 (Moreno et al. 2003) 8 F2 (S X BN) F 99 12-13 LS,HS UV Rf39 D8Mit16 4 48 (Moreno et al. 2003) 8 F1 (S x SHR) x S M 276 8-16 LS,HS UAE Uae8 D8Rat62 5 32-99 (Garrett et al. 2003; Garrett et al. 2006) 8 F1 (S x SHR) x S M 276 16 LS,HS KLG D8Rat62 3 32-99 (Garrett et al. 2003; Garrett et al. 2006) 8 F2 (S x SHR) M 539 14 LS,HS UAE Uae15 D8Rat46 3 31-74 (Poyan Mehr et al. 2003; Siegel et al. 2004) 8 F1 (MWF x SHR) x MWF M 215 8-24 LS UAE Uae24 D8Rat35 6 20-99 (Schulz et al. 2003)

9 F1 (MWF x SHR) x MWF M 215 24 LS GN Glom6 D9Rat5 3 70-100 (Schulz et al. 2003) 9 F1 (S x SHR) x S M 276 12-16 LS,HS UAE Uae9 D9Uia4 5 22-106 (Garrett et al. 2003; Garrett et al. 2006) 9 F1 (S x SHR) x S M 276 16 LS KLG D9Rat97 5 77-106 (Garrett et al. 2003) 9 F2 (S x SHR) M 539 14 LS,HS UAE Uae16 D9Rat10 8 56-100 (Poyan Mehr et al. 2003; Siegel et al. 2004) 9 F1 (MWF x SHR) x MWF M 215 8-24 LS UAE Uae25 D9Rat10 4 22-100 (Schulz et al. 2003)

10 F2 (S X BN) F 99 12-13 LS,HS UV Rf18 D10Rat17 3 82-103 (Moreno et al. 2003) 10 F2 (S X BN) F 99 12-13 LS,HS RVR Rf40 D10Mgh11 4 30 (Moreno et al. 2003) 10 F1 (S x SHR) x S M 276 8-12 LS,HS D10Mco57 5 32-82 (Garrett et al. 2003; Garrett et al. 2006) 10 F2 (S x SHR) M 539 14 LS UAE Uae17 D10Rat30 4 82-89 (Poyan Mehr et al. 2003) 10 F2 (SHR x SHRSP) B 154 10 MS KLG D10Rat21 84 (Gigante et al. 2003)

11 F2 (S X BN) F 99 12-13 LS,HS CC Rf19 D11Rat67 3 42-85 (Moreno et al. 2003) 11 F2 (S X BN) F 99 12-13 LS,HS GD Rf20 D11Rat71 4 30-62 (Moreno et al. 2003)

48 Chr Cross Sex Pop Age Diet/ QTL QTL Peak LOD Postion Reference Size Condition Type Symbol Marker (Mb)

11 F1 (S x SHR) x S M 276 8-16 LS UAE Uae10 D11Rat67 6 28-85 (Garrett et al. 2003; Garrett et al. 2006) 11 F1 (S x SHR) x S M 276 16 LS,HS KLG D11Mgh3 6 62 (Garrett et al. 2003; Garrett et al. 2006) 11 F2 (S x SHR) M 539 14 LS UAE Uae18 D11Mit2 4 17-45 (Poyan Mehr et al. 2003)

12 F2 (S X BN) F 99 12-13 LS,HS RBF Rf21 D12Mgh5 4 10-33 (Moreno et al. 2003) 12 F2 (S X BN) F 99 12-13 LS,HS RBF Rf41 D12Mgh9 3 29 (Moreno et al. 2003) 12 F1 (MWF x LEW) x LEW M 213 8-24 LS UAE Uae3 D12Rat37 5 0-11 (Schulz et al. 2002)

13 F1 (MWF x LEW) x LEW M 215 24 LS GN Glom3 D13Rat62 3 46-76 (Schulz et al. 2002) 13 F1 (BUF x WKY) x BUF M 167 20 UPE Pur1 D13Mgh4 18 0-9 (Murayama et al. 1998) 13 F2 (LH X LN) 327 29-31 LS KRC Rf47 D13Rat26 4 106 (Bilusic et al. 2004) 13 F1 (S x SHR) x S M 276 8-16 LS,HS UAE Uae11 D13Mgh5 6 46-81 (Garrett et al. 2003; Garrett et al. 2006) 13 F1 (S x SHR) x S M 276 16 LS,HS KLG D13Mgh5 6 46-81 (Garrett et al. 2003; Garrett et al. 2006)

14 F2 (FHH x ACI) M 337 14 UNX UPE Rf4 D14Mgh7 4 12 (Shiozawa et al. 2000) 14 F2 (S X BN) F 99 12-13 LS,HS RVR Rf22 D14Rat90 4 45-102 (Moreno et al. 2003)

15 F2 (S X BN) F 99 12-13 LS,HS RVR Rf23 D15Mgh9 4 46-106 (Moreno et al. 2003) 15 F2 (S X BN) F 99 12-13 LS,HS RBF Rf42 D15Mgh11 3 19 (Moreno et al. 2003) 15 F1 (MWF x SHR) x MWF M 215 8-24 LS UAE Uae26 D15Rat102 2 83-110 (Schulz et al. 2003)

16 F2 (S X BN) F 99 12-13 LS,HS CC Rf24 D16Rat87 4 3-23 (Moreno et al. 2003) 16 F2 (SHR x SHRSP) B 154 10 MS KLG D16Mit2 9 (Gigante et al. 2003)

17 F2 (FHH x ACI) M 337 14 UNX UPE Rf5 D17Mit12 3 48-78 (Shiozawa et al. 2000) 17 F2 (S X BN) F 99 12-13 LS,HS CC Rf25 D17Rat47 3 82-93 (Moreno et al. 2003) 17 F2 (S X BN) F 99 12-13 LS,HS RVR Rf43 D17Rat59 3 21 (Moreno et al. 2003) 17 F2 (LH X LN) 327 29-31 LS SC Rf49 D17Rat102 3 54 (Bilusic et al. 2004)

49 Chr Cross Sex Pop Age Diet/ QTL QTL Peak LOD Postion Reference Size Condition Type Symbol Marker (Mb)

17 F1 (MWF x LEW) x LEW M 213 8-24 LS UAE Uae4 D17Rat58 5 42-81 (Schulz et al. 2002) 17 F2 (SBH X SBN) M 75 8-40 MS, UNIX UPE D17Rat61 5-14 6-28 (Yagil et al. 2006)

18 F2 (S X BN) F 99 12-13 LS,HS UKE Rf26 D18Rat91 3 55-69 (Moreno et al. 2003) 18 F2 (S X BN) F 99 12-13 LS,HS UPE Rf44 D18Mgh9 3 16 (Moreno et al. 2003)

19 F2 (S X BN) F 99 12-13 LS,HS RBF Rf45 D19Mit10 3 17 (Moreno et al. 2003) 19 F1 (S x SHR) x S M 276 8-16 LS UAE Uae12 D19Rat29 5 0-23 (Garrett et al. 2003; Garrett et al. 2006) 19 F1 (S x SHR) x S M 276 16 LS,HS KLG D19Rat29 5 0-23 (Garrett et al. 2003; Garrett et al. 2006) 19 F2 (S x SHR) M 539 14 LS,HS UAE Uae19 D19Rat75 6 8-45 (Poyan Mehr et al. 2003; Siegel et al. 2004)

20 F2 (SBH X SBN) M 75 8-40 MS, UNIX UPE D20Rat3 4-16 5-18 (Yagil et al. 2006)

X F1 (MWF x LEW) x LEW M 213 24 LS GN Glom1 DXRat96 3 9 (Schulz et al. 2002) X F1 (MWF x SHR) x MWF M 215 8-24 LS UAE Uae27 DXRat8 3 26-56 (Schulz et al. 2003)

Chr, chromosome number; Pop Size, number of animals used for linkage analysis. M, male; F, female; B, male and female; LS, low-salt diet (<1% NaCl); MS, medium-salt diet (>1and ≤2% NaCl); HS, high-salt diet (>2% NaCl); UNIX, unilateral nephrectomy; GN, glomeruli number; GD, glomerular damage; KLG, kidney lesion grade; RIF, renal interstitial fibrosis; UPE, urinary protein excretion, UAE, urinary albumin excretion; UKE, urinary potassium excretion; UV, urine volume; CC, creatinine clearance; SC, serum creatinine; RBF, renal blood flow; RVR, renal vascular resistance; KRC, kidney renin concentration. Table partially compiled with information from the Rat Genome Database (rgd.mcw.edu). Data highlighted in red is derived from work in this dissertation.

50 experimental cross is the only population studied to date involving two normotensive

strains.

Two linkage analyses were done using the MWF crossed with either the Lewis

(LEW) rat (Schulz et al. 2002) or SHR (Schulz et al. 2003). The LEW rat is normotensive

and experiences no overt renal damage. In contrast, despite systemic hypertension, the

SHR fails to develop significant renal damage (Schulz et al. 2003; Sterzel et al. 1988).

UAE QTL were observed on chromosome 1, 6, 12, and 17 for the F1 (MWF x LEW) x

LEW population. None of the UAE QTL co-localized with BP QTL. For the F1 (MWF x

SHR) x SHR population, QTL were observed on chromosome 1, 4, 6, 7, 8, 9, 15, and X.

Genome scans done using an F1 (S x SHR) x S population yielded ten QTL for

UAE and/or UPE with variable time-course patterns on chromosome 1, 2, 6, 8, 9, 10, 11,

13, and 19 when raised on a low-salt diet (Garrett et al. 2003). Most UAE QTL co-

localized with QTL for kidney lesion grade (KLG, integrated assessment of kidney

pathology). A second backcross population raised on a high-salt diet (2% NaCl) found that UAE QTL on chromosomes 2, 11, and 19 were influenced by salt-loading (Garrett et al. 2006). Additionally, two genome scans for UAE were done using F2 (S x SHR)

populations. The first F2 (S x SHR) population was raised on a low-salt diet. UAE QTL

were observed on chromosomes 2, 6, 8, 9, 10, 11, and 19 (Poyan Mehr et al. 2003),

consistent with QTL observed in the backcross population. A second F2 (S x SHR) population was raised on a high-salt diet (4% NaCl) and a genome scan identified UAE

QTL on RNO 3, 6, 8, 9, and 19 . Thus, QTL for UAE on RNO2, 10, and 11 were not detected in the salt-loaded population (Siegel et al. 2004). Based on all the experimental crosses involving the S and SHR, the data appears to show that UAE and BP QTL map to

51 the same region on chromosomes 6, 8, 9, 10, and 11, whereas UAE QTL on

chromosomes 1, 2, 13, and 19 appear not to co-localize with BP QTL (Garrett et al. 2006;

Garrett et al. 2000).

Recently, a complete genome scan for UPE was carried out using an F2 (SBH x

Sabra Hypertensive Resistant [SBN]) population (Yagil et al. 2006). Animal were

subjected to UNIX and followed for a period of 9 months. UPE QTL were observed on

chromosomes 2, 3, 17, and 20. One of the most extensive linkage studies conducted to

date used an all female F2 (S x Brown Norway [BN]) population. A total of 236 neuroendocrine, renal, and cardiovascular traits related to arterial BP were investigated

(Moreno et al. 2003). Renal function/damage QTL were observed on all chromosomes

except on 7, 9, 13, and X. An F2 (LH x Lyon Normotensive [LN]) population found

renal function QTL on chromosomes 1, 2, 13, and 17 (Bilusic et al. 2004). Finally, using an F2 (SHR x SHRSP) population QTL for renal damage were observed on chromosomes

1, 10, and 16 (Gigante et al. 2003).

The major findings from these multiple linkage analyses can be summarized as

follows: (1) every rat chromosome harbors at one least renal related QTL, confirming that

the overall genetic control of renal function is complex and facilitated by a large number

of genetic elements; (2) some renal function QTL, such those on chromosome 1, 2, 6 and

8 are repeatedly detected in multiple genetic linkage analyses using different strains

which demonstrates that these QTL may play a more important role in renal function

compared to other chromosome; (3) the type of segregating populations studied has an

important effect on whether a given QTL will be detected. For example, the UPE QTL

detected on chromosome 1 using a backcross population involving the S and SHR was

52 not detected using an F2 population; (4) environmental changes, such as diet (salt- feeding) and/or surgical intervention (UNIX) can influence the observation of a QTL; (5) renal function and/or damage QTL can be sex dependent; and (6) some QTL may be developmental specific genetic factors, that is, they are only observed at specific ages.

Summary of Congenic Strains Developed for the Study of CKD

Figure 9 depicts the consomic and congenic strains that have been developed for

the study of kidney disease or related traits in the rat. The majority of these strains were

developed by the PhysGen project, “Physiogenomics of Stressors in Derived Consomic

Rats” (pga.mcw.edu; part of the NIH program for Genomic Applications [PGA]

initiative). Consomic strains derived from the S and BN and FHH and BN were

characterized using 214 phenotypes specific to , lung, kidney, vasculature, and blood

in response to environmental stressors such as hypoxia, exercise, and salt intake

(PhysGen 2006). The approach was systematic, that is each chromosome was replaced in

the S with BN alleles or FHH with BN alleles and was not based on linkage analysis.

S.BN consomic strains on chromosome 5, 6, 7, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and

X demonstrated a significant difference in UPE compared to the S control (PhysGen

2006). The S.BN(18) consomic experienced the greatest reduction in UPE compared to

the other consomic strains. The S allele conferred susceptibility to UPE on all

chromosomes, except on chromosome 12, 17, and X where the BN allele increased UPE.

FHH.BN consomic strains on chromosome 1, 14, 15, 16, and 20 demonstrated a

significant reduction in UPE compared to the parental FHH (PhysGen 2006). The

53 Figure 9. Summary of Congenic Strains for CKD

Schematic drawing of the location of consomic or congenic strains developed to study the genetics of CKD. Colored bars represent the introgressed region of each respective congenic strain, i.e., red bars represent the region of chromosome transferred from the BN rat onto the genetic background of the S. 1A double congenic strain derived from ACI.FHH(1) and (3) demonstrated an interactive effect on renal susceptibility. 2 Congenic strains available, but not tested. 3 This strain increases UPE compared to S, i.e., SHR contributed susceptibility alleles. 4A double congenic strain derived from ACI.FHH(1) and (14) demonstrated an interactive effect on renal susceptibility. 5 S.BN(12,17, and X) increase UPE compared to the S rat.

FHH.BN(1) consomic had the largest effect on UPE compared to the other four consomic strains.

Most congenic analysis has been directed to chromosome 1, specifically the Rf-1 and Rf-2 regions (Figure 9). These studies include the following congenic strains:

54 SBN.SBH(1) (Yagil et al. 2002), SHR.BN(1)-D1Mit3/Igf2 (St. Lezin et al. 1999) ,

FHH.BN(1)-B (Lopez et al. 2006) , ACI.FHH(1) (Van Dijk et al. 2005). One study in particular developed a very small congenic strain around the Rf-2 region encompassing a region < 1 Mb containing approximately eight genes (Rangel-Filho et al. 2005). The authors identified a point mutation in the start codon of Rab38 that results in no protein being expressed. They speculate that this mutation underlies the Rf-2 QTL by altering tubular reuptake and processing of filtered protein in the FHH rat (Rangel-Filho et al.

2005).

A recent study using two congenic strains, ACI.FHH(1) and ACI.FHH(3) found that each strain alone demonstrated a slight effect on renal susceptibility (UPE and renal blood flow) (Van Dijk et al. 2006). A double congenic strain (a strain carrying both chromosome 1 and 3 from the FHH) was constructed to examine the interaction between

QTL observed in the linkage analysis. The double congenic demonstrated a major effect on renal susceptibility, confirming that a synergistic interaction exists between the QTL

(Van Dijk et al. 2006). Additionally, a congenic strain on chromosome 14

[ACI.FHH(14)] was found not to have a direct effect on renal susceptibility, but a significant effect on renal susceptibility was demonstrated using a double congenic

(chromosome 1 and 14) (Van Dijk et al. 2005). Finally, congenic strains were developed using the S and SHR on chromosome 2, 6, 9, 11, and 13 (Garrett et al. 2006). All the congenic strains had significantly different UPE compared to the S control. The

S.SHR(2) congenic was found to have the largest effect on UPE compared to the other four congenic strains.

55 In summary, except for the Rf-2 region, the primarily role of these congenic strains were to confirm the linkage analysis. These strains can now provide the starting material to conduct future substitution analysis, and the opportunity to lead to the gene causative to each QTL.

56

Time-course genetic analysis of albuminuria in Dahl salt-

sensitive rats on low salt diet

Running Title: Albuminuria in Dahl rats

Subjects: Molecular medicine, genetics and development

Authors: Michael R. Garrett, Howard Dene and John P. Rapp

Department of Physiology & Molecular Medicine

Medical College of Ohio, Toledo, Ohio

Corresponding Author: Michael R. Garrett, M.S., M.B.A.

Department of Physiology & Molecular Medicine

Medical College of Ohio

3035 Arlington Avenue

Toledo, Ohio 43614-5804

Telephone: 419-383-4026

Fax: 419-383-6168

E-mail: [email protected]

Manuscript originally published in J Am Soc Nephrol 14: 1175-1187, 2003. Reproduced with permission from the American Society of Nephrology via Copyright Clearance

Center. 57 Abstract

The Dahl salt sensitive hypertensive (S) rat develops albuminuria early in life even on a low salt diet. In contrast, the spontaneously hypertensive rat (SHR) is highly resistant to developing albuminuria in spite of elevated blood pressure (BP). An F1 hybrid of S and SHR showed a low urinary albumin excretion (UAE) and low urinary protein excretion (UPE) similar to SHR, i.e., SHR was dominant. A genetic analysis was carried out on a large population (n=276) obtained by backcrossing F1 rats to the

recessive S strain; the population was fed a low salt diet. Genome scans done at 8, 12

and 16 weeks of age yielded ten quantitative trait loci (QTL) for UAE and/or UPE with

variable time-course patterns on nine rat chromosomes (RNO), i.e., RNO1, RNO2,

RNO6, RNO8, RNO9, RNO10, RNO11, RNO13 and RNO19. There were two UPE

QTL on RNO6. At most of the UAE and/or UPE QTL the S allele was associated with

increased excretion, except for one of the QTL on RNO6 and the QTL on RNO11 where

the S allele caused decreased excretion. Only the UAE and UPE QTL on RNO10 co-

localized with a BP QTL. The S allele on RNO10 caused higher BP and higher UAE.

Two additional BP QTL were detected on RNO1 and RNO6. Most of the UAE and UPE

QTL co-localized with QTL for kidney lesions characteristic of S rats. Multiple

interactions were observed for UAE, many of which involved RNO2. In summary, UAE

is highly polygenic and the majority of the QTL altering UAE do not co-localize with

QTL for BP as evaluated by tail-cuff measurements of BP.

Key Words not in title: S rats, spontaneously hypertensive rats, SHR, proteinuria, blood

pressure, hypertension, quantitative trait loci, QTL, kidney weight, heart weight 58

Introduction

End stage renal disease (ESRD) is an important medical problem. Clinical observations suggest that malignant hypertension leads to renal failure, and that less severe hypertension will accelerate the progression of renal disease (1-4). Moreover, the clinical impression is that there are factors other than hypertension also influencing the progression of renal disease. The best example being the high incidence of ESRD in hypertensive blacks compared to hypertensive whites, (5-12) leading to the idea that genetic factors independent of blood pressure (BP) influence the progression of renal disease. Also, the relatives of people with ESRD have an increased risk of ESRD in both black (13) and white (14) Americans.

In the 1960's Dahl selectively bred rats for sensitivity (S rats) to the hypertensive effect of high salt diet (15). Besides fulminant hypertension S rats on high salt diet develop progressive proteinuria (16,17) and severe renal vascular and glomerular lesions, and associated renal tubular lesions (18-21). In a careful longitudinal study Sterzel et al.

(17) showed that 5-6 week old S rats have proteinuria associated with segmental retraction of podocyte foot process. The early phase of hypertension was not associated with an overall loss of renal function, lower number of glomeruli or glomerular hypertension. Of course, as hypertension progressed vascular and glomerular lesions and loss of renal function became marked (17). BP of S rats has undergone extensive genetic analysis as has been reviewed (22) and more recently summarized (23). A genetic 59 analysis of progressive renal disease in the Dahl S rat has, however, not been done

previously and is the focus of the present work.

In order to perform a genetic analysis it is often useful to produce a segregating

population by crossing S rats to a contrasting strain. There is an interesting contrast

between the Dahl S rat and the spontaneously hypertensive rat (SHR). In spite of the

hypertension in SHR the progression of renal lesions appears slow (24) compared to the

development of such lesions in S rats. Karlsen et al. (25) have directly compared S and

SHR fed a high salt diet for 4 weeks. Histologically the SHR kidneys were essentially

normal, whereas the S kidneys showed significant lesions. As will be seen such

differences are dramatically reflected by differences in urinary albumin excretion (UAE)

between the two strains.

Our experimental design here was to produce a segregating population derived

from S and SHR and to follow UAE longitudinally. In this initial work the rats were fed

a low salt diet in order to minimize changes in BP. A genetic analysis at each time point

yielded several strong quantitative trait loci (QTL) controlling UAE, most of which were

independent of known QTL for BP.

Materials and Methods

Animals

Inbred Dahl salt-sensitive (SS/Jr) rats were from our colony at the Medical

College of Ohio, Toledo, and will be referred to as S. Spontaneously hypertensive rats

(SHR/NHsd) were from Harlan Sprague-Dawley (Indianapolis, IN) and will be referred to as SHR. To determine UAE in F1 animals, S females were crossed with SHR males to 60

produce F1(SxSHR) animals. S, F1(SxSHR) and SHR (n=6 in each group) males (all age

matched) were studied for UAE, urinary protein excretion (UPE) and BP at 8, 12, 20 and

26 weeks of age.

A backcross population, F1(SxSHR)xS, of 276 male rats was produced by crossing F1 females to S males. Rats were weaned at 30 days of age and placed on a low

salt (0.3% NaCl) diet (diet TD 7034, Harlan Teklad, Madison, WI) for the duration of the

experiment. The backcross population was studied for the progression of UAE, UPE and

BP at 8, 12 and 16 weeks of age.

The 276 rats were handled in 4 blocks of 69 animals. Of the 69 rats in each block,

a subset of 48 were selected at random for BP determination, but urine was collected on

all 69 rats in each block. Rats in each block were closely age matched, but between

blocks the average ages differed by a few days. This allowed BP and urine collections to

proceed on all rats at approximately the same ages. Block effects, if any, were removed

statistically before further analysis.

Rats were killed by CO2 inhalation. Liver and kidneys were harvested and

weighed. Pieces of liver were archived at -80C for subsequent DNA extraction. The

right kidney was fixed and embedded in paraffin for subsequent sectioning and staining.

Body, heart and kidney weights were measured. Adjusted heart weight or adjusted

kidney weight was calculated by adjusting organ weight for differences in body weight by regression analysis using programs from SPSS (Chicago, IL).

61 Blood Pressure (BP)

A subset consisting of 192 backcross animals were selected (as noted above) to measure systolic BP by the tail-cuff method on conscious restrained rats warmed to 28C.

BP measurements were made between 7AM and 12PM. Starting at 8 weeks of age BP was measured on 2 consecutive days by 2 operators at 2 workstations. Each rat had one session with each operator. Three to four consistent BP measurements were taken at each session and averaged for that day’s BP reading. The final BP of the rat was the average of both sessions. BP measurements were again taken at 12 and 16 weeks of age for all

192 animals.

UAE and UPE determination

Urine was collected for all 276 backcross animals. To collect urine, animals were kept in metabolism cages (Lab Products, Seaford, Delaware) for 24 hours. Sodium azide was added to the collection vials to provide about 0.1% in the urine. Food was withheld, but the animals had free access to water. UPE was determined colorimetrically using pyrogallol red/molybdate complex (Quantimetrix, Redondo Beach, CA). UPE was expressed as mg protein/24 hours. At 8 weeks of age UAE was determined by a rat albumin EIA kit (SPI-bio, France; http://www.spibio.com) and also by SDS-PAGE. The correlation between the albumin determined using the EIA kit and SDS-PAGE was r=0.89. Subsequent UAE determinations (weeks 12 and 16) were done using SDS-PAGE because it was more efficient and cost effective than the EIA. All albumin measurements reported here were from SDS-PAGE and done as follows. Urine samples were loaded on

10% Criterion Tris-HCL precast gels (Biorad, Hercules, CA) along with albumin standards. The gels were stained with Bio-Safe Coomassie Stain (Biorad, Hercules, CA), 62 destained in water, and then scanned using a HP scanjet 5300C scanner (Hewlett

Packard, Palo Alto, CA). The gels were analyzed using Scion Image software (Scion

Corp, Frederick, Maryland; http://www.scioncorp.com). UAE is expressed as mg albumin/24 hours.

Histology

Kidney sections were stained with hematoxylin and eosin and graded in a blinded fashion on an arbitrary semiquantitative scale from 0 to 4 for kidney lesions. One central longitudinal section was made through the right kidney and the entire section was examined. The lesions observed were characteristic of S rats and apparently start as glomerular sclerosis in isolated glomeruli. These glomeruli leak protein, leading to tubular casts of protein, tubular cell degenerative changes, and areas of pronounced tubular regeneration. Vascular lesions include arterial wall thickening, and necrosis. The glomerular and tubular changes occur in individual nephrons with adjacent glomeruli and their tubules intact. Thus the cortex becomes streaked with areas of damage interspersed with relatively normal tissue. Foci of lymphocytic infiltration are sometimes present. The kidney lesion grades (KLG) were 0=normal, 1=mild,

2=moderate, 3=marked, 4=severe. It was possible to assign half grades so the grades were 0, 0.5, 1, 1.5, etc. The grades represent a visually integrated assessment of the severity and extent of the lesions. No attempt was made to grade the individual components (e.g., glomeruli, tubules, vasculature) separately.

63 Genotyping

Genomic DNA was prepared from liver samples using DNeasy 96 tissue kits

(Qiagen, Valencia, CA). Genotyping was done using microsatellite markers amplified by the polymerase chain reaction (PCR) and evaluated by electrophoresis as previously reported (26). Markers polymorphic between S and SHR were selected from the following sources: (1) The Whitehead Institute for Biomedical Research

(http://www.genome.wi.mit.edu); (2) Wellcome Trust Centre for Human Genomics

(Oxford, UK; http://www.well.ox.ac.uk); (3) Medical College of Ohio, Department of

Physiology (http://www.mco.edu/depts/physiology/research) and (4) from the following articles (27,28). A total of 174 markers approximately evenly spaced throughout the genome were used.

Linkage and Statistical Analysis

Linkage analysis and QTL localization were performed using Mapmaker/EXP and Mapmaker/QTL programs (29-31) and Map Manager QTX

(http://mapmgr.roswellparks.org) (32). The Map Manager QTX program offers an easier method than Mapmaker for conducting QTL analysis on multiple traits. Once a preliminary QTL analysis was done using Map Manager and potential QTL were identified, Mapmaker/QTL was used to generate a LOD plot. For a backcross population a LOD score of at least 1.9 is considered evidence for suggestive linkage and a LOD score of 3.3 or above indicates significant linkage between phenotype and genotype (33).

Determination of the "phenotypic effect" for phenotypes with suggestive or significant linkage to a chromosome was done by selecting one index marker at or near the QTL 64 peak. The phenotypic effect was calculated as the average phenotype of rats with the S/S

genotype minus the average phenotype of rats with the S/SHR genotype at the selected

marker.

Interactions

Interactions throughout the genome were examined using a computer program

provided by Dr. Gary Churchill (http://www.jax.org/research/churchill). The method

examines all pairs of marker loci for an interaction with a given phenotype, in this case,

UAE. The program calculates an Fall statistic from a full regression model which

assumes that the marker pair represents two QTL interacting to affect UAE versus the

null hypothesis of no effect at either locus. The significance threshold for Fall was

determined by permutation analysis. For marker pairs that had an Fall statistic above the

significance threshold, a second F statistic (Fint) was computed to compare a model wherein the marker pair represent two interacting QTL to a model wherein the marker

pair represent two QTL acting additively to affect UAE. Interactions between two

markers were accepted when both the Fall statistic and Fint were above the significance

threshold as determined by permutation analysis.

Results

Table 1 gives data for BP and body weights for S, SHR and their F1 cross, i.e.,

F1(SxSHR), for an 18-week period starting at 8 weeks of age. There was little difference

in BP between S and SHR except that S were higher at week 8, and the development of

hypertension in F1(SxSHR) rats lagged behind the parental strains at weeks 12 and 20. 65

Body weight was always larger in the F1 hybrids than either parental strain. At the end of the 18 week period there were no meaningful differences in heart or kidney weight

adjusted for differences in body weight. The data demonstrate that, like the SHR, the inbred Dahl S rats from our colony slowly develop hypertension even on a low salt diet.

In contrast to the similarity of BP between S, SHR and their F1 hybrid, there were

dramatic differences in UAE and UPE between groups. S rats have markedly higher

UAE and UPE than SHR at all time points (Figure 1) regardless of only minor strain

differences in BP (Table 1). The F1(SxSHR) were indistinguishable from SHR with regard to UAE or UPE, that is, the SHR phenotype was strongly dominant to the S phenotype at all time points. In this situation a backcross to the recessive (S) strain is appropriate for a genetic analysis.

A large F1(SxSHR)xS population of male rats (n=276) was produced; population

data for BP, UAE, UPE, body weight and KLG are given in Table 2. The distributions of

UAE and UPE at all time points were markedly skewed to the right for this population.

Thus all analyses of UAE and UPE for linkage and interactions were carried out on the

natural logarithm (ln) of these data in order to approximate normal distributions.

Representative frequency distributions are shown for UAE (Figure 2A) and lnUAE

(Figure 2B) at week 12. The ln transformed data are also suggestive of a bimodal

distribution with 3/4th of the rats in the lower mode and 1/4th in the higher mode. This

bimodality was seen only with the UAE distributions and was not present in the UPE

distributions.

Genome scans for BP, lnUAE and lnUPE were done at weeks 8, 12 and 16 on the

F1(SxSHR)xS population maintained on low salt (0.3% NaCl) diet. Kidney lesion grade 66 (KLG) was also studied at week 16 after killing the animals. The data are presented as

LOD plots in Figure 3 for all rat chromosomes (designated RNO for Rattus norvegicus)

on which a quantitative trait locus (QTL) was observed for any measurement. Weak

signals for BP QTL (dark blue lines in Figure 3) were seen on RNO1 at all time points,

weakly on RNO2 only at week 16, and on RNO6 becoming progressively stronger with

time. The BP QTL on RNO10 was particularly interesting as it was undetectable at week

8, became highly significant at week 12, and then attenuated at week 16.

In general QTL for UAE and UPE (red and orange lines respectively in Figure 3) were detected together, although higher LOD scores were usually seen with UAE. The strongest UAE QTL was on RNO2, which gave LOD scores from 10 to 13 from week 8 persisting through week 16. Significant UAE and/or UPE QTL were also seen on RNO1,

RNO6, RNO8, RNO9, RNO10, RNO11, RNO13 and RNO19. Most of these QTL were either present at week 8 and persisted through week 16 or became progressively more prominent with time. Two interesting exceptions were RNO10 and RNO13. On RNO10 the UAE and/or UPE QTL appeared strong early and were weaker at week 16. The UAE

QTL on RNO13 was similarly very strong at week 8 but much reduced after that. RNO6 was unique in showing two UPE QTL on the same chromosome. The percentage of the total population variance explained by all the QTL was 25% for UPE and 68% for UAE at week 8, and varied from 57% to 70% for UPE and UAE at weeks 12 and 16.

At 16 weeks of age the backcross rats were killed and kidneys were studied histologically. The population parameters for KLG are given in Table 2 and KLG was also analyzed for QTL at week 16 (light blue in Figure 3). RNO2 had the strongest KLG

QTL corresponding to the UAE and UPE QTL. KLG QTL were observed at suggestive 67 levels of significance concomitant with most other UAE or UPE QTL, i.e., those on

RNO6, RNO8, RNO9, RNO11, RNO13 and RNO19. The exceptions were RNO1 and

RNO10 where KLG QTL were not observed in the presence of the UAE and UPE QTL.

Besides the LOD plots, Figure 3 also gives the magnitude and direction of the

observed effects for a microsatellite marker at or near the LOD peak. "Phenotypic effect"

values are given only if the LOD plot was at least of suggestive significance. The phenotypic effect was defined at a given marker as the average phenotypic value for all rats homozygous for the S allele (S/S genotype) minus the average phenotypic value for all rats that were heterozygous, i.e., carried one allele from S and the other from SHR

(S/SHR genotype). Because this was a backcross to S, these are the only genotypes present at each locus. A positive value for phenotypic effect means that S/S rats were higher than S/SHR; a negative value means that S/S were lower than S/SHR.

In general, BP effects of each QTL were modest (6-10 mm Hg), and the S/SHR genotype was associated with increased BP on RNO1 and RNO6, and the S/S genotype increased BP on RNO2 and RNO10. Only the BP QTL on RNO10 was aligned with the

UAE and UPE QTL and the S/S genotype increased BP, UAE and UPE.

