THE ROLE OF TM6SF2, MBOAT7, SERPINA1 AND HSD17B13 GENETIC VARIANTS IN LIVER FIBROSIS AND CIRRHOSIS

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES

Viktorija Basytė-Bacevičė

THE ROLE OF TM6SF2, MBOAT7,

SERPINA1 AND HSD17B13 GENETIC

VARIANTS IN LIVER FIBROSIS AND

CIRRHOSIS

Doctoral Dissertation Medical and Health Sciences,

Medicine (M 001)

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Dissertation has been prepared at the Department of Gastroenterology of Medical Academy of Lithuanian University of Health Sciences during the period of 2017–2020.

Dissertation is defended extramurally. Scientific Consultant

Prof. Dr. Juozas Kupčinskas (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001).

Dissertation is defended at the Medical Research Council of the Lithuanian University of Health Sciences:

Chairperson

Prof. Habil. Dr. Virgilijus Ulozas (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001).

Members:

Prof. Dr. Antanas Mickevičius (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001);

Prof. Dr. Rasa Ugenskienė (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine – M 001);

Prof. Dr. Ligita Jančorienė (Vilnius University, Medical and Health Sciences, Medicine – M 001);

Prof. Dr. Mara Pilmane (Riga Stradins University, Medical and Health Sciences, Medicine – M 001).

Dissertation will be defended at the open session of the Medical Research Council of Lithuanian University of Health Sciences on 11th_{February 2021}

at 2 p.m. in the auditorium No. A-204 of the Centre for the Advanced Pharmaceutical and Health Technologies of Lithuanian University of Health Sciences.

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LIETUVOS SVEIKATOS MOKSLŲ UNIVERSITETAS

Viktorija Basytė-Bacevičė

TM6SF2, MBOAT7, SERPINA1 IR

HSD17B13 GENŲ POLIMORFIZMŲ

SĄSAJOS SU KEPENŲ FIBROZE

IR CIROZE

Daktaro disertacija Medicinos ir sveikatos mokslai,

medicina (M 001)

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Disertacija rengta 2017–2020 metais Lietuvos sveikatos mokslų universitete Medicinos akademijos Gastroenterologijos klinikoje.

Disertacija ginama eksternu.

Mokslinis konsultantas

prof. dr. Juozas Kupčinskas (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001).

Disertacija ginama Lietuvos sveikatos mokslų universiteto medicinos mokslo krypties taryboje:

Pirmininkas

prof. habil. dr. Virgilijus Ulozas (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001).

Nariai:

prof. dr. Antanas Mickevičius (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001);

prof. dr. Rasa Ugenskienė (Lietuvos sveikatos mokslų universitetas, medicinos ir sveikatos mokslai, medicina – M 001);

prof. dr. Ligita Jančorienė (Vilniaus universitetas, medicinos ir sveikatos mokslai, medicina – M 001);

prof. dr. Mara Pilmane (Rygos Stradinio universitetas, medicinos ir sveikatos mokslai, medicina – M 001).

Disertacija ginama viešame Lietuvos sveikatos mokslų universiteto Medi-cinos mokslo krypties tarybos posėdyje 2021 m. vasario 11 d. 14 val. Lietuvos sveikatos mokslų universiteto ligoninės Naujausių farmacijos ir sveikatos technologijų centro A-204 auditorijoje.

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ABBREVIATONS

AAT – alpha-1 antitrypsin

AATD – alpha-1 antitrypsin deficiency a-SMA – alpha-smooth muscle actin aHSCs – activated hepatic stellate cells ALD – alcoholic liver disease

ALT – alanine aminotransaminase ANOVA – analysis of variance

aOR – adjusted odds ratio C – concentration

CCL2 – C-C motif chemokine CI – confidence interval ECM – extracellular matrix

EMT – epithelial-mesenchymal transition ER – endoplasmic reticulum

gDNA – genomic DNA

GWAS – genome-wide association studies HBV – hepatitis B virus

HCC – hepatocellular carcinoma HCV – hepatitis C virus

HSCs – hepatic stellate cells

HSD17B13 – 17-beta-hydroxysteroid dehydrogenase 13 ILC – innate lymphoid cells

ILs – interleukins INF-α – interferon-α

LysoPI – lysophosphatidylinositol

MBOAT7 – membrane-bound O acyltransferase domain containing protein 7

miRNA – microRNAs

MMPs – matrix metalloproteinases MSCs – mesenchymal stem cells NA – not available

NAFLD – non-alcoholic fatty liver disease NASH – non-alcoholic steatohepatitis NGS – next-generation sequencing NTC – no template controls OD260 – optical density at 260 nm OR – odds ratio

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PCR – polymerase chain reaction PDGF – platelet-derived growth factor

PNPLA3 – patatin-like phospholipase domain containing 3 PSC – primary sclerosing cholangitis

RT-PCR – real-time polymerase chain reaction SD – standard deviation

SECs – sinusoidal endothelial cells

SERPINA1 – serine protease inhibitor, group A, member 1 SNP – single nucleotide polymorphism

TM6SF2 – transmembrane 6 superfamilymember 2 TGF- β – tumor growth factor-β

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INTRODUCTION

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(PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2) and membrane-bound O-acyltransferase domain-containing protein 7 (MBOAT7) genes are significant in the pathogenesis of chronic liver diseases (12,14–16). More recently, the significance of heterozygous carriage of serine protease inhibitor, group A, member 1 (SERPINA1) gene Pi*Z and Pi*S variants in liver cirrhosis, caused by NAFLD and alcohol misusers was established in a study of Western European population (17). Another recent large-scale study used exome sequencing method in four independent cohorts to identify associations between 17-beta-hydroxysteroid dehydrogenase 13 (HSD17B13) variants and protective trait against chronic liver injury (18).

Although worldwide GWAS studies have identified a number of significant associations between gene variants and the development of liver fibrosis and cirrhosis, these findings generally need to be confirmed (replicated) in individual populations. In this work, we chose to investigate the associations of less studied TM6SF2, MBOAT7, SERPINA1 and

HSD17B13 gene polymorphisms with liver fibrosis and cirrhosis in patients

from Lithuania.

The aim of the study

To investigate the role of genetic variants in TM6SF2, MBOAT7,

SERPINA1 and HSD17B13 genes in patients with liver fibrosis and liver

cirrhosis.

The objectives of the study

1. To determine the association between TM6SF2 variant rs58542926,

MBOAT7 variant rs641738 and liver fibrosis and cirrhosis.

2. To evaluate the association of hepatic fibrosis and liver cirrhosis risk genotype in PNPLA3 gene and TM6SF2 rs58542926 and MBOAT7 rs641738 risk variants.

3. To investigate the association between SERPINA1 (Pi*Z rs28929474 and Pi*S rs17580), HSD17B13 rs72613567 variants and the risk of hepatic fibrosis or liver cirrhosis of different aetiology.

Novelty of the study

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multiple cofounding factors, affecting disease course (alcohol consumption, diet, lifestyle, etc.) (6–8). The aforementioned GWAS provide an opportunity to identify causative gene variants that affect the pathogenesis of complex diseases. Each gene variant has a small effect in isolation, but their interactions in different types of liver injury and the impact of environmental or individual risk factors confers an overall predisposition to clinical case. Identification of different underlying gene variants and their functional role in progression of chronic liver disease helps to better understand the pathogenesis of the disease potentially identify new therapeutic targets (12). To date, several genetic risk loci have been linked with liver fibrosis and cirrhosis (14,19–21). In first part of this study, we estimated the association between SNPs in TM6SF2 (rs58542926), MBOAT7 (rs641738) and the risk of hepatic fibrosis or liver cirrhosis of different aetiology. We also determined whether TM6SF2 and MBOAT7 SNPs modify the effects of the PNPLA3 rs738409 risk variant on the development of hepatic fibrosis and liver cirrhosis, using results of previous research of the same cohort (21). The second part was dedicated to evaluate SERPINA1 (rs2892947, rs17580) association with chronic liver disease and possible HSD17B13 rs10433937 protective trait of hepatic fibrosis and cirrhosis. This is the first study in a Lithuanian population that evaluated TM6SF2 (rs58542926) and MBOAT7 (rs641738) polymorphisms impact on chronic liver injury and replicated the findings of a large case-control study of Western European population on

