A Thesis

entitled

Regulatory Mechanisms of Cardiotonic Steroids in Chronic Kidney Disease

by

Subhanwita Ghosh

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Science Degree in

Bioinformatics & Proteomics/Genomics

______Dr. David J. Kennedy, Committee Chair

______Dr. Sadik Khuder, Committee Member

______Dr. Levison Bruce, Committee Member

______Dr. Amanda Bryant-Friedrich, Dean College of Graduate Studies

The University of Toledo

August, 2017

Copyright 2017, Subhanwita Ghosh

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of

Regulatory Mechanisms of Cardiotonic Steroids in Chronic Kidney Disease

by

Subhanwita Ghosh

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Bioinformatics & Proteomics/Genomics

The University of Toledo

August, 2017

Introduction: Cardiotonic steroids (CTSs) are steroid hormones which are elevated in chronic kidney disease (CKD). The 2-pyrone ring structure of CTS is critical for its binding to the Na+/K+-ATPase and subsequent initiation of profibrotic signaling which promotes tissue fibrosis and organ dysfunction underlying cardiac and renal disease.

Paraoxonases (PONs) are a family of hydrolytic enzymes which can regulate levels of 2- pyrone structures like those found in CTS, and we have recently discovered that the activity of these enzymes is diminished in setting such as CKD where CTSs are elevated.

Hypothesis: We hypothesized that dysregulated expression in the PON gene family is associated with renal disease and that PONs, via their hydrolytic activity, participate in the metabolism and regulation of CTSs.

Methods/ Results: We used molecular, biochemical, and bioinformatic approaches to study how PONs regulate CTSs in the setting of renal disease. We first performed a meta- analysis (accessed from NCBI’s Gene Expression Omnibus (GEO) Datasets) of normalized PON isoform gene expression from whole genome microarray data of both peripheral blood mononuclear cells (PBMC’s, n=4 studies, 174 total patients) and renal

iii biopsy samples (n=4 studies, 207 total patients), in case-control studies of patients with chronic kidney disease (CKD stage 2-5) and the respective non-CKD controls. From these studies, we noted that PON-1 gene expression was significantly decreased in

PBMCs of CKD (Stage 2-3) patients vs non-CKD controls (0.8±0.03 vs 1.0±0.03, p<0.05), however in renal biopsy specimens PON-1 (2.7±0.42 vs 1.0±0.02, p<0.05) and

PON-2 (1.5±0.06 vs 1.0±0.04, p<0.01) was significantly increased in CKD (Stage 2-3) patients vs non-CKD controls. We also searched for the genes which are highly correlated such as GADD45B, STK26, IFITM2///IFITM1, ZNF418, etc. with PON genes in the setting of chronic kidney disease. Next, we measured circulating PON-1 protein

(ELISA) and activity (as measured with 7-hydroxycoumarin, a specific fluorogenic substrate of PON lactonase/2-pyronase activity) in CKD patients (n=31) and non-CKD controls (n=15). Here we found that PON-1 circulating protein was significantly lower compared to non-CKD controls (p<0.001), and that PON lactonase/2-pyronase activity was also significantly decreased in CKD patients (p<0.0001). Finally, we developed an

LC-MS-MS based assay to monitor the potential metabolism of CTSs by PONs.

Incubation of the CTS telocinobufagin (TCB) with PON-1 overexpressing HEPG2 cells led to a 2-fold decrease in the intact 2-pyrone form of TCB vs control (p=0.0054).

Conclusion: While PON-1 and PON-2 gene expression is increased in renal biopsy samples of CKD patients, PON-1 gene expression is decreased in PBMC’s of CKD patients’ vs controls. Furthermore, circulating levels of PON1 and lactonase activity of

PON1are decreased in CKD patients. There are numbers of genes that are differentially expressed in chronic kidney disease which are correlated with PON gene expression including GADD45B, STK26, IFITM2///IFITM1, ZNF418, GDAP1, etc. Furthermore,

iv

CTS appear to be physiologic substrates for PON’s hydrolytic lactonase/2-pyronase activity and this may represent a novel regulatory mechanism for CTS.

v

Acknowledgements

I express my deepest appreciation to my Master’s thesis adviser, Dr. David J. Kennedy for his invaluable help and guidance throughout my research work. Having him as an adviser is an honor. I also express my heartfelt thanks to my committee members, Dr.

Levison Bruce and Dr. Sadik Khuder for their assistance with this thesis. Without their immense help and insightful comments this work could not be completed. My sincere thanks to Dr. Haller for useful suggestions during the research lab meetings that helped in improving this thesis.

I also express my sincere thanks to my fellow labmates for stimulating ideas and discussions. It was a great fun to work in Dr. Kennedy’s lab.

Finally, I express my sincere gratitude to my family for always being supportive.

vi

Table of Contents

Abstract ...... iii

Acknowledgements ...... vi

Table of Contents ...... vii

List of Tables ...... x

List of Figures ...... xi

List of Abbreviations ...... xiii

1 Introduction ………………...... 1

2 The Paraoxonase gene family ...... 6

3 Methods

3.1 Bioinformatics approaches...... 9

3.1.1 Study of PON gene expressions in different tissues in human body ...... 9

3.1.2 PON genes dysregulation is related to heart and kidney disease ...... 9

3.1.3 Comparison of PON gene expressions in renal biopsy samples and peripheral blood samples taken from CKD patients and non-CKD controls ...... 10

3.1.4 An approach to look at the regulatory network of genes that may involve in

CKD...... 15

3.2 Biochemical and molecular biology approaches ...... 17

3.2.1 Animal tissue culture ...... 17

3.2.2 Method development to measure protein level expression of PON1 in tissue culture samples and human plasma/ serum ...... 19

vii 3.2.3 Method development to measure PON1 lactonase activity in human plasma and tissue culture sample ...... 24

3.2.4 Measurement of PON1 protein expression in CKD vs non-CKD human plasma…………………………………………………………………………..………. 30

3.2.5 Measurement of PON1 lactonase activity in CKD vs non-CKD human plasma…………………………………………………….………………….…………. 31

3.2.7 Measurement of PON1 protein expression in tissue culture ...... 33

3.2.8 Measurement of PON1 lactonase activity in tissue culture ...... 34

3.2.9 Method development for measuring TCB by LC-MS ...... 35

3.2.10 Making Standard curve for TCB standards ...... 37

3.2.11 Measuring CTS degradation by tissue culture that produce PON1……….37

4 Results

4.1 Bioinformatics approaches...... 39

4.1.1 Studying the PON genes using bioinformatics tools provided in www.ncbi.nlm.nih.gov ...... 39

4.1.2 Study of PON gene expressions in different tissue in human body ...... 42

4.1.3 Comparison of PON gene expressions in renal biopsy samples and peripheral blood samples taken from CKD patients and non-CKD controls ...... 44

4.1.4 An approach to look at the highly correlated genes that may involve in

CKD...... …………………………………………………………………...…….46

4.2 Biochemical and molecular biology approaches ...... 51

4.2.1 Method development to measure PON1 protein expression in human plasma samples………………………………………………………………………51

viii 4.2.2 Method development to measure PON1 lactonase activity in human plasma samples…………………………………………………………………………. 51

4.2.3 Measurement of PON1 protein expression in CKD vs non-CKD human plasma………...... 51

4.2.4 Measurement of PON1 lactonase activity in CKD vs non-CKD human plasma…………………………………………………….…………………...… 52

4.2.5 Measurement of PON1 protein expression in tissue culture ...... 52

4.2.6 Measurement of PON1 lactonase activity in tissue culture ...... 53

4.2.7 Method development for measuring TCB by LC-MS ...... 53

4.2.8 Making Standard curve to measure TCB ...... 53

4.2.9 Measuring CTS degradation by tissue culture that produce PON1 ..54

Conclusion ...... 57

References ...... 58

A The R-code to find out the correlated genes and save the corresponding values in

*.csv files ...... 62

B The correspondence values of differentially expressed genes ...... 64

C The ELISA report of CKD patients’ vs non-CKD control plasma ...... 70

D The lactonase activity assay CKD patients’ vs non-CKD control plasma...... 71

ix

List of Tables

2.1 PON genes and their position in chromosome 7 ...... 6

3.1 GFR value distribution at different stages of CKD ...... 14

3.2 Data matrix of studies with PBMC samples ...... 15

3.3 Data matrix of studies with renal biopsy samples ...... 15

3.4 Experiment design for lactonase activity assay method development ...... 27

4.1 Correspondence values of differentially expressed genes with PON genes in study with PBMC samples ...... 46

4.2 Correspondence values of differentially expressed genes with PON genes in study with renal biopsy samples ...... 49

4.3 TCB standards measured by LC-MS ...... 53

4.4 TCB degradation by PON produced by high -PON-HEPG2 ...... 55

B.1 Correspondence value from GSE 15072 (CKD vs non-CKD) ...... 64

B.2 Correspondence value from GSE 15072 (CKD vs non-CKD) ...... 65

B.3 Correspondence value from GSE43484...... 67

B.4 Correspondence value from GSE32591...... 68

B.5 Correspondence value from GSE66494...... 69

C.1 ELISA report of CKD patients’ and non-CKD controls’ plasma ...... 70

D.1 Lactonase activity assay of CKD patients’ and non-CKD controls’ plasma ...... 71

