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ASSOCIATION STUDY AND

FUNCTIONAL VALIDATION OF

GENETIC MARKERS RELATED TO

NON-ALCOHOLIC FATTY LIVER

DISEASES

Angel Bardasco Blazquez

(Tutor: Ana M. Aransay, CIC bioGUNE)

1

ABSTRACT

Non-alcoholic fatty liver diseases (NAFLD) are the most common causes of

chronic liver disease in several western countries. Up to now, the only way to diagnose

NAFLD with certainty is liver biopsy. Therefore, the aim of the present project is to find

genetic markers that could help to develop a non-invasive diagnostic method for these

disorders. To achieve this objective we have carried out a candidate gen association

approach that yielded 6 potentially associated genes, being the first time that SLC2A1

has been associated with NAFLD. In addition, we have investigated the regulation of

those genes in liver biopsies at transcriptomic level. We have detected that all the

studied genes are regulated in NAFLD, and that the regulation is higher in advanced

stages of the disease. This suggests that the identified genes could serve as potential

markers for the diagnosis of the phases of NAFLD development.

2

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) includes a wide spectrum of lesions

including steatosis (ST), non-alcoholic steatohepatitis (NASH), fatty liver and

inflammation, as well as a high number of cryptogenic cirrhoses 1. NAFLD represents

the hepatic manifestation of the metabolic syndrome with insulin resistance as a

common feature, namely, central obesity, insulin resistance, dyslipidemia and

hypertension 2, 3

. ST is an accumulation of fatty acids in the liver 4 and NASH is a term

that describes a form of liver disease that is histologically indistinguishable from

alcoholic hepatitis, but occurs in persons who do not consume excess of ethanol 4, 5

(less than 40g of ethanol per week 3).

Prevalence of NAFLD

NAFLD is the most common cause of chronic liver disease in several western

countries 5 and it has been reported in all age groups including children, although the

highest prevalence is described in individuals between 40 and 60 years 3. About 75% of

obese patients have NAFLD 6 (the prevalence augments with increasing body weight

3).

In general population, the prevalence of NAFLD is estimated about 20% 7. The

prevalence of NAFLD is increasing in industrialized countries 2 due probably to social

and environmental agents like alcohol, industrial toxins and hepatotrophic viruses 5,

and also to metabolic syndromes like obesity and type 2 diabetes (T2D) 8. Nugent et al.

6 showed that patients with T2D have high risk to develop NAFLD, since NAFLD was

detected in 62% of patients with newly diagnosed T2D and T2D has been described in

34% to 75% of patients with NASH 3. Obesity and diabetes mellitus are not

predisposing factors only to develop NAFLD, but they are also potential risk factors to

develop severe hepatic fibrosis and cirrhosis 9. All of these previous mentioned factors,

hyperlidipemia and other conditions associated with insulin resistance are generally

present in patients with NAFLD 3: at the time of NASH diagnosis, up to one third of the

patients have diabetes or fasting hyperglycemia 4, between 39 and 100% are

overweighted or obese, and between 20 and 80% have abnormalities of lipid

metabolism 4.

3

Development and progression of NAFLD

The pathogenesis of NAFLD responds to a two-hit hypothesis. First of all, an

imbalance in fatty acid metabolism 10

involves accumulation of fat in the liver

(steatosis), likely, as a result of insulin resistance and increased fat mass 5, 11

. Secondly,

hepatocyte necrosis and apoptosis is driven by oxidative stress (and subsequent lipid

peroxidation), deregulated proinflammatory cytokines production by Kupffer cells

(principally the tumor necrosis factor alpha, TNFα) and hormones derived from

adipose tissue (adipocytokines) that result from efforts to compensate the altered lipid

homeostasis 1, 3, 11, 12

11

. Jou et al.12

proposed that the hepatocyte death is the third and

decisive step in NAFLD pathogenesis because this event drives progression from NASH

to cirrhosis.