In evaluating magnitude and direction of effects of UAE or UPE QTL the mean of the ln transformed values for each genotype was converted back to mg/24 hrs by taking the antilog. This yields the geometric mean value for each genotype. This was done in order to avoid giving results in ln units which are difficult to envisage in physiological terms. Thus the phenotypic effect values in Figure 3 for UAE or UPE are the difference between the geometric means for the two genotypes for a marker at or near the LOD peak. Most of the effects are modest in the range of 1 to 5 mg/24 hrs. As one might 68 expect, the S/S genotype was associated with increased UAE or UPE at most QTL, i.e.,

on RNO1, RNO2, QTL 2 on RNO6, RNO8, RNO9, RNO10, RNO13 and RNO19. Only

on RNO11, and QTL 1 on RNO6, was the S/SHR genotype associated with increased

UAE and/or UPE. RNO6 was interesting in that for the two UPE QTL on RNO6, the S/S

genotype decreased UPE at QTL 1 and the S/S genotype increased UPE at QTL 2 (Figure

3). In general one does not expect to resolve such offsetting effects on the same

chromosome unless, as in this case, the QTL are spaced far apart. For all UAE and/or

UPE QTL the direction of the phenotypic effect was always concordant with the direction

of the phenotypic effect for the KLG QTL (Figure 3).

Figure 4 summarizes the pair-wise interactions observed in the F1(SxSHR)xS

population for UAE. The figure plots a symbol in a position on a two-dimensional

chromosomal grid corresponding to the two chromosomes involved in the interaction.

Different symbols indicate interactions seen at weeks 8, 12 or 16. The microsatellite

markers involved on each chromosome are indicated. Several pair-wise interactions

involved markers both of which were at or near UAE QTL found by the initial linkage analysis. There were also several interactions involving only one marker near a QTL, the other marker of the pair being on a chromosome where no QTL was observable except through its interaction. RNO2 was highly interactive with several other chromosomes.

Figure 5 summarizes LOD plots for heart weight and kidney weight adjusted for differences in body weight. Of the six heart weight QTL observed, only one at D1Rat36 corresponded to a BP QTL. Of the three kidney weight QTL observed, two of them (at

D1Rat86 and D19Uia1) corresponded reasonably well with UAE and/or UPE QTL. In both cases, increased kidney weight was associated with increased UAE. Where a heart 69 weight or a kidney weight QTL fell on a chromosome shown in Figure 3 the position of the organ weight QTL is also indicated in Figure 3.

QTL for body weight were also observed in the backcross population. The following is a list of markers at the LOD peak if a body weight QTL was observed at any of the three time points studied: D2Rat82, D3Rat160, D10Mco57, D12Mgh3, D15Rat5,

D17Mit7, D18Rat57. Markers D2Rat82 and D10Mco15 fall within the UAE and UPE

QTL (Figure 3) and had their maximum phenotypic effect of -10g at week 16 on RNO2 and 7.5g at week 4 on RNO10.

Discussion

Our purpose in designing this work was to take advantage of the marked strain difference in UAE between S and SHR, and to look early in the development of the rat for initial genetic causes of proteinuria. Which QTL and how many QTL are observed for any trait of course depends on the strains that are studied (22,34). Ten QTL for UAE and UPE were found all with modest but significant effects. In general, a large number of QTL influencing a quantitative trait is not surprising. BP in S rats is influenced by as many as 16 QTL as summarized recently (23) and diabetes in the non-obese diabetic mouse, for example, is controlled by 17 QTL (35). As expected, at the majority of QTL the S/S genotype was associated with increased UAE and UPE. Considering the modest quantitative effects of each QTL and the young age of the rats, the correspondence of kidney lesion grade (KLG) QTL with the UAE and UPE QTL was striking. Our KLG grading system did not dissect lesions into individual components (e.g., glomerular versus tubular lesions) but rather was a semiquantitative composite of all lesions. As 70 such, the main usefulness of the KLG was to show that the modest degrees of change in

UAE and UPE associated with each QTL were sufficient to be reflected in histologically observable secondary renal damage.

UPE has been studied in several other rat strains. The Milan normotensive strain

(MNS) develops glomerular sclerosis and severely increased UPE with aging in the absence of hypertension (36,37). Similarly aged BUF/Mna rats develop glomerular sclerosis and markedly increased UAE in the absence of hypertension. A genetic cross indicated that high UAE was strongly recessive (38), similar to the present work. A genome scan involving a backcross of F1(BUFxWKY) to BUF yielded significant linkage only to a broad region of RNO13 (39) which is roughly consistent with our linkage result for UAE on RNO13.

The Fawn-Hooded hypertensive rat (FHH) develops progressive glomerular sclerosis and hypertension with aging. Again, proteinuria appeared recessive in a cross with ACI rats and a genome scan for QTL was done on a backcross of F1(FHHxACI) to

FHH (40) as well as on an F2(FHHxACI) population that had undergone unilateral nephrectomy (41). Both populations yielded strong evidence for two UPE QTL on

RNO1. The QTL were named Rf-1 and Rf-2 for renal failure 1 and 2. Rf-1 gave the strongest signal and was not associated with a QTL for BP. A congenic strain for the

FHH QTL allele on the ACI background at Rf-1 was subsequently made and was shown to increase proteinuria after unilateral nephrectomy or induction of hypertension by inhibition of nitric oxide synthase (42).

Our results for the UAE and UPE QTL on RNO1 agree exceptionally well with

Rf-1 data as to the location of the QTL and the absence of a co-located BP QTL. Rf-2 in 71 contrast was co-localized with a QTL for BP (40,41). The BP QTL we observed on

RNO1 is in the same location as Rf-2. Although we did not observe a second UAE or

UPE QTL in the Rf-2 location, it is likely that such an effect would be observable if our rats were comparable to those in the FHH studies, i.e., older and/or unilaterally nephrectomized. In the more recent study on FHH (41) where the population studied was an F2(FHHxACI) cross, and the rats had been unilaterally nephrectomized, additional

QTL for UAE and UPE were seen on RNO3, RNO14 and RNO17. No comparable QTL

were seen in our work.

There are two strains of Sabra rats, SBH and SBN selectively bred for

susceptibility or resistance, respectively, to the hypertensive effect of deoxycorticosterone plus increased dietary NaCl treatment. SBH rats develop proteinuria

as well as salt-induced hypertension. Although a genome scan for UPE QTL was not

done on these strains, consomic strains placing either RNO1 or RNO17 from SBH onto

the SBN background were constructed based on the location of previously described BP

QTL in Sabra rats and on the work with the FHH rats noted above. Under the condition

of unilateral nephrectomy and aging the consomic rats showed increased proteinuria

compared to the SBN controls, confirming the presence of UPE QTL on RNO1 and

RNO17 (43).

The susceptibility of the kidney to damage by hypertension has been compared by

kidney cross transplantation studies between histocompatible SHR and Brown Norway

(BN) rats. It was concluded the BN kidney was inherently more susceptible to damage

resulting from hypertension than the SHR kidney (44). A congenic strain with a segment

of RNO1 that probably includes the Rf-2 region from BN was introgressed into SHR. 72 This congenic strain was more susceptible to hypertension-induced renal damage than

SHR (45).

In all of the above work quoted from the literature the rat models are either a)

studied with advanced age, b) unilaterally nephrectomized, or c) treated to exacubate

hypertension. This has its merits in order to stimulate phenotypic expression of UAE and

UPE. In our experiment the genetic background of 75% S and 25% SHR in the

backcross population is obviously permissive for hypertension, but we kept the salt

sensitive component of hypertension to a minimum by using a low salt diet and initiating

studies relatively early in the life of the rat. It was, therefore, interesting in our study to

observe many QTL for UAE and UPE with strong statistical support for their existence

without additional exacerbating factors. Similarly, a recent study using Munich Wistar

Frömter (MWF) rats also looked for UAE QTL over time starting at 8 weeks of age

without exacerbating factors (46). The MWF strain has a reduced number of glomeruli

and develops proteinuria and mild hypertension with aging. In a backcross study

involving MWF and Lewis rats, high UAE was strongly recessive as in the present work and QTL for UAE were found on RNO1, RNO6, RNO12 and RNO17. None of these co- localized with BP QTL. Schulz et al. (46) state that the RNO1 QTL co-localizes with Rf-

2 noted above in work with the FHH strain and the RNO17 QTL co-localizes with a QTL

also described in FHH. The RNO6 QTL for UAE in the MWF rat study apparently falls

between the two UPE QTL seen here on RNO6.

In the present study, the high UAE of the S rat was strongly recessive to the low

UAE of SHR. This pattern of high UAE being recessive is strongly and consistently seen

in all other proteinuria rat models where F1 rats were studied, i.e., in BUF (38), FHH (41) 73 and MWF (46) and MNS (47). The frequency distribution in Figure 2B is suggestive of

the segregation of two recessive genes in the backcross population. Matsuyama et al.

(38) interpreted their data on BUF rats to indicate two recessive genes, but were

ultimately able to locate only one QTL on RNO13 (39). Schulz et al. (46) suggested that

if any three out of the four UAE QTL described in MWF rats were homozygous

recessive, then high UAE was achieved. In the present work it was not possible to

explain the bimodal distribution in Figure 2B by any two particular QTL out of the ten

observed and the origin of the bimodality is obscure.

It is well known that hypertension can cause/exacerbate glomerular damage leading to proteinuria. It has, however, also been established in the work with the FHH rat that the Rf-1 QTL for proteinuria on RNO1 is not associated with a BP QTL (40,41).

In the recent study on the MWF rat, none of the UAE QTL co-localized with BP QTL

(46). Similar results were seen here. In fact, under the conditions of our experiment, of the 10 QTL observed for UAE and UPE, only the QTL on RNO10 obviously co-localized with a BP QTL (Figure 3). We have, however, studied an F2(SxSHR) population fed an

8% NaCl diet for BP QTL (UAE was not available from this population). BP QTL were

seen on RNO3, RNO8 and RNO9 (28). The BP QTL on RNO3 was not co-localized

with the UAE or UPE QTL described here. The BP QTL on RNO8 seen on high salt diet

was, however, exactly co-localized with the UAE and UPE QTL on RNO8, and the BP

QTL on RNO9 overlaps with the UAE and UPE QTL on RNO9. Thus, based on existing data it appears that only a minority of UAE and UPE QTL are also linked to BP. It is emphasized, however, that in all of these studies the tail-cuff method was used to 74 measure BP. This has the limitation of providing information on only systolic BP

measured during one part of the diurnal cycle (between 7AM and 12PM in our case).

The resistance of the SHR kidney to the development of glomerular sclerosis and

subsequent proteinuria in spite of sustained systemic hypertension is striking when compared to normotensive WKY (24) or hypertensive S rats (25). Similar results are also seen in the present data in which systemic hypertension in SHR (Table 1) is not translated into albuminuria (Figure 1). This phenomenon has been attributed to increased renal vascular resistance in SHR due largely to preglomerular vasoconstriction in response to systemic hypertension (48). Subsequent work supported this concept (49-52). Such a

preglomerular constriction would protect the SHR from glomerular hypertension and

consequent glomerular damage. The S/SHR genotype was associated with lower UAE or

UPE at most of the QTL described here. Which QTL may involve preglomerular

vasoconstriction (if any) cannot be determined from the present data.

It is also interesting that the most significant heart weight QTL, which was on

RNO9 (Figure 5), was not associated in the present work with a BP QTL. In our previous study (28) of an F2(SxSHR) population raised on 8% NaCl diet there was a

prominent BP QTL on RNO9 overlapping with the heart weight QTL observed here.

Thus it appears possible that on RNO9 genetically controlled differences in heart weight seen here on low salt diet, precede the BP differences induced by a high salt diet.

A description of QTL for a trait is incomplete without considering the interactions

among the QTL. The multiple interactions observed here serve to stress the ultimate

complexity of the genetic control of a quantitative trait (53). This complexity includes 75 chromosomal regions harboring QTL that are observable only through their interactions which here were on RNO5, RNO7, RNO18, RNO20 and RNOX (Figure 4).

In summary of the present work and the other rat strains showing albuminuria, it is consistently observed that a) albuminuria, although highly polygenic, is strongly recessive; b) most, but not all, of the QTL regulating albuminuria are not co-localized with QTL influencing BP.

76 Acknowledgements

This work was supported by a grant from the National Institute of Health to J.

Rapp and by the Helen and Harold McMaster Endowed Chair in Biochemistry and

Molecular Medicine to J. Rapp. 77 Figure Legends

Figure 1. Bar graphs showing urinary protein excretion (UPE) (Panel A) and urinary albumin excretion (UAE) (Panel B) for S, F1(SxSHR) and SHR rats at 8, 12, 20 and 26

weeks of age. Rats were fed a low salt (0.3% NaCl) diet. In panel B, note that the scale

has been divided into two sections to accommodate a wide range of values for UAE.

Data are expressed as mg/24 hours. Error bars are SE; n=6 rats in each group.

Figure 2. Frequency distributions for urinary albumin excretion (UAE) (Panel A) and

the natural logarithm (ln) of UAE (Panel B) for the F1(SxSHR)xS population of 276 rats

raised on low salt (0.3% NaCl) diet. Note in panel B that the scale is logarithmic, but the

cells are labeled with both the transformed and untransformed values of UAE. The

population mean ± SD is given on each graph.

Figure 3. Genome scan LOD plots for urinary albumin excretion (UAE), urinary protein

excretion (UPE), blood pressure (BP) and kidney lesion grade (KLG) at 8, 12 and 16

weeks of age for the F1(SxSHR)xS population (n=276) on 0.3% NaCl diet. Only rat

chromosomes (RNO) for which there was evidence for quantitative trait loci (QTL) are

shown. The horizontal dashed line at LOD=1.9 is the threshold for suggestive

significance and the solid line at LOD=3.3 is the threshold for significance. For each

chromosome the positions of genetic markers are given at the bottom, and the genetic

distance in centiMorgan (cM) is given at the top. LOD plots for phenotypes are color

coded: BP is dark blue, UPE is orange, UAE is red, and KLG at week 16 is light blue.

Where the LOD plot showed suggestive significance or better, the phenotypic effect is 78 given on each panel. Phenotypic effect is in mmHg for BP, and mg/24 hours for UAP

and UAE. KLG is on an arbitrary scale. The phenotypic effect was calculated at a

marker which was at or near a LOD plot peak and is the average phenotype of rats with

S/S genotype minus the average phenotype rats with S/SHR genotype. The phenotypic

effect values are given on each panel next to the color coding key. A positive phenotypic

effect indicates that S/S rats were higher than S/SHR; a negative phenotypic effect

indicates that S/S rats were lower than S/SHR. Where no phenotypic effect is given, the

LOD plot for that phenotype did not reach the LOD=1.9 threshold. The positions of QTL

for adjusted heart weight (AdjHW) and adjusted kidney weight (AdjKW) from Figure 4

are also indicated as 1-LOD intervals on appropriate graphs; the peak LOD values for

these QTL are in parenthesis.

Figure 4. Pairwise interactions for UAE from the F1(SxSHR)xS population.

Calculations were done on the ln transformed data. The rat chromosomes (approximately to scale) are plotted on both axes to create a grid for locating the pairwise interactions.

Different symbols indicate significant interactions observed when the rats were 8, 12 or

16 weeks of age. The interacting pairs of markers are indicated next to each symbol.

Only highly significant interactions represented by Fall and Fint both P<0.01 are shown. A

marker which is underlined is one which can be found on Figure 3; markers not

underlined were on chromosomes not represented in Figure 3 because QTL were not

individually detectable on these chromosomes.

79 Figure 5. Total genome LOD plots for adjusted heart and kidney weights at 16 weeks of age for the F1(SxSHR)xS population on 0.3% NaCl diet. The horizontal dashed line and

solid lines are the thresholds for suggestive significance and significance respectively.

Markers at or near LOD peaks are given. Phenotypic effects at these markers are also

indicated. The figure was prepared using R/qtl developed by Karl W. Broman, available

at http://biosun01.biostat.jhsph.edu/~kbroman/qtl.

80

Figure 1

81

Figure 2

82 Figure 3

83

Figure 3 [continued]

84 Figure 3 [continued]

85 Figure 4

86 Figure 5

87 Table 1. Comparison of blood pressure, body weight, and adjusted heart and kidney

weights for S, F1(SxSHR) and SHR raised on 0.3% NaCl diet.

One-way S F1(SxSHR) SHR ANOVA, P Week 8

Blood pressure, mm Hg 168 ± 5.1 152 ± 5.4 148 ± 4.7 0.039

Body weight, g 293 ± 9.8 304 ± 6.4 203 ± 6.0 < 0.0001

Week 12

Blood pressure, mm Hg 182 ± 4.1 163 ± 5.6 187 ± 5.2 0.009

Body weight, g 384 ± 8.9 408 ± 6.1 285 ± 6.0 < 0.0001

Week 20

Blood pressure, mm Hg 193 ± 10.5 179 ± 7.5 182 ± 6.9 0.49

Body weight, g 451 ± 18.5 503 ± 7.3 375 ± 13.0 < 0.0001

Week 26

Blood pressure, mm Hg 208 ± 4.6 192 ± 6.2 193 ± 9.5 0.28

Body weight, g 485 ± 11.9 519 ± 7.9 392 ± 7.0 < 0.0001

Adjusted heart weight, g 1.675 ± 0.018 1.599 ± 0.023 1.626 ± 0.012 0.042

Adjusted kidney weight, g 3.306 ± 0.140 3.130 ± 0.030 3.173 ± 0.027 0.273

n=6 rats per group. Data are mean ± SE. Data were analyzed by a one-way analysis of variance (ANOVA). Data are from the same rats used in Figure 1. Weeks given are the ages of the rats. 88 Table 2. Descriptive statistics for blood pressure, UAE, UPE, body weight and KLG for the F1(SxSHR)xS population of 276 rats raised on 0.3% NaCl diet.

Mean SD Minimum Maximum Blood Pressure, mm Hg

Week 8 147 15.2 106 187

Week 12 156 16.4 113 206

Week 16 159 16.1 129 212

UAE, mg/24 hours

Week 8 3.6 5.37 0.09 46.8

Week 12 4.0 5.78 0.0 37.7

Week 16 4.9 7.04 0.05 40.1

UPE, mg/24 hours

Week 8 9.4 6.39 2.0 58.9

Week 12 21.9 9.83 7.8 62.1

Week 16 23.5 12.78 9.2 110.3

Body Weight, g

Week 8 250 20 205 296

Week 12 371 17 319 427

Week 16 428 22 362 490

KLG

Week 16 0.70 0.59 0 3.0

UAE, urinary albumin excretion; UPE urinary protein excretion; KLG, kidney lesion

grade. 89

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96 Genetic Linkage of Urinary Albumin Excretion in Dahl Salt-Sensitive

Rats: Influence of Dietary Salt and Confirmation using Congenic

Strains

Michael R. Garrett, Bina Joe, and Shane Yerga-Woolwine

Department of Physiology and Cardiovascular Genomics, Medical University of Ohio,

Toledo, Ohio 43614-5804.

Running Head: Urinary Albumin QTL and Congenic Strains

Corresponding Author: Michael R. Garrett

Department of Physiology and Cardiovascular Genomics

Medical University of Ohio

3035 Arlington Avenue, Toledo, OH 43614-5804

Phone: 419-383-4144

E-mail: [email protected]

Fax: 419-383-6168

Manuscript originally published in Physiol Genomics 25: 39- 49, 2006. Reproduced with permission from the American Physiological Society via Copyright Clearance Center.

97

Abstract

Previously, we reported a linkage analysis for urinary albumin excretion (UAE)

from a backcross population derived from the Dahl salt-sensitive (S) rat and the

spontaneously hypertensive rat (SHR) raised on a low-salt diet. The current study sought

to examine the effect of salt-loading on the observation of UAE quantitative trait loci

(QTL) using a F1(SxSHR)xS backcross population (n=228) raised on 2% NaCl. Parental strain data demonstrated that S rats have significantly higher blood pressure (BP) and

UAE compared to either the F1(SxSHR) or SHR at 8 weeks of age, and this difference is

exacerbated by 12 weeks of age in response to a high-salt diet (2% NaCl). Genome scans done at 8, 12, and 16 weeks of age yielded eight QTL for UAE. At week 8 (low-salt),

QTL for UAE were observed on rat chromosomes (RNO) 1, 2, 6, 8, 9, 11, 13 and 19.

Week 8 linkage analysis confirmed the previous linkage data and provided a baseline to

examine the effect of salt-loading at subsequent time points. At week 12 and 16 (after

salt-loading), QTL for UAE were observed on RNO1, 6, 8, 9, and 13. Surprisingly, UAE

QTL were no longer observed on RNO2, 11, and 19 following salt-loading, suggesting

that these QTL are attenuated by increased salt intake. The effects of UAE QTL on

RNO2, 6, 9, 11, and 13 were examined using congenic strains whereby the SHR alleles at

each QTL were placed on the S background. These congenic strains demonstrated large

and significant effects on UAE compared to the S rat, proving that QTL for UAE reside

on these chromosomes.

Key Words not in title: S rats, spontaneously hypertensive rats, SHR, albuminuria,

proteinuria, blood pressure, hypertension, quantitative trait loci, QTL, kidney weight

98

Introduction

Salt-sensitive hypertension is known to be a contributing factor for the progression of kidney disease, as well as for cardiovascular diseases (5, 15, 21). Clinical studies suggest that those patients who exhibit salt-sensitive hypertension are more prone to develop severe hypertension related target organ damage (2, 6, 8, 22). This observation is supported by experimental rat models that exhibit salt-sensitive hypertension and associated renal damage. One such model is the Dahl salt-sensitive (S)

rat. The S rat was originally bred for sensitivity to the hypertensive effects of a high-salt

diet (8% NaCl) (7). In response to high-salt, these animals exhibit a marked and rapid

increase in blood pressure associated with severe renal damage (28, 34, 35). On low-salt,

these rats still develop hypertension and renal damage with age (12), but the onset and

progession is slower compared to their response on high-salt. The spontaneous

hypertensive rat (SHR) offers an interesting contrast to the S rat because despite

developing hypertension, it is resistant to the development of renal damage (11, 12).

It is well known that an early sign of deteriorating kidney function in certain

disease states is the presence of small amounts of albumin in the urine (24). As kidney

function declines, the amount of albumin in the urine increases and eventually develops

into the abnormal range, referred to as “albuminuria.” This threshold is well defined in

humans (24), but it is not so clear for studies involving rodents. For the purpose of our

work, urinary albumin excretion (UAE) is used as a marker of kidney function without

necessarily implying the existence of a disease state.

99

We previously conducted a linkage analysis for urinary albumin excretion (UAE)

using a backcross population derived from the S and the SHR raised on low-salt diet (12).

The study identified eight quantitative trait loci (QTL) where the S-rat-allele increased

urinary albumin excretion (UAE) and two QTL where the SHR carried the allele for

increased UAE.

The aim of the present study was to perform a second linkage analysis to

determine if QTL for UAE would be influenced by salt-loading either by altering the

time-course pattern of known QTL or by identifying QTL that were not detected on low-

salt. A second aim was to perform congenic strain analysis for several UAE QTL to

confirm the linkage analysis (on rat chromosomes 2 6, 9, 11, and 13) and to demonstrate

the magnitude of the effect of each QTL once isolated on the S background.

Materials and Methods

Animals

Inbred Dahl salt-sensitive (SS/Jr or S) rats were maintained in our animal facility at the Medical University of Ohio at Toledo (MUOT). Spontaneously hypertensive rats

(SHR/NHsd or SHR) were originally obtained from Harlan Sprague-Dawley

(Indianapolis, IN) and have been inbred for >20 generations at MUOT. To determine the

effect of salt-loading in F1(SxSHR) animals on systolic blood pressure (BP), urinary

protein excretion (UPE) and urinary albumin excretion (UAE), S females were crossed

to SHR males to produce F1(SxSHR) animals. Two groups of age matched S, F1, and

SHR male rats (n=6 for each strain, per group) were studied for BP, UPE and UAE at 12

100

weeks of age. Group 1 was fed a low-salt (0.3% NaCl; TD7034; Harlan Teklad, Madison,

WI) diet from weaning (30 days) and Group 2 animals were on a low-salt diet from

weaning until eight weeks of age and then fed a 2% NaCl diet (TD 94217, Harlan

Teklad, Madison, WI) for 4 weeks.

An F1(SxSHR)xS backcross population of 228 male rats was produced by

crossing F1 females with S males. The backcross animals were weaned at 30 days of age and placed on a low-salt diet (0.3% NaCl) until the animals were eight weeks of age. At eight weeks of age the animals were studied for BP, UPE, and UAE. Once testing was complete, the animals were placed on a high-salt diet (2% NaCl) for the duration of the experiment. The animals were studied again for BP, UPE, and UAE at 12 and 16 weeks of age.

The 228 rats were handled in four blocks of 57 animals. Rats in each block were aged-matched and there was little difference in age between blocks. This allowed for phenotyping on all rats at approximately the same ages. Block effects, if any, were removed statistically.

Rats were killed by CO2 inhalation. Liver was harvested and archived at -80ºC for

subsequent DNA extraction. Body, heart and kidney weights were measured. The right

kidney was fixed in 10% buffered formalin and embedded in paraffin for sectioning and

staining.

Congenic Strains

Congenic strains were developed using a speed-congenic approach (19, 37),

whereby a genome scan using 100-114 microsatellite markers was performed at each

generation. This approach allowed for the selection of animals that had incorporated the

101

greatest amount of background genome (S alleles) while still containing the introgressed

region of interest (alleles from SHR). Female S rats were bred with male SHR to

produce F1 rats. Female F1 rats were backcrossed to male S rats (ensuring that all

subsequent male animals had a Y chromosome derived from the S rat) to produce the first

backcross generation (BC1). This is important in dealing with SHR because the SHR Y-

chromosome has been shown to influence BP in some experimental crosses (10) and

differences in BP between strains could confound the analysis of renal damage.

At BC1, animals were selected that retained the greatest amount of S genome

while being heterozygous S/SHR for either RNO2, 6, 9, 11, or 13. These animals were

designated as the best breeders for subsequent pairing with S rats to produce BC2

animals. The same process continued for BC2 thru BC5. At BC5, the background

genome for each strain that had the S/SHR genotype on either RNO2, 6, 9, 11, or 13 was

found to be homozygous SS. These animals were bred to S rats to generate additional

animals heterozygous for the introgressed region. The heterozygous animals for each

region (either RNO2, 6, 9, 11,or 13) were intercrossed and animals that were

homozygous for SHR alleles throughout the introgressed region were selected to fix the

SHR alleles on the S background. The five strains produced are denoted as: S.SHR(2),

S.SHR(6), S.SHR(9), S.SHR(11), and S.SHR(13).

For testing, S, SHR and all five congenic strains were bred concomitantly. At 30 days of age, the rats were weaned and placed on a low-salt diet. Urine was collected on both male and female animals 51-53 days of age for determination of UPE and UAE.

Phenotyping

Blood Pressure

102

BP was measured by tail-cuff method on conscious restrained rats warmed to

28ºC. The procedure for BP determination for the initial study involving the effects on

salt-loading on S, F1 and SHR rats and for the backcross population was the same. Of the

57 rats in each block for the backcross population (as noted above), a subset of 48

animals was selected at random from each block for BP determination. A total of 192

animals had their BP measured. Starting at 8 weeks of age, BP was measured on two consecutive days by two operators. Each operator had one session with each rat. The

final BP of each rat was the average of both sessions. The procedure was repeated at

week 12 and 16.

UPE and UAE determination

To collect urine, animals were kept in metabolism cages (Lab Products, Seaford,

Delaware) for 24 hours with free access to water. Sodium azide was added to the

collection vials for a final concentration of approximately 0.1% in the urine. Total UPE

was determined colorimetrically using pyrogallol red/molybdate complex (Quantimetrix,

Redondo Beach, CA) and expressed as mg protein/24 hours. UAE was determined by a

rat albumin EIA kit (SPI-bio, France) and also by SDS-PAGE as reported previously

(12). UAE is expressed as mg albumin/24 hours.

Histology

Kidneys were cut into 3-µm sections and stained with hematoxylin and eosin. One

central longitudinal section from the right kidney was examined in a blinded fashion on

an arbitrary semiquantitative scale from 0 to 4 for kidney lesions as reported earlier (12).

The grades represent a visually integrated assessment of the severity and extent of the

lesions and no attempt was made to grade the individual components (e.g., glomeruli,

103

tubules, and vasculature) separately. The primary use of the histology data was to serve

as a confirmatory phenotype and as an indication of whether or not QTL for UPE were

associated with any overt histological changes. The kidney lesion grades (KLG) were

0=normal, 1=mild, 2=moderate, 3=marked, 4=severe. It was possible to assign half

grades so the grades were 0, 0.5, 1, 1.5, etc.

Genotyping

Genomic DNA for the backcross population (liver) or for the development of

congenic strains (tail biopsy) was prepared using DNeasy 96 tissue kits (Qiagen,

Valencia, CA). Genotyping was done using two methods: (1) a standard approach using

agarose or polyacrylamide gel electrophoresis as previously described (9); and (2) a

fluorescent-based approach using a Beckman Coulter CEQ8000 capillary sequencer (3).

Using the fluorescent-based approach, the forward primer of a microsatellite marker is

tagged 5’ with an M13 primer (CACGACGTTGTAAAACGAC). The M13-forward

primer is used in combination with an M13 primer that is fluorescently labeled (dye D4-

PA, Beckman Coulter, Fullerton, Ca) to produce a PCR product that can be detected

using the CEQ system (3). The M13 dye labeled primer can be used with any PCR primer

that is 5’ tagged with M13, eliminating the need to purchase individually labeled primer

sets.

PCR reactions for the fluorescent based approach were prepared as follows in a

10 µl reaction: 1X Buffer containing 1.5mM MgCl2 (Promega, Madison, WI), 0.2mM

dNTP’s (Sigma, St Lois, MO), 2.5pmol M13-D4 labeled dye (Research Genetics,

Huntsville, AL), 0.1pmol M13-forward primer, 2.5pmol-reverse primer, and 0.25 U Taq

DNA polymerase (Promega, Madison, WI). PCR amplification was performed as

104

follows: 95ºC for 5 min and continued for 5 cycles of 94ºC for 40 sec, 55ºC for 40 sec,

72ºC for 1 min, after which an additional 30 cycles of 94ºC for 40 sec, 50ºC for 40 sec,

72ºC for 1 min was performed. After amplification, the PCR products were diluted 1:10 in water, 1 µl of each diluted product (4-6 different PCR products ranging in size from

100-400 bp) was added to 40 µl SLS buffer (Beckman Coulter, Fullerton, CA) containing

DNA size standard, loaded on CEQ sequencer, and then analyzed using CEQ fragmentation analysis software.

A complete genome scan of all 21 chromosomes (except Y) was performed using a total of 174 markers that were approximately evenly spaced throughout the genome.

The information on microsatellites markers were obtained from the following databases:

(1) Rat Genome Database (RGD) at the Medical College of Wisconsin

(http://www.rgd.mcw.edu); (2) Wellcome Trust Centre for Human Genomics (Oxford,

UK; http://www.well.ox.ac.uk); and (3) Medical University of Ohio, Department of

Physiology and Cardiovascular Genomics

(http://www.meduohio.edu/depts/physiology/research).

Linkage and Statistical Analysis

Linkage analysis and QTL localization were performed using Map Manager QTX

(http://mapmgr.roswellparks.org) (18). The likelihood ratio statistic (LRS) generated by

Map Manager QTX as a measure of the significance of a QTL was converted into a LOD score (LOD= LRS/4.6) for reporting purposes. For a backcross population a LOD score of at least 1.9 was considered evidence for suggestive linkage and a LOD score of 3.3 or

above indicate significant linkage between phenotype and genotype (17). Determination

105

of the "phenotypic effect" (average effect of animals S/S minus average effect of animals

S/SHR) for phenotypes with suggestive or significant linkage to a chromosome was done

by selecting one index marker at or near the QTL peak and performing an independent t-

test using SPSS (Chicago, IL). KLG grade scores were additionally analyzed by

nonparametric Kruskal-Wallis test.

All other data were analyzed by one-way analysis of variance followed by post hoc

multiple comparisons using Tukey’s test (SPSS, Chicago, IL). All data are presented as mean ± SE.

Results

Effect of a High-Salt Diet on BP, UPE, and UAE in S, F1(SxSHR), and SHR

The data for BP, UPE and UAE for both parental strains and F1(SxSHR) are

presented in Figure 1. On a low-salt diet, the S and SHR at 12 weeks of age show similar

BP values (Figure 1A). F1 rats have BP values slightly lower than either of the parental

strains. On a high-salt diet, hypertension in the S rat was exacerbated, but no significant effect was seen for the F1 or SHR. The BP of 12-week-old salt-loaded S rats was 38 ±

10.1 mmHg (p< 0. 001) higher than low-salt fed S rats of the same age (Figure 1A).

In contrast to BP, UPE and UAE levels showed a significant difference between

strains even on a low-salt diet (Figure 1B and 1C). For purposes of our analysis we define

UPE >20mg/24 hours as being “proteinuria” and UAE > 5 mg/24 hours as being

“albuminuria.” The proteinuria threshold has been used previously by others (38) and

values below either of these thresholds appear to correspond to the normal range of UPE

and UAE in several rat inbred strains (25). These threshold values provide some value

106

with regard to classifying what is normal and what is abnormal. S rat UPE was 109 ±

10.5 mg/24 hours, clearly well above the threshold to be considered having proteinuria.

The S rat had 5-fold higher UPE than either the F1 hybrid (17 ± 2.1 mg/24 hours) or the

SHR (19 ± 1.50 mg/24 hours) (Figure 1B). Remarkably, F1 UPE was below the 20 mg/24

hour threshold and would be considered normal. The UAE difference between the strains

was dramatically larger than UPE. However, UAE for the F1 or SHR are below what

would be considered “albuminuria” and fall in the normal range. There was no significant

difference between the F1 and SHR for either UPE or UAE on a low-salt diet.