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1. REVIEW OF LITERATURE

1.1. Liver fibrosis pathogenesis

Liver consists of parenchymal cells known as hepatocytes. Between the plates of hepatocytes are hepatic microvascular units, called sinusoids. The walls of sinusoids are lined by sinusoidal endothelial cells, Kuppfer cells and hepatic stellate cells. Sinusoids are separated from hepatocytes by subendo-thelial space of Disse. Normal liver architecture is disrupted by the presence of liver fibrosis. The type of fibrosis progression depends on various factors, such as alcohol consumption, non-alcoholic steatohepatitis (NASH), HBV, HCV, autoimmune hepatitis and cholestatic liver diseases (primary biliary cholangitis, primary sclerosing cholangitis) (22). The common effect of all of these factors on the liver is the generation of a chronic inflammation resulting in abnormal wound healing response. Chronic viral hepatitis results in bridging fibrosis, which is characterised by interface hepatitis, portal-central vein bridging necrosis and the formation of portal-central fibrotic septa (23). Alcohol-induced injury and NAFLD typically cause perisinusoidal or pericellular fibrosis (23). Fibrosis secondary to cholestatic diseases develops a portal to portal pattern and results in pre-sinusoidal resistance to portal flow (23). Different cell types and mediators participate to this injury. The generation of fibrotic response in the liver gives the rise to the accumulation of extracellular matrix (ECM) in the space of Disse (24). ECM deposits distort the hepatic architecture and lead to capillarization of the sinusoids that is described as the impairment of metabolic exchange between hepatocytes and portal venous flow (25,26).

1.1.1. Origin of fibrogenesis in liver

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migrate and originate myofibroblasts through a process called epithelial-mesenchymal transition (EMT) (32). This process significantly enhances the percentage of myofibroblasts in the fibrotic liver. Bone marrow-derived cells, like mesenchymal stem cells (MSCs) represent a substantial fraction of the total fibrogenic population in a chronic liver injury. MSCs are multipotent cells that differentiate into osteoblasts, adipocytes, myocytes and chondro-cytes. Recent study reported that MSCs may protect the fibrotic tissue from more advanced disease (33). Fibrocytes express fibronectin, vimentin, collagen type 1 together with hematopoietic cell markers like CD45, MHCII, CD34, CD11b, Gr1, CD86, CCR2, Ly6c, CD54, CD80, CCR1, CCR7, and CCR5 (34, 35) Recruitment of fibrocytes and expression of alpha-smooth muscle actin (a-SMA) are triggered in cholestatic and carbon tetrachloride (CCl4)-induced liver injury, which contributes to scar formation in liver. Endogenous portal fibroblasts are localised underneath the bile duct epithelium also give rise to myofibroblasts in the cholestatic liver disease (32). During biliary fibrosis, portal fibroblasts start expressing a-SMA and produce EMC (36-38). Another cell group, that is important in liver capsule fibrosis, are mesothelial cells. Mesothelial cells are similar to endothelial cells and can be found in internal organs and serous cavities. Studies suggest that HSCs and myofibroblasts originate from mesothelial cells (39, 40).

1.1.2. Mechanisms of liver fibrosis

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Fig. 1.1.2.1. Pathogenesis of liver fibrosis ILC,innate lymphoid cells; PDGF, platelet-derived growth factor; VEGF, vascular endothelial growth factor; ECM, extracellular matrix;

HSC, Hepatic stellate cells (Dhar et al).

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1.2. Liver cirrhosis pathogenesis

The onset of liver fibrosis is usually insidious and related morbidity and mortality occur after the development liver cirrhosis. Liver cirrhosis is the final pathological result of various chronic liver diseases. The main aetio-logical factors of cirrhosis are alcohol, chronic HBV and HCV infections, NAFLD and NASH. Other causes include inherited diseases, such as hemo-chromatosis and Wilson’s disease (57-61), primary biliary cholangitis, primary sclerosing cholangitis (62-65) and autoimmune hepatitis (61,66). Some cases are idiopathic or cryptogenic. Fibrosis as a precursor of cirrhosis is a crucial pathological process in the evolution of all chronic liver diseases to cirrhosis (67). To date, strategies to treat liver cirrhosis still lacks effect, partly because of poor understanding of the molecular mechanisms leading to cirrhosis. Poor treatment options lead to complications, such as ascites, renal failure, hepatic encephalopathy and variceal bleeding (52). Better under-standing would facilitate the development of more effective treatment options.

1.2.1. Cell types that contribute to liver cirrhosis

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infection (76). Kupffer cells mediate hepatic inflammation and aggravate liver injury (77). In addition, Kupffer cells release thromboxane A2 and increase portal pressure in cirrhotic liver (78). Hepatocytes are the primary liver parenchymal cells, and play complicated roles in fibrosis and cirrhosis. Hepatotoxic agents, such as hepatitis viruses, alcohol and bile acids primary affect hepatocytes (27). Damaged hepatocytes release reactive oxygen species, fibrogenic mediators, induce activation of HSCs, and stimulate the fibrogenic actions of myofibroblasts (27). This contributes to scar tissue inflammation, fibrogenesis, and development of cirrhosis. Chronic HCV infection can impair hepatocellular function and limit hepatic regeneration (79, 80). Hepatocytes are the major sources of matrix metalloproteinases (MMP-2, MMP-3 and MMP-13) and tissue inhibitors of matrix metallo-proteinases (TIMP-1 and TIMP-2), all of which are involved in the patho-genesis of liver cirrhosis in CCl4- induced liver cirrhosis in rats (81). In the last fibrotic stage or cirrhosis, damaged hepatocytes start to release high amounts of TGF-β1, which further exacerbates hepatic fibrogenesis (82). Vascular and cell architecture changes in liver are depicted in Fig. 1.2.1.1.

Fig. 1.2.1.1. Vascular and architectural changes in liver A – healthy liver. Terminal portal tract blood runs through hepatic sinusoids where fenestrated sinusoidal endothelia that rest on loose connective tissue (space of Disse) allow

for extensive metabolic exchange with the lobular hepatocytes.

B – cirrhotic liver. Activated myofibroblasts proliferate and produce excess extracellular matrix. This event leads to fibrous portal-tract expansion, central-vein fibrosis and

capillarisation of the sinusoids, characterised by loss of endothelial fenestrations, congestion of the space of Disse with ECM, disrupted blood flow and loss of metabolic

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1.2.2. Role of cytokines and interleukins in liver cirrhosis

As in liver fibrosis, PDGF plays important role in liver remodeling and progression towards liver cirrhosis. PDGF activates corresponding signal molecules and transcription factors, leading to the activation of its downstream target genes and activation of HSCs (84, 85). TGF-β is the strongest known inducer of fibrogenesis in hepatic fibrosis (86, 87). The expression level of TGF-β1 is increased in fibrotic liver and reaches a maximum at cirrhosis (82). The primary effect of TGF-β1 is stimulation and activation of HSCs. This process results in TGF-β1 autocrine loop in activated HSCs and to the progression of liver fibrosis to cirrhosis (88, 89). In addition, β1 inhibits DNA synthesis and induces apoptosis of hepatocytes. TGF-β1-induced apoptosis is thought to be responsible for tissue loss and decrease in liver size in cirrhosis patients (86). TNF-α is mainly produced by monocyte, macrophage, HSCs, and Kupffer cells. It has proinflammatory activities and cytotoxic effects. TNF-α plays an important role in the activation of HSCs and synthesis of ECM (90, 91). However, the effects of TNF-α on HSCs and fibrosis are complicated and controversial, because TNF-α could induce apoptosis in HSCs (92). Interleukins (ILs) are a group of cytokines initially found to be expressed by leukocytes, but later on were shown to be produced by a wide variety of cells, such as CD4 T lymphocytes, monocytes, macrophages, and endothelial cells. Kupffer and SECs can rapidly produce ILs in response to liver tissue damage. IL-1 can directly activate HSCs and stimulate them, resulting in liver fibrogenesis (93,94). High blood levels of IL-22 and IL-33 are observed in cirrhotic patiens (95-97).

1.2.3. miRNAS in liver cirrhosis

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1.3. Genetic predisposition in liver injury

Huge advances in past decades have been made in understanding the genetic background of live diseases. The first mapped and cloned genes of monogenic liver diseases were the Wilson disease gene ATP7B and haemochromatosis gene HFE (103-105). These discoveries helped to fully characterise copper and hepatic iron metabolism and its regulators in liver. Hereditary haemochromatosis is most often caused by a p.C282Y mutation, while Wilson disease is heterogenic disease with more that 500 mutations in

ATP7B gene (106,107). In contrast to monogenic diseases, a large ensemble

of gene variants affects polygenic liver diseases. The advancements in genotyping technology has led to the introduction of GWAS, which have been applied to various liver diseases in recent years. GWAS is unique in a way that no prior hypothesis is needed to start a study.