In the hepatic ST, vesicles of fat, predominately triglycerides, accumulate within

hepatocytes (without causing considerable hepatic inflammation) causing liver cell

death 5 and activating mechanisms of hepatocyte regeneration

12. This regenerative

respond activates hepatic stellate cells to myofibroblasts, causing liver fibrosis and

expanding hepatic progenitor populations. Subsequently , several chemoattractans are

produced to recruit various types of immune cells into the liver, inducing hepatic

inflammation that drives to NASH 12

. ST can progress to NASH due to hepatocyte injury

and apoptosis, and hepatic infiltration by inflammatory cells. It is unclear why some

patients who develop ST go on developing NASH while others do not 11

. The next stage

could be that NASH develops to cirrhosis as a result of an incomplete repair of

metabolic liver injury 12

. In most chronic liver diseases that lead to cirrhosis, there is an

increased risk of developing hepatocellular carcinoma (HCC) that is an irreversible

state of liver damage 13

. Hepatocyte DNA damage and expansion of liver progenitor

cells have been demonstrated in early NASH and this suggests that NASH provides

fertile ground for neoplastic transformation of hepatocytes at several stages of

differentiation 12

. Progression of simple ST to NASH increases the risk to develop

cirrhosis and consequent liver-related morbidity and mortality 12

.

4

Figure 1. Lipid metabolism within the hepatocytes. Liver lipid content is determined

by the equilibrium of several processes: import of FFA from the adipose tissue, de

novo synthesis of FFA in hepatocytes, β-oxidation of FFAs, esterification of FFA into

triglycerides and export of triglycerides as vLDL. Hepatic ST is a consequence of

imbalance in those processes in favor of excessive triglyceride accumulation. Insulin

resistance and resulting hyperinsulinemia lead to hepatocyte lipid accumulation in

the liver by several mechanisms. In adipose tissue, insulin resistance enhances

triglyceride lipolysis and inhibits esterification of FFAs. The result is the increased

levels of circulating FFAs, which are then taken up by the liver. In hepatocytes, the

hyperinsulinemia increases the “de novo” synthesis of fatty acids and inhibits their β-

oxidation. The consequence of reduced vLDL production and triglyceride export is

the accumulation of FFAs within hepatocytes.

The normal lipid metabolism in the liver involves hepatocyte uptake and de

novo synthesis of free fatty acids (FFA), disposal of FFA via oxidation or de novo

triglyceride synthesis and export of triglycerides as a very low density lipoproteins

(vLDL) from hepatocytes (Figure 1) 12

. As soon as the rate of triglyceride synthesis

overwhelms the capacity of vLDL export, triglycerides accumulate within hepatocytes

causing ST 12

. When the insulin resistance occurs, hepatic FFA concentration increases

by the movement of FFA from adipocytes by lipolysis (and, consequently, increasing

hepatic import) and/or hepatic endogenous synthesis 4. Triglycerides by themselves

are not hepatotoxic but they are biomarkers of increased hepatic exposure to

potential toxic FFA. An enzyme for esterification (Acyl-coA:diacylglycerol

acylltransferase, DGAT) is required to transform FFA in triglycerides and to join vLDL12

,

5

then, the capacity of vLDL export can be overwhelmed leading to FFA accumulation

within hepatocytes.

The molecular mechanism of the insulin resistance is complex and has not

been elucidated completely. Several molecules appear to interfere with the insulin

signaling pathway and it has been found that adiponectin plays a key role in insulin

sensitivity 3, 11

. FFA and their metabolites are ligands of peroxisome proliferator-

activated receptor alpha (PPARα), a transcription factor that activates genes involved

in fatty acid oxidation. When PPARα is up-regulated, there is more FFA oxidation,

which is translated in increased oxidative stress, elimination of FFA and progression

from ST to NASH 3.

