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Highly active anti-retroviral therapy and liver mitochondrial toxicity in human immunodeficiency virus… Matsukura, Motoi 2008

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HIGHLY ACTIVE ANTI-RETROVIRAL THERAPY AND LIVER MITOCHONDRIAL TOXICITY IN HUMAN IMMUNODEFICIENCY VIRUS / HEPATITIS C VIRUS CO-INFECTION by MOTOI MATSUKURA B.Sc. (Honours), University of Calgary, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Pathology and Laboratory Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2008 © Motoi Matsukura, 2008  ABSTRACT  Background: A third of HIV-infected patients are co-infected with HCV in the developed world, and more of co-infected patients than ever before are dying because of liver related diseases today. Drug-related hepatotoxicity is a growing concern among human immunodeficiency virus (HIV) / hepatitis C virus (HCV) co-infected population. Nucleotide analogues containing HIV antiretroviral therapy, namely highly active anti-retroviral therapy (HAART), can induce mitochondrial toxicity. However, little is known about the effect of nucleotide analogues on the liver at the cellular and molecular level, and how it may affect treatment. Objective: To investigate whether liver tissue from HIV/HCV co-infected individuals will show greater liver mitochondrial toxicity if currently receiving antiviral HIV medication, compared to those who are not taking it. Methods: Liver biopsies were collected from 23 HIV/HCV co-infected males. Fourteen patients were on stable HAART (ON-HAART) and 9 were OFF-HAART, including 4 who stopped HAART >6 months prior and 5 who were HAART-nave. Liver mitochondrial toxicity was assessed by transmission electron microscopy-based quantitative stereological analyses of hepatocyte and mitochondrial morphometry, as well as by mitochondrial DNA (mtDNA) and mtRNA (COX1/(3-actin) real-time-PCR quantification. Results: Hepatocytes tended to be larger in the ON-HAART group than in the OFF-HAART group ()=0.05), but they both showed similar mitochondrial volume fraction of the cell and mitochondrial crista density. Liver mtDNA and mtRNA levels were not significantly different between ON-HAART and OFF-HAART. Hepatocyte lipid accumulation was significantly higher in HCV genotype 3 compared to genotype 1 infection ()=0.002), but was not associated with HAART status. ii  Conclusions: We found no evidence or trend of increased mitochondrial toxicity in HIV/HCV co-infected individuals currently on HAART compared to those who are not. This finding could be relevant to the decision-making process with respect to initiating HCV therapy in this population.  iii  TABLE OF CONTENTS  Abstract ^ Table of contents ^ List of tables ^ List of figures ^ Abbreviations Acknowledgements ^ Dedication ^  ii iv vi ..vii ^ ix xi xii  I. CHAPTER ONE: INTRODUCTION 1.1 Human immunodeficiency virus (HIV) epidemiology ^ 1.2 HIV pathology ^ 1.3 Highly active anti-retroviral therapy (HAART) 1.3.1 Non-nucleotide reverse transcriptase inhibitors (NNRTIs) ^ 1.3.2 Protease inhibitors (PIs) ^ 1.3.3 Nucleotide reverse transcriptase inhibitors (NRTIs) ^ 1.4 HIV/ hepatitis C virus (HCV) co-infection epidemiology ^ 1.5 HCV pathology ^ 1.6 HCV treatment ^ 1.7 Hepatotoxicity in HIV/HCV co-infection ^ 1.7.1 Impact of hepatotoxicity in HIV/HCV co-infected population ^ 1.7.2 Immune reconstitution injury ^ 1.7.3 Hypersensitive reactions by NNRTIs ^ 1.7.4 Direct hepatocellular idiosyncratic injury by PIs ^ 1.8 Mitochondrial toxicity by NRTIs ^ 1.8.1 Mechanisms of mitochondrial toxicity ^ 1.8.2 Metabolite accumulations with mitochondrial toxicity ^ 1.8.3 Hyperlactatemia ^ 1.8.4 Glycogen accumulation ^ 1.8.5 Hepatosteatosis ^  1 .4 9 . 11 .11 ..^11 .12 13 17 18 18 21 21 22 22 22 26 ..28 28 29  II. CHAPTER TWO: OBJECTIVE AND HYPOTHESIS 2.1 Rationale ^ 2.2 Objective ^ 2.3 Hypothesis ^  31 . 32 33  iv  III. CHAPTER THREE: MATERIALS AND METHODS 3.1 Study population ^ 3.2 Liver biopsy ^ 3.3 Mitochondrial DNA (mtDNA) extraction ^ 3.4 mtDNA assay ^ 3.5 mtRNA assay ^ 3.6 Long polymerase chain reaction (PCR) ^ 3.7 Transmission electron microscopy (TEM) ^ 3.8 Stereology ^ 3.8.1 Star volume ^ 3.8.2 Point counting ^ 3.8.3 Line intercept ^ 3.8.4 Perimeter measurement ^ 3.9 Statistics ^  34 36 36 37 40 ^ 40 .43 44 46 .49 54 58 59  IV. CHAPTER FOUR: RESULTS 4.1 Study population ^ 4.2 ON-HAART vs. OFF-HAART ^ 4.2.1 mtDNA ratio and mtRNA ratio ^ 4.2.2 Pathology score ^ 4.2.3 TEM morphometry ^ 4.3 HCV genotype 1 vs. 3 ^ 4.4 mtDNA deletion  60 62 . 62 64 65 69 72  V. CHAPTER FIVE: DISCUSSION 5.1 Findings ^ 5.2 Morphometric methodology development ^ 5.3 Limitations ^ 5.4 Future directions ^ 5.5 Conclusion ^  REFERENCES ^ APENDIX A: Ethics certificate of expedited approval ^  74 78 80 .81 82  83 ..95  v  LIST OF TABLES  Table 1^Sequences of primers and probes used in the present project ^ 39 Table 2^Compatibility between blocks and reproducibility of TEM morphometry ^ 57 Table 3^Summary of TEM stereological methodology for each parameter ^ 58 Table 4^Characteristics of study population ^  61  Table 5 Comparisons of mtDNA and mtRNA ratios between ON-HAART and OFF-HAART groups ^ 62 Table 6^Results of pathological examinations ^  64  Table 7^TEM morphometric analysis results between ON-HAART and OFF-HAART.... 66 Table 8 Comparisons of RT-PCR and TEM results between HCV genotype 1 and 3 groups ^ 70 Table 9 Severity grading of mtDNA deletion between ON-HAART and OFF73 HAART ^ Table 10 Severity grading of mtDNA deletion between HCV genotype 1 and 3a ^ 73  vi  LIST OF FIGURES  Figure 1 HIV prevalence in the world ^  3  Figure 2 Images of HIV ^  5  Figure 3 Schematic diagram showing HIV infection and the replication cycle in the host cell ^ 6 Figure 4 The changes in viral load, antibody level, CD4 + and CD8 + T cells over the course of HIV infection without antiretroviral treatment 8 Figure 5 Changes in the mortality rates of HIV infection with HAART introduction in the United States 10 Figure 6 Progression of HCV related liver diseases in the HCV mono-infected population ^ 16 Figure 7 Schematic diagram summarizing potential factors that can cause liver damage in HIV/HCV co-infected patients who are taking HAART ^ 20 Figure 8 Examples of NRTIs  25  Figure 9 Diagram of a liver cell with mitochondrial dysfunction ^ 27 Figure 10 Overall scheme showing the original and present MSc project ^ 32 Figure 11 Study population from the enrolment up to the collection of research samples ^ 35 Figure 12 Long-template PCR amplification of mtDNA fragments ^ 42 Figure 13 Hepatocyte TEM image with Star volume grid ^  47  Figure 14 Change in coefficient error (CE) of hepatocyte volumes with the increasing sample size ^ 48 Figure 15 Point counting method ^  50  Figure 16 Changes in CEs of hepatocyte cytoplasm, glycogen, mitochondria and lipid volume fractions with the increasing sample size ^ 51 Figure 17 Comparing volume fractions of cellular constituents at different grid point number ^ 53 Figure 18 Measuring crista surface density using the line intercept ^ 55  vi i  Figure 19 Change in CE of mitochondrial crista density with increasing sample size... 57 Figure 20 Positive correlation between cyochrome C oxidase subunit (COX) 1 and COX8 expression levels ^ 63 Figure 21 Comparing hepatocyte volumes between the ON-HAART and OFF^HAART groups 21 Figure 22 Representative TEM images of hepatocyte mitochondria ^ 67 Figure 23 Longitudinal and transversal sections of crystalline structure inside mitochondria from OFF-HAART/HCV-1 subjects ^ 68 Figure 24 TEM images showing different lipid and glycogen accumulation patterns based on HCV genotype ^ 70 Figure 25 Lipid volume fraction differences in hepatocytes based on HCV genotype profile ^ 71  viii  ABBREVIATION  3TC^Lamivudine ABC^Abacavir AIDS^Acquired Immune Deficiency Syndrome ALT^ALanine Transaminase ASPG^Accessory Subunit of the Polymerase Gamma AST^Aspartate Transaminase AZT^Zidovudine BC^British Columbia BCMH^British Columbia Ministry of Health CD#^Cluster of Differentiation CDC^Center for Disease Control cDNA^Complementary DNA CE^Coefficient of Error COX1 / 8^Cyochrome C Oxidase subunit I or VIII CTL^Cytotoxic T-Lymphocyte d4T^Stavudine ddC^Zalcitabine, also known as Dideoxycystidone ddI^Didanosine DNA^DeoxyriboNucleic Acid EM^Electron Microscope ESLD^End Stage Liver Diseases HAART^Highly Active Anti-Retroviral Therapy HCV^Hepatitis C Virus ix  HIV^Human Immunodeficiency Virus IDU^Injection Drug User IMP^Inosine MonoPhosphate IQR^Inter-Quartile Range MSM^Men who have Sex with Men mtDNA^Mitochondrial DNA mtRNA^Mitochondrial RNA nDNA^Nuclear DNA MT^MiTochondria NEFA^NonEsterified Fatty Acids NNRTI^Non-Nucleotide Reverse Transcriptase Inhibitor NRTI^Nucleotide Reverse Transcriptase Inhibitor PCR^Polymerase Chain Reaction PI^Protease Inhibitor RNA^RiboNucleic Acid ROS^Reactive Oxygen Species TEM^Transmission Electron Microscope UNAIDS^The Joint United Nations Programme on HIV and AIDS WHO^World Health Organization  x  ACKNOWLEDGEMENTS I am sincerely grateful to my supervisors Dr. Haêne COtè and Dr. David Walker for their generosity of time, advice and passion that they have patiently provided throughout the course of the project. Special thanks to Izabelle Gadawski for her generous supports and encouraging words. Many thanks are also extended to the following people who have involved in this project and dedicated their much appreciated efforts.  Committee: Drs. Susan Porter, Valentina Montessori and Mike Allard  The COte Lab: Dr. Eszter Papp, Dr. Courtney Kang, Brian Schick, Marissa Jitratkosol, Henry Stringer, May Au, Helen Lu, Jennifer Chen, Monica Raj, Nancy Wang, Carmen Li and Beheroze Sattha.  The Walker Lab: Fanny Chu, Erin Tranfield and Ali Behzad  Farley Clinic: Dr. John Farley and Wendy Shum  B.C. Centre for Excellence in HIV/AIDS: Dr. Richard Harrigan, Dr. Rolando Barrios, Dr. Julio Montaner, Dr. Mark Hull, Sonja Rietkerk, Parminder Thind, Benita Yip, Marianne Harris and Nathalie Jahnke  xi  DEDICATION  To my parents  xii  I. CHAPTER ONE: INTRODUCTION  1.1 HIV epidemiology  Since cases of acquired immune deficiency syndrome (AIDS) presented in the United States in 1981 (CDC 1981), 33 million people are now estimated to be living with human immunodeficiency virus (HIV) and more than two million people worldwide are dying of AIDS every year. (Figure 1) (UNAIDS 2007). It is projected that by 2030, HIV will join heart disease and stroke as one of the top three causes of death worldwide (Mathers and Loncar 2006). The HIV epidemic is thought to have started between 1915-1941 in the west equatorial region of Africa, encompassing Gabon, Equatorial Guinea, Cameroon and the Republic of Congo, however answering how and why it first appeared in the late 20 th century on a global pandemic scale is a challenging task (Elswood and Stricker 1994; Zhu, Korber et al. 1998; Hahn, Shaw et al. 2000; Korber, Muldoon et al. 2000; De Cock 2001; Holmes 2001). Today, this virus is mostly damaging developing countries, especially in the Sub-Saharan African region, where two thirds of the global HIV population is concentrated. In some countries, as much as 33% of the adult population (age 15-49)—for example in Swaziland—is infected with HIV (UNAIDS 2007), and women are disproportionately affected. Outside of Africa, South and Southeast Asia have the largest HIV population in the world, representing four million infected individuals. Although the major route of HIV infection is through heterosexual transmission, injection drug use (IDU) also accounts for a large number of the HIV transmission in this area. IDU is the major course of HIV transmission in Eastern Europe and Central Asia, representing 62% out of total 1.6 million HIV infection cases (UNAIDS 2007). Although the HIV epidemic is less severe in Western Europe and North America compared to other parts of the world, the total number of people living with HIV is 1  increasing every year in these regions. This increase is attributed to the life prolonging effect of antiretroviral therapy added with the stable number of new HIV infection cases each year. Up to 50% of all newly diagnosed HIV infections are among men who have sex with men (MSM), contributing to a total two million people living with HIV in Western Europe and North America (UNAIDS 2007). In Canada, approximately 58,000 people are living with HIV today (Archibald 2007). Half of the infections are occurring among MSM, followed by IDU and heterosexual transmission (17% and 15% respectively), and 20% of all infection cases are women. HIV infection is prevalent among the aboriginal population (7.5% of HIV cases), despite the fact that aboriginals only make up 3.3% of the Canadian population, mainly driven by IDU. (Archibald 2007). There are approximately 10,000 people living with HIV in British Columbia (BC), which represents about 18% of Canadian HIV cases. As this province is home for 13% of the overall Canadian population, the HIV burden in BC is disproportionately high (BCMH 2006). Twenty seven per cent of HIV infected people are unaware of their HIV infection in Canada (Archibald 2007), therefore it is important to address the logistics of HIV testing, delivering appropriate care and supporting this population to minimize the impact of the HIV epidemic in Canada.  2  ADULTS AND CHILDREN ESTIMATED TO BE LIVING WITH HIV IN 2007  Western and Central Europa  760 000  North America  [600 000-1.1 million]  1.3 million  Eat.torn Europe and Contra! Asia  1.6 million [1.2-2.1 million]  East Asia  800 000  [480 000-1.9 million]  [620 000-960 000] Middle East and North Africa  Caribbean  380 000  230 000  [270 000-500 000]  [210 000-270 000]  Latin America  South and South-East Asia  4.0 million [3.3-5.1 million]  Sub-Saha ran Africa  1.6 million [1.4-1.9 million]  22.5 million  L)caania  [20.9-24.3 million]  75 000 [53 000-120 000]  Total: 33.2 (30.6-36.1) million  tr,^10 MOO J0011 UNIT10^tvocithoolt^4.Aleas  World Health Organization  Figure 1: HIV infection prevalence around the world (image source: http://www.unaids.org/en/ ).  