1 Presented by M.Sc. Department of Experimental Medicine, Mcgill

UTILIZATION OF PRIMARY CULTURES OF MACROPHAGES FOR BIOLOGIC

VALIDATION OF CANDIDATE GENES LINKED TO LEFT VENTRICULAR

HYPERTROPHY

Presented by

SWATI GUPTA

M.Sc.

Department of Experimental Medicine, McGill University, Montreal

September 2011

A thesis submitted to McGill University in partial fulfillment of the requirement of the degree of Master of Science.

1 TABLE OF CONTENTS

I Acknowledgement………………………………………………………………………………9

II Abstract……………………………………………………………………………………………11

III Review Of Literature………………………………………………………………………15

1 Genetics Of Complex Traits……………………………………………………………………15

2 Genetics Of Gene Expression………………………………………………………………....16

2.1 Quantitative Trait Transcript Analysis………………………………………………...16

2.2 Quantitative Trait Locus Mapping……………………………………………………….16

2.3 Candidate Genes………………………………………………………………………………...17

2.4 Gene Networks…………………………………………………………………………………..17

3 Left Ventricular Hypertrophy………………………………………………………………...18

3.1 Significance Of Left Ventricular Hypertrophy………………………………………18

3.2 LVM As A Complex Trait……………………………………………………………………..19

4 Biological Validation Of Results From Genetic Analyses………………………….19

5 Gist………………………………………………………………………………………………………20

IV Introduction……………………………………………………………………………………21

1 Connector……………………………………………………………………………………………..21

1.1 Experimental Model…………………………………………………………………………...21

1.2 Macrophages……………………………………………………………………………………...22

1.3 Potential Roles Of Macrophages in Cardiac Remodelling………………………23

1.4 Primary Cultures Of Macrophages……………………………………………………….24

2 2 Hypothesis……………………………………………………………………………………………24

V Materials and Methods…………………………………………………………………….26

1 Extraction Of Bone-Marrow Derived Macrophages (BMDMΦ)………………...26

2 Culture And Activation Of BMDMΦ From Mice……………………………………….27

3 RT-PCR Analysis…………………………………………………………………………………...27

4 Down-Regulation Of Candidate Gene In BMDMΦ……………………………………28

5 Abundance Of miRNA Transcripts………………………………………………………….31

6 Statistical Analysis……………………………………………………………………………..…31

VI Results…………………………………………………………………………………………….34

1 Biologic Validation Of Genomic Results (First Phase)……………………………...36

2 Biologic Validation Of Genomic Results (Second Phase)………………………….51

VII Discussion……………………………………………………………………………………..61

VIII Conclusion……………………………………………………………………………………71

IX References (Literature Review)………………………………………………………72

3 LIST OF TABLES

Table 1. Dicer si-RNA duplex information………………………………………………..32

Table 2. RT-PCR primer information………………………………………………………..32

Table 3. Taqman® MicroRNA Assay information……………………………………..33

Table 4. Function, chromosomal site and location of candidate genes……….35

Table 5. Percentage knockdown of Fastkd3 and Twistnb…………………………..51

4 LIST OF FIGURES

Fig 1. The QTL mapping for LVM……………………………………………………………...34

Fig 2. Graphic representation of all cis-eQTLs detected in all RIS in first phase………………………………………………………………………………………………………36

Fig 3. Appropriate cell type expression of 32 genes…………………………………..38

Fig 4. mRNA levels of Fastkd3 and 4930486L24RIK in control and stimulated cells………………………………………………………………………………………………………...40

Fig 5. Dose-response curve of Fastkd3 and 4930486L24RIK in stimulated cells………………………………………………………………………………………………………...42

Fig 6. Correlation across dose-response conditions for Fastkd3 and 4930486L24RIK……………………………………………………………………………………….44

Fig 7. Transfection via electroporation to downregulate Twistnb………………46

Fig 8. Transfection via lipofectamine LTX to downregulate Fastkd3 and Twistnb in control cells…………………………………………………………………………….48

Fig 9. mRNA levels of Fastkd3 and Twistnb in Fastkd3 and Twistnb knockdown cells under both control and stimulated conditions………………...50

Fig 10. Graphic representation of all cis-eQTLs detected in all RIS in second phase………………………………………………………………………………………………………52

Fig 11. Schematic representation of cis-eQTLs with respect to phenotypic QTL peak…………………………………………………………………………………………………53

Fig 12. Appropriate cell type expression of 8 genes…………………………………..54

Fig 13. mRNA levels of CTSL, 4930486L24RIK and CCRK in control and stimulated cells………………………………………………………………………………………..56

Fig 14. Relative distribution of 10 cis-eQTLs vs. the locus of miRNA cluster.57

5 Fig 15. Polymorphisms within transcription factor binding sites in microRNA cluster promoter in between A/J and C57BL/6J mice………………………………..58

Fig 16. Comparison of four endogenous controls for microRNA assay in control cells……………………………………………………………………………………………..59

Fig 17. Abundance of miR-23b-24-1-27b cluster in control cells………………..60

6 LIST OF ABBREVIATIONS

Abbreviation or Symbol Term

BP Bacteriological Plastic

BMDMΦ Bone Marrow Derived Macrophages chr Chromosome cis-eQTL cis-acting eQTL

CLP Common Lymphoid Progenitor

CMP Common Myeloid Progenitor

D-siRNA Dicer-siRNA eQTL expression QTL

G Gage

GWAS Genome Wide Association Studies

HSC Hematopoietic Stem Cells

LOD Log Of Odds Ratio

LVH Left Ventricular Hypertrophy

LVM Left Ventricular Mass

M-CSF Macrophage Colony Stimulating Factor

MI Mendelian Inheritance

QTL Quantitative Trait Locus

QTT Quantitative Trait Transcript

RIS Recombinant Inbred Strains

7 RISC RNA-Induced Silencing Complex

SEM Standard Error Around The Mean siRNA Small Interfering RNA

SNP Single Nucleotide Polymorphism

TC Tissue Culture

3’ UTR 3’ Untranslated Region

8 I ACGKNOWLEDGEMENTS

First and foremost, I would like to thank Almighty God who was always with me at all my good and bad times, in all my difficulties and this time also for sure. I would like to thank him for all the gifts he has showered on me and for providing all the strengths to surge forward in life.

I express my profound sense of reverence to my supervisor Dr. Christian Deschepper, research unit director, IRCM for constant encouragement and supervision. With his enthusiasm, inspiration and sincere efforts to explain things clearly and simply, he made research a fun for me, without which I would have been lost. It’s very difficult for me to write a vote of thanks for him in such a small space. I can write a separate chapter if I try to thank him. He is the most important person who helped me in all the ways during my masters. He is optimistic in science. I can never forget his everlasting-enthusiastic smile. I learnt to remain optimistic in tough times from him. His sense of patience, wit, cheerful temper and optimistic attitude are few qualities, which will continue to inspire me in all spheres of my life. His attitude of encouraging independent thinking has always stood me in good stead. It is because of him that I have been able to complete this work. It has been indeed an enriching experience working with him.

No words of appreciation would be enough to Sylvie Picard, for providing me with intense technical assistance and knowledge in the field of molecular biology. It would have been very difficult for me to conduct some of the experiments without her help. She helped me to solve my problems whenever I was in need.

I am thankful to Marie-Pier Scott-Boyer, for the valuable suggestions, help and encouragement she provided throughout the study. She contributed a lot to this project by providing the most precious statistical data without which this study could have been really impossible.

9 I am intensely grateful to Smitha Giridhar for being wonderful colleague and friend. She is extremely generous, creative, co-operative, understanding person. She helped me a lot through my experiments.

I would also like to acknowledge Samantha Praktiknjo for guiding me to advanced molecular technique (RT-PCR), also Sophie Cardin, who is very calm and possesses a positive attitude in dealing with scientific problems.

I am thankful to them for making the lab environment more alive and joyful during my stay at IRCM. They provided me the inspiration and mental toughness to stand against all odds. The time I spent with them will always remain as a wonderful memory in my mind.

Finally, I thank my parents for giving me life in the first place, for unconditional support, love and encouragement to pursue my interests, for educating me with aspects from both arts and sciences, even when the interests went beyond the boundaries of language, field and geography. I am deeply and extremely thankful to my sweet elder sister, Jyotsana for being a constant source of motivation and encouragement throughout my life, for listening to my complaints and frustrations during my courses. I know that any attempt to express my gratitude for the help and love she offered me with words is a futile exercise. Thanks are always reserved for my elder brother, Manish; sister–in-law, Gunjan; and brother-in-law, Prashant whose moral support was with me throughout my masters. I really thank my niece Priyanshi and my nephew Pratyaksh for their ever-lasting smile, which used to relieve me whenever I was frustrated. To them, I dedicate this thesis.

10 II ABSTRACT

Abstract (English)

Cardiac left ventricular mass (LVM) correlates tightly with cardiovascular morbidity and mortality in human populations, and constitutes an important independent predictor of cardiovascular risk. Using 24 mouse AxB/BxA recombinant inbred strains (RIS) (derived from crosses between A/J and C57BL/6J parents), our laboratory previously detected one major QTL linked to LVM on Chromosome (chr) 13. To extend these data, our laboratory used Illumina microarrays to obtain the profile of gene expression in 4 hearts of male mice from all RIS. By using first a publicly available genomic map, our laboratory identified initially a total of 456 cis-acting eQTLs (cis-eQTLs), 15 of which showing expression levels correlating with LVM. Although little functional annotation was available for these genes, the SymAtlas database indicated that most of them were expressed at higher levels in cells from the myeloid lineage (i.e. macrophages and/or mast cells) than in cardiac myocytes. By further using bone-marrow derived macrophages (BMDMΦ) from Balb/c mice, we: 1) verified that these genes were indeed expressed in such cells; 2) observed that their expression level was modulated by conditions known to affect the phenotypic properties of macrophages; 3) demonstrated that their expression could be knocked down by transfection with Dicer silencing RNAs.