Oxidative stress

Hepatic mitochondria of patients with NASH exhibit ultrastructural lesions with

the presence of para-cristalline inclusions in the megamitochondria, while

mitochondria of patients with simple ST are normal 14

. Although the mechanisms for

hepatic mitochondrial dysfunction in NASH are still unknown, it may involve lipid

peroxidation, TNFα and reactive oxygen species (ROS). TNFα increases permeability of

the mitochondrial membranes and the blocking of the electron flow from complex II to

complex IV 14

. Those factors are supposed to alter mitochondrial DNA and

mitochondrial oxidative phosphorylation, producing the structural alterations

mentioned abobe3.

It has been shown that 30% of the patients with NASH have elevated ferritin

levels (marker of iron overload) 11

, which plays a role in oxidative stress and may play a

function in pathogenesis of NASH 15

. This iron overload generates reactive oxygen

species and subsequent lipid peroxidation. In addition, iron has harmful effects on the

mitochondria activity 11

.

6

Diagnosis of NAFLD

Most of the diagnosis of NAFLD is done by exclusion of other liver diseases

taking into consideration parameters through a scoring system 11

like biochemical tests

based on serum markers, imaging techniques such as ultrasound, and measurement of

liver stiffness by transient elastography 8, 13

. The gold standard for accurate diagnosis

of NAFLD is liver biopsy. This is the only way to distinguish between fatty liver

(steatosis) alone and NASH. There are not specific and sensitive noninvasive tests

(there are some, but their efficiency has not been demonstrated) 3, 5

. The problems

with liver biopsy are that is painful, invasive and, given the increasing number of

patients with NAFLD, it is not an efficient method 8. Therefore, studies of these

diseases are limited by the inability to make a definitive diagnosis of NAFLD3.

Additionally, distinction of NASH from simple ST is important because their prognoses

and clinical management are different 8, 13

. Studies that have used strict definitions for

diagnosis, including biopsies, were most often based on specific subsets of the

population (like diabetics, obese individuals, alcoholic liver diseases, etc.) and, so far,

they cannot be applied to the general population 3.

Treatment

Although there is no consensus treatment for ST and NASH, most of the applied

therapies include specific diet and exercise for weight loss and sometimes, it could be

enough to treat hepatic ST 16

. It is also recommended to stop smoking and alcohol-

drinking during treatment 3. When specific diet and exercise is not enough,

pharmacological treatment should be initiated. Several drugs such as antioxidants and

lipid-lowering drugs have been tried for the treatment of NAFLD 3, 13

. There is quite a

controversy about NAFLD pharmacological treatment because the response of patients

is variable and often there are important secondary effects.

7

Genetic of liver diseases

It has been shown that most evidences of genetic association with NAFLD

derive from family clustering analysis 2. There are different families of genes involved

in ST and NASH: genes influencing lipid metabolism, genes affecting oxidative stress,

genes coding for bacterial receptors and genes influencing extracellular matrix

synthesis and degradation 2. Modifications of those gDNA gene sequences have been

associated with liver diseases:

• The polymorphism (-493G/T) in the promotor of microsomal triglyceride

transfer protein (MTP) has been associated with NAFLD 17

. This enzyme adds

triglycerides to nascent apolipoprotein B, producing vLDL. Thus, decreased

activity of MTP may lead to lipid accumulation 17

.

• The (1183T/C) polymorphism in the manganese superoxide dismutase

(MnSOD), located in mitochondria and implicated in scavenging excessive

oxidative stress to hepatocytes, has been related with NAFLD 17

.

• Polymorphisms (1031T/C) and (863C/A) in tumor necrosis factor alpha

(TNFα) were reported as associated with NASH in Japanese and Italian

population 18, 19

. TNFα has been shown to induce insulin resistance, involved

in development of NASH.

• A SNP (V175M) in exon 8 of posphatidylethanolamine N-methyltransferase

(PEMT), that play a role in lipoprotein secretion from liver, has been seen

associated with NAFLD 20

. This SNP it is a non-synonymous polymorphism

(aminoacid change: V175M) and generates a loss of function of PEMT.

• Mice exposed to a lipid rich diet developed severe NASH with fibrosis that

has been associated with overexpression of Cytochrome P450 2E1 (CYP2E1)

4. Polymorphisms within this enzyme could be associated with risk of liver

disease.