E.1 The lactonase activity of high PON recombinant cells …………….……...... 72

x

List of Figures

1-1 Balance between natriuretic effect and CTS and the “trade-off” ...... 2

1-2 Binding of CTS with Na/K-ATPase triggers fibrosis in tissues ...... 3

1-3 Closed ring and open ring structure of CTS such as TCB ...... 4

2-1 PON genes position in human chr7 ...... 6

2-2 Nucleotide BLAST of PON genes ...... 6

2-3 Protein BLAST of PON protein ...... 7

3-1 NCBI nucleotide search ...... 9

3-2 Selecting data for analysis from a data set ...... 14

3-3 PON1 expression values from a study ...... 15

4-1 The conserved domain of PON1 protein ...... 40

4-2 The conserved domain of PON2 protein ...... 41

4-3 The conserved domain of PON3 protein ...... 42

4-4 Tissue specific expression of PON1 in human body ...... 42

4-5 Tissue specific expression of PON2 in human body ...... 43

4-6 Tissue specific expression of PON3 in human body ...... 43

4-7 Comparison of PON gene expression in heart, kidney and liver ...... 44

4-8 PON gene expression in PBMC from CKD and non-CKD ...... 44

4-9 PON gene expression in renal biopsy from CKD and non-CKD ...... 45

4-10 Network of PON1 with differentially expressed genes in PBMC samples ...... 47

xi 4-11 Network of PON2 with differentially expressed genes in PBMC samples ...... 48

4-12 Network of PON3 with differentially expressed genes in PBMC samples ...... 48

4-13 Network of PON1 with differentially expressed genes in renal biopsy samples ...50

4-14 Network of PON3 with differentially expressed genes in renal biopsy samples ...50

4-15 PON1 protein expression in CKD and non-CKD plasma samples ...... 51

4-16 PON1 lactonase activity in CKD and non-CKD plasma samples ...... 52

4-17 PON1 expression by high PON-CHO cell lines ...... 52

4-18 PON1 expression by high PON-HEPG2 vs HEPG2 parental cell lines ...... 52

4-19 PON1 lactonase activity by recombinant high-PON cell lines…………………. 53

4-20 Chromatogram of 10ng/ml TCB ...... 54

4-21 Chromatogram of 5ng/ml TCB ...... 54

4-22 TCB standard curve ...... 54

4-23 TCB degradation by PON produced by high PON-HEPG2 cells ...... 56

B-1 Network with PON1 (GSE15072) ...... 65

B-2 Network with PON2 (GSE15072) ...... 66

B-3 Network with PON3 (GSE15072) ...... 66

B-4 Network with PON3 (GSE43484) ...... 68

B-5 Network with PON3 (GSE32591) ...... 69

B-6 Network with PON3 (GSE66494) ...... 70

xii

List of Abbreviations

APCI………………… Atmospheric Pressure Chemical Ionization

BLAST………………. Basic Local Alignment Search Tool

CE……………………. Capillary Electrophoresis CHO………………… Chinese hamster ovary CKD…………………. Chronic Kidney Disease CTS…………………. Cardiotonic Steroids

DMSO………………. Dimethyl sulfoxide DNA…………………. Deoxyribonucleic Acid

EDTA………………. Ethylenediaminetetraacetic acid eGFR………………...Estimated Glomerular Filtration Rate ELISA………………. Enzyme-Linked Immunosorbent Assay EMEM………………. Eagle's Minimum Essential Medium ESI…………………… Electrospray Ionization Ex/Em……………… Excitation/Emission

FBS………………….Fetal Bovine Serum

GEO…………………Gene Expression omnibus

HD……………………Hemodialysis HDL…………………. High Density Lipid HEPG2……………… Human Liver Cancer Cell Line HRP…….……………Horseradish peroxidase HT……………………Hypertensive

LC-MS………………. Liquid Chromatography–Mass Spectrometry LDL…………………...Low Density Lipid LRE……………………Luciferin-Regenerating

xiii

Na/K ATPase…………. Sodium-Potassium Adenosine Triphosphatase NCBI………….………. National Center for Biotechnology Information

PBMC……………… Peripheral Blood Mononuclear Cell PBS………………… Phosphate-Buffered Saline PC…………………. Positive Control pH…………………. Potential of Hydrogen PON…………………Paraoxonase

QI……………………. Quadrupole Ion

RefSeq ………………. Reference Sequence RIPA………………… Radioimmunoprecipitation Assay RNA…………………. Ribonucleic Acid

SGL…………………...SMP-30/Gluconolaconase/LRE SMP-30………………. Senescence Marker Protein 30 Src…………………… Sarcoma viral oncogene kinase Str_Synth………………Strictosidine synthase

TCB…………………. Telocinobufagin

xiv

xv Chapter 1

Introduction

The maladaptation of the body’s ability to handle sodium (salt) and volume (water) loads is linked with devastating diseases of heart failure and renal failure. This process includes an elegant regulatory system composed of effector steroid hormones – known as cardiotonic steroids (CTS) – and their receptor complex, the sodium-potassium adenosine triphosphatase (Na+/K+ ATPase). CTS are ligands of the Na+/K+ ATPase. The production of these hormones are a compensatory mechanism for natriuresis. Natriuresis is excretion of sodium in the urine via action on the Na+/K+ ATPase in the kidney and vascular tone in volume-expanded states such as salt-sensitive hypertension and chronic kidney disease, as well as edematous states like heart failure and pre-eclampsia.

Chronic stimulation of Na+/K+ ATPase signaling by CTS has important implications which are not limited to the natriuretic response to increased salt and water load. There is a “trade-off” pathological adaptation to volume expansion, that includes hypertension, hypertrophy, and fibrosis (23). It has been evident that the pro-inflammatory and pro- fibrotic pathways initiated by these steroid hormones in both cardiac and renal tissue and that makes them attractive therapeutic targets for researches in cardiac and renal disease.

Natriuresis is the natural process to excrete Na+ from our body. If Na+ is not removed 1

from our body it causes different kind of problems related to sodium homeostasis such as hypertension. This ultimately cause adverse cardiovascular outcomes. The increased sodium load can be balanced by increased production of CTS. But when there is an excess of CTS in our body it binds with Na+/K+ ATPase and that activates Src (Sarcoma viral oncogene kinase). Activation of Src leads the downstream pro-fibrotic and pro- inflammatory signals in our important organs like heart and kidney.

Figure 1-1: Illustration of the balance between the natriuretic effect of cardiotonic steroids (CTS) and trade-off of inducing Na+/K+ ATPase -mediated signal transduction leading to cardiac and renal fibrosis, eventually contributing to the development of hypertension and adverse cardiovascular outcomes.

Progressive cardiac and renal compromise (referred to as “cardio-renal syndrome”) in patients with heart failure and chronic kidney disease are very common and leading to recurrent hospitalizations and clinical deterioration. The contemporary therapies of neurohormonal blockade fail to adequately address cardio-renal syndrome. The progression and regulation of CTS in volume-expanded states such as heart failure and chronic kidney disease is unknown. Thus, the understanding of a fundamental, integrated,

2

and mechanistic understanding of the CTS- Na+/K+ ATPase effector/receptor complex is very much important.

One of the common structural features of CTS is the presence of a lactone ring which is critical for its binding to the Na+/K+ ATPase and subsequent signaling. The unique δ- lactone ring structure of CTS such as TCB binds with the Na+/K+ ATPase, which leads

Src activation and cause profibrotic and proinflammatory signal in kidney, heart and circulatory systems. As the δ-lactone ring is necessary to bind with the Na+/K+ ATPase and producing profibrotic and pro-inflammatory signals in kidney and heart tissue, our goal is to determine if lactonase enzymes can hydrolyze the δ-lactone ring and thus render the CTS unable to bind with the Na+/K+ ATPase.

TCB

Profibrotic and proinflammatory signals

Figure 1-2: The binding of TCB with the Na+/K+ ATPase, leads profibrotic and proinflammatory signal in kidney, heart and circulatory systems.

Paraoxonases (PON) can function as antioxidant enzymes and due to this ability, they have been a topic of interest in cardiovascular research over the past two decades. 3

Besides this recent point of interest, PON’s initially were subject of toxicology studies in the field of organophosphate poisoning in the post-World War II era and were identified as an organophosphate hydrolyzing enzymes (15). The organophosphates were first synthesized as insecticides in the 1930s. These insecticides have direct neurotoxic effect.

This discovery led to the subsequent identification of PON in human serum in 1953 (2).

PON1 is produced in liver and excreted attached to HDL.

HDL is known to carry important detoxifying enzymes like PON which have lactonase activities. The liver makes PON which is eventually attached to HDL and thus confers important antioxidant properties to HDL.

In these studies, we have attempted to address the following topics: While circulating

PON lactonase activity is decreased in CKD and is associated with increased mortality in this setting, little is known about what causes this decreased activity (e.g. is it due to either decreased PON expression levels or dysfunctional PON protein)? Further, while decreased PON lactonase activity is associated with increased levels of lactone containing CTS in CKD, does this group of hydrolytic enzymes regulate CTS? This would represent the first known native physiologic substrate for these enzymes.

Figure 1-3: Closed lactone ring

structure of CTS such as TCB and

its hydrolyzed structure

In 2014 Kennedy et al found that the enzymatic activity of the HDL associated PON protein was decreased in patients with chronic kidney disease, and that those with low

PON activity have significantly worse survival rates and cardiovascular outcomes (11). 4

As CTS are elevated in these settings where PON activity is diminished, we sought to determine if there was a potential role for PON lactonase activity in regulating CTS levels.

The hydrolysis of CTS may be useful to prevent CTS from binding to the Na+/K+

ATPase. If PON demonstrates the ability to hydrolyze CTS, it may be useful therapeutically. In this current study, we hypothesized that genetic polymorphisms in the

PON gene family are associated with cardiovascular and renal disease; and PON’s, via their enzymatic lactonase/hydrolytic activity, participate in the metabolism and regulation of cardiotonic steroids (CTS). In this current study, we tested two hypotheses. The first one tests if PON activity is associated with decreased PON expression in the setting of renal disease and second one tests if PON’s, via their hydrolytic activity, participate in the metabolism and regulation of CTS.