In a second phase of the project, the laboratory obtained a denser genetic map that allowed for the identification of a total of 1195 cis-eQTLs. Among corresponding genes, 33 showed significant correlation (p < 0.05) with LVM, and 10/33 genes clustered within a 10 MB interval centered around the QTL linked to LVM on chr 13. Further RT-PCR analyses revealed that the expression levels of these genes varied in BMDMΦ cells according to the A/B genotype. Since the clustering of 10 cis-eQTLs all correlating with LVM suggested the presence of a common regulator, we further analyzed

11 genome databases and noticed that the same interval contained the locus of the mir-23b-27b-24-1 microRNA gene cluster. Interestingly, mir-27b has been reported to be functionally related to changes in LVM.

Altogether, these experiments establish the feasibility of using primary macrophage cultures for further biologic validation of the roles and/or properties of candidate genes linked to LVM. Using these cells, it will be possible to test whether alterations in the intracellular concentration of miR-27b may regulate the abundance of the transcripts of other candidate genes from our locus of interest. In light of evidence reporting that macrophages may play a role in left ventricular remodeling, this may lead to future experiments testing how these genes may contribute via expression within macrophages to changes in LVM.

12 Abstract (French)

La masse ventriculaire gauche (MVG) du coeur corrèle étroitement avec la mortalité et morbidité cardio-vasculaires chez l’humain, et constitue un important facteur de risque indépendant. En utilisant une collection de 24 souches de souris recombinantes consanguines AxB/BxA (dérivées de croisements entre les cousches parentales A/J et C57BL/6J), notre laboratoire avait précédemment identifié sur le chromosome (chr) 13 un locus majeur en liaison avec la MVG. Pour élargir ces données, notre laboratoire a utilisé des micropuces à ADN Illumina pour mesurer le profil d’expression de gènes de 4 cœurs de souris mâles de chacune des souches recombinantes consanguines. Sur la base d’une analyse préliminaire (utilisant une carte génomique d’une banque de données publique, notre laboratoire utilisa des puces à ADN Illumina pour analyser les profils d’expressions des gènes de 4 cœurs de souris mâles de chacune des souches recombinantes consanguines. Une analyse préliminaire (utilisant une carte génomique disponible d’une banque de données publiques) permit ainsi d’identifier un total de 456 “cis-acting eQTLs » (cis-eQTLs), parmi lesquels 15 avaient des niveaux d’expression corrélant avec les valeurs de MVG. Bien que les banques de données contenaient peu d’annotation concernant ces gènes, la banque de données SymAtlas indiquait que la plupart d’entre eux étaient exprimés de façon plus importante dans les cellules provenant de la lignée myéloïde (comprenant les macrophages et les mastocytes) que dans des myocytes cardiaques. En faisant d’autres investigations à l’aide de cultures primaires de macrophages dérivées de moëlle osseuse de souris Balb/c, nous pûmes : 1) vérifier que ces gènes étaient effectivement exprimés dans ces cellules ; 2) observer que leur niveau d’expression était modulé par des conditions modifiant le phénotype des macrophages ; et 3) démontrer que leur niveau d’expression peut être diminué en transfectant les cellules par des « Dicer silencing RNAs ».

13 Lors d’une deuxième phase du projet, notre laboratoire obtint une carte génomique plus dense, ce qui permit d’identifier un total de 1195 cis- eQTLs. Parmi ces gènes, 33 corrélaient de façon significative (P < 0.05) avec les valeurs de MVG, et 10/33 étaient concentrés dans un intervalle de 10 MB centré autour du locus du chr13 en liason avec la MVG. D’autres analyses de RT-PCR révélèrent que l’expression de ces gènes dans des cultures primaires de macrophages variaient en fonction du génotype A/B de ces cellules. Comme une telle concentration de multiples cis-eQTLs corrélant tous avec les valeurs de MVG suggérait la présence d’un régulateur commun, nous avons poursuivi nos recherches dans des banques de données génomiques, et avons pu constater que ce même intervalle de 10 MB contenait le locus du gène de micro-ARN mir-23b-27b-24-1. De façon intéressante, il a été rapporté que le micro-ARN mir-27b pouvait être lié à des changements de MVG.

En résumé, ces travaux ont confirmé qu’il était faisable d’utiliser des cultures primaires de macrophages pour faire des validations biologiques concernant les rôles et/ou les caractéristiques de gènes candidats liés à la MVG. L’utilisation de telles cellules permettra de tester si des altérations de la concentration intracellulaire de mir-27B peut réguler l’abondance des transcrits de certains de nos autres gènes candidats. Puisque d’autres données de la littérature indiquent que les macrophages peuvent jouer un rôle dans les processus de remodelage ventriculaire, ces résultats pourront mener à d’autres expériences menant à déterminer si ces gènes peuvent mener à des changements de MVG via leur niveau d’expression dans les macrophages.

14 III REVIEW OF LITERATURE

1 Genetics Of Complex Traits

In recent years, numerous studies carried out in family pedigrees have allowed us to look into the causes and the mechanisms of several genetic diseases with Mendelian inheritance (MI). Accordingly, more than 12,000 genes have been linked to genetic MI diseases, as cataloged in the Online Mendelian Inheritance in Man (OMIM) 1 database. In these diseases, one single gene variant is generally responsible for the presence or absence of a given trait. Given the “discontinuous” nature of such traits, one can generally make a clear distinction between healthy and affected individuals. Likewise, these diseases usually have important clinical manifestations but affect only one small percentage of the population.

Many phenotypic characteristics of living organisms correspond to the definition of complex traits, which result from the interactions of many gene variants (each of them having generally weak effects) with environmental factors. Examples of such traits include certain cardiovascular diseases (such as hypertension), type II diabetes, Alzheimer’s disease, etc. Such diseases represent common disorders that affect large parts of the general population.

Complex traits are usually analyzed (in contrast to Mendelian discontinuous traits) as continuous quantitative traits that can be genetically linked in either genetic animal crosses or human populations to quantitative trait loci (QTLs), i.e. DNA sequences that either contain responsible genes or are in linkage disequilibrium with them. However, the limits of linkage analyses are such that typical QTLs are still relatively large regions that still contain hundreds of genes. Consequently, it is very difficult to witness within these intervals which exact genes are responsible for the trait of interest. Recently, genome-wide association studies (GWAS) have allowed for the identification in human populations of many markers that associate with

15 disease traits. However, the markers analyzed by this technique typically explain only a small portion of the variance, and in many cases, it has not been possible to explain how a gene close to the marker relates biologically to the trait of interest.

2 Genetics Of Gene Expression

More recently, microarray based studies have made it possible to monitor the expression of thousands of genes simultaneously within tissue extracts. As an alternative to (and in parallel) to QTL linkage studies, microarray expression studies have been used to facilitate the identification of genes contributing to complex traits and diseases 2-4.

2.1 Quantitative Trait Transcript Analysis

Gene expression data from microarrays can be used as “intermediate phenotypes” to connect genetic variants to phenotypic traits. In first step, the approach called quantitative trait transcript (QTT) analysis correlates gene expression with quantitative traits. In other words QTT select for the genes that have the highest correlation with particular traits, allowing the feasible identification of genetic variants that contribute to complex traits and diseases 2,5.

2.2 Quantitative Trait Locus Mapping

The next step involves the integration of gene expression data with genomic maps. This approach treats all gene expression data obtained by microarrays as quantitative phenotypes, and maps QTLs linked to the abundance of transcripts in a given tissue. Corresponding QTLs are called “expression QTLs” (eQTLs), which correspond to genomic loci that regulate expression levels of mRNAs 6,7. These eQTLs can be of two types: 1) when the location of an eQTL corresponds to the locus of the very gene whose transcript abundance is measured, it is characterized as a cis-acting eQTL (cis-eQTL), meaning that genetic variation in the neighborhood of that gene

16 is very likely to be responsible to variation in the abundance of the transcript of that gene; 2) when the location of an eQTL is different from that of the gene whose transcript abundance is measured, it is characterized as a trans- acting eQTL (trans-eQTL), meaning that variation in the abundance of the transcript of the QTL gene does not result from a mutation in the regulatory regions of the gene itself, but is likely to be a consequence of changes in expression of another gene in a different locus 3,8-11.

2.3 Candidate Genes

When a cis-eQTL: 1) corresponds to a gene whose transcript abundance correlates with the phenotypic trait; and 2) has a location that corresponds to that of a phenotypic locus (either a QTL identified in a linkage study or a marker associated with a trait in a GWA study), the gene corresponding to the cis-eQTL can be ascertained as a candidate gene for that trait in human or animal populations 12-14. Nonetheless, additional biologic validation work is required to ascertain whether candidate genes within the QTL interval really constitute causal links between the QTL and the trait of interest.

2.4 Gene Networks

Lessons from GWAS studies indicate that most gene variants associated with complex traits typically account for only a small portion of the variance of phenotypic traits. Retrospectively, this may not be all that surprising, since a genome exerts its functions not through particular genes or proteins, but through highly complex networks that produce a range of responses 15,16. Therefore, an approach that considers only individual eQTLs or candidate genes one at a time will not be able to account for these multiple interacting genomic influences. In order to circumvent this concern, researchers have developed computational model to define gene co- expression networks which, when correlated with phenotypic traits, have led to the identification of putative regulatory networks. When there is within

17 such networks an enrichment of genes sharing similar functions (on the basis of annotation terms from databases), it becomes possible to correlate perturbation of cellular functions to quantitative genes 17.

3 Left Ventricular Hypertrophy (LVH)

3.1 Significance Of LVH

In adult humans, numerous studies have repeatedly and convincingly shown that increased left ventricular mass (LVM) is highly predictive of cardiovascular morbidity and mortality 18-22. Inappropriately high LVM is known as left ventricular hypertrophy (LVH). This condition is very prevalent, as it is diagnosed by 2D-echocardiography in ~15% of middle- aged subjects 20. Why exactly LVH is so predictive of cardiovascular outcome is not exactly clear, but it is generally believed to be due to a combination of several factors, including increased oxygen requirement of the myocardium, abnormal contractility and/or filling properties, mechanical impairment due to concomitant fibrosis, as well as greater risk for arrhythmias and metabolic remodeling 20,23,24. One exception to the association between LVM and negative outcome appears to be that of the so-called “physiological hypertrophy” that occurs during pregnancy and in trained athletes, although even this type of LVH may not be entirely devoid of negative consequences 25.