3  1.2 HIV pathology  HIV is a member of the lentivirus family and contains RNA molecules encoding an HIV viral protease, a reverse transcriptase, an integrase among other proteins (Figure 2) (Sierra, Kupfer et al. 2005). The virus enters the host through body fluid contact, such as blood, blood products, semen, vaginal fluid and breast milk. Entry into the cell is mediated by an interaction between the viral envelop glycoprotein gp120 and the cellular CD4 receptor on the cell membrane (Lusso 2006). T helper lymphocytes, macrophages and dendritic cells are the major cells expressing the CD4 receptor (Burger and Poles 2003). This molecular interaction triggers a conformational change in gp120, displacing a previously hidden region that acts as a binding site for specific co-receptor and allows the virus to fuse with the cellular membrane which consequently leads to the insertion of the viral core into the host cell (Doms and Moore 2000). Once inside the host cell, the viral RNA genome is reverse-transcribed into a full-length doublestranded DNA by the viral reverse transcriptase. The viral DNA then enters the nucleus through a nuclear pore and integrates into the host's DNA using viral integrase (Figure 3). Integrated viral genome is either transcribed by the cellular machinery, producing more of viral proteins and genomic RNA, or it can settle into the non-productive latent phase that allows the virus to survive in the host for long time (Sierra, Kupfer et al. 2005). Inactive naïve T cells and monocytes contain this latent virus inside, and this is also a reason why achieving complete HIV eradication is difficult if not impossible. When sufficient amount of viral envelop proteins accumulate in the cytoplasm, they migrate and insert themselves into the plasma membrane, in preparation to bud out. Viral polyproteins, enzymes and genomic RNA are also transported to the cellular membrane and assemble into the immature core complex (Sierra, Kupfer et al. 2005). The mature HIV virion is generated when the viral protease autocatalytically cleaves the  4  polyproteins during budding and release of viral particles from the cell (Sierra, Kupfer et al. 2005).  RNA  Figure 2: Images of HIV. (a) Diagram of a mature HIV virion (100-120 nm diameter) and the lipid bilayer of cellular origin. Reverse transcriptases (RT) and two copies of the viral RNA are contained inside the core. (CA=capsid protein, NC=nucleocapsid protein and MA=matrix protein) (Sierra 2005). (b) Cross section of a mature HIV virion under transmission electron microscope, magnification at 170,000x (Orenstein 2002).  5  1. Virus attach nd fusion O  •  0  /".  2. Rev^tra^ription  3. late  4. ranscription  5. Tran  i) 160  ♦•  Pk 6. Assemble, budding and maturation  Figure 3: Schematic diagram showing the HIV infection and replication cycle in the  host cell (RT=Reverse Transcriptase, IN=Integrase and PR=Protease) (Sierra 2005).  6  The natural course of HIV disease takes years, and without treatment, average survival time after HIV infection is about 10-12 years, however this length substantially varies among individuals (Alaeus, Lidman et al. 1999). HIV infection is characterized by three phases: primary (acute) infection phase, asymptomatic phase and symptomatic phase (Figure 4). Primary infection generally lasts 2-8 weeks, with a rapid increase in the number of copies of HIV RNA in the circulation, referred to as plasma viral load (Touloumi and Hatzakis 2000; Burger and Poles 2003). Fifty percent of primary HIV infections remain asymptomatic and 50% patients develop flu like symptoms within the first four weeks after infection, which include fever, myalgia, fatigue, pharyngitis, weight loss and headache. A rapid CD4 + T cell decline is also observed during this phase, mirrored with the increase of plasma HIV RNA level, however this tendency reverses within a few weeks when CD8 + T cells develop the immunity against HIV and suppress viral replication in the host. Approximately at six months of post-infection, plasma HIV RNA levels stabilize around a so-called "set point" during the asymptomatic phase. A gradual and slow depletion of CD4 + T cells is observed during this phase, until the onset of the symptomatic phase, or about 18 months prior to the development of AIDS, when homeostasis of T cells breaks down with total loss of CD4 + T cells and eventually the immune system collapses. This allows the HIV RNA levels to increase again in the host's circulation. Most AIDS-defining diseases, such as pneumocystis pneumonia, tuberculosis and non-Hodkin's lymphoma, appear when the CD4 + T cell level drops below 200 cells/µl (Touloumi and Hatzakis 2000; Burger and Poles 2003; Simon, Ho et al. 2006). The introduction of highly active antiretroviral therapy (HAART), has improved the overall survival time of HIV infected individuals up to 36 years in developed countries today (Burger and Poles 2003). However in Africa, where the majority of HIV-infected populations live, infection rates have not changed even after HAART, largely due to poor access to HAART (Morgan, Mahe et al. 2002).  7  ^  CD4+ T cell ^ CDS' T cell — — — Antibody Viral load "  -----------  „  ---  ---  •  --------  ••• .......................  0^1^2^1^2^3^4^5^6^7^8^9^10 Time (months)^ •  Time (years)  ► •^ Primary^ infection^  ► •^► Asymptomatic^ (clinical latency)^  Symptomatic (AIDS)  Figure 4: Changes in viral load, anti-HIV antibody level, CD4 + and CD8 + T cells over the course of the HIV infection in the absence of antiretroviral treatment.  8  1.3 HAART  Since HAART was introduced in 1996 (Yeni 2006), dramatic improvements in HIV related morbidity and mortality have been achieved; mortality rates have decreased from 29.8 deaths per 100 persons-years in 1995 to 8.8 deaths per 100 person-years in the second half of 1997 (Palella, Delaney et al. 1998). Lichtenberg (2003) also showed rapid decline of HIV related mortality in the USA after HAART was introduced in 1996 (Figure 5). HAART is a combination therapy consisting of at least three antiretroviral drugs, and its goal is to achieve and maintain an HIV-RNA copy number in plasma (viral load) at a level below detection with current methods (<50 copies/m1) (Mocroft and Lundgren 2004; Yeni, Hammer et al. 2004). It is a combination of either a non-nucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI) in addition to two different nucleotide reverse transcriptase inhibitors (NRTI). The therapy is usually initiated before patients reach CD4 + cell level below 200 cells/ml. It is estimated that at least 60 years of continuing HAART would be necessary to achieve complete eradication of HIV from patients, therefore HIV is a lifelong infection with today's therapy (Finzi, Blankson et al. 1999).  9  HIV mortality, 1987-1998 45. COO 40.000 3514)0 30 COO 25 000 20 COO 15. COO 10.000 5.000  1987^1988^1989^1990^1991^1992^?993^1994^1995^1996^1997^1998 Year  Figure 5: Mortality change in HIV with the introduction of HAART in the United States. HIV mortality rapidly declined after HAART was introduced in 1996 (Lichtenberg 2003).  10  1.3.1 NNRTIs NNRTIs inhibit the viral reverse transcriptase by allosterically interacting with the enzyme outside the substrate (i.e. nucleotides) binding site and negatively influencing its structural function (De Clercq 2004). NNRTI binding site is also closely located from the substrate binding site, therefore the cooperative interaction between these two sites influences the effectiveness of NNRTI and NRTI in combination therapy (Spence, Kati et al. 1995; De Clercq 2004).  1.3.2 PIs As described above, to become a mature virus, HIV must undergo a final step during the budding process and cleave its polyproteins. This is accomplished by the viral protease to produce functional enzymes, and PIs are the class of antiretroviral drugs that inhibit the HIV protease (Abdel-Rahman, Al-karamany et al. 2002). Because the virus can mutate and develop drug resistance, HIV therapy is only effective when it contains at least three drugs aimed at a minimum of two targets within the HIV replication cycle, hence it is important to use combination therapy.  1.3.3 NRTIs NRTIs were the first type of drugs introduced as the HIV antiviral medication in 1985. As their name imply, NRTI is the generic name of the antiretroviral drugs that act as alternative substrates for HIV reverse transcriptase and inhibit further transcription. This is because NRTIs lack a 3'hydroxyl group that is required for the addition of the subsequent nucleotide during DNA elongation (Figure 8) (Chinen and Shearer 2002). Thus this structural similarity of NRTIs to normal nucleotides impairs the transcription of the HIV genome in two ways: 1) by competing with endogenous nucleic acids for incorporation; 2) by prematurely terminating 11  chain elongation once incorporated (Kakuda 2000). All antiretroviral drugs belonging to the NRTI class require phosphorylation by the cells before exhibiting their function as an antiretroviral.  1.4 HIV/HCV co-infection epidemiology  More than 170 million people are infected with Hepatitis C Virus (HCV) in the world, with the highest prevalence in Africa and Middle East, especially Egypt where 17-26% of the population is HCV-infected (WHO 2000; Dehesa-Violante and Nunez-Nateras 2007). About 3.5 million people live with HCV in North America, and among those, 240,000 infection cases are reported in Canada (Health-Canada 2006; Wasley, Miller et al. 2007). In British Columbia, it is estimated that 40,000 people are infected with HCV and 100 infected individuals are dying every year because of the infection (BCMH 2004). Both HIV and HCV share the same parenteral transmission route, therefore HCV co-infection rate among the HIV population is much higher than in the general population: about 30% of HIV patients are co-infected with HCV in developed countries, and this rate reaches 50% in Vancouver, BC (Verucchi, Calza et al. 2004; Braitstein, Justice et al. 2006). The major risk factors of HCV transmission are IDU (60%), sexual transmission (30%) and accidental needle-stick exposure, haemodialysis, household and perinatal spread (10%) (Zakim and Boyer 2003). Blood-contact transmission of HCV is ten times more infectious than HIV (Aceijas and Rhodes 2007), and because of this, the HCV co-infection rate among HIV infected IDUs is exceptionally higher compared to other population groups, as high as 70-90%, and 82% in Vancouver (Patrick, Tyndall et al. 2001; Verucchi, Calza et al. 2004).  12  1.5 HCV pathology  HCV was identified in 1974 and originally given the name of non-A non-B hepatitis virus (Zakim and Boyer 2003). HCV is a member of the family Flaviviridae, genus Hepacivirus and has a single-stranded positive sense RNA with a single open reading frame as its genome. The HCV genome produces a polyprotein which is further cleaved into at least ten viral proteins by cellular and viral proteases (Suzuki, Aizaki et al. 2007). HCV circulates in serum at a relatively low level, and this has made direct visualization of the virus difficult. Studies of this virus now suggest that HCV is a RNA virus, about 50-60 nm diameter, and the virus may circulate in various forms, such as free mature virions, non-enveloped nucleocapsids, or complexed with antibodies and lipoprotein-rich particles (Penin, Dubuisson et al. 2004). Its replication cycle at the molecular level is poorly understood. CD81 and the low density lipoprotein receptor are thought to be the HCV receptors (Zakim and Boyer 2003). Uncoating of HCV is probably happening in acidic endosomes in which pH changes trigger the shedding of viral envelope and nucleocapsid followed by release of RNA. Inside the cytoplasm, the viral RNA associates with ribosomes and other cellular proteins leading to translation of viral proteins. After the replicative complex of viral enzymes is synthesized and assembled, HCV RNA molecules bind to viral polymerase and negative strand RNA molecules are produced. Negative daughter strand RNA molecules are then used as templates for positive strand RNA synthesis in the same replicative complex. Assembly of the virion requires formation of a nucleocapsid consisting of the core protein that binds to the positive strand RNA. The envelope proteins retained inside the endoplasmic reticulum membrane then associate with the nucleocapsid. After the virion is assembled, it is probably released from the cell via host cellular exocytosis pathways (Zakim and Boyer 2003; Penin, Dubuisson et al. 2004; Suzuki, Aizaki et al. 2007). 13  Six major HCV genotypes have been found in the world: 1 and 2 are found worldwide; 3 is common in south and southeast Asia and Canada; 4 is the main genotype in Africa; 5 is largely found in South Africa; and 6 is in Hong Kong and Vietnam (Zakim and Boyer 2003). Genotype 1 is the most common genotype in the USA, accounting for at least 70% of infections. Hepatosteatosis, the accumulation of lipid particles in hepatocytes, is often associated with HCV infection especially with HCV genotype 3; as much as 74% of HCV genotype 3 patients show steatosis compared to 48% for other genotypes, contributing overall to 56% of steatosis prevalence among HCV patients (Lonardo, Loria et al. 2006; Bjornsson and Angulo 2007). HCV genotype 3-associated steatosis is induced by the virus itself through a direct cytopathic effect, while cases in other genotypes are caused by pre-existing risk factors, such as obesity, diabetes, alcohol and other host-mediated factors (Patton, Patel et al. 2004; Hung, Kuo et al. 2006; Leandro, Mangia et al. 2006). HCV is a common cause of both acute and chronic liver diseases, which may develop into cirrhosis, end-stage liver disease and hepatocellular carcinoma. In 12-16% of acute HCV infections, the virus is naturally eradicated from the host within several months of the infection (Ascione, Tartaglione et al. 2007). The diagnosis of acute hepatitis C is suggested by the presence of anti-HCV or HCV RNA in serum, however neither anti-HCV nor HCV RNA testing can reliably distinguish between acute and chronic hepatitis C. The majority of the HCV infection cases (about 85%) develop chronic hepatitis C which is represented with the persistence of alanine transaminase (ALT) elevation or HCV RNA in serum for 6 months or longer period (Seeff 1999; Zakim and Boyer 2003; Ascione, Tartaglione et al. 2007). HCV infection itself appears to have little direct cytotoxicity on hepatocytes, however the immune response (cytokines, neutrophils, macrophages, natural killer cells) to HCV infection is believed to be a major determinant of severity, course and outcome of acute and chronic hepatitis C. Resolution of the infection is associated with a vigorous and broadly 14  based CD8 cytotoxic T-lymphocyte (CTL) response, and the persistence of a low-level, ineffective immune response to HCV is likely to be responsible for chronic hepatic injury rather than a direct cytopathic effect of HCV. Also, although the pathology of hepatitis C involves hepatocellular injury, necrosis, unrest, portal and parenchymal inflammation, fibrosis, cirrhosis and steatosis, needle biopsy alone may not be reliable in assessing chronic hepatitis C progression because of the potential sampling error as the hepatic fibrosis is irregularly distributed in the liver. The presence of fibrosis on liver biopsy is also correlated with patient age, age at onset of infection, male sex, history of heavy alcohol intake, and co-infection with other viruses, such as HIV and hepatitis B virus (Alberti, Chemello et al. 1999; Seeff 1999; Ascione, Tartaglione et al. 2007). After chronic HCV infection is established, spontaneous resolution is uncommon, and up to 20% of patients with chronic HCV infection develop cirrhosis within 10 years of onset and 10-25% of cirrhotic patients develop hepatocellular carcinoma within five years (Seeff 1999; Hung, Kuo et al. 2006). Serious complications are more likely to occur in the third or fourth decade after the infection (Figure 6) (Seeff 1999; Ascione, Tartaglione et al. 2007).  