5

Chapter 2

The Paraoxonase Gene Family

The PON gene family includes 3 members: PON1, PON2, and PON3. PON genes are located as cluster in human chromosome 7. They are Calcium-dependent esterases with approximate molecular mass of 40-45 kDa. PON reflects the ability to hydrolyze paraoxon, though it is the substrate primarily for PON1. Lactones are substrate for all three isoforms. PON1 has been shown to protect lipoproteins (not only LDL but also

HDL) from lipid peroxidation by degrading specific oxidized cholesteryl esters and phospholipids. On a per microgram basis, PON3 is protecting LDL against oxidation and in lactonase activity, approximately 100 times more active than PON1 (18).

Table 2.1: PON genes and their positions in human chromosome 7

Symbol Gene ID Map Start End

PON1 5444 7q21.3 95,297,676 95,297,698

PON2 5445 7q21.3 95,404,863 95,435,329

PON3 5446 7q21.3 95,359,944 95,396,368

6

Figure 2-1: PON genes position in chromosome 7 by Illumina genome browser

Paraoxonases have over 80% similarity at the nucleotide level. PON2 is 93% identical to

PON3 and 84% identical to PON1. Query cover is very low in both cases.

Figure 2-2: BLAST result comparing the nucleic acid sequence of PON1 and PON3 with that of PON2 showing over 80% similarity in DNA sequence level.

Paraoxonases have over 66% similarity at the protein level. Both PON1 and PON3 are

66% identical to PON2. It covers 100% of the query sequence and that signifies very similar structure of the PON proteins.

7

Figure 2-3: BLAST result of PON1 and PON3 showing 66% similarity at protein sequence level

In 2013, Martinelli and cowrokers published the multifunctional role of PON enzymes.

Initially they were characterized for the ability to hydrolyze organophosphates. The name

PON reflects its ability to hydrolyze paraoxon, a metabolite of the insecticide parathion, as well as hydrolyzing several other insecticides and nerve agents, though PON1 is the one to act on organophosphates like paraoxon. PON2 and PON3 are not able to degrade paraoxon. PON1, PON2, and PON3 also share an ability to hydrolyze aromatic and long- chain aliphatic lactones, and thus the term “lactonase” may be more appropriate, but native physiologic substrate is not well established. Aromatic esters are another common substrate for all three (14, 18)

8

Chapter 3

Methods

3.1 Bioinformatics approaches

3.1.1 Studying the PON genes using bioinformatics tools provided in www.ncbi.nlm.nih.gov:

PON1: PON1 is searched under nucleotide option and the search is limited by choosing homo sapiens as species, genomic DNA/RNA as molecular type and RefSeq as source database. The PON1 DNA has the NCBI reference number NG_008779.1.

Figure 3-1: The NCBI search result on human PON1 nucleotide (DNA)

3.1.2 Study of PON gene expressions in different tissues in human body:

BioGPS is a bioinformatic tool to accumulate tissue specific mRNA expression data by using customized oligonucleotide array to analyze samples from 79 human and 61 mouse tissues. This database is based on an experiment done by Su AI et. Al., with name “A

9

gene atlas of the mouse and human protein-encoding transcriptomes” (21). BioGPS is described as an extensive gene query tool by Wu et. Al in 2009 (22).

For this current study, we used the gene names (PON1, PON2 and PON3) as queries in the search box for tissue specific expression.

3.1.3 Comparison of PON gene expressions in renal biopsy samples and peripheral blood samples taken from CKD patients and non-CKD controls:

Geo Datasets from NCBI: GEO (Gene expression omnibus) is an international public repository that archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomics data submitted by the research community. The GEO Datasets database stores original submitter-supplied records

(Series, Samples and Platforms) as well as curated Datasets

(https://www.ncbi.nlm.nih.gov/geo/info/datasets.html).

Goal: To study the PON gene expression in CKD vs non-CKD subjects

Studies:

1. We selected 4 studies related to CKD that are using peripheral blood samples:

GSE15072, GSE37171, GSE43484, GSE70528

2. We also selected 4 studies related to CKD that are using renal biopsy samples:

GSE12682, GSE20602, GSE32591, GSE66494

Study designs:

1. Studies with peripheral blood samples from CKD vs non-CKD controls

10

a) GSE15072: This is a combined study comprising two studies. The study included

8 healthy subjects (NORM), 9 patients with Chronic kidney disease (CKD) at

stage II-III (CKD II-III) (mean±SD of estimated GFR by MDRD formula:

41.4±4.3 ml/min) and 17 patients undergoing hemodialysis treatment (HD). All

HD patients were under stable treatment, for at least 1 year, three times a week (4-

5 hours per session) and synthetic membrane dialyzers were used. No CKD

patients received dialysis treatment. The patients were screened for infectious

diseases, diabetes, chronic lung diseases, neoplasm, or inflammatory diseases and

patients receiving antibiotics, corticosteroids, or nonsteroidal anti-inflammatory

agents and were excluded. No patients had symptomatic coronary artery diseases

or a family history of premature cardiovascular diseases. 20 ml of whole blood

were collected from each patient. For HD patients, the blood samples were

obtained at the beginning of the second HD session of the week. Total RNA was

isolated from PBMC’s by RNeasy mini kit (Qiagen, Oslo Norway) from a

minimum of 5 million cryopreserved PBMC. Next, the isolated RNA was,

processed and hybridized to the GeneChip Human Genome Plus 2.0 or U133A

oligonucleotide microarray (Affymetrix, Santa Clara, CA, USA) (7, 24). b) GSE37171: This study was conducted at the University of British Columbia and

approved by the human ethics research board. The gene expression in patients

with CKD and healthy controls was compared by a 3:1 case-control design. A

group of Patients with stage 5 renal disease aged 18 to 75 years were involved.

The patients were clinically stable awaiting renal transplantation, and were not

receiving immunosuppressive medications. They all provided written informed 11

consent and those that were enrolled into the study were treated per Canadian

Guidelines for Chronic Kidney Disease. Normal controls served as comparators,

they were selected based on comparable age and gender matched to the patients

who were screened to ensure freedom from known illness and medical therapy

(19). c) GSE43484: This study was performed in patients with CKD who have

significantly increased morbidity and mortality resulting from infections and

cardiovascular diseases. Here the gene expression profile in PBMC was explored

and to identify differences in activated pathways in monocytes relevant to the

pathophysiology of atherosclerosis and increased susceptibility to infections. The

peripheral blood from CKD patients (stages 4 and 5, eGFR <20 ml/min x 1.73

m2) and healthy donors were collected to isolate monocytes. Genespring software

and Panther tools website were used to interpret the Microarray gene expression

profile data (1). d) GSE70528: This study involved RNA microarray analysis in 19 patients without

diabetes and they were divided into three groups: HT group (8 hypertension

patients with eGFR of >60 mL/min/1.73 m2), CKD group (7 patients not on

dialysis with eGFR of <60 mL/min/1.73 m2) and HD group (4 HD patients). (12)

2. Studies with renal biopsy samples from CKD vs non-CKD controls a) GSE12682: 42 kidney samples from healthy living transplant donors,

nephrectomies and from diagnostic kidney biopsies were collected. The tissue

samples were grouped based on the histological readings of the kidney biopsies. 12

Samples with evidence of glomerular and tubulo-interstitial fibrosis were selected

into the diseased group. The tissue from glomerular and tubulointerstitial fractions

were micro-dissected and expression arrays were performed separately (20).

b) GSE20602: There were 14 human kidney biopsies of patients with

nephrosclerosis and 4 human kidney biopsies of patients with tumor nephrectomy.

Samples were used for RNA extraction and hybridization on Affymetrix

microarrays. Gene expression data was then analyzed using Genepattern and

Genomatix software (17).

c) GSE32591: This study used glomeruli and tubulointerstitial compartments to

extract RNA. The RNAs were then processed for hybridization on Affymetrix

microarrays (4).

d) GSE66494: Renal biopsy specimens were used to explore gene expression

profiles in human chronic kidney disease. Two independent sets …………

discovery set and validation set were performed (16).

Method:

1. 8 studies are selected for Bioinformatic meta-analysis. The studies are

accessed from NCBI’s Gene Expression Omnibus (GEO) Datasets).

2. PON expression from whole genome microarray data of both peripheral

blood mononuclear cells (PBMC’s, n=4 studies) and renal biopsy (n=4

studies) in patients with chronic kidney disease (CKD stage 2-5) and the

respective non-CKD controls are retrieved.

13

Figure3-2: Selecting CKD and non-CKD samples from data set

3. PON gene expressions from each sample are retrieved from gene

expression profile of each studies by using the ID’s of PON genes

Figure 3-3: PON1 expression of each samples in a study

4. The PON expressions are normalized and plotted.

5. CKD stages are classified according to the glomerular filtration rate

(GFR).

Table 3.1: GFR value distribution in different stages of CKD

CKD Stages for grouping: Stage 1 with normal or high GFR (GFR > 90 mL/min) Stage 2 Mild CKD (GFR = 60-89 mL/min) Stage 3A Moderate CKD (GFR = 45-59 mL/min) Stage 3B Moderate CKD (GFR = 30-44 mL/min) Stage 4 Severe CKD (GFR = 15-29 mL/min) Stage 5 End Stage CKD (GFR <15 mL/min) 6. The data matrix made from the studies is as follows…………………….

14

Table 3.2: Data matrix of the studies with PBMC samples from CKD patents and non-

CKD controls

Table 3.3: Data matrix of the studies with renal biopsy samples from CKD patents and non-CKD controls.

3.1.4 An approach to look at the highly correlated of genes that may affect PON1:

Goal: To determine genes which are highly correlated with PON genes regulation

Materials: The same studies as above are used for the analysis. R-program is used with c3net, bc3net and igraph packages are used in this analysis (3,6,10).

Target file:

1. The data sets are analyzed by GEO2R provided in the Web site Bonferroni method is

used to select the top 250 significantly differentially expressed genes.