Demographic and anthropometric variables can, in conjunction with cardiovascular non-genetic risk factors, explain 25-50% of the variance of LVM, but the remaining 50-75% of the variance must be accounted for by other factors, presumably genetic 20,26,27. Accordingly, heritability of LVM is high, and twin studies have shown that genetic factors contribute ~ 60% of the variance of LVM in either normotensive pre-adolescent subjects or middle-aged adults 28,29. In some (relatively infrequent) cases, LVH may be the result of high-penetrance mutations of single genes coding mostly for sarcomeric, cytoskeletal and/or metabolic proteins, leading to either hypertrophic cardiomyopathy (present in ~0.2 % of the population) or

18 dilated cardiomyopathy (present in ~0.04 % of the population) 30,31. However, little is known about the genetics of LVM outside the narrow confines of inherited cardiomyopathies 29.

3.2 LVM As A Complex Trait

Given that LVM is in fact a complex quantitative trait, identification of its genetic determinants will not rely on the methods designed to study diseases with medelian inheritance in family pedigrees. To date, we are aware of only one large GWAS study performed in human populations 32. Some gene candidates have been identified by this study, but no genetic variant has yet been fully validated as being linked to variations in LVM.

Beyond human studies, one interesting alternative is to to use animal models derived from genetic crosses. One example is that of panels of recombinant inbred strains (RIS). To construct such panels, one needs to first cross two distinct inbred parental strains to generate an F1 progeny. Brother-sister mating of animals from the F1 generation are then performed for 20 generations. In the end, this generates different RIS, each carrying equal proportions of genetic variations from two initial parental inbred stains, but having their own distinct genotypic and phenotypic characteristics. In contrast to human, RIS mouse models 1) facilitates the identification of the parental origin of each genetic allele (since there are only 2 possibilities), and 2) increases the genetic possibilities of recombination within same initial progeny. These features represent favorable conditions to identify QTLs, as they contribute to increase the statistical significance of linkage 33,34. By identifying candidate genes in animal models, it then become possible to further extend the study within human populations.

4 Biological Validation Of Results From Genetic Analyses

A classical approach for identifying candidate genes is to test (1) whether there are quantitative differences in level of expression of genetic

19 expression in the tissue of interest, (2) whether there are genetic polymorphism(s) in coding or regulatory regions that can account for differences in gene expression, (3) whether functions of the gene may lead to differences in the phenotype of interest. Additional experiments may involve functional studies to test the function and/or expression of alternative alleles. Such experiments may involve the use of either: 1) animal models like transgenic, knockout and knockin animals, in which the gene of interest can be either overexpressed, inactivated and replaced with a variant allele, respectively 35; and/or 2) in vitro experiments in cultured cells, where various manipulations can be performed to test the impact of a particular genetic variant on the expression and/or function of corresponding genes..

In the case of gene networks, one common approach has been to to test whether there is within the network an enrichment of genes that have been assigned to biologic functions on the basis of annotation terms. However, this approach relies mostly on pre-existing data deposited into annotation databases and therefore is useless for the genes for which there is no available functional annotation. In such cases, additional experimentation approaches become prerequisite to further understand the possible functions of candidate genes, and the impact of genetic variations.

5 Gist

Remarkably, the procedure from QTL to gene involves five discrete steps (1) mapping the trait to the chromosome segments, (2) one QTL at a time (3) high resolution mapping (4) identify candidates and (5) validate candidates.

20 IV INTRODUCTION

1 Connector

1.1 Experimental Model

For genetic analyses of LVM, we used a panel of 24 mouse RIS, comprising 13 AxB and 11 BxA mice. These mice originated from reciprocal crossings between mouse inbred strain A/J and C57BL/6J (the original father being C57BL/6J or A/J in the mice AxB and BxA, respectively) 36. Using this panel, our laboratory has previously detected one major phenotypic QTL linked to LVM on chromosome (chr) 13, and two minor QTLs were also observed on chr 12 and 16 37. The interest of the QTL region on chr13 itself is reinforced by the fact that the same syntenic region has been linked to LVM in a panel of rat RIS 12.

To complement the initial genotypic data, our laboratory measured gene expression in hearts of 4 male mice from each strain, using Illumina MouseRef v2.0 microarrays. The expression data were then integrated with genomic data to identify cis-eQTLs, and analyze which ones among those correlated significantly with LVM values. However, the project evolved in two phases. For the early initial phase, we used genotype data from the already publicly available database released by the Wellcome Trust Foundation (http://www.informatics.jax.org/ and http://mus.well.ox.ac.uk/mouse/INBREDS). This database reported a total of 800 single nucleotide polymorphisms (SNPs) that were polymorphic between the A/J and C57B/6J strains, and genotyped in all 24 strains from the RIS AxB/BxA panel. On the basis of that early analysis, we identified several cis-eQTLs that all correlated significantly with LVM. Since many of these genes were known to express predominantly in cells of macrophagic origin, we explored the possibility to use primary cultures of macrophages to

21 perform biologic validations of the functions and/or expression levels of these genes.

In the course of the evolution of this project, other researchers identified additional SNPs in mouse inbred strains 34, and used these data to design the Mouse Diversity Genotyping Array. Altogether, this chip detects a total of 623,124 SNPs and captures a greater part of the genetic diversity in available mouse laboratory strains. We thus used this new resource to further analyze genomic DNA in individuals from all 24 RIS, which allowed our laboratory to identify 945 informative SNPs in A/J versus C57BL/6J (instead of the 800 previously found in the Wellcome Trust database). These new data allowed us obtain a better and denser genetic map, and thus improve the identification of candidate genes (This work was conducted by Marie-Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal). We thus continued biologic validation experiments for these new candidate genes, taking advantage of the experimental setup we have developed in the first phase of our project.

1.2 Macrophages

White blood cells or leukocytes have a common origin in hematopoietic stem cells (HSC). The mononuclear phagocyte system (monocyte-macrophage lineage) constitute a subgroup of leukocytes that develop from HSC along distinct differentiation pathways in response to internal and external cues. HSC are located in the bone marrow, may differentiate either into common myeloid progenitor (CMP) or common lymphoid progenitor (CLP). Eventually, CMP become able to differentiate into monocytes and migrate into the blood stream . Upon migration of these monocytes into specific tissue, they can differentiate either into interstitial dendritic cells or macrophages , depending on the cues and/or nature of the tissue 38. Macrophages have broad range of pathogen-recognition receptors that make them efficient at phagocytosis (engulfing of foreign particles) and

22 inflammation (production of inflammatory cytokines) 39. Macrophage harbors numerous receptors on their surface, and can become activated upon stimulation of these receptors. Classically, several different types of activation programs have been identified, comprising the programs of innate activation, humoral activation, classical activation, alternative activation and innate/acquired deactivation 40. Depending on the nature of activating stimuli, macrophages can develop either as classically activated M1 macrophages or innately and alternatively activated M2 macrophages, 41,42.

1.3 Potential Roles Of Macrophages In Cardiac Remodelling

Increased LVM is a cardiovascular trait that corresponds to increased mass as measured at the level of the entire organ. Cardiac myocytes represent the largest cell in hearts and, since they represent about 50% of all cells within the murine myocardium cells, they contribute importantly to overall LVM 43. However, the other 50% of cells within hearts comprise many types of non-cardiomyocytes, including fibroblasts, vascular smooth muscle cells, endothelial cells and cells from the myeloid lineage. Among those, there is evidence that macrophages can contribute to ventricular remodeling 44. Among other things, recent publications indicate that these cells play important functions in cardiac remodeling after myocardial infarction 45,46. However, their possible contribution to differences in LVM has not been explored until now.

Our preliminary studies on gene expression have provided indications that several genes previously known to express mostly within cells from the myeloid lineage can associate with differences in LVM. Interestingly, there are important genetically driven differences in the properties of macrophages from either A/J or C57BL/6J mice 47. One could thus consider and explore the hypothesis that particular sets of genes can, via their differential expression in cardiac macrophages, lead to differences in LVM.

1.4 Primary Cultures Of Macrophages

There are several available methods that have been traditionally used to obtain macrophages in primary cultures. One method consists of obtaining bone marrow-derived macrophages (BMDMΦ) from bone marrow progenitors. The first step consists of harvesting immature cells from the femurs and tibias of mice 48,49. After plating, these immature cells are further cultured in the presence of the lineage-specific growth factor Macrophage Colony Stimulating Factor (M-CSF), which allows for the proliferation and differentiation of committed myeloid progenitors into macrophage/monocyte cell lineage 50,51.

2 Hypothesis

The basis of our overall research hypothesis is that the identification of genetic factors associated with LVM might lead to the identification of the factors associated with increased cardiovascular risk. Although candidate genes can be first identified on the basis of genetic and statistical analyses, biologic validation remains an essential component to determine whether these genes can be causally linked to LVM. Thus, the main goal of my project was to set up a biologic experimental system to validate the results of bioinformatics analyses performed by other members of the laboratory. Given evidence indicating that many of our candidate genes were expressed preferentially and at higher levels in macrophages rather than in cardiac myocytes, we focused on using primary cultures of macrophages as experimental tools. Among other things, we used primary cultures of these cells to: 1) test the effect of genotype on the expression of candidate genes in these cells; 2) explore whether the expression of these genes varied according to different states of activation of macrophages; and 3) test to which extent candidate genes participated to biologic functions of macrophages. In addition, since many of our candidate genes were predicted

24 to show strong levels of co-expression within a network structure, we were interested to test the possible effects of knockdown of one gene on the expression of other genes in the network.