15  ^ ^ ^ ^  ^,  ^-  ^.  U)  ca in tAW^ 0^ .0^ 0^ ni^  V) CD 0  11) N-  ._c 0  )  o V _a C < . C 45 0 F2 t-Ti a.9  >1 a0  0 co 0_ -0 La5 a) C.) n3 CU v^C C^' 7 (..) O 1 c ^0 a3  L-  (..)^t..715 t.) v  a) a)  W 1^a) co cn D XI 4:^as as th^ (...) 4-, tn u) W^LS 0  —_  c0 c 3 .... i^cn  0 at3^E E 0 2J^> o 0 4-^0 .c 3 >^i  01  .0 6.tii^ 6.^ g:".  0 CNI  co^  t^ 0^  ^(..)^ ^c.14-'^ .(..) to  7."..^  a^  4.1^  ^E ^0  ^.c  c^ 0^  cr-^ in^ V"..^  0. 1■ (i)^  IA^ 0 4)^  Ts  L: a) a) ..ca (-)  --2.7.).0C >  92  -C  (,) >,  . CT)  C5. ca_. c -c ..E. ^ O -(11  m c^0 a) = ^i > 2.2^ 07 (3  tn^  O w^ m^> (1) ^.0^♦an^_._ _c  ^O ^C  .0^  ...... 42)^ O  > (13  0) CD 0 '_E ct 0 is TD  M  §  =  16  1.6 HCV treatment  Currently an HCV vaccine is in a developmental stage and no effective vaccine is yet available, due to the lack of absolute immunity to re-infection with this virus and the frequency with which the virus can mutate and escape immune clearance (Zakim and Boyer 2003; Houghton and Abrignani 2005). Unlike HIV, however, HCV can be eradicated with interferona and ribavirin combination therapy. Knowing that HIV/HCV co-infected patients are at significantly higher risk of developing liver disease, co-infected patients in the early stages of HIV disease (i.e. 350 CD4 cells/nil) might greatly benefit from starting HCV therapy as soon as possible. The recommended duration of HCV therapy is 24 weeks for HCV genotype 2 and 3, and 48 weeks for genotype 1 and 4, as genotype 1 and 4 are known to be more resistant to interferon-a (Bruno, Sacchi et al. 2002). However, if there is continued presence of HCV RNA after 24 weeks of treatment, treatment cessation should be considered as there is a high potential of non-responsiveness, and the long-term benefit of therapy becomes questionable (Tural 2007). In the HIV/HCV co-infected population, about 30% of genotype 1 and 60-70% of genotype 3 patients that undergo the therapy attain successful eradication of HCV (Soriano, Puoti et al. 2007). HCV therapy is difficult. Most patients experience an influenza-like syndrome after the initial injection of interferon-a. Twenty to thirty percent of patients receiving the therapy develop some degree of depression, irritability, anxiety or moodiness (Zakim and Boyer 2003). Interferon-a is myelosuppressive and the doses used to treat chronic hepatitis C regularly result in decreases in red blood cells, white blood cells and platelet counts. Ribavirin can cause a dosedependent red blood cell haemolysis, apparently resulting from a high concentration of ribavirin triphosphate inside the red blood cell (Zakim and Boyer 2003).  17  1.7 Hepatotoxicity in HIV/HCV co-infection  1.7.1 Impact of hepatotoxicity in HIV/HCV co-infected population Today HIV infected people are increasingly dying of end-stage liver diseases and this is especially true in the HIV/HCV co-infected population. The rate of end-stage liver disease deaths has increased from 8% of HIV patient deaths in 1997 to 25-50% in 2004 (Bica, McGovern et al. 2001; Aguero 2007). This mortality rate is driven by the fact that liver diseases among HIV-infected patients is eleven times more prevalent than in the general population of a similar age (Aguero 2007). Some of the suggested reasons for this trend are: i) longer life expectancy of HIV patients, in such a decline in opportunistic infections has led to an increase in the clinical presentation of other underlying co-morbid conditions, such as advanced liver diseases; ii) accelerated liver injury with HCV infection and/or hepatitis B virus; and iii) idiosyncratic hepatotoxic reactions (Bica, McGovern et al. 2001; Monga, Rodriguez-Barradas et al. 2001; Sherman, Peters et al. 2007). This issue is prominent and important among HIV/HCV co-infected patients, as one recent study found that 93% of HIV patients who died with endstage liver diseases also had chronic HCV infection (Rosenthal, Pialoux et al. 2007). Several studies have shown accelerated hepatitis C progression with HIV infection, such as higher rate of liver fibrosis, steatosis, cirrhosis, hepatocellular carcinoma and fulminant hepatic failure, and shorter survival among HIV/HCV co-infected patients compared to HIV or HCV mono-infected patients (Monga, Rodriguez-Barradas et al. 2001; Kramer, Giordano et al. 2005; Pineda, Romero-Gomez et al. 2005; Poizot-Martin 2007; Thein 2007). However, HCV influence on HIV progression is controversial (Rockstroh 2007). Poor liver condition in HIV/HCV coinfected patients might be associated with a decreased tolerance for HIV therapy, as HIV/HCV co-infected patients show a three times higher incidence of HAART related hepatotoxicity than HIV mono-infected patients (Sulkowski and Benhamou 2007). HIV/HCV co-infected patients 18  may also respond less to HCV therapy compared to HCV mono-infected patients; sustained HCV virologic response among HIV/HCV co-infected patients is achieved in only 27-40 % of patients treated with HCV therapy and in as few as 14% of HCV genotype 1 patients (Luetkemeyer, Hare et al. 2006). In HIV therapy, hepatotoxicity is a term broadly describing all clinical and biochemical liver adverse events attributed to antiretroviral therapy, and this vague concept can cause patients and physicians to avoid and interrupt HIV therapy when it is not truly necessary (Cooper 2007). Complex mechanisms and interactions between multiple factors causing liver damage are making it difficult to define HAART-related hepatotoxicity (Figure 7). Increasing our understanding about the extent to which HAART causes hepatotoxicity and how this may influence HCV disease progression and HCV treatment outcome in HIV/HCV patients would be beneficial. There are four primary mechanisms of HAART associated hepatotoxicity (Nunez 2006; Vallet-Pichard and Pol 2006; Sherman, Peters et al. 2007).  i)  Immune reconstitution injury.  ii)  Hypersensitivity reactions leading to liver injury with either nevirapine or abacavir.  iii)  Direct hepatocellular idiosyncratic injury by PIs.  iv)  Mitochondrial toxicity by NRTIs.  19  (111111111101■Ir  I ••••■•••=e  0) -CD C C  L'  <  a2 ( _C  @  ( )  CD  (13 (1)  a)  0 (›.. 1) 'Es  cn 7 c 0  o *-  CD0  t5  -  (1) o C o  Onc (-) — o > o) . (7) co  f3 Cl)  T.) < C• •-  o  a) •3 3 .0 T<  E33  o 176 a_ ta)^_c c crs -  > -0  E  )  a) CI OD Cr) a C.) al LC  Co  11-3' (13 __C -"1-0 C (I) FL) 0 0 -C 0 CD t). 0 0  E (13^(.)  _C 0_ - C.)  2  0  0  .(-1)  E /E .c o N • C1) Cl - a) CO ‘-  E  = E z co E E  -  CD 0 0  o  ,,,^4 %. o  E •(13 n 2c g  E  (/)^4-7; 0 ..^.7)  a)  -CO  cu^clan) -o a) o M of  a) „D-  E  20  1.7.2 Immune reconstitution injury Immune reconstitution injury occurs with the recovery of CD4 cells through effective HAART, which then leads to the clearance of hepatocytes by an immune-mediated pathway (Sherman, Peters et al. 2007). This effect is associated with elevation of liver enzymes in plasma, and HCV-infected hepatocytes could be a target of the recovered immune system in HIV/HCV co-infected patients (Sulkowski and Benhamou 2007). Clearance of HCV from the liver by immune reconstitution has been observed by several researchers, lowering the progression of fibrosis and liver-related mortality (Benhamou, Di Martino et al. 2001; Qurishi, Kreuzberg et al. 2003), but others also observed the re-emergence of plasma HCV-RNA after the initiation of HAART (Cooper and Cameron 2004). Therefore, the overall benefit of HAART in terms of immune reconstitution and HCV clearance is still unclear (Rockstroh 2007).  1.7.3 Hypersensitive reactions by NNRTIs Hypersensitivity reactions are idiosyncratic reactions of the host which are not related to the amount of drug intake. Sulpha drugs are prototypical drugs inducing these immune-mediated reactions involving the liver (Nunez 2006). Drug related hypersensitivity repeatedly occurs 100 times more frequently in HIV patients (Phillips and Mallal 2007). Injury is manifested by the development of hepatitis, and can be accompanied with fever, skin rash and jaundice. Immune mediated drug reactions may involve the generation of neoantigens formed by the reaction of liver proteins with reactive drug metabolites (Bissell, Gores et al. 2001). In HIV therapy, some NNRTIs are reported to evoke hypersensitivity reactions which are associated with the genetic background of the patients (Hetherington, Hughes et al. 2002; Mallal, Nolan et al. 2002).  21  1.7.4 Direct hepatocellular idiosyncratic injury by PIs Although over 90% of HIV/HCV co-infected patients receiving PI-based therapy do not develop hepatotoxicity, concerns are often raised about PI use in those with liver diseases (Cooper 2007). PIs interrupt liver enzymes, such as cytochrome p450 3A4—a liver enzyme that is responsible for metabolizing 60% of currently known therapeutic drugs (Zhou, Yung Chan et al. 2005)—which could influence the pharmacokinetic and metabolic effects and may contribute to hepatotoxicity by increasing drug concentrations or interfering with liver function (Sulkowski, Thomas et al. 2000). PIs are associated with elevated liver enzymes and bilirubin levels in the blood when high doses are taken by patients (Sulkowski, Mehta et al. 2004; Rodriguez-Novoa, Barreiro et al. 2006).  1.8 Mitochondrial toxicity by NRTIs  1.8.1 Mechanisms of mitochondrial toxicity NRTIs, as part of antiretroviral therapy, are responsible for causing mitochondrial toxicity (Lewis, Day et al. 2003). Mitochondrial toxicity is one of the factors that can lead to serious health conditions, not only liver-related such as hyperlactatemia/lactic acidosis and liver failure, but also peripheral neuropathy, peripheral lipoatrophy, bone marrow toxicity, pancreatitis, and sometimes death. Peripheral neuropathy is the first indicator of NRTI related toxicity in many cases, –6-8 weeks after starting the therapy (Petit, Fromenty et al. 2005). Highly metabolic tissues often show the signs of mitochondrial toxicity, and cases have been described involving the liver (Lewis 2003). The key mechanism of NRTIs induced mitochondrial toxicity is attributed to the similarity between HIV reverse transcriptase and mitochondrial DNA polymerase. NRTIs are intended to be incorporated in elongating DNA and cause chain termination during the 22  transcription of HIV RNA into DNA by the HIV reverse transcriptase. This effectively inhibits HIV replication. However, these nucleoside analogues can also have the unwanted effect of inhibiting human DNA polymerase y, the only enzyme in human cells responsible for mitochondrial DNA (mtDNA) replication (Lewis, Day et al. 2003; Cherry, Lala et al. 2005). DNA polymerase y shares characteristics with not only DNA-dependent DNA polymerases, but also with RNA-dependent DNA polymerases. Since HIV reverse transcriptase is in effect an RNA-dependent polymerase, this similarity may be one of the reasons why this enzyme is susceptible to NRTIs (Fridlender, Fry et al. 1972; Kakuda 2000; Falkenberg, Larsson et al. 2007). Furthermore, unlike nuclear DNA polymerases (i.e. DNA polymerase a, (3, a and c) that exert strict proofreading and repair of basepair mismatches during the replication, DNA polymerase y has a comparatively poor DNA repair capability. The order of general inhibitory effect of NRTIs on polymerases is: HIV reverse transcriptase >> DNA polymerase y > DNA polymerase 13 > DNA polymerase a = DNA polymerase c (Kakuda 2000). The following mechanisms between NRTIs and DNA polymerase y may cause the acquired mitochondrial toxicity (Lewis, Day et al. 2003).  i)  Inhibition of DNA polymerase y by competing with natural nucleotides.  ii)  Chain termination by NRTIs incorporation into DNA polymerase y.  iii)  Decreasing fidelity of DNA synthesis by DNA polymerase y.  iv)  Persisting existence of NRTIs in mtDNA due to inefficient excision.  These characteristics of DNA polymerase y and NRTIs play a role in the fact that NRTI exposure can result in mtDNA depletion and possibly mtDNA deletion and mutation. The likely  23  consequence of this is reduced mitochondrial structural integrity and function (Montaner, Cote et al. 2003). Those of the dideoxynucleotide type NRTIs (D-drugs), including stavudine (d4T), didanosine (ddI) and zalcitabine (also known as dideoxcycystidone or ddC) have been shown to be more toxic to the host and to cause mitochondrial toxicity through mtDNA depletion than non-D-drug types (zidovudine (AZT), lamivudine (3TC) and abacavir (ABC)) (Figure 8) (Walker, Setzer et al. 2002; Walker, Bauerle et al. 2004). A kinetic study shows that D-drug types are utilized by DNA polymerase y as efficiently as natural nucleotides in vitro, whereas the non-D-drug types only act as moderate inhibitors of DNA chain elongation (Lim and Copeland 2001). Production of ddC has been stopped since 2006 because of its severe side effects on patients.  24  NH 2  HO — N  ch.0.7  OH Dideoxycytidine (ddC)  Deoxycytidine  ') HO  —  0  0^  0  NH  .  1^NH^  N0^HO— N  \N  HO^  cr.0.2  O  OH  N3  Deoxythymine  Natural nucleotides  NH ..L  Zidovudine (AZT)  Stavudine (D4T)  i  NRTIs  Figure 8: Examples of chemical structures of NRTIs. Compared to natural nucleotides, the 3' OH group on the pentose is missing in NRTIs, preventing the elongation of DNA. Base structures are maintained in NRTIs.  