2. The raw values of each genes are retrieved along with PON1, PON2, PON3.

3. The same method is followed for all eight studies.

15

Method:

1. The target files are saved as *.txt files and the following R-code is used to find

out highly correlated genes with PON genes

2. Filtering the target files: Some of the genes are duplicated in the target files

because of different IDs with same genes in the microarray chip. The flowing

code is used to find out the duplications. Only gene ID’s with lowest probability

are kept for each of the duplicated genes in the target files.

R-code :

expmat = read.csv("GSE15072-HD.csv", header=T)

table(expmat[,1])

3. The duplication of gene names are removed on the basis of the filtering process.

Now the target files are ready to be used to find out the genes with higher

correspondence values with PON genes.

4. The R-code shown in Appendix A is used to find out the correlated genes and

saved in *.csv files. In this code bc3net, c3net and igraph packages are used.

5. In some cases, 0.25 value is not enough to obtain correlations of PON genes with

other genes by the code………….

expmat = expmat[avgexp > quantile(avgexp, prob=0.25),]

In that cases, different values (<0.25) are examined.

6. Selecting the correlated genes: the correlated genes are having higher

corresponding values with PON genes in both a negative and positive way, that

means the positive values shows direct correlation and negative values shows

16

reverse correlation. We are selecting the values which are higher than 0.4 and

higher than -0.4.

7. Differentially expressed genes that are with higher correspondence values are

searched through “Lens”, a tool for enrichment and network studies of proteins.

This tool is developed by Adam Handen and Madhavi K Ganapathiraju and

published in BMC Medical Genomics in 2015 (8).

3.2 Biochemical and molecular biology approaches

3.2.1 Animal tissue culture

Goal: Expanding cell lines storage for future use and further experiments.

Materials:

Materials:

1. Cell lines:

a. High PON-CHO: These are recombinant Chinese hamster

ovarian cell line expressing PON genes (5).

b. High PON-HEPG2: These are recombinant hepatic cell

lines over expressing PON genes (9).

2. Media: EMEM with 10% FBS and 1% penicillin + streptavidin solution

3. Container for culture: T-75 flasks, Cryo tubes

Media preparation:

We are using EMEM media for this purpose. The media is opened only under hood and 10% FBS and 1% penicillin-streptavidin solution is added to it. Normally we

17

make 500 ml media and add 50ml FBS and 5ml penicillin-streptavidin. We filtered this media to sterilize.

Method:

1. 30ml fresh media 1 in two t-75 flasks was poured.

2. cells in cryo tube were thawed down and 1ml fresh media was

added and mixed thoroughly.

3. 1ml of suspended cells in each flask was added. The flask with

passage number was marked and was incubated at 37C

incubator

4. The media was changed when cells were 60% confluent and

incubated until 80-90% confluent

5. When the cells are 80-90% confluent, they are ready to be

stored or to be used for other experiments.

6. For storing the cells, the old media was sucked out and washed

with 4ml sterile PBS.

7. After washing with PBS, 4ml trypsin 1X was added to the flask.

The cells were incubated for 4-5 mins with trypsin.

8. After 4-5mins incubation the cells were detached from the flask

surface by vigorous shaking and 5ml fresh media was added to

eliminate the trypsin effect.

9. The detached cells were taken out in sterile tube and centrifuged

at 200Xg for 5mins at room temperature

18

10. The supernatant was sucked out carefully without disturbing the

cell Pilate.

11. 100μl sterile DMSO with 900μl FBS was added to re-suspend

the cells.

12. The cells in were transferred in cryo and was stored at -80◦C

and latter transfer in liquid nitrogen

3.2.2 Method development to measure protein level expression of PON1 in tissue culture samples and human plasma/ serum

Goal: To identify the sample concentration to be used for assay

Materials:

1. PBS- 137 mM NaCl, 2.7 mM KCl, 8.1 mM Na2HPO4,

1.5mM KH2PO4, pH 7.2-7.4, 0.2μM filtered.

2. Wash Buffer- 0.05% Tween 20 in PBS, pH 7.2- 7.4 (R&D

Systems, Catalog #WA126).

3. Block Buffer- 1% BSA, 0.05% NaN3, in PBS, pH7.2-7.4

4. IC Diluent #1- 1% BSA in PBS, pH 7.2-7.4, 0.2μm filtered

5. IC Diluent #4- 1mM EDTA, 0.5% Triton X-100 in PBS, pH

7.2-7.4

6. Lysis Buffer- 1mM EDTA, 0.5% Triton X-100, 10ug/mL

Leupeptin, 10 μg/ml Pepstatin, 100μM PMSF, 3μgmL

Aprotinin in PBS, pH 7.2-7.4

7. Total PON1 capture antibody- rat anti-human PON1

19

8. Total PON1 Detection antibody- goat anti-human PON1

9. Total PON1 standard- recombinant human PON1

10. Streptavidin-HRP- streptavidin conjugated to horseradish-

peroxidase

Reagent preparation: All reagents were purchased from R&D systems. The reagents are described below with the catalog numbers…………………...

1. Wash buffer: WA126 was used to make wash buffer. For

1 liter wash buffer 1 pouch of WA126 is dissolved in 1

liter of DD water and NaN3 is added to get 0.05% final

concentration.

2. IC diluent#1: 10X DY995 is diluted to 1X concentration

by adding DD water.

3. IC diluent#4: 5X DYC001 is diluted to 1X concentration

by adding DD water.

4. Block Buffer: 10% NaN3 is added to IC diluent 1 to get

final 0.05% NaN3 concentration

5. Substrate solution: DY999 contains two types of vials

marked as A and B. vial A and B were used in 1:1

20

concentration to get substrate solution. The substrate

solution was made right before applying to the plate.

6. Stop Solution: DY994 is the stop solution and contains

Acid.

7. Capture antibody: The Total Human PON1 ELISA duo

Kit from R&D system provides capture antibody in

powder form. Each vail gives 360ug/ml capture antibody

when reconstituted with 200ul PBS. This can be stored in

-20C. The working concentration 2μg/ml is achieved by

further dilution as required.

8. Detection Antibody: The Total Human PON1 ELISA

duo Kit from R&D system provides detection antibody

in powder form. Each vail gives 21.6μg/ml capture

antibody when reconstituted with 1ml IC diluent 1. This

can be stored in -20C. The working concentration

600ng/ml is achieved by further dilution as required.

9. PON1 standards: The Total Human PON1 ELISA duo

Kit from R&D system provides recombinant PON1 in

powder form. Each vial contains 140ng/ml recombinant

PON1 protein when reconstituted with 500ul of IC

diluent 4. Further dilutions are required for the assay.

Sample preparation: Human plasma samples are diluted as follows…………….

21

1. 10 % sample: 100μl plasma in 900μl IC diluent#4

2. 1% sample: 10μl plasma in 990μl IC diluent#4

3. 0.1% sample: 1μl plasma in 999μl IC diluent#4

Conditioned media and the cell lysate are used in undiluted form.

Method:

Plate preparation

1. The capture Antibody was diluted to working concentration

of 2.0μg/ml in PBS, without carrier protein. 100μL per well

of the diluted Capture Antibody was used immediately to

coat a 96-well microplate with. The plate was sealed and

incubated overnight at room temperature.

2. Each well was aspirated and washed with Wash Buffer,

repeating the process two times for a total of 3 washes. The

process was done by 96well plate washer (auto washer) by

400μL wash buffer in each well. Any remaining was

removed by aspirating.

3. The plate was blocked by adding 300μL of Block Buffer to

each well and incubated at room temperature for 1-2 hours.

4. step 2 was repeated. The plates are now ready for sample

addition.

22

Assay Procedure

1. 100uL of sample or standards in IC Diluent#4 per well was

added. IC Diluent#4 is used as the zero standard. The plate

was sealed and incubated for 2 hours at room temperature.

Note: A seven-point standard curve using 2-fold serial dilutions and a high standard of

10,000 pg/mL is recommended.

2. Aspiration/wash step was repeated.

3. Detection Antibody was diluted to a working concentration

of 600ng/mL in IC diluent # 1 before use. 100μL of the

diluted Detection Antibody was added to each well. The

plate was sealed with a new plate sealer and incubated for 2

hours at room temperature.

4. Aspiration/wash step was repeated.

5. Immediately before use, the Streptavidin-HRP was diluted

to the working concentration specified on the vial label

using IC Diluent #1. 100μL of the diluted Streptavidin-

HRP was applied to each well and incubated for 20 minutes

at room temperature in dark.

6. Aspiration/wash step was repeated. 7. 100 μL of Substrate Solution was added to each well and

incubated for 20 minutes at room temperature in dark.

23

8. 50μL of Stop Solution was added to each well and the plate

is gently tapped to ensure thorough mixing.

9. The optical density of each well was measured

immediately, using a microplate reader set to 450nm.

3.2.3 Method development to measure PON1 lactonase activity in human plasma and tissue culture sample

Goal: Developing method to measure PON1 lactonase activity

Materials:

1. Paraoxonase assay buffer

2. Fluorescence standard

3. PON1 inhibitor (2-hydroxyquinoline)

4. PON1 substrate

5. Paraoxonase Positive Control

6. Samples (cell lysate, human serum/plasma - without

EDTA)

7. Anhydrous DMSO

8. Black 96-well plates

Storing: Kit should be stored at -20◦C and protect from direct light. Reconstituted fluorescence standard, PON1 inhibitor and PON1 substrate should be stored at -20◦C as well. Reconstituted paraoxonase positive control is aliquoted and stored at -80◦C.

24

Reagent preparation:

1. Fluorescence standard: a flouregenic standard is used

for this assay. It has a similar 2-pyrone ring as CTS.