25 V MATERIALS AND METHODS

1 Extraction Of Bone-Marrow Derived Macrophages (BMDMΦ)

The femur and tibia bones of 8-12 weeks old A/J, C57BL/6J and Balb/c mouse (purchased form Jackson Laboratory) were used as a source of BMDMΦ. On day 1, mice were sacrificed and euthanized by cervical dislocation, the abdomen was sterilized with 70% ethanol. Muscle on each hind leg was removed by exposing the skin over the leg and abdomen, and the muscles attaching the hind limb to the pelvis and tibia were removed as well. Then, intact femur and tibia were removed from the mouse by cutting through the pelvic bone close to the hip joint and the ankle, and the femur and tibia were kept separately on ice. To maintain the sterility, the bones were placed in 70% ethanol for 1 minute, then washed twice in sterile 1X PBS (Gibco, Invitrogen Ltd., USA). Both epiphyses were removed by performing clean horizontal cuts using scissors and forceps. The bone marrow cells were flushed by forcing 10mL of RPMI complete (Roswell Park Memorial Institute (RPMI) 1640 containing 10% Heat Inactivated (HI) -FBS, 50 IU Penicillin, 50μg Streptomycin, 5mL of 1M HEPES, purchased from Gibco, Invitrogen Ltd., USA) in 50 mL polypropylene tube using a syringe with 25-gage (G) needle and 26-G needle for femur and tibia respectively, until the bones were white. The marrow plugs from two femurs and two tibias (contained in 20mL of RPMI complete) were mechanically disrupted by passing through a 18-G needle twice, centrifuged at 400g for 5 minute, resuspended in 50mL RPMI complete supplemented with M-CSF (5ng/mL, and plated on two 15 cm bacteriological plastic (BP) dishes (25 mL per dish). Culture medium was replaced on days 3 and 6. On day 7, the cells (which have differentiated into BMDMΦ) were detached by 10 min incubation with 10mL of 1X PBS containing 10mM EDTA and 4mg/mL Lidocaine-HCL, followed by vigorous pipetting. The resulting cell suspension was centrifuged at 400g for 5 min, and BMDMΦ were replated in six-well of tissue culture

26 (TC) dish 50, using 5X105 cells per well.

2 Culture And Activation Of BMDMΦ From Mice

BMDMΦ from Balb/c mice replated on day 7 were used to study the effects of several activating agents. Cells were provided with fresh RPMI complete on day 8. On day 9, the medium was replaced for either control medium or medium supplemented with seven different activators, i.e. LPS (100ng/μL), IFNγ (1ng/μL), LPS and IFNγ (100ng/μL and 1ng/μL, respectively), IL-4 (20ng/mL), IL-13 (20ng/mL), M-CSF (5ng/mL) and Zymosan A (500μg/mL) (LPS and Zymosan A were purchased from Sigma- Aldrich Corp., USA and all other activators from PeproTech Inc.-Cedarlane, USA). After 6 hours and 24 hours of incubation with these different media, cells were collected for further reprocessing and analysis. For dose-response experiments, cells were stimulated with different concentrations of either LPS (100ng/μL, 50ng/μL, 25ng/μL, 12.5ng/μL, 6.25ng/μL and 3.125ng/μL) or Zymosan A (500μg/mL, 250μg/mL, 125μg/mL, 62.5μg/mL, 31.25μg/mL and 15.62μg/mL) for 24 hours. To study the effect of genotype on gene expressions, BMDMΦ extracted from the three mouse strains A/J, C57BL/6J and Balb/c were kept with either control medium or in the presence of LPS (100ng/μL).

Total RNA was extracted from harvested cells using the RNA Micro extraction kit (Qiagen, Germany). RNA concentration (ng/μl) is quantified by measuring the absorbance (A) at 260 nm and 280 nm in a Nanodrop. The purity of RNA samples was estimated by the ratio A260/A280 (purity=1.8 to 2.0). 10mM Tris HCL pH7.5 was used as a blank and samples were 1/10 diluted before reading.

3 RT-PCR Analysis

Aliquots containing 0.2μg of total RNA were reverse-transcribed using SuperScriptTM II Reverse Transcriptase (Invitrogen Ltd., USA). Gene

27 expression was quantified by RT-PCR (MxProTM QPCR, Stratagene, USA), which allowed accurate quantification of starting amounts of cDNA targets. PCR products was detected using SYBR Green fluorescent dye (QuantiTect SYBR Green PCR Kit, Qiagen, Germany) that bind to double-stranded DNA with the excitation and emission maxima at 494 nm and 521 nm, respectively. For relative quantification, the comparative or ΔΔCT method was used, which relies on direct comparison of CT values. The standard curve was used to determine the amplification efficiencies of the target and endogenous reference (S-16) genes in an initial experiment. Since, amplification efficiencies were comparable, therefore, in all subsequent experiments, the ΔΔCT value [ΔΔCT = ΔCT (gene of Interest) – ΔCT (endogenous control)] for each sample is determined. Then, the normalized

-ΔΔC level of target gene expression [2 T] was calculated and used to compare differential expression of a gene in differentially treated cells.

4 Down-Regulation Of Candidate Genes In BMDMΦ

RNA interference mediated by small interfering RNAs (siRNA) has emerged as a powerful tool to target specific knockdown of gene expression in cell culture. However, macrophages constitute a particular case, as these cells contain RNA-sensing immunoreceptors from the Toll-like receptor family. Consequently, exposure of macrophages to siRNA induces the secretion of proinflammatory cytokine in these cells via an innate immunity reaction 52,53.

Several formulations of siRNAs are currently available. Dicer siRNAs (D-siRNAs) are potent mediators of gene-specific silencing. These molecules are 27-mer double stranded RNAs that are cleaved by dicer endonuclease, a member of the RNase III family into smaller functional siRNAs (21-mer) 54. D- siRNA have a unique and asymmetric design that includes a 3’ overhang of 2- base preferably on antisense strand and presence of 2 DNA residues to the 3’ end of the other strand preferably sense strand in the blunt end of the duplex

28 55, preventing heterogeneity of the cleaved siRNA product. Blunt end is not appropriate for binding of dicer endonuclease, thus, cleavage preferentially occurs 21–22 bases from the 3’ overhang. In addition to cleaving D-siRNAs, dicer endonuclease facilitates the loading of siRNAs onto RNA-induced silencing complex (RISC), which targets any homologous mRNA for degradation 55-57.

Although siRNAs can activate proinflammatory responses in macrophages, these non-specific responses are concentration-dependent 58. One particular advantage of D-siRNAs is that they are active at much lower concentrations (sometimes as low as 100pM) than standard siRNAs, which reduces the potential for off-target or nonspecific effects. Accordingly, it has been shown that D-siRNAs silence gene expression more potently than 21- mer siRNAs without inflammatory side-effects 59,60.

Thus, to obtain potent gene silencing without non-specific off-target effects, gene knockdown experiments in BMDMΦ cells were performed by transfection with D-siRNA (Integrated DNA Technologies, Inc., USA), either by electroporation or by using lipofectamine LTX (the latter being described as one of the best transfection agents for macrophages) 61.

Initial optimization studies involved transfections with: 1) three different Dicer siRNA Cy3TM DS transfection controls (10nM) (i.e. fluorescently-labeled double –stranded control RNA duplexes; 2) HPRT-S1 DS (10nM) (i.e. a positive control Dicer-Substrate RNA duplex which uses a site in the hypoxanthine guanine phosphoribosyltransferase 1 (HPRT1) gene that is common between human, mouse, and rat; and 3) DS scrambled-neg (10nM) (i.e. scrambled universal negative control RNA duplex which does not exist in the human, mouse, and rat genomes). Knockdown experiments were performed for two potential candidate genes, i.e Fastkd3 (from chr13) and Twistnb (from chr12). Three different D-siRNAs were used for each target gene.

29 Electroporation optimization trials involved the testing of three different concentrations (10nM, 1nM and 0.1nM) of each D-siRNA. D-siRNA duplexes were transferred to a 4-mm cuvette (Bio-Rad Life Science, Germany) and filled up to a final volume of 50 μL with Opti-MEM (Gibco, Invitrogen Ltd., USA). 50 μL of the 2 × 106 BMDMΦ suspension from Balb/c on day 7 were added and pulsed at 300 V, 150 μF, and 100 Ω in a Gene Pulser Xcell Total System (Bio-Rad Life Science, Germany). After addition of RPMI complete, the cells were centrifuged at 500g for 5 minutes, resuspended in RPMI complete and plated on 10-cm TC dishes for 24 hours 62,63. Cells were harvested and total RNA was extracted using the RNeasy Mini kit (Qiagen, Germany). Gene quantification was performed by RT-PCR.

For optimization of the transfection procedure with lipofectamine LTX, three different concentrations (10nM, 5nM and 2.5nM) of each D-siRNA were used to knockdown Fastkd3 and Twistnb. For this study BMDMΦ derived from Balb/c on day 7, were replated in six-well of TC with the density of 5X105 BMDMΦ per well. On Day 8, these cells are provided with fresh RPMI incomplete (without 10% HI-FBS, 50 IU Penicillin, 50μg Streptomycin, 5mL of 1M HEPES). In parallel to this, D-siRNA duplexes and lipofectamine LTX were diluted in Opti-MEM separately, followed by incubation of D-siRNA with lipofectamine LTX for 30 min to allow formation of complex. The resulting complex was then added to these BMDMΦ. After 4- 6 hours of incubation, cells were provided with RPMI complete for 24 hours (protocol from ‘LipofectamineTM LTX Reagent’, Invitrogen Ltd., USA). Cells were harvested and RNA (RNA micro Handbook, Qaigen, Germany) was extracted, followed by reverse transcriptase and gene expression analysis through RT-PCR.