25  There is also a concern of mitochondrial toxicity with the use of ribavirin in HCV therapy because ribavirin is also a nucleoside analogue and as such, it may also inhibit mtDNA synthesis (Zakim and Boyer 2003). The combined effects of ribavirin and HAART on mitochondria, along with the potentiation of other nucleoside activities resulting from ribavirin's promotion of nucleoside phosphorylation may act synergistically to increase mitochondrial toxicity (Zakim and Boyer 2003; Bani-Sadr, Carrat et al. 2005). When didanosine (ddI), one of the NRTIs, is concurrently used with ribavirin for HCV treatment, increased mitochondrial toxicity may further be accelerated because ribavirin can potentiate the efficacy of ddI by promoting its phosphorylation (Salmon-Ceron, ChauvelotMoachon et al. 2001). Ribavirin inhibits inosine monophosphate (IMP) dehydrogenase and this leads to an increase in the IMP pool which acts as a phosphate donor for ddI. Phosphorylated ddI is a nucleotide analogue that can inhibit DNA polymerase y activity. Today, the combination use of ddI and ribavirin is not recommended for this reason (Soriano, Puoti et al. 2007). However ddI is still is being used in developing countries and combination treatment with ribavirin is possibly accessible (Cooper 2007).  1.8.2 Metabolite accumulations with mitochondrial toxicity The mitochondrion is an organelle responsible for cellular metabolism and energy production, and abnormal mitochondrial function may result in accumulation of metabolites (lipids and glycogens) and increased lactic acid levels as cells are forced to generate energy through non-oxidative pathway, such as glycolysis (Figure 9). Some mitochondrial toxicity symptoms (e.g. steatosis, hyperlactatemia / lactic acidosis) are believed to be a consequence of mitochondrial dysfunction, particularly in hepatic cells.  26  Glycolysis  Liver cell Glycogen  Oxidative phosphorylation  ROS  30 ATP + CO 2 + H 2 0 Fatty Acids  1  ••■•••■triglycerides  Normal mitochondrial function Mitochondrial dysfunction  Figure 9: Diagram of a liver cell with mitochondrial dysfunction. Decreased mitochondrial function causes elevation of lactate, as cells are forced to generate energy through glycolysis, and pyruvate is converted to lactate. Increase of glycogen and lipid levels in hepatocytes would be expected because of the abnormal metabolisms. Dysfunctional mitochondria are also prone to generate reactive oxygen species (ROS), which can further damage the cell.  27  1.8.3 Hyperlactatemia Hyperlactatemia is a condition in which abnormally high lactate concentration is detectable in blood. The clinical consequence, if it develops into lactic acidosis (acidification of blood), includes gastrointestinal symptoms (abdominal distention, pain, nausea, vomiting, diarrhea, hepatomegaly), respiratory symptoms (cough, dyspnea), muscular symptoms (myalgias, cramps, generalized weakness, and fatigue), and rapid weight loss (Schiller 2004). Dysfunctional mitochondria can result in lactate elevation, as cells are forced to generate energy through glycolysis. A significant association between decreased blood cell mtDNA levels and symptomatic NRTI-related hyperlactatemia has been found in HIV patients taking HAART (Cote, Brumme et al. 2002).  1.8.4 Glycogen accumulation The liver is a major organ balancing the homeostasis of glucose concentration in blood by converting it back and forth to glycogen (Ferrer, Favre et al. 2003). High levels of glucose in the blood from decreased mitochondrial activity may increase glycogen amount in hepatocytes. Under electron microscopy, Verruci G. et al (2004) qualitatively observed increased glycogen accumulation in hepatocytes among HIV/HCV patients with concomitant HAART compared to co-infected naive group. Menstruation cycle affects glycogen repletion in women—greater in the luteal phase compared with the follicular phase (Tarnopolsky, Atkinson et al. 1995)—and a rapid glycogen accumulation is a physiological response in mammals to the increase in blood glucose concentration that occurs after a meal in mammals (Ferrer, Favre et al. 2003). Consequently, these two factors may need to be considered when measuring glycogen amount.  28  1.8.5 Hepatosteatosis  Hepatosteatosis—lipid accumulation in hepatocytes—can be a consequence of mitochondrial toxicity. There are two forms of steatosis: macrovesicular steatosis and microvesicular steatosis. Macrovesicular steatosis represents large vacuoles of lipid that occupies the cytoplasm and displaces the nucleus to the cell periphery (Pessayre, Mansouri et al. 1999). Alcohol abuse, obesity and diabetes are major causes of this form of steatosis (Fromenty, Berson et al. 1997). The possible mechanisms behind the development of macrovesicular steatosis are (Fromenty, Berson et al. 1997):  i)  Increased mobilization of fat from adipose tissue.  ii)  Increased hepatic synthesis of fatty acids.  iii)  Increased esterification of fatty acids into triglycerides.  iv)  Decreased egress of triglycerides from the liver, in the absence of or mild decrease in mitochondrial p-oxidation.  Microvesicular steatosis occurs when tiny lipid vesicles accumulate and make a hepatocytes "foamy", giving them a spongiocytic appearance caused by the severe impairment of mitochondrial fatty acid 13-oxidation. As a consequence, nonesterified fatty acids (NEFA) are poorly oxidized in mitochondria. Hepatic triglycerides accumulate as small lipid vesicles and their abnormal accumulation within the hepatocytes is thought to play a significant role in the physiology of microvesicular steatosis (Pessayre, Mansouri et al. 1999). The size of vesicles is 0.3-4.0 pm in diameter (Rolfes and Ishak 1985). In severe cases, it may develop to liver failure, coma and eventually death (Fromenty, Berson et al. 1997). While some hepatocytes are filled up with numerous small lipid vesicles, other hepatocytes may still exhibit macrovesicular steatosis  29  in the same patient. Such mixed case would be also classified as microvesicular steatosis. When I3-oxidation is impaired by decreased mitochondrial function, free fatty acids accumulate and interfere with the assembly, transport and secretion of low density lipoproteins by the liver. This can additionally cause fat deposits (Fromenty, Berson et al. 1997). It has been suggested that decreased mitochondrial I3-oxidation with HAART induced mitochondrial toxicity may cause hepatosteatosis. However, this association is controversial. Some data indicates significant increases in lipid accumulation in hepatocytes among HIV/HCV co-infected patients taking HAART (Sulkowski, Mehta et al. 2005; McGovern, Ditelberg et al. 2006), but others do not find any relationsip between these two factors (Monto, Dove et al. 2005; Bani-Sadr, Carrat et al. 2006; Neau, Winnock et al. 2007). Certainly, severe hepatic steatosis is a hallmark of HAART-induced lactic acidosis (Cote 2005). There is a general agreement that steatosis is associated with HCV genotype 3 infection, older age, increased body mass index and severity of liver diseases. Gender may play a role in the different levels of lipid accumulation in cells, since the preference of fat utilization as an energy source is different between genders. For example, women oxidize more lipid than carbohydrate during exercise compared to men (Tarnopolsky and Ruby 2001). Female gender is also significantly associated with increased steatosis among HCV mono-infected patients (Matos, Perez et al. 2006). Much remains to be elucidated with respect to the HAART related hepatotoxicity and liver mitochondrial toxicity in HIV/HCV co-infected population, and how this may affect their health.  30  II. CHAPTER TWO: OBJECTIVE AND HYPOTHESIS  2.1 Rationale  Despite the significant co-infection of HCV within the HIV population, the degree to which HIV and HCV antiretroviral medications may negatively affect the liver is not well understood. This is of special concern for HIV/HCV co-infected patients as their liver is already in a vulnerable state due to their HCV infection. Hepatotoxicity in HIV/HCV co-infected patients is not clearly defined, and this may also be causing unnecessary fear to both patients and physicians when starting HCV treatment. The original objective of this research project was to investigate the impact of HCV therapy on hepatic mitochondrial toxicity in HIV/HCV co-infected patients, whether on HAART or not, and to compare the condition of patients' livers before and after HCV therapy. Ultimately, this research should help understand the mechanism of hepatotoxicity in this particular population, and could inform the choice of drug combinations to minimize hepatotoxicity in HIV/HCV co-infected patients. However, as previously mentioned, the entire HCV treatment lasts at least 24 weeks and up to 48 weeks. This requires a long-term collection process in order to obtain pre- and post- HCV therapy samples and data from the study participants. Therefore, considering the time available for this master's program, my project has focused on collecting the baseline data of these patients and studying whether HIV therapy alone, prior to HCV treatment is associated with increased liver mitochondrial damage in HIV/HCV co-infected patients (Figure 10).  31  ^  Pre-HCV therapy HIV/HCV patients ^ Needle liver biopsy (ON-HAART N=14, ^ ( 1mn1 2 x 20mm ) OFF-HAART N=9 )  MSc. project Baseline data collection  HCV therapy ( 24 to 48 weeks )  Post-HCV therapy Needle liver biopsy ( lm m 2 x 20mm )  Figure 10: Overall scheme showing the original 5-years research project liver sample collection and how the present MSc. project is integrated within it. A liver biopsy is collected before HCV therapy and another biopsy is done after HCV therapy, to compare mitochondrial conditions before and after HCV therapy. HCV therapy usually takes about a year to complete, and considering the time available for this MSc. program, I focused on collecting the baseline data and asked whether HAART alone can damage mitochondria in HIV/HCV co-infected patients.  2.2 Objective  The molecular and cellular mechanisms of mitochondrial toxicity in HIV/HCV co-  infected populations and its effect on overall liver-related morbidity and mortality still remains unclear. Although abnormal mitochondrial structure (e.g. cristae reduction, polymorphism, crystalline inclusion) has been demonstrated in HIV/HCV patients by electron microscopy (Verucchi, Calza et al. 2004; Van Huyen, Batisse et al. 2006), it is unclear what damage is caused by HCV infection versus NRTI-related mitochondrial toxicity. Our objective in this present research was to examine whether HIV/HCV co-infected patients who are currently receiving HAART would show greater liver mitochondrial damage than those who are currently off HAART or HAART naïve. This was done by investigating liver mitochondria through the study of their DNA and RNA levels and their ultrastructure.  32  2.3 Hypothesis I hypothesized that HIV/HCV co-infected patients who are currently receiving HAART will show greater liver mitochondrial damage than those who are not on HAART.  33  III. CHAPTER THREE: MATERIALS AND METHODS  3.1 Study population  This was an open, prospective observational cohort study, which was a part of the original HIV/HCV co-infection study funded by a 5-year grant comparing the antiretroviral therapy associated hepatocyte mitochondrial toxicity before and after HCV therapy among the HIV/HCV co-infected population (CIHR (HOP-75347), title: Mitochondrial toxicity in HIV/HCV co-infection antiviral therapy, PI H6lene The majority of the HIV/HCV co-infected study participants were consented and enrolled at the St. Paul's Hospital John Ruedy Immunodeficiency Clinic, except for one participant who was enrolled at Dr. Farley's clinic in Vancouver. Participants were asked to undergo a double liver biopsy at the time of their standard-of-care biopsy; the first obtained biopsy was for their pathological examination and the second one was for research purposes. At the time of their liver biopsies, all patients were HCV therapy naïve and being evaluated to start HCV therapy. Inclusion and exclusion criteria for the study were as summarized below:  Inclusion criteria i)  HIV infected; and  ii) Anti-HCV and anti-HIV antibody positive as well as HCV and HIV RNA-positive; and iii) HCV therapy naïve; and iv) HAART naïve or off HAART for at lest 6 months, unless currently on stable HAART for at least 6 months.  34  Exclusion criteria i) Other causes of chronic liver diseases (e.g. hepatitis B virus, metabolic liver disease, autoimmune liver disease) are present; or ii) Had one or more opportunistic infections within the last month; or iii) Pregnant, or will not avoid seeking pregnancy during the study.  A total of 45 participants were enrolled between July 2003 and June 2007; 36 individuals underwent their "standard of care" pre-HCV treatment screening liver biopsy (hereafter referred to as the first biopsy) while the other individuals were either lost to follow-up (N=8) or died (N=1). Five individuals declined to undergo a second biopsy so that no "research biopsy" was collected. Females (N=8) were excluded from this analysis as their number was relatively small and because metabolic mechanism(s) can be influenced by menstrual cycle (Figure 11) (Tarnopolsky and Ruby 2001). A total of 23 male subjects were included in the analyses presented in this thesis.  45 Enrolled^36 pre-HCV^31 research —> individuals^treatment biopsies^biopsies  st,  9 lost to follow-up (deceased, no show, etc.)  5 opted out of having 2" biopsy(Pathology biopsy only)  23 males studied  8 females  Figure 11: Study population from the enrolment up to the collection of research samples.  35  These 23 subjects were chosen as they were either HAART-naive (N=4), off HAART for more than 6 months (N=5) or on stable HAART for at least 6 months (N=14). All procedures and protocols were approved by the University of British Columbia and Providence Health Care Research Ethics Boards. All participants provided written informed consent.  3.2 Liver biopsies  Ultrasound-guided liver biopsies were performed at St. Paul's Hospital. All biopsies took place before noon and patients were instructed to fast the night before and on the morning of the biopsy. Two needle biopsies using a spring-loaded needle biopsy gun (10 mm core 18 gauge) were obtained under local anaesthesia. The first needle biopsy sample was used for the standard histological pathology examination using the modified Ishak-Knodell scale for scoring (Ishak, Baptista et al. 1995; Brunt, Janney et al. 1999; Brunt 2000), while the second one was immediately cut into aliquots. Within 5 minutes of the biopsy collection, one aliquot (1mm 2 x 5mm) was frozen at -80°C, for later use to quantify mitochondrial DNA (mtDNA). A second aliquot (1mm 2 x 5mm) was immersed in RNAlater TM (QIAGEN, Mississauga, Ont., Canada) overnight at 4°C before being frozen at -80°C for the mitochondrial RNA (mtRNA) quantification. A third aliquot (1mm 2 x 5mm) was immersed in 2.5% glutaraldehyde fixative for transmission electron microscope (TEM) and stereological image analysis (see section 3.8).  