This standard is provided in PON1 activity assay kit

(should be stored in -20C) by BioVision. Each vial of

standard gives 50mM stck when reconstituted with

55ul of DMSO

2. PON1 inhibitor: 2-hydroxyquinoline is a PON

inhibitor. A vial of this compound is also included in

the above assay kit. Each vial gives 50mM stock

when reconstituted with 110ul of DMSO. To get

2mM working concentration we need to dilute 40ul

stock with 960ul of distilled water yielding 1ml work

solution. The stock can be stored in -20C and is good

for 3 freeze/ thaw cycles

3. PON1 substrate: PON1 substrate is available in the kit

as powder. It gives 250X stock when reconstituted

with 44ul of DMSO. The stock is diluted to 5X

working concentration before adding to the wells.

4. Paraoxonase positive control: It is the recombinant

PON enzyme and should be reconstituted with 110 ul

of assay buffer provided in the kit. This positive

25

control should be aliquoted and stored in -80 C for

further use.

Sample preparation:

Undiluted portions of samples are used for lactonase activity assay.

For Plasma, samples should be EDTA or Ca+2 chelating coagulant free.

Method:

Standard curve preparation:

1. The fluorescence standard was diluted to working

concentration by adding 10μl of the 5mM stock to 990μl

Paraoxonase Assay Buffer to obtain a 50 pmol/ul

Standard solution. 0, 2, 4, 6, 8, 12, 16, and 20μl of the 50

pmol/μl solution into a series of wells in a black 96-well

plate was added and the volume of each well was

adjusted to 100 μl with Paraoxonase Assay Buffer,

yielding 0, 100, 200, 300, 400, 600, 800, and

1000pmol/well Fluorescence Standard.

Sample preparation:

2. Plasma samples are collected by standard methods and

store at -80◦C. Samples should be heparinized for

storing. No EDTA or Ca+2 chelating anticoagulants are

usable as PON1 is Ca+2 dependent enzyme. The cell

26

lysate and conditioned media can be collected fresh or

can be stored at -20C for future use.

3. Assay reaction wells were prepared according to the

table below. In addition to the sample wells, a

background control (no enzyme) well was prepared to

correct for potential non-enzymatic substrate hydrolysis.

For verification of PON1 specific activity, I examined

the PON 1 inhibition, using the 2-hydroxyquinoline

Positive control (PC) and PC+ inhibitor wells using the

reconstituted Paraoxonase positive Control were also

investigated. The volume of all reaction wells to 80

μl/well were adjusted by assay buffer.

Table 3.4: Experiment design for lactonase activity assay method development

Test + PON1 Background +ve PC+inhibitor

sample inhibitor control

Samples (serum/ plasma) 2-10μl 2-10μl ------

Paraoxonase +ve control ------10μl 10μl

2-hydroxyquinoline ---- 10μl ------10μl solution (10X)

Paraoxonase assay buffer 78-70μl 68-60μl 80μl 70μl 60μl

Total 80μl 80μl 80μl 80μl 80μl

27

Reaction mix:

4. The plate was preincubated for 10min at 37◦C to pe-

warm samples and allow the the inhibitor to interact with

sample PON1. During the preincubation, a 5X

concentrated PON1 substrate solution was prepared by

diluting the reconstituted 250X PON1 Substrate stock

solution at a 1:50 ratio with the assay buffer.

5. The reaction was started by adding 20ul of 5X PON1

Substrate solution to each reaction well using a

multichannel pipette, yielding a final volume of

100ul/well. The Substrate solution was not added to the

standard curve wells.

Measurement:

6. Immediately (within 1 min) the fluorescence at

Ex/Em=368/460 nm is measured for 60 min at 37◦C.

The readings were taken at specific time interval for 1

hour. Ideal measurement time for linear range may vary

depending upon the sample. The standard curve wells

may be read in endpoint mode (EX/EM=368/460nm).

Calculations:

7. For the fluorescence, standard curve, the zero standard

(0 pmole/well) reading from the standard readings was

28

subtracted, background-subtracted values are plotted and

the slope of the standard curve is calculated.

8. For the reaction wells (including background control),

two time points (t1 and t2) in the linear phase of the

reaction progress curves were identified and the

corresponding fluorescence values at those points

(RFU1 and RFU2) were selected to determine the

change in fluorescence over the time interval:

ΔF=RFU2-RFU1.

9. The specific fluorescence (Cs) was calculated by

subtracting the background control from each sample:

Cs=ΔFs-ΔFBC. Paraoxonase activity was obtained by

applying the Cs values to the fluorescence standard

curve to get B pmol of substrate metabolized during the

reaction time.

Sample Paraoxonase 1 Activity= ∗ =pmol/min/ml=μU/ml ∗

Where B is the amount of metabolite produced, calculated from standard curve (in pmol)

ΔT is the linear phase reaction time t2-t1 (in minutes)

V is the volume of sample added to t he well (in ml)

D is the sample dilution factor (if applicable)

Paraoxonase unit definition: One unit of paraoxonase activity is the amount of enzyme that generates 1 μmole of fluorescence product per min at 37◦C and pH 8.

29

3.2.4 Measurement of PON1 protein expression in CKD vs non-CKD human plasma

Goal: To measure the PON1 protein expression in plasma and compare that between

CKD and non-CKD subjects.

Materials:

The same materials were used as described in ELISA method

development part in section 3.2.2

Reagent preparation:

The same reagents were prepared and used as described in

ELISA method development part in section 3.2.2

Sample preparation: The plasma samples were diluted 100 times, to be used in ELISA experiment. 10μl of each plasma samples was diluted in 1000μl IC diluent#4 solution to achieve 100 times diluted samples. There are 15 non-CKD controls and 31 CKD samples.

The CKD samples are divided in to stage2- 3 (moderate, n=10) and stage 4-5 (severe- end stage, n=31) according to the GFR (glomerular filtration rate) values.

Method:

The same method as described in ELISA method development section in 3.2.2 was used to measure the PON 1 protein level in CKD plasma samples and non-CKD plasma samples. Each sample was measured in triplicate and the average value

30

was used to estimate the protein concentration in plasma sample. The values are statistically compared and plotted.

3.2.5 Measurement of PON1 lactonase activity in CKD vs non-CKD human plasma

Goal: To compare PON1 lactonase activity of human plasma from non-CKD controls and CKD patients.

Materials:

The same materials were used as described in Lactonase

activity assay method development part in section 3.2.3

Sample preparation: Samples are not diluted in this case. I am using the plasma samples at their own concentrations.

Method:

Standard curve preparation

1. The fluorescence standard was diluted to working

concentration by adding 10μl of the 5mM stock to

990μl Paraoxonase Assay Buffer to obtain a 50

pmol/ul Standard solution. 0, 2, 4, 6, 8, 12, 16, and

20μl of the 50pmol/ul solution into a series of

wells in a black 96-well plate was addaed and the

volume of each well was adjusted to 100 ul with

31

Paraoxonase Assay Buffer, yielding 0, 100, 200,

300, 400, 600, 800, and 1000pmol/well

Fluorescence Standard.

Sample preparation

2. 5ul plasma from each sample were used in sample

wells. The samples are EDTA free.

3. In addition to the sample wells, a background

control (no enzyme) well was prepared to correct

for potential non enzymatic substrate hydrolysis.

The volume of all reaction wells were adjusted to

80 μl/well with Paraoxonase Assay Buffer:

Reaction mix

4. The same method as described in 3.2.3 was

followed for reaction mix and for adding the

substrate solution. (substrate solution is not for

adding to the standard curve wells)

Measurement

5. The method for taking the reading is described in

the 3.2.3

32

Calculations

6. The lactonase activity was calculated as described

before in method development part in 3.2.3 and the

values are statistically compared and plotted.

3.2.6 Measurement of PON1 protein expression in tissue culture

Goal: To express PON1 protein in-vivo

Materials:

1. High-PON-CHO cell lines and low-PON-CHO or parental CHO

cell lines

2. The materials for animal tissue culture described in 3.2.1

3. The materials for ELISA to measure protein expression as

described in 3.2.2

Reagent preparation:

Same as described in 3.2.1 for cell culture and in 3.2.2 for ELISA

Sample preparation:

The cells are pelleted by centrifuging at 200Xg for 5 minutes and supernatant is taken out as conditioned media. The pelleted cells are lysed with RIPA lysis buffer. The cell lysate and the conditioned media are directly used as samples.

33

Method:

Plate preparation

1. The same ELISA method described in 3.2.2 was used to

measure the PON1 protein concentration in cell lysate and

conditioned media. The samples were used in undiluted

form.

3.2.7 Measurement of PON1 lactonase activity in tissue culture

Goal: To measure PON 1 lactonase activity of PON1 produced by recombinant cell lines producing PON

Materials:

1. High-PON-CHO cell lines and low-PON-CHO or

parental CHO cell lines

2. The materials for animal tissue culture described in 3.2.1

3. The materials for ELISA to measure protein expression

as described in 3.2.3

Reagent preparation:

The reagents were prepared as described in 3.2.1 and 3.2.3

Sample preparation:

The cells are pelleted by centrifuging at 200Xg for 5 minutes and supernatant is taken out as conditioned media. The pelleted cells are lysed with RIPA lysis buffer. The cell lysate and the conditioned media are directly used as samples.

34

Method:

The method is described in 3.2.3 section. In this experiment the standards, the samples and a blank well were used for the assay.

3.2.8 Method development for measuring TCB by LC-MS

Goal: To develop a method to measure TCB by LC-MS

Materials: Telocinobufagin (TCB), Digoxigenin, formic acid, H2O (LC-MS grade), acetonitrile (LC-MS grade), Shimadzu LC-MS 8050

Reagent preparation:

1. Organic phase: Acetonitrile with 0.1% formic acid

2. Aqueous Phase: water with 0.1% formic acid

3. TCB standard: 7-point serial dilution of TCB from 10ng/ml to

0.15ng/ml were made by dissolving TCB in acetonitrile

4. Digoxigenin at 10ng/ml concentration, dissolved in acetonitrile

Method:

1. The machine was run with following setting for the internal satandard

Digoxigenin…………………

a) m/z of precursion ion = 391.00

b) product at 43

c) dwelling time 100 min

d) ESI and APCI interfaces at default values

35

e) Total flow =0.8ml/min

f) Pump B =80% and Pump A=20%

g) The column oven temperature is 30◦C to maximum 60◦C.

h) Autosampler temperature is 15C

i) Controler is started with “Event 3”

2. The method was downloaded and used for optimization while

searching f or the product ion.