Then, experiments were designed in which 2.5nM concentration of D- siRNAs Fastkd3 (2) and Twistnb (1) were used to knockdown Fastkd3 and Twistnb in cells, followed by incubation of each under control and stimulated LPS (100ng/μL) and Zymosan A (500μg/mL) conditions. For this study

30 BMDMΦ derived from Balb/c on day 7, were replated in six-well of TC with the density of 5X105 BMDMΦ per well. On Day 8, these cells are provided with fresh RPMI incomplete. In parallel to this, D-siRNA duplexes and lipofectamine LTX were diluted in Opti-MEM separately, followed by incubation of D-siRNA with lipofectamine LTX for 30 min to allow formation of complex. The resulting complex was then added to these BMDMΦ. After 4- 6 hours of incubation, cells were provided with RPMI complete for 24 hours. On day 9, the medium was replaced for each followed by incubation of each for 24 h with two different activators LPS (100ng/μL) and Zymosan A (500μg/mL). Cells were harvested and RNA (RNA micro Handbook, Qaigen, Germany) was extracted, followed by reverse transcriptase and gene expression analysis through RT-PCR.

5 Abundance Of MiRNA Transcripts

Some experiments involved the quantification of the abundance of several miRNA transcripts in BMDMΦ from A/J, C57BL/6J and Balb/c mice. Total RNA was extracted using the mirVANA kit (Ambion, USA). MiRNA transcripts to be quantified were either endogenous controls (snoRNA202, snoRNA234, snoRNA135 and rRNA 18S) or the transcripts of miR-23b, miR- 24-1, and miR-27b. For each transcript, total RNA was reverse transcribed, using a specific Taqman microRNA Reverse Transcriptase Kit (Ambion, USA) and further amplified, using a specific Taqman microRNA assay kit (Ambion, USA) (ViiA™ 7 Dx Real-Time PCR Instrument, Applied Biosystems, USA). The control small RNA was chosen by comparing all four potential endogenous control, and selecting the one that showed the least amount of variation across cells from all 3 strains of mice and across several experimental conditions (stimulation with either LPS, M-CSF, IL-4 or IL-13).

6 Statistical Analysis

Differences in the relative expression of mRNA level during RT-PCR analysis were statistically detected by one-way ANOVA analysis, followed by

31 Tukey’s multiple comparison test in which P value less than 0.05 was considered as the significant level of difference in between samples.

Table 1. Summary of D-siRNA duplexes purchased from Integrated DNA Technologies, Inc., USA.

Dicer si-RNA Type Catalog ID Targeted: Region/Exon Cy3TM DS Fluorescent control HPRT-S1 DS Positive control DS scrambled-neg Negative control Fastkd3 MMC.RNAI.N027123.12.1 (1) CDC/2 MMC.RNAI.N027123.12.2 (2) 3’UTR/7 MMC.RNAI.N027123.12.3 (3) 3’UTR/7 Twistnb MMC.RNAI.N172253.12.1 (1) 3’UTR/4 MMC.RNAI.N172253.12.2 (2) 3’UTR/4 MMC.RNAI.N172253.12.3 (3) 3’UTR/4

Table 2. Summary of RT-PCR primers ordered from Invitrogen Ltd., USA.

RT-PCR Primer Gene Amplified Region : Oligo Sequence Junction Twistnb Exon3/Exon4 5’AGGCGATGAACTGGAATTTG3’ (FP) 5’CAACCTCTTCAACGACAGTTTC3’ (RP) Fastkd3 Exon6/Exon7 5’AGACACCTGCGGTTACTTGG3’ (FP) 5’GCGGAGCTTTGAGAAAACAG3’ (RP) Testin-2 Exon7/Exon8 5’TGGTCGGCTATGGTTTTGAG3’ (FP) 5’TGTGGCAAGTGTAGCAATCC3’ (RP) CTSL Exon7/Exon8 5’GGTTCTGTTGGTGGGCTATG3’ (FP) 5’TCCATACCCCATTCACTTCC3’ (RP) CCRK Exon7/Exon8 5’TCCTCTACCCTCCACGACAG3’ (FP) 5’CAGAGGCGCTGTGAAGAAG3’ (RP) GOLM1 Exon10/Exon11 5’CATGGATGAAAACGAAGCAG3’ (FP) 5’AATGTGGCTCTGCCTTTCAC3’ (RP)

Table 3. Summary of TaqMan® MicroRNA Assay purchased from Ambion, USA.

TaqMan® MicroRNA Assay Assay Name Assay ID Targeted Sequence snoRNA202 001232 GCTGTACTGACTTGATGAAAGTACTTT TGAACCCTTTTCCATCTGATG snoRNA234 001234 CTTTTGGAACTGAATCTAAGTGATTTA ACAAAAATTCGTCACTACCACTGAGA snoRNA135 001230 CTAAAATAGCTGGAATTACCGGCAGAT TGGTAGTGGTGAGCCTATGGTTTTCTGAA rRNA 18S Part #: 4352407 hsa-miR-23b 000400 mmu-miR-23b: AUCACAUUGCCAGGGAUUACC hsa-miR-189 000488 mmu-miR-24-1: GUGCCUACUGAGCUGAUAUCAGU hsa-miR-27b 000409 mmu-miR-27b: UUCACAGUGGCUAAGUUCUGC

33 VI RESULTS

Each RIS has a different phenotype, which is identified as the LVM normalized by the body weight. Our laboratory has previously detected one major phenotypic QTL linked to LVM on chr 13 with the peak at 57.8MB, and two minor QTLs on chr 12 and 16, in a panel of 24 RIS 37 (Fig. 1).

Figure 1. Single-locus genome wide scans in males for LVM. On major peak showing significant linkage (P!= 0.05) at chr 13 (highlighted in red) have been identified by a QTL mapping. Dotted lines at top and bottom represent significant (P"= 0.05) and suggestive (P = 0.63) thresholds, respectively. LOD, log of odds ratio 37.

The results presented herein correspond to consecutive time periods in the laboratory. During the initial early phase of the project, preliminary analyses of our data were based on results genomic maps obtained from the Wellcome Trust Database. Accordingly, we identified a total of 800 informative SNPs showing polymorphisms in the A/J versus C57B/6J mouse strains, and to obtain the corresponding genomic maps in all 24 RIS. To extend our previous phenotypic LVM data, our laboratory used Illumina microarrays to measure (in hearts from 4 individuals from each RIS) the mRNA abundance of all genes expressed in myocardium. Of note, expression levels of genes within tissues can themselves be considered as quantitative traits. This has led to studies where investigators identified so-called “eQTLs”, which has provided greater insights into the biology of gene regulation and/or complex traits 2,6,7

When an eQTL locus corresponds to that of the gene whose transcript abundance is measured, it is identified as a ”cis-eQTL”. This means that a genetic variation in the neighborhood of the gene is associated with the

34 differential abundance of its transcript. When the locus of a cis-eQTL matches that of the phenotypic QTL, it increases the likelihood for the corresponding gene to be considered as a candidate gene within the phenotypic QTL interval 12-14. Another interesting behavior of a gene corresponds to the situation where its expression level correlates quantitatively with the phenotype of interest. In such situation, the gene can be considered as “QTT” 2,5.

In the course of the project several genes have been tested in biologic experiments. Table 4 summarizes these various genes, and indicates in which experiments they have been tested.

Table 4. Table indicating function, chromosomal site and location of the twelve cis-eQTLs. Last 10 cis-eQTLs, showed significant correlation with LVM and also fell within confidence interval of phenotypic QTL.

Gene Function Chr Location Genes Tested Twistnb (TWIST DNA directed RNA 12 34,114,489- Fig. 7, 8 and neighbor) polymerase activity 34,124,245 9 FASTKD3 (Fast ATP binding 13 68,721,125- Fig. 4, 5, 6,8 kinase domain 3) 68,731,156 and 9 4930486L24RIK Cysteine-type peptidase 13 60,943,973- Fig. 4, 5, 6 activity 60,965,777 and 13 TGFBI (Transforming Extracellular matrix binding 13 56,710,920- growth factor) 56,740,923 CPLX2 (Complexin 2) SNARE binding 13 54,472,713- 54,485,278 GOLM1 (Golgi Molecular function 13 59736357- membrane protein 1) 59777145 HABP4 (Hyaluronic Hyaluronic acid binding 13 64,263,174- acid binding protein 64,287,844 4) CDC14B (Cell division Protein 13 64293853- cycle 14 homolog B) tyrosine/serine/threonine 64376296 phosphatase activity ZFP367 (Zinc finger DNA binding 13 64,234,330- protein 367) 64,254,510 CTSL (Cathepsin L) Cysteine-type peptidase 13 64464522- Fig. 13 activity 64471614 CCRK (CDK20, cyclin- ATP binding 13 64533861- Fig. 13 dependent kinase 20) 64541029 1110018J18RIK Molecular function 13 64,391,124- 64,414,018

35 1 Biologic Validation Of Genomic Results (First Phase)

By analyzing the microarray data in the context of the Wellcome Trust genomic maps for all 24 RIS, our laboratory identified a total of 456 cis- eQTLs (LOD > 3.0); out of those, we identified a total of 15 genes whose expression correlated significantly with LVM. These genes thus behaved both as cis-eQTLs and QTTs. Two genes among the latter (Fastkd3 and 4930486L24RIK) were contained within the boundaries of the major LVM- linked phenotypic QTL previously found on chr 13 (Fig. 2)

Figure 2. Graphic representation of all cis-eQTLs detected in 24 RIS. The genomic map was obtained from the Wellcome Trust. X-axis is the position of each cis-eQTL on the genome and Y-axis is the correlation coefficient of the expression of each gene with the values of LVM. Dotted line represents significant (P"= 0.05) threshold, gene symbol is indicated for each cis- eQTL showing significant (P"= 0.05) correlation coefficient. The highlighted region contains the cis-eQTL at chr13 whose position overlap that of the major LVM-linked phenotypic QTL previously found. Results have been obtained from Marie-Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal.

36 In the case of complex traits, it is rare to find situations where one single gene variant is responsible for a large part of the variance of a given phenotypic trait. We were therefore interested to test whether all cis-eQTLs linked to LVM could contribute collectively to LVM. To test this hypothesis, we built a gene co-expression network with all genes showing significant co- expression with 2 or more of the 15 cis-eQTLs. This analysis revealed that 13 out of these 15 cis-eQTL genes (including Fastkd3 and 4930486L24RIK) displayed interconnections between themselves and with a set of 17 other co-expressed genes. Interestingly, we found that 27/32 of these interconnected genes were (according to data from the SymAtlas database) preferentially expressed within macrophages and other cells of myeloid origin rather than in cardiac myocytes (Fig.3).