On a high-salt diet, UPE in the S rat was greatly influenced by salt-loading. UPE

for 12-week-old salt-loaded S rats was 53 ± 10.1 mg/24 hours (p<0.0001) higher than

low-salt fed S rats of the same age. Neither F1 nor SHR animals experienced a significant

increase in UPE beyond their low-salt values. Consequently, there was a greater fold

difference between the S rat and F1 or SHR compared to low-salt feeding. The S rat had

~7-fold higher UPE than either the F1 hybrid (19.5 ± 1.1 mg/24 hours) or the SHR (22.6

± 3.2) (Figure 1B). Similarly, UAE increased significantly in the S (Figure 1C), but

remained unchanged in the F1 or SHR.

Effect of a High-Salt Diet on BP and UAE on the F1(SxSHR)xS Population

A backcross population (n=228) of male rats was challenged with a high-salt diet

(2% NaCl) to study the effect of blood pressure changes on kidney function, i.e. UPE and

UAE. The population was initially raised on a low-salt diet until week 8 phenotyping

was complete and then the animals were placed on a 2% NaCl diet until week 16.

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Figure 2 illustrates the effect that a high-salt diet had on the backcross population

for BP and UAE compared to the previously published low-salt population (12). For the low-salt population, there was a slight, but significant increase in BP from 146 ± 1.1 mmHg in week 8 to 155 ± 1.2 mm Hg in week 12 (Figure 2A). From week 8 to 12 there was also a slight increase in the proportion of animals with higher BP. In contrast, the

BP of the high-salt population (after four weeks of salt-loading) markedly increased from

140 ± 1.1 mmHg to 165 ± 1.5 mmHg, a difference of 24.7 ± 1.9 mm Hg (p<0.0001) .

Additionally, there was a dramatic shift in the number of animals that had higher BP

compared to week 8 (Figure 2A).

At week 8, the average UAE value for the low-salt population was 3.6 ± 0.32

mg/24 hours and this essentially remained unchanged (3.9 ± 0.35 mg/24hours) through

week 12 (Figure 2B) even though average BP in the population was significantly higher

than week 8. In comparison, average UAE for the high-salt population (after four weeks

of salt-loading) tripled from 3.4 ± 0.32 mg/24 hours to 9.6 ± 0.66 mg/24 hours,

presumably a result of the large increase in average BP in the population.

Figure 3 gives a more detailed presentation of the characteristics of the present backcross population including data for BP, UPE, UAE, body weight (BW), heart weight

(HW), total kidney weight (TKW) and KLG at each time point studied. A main point to note in Figure 3 is that many rats had UPE and UAE values which are above the normal range of ~20 mg/24 hours for UPE and above ~5 mg/24 hours for UAE. Thus a majority of animals in this population would be classified as having proteinuria and albuminuria based on these thresholds. Additionally, the scatter plots for UPE and UAE show that the distributions for each measure are skewed to the right (i.e. more lower values). For this

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reason, subsequent analysis was carried out on the natural logarithm (ln) of UPE and

UAE data to approximate a normal distribution.

Time Course Genome Scan for BP, UPE, and UAE on the F1(SXSHR)XS Population

raised on High-Salt

Genome scans for BP, UPE, and UAE were done at 8, 12, and 16 weeks of age

for the backcross population maintained on a high-salt (2% NaCl) diet. Additionally,

heart weight (HW), and total kidney weight (TKW), and kidney lesion grade (KLG) were

studied at week 16 after the animals were sacrificed. Figure 4 gives a summary of the quantitative trait loci (QTL) identified at all time points. Suggestive or significant QTL with linkage to UPE (gray line) were identified on rat chromosomes (RNO) 2, 6, and 9 for week 8. UAE (red line) QTL co-localized with UPE for these chromosomes, and were also observed on RNO1, 8, 11, 13, and 19. At week 12, after four weeks of a high-salt diet, QTL for UPE and UAE were observed on RNO1, 6, 8, and 9. A QTL on RNO13 was only observed for UAE. QTL for UPE and UAE on RNO2, 11, and 19 were no longer observed following four weeks of a high-salt diet. The same UPE and UAE QTL

observed at week 12 were also seen at week 16 (eight weeks of a high-salt diet ). Only

one suggestive QTL for BP was observed on RNO11 at week 12, but not at week 16.

At 16 weeks of age backcross animals were sacrificed and their kidneys were

scored for KLG. The KLG represents an integrated assessment of kidney pathology and

was intended solely as a confirmatory phenotype to UPE and UAE. Most UPE and UAE

were associated with a QTL for KLG demonstrating that it at least served this limited

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purpose. KLG (purple line in Figure 4) QTL were observed on RNO1, 2, 8, 9, and 13.

The KLG QTL on RNO8 was the only KLG QTL to meet the criteria for significant

linkage (LOD >3.3). All other KLG QTL reached only a suggestive level of significance.

Overall, the UPE effects at each QTL were modest, in the range of 2 to 12 mg/24 hours, depending upon the time point (Table 1). The UAE effects were in the range of 1 to 3 mg/24 hours. The effect of each UPE or UAE QTL at week 12 was ~3-fold higher than that of week 8 (Table 1). Additionally, the phenotypic effect at each QTL was slightly higher for week 16 compared to week 12 for UPE, but remained unchanged for

UAE. The UAE QTL on RNO8 had the largest effect of all the QTL observed and ranged from 8 to 12 mg/24 hour (week 12 to 16). The RNO8 QTL accounted for 8% of the phenotypic variance in UAE (out of ~40% from all QTL).

The S/S genotype was associated with increased UPE and UAE at all QTL detected on RNO1, 2, 6, 8, 9, 13, and 19 (Table 1). However, for the QTL on RNO11 the

S/SHR genotype was associated with increased UPE and UAE (i.e., the SHR allele was associated with increased UPE/UAE and the S allele with decreased UPE/UAE). The

KLG effect at each QTL was modest (~0.5 unit). The direction of the effect of each KLG

QTL was always concordant with that of the UPE and UAE QTL (Table 1).

Phenotypic Analysis of Congenic Strains

The UPE and UAE data for S, SHR, and five congenic strains are shown in Figure

5 and corresponds to the QTL analysis performed at week 8 before the animals were placed on high-salt. Both male and female rats were tested to examine the effect of sex on each QTL. Data is only shown for male rats; however, the results between males and

110 females were consistent. The S.SHR(2) congenic had significantly lower (p<0.0001)

UPE and UAE compared to S (Figure 5A). UPE for the S.SHR(2) congenic was not significantly different from the SHR at this age. However, UAE was significantly different between the congenic and SHR. The S.SHR(2) congenic strain lowered UPE and UAE by ~75% compared to the control S rats. This is the direction of change expected based on the genome scan (i.e. the SHR allele significantly lowered UPE compared to the S allele on RNO2). The RNO2 congenic strain had the largest effect in reducing UPE compared to the other four congenic strains.

The congenic strains S.SHR(6), S.SHR(9), and S.SHR(13) had a similar reduction in UPE, ranging from 16 to 22 mg/24 hours (Figure 5B, C and E). However, all three strains had a greater fold reduction in UAE (than UPE) compared to the control S rats.

For example, the S.SHR(6) congenic strain had a 6.8-fold reduction in UAE compared to the control S, whereas only a 2-fold difference was seen when UPE was compared between the two strains.

The S.SHR(11) congenic strain had significantly higher (p<0.0001) UPE and

UAE (Figure 5D) compared to the control S strain. This QTL corresponds to the only

QTL observed in the current study where the SHR allele was found to increase UPE and

UAE. The S.SHR(11) congenic had a 65% increase in UPE over the control S strain. A

200% increase was observed when UAE was examined (Figure 5D). Interestingly, this congenic strain is more susceptible to kidney damage (as measured by UPE and UAE) than the control S rats, which are already highly prone to kidney damage.

111

Discussion

We previously reported a time course genome scan on an F1(SxSHR)xS

population raised on a low-salt diet (12). The objective for the earlier study was to monitor the onset and development of UAE QTL, while minimizing the effect of increased blood pressure on renal damage. The present study sought to determine the effect of salt-induced high blood pressure on the identification of UAE QTL. The backcross population, once placed on 2% NaCl, did have a greater increase in average BP from week 8 to week 12 than was observed in the low-salt population for the same period. The shift in the population’s average BP from week 8 to week 12 was modest, but

was associated with a large and significant effect on UAE. Interestingly, the population

mean UAE was low (~10.0 mg/ 24hour) compared to the UAE of the S rat at the same

age (~80 mg/24 hours), demonstrating that SHR alleles have a remarkable ability to

confer resistance to UAE in the presence of the highly permissive S background which

readily develops hypertension and renal damage.

Figure 6 summarizes UAE QTL identified in the current study compared to QTL

observed in the previously described low-salt population (12). For clarity, only UAE is

presented because all UPE QTL were concordant with UAE QTL. The initial time point

(week 8 without salt-loading) served to confirm the previous linkage analysis and also to

provide a baseline for the effect of salt-loading on the identification of UAE QTL.

The majority (8 out of 9) of QTL for UAE found in the low-salt population were

also observed in the current study, with the exception of the RNO10 UAE QTL. The

RNO10 QTL was only detected at the suggestive level (LOD=1.9 to 3.0) in the low-salt

112 population at week 8 and 12 and the reduced number of animals in the high-salt population most likely lacked the statistical power to provide evidence for this QTL.

The UAE QTL identified after salt-loading at week 12 and 16 were consistent with those found using the low-salt population (Figure 6). UAE QTL on RNO1, 6, 8, 9 and 13 were seen throughout the period of salt-loading, but those on RNO2, 11, and 19 were undetected in the high-salt population, demonstrating that these QTL were influenced by increased salt intake. For UAE QTL on RNO2 and 19, it is not readily apparent how salt-loading acts to attenuate these QTL. However, a reasonable speculation can be made regarding the UAE QTL on RNO11. The SHR allele on RNO11 was found to be associated with increased UAE and once dietary NaCl was increased, the

QTL was no longer observed, suggesting that the SHR allele on RNO11 no longer conferred susceptibly to UAE. It is known that the SHR has the ability to resist the development of proteinuria in the presence of systemic hypertension because it experiences increased renal vascular resistance largely due to preglomerular vasoconstriction (1, 14, 36). SHR alleles of RNO11 gene(s) may be a mediator of preglomerular vasoconstriction and protect the SHR from renal damage in response to an increased BP.

The congenic strain data confirmed the presence of UPE and UAE QTL on

RNO2, 6, 9, 11, and 13 (comparable with week 8 of the genome scans). All the congenic strains had significantly different UPE and UAE compared to the S, with effects ranging from 15 to 40 mg/ 24 hours for UAE. In contrast, for either genome scan, the effect of each QTL was at most 3.3 mg/24 hours. This difference is best explained by the strong ability of the SHR alleles to exert their effects on the highly permissive S background.

113

The genetics of kidney disease has been studied using several rat models and this

work has been recently reviewed (16). Genetic studies involving the Buffalo (BUF/Mna)

(20, 23), Fawn hood hypertensive (FHH) (4, 31), Munich Wistar Frömter (MWF) (29,

30), and Milan normotensive strain (MNS) (33) have consistently shown that high UAE

is strongly recessive. This also holds true for the current work with the S rat. Many of

these studies have not gone beyond the stage of linkage analysis, except for one (27). One

of the earliest linkage analysis came from a cross involving the FHH strain, in which five

QTL for UAE were identified (Rf-1 through Rf-5) (4, 31). Interesting, Rangel-Filho et al.

recently published data suggesting that a naturally occurring mutation in the start codon of Rab38 (which leads to a knockout in the FHH strain) underlies the Rf-2 locus located on RNO1 (27). They suggest that Rab38 is involved in altering tubular reuptake and processing of filtered protein, though further studies are required to elucidate the causative mechanism.

Of particular interest are two linkage analyses that involve genome scans for UAE using F2(SxSHR) populations. The first F2(SxSHR) population was raised on a low-salt diet and a genome scan identified UAE QTL on RNO2, 6, 8, 9, 10, 11, and 19 (26), consistent with QTL we identified using a backcross population (12). A second

F2(SxSHR) population was raised on a high-salt (4% NaCl) diet and a genome scan

identified UAE QTL on RNO 3, 6, 8, 9, and 19 (32). Thus, QTL for UAE on RNO2, 10,

and 11 were not detected in the salt-loaded population, but were observed in the low-salt

F2 population. The current study confirms the observation that UAE QTL on RNO2 and

11 are influenced by salt-loading. In contrast, Siegal et al. (32) detected the UAE QTL

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on RNO19 in both populations, whereas we no longer detected this QTL after salt-

loading.

Siegal et al. (32) found that QTL for BP co-localized with UAE on RNO3, 6, and

9. In contrast, no BP QTL was observed on either RNO6 or 9 in the current study, but

was observed on RNO11. BP QTL have also been observed on RNO3, 8, and 9 from a

linkage analysis using an F2(SxSHR) raised on 8% NaCl (13). The BP QTL on RNO3

and 9 are consistent with the F2(SxSHR) raised on 4% NaCl (32), but RNO6 is unique to

the 4% NaCl population and RNO8 is unique to the 8% NaCl population. The

discordance between the BP QTL observed between these studies is most likely a result

of differences in the background genome (backcross versus F2) and/or the degree of salt-

feeding (0.3%, 2%, 4%, or 8%). Taken together, the data from all the crosses involving the S and SHR suggest that UAE and BP QTL map to the same region on RNO6, 8 and 9,

10, and 11. The UAE QTL on RNO1, 2, 13, and 19 appear not to co-localize with BP

QTL.

The UAE QTL reported in the current study control the trait of urinary albumin excretion which is variable from ‘low’ in the SHR to ‘high’ in the S rat. From the data presented it is not readily apparent whether factors underlying each of the UAE QTL detected in this study are genetic determinants of abnormal levels of urinary albumin indicative of impaired renal function or determinants of only small changes in baseline levels of albumin. The observed difference in UAE between the S and the congenic strains are modest (15-40 mg/24hours). However, the S and congenic strains were

compared at an early age (~8 weeks) when levels of UAE are relatively low in the S rat.

The question then arises—are the allelic variants described in this study responsible for

115 the substantial albuminuria and progressive renal lesions that ultimately develop in S rats as they age? Unfortunately, this question can not be answered by the present data and will require future aging studies in congenic rats carrying individual QTL allelic variants.

Nevertheless, the present linkage analysis did show that QTL for KLG mapped to most of the regions linked to UAE, demonstrating that even low levels of UAE (~10 mg/24hours) can be associated with overt, albeit early, kidney pathology.

The current study sought to identify QTL for several cardiovascular and renal phenotypes. The data demonstrated that factors such as age and diet can influence when and if a QTL is observed. Additionally, we clearly established that UAE QTL were present on RNO2, 6, 9, 11 and 13 by congenic strain analysis. Nevertheless, a significant amount of work is still required to further characterize each congenic strain and ultimately identify the underlying genetic factors responsible for susceptibility to renal damage in the S rat.

116

Acknowledgements

This work was supported by a grant (HL066998) from the National Institutes of

Health to M. Garrett. The authors thank Dr. John Rapp for his support and critical reading of this manuscript. Technical assistance by Hilary Good and Kris Farms is sincerely appreciated.

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Figure Legend

Figure 1. Effect of a high-salt diet (2% NaCl) on systolic blood pressure (BP) (panel A), urinary protein excretion (UPE) (panel B), and urinary albumin excretion (UAE) (panel

C) for S, F1(SxSHR) and SHR rats at 12 weeks of age. Salt-loaded rats were placed on a

high-salt diet (2% NaCl) at 8 weeks of age and continued through 12 weeks of age. The

low and high-salt groups were raised and studied concomitantly. In panel B and C, the

dashed line represents the threshold for “proteinuria” (> 20 mg/24hour) and

“albuminuria” (>5 mg/24hour), respectively. Statistical analysis was done using a

oneway ANOVA followed by post hoc multiple comparisons using Tukey’s test. Error

bars are SE; n=6 rats in each group. a, significantly different from each other; b, not

significantly different from each other; c, significantly different from b; p<0.01.

Figure 2. Box plot comparing blood pressure (BP) and urinary albumin excretion (UAE)

between the backcross population raised on low-salt [previously published,(12)] and the

backcross population raised on high-salt diet (this study). For the high-salt population the

rats were maintained on low-salt until 8 weeks of age and then placed on 2% NaCl diet

until 16 weeks of age. Open plots are week 8 data and shaded plots are week 12 data. The box itself contains the middle 50% of the data. The upper hinge indicates the 75th

percentile of the data set and the lower hinge represents the 25th percentile. The line in the

box indicates the median value of the data. The + symbol represents the mean value of

the population.

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Figure 3. Scatter plots showing time course population data for systolic blood pressure

(BP), urinary protein excretion (UPE), urinary albumin excretion (UAE), and body weight (BW) for the backcross population raised on high-salt. For this high-salt population the rats were maintained on low-salt until 8 weeks of age and then placed on

2% NaCl diet until 16 weeks of age. At week 16, the backcross animals were sacrificed and heart weight (HW), total kidney weight (TKW) and kidney lesion grade (KLG) were measured. HW, TKW, and KLG data are depicted in the histograms. The arrowheads

represent the location of the mean value of the population. Mean values are given ± SD.

Figure 4. Summary of genome scan for urinary protein excretion (UPE), urinary

albumin excretion (UAE), systolic blood pressure (BP), kidney lesion grade (KLG), heart

weight (HW) and total kidney weight (TKW) at 8, 12 and 16 weeks of age for the

backcross population (n=228) raised on 2% NaCl diet. The animals were placed on 2%

NaCl diet after the initial phenotype data were collected for BP, UPE, and UAE at 8

weeks of age. Thus, only the data at time points 12 and 16 week were from rats on the

high-salt diet. The lines to the right of the linkage maps represent the 2-LOD interval of

each QTL identified which achieved at least suggestive significance (LOD >1.9). The

QTL that achieved a significant level of significant (LOD >3.3) are denoted by an

asterisk (*). Traits that were observed at only one or two time points are labeled with the

time points at which the QTL was observed. If no time point is specified then the QTL

was observed at all three time points.

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Figure 5. Congenic strain data for urinary protein excretion (UPE) and urinary albumin

excretion (UAE) QTL on RNO2 (panel A), RNO6 (panel B), RNO9 (panel C), RNO11

(panel D), and RNO13 (panel E). The introgressed region of each congenic strain is shown next to the linkage map in each panel. The black bars designate the extent of introgressed SHR alleles on the S background. The open regions on the ends of these bars represent the interval in which recombination occurred. Bar graphs show the effect of each congenic (C) strain compared to both parental strains, S and SHR. The left y-axis plots the effect of UPE and the right y-axis plots the effect of UAE. All the rats studied were bred concomitantly and age matched for urine collection. At 30 days of age the rats were weaned and placed on a low-salt (0.3% NaCl) diet. Urine was collected on animals

51-53 days of age for determination of UPE and UAE. The number of animals used for each strain were as follows: S (n=18), S.SHR(2) (n=18), S.SHR(6) (n=16), S.SHR(9)

(n=18), S.SHR(11) (n=12), and S.SHR(13) (n=16). Error bars are SE. * denotes S versus congenic comparison; significantly different at p<0.0001. # denotes congenic versus SHR comparison; significantly different at p<0.0001. P-values are from a one-way analysis of variance followed by post hoc multiple comparisons using Tukey’s test.

Figure 6. Comparison of the urinary albumin excretion (UAE) QTL observed in the low-salt (LS) backcross population (12) and those observed in the high-salt (HS) backcross population (present study). The blue boxes indicate that the QTL was observed at a given time point. The yellow boxes represent indicate that the QTL was not observed.

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Figure 1

121

Figure 2

122

Figure 3

123

Figure 4

124

Figure 5

125

Figure 6

126

Table 1. Phenotypic Effect by Genotype for BP, UPE, UAE, HW, TKW, and KLG in the backcross population of 228 rats raised on 2% NaCl diet

Genotype

Week Phenotype RNO Marker S/S S/SHR Effect P Value

8 UPE (mg/24 hours) 2 D2Rat179 18.9 (1.01) 16.5 (1.03) +2.4 0.001 6 D6Uia5 25.2 (1.05) 21.6 (1.05) +3.6 0.007 9 D9Uia6 19.0 (1.04) 15.7 (1.04) +3.3 0.001

UAE (mg/24 hours) 1 D1Rat86 1.7 (1.15) 1.1 (1.15) +0.6 0.006 2 D2Rat179 1.9 (1.14) 1.0 (1.15) +0.9 0.001 6 D6Uia5 2.0 (1.16) 1.0 (1.14) +1.0 0.001 8 D8Rat62 1.8 (1.14) 1.0 (1.14) +0.8 0.001 9 D9Uia6 1.6 (1.13) 1.0 (1.17) +0.6 0.005 11 D11Rat50 1.1 (1.15) 1.7 (1.15) -0.6 0.003 13 D13Wox5 1.9 (1.17) 0.9 (1.15) +1.0 0.001 19 D19Rat74 1.8 (1.17) 1.0 (1.14) +0.8 0.007

12 UPE (mg/24 hours) 1 D1Rat86 26.5 (1.06) 20.4 (1.04) +6.1 <0.0001 6 D6Uia5 25.2 (1.05) 21.6 (1.06) +3.6 0.007 8 D8Rat62 27.9 (1.06) 19.7 (1.04) +8.2 <0.0001 9 D9Uia6 25.7 (1.05) 20.4 (1.05) +5.3 0.002

UAE (mg/24 hours) 1 D1Rat86 6.1 (1.13) 3.2 (1.15) +2.9 0.001 6 D6Uia5 5.6 (1.14) 3.7 (1.15) +1.9 0.002 8 D8Rat62 5.8 (1.14) 3.4 (1.14) +2.4 0.001 9 D9Uia6 5.7 (1.13) 3.2 (1.16) +2.5 0.003 13 D13Wox5 6.4 (1.16) 3.4 (1.15) +3.0 0.002

BP (mm Hg) 11 D11Rat50 159 (1.9) 169 (2.0) -10.0 <0.0001

16 UPE (mg/24 hours) 1 D1Rat86 32.0 (1.07) 24.8 (1.04) +7.2 0.003 6 D6Uia5 30.3 (1.06) 26.9 (1.07) +3.4 0.001 8 D8Rat62 35.2 (1.06) 22.8 (1.05) +12.4 <0.0001 9 D9Uia6 32.9 (1.06) 23.0 (1.05) +9.9 0.005

UAE (mg/24 hours) 1 D1Rat86 5.3 (1.13) 4.4 (1.11) +1.0 <0.0001 6 D6Uia5 5.4 (1.12) 4.4 (1.14) +1.0 <0.0001 8 D8Rat62 6.9 (1.12) 3.5 (1.12) +3.4 <0.0001 9 D9Uia6 6.0 (1.11) 3.7 (1.14) +2.3 <0.0001 13 D13Wox5 6.9 (1.16) 3.6 (1.15) +3.3 <0.0001

HW (mg) 1 D1Rat158 1494 (8.1) 1446 (9.5) +48 <0.0001 9 D9Uia6 1494 (8.7) 1430 (7.9) +64 <0.0001

TKW (mg) 1 D1Mco35 3377 (31.1) 3201 (22.1) +176 <0.0001 13 D13Wox5 3404 (33.4) 3218 (25.4) +186 <0.0001 19 D19Rat74 3366 (29.0) 3213 (24.1) +153 <0.0001

KLG ( scale 0 to 4) 1 D1Rat86 1.4 (0.08) 1.0 (0.08) +0.4 0.001 2 D2Rat50 1.3 (0.09) 1.0 (0.08) +0.3 0.004 8 D8Rat62 1.4 (0.09) 0.9 (0.07) +0.5 <0.0001 9 D9Mco6 1.3 (0.08) 0.9 (0.09) +0.4 0.001 13 D13Wox5 1.4 (0.10) 0.9 (0.07) +0.5 <0.0001 UPE and UAE values were not normally distributed. The data was transformed using natural logarithm (ln) to better approximate a normal distribution. In evaluating the magnitude and direction of the effect of UPE and UAE QTL the mean of the ln values for each genotype was converted back to mg/24 hours by taking the antilog, yielding the geometric mean value for each genotype. The phenotypic effect is defined as the geometric mean of animals homozygous S/S minus the geometric mean of animals heterozygous S/SHR for markers at or near the QTL peak. P-values are from independent t- test. SE (geometric) are shown in parenthesis.

127

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132

Dissection of a Genetic Locus Influencing Renal Function in the Rat and

its Concordance with Kidney Disease Loci on Human Chromosome

1q21

Michael R. Garrett1, William T. Gunning2, Tracy Radecki1, and Arti Richard1

1Department of Physiology, Pharmacology, Metabolism and Cardiovascular Sciences,

and 2Department of Biochemistry and Biology, University of Toledo, Health

Science Campus, Toledo, Ohio 43614-5804.

Running Head: Genetic Dissection of a Rat Locus Involved in Renal Function

Keywords: Dahl salt-sensitive rat, QTL, albuminuria, proteinuria

Corresponding Author: Michael R. Garrett

Department of Physiology, Pharmacology, Metabolism and

Cardiovascular Sciences

University of Toledo, Health Science Campus

3035 Arlington Avenue, Toledo, OH 43614-5804

Phone: 419-383-4144

E-mail: [email protected]

Fax: 419-383-6168

To be submitted for publication in Genome Research

133 Abstract

Previously, we conducted a genome scan on a population derived from the Dahl

salt-sensitive hypertensive (S) and the spontaneously hypertensive rat (SHR) using

urinary albumin excretion (UAE) as our primary measure of renal function. We identified

10 quantitative trait loci (QTL) linked to several renal and/or cardiovascular traits. In

particular, linkage and subsequent congenic strain analysis demonstrated that the loci on

chromosome 2 had a large and significant effect on UAE compared to the S rat. The

present work sought to characterize the chromosome 2 congenic strain [S.SHR(2)] by

conducting a time-course analysis (week 4 to 20), including evaluating additional renal

parameters, histology, electron microscopy, and gene expression/ pathway analysis.

Throughout the time course the congenic strain consistently maintained a two-fold

reduction in UAE compared to S rats and was supported by the histological findings of

significantly reduced glomerular, tubular and interstitial changes. Gene

expression/pathway analysis performed at week 4, 12, and 20 revealed that pathways involved in cellular assembly and organization, cellular movement, and immune response were controlled differently between the S and congenic. Considering all the data, the chromosome 2 congenic appears to attenuate renal damage primarily through an altered fibrotic response. Recombinant progeny testing (RPT) was employed to reduce the QTL to ~ 5 Mb containing several interesting candidate genes. The concordance of this rat

QTL with renal disease loci on human chromosome 1q21 demonstrate that elucidating the causative gene and mechanism of the rat QTL may be of particular importance for understanding kidney disease in humans.

[Supplemental material is provided with this manuscript]

134

Introduction

Chronic kidney disease (CKD) is a worldwide healthcare problem with increasing incidence and prevalence (NKF 2002). It is characterized by a gradual loss of kidney function, usually over years, and can culminate in renal failure. Additionally, CKD is an important risk factor for the development of cardiovascular disease and overall mortality.

Interestingly, most CKD cases are not associated with primary renal disease, but with systemic conditions like diabetes and hypertension (NKF 2002; USRDS 2003). Essential hypertension can cause renal injury, but renal susceptibility genes most likely determine occurrence and severity of renal damage (Tylicki et al. 2002).

It has been well established, through familial studies, that CKD has a strong genetic component (Bergman et al. 1996; Ferguson et al. 1988; Freedman et al. 1993;

Spray et al. 1995). Investigation of congenital and familial forms of kidney disease has led to the identification of genes required for proper functioning of the glomerular filtration barrier (Chow et al. 2005). These studies have provided insight into mechanisms of proteinuria and glomerulosclerosis. However, these mutations have not been linked to

CKD in the general population, demonstrating that CKD is most certainly a complex trait and as such makes genetic studies in humans more difficult (Chow et al. 2005). On the other hand, the rat provides a particularly fertile model to study disease because it overcomes some of the limitations associated with using human subjects. There are many well-defined inbred rat strains currently being used to study the genetics of CKD (Brown et al. 1996; Matsuyama et al. 1990; Murayama et al. 1998; Schulz et al. 2002; Schulz et al. 2003; Shiozawa et al. 2000; Stella et al. 1991). In particular, the Dahl salt-sensitive rat

135 (S) was selectively bred as a model to study the genetics of salt-sensitive hypertension

(Dahl et al. 1962a; Dahl et al. 1962b). However, the S rat also develops progressive proteinuria, severe glomerular, vascular, and tubular lesions, beside the development of hypertension, and consequently can serve as a model of hypertension related renal disease (Hampton et al. 1989; Sterzel et al. 1988)

Recently, a genetic analysis of renal and cardiovascular traits was conducted using the S and the spontaneously hypertensive rat (SHR) by our group (Garrett et al.

2003) and others (Poyan Mehr et al. 2003; Siegel et al. 2004). Linkage analysis identified quantitative trait loci (QTL) on multiple chromosomes (1, 2, 6, 8, 9, 10, 11, 13, and 19) for urinary protein excretion (UPE) and/or urinary albumin excretion (UAE) with variable time-course patterns (Garrett et al. 2003). A second study found that several

QTL (chromosome 2, 11, and 19) were influenced by salt-loading, presumably a result of changes in blood pressure in response to the high-salt diet (Garrett et al. 2006). Congenic strain analysis established that the QTL on chromosome 2 had a major influence on renal function in the S rat (Garrett et al. 2006).

The first aim of the present work was to characterize the chromosome 2 congenic strain by conducting a time-course analysis and establishing onset and progression of renal disease in comparison to both parental strains. We employed a comprehensive approach, including evaluation of additional renal parameters, histology, electron microscopy, gene expression analysis, and gene pathway analysis to characterize the strain. A second aim was to employ recombinant progeny testing (RPT) to reduce the

QTL to a small genomic region to aid in gene identification.

136 Results

Characterization of S, S.SHR(2) and SHR

A time course for UPE, UAE, and BP of each group of rats is presented in Figure

1. No detectable difference in UPE (Fig. 1A) was observed between the groups at week

4. Starting at week 5 and continuing through week 20, the S rat had significantly higher

UPE compared to either the S.SHR(2) congenic or SHR. The S.SHR(2) congenic

consistently maintained at least a 2-fold reduction in UPE compared to the S rat. The S

rat rapidly progressed beyond the “proteinuria” threshold (>20mg/ 24 hours) between

week 6 to 8, while the congenic reached this point between week 8 to 12. The SHR never

progressed beyond this threshold. At week 20 S rat UPE was 270±22.9 mg/24hrs compared to 92.3±8.3 in the congenic. Differences between groups for UAE (Fig. 1B)

were similar to UPE, although the congenic maintained a greater fold reduction (3-fold)

compared to the S rat. Despite the large difference between the S and congenic with

respect to UPE, no detectable difference in systolic BP (Fig. 1C) or associated

parameters, such as diastolic BP, pulse pressure, or heart rate was observed (data not

shown).

Table 1 shows additional experimental parameters between the groups at week 20.

Body weight (BW) between the S and S.SHR(2) congenic were similar, but were

significantly greater than the SHR. No significant difference in heart weight (adjusted for

BW differences) between the groups were observed, but kidneys from the S rat were

significantly larger than either the congenic or SHR. Plasma sodium and potassium were

significantly higher in the S compared to either the congenic or SHR. Blood urea nitrogen

137 (BUN) was also higher in the S rat. Creatinine clearance, normalized to kidney weight,

was not different between the groups, although plasma creatinine was significantly reduced in the SHR.

Kidney from S, S.SHR(2), and SHR were examined and evaluated at week 4, 12, and 20 for glomerular, tubular, vascular, and interstitial damage (Fig. 2). Representative

histological images for week 20 animals are shown in Figure 3. At week 4, all animals

showed some evidence of immature glomeruli demonstrated by small, hypercellular glomerular structures. No significant pathology was observed between the S and the

S.SHR(2) congenic (Fig. 2), except significant interstitial changes were observed between these two strains. At week 12, glomerular damage of the congenic (1.0± 0.02) was significantly lower than the S (1.3± 0.01), although both exhibited focal glomerulosclerosis. Tubular and interstitial changes were significantly attenuated (~1.5- fold) in the congenic compared to the S (Fig. 2). No significant vascular changes were seen among any of the groups. At week 20, the S rat had approximately twice the glomerular damage as the congenic (2.7± 0.04 versus 1.6 ± 0.04). Most notable was that the S rat experienced a significant increase in fibrosis from week 12 (1.8± 0.17) to 20

(2.6± 0.24), while there was essentially no change in the congenic (1.2± 0.17 versus 1.3±

0.24). The SHR showed similar glomerular pathology to the congenic at week 12. Again, no significant vascular changes were seen between groups.

An ultrastructural examination of kidney from S, S.SHR(2), and SHR confirmed the light microscopy data and provided more specific detail on pathological changes between the groups (Fig. 3). At week 4, little or no pathology was observed between groups. Glomeruli appeared to be within normal limits regarding general appearance of

138 discrete foot processes of the visceral epithelial cells. At week 12, significant differences were seen between the S and congenic. Changes were focal in nature and did not involve all glomeruli. Little or no pathology was seen in the SHR. At week 20, significant glomerular disease including evidence of visceral cell effacement, significant glomerulosclerosis, focal mesangial matrix increase, and diffuse mesangial electron dense deposits were observed in the S (Fig. 3). S.SHR(2) kidneys were also found to have focal visceral cell effacement, focal glomerulosclerosis, focal mesangial matrix increase, and focal mesangial electron dense deposits; however, the pathologic changes were not nearly as severe as the S. SHR had some glomerular pathology including minimal effacement of foot processes, focal mesangial matrix increase and rare mesangial electron dense deposits.