3. The running time was 2minutes and the injection volume was 10ul

4. For TCB the transitions are used as described by Komiyama et. Al. in

2004 (13). The transitions are ……………………………….

403.2>331.2

403.2>349.2

403.2>367.2

Q1, CE and Q3 are -14, -20, and -25 respectively.

5. The method was run to optimize.

6. The TCB and Digoxigenin peaks are tried to get at well-spaced

retention time by trying with different injection volumes and different

pump concentrations and different pump pressures.

36

3.2.9 Making standard curve to measure TCB

Goal: To make standard curve to measure TCB

Materials:

1. 7 point TCB standards with different concentrations from 10ng/ml to 0.15ng/ml

achieved by serial dilution

2. The mobile phases are described as before in 3.2.8

Method:

1. The TCB standards are run through LC-MS by using the developed method with

1200 psi pump pressure, 50%pump concentrations, 0.6ml/min flow rate and 10ul

injection volume.

3.2.10 Measuring CTS degradation by tissue culture that produce PON1

Goal: To measure in-vivo TCB degradation

Materials: High-PON-HEPG2 cells, EMEM with FBS and 1% penicillin + streptavidin, solution, sterile Hank’s modified media (8.0gm NaCl, 0.4gm KCl, 0.14gm CaCl2, 0.1gm

MgSO4.7H2O, 0.1gm MgCl2.6H2O, 1.0gm glucose, 0.35gm NaHCO3, 1000ml distilled water, filtered), 0.5mM TCB stock solution in DMSO, ice cold acetonitrile (LC-MS grade),

Reagent preparation:

1. The reagents for cell culture are described in section 3.2.1 .

Modified Hank’s media is the phosphate free Hank’s media which

37

is required to eliminate the ion suppressing effect of phosphate

while using LC-MS.

2. The reagents for LC-MS is same as described in section 3.2.8

Method:

1. Cells were grown in EMEM with 1%PS and 10%FBS until 80% confluent in 6-

well plates

2. The cultures were washed and changed to modified Hank’s media with HDL

3. All cells treated with 4ul of 0.5mM TCB per well

4. The reactions were stopped either immediately or after overnight incubation

5. The cells were scraped and the collected with media, followed by sonication and

adding 6X excess ice-cold acetonitrile prior to centrifugation and measured by

LC-MS.

6. The same LC-MS method and reagents as described before are used to measure

TCB in samples.

38

Chapter 4

Results

4.1 Bioinformatics approaches

4.1.1 Studying the PON genes using bioinformatics tools provided in www.ncbi.nlm.nih.gov:

We examined the conserved domain, mRNA sequence and protein sequence by clicking on the search options listed in the right panel of the web page shown above.

The procedure is followed to study the PON2 and PON3 genes. The gene is located in chromosome 7 and 33216 base pairs long with 9 exons. The enzyme encoded by this gene is an arylesterase. Arylesterase mainly hydrolyzes paraoxon to produce p- nitrophenol. Paraoxon is an organophosphorus anticholinesterase compound. This compound is produced in vivo by oxidation of the insecticide parathion. Polymorphisms in this gene are a risk factor in coronary artery disease. The gene is found in a cluster of three related paraoxonase genes at 7q21.3.

The mRNA is 1769 bases long the NCBI accession number is NM_000446.5. The protein sequence is 355 amino acids long the NCBI accession number is NP_000437.3. The conserved domain consists of arylesterase, SGL superfamily and Str_Synth superfamily.

39

Figure 4-1: The conserved domain in PON1

Arylesterases are the enzymes that hydrolyse organophosphorus esters such as paraoxon.

These enzymes are found in the liver and blood. They confer resistance to organophosphate toxicity. Human arylesterase (PON1) is associated with HDL and may protect against LDL oxidation. SGL superfamily is found in proteins expressed by a variety of eukaryotic and prokaryotic species and include various enzymes, such as senescence marker protein 30 (SMP-30), gluconolactonase and luciferin-regenerating enzyme (LRE). SMP-30 is known to hydrolyse diisopropyl phosphorofluoridate in the liver. It also has sequence similarity, in the region described in this family, with PON1 and LRE. Strictosidine synthase (Str_Synth superfamily) is a key enzyme in alkaloid biosynthesis. It catalyzes the condensation of tryptamine with secologanin to form strictosidine.

PON2: By using the same way as above the PON2 gene is studied from NCBI. The gene is located in chromosome 7 and 37211 bp base pairs long with 9 exons with NCBI accession number. This gene encodes a member of the paraoxonase gene family, which includes three known members located adjacent to each other on the long arm of chromosome 7. The encoded protein is ubiquitously expressed in human tissues, 40

membrane-bound. PON2 may act as a cellular antioxidant, protecting cells from oxidative stress. It has hydrolytic activity against acylhomoserine lactones and it is an important bacterial quorum-sensing mediator. These indicate the protein may also play a role in defense responses to pathogenic bacteria. Mutations in this gene may be associated with vascular disease and a number of quantitative phenotypes related to diabetes [provided by RefSeq, Jul2008]. The mRNA is 1669 bases long. The protein sequence is 354 amino acids long. The conserved domain consists of arylesterase, and

Str_Synth superfamily. Arylesterases hydrolyse organophosphorus esters such as paraoxon and are found in the liver and blood. They confer resistance to organophosphate toxicity. Human arylesterase is associated with HDL and may protect against LDL oxidation. Strictosidine synthase (Str_Synth superfamily) is a key enzyme in alkaloid biosynthesis and catalyzes the condensation of tryptamine with secologanin to form strictosidine.

Figure 4-2: The conserved domain in PON2

PON3: The gene is located in chromosome 7 and 43504 base pairs long with 9 exons with NCBI accession number NG_008726.1. This gene is a member of the paraoxonase family and lies in a cluster on chromosome 7 with the other two family members. The encoded protein is secreted into the bloodstream and associates with high-density

41

lipoprotein (HDL). The protein rapidly hydrolyzes lactones and can inhibit the oxidation of low-density lipoprotein (LDL), a function that is believed to slow the initiation and progression of atherosclerosis [provided by RefSeq, Jul 2008]. The mRNA is 1201 bases long. The protein sequence is 354 amino acids long. The conserved domain consists of arylesterase, and Str_Synth superfamily which are similar as PON2.

Figure 4-3: Conserved domain in PON3

4.1.2 Study of PON gene expressions in different tissues in human body:

PON1 is highly expressed highly in liver and in kidney and heart. By BioGPS, the expression of PON1 in human is shown below…………………………….

Figure 4-4: Tissue specific PON1 expression in human body

42

PON2 is highly expressed in liver. By BioGPS, the expression of PON2 in human is shown below…………………………….

Figure 4-5: Tissue specific PON2 expression in human body

By BioGPS, the expression of PON3 in human is shown below………………………

Figure 4-6: Tissue specific PON3 expression in human body

The overall expression of PON enzymes in our body shows high expression of PON1 and

PON3 in liver whereas PON2 is high in cardiomyocytes as well as in liver.

43

Figure 4-7: Comparison of PON genes expressions in liver, heart and kidney

4.1.3 Comparison of PON gene expressions in renal biopsy samples and peripheral blood samples taken from CKD patients and non-CKD controls:

Results:

1. From the studies with peripheral blood samples the PON1 gene expression

is significantly lower in CKD stage 2-3 than the non-CKD controls. No

significant changes were seen with PON2 and PON3 expression. In CKD

stage 4-5, the expression levels may be difficult to interpret as the patients

are on dialysis and medications.

Figure 4-8: Showing the result of PON genes expressions in peripheral blood samples from CKD and non-CKD controls.

44

2. From the studies with renal biopsy samples the PON1 gene expression is

significantly higher in CKD stage 2-3 than in non-CKD controls. PON2

gene expression is also significantly higher in CKD stage 2-3 than in non-

CKD controls. PON3 gene expression is not significantly altered.

Figure 4-9: showing the result of PON genes expressions in renal biopsy samples from

CKD and non-CKD controls.

45

4.1.4 An approach to look at the highly correlated genes that may affect PON1

1. GSE 70528: A study with PBMC from CKD patients and non-CKD controls

CKD vs non-CKD controls

Table 4.1: Correspondence values of differentially expressed genes with PON genes in study GSE70528

PON1 PON2 PON3 GADD45B -0.48214 GADD45B -0.48214 ZC3H13 -0.44286 STK26 -0.41786 STK26 -0.41786 GDAP1 0.482143 IFITM2///IFITM1 -0.51786 IFITM2///IFITM1 -0.51786 PON1 0.675 ING5 -0.42857 ING5 -0.42857 PON2 0.675 IRF7 -0.42857 IRF7 -0.42857 HNRNPA0 -0.42143 HNRNPA0 -0.42143 IFITM1 -0.46429 IFITM1 -0.46429 ZNF418 0.542857 ZNF418 0.542857 MOXD1 0.403571 MOXD1 0.403571 SLC19A1 -0.41071 SLC19A1 -0.41071 UMAD1 -0.44286 UMAD1 -0.44286 FLOT1 -0.42857 FLOT1 -0.42857 ZBTB7B 0.432143 ZBTB7B 0.432143 GDAP1 0.503571 GDAP1 0.503571 PON3 0.675 PON3 0.675 Plotting a gene regulatory network is important, because it helps us to understand the gene function and interactions between 2 or more genes.