1.4. Genome-wide association studies

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associations adjusted P value of <5x10-8_{is used (13,112). To validate GWAS}

results it is important to replicate these findings in different cohorts. A significant number of GWAS have already been conducted in the field of hepatology. The first GWAS was performed in 2007 to investigate the relationship between the hepatic cholesterol hemitransporter gene ABCG5/G8 variant and gallbladder stones (113). GWAS has also been used to identify several risk gene variants in primary biliary cholangitis (PBC) (110), primary sclerosing cholangitis (PSC) (114, 115) and autoimmune hepatitis (116). GWAS contributed by identifying single nucleotide polymorphism located in

interleukin-28B (rs12979860), which is strongly associated with positive

interferon-α (INF-α) treatment response and spontaneous HCV clearance in patients with chronic HCV infection (117). Subsequent GWAS linked variant in PNPLA3 (rs738409), TM6SF2 (rs5854296) and MBOAT7 (rs641738) with the development of alcoholic liver disease and non-alcoholic steatohepatitis (113). More recently novel genetic risk factors in genes SERPINA1 (rs2892947, rs17580) and HSD17B13 (rs10433937) were determined (17,18).

Table 1.3.1. Phases in the initiation and analysis of a genome-wide asso-ciation study

Sample panel building

Cases and healthy controls of same ethnicity

Enrichment with early onset cases and/or familiar cases Keep variability in phenotype at a minimum

Establish replication cohort Genotyping

Sample preparation (DNA extraction, calibration) Genotyping chip

Genetic coverage Initial quality control

Exclude samples failing specific measures Exclude samples with low call-rate Exclude SNPs with low genotyping rate

Exclude SNPs with a low minor allele frequency and out of Hardy-Weinberg equilibrium

Statistical analysis

Imputation of non-genotyped SNPs using HapMap Inclusion of covariates (e.g. gender, sex, smoking etc.)

Select 1-2 SNPs from each associated locus to take forward in replication Replication

Genotype

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Fig. 1.3.1. Schematic Manhattan plot presenting the results

of a hypothetical genome-wide association study.

The horizontal axis shows the relative lengths of the chromosomes. Each dot represents a different single nucleotide polymorphism that was genotyped successfully in the study.

The vertical axis shows the P values for each single nucleotide polymorphism on a logarithmic scale. The dashed red line indicates the threshold for genome-wide statistical

significance, which takes into account the effects of multiple testing. In this example, only one predominating locus exists in which the strength of the association exceeds

the threshold (Krawczyk et al (118)).

1.5. TM6SF2 gene in liver diseases

TM6SF2 gene is localised in chromosome 19 and is involved in the

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Study by Coppola et al. showed that TM6SF2 risk variant is associated with severe liver steatosis in patients infected with chronic HCV, however association between TM6SF2 and severe liver necroinflammation or fibrosis was not found (125). Milano et al. used larger cohort of 819 patients with chronic HCV infection and showed that rs58542926 variant was also relevant in developing severe fibrosis in individuals with chronic HCV as well as had an impact on developing liver steatosis (126). Sookoian and colleagues observed that TM6SF2 risk allele has only modest effect on NAFLD, suggesting that carriers of risk allele are slightly more likely to develop NAFLD (127). Moreover, in Japanese cohort with biopsy-proven NAFLD,

TM6SF2 risk genotype had no impact on histological liver injury (128). Study

performed by Eslam et al. used a large cohort of Caucasians with NAFLD, chronic HCV or HBV infections and revealed that rs58542926 variant has more influence on the serum metabolic profile and predispose hepatic steatosis, rather than acts directly on liver inflammation and fibrosis (129). In another cohort of patients with chronic HCV or NAFLD no link between the

TM6SF2 risk variant and hepatic fibrosis was found (130). Another recent

study showed that TM6SF2 rs58542926 do not predispose to liver injury in patients with primary sclerosing cholangitis (131). The role of TM6SF2 rs58542926 chronic liver disease have been summarized in several meta-analyses (132-134). Hypothesizing that this SNP might contribute to the development of liver damage in other chronic liver diseases, we genotyped

TM6SF2 rs58542926 in our cohort of patients with liver fibrosis and cirrhosis.

1.6. MBOAT7 gene in liver diseases

MBOAT7 family comprises 11 genes that are involved in variety of

biological processes. MBOAT7 is expressed in many human tissues, including liver, and is attached to endoplasmic reticulum, mitochondria-associated membranes and lipid droplets. MBOAT7 catalyses the transfer of acyl-CoA to lysophosphatidylinositol (LysoPI) and has a preference for arachidonoyl-CoA. This might be one of the mechanisms by which pro-inflammatory free arachidonic acid and eicosanoid levels are regulated. Increased levels of arachidonic acid lead to release of prostaglandins, leukotrienes and inflammation. A recent GWAS by Buch et al. confirmed MBOAT7 rs641738 risk variant link to alcohol induced liver injury (113). Following study by

Mancina et al. found association between MBOAT7 risk variant and entire

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elevated hepatic fat content (135). Following study confirmed MBOAT7 and risk of liver fibrosis, but not with hepatic steatosis (12). A study by Thabet and colleagues showed that MBOAT7 rs641738 is a novel risk variant for hepatic inflammation and fibrosis in HCV infected patients with risk allele T (16). However, in cohort of Mediterranean patients no association between

MBOAT7 risk allele and progression of liver disease in patients with chronic

HCV and HBV infections were found (136). We genotyped MBOAT7 rs641738 SNP in our cohort of patients with different aetiology chronic liver fibrosis and cirrhosis.

1.7. SERPINA1 gene in liver diseases

SERPINA1, also named as human alpha-1 antitrypsin (AAT), is a water

soluble, tissue-diffusible, circulating glycoprotein. AAT is mainly synthe-sized by hepatocytes. AAT is a major inhibitor of leucocyte elastase, proteinase-3 and bacterial serine proteases and also has anti-inflammatory and immunomodulatory effect (137). SERPINA1 gene has two alleles, which are transmitted by autosomal codominant Mendelian inheritance. The presence of SERPINA1 variants lead to decreased serum AAT concentrations and cause systemic disease, which affects lungs and liver (138). Most prevalent normal alleles are named M and are present in 85-90% of individuals, while most prevalent pathologic alleles are termed S and Z. Genotypes are described as combinations of these alleles: MM (normal genotype), MS, SS, MZ, SZ and ZZ (five deficiency genotypes). The un-conventional nomenclature of SERPINA1 alleles is based on electrophoretic protein variants that were identified long before the gene SERPINA1 was known. Alleles were named with the prefix PI* (protease inhibitor*). Most relevant variants are Pi*Z (rs28929474) and Pi*S (rs17580). Pi*Z allele has prevalence of up to 8% in Northern Europe (139), while Pi*S is more common in Southern Europe with prevalence up to 20% (140,141). Approximately 120 million people worldwide carry at least one risk allele (139, 141).

1.7.1. Pi*Z allele

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aberrantly folded AAT. Misfolded AAT accumulates in the endoplasmic reticulum (ER) of hepatocytes, which causes cellular damage and toxicity (143). As a result, circulating AAT level in blood is reduced or undetectable, leading to inadequate proteinase inhibition, which damages the protective layer in the lungs. Recent study in a large cohort of Western European patients found that Pi*Z variant is a strong risk factor for cirrhosis in NAFLD and alcohol misuse, although clear mechanism how heterozygous AAT variants predispose liver damage is not known (17). SERPINA1 Pi*Z variant association with chronic liver disease was also confirmed in several previous studies. Fischer et al. analysed Pi*Z deposits in liver biopsies and concluded that carriage of single Pi*Z allele has increased chronic liver injury risk (144). These findings were supported by study of patients with excessive alcohol consumption (145). Study by Regev et al. found that in patients with HCV or NAFLD induced chronic liver injury heterozygous state for Pi*Z allele was associated with more severe disease course, often requiring liver trans-plantation (146). In another study of patients with Pi*Z or Pi*S mutations and chronic NAFLD, no link to severe liver damage was found; however novel association with A1AT mutations and increased sinusoidal iron accumulation risk was reported (147). Schaefer et al. concluded that genotype Pi*MZ is genetic risk factor for more advanced and decompensated liver cirrhosis and is associated with higher death and liver transplantation risk (148). However, study by Al-Jameil et al. could not establish association between hetero-zygous A1AT genotypes and liver cirrhosis (149). We tested if Pi*Z allele (rs28929474) has an impact on chronic liver fibrosis and cirrhosis of different aetiology in our cohort of patients.