Expression Profiling and Pathway Analysis

Gene expression profiling was performed at week 4, 12, and 20 using kidney from the S and S.SHR(2) congenic to: (1) identify expression differences of positional candidates within the QTL region; (2) correlate temporal gene expression changes between the S and congenic with degree of renal damage; and (3) identify biochemical pathways potentially involved in the attenuated renal damaged observed in the congenic.

Table 2 gives a summary of the number of genes found to be differentially expressed throughout the genome and on chromosome 2, striated by fold-change and statistical significance. On average, depending on fold-change threshold and statistical tests, ~40% of differentially expressed genes map within the congenic interval. A total of 727 protein-

139 coding genes (www.ensemble.org) reside in the congenic interval and are represented on the Affymetrix Genechip®, of these, anywhere from 48 or 6.6% (week 4) to 100 or

13.7% (week 20) of genes were found to be differentially expressed.

A detailed description of genes found to be differentially expressed and that map within the congenic interval are shown in Table 3. Genome wide analyses of differentially expressed genes are shown in supplemental Table 1. A total of 37 genes/expressed sequence tags (EST) on chromosome 2 (fold-change ± 2, FDR, p<0.05) were observed to be differentially expressed between the S and S.SHR(2) throughout the time course. Seven of these genes (Table 2, #31-37) were consistently down-regulated at all three time points. In general, genes that were observed to be differentially expressed at one time point were also found to be differentially expressed at an additional time point

(serving to confirm the expression data). However, there were many genes observed in entire data set (supplemental Table 1) where expression differences were time specific.

To gain insight into the major biological processes and pathways linked to genes differentially expressed between the S and S.SHR(2) congenic, the gene expression data was evaluated using the Ingenuity Pathway Analysis (IPA) program (see material and methods for details). Multiple networks were discovered in each of the three dataset

(week 4, 12, and 20). Table 4 summarizes (additional information in supplemental Fig.1 and supplemental Table 2) top functions associated with these networks. Cellular movement, cellular growth and proliferation, and connective tissue development and function were recurring top scoring functions from week 4 to 20. Additionally, canonical pathways (which are the most relevant pathways based on the entire dataset) for Wnt/ß - catenin signaling, amyloid processing, and antigen presentation were identified in week

140 4, Wnt/ß -catenin signaling pathway and glutathione metabolism in week 12, and nitric

oxide signaling in week 20.

A two-way ANOVA was performed on the time course microarray data to identify genes that were significantly different between strains [S and S.SHR(2)] and

over time [week 4, 12, and 20], i.e. strain X time interaction. A total of 186 genes were

identified using fold-change ±1.5 or greater and a p<0.01 (supplemental Table 3).

Approximately one-third of these genes were not previously observed to be different

when examined at each time point individually. Figure 4 depicts the top scoring networks

and associated high level functions observed in the dataset. Top functions identified

involved cellular assembly, cellular movement, and immune related functions.

Fine Mapping the Renal Function Locus

Recombinant progeny testing (RPT) was employed to refine the genomic region

containing the QTL responsible for the observed difference in renal function between the

S and S.SHR(2) congenic. The basis of RPT is to find recombinants in an interval derived

from the S.SHR(2) congenic, to propagate these recombinants, then to measure the

phenotypes of their progeny in order to determine the genotypic value of each

recombinant. A large backcross population (n=971) was developed from the S.SHR(2)

congenic strain and the S rat to identify recombinant animals and to reduce the confidence interval for the QTL region. Bootstrapping analysis of the original linkage

analysis (backcross population, n=276) (Garrett et al. 2003) localized the 95% confidence

interval (CI) to a ~14cM region delimited approximately by markers D2Rat220 and

141 D2Rat50 (Fig. 5). The new backcross population derived from the S.SHR(2) congenic reduced the 95% CI to ~1.5 cM, flanked by D2Arb11 and D2Rat284. A total of 20 recombinant animals were selected from this population for RPT. All recombinant families were evaluated for UPE at 6 weeks of age for an initial survey of the families

(Fig. 5). Recombinant families 1-4 (shown in red) all demonstrated a significant reduction (40-60%) in UPE, that is, animals that carried the recombinant chromosome

(congenic-like) had significantly lower UPE compared to non-recombinant littermates (S- like). Families 5-17 (shown in yellow) showed no significant effect on UPE, which eliminates the region above D2Arb11, and below D2Rat108, for contributing to the QTL.

Families 18-20 (also shown in red) demonstrated a significant effect on UPE.

Figure 6 shows an enlargement of the QTL region and the four recombinant families (4, 6, 17, and 18) important for delimiting the QTL. To confirm the data obtained from the initial survey (Fig. 5), additional animals (n=43-84) were tested from each family of the four critical families. UPE was evaluated in these families at 6 and 12 weeks of age to determine if there was a sustainable effect on UPE in older animals (Fig.

6). Families 4 and 18 demonstrated a significant effect on UPE at week 6, which continued through week 12. For family 4 (week 12), the average UPE for non- recombinant animals was 95.3± 4.94 mg/24 hours versus 56.70± 4.45 mg/24 hours for recombinant animals. For family 18, the average UPE for non-recombinant animals was

95.3± 4.84 mg/24 hours versus 54.70± 4.04 mg/24 hours for recombinant animals. No significant effect on UPE was observed for family 5 or 17 at either week 6 or 12, demonstrating that the QTL lies in the interval flanked by D2Rat46 and D2Rat230 (Fig.

6).

142 The refined QTL region spans 1.5 cM or ~ 5.0 Mb, which contains 64 known

and/or predicted genes. The locations of these genes are depicted in Fig. 6 (additional

information provided in supplemental Table 4). Of the 37 differentially expressed found

to map on RNO2, only two of these map to the refined QTL region (Table 3). They are

secreted frizzled-related protein 2 (Sfrp2) and chaperonin containing TCP1, subunit 3

(Cct3). The region also contains several candidate genes based on known function or renal involvement including, Neph1, Ras-like protein in 25 (Rab25), and IQ motif

containing GTPase activating protein 3 (Iqgap3).

Discussion

For this study we sought to investigate differences in onset and progression of

renal disease in the Dahl S, S.SHR(2) congenic, and SHR using a time-course. Our data

clearly demonstrates that the locus on chromosome 2 has a major and sustained ability to

attenuate renal damage. As early as week 4, significant interstitial changes were observed

between the S and the congenic which preceded any significant difference in UPE. This

suggests that the QTL may attenuate renal damage primarily through controlling fibrosis,

as opposed to controlling glomerular permeability. This is further supported by the fact

the congenic experienced increased glomerular damage from week 12 to 20, with no

significant change in interstitial injury. Another finding was that no significant difference

in systolic BP was observed between the S and congenic. This data demonstrates that the

reduced renal damage in the congenic is not simply a consequence of lower BP compared

to the S suggesting the underlying mechanism of the QTL is unlikely to be mediated

through BP regulation.

143 In recent years, several groups have used a combined QTL and gene expression

approach to prioritize candidate genes for further study within a QTL region (Joe et al.

2005; McBride et al. 2003; Yagil et al. 2005), while others have successfully used the

approach to expedite the identification of disease causing genes (Aitman et al. 1999; Fehr et al. 2002; Karp et al. 2000; Rozzo et al. 2001). More recently, two groups have utilized a large scale integrated approach using either a panel of recombinant inbred strains

(Hubner et al. 2005) or chromosome substitution strains (Malek et al. 2006) with gene expression profiling to gain insight into genes and pathways underlying disease processes. To this end, we sought to utilize gene expression analysis to identify differential expressed genes linked to the QTL and establish biochemical pathways responsible for the renal protective effect of the congenic strain.

Network analysis of differentially expressed genes consistently identified cellular movement and cellular growth and proliferation as top functions associated with gene networks at each week. Additionally, networks linked to genes that were significantly different between strains [S and S.SHR(2)] and over time (week 4, 12, and 20) were

cellular assembly and organization, cellular movement, and immune response. The

individual analysis provided a “snapshot” of genes differentially expressed at each time

point, while the second analysis (strain X time) placed the networks in context of what

occurred over time and identified additional genes of interest. The purpose of conducting

the gene expression analysis at week 4 before the onset of detectable differences in UPE

was to address the issue of “cause and effect”. The assumption being that networks

identified at the early time point are more likely to reflect the “cause” and not the “effect”

144 of differences in renal function such as networks observed at later time points (week 12

and 20).

At week 4 (and week 12) the Wnt/ ß-catenin signaling pathway was identified as

the most significant pathway based on the entire dataset (Table 4). Sfrp2 and Wnt2b are

genes that participate in this pathway, were differentially expressed, and mapped within

the QTL interval (Table 3). Wnts encode secreted proteins that bind members of the

Frizzled receptor family which regulates cell proliferation and differentiation (Brembeck

et al. 2006; Nelson and Nusse 2004). Conventional Wnt signaling is mediated by

stabilization of ß-catenin, which then translocates into the nucleus and interacts with T-

cell factor (TCF)/lymphoid enhancing factor (LEF) family to alter gene expression

(Nelson and Nusse 2004). ß-catenin also mediates the interplay of adheren junctions with

the actin cytoskeleton through E-cadherin (Nelson and Nusse 2004). The Wnt/ ß-catenin

pathway has been implicated in playing in a role in renal fibrosis by driving tubular cells

to undergo an epithelial-mesenchymal transition (EMT) into activated fibroblasts

(Surendran et al. 2005).

Renal fibrosis represents the pathological event most correlated with loss of renal function considering all forms of renal disease (Liu 2006; Zeisberg et al. 2001). So, does proteinuria in itself cause renal fibrosis? An attractive explanation is that albumin being the principal component of ultrafiltered protein during proteinuria causes tubular cell injury. This hypothesis has been supported by several in vitro studies (Donadelli et al.

2000; Zoja et al. 2003). Another more interesting hypothesis involves the translocation of

growth factors accompany proteinuria [including insulin-like growth factor 1 (IGF-I);

hepatocyte growth factor (HGF), and transforming growth factor ß (TGF-ß), ] into the

145 tubular fluid causing activation of tubular cells and recruitment of circulating fibrocytes

(Hirschberg and Wang 2005). The “activated” tubular cells secrete chemokines [namely, monocyte chemoattract protein -1 (MCP-1, CCL2) and RANTES (CCL5)] that activate macophages to secrete TGF-ß, promoting tubular EMT and producing myofibroblasts

(Hirschberg and Wang 2005). These highly differentiated myofibroblasts cells produce extracellular matrix and in coordination with matrix degrading enzymes result in fibrosis.

The gene expression/network analysis, along with the histology data seems to support a hypothesis by which the congenic experiences reduced kidney damage primarily through controlling fibrosis. While the congenic does have significantly less

UPE compared to the S, it still develops proteinuria and accompanying kidney pathology.

This is not surprising given that the congenic still contains other S susceptibility alleles at other QTL (on chromosome 1, 6, 8, 9, 13, and 19 which act to increase UPE). Consistent with the above hypothesis, growth factors make it into the tubular fluid of both the S and

S.SHR(2) congenic strain. However, the congenic responds differently to these growth factors and associated signaling pathways, attenuating fibrosis, which results in less renal scarring and associated glomerular damage.

Recombinant progeny testing (RPT) was used as an alternative to conventional substitution analysis to reduce the QTL interval. RPT has been suggested as one approach for fine mapping QTL with large and dominant effects (Darvasi 1998), both of which are the case for the chromosome 2 QTL (Garrett et al. 2006). Slight variations of this approach (RPT using congenic strains or interval specific congenic strain) have been utilized to substantially narrow QTL regions to small intervals (Christians et al. 2003;

Christians and Keightley 2004; Fehr et al. 2002; Liu et al. 2001; Oliver et al. 2005). The

146 main benefit over conventional substitution mapping is the rapid development and testing

of many recombinant animals. The benefit demonstrated by the current work was that a

single male recombinant animal could be bred to multiple females, rapidly producing offspring and allowing for more recombinant families to be tested at a given time. The

scheme also had the advantage of minimizing environmental noise because the effects of the recombinant chromosomes were compared to non-recombinant littermates.

After one round of RPT the QTL interval was significantly reduced from an initial size of ~80 cM down to ~1.5 cM, eliminating many of the genes found to be differentially expressed as causative to the QTL. The region still contains many more

genes than are feasible to comprehensively evaluate without further work. However, the

region does contain several interesting candidate genes, two of which were differentially

expressed. These were Sfrp2 and Cct3. SFRPs act as soluble modulators of Wnt

signaling, so Sfrp2 could play a role in the proposed QTL mechanism discussed earlier.

Cct3 belongs to a protein complex (composed of eight subunits) involved in protein

folding of actin and tubulin, suggesting a possible role in maintaining cytoskeleton

integrity (Camasses et al. 2003). Additionally, CCT promotes the activation of the

anaphase promoting complex, a complex in which inversin (INVS) also interacts

(Camasses et al. 2003). Mutations in INVS cause nephronophthisis type 2 (NPHP2), an autosomal recessive form of cystic kidney disease (Otto et al. 2003). Interestingly, besides kidney cysts, a prominent feature of the disorder is the development of renal interstitial fibrosis (Otto et al. 2003).

The most interesting gene, Neph1, is structurally related and interacts with

Nephrin (NPHS1), a protein that localizes to the slit-diaphragm, and was found to be

147 mutated in congenital nephrotic syndrome type 1 (Kestila et al. 1998). Additionally, mice deficient in Neph1 develop severe proteinuria and die within the first weeks of life

(Donoviel et al. 2001). Rab25 belongs to a family of small GTPases and are involved in intracellular vesicular transport and trafficking (Stein et al. 2003). Recently, a null mutation in Rab38 (family member of Rab25) has been shown to be a strong candidate for the development of proteinuria in the fawn-hooded hypertensive (FHH) rat by potentially altering the tubular re-uptake of filtered protein (Rangel-Filho et al. 2005).

The fact that neither Neph1 nor Rab25 was differentially expressed or contained coding sequence variants between the S and congenic (data not shown) most likely eliminate them being causative to the QTL. Lastly, Iqgap3 belongs to a family of proteins that are integral components of cytoskeletal regulation which has been linked to E-cadherin mediated cell-cell adhesion and ß-catenin mediated transcription (Briggs and Sacks

2003).

Of importance to the current study is its relevance to human kidney disease. Rat chromosome 2 around the QTL is homologous to both human chromosome 1 and 4 (Fig.

7). The break point (between 178.523 and 179.114 Mb) occurs around the middle of the rat QTL. Interestingly, only human chromosome 1 has been linked to renal disease, whereas human chromosome 4 (homologous region to rat QTL) has not been linked to renal disease. An autosomal dominant form of medullary cystic kidney disease (MCDK1) was mapped to chromosome 1q21 using several kindreds (Kiser et al. 2004; Wolf et al.

2003; Wolf et al. 2004). The disease is characterized by tubulointerstitial nephropathy that causes renal salt-wasting and end-stage renal failure (Kiser et al. 2004). Cysts are common, but not always present. The presence of proteinuria is variable. Recently, high

148 resolution haplotype analysis was performed on 16 kindreds with MCDK1 (Wolf et al.

2006). Mutational analysis of 37 genes identified variations in three different genes, including CCT3 (Wolf et al. 2006). Another group mapped an autosomal dominant form of progressive renal failure and hypertension to the same region using a large Israeli family (Cohn et al. 2000). Clinically, all affected members present with hypertension with varying degrees of renal pathology (glomerulosclerosis, interstitial fibrosis, and tubular atrophy). A third study, which conducted a genome scan for renal function among hypertensive individuals (HyperGen study) found linkage of creatinine clearance to the same region on chromosome 1 (DeWan et al. 2001).

The co-localization of multiple renal disease loci clearly demonstrates the importance of this region in renal function. The fact that there are disparate etiologies to these disorders suggests that the gene(s) that underlie each disorder are not the same.

However, the possibility does exist that a common gene could underlie all the disorders.

Multiple alleles of the “gene” could have a distinct effect on the observed phenotype or the same allele could simply be influenced by the genetic background of the individual, resulting in a distinct disorder. Regardless, co-localization of these renal disease loci provide a means to prioritize the analysis of genes in the QTL for study until subsequent

RPT can help delimit the QTL further. So, if one assumes the same gene underlie all the different phenotypes, the QTL can be reduced to 600 Kb containing 18 genes (Fig.7, supplemental Table 4).

A limitation of the current study relates to the relatively large size of the congenic interval used to conduct the gene expression analysis. Only two of the 37 differentially expressed genes mapped to the refined QTL. A logical question to ask is to what extent

149 does expression differences in the other 35 differentially expressed genes (or even

sequence variants in genes outside the refined QTL) influence the major gene networks observed in this study? That is, are the observed biochemical pathways unrelated to the attenuated renal damage in the S.SHR(2) congenic? Unfortunately, this question can only really be answered once the causative gene is identified and its mechanism understood.

So, while the gene expression/network analysis does provide a clue to a potential mechanism, the data should be approached with some caution.

We have attempted to use a combined approach using congenic strain analysis

(RPT) and gene expression/network analysis to expedite the identification of the gene causative to the QTL on chromosome 2. The concordance of the rat QTL with several human renal disease loci demonstrate that elucidating the causative gene and mechanism of the rat QTL may be of particular importance for understanding human kidney disease..

Methods

Animals

The Dahl salt-sensitive (SS/Jr or S), spontaneously hypertensive rat (SHR/NHsd or SHR) and S.SHR(2) congenic strain were maintained in our animal facility at the

Medical University of Ohio at Toledo (MUOT). All experiments had approval of our

Institution Animal Care and Use Committee. At 4 weeks of age, a group of age matched males S (n=50), S.SHR(2) (n=50), and SHR (n=50) and animals were weaned to a low- salt diet (0.3% NaCl; TD7034; Harlan Teklad, Madison, WI) and studied for several renal and cardiovascular traits at 4,5,6, 8, 12, 16 and 20 weeks of age.

150 At each time point a subset of animals (n=6 to 12) were euthanized (overdose of

sodium pentabarbitol) for the collection tissue. Kidney samples were processed for

histological, electron microscopy, and microarray analysis. Serum samples were obtained

to measure blood parameters. Additionally body, heart and kidney weights were also measured.

For recombinant progeny testing (RPT), a large F1[S x S.SHR(2)] x S backcross population (n=971) was developed to establish recombinant animals within the introgressed region on chromosome 2. Male S.SHR(2) animals were crossed to S females to produce F1[S x S.SHR(2)] animals. The F1 rats were backcrossed to the S to produce the F1[S x S.SHR(2)] x S population. Additionally, urine parameters were collected on this population to reduce the confidence interval for the QTL region. The

971 rats were handled in 6 blocks of ~162 animals. Rats in each block were closely age matched, but between blocks the average ages differed by a few days. This allowed urine collections to proceed on all rats at approximately the same ages. Block effects, if any, were removed statistically before further analysis.

Phenotyping

Blood Pressure

BP was also collected using a telemetry system (Data Sciences International, St.

Paul, MN). At 61- 63 days of age a transmitter was surgically implanted in S (n=8) and

S.SHR(2) congenic (n=8) animals. Surgery was performed under 1.5% isoflurane anesthetic in 100% O2. The probe body was placed subcutaneously in the flank and the

probe catheter was inserted into the femoral artery and the tip of the probe was advanced

to the lower abdominal aorta as done previously (Joe et al. 2003). Data on systolic BP,

151 diastolic BP, mean BP, pulse pressure, and heart rate were collected. Readings for each

BP parameter was collected for a 24-hour period (5 minute time intervals for 10 seconds)

at 8, 12, 16, and 20 weeks of age.

Urine and Blood Parameters

To collect urine, animals were kept in metabolism cages (Lab Products, Seaford,

Delaware) for 24 hours with free access to water. Sodium azide was added to the

collection vials for a final concentration of approximately 0.01% in the urine as a

perservative. Urinary protein excretion (UPE) was determined colorimetrically using

pyrogallol red/molybdate complex (Quantimetrix, Redondo Beach, CA). Urinary

albumin excretion (UAE) was determined by rat specific albumin EIA kit (SPI-bio,

France). UPE and UAE are expressed as mg /24 hours. Urine creatinine was determined by the Jaffe’ method (Cayman Chemical, Ann Arbor, MI). Following 24-hour urine

collection, blood was obtained by cardiac puncture. Blood parameters (Table 1) were

determined by standard methods using an Alfa Wassermann ACE® automated chemistry

analyzer (BioReliance, Rockville, MD).

Histology

Kidneys were fixed in 10% buffered formalin and embedded in paraffin, cut into

3-µm sections and stained with hematoxylin and eosin or Masson’s trichrome. Two

central longitudinal sections from each kidney (n=6, each group) were examined in a

blinded fashion. Glomerular damage (glomerulosclerosis and mesangial expansion) was

accessed as follows: grade 0, no changes; grade 1, lesions involving less than 25% of

glomeruli; grade 2; lesions affecting 25- 50%; grade 3; lesions affecting 50- 75%; and

grade 4; lesions affecting >75%. On average, 895 glomeruli were evaluated per group.

152 Vascular, tubular, and interstitial injury was evaluated separately on a semi-quantitative

scale from 0 (normal) to 4 (severe). Vascular compartments were assessed for vessel wall

thickening, sclerotic involvement, and vasculitis/perivascular inflammation. Tubules

were evaluated for the presence of necrosis, hydropic change, and/or tubular casts.

Finally, the interstitium was assessed for inflammatory cell infiltrates and/or evidence of fibrosis.

Electron microscopy

For transmission electron microscopy (EM), a 1cm square region of kidney cortex was obtained from each animal (n=3, each group). Sample were fixed in 3% glutaraldehyde for 1 hour and washed three times for 10 minutes with 0.2 M sodium cacodylate, post-fixed for 2 hours with 1% OsO4 followed by 1 hour with saturated uranyl acetate. Dehydration was carried out by a graded series of chilled ethanol solutions

(30-100%) and a final dehydration with 100% acetone. Cortical samples were infiltrated

overnight in Spurr's resin (Electron Microscope Sciences) and ultra-thin sections were

obtained and collected on copper 300-mesh support grids. Sections were stained with

uranyl acetate and lead citrate, and examined using a Philips CM 10 transmission electron

microscope.

Microarray and Analysis

DNA microarray analysis was performed using Affymetrix Genechip® Rat

Genome 230 2.0 array at three timepoints. The chip contains 31,000 probe sets from

more than 30,000 transcripts on one array. Three male S and three male S.SHR(2)

congenic were selected at random from each group at week 4, 12 and 20. Kidney was cut

into ~ 0.5cM size cubes, suspended in RNAlater (Ambion, Austin, TX) and stored

153 overnight at 4ºC. RNA was extracted using Trizol® reagent (Invitrogen, Carlsbad, CA)

and purified using Mini RNeasy kit (Qiagen,Valencia, CA ) according to manufacturer’s

protocols. RNA quality was assessed by an OD260/280 ratio > 2.0 and visually by ethidium

bromide staining on an agarose gel.

Biotinylated cRNA was synthesized from 10 µg of total RNA using the One-

Cycle Target Labeling Kit (Affymetrix, Santa Clara, CA) as directed by the user manual. cRNA quality for each sample was assessed by hybridization of 5µg adjusted kidney cRNA to Affymetrix Genechip® Test3 array. Subsequently, 15µg adjusted kidney cRNA was hybridized to the Genechip® Rat 230 2.0 array. Hybridized chips were automatically washed, stained and scanned at the Medical University of Ohio Bioinformatics &

Proteomics/Genomics Program gene array facility using Affymetrix equipment. Data obtained from these gene expression studies are deposited in the Gene Expression

Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) with the GEO accession

number XXXXXXXX.

Microarray analysis was performed using the commercially available

GeneSifter.net software platform (http://www.genesifter.net). Expression values were

generated by probe level analyses using GC- RMA (robust multi-array averaging with an

adjustment for GC content). The GC-RMA method converts the intensities from multiple

probes from a probe set into a single expression value and uses both perfect-match (PM)

and mismatch (MM) probe information. The data is normalized by quantile normalization

and log2 transformed. Following the normalization process, differentially expressed gene were statistically identified by t-test using two methods at each time point: (1) FWER

(family-wise error rate) procedure, p<0.05 and fold-change ±1.5 or greater; and (2) a

154 more stringent procedure, using Benjamani and Hochberg FDR (false discovery rate)

which corrects for multiple comparison, using p<0.05, and fold-change ±1.5 or greater.

Additionally, a two-way ANOVA using a p<0.01 was performed to identify strain X time

interaction effects between the S and S.SHR(2) from week 4 to week 20.

Gene networks and functional analysis were generated through the use of

Ingenuity Pathways Analysis (Ingenuity® Systems, www.ingenuity.com). Gene

identifiers and corresponding expression values from data sets analyzed at week 4, 12, 20 or the strain x time interaction from week 4 to 20 from GeneSifter.net was uploaded into

in the application. Each gene identifier was mapped to its corresponding gene object in

the Ingenuity Pathways Knowledge Base. These genes, called focus genes, were overlaid

onto a global molecular network developed from information contained in the Ingenuity

Pathways Knowledge Base. Canonical pathways analysis identified the pathways from

the Ingenuity Pathways Analysis library of canonical pathways that were most significant

to the data set. The significance of the association between the data set and the canonical

pathway was measured in two ways: (1) A ratio of the number of genes from the data set

that map to the pathway divided by the total number of genes that map to the canonical

pathway; and (2) A Fischer’s exact test was used to calculate a p-value determining the

probability that the association between the genes in the dataset and the canonical

pathway is explained by chance alone.

Genotyping

Genomic DNA was obtained by tail biopsy from F1[S x S.SHR(2)] x S backcross

and RPT animals and prepared using Wizard SV 96 Genomic DNA kit (Promega, San

Luis Obispo, CA). Genotyping was done by multiplexing (4 primer set per PCR reaction)

155 using a fluorescent-based approach on a Beckman Coulter CEQ8000 XL capillary sequencer. Briefly, each primer is labeled with a fluorescent dye (dye D4-PA, Beckman

Coulter, Fullerton, Ca) which produces a PCR product that can be detected using the

CEQ system. Given only one fluorescent dye was utilized, primers for multiplex PCR were selected to vary in size and allow multiple PCR products to be identified during any given CEQ run. A total of 24 markers spaced at ~5 cM intervals on chromosome 2 were used to identify recombinant animals in the F1[S x S.SHR(2)] x S backcross population which were subsequently used for RPT.

PCR reactions were prepared as follows in a 10 µl reaction: 1X Buffer containing

1.5mM MgCl2 (Promega, Madison, WI), 0.2mM dNTP’s (Sigma, St Lois, MO), 2.5pmol

D4-PA each forward primer (Sigma-Proligo, Boulder, CO), 2.5pmol each reverse primer, and 0.25 U Taq DNA polymerase (Promega, Madison, WI). PCR amplification was performed as follows: an initial step at 95ºC for 3 min and continued for 35 cycles of

94ºC for 40 sec, 55ºC for 40 sec, 72ºC for 1 min. After amplification, the PCR products were diluted 1:10 in water, 1-3 µl of diluted multiplex PCR product was added to 40 µl deionized formamide containing DNA size standard (Beckman Coulter, Fullerton, CA), loaded on CEQ sequencer, and then analyzed using CEQ fragmentation analysis software.

Statistical Analysis Linkage analysis and QTL localization were performed using Map Manager QTX

(http://mapmgr.roswellparks.org) (Manly et al. 2001). The approximate confidence internal of the chromosome 2 QTL peak was estimated using the bootstrap method of the

Map Manager QTX program. S, S.SHR(2) and SHR time course data was evaluated by

156 one-way analysis of variance followed by post hoc multiple comparisons using Tukey’s test (SPSS, Chicago, IL). RPT data was evaluated using an independent t-test using SPSS

(Chicago, IL). All data are presented as mean ± SE.

157

Acknowledgements

Grant support to M. Garrett from the National Institutes of Health (RO1-HL-

066998) and Medical University of Ohio (MUOT) Bioinformatics and

Proteomics/Genomics program is greatly appreciated. The authors thank Dr. Bina Joe for the implantation of the telemetry probes for blood pressure measurements; Dr. David

Weaver, director of the genomics core laboratory at MUOT for help conducting the

Affymetrix Gene chip protocols; and Dr. John Rapp for critical reading of this manuscript.

158

Figure Legends

Figure 1. Time course for urinary protein excretion (UPE), panel A; urinary albumin

excretion (UAE), panel B; and systolic blood pressure (BP), panel C in male S,

S.SHR(2), SHR rats. A schematic of the congenic strain is shown in panel A inset. The

black bar designates the extent of introgressed SHR alleles on the S background. The open region on each end of the congenic segment represents the recombination interval.

In panel A, the dashed line represents the threshold for “proteinuria” (> 20 mg/24hour).

Rats were maintained on low-salt diet (0.3% NaCl) for entire course of the experiment.

The number of animals at each time varied from n= 50 per group (week 4) to n=12 per group (week 20). Systolic BP was measured by telemetry (n=8 per group). Mean values ±

SE

Figure 2. Time course histological examination of male S, S.SHR(2), and SHR rats.

Glomerular injury score, panel A; tubular injury score, panel B; vascular injury score,

panel C; interstitial injury score, panel D. Panel E presents an intergraded assessment of

kidney pathology calculated as the average of measures in panel A-D. Bar graphs show

the effect of the S.SHR(2) congenic (C) strain compared to both parental strains, S and

SHR. Kidneys from six animals were evaluated at each time point. ND, not detectable. a,

significantly different from S.SHR(2) and SHR at p<0.05; b, significantly different from

S and S.SHR(2) at p<0.05; c, significantly different from SHR at p<0.05. P-values are

from a one-way analysis of variance followed by post hoc multiple comparisons using

Tukey’s test. Mean values ± SE.

159

Figure 3. Representative light (20X original magnification) and electron microscopy

(3000 X) images of week 20 kidney samples from male S, S.SHR(2), and SHR rats.

Hematoxylin and eosin (H& E), left; Masson’ trichrome, center; and electron microscopy

images, right.

Figure 4. Functional network analysis of genes differentially expressed between S and

S.SHR(2) and over time (strain X time). Nodes represent genes, with their shape

representing the functional class of the gene product (see figure legend , supplemental

Fig. 1). The lines connecting the nodes indicate the biological relationship between each

node. Shaded nodes are genes found to be differentially expressed between strains and

over time. High level functions for each network and significance level are shown. Top

functions in the entire data set are also shown.

Figure 5. Fine mapping of renal function QTL using recombinant progeny testing

(RPT). The physical map of rat chromosome 2 is shown on the left. The solid bars to the

right of the physical map indicate the extent of the SHR-donor regions for each recombinant family. The open region represents the recombination interval. Urinary protein excretion (UPE) was measured at week 6. The solid black bar denotes the location of the QTL based on the RPT. The 95% confidence interval (CI) obtained from the original linkage analysis (Garrett et al. 2003) and the present study is shown to the right of the figure. Red bars denote that recombinant animals (congenic-like) had significantly (p < 0.01) lower UPE compared to non-recombinant (S-like) littermates.

160 Yellow bars denote there was not a significant difference in UPE between recombinant

and non-recombinant littermates. The number of animals tested from each recombinant

family ranged from 16 to 29.

Figure 6. Enlargement of the QTL region and the four recombinant families important in delimiting the QTL region. The physical map of rat chromosome 2 is shown on the left, with an enlargement of the physical map of the QTL region to the right . Map distances are in basepairs (www.ensembl.org, Ensembl v38 - Apr 2006). Urinary protein

excretion (UPE) was measured at week 6 and week 12. Data for “UPE Effect” below

each bar is UPE of recombinant rats (congenic-like) minus the UPE of non-recombinant

(S-like) littermates. A negative value indicates that recombinant rats had lower UPE than

non-recombinant rats, and when this is significant this indicates that the QTL is in the

congenic interval. The numbers of animals tested are shown below the effect. P-values

are from an independent t-test. * denotes p<0.0001; NS is not significant. Data ± SEM.

Figure 7. Comparative map showing overlap of renal susceptibility loci between rat and

human. The physical map of the rat QTL is shown on the left. The region in human that is

homologous to the rat QTL lies on both human chromosome 1 and 4. Map distances are in basepairs (www.ensembl.org, Ensembl v38 - Apr 2006). Adult onset nephropathy and

hypertension (Cohn et al. 2000); MCDK1, medullary cystic kidney disease (Kiser et al.

2004; Wolf et al. 2003; Wolf et al. 2004), and creatinine clearance (HyperGen study)

(DeWan et al. 2001).

161 Supplemental Figure 1. Functional network analysis of genes differentially expressed between S and S.SHR(2) congenic at week 4, 12 , and 20. Nodes represent genes, with their shape representing the functional class of the gene product (see legend). The lines connecting the nodes indicate the biological relationship between each node (see legend).

Nodes are color coded based on degree and direction of fold change [red, S.SHR(2) is overexpressed compared to S; and green, S.SHR(2) is underexpressed compared to S].

Note: each network is numbered (e.g. 1, 2…) based on the number of focus genes it contains. So, network 1 at week 4 and network 1 at week 12 need not be related.