Figure 3. Color-coded profiles of expression of cis-eQTL and interconnected genes across several tissues (per SymAtlas database). Each gene on X-axis is plotted against different tissue types on Y-axis. Green color means high expression whereas red color means low expression of gene. The right part of the figure highlights cell types belonging to the myeloid lineage It can be seen that only 5/32 genes show higher levels of expression in heart than in cells of myeloid origin. on The left part of the figure is a magnification showing the expression levels of Fastkd3 and 4930486L24RIK (cis-eQTLs at chr13) in heart, macrophages and mast cells. For the purpose of current project the important information has been magnified in the figure on left form figure on right. Results have been obtained from Marie- Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal.

Whereas the previous analysis indicated that Fastkd3 and 4930486L24RIK were potential candidate genes within the QTL interval, there are particular challenges to further test their possible contributions to LVM. Indeed, very little information is currently available about the nature and/or the functions of these two candidate genes. We therefore reasoned the fact that they are preferentially expressed in macrophages might constitute a starting point for further investigations. We thus proceeded to setup primarily cultures of mouse BMDMΦ to: 1) verify that these genes are

38 indeed expressed in these cells; and 2) test whether their expression level changes as a function of conditions known to affect the functions and properties of macrophages. If so, these cultures could provide a cell-based system to further validate hypotheses derived from our genetic experiments.

As a starting point, we prepared primary cultures from Balb/c mice. This strain was chosen because: 1) most published methods used this particular strain; and 2) it may in some cases be a particular advantage to have cells with a genetic origin other than the two parental strains (A/J and C57BL/6J) from our RIS 64,65. After one week of differentiation in the presence of M-CSF, BMDMΦ cells were plated (at a concentration of 5X 105 cells / well) in six-well plates. The cells were incubated for 6 h and 24 h in the presence of either control medium or medium supplemented with one of the seven following macrophage activators: LPS (100ng/μL), IFNγ (1ng/μL), LPS and IFNγ (100ng/μL and 1ng/μL, respectively), IL-4 (20ng/mL), IL-13 (20ng/mL), M-CSF (5ng/mL) and Zymosan A (500μg/mL) (using 3 wells for each condition). After incubation, total RNA was extracted from cells in each well, and the concentration of the mRNA transcripts of either Fastkd3 or 4930486L24RIK were quantified by RT-PCR. We confirmed the expression of Fastkd3 (Fig. 4a) and 4930486L24RIK (Fig. 4b) in macrophages and their expression levels are indeed regulated in the presence of stimulating agents known to activate macrophages. The highest levels of activation (P < 0.0001) were obtained by 24 h exposure to LPS, LPS with IFNγ and Zymosan A for both the candidate genes. For Fastkd3, the strongest stimulation was obtained with 24 h exposure to either LPS (with or without IFNγ) or Zymosan; for 4930486L24RIK, strongest stimulation was obtained with 24 h exposure to Zymosan (Fig. 4).

Figure 4. Bar graph representation of mRNA levels of either Fastkd3 (a) or 4930486L24RIK (b) in 5X 105 BMDMΦ in presence of seven stimulating agents: LPS (100ng/μL), IFNγ (1ng/μL), LPS and IFNγ (100ng/μL and 1ng/μL, respectively), IL-4 (20ng/mL), IL-13 (20ng/mL), M-CSF (5ng/mL) and Zymosan A (500μg/mL) at 6 h and 24 h of incubation. Each bar represents the mean of three replicates with error bar representing Standard error of the mean (SEM). Differences in mRNA levels were detected by one-way ANOVA analysis, followed by Tukey’s multiple comparison test. Significant increases (P value < 0.0001 versus control) in mRNA levels were observed for both Fastkd3 and 4930486L24RIK in presence of LPS, LPS with IFNγ and Zymosan A at 24 h.

In subsequent experiments, we activated 5X 105 BMDMΦ from Balb/c in a dose-related fashion with 24 H exposure to either LPS (for Fastkd3) or

40 Zymosan A (for 4930486L24RIK), . Accordingly, we measured expression of Fastkd3 against different concentrations of Zymosan A (500μg/mL, 250μg/mL, 125μg/mL, 62.5μg/mL, 31.25μg/mL and 15.62μg/mL) (Fig. 5a). 4930486L24RIK was quantified against different concentrations of LPS (100ng/μL, 50ng/μL, 25ng/μL, 12.5ng/μL, 6.25ng/μL and 3.125ng/μL) (Fig. 5b) (using 3 wells for each condition). We found that expression of the Fastkd3 (r2 = 0.93 and P < 0.001) and 4930486L24RIK (r2 = 0.95 and P < 0.001) correlated significantly and in a linear fashion to the log (conc) of stimulating agent Zymosan A and LPS respectively.

Figure 5. Graphical representation showing the dose-response correlation between mRNA levels of two candidate genes and different concentrations of activating agents 5X 105 BMDMΦ were stimulated with different concentrations of (a) Zymosan A (500μg/mL, 250μg/mL, 125μg/mL, 62.5μg/mL, 31.25μg/mL and 15.62μg/mL) and (b) LPS (100ng/μL, 50ng/μL, 25ng/μL, 12.5ng/μL, 6.25ng/μL and 3.125ng/μL) for 24 h. Each circle represents the mean of three replicates with error bar representing SEM. Log (conc) of Zymosan A (a) and LPS (b) (X-axis) plotted against percentage of control (Y-axis) for either Fastkd3 (r2 = 0.93 and P < 0.001) or 4930486L24RIK (r2 = 0.95 and P < 0.001).

Our previous analyses in the hearts of RIS had indicated that there could be some correlation in the expression levels of our candidate genes. One possible explanation for such correlation would be situations where all concerned genes contribute to similar biologic processes. We thus tested whether conditions found to activate Fastkd3 affected expression of 4930486L24RIK in BMDMΦ (and vice-versa). 5X 105 BMDMΦ from Balb/c

42 were activated in a dose-related fashion with the stimulating agent LPS and Zymosan A at 24 h (using 3 wells for each condition). Accordingly, we measured expression of Fastkd3 and 4930486L24RIK against different concentrations of LPS (100ng/μL, 50ng/μL, 25ng/μL, 12.5ng/μL, 6.25ng/μL and 3.125ng/μL) as well as Zymosan A (500μg/mL, 250μg/mL, 125μg/mL, 62.5μg/mL, 31.25μg/mL and 15.62μg/mL). In the presence of Zymosan A, we found a significant correlation in the expression levels of both genes (r2 = 0.61 and P < 0.05) (Fig. 6b). In the presence of LPS, the correlation in the expression levels of both genes did not reach statistical significance (r2 = 0.46 and P = 0.0936) (Fig. 6a).

Figure 6. Plot showing the correlation in expression level of 2 candidate genes in the presence of different concentrations of different stimulating agents. mRNA levels of 4930486L24RIK (X-axis) plotted against mRNA levels of Fastkd3 (Y-axis) in 5X 105 BMDMΦ kept across different dose response conditions of (a) LPS (100ng/μL, 50ng/μL, 25ng/μL, 12.5ng/μL, 6.25ng/μL and 3.125ng/μL) and (b) Zymosan A (500μg/mL, 250μg/mL, 125μg/mL, 62.5μg/mL, 31.25μg/mL and 15.62μg/mL) for 24 h. Significant correlation was observed in presence of Zymosan A (r2 = 0.61 and P < 0.05) but not in presence of LPS (r2 = 0.46 and P = 0.0936).

Additional insights concerning the functions of genes with unknown functions can be derived from experiments where their level of expression is manipulated by genetic means. We thus explored the possibility of knocking down expression of Fastkd3 and Twistnb in BMDMΦ with silencing RNAs. To optimize the procedure, we also compared two different methods of siRNA

44 delivery, i.e. electroporation of cells in a cuvette or transfection of plated cells with lipofectamine LTX.

During electroporation, 2X106 BMDMΦ were put into an electroporation cuvette, and transfected with either DS scrambled siRNA (a negative control duplex) or three different D-siRNAs duplexes targeting either Fastkd3 or Twistnb. The sequences of the three D-siRNA designed for specific down-regulation of Fastkd3 and Twistnb are presented in Table 1. The Fastkd3 and Twistnb specific D-siRNA were used at three different concentrations (10nM, 1nM and 0.1nM). After transfection, the cells were further cultured for 24 h (using 3 wells for each condition). Total RNA was extracted from each well, and gene expression was quantified by RT-PCR.

One example showing results obtained with the Twistnb specific D- siRNA is shown in Fig. 7. As shown, expression of Twistnb was significantly (P < 0.001) decreased using a 10nM concentration for each D-siRNA. However, experiments using the Fastkd3-specific D-siRNA yielded much less consistent results (results not shown). On one occasion, we observed that expression of Fastkd3 was decreased in the presence of some concentrations of D-siRNA, but the effects were not consistent across the several doses tested. On one other occasion, we did not observe any reduction in Fastkd3 expression at any of the doses tested.

Figure 7. Transfection via. electroporation. Bar graph representating candidate gene mRNA levels. 2X106 BMDMΦ were transfected with negative control D-siRNA and three D-siRNA duplexes Twistnb (1), (2), (3), each at three different concentrations (10nM, 1nM and 0.1nM). Each bar represents the mean of three replicates with error bar representing SEM. Differences in mRNA levels were detected by one-way ANOVA analysis, followed by Tukey’s multiple comparison test. Significant (P < 0.0001) decrease in mRNA level of Twistnb was observed at 10nM concentration of all three D-siRNA duplexes.