1.7.2. Pi*S allele

The role of Pi*S variant in developing liver injury remains unclear, because the rate polymerisation of AAT in carriers of Pi*S allele is much lower than in Pi*Z mutation (139,150). This explains the absence of liver injury, however, there are a few studies, that investigated liver disease risk.

Strnad et al. reported borderline association with alcohol-related cirrhosis

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1.8. HSD17B13 gene in liver diseases

HSD17B13 dehydrogenases are group of enzymes that catalyses the

conversion between 17-keto- and 17-hydroxysteroids. To date, 15 HSD17B13 members have been identified. Most members of this family are involved in regulation of sex hormone metabolism (152). Other members are also important in fatty acid metabolism, cholesterol synthesis, bile acid production (153). HSD17B13 is expressed mainly in liver and located on the surface of liver lipid droplets (154-156). Previous studies showed this gene and its variants association with increased lipogenesis in mouse liver and cultured hepatocytes (156) and with histological features of NAFLD (157). Increasing interest in functional role of HSD17B13 gene variants has led to studies, which confirmed possible protective trait against chronic liver injury. Study by Pirola et al. found that HSD17B13 rs72613567 variant reduces the risk of NAFLD and progressive liver damage (158). A novel HSD17B13 rs72613567 link to reduced chronic liver disease risk, reduced progression from steatosis to steatohepatitis and decreased alanine aminotransferase (ALT) levels was reported (18). Another study revealed that HSD17B13 variant rs10433879 risk allele was associated with lower ALT results in hazardous drinkers (159). These findings were confirmed in large cohort of Danish population with high risk of fatty liver disease – ALT-lowering effect of HSD17B13 rs72613567 was determined and in prospective analysis HSD17B13 variant was asso-ciated with lower rates of liver-related mortality in the general population, and reduced liver-related mortality in patients with cirrhosis (160). Yang et

al. also confirmed HSD17B13 rs72613567 protective trait against

alcohol-induced liver injury and NAFLD (161). We used HSD17B13 rs10433879, that is in a close proxy to HSD17B13 rs72613567 (r2_{=0.96), for genotyping}

patients with hepatic injury in order to determine possible protective trait against chronic liver disease.

1.9. PNPLA3 gene in liver diseases

The PNPLA3 gene encodes transmembrane protein adiponutrin in humans which is highly expressed in the liver and adipose tissues. In humans,

PNPLA3, a 481-residue protein, is observed in various tissues but is mostly

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(lipase) activity against triglycerides and retinyl esters in hepatic stellate cells (164,165).

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2. METHODS

2.1. Ethics

The study was approved by the Lithuanian Bioethics Committee (Protocol No. 2/2008) and Kaunas Regional Ethics Committee of Biomedical Surveys (Protocol No. BE-10-2, approval date: 08 March 2011). All patients and controls gave their informed consent to take part in this study.

2.2. Design of the study 2.2.1. Patients

Our study included patients with chronic liver disease regardless of aetiology, who were recruited during the period 2012–2019 at the Department of Gastroenterology in Lithuanian University of Health Sciences Hospital. Patients with liver cirrhosis, patients with liver fibrosis and healthy control individuals were included in the study. The diagnosis and aetiology of chronic liver disease was confirmed by laboratory tests, clinical features, radiological imaging and liver biopsy. Alcohol induced liver injury was diagnosed when daily consumption of alcohol was >30g/20g/day for males/females, respectively, as confirmed by at least 1 family member of affected individual. The patients in liver fibrosis group underwent percutaneous liver biopsy and were included in the study if stages of fibrosis from 1 to 3 were documented by histological evaluation using Metavir score (167). All individuals that had fibrosis stage IV according to Metavir score were transferred to liver cirrhosis group. The patients in liver cirrhosis group underwent percutaneous or transjugular liver biopsy. For patients who had significant coagulation disorders, diagnosis of liver cirrhosis was confirmed by laboratory tests, clinical features and radiological imaging. The study consists of two parts. In the first part of our study 550 controls, 334 patients with liver cirrhosis and 128 patients with liver fibrosis were investigated to determine the association between TM6SF2 rs58542926 and MBOAT7 rs641738 SNPs and chronic liver injury. Additionally, we ran combined analysis of carriers of PNPLA3 rs738409 and TM6SF2 rs58542926 or MBOAT7 rs641738 risk genotypes. For this analysis, we used data from our previous study with the same cohort (21) and included 798 individuals (401 controls and 269 patients with liver cirrhosis and 128 patients with liver fibrosis). Second part of our study included 548 controls, 302 patients with liver cirrhosis and 127 patients with liver fibrosis in order to determine the association between SNPs in

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risk for liver fibrosis and liver cirrhosis. Fig. 2.2.1.1 illustrates number of individuals in different stages of the study. The numbers differ between groups because some of the DNA samples were lost, some have evaporated and DNA was no longer detectable. Control groups consisted of voluntary, unrelated Lithuanian blood donors from the National Blood Centre collected during the period 2008 to 2009. The genotyping was performed at the Institute for Digestive Research at Lithuanian University of Health Sciences.

Fig. 2.2.1.1. Number of individuals in different stages of the study

2.2.2. Deoxyribonucleic acid extraction

Patients’ blood samples were collected in EDTA-containing vacutainer tubes and stored at –80°C until DNA extraction procedure. Genomic DNA (gDNA) was isolated from whole blood mononuclear cells by using salting-out method. Samples were stored at −20°C until analysis.

Equipment:

1. Automatic pipettes “Eppendorf research” 100 μl – 1000 μl (Eppendorf AG, Germany).

2. Automatic dispenser “Ependorf Multipette plius” 500 μl – 10 ml (Eppendorf AG, Germany).

3. Centrifuge “Eppendorf Centrifuge 5424” (Eppendorf AG, Germany) 4. Mikrocentrifuge “Eppendorf Centrifuge 5810R” (Eppendorf AG,

Ger-many).

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6. Plastic centrifuge tubes with caps, 50 ml. 7. Eppendorf type tubes, 1.5 ml–2 ml.

8. Fume hood “ESCO AURSTREAM“ (Esco, Singapore). 9. Thimble 10 ul –100 ul, 100 ul –1000 ul.

10. Termomixer “Eppendorf Thermomixer comfort” (Eppendorf AG, Ger-many).

11. Mixer “IKA MS 3 basic” (IKA, USA). 12. Magnetic mixer MMS-3000 (Biosan, Latvia).

13. pH meter METTLER TOLEDO (FiveEasy, Germany). 14. Electronic scales KERN 440-35N (KERN, Germany).

Reagents:

1. Ammonium chloride (NH4Cl) M=53.49 g/mol (Carl Roth GmbH + Co, Germany).

2. Potassium bicarbonate (KHCO3) M=100.12 g/mol (Sigma-ALORICH Chemie GmbH, Germany).

3. Etilendiaminum tetraacetatatum (EDTA) (C10H16N2O8) M=292.25 g/mol (Carl Roth GmbH + Co,Germany).

4. Three-hydrogen chloride (Three HCl) (C4H11NO3HCl) M=157.6 g/mol (Carl Roth GmbH + Co, Germany).

5. Sodium chloride (NaCl) M=58.44 g/mol (Merck KGaA, Germany). 6. Proteinase K 934 u/ml, C=18.5 mg/ml (Fermentas, Lithuania). 7. 96% ir 70% ethanol (A.B. Stumbras, Lithuania).

8. Sodium lauryl sulfate (SDS) (C12H25NaO4S) M=288.38 g/mol 9. Three – EDTA buffer “Roti stoch” (Carl Roth GmbH + Co, Germany) 10. Caustic soda (NaOH) (Merck KGaA, Germany).

11. 99.9 % chloroform (Scharlau Chemie S.A., Spain DNA extraction protocol:

Day – 1

1. The blood is cooled down until 4°C temperature. The cooled blood is transfused to the centrifugal test-tube of 50 ml volume with a cap. 2. 20 ml Lysis I buffer (composition: 155 mM NH4Cl, 10 mM KHCO3,

1 mM EDTA, pH 7.4) is poured to the blood and the blood is incubated for 30 minutes at 4°C temperature.