162 Figure 1

163

Figure 2

164 Figure 3

165 Figure 4

166 Figure 5

167 Figure 6

168 Figure 7

Supplemental Figure 1 169

170 Table 1- Comparison of Blood and Urine Parameters for S, S.SHR(2), and SHR at Week 20

S S.SHR(2) SHR Weight Body weight,g 421±11.1 435 ± 10.0 339 ± 9.3 * Heart weight,g 1.3 ± 0.02 1.3 ± 0.02 1.3 ± 0.01 Total kidney weight,g 2.8 ± 0.11 † 2.6 ± 0.07 2.6 ± 0.04

Blood Total Protein 6.7 ± 0.13 6.4 ± 0.17 6.4 ± 0.12 Creatinine, mg/dl 0.54 ± 0.01 0.53 ± 0.02 0.4 ± 0.02 * Blood urea nitrogen, mg/dl 33.4 ± 1.50 † 29.7 ± 0.49 29.3 ± 0.76 Sodium, meq/l 149.6 ± 2.36 † 142.8 ± 1.05 144.2 ± 0.60 Potassium, meq/l 4.6 ± 0.13 † 5.4 ± 0.22 5.3 ± 0.21

Bicarbonate (CO2), meq/l 26.6 ± 1.09 † 23.1 ± 0.47 23.5 ± 0.16

Urine Creatinine clearance, ml/min/gtkw 0.75 ± 0.018 0.78 ± 0.025 0.80 ± 0.027 Urine pH 6.6 ± 0.07 6.8 ± 0.07 7.2 ± 0.10 * Sodium Excretion, meq/day 0.43 ± 0.03 0.45± 0.02 0.82 ± 0.05 *

Values are mean ± SE; n=6 per group. Heart weight and total kidney weight were adjusted for differences in body weight between the groups. Creatinine clearance was normalized to total kidney weight (TKW) to adjust for differences in animal size. *P <0.05 versus S or S.SHR(2), †P<0.05 versus S.SHR(2) or SHR

171 Table 2- Number of Differentially Expressed Genes in Kidney between S and S.SHR(2)

Striated by Fold-Change and Statistical Significance

FWER, p<0.05 FDR, p<0.05

All All Analyisis Criteria Time Chromosomes RN02 Chromosomes RN02

Fold Change ±1.5 Week 4 168 48 4

Week 12 109 50 72 41

Week 20 360 100 152 53

Fold Change ±1.8 Week 4 62 26 1

Week 12 54 30 42 27

Week 20 105 44 54 30

Fold Change ±2.0 Week 4 35 18 1

Week 12 44 25 35 22

Week 20 58 28 35 21

Table 3- Time Course of Genes Differentially Expressed in Kidney between S and S.SHR(2) located on Chromosome 2

Week 4 Week 12 Week 20

Num Acc # Gene Fold p- Fold p- adj p- Fold p- adj p- Description Name Change value Change value value Change value value

1 AI501417 Mgst2? +2.4 0.031 EST 2 BE107853 +5.4 0.001 +6.4 0.0002 0.006 EST 3 BF415556 +5.5 0.001 +4.7 0.0001 0.006 EST 4 BG374101 +3.6 0.006 +2.7 0.0008 0.009 EST 5 AA859982 +2.6 0.024 +2.2 0.0003 0.007 EST 6 AW141832 +2.1 0.0012 0.012 similar to RIKEN cDNA 2310050P13 (LOC310808) 7 BF551322 +2.2 0.0040 0.023 similar to CG5805-PA (LOC365841) 8 BG379771 +2.8 0.0038 0.022 EST 9 BF553729 Wnt2b +2.7 0.00002 0.011 EST AI555464 Wnt2b +2.4 0.00553 0.041 EST 10 BE097574 Kcnab1 +2.4 0.00974 0.049 EST 11 AA819288 +2.3 0.00309 0.033 Retinoic acid receptor responder protein 1 12 BF411765 Celsr2 +2.1 0.00022 0.017 cadherin EGF LAG seven-pass G-type receptor 2 13 AI715140 +2.0 0.00786 0.047 similar to histone protein Hist2h3c1 (LOC310679) 14 BF284175 +2.3 0.001 +2.4 0.0001 0.006 +2.2 0.00034 0.017 similar to group XII-1 phospholipase A2 (LOC362039) 15 BF396545 Sfrp2 -2.4 0.005 secreted frizzled-related protein 2 16 AI511405 -13.2 0.000 -6.3 0.0006 0.009 similar to hypothetical protein FLJ22693 (LOC310467) 17 BF522861 -2.1 0.006 -2.1 0.0033 0.021 18 BF388224 -7.4 0.002 -8.7 0.0044 0.023 19 AI454310 -2.0 0.031 -2.5 0.0002 0.006 EST 20 BF402235 Sars1 -2.1 0.0005 0.009 EST 21 AI172217 -2.9 0.0094 0.034 similar to protein beta-galactosidase, alpha 22 U86635 Gstm5 -2.1 0.0052 0.024 glutathione S-transferase, mu 5 23 NM_021584 -2.0 0.0456 0.084 -2.8 0.00219 0.033 activity/neurotransmitter-induced early gene protein 4 24 BF290410 -2.2 0.00327 0.033 EST 25 BE120370 -2.1 0.00578 0.042 EST 26 NM_024157 Cfi -4.0 0.00767 0.046 complement factor I 27 NM_012889 Vcam1 -2.1 0.00515 0.039 vascular cell adhesion molecule 1 28 AF367210 Il7 -2.1 0.00405 0.036 interleukin 7 29 NM_031120 Ssr3 -2.9 0.00623 0.043 TRAP-complex gamma subunit 30 U05675 Fgb -2.3 0.01057 0.049 fibrinogen, beta polypeptide 31 BI288681 -7.3 0.001 -6.7 0.0006 0.009 -3.8 0.00123 0.028 transcription activator of D-serine dehydratase? 32 BE100973 -5.8 0.010 -8.7 0.0001 0.006 -8.4 0.00027 0.017 Rattus norvegicus transcribed sequences 33 AI454769 -4.3 0.019 -4.8 0.0011 0.011 -3.9 0.00047 0.018 Rattus norvegicus transcribed sequences 34 AI043891 Cct3 -2.8 0.010 -2.8 0.0019 0.015 -2.0 0.02750 0.073 35 AI232217 -4.2 0.021 -3.7 0.0001 0.006 -2.2 0.00175 0.032 Rattus norvegicus transcribed sequences 36 NM_031154 Gstm3 -5.1 0.004 -4.7 0.0027 0.019 -3.4 0.00444 0.037 glutathione S-transferase, mu 3(Yb3) 37 M28241 Gstm1 -3.2 0.007 -3.8 0.0037 0.022 -3.6 0.00332 0.033 glutathione S-transferase, mu 1

A positive fold change means that the transcript was expressed higher in the S.SHR(2) congenic compared to the S. Conversely, a negative value means that the S transcript was expressed at a higher level than the congenic. Transcripts found to have a fold-change ±2.0 or greater are included in the table; see supplemental Table 1 for a complete gene list. Statistical calculations were performed by two methods (see material and methods for more detail): p-value (FWER) and an adjusted p-value (FDR using Benjamani and Hochberg correction for multiple corrections) 172

Table 4- Top Scoring Functions and Associated Canonical Pathways of Networks Identified from Genes Differentially

Expressed in Kidney between S and S.SHR(2)

Week Top Functions (All Networks) Significance Canonical Pathway (p<0.05) Node

4 Cellular Movement 1.62E-5 - 2.85E-2 Wnt/B-catenin Signaling Crebbp(2), Csnk1e(1), Sfrp2(3), Wnt2b(3) Small Molecule Biochemistry 6.34E-4 - 2.85E-2 Amyoid Processing Csnk1e(1), Psen(1) Cancer 6.34E-4 - 2.85E-2 Antigen Presentation HLA-E(1), Psmb9(1) Cellular Growth and Proliferation 3.04E-4 - 2.85E-2

12 Gene Expression 8.19E-13 - 1.04E-5 Wnt/B-catenin Signaling Tcf4(2), Tgfb3(1) Cell-to- and Interaction 2.71E-11 - 8.91E-6 Glutathione Metabolism Gstm1(2), Gstm2 (2) Glycine, Serine, and Threonine Alas2(1), Sars Cellular Growth and Proliferation 2.71E-11 - 1.25E-5 Metabolism Connective Tissue Development and Function 2.71E-11 - 3.42E-6

20 Nitric Oxide Signaling in Gucy1a3(3), Gucy1b3(3) Cellular Growth and Proliferation 3.40E-11 - 4.16E-5 Cardiovascular System Connective Tissue Development and Function 7.21E-11 - 4.11E-5 Cellular Movement 8.58E-11 - 3.60E-5 Immune and Lymphatic Development and Function 1.49E-10 - 4.18E-5 173 Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 1 BF399429 - 1389026_at 149425862 Up 1.8 0.0157 0.1555 Rattus norvegicus similar to RIKEN cDNA 4933417L02 (LOC293117), mRNA 1 AI599126 - 1383292_at 212326840 Up 1.7 0.0427 0.2103 Rattus norvegicus similar to inner centromere protein-B (LOC293733), mRNA 1 BI289559 - 1393376_at 173556971 Up 1.5 0.0141 0.1498 Rattus norvegicus similar to SOX6 (LOC293165), mRNA 1 AI230728 Snrpa 1388436_at 82265660 Up 1.5 0.0193 0.1677 Rattus norvegicus similar to U1 small nuclear ribonucleoprotein A (U1 snRNP A protein) (LOC292729), mRNA 1 BF389640 - 1379485_at 267369410 Down 2.6 0.0021 0.0604 Rattus norvegicus similar to eukaryotic initiation factor 3, subunit 10 theta, 150/170kDa 1 AF394783 Sult1a1 1370019_at 185829205 Down 2.1 0.0298 0.1894 sulfotransferase family 1A, phenol-preferring, member 1 1 AI408286 - 1378193_at 213828862 Down 1.8 0.0461 0.2103 Rattus norvegicus similar to RIKEN cDNA A430103C15 (LOC293744), mRNA 1 NM_019232 Sgk 1367802_at 23501262 Down 1.7 0.0181 0.1640 serum/glucocorticoid regulated kinase 1 AI145746 - 1392249_at 20355444 Down 1.6 0.0007 0.0593 Rattus norvegicus transcribed sequences 1 AA964142 - 1389888_at 157532994 Down 2.1 0.0011 0.0593 1.7 0.0044 0.0372 Rattus norvegicus similar to RIKEN cDNA 2610209A20 (LOC368121), mRNA 1 X04440 Prkcb1 1370585_a_at 181118102 Down 1.7 0.0027 0.0711 1.6 0.0302 0.0760 protein kinase C, beta 1 1 AA858962 Rbp4 1371762_at 242443798 Down 2.4 0.0039 0.0217 retinol binding protein 4 1 NM_033234 Hbb 1367553_x_at 161590658 Down 2.4 0.0165 0.0461 hemoglobin beta chain complex 1 BI287300 Hbb 1371245_a_at 161578261 Down 2.4 0.0074 0.0282 Rat hemoglobin beta-chain mRNA 1 U13253 Fabp5 1370281_at 216825748 Down 1.6 0.0059 0.0241 fatty acid binding protein 5, epidermal 1 AI230048 Dbp 1387874_at 96173573 Up 2.3 0.0252 0.0574 2.3 0.0007 0.0216 D site albumin promoter binding protein 1 AB012600 Arntl 1370510_a_at 171132014 Down 1.6 0.0221 0.0538 1.6 0.0120 0.0522 aryl hydrocarbon receptor nuclear translocator-like 1 AI169562 rGK-4 1371080_at 94412803 Up 3.0 0.0204 0.0639 kallikrein 1 NM_019184 Cyp2c 1387328_at 243281320 Up 2.6 0.0034 0.0327 Cytochrome P450, subfamily IIC (mephenytoin 4-hydroxylase) 1 BM385272 - 1393139_at 78979034 Up 2.0 0.0051 0.0392 Rattus norvegicus similar to Apolipoprotein C2 (LOC292697), mRNA 1 BI281129 - 1373309_at 97604477 Up 2.0 0.0145 0.0554 Rattus norvegicus similar to RIKEN cDNA 1810054O13 (LOC308602), mRNA 1 BM390571 - 1376248_at 96197898 Up 1.9 0.0410 0.0888 Rattus norvegicus similar to cytosolic sulfotransferase (LOC292915), mRNA 1 AW522786 - 1378887_at 140157974 Up 1.9 0.0082 0.0477 Rattus norvegicus transcribed sequences 1 BF285952 - 1393474_at 96197898 Up 1.7 0.0122 0.0526 Rattus norvegicus similar to cytosolic sulfotransferase (LOC292915), mRNA 1 AI060133 - 1382476_x_at 207556536 Up 1.7 0.0242 0.0700 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21- Symmetric Operator Dna, Alpha Carbons Only 1 BG378791 - 1390596_at 233410939 Up 1.6 0.0308 0.0760 Rattus norvegicus similar to Melanoma antigen recognized by T-cells 1 (MART-1) (Melan-A protein) (Antigen SK29-AA) (Antigen LB39-AA) (LOC293890), mRNA 1 NM_134369 Cyp2t1 1368265_at 82229589 Up 1.6 0.0017 0.0323 cytochrome P450 monooxygenase CYP2T1 1 BF561222 - 1383242_a_at 87626241 Up 1.6 0.0002 0.0173 Rattus norvegicus transcribed sequence with moderate similarity to protein pir:I60307 (E. coli) I60307 beta-galactosidase, alpha peptide - Escherichia coli 1 NM_031711 Arl2 1368166_at 208909458 Up 1.6 0.0283 0.0742 ADP-ribosylation-like 2 1 AW435479 Aplp1 1389307_at 85390513 Up 1.6 0.0458 0.0937 Rattus norvegicus transcribed sequence with strong similarity to protein ref:NP_005157.1 (H.sapiens) amyloid beta (A4) precursor-like protein 1 [Homo sapiens] 1 BE098025 - 1374924_at 22263990 Up 1.5 0.0093 0.0493 Rattus norvegicus transcribed sequences 1 BE112982 - 1380808_at 201997982 Up 1.5 0.0264 0.0723 Rattus norvegicus similar to TOLLIP protein (LOC361677), mRNA 1 J02827 Bckdha 1370897_at 80837907 Up 1.5 0.0008 0.0221 branched chain keto acid dehydrogenase subunit E1, alpha polypeptide 1 BI283664 - 1377209_at 131370265 Up 1.5 0.0029 0.0327 Rattus norvegicus transcribed sequences 1 AI072405 Aqp11 1384877_at 154973796 Up 1.5 0.0100 0.0493 11 1 BE099174 - 1382115_at 245725493 Up 1.5 0.0031 0.0327 Rattus norvegicus similar to RIKEN cDNA 4930521E07 (LOC309486), mRNA 1 AI008432 - 1392843_at 31940826 Down 2.2 0.0174 0.0610 Rattus norvegicus transcribed sequences 1 BM389322 - 1380768_at 1481655 Down 2.1 0.0082 0.0477 Rattus norvegicus transcribed sequences 1 BM383349 - 1383688_at 168931643 Down 1.9 0.0119 0.0522 Rattus norvegicus transcribed sequences 1 BF397243 - 1395047_at 241156969 Down 1.9 0.0007 0.0216 Rattus norvegicus similar to tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase 2; tankyrase 2 (LOC309512), mRNA 1 AW920064 Ctsc 1397808_at 144640775 Down 1.9 0.0415 0.0888 Rattus norvegicus transcribed sequences 1 BE097460 - 1398700_at 40899401 Down 1.8 0.0068 0.0448 Rattus norvegicus transcribed sequences 1 AI714002 - 1374775_at 195310301 Down 1.8 0.0134 0.0546 Rattus norvegicus similar to Ki-67 (LOC291234), mRNA 1 AI511069 - 1378391_at 109033934 Down 1.7 0.0070 0.0448 Rattus norvegicus transcribed sequences 1 AI170394 Mpeg1 1389006_at 215387912 Down 1.7 0.0098 0.0493 macrophage expressed gene 1 1 AA996491 - 1385381_at 46388118 Down 1.7 0.0431 0.0893 Rattus norvegicus transcribed sequences 1 BI289045 - 1381557_at 219904665 Down 1.6 0.0379 0.0846 Rattus norvegicus transcribed sequences 1 AI008409 - 1389986_at 208427738 Down 1.6 0.0257 0.0712 Rattus norvegicus transcribed sequence with strong similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 1 BM390792 - 1397453_at 245429376 Down 1.6 0.0004 0.0182 Rattus norvegicus transcribed sequences 1 AI171480 - 1390909_at 162170846 Down 1.5 0.0007 0.0216 similarity to protein pdb:1JZ5 (E. coli) B Chain B, E. Coli (Lacz) Beta-Galactosidase In Complex With D- Galctopyranosyl-1-On 1 BE113281 Qki 1372542_at 45029384 Down 1.5 0.0326 0.0788 Rattus norvegicus transcribed sequences 1 AF245172 Gda 1387659_at 224579743 Down 1.5 0.0418 0.0888 guanine deaminase 1 BG670294 - 1390107_at 147022234 Down 1.5 0.0095 0.0493 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 2 AI501417 - 1396831_at 140736218 Up 2.4 0.0312 0.1909 Rattus norvegicus transcribed sequences 2 BF282918 - 1382738_at 180709895 Up 1.9 0.0252 0.1818 Rattus norvegicus transcribed sequences 2 BE112158 - 1374640_at 189287027 Up 1.7 0.0428 0.2103 Rattus norvegicus transcribed sequences 2 BF543355 Igsf10 1390901_at 148704637 Up 1.6 0.0070 0.1053 bone specific CMF608 2 AI576368 - 1395636_at 232939449 Up 1.6 0.0034 0.0764 Rattus norvegicus transcribed sequences 2 BI284800 - 1371071_at 118791451 Up 1.6 0.0226 0.1748 Rattus norvegicus guanine nucleotide binding protein beta 4 subunit mRNA, partial cds 2 BF408099 - 1384824_at 138616556 Up 1.5 0.0018 0.0604 Rattus norvegicus transcribed sequences 2 BF396545 Sfrp2 1396614_at 175479529 Down 2.4 0.0047 0.0922 secreted frizzled-related protein 2 2 AF140232 S100a6 1367661_at 182895080 Down 1.8 0.0481 0.2103 calcium binding protein A6 (calcyclin) 2 NM_020538 Aadac 1369492_at 149348935 Down 1.7 0.0205 0.1715 arylacetamide deacetylase (esterase) 2 AA893529 - 1380254_at 192441767 Down 1.6 0.0021 0.0604 Rattus norvegicus transcribed sequence with weak similarity to protein sp:Q92664 (H.sapiens) TF3A_HUMAN Transcription factor IIIA (Factor A) (TFIIIA) 2 NM_012618 S100a4 1367846_at 182885070 Down 1.6 0.0276 0.1858 S100 calcium-binding protein A4 2 AI407985 - 1371480_at 112237120 Down 1.6 0.0219 0.1738 Rattus norvegicus transcribed sequence with strong similarity to protein pir:A36670 (H.sapiens) A36670 cell division control protein CKS1 - human 2 BF390321 - 1382282_at 191399944 Down 1.5 0.0110 0.1393 Rattus norvegicus similar to hypothetical protein MGC46719 (LOC295283), mRNA 2 BF553729 Wnt2b 1397820_at 200218630 Up 1.7 0.0012 0.0593 2.7 0.0000 0.0109 Rattus norvegicus transcribed sequences 2 AI555464 Wnt2b 1394801_at 200215823 Up 2.4 0.0055 0.0410 Rattus norvegicus transcribed sequences 2 AI715140 - 1384367_at 191053229 Up 1.6 0.0179 0.1640 2.0 0.0079 0.0470 Rattus norvegicus similar to histone protein Hist2h3c1 (LOC310679), mRNA 2 NM_031114 S100a10 1386890_at 186645674 Down 2.0 0.0470 0.2103 1.9 0.0210 0.0647 S-100 related protein, clone 42C 2 AF084544 Cspg2 1371232_a_at 19712629 Down 1.8 0.0424 0.2103 1.6 0.0140 0.0551 chondroitin sulfate proteoglycan 2 () 2 AI556018 - 1376996_at 231016070 Down 1.6 0.0140 0.1498 1.6 0.0196 0.0632 Rattus norvegicus similar to KIAA1546 protein (LOC310859), mRNA 2 NM_012769 Gucy1b3 1369097_s_at 173684395 Down 1.6 0.0151 0.1530 2.0 0.0025 0.0327 guanylate cyclase 1, soluble, beta 3 2 BF399387 Gucy1b3 1374389_at 173684395 Down 1.5 0.0070 0.0279 1.7 0.0028 0.0327 guanylate cyclase 1, soluble, beta 3 2 BG374101 - 1381262_at 181637899 Up 3.6 0.0061 0.0977 2.7 0.0008 0.0097 Rattus norvegicus transcribed sequences 2 AA859982 - 1385871_at 152124473 Up 2.6 0.0238 0.1788 2.2 0.0003 0.0072 Rattus norvegicus transcribed sequences 2 AA900057 Snx27 1379804_at 189474923 Up 1.9 0.0062 0.0977 1.8 0.0015 0.0134 PDZ protein Mrt1 2 AI176320 - 1373416_at 113315728 Up 1.8 0.0012 0.0593 1.6 0.0048 0.0225 Rattus norvegicus transcribed sequences 2 BF522861 - 1386766_at 212899950 Down 2.1 0.0057 0.0977 2.1 0.0033 0.0214 --- 2 BI285792 - 1375909_at 203606404 Down 1.9 0.0020 0.0604 1.6 0.0048 0.0225 similarity to protein pdb:4GTU (H.sapiens) G Chain G, Ligand-Free Homodimeric Human Glutathione S-Transferase M4- 4 (E.C.2.5.1.18) 2 BF402235 Sars1 1392262_at 204000299 Down 1.8 0.0012 0.0593 2.1 0.0005 0.0097 Rattus norvegicus transcribed sequences 2 AI172217 - 1382682_at 225153968 Down 1.8 0.0294 0.1894 2.9 0.0094 0.0340 similarity to protein pir:I60307 (E. coli) I60307 beta-galactosidase, alpha peptide - Escherichia coli 2 BE107904 - 1392040_at 212825827 Down 1.7 0.0292 0.1894 1.7 0.0256 0.0574 Rattus norvegicus similar to 2810453L12Rik protein (LOC310807), mRNA 2 U76206 Gpr105 1370449_at 148502460 Down 1.6 0.0268 0.1826 1.7 0.0099 0.0346 G protein-coupled receptor 105 2 AI234811 - 1394625_at 171237013 Down 1.5 0.0140 0.1498 1.5 0.0452 0.0840 similarity to protein ref:NP_060812.1 (H.sapiens) hypothetical protein FLJ11155 [Homo sapiens] 2 AA945938 - 1382611_at 192382906 Down 1.5 0.0108 0.1393 1.6 0.0007 0.0097 Rattus norvegicus transcribed sequences 2 BI286387 - 1371248_at 185464256 Up 2.2 0.0376 0.0742 Rattus norvegicus similar to Cornifin alpha (Small proline-rich protein 1) (SPRR1) (LOC365848), mRNA 2 BF551322 - 1393178_at 180514438 Up 2.2 0.0040 0.0217 Rattus norvegicus similar to CG5805-PA (LOC365841), mRNA 2 BE111565 - 1390886_at 120116931 Up 1.6 0.0144 0.0432 Rattus norvegicus transcribed sequences 2 BE102993 Snx16 1382643_at 93274839 Up 1.5 0.0053 0.0235 sorting nexin 16 2 BG374304 - 1377310_at 212082882 Up 1.5 0.0156 0.0441 Rattus norvegicus transcribed sequences 2 U86635 Gstm5 1370813_at 203456122 Down 2.1 0.0052 0.0235 glutathione S-transferase, mu 5 2 BM390462 - 1383165_at 204021188 Down 1.9 0.0047 0.0225 Rattus norvegicus similar to KIAA1324 protein (LOC362019), mRNA 2 NM_012624 - 1387263_at 181214402 Down 1.8 0.0036 0.0217 pyruvate kinase, liver and RBC 2 BF286009 - 1383218_at 120996329 Down 1.7 0.0003 0.0078 Rattus norvegicus LOC361928 (LOC361928), mRNA 2 AI602542 - 1382211_at 153116455 Down 1.6 0.0007 0.0097 Rattus norvegicus transcribed sequences 2 AA858645 - 1389469_at 192382906 Down 1.5 0.0013 0.0123 Rattus norvegicus similar to chromodomain helicase DNA binding protein 1-like (LOC310707), mRNA 2 BF415512 - 1396529_at 116866938 Down 1.5 0.0333 0.0676 Rattus norvegicus transcribed sequences 2 BG379771 - 1393262_at 144033146 Up 2.8 0.0038 0.0217 1.7 0.0006 0.0211 Rattus norvegicus transcribed sequences 2 AI578135 - 1383747_at 112970425 Up 2.2 0.0229 0.0552 2.0 0.0191 0.0630 Rattus norvegicus transcribed sequences 2 BE097574 Kcnab1 1378738_at 154697136 Up 1.9 0.0018 0.0151 2.4 0.0097 0.0493 Rattus norvegicus transcribed sequences Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 2 AA819288 - 1382274_at 157331763 Up 1.8 0.0008 0.0097 2.3 0.0031 0.0327 Retinoic acid receptor responder protein 1 (Tazarotene-induced gene 1 protein) (RAR-responsive protein TIG1) (LOC310486), mRNA 2 AW141940 - 1375954_at 182787814 Up 1.7 0.0285 0.0616 1.9 0.0100 0.0493 Rattus norvegicus similar to 8KDa amlexanox-binding protein (LOC295213), mRNA 2 NM_021584 - 1387276_at 144686423 Down 2.0 0.0456 0.0840 2.8 0.0022 0.0327 activity and neurotransmitter-induced early gene protein 4 (ania-4) 2 BE102803 - 1383867_at 116157860 Down 1.9 0.0145 0.0432 1.9 0.0013 0.0282 Rattus norvegicus similar to eIF-5A2 protein (LOC310261), mRNA 2 BF409007 - 1393063_at 116159119 Down 1.7 0.0059 0.0241 1.7 0.0030 0.0327 Rattus norvegicus transcribed sequences 2 BE120508 - 1379781_at 118931483 Down 1.6 0.0209 0.0528 1.6 0.0443 0.0915 Rattus norvegicus similar to BAF53a (LOC361925), mRNA 2 NM_017020 Il6r 1386987_at 182078051 Down 1.5 0.0104 0.0351 1.8 0.0138 0.0549 interleukin 6 receptor