• Mutations in gen hemochromatosis (HFE) (C282Y and H63D), related with

iron overload, have been associated with hepatic fibrosis 15

.

8

• It was shown that polymorphism (667C/T) in the methylenetetrahydrofolate

reductase (MTHDR) has been associated with mayor risk to develop

hepatocellular carcinoma in patients with alcoholic cirrhosis 21

, and some

alleles have been significantly associated with NASH 22

.

Rubio et al. 23

suggest that the alterations in gene expression associated with

NASH are broad and selective, and they found that many of the identified genes are

associated with mitochondrial function, insulin action and oxidative stress. Expression

of proliferator-activated receptor gamma (PPARγ) at mRNA level was significantly

lower in subjects with ST than in those without 24

. Genes involved in scavenging of

reactive oxygen species (like catalase or glutathione peroxidase), as well as genes

involved in glucose (alcohol dehydrogenase 1 and glucose-6-phosphatase) and fatty

acid metabolism (like 3-hydroxy-3-,ethylglutaryl coenzyme A, mitochondrial 3-oxoacyl-

CoA thiolase and long-chain acyl-CoA synthetase) are down-regulated in NASH

patients 25

. However, genes involved in protein synthesis, degradation pathways and

complement activation are up-regulated in NASH patients 25

. It is interesting to

emphasize that patients with liver ST have a gene-expression pattern intermediate

between those patients with NASH and healthy controls 23

. However, Rubio el al.23

described that all patients with ST do not develop NASH, and only those that have a

similar gene-expression pattern to the one associated to NASH seem to have a higher

risk to develop NASH.

Cytochrome P450 2E1 (CYP2E1) is up-regulated in patients with NASH while in

patients with ST is normal. Its activity has been associated with oxidative stress, insulin

resistance and hepatic lipid peroxidation 12

. In addition, it has been seen that several

genes that are important for the mitochondrial function are down-regulated in NASH

patients 3.

9

Association Studies

The development of common diseases results from complex interactions

between numerous environmental factors and variation of several genes, and,

therefore, it is very interesting identifying the associated variations to understand the

biology of those diseases 26

.

The Human Genome Project has deposited millions of Single Nucleotide

Polymorphisms (SNP) into public databases like dbSNP

(http://www.ncbi.nlm.nih.gov/sites/entrez) or International HapMap Project

(http://www.hapmap.org/) 27

. The goal of the international HapMap Project is to

determine the common patterns of DNA sequence variation in the human genome and

to make this information freely available in the public domain. The data base contains

a map of these patterns across the genome by determining genotypes, their

frequencies and the degree of variability in different populations 28

. The phase II

HapMap has characterized over 3.1 million of SNPs by genotyping 270 individuals from

four geographically diverse populations and includes 25-35% of common SNP variation

in the populations surveyed 29

. In addition, HapMap Project characterized the linkage

disequilibrium (LD) patterns of different population based in the obtained genotypes.

LD means a nonrandom relationship of alleles at two or more loci that is inherited as

one single block. It is possible to do an association study of a significant proportion of

the common variation of a large number of genes that occurs in regions of high LD

where it is not necessary to genotype all SNPs within an LD-block but just one or two

representatives of each region, which are called haplotype tag SNPs (htSNPs) 26, 30

.

When multiple markers in a chromosomal region are studied to assess the association

between this region and disease, a statistical analysis based on haplotypes may be

more informative than separate analyses of the individual markers 27

.

Samples used for association should be selected with care:

• Case and control groups should be of the same ethnical population,

because if different population are mixed-up their different genetic

background can drive to false marker association (population

stratification) 26

.

10

• The inclusion criteria for case and control selection should be very strict

according to their clinical parameters. The more phenotypical

information we have, the better.

The results obtained in a genetic association study should be validated by

testing the function of the associated genes within an in vivo system.