3. The sample is centrifuged for 15 minutes at 4°C temperature using the 2500 g – 3000 g power.

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times or as many times as necessary in order for sediment and upper layer to lose red color.

5. 6 ml of Lysis II buffer is poured to each test-tube containing sediment from white blood cells and cellular membranes. The mixture is well mixed to get it homogeneous and then 400 μl 10% SDS solution and 30 μl proteinase K solution are added.

6. The test-tube is covered, well mixed and incubated in moving water bath for 16 hours at 37°C temperature.

Day – 2

1. The samples are incubated for 1 hour at 55°C temperature. 2 ml NaCl solution is added, well mixed and centrifuged at 3000 g speed for 15 minutes in the centrifuge at 16°C temperature.

2. 2 ml chloroform is added to the sample in the laminar of vertical air flow and then mixed. The sample is centrifuged at 3000 g speed for 20 minutes in the centrifuge at 16°C temperature.

3. The upper layer is transferred carefully to the clean 50 ml centrifugal test-tube. The upper layer is mixed with 20 ml of cold 96% isopropanol solution. The test-tube is rotated until DNA threads become evident. 4. The precipitated DNA is taken with the help of tip and moved to the

1.5 ml test-tube.

5. 1 ml of 70% ethanol solution is poured in order to rinse DNA. It is carefully shaken and centrifuged in the micro-centrifuge.

6. Ethanol is poured off carefully. The test-tube containing DNA sediment is carefully turned over, air dried until ethanol evaporated.

7. DNA is melted in 500 μl 1 × TE buffer.

8. DNA sample is kept in the refrigerator at 4°C temperature; it can be frozen for longer periods in the –20°C freeze.

2.2.3. Evaluation of DNA concentration and purity by spectrophotometer

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The following calculation was performed to determine the amount of DNA in the original sample:

CDNA = 50 μg/ml × optical density at 260 nm (OD260) × dilution factor. Equipment:

1. NanoDrop™ 2000 Spectrophotometers (Thermo Scientific™) 2. Vortex mixer “IKA MS 3 basic” (IKA, USA)

3. Automatic pipettes “Eppendorf research” 1 μl – 10 μl (Eppendorf AG, 4. Germany),

5. Pipette tips 10 μl – 100 μl Reagents:

1. 1 × TE buffer Roti stock (Carl Roth GmbH + Co, Germany) Method:

1. The upper and lower optical surfaces of the spectrophotometer sample retention system are cleaned by pipetting 2 to 3 µL of clean deionized water onto the lower optical surface.

2. The lever arm is closed, ensuring that the upper pedestal comes in contact with the deionized water. Then the lever arm is lifted and both surfaces are wiped off with clean, dry, lint-free lab wipe.

3. Vortexed DNA sample of 2 µL is dispensed onto the lower optical pedestal and the lever arm is closed. The sample needs to bridge the gap between the two optical surfaces.

4. The measurements are done by device software.

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Fig. 2.2.3.1. Spectrophotometer „Nanodrop 2000“

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2.2.4. Plate design

93 DNA samples were arranged in a 96 wells plate layout before geno-typing. Patients with the same diagnosis were kept on the same plate. Three empty wells (no template controls, NTC) were used for quality control, to reveal potential contaminations. Each plate was labeled with a unique plate name for database storage (Table 2.2.4.1).

Table 2.2.4.1. Plate layout. Wells A6, E2 and H12 were used as negative

controls

1 2 3 4 5 6

A CIR1098 CIR1099 CIR1100 CIR1102 CIR1103 NTC B CIR1110 CIR1111 CIR1112 CIR1113 CIR1114 CIR1115 C CIR1123 CIR1124 CIR1125 CIR1126 CIR1127 CIR1128 D CIR1135 CIR1136 CIR1137 CIR1138 CIR1139 CIR1140 E CIR1147 NTC CIR1148 CIR1149 CIR1150 CIR1151 F CIR1163 CIR1164 CIR1165 CIR1166 GAP0002 GAP0010 G GAP0060 GAP0062 GAP0069 GAP0077 GAP0078 GA0099 H GAP0182 GAP0183 GAP0239 GAP0263 FS225 FS237

7 8 9 10 11 12

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2.3. Genotyping

In this study TaqMan® genotyping method for TM6SF2 (rs58542926),

MBOAT7 (rs641738), SERPINA1 (rs28929474, rs17580) and HSD17B13

(rs72613567) was used. TaqMan® is a fluorescence-based genotyping assay (single-tube polymerase chain reaction (PCR) assay). TaqMan® includes two allele-specific dual-labeled oligonucleotide probes and a PCR primer pair for detection of specific SNP targets. TaqMan® probes are hydrolysis probes that are designed to increase the specificity of quantitative PCR. TaqMan® probes consist of a fluorophore covalently attached to the 5’-end of the oligo-nucleotide probe and a quencher at the 3’-end. Several different fluorophores (FAM™ - carboxyfluorescein or VIC®) and quenchers (TAMRA™ - 6-carboxytetramethylrhodamine, succinimidyl ester) are available. TaqMan® genotyping assays consist of pre-optimized PCR primer pairs and two probes for allelic discrimination and are used to amplify and detect specific alleles in genomic gDNA. The genotyping in this study was performed on 7500TM Fast real-time polymerase chain reaction (RT-PCR) system. All stages of the reaction were carried out in accordance with the manufacturer's protocols.

2.3.1. Genotyping procedure using RT-PCR Reagents

1. TaqMan® Universal PCR Master Mix (TaqMan® Universal PCR; Master Mix, No AmpErase® UNG).

2. Sigma® Water, BPC Grade 100 ml.

3. TE Buffer (“Roti stock 100×TE ready to use”) 1000 ml. 4. The sets of factory-validated primers and probes. Equipment

1. Real-time PCR thermocycler “Applied Biosystems 7500; Fast”. 2. Centrifuge “Eppendorf Centrifuge 5810 R”.

3. Vortex mixer “IKA Yellow line lab dancer vario”.

4. Vertical laminar Flow “Airstream Class II Biohazard Safety. Cabinet”.

5. Automatic pipettes “Eppendorf Research” 10–100 μl, 0.5–10 μl. 6. PCR plate “Microamps ™ Fast Optical 96-Well”, 0.1 ml.

7. PCR plate base “MicroAmp™ Splash Free 96-Well Base”.

8. Transparent adhesive film “MicroAmps ™ Optical Adhesive Film”. 9. Automatic pipettes tips 10 μl –100 μl, 0.1 μl –10 μl.

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Method

Reaction mixture is prepared according to the protocol presented in Table 2.3.1.1 (5 μl/sample) and divided in 96-well PCR plate. 1 μl/sample of test DNA is added (TE Buffer is used to dilute DNR to concentration of 10 ng/μl).

Table 2.3.1.1. Preparation of the reaction mix

Reagents Volume (μl) for 1 reaction

TaqManUniversal PCR Master Mix 3.25

Primer and TaqMan Probe dye mix 0.16

Nuclease free water 1.59

Total 5

1. The gene amplification is carried out according to PCR protocol (Table 2.3.1.2).

Table 2.3.1.2. TaqMan® PCR protocol

Step Temperature Time Cycle(s) Function

1 95°C 10 min 1 Start

2 92°C 15 sec

45

Denaturation

3 60°C 90 sec Elongation, nucleolytic cleavage _{of hybridized probes}

4 4°C ∞ 1 Finish

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Fig. 2.3.1.1. Allelic Discrimination Plot

2.4. Statistical analysis

Statistical analysis of the genotyping data was performed by using PLINK software version 1.07 (169). The distribution of TM6SF2 (rs58542926),

MBOAT7 (rs641738), SERPINA1 (rs28929474, rs17580) and HSD17B13

(rs72613567) genotypes in cases and controls was examined for consistency with the Hardy-Weinberg equilibrium (HWE). Alleles and genotypes frequencies between groups were compared using Pearson’s goodness-of-fit χ2 and Fisher exact tests. Associations between control and cases groups with

TM6SF2, MBOAT7, SERPINA1 and HSD17B13 SNP alleles and genotypes

were calculated by using logistic regression analysis with adjustment for age and sex. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were reported. Age between groups was compared using analysis of variance (ANOVA) and is shown as mean with standard deviation. Gender distri-butions were compared using χ2_{tests. In TM6SF2 and MBOAT7 genes SNP}

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3. RESULTS

3.1. Characteristics of study groups

In the first part of our study we included: 334 patients with liver cirrhosis, 128 patients with liver fibrosis and 550 controls. Liver cirrhosis group consisted of 166 men and 168 women with a mean age of 52 years. Liver fibrosis group consisted of 79 men and 49 women with a mean age of 47 years. The control group consisted of 550 individuals: 271 men and 279 women with a mean age of 47 years. Men were predominant in liver fibrosis group and accounted for 61.7%. Liver cirrhosis patients were significantly (P<0.001) older than liver fibrosis and control patients.