2 BF411765 Celsr2 1371018_at 203960524 Up 2.1 0.0002 0.0173 cadherin EGF LAG seven-pass G-type receptor 2 2 BF549971 - 1377706_x_at 913063 Up 1.9 0.0212 0.0647 --- 2 AI112346 - 1380852_at 89037914 Up 1.8 0.0030 0.0327 Rattus norvegicus transcribed sequences 2 AI137898 - 1393330_at 84724179 Up 1.8 0.0153 0.0574 Rattus norvegicus transcribed sequences 2 BI284261 - 1377869_at 140064501 Up 1.7 0.0336 0.0802 Rattus norvegicus similar to carbon catabolite repression 4 protein homolog (LOC310395), mRNA 2 BG371995 - 1383499_at 203756191 Up 1.7 0.0098 0.0493 Rattus norvegicus transcribed sequences 2 AI137642 - 1393164_at 30341592 Up 1.7 0.0311 0.0763 Rattus norvegicus similar to mitochondrial ribosomal protein S27; mitochondrial 28S ribosomal protein S27 (LOC361883), mRNA 2 BI294747 - 1390973_at 195972223 Up 1.7 0.0490 0.0966 Rattus norvegicus similar to tripartite motif-containing 45 (LOC295323), mRNA 2 BG374448 - 1374760_at 83363798 Up 1.6 0.0009 0.0232 Rattus norvegicus transcribed sequences 2 AI602131 - 1384603_at 218712747 Up 1.6 0.0280 0.0740 Rattus norvegicus similar to ATP-binding cassette transporter (LOC310836), mRNA 2 BF284064 Mccc1 1395844_at 122437985 Up 1.6 0.0104 0.0493 Rattus norvegicus similar to methylcrotonoyl-Coenzyme A carboxylase 1 (alpha) (LOC294972), mRNA 2 BE116137 - 1378929_at 180446723 Up 1.6 0.0493 0.0966 Rattus norvegicus transcribed sequences 2 NM_130747 rACH 1369485_at 21984849 Up 1.6 0.0144 0.0554 cytoplasmic acetyl-CoA hydrolase 2 AI228548 S100a1 1388456_at 182784663 Up 1.6 0.0155 0.0575 Rattus norvegicus similar to S-100 protein, alpha chain (LOC295214), mRNA 2 NM_024157 Cfi 1368205_at 227308641 Down 4.0 0.0077 0.0463 complement factor I 2 NM_031120 Ssr3 1369718_at 155149234 Down 2.9 0.0062 0.0428 TRAP-complex gamma subunit 2 BE109107 - 1384969_at 243721308 Down 2.5 0.0468 0.0946 Rattus norvegicus transcribed sequences 2 NM_012532 Cp 1368420_at 105086278 Down 2.3 0.0170 0.0608 ceruloplasmin 2 AF202115 Cp 1368418_a_at 105086278 Down 1.9 0.0189 0.0629 ceruloplasmin 2 AF202115 Cp 1368419_at 105086278 Down 1.9 0.0090 0.0493 ceruloplasmin 2 U05675 Fgb 1370511_at 174767192 Down 2.3 0.0106 0.0493 fibrinogen, beta polypeptide 2 BF290410 - 1386052_at 59105666 Down 2.2 0.0033 0.0327 Rattus norvegicus transcribed sequences 2 AF367210 Il7 1369208_at 96356083 Down 2.1 0.0041 0.0364 interleukin 7 2 AI170450 - 1391587_at 77391890 Down 2.1 0.0218 0.0653 Rattus norvegicus transcribed sequences 2 NM_012889 Vcam1 1368474_at 212277654 Down 2.1 0.0051 0.0392 vascular cell adhesion molecule 1 2 BE120370 - 1384393_at 202489626 Down 2.1 0.0058 0.0417 Rattus norvegicus transcribed sequences 2 NM_053772 Pkia 1368982_at 96524399 Down 2.0 0.0426 0.0893 protein kinase inhibitor, alpha 2 AA893743 Pkia 1373082_at 96521715 Down 2.0 0.0024 0.0327 protein kinase inhibitor, alpha 2 AW528482 - 1381668_at 236366764 Down 2.0 0.0278 0.0740 Rattus norvegicus transcribed sequences 2 BF283694 - 1379444_at 171368285 Down 1.9 0.0111 0.0501 Rattus norvegicus transcribed sequences 2 BE109744 - 1392552_at 182597259 Down 1.9 0.0025 0.0327 Rattus norvegicus similar to transcription repressor p66 (LOC310614), mRNA 2 BM385061 - 1391106_at 56080700 Down 1.9 0.0124 0.0528 Rattus norvegicus transcribed sequences 2 BF282631 C7 1383291_at 54147534 Down 1.9 0.0273 0.0736 Rattus norvegicus similar to Complement component C7 precursor (LOC310126), mRNA 2 BI274436 - 1393109_at 219774188 Down 1.9 0.0292 0.0752 similarity to protein pir:RGECDW (E. coli) RGECDW transcription activator of D-serine dehydratase - Escherichia coli 2 H31722 - 1380404_at 213164026 Down 1.8 0.0259 0.0714 Rattus norvegicus transcribed sequences 2 BI282029 - 1395388_at 88552849 Down 1.8 0.0336 0.0802 Rattus norvegicus transcribed sequences 2 AA957384 Itga1 1377278_at 47172364 Down 1.8 0.0072 0.0448 Rattus norvegicus transcribed sequences 2 NM_030994 Itga1 1387144_at 47107864 Down 1.6 0.0166 0.0598 integrin alpha 1 2 M57405 Gucy1a3 1387079_at 173755007 Down 1.8 0.0003 0.0173 guanylate cyclase 1, soluble, alpha 3 2 NM_019225 Slc1a3 1368565_at 58270236 Down 1.8 0.0286 0.0747 solute carrier family 1, member 3 2 AW535280 - 1370986_s_at 338357 Down 1.8 0.0093 0.0493 Rattus norvegicus retroviral-like c-Ha-ras proto-oncogene mRNA, partial sequence 2 AI237532 - 1385872_at 152125352 Down 1.8 0.0002 0.0173 Rattus norvegicus transcribed sequences 2 U78857 Ania4 1369686_at 144267748 Down 1.7 0.0080 0.0475 activity and neurotransmitter-induced early gene protein 4 (ania-4) 2 BF284922 - 1388557_at 118026379 Down 1.7 0.0106 0.0493 Rattus norvegicus transcribed sequences 2 BM389026 - 1373911_at 143537110 Down 1.7 0.0149 0.0565 Rattus norvegicus similar to osteoblast specific factor 2 precursor (LOC361945), mRNA 2 AW532618 - 1378133_at 139240746 Down 1.7 0.0369 0.0845 Rattus norvegicus transcribed sequences 2 AA858564 - 1379942_at 120969384 Down 1.7 0.0045 0.0376 Rattus norvegicus transcribed sequences 2 BE108581 - 1382746_s_at 224929644 Down 1.7 0.0211 0.0647 Rattus norvegicus transcribed sequences 2 BI281702 Map1b 1373363_at 30415255 Down 1.7 0.0341 0.0803 -associated protein 1b 2 AA851404 - 1391501_at 158892675 Down 1.7 0.0028 0.0327 Rattus norvegicus similar to RIKEN cDNA 4921524P20 (LOC295106), mRNA 2 AI409635 - 1375028_at 176487312 Down 1.7 0.0172 0.0609 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 2 BI282157 Cdh6 1378480_at 62543102 Down 1.6 0.0497 0.0966 Rattus norvegicus transcribed sequences 2 AA964378 Cdh6 1379614_at 62544084 Down 1.6 0.0154 0.0574 Rattus norvegicus transcribed sequences 2 AI574991 - 1379356_at 204567499 Down 1.6 0.0019 0.0325 Rattus norvegicus transcribed sequences 2 NM_019243 Ptgfrn 1367986_at 196091505 Down 1.6 0.0228 0.0667 prostaglandin F2 receptor negative regulator 2 BM388077 - 1383146_at 144831075 Down 1.6 0.0275 0.0736 Rattus norvegicus transcribed sequences 2 AA859029 - 1393751_at 93466787 Down 1.6 0.0031 0.0327 similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 2 AI178422 - 1381771_at 148600612 Down 1.6 0.0491 0.0966 Rattus norvegicus transcribed sequences 2 AW915435 - 1392554_a_at 27599983 Down 1.6 0.0016 0.0323 --- 2 BM385125 - 1394525_at 4899 Down 1.6 0.0256 0.0712 --- 2 BM389035 - 1378453_at 47107864 Down 1.6 0.0477 0.0957 Rattus norvegicus transcribed sequences 2 AA851345 - 1391979_at 55951395 Down 1.6 0.0494 0.0966 Rattus norvegicus transcribed sequences 2 BI282008 - 1389003_at 2911103 Down 1.5 0.0005 0.0182 Rattus norvegicus transcribed sequences 2 BE102621 - 1376153_at 140408472 Down 1.5 0.0197 0.0632 Rattus norvegicus transcribed sequences 2 AW526268 - 1380100_at 116006188 Down 1.5 0.0198 0.0632 similar to misshapen/NIK-related kinase isoform 2; GCK family kinase MINK; serine/threonine protein kinase (LOC294917), mRNA 2 NM_012716 Slc16a1 1386981_at 199860337 Down 1.5 0.0001 0.0109 solute carrier family 16, member 1 2 AI102620 Map3k1 1375673_at 43062252 Down 1.5 0.0177 0.0614 Rattus norvegicus transcribed sequences 2 AI232784 - 1372613_at 232765165 Up 1.5 0.0105 0.0493 Rattus norvegicus similar to RIKEN cDNA 1810026B04 (LOC295458), mRNA 2 BE107853 - 1375497_at 142404403 Up 5.4 0.0013 0.0593 6.4 0.0002 0.0059 1.6 0.0006 0.0207 Rattus norvegicus transcribed sequences 2 BF284175 - 1373810_at 227331056 Up 2.3 0.0007 0.0593 2.4 0.0001 0.0059 2.2 0.0003 0.0173 Rattus norvegicus similar to group XII-1 phospholipase A2 (LOC362039), mRNA 2 AW141832 - 1395589_at 212902947 Up 1.9 0.0243 0.1788 2.1 0.0012 0.0118 1.9 0.0039 0.0362 Rattus norvegicus similar to RIKEN cDNA 2310050P13 (LOC310808), mRNA 2 AI511405 - 1385407_at 155315259 Down 13.2 0.0005 0.0593 6.3 0.0006 0.0097 1.8 0.0020 0.0327 Rattus norvegicus similar to hypothetical protein FLJ22693 (LOC310467), mRNA 2 BF388224 Ppp3ca 1379175_at 234240301 Down 7.4 0.0023 0.0617 8.7 0.0044 0.0225 1.7 0.0256 0.0712 Rattus norvegicus transcribed sequences 2 BI288681 - 1395073_at 114884954 Down 7.3 0.0013 0.0593 6.7 0.0006 0.0097 3.8 0.0012 0.0278 similarity to protein pir:RGECDW (E. coli) RGECDW transcription activator of D-serine dehydratase - Escherichia coli 2 BE100973 - 1377133_at 232935709 Down 5.8 0.0096 0.1324 8.7 0.0001 0.0059 8.4 0.0003 0.0173 Rattus norvegicus transcribed sequences 2 NM_031154 Gstm3 1387023_at 203491368 Down 5.1 0.0037 0.0783 4.7 0.0027 0.0190 3.4 0.0044 0.0375 glutathione S-transferase, mu type 3 (Yb3) 2 AI454769 - 1394671_at 225150560 Down 4.3 0.0186 0.1643 4.8 0.0011 0.0110 3.9 0.0005 0.0182 Rattus norvegicus transcribed sequences 2 AI232217 - 1389896_at 212083663 Down 4.2 0.0211 0.1715 3.7 0.0001 0.0059 2.2 0.0018 0.0323 Rattus norvegicus transcribed sequences 2 M28241 Gstm1 1386985_at 203575444 Down 3.2 0.0071 0.1053 3.8 0.0037 0.0217 3.6 0.0033 0.0327 glutathione S-transferase, mu 1 2 AI043891 Cct3 1392091_at 180425783 Down 2.8 0.0101 0.1324 2.8 0.0019 0.0151 2.0 0.0275 0.0736 --- 2 AI454310 - 1382171_at 147798045 Down 2.0 0.0306 0.1906 2.5 0.0002 0.0059 1.6 0.0056 0.0410 Rattus norvegicus transcribed sequences 2 BE116408 - 1376584_at 159661795 Down 1.6 0.0176 0.1640 1.5 0.0419 0.0802 1.7 0.0417 0.0888 Rattus norvegicus transcribed sequences 3 BF284124 - 1377018_at 87795505 Up 1.9 0.0127 0.1473 Rattus norvegicus similar to E430002G05Rik protein (LOC311252), mRNA 3 BE349704 - 1374354_at 13905403 Up 1.7 0.0415 0.2103 Rattus norvegicus transcribed sequences 3 NM_053383 C1qr1 1368393_at 137188915 Up 1.6 0.0352 0.2016 lymphocyte antigen 68 3 BF390450 - 1382267_at 87640753 Up 1.6 0.0468 0.2103 Rattus norvegicus similar to four jointed box 1 (LOC366140), mRNA 3 AI169620 - 1373053_at 85065076 Up 3.0 0.0029 0.0718 2.2 0.0468 0.0852 --- 3 NM_053585 Madd 1369066_at 75498321 Down 2.0 0.0242 0.1788 MAP-kinase activating death domain 3 AI170535 - 1375420_at 77598689 Down 1.6 0.0315 0.1909 similar to endoplasmic reticulum membrane protein with at least 3 transmembrane domains of bilaterial origin like (XB300) (LOC311209), mRNA 3 BF400750 - 1395249_at 149100353 Down 1.6 0.0392 0.2103 Rattus norvegicus LOC362256 (LOC362256), mRNA 3 BG377397 - 1393795_at 25513009 Down 1.6 0.0208 0.1715 Rattus norvegicus similar to mKIAA0569 protein (LOC311071), mRNA 3 BF419641 - 1392024_at 64130650 Down 1.6 0.0338 0.2002 Rattus norvegicus transcribed sequences 3 BI275559 - 1393226_at 67024503 Down 1.6 0.0480 0.2103 Rattus norvegicus transcribed sequences Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 3 BF415891 - 1392441_at 25654295 Down 1.5 0.0021 0.0604 similarity to protein sp:O60315 (H.sapiens) SIP1_HUMAN Zinc finger homeobox protein 1b (Smad interacting protein 1) (SMADIP1) 3 BF553538 - 1386123_at 99861095 Down 1.7 0.0303 0.0635 Rattus norvegicus transcribed sequences 3 NM_133572 Cdc25b 1370034_at 118893716 Down 3.0 0.0040 0.0217 1.5 0.0142 0.0551 cell division cycle 25B 3 AI234249 - 1374630_at 3622883 Up 1.7 0.0375 0.0846 Rattus norvegicus similar to chloride intracellular channel 3 (LOC296566), mRNA 3 NM_017092 Tyro3 1367953_at 106309671 Up 1.6 0.0043 0.0372 TYRO3 protein tyrosine kinase 3 3 AI071620 - 1385580_at 142059065 Up 1.6 0.0476 0.0957 Rattus norvegicus transcribed sequences 3 BI275952 - 1377088_at 142360679 Up 1.6 0.0025 0.0327 Rattus norvegicus similar to RIKEN cDNA 2310046K01 (LOC311536), mRNA 3 NM_134403 Abtb2 1368111_at 88858336 Up 1.6 0.0029 0.0327 Cca3 protein 3 AF067650 Sardh 1370573_at 6075853 Up 1.5 0.0211 0.0647 sarcosine dehydrogenase 3 BE105446 Fnbp1 1377342_s_at 10091465 Up 1.5 0.0275 0.0736 rapostlin 3 BI296106 - 1383397_at 75396998 Up 1.5 0.0343 0.0804 Rattus norvegicus similar to nicotinic acetylcholine receptor-associated 46K protein - mouse (LOC362161), mRNA 3 NM_031055 Mmp9 1398275_at 155985473 Up 1.5 0.0208 0.0646 matrix metalloproteinase 9 (gelatinase B, 92-kDa type IV collagenase) 3 BI294934 - 1380612_at 169762453 Down 1.6 0.0174 0.0610 Rattus norvegicus transcribed sequences 3 BF543871 - 1394352_at 44547684 Down 1.6 0.0167 0.0598 --- 3 BF565756 - 1386695_at 42002053 Down 1.6 0.0201 0.0639 Rattus norvegicus transcribed sequences 3 BF542618 - 1394845_at 100002831 Down 1.5 0.0068 0.0448 Rattus norvegicus similar to mKIAA0560 protein (LOC366163), mRNA 3 BF552877 - 1372327_at 112387545 Down 1.5 0.0364 0.0837 Rattus norvegicus similar to Na-Ca exchanger 5 (LOC311387), mRNA 4 AI012630 - 1377012_at 94611607 Up 1.6 0.0159 0.1557 Rattus norvegicus transcribed sequences 4 BF397628 - 1396422_at 121746926 Up 1.6 0.0228 0.1748 similarity to protein ref:NP_002680.1 (H.sapiens) polymerase (DNA-directed), alpha (70kD) [Homo sapiens] 4 NM_057115 Ptpn12 1369496_at 9517633 Down 1.8 0.0001 0.0312 protein tyrosine phosphatase, non-receptor type 12 4 AI011345 Prkwnk1 1368140_at 156297841 Down 1.7 0.0372 0.2071 protein kinase, lysine deficient 1 4 BE116205 - 1384795_at 118125137 Down 1.7 0.0283 0.1877 Rattus norvegicus similar to nuclear protein, NP220 (LOC312491), mRNA 4 BE109884 - 1379651_at 133772259 Down 1.7 0.0306 0.1906 Rattus norvegicus similar to forkhead-related transcription factor 1A (LOC297480), mRNA 4 BG670960 MGC95152 1395519_at 83087757 Down 1.6 0.0443 0.2103 Rattus norvegicus similar to B230212L03Rik protein (LOC297109), mRNA 4 BE115521 - 1391170_at 148981770 Down 1.6 0.0472 0.2103 Rattus norvegicus similar to mKIAA1757 protein (LOC297514), mRNA 4 AI714037 - 1394729_at 156304911 Down 1.6 0.0088 0.1249 Rattus norvegicus transcribed sequences 4 NM_017335 Slc6a12 1387295_at 157781292 Down 1.5 0.0414 0.2103 GABA transporter 4 AW529483 - 1378365_at 117432385 Down 1.9 0.0138 0.1498 1.6 0.0218 0.0538 Rattus norvegicus transcribed sequences 4 AI385379 - 1398107_at 83462977 Down 1.9 0.0455 0.0840 Rattus norvegicus similar to RIKEN cDNA A030007L17; EST AA673177 (LOC362370), mRNA 4 BI281250 - 1384960_at 43909617 Down 1.8 0.0124 0.0395 --- 4 AI548449 - 1384851_at 94549854 Up 1.7 0.0131 0.0540 similarity to protein ref:NP_064544.1 (H.sapiens) hypothetical protein DKFZp762K2015 [Homo sapiens] 4 BF400819 Galnt11 1396602_at 5272176 Up 1.6 0.0069 0.0448 Rattus norvegicus transcribed sequences 4 AF187814 Cml3 1370991_at 119964675 Up 1.6 0.0034 0.0328 camello-like 3 4 AF023090 - 1372293_at 8964935 Up 1.6 0.0178 0.0614 Rattus norvegicus transcribed sequence 4 AI235658 - 1383023_at 57124082 Up 1.6 0.0380 0.0846 similar to Ubiquitin-conjugating enzyme E2 H (Ubiquitin-protein ligase H) (Ubiquitin carrier protein H) (UBCH2) (E2-20K) (LOC296956), mRNA 4 AI232716 - 1373975_at 83934018 Up 1.5 0.0070 0.0448 Rattus norvegicus similar to thioether S-methyltransferase (LOC297114), mRNA 4 AI230054 - 1373902_at 158901835 Up 1.5 0.0288 0.0749 Rattus norvegicus similar to hypothetical protein MGC47816 (LOC302036), mRNA 4 AA848944 - 1379584_at 166128286 Down 2.1 0.0257 0.0712 Rattus norvegicus transcribed sequences 4 AI639113 - 1392053_at 89856142 Down 2.1 0.0157 0.0575 similarity to protein pir:A57384 (H.sapiens) A57384 multimerin, endothelial cell, precursor - human 4 AW915454 - 1376394_at 166697703 Down 1.9 0.0188 0.0629 Rattus norvegicus transcribed sequences 4 U56863 Klra5 1370740_at 168640017 Down 1.7 0.0110 0.0500 Ly-49.9 antigen 4 AI716194 Fgl2 1392894_at 9176331 Down 1.7 0.0193 0.0630 fibrinogen-like 2 4 NM_013080 Ptprz1 1368350_at 49334202 Down 1.7 0.0003 0.0173 protein tyrosine phosphatase, receptor-type, Z polypeptide 1 4 BM392106 Cald1 1368824_at 62062872 Down 1.6 0.0069 0.0448 Rattus norvegicus similar to talin 2 (LOC315776), mRNA 4 BI291848 Cald1 1371969_at 62062872 Down 1.6 0.0143 0.0554 caldesmon 1 4 AF400662 Cacna2d1 1369649_at 15139876 Down 1.6 0.0108 0.0497 calcium channel, voltage-dependent, alpha2/delta subunit 1 4 BF281550 - 1390810_at 25065233 Down 1.6 0.0197 0.0632 Rattus norvegicus transcribed sequences 4 AA943147 - 1382902_at 87279682 Down 1.6 0.0043 0.0372 Rattus norvegicus similar to hypothetical protein FLJ20637 (LOC362376), mRNA 5 AI598405 - 1389835_at 171879446 Up 1.7 0.0028 0.0711 Rattus norvegicus similar to RER1 homolog (LOC298675), mRNA 5 BF401989 Ncoa2 1396254_at 5248662 Up 1.6 0.0442 0.2103 Rattus norvegicus transcribed sequences 5 AA900547 - 1382718_at 58407667 Up 1.6 0.0118 0.1401 Rattus norvegicus similar to cysteine-rich protein NFX-1 (LOC313166), mRNA 5 AI411618 MGC105681 1373025_at 155656101 Down 2.0 0.0425 0.2103 Rattus norvegicus similar to C1q C chain (LOC362634), mRNA 5 BM386775 - 1379226_at 152821172 Down 1.6 0.0368 0.2071 Rattus norvegicus similar to absent in melanoma 1 (LOC298543), mRNA 5 NM_053718 Mllt3 1368279_at 107070479 Down 1.5 0.0431 0.2103 myeloid/lymphoid or mixed-lineage leukemia (trithorax (Drosophila) homolog); translocated to, 3 5 BI281950 - 1372911_at 165129561 Up 1.5 0.0294 0.0629 Rattus norvegicus transcribed sequences 5 X55812 Cnr1 1369677_at 50427173 Down 1.7 0.0151 0.0433 cannabinoid receptor 1 5 BI277879 Slc30a2 1378896_at 153087073 Up 2.0 0.0032 0.0327 solute carrier family 30, member 2 5 NM_012890 Slc30a2 1398264_at 153087073 Up 1.9 0.0192 0.0630 solute carrier family 30, member 2 5 BM384214 Matn1 1380579_at 149805644 Up 1.6 0.0005 0.0182 Rattus norvegicus similar to Matrilin 1, cartilage matrix protein 1 (LOC297894), mRNA 5 BF284337 - 1373963_at 79494958 Up 1.6 0.0099 0.0493 Rattus norvegicus similar to RIKEN cDNA 2810435D12 (LOC298097), mRNA 5 NM_021653 Dio1 1369259_at 128385247 Up 1.6 0.0025 0.0327 deiodinase, iodothyronine, type I 5 AI145487 - 1393945_at 40706424 Down 2.2 0.0072 0.0448 Rattus norvegicus transcribed sequences 5 NM_053977 Cdh17 1369224_at 26047160 Down 1.9 0.0126 0.0532 cadherin 17 5 AI230669 Gnb1 1382982_at 172366934 Down 1.7 0.0035 0.0331 Rattus norvegicus transcribed sequences 5 BF396154 - 1378142_at 129921739 Down 1.6 0.0198 0.0632 Rattus norvegicus transcribed sequences 5 AI103530 - 1382431_at 70490031 Down 1.6 0.0041 0.0365 similarity to protein sp:O95477 (H.sapiens) ABC1_HUMAN ATP-binding cassette, sub-family A, member 1 (ATP-binding cassette transporter 1) 5 BF397925 - 1391139_at 105424702 Down 1.6 0.0387 0.0857 Rattus norvegicus transcribed sequences 5 BE115159 - 1384059_at 102304948 Down 1.6 0.0000 0.0109 Rattus norvegicus transcribed sequences 5 AA874945 - 1382987_at 12379423 Down 1.5 0.0128 0.0535 Rattus norvegicus transcribed sequences 6 AI058428 - 1381085_at 96508955 Up 1.6 0.0262 0.1826 Rattus norvegicus transcribed sequences 6 BF386238 - 1397015_at 57765343 Down 2.1 0.0309 0.1906 Rattus norvegicus transcribed sequences 6 BE116294 - 1396253_at 51807151 Down 1.8 0.0098 0.1324 Rattus norvegicus transcribed sequences 6 BI303268 Bzw2 1370258_at 54817272 Down 1.7 0.0444 0.2103 Hfb2 protein 6 BG378238 - 1372524_at 40250862 Down 1.6 0.0330 0.1971 Rattus norvegicus similar to Kiaa0575 (LOC313977), mRNA 6 BM384008 - 1392864_at 72685228 Down 1.6 0.0020 0.0604 Rattus norvegicus similar to p190-B (LOC299012), mRNA 6 NM_019163 Psen1 1368792_at 107725733 Down 1.6 0.0391 0.2103 presenilin 1 6 BM386731 - 1385089_at 42217273 Up 2.2 0.0034 0.0764 2.1 0.0118 0.0383 6 BM383749 - 1378128_at 42262080 Up 2.0 0.0183 0.1640 1.6 0.0386 0.0746 Rattus norvegicus transcribed sequences 6 NM_013174 Tgfb3 1367859_at 110173443 Up 1.5 0.0098 0.0346 transforming growth factor, beta 3 6 BM391858 Dnahc11 1389755_at 145176836 Down 1.7 0.0191 0.0507 Rattus norvegicus similar to left-right dynein (LOC314527), mRNA 6 AI716595 Rgs6 1379517_at 106987205 Up 1.8 0.0313 0.0764 similarity to protein sp:P49758 (H.sapiens) RGS6_HUMAN REGULATOR OF G-PROTEIN SIGNALING 6 (RGS6) (S914) 6 AI008479 - 1386311_at 111086323 Up 1.6 0.0430 0.0893 --- 6 AI013980 - 1382676_at 135520809 Up 1.5 0.0097 0.0493 Rattus norvegicus similar to WD repeat domain 20 isoform 1 (LOC314453), mRNA 6 AA800587 Gpx2 1374070_at 99372423 Down 2.1 0.0323 0.0782 glutathione peroxidase 2 6 BE115243 - 1391039_at 14047672 Down 1.6 0.0036 0.0338 Rattus norvegicus transcribed sequences 7 BI298372 - 1375467_at 140961150 Up 1.5 0.0073 0.1053 Rattus norvegicus similar to oriLyt TD-element binding protein 7 (LOC366994), mRNA 7 AF305418 Col2a1 1387767_a_at 136679219 Up 1.5 0.0442 0.2103 procollagen, type II, alpha 1 7 NM_053629 Fstl3 1370120_at 11437262 Down 1.9 0.0062 0.0977 follistatin-like 3 7 AW534107 - 1394742_at 105805046 Down 1.8 0.0212 0.1715 Rattus norvegicus transcribed sequences 7 BF402538 - 1381189_at 9816510 Down 1.7 0.0130 0.1473 Rattus norvegicus similar to fizzy-related protein (LOC314642), mRNA 7 BF565397 Csnk1e 1386693_at 117401336 Down 1.6 0.0459 0.2103 casein kinase 1, epsilon 7 BG381296 Nfic 1375342_at 9768135 Down 1.6 0.0220 0.1738 nuclear factor I/C 7 M15481 Igf1 1370333_a_at 24531690 Down 1.6 0.0101 0.1324 1.5 0.0072 0.0282 insulin-like growth factor 1 7 AI412155 Cry1 1392640_at 20644316 Down 1.9 0.0025 0.0178 similarity to protein ref:NP_004066.1 (H.sapiens) cryptochrome 1 (photolyase-like) [Homo sapiens] 7 AI554981 - 1394962_at 21768846 Up 1.8 0.0241 0.0700 Rattus norvegicus transcribed sequences 7 AA997236 Tef 1385374_at 120169280 Up 1.7 0.0119 0.0522 --- 7 NM_012793 Gamt 1368253_at 10959990 Up 1.5 0.0128 0.0535 guanidinoacetate methyltransferase 7 AI411286 Gna15 1367504_at 9663886 Up 1.5 0.0337 0.0802 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 7 AW525471 - 1382325_at 117003627 Up 1.5 0.0224 0.0662 Rattus norvegicus similar to 2-amino-3-ketobutyrate-coenzyme A ligase (LOC366959), mRNA 7 BE099085 - 1374474_at 128885958 Down 1.8 0.0271 0.0736 Rattus norvegicus transcribed sequences 7 AI059060 - 1384599_at 62448645 Down 1.6 0.0255 0.0712 Rattus norvegicus transcribed sequences 7 BI294763 - 1378540_at 62754313 Down 1.5 0.0235 0.0685 Rattus norvegicus transcribed sequences Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 7 BI291849 - 1391539_at 25628629 Down 1.5 0.0244 0.0703 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 8 BI291272 Rcn2 1377512_at 59648572 Up 1.9 0.0265 0.1826 Rattus norvegicus transcribed sequences 8 BF283259 - 1397187_at 63564060 Up 1.6 0.0151 0.1530 Rattus norvegicus transcribed sequence with weak similarity to protein pir:T33900 (C.elegans) T33900 hypothetical protein Y48A5A.1 - Caenorhabditis elegans 8 BF542912 Scn3b 1383435_at 43231453 Up 1.6 0.0204 0.1715 Rattus norvegicus transcribed sequence with moderate similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 8 BF408587 - 1391021_at 76091646 Down 1.5 0.0037 0.0783 Rattus norvegicus similar to KIAA1749 protein (LOC315795), mRNA 8 AW435415 - 1380306_at 51928308 Up 2.9 0.0312 0.0640 Rattus norvegicus transcribed sequences 8 AA875647 - 1397225_at 51926077 Up 2.5 0.0380 0.0742 similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 8 AI232305 Panx1 1392001_at 11812788 Down 1.7 0.0217 0.0538 similarity to protein ref:NP_036135.1 (M.musculus) cofactor required for Sp1 transcriptional activation subunit 2 (150 kDa) [Mus musculus] 8 BM388029 - 1374159_at 115271908 Down 1.6 0.0184 0.0499 Rattus norvegicus transcribed sequences 8 AI502723 - 1393220_at 104382061 Down 1.5 0.0207 0.0528 Rattus norvegicus transcribed sequences 8 BI291842 Cish 1378586_at 112538514 Up 3.3 0.0129 0.0536 Rattus norvegicus transcribed sequences 8 BM385405 - 1381811_at 47052136 Up 2.0 0.0375 0.0846 ubiquitin specific protease 2 8 AF106659 Usp2 1387703_a_at 47052136 Up 1.8 0.0388 0.0857 ubiquitin specific protease 2 8 AA997421 - 1379721_at 21058321 Up 1.8 0.0061 0.0425 Rattus norvegicus similar to 2310047B19Rik protein (LOC363029), mRNA 8 NM_017186 Gcm1 1369506_at 83018975 Up 1.8 0.0459 0.0937 glial cells missing (Drosophila) homolog a 8 BF283108 - 1394389_at 68596965 Up 1.8 0.0308 0.0760 Rattus norvegicus transcribed sequences 8 AI030103 MGC95155 1393061_at 47755143 Up 1.6 0.0009 0.0232 Rattus norvegicus similar to cDNA sequence BC021608 (LOC300676), mRNA 8 BF403998 - 1389166_at 58093942 Up 1.6 0.0076 0.0461 Rattus norvegicus similar to calcium binding protein Kip 2 (LOC300719), mRNA 8 BF386242 - 1382569_at 100650642 Up 1.6 0.0099 0.0493 Rattus norvegicus similar to membrane receptor (5H375) (LOC315904), mRNA 8 AF368860 - 1370349_a_at 36892614 Down 7.3 0.0091 0.0493 ATPase inhibitor 8 NM_130422 Casp12 1387605_at 2083906 Down 2.3 0.0184 0.0622 caspase 12 8 BE110921 - 1385029_at 2112379 Down 2.2 0.0375 0.0846 --- 8 AA893518 - 1389270_x_at 36892614 Down 2.1 0.0017 0.0323 Rattus norvegicus similar to Urinary protein 3 precursor (RUP-3) (LOC300504), mRNA 8 NM_053963 Mmp12 1368530_at 4249938 Down 1.8 0.0450 0.0923 matrix metalloproteinase 12 8 BF405592 - 1377531_at 32289429 Down 1.7 0.0146 0.0556 Rattus norvegicus transcribed sequences 8 NM_021866 Ccr2 1387742_at 128892784 Down 1.6 0.0496 0.0966 chemokine receptor CCR2 gene 8 BF395218 Tpm1 1395794_at 71339094 Down 1.6 0.0400 0.0876 tropomyosin 1, alpha 8 BF409530 Ncam1 1397120_at 53014649 Down 1.6 0.0106 0.0493 --- 8 BF289445 - 1394659_at 93733149 Down 1.5 0.0280 0.0740 Rattus norvegicus transcribed sequences 8 AI717707 - 1395410_at 106062791 Down 1.5 0.0293 0.0752 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 8 AI235560 - 1392727_at 92449550 Down 1.5 0.0478 0.0957 Rattus norvegicus similar to KIAA1009 protein (LOC300880), mRNA 9 BE099410 - 1396770_at 73913631 Up 1.8 0.0450 0.2103 Rattus norvegicus transcribed sequences 9 AI385237 - 1378346_at 110454706 Up 1.5 0.0009 0.0593 Rattus norvegicus transcribed sequences 9 AI408959 - 1396942_at 13460037 Down 2.4 0.0032 0.0761 Rattus norvegicus hypothetical LOC301267 (LOC301267), mRNA 9 BF523991 - 1396268_at 84258233 Down 2.3 0.0019 0.0604 Rattus norvegicus similar to putative nuclear protein (LOC301570), mRNA 9 AI556441 - 1395058_at 44843123 Down 2.1 0.0152 0.1530 Rattus norvegicus similar to CG12050-PA (LOC314545), mRNA 9 AA859909 - 1383734_at 4650918 Down 1.9 0.0445 0.2103 4.8 0.0130 0.0407 Rattus norvegicus transcribed sequences 9 BI278550 - 1383439_at 38355753 Down 1.8 0.0256 0.0574 Rattus norvegicus transcribed sequences 9 BE105373 - 1376540_at 11583413 Down 1.6 0.0193 0.0507 Rattus norvegicus transcribed sequences 9 AW527151 - 1374635_at 62252015 Down 2.3 0.0010 0.0109 2.8 0.0030 0.0327 similarity to protein ref:NP_003803.1 (H.sapiens) a disintegrin and metalloproteinase domain 23 preproprotein [Homo sapiens] 9 BE106041 - 1394717_at 4484968 Down 2.6 0.0032 0.0214 2.0 0.0073 0.0448 Rattus norvegicus transcribed sequences 9 AA955211 - 1393271_at 74287921 Up 1.8 0.0116 0.0519 Rattus norvegicus transcribed sequences 9 AI639159 - 1386454_at 74409235 Up 1.8 0.0105 0.0493 Rattus norvegicus similar to YSPL-1 form 1 (LOC367298), mRNA 9 NM_057125 Pex6 1368264_at 10130773 Up 1.6 0.0138 0.0549 peroxisomal biogenesis factor 6 9 BG381010 - 1376104_at 105454273 Up 1.6 0.0308 0.0760 Rattus norvegicus transcribed sequences 9 BF565191 - 1386691_at 84085615 Up 1.6 0.0318 0.0773 Rattus norvegicus similar to DNA segment, Chr 1, ERATO Doi 757, expressed (LOC363268), mRNA 9 NM_019278 Resp18 1367904_at 74551809 Up 1.5 0.0040 0.0364 regulated endocrine-specific protein 18 9 BF566725 - 1385624_at 11492917 Up 1.5 0.0306 0.0760 Rattus norvegicus similar to Transcription initiation protein SPT3 homolog (SPT3-like protein) (LOC301257), mRNA 9 BF288995 - 1382513_at 8059023 Down 1.7 0.0092 0.0493 Rattus norvegicus similar to triggering receptor TREM-2A (LOC301227), mRNA 9 BF284017 - 1374525_at 59083213 Down 1.7 0.0360 0.0833 Rattus norvegicus similar to amyotrophic lateral sclerosis 2 chromosome region candidate 9; putative GTPase regulator (LOC363239), mRNA 9 BM384831 - 1393653_at 83412793 Down 1.7 0.0010 0.0238 Rattus norvegicus transcribed sequences 9 AF053312 Ccl20 1369814_at 82440964 Down 1.6 0.0158 0.0578 small inducible cytokine subfamily A20 9 AI548940 - 1385143_at 79840001 Down 1.6 0.0246 0.0704 Rattus norvegicus transcribed sequence with moderate similarity to protein pir:I60486 (R.norvegicus) I60486 gene trg protein - rat (fragment) 9 BI275716 Col3a1 1370959_at 44281582 Down 1.5 0.0293 0.0752 collagen, type III, alpha 1 9 AA997266 - 1392655_at 84439277 Down 1.5 0.0022 0.0327 Rattus norvegicus similar to Nuclear autoantigen Sp-100 (Speckled 100 kDa) (Nuclear dot-associated Sp100 protein) (LOC363269), mRNA 10 AI703949 - 1397956_at 15129199 Up 1.7 0.0175 0.1640 --- 10 BE112998 Emp2 1377752_at 5311157 Up 1.7 0.0462 0.2103 Rattus norvegicus similar to XMP (LOC360468), mRNA 10 BF563316 LOC303163 1396163_at 43828832 Down 2.1 0.0183 0.1640 Rattus norvegicus similar to Ac2-233 (LOC303163), mRNA 10 NM_031116 Ccl5 1369983_at 71605827 Down 2.0 0.0382 0.2095 chemokine (C-C motif) ligand 5 10 NM_138916 Crk7 1387546_at 86972428 Down 1.9 0.0243 0.1788 --- 10 BF566908 Crebbp 1385852_at 11598703 Down 1.8 0.0478 0.2103 CREB binding protein 10 AA998678 - 1384890_at 90213177 Down 1.8 0.0115 0.1401 Rattus norvegicus similar to Enx-2 (LOC303547), mRNA 10 AW525662 - 1382664_at 93679262 Down 1.6 0.0267 0.1826 Rattus norvegicus similar to Protein CDC27Hs (Cell division cycle protein 27 homolog) (H-NUC) (LOC360643), mRNA 10 AI236624 - 1390813_at 76775778 Down 1.6 0.0395 0.2103 Rattus norvegicus similar to RNA-binding protein Musashi2-L (LOC360596), mRNA 10 AI407339 - 1391489_at 34132512 Down 1.6 0.0073 0.1053 Rattus norvegicus similar to LRG-47 (LOC303090), mRNA 10 BI285135 - 1374073_at 64576051 Down 1.5 0.0317 0.1909 Rattus norvegicus similar to DNA segment, Chr 11, ERATO Doi 18, expressed (LOC303333), mRNA 10 BG377259 - 1382185_at 28584909 Up 1.6 0.0476 0.0859 C1q and tumor necrosis factor related protein 2; complement-c1q tumor necrosis factor-related protein 2 [Homo sapiens] 10 AI237401 - 1375519_at 15558732 Down 2.5 0.0081 0.0305 Rattus norvegicus similar to alpha globin (LOC287167), mRNA 10 AI577319 Hba-a1 1388608_x_at 15558740 Down 2.4 0.0103 0.0351 hemoglobin, alpha 1 10 NM_031010 Alox15 1387796_at 57185939 Down 1.6 0.0048 0.0225 arachidonate 12-lipoxygenase 10 BM388946 - 1374204_at 65428043 Down 1.6 0.0303 0.0635 1.7 0.0120 0.0522 Rattus norvegicus similar to WSB-1 (LOC303336), mRNA 10 BG376453 - 1385190_at 18406306 Down 1.7 0.0021 0.0167 2.2 0.0105 0.0493 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 10 NM_012503 Asgr1 1370149_at 56899266 Up 1.9 0.0106 0.0493 asialoglycoprotein receptor 1 10 AI180413 MGC108825 1389350_at 97725096 Up 1.9 0.0060 0.0425 Rattus norvegicus similar to beta-2-glycoprotein I (LOC287774), mRNA 10 M25804 Nr1d1 1370816_at 87541398 Up 1.8 0.0215 0.0647 nuclear receptor subfamily 1, group D, member 1 10 NM_012851 Hsd17b1 1369292_at 90094249 Up 1.8 0.0159 0.0578 hydroxysteroid 17-beta dehydrogenase 1 10 AA925026 Tmprss8 1384775_s_at 13226053 Up 1.7 0.0476 0.0957 transmembrane serine protease-1 10 BE112523 - 1383654_a_at 110673934 Up 1.7 0.0096 0.0493 Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_071441.1 (H.sapiens) fructosamine-3-kinase [Homo sapiens] 10 BI275257 - 1375206_at 58616915 Up 1.6 0.0301 0.0760 Rattus norvegicus hypothetical LOC287466 (LOC287466), mRNA 10 L04796 Gcgr 1370522_at 109928085 Up 1.6 0.0115 0.0516 glucagon receptor 10 AA893634 - 1372305_at 85612425 Up 1.5 0.0215 0.0647 Rattus norvegicus similar to nonclathrin coat protein zeta2-COP (LOC360611), mRNA 10 AF035963 Havcr1 1387965_at 31825130 Down 3.2 0.0250 0.0709 kidney injury molecule 1 10 BE107457 - 1379957_at 71192701 Down 2.1 0.0135 0.0546 Rattus norvegicus similar to schlafen 8 (LOC303378), mRNA 10 AI406967 Ebf 1394709_at 29836596 Down 2.0 0.0415 0.0888 Rattus norvegicus transcribed sequences 10 BG372455 - 1390117_at 75206538 Down 2.0 0.0016 0.0323 Rattus norvegicus transcribed sequences 10 BG671264 - 1390783_at 99478336 Down 1.8 0.0021 0.0327 Rattus norvegicus similar to ATP-binding cassette transporter sub-family A member 8a (LOC303638), mRNA 10 BM386570 Nf1 1376943_at 65700694 Down 1.6 0.0308 0.0760 Rattus norvegicus transcribed sequence with weak similarity to protein pir:A38445 (H.sapiens) A38445 EV12B protein precursor - human 10 BF398435 - 1381048_at 32085164 Down 1.6 0.0293 0.0752 --- 10 BI278962 - 1388880_at 71177086 Down 1.5 0.0400 0.0876 Rattus norvegicus transcribed sequences 10 BG380279 - 1375672_at 62783889 Down 1.8 0.0001 0.0216 1.8 0.0007 0.0097 2.3 0.0001 0.0109 Rattus norvegicus transcribed sequence with weak similarity to protein pir:RGECDW (E. coli) RGECDW transcription activator of D-serine dehydratase 10 AI146250 - 1383744_at 62784324 Down 2.5 0.0002 0.0312 2.3 0.0024 0.0178 2.4 0.0001 0.0109 Rattus norvegicus transcribed sequence with weak similarity to protein pir:RGECDW (E. coli) RGECDW transcription activator of D-serine dehydratase 11 AI137518 Cxadr 1384816_at 17193505 Up 2.1 0.0194 0.1677 Rattus norvegicus transcribed sequences 11 BG665433 Cxadr 1374273_at 17231126 Down 1.6 0.0350 0.0817 Rattus norvegicus transcribed sequence with moderate similarity to protein pir:I60307 (E. coli) I60307 beta-galactosidase, alpha peptide - Escherichia coli 11 AI599448 - 1380049_at 69672555 Up 1.5 0.0445 0.2103 Rattus norvegicus transcribed sequences 11 BI276069 Eif2b5 1376145_at 82535714 Down 1.6 0.0129 0.1473 initiation factor eIF-2Be 11 AA891032 - 1386503_at 84611466 Up 1.6 0.0203 0.0639 --- 11 AA818342 - 1373628_at 43736017 Down 2.1 0.0222 0.0658 Rattus norvegicus transcribed sequences 11 AI176265 - 1378032_at 45794262 Down 2.1 0.0360 0.0833 Rattus norvegicus similar to molecule possessing ankyrin-repeats induced by lipopolysaccharide; IkappaB-zeta (LOC304005), mRNA 11 BF291041 - 1382296_at 25498273 Down 2.1 0.0380 0.0846 Rattus norvegicus transcribed sequences 11 BF283405 - 1379779_at 25557901 Down 1.9 0.0033 0.0327 Rattus norvegicus transcribed sequence with moderate similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 11 NM_012696 Kng1 1387050_s_at 80109746 Down 1.9 0.0402 0.0877 T-kininogen 11 AW535310 Adamts5 1394483_at 25503380 Down 1.8 0.0046 0.0376 a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 5 (aggrecanase-2) 11 AI502798 - 1392296_at 56684734 Down 1.7 0.0225 0.0662 --- Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 11 AA996697 - 1384122_at 70386039 Down 1.6 0.0204 0.0639 Rattus norvegicus transcribed sequences 11 AA850766 - 1384724_at 25503380 Down 1.6 0.0216 0.0647 Rattus norvegicus transcribed sequences 11 BG669292 - 1388879_at 44854445 Down 1.6 0.0313 0.0764 Rattus norvegicus similar to 5033411B22Rik protein (LOC363767), mRNA 11 BF561264 - 1393285_at 23905377 Down 1.6 0.0251 0.0709 --- 11 BE105661 - 1390443_at 17447730 Down 1.6 0.0343 0.0804 Rattus norvegicus transcribed sequences 11 U31554 Lsamp 1370550_at 60165324 Down 1.6 0.0003 0.0173 limbic system-associated membrane protein 11 BM389685 - 1376937_at 43764257 Down 1.6 0.0497 0.0966 Rattus norvegicus similar to downregulated in 1 (LOC304020), mRNA 11 BI303277 - 1392025_x_at 23905377 Down 1.6 0.0046 0.0376 --- 11 BE109290 Dlgh1 1397135_at 70775877 Down 1.5 0.0192 0.0630 Rattus norvegicus transcribed sequences 11 BF388632 - 1385742_at 86161377 Down 1.5 0.0400 0.0876 --- 12 NM_053962 Sds 1369864_a_at 37281250 Down 1.9 0.0278 0.1860 serine dehydratase 12 BE103748 - 1392941_at 22421151 Down 1.6 0.0297 0.1894 Rattus norvegicus transcribed sequences 12 AW531730 - 1394764_at 10757471 Up 1.7 0.0108 0.0358 Rattus norvegicus similar to kinase phosphatase inhibitor 2 (LOC304286), mRNA 12 AI235340 - 1384634_at 43276320 Down 1.7 0.0309 0.0640 Rattus norvegicus transcribed sequences 12 BF288115 - 1376688_a_at 19885349 Down 1.6 0.0148 0.0432 Rattus norvegicus similar to paired immunoglobin-like type 2 receptor alpha; cell surface receptor FDF03 (LOC288552), mRNA 12 BF521655 - 1385670_at 37302260 Up 1.5 0.0184 0.0622 Rattus norvegicus similar to serine dehydratase related sequence 1 (LOC360816), mRNA 12 AI060117 - 1381471_at 94144 Down 1.5 0.0336 0.0802 Rattus norvegicus transcribed sequences 13 NM_031118 Soat1 1369002_at 71420862 Down 2.3 0.0402 0.2103 acyl-coenzyme A:cholesterol acyltransferase 13 NM_053843 Fcgr3 1398246_s_at 86809011 Down 2.0 0.0370 0.2071 Fc receptor, IgG, low affinity III 13 AI231826 Rgs5 1393167_at 85445325 Down 1.6 0.0342 0.2009 regulator of G-protein signaling 5 13 AA894335 - 1375590_at 46546034 Down 1.6 0.0049 0.0932 Rattus norvegicus similar to RIKEN cDNA 5730454B08 (LOC310649), mRNA 13 AI704885 - 1381542_at 40948540 Down 1.5 0.0062 0.0977 Rattus norvegicus similar to RIKEN cDNA 1300013G12 (LOC304766), mRNA 13 BF400061 Capon 1396643_at 86360648 Down 1.8 0.0132 0.0407 Rattus norvegicus transcribed sequences 13 AI717113 F5 1374320_at 79992913 Down 1.8 0.0237 0.0563 coagulation factor 5 13 AI406858 - 1390987_at 86139707 Up 1.5 0.0142 0.0551 Rattus norvegicus transcribed sequences 13 AI407002 - 1373908_at Down 2.2 0.0306 0.0760 --- 13 BF288130 Ptprc 1390798_at 51247016 Down 1.9 0.0283 0.0742 protein tyrosine phosphatase, receptor type, C 13 AA893579 - 1392794_at 86131914 Down 1.8 0.0091 0.0493 Rattus norvegicus transcribed sequences 13 BF417625 - 1393187_at 89646247 Down 1.8 0.0416 0.0888 Rattus norvegicus transcribed sequences 13 AI639103 - 1379387_at 58607898 Down 1.7 0.0269 0.0734 Rattus norvegicus transcribed sequences 13 BF406641 - 1377301_at 48378522 Down 1.6 0.0032 0.0327 Rattus norvegicus transcribed sequences 13 BE108956 - 1392489_at Down 1.6 0.0184 0.0622 Rattus norvegicus transcribed sequences 13 AA943075 - 1382368_at 57586629 Down 1.6 0.0026 0.0327 Rattus norvegicus transcribed sequences 13 BI275261 - 1379497_at 237646 Down 1.6 0.0003 0.0173 --- 13 BM390782 - 1394972_at 57586629 Down 1.6 0.0051 0.0392 Rattus norvegicus transcribed sequences 13 NM_017170 Sap 1367804_at 89112726 Down 1.5 0.0172 0.0609 serum amyloid P-component 13 AI012859 Rnasel 1380425_at 68822011 Down 1.5 0.0419 0.0888 ribonuclease L (2, 5-oligoisoadenylate synthetase-dependent) 13 BG380581 - 1371472_at 85484906 Down 1.5 0.0031 0.0327 Rattus norvegicus transcribed sequences 14 BI299600 - 1378313_at 18662186 Up 1.5 0.0014 0.0604 Rattus norvegicus transcribed sequence with weak similarity to protein sp:O08816 (R.norvegicus) WASL_RAT Neural Wiskott-Aldrich syndrome protein (N-WASP) 14 BI290769 - 1380263_at 81898783 Down 1.6 0.0062 0.0977 Rattus norvegicus hypothetical LOC305452 (LOC305452), mRNA 14 AA892854 - 1398390_at 14995496 Down 2.1 0.0176 0.0484 small inducible cytokine B subfamily (Cys-X-Cys motif), member 13 (B-cell chemoattractant); B-cell-homing chemokine (ligand for Burkitts lymphoma re 14 X76456 - 1371266_at 19049664 Up 1.6 0.0054 0.0406 --- 14 BI288076 - 1376870_at 83909631 Up 1.6 0.0157 0.0575 Rattus norvegicus similar to transcription factor MAZR (LOC305471), mRNA 14 BI289418 Cd38 1390325_at 72320479 Down 1.7 0.0489 0.0966 CD38 antigen 14 AB001382 Spp1 1367581_a_at 6423008 Down 1.7 0.0425 0.0893 secreted phosphoprotein 1 14 AA964882 - 1384044_at 36959936 Down 1.6 0.0153 0.0574 Rattus norvegicus similar to beta-sarcoglycan (LOC305305), mRNA 14 BF523573 - 1386061_at 110494799 Down 1.6 0.0307 0.0760 Rattus norvegicus transcribed sequences 14 BE110655 - 1388317_at 87224018 Down 1.5 0.0431 0.0893 Rattus norvegicus transcribed sequences 15 BF391447 - 1392162_at 35694709 Up 1.7 0.0148 0.1530 Rattus norvegicus transcribed sequences 15 BM387164 - 1378087_at 56803906 Down 1.6 0.0243 0.0572 Rattus norvegicus transcribed sequences 15 AI411212 Pdlim2 1375367_at 50564108 Up 1.7 0.0093 0.0493 Rattus norvegicus similar to PDZ and LIM domain 2 (LOC290354), mRNA 15 AI111644 - 1383390_at 39469179 Up 1.7 0.0466 0.0946 Rattus norvegicus transcribed sequences 15 AA924588 - 1392725_at 108030444 Up 1.7 0.0466 0.0946 Rattus norvegicus similar to cDNA sequence BC006662 (LOC290500), mRNA 15 NM_012770 Gucy1b2 1368779_a_at 41612400 Up 1.6 0.0264 0.0723 guanylate cyclase, soluble, beta 2 15 AA859589 - 1393869_at 87382731 Up 1.6 0.0187 0.0629 Rattus norvegicus transcribed sequences 15 NM_053907 Dnase1l3 1368294_at 18909363 Down 2.2 0.0058 0.0417 deoxyribonuclease I-like 3 15 BG378310 - 1385099_at 87547816 Down 1.8 0.0073 0.0448 similarity to protein ref:NP_003834.1 (H.sapiens) sciellin [Homo sapiens] 15 BI303923 - 1383708_at 108785601 Down 1.7 0.0031 0.0327 strong similarity to protein ref:NP_004782.1 (H.sapiens) integrin, beta-like 1 (with EGF-like repeat domains) [Homo sapiens] 15 AF314657 Clu 1367784_a_at 45399691 Down 1.6 0.0338 0.0802 clusterin 15 BE104961 - 1379538_at 35487615 Down 1.5 0.0365 0.0837 Rattus norvegicus similar to centromere protein J; centrosomal P4.1-associated protein; LYST-interacting protein LIP1; LAG-3-associated protein (LOC305909), mRNA 16 AI137275 - 1375115_at 10336818 Up 1.6 0.0354 0.2016 Rattus norvegicus similar to wings apart-like CG3707-PA (LOC290577), mRNA 16 AF209114 LOC192253 1388040_a_at 83812189 Up 1.6 0.0256 0.1821 myosin heavy chain Myr 8 16 U02323 Nrg1 1370607_a_at 63053630 Down 1.5 0.0468 0.2103 neuregulin 1 16 AI180253 - 1379411_at 20463328 Up 1.9 0.0266 0.0582 2.2 0.0072 0.0448 Rattus norvegicus similar to putative protein family member (XC177) (LOC306366), mRNA 16 AI710682 - 1371354_at 6639336 Up 1.7 0.0028 0.0327 Rattus norvegicus similar to troponin C, - mouse (LOC290561), mRNA 16 AI030212 - 1383031_at 62307869 Up 1.6 0.0096 0.0493 --- 16 AA892204 - 1386352_at 5892391 Up 1.5 0.0372 0.0846 --- 16 BE118080 LOC207125 1371091_at 83402975 Up 1.5 0.0178 0.0614 unknown protein 16 BI303853 - 1381556_at 30959054 Down 1.8 0.0416 0.0888 Rattus norvegicus transcribed sequences 16 NM_013128 Cpe 1386921_at Down 1.8 0.0357 0.0831 carboxypeptidase E 16 AI237698 - 1382601_at 56307360 Down 1.7 0.0050 0.0392 Rattus norvegicus transcribed sequences 17 BF388256 - 1381316_at 41225418 Up 1.9 0.0346 0.2016 Rattus norvegicus transcribed sequences 17 BF397269 - 1393811_at 6748412 Down 2.3 0.0380 0.2095 Rattus norvegicus LOC361197 (LOC361197), mRNA 17 BF553179 - 1383759_at 11063085 Down 2.1 0.0112 0.1393 Rattus norvegicus similar to nuclear ATP/GTP-binding protein (LOC290986), mRNA 17 AA997691 - 1396180_at 47923987 Down 1.7 0.0019 0.0604 Rattus norvegicus similar to leucine rich repeat containing 16 (LOC306941), mRNA 17 AA997590 - 1385248_a_at 20987962 Down 1.6 0.0487 0.2103 Rattus norvegicus transcribed sequence with moderate similarity to protein pir:B35272 (H.sapiens) B35272 osteoinductive factor - human 17 AW140475 Cugbp2 1388195_at 83026864 Up 1.7 0.0495 0.0886 CUG triplet repeat,RNA-binding protein 2 17 AI029126 - 1379518_at 33137357 Up 1.6 0.0009 0.0232 Rattus norvegicus transcribed sequences 17 AI007775 - 1393348_at 36881748 Up 1.6 0.0059 0.0419 Rattus norvegicus transcribed sequences 17 AA926109 - 1383486_at 57239713 Down 3.0 0.0183 0.0622 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 17 BI294018 - 1392648_at 88367101 Down 2.1 0.0449 0.0923 Rattus norvegicus similar to macrophage mannose receptor precursor (LOC291327), mRNA 17 AW522589 - 1391162_at 46899255 Down 1.9 0.0068 0.0448 Rattus norvegicus transcribed sequences 17 BE116471 - 1395994_at 7961472 Down 1.7 0.0307 0.0760 Rattus norvegicus transcribed sequences 17 BM383122 LOC191574 1389500_at Down 1.6 0.0204 0.0639 --- 18 BE118820 - 1385202_at 14138699 Up 1.6 0.0258 0.1821 --- 18 AW920883 - 1394907_at 56545079 Down 1.9 0.0057 0.0977 Rattus norvegicus transcribed sequences 18 BI304009 Lox 1368172_a_at 47893877 Down 1.6 0.0011 0.0593 lysyl oxidase 18 BM391907 - 1373692_at 29303561 Down 1.6 0.0201 0.1714 similarity to protein ref:NP_003723.1 (H.sapiens) eukaryotic translation initiation factor 4E binding protein 3; eukaryotic initiation factor 4E-binding protein 3 18 BI281497 - 1384177_at 52202307 Up 1.6 0.0008 0.0097 Rattus norvegicus similar to RIKEN cDNA 9130427A09 (LOC307288), mRNA 18 AA964219 - 1390790_a_at 71794564 Down 2.0 0.0055 0.0239 Rattus norvegicus transcribed sequences 18 AA925710 - 1384098_at 12711851 Down 1.7 0.0370 0.0742 Rattus norvegicus similar to hypothetical protein (LOC361296), mRNA 18 BE108561 Tcf4 1396660_at 66272171 Down 1.6 0.0437 0.0827 Rattus norvegicus transcribed sequences 18 AW435211 Tcf4 1397286_at 66240259 Down 1.5 0.0096 0.0493 transcription factor 4 18 BG377887 Gfra3 1377680_at 27167576 Up 1.5 0.0139 0.0551 GDNF-family receptor alpha 3 18 AI231438 MGC93742 1390569_at 81160681 Up 1.5 0.0082 0.0477 Rattus norvegicus transcribed sequences 18 BG378232 Mapk4 1374724_at 70707526 Up 1.5 0.0220 0.0655 Rattus norvegicus transcribed sequences 18 AI501237 - 1377994_at 62920344 Down 1.7 0.0381 0.0846 similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 19 BF405113 - 1390605_at 2354534 Down 1.8 0.0018 0.0604 Rattus norvegicus transcribed sequences 19 AW532828 - 1374167_at 35719480 Down 1.6 0.0118 0.1401 Rattus norvegicus hypothetical LOC291982 (LOC291982), mRNA 19 BI288751 LOC291840 1381543_at 9665175 Up 1.6 0.0058 0.0241 Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_060701.1 (H.sapiens) hypothetical protein FLJ10815 [Homo sapiens] 19 BF291194 - 1395122_s_at 53544745 Up 1.6 0.0110 0.0500 Rattus norvegicus similar to PISSLRE (LOC361434), mRNA Supplemental Table1