In light of the inconsistent results obtained by electroporation, we tested whether transfection with lipofectamine LTX could be used as an alternative. Aliquots containing 5X105 BMDMΦ were replated in six-well of TC, provided with fresh RPMI incomplete media next day and transfected with negative control duplex and three different D-siRNAs duplexes targeting either Fastkd3 or Twistnb. After 4-6 hours of incubation, cells were provided with RPMI complete for 24 h. The Fastkd3 and Twistnb specific D-siRNA were used at three different concentrations (10nM, 5nM and 2.5nM) (using 3 wells for each condition). Total RNA was extracted from each well, and gene expression was quantified by RT-PCR.

For Fastkd3, we observed that expression of the gene was significantly knocked down by all three corresponding D-siRNA duplexes, and that the effect appeared to be dose-dependent for 2/3 of the D-siRNA duplexes (Fig. 8a). Likewise, expression of Twistnb was significantly knocked down by all

46 corresponding three D-siRNA duplexes, although there was no clear dose- response relationship in this case. Nonetheless, significant down-regulation of both genes could be obtained in each case using D-siRNA duplexes at a 2.5 nM concentration. This allowed us to avoid using D-siRNA duplexes at the higher 10 nM concentration in subsequent experiments, and thus avoid possible off-targets non-specific effects that may occur in macrophages at that dose 58.

Figure 8. Transfection of D-siRNA duplexes by lipofectamine LTX. The bar graphs represent the mRNA levels for each candidate gene. 5X105 BMDMΦ were transfected with negative control D-siRNA and three D-siRNA duplexes (a) Fastkd3 (1), (2), (3) and (b) Twistnb (1), (2), (3) and each having three different concentrations (10nM, 5nM and 2.5nM) specific for suppressing the expression of Fastkd3 and Twistnb respectively (Table 1) at 24 h. Each bar represents the mean of three replicates with error bar representing SEM. Differences in mRNA levels were detected by one-way ANOVA analysis, followed by Tukey’s multiple comparison test. Significant decrease in mRNA level of Fastkd3 (P < 0.0001) and Twistnb (P < 0.002) were observed at all concentrations of all three D-siRNA duplexes.

We further tested whether transfection of D-siRNA duplexes with lipofectamine LTX could knock down expression of corresponding genes not

48 only in control condition, but also in the presence of agents that activate macrophages, i.e LPS or Zymosan A. 5X105 BMDMΦ were replated in six- well of TC, provided with fresh RPMI incomplete media next day and transfected with three different kinds of D-siRNAs at the 2.5 nM concentration (either the negative control duplex, Fastkd3(2) (Fig. 9a) or Twistnb(1) (Fig. 9b). After 4-6 hours of incubation, cells were provided with RPMI complete for 24 h, followed by 24 h incubation of each under control and stimulated LPS at the concentration of 100ng/μL and Zymosan A at the concentration of 500μg/mL (using 3 wells for each condition). Total RNA was extracted from each well, and gene expression was quantified by RT-PCR. Although both LPS and Zymosan A stimulated expression of both genes, expression of Fastkd3 and Twistnb was about 50% lower than in cells transfected with the negative control, both in unstimulated and stimulated conditions (Fig. 9) (Table 4).

Figure 9. Bar graph representating candidate gene mRNA levels. 5X105 BMDMΦ were transfected with negative control D-siRNA at 10nM and D-siRNA Fastkd3 (2) at 2.5nM (a) or D-siRNA Twistnb (2) at 2.5nM (b) at 24 h, followed by 24 h incubation of each under control and stimulated (LPS – 100ng/μL and Zymosan A - 500μg/mL). Each bar represents the mean of three replicates with error bar representing SEM. Posing that the knockdown of Fastkd3 and Twistnb occurs in specific fashion.

50 Table 5. Knockdown of Fastkd3 and Twistnb mRNA levels in BMDMΦ under control conditions. Relative expression of Fastkd3 and Twistnb were normalized using DS Scrambled Negative as baseline (see Fig. 9).

Cell Type Fastkd3 Twistnb % Knockdown Control 57 44

2 Biologic Validation Of Genomic Results (Second Phase)

In our initial experiments with macrophages, we focused on studying the expression of Fastkd3 and 4930486L24RIK, since our initial genomic analyses had indicated that they behaved both as cis-eQTLs and QTTs. As these experiments were underway, we obtained additional genomic data, as each RIS was analyzed with the newly developed Mouse Diversity Genotyping array (The Jackson Laboratory). This analysis allowed us to identify a total of 945 informative SNPs in A/J versus C57BL/6J (instead of just 800 from the Wellcome Trust database). Accordingly, the laboratory obtained a better and denser genetic map, in particular within the interval of our phenotypic QTL. By re-analyzing our microarray gene expression data with the new genetic map, our laboratory identified a total of 1,195 cis-eQTLs (instead of 465 cis-eQTLs obtained previously with the genomic map from Wellcome Trust). Among these, we found that a total of 33 cis-eQTLs showed significant correlation with LVM (p < 0.05 by permutation test) (Fig. 10).

Figure 10. Graphic representation of all cis-eQTLs detected in 24 RIS, using a genomic map obtained using the Mouse Diversity Genotyping array from the Jackson Laboratory. X-axis is the position of each cis-eQTL on the genome and Y-axis is the correlation coefficient of the expression of each gene with the values of LVM. The dotted line represents the threshold for significant correlation with LVM (P"< 0.05), The closed circles above this threshold represents cis-eQTL showing significant (P"< 0.05) correlation coefficient. The green arrow points to a group of cis-eQTLs onchr13, where we had previously detected a major phenotypic QTL linked to LVM. Results have been obtained from Marie-Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal.

Interestingly, 10 out of these cis-eQTLs/QTTs (i.e.TGFBi, CPLX2, GOLM1, HABP4, CDC14B, ZFP367, CTSL, CCRK, 1110018J18RIK, 4930486L24RIK) clustered within a 10 MB interval centered around the phenotypic QTL linked to LVM on chr 13. 4930486L24RIK was included in this interval, but Fastkd3 was now positioned just outside of the interval (Fig. 11).

Figure 11. Schematic representation of cis-eQTLs with respect to phenotypic QTL peak. The box on top of the figure shows the profile of the QTL peak linked to LVM linked phenotypic on chr 13. The following boxes show the profiles of all cis-eQTLs on chr 13 having a significant correlation with LVM. The green box highlights the 10 cis-eQTLs falling within the 10MB confidence interval centered around the peak of the phenotypic QTL. Results have been obtained from Marie-Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal.

Although the analysis using the new genomic map detected more cis- eQTLs that the analysis performed previously, we observed once again (on the basis of the SymAtlas database) that most of these genes were expressed within macrophages and other cells of myeloid origin, and most often at higher levels than in cardiac myocytes. In the particular case of the candidate gene CTSL , expression was not only higher than in heart, but also appeared to be exclusively restricted to macrophages (Fig. 12).

Figure 12. Relative abundance of the mRNA of 8 genes is several tissues (as per SymAtlas database). Each row represents different tissue types. The green boxes highlight expression of each candidate gene in heart and macrophage respectively. Repeatedly, preferential expression of candidate genes was observed in macrophages and other myeloid lineage cells rather than heart.

Our data on gene expression in the hearts of RIS indicated that a particular locus on chr 13 was polymorphic in A/J vs. C57BL/6J and affected their level of expression in hearts. To verify the influence of the genetic background, we tested whether the level of expression of these genes in BMDMΦ would be affected by the genetic origin of the cells. Among the 10 cis-eQTLs described above, three candidate genes (namely, CTSL, 4930486L24RIK, and CCRK) were of particular interest, because of their high level of expression in macrophages as well as evidence in the literature showing that they may be involved in cardiac remodeling. Indeed: 1) knockout of CTSL in mice affects cardiac morphology, induces a cardiomyopathic dilation and affects cardiac function 66; 2) 4930486L24RIK exhibits 60% similarity with CTSL at the amino acid level, and thus may similarly be involved in cardiac repair and remodeling and 3) the heart expresses a splice variant of CCRK that promotes cardiac cell growth and survival 67.

54 We prepared BMDMΦ from three mouse strains, i.e. A/J and C57BL/6J (the parental strains of the RIS) and the third genetically distinct Balb/c strain. After one week of differentiation in the presence of M-CSF, BMDMΦ cells were plated (at a concentration of 5X 105 cells / well) in six-well plates. The cells were incubated for 24 h in the presence of either control medium or medium supplemented with stimulating agent LPS (100ng/μL) (using 3 wells for each condition). After incubation, total RNA was extracted from cells in each well, and the concentration of the mRNA transcripts of CTSL, 4930486L24RIK and CCRK were quantified by RT-PCR, both in control condition and after stimulation. Altogether, we found that the basal levels of expression as well as the magnitude of the response to LPS stimulation showed significant differences according to the genetic origin of macrophages, and were in all cases different in cells obtained from A/J and C57BL/6J mice (Fig. 13). This also suggested that the respective genotypes of these 2 strains of mice could affect the expression of these 3 genes in a cell- autonomous fashion.

CTSL

55 4930486L24RIK

CCRK

Figure 13. Bar graph representation of mRNA levels of 3 candidate genes in macrophages from 3 different mouse strains. 5X 105 BMDMΦ were either in control medium or in presence of stimulating agents LPS (100ng/μL) for 24 h, followed by quantification of CTSL, 4930486L24RIK and CCRK in control condition (graphs on left) and LPS stimulated conditions (graphs on right). Each bar represents the mean of three replicates with error bar representing Standard error around the mean (SEM). Differences in mRNA levels were detected by one-way ANOVA analysis, followed by Tukey’s multiple comparison test.

Of note, the fact that 10 cis-eQTLs/QTTs all cluster within an interval as small as 10 MB was intriguing, as this is unlikely to happen just by chance. We reasoned that there might be a common regulator for all genes within that interval. Upon further analysis of genomic databases, we noticed that a non-coding gene corresponding to the miR-23b-27b-24-1 microRNA cluster

56 was also located within the same 10 MB interval on chr 13 containing the 10 cis-eQTLs(Fig.14).