The most common cause of liver injury in liver cirrhosis group was alcohol, in liver fibrosis group – HCV infection. To eliminate the potential bias of differences in age and sex distribution among the groups, these parameters were included as covariates in further logistic regression analysis. Demographic and clinical characteristics of the study group are presented in the Tables 3.1.1 and 3.1.2.

Table 3.1.1. Characteristics of liver cirrhosis and hepatic fibrosis cohort

Liver cirrhosis

(n=334) Liver fibrosis (n=128) Controls (n=550) P value

Age (Mean ± SD), years 51.8 ± 13.2 47.2 ± 13.4 47.3 ± 9.0 <0.001 Gender (n, %)

Male

Female 166 (48.3) 168 (51.7) 79 (61.7) 49 (38.3) 271 (49.3) 279 (50.7) <0.05 SD, standard deviation.

Table 3.1.2. Aetiology of chronic liver disease

Aetiology of liver disease, n (%) Liver cirrhosis Liver fibrosis

(38)

In the second part of our study 302 patients with liver cirrhosis, 127 patients with liver fibrosis and 548 controls were included. Liver cirrhosis group consisted of 150 men and 152 women with a mean age of 50 years. Liver fibrosis group comprised 78 men and 49 women with a mean age of 48 years. The most common cause of liver injury in liver cirrhosis group was alcohol, in liver fibrosis group – HCV infection. The control group included 548 individuals: 278 men and 270 women with a mean age of 47 years. Liver cirrhosis patients were significantly (P<0.001) older than liver fibrosis and control patients. To eliminate the potential bias of differences in age and sex distribution among the groups, these parameters were included as covariates in further logistic regression analysis. Demographic and clinical characte-ristics of the study group are presented in the Tables 3.1.3 and 3.1.4.

Table 3.1.3. Characteristics of patients with liver cirrhosis, fibrosis and controls

Liver cirrhosis

(n=302) Liver fibrosis (n=127) Controls (n=548) P value

Age (Mean ± SD), years 50.3±12.7 47.5±12.1 47.2±8.9 <0.001 Gender (n, %)

Male

Female 150 (49.7) 152 (50.3) 78 (61.4) 49 (38.6) 278 (50.7) 270 (49.3) 0.06 SD, standard deviation.

Table 3.1.4 Aetiology of liver injury

Aetiology of liver disease, n (%) Liver cirrhosis Liver fibrosis

Alcohol 180 (56.3) HCV 122 (43.7) 116 (91.3) Other causes Autoimmune Cryptogenic Steatohepatitis 4 (3.1) 4 (3.1) 3 (2.5) HCV, hepatitis C virus.

The observed genotype frequencies for all SNPs in: TM6SF2, MBOAT7,

SERPINA1 and HSD17B13 genes included in the study were in

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3.2. Association of TM6SF2 gene single nucleotide polymorphism with liver fibrosis and cirrhosis

Tables 3.2.1–3.2.4 presents the frequencies of alleles and genotypes of

TM6SF2 SNP in controls, liver fibrosis and different aetiology liver

cirrhosis groups. TM6SF2 rs58542926 risk T allele was determined 7.45% in control, 5.47% in liver fibrosis, 8.38% in liver cirrhosis, 8.09% in alcohol induced and 7.50% HCV induced liver cirrhosis groups. TM6SF2 rs58542926 was not linked with the liver fibrosis (aOR: 0.67, CI: 0.37–1.23, P=0.19) or cirrhosis (aOR: 1.18, CI: 0.83–1.68, P=0.36).

Table 3.2.1. Distribution of TM6SF2 gene polymorphism in liver fibrosis and control groups Allele/Genotype Controls (n=550), n (%) Fibrosis (n=128), n (%) aOR (95% CI) P TM6SF2 rs58542926 T 41 (7.45) 7 (5.47) 0.67 (0.37-1.23) 0.19 C 509 (92.55) 121 (94.53) TT 2 (0.36) 0 (0) 0.81 (0.04-16.98) 0.48 TC 77 (14.0) 13 (10.16) 0.69 (0.37-1.28) 0.23 CC 471 (85.64) 115 (89.84) 1 (Reference)

aOR – adjusted odds ratio; CI – confidence interval.

Table 3.2.2. Distribution of TM6SF2 gene polymorphism in liver cirrhosis and control groups

Allele/Genotype Controls (n=550), n (%) Cirrhosis (n=334), n (%) aOR (95% CI) P TM6SF2 rs58542926 T 41 (7.45) 28 (8.38) 1.18 (0.83-1.68) 0.36 C 509 (92.55) 306 (91.62) TT 2 (0.36) 2 (0.60) 1.69 (0.24-12.09) 0.59 TC 77 (14.0) 53 (15.87) 1.17 (0.80-1.71) 0.43 CC 471 (85.64) 279 (83.53) 1 (Reference)

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Table 3.2.3. Distribution of TM6SF2 gene polymorphism in alcoholic cir-rhosis and control groups

Allele/Genotype Controls (n=550) n (%) Alcoholic cirrhosis (n=171), n (%) aOR (95% CI) P TM6SF2 rs58542926 T 41 (7.45) 14 (8.09) 1.13 (0.72-1.77) 0.60 C 509 (92.55) 157 (91.81) TT 2 (0.36) 0 (0) 0.66 (0.03-13.86) 0.44 TC 77 (14.0) 28 (16.37) 1.21 (0.75-1.93) 0.44 CC 471 (85.64) 143 (83.63) 1 (Reference)

Table 3.2.4. Distribution of TM6SF2 gene polymorphism in HCV induced cirrhosis and control groups

Allele/Genotype Controls (n=550) n (%) HCV induced cirrhosis (n=120) n (%) aOR (95% CI) P TM6SF2 rs58542926 T 41 (7.45) 9 (7.50) 0.96 (0.56-1.65) 0.88 C 509 (92.55) 111 (92.50) TT 2 (0.36) 1 (0.83) 2.26 (0.20-25.21) 0.49 TC 77 (14.0) 15 (12.50) 0.88 (0.49-1.60) 0.68 CC 471 (85.64) 104 (86.67) 1 (Reference)

aOR – adjusted odds ratio; CI – confidence interval; HCV – hepatitis C virus.

3.3. Association of MBOAT7 gene single nucleotide polymorphism with liver fibrosis and cirrhosis

The distributions of alleles and genotypes of MBOAT7 SNP were similar between the control, liver fibrosis and cirrhosis groups. The risk allele T of

MBOAT7 rs641738 was detected in 43.27% of controls, in 41.1% of the liver

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differences between MBOAT7 rs641738 alleles and genotypes among liver fibrosis (aOR: 0.94, CI: 0.71–1.24, P=0.65) or cirrhosis (aOR: 1.05, CI: 0.87– 1.28, P=0.61) groups were observed.

Table 3.3.1. Distribution of MBOAT7 gene polymorphism in liver fibrosis and control groups

Allele/Genotype Controls (n=550), _{n (%)} Fibrosis (n=128), _{n (%)} _{(95% CI)}aOR P

MBOAT7 rs641738 T 238 (43.27) 53 (41.41) 0.94 (0.71-1.24) 0.65 C 312 (56.73) 75 (58.59) TT 108 (19.64) 20 (15.63) 0.79 (0.45-1.43) 0.44 TC 261 (47.45) 66 (51.56) 1.11 (0.72-1.70) 0.65 CC 181 (32.91) 42 (32.81) 1 (Reference) aOR – adjusted odds ratio; CI – confidence interval.