Week4 Week12 Week20 Chromosome Gene Identifier Gene ID Other ID Position (Mb) Direction Ratio p-value adjp-value Ratio p-value adjp-value Direction p-value adjp-value Gene Description 19 BE108555 ZD10B 1396661_at 40905883 Up 1.6 0.0123 0.0528 Rattus norvegicus similar to Ddx19 protein (LOC292022), mRNA 19 BE105838 - 1379078_at 40888933 Up 1.6 0.0408 0.0886 Rattus norvegicus transcribed sequences 19 AI575458 - 1383083_at 52213643 Up 1.5 0.0105 0.0493 Rattus norvegicus similar to SMAR1 (LOC292064), mRNA 19 BI289613 - 1372600_at Up 1.5 0.0028 0.0327 --- 19 AI071216 - 1383973_at 36520052 Up 1.5 0.0215 0.0647 Rattus norvegicus similar to hypothetical protein E330010G16 (LOC307816), mRNA 19 BF414702 - 1383641_at 32107286 Down 1.6 0.0420 0.0888 endothelin receptor type A 19 BI275867 - 1390733_at 44899645 Down 1.6 0.0431 0.0893 Rattus norvegicus transcribed sequences 19 BF548891 - 1393415_at 32046233 Down 1.6 0.0062 0.0428 endothelin receptor type A 20 AI012393 Btnl7 1376373_at 4522079 Up 1.6 0.0446 0.2103 similarity to protein sp:P14373 (H.sapiens) RFP_HUMAN Zinc-finger protein RFP (Ret finger protein) (Tripartite motif protein 27) 20 BE096729 Ftcd 1387877_at 12470290 Down 1.6 0.0040 0.0809 formiminotransferase cyclodeaminase 20 AI599350 Psmb9 1370186_at 4801220 Down 1.6 0.0048 0.0922 proteosome (prosome, macropain) subunit, beta type 9 20 AW252296 Col18a1 1381431_at 11974608 Down 1.6 0.0000 0.0216 Rattus norvegicus transcribed sequences 20 BE108058 Col18a1 1398394_at 11973497 Down 1.6 0.0250 0.0709 Rattus norvegicus transcribed sequences 20 AJ243973 RT1.S3 1388213_a_at 2815982 Down 1.5 0.0499 0.2103 MHC class Ib RT1.S3 20 AI548626 - 1398128_at 43870890 Down 1.5 0.0176 0.1640 Rattus norvegicus transcribed sequences 20 BI290161 - 1382041_at 10731287 Up 1.7 0.0019 0.0325 Rattus norvegicus similar to 1-acylglycerol-3-phosphate-gamma (LOC294324), mRNA 20 AI113219 Spgl1 1382843_at 28459734 Up 1.6 0.0087 0.0493 sphingosine-1-phosphate lyase 1 20 AF413572 Pkib 1369105_a_at 36655515 Down 2.4 0.0091 0.0493 protein kinase (cAMP dependent, catalytic) inhibitor beta 20 AI411352 Gja1 1372002_at 35409815 Down 1.8 0.0299 0.0760 protein alpha 1 20 AW532939 - 1392736_at 30044615 Down 1.8 0.0136 0.0546 --- 20 BM383464 - 1382437_at 3423932 Down 1.7 0.0124 0.0528 RT1 class Ib gene(Aw2) 20 NM_019309 Grik2 1369036_at 53229999 Down 1.5 0.0013 0.0282 glutamate receptor, ionotropic, kainate 2 X AI169241 - 1372637_at 39886845 Up 1.9 0.0459 0.2103 Rattus norvegicus transcribed sequence with weak similarity to protein pir:I38488 (H.sapiens) I38488 trophinin - human X BG371620 - 1383675_at 147273184 Up 1.5 0.0481 0.2103 --- X NM_053428 Fgf13 1368114_at 144200839 Up 1.5 0.0351 0.2016 fibroblast growth factor 13 X BF396777 MGC94181 1395291_at 48895398 Up 1.5 0.0227 0.1748 Rattus norvegicus transcribed sequences X AI170324 - 1373882_at 50829626 Down 1.5 0.0057 0.0977 similarity to protein sp:P97946 (M.musculus) VEGD_MOUSE Vascular endothelial growth factor D precursor (VEGF-D) (c-fos induced growth factor) (FIGF) X NM_013197 Alas2 1367985_at 39799426 Down 2.7 0.0201 0.0523 1.9 0.0341 0.0803 aminolevulinic acid synthase 2 X NM_021670 Bmp15 1387734_at 29003933 Up 1.9 0.0425 0.0893 bone morphogenetic protein 15 X NM_012521 Calb3 1368339_at 52446817 Up 1.8 0.0247 0.0704 calbindin 3 X BF389476 MGC94465 1380835_at 110744008 Up 1.8 0.0133 0.0546 Rattus norvegicus similar to hypothetical protein FLJ12671 (LOC317205), mRNA X BG379491 - 1374218_at 41701219 Up 1.6 0.0007 0.0216 Rattus norvegicus transcribed sequences X BI274139 - 1375276_at 26630886 Up 1.6 0.0019 0.0325 Rattus norvegicus similar to mitochondrial inner membrane translocase component Tim17b (LOC317374), mRNA X NM_017251 Gjb1 1387145_at 89448909 Up 1.6 0.0050 0.0392 gap junction membrane channel protein beta 1 X BG373822 - 1376706_at 67548001 Down 1.6 0.0486 0.0966 Rattus norvegicus transcribed sequences X BF415976 - 1380693_at 56821495 Down 1.6 0.0482 0.0962 --- UN BM388500 - 1383977_a_at Up 1.9 0.0252 0.1818 --- UN BF551160 - 1394056_at Down 1.7 0.0299 0.1894 --- UN U15660 Nr1d2 1370540_at Up 2.2 0.0086 0.0318 nuclear receptor subfamily 1, group D, member 2 UN U20796 Nr1d2 1370541_at Up 1.7 0.0149 0.0432 nuclear receptor subfamily 1, group D, member 2 UN BF284190 Nr1d2 1390430_at Up 1.6 0.0380 0.0742 --- UN AI237240 - 1395238_at Down 2.7 0.0253 0.0574 --- UN BE116619 Rap2b 1392922_at Down 1.7 0.0259 0.0574 Rattus norvegicus transcribed sequences UN AF361355 Pr1 1388103_at Up 1.5 0.0048 0.0225 1.7 0.0026 0.0327 voltage-dependent calcium channel gamma subunit-like protein UN AI712791 - 1384303_s_at Up 1.7 0.0376 0.0846 --- UN AI712791 - 1384302_at Up 1.6 0.0049 0.0391 --- UN BF416508 Abp10 1389441_at Down 1.5 0.0010 0.0238 annexin V-binding protein ABP-10 Supplemental Table2

Week Total Networks Top Networks High Level Functions Significance Top Function Significance Canonical Pathway (p<0.05) Node 4 10 1 Immune Response 3.87E-8 - 2.88E-2 Development Disorder 3.46E-15 - 3.15E-5 Wnt/B-catenin Signaling Crebbp(2), Csnk1e(1), Sfrp2(3), Wnt2b(3) Cell-to-Cell Signaling and Interaction 1.27E-6 - 2.88E-2 Cellular Growth and Proliferation 2.09E-14 - 5.88E-5 Amyoid Processing Csnk1e(1), Psen(1) Immune and Lymphatic Development and Function 1.27E-6 - 2.88E-2 1.87E-13 - 5.88E-5 Antigen Presentation HLA-E(1), Psmb9(1) Cellular Development 8.90E-13 - 6.36E-5

2 Gene Expression 1.15E-7 - 3.84E-3 Viral Function 6.30E-6 - 3.84E-3 Cancer 1.43E-5 - 3.84E-3

3 Cellular Development 8.04E-8 - 3.84E-3 Developmental Disorder 2.64E-7 - 3.84E-3 Cellular Growth and Proliferation 2.87E-7 - 3.84E-3

12 6 1 Gene Expression 2.68E-10 - 6.60E-5 Gene Expression 8.19E-13 - 1.04E-5 Wnt/B-catenin Signaling Tcf4(2), Tgfb3(1) Cell-to-Cell Signaling and Interaction 9.15E-10 - 8.57E-5 Cell-to-Cell Signaling and Interaction 2.71E-11 - 8.91E-6 Glutathione Metabolism Gstm1(2), Gstm2 (2) Cellular Growth and Proliferation 9.15E-10 - 8.57E-5 Cellular Growth and Proliferation 2.71E-11 - 1.25E-5 Glycine, Serine, and Threonine Metabolism Alas2(1), Sars Connective Tissue Development and Function 2.71E-11 - 3.42E-6 2 Cardiovascular Disease 5.17E-7 - 3.67E-3 Drug Metabolism 5.17E-7 - 1.46E-3 Hematological Disease 5.17E-7 - 3.84E-3 Small Molecule Biochemistry 5.17E-7 - 3.84E-3

20 10 1 Cell Morpholgy 2.07E-7 - 3.84E-3 Cellular Growth and Proliferation 3.40E-11 - 4.16E-5 Nitric Oxide Signaling in Cardiovascular System Gucy1a3(3), Gucy1b3(3) 1.03E-6 - 3.84E-3 Connective Tissue Development and Function 7.21E-11 - 4.11E-5 Endocrine System Development and Function 1.03E-6 - 2.36E-3 Cellular Movement 8.58E-11 - 3.60E-5 Cellular Development 2.18E-6 - 3.49E-3 Immune and Lymphatic Development and Function 1.49E-10 - 4.18E-5

2 Gene Expression 1.99E-8 - 9.24E-4 Cell Morphology 5.20E-8 - 1.43E-5 Cell-to-Cell Signaling and Interaction 2.07E-7 - 9.54E-4

3 Cellular Movement 1.31E-8 - 3.16E-3 Cellular Development 1.31E-7 - 3.84E-3 Immune and Lymphatic Development and Function 1.31E-7 - 3.84E-3 Supplemental Table3