3.3 mtDNA extraction  Total DNA was extracted from these liver samples using the QIAamp or the DNA/RNA Allprep extraction kits (QIAGEN, Mississauga, ON, Canada), according to the manufacture's protocol. Samples were homogenized with the rotator-stator homogenizer Polytron PT2100  36  (Kinematice AG, Switzerland) for 40 sec in the provided buffer. The knife of the homogenizer was always cleaned between homogenizations of samples, by running it under dH2O for 20 sec, then 70% ethanol for 20 sec and finally in dH2O for 20 sec.  3.4 mtDNA assay  Extracted DNA was subjected to quantitative PCR in a LightCycler 480 (Roche Applied Science, Laval, Que.) to quantify a nuclear gene (Accessory Subunit of the Polymerase Gamma [ASPG]) and a mitochondrial gene (Cyochrome C Oxidase subunit I [COX1]) (Cote, Brumme et al. 2002; Cote, Yip et al. 2003). Sequences of primers and probes are summarized in Table 1. The amplified products were quantified against a standard curve built from six 10-fold serial dilutions (31310000) of Topo plasmid (Invitrogen, Burlington, Ont) containing these two genes. All real-time PCR reactions were done using the LightCycler 480 Probes Master kit (Roche Applied Science) in 10 til volume (i.e. 2 p,1 DNA sample, 5 mM Mg 2+ , 1 ii,M each primer, 0.2 jiM fluorescein probe and 0.4 i_tM LC Red 640/705 probe). The PCR conditions were 95°C/10min, 45 amplification cycles of 95°C/5sec, 60°C/10sec and 72°C/5sec. A single fluorescence acquisition, to monitor the DNA quantity change by PCR, was performed at the end of each annealing step. A negative control and two internal controls were always included in each run. Each sample was assayed in duplicate and results of these two duplicates were accepted if less than 25% apart. The mean of these two results was regarded as the DNA copy number of the sample. If readings fell outside the standard curve range, the sample was re-assayed after adequately diluting. The mtDNA content of each sample was expressed as the ratio between mtDNA and nuclear DNA (mtDNA/nDNA). Change in the ratio theoretically reflects change in  37  mtDNA amount, as cells are assumed to contain a constant amount of nDNA. All real-time PCR assays (mtDNA and mtRNA) had a coefficient of variation below 15%.  38  E  ei ■ >  0  CC  ■ 0  ,(7) 0-  ,,, (1) —  4=' 1  (  Z  u)  ■ z  C/3  E  0  o 0  4,  C.) < 0 0 *  (12 < 0 <  I-  0*  in  in  0  H H  H  < < < < Lo  or  On  ,r  a_  I__  1  0  I-  H  0 0 II< 0  (9  in  0  0 1_  11 0  0  , ,„  <  0  I—  in.  e?  0  c)  0, U C) 8  0 0  i0 () ix)  f2 1— 0  (,_.2 < 0  0 i—  <  1--  in  ;I, 0 CD 0  rn  i c9  10  g o  IH  < I— 0 0  y in w co 0-  .--• H  co  to CO n  ei? < 0 < ° 0  H  e?  r•-  E6  cc' 0.  1_  g-3  0 < co  en „  i_  < 03  w ,,  -r.  0  0 0 ' in  H  H 0 0  o(-3  1_0  0  0. < co x 0 0  cNi  in  I0 (...) < o 2,  r o  0 H c)  0  (D e0  0  5 .4. 0 ' it)  ce cc)  8 0  en 6 <  *()* 01— 0 0 0 0 1—  ar 0_. < 0 < En (.9 >1c_ 0 r„„ F-0_ 1— 0 1=0  -,,,.) 0 < in k 0 (_) 0 . 0 F0 (9 *00< 0 f....) 0  E a-  w  IQ  0  < o <  < 0 < f , ="' It)  <  (12 0  <*  1.:c.,= 0 (9 < 0  0._  o  I—  0*  < 0 < C) (...) < CD (-) *  Ln  5  CN1  Es o  CV  ce ci0  (-) 0  ct  Nr 0 ii. 0  E.,) 0 < <  CD  I-  <  0 C.) 0  <  ° <  . 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G I 0 0  ° 0  e 0 E 71. a '6  °em  0 0  to (1) 2 a-  c° x^x ca  a z  0(.0 <  a.c.  )7  < z  in GL,  )7  < < z^z ce E  < 0:1).  < z  °cm C 3 ce E  < (IL  4 z  E ,,,, ••• E ■ 12 E .0 .- 0_. 8: 0 2 ,,, Cl) a-^  — < < < < z z ci a  C  0 0  < 1-1_0 t1 : 0 0 0 0 0 H o 000000000 w<0 1—<<01—< 1—(-)001—o  0  0 o  0 0 i 6 0 0 o * o < < H. 0ct, ..‹. <zr H<cr _ < p < 00 I— C.) (...) 0 0 U 0 I— r1 0 IH d 61.7t0<c),„ t- 1-- 0 o 0 o 0 H .= c H , < < 000001'z woR00 0 = 0 0 < < 0 0 H 1— 0 -00-000000<0001_0 H 0 01 CD 0 H f— < < < ,- a) rn I— as  o  u_  Cfi  a)  .0  2 a_  E a) E  a z  E  9,  39  3.5 mtRNA assay  Six samples collected in the early stage of the project were not treated with RNAlater TM (QIAGEN), therefore only a total 17 RNAlater TM treated samples were analyzed. RNAlater TM is a reagent that stabilizes the RNA structure to minimize its degradation. RNA was either extracted using the RNEasy kit (QIAGEN), then treated with DNAse and total cDNA prepared by reverse transcription (Roche Applied Science) using a random (N6) primer, or was extracted with the Allprep DNA/RNA kit (QIAGEN), and total cDNA was prepared using the Quantitect® RT kit (QIAGEN). The cDNA quantification protocol using real-time PCR was the same as the one described in the mtDNA assay section. Briefly, cDNA was quantified for both the mtDNA-encoded gene COX1 and the nuclear DNA-encoded gene Cytochrome C Oxidase subunit 8 (COX8). The standard curves were built by serial dilution of Topo plasmids (Invitrogen) containing the genes of interest. Results were expressed relative to the house keeping gene I3-actin mRNA level (COX1 or COX8 mRNA/(3-actin mRNA). Most housekeeping genes are expressed similarly in the liver in a sex dependent manner (Verma and Shapiro 2006). We are only using male subjects, therefore we decided to use 13-actin whose primers were already available in our lab. Primers and probes used for the cDNA quantification were summarized in Table 1.  3.6 Long PCR  Two overlapping 8.5 kb mtDNA fragments that cover the whole mitochondrial genome were amplified by PCR (Figure 12). The PCR master mix was made using the Expand Long Template PCR System Kit (Roche) in a 50 pl volume (i.e. 2 ml DNA sample, 5 gl buffer  40  solution [provided by the kit], 0.3 [tM each primer, 0.35 1..tM dNTP and 2 U Taq polymerase). Sequences of the four primers are presented in Table 1. The prepared master mix was then amplified under the following PCR conditions: 10 amplification cycles of 95°C/l0sec, 58°C/30sec and 68°C/360sec, then 25 amplification cycles of 95°C/10sec, 58°C/30sec and 68°C/380sec. Negative (H2O) and positive (Topo plasmid with COX1 primers) controls were always run together. PCR products were visualised on a 0.75% agarose gel containing 0.5 gg/m1 ethidium bromide at 80V for one hour. The gel image was digitally captured and the amount of mtDNA deletion was measured using an arbitrary qualitative grading scale ranging from 0 (meaning no extra band visible) to 4 (multiple extra bands of high intensity visible on the gel). To validate the consistency and reproducibility of grading, two different blinded examiners practiced the scoring of the gel based on the protocol above, and comparable grading results were obtained from two examiners.  41  )alert 110  e juaw6eJd  ^1  CO  I  WO* op  q luaLu6ei j  • -  ui Ol tc CC 0 C  0 a) _c _  0) C  C  2 Co 0 C C 0  _c cn  a) O a3 6 .E C EQ •• -c Cl)  a) < c Z .  )  E can O < 0  o 4— F. CO  • -c E O  •  C  CL a) C.) -c a.) E •  -  o o c z  —1 Co CO  •  E ,a) 42  3.7 TEM  All procedures in this section were performed inside the fume hood, as reagents used for the TEM fixation are toxic. These reagents were discarded in appropriate waste containers after use for proper disposal. As soon as the liver biopsy was obtained, about one fourth of the liver tissue (1 mm 2 x 5 mm) was immediately cut into pieces about 1 mm 3 in size or smaller on a wax board using a disposable scalpel and immersed in 2.5% glutaraldehyde fixative (1 ml 25% Glutaraldehyde + 4 ml H2 O + 5 ml 0.2 M Sodium Cacodylate) for at least one hour. Care was taken to avoid drying of the tissue (i.e. the sample was cut in a droplet of fixative on the board). After primary fixation with glutaraldehyde, samples were immersed in 0.1 M Sodium Cacodylate for 10 min x 3 using new buffer each time. Each sample was always incubated in the rotary wheel. For post fixation, an 0s04 / KFeCN 6 solution was prepared just before use (0.04g KFeCN6 + 2 ml 0.2 M Sodium Cacodylate + 2 ml 2% 0s0 4 ). After finishing the buffer rinse, freshly made 0s04 / KFeCN6 solution was added to the bottle with the specimen and incubated in a rotary wheel for one hour. The bottle was covered with foil as the solution is light sensitive. The samples were then washed with dH 2 O for 15 min x 3 using new water each time, dehydration was through increasing concentrations of 30%, 50%, 70% and 90% of acetone (5 ml) for 15 min each and then with 100 % acetone 15 min x 3. Next, the specimens were infiltrated with a solution of 50% epon (2.5 ml acetone + 2.5 ml epon) for 1-2 hours and then with a 67% epon solution overnight. The vials were capped with a lid with a hole in the center to allow gradual evaporation of acetone. Finally, the tissues were infiltrated with pure epon for at least five hours or overnight. The infiltrated specimens were next placed in embedding boats for polymerization. The blocks were polymerized at 65-70 °C overnight. 43  Excess epon around the specimen was cut away first using a razor-blade for trimming. Then using the EM UC6 ultramicrotome (Leica, Richmond Hill, Ont.) with glass knives, the block was cut until the surface of the specimen was reached and sufficient area of the specimen occupied the sectioned area. Before thin sectioning, 500 nm thick sections were done to determine the quality of fixation by light microscopy. Several sections were cut and placed on a glass slide and dried on a hot plate. The dried sections were then stained with toluidine blue 0 (1.0g TBO powder + 1.0g Sodium Borate + 100mL dH2O). Dye was washed off with distilled water and dried on a hot plate. Sections were then checked with a light microscope. If no problem was seen under the light microscope, 60 nm thin sections were cut next with a Diatome diamond knife (Diatome, Biel, Switzerland). Sections were picked up on copper mesh grids. All sections were stained with 2% uranyl acetate for 5 min. Grids were rinsed with distilled water and dried completely. Next grids were stained with Reynold's lead citrate (1.33g Pb(NO3)2 (lead nitrate) + 1.33g Pb(NO3)2 (lead nitrate) + 30mL CO2-free dH 2 O) in a small petri dish with NaOH pellets placed inside the petri dish to absorb CO2 molecules. Grids were also stained for 5 min, rinsed with distilled water and dried completely. Stained sections were viewed in a Tecnai 12 TEM (FEI Company, Oregon, USA) at 80 kV. Digital images were acquired using the digital camera inserted above the viewing screen. Images were captured in a standardized random fashion at 2,400x, 5,800x, 18,500x and 46,000x magnification.  3.8 Stereology  The degree of hepatotoxicity and mitochondrial integrity were assessed using TEM stereological morphometry with the star volume, point counting and line intercept methods. 44  Hepatocyte volume was measured using star volume, while volume fractions of hepatocyte constituents were measured using point counting and mitochondrial crista density was measured using the line intercept method. The number of images to be analyzed for each parameter was determined as the image sample size that provided the lowest stable coefficient of error (CE) (Table 3 on page 58). All morphometry was done by a single examiner who was blinded from the information on the study subject. Analyses were done with the computer software Image-Pro (MediaCybernetics, Bethesda, MD).  45  3.8.1 Star volume Star volume is defined as the mean volume of all parts of an object which can be seen unobscured in all directions from a particular point (Gundersen, Bendtsen et al. 1988). By measuring the length of 16 radial lines from a center point randomly chosen within the cell to the cell membrane, as shown in the Figure 13, we can approximate the radius of irregularly shaped cells and then estimate the volume according to the following mathematical formula.  Cell volume =4/3 (,rr 3)  r = average length of 16 radii  This measurement was performed to investigate the relationship between progression of liver/mitochondrial damage and the hepatocyte volume. TEM images at a magnification of 2,400x were used to measure the cellular radii, as this was the minimum magnification under which whole cells could still be captured in a single image. Random sampling was important to avoid incorporating sampling bias into the data, thus the images were always taken at the lower right corner of the field in the grid under TEM (Howard and Reed 2005). Thirty cells were analyzed and then randomly picked in descending numbers (i.e. randomly pick 27 samples out of 30, and then 24, 21, and so on) so that we could estimate the minimum sample numbers that sustain the minimum CE. As shown in the Figure 14, sample size 21 showed the smallest number with the lowest CE value, which led us to consider that the minimum sample size we needed to satisfy the estimation star volume is around 20.  46  Figure 13: Hepatocyte TEM image with Star volume grid (2,400x). By measuring the length of 16 radial lines from a center point randomly chosen within the cell to the cell membrane, we can approximate the radius of irregularly shaped cells and then estimate the volume with the mathematical formula.  47  0.60 0.50 0.40 w 0.30 0 0.20 0.10 0.00  0  ^ ^ 5 10^15^20^25^30^35 Sample size  Figure 14: Change in CE of hepatocyte volumes with increasing sample size. CE stabilized around 0.10 at sample size 21, which was the number of cells we decided for sampling cell volume for each liver sample.  48  3.8.2 Point counting  Point counting is a stereological method that allows us to estimate the volume fraction of each organelle in cells by monitoring the chance of encounters between points and the features of interest (Reith and Mayhew 1988). For example to, estimate the lipid volume fraction, we counted the number of points that fall on lipid droplets and calculate the ratio of the lipid point number to the number of points on the whole cell. This stereologically estimates the fraction of lipid in the cells (Figure 15). However, since a single section is an unlikely representation of cell composition, the morphometric data must be colleted from sets of image sections in a randomized manner, just as with the other cases (Reith and Mayhew 1988). TEM images for this purpose were taken at 5,800x magnification, which was the point at which we found the magnification allows us to distinguish the details of cellular composition with an adequate field of view. To randomize the sampling, the images were taken at the right lower corner of the field in each grid square, just when the lines of grid bars disappeared from the field of view. The minimum sample size was also estimated in a similar manner as for the star volume (Figure 16). As a general trend, CEs seemingly stabilized at a sample size of 6. Despite further declining tendency of mitochondrial CE value, its CE was already in the acceptable range (0.08) at a sample size of 6. Because of the rare event of lipid in the cells, further increases in sample size did not improve the lipid CE (data not shown). Therefore we decided to use 6 images for sampling the volume fractions of hepatocyte constituents.  