46

Network with PON1 from study GSE70528: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/59613fb766043/admin_59613f b76642b

APOA1 is one of the connector genes and is associated with cholesterol, coronary heart disease, hematological and biochemical traits, hypertriglyceridemia, lipid metabolism phenotypes, metabolite levels, and triglycerides.

Figure 4-10: Network with PON1 and differentially expressed genes with higher corresponding values from GSE70528. Red dots are candidate genes and gray dots are common connectors.

Network with PON2 from study GSE70528: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/5961410bd5c4b/admin_59614

10bd6030

HTR2A is directly connected with PON2 and is associated with cardiac hypertrophy.

BCL6 relates to PON2 by a candidate gene ZBTB7B and is associated with renal function and pulmonary function.

47

Figure 4-11: Network with PON2 and differentially expressed genes with higher corresponding values from GSE70528. Red dots are candidate genes and gray dots are common connectors.

Network with PON3 from study GSE70528: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/5961422c7d421/admin_59614

22c7d807

On the contrary to other two genes we can see clearly that PON3 is not related to any of the differentially expressed genes in the setting of CKD.

Figure 4-12: Network with PON3 and differentially expressed genes with higher corresponding values from GSE70528. Red dots are candidate genes and gray dots are common connectors.

48

2. GSE12682: A study with renal biopsy samples from CKD patients and non-

CKD controls:

Table 4.2: Correspondence values of differentially expressed genes with PON genes in study GSE12682

PON1 PON3 VAC14 0.419589 SAFB2 0.414672 PLAC4 0.415701 AASS 0.410811 CNGB3 0.455856 HOXD9 -0.4381 AMELY 0.424453 FAM134B 0.453539 CDH10 0.433462 PRR5-ARHGAP8 -0.43089 SPDEF 0.422909 RARA 0.417503 POU3F1 0.480051 PON2 -0.40824 In this case PON3 is found to be very much nonspecific. Even with a very low value to select average gene expression value, it was not possible to retrieve the correspondence value of PON3 with other differentially expressed genes.

Network with PON1 from study GSE12682: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/596147e5ee8e3/admin_596147 e5eecc9

There are some important targets in the setting of CKD. The following are the genes that are found to be important……………………

ADAM12 is associated with thiazide-induced adverse metabolic effects in hypertensive patients. BET1L associated with uterine fibrosis. KCNAB1 is associated with voltage gated K+ channel activity.

49

Figure 4-13: Network with PON1 and differentially expressed genes with higher corresponding values from GSE12682. Red dots are candidate genes and gray dots are common connectors. Blue dots are direct targets in the setting of CKD.

Network with PON3 from study GSE12682: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/5961493fa3e1c/admin_596149

3fa4202

As noted in the PBMC studies PON3 stands outside of the network in the setting of CKD.

Figure 4-14: Network with PON3 and differentially expressed genes with higher corresponding values from GSE12682.

Other results and plots from this analysis are given in the Appendix B attached at the end.

50

4.2 Biochemical and molecular biology approaches

4.2.1 Method development to measure PON1 protein expression in human plasma samples:

100 times dilution gives consistent result for PON1 protein expression. So, for next experiments we used 100 timed diluted plasma samples for ELISA.

4.2.2 Method development to measure PON1 lactonase activity in human plasma and tissue culture samples:

5ul plasma sample in each well shows consistent result. So, we used 5ul samples for further analysis. We preferred to use same amount for tissue culture samples to measure the lactonase activity.

4.2.3 Measurement of PON1 protein expression in CKD vs non-CKD human plasma :

According to the ELISA experiment on diabetic nephropathy samples we can see that the protein expression level of PON1 is lower in CKD patients than in non-CKD controls.

P-value for stage 3 vs *** *** control:0.0002 P-value for stage 4-5 vs control: <0.0001

Figure 4-15: ELISA report showing lower circulating levels of PON protein in human plasma of CKD stage 3 and CKD stage 4-5 patients than in non-CKD controls.

51

4.2.4 Measurement of PON1 lactonase activity in CKD vs non-CKD human plasma

*** *** P-value for stage 3 vs control:<0.0001 P-value for stage 4-5 vs control: <0.0001

Figure 4-16: Lactonase activity assay showing significantly lower level of PON1 lactonase activity in human plasma of CKD stage 3 and CKD stage 4-5 patients than in non-CKD controls.

4.2.5 Measurement of PON1 protein expression in tissue culture:

High PON-CHO cells produced measurable amounts of PON1 and it is measured as

8.307 ng/ml in 100 ul of cell lysate and 5.959 ng/ml in 100 ul of conditioned media.

Figure 4-17: ELISA showing PON1 protein expression in recombinant high PON-CHO cells

Figure 4-18: PON1 expression by HEPG2 cell lines are also checked by PCR. 52

PON over expressing HEPG2 or High PON HEPG2 cell lines are producing higher amount of PON1 protein

4.2.6 Measurement of PON1 lactonase activity in tissue culture

Figure 4-19: PON1 lactonase activity by recombinant high-PON cell lines

4.2.7 Method development for measuring TCB by LC-MS

The method is optimized with 1200 psi pump pressure, 50%pump concentrations,

0.6ml/min flow rate. Digoxigenin is not found to be a very good internal standard as it may degrade over time.

The optimized method is saved to measure TCB with different concentrations to make standard curve. And this method was also used to measure TCB degradation by different recombinant animal cell lines producing PON enzymes.

4.2.8 Making standard curve to measure TCB

Table 4.3: The TCB standards measured by LC-MS

TCB concentration Ret. Time Area Height found Conc.

0.15ng/ml 5.068 1547 162 0.041

0.31ng/ml 5.094 2858 339 0.223

53

0.625ng/ml 5.072 5584 559 0.603

1.25ng/ml TCB 5.127 10664 1131 1.31

2.5ng/ml TCB 5.078 19052 1863 2.477

5ng/ml TCB 5.081 39725 3643 5.355

10ng/ml TCBN 5.091 71842 6540 9.826

Figure 4-20: chromatogram for 0.15ng/ml TCB

Figure 4-21: Chromatogram for 10ng/ml TCB

Figure 4-22: Standard curve obtained by running TCB standards (conc.= 10ng/ml,

5ng/ml, 2.5ng/ml, 1.25ng/ml, 0.625ng/ml, 0.31ng/ml, 0.15ng/ml)

54

4.2.9 Measuring CTS degradation by tissue culture that produce PON1

Measurement of TCB at m/z: 403.30>349.20

Table 4.4: TCB degradation in High-PON-HEPG2 cell culture is measured by LC-MS sample composition TCB added incubation Ret. Area Height Conc.

time Time (ng/ml)

Acetonitrile No n/a ------0 blank media +TCB yes o/n 5.095 581916 55524 80.833 blank media +TCB yes o/n 5.112 548957 52574 76.245 blank media no o/n ------0

-no TCB blank media no stop ------0

-no TCB blank media +TCB yes stop 5.106 599404 57332 83.267 blank media +TCB yes stop 5.12 576671 54768 80.103 culture+TCB yes o/n 5.113 218707 21440 30.271 culture+TCB yes o/n 5.107 176028 16901 24.33 culture- no TCB no o/n ------0 culture- no TCB no o/n ------0 culture+TCB yes stop 5.108 534625 50867 74.25 culture+TCB yes stop 5.111 511973 48541 71.096

Table 4.4: TCB degradation in High-PON-HEPG2 cell culture is measured by LC-MS

55

Figure 4-23: TCB is degraded in significant amount over night (P=0.0054) by High PON-

HEPG2.

56

Conclusion

1. Based on microarray data from human peripheral blood samples, expression of

PON-1 is decreased in Stage 2-3 CKD, while no significant changes are seen in

PON-2 and PON-3 vs non-CKD controls.

2. In renal biopsy samples PON-1 and PON-2 are elevated in CKD stage 2-3

patients’ vs non-CKD controls samples.

3. From the network analysis, PON1 and PON2 have correlations with differentially

expressed genes in the setting of CKD but PON3 stands out from the network.

From the network study, it is possible to find out important genes that may

regulate PON genes.

4. Based on ELISA data from human serum samples circulating levels of PON-1

protein are significantly decreased in CKD vs non-CKD controls.

5. Based on the lactonase activity assay from human serum samples circulating

levels of PON lactonase activity are significantly decreased in CKD vs non-CKD

controls.