NAFLD association study at CIC bioGUNE

The present project is part of a study that is being carried out at CIC bioGUNE

for the association of genetic variations with NAFLD.

According to previous experimental studies (including knockout models,

transcriptomics, proteomics, and metabolomics), a list of candidate genes involved in

the pathogenesis of NASH was identified. Ninety two genes were considered according

to the following criteria: 62 genes were previously identified to be differentially

expressed in liver samples from patients with NASH and/or ST compared to controls 23

;

17 genes are involved in hepatic One-Carbone metabolism, compromising the

methylation and folate cycles; and 13 genes had been associated to liver injury.

A total of 3,072 htSNPs were selected within those aforementioned candidate

genes based on the information available at international HapMap Project for the

European and Asian Populations.

Among all the samples obtained from the collaborative hospitals (Principe de

Asturias Hospital, Madrid, Spain; Clinic Hospital, Barcelona, Spain; Hospital de

Galdakao, Galdakao, Spain) only those with certain diagnosis for ST and/orNASH after

biopsy were genotyped. DNA from control individuals was purchased from the DNA

bank of BIOEF Foundation (Sondika, Spain). The inclusion criteria for the controls were

absence of Insulin Resistance Syndrome (no traces of hyperglycemia, hypertension or

obesity), normal liver activity tested by measuring the levels of transaminases and

Body Mass Index (BMI) ≤30 kg/m2.

11

A total of 69 patients and 217 controls were successfully genotyped by Golden

Gate technology following Illumina Inc.’s protocols.

Aim of the study

The general objective of CIC bioGUNE project is to find some genetic

association with non-alcoholic hepatic disease that can be used as a non-invasive

diagnosis method, following a candidate-gene association approach

The specific aim of the present work is to analyze the results of the association

study (statistics) and to test the mRNA expression level of the resulting associated

genes in liver biopsies of diagnosed individuals.

12

MATERIALS AND METHODS

Association study

Data obtained by GoldenGate Assay were decoded and corrected in Genome

Studio (2008 (c) Illumina, Inc. 2003-2008) software. Only good quality markers were

considered for further analysis.

Obtained genotypes and allele frequencies were compared between ST/NASH

cases and controls using PLINK Software v. 1.05. The analysis was done using allelic

test of single-marker and multi-marker association including all individuals. The data

filtering criteria were minor allele frequency (MAF) ≥ 0.01 and Hardy-Weinberg

equilibrium (HWE) ≥ 0.001.

Calculation of r2 and Gabriel et al. LD-block estimation31

were analyzed in

Haploview v. 4.1 (MAF ≥ 0.01 and HWE ≥ 0.001).

Differential expression

Total-RNA extracts were obtained from liver biopsies of control individuals, ST

and NASH patients. Human Universal Reference RNA (HUR) of Clontech (Stratagene:

740000) was used as a positive control.

Retro-transcription (RT) of samples and HUR was done following this protocol:

• 275 ng of total-RNA, 1µl of Oligo (dT) 12-18 (500µg/ml), 1µl dNTP mix

(10mM) and sterile distilled water were added per tube

• Mixture was heated at 65ºC for 5 min and contents were collected by brief

centrifugation

• 4µl of 5x First-Strand Buffer, 24µl of 0.1 M DTT and 1µl of RNaseOUTTM

(40

units/µl) were added per tube

• Tubes were mixed gently and incubated at 42ºC for 2 min

13

• 1µl of SuperScriptTM

(200 units, Invitrogen, Cat. No. 10777-019) was added

per tube and mixed

• Tubes were incubated at 42ºC for 50 min and 70ºC for 15min in order to

inactivate the enzyme

Then, 17µl of each cDNA product were diluted in 300µl of water.

cDNA of HUR was diluted as follows in order to have a standard curve of each

quantitative PCR (qPCR) reaction:

20µl of original cDNA + 400µl of distilled water

200µl of previous dilution + 200 µl of distilled water

200µl of previous dilution + 200 µl of distilled water

200µl of previous dilution + 200 µl of distilled water

200µl of previous dilution + 200 µl of distilled water

cDNA of cases and controls was analyzed in iCycler Thermal Cycler with iCycler

iQ Module developed by Bio-Rad.