Table 3.3.2. Distribution of MBOAT7 gene polymorphism in liver cirrhosis and control groups

Allele/Genotype Controls (n=550), _{n (%)} _{(n=334), n (%)}Cirrhosis _{(95% CI)}aOR P

MBOAT7 rs641738 T 238 (43.27) 149 (44.61) 1.05 (0.87-1.28) 0.61 C 312 (56.73) 185 (55.39) TT 108 (19.64) 69 (20.66) 1.10 (0.75-1.62) 0.62 TC 261 (47.45) 160 (47.90) 1.05 (0.77-1.43) 0.76 CC 181 (32.91) 105 (31.44) 1 (Reference)

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Table 3.3.3. Distribution of MBOAT7 gene polymorphism in alcoholic cirrhosis and control groups

Allele/Genotype _{(n=550), n (%)}Controls Alcoholic cirrhosis _{(n=171), n (%)} aOR (95% CI) P

MBOAT7 rs641738 T 238 (43.27) 72 (42.11) 0.96 (0.75-1.23) 0.74 C 312 (56.73) 99 (57.89) TT 108 (19.64) 31 (18.13) 0.91 (0.55-1.50) 0.72 TC 261 (47.45) 83 (48.54) 0.99 (0.68-1.47) 0.99 CC 181 (32.91) 57 (33.33) 1 (Reference)

Table 3.3.4. Distribution of MBOAT7 gene polymorphism in HCV induced cirrhosis and control groups

Allele/Genotype Controls (n=550), _{n (%)} cirrhosis (n=120), HCV induced n (%) aOR (95% CI) P MBOAT7 rs641738 T 238 (43.27) 55 (45.83) 1.11 (0.83-1.46) 0.48 C 312 (56.73) 65 (54.17) TT 108 (19.64) 23 (19.17) 1.17 (0.65-2.09) 0.60 TC 261 (47.45) 64 (53.33) 1.35 (0.85-2.13) 0.21 CC 181 (32.91) 33 (27.50) 1 (Reference)

3.4. Combined analysis of PNPLA3 rs738409 and MBOAT7 or TM6SF2 single nucleotide polymorphisms

(43)

Table 3.4.1. Distribution of PNPLA3 and TM6SF2 gene polymorphisms in fibrosis and control groups

PNPLA3

rs738409 rs58542926 TM6SF2 Fibrosis n (%) Controls n (%) (95% CI) aOR P

CC CC 65 (87.83) 241 (87.31) 1.05 (0.48-2.29) 0.16 CC TC+TT 9 (12.16) 35 (12.68)

GC+GG CC 50 (92.59) 103 (82.4) 2.66 (0.87-8.16) 0.04 GC+GG TC+TT 4 (7.4) 22 (17.6)

Table 3.4.2. Distribution of PNPLA3 and TM6SF2 gene polymorphisms in cirrhosis and control groups

PNPLA3

rs738409 rs58542926 TM6SF2 Cirrhosis n (%) Controls n (%) (95% CI) aOR P

CC CC 121 (85.82) 241 (87.32) 0.88 (0.49-1.59) 0.67 CC TC+TT 20 (14.18) 35 (12.68)

GC+GG CC 107 (83.59) 103 (82.40) 1.09 (0.57-2.10) 0.80 GC+GG TC+TT 21 (16.41) 22 (17.60)

Table 3.4.3. Distribution of PNPLA3 and TM6SF2 gene polymorphisms in alcoholic cirrhosis and control groups

PNPLA3

rs738409 rs58542926 TM6SF2 Alcoholic cirrhosis n (%) Controls n (%) (95% CI) aOR P

CC CC 63 (86.30) 241 (87.32) 0.92 (0.43-1.95) 0.82 CC TC+TT 10 (13.70) 35 (12.68)

GC+GG CC 57 (80.28) 103 (82.40) 0.87 (0.41-1.83) 0.71 GC+GG TC+TT 14 (19.72) 22 (17.60)

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Table 3.4.4. Distribution of PNPLA3 and TM6SF2 gene polymorphisms in HCV induced cirrhosis and control groups

PNPLA3

rs738409 rs58542926 TM6SF2 HCV induced cirrhosis n (%) Controls n (%) (95% CI) aOR P

CC CC 44 (93.62) 241 (87.32) 2.13 (0.63-7.23) 0.10

CC TC+TT 3 (6.38) 35 (12.68)

GC+GG CC 36 (83.72) 103 (82.40) 1.10 (0.43-2.79) 0.18 GC+GG TC+TT 7 (16.28) 22 (17.60)

Table 3.4.5. Distribution of PNPLA3 and MBOAT7 gene polymorphisms in fibrosis and control groups

PNPLA3

rs738409 MBOAT7 rs641738 Fibrosis n (%) Controls n (%) (95% CI) aOR P

CC CC 23 (31.51) 87 (31.52) 0.99 (0.57-1.741) 1.00 CC TC+TT 50 (68.49) 189 (68.48)

GC+GG CC 18 (32.73) 43 (34.40) 0.93 (0.47-1.82) 0.83 GC+GG TC+TT 37 (67.27) 82 (65.60)

Table 3.4.6. Distribution of PNPLA3 and MBOAT7 gene polymorphisms in cirrhosis and control groups

PNPLA3

rs738409 MBOAT7 rs641738 Cirrhosis n (%) Controls n (%) aOR (95% CI) P

CC CC 57 (40.43) 87 (31.52) 1.47 (0.97-2.25) 0.07 CC TC+TT 84 (59.57) 189 (68.48)

GC+GG CC 32 (25.00) 43 (34.40) 0.64 (0.37-1.09) 0.10 GC+GG TC+TT 96 (75.00) 82 (65.60)

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Table 3.4.7. Distribution of PNPLA3 and MBOAT7 gene polymorphisms in alcoholic cirrhosis and control groups

PNPLA3

rs738409 MBOAT7 rs641738 Alcoholic cirrhosis n (%) Controls n (%) (95% CI) aOR P

CC CC 31 (42.47) 87 (31.52) 1.60 (0.95-2.72) 0.08 CC TC+TT 42 (57.53) 189 (68.48)

GC+GG CC 18 (25.35) 43 (34.40) 0.65 (0.34-1.96) 0.19 GC+GG TC+TT 53 (74.65) 82 (65.60)

Table 3.4.8. Distribution of PNPLA3 and MBOAT7 gene polymorphisms in HCV induced cirrhosis and control groups

PNPLA3 rs738409 MBOAT7 rs641738 HCV induced cirrhosis n (%) Controls n (%) aOR (95% CI) P CC CC 18 (38.30) 87 (31.52) 1.35 (0.71-2.56) 0.36 CC TC+TT 29 (61.70) 189 (68.48) GC+GG CC 8 (18.60) 43 (34.40) 0.44 (0.19-1.02) 0.03 GC+GG TC+TT 35 (81.40) 82 (65.60)

3.5. Association of SERPINA1 rs2892947 single nucleotide polymorphism with liver fibrosis and cirrhosis

(46)

Table 3.5.1. Distribution of the SERPINA1 rs28929474 in liver fibrosis and control groups

Allele/Genotype _{(n=548), n (%)}Controls _{(n=127), n (%)}Fibrosis _{(95% CI)}aOR P

SERPINA1 rs28929474 T 6 (1.09) 2 (1.57) 1.07 (0.30-3.87) 0.92 C 542 (98.91) 125 (98.43) TT 0 (0) 0 (0) NA NA TC 12 (2.19) 3 (2.36) 1.08 (0.30-3.89) 0.91 CC 536 (97.81) 124 (97.64) 1 (Reference)

aOR – adjusted odds ratio; CI – confidence interval; NA – not available.

Table 3.5.2. Distribution of the SERPINA1 rs28929474 in liver cirrhosis and control groups

Allele/Genotype _{(n=548), n (%)}Controls _{(n=302), n (%)}Cirrhosis _{(95% CI)}aOR P

SERPINA1 rs28929474 T 6 (1.09) 4 (1.32) 1.13 (0.45-2.81) 0.80 C 542 (98.91) 298 (98.68) TT 0 (0) 0 (0) NA NA TC 12 (2.19) 7(2.32) 1.06 (0.41-2.72) 0.90 CC 536 (97.81) 295 (97.68) 1 (Reference)

Table 3.5.3. Distribution of the SERPINA1 rs28929474 in alcoholic cirrhosis and control groups

Allele/Genotype _{(n=548), n (%)}Controls Alcoholic cirrhosis _{(n=180), n (%)} _{(95% CI)}aOR P

(47)

Table 3.5.4. Distribution of the SERPINA1 rs28929474 in HCV induced cirrhosis and control groups

Allele/Genotype Controls (n=548) n (%) HCV induced cirrhosis (n=122), n (%) aOR (95% CI) P SERPINA1 rs28929474 T 6 (1.09) 1 (0.82) 0.62 (0.13-2.93) 0.55 C 542 (98.91) 121 (99.18) TT 0 (0) 0 (0) NA NA TC 12 (2.19) 1 (0.82) 0.37 (0.05-2.87) 0.32 CC 536 (97.81) 121 (99.18) 1 (Reference)

aOR – adjusted odds ratio; CI – confidence interval; HCV – hepatitis C virus; NA – not available.