6. Based on the LC-MS-MS assay, PON-1 overexpressing HEPG2 cells can degrade

TCB

57

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61

Appendix A

The R-code to find out the correlated genes and save the corresponding values in

*.csv files

expmat = read.table("GSE15072-HD.txt", header=T)

avgexp = rowMeans(expmat, na.rm = TRUE)

expmat = expmat[avgexp > quantile(avgexp, prob=0.25),]

expmat

library(bc3net)

net.A = bc3net(expmat, verbose=TRUE)

net.A

library(igraph)

net = subgraph.edges(net.A,

eids = which(E(net.A)$weight>0.3))

net = getgcc(net)

l = layout.fruchterman.reingold(net)

l = layout.norm(l)

plot(net, layout=l, vertex.size=0.1,

vertex.shape="none", vertex.label=NA, edge.width=0.6)

dcol = colorRampPalette(c("dodgerblue2","white")) 62

points(l, col = densCols(l, colramp = dcol), pch = 20, cex=0.2, xaxt='n', yaxt='n', ylab="", xlab="") getgcc = function(net){ mem = which.max(clusters(net)$csize) genes.gcc = V(net)$name[clusters(net)$membership==mem] net = igraph::induced.subgraph(net,genes.gcc) return(net)} library(c3net) expdata <- copula(expmat) mim <- makemim(expdata)

Ic <- mean(mim[upper.tri(mim)]) #Example cut-off for the first step of C3NET

# Ic <- 2 can be set for the example. mim[mim < Ic] <-0 #nonsignificant values eliminated wrt C3NET step 1. net <- c3(mim) # regulatory network inferred (non-zero elements stand for links of the predicted network) plot(net) nt1 = cor(t(expmat), method="spearman") g1 = graph.adjacency(nt1, weighted=TRUE, mode="lower") plot(g1) plot(g1, vertex.size=3, edge.width=3, vertex.color="Green") write.csv(nt1, file="nt1-HD.csv")

63

Appendix B

The correspondence values of differentially expressed genes and network found for each study B.1 Studies with PBMC a) GSE15072:

Table B.1: Correspondence values of differentially expressed genes with PON genes in study GSE15072 (CKD vs non-CKD)

CKD vs non-CKD controls PON1 PON2 MUC3B -0.4387 ACR -0.4191 DRP2 0.47059 CDK16 -0.4779 HOXB6 -0.701 MUC7 -0.402 PARVB 0.52451 RNF5 -0.424 CENPT 0.5098 DEAF1 0.44363 LTB4R -0.5319 CASP2 -0.4363 MRPL2 0.48529 PON2 1 MAST4 0.40686 PON3 CDK16 0.41667 MRPL2 0.41177 S100A12 -0.527 LAPTM4B 0.40931 NMT2 0.43628 GSTM2 0.40441 SLC6A12 0.79902 RPS6KA3 -0.4044 GSTM2 0.46814 ZNF16 -0.4657 MUC7 -0.5417 VGLL4 0.41177 RAB31 -0.5833 PTP4A3 -0.4167 LILRA2 -0.4976 CDC42BPA 0.4902 ZNF16 -0.5049 SLCO5A1 0.46569 DEAF1 0.57598 CLDN14 0.41912 POLR3C 0.49755 RYR3 0.46814 CDC42BPA 0.53922 CHAF1A 0.43382 SLCO5A1 0.45588 ADGRL1 0.41667 ARHGAP19 -0.6593 CLDN14 0.65196 RYR3 0.68382 Table B.2: Correspondence values of differentially expressed genes with PON genes in study GSE15072 (HD vs non-CKD) 64

HD vs non-CKD controls

PON2 RPL34 0.409023 POLR2K 0.439098 ANXA1 0.463158 XPO6 -0.48571 NDE1 -0.4391 PSMA3 0.458647 KTN1 0.431579 RPF1 0.41203 MMP25 -0.41053 PDCD6 -0.40451 SNRPD1 0.407519 TBCA 0.455639 NDUFA1 0.419549 ELOVL5 -0.41654 TAX1BP1 0.422556 TRAPPC10 -0.4015 RPL22 0.47218 MRPL42 0.434586 JUN 0.416541 EVI2A 0.449624 KLF10 0.406015 N4BP1 -0.47218 COPS2 0.43609 RPS25 0.407519 RUFY2 -0.44812 FAM65B -0.47669 FXR1 0.487218 CCNC 0.439098 MBD2 0.419549 BCAS2 0.409023 MCTS1 0.44812 METTL5 0.407519 FAM129A -0.53835 In HD vs non-CKD control the genes correlations with PON1 and PON3 are very much nonspecific.

Network with PON1 from study GSE15072: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/5960f21908347/

Figure B-1: Network with PON1 and differentially expressed genes with higher corresponding values from GSE15072.

Network with PON2 from study GSE15072: The network result can be seen online at

65

http://severus.dbmi.pitt.edu/LENS/index.php/results/view/59613b13015f0/admin_59613b

13019d6

Figure B-2: Network with PON2 and differentially expressed genes with higher corresponding values from GSE15072

Network with PON3 from study GSE15072: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/59613df8807c1/admin_59613d f880ba7

Figure B-3: Network with PON3 and differentially expressed genes with higher corresponding values from GSE15072

66

B.1 Studies with PBMC

a) GSE 37171:

In this CKD vs non-CKD study, PON genes correlation with other genes are non-

specific.

b) GSE43484:

Table B.3: Correspondence values of differentially expressed genes with PON genes in study GSE43484

PON3 HTN1 -0.48571 VAV2 0.485714 TMEM92-AS1 -0.6 KCNAB1 0.6 RPRM 0.485714 RGS9 0.485714 MIR6872//SEMA3B -0.6 HSF4 -0.6 CMA1 0.714286 ZER1 0.485714 KCNV1 -0.6 ANKRD7 0.6 HCN2 0.714286 BET1L 0.714286 GCNT4 0.485714 ADAM12 0.485714 FARS2 0.6 TRPC3 -0.6 DCAF13 -0.48571 PPP1R10 0.485714 CCKAR -0.6 GRIN2A -0.6 TLK1 -0.48571 KIR2DS2 -0.6 DRD3 0.6 LCN2 -0.48571 LMO4 -0.48571 MMP10 -0.48571 TACR3 0.6 LIMCH1 0.6 NPR3 -0.6 SIRPA 0.485714 TNS4 0.714286 EDNRA -0.48571 APOE 0.6 BTF3P12 0.6 CPLX3 0.6 ERCC4 -0.48571 IFT74 0.6 MIR664B//SNORA56//DKC1 0.6 MCM3AP -0.71429 SAGE1 -0.48571 ZNF76 0.485714 OR1E2//OR1E1 0.6 XRCC3 0.714286 DGKH 0.485714 DIO2 0.6 DBT -0.48571 PARK2 -0.71429 PON1 and PON2 correlation with other genes in this study are non-specific.

Network with PON3 from study GSE43484: The network result can be seen online at 67

http://severus.dbmi.pitt.edu/LENS/index.php/results/view/5961444b5025d/admin_59614

44b50643

Figure B-4: Network with PON3 and differentially expressed genes with higher corresponding values from GSE43484

B.1 Studies with renal biopsy

a) GSE20602: PON genes correlations with other genes are non-specific.

b) GSE32591:

Table B.4: Correspondence values of differentially expressed genes with PON genes in study GSE32591

PON3 PLSCR1 -0.4033 MS4A6A -0.41703 HHLA1 0.502785 B2M -0.46494 C1QB -0.41665 WNT10B 0.411402 LYZ -0.43019 DDX60 -0.41079 TYROBP -0.40615 NMI -0.44342 CTSS -0.47513 RPS19 -0.40293 LY96 -0.46255 SLC37A4 0.443526 SP110 -0.40379 NRP1 -0.40182 TGFBI -0.40191 STAT1 -0.40619 CLEC2B -0.46911 QPCT -0.46386 PAK4 0.429366 RIN2 -0.40333 HLA-DQB1 -0.4856 ITGB2 -0.48593 COL6A3 -0.40464 RNASE6 -0.47945 TRIM22 -0.41058 CSF1R -0.47171 CD53 -0.41543 FCER1G -0.41668 COL1A2 -0.41194 PSME2 -0.414 PNPLA2 0.447405 PTPRC -0.50333 TNFAIP8 -0.40284 CD14 -0.4301 CYP11B2 0.416445 PON1 and PON2 correlations with other genes are non-specific.

Network with PON3 from study GSE32591: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/59614a3c1eeaf/admin_59614a

3c1f296 68

Figure B-5: Network with PON3 and differentially expressed genes with higher corresponding values from GSE32591

c) GSE66494

Table B.5: Correspondence values of differentially expressed genes with PON genes in study GSE66494

PON3 SLC30A6 0.464252 YME1L1 0.409836 DNAJC21 0.408144 RAD23B 0.434373 ZNF561 0.430196 ZXDA 0.401216 PLCG1 -0.55198 TM9SF4 0.408197 ALG13 0.412004 FAM199X 0.531782 PON1 and PON2 correlations with other genes are non-specific in this study

Network with PON3 from study GSE66494: The network result can be seen online at http://severus.dbmi.pitt.edu/LENS/index.php/results/view/59614d47b8044/admin_59614 d47b842a

69

Figure B-6: Network with PON3 and differentially expressed genes with higher corresponding values from GSE66494

Appendix C

The ELISA report of CKD patients vs non-CKD control plasma :

Table C.1: PON1 protein expression in ng/ml in plasma samples from CKD patients and non-

CKD controls

CKD CKD Control stage 3 stage 4-5 1041.91 879.6815 977.3459 1008.926 940.9703 977.8137 983.194 733.9451 972.0825 958.7487 881.3 910.0919 1018.634 799 933.8355 948.3389 888.1 914.5365 977.3459 929.5 952.3157 980.153 807.1 920.5017 991.6154 313.8 707 969.0415 644 724.7 958.7487 815.2 665.6 959.6844 542.8 991.6154 591.9 999.9198 610.2 992.3172 501.1 657.2 70

593.7 575.1 464.1 418.3

Appendix D

The lactonase activity assay report of CKD patients’ vs non-CKD control plasma:

Table D.1: Lactonase activity assay in mU/ml in plasma samples from CKD patients and non-

CKD controls

CKD CKD Control stage 3 Stage 4-5 2254.791 1737.16 1221.431 2238.647 1428.691 1412.327 2335.213 1572.772 1899.327 2304.212 1265.311 2065.864 2449.094 1262.024 1879.81 2041.347 1298.218 1190.803 2025.693 1622.338 904.1848 1887.611 1579.918 1099.771 2289.301 1104.993 1214.797 1918.218 1559.784 1145.185 2056.33 879.8891 1123.182 1944.44 1468.646 2491.087 1941.136 2088.403 1808.568 2405.242 2363.429 803.067 1254.409 998.8877 941.6759 1885.211

71

Appendix E

The lactonase activity assay report of high PON recombinant cells:

Table E.1: The lactonase activity measured in high PON recombinant cell lysate and conditioned media (CM)

Sample Activity (mU/ml) high PON-HEPG2-lysate 209.6219 highPON-CHO_-lysate 24.38572 high-PON-HEPG2-CM 3.831456 high-PON-CHO-CM 6.701106

72