Primers for measuring the mRNA expression of Cytochrome P450, family 2,

subfamily E, polypeptide 1 (CYP2E1), Serine/threonine kinase 11 (STK11), Solute carrier

family 2 (facilitated glucose transporter), member 1 (SLC2A1), Asparaginase synthetase

(ASNS) and 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR) were

purchased from Qiagen as QuantiTec® Primer Assay numbers QT01669962,

QT01008980, QT00068957, QT00084546, and QT00072156 respectively. The mRNA

expression of Glyceraldehyde-3-phosphate dehydrogenase (GADPH) and acidic

ribosomal phosphoprotein (ARP), as housekeeping genes, was analyzed using in-house

sets of primers.

For the qPCR, reactions were set in triplicates, and standard curve dilutions

were analyzed in duplicates. All reactions were done in a total volume of 20µl including

the following reagents:

14

For QuantiTec® Primer Assay primer sets (per tube):

1µl of EvaGreen 20x (Biotisem: 31000)

10µl of Hot Start MasterMix 2x (Metabion, mi-E8011)

2µl of Forward and Reverse primers 10X

5µl of cDNA sample

2µl of distilled water

For housekeeping genes primer sets (per tube):

1µl of EvaGreen 20x (Biotisem: 31000)

10µl of Hot Start MasterMix 2x (Metabion, mi-E8011)

0.54 µl of Forward primer 100mM

0.54 µl of Reverse primer 100mM

5µl of cDNA sample

2.92µl of distilled water

An automatic pipetting system (Eppendorff epMottion 5070) was used in order

to increase the reproducibility of the assays.The amplification cycling conditions for

the qPCR were:

• For ASNS, CYP2E1, STK11 and MTR: 40 cycles of 15s at 94ºC, 30s at 55ºC and

30s at 72ºC.

• For SLC2A1: 40 cycles of 15s at 94ºC, 30s at 61ºC and 30s at 72ºC.

• For housekeeping genes (GADPH and ARP): 40 cycles of 15s at 94ºC, 30s at 60ºC

and 30s at 72ºC.

Differential relative expression of each tested gene was estimated based on

the obtained Ct values, by the delta-delta-Ct method 32

.

15

RESULTS

Association Study

A total of 1536 SNPs were successfully genotyped in all studied individuals (69

cases and 217 controls).

Eleven SNPs showed to be significantly associated (p < 10-4

) with NAFLD for the

single-marker allelic test. These significant SNPs were located in the following genes:

Cytochrome P450, family 2, subfamily E, polypeptide 1 (CYP2E1), Smg-7 homolog,

nonsense mediated mRNA decay factor (SMG7), Solute carrier family 2 (facilitated

glucose transporter), member 1 (SLC2A1), 5-methyltetrahydrofolate-homocysteine

methyltransferase (MTR) and Serine/threonine kinase 11 (STK11) (see table 1). Seven

out of the eleven associated SNPs are located in SLC2A1 and all of them are in high LD

(figure 2).

CHR SNP gene A1 F_A F_U A2 CHISQ P Position

10 rs28969387 CYP2E1 A 0.063 0 T 27.52 1.56E-07 Exon 9

1 rs1044879 SMG7 G 0.627 0.406 C 17.43 2.98E-05 3'UTR

1 rs1770810 SLC2A1 A 0.278 0.127 G 16.4 5.13E-05 Intron

1 rs841856 SLC2A1 A 0.271 0.124 C 15.39 8.73E-05 Intron

1 rs3754255 MTR A 0.258 0.442 G 13.59 2.28E-04 Intron

19 rs7259033 STK11 G 0.33 0.521 C 12.93 3.24E-04 Intron

1 rs841858 SLC2A1 A 0.234 0.113 C 12.07 5.12E-04 Intron

1 rs4658 SLC2A1 G 0.286 0.152 C 11.71 6.23E-04 3'UTR

1 rs841848 SLC2A1 A 0.281 0.145 G 11.57 6.69E-04 Intron

1 rs3754223 SLC2A1 A 0.273 0.145 T 11.41 7.29E-04 Intron

1 rs2229682 SLC2A1 A 0.265 0.141 G 11 9.12E-04 Exon 6

Table 1. Results of the single-marker analysis. Only significant (p < 10-4

) SNPs are shown.