49  Figure 15: Point counting method. It is a stereological method; counting the number of points of the grid that fall within the organelle and estimate the fraction of the point number out of the total point number in the image which stereologically represents the volume fraction of the organelle. For example, for the cell at the bottom of the picture, pink circles are used for counting lipid, blue circles are used for mitochondria, and yellow circles are used for cytoplasm. White solid line represents the cell border.  50  4,1 N ci) CI  '3. E co  E to a 6  4  0390  0 0 0  I ski:  88*8R.`28 o 0 © © co  3 30 0 0 0 0 0  00 412 Y 0NI ©^s2  0  o  .41  0  n.  .2  co  O  aI o. E  O  2  O U.1  •  8 Et 8 gRo 30  0  0  to  0  CDo  -•r  O  - C■1  12 8 8 g 8 cz; 0 0 0 Q^ Oci  2 A  U)  ••^  §  aj^41) c,cti N'  7) ;  E  c  0. To^> E > ca 1-1-1 L u) 0 _c  o =  9C Cii  • .5 •(.7) -0 "  _c  8  -C a) U) (.)^C.) 0 a)  Q)  co "5^oCC17)3) C >, (.) o E o c = •  15 , co EL3 -c)^co C a) a) "E. ID  4—  a) 11-3 D a)  E ca) > S) O C 11) • 'E^°  •  L  0. D 7 _c O^a) c^45 -o  • Q) co^U) L=  as a) C a 0 O co a) O _ c ) co 11) (.) o ° CDa) (6^ 95 1_ 5 a) .  C N — a) N (m a) .(/) O 0) a) 0 _C  <-) a) cp_ >, cr,^E E C E ^ • (1) co co  -  r0  QS co 1-€.1 0.^-0  u) 0  CL) 0_ m c  -0 a W c..) a.)^ a) N ci 0 >, il a) 72 (13 0.) o O ...,, c = .,2 w co^ 12 w -E, 0. >,^_c o) a) -a)^ _c c o (1) a) > ..m -L 2 F,. "E) E - • aco  W 8 .E o O u) cu^o .N LLJ^ — ^o a) 0 (..) _c C 5, c.) 0a)) -O. C 72 o c c .— -a as  U)  Ifs  a C3  a)  QS 2 0_ ^w 0 _c .-• .- co .N _c (..) rn 2 (n 45  1.6 a) .1— c th (1)  0) a3 E o = cu^as ...e....) 0) cf) LLJ 11-3. 4—  E < 0 .c  51  The grid point number was determined when the lowest point number attained consistent volume fraction of our target cellular constituents and increasing the point number did not improve the value any longer. The volume fractions obtained were compared among the point number 121, 169, 324, 676 and 2704 (Figure 17). Generally, all point numbers showed consistent volume fraction for all cellular constituents, except peroxisome whose 1% fluctuation at the different point number represented 50% volume change because of its rare encounter in the cell (Figure 17). Peroxisome volume fraction stabilized after the point number 676, therefore we decided to use the point number 676. Counting peroxisome was later cancelled, because it was found that distinguishing peroxisomes and mitochondria was difficult at this TEM magnification. Volume fraction of peroxisomes in normal hepatocyte is less than 2% (Zakim and Boyer 2003), therefore its influence on mitochondrial volume fraction in the case of peroxisomes being miscounted as mitochondria is minimal (less than 10% of mitochondrial volume in case all peroxisomes were miscounted).  52  (Page omitted by student)  53  3.8.3 Line intercept Crista surface density was measured using a linear grid and by counting the number of intersections between grid lines and crista membranes and counting the points that fell within the mitochondria (Figure 18). This counting then allows for a stereological estimate of the surface density of the cristae according to the following mathematical formula (Howard and Reed 2005).  Surface density = (2 Z /) / (z E P) I = number of intercepts, z = linear grid length per point,  P = number of points hitting the reference space  The tendency of anisotropy (i.e. tendency to a given orientation) in a target component has to be carefully considered when one is using this stereological method. The linear grid has a high degree of anisotropic tendency, and if this grid is used for estimating the surface of components which themselves also show a certain degree of anisotropy, there is a danger that the data is contaminated with a systematic bias. This is because to a certain degree, the counted intercepts are following a pattern generated between lines and the component, and so are not random in manner (Weibel 1979; Howard and Reed 2005). This problem can be resolved by using a cycloid grid which shows the isotropic character in such way as to make intercepts based on the anisotropic pattern of the target component (Howard and Reed 2005). In this project, our target component was the mitochondrial crista membrane which does not follow an anisotropic pattern inside the mitochondria, therefore we could still use the linear grid in our study.  54  Figure 18: Measuring crista surface density using a linear grid. By counting the number of intersections between the linear grid line and crista membranes (red circles), we can stereologically estimate the surface density of the cristae according to the mathematical formula provided.  55  After taking the image at 5,800x magnification for the point counting, we increased the magnification to 18,500x magnification, without moving the location of the field. We captured an image at this magnification to record all mitochondria in this field of view so that the next images of all the mitochondria seen in the field at x18,000 were captured at 46,000x magnification to measure the surface density of crista membranes. This process allowed random sampling. Linear grid length 2.25 gm, with a space interval of 62 horizon and 22 vertical (settings in Image-Pro, MediaCybernetics) was imposed on top of the 1024 x 1024 resolution image at the magnification of 46,000x. This length of the line and space between lines in the grid were used for settings, as this size allowed us to count the intercepts and points in the smallest mitochondria yet did not over fill the lines in the average size mitochondria. The left end of the line was used as the point to count the mitochondrial matrix it is hitting (P value in the formula above). To validate the use of the linear grid in our case, we compared crista density results estimated by the linear grid with results by the cycloid grid, using the same 33 mitochondria. These two results were comparable and did not show statistical difference (p = 0.93, t-test, two-tailed, paired). Therefore we used the line intercept method. CE evaluation was also performed to find the minimum sample size, and as shown in Figure 19, the CE stabilized with a sample size of around 37. Therefore, we used 40 samples as the minimum sample size for this surface density measurement. These stereological methods were reproducible and results between two different blocks from the same sample showed similar results (Table 2).  56  0.25 -  0.20 -  0.15 w 0.10 -  0.05 -  0.00 0^10^20^30^40^50^60^70  Sample size  Figure 19: Change in CE of mitochondrial crista density with increasing sample size. CE stabilized to 0.06 at the sample size 40, which was the number of mitochondria we decided to use for sampling crista density for each liver sample.  Table 2: Compatibility between blocks and reproducibility of TEM morphometry Reproducibility (Mean) Parameter  Between blocks (Mean) pa  1 8t time^rd time^  Block 1^Block 2^p  b  Cell size (01 3 )  3211  3287  0.15  2703  3682  0.16  Crista density (um 2/um 3 )  6.23  6.38  0.59  4.47  4.69  0.64  Mt. Volume fraction (%)  18.0  18.2  0.82  15.0  18.8  0.09  Glycogen (%)  11.7  10.9  0.37  23.5  20.5  0.56  Lipid (%)  11.0  11.1  0.23  0.6  3.5  0.29  a. t-test, paired, two-tails b. t-test, unpaired, two-tails  57  3.8.4 Perimeter measurement  Perimeters of mitochondria were measured as an indicator of changes in the average size of mitochondria from each patient. The larger the mitochondrion, the longer the perimeter of the mitochondrion is expected. This measurement was done to quickly check for a tendency of mitochondria size in different groups, and we intended to expand the experiment with proper CE value based on image number determination if the results indicated differences between groups. Four mitochondria images from 46,000x were randomly chosen and the perimeters of all mitochondria in these images were measured using a program in Image-Pro (MediaCybernetics, Bethesda, MD). As no tendency of different mitochondria size in different groups was seen, we did not pursue this measurement.  Table 3: Summary of TEM stereological methodology for each parameter. Magnification  Number of samples  Coefficient of error (CE)  Method  Cell size (pm 3 )  2,400x  20 cells  0.10  Star volume  Mitochondrial volume fraction (%)  5,800x  6 images  0.08  Point counting  Glycogen volume fraction (%) a  5,800x  6 images  0.19  Point counting  Lipid volume fraction (%)  5,800x  6 images  0.15b  Point counting  Cytoplasm volume fraction (%)  5,800x  6 images  0.06  Point counting  46,000x  40 mitochondria  0.07  Line intercept  Parameter (unit)  Crista density (pm t/pm 3)  a. Two samples were not analyzed as they were not fixed with potassium ferrocyanate. b. The same images were used to calculate mitochondria, glycogen, lipid and cytoplasm volume fractions. CE of lipid volume fraction was artificially increased (up to 0.50) for samples with low hepatocyte lipid content due to the majority of images showing no lipid at all.  58  3.9 Statistics  All results were shown in medians and inter-quartile ranges (IQR), and statistical comparisons between groups were done with the Kruskal-Wallis test using the computer software XLSTAT (Addinsoft, New York, NY). As mentioned previously, means of duplicate experiments were used for the nucleic acid measurements. Mean values from all images were used for the morphometric measurements, and similar results were obtained when medians were instead used. To study the relationship between COXI and COX8 expression levels, Pearson's correlation test was used (XLStat, Addinsoft, New York, NY). Statistical significance was considered when the alpha (p) value was less than 0.05.  59  IV. CHAPTER FOUR: RESULTS  4.1 Study population  A total of 23 biopsies obtained from HIV/HCV co-infected males being evaluated for HCV antiviral therapy were examined in the present study. Table 4 shows the characteristics of the study population. The vast majority of the participants had experienced IDU, possibly explaining the common transmission route of HIV and HCV. No active use of drugs or alcohol were reported when participants were enrolled. Seventy four percent of the study population (N=17) was co-infected with HCV genotype 1 and the remainder (26%) were infected with HCV genotype 3a. Due to a change in HCV treatment guidelines after the study was initiated, obtaining liver biopsy samples from genotype 2 and 3 became more difficult. The general prevalence of HCV genotype 1 in the USA is about 70%, HCV genotype 3 is up to 10% (Zakim and Boyer 2003). Therefore, our HCV genotype population appears to reflect the prevalence of each genotype in the general population. Participants were grouped according to HAART status, either ON-HAART or OFF-HAART. No differences were observed when the two groups were compared by age, CD4 cell count, liver tests, platelets and serum lactate levels (Table 4). Two participants were receiving a D-drug (stavudine (d4T) in both cases)-containing regimen among ON-HAART subjects, and remainders were on non-D-drug regimens. Median aminotransaminases (ALT and AST) levels were above the upper limit of normal in both groups (Table 4).  60  Table 4: Characteristics of the study population. ON-HAART (Median [IQR])  OFF-HAARTa (Median [1Q11])  Sample size  14  9  Age (years)  43 [39-47]  48 [46-53]  9  7  10/0/4/0  7/0/2/0  CD4 cell count (cells/pL)  390 [320-580]  400 [340-410]  0.87  Plasma HIV RNA (copies/ml)  <50 [<50-<50]  48200 [20500-88400]  <0.05  42.0 [40.5-44.0]  41.0 [40.0-42.0]  0.86  Bilirubin ( ilmol/L ) b  12.0 [8.5-17.5]  9.0 [5.8-13.0]  0.15  Platelet ( g/L ) b  174 [150-203]  210 [149-224]  0.58  AST ( U/L ) b  71.0 [45.5-89.8]  53.0 [49.0-79.0]  0.64  ALT ( U/L ) b  74.0 [60.0-176.0]  58.0 [50.0-88.0]  0.68  1.50 [1.23-2.05]  1.70 [1.35-1.75]  0.88  28 [11-94]  0  22 [--]  0  PI (N=10) (months) d  23 [15-40]  0  NNRTI (N=6) (months) °  22 [10-35]  0  IDU experienced individuals HCV genotype (1 / 2 / 3 / 4)  Albumin ( gIL  )  b  Lactate ( mmol/L ) b  P  0.09  Duration of HAART at the time of biopsy Total (I4=14) (months) D-drugs (N=2) (months)`  a. HAART naïve (N=5) + >6 months off HAART (N=4) at the time of biopsy. b. Normal range: Albumin 35-48 g/L, Bilirubin Total <20 gmol/L, Platelet 150-400 g/L, AST <40 U/L, ALT 7-56 U/L, Lactate 0.5-2.1 mmol/L. c. D-drugs = stavudine (d4T), didanosine (ddl) or zalcitabine (ddC). D4T in both cases. d. Two patients were taking both PI and NNRTI at the same time.  61  4.2 ON-HAART vs. OFF-HAART  Signs of liver mitochondrial toxicity were compared among HIV/HCV co-infected individuals based on their HAART status. Liver mitochondrial toxicity was assessed by mitochondrial DNA (mtDNA) and mtRNA (COX141-actin) real-time-PCR quantification, Ishak-Knodel pathology exam, mtDNA deletions, and TEM-based quantitative stereological analyses of hepatocyte and mitochondrial morphometry. Overall, no statistically significant differences were observed between ON-HAART and OFF-HAART liver mitochondria characteristics, except for cell size which was marginally larger in ON-HAART samples.  4.2.1 mtDNA ratio and mtRNA ratio COX1 (mtDNA) and ASPG (nDNA) were quantified using real-time PCR and the mtDNA/nDNA ratio was calculated to express the mtDNA content in each individual. The median ratio was approximately 500 in both ON-HAART and OFF-HAART groups (Table 5).  Table 5: Comparisons of mtDNA and mtRNA ratios between ON-HAART and OFF-HAART  groups. ON-HAART^OFF-HAART ^ (N=14)^(N=9) p (Median [IQR])^(Median [IQR]) mtDNA / nDNA  506 [381-804]  508 [394-823]  0.90  COX1 mtRNA/ 13-actin RNA a  25.7 [17.0-41.6]  34.6 [22.9-37.9]  0.80  COX8 mtRNA / (3-actin RNA a  1.0 [0.8-1.4]  1.1 [0.6-1.6]  0.83  a. Total 17 samples RNAlater treated were analyzed (N=10 ON-HAART, N=7 OFF-HAART).  62  Similarly, reverse transcribed hepatocyte mtRNA expression levels were measured, by quantifying the ratio of COX 1 mtRNA to housekeeping RNA 13-actin (COX1/(3-actin). A total of 17 samples were used instead of 23, as six samples were not treated with RNAlater TM (QIAGEN) in the early stage of the study. The medians of mtRNA in ON-HAART and OFFHAART groups were similar, showing no statistical difference (25.7 [17.0-41.6] and 34.6 [22.9-37.9] respectively, p=0.80). Expression level of COX8 was also examined in the same way. Both COX1 and COX8 are subunits of the mitochondrial respiratory chain complex IV, also called as cytochrome c oxidase (COX). However the former is encoded by the mtDNA while the latter is encoded by the nuclear DNA. As shown in Table 5, both ON-HAART and OFF-HAART groups show similar expression level of COX8 (p=0.70). When the correlation between COX1 and COX8 expression levels was examined, an overall positive significant correlation of expression levels was observed between these two genes (R=0.63, p=0.006) (Figure 20).  