Chromosome Gene Identifier Gene ID Other ID Position (Mb) Gene Description 1 NM_021663 NUCB2_RAT 1370000_at 174624096 NEFA precursor 1 J02827 ODBA_RAT 1370897_at 80837907 branched chain keto acid dehydrogenase subunit E1, alpha polypeptide 1 AI169562 Q63274_RAT 1371080_at 94412803 kallikrein 1 BF389640 Q4G020_RAT 1379485_at 267369410 Rattus norvegicus similar to eukaryotic translation initiation factor 3, subunit 10 theta, 150/170kDa; eukaryotic translation initiation factor 3, subunit 10 (theta, 170kD); Eukaryotic translation initiation factor 3, subunit 10, 170kD; eukaryotic tr 1 BE099664 1381264_at 264609833 Rattus norvegicus transcribed sequences 1 AI060133 1382476_x_at 207557036 Rattus norvegicus transcribed sequence with moderate similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 1 AA956308 XP_218291.3 1384118_at 72699408 Rattus norvegicus similar to RIKEN cDNA 2810439M05 (LOC308361), mRNA 1 BF399429 XP_215000.3 1389026_at 149425862 Rattus norvegicus similar to RIKEN cDNA 4933417L02 (LOC293117), mRNA 1 AI233266 Q2V057_RAT 1389645_at 85545612 Rattus norvegicus similar to proline dehydrogenase (oxidase) 2 (LOC361538), mRNA 1 AA964142 XP_344925.2 1389888_at 157532994 Rattus norvegicus similar to RIKEN cDNA 2610209A20 (LOC368121), mRNA 1 AI412174 CI071_RAT 1390801_at 228097480 Rattus norvegicus transcribed sequences 1 AI145746 1392249_at 20355444 Rattus norvegicus transcribed sequences 1 AA893579 1392794_at 203379419 Rattus norvegicus transcribed sequences 1 AA818059 1392982_at 101801286 Rattus norvegicus transcribed sequences 1 BF285952 NP_001034754.1 1393474_at 96197898 Rattus norvegicus similar to cytosolic sulfotransferase (LOC292915), mRNA 1 AI045155 NP_001013163.1 1393929_at 47115584 Rates norvegicus similar to G protein-coupled receptor KY411 (LOC308163), mRNA 1 BF284064 1395844_at 103864540 Rattus norvegicus similar to methylcrotonoyl-Coenzyme A carboxylase 1 (alpha) (LOC294972), mRNA 1 BE097460 1398700_at 40899901 Rattus norvegicus transcribed sequences 2 NM_031620 SERA_RAT 1367811_at 124600017 3-phosphoglycerate dehydrogenase 2 NM_017090 GCYA3_RAT 1368154_at 173755007 guanylate cyclase 1, soluble, alpha 3 2 AF202115 CERU_RAT 1368419_at 105086278 ceruloplasmin 2 NM_012889 VCAM1_RAT 1368474_at 212277654 vascular cell adhesion molecule 1 2 AF367210 IL7_RAT 1369208_at 96356083 interleukin 7 2 U78857 1369686_at 144267748 activity and neurotransmitter-induced early gene protein 4 (ania-4) 2 BF411765 CELR2_RAT 1371018_at 203960524 cadherin EGF LAG seven-pass G-type receptor 2 2 BI281702 MAP1B_RAT 1373363_at 30415255 microtubule-associated protein 1b 2 AI176320 XP_226988.3 1373416_at 113315728 Rattus norvegicus transcribed sequences 2 BE107853 1375497_at 142404903 Rattus norvegicus transcribed sequences 2 AA957384 1377278_at 47172864 Rattus norvegicus transcribed sequences 2 BF388224 1379175_at 234240301 Rattus norvegicus transcribed sequences 2 BG374101 1381262_at 181637899 Rattus norvegicus transcribed sequences 2 AA819288 1382274_at 157332263 Rattus norvegicus similar to Retinoic acid receptor responder protein 1 (Tazarotene-induced gene 1 protein) (RAR-responsive protein TIG1) (LOC310486), mRNA 2 BM383464 1382437_at 208517037 RT1 class Ib gene(Aw2) 2 AI511405 XP_227217.3 1385407_at 155315259 Rattus norvegicus similar to hypothetical protein FLJ22693 (LOC310467), mRNA 2 AI237532 XP_227203.2 1385872_at 152125352 Rattus norvegicus transcribed sequences 2 BF290410 XP_226824.3 1386052_at 59105666 Rattus norvegicus transcribed sequences 2 BF284922 1388557_at 118026879 Rattus norvegicus transcribed sequences 2 BI282008 1389003_at 2911603 Rattus norvegicus transcribed sequences 2 BF402235 1392262_at 204000799 Rattus norvegicus transcribed sequences 2 BF409007 1393063_at 116159619 Rattus norvegicus transcribed sequences 2 BF415556 1395436_at 234263853 Rattus norvegicus transcribed sequences 2 BF553729 Q9QXK5_RAT 1397820_at 200218630 Rattus norvegicus transcribed sequences 3 NM_032066 NP_114455.1 1368051_at 78436634 smooth muscle-specific 17 beta-hydroxysteroid dehydrogenase type 3 3 NM_053498 ENP6_RAT 1368315_at 141385480 ectonucleoside triphosphate diphosphohydrolase 6 3 BM389019 NP_114013.1 1368829_at 112608480 fibrillin-1 3 BI295949 NP_001011965.1 1373590_at 14393734 Rattus norvegicus transcribed sequences 3 BF284124 XP_230325.3 1377018_at 87795505 Rattus norvegicus similar to E430002G05Rik protein (LOC311252), mRNA 3 BI273752 XP_216007.3 1379364_at 4100013 Rattus norvegicus similar to CG33130-PA (LOC296580), mRNA 3 BI294934 1380612_at 169762453 Rattus norvegicus transcribed sequences 3 AW527286 1380899_at 143411116 Rattus norvegicus similar to Protein C20orf160 (LOC311550), mRNA 3 BF553538 CXA9_RAT 1386123_at 99861095 Rattus norvegicus transcribed sequences 3 BF565756 1386695_at 42002053 Rattus norvegicus transcribed sequences 4 AI011345 WNK1_RAT 1368140_at 156297841 protein kinase, lysine deficient 1 4 NM_013080 PTPRZ_RAT 1368350_at 49334202 protein tyrosine phosphatase, receptor-type, Z polypeptide 1 4 BI291848 1371969_at 62062872 caldesmon 1 4 BG378920 MET_RAT 1374065_at 43134101 Rattus norvegicus transcribed sequences 4 AI012630 PTGD2_RAT 1377012_at 94611607 Rattus norvegicus transcribed sequences 4 AW529483 NP_997677.1 1378365_at 117432385 Rattus norvegicus transcribed sequences 4 BF389527 1381723_at 182898769 --- 4 BI288769 NP_001034433.1 1383401_at 42700914 --- 4 BE116205 XP_242556.3 1384795_at 118125137 Rattus norvegicus similar to nuclear protein, NP220 (LOC312491), mRNA 4 AW524822 1390865_at 49659289 Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_036191.1 (M.musculus) Ca<2+>dependent activator protein for secretion; Ca2+-dependent activator protein for secretion [Mus musculus] 4 AI639113 1392053_at 89856142 Rattus norvegicus transcribed sequence with moderate similarity to protein pir:A57384 (H.sapiens) A57384 multimerin, endothelial cell, precursor - human 4 BF283618 1392510_at 62729242 Rattus norvegicus similar to hypothetical protein B230314O19 (LOC362336), mRNA 4 BF400819 1396602_at 5272676 Rattus norvegicus transcribed sequences 5 NM_053977 CAD17_RAT 1369224_at 26047160 cadherin 17 5 NM_021653 IOD1_RAT 1369259_at 128385247 deiodinase, iodothyronine, type I 5 BF283756 NP_852143.1 1373923_at 2286373 Rattus norvegicus transcribed sequences 5 BF284337 1373963_at 79494958 Rattus norvegicus similar to RIKEN cDNA 2810435D12 (LOC298097), mRNA 5 BE105524 1380487_at 61951151 Rattus norvegicus transcribed sequences 5 BM384214 NP_001006980.1 1380579_at 149805644 Rattus norvegicus similar to Matrilin 1, cartilage matrix protein 1 (LOC297894), mRNA 5 AI028979 Q5BK25_RAT 1383395_at 160646328 Rattus norvegicus similar to Agmatinase, mitochondrial precursor (Agmatine ureohydrolase) (AUH) (LOC298607), mRNA 5 AI547423 BSND_RAT 1393209_at 127542219 barttin 5 BF401989 1396254_at 5249162 Rattus norvegicus transcribed sequences 5 BG378899 1399092_at 148815020 Rattus norvegicus similar to hypothetical protein FLJ10315 (LOC362608), mRNA 6 BG378238 1372524_at 40251362 Rattus norvegicus similar to Kiaa0575 (LOC313977), mRNA 6 BE107255 XP_234275.3 1374480_at 94024421 Rattus norvegicus similar to hypothetical protein E130308H01 (LOC314212), mRNA 6 BG667451 1377185_at 101599793 Rattus norvegicus transcribed sequences 6 AI103284 XP_216791.3 1381376_at 136196829 Rattus norvegicus similar to Tnfaip2 protein (LOC299340), mRNA 6 BM391545 1392733_at 77959323 Rattus norvegicus transcribed sequences 6 BM384008 XP_216688.3 1392864_at 72685228 Rattus norvegicus similar to p190-B (LOC299012), mRNA 7 AA819268 1371776_at 32601895 phosphatidylinositol 3-kinase, regulatory subunit, polypeptide 1 7 BG381296 Q9QY87_RAT 1375342_at 9768135 nuclear factor I/C 7 BI298372 1375467_at 140961650 Rattus norvegicus similar to oriLyt TD-element binding protein 7 (LOC366994), mRNA 7 BM384525 XP_216859.1 1395045_at 16321201 Rattus norvegicus similar to NADH oxidoreductase (LOC299643), mRNA 8 NM_053963 MMP12_RAT 1368530_at 4249938 matrix metalloproteinase 12 8 BI291272 1377512_at 59649072 Rattus norvegicus transcribed sequences 8 BF405592 1377531_at 32289929 Rattus norvegicus transcribed sequences 8 AA997421 XP_343361.2 1379721_at 21058321 Rattus norvegicus similar to 2310047B19Rik protein (LOC363029), mRNA 8 BF542912 SCN3B_RAT 1383435_at 43231453 Rattus norvegicus transcribed sequence with moderate similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 8 NM_130422 NP_569106.1 1387605_at 2083906 caspase 12 8 NM_021866 CCR2_RAT 1387742_at 128892784 chemokine receptor CCR2 gene 8 AA893518 UP3_RAT 1389270_x_at 36892614 Rattus norvegicus similar to Urinary protein 3 precursor (RUP-3) (LOC300504), mRNA 8 AA850618 Q9R0N2_RAT 1390710_x_at 44962880 Rattus norvegicus similar to gp250 precursor (LOC300652), mRNA 8 BF408587 1391021_at 76091646 Rattus norvegicus similar to KIAA1749 protein (LOC315795), mRNA 9 BI275716 CO3A1_RAT 1370959_at 44281582 collagen, type III, alpha 1 9 AW527151 1374635_at 62252515 Rattus norvegicus transcribed sequence with strong similarity to protein ref:NP_003803.1 (H.sapiens) a disintegrin and metalloproteinase domain 23 preproprotein [Homo sapiens] 9 AI385237 1378346_at 110455206 Rattus norvegicus transcribed sequences Supplemental Table3

9 AI639159 XP_346062.2 1386454_at 74409235 Rattus norvegicus similar to YSPL-1 form 1 (LOC367298), mRNA 9 BM384831 1393653_at 83412793 Rattus norvegicus transcribed sequences 9 BF523991 1396268_at 84258233 Rattus norvegicus similar to putative nuclear protein (LOC301570), mRNA 9 BE099410 1396770_at 73914131 Rattus norvegicus transcribed sequences 9 AI408959 1396942_at 13460537 Rattus norvegicus hypothetical LOC301267 (LOC301267), mRNA 10 AI010414 1371545_at 96056298 Rattus norvegicus transcribed sequences 10 BI285135 HCP1_RAT 1374073_at 64576051 Rattus norvegicus similar to DNA segment, Chr 11, ERATO Doi 18, expressed (LOC303333), mRNA 10 BE116569 XP_340743.2 1374208_at 4372881 Rattus norvegicus similar to RIKEN cDNA A430104C18 (LOC360466), mRNA 10 BI298463 1374356_at 15207723 Rattus norvegicus similar to tripartite motif protein 32 (LOC302999), mRNA 10 BG380279 1375672_at 62782723 Rattus norvegicus transcribed sequence with weak similarity to protein pir:RGECDW (E. coli) RGECDW transcription activator of D-serine dehydratase - Escherichia coli 10 BI294949 NP_001008361.1 1375674_at 10891559 Rattus norvegicus similar to chromosome 16 open reading frame 5 (LOC360480), mRNA 10 BE100978 1379644_at 40248088 Rattus norvegicus transcribed sequences 10 AW528010 1381233_at 97298015 Rattus norvegicus transcribed sequences 10 BF408881 XP_340881.2 1382957_at 82931188 Rattus norvegicus similar to cisplatin resistance-associated overexpressed protein (LOC360602), mRNA 10 BE112523 1383654_a_at 110674434 Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_071441.1 (H.sapiens) fructosamine-3-kinase [Homo sapiens] 10 AI180413 APOH_RAT 1389350_at 97725096 Rattus norvegicus similar to beta-2-glycoprotein I (LOC287774), mRNA 10 BF419586 1389982_at 72806191 LIM homeobox protein 1 10 BG372455 1390117_at 75207038 Rattus norvegicus transcribed sequences 10 BG671264 1390783_at 99478336 Rattus norvegicus similar to ATP-binding cassette transporter sub-family A member 8a (LOC303638), mRNA 10 AI703949 1397956_at 15129199 --- 11 NM_133428 NP_596919.1 1368583_a_at 80230659 histidine-rich glycoprotein 11 BF283405 1379779_at 25558401 Rattus norvegicus transcribed sequence with moderate similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 11 AI599448 1380049_at 69673055 Rattus norvegicus transcribed sequences 11 AI137518 CXAR_RAT 1384816_at 17193505 Rattus norvegicus transcribed sequences 11 AA891032 XP_341011.2 1386503_at 84611466 --- 11 BG669292 1388879_at 44854445 Rattus norvegicus similar to 5033411B22Rik protein (LOC363767), mRNA 11 BI303277 1392025_x_at 23905377 --- 12 NM_053626 OXDA_RAT 1369491_at 43628160 D-amino acid oxidase 12 AI103040 1376840_at 9668106 Rattus norvegicus transcribed sequences 12 BF553979 Q6QI44_RAT 1383446_at 36233484 Rattus norvegicus transcribed sequences 12 BF283000 XP_347254.1 1399083_at 22777345 Rattus norvegicus similar to Williams-Beuren syndrome critical region protein 21 (LOC368083), mRNA 13 BE099979 XP_213921.3 1375428_at 81491327 Rattus norvegicus similar to cellular repressor of E1A-stimulated genes CREG (LOC289185), mRNA 13 BF406641 1377301_at 48379022 Rattus norvegicus transcribed sequences 13 BF389856 1377807_a_at 81082440 Rattus norvegicus transcribed sequence with strong similarity to protein sp:P00722 (E. coli) BGAL_ECOLI Beta-galactosidase (Lactase) 13 AI704885 NP_001012025.1 1381542_at 40948540 Rattus norvegicus similar to RIKEN cDNA 1300013G12 (LOC304766), mRNA 13 BF542890 1394400_at 96950732 Rattus norvegicus transcribed sequences 14 AI235294 XP_223611.1 1376161_at 86998069 Rattus norvegicus similar to RIKEN cDNA 1110014L17 (LOC305502), mRNA 14 BI291600 1377262_at 110170474 Rattus norvegicus similar to KIAA2010 protein (LOC360993), mRNA 14 BI299600 1378313_at 18662686 Rattus norvegicus transcribed sequence with weak similarity to protein sp:O08816 (R.norvegicus) WASL_RAT Neural Wiskott-Aldrich syndrome protein (N-WASP) 14 BF401106 1383753_at 110139395 Rattus norvegicus transcribed sequences 14 AI012338 PURB_RAT 1391303_at 87231825 Rattus norvegicus transcribed sequence with strong similarity to protein ref:NP_150093.1 (H.sapiens) purine-rich element binding protein B [Homo sapiens] 15 NM_053907 DNSL3_RAT 1368294_at 18909363 deoxyribonuclease I-like 3 15 BI303923 ITGBL1 1383708_at 108785601 Rattus norvegicus transcribed sequence with strong similarity to protein ref:NP_004782.1 (H.sapiens) integrin, beta-like 1 (with EGF-like repeat domains) [Homo sapiens] 15 AI058292 XP_224283.1 1384415_at 43208046 Rattus norvegicus transcribed sequences 15 BG378310 1385099_at 87547816 Rattus norvegicus transcribed sequence with strong similarity to protein ref:NP_003834.1 (H.sapiens) sciellin [Homo sapiens] 15 AI060175 NP_001012040.1 1393932_at 2569584 Rattus norvegicus similar to RIKEN cDNA 1700010P07 (LOC305684), mRNA 16 BF283341 TKT_RAT 1376635_at 5908757 transketolase 16 BE114427 1377333_at 3758163 Rattus norvegicus transcribed sequences 16 AI180253 1379411_at 20463828 16 U17260 ARY1_RAT 1387187_a_at 23845894 N-acetyltransferase 1 (arylamine N-acetyltransferase) 17 AI029126 1379518_at 33137857 Rattus norvegicus transcribed sequences 17 AA926109 1383486_at 57240213 Rattus norvegicus transcribed sequence with moderate similarity to protein pdb:1LBG (E. coli) B Chain B, Lactose Operon Repressor Bound To 21-Base Pair Symmetric Operator Dna, Alpha Carbons Only 17 BF560932 1384000_at 42176820 Rattus norvegicus similar to sox-4 protein - mouse (LOC364712), mRNA 18 NM_030858 SMAD7_RAT 1368896_at 72294803 MAD homolog 7 (Drosophila) 18 BM391907 1373692_at 29303561 Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_003723.1 (H.sapiens) eukaryotic translation initiation factor 4E binding protein 3; eukaryotic initiation factor 4E-binding protein 3 [Homo sapiens] 18 BI281497 GRAM3_RAT 1384177_at 52202307 Rattus norvegicus similar to RIKEN cDNA 9130427A09 (LOC307288), mRNA 18 AI231438 NP_001007688.1 1390569_at 81160681 Rattus norvegicus transcribed sequences 18 AW920883 1394907_at 56545579 Rattus norvegicus transcribed sequences 19 L07316 MDP1_RAT 1368281_at 53503490 dipeptidase 1 19 AY081218 NP_612513.1 1370336_at 49682142 pregnancy-induced growth inhibitor 19 AW532828 Q6QI93_RAT 1374167_at 35719480 Rattus norvegicus hypothetical LOC291982 (LOC291982), mRNA 19 BF548891 EDNRA_RAT 1393415_at 32046233 endothelin receptor type A 19 BE108555 1396661_at 40906383 Rattus norvegicus similar to Ddx19 protein (LOC292022), mRNA 20 AF413572 IPKB_RAT 1369105_a_at 36655515 protein kinase (cAMP dependent, catalytic) inhibitor beta 20 AB047324 NP_620186.1 1370548_at 44211646 solute carrier family 16, member 10 20 BM383785 1376432_at 8450329 Rattus norvegicus similar to Tex27 protein (LOC361816), mRNA 20 BI290161 XP_215367.2 1382041_at 10731287 Rattus norvegicus similar to 1-acylglycerol-3-phosphate-gamma (LOC294324), mRNA 20 BI289595 VPS26_RAT 1382099_at 29709095 Rattus norvegicus similar to vacuolar protein sorting 26; vacuole protein sorting 26; H beta 58 (LOC361846), mRNA 20 BE096729 FTCD_RAT 1387877_at 12470290 formiminotransferase cyclodeaminase 20 AA901176 XP_215372.3 1398511_at 13435983 Rattus norvegicus similar to Sushi domain (SCR repeat) containing (LOC294335), mRNA X AI170324 VEGFD_RAT 1373882_at 50829626 Rattus norvegicus transcribed sequence with moderate similarity to protein sp:P97946 (M.musculus) VEGD_MOUSE Vascular endothelial growth factor D precursor (VEGF-D) (c-fos induced growth factor) (FIGF) UN AW535280 1370986_s_at Rattus norvegicus retroviral-like c-Ha-ras proto-oncogene mRNA, partial sequence UN AI716098 1378132_at Rattus norvegicus transcribed sequences UN AI145015 1381418_at Rattus norvegicus transcribed sequences UN BE096055 1383665_at --- UN AI169075 1388526_at --- UN BF416508 1389441_at annexin V-binding protein ABP-10 UN BM383122 1389500_at --- UN BI289762 1390174_at --- UN AI232305 1392001_at Rattus norvegicus transcribed sequence with moderate similarity to protein ref:NP_036135.1 (M.musculus) cofactor required for Sp1 transcriptional activation subunit 2 (150 kDa) [Mus musculus] UN AW915435 1392554_a_at --- UN AI008432 1392843_at Rattus norvegicus transcribed sequences UN AA943800 1394127_at Rattus norvegicus transcribed sequences UN AI236935 1394526_at Rattus norvegicus transcribed sequences UN AI237240 1395238_at --- UN BE116471 1395994_at Rattus norvegicus transcribed sequences UN BF288545 1396127_at --- UN BI276183 1397744_at Rattus norvegicus transcribed sequences

Regions highlighted in yellow were not observed to be differentially expressed when gene expression data was analyzed individually at each week Supplemental Table4

Number Gene Identifier Gene ID Position (Mb) Gene Description 1 XM_579199.1 Fga 174737640 174753599 fibrinogen, alpha polypeptide 2 NM_020071.1 Fgb 174768160 174775103 fibrinogen, B beta polypeptide 3 NM_021757.2 Plrg1 174780415 174796746 pleiotropic regulator 1 homolog (Arabidopsis) 4 XM_227313.3 LOC310550 174836071 175056529 similar to protocadherin 16 precursor; protocadherin 16; fibroblast cadherin FIB1; fibroblast cadherin 1; dachsous homologue 5 XM_227314.3 Sfrp2 175479310 175486882 secreted frizzled-related protein 2 6 NM_198769.2 Tlr2 175607990 175613992 toll-like receptor 2 7 XM_579995.1 LOC499646 175800216 175801906 LOC499646 8 XM_215595.3 RGD1309007_predicted 175841812 175906450 similar to RIKEN cDNA 2610034E18 gene (predicted) 9 XM_342268.2 Trim2_predicted 175913283 175987825 tripartite motif protein 2 (predicted) 10 XM_215594.3 RGD1311955_predicted 176220891 176253825 similar to CG32384-PA (predicted) 11 NM_021763.1 LOC60382 176275425 176353675 arfaptin 1 12 XM_227318.2 Tigd4_predicted 176369448 176371430 tigger transposable element derived 4 (predicted) 13 XM_579996.1 LOC499647 176437962 176444681 LOC499647 14 XM_342269.2 LOC361972 176445662 176485867 similar to RIKEN cDNA 9930117H01 gene 15 XM_579140.1 Dear 176736639 176739891 dual endothelin 1, angiotensin II receptor 16 XM_574968.1 LOC361973 177242730 177243851 LOC361973 17 XM_342270.2 Pet112l_predicted 177408100 177433793 PET112-like (yeast) (predicted) 18 XM_345227.2 LOC365834 177632475 177669341 similar to KIAA1759 protein 19 XM_227324.3 LOC295167 177769873 177774358 similar to S-ANTIGEN PROTEIN PRECURSOR 20 XM_215597.3 RGD1304885_predicted 177970585 178081504 similar to SH3 domain protein D19 (predicted) 21 NM_017153.1 Rps3a 178092351 178096720 ribosomal protein S3a 22 XM_342271.2 Lrba_predicted 178267690 178787634 LPS-responsive beige-like anchor (predicted) 23 XM_579311.1 RGD1308384_predicted 178793765 178923600 similar to RIKEN cDNA 6330415M09 (predicted) 24 NM_017079.1 Cd1d1 179011165 179014672 CD1d1 antigen 25 XM_574969.1 LOC499649 179088285 179089078 similar to high mobility group protein 26 NM_207606.1 Neph1 179114757 179169185 nephrin 1 27 XM_227485.3 Msr2_predicted 179278607 179293565 macrophage scavenger receptor 2 (predicted) 28 XM_227484.3 Cd5l_predicted 179363734 179425297 CD5 antigen-like (predicted) 29 XM_574970.1 LOC499650 179450738 179452834 LOC499650 30 XM_227483.3 Spap1_predicted 179483570 179576639 SH2 domain containing phosphatase anchor protein 1 (predicted) 31 XM_574971.1 LOC499651 179591714 179603714 similar to FLJ16478 protein 32 XM_227480.3 LOC295295 179624973 179625738 similar to phosphoglycerate mutase (EC 5.4.2.1) B chain - rat 33 NM_023982.1 Arhgef11 179738870 179796785 Rho guanine nucleotide exchange factor (GEF) 11 34 XM_227479.3 RGD1309453_predicted 179798792 179808808 similar to hypothetical protein FLJ32884 (predicted) 35 XM_215648.3 RGD1305653_predicted 179810529 179834374 similar to MEGF12 (predicted) 36 NM_021589.1 Ntrk1 179838740 179855545 neurotrophic tyrosine kinase, receptor, type 1 37 XM_574972.1 Insrr 179857574 179875876 insulin receptor-related receptor 38 NM_207605.3 Sh2d2a 179916108 179924623 SH2 domain protein 2A 39 XM_227476.3 Prcc_predicted 179931946 179957221 papillary (translocation-associated) (predicted) 40 NM_053707.2 Hdgf 179981346 179991892 hepatoma-derived growth factor 41 NM_001007637.1 mrpl24 179995713 180001745 mitochondrial ribosomal protein L24 42 XM_342272.2 RGD1311265_predicted 180001177 180009023 similar to CGI-41 protein (predicted) 43 NM_001007741.1 MGC94465 180009276 180018762 similar to hypothetical protein FLJ12671 44 NM_017244.1 Crabp2 180029801 180034147 cellular retinoic acid binding protein 2 45 NM_012987.1 Nes 180051907 180060947 nestin 46 XM_579358.1 LOC497759 180072888 180080749 hypothetical gene supported by NM_012916 47 NM_022285.1 Hapln2 180104684 180110115 hyaluronan and proteoglycan link protein 2 48 XM_215614.3 RGD1311086_predicted 180117561 180131693 similar to RIKEN cDNA 2610029K21 (predicted) 49 XM_215635.3 Apoa1bp_predicted 180132556 180144393 apolipoprotein A-I binding protein (predicted) 50 XM_227396.3 Iqgap3_predicted 180155674 180198814 IQ motif containing GTPase activating protein 3 (predicted) 51 NM_030860.2 Mef2d 180221078 180246917 myocyte enhancer factor 2D 52 NM_183054.1 Rhbg 180320787 180333050 Rh type B glycoprotein 53 XM_342276.2 RGD1304953_predicted 180371109 180380696 similar to SSTK-interacting protein (predicted) 54 NM_199091.1 Cct3 180381044 180434156 chaperonin containing TCP1, subunit 3 (gamma) 55 NM_001004226.1 0610031j06rik 180438070 180441660 kidney predominant protein NCU-G1 56 XM_227398.3 RGD1309886_predicted 180442734 180446076 similar to RIKEN cDNA 2310042N02 (predicted) 57 NM_013414.1 Bglap2 180482313 180483290 bone gamma-carboxyglutamate protein 2 58 XM_345231.2 RGD1307244_predicted 180514428 180526708 similar to CG5805-PA (predicted) 59 XM_578027.1 LOC502539 180537560 180545828 similar to IQ motif containing GTPase activating protein 3 60 XM_574973.1 Sema4a_predicted 180552405 180573625 sema domain, immunoglobulin domain (Ig), transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4A (predicted) 61 NM_001002016.1 Lmna 180595724 180616354 lamin A 62 XM_227403.3 LOC310631 180645534 180653100 similar to KIAA2031 protein 63 XM_227404.3 Rab25_predicted 180657133 180662995 RAB25, member RAS oncogene family (predicted) 64 XM_215630.3 LOC295234 180665358 180668716 similar to late endosomal/lysosomal MP1 interacting protein

Area shown in yellow denotes region of overlap between the rat QTL and all human kidney disease located on human chromosome 1q21 Genes shown in bold denote that these genes were differentially expressed between the S and S.SHR(2) congenic (see supplemental Table 1)

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193

SUMMARY

The S rat provides a good model to study the genetics of hypertension related

renal disease. The purpose of the current work was to dissect the genetic basis of renal

damage (proteinuria) in the S rat using the SHR as a contrasting strain. A time course

study was performed to determine onset and progression of renal damage for these strains

and F1 offspring (Manuscript 1). The S rat, even at an early age showed high UPE and

UAE compared to either the SHR or F1(S x SHR) hybrid (Manuscript 1). Additionally, no significant difference was observed between SHR and F1 for UPE and/or UAE at any

time point, demonstrating that the SHR phenotype is completely dominant to the S

phenotype. For this purpose it was necessary to conduct a genetic analysis using a

backcross to the S.

The first backcross population was raised on a low-salt diet to minimize changes in blood pressure (BP) that might obscure those genes that control susceptibility to

kidney damage independent of blood pressure (Manuscript 1). Genome scans were

performed when the animals were 8, 12 and 16 wk of age. In general, QTL for UPE and

UAE were detected together, although higher LOD scores were usually seen with UAE.

The strongest UAE QTL was on RNO2, which gave LOD scores from 10 to 13 from wk

8 persisting through wk 16. Significant UPE and/or UAE QTL were also seen on RNO1,

6, 8, 9, 10, 11, 13 and 19. The SS genotype was associated with increased UPE or UAE

at most QTL. Only on RNO6 and 11 was the SSHR genotype associated with increased

UPE and/or UAE. Most UAE QTL co-localized with KLG, demonstrating that degree of

UAE correlated with observable histological damage in the kidney.

194 A QTL for BP was observed on RNO1 at all time points, weakly on RNO2 only

at wk 16, and on RNO6, which become progressively stronger with time. The BP QTL

on RNO10 was particularly interesting as it was undetectable at wk 8, became highly

significant at wk 12, and then attenuated at wk 16.

The second backcross population was raised on a high-salt (2% NaCl) diet to study the effect of salt-induced high blood pressure on kidney damage (Manuscript 2).

This backcross population was initially raised on a low-salt diet until phenotyping was done at 8 wk of age for two reasons: (1) to confirm the previous linkage analysis; and (2)

to provide a baseline for analyzing the effect of salt-loading on the identification of UAE

QTL. At wk 8 the mean UAE value for second population prior to being salt-loaded was

not statistically different from the wk 8 mean UAE for the low-salt population. However,

salt-loading caused a significant increase in UAE from wk 8 to 12, whereas no significant

change in UAE was observed in the same period for the low-salt population. A similar

effect was observed for BP. The implication is that the increased UAE observed in the

second population resulted from increased blood pressure induced by salt-loading. The

mean KLG score for the high-salt population was two-fold higher than the low-salt

population indicating that more severe pathology correlated with increase UAE.

QTL observed at wk 8 were consistent with the QTL found at wk 8 for the low-

salt population (UPE and/ or UAE QTL on RNO1, 2, 6, 8, 9, 11, 13, and 19), except that

no UPE or UAE QTL was detected on RNO10. UPE and UAE QTL on RNO1, 6, 8, 9

and 13 were seen throughout the period of salt loading, but those on RNO2 and RNO11

went undetected, suggesting that these QTL are attenuated by the effect of increased

blood pressure in response to increased salt intake.

195 To demonstrate the effect of UAE QTL on RNO2, 6, 9, 11, and 13, congenic

strains were developed whereby the SHR alleles at each QTL were placed on the S

background (Manuscript 2). The congenic strain data confirmed the presence of UPE and

UAE QTL on RNO2, 6, 9, 11, and 13 (comparable with wk 8 of the genome scans). All

the congenic strains had significantly different UPE and UAE compared to the S. The

UAE effect ranged from 15 to 40 mg/ 24 h. In contrast, for either genome scan, the effect

of each QTL was at most 3.3 mg/24 h demonstrating that SHR alleles have a strong

ability to exert their effects on the highly permissive S background. The S.SHR(2)

congenic lowered UPE to a greater extent than any of the other congenic strains and had

three-fold lower UPE compared to control S rats (16±1.3 versus 47±3.9 mg/24h,

p<0.0001) .

To characterize the S.SHR(2) congenic strain more thoroughly, a time course study of S, S.SHR(2), and SHR for 20 wk was conducted to examine onset and progression of proteinuria (Manuscript 3). A detailed analysis of renal histology and

electron microscopy also was performed. No detectable difference in UPE was observed

at wk 4; however, starting at wk 5 there was a slight, but significant difference in UPE.

At subsequent weeks, the congenic strain consistently maintained at least a 50%

reduction in UPE compared to control S rats (week 20, 92.3±8.3 versus 270±22.9 mg/24h, p<0.0001). The UPE data were supported by the histological findings of

significantly reduced glomerular, tubular and interstitial changes in the congenic. Systolic

BP was measured at wk 10, 12, 16, and 20 by telemetry. No difference in systolic BP was detected between the S.SHR(2) and control S rats. This data suggests that the QTL exerts a renal protective effect without influencing BP.

196 Gene expression/pathway analysis performed at wk 4, 12, and 20 revealed that

pathways involved in cellular assembly and organization, cellular movement, and immune response were controlled differently between the S and congenic. A total of 37 genes/EST on chromosome 2 were observed to be differentially expressed between the S and S.SHR(2) throughout the time course. At wk 4 (and wk 12) the Wnt/ ß-catenin signaling pathway was identified as the most significant pathway based on the entire dataset. In line with the histological findings, alterations in the Wnt/ ß-catenin pathway may help explain the reduced fibrosis exhibited by the congenic, since this pathway has been linked to renal fibrosis by driving tubular cells to undergo EMT.

Recombinant progeny testing was carried out using 20 recombinant families to fine-map the QTL. The QTL was successfully reduced from ~80 cM (progenitor congenic) to < 1.5 cM, containing 64 known and/or predicted genes. Only two of the 37 differentially expressed genes identified by gene expression analysis mapped to this reduced region, eliminating many of the genes found to be differentially expressed as causative to the QTL. The importance of this work is highlighted by the fact that the rat

QTL shows concordance with several renal disease loci on human chromosome 1q21.

While this work represents a major step forward in positional cloning of the rat chromosome 2 UPE QTL, further fine-mapping and additionally techniques will be required to identify the gene(s) causative to QTL.

In summary, the work presented in this thesis provides new insight into the genetics of renal disease in the S rat. The major findings of this thesis are: (1) 10 QTL for

UPE and/or UAE with variable time-course patterns were detected on RNO1, 2, 6, 8, 9

,10, 11, 13, and 19 in the absence of salt-loading; (2) the S allele is associated with

197 susceptibility at most UPE and/or UAE QTL, except on RNO6 and 11; (3) most UPE and/or UAE QTL co-localize with observable histological damage (KLG); (4) UPE and/or UAE QTL on RN02, 11, and 19 are influenced by salt-loading, presumably as a result of increased BP; (5) when each QTL is isolated on the S background (congenic strain) larger effects on UPE are observed compared to those seen in the linkage analyses

(RNO2, 6, 8, 9, and 11); (6) the QTL on RNO2 appears to influence susceptibility to renal damage without affecting BP; (7) UPE and/UAE are highly predictive of renal damage for the RNO2 QTL as illustrated by time-course histological and electron microscopy analysis; (8) gene expression analysis suggests that RNO2 QTL mechanism is related to attenuating renal interstitial fibrosis; and (9) RPT provides a rapid and effective method to fine map QTL to smaller genomic intervals.

198

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234 ABSTRACT

Chronic kidney disease (CKD) is an important medical problem worldwide that can, left undiagnosed and untreated, develop into end stage renal failure. Unfortunately, knowledge of the genetic factors that cause CKD is limited, except for some monogenic forms of kidney disease. It is our goal to understand the genetic basis of renal disease observed in the Dahl salt-sensitive (S) rat, a model of hypertension and renal disease and ultimately apply this knowledge to humans. An initial linkage analysis for urinary protein excretion (UPE), including several other renal and cardiovascular traits was performed at multiple time points using a backcross population derived from the S rat and the spontaneously hypertensive rat (SHR). The study identified 10 quantitative trait loci

(QTL) linked to renal damage and/or function independent from the effects of salt- loading. A second linkage analysis sought to examine the effect of salt-loading on the observation of UPE QTL using a backcross population raised on 2% NaCl. The second linkage analysis confirmed the results of the first and identified that UPE QTL on chromosome 2, 11, and 19 were influenced by salt-loading. Congenic strains were developed on select chromosomes (2, 6, 9, 11, and 13) to confirm the linkage analysis and to examine the effect of each QTL individually on the S background. These congenic strains demonstrated large and significant effects on UPE compared to the S rat. Further work sought to characterize the chromosome 2 congenic [S.SHR(2)] strain by conducting a time-course analysis (wk 4 to 20), including evaluating several renal parameters, histology, electron microscopy, and gene expression/pathway analysis. Throughout the time course the congenic strain consistently maintained a two-fold reduction in UPE, and significantly reduced glomerular, tubular and interstitial changes. Gene

235 expression/pathway analysis revealed that pathways involved in cellular assembly and organization, cellular movement, and immune response were controlled differently

between the S and congenic providing a potential mechanism of the QTL. Fine-mapping

using recombinant progeny testing (RPT) further reduced the QTL region to contain 64

known and/or predicted gene, including several interesting candidate genes.