Multi-marker association was analyzed by sliding window from 2 to 10 SNPs-

windows, and p < 10-4

value was used as a threshold. This test revealed one group of 3

SNPs located in an intronic region of Asparaginase synthetase (ASNS) gene, which

resulted to be in total LD in the studied population (see table 2 and figure 2).

16

LOCUS gene start gene end HAPLOTYPE F_A F_U P Sliding windows

WIN1246 ASNS ASNS TAG 0.1176 0.2857 7.09E-05 rs7781469 rs4727377 rs7810919

Table 2. Results of the sliding-windows analysis. Only significant (p < 10-4

) windows are shown.

Figure 2. a) LD values (r2) of SLC2A1 (red color without number means 100% of LD) and b) of ASNS

significantly associated SNPs.

Differential expression

Differential expression analysis was done for all the genes represented by

several (more than one) associated SNPs with a p<10-2

(3 SNPs ofCYP2E1; 11 SNPs of

SLC2A1; 12 SNPs of MTR; 3 SNPs of STK11; 11 SNPs of ASNS. Therefore, although

rs1044879, which is a proxy of neutrophil cytosolic factor 2 (NCF2), was one of the

most significant SNPs (p=2.98 x 10-5

), it was not considered for subsequent analysis

because NCF2 was represented uniquely by that polymorphism.

RT-qPCR for the 5 associated genes (CYP2E1, STK11, SLC2A1 ASNS MTR) and 2

housekeeping genes (GADPH and ARP) was carried out using the RNA extracted from

liver biopsies of a total of 5 ST and 5 NASH patients and 6 controls.

17

In figure 3 we represent the ratio (Cthousekeeping/Ct target gene) based on the media

Ct values obtained for each group of samples (ST, NASH and controls). These results

show that the Ct values obtained for both housekeeping genes (GADPH and ARP) were

very similar. Consequently, a media value of the 2 housekeeping Cts was used to

normalize the expression levels of the targeted genes (Figure 4).

Figure 3. mRNA expression ratio (Ct housekeeping / Ct target gene) of all the studied genes.

18

Figure 4. Results of differential expression analysis obtained by delta-delta-Ct method 32

.

The differential expression analysis showed that CYP2E1 has similar regulation

in controls and in ST, while it is up-regulated in NASH. We can also observe that ASNS,

STK11 and MTR are up-regulated in NASH. However, ASNS is down-regulated in ST, and

STK11 and MTR have similar expression in ST and controls. Additionally, SLC2A1 is

down-regulated in ST and NASH patients.

19

DISCUSSION

In association studies the possibility of false positive findings arises from a

combination of the characterization of small sample sizes, the poor description of case

and control samples, and the overestimation of the risks of genetic effects. In the

present study, the quality of the patient biological material was low and that is why

we got very small genotyping call rate of those samples, and therefore, the proportion

of the total genotyping success was very reduced (about 50%).

Among the obtained results, it is outstanding that the associated SNP located in

exon 9 of CYP2E1 (rs28969387) is a non-synonymous SNP that produces an

aminoacidic change (H457L). Structural changes or loss of function of this protein could

be related to this or other aminoacidic changes. We have seen that expression of

CYP2E1 is significantly up-regulated in NASH samples, however, it does not seem to be

altered at all in ST patients (see figure 3). Those results agree with previous studies 12

in which it is suggested that CYP2E1 could initiate oxidative stress leading to ST to

NASH by production of reactive oxygen species (ROS) 7, 12

. Blocking CYP2E1 activity

prevents necroinflammatory changes in rats 4. One study by Jörn et al.