2.5  •  2.0  co  •  R=0.63 p = 0.006  •  1.5  x 01.0 1.0 0.5 0.0 0.0  10.0^20.0^30.0^40.0^50.0 COX1  Figure 20: Positive correlation between COX1 and COX8 expression levels. 63  4.2.2 Pathology score As described previously, when two biopsy samples were obtained, the first liver biopsy sample was subjected to pathological examination by a pathologist who used the adjusted IshakKnodell scoring system to grade the histological damage of the liver sample. The test examines the severity of fibrosis, inflammation and necrosis in the liver but is not designed to examine the severity of steatosis. Samples were graded from 0 (no damage) to 24 (most severe). The IshakKnodell scores between ON-HAART and OFF-HAART groups were not significantly different, both showing a median score of 7.0 (p=0.44) (Table 6).  Table 6: Results of the pathological examinations.  ON-HAART^OFF-HAART ^ (N=14)^ (N=9) p (Median [IQR])^(Median [IQR]) Ishak-Knodell score^7.0 [5.0-9.0]  ^  7.0 [6.0-12.3]^0.44  64  4.2.3 TEM morphometry A quarter of each liver sample prepared and sectioned for the TEM was subjected to stereological morphometric analysis. Sectioned samples were examined under different TEM magnifications, to analyze hepatocyte volume at 2,400x, cellular constituents volume fractions at 5,800x and mitochondria crista density at 46,000x. The hepatocyte volumes between ON-HAART and OFF-HAART were compared, and hepatocytes were marginally significantly larger in the ON-HAART group (4425 [3369-5317] um 3 ) compared to the OFF-HAART group (3369 [2721-3596] um 3 , p=0.05) (Figure 21). It is noteworthy that the variability was much greater in the ON-HAART group.  6000 -  5500 5000  -  ri 4500 1 4000 E 7 3500 > 3000 0  p= 0.05  ( )  2500 2000  1 ^ OFF-HAART O N-HAART ^ (N=9) ( N = 14 )  Figure 21: Comparing hepatocyte volumes between ON-HAART and OFF-HAART groups.  65  The mitochondria volume fraction and crista density analysis showed statistically similar mitochondrial qualities in both ON-HAART and OFF-HAART groups. Mitochondria occupied about 18% of the volume in hepatocytes in both groups. ON-HAART showed 28% lower crista density median compared to OFF-HAART (Table 7), but this difference between groups did not reach statistical difference between groups. Volume fractions of hepatocyte metabolic constituents did not significantly differ between ON-HAART and OFF-HAART samples. For example, liver glycogen was similar between ON-HAART (24.7 [22.5-30.0] %) and OFFHAART (22.2 [18.7-30.8] %, p=0.61); and so was lipid volume, (2.0 [0.8-5.0] % vs. 2.1[0.87.0] %, p=0.87) (Table 7). Despite these similarities, qualitative differences in mitochondrial morphology were seen by TEM observation, showing that some samples have apparently more regular mitochondrial morphological integrity (i.e. consistent mitochondrial shape and size, defined cristae structures inside) than others, even within the same group (Figure 22). Crystalline inclusions inside mitochondria were also seen in two samples, one from each group (Figure 23).  Table 7: TEM morphometric analysis results between ON-HAART and OFF-HAART. ON-HAART (N=14) (Median [IQR])  OFF-HAART (N=9) (Median [IQR])  P  17.3 [15.8-20.0]  18.4 [16.3-22.2]  0.49  4.7 [4.5-6.0]  6.5 [5.0-7.1]  0.23  Mitochondria perimeter (pm)  2.24 [1.97-2.43]  2.20 [2.02-2.26]  0.80  Glycogen (%)  24.7 [22.5-30.0]  22.2 [18.7-30.8]  0.61  2.0 [0.8-5.0]  2.1 [0.8-7.0]  0.87  4425 [3369-5317]  3369 [2721-3596]  0.05  Mitochondria volume fraction (%) Crista density (pm t/pm 3 )  Lipid (%) Cell size (pm 3 )  66  Figure 22: Representative TEM images of hepatocyte mitochondria. (a) Healthy mitochondria! morphology (volume fraction = 18.1%), with defined and consistent mitochondria shape. The sample image was taken from an OFF-HAART/HCV-3 subject, at 18,500x magnification. (b) Cristae structure is defined and its density consistent throughout the mitochondrial matrix (crista density = 6.38 pm 2 /pm 3 ), from the same sample as (a). Magnification 46,000x. (c) Example of less clear mitochondria. Irregular and enlarged mitochondria! morphology (volume fraction = 17.4%). Sample from ON-HAART/HCV-3 subject, at 18,500x magnification. (d) Higher magnification of sample (c) at 46,000x, showing weakly defined cristae with a low density (4.67 pm 2 /pm 3 ).  67  Figure 23: Longitudinal (arrow) and transverse (arrow head) sections of crystalline structures  inside mitochondria from an OFF-HAART/HCV-1 subject. Magnification 59,000x.  68  4.3 HCV genotype 1 vs. 3  Mitochondrial DNA and RNA assays as well as TEM stereological morphometric analyses were also compared according to HCV genotype status. All study subjects were infected with either HCV genotype 1 (N=17) or HCV genotype 3a (N=6), therefore we compared differences in hepatocyte characteristics based on these two genotype groups. No differences were seen between the two groups with respect to mtDNA/nDNA ratio or COX1 and COX8 gene expression level (Table 8). The mitochondrial volume fractions in the cells, however, were significantly lower in the HCV genotype 3a group (16.1 [14.1-18.0] %) than HCV genotype 1 (18.7 [17.1-22.2] %, p=0.05). This difference in mitochondria volume fraction was not accompanied by a difference in the crista density between groups ()=0.83). When volume fractions of metabolic constituents were compared, HCV genotype 3a was associated with significantly higher intra-hepatocyte lipid accumulation (12.0 [8.0-14.1] %) compared to genotype 1 (1.3 [0.6-2.3] %, p=0.002) (Figure 24 and 25), along with a trend toward larger hepatocyte size (4787 [3894-5317] vs. 3382 [2721-4362]  1.11113 ,  p=0.08). There  was no difference in glycogen store volume fraction between HCV genotype 1 (23.5 [21.530.8] %) and genotype 3a (24.0 [21.8-26.2] %, p=0.32).  69  Table 8: Comparisons of RT-PCR and TEM results between HCV genotype 1 and 3 groups. HCV genotype 1 (N=17) (Median [IQR])  HCV Genotype 3 (N=6) (Median [IQR])  532 [384-823]  399 [383-490]  0.26  COX1 mRNA/ 6-actin mRNA  29.2 [20.4-37.0]  40.0 [22.7-43.1]  0.89  COX8 mRNA / [3 -actin mRNA  1.1 [0.7-1.4]  1.5^[0.7-2.1]  0.57  Mitochondria volume fraction (%)  18.7 [17.1-22.2]  16.1 [14.1-18.0]  0.05  Crista density (pm 2 lpm 3 )  5.25 [4.60-6.24]  5.21 [4.49-6.84]  0.84  Mitochondria perimeter (pm)  2.16 [2.01-2.33]  2.34 [2.14-2.48]  0.40  Glycogen (%)  23.5 [21.5-30.8]  24.0 [21.8-26.2]  0.26  1.3 [0.6-2.3]  12.0 [8.0-14.1]  0.002  3382 [2721-4362]  4787 [3894-5317]  0.08  6.5 [5.0-9.0]  9.0 [7.0-9.0]  0.20  mtDNA / nDNA  Lipid (%) Cell size (pm 3 ) Ishak-Knodell score  Figure 24: TEM images at 2,400x, showing different lipid (white vesicles) and glycogen (black solid dots) accumulation pattern based on HCV genotype: genotype 1 (a: lipid volume fraction = 0.5%, glycogen volume fraction = 22.5%) and genotype 3 (b: lipid volume fraction = 14.5%, glycogen volume fraction = 27.8%).  70  •  25.0 •  20.0  ON-HAART OFF-HAART  c■  )  C O  15.0  * p = 0.002  a-  E 0  •  •  10 . 0  5.0  0.0  • C  •  (X.tog-K: 0  •  HCV-1^HCV-3 (N=17)^(N=6)  Figure 25: Lipid volume fraction difference in hepatocytes based on HCV genotype profile. HCV-3 shows significantly increased lipid accumulation compared to HCV-1. Samples from ON-HAART patients are shown in black while OFF-HAART are shown in grey. The horizontal bar represents the median.  71  4.4 mtDNA deletion  Using long PCR and amplifying the whole genome mtDNA (in two fragments) (Figure 12-b), the presence of mtDNA with deleted region in each liver sample was investigated. The long PCR products were run on a gel, and the principle behind this is that mtDNA fragments with a deleted region should be smaller than normal mtDNA fragments and travel further on the gel (Figure 12-a). The degree of severity of the mtDNA deletion was graded on a scale of zero to four, as described previously. When the mtDNA deletion score was compared between ON-HAART and OFFHAART groups, there was a tendency toward more mtDNA deletions in the ON-HAART group than the OFF-HAART group (p=0.14) (Table 9). However, the degree of mtDNA deletions was not correlated with mtDNA quantity level (mtDNAJnDNA ratio), mitochondria volume fraction or mitochondria crista density. No significant difference in mtDNA deletion was observed between HCV genotype groups (Table 10).  72  Table 9: Severity grading of mtDNA deletions between ON-HAART and OFF HAART groups Fragment a^Fragment b^Sum  ^pa  ON-HAART (N=14)^1.0 [0.0-1.0]^1.0 [0.3-2.0]^2.5 [1.0-3.0] (Median [IQR])^ OFF HAART (N=9)^0.0 [0.0-1.0]^0.0 [0.0-1.0]^1.0 [0.0 2.0] (Median [1Q12])  0.14  -  -  a. Comparing the Long-PCR score sum between groups  Table 10: Severity grading of mtDNA deletions between HCV genotype 1 and 3a Fragment a^Fragment b^Sum  ^p a  HCV-1 (N=17) (Median [IQR])  1.0 [0.0-1.0]^1.0 [0.0-2.0]^1.0 [0.0-3.0]  HCV-3a (N=6) (Median [IQR])  0.0 [0.0-0.8]^1.5 [1.0-2.0]^2.0 [1.3-2.8]  0.72  a. Comparing the Long-PCR score sum between groups  73  V. CHAPTER FIVE: DISCUSSION  5.1 Findings  In the present study of liver mitochondrial alterations at the nucleic acid, ultrastructural and related metabolic level among HIV/HCV co-infected individuals, we found no evidence of increased liver mitochondrial damage in association with current HAART, except enlarged hepatocyte volume in the ON-HAART group. At first glance, our results may seem to contradict previous findings that HIV/HCV co-infected individuals are more at risk of hepatotoxicity than HIV or HCV mono-infected ones (den Brinker, Wit et al. 2000; Greub, Ledergerber et al. 2000; Monga, Rodriguez-Barradas et al. 2001; Brau 2005; Kramer, Giordano et al. 2005; Pineda, Romero-Gomez et al. 2005). It has also been hypothesized that NRTI-induced liver mitochondrial toxicity may further exacerbate liver damage and contribute to lower tolerability of HCV therapy (Lafeuillade, Hittinger et al. 2001; Luetkemeyer, Hare et al. 2006; Sulkowski and Benhamou 2007). In spite of these indications of hepatotoxicity with HAART in the HIV/HCV population, however, HAART seems to slowdown hepatic fibrosis progression in this population, which would be a benefit to the patients (Qurishi, Kreuzberg et al. 2003; MarineBarjoan, Saint-Paul et al. 2004; Brau, Salvatore et al. 2006). In fact, the primary reason for the development of end stage liver diseases (ESLD) in HIV/HCV co-infected population is the chronic progression of HCV. To what extent HAART-related hepatotoxicity of the current regimen may contribute to ESLD is unclear. HAART regimens have been adjusted and improved upon as physicians encountered adverse effects in their patients. Therefore, today's HAART regimens may not be as toxic as before, when D-drugs were more commonly used and the long-term toxicity of HIV medications was not well described. 74  It is known that different NRTIs used in HAART may show different levels of mitochondrial toxicity to patients. As previously mentioned, D-drug NRTIs are blamed for severe mitochondrial toxicity in both clinical and in vitro studies (Birkus, Hitchcock et al. 2002; Cote, Brumme et al. 2002; Walker, Setzer et al. 2002). Walker et al have shown decreased liver mtDNA levels in HIV/HCV co-infected patients receiving D-drugs, but not in those on non-Ddrug-containing HAART (Walker, Bauerle et al. 2004). Because of their structural similarity to deoxynucleotides, D-drugs not only interrupt the cellular DNA polymerase 7 but also may act as antagonist against natural nucleotides at the time of phosphorylation stage or uptake into mitochondria by transporters, and these mechanisms may reinforce mitochondrial toxicity (Kakuda 2000; Dolce, Fiermonte et al. 2001; Walker, Setzer et al. 2002). In our study, we observed that mtDNA levels were essentially the same whether ON- or OFF-HAART. Considering that the majority of ON-HAART subjects were receiving non-D-drug HAART regimens, our results are consistent with Walker's findings (Walker, Bauerle et al. 2004). Mitochondrial gene expression of both a nuclear DNA (COX8) and a mtDNA-encoded gene (COX1) did not differ between the two groups, yet showed a significant correlation between them, suggesting the absence of mtDNA-specific alterations in expression patterns. Although the sample size was limited, no relationship was detected between mtDNA and PI or NNRTIbased regimens. Our results suggest that non-D-drug related mitochondrial toxicity is minimal in the liver. We rigorously and objectively evaluated hepatocyte cell volume, mitochondria volume fraction, mitochondrial perimeter, crista density, lipid and glycogen volume fractions by quantitative TEM morphometric analyses. No difference in any of the studied parameters was detected between ON- and OFF-HAART liver samples, except for the cell volume which was marginally larger in ON-HAART samples. This appears to be inconsistent with two studies in HIV/HCV co-infected individuals that suggested an association between HAART and increased 75  liver mitochondrial ultrastructural alterations (Verucchi, Calza et al. 2004; Van Huyen, Batisse et al. 2006). Only two of our subjects received d4T and none were on ddI or ddC—the two most toxic ones. Exposure to different NRTIs in these small studies may partially explain this difference. Also, there are differences in evaluation methods between our group and the others; the two research groups listed above evaluated mitochondrial toxicity using more qualitative methods based on TEM observation, while we applied quantitative methods using stereological morphometry to evaluate mitochondrial integrity focusing on parameters described above. We also blinded ourselves during the analyses. Differences in methodologies could influence sensitivity as well as objectivity of measurements which could affect the observations. The addition of potassium ferrocyanide to our osmium fixation resulted in the preservation of glycogen, an important energy storage form reflecting metabolic conditions in hepatocytes. We noted that in areas of hepatocytes where glycogen was abundant, mitochondrial staining was noticeably lighter, making the identification of cristae more difficult than for mitochondria not surrounded by glycogen. In spite of this, our quantitative analyses demonstrated that there were no significant differences in mitochondrial size, volume fraction or