Table 3.5.5. Distribution of the SERPINA1 rs28929474 in combined liver fibrosis + liver cirrhosis and control groups

Allele/Genotype Controls (n=548) n (%) Fibrosis and cirrhosis (n=429), n (%) aOR (95% CI) P SERPINA1 rs28929474 T 6 (1.09) 6 (1.40) 0.88 (0.40-2.05) 0.77 C 542 (98.91) 423 (98.60) TT 0 (0) 0 (0) NA NA TC 12 (2.19) 10 (2.33) 1.07 (0.46-2.49) 0.88 CC 536 (97.81) 419 (97.67) 1 (Reference)

3.6. Association of SERPINA1 rs17580 single nucleotide polymorphism with liver fibrosis and cirrhosis

Tables 3.6.1-3.6.5 present the frequencies of alleles and genotypes of

SERPINA1 Pi*S (rs17580) SNP in controls, liver fibrosis, different aetiology

(48)

Pi*S risk allele was also less frequent in controls (1.1%) than in liver fibrosis group (3.15%) (aOR: 3.42, CI: 1.34-8.76, P=0.01). The frequency of the

SERPINA1 rs17580 heterozygous genotype AT was significantly lower in

controls (2%) than in patients with liver fibrosis (6.3%) (aOR 3.28, CI: 1.29-8.34, P=0.008). Frequency of SERPINA1 SNP genotype AT was also

significantly lower in controls than in combined liver fibrosis and cirrhosis group (aOR: 2.77, CI: 1.33-5.74, P=0.004). We also observed tendency of risk allele A association with liver cirrhosis overall (aOR: 2.59, CI: 1.16-5.77, P=0.02), and alcohol induced cirrhosis (aOR: 2.59, CI: 1.05-6.40, P=0.04), but not with HCV induced cirrhosis (aOR: 2.67, CI: 0.93-7.65, P=0.07). Moreover, genotype SERPINA1 genotype AT showed trend towards increased overall liver cirrhosis risk (aOR: 2.55, CI: 1.16-5.63, P=0.02) and alcohol induced liver cirrhosis risk (aOR: 2.57, CI: 1.05-6.30, P=0.03). No patients had homozygous Pi*SS genotype.

Table 3.6.1. Distribution of the SERPINA1 rs17580 in liver fibrosis and control groups

Allele/Genotype Controls (n=548), _{n (%)} Fibrosis (n=127), _{n (%)} _{(95% CI)}aOR P

SERPINA1 rs17580 A 6 (1.10) 4 (3.15) 3.42 (1.34-8.76) 0.01 T 542 (98.90) 123 (96.85) AA 0 (0) 0 (0) NA NA AT 11 (2.01) 8 (6.30) 3.28 (1.29-8.34) 0.008 TT 537 (97.99) 119 (93.70) 1 (Reference) aOR – adjusted odds ratio; CI – confidence interval; NA – not available

significant P-values are marked in bold.

Table 3.6.2. Distribution of the SERPINA1 rs17580 in liver cirrhosis and control groups

Allele/Genotype Controls (n=548) _{n (%)} Cirrhosis (n=302), _{n (%)} aOR (95% CI) P

SERPINA1 rs17580 A 6 (1.10) 7 (2.32) 2.59 (1.16-5.77) 0.02 T 542 (98.90) 295 (97.68) AA 0 (0) 0 (0) NA NA AT 11 (2.01) 15 (4.97) 2.55 (1.16-5.63) 0.02 TT 537 (97.99) 287 (95.03) 1 (Reference)

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Table 3.6.3. Distribution of the SERPINA1 rs17580 in alcoholic cirrhosis and control groups

Allele/Genotype Controls (n=548) _{n (%)} Alcoholic cirrhosis _{(n=180), n (%)} _{(95% CI)}aOR P

SERPINA1 rs17580 A 6 (1.10) 5 (2.78) 2.59 (1.05-6.40) 0.04 T 542 (98.90) 175 (97.22) AA 0 (0) 0 (0) NA NA AT 11 (2.01) 9 (5.00) 2.57 (1.05-6.30) 0.03 TT 537 (97.99) 171 (95.00) 1 (Reference)

Table 3.6.4. Distribution of the SERPINA1 rs17580 in HCV induced cirrhosis and control groups

Allele/Genotype Controls (n=548) _{n (%)} cirrhosis (n=122), HCV induced n (%) aOR (95% CI) P SERPINA1 rs17580 A 6 (1.10) 3 (2.46) 2.67 (0.93-7.65) 0.07 T 542 (98.90) 119 (97.54) AA 0 (0) 0 (0) NA NA AT 11 (2.01) 6 (4.92) 2.53 (0.92-6.97) 0.06 TT 537 (97.99) 116 (95.08) 1 (Reference)

aOR – adjusted odds ratio; CI – confidence interval; HCV – hepatitis C virus; NA – not available.

Table 3.6.5. Distribution of the SERPINA1 rs17580 in combined liver fibrosis + liver cirrhosis and control groups

Allele/Genotype Controls (n=548) _{n (%)} Fibrosis and cirrhosis (n=429), n (%) aOR (95% CI) P SERPINA1 rs17580 A 6 (1.10) 11 (2.56) 2.61 (0.40-5.55) 0.016 T 542 (98.90) 418 (97.44) AA 0 (0) 0 (0) NA NA AT 11 (2.01) 23 (5.36) 2.77 (1.33-5.74) 0.004 TT 537 (97.99) 406 (94.64) 1 (Reference) aOR – adjusted odds ratio; CI – confidence interval; NA – not available;

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3.7. Association of HSD17B13 rs10433937 single nucleotide polymorphism with liver fibrosis and cirrhosis

Alleles of HSD17B13 rs10433937 were distributed evenly between the study groups. Frequency of HSD17B13 rs10433937 risk allele G was 26.8% in control group, while in liver fibrosis, cirrhosis, combined liver fibrosis and cirrhosis, alcohol and HCV induced liver injury groups accounted for 22.8%, 27.15%, 25.8%, 24.4% and 31.1%, respectively. (Tables 3.7.1–3.7.5). No significant associations between HSD17B13 rs10433937 alleles and geno-types and increased liver disease was determined. However, genotypic analysis revealed that risk genotype GG had a tendency to be protective against liver fibrosis (aOR: 0.37, CI: 0.14-0.96, P=0.03), however P value did not achieve statistically significant level attributed to this study (P<0.016).

Table 3.7.1. Distribution of the HSD17B13 rs10433937 in liver fibrosis and control groups

Allele/Genotype Controls (n=548) _{n (%)} Fibrosis (n=127), _{n (%)} _{(95% CI)}aOR P

HSD17B13 rs10433937 G 147 (26.82) 29 (22.83) 0.81 (0.60-1.10) 0.18 T 401 (73.18) 98 (77.17) GG 55 (10.04) 5 (3.94) 0.37 (0.14-0.96) 0.03 GT 182 (33.21) 46 (36.22) 1.03 (0.69-1.56) 0.87 TT 311 (56.75) 76 (59.84) 1 (Reference)

Table 3.7.2. Distribution of the HSD17B13 rs10433937 in liver cirrhosis and control groups

Allele/Genotype _{(n=548), n (%)}Controls _{(n=302), n (%)}Cirrhosis _{(95% CI)}aOR P

HSD17B13 rs10433937 G 147 (26.82) 82 (27.15) 0.99 (0.81-1.22) 0.95 T 401 (73.18) 220 (72.85) GG 55 (10.04) 38 (12.58) 1.22 (0.78-1.920 0.39 GT 182 (33.21) 88 (29.14) 0.85 (0.62-1.17) 0.33 TT 311 (56.75) 176 (58.28) 1 (Reference)

THE ROLE OF TM6SF2, MBOAT7, SERPINA1 AND HSD17B13 GENETIC VARIANTS IN LIVER FIBROSIS AND CIRRHOSIS

Viktorija Basytė-Bacevičė