33 based in

overexpression of CYP2E1 in a hepatocyte cell line, reveals that increased CYP2E1

expression results in the down-regulation of insulin signaling, potentially contributing

to the insulin resistance associated with NAFLD. Factors related with NAFLD such as

ethanol exposure, a high-lipid and high-carbohydrate diet, fasting lost weigh and

diabetes, can increase CYP2E1 levels 4.

As shown in this study and in previous works 23

, SLC2A1 is down-regulated in ST

and NASH patients (see figure 4). However, SLC2A1 has been found to be up-regulated

in obesity and diabetes patients 34

, both diseases associated with NAFLD. The proper

understanding of this result could be very interesting since the oposite regulation of

SLC2A1 in ST/NASH compared to obesity/diabetes could suggest the potentiality of this

gene as a genetic marker to identify NAFLD.

When ST progresses to NASH, the insulin resistance occurs 4 and in this

conditions, Cui et al.35

found over-expression of ASNS and, in addition, Sreekumar et

al. 25

found general up-regulation in genes involved in protein synthesis in NASH

20

patients. Those data agree with our results, in which we found ASNS down-regulated in

ST while it is up-regulated in NASH (see figure 3).

MTR enzyme has been shown down-regulated in alcoholic liver diseases 36, 37

due probably to the direct effect of the alcohol in the regulation of this enzyme. In the

contrary, we saw that MTR was more expressed in the studied ST/NASH liver biopsies

than in the controls (Figure 3). This result could be understood if we consider that

NAFLD patients may drink from 0 to 40 gr of ethanol per week and we do not have this

information for the studied samples.

Nakau et al. 38

suggest that lack of STK11 activity is a mechanism for HCC

development. In addition, STK11 plays a key role in the p53-dependent apoptosis 38

,

over-expression of STK11 in tumor cell lines results in cell cycle arrests 38

and up-

regulation of STK11 could delay progression from NASH to HCC. What is more, STK11

phospholyration is related with an increase in expression of fatty acid synthase (FAS)

39. Therefore, the up-regulation of this enzyme detected in the present study could be

the cause of an increasing lipid accumulation within hepatocytes in ST and NASH

patients.

It will be interesting to get the associated SNPs genotypes for the liver biopsies

where regulation of the corresponding genes was tested, in order to correlate

genotype to phenotype.

The obtained results open some new perspectives into the NAFLD research. It

will be required to describe the implications of the mentioned genes in the

pathogenesis of NAFLD, and hopefully, the resulting knowledge will even reveal some

new therapeutic targets.

21

CONCLUSIONS

The present project demonstrates for the first time that SLC2A1 is associated

with NAFLD, since 7 SNPs located within this gene showed significant association (p <

10-4

).

Regulation of SLC2A1 in ST and NASH is opposite than in obese people. This

means that SLC2A1 could be a potential specific marker of NAFLD.

The regulation of the studied genes is always higher in NASH samples than in ST

ones. This could suggest that metabolism in NASH patients is more unbalanced than in

ST.

OUTLOOK

This project will continue first validating significant SNPs in other cohort of

case/control samples. Afterwards, functional studies of associated genes should be

carried out by either silencing down-regulated genes or over-expressing up-regulated

ones, followed by the analysis of the consequences of these regulations at genome-

wide level. The analysis of the transcriptome expression of the chosen in-vivo models

will be examined using high-throughput arrays.

Special efforts will be carried out to test that the regulation of associated genes

seen in liver biopsies is somehow reflected in the blood. This will be crucial to develop

non-invasive diagnostic tools.

AKNOWLEDGMENTS

This work was supported by a grant of Fundacion La caixa (obra social, number

BM06-227-0) coordinated by Mari Luz Martínez-Chantar. I would like to thank Ana M.

Aransay’s and Mari Luz Martinez-Chantar’s research groups for teaching me the

background, the hypothesis and the appropriate techniques that made this work

possible.

22

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