surface density of cristae between the groups, whether ON vs. OFF-HAART, or HCV genotype 1 vs. 3a. If one had not used potassium ferrocyanide as in (Verucchi, Calza et al. 2004; Van Huyen, Batisse et al. 2006), we suspect that this may have led not only to apparent differences in mitochondrial staining but also to seemingly empty regions in the cytoplasm, from which glycogen would have leached out during processing. This may in turn affect the definition and apparent structure of surrounding organelles, which could be misinterpreted as a sign of mitochondrial toxicity. It is well established that hepatic glycogen turnover is sensitive to blood glucose concentration (Ferrer, Favre et al. 2003). Genetic disorders of glycogen storage can result in early onset of hyperlactatemia and abnormal glycogen accumulation in the liver, causing 76  hepatomegaly (Beauchamp, Taybert et al. 2007). HAART-related mitochondrial toxicity can also lead to hyperlactatemia (Cote, Brumme et al. 2002). To our knowledge, the relationship between mitochondrial dysfunction and glycogen accumulation in the liver has not been well defined. Mitochondria are important organelles, contributing to cellular glucose metabolism and energy production. An inverse relationship between mitochondrial function and glycogen accumulation in the liver might be expected. Participants in this study were instructed not to eat before their biopsies, therefore, if they in fact complied, the rapid glycogen turnover associated with food intake should be minimal in these subjects. Although we did observe some samples with seemingly elevated amounts of glycogen, morphometric analysis did not reveal any statistically significant correlation between the volume fraction of glycogen and mtDNA or any association with HAART. Hepatomegaly has been observed in HIV patients (Wnuk 2001). Co-infection with HCV increases the risk of steatosis or steatohepatitis, which may enlarge hepatocytes and lead to hepatomegaly. However, the relationship between hepatomegaly and HAART remains unclear (Wnuk 2001; Perlemuter, Bigorgne et al. 2007; Rosenthal, Pialoux et al. 2007) since abnormal mitochondrial function can also to be involved in the development of steatosis and steatohepatitis (Fromenty, Berson et al. 1997; Perlemuter, Bigorgne et al. 2007). Although our data do not suggest increased mitochondrial damage per se in ON-HAART individuals, it is possible that hepatocytes in this group may show weak hepatotoxicity reflected by enlarged hepatocytes. The well-described increase in steatosis associated with HCV genotype 3 (Abid, Pazienza et al. 2005; Rosenthal, Pialoux et al. 2007) was evident in our morphometric analysis, with HCV genotype 3a biopsies showing significantly increased intracellular lipid accumulation compared to genotype 1 biopsies. This contributes to validating the morphometric analytic method used in this study. Although we observed a tendency toward larger mitochondria 77  volume fraction in the HCV genotype 1 group, no other parameters, such as mtDNA level, mtDNA expression level and mitochondrial crista density, showed any significant difference between groups, suggesting a lack of association between mitochondrial profile and lipid accumulation in HCV genotype 3a. The mechanism by which intra-hepatic lipids accumulate in HCV genotype 3 involves the interruption of the hepatic assembly and secretion of triglyceriderich very low density lipoproteins by HCV genotype 3 core protein and is independent of mitochondrial dysfunction (Moriya, Yotsuyanagi et al. 1997; Perlemuter, Sabile et al. 2002).  5.2 Morphometric methodology development  When we determined the number of images that should be examined in the point counting method, CE values for cellular constituents stabilized at the image number of six, except for lipid and mitochondria volumes. Although there was a tendency toward lower CE at higher image number, since the mitochondrial volume CE was already in an acceptable range (0.08) at a sample size of six, we judged it adequate. The presence of lipids was very variable between samples and between images. Some showed a high lipid content while others showed almost none. We increased the number of images analyzed for the lipid determination by duplicating the obtained lipid data, therefore theoretically doubling or tripling the number of images, to compare change in lipid CE value. However, this did not improve the lipid CE value. In the case of rare events, when there is no presence of target (lipid) in most images but that some appears once in a while, this tends to dramatically increase the CE value. In such cases, determining the image number based on the CE value would not be practical. When we repeated our CE determination using only samples with highly prevalent lipids in the cell, the lipid CE value decreased as low as 15% with six images. Therefore, we decided to use six as the minimum sample size to fulfill the validity of point counting in this study. 78  As mentioned previously, we decided to use the grid with 676 points in the point counting method (Figure 17). This judgment was driven by determining at which point number peroxisome volume fraction stabilizes. However, as we eventually opted to not measure peroxisome volume fraction, we could possibly reconsider how many points in the grid should be used. It would most likely decline since the volume fractions of other cellular constituents were fairly stable among different numbers of grid points, even with the lowest grid points (121 points) in our measurement (Figure 17). Point counting is a time consuming procedure, therefore it would be more efficient if the lowest possible number of point was used. Readjustment of the number of grid points in this method should be considered in future. The volume fractions of cellular organelle we measured were close to literature values. For example, values of human hepatocyte nucleus and mitochondria volume fractions of 5-10% and 20% respectively have been reported (Zakim and Boyer 2003), and our data showed mean values of 6% nucleus and 19% mitochondria volume fractions in a total of 23 samples. Also, before we canceled measuring the peroxisome volume fraction, we obtained -2% of peroxisome volume fraction from several images during the early stage of this project. This number was also close to the literature value 1.5-2% (Zakim and Boyer 2003). These close values between our data and the literature increase our confidence in our methodology of volume fraction measurement. Healthy human hepatocytes have a volume of approximately 5000 iim 3 (Zakim and Boyer 2003). We obtained a smaller hepatocyte volume than the value shown in the literature, mean value of 3920 gm 3 in a total of 23 samples. This difference may be attributed to differences in methodology. We tried to contact the authors of the above book to ask about their measurement methodology but this was not successful. It is also a possible innate nature of our methodology using TEM that we have more chances of obtaining non-middle cross section images of the cell because of the round shape of the cell. From the three-dimensional 79  perspective, these non-middle sections are expected to have smaller diameters of the cell than the middle part one, therefore our methodology may have a tendency of measuring the cell volume smaller than its actual volume. Clinical conditions of our samples can also influence the cell size, however to our best knowledge, there is no data in the literature showing HIV or HCV infected human hepatocyte volume.  5.3 Limitations  mtDNA levels were reported as the ratio between mtDNA and nDNA. This assumes that nDNA copy number per cell is relatively stable within the tissue. However, hepatocytes are known to have a heterogeneous nuclear DNA content (Gupta 2000), which could affect the mtDNA ratio independently of a drug-related mtDNA effect. As such, it is a potential confounder, since liver mononuclear polyploidy can vary depending on the severity of chronic hepatitis and fibrosis (Anti, Marra et al. 1994; Toyoda, Bregerie et al. 2005). As much as 31% of liver cells are binuclear according to Seglen et al (Seglen 1997). During the random analysis of more than 460 cells in our TEM images, we observed that only 1.7% of hepatocytes were binucleate, and these hepatocytes were seen with similar frequency in both ON- and OFFHAART samples. In general, binuclear hepatocytes appeared larger than mononuclear hepatocytes, which would suggest that the actual ratio of organelle to nucleus volume may be minimally affected. Thus the mtDNA/nDNA ratio between mononuclear and binuclear hepatocytes could be comparable. Polyploidy would not affect the other measurements of mitochondrial damage in our study. The relatively small sample size in this project may also be limiting our analysis at this stage. However, the entire project is a long term prospective project over a five year period, and we will collect more samples to increase the statistical power that would lead us to have more 80  reliable data analysis in future. We observed an interesting yet non-significant trend of lower mitochondrial crista surface density median in ON-HAART group compared OFF-HAART group (4.68 µm 2 /µm 3 vs. 6.38 µm 2 /µm3 , p=0.23) and increasing sample size may confirm whether this trend is maintained. Regardless, having little signs of mitochondrial toxicity in ONHAART group and overall close median values of each parameter compared to OFF-HAART group may imply minimal hepatotoxic effect of the current HAART regimen. A liver sample from needle biopsy is a small portion of the organ and represents only the local condition of the liver. Since adverse liver condition is irregularly distributed in the liver, it is possible that the samples studied may not reflect the state of the livers at other locations. However, liver biopsy is still considered as the gold standard for evaluating liver condition. Duration of HIV and HCV infection could influence the condition of liver sample but that information is not available for most patients. Other factors that may affect the liver such as overall lifelong HAART exposure or specific drug exposure (i.e. PI vs. NNRTI) could not be addressed with this small sample size. Our control group is limited to HIV/HCV co-infected OFF-HAART patients. Although it would have been interesting to compare data in healthy control liver, this was not possible. Liver biopsy involves a possible life-threatening risk, even if it is a very low possibility, and only patients with liver problems are allowed to be subjected to this procedure today. Therefore we could not ethically obtain liver control samples from healthy or even HIV mono-infected individuals.  5.4 Future direction  As previously mentioned, the objective of the original research proposal that the present project is a part of is to compare liver mitochondrial toxicity among HIV/HCV individuals 81  before and after HCV therapy, and to determine the influence of concurrent HAART. We are continuously collecting not only post-HCV therapy liver samples, but also enrolling new patients and increasing the overall sample size. Having a larger sample size will increase our power and allow a more accurate analysis. Increasing our knowledge about the potential hepatotoxicity from HIV and HCV therapy in HIV/HCV co-infected population is important, as HCV is significantly affecting the morbidity and mortality in this population today. Adding to our understanding of liver toxicity would be beneficial and could lead to improved treatment guidelines to help HIV/HCV co-infected patients access treatment with confidence.  5.5 Conclusion  In conclusion, the morphometric analyses employed in this study provided no evidence to support the hypothesis that subjects co-infected with HIV and HCV would show increased liver mitochondrial toxicity if on concomitant HAART. According to Cooper (2007), initiating HCV treatment in HIV/HCV population is often feared and unnecessarily avoided by both patients and physicians, because of the fear of potential hepatotoxicity (Cooper 2007). Our results suggest that HAART per se, in this case with the predominantly non-D-drug-containing regimen, may not be a reason to delay or avoid the start of HCV antiviral therapy in co-infected individuals. 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Nature 391(6667): 594-7.  94  APENDIX A: Ethics certificate of expedited approval  C • q^PROVIDENCE HEALTH CARE  *44.* Research Institute  UBC-Providence Health Cant Research Institute Office of Research Services 11th Floor Homby Site - SPH rib 1081 Sward St. Vancouver, BC V6Z 1 ye  Tel: (604) 806-8567 Fax: (604) 8064568  ETHICS CERTIFICATE OF EXPEDITED APPROVAL: ANNUAL RENEWAL PRINCIPAL INVESTIGATOR:  DEPARTMENT:  UBC-PHC REB NUMBER:  Valentina Montessori  UBC/Medicine, Faculty of Medicine, Department of Infectious Diseases  H03-50055  INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT: Institution^  Providence Health Care^  Site  I  St. Paul's Hospital  Other locations when the research will be conducted:  NIA  CO - INVESTIGATOR(S): Marissa Jitratkosol Helene Cote Mark Hull  SPONSORING AGENCIES: Canadian Institutes of Health Research (CIHR) - "Mitochondria! toxicity in HIV/HCV coinfection antiretroviral therapy PROJECT TITLE: Mitochondrial toxicity in HIV/HCV coinfection antiretroviral therapy EXPIRY DATE OF THIS APPROVAL: February 28, 2009 APPROVAL DATE: February 28, 2008 CERTIFICATION: 1.The membership of the UBC-PHC REB complies with the membership requirements for research ethics boards defined in Part C Division 5 of the Food and Drug Regulations of Canada. 2.The UBC-PHC REB carries out its functions in a manner fully consistent with Good Clinical Practices. 3.The UBC-PHC REB has reviewed and approved the research project named on this Certificate of Approval including any associated consent form and taken the action noted above. This research project is to be conducted by the principal investigator named above at the specified research site(s). This review of the UBC-PHC REB have been documented in writing. The UBC - PHC Research Ethics Board Chair or Associate Chair, has reviewed the documentation for the above named project. The research study, as presented in the documentation, was found to be acceptable on ethical grounds for research involving human subjects and was approved for renewal. Approval of the UBC-PHC Research Ethics Board or Associate Chair, verified by the signature of one of the following:  Dr. I. Fedoroff, ^ ^ Chair  Dr. J. Kemahan, Associate Chair  95  

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