Figure 14. Relative distribution on chr 13 of all 10 cis-eQTL/QTT genes vs. the locus of the miR-23b-27b-24-1 microRNA cluster (in red). The figure represents a screenshot from the information available in the US Santa Cruz Genome Browser. The green stars highlight two candidate genes (CDC14B and GOLM1) reported to have a miR-27b binding site within the 3’UTR region of their respective mRNA transcript.

In addition, we compared the reported sequences of the promoter region of the miR-23b-27b-24-1 miRNA gene for the A/J and C57BL/6J mouse strains. We obtained mouse genomic DNA sequences from the Mouse Genome Project run at the Wellcome Trust Sanger Institute and the raw data (deposited in the European Nucleotide Archive) from FTP transfer. We performed the sequence alignments between two mouse strains using the Blast2 tool from NCBI and observed the polymorphisms at the promoter region in between 2 strains. Then, we used the DBD Transcription Factor Prediction Database (www.transcriptionfactor.org) to predict the transcription factor binding sites and observed that several transcription factors (CDxA, Sry and Ik-2) binding sites were perturbed in between 2 strains (Fig. 15).

Figure 15. Comparison of microRNA cluster gene promoter region sequences in between A/J (top) versus C57BL/6J (bottom). Highlighted are the sequences where the transcription factor GATA-1 (yellow), CDxA (blue), Sry (green), Ik-2 (pink) and C/EBP (grey) are predicted to bind. The transcription factor highlighted in green boxes, have perturbed gene sequences in between A/J and C57BL/6J. The alignment was obtained using Blast2 tool from NCBI.

We further test whether transcripts from the miR-23b-27b-24-1 miRNA gene could (similarly to cis-eQTL/QTT genes) also show differences in abundance in macrophages obtained from different mouse strains. One prerequisite for the quantification of the abundance of a given miRNA transcript is to identify one control endogenous RNA that can be used to normalize the results. In this case, the required characteristics of such control RNA should be to show little variation in abundance when measured in BMDMΦ cells derived from different mouse strains and maintained in different culture conditions. In the course of preliminary experiments, we therefore measured three different small non-coding RNAs (snoRNA202, snoRNA234, snoRNA135) and one ribosomal RNA (18S) to test for their suitability as controls. 5X 105 BMDMΦ from A/J and Balb/c mouse were treated with control conditions or stimulation with LPS (100ng/μL), M-CSF (5ng/mL), IL-4 (20ng/mL) and IL-13 (20ng/mL) at 24 h (using 3 wells for each condition). After incubation, total RNA was extracted from cells in each well, and the concentration of the mRNA transcripts of snoRNA202,

58 snoRNA234, snoRNA135 and rRNA 18S were quantified by RT-PCR. On the basis of these preliminary results (Fig. 16), we chose to use snoRNA202 as the endogenous RNA control in subsequent quantification experiments.

Figure 16. Quantification of four endogenous RNA controls, (snoRNA202, snoRNA234, snoRNA135 and rRNA 18s) in 5X 105 BMDMΦ from A/J and Balb/c mice, either in control medium or in presence of four different stimulating agents LPS (100ng/μL), M-CSF (5ng/mL), IL-4 (20ng/mL) and IL-13 (20ng/mL). SnoRNA202 was observed as a least varying endogenous control for both types of cells under different conditions.

In subsequent experiments, we measured by RT-PCR the abundance of miR-23b, miR-24-1 and miR-27b in BMDMΦ from A/J, C57BL/6J and Balb/c mice in control conditions. No expression of miR-24-1 was found in macrophages from either strain. In contrast, the miR-23b and miR-27b were readily detectable, and were more abundant in macrophages from A/J than in counterparts from C57BL/6J mice, thus indicating that expression of this gene showed strain-specific differences (Fig. 17)

Figure 17. Bar graph representation of mRNA levels for microRNA cluster genes. Quantification of miR-23b, miR-24-1 and miR-27b in 5X 105 BMDMΦ from A/J, C57BL/6J and Balb/c under control conditions. Each bar represents the mean of three replicates with error bar representing Standard error of the mean (SEM). Strain-specific differences in expression of miRNA cluster were observed.

60 VII DISCUSSION

So far, several studies have made it possible to identify loci linked to complex traits and variants associated to complex traits in either animal or human studies as mentioned before. However, within genetic regions, it has been much more difficult to find which variants are causally related to traits of interest 68. This is due to a variety of reasons: 1) recent GWAS studies have shown that individual contributions of genetic variants towards complex trait determination is typically small; 2) genetic regions linked to complex traits still often contain great numbers of possible candidate genes (or in some converse cases, linkage may concern regions without known protein coding genes); and 3) Even when the number of protein-coding genes within a linked genetic regions is small, it is not uncommon that the roles and functions of these genes is not clear. Consequently, it has been suggested recently that the integration of GWAS and/or linkage studies with gene expression data could help find the appropriate genetic variants responsible for the trait of interest 69-71.

More recently, the identification of candidate genes within QTLs has been improved by an increasingly popular approach of expression data (microarray expression studies) to complement the analysis by eQTL studies 2-4. However, very few SNPs discovered by eQTL mapping have been validated to date. In addition, integration of expression data and disease traits still remains challenging for the several following reasons: 1) because of linkage disequilibrium, one can never be sure that a particular gene variant represent the causal variant; 2) there have been many examples so far where the function of a given candidate gene is not known, or where its function concerns a mechanism with no obvious connection with the trait under study; 3) a given gene may be related to a trait of interest via its expression level in a very particular cell-type, but the same may not be true in all cells within a particular organ; and 4) expression of a given gene may

61 have consequences only within the particular context of the expression of other networked genes 72. One further problem is that the probability for eQTLs to overlap with complex trait loci just by chance is still relatively high 73. All the above highlight the importance of having a robust biologic experimental system to test the functions of possible candidate genes and elucidate their possible contribution to complex traits.

In a previous study, we have determined that one major phenotypic QTL on chr 13 was responsible for 30% of the variance of LVM in the panel of 24 AxB/BxA RIS 37. In the current study, we found that several cis-eQTL are comprised within the region of the QTL, and also correlate with values of LVM. In the first phase of our project, we thus indented a total of 456 eQTLs, among which 15 correlated significantly with values of LVM. In a second phase of our project, the availability of a denser and more precise genetic map allowed us to identify a total of 1,195 eQTLs, among which 33 correlated significantly with values of LVM. Intriguingly, the region containing the highest density of cis-eQTL/QTT genes overlapped greatly with the QTL linked to LVM.

Interest in this region is increased by the fact that in another independent study in rat RIS, the same syntenic region was linked to LVM 74. In that previous study, the authors had identified osteoglycin as a possible candidate gene in that region. In our own mouse study, although we observed that Ogn expression levels correlated with LVM, Ogn itself was not a cis-eQTL. We therefore further studied the genes identified by ourselves in that region to be cis-eQTLs and/or QTTs. In particular, we performed experiments to further understand their properties and functions, and possibly understand how they could related to changes in LVM.

Early in the course of our investagations, Fastkd3 and 4930486L24RIK emerged as possible candidate genes, as they were both cis-eQTLs and QTTs within the confidence interval of our LVM linked phenotypic QTL on chr 13.

62 As discussed previously, very little information was available concerning these two genes. The protein product of 4930486L24RIK is identified in protein databases as “testin-2 precursor”. However, BLAST searches reveal that this cDNA displays 73% and 68% identity with cathepsin L-like 3 (Ctsll3) and cathepsin L. In addition, the protein product of 4930486L24RIK is 100% homologous to that of Ctsll3, and both genes are located in tandem in a locus in chr13 that contains the genes of many cathepsins. The information relating 4930486L24RIK to testin-2 precursor may be a misnomer, because there is no homology between 4930486L24RIK and testin-1. Although there was little information about Fastkd3 at the time the current project was initiated, it has since been identified as one member of a family of 5 related proteins that includes Fastkd1-5, all of which are found in mitochondria 75. On the basis of siRNA mediated transfections (knockdown experiments), it was found that knockdown of Fastkd3 severely blunts basal and stress- induced mitochondrial oxygen consumption, and tandem affinity purification revealed that Fastkd3 interacts with components of mitochondrial respiratory and translation machineries 75.

Despite the paucity of annotation information for these 2 genes, there was some information in the SymAtlas/BioGPS database concerning their tissue specific profile of expression. Accordingly, Fastkd3 was reported to be expressed at highest levels in macrophages and myeloid progenitors, 4930486L24RIK appeared to be expressed mostly in mast cells, and in both cases they were expressed at higher levels in these cells than in cardiomyocytes.

Both mast cells and macrophages belong to the myeloid cell lineage and derive from CD34+ bone marrow progenitors 76. Upon differentiation, the latter enter the blood stream as monocytes. In response to chemotactic signals, monocytes from the blood stream can invade injured tissues, where they differentiate into macrophages 76-78. Upon stimulation with activators,

63 macrophages can further differentiate into “polarized macrophages”, the most recognized being M1 and M2 macrophages 40-42.

Primary cultures of macrophages can be obtained by culturing bone marrow progenitor cells in the presence of macrophage cell stimulation factor M-CSF 50,51. Such cultures have proved convenient to explore some of the properties of macrophagic cells. For instance, it has been shown that these cells can acquire in vitro the properties of M1 macrophages upon stimulation with LPS 40-42. To gain further information about Fastkd3 and 4930486L24RIK, we thus tested whether their expression can be detected in mouse primary macrophages. As this was indeed the case, we further tested whether known activators of macrophages would affect their level of expression, and whether they were regulated in a coordinate fashion.

Indeed, macrophages are very plastic cells, and several bioactive molecules activate them in different ways. M-CSF is a pro-survival factor that maintains macrophages in a differentiated state 79. IFN-γ and LPS (either alone or in conjunction) activate macrophages into “M1” macrophages via the “classical” activation program, whereas IL-4 and/or IL-13 activate the cells into “M2” macrophages via the “alternate” activation program 40. Zymosan A activates macrophages into a phenotype that is somewhat intermediate between the M1 and M2 programs 80. Our own experiments showed that indicated that Fastkd3 and 4930486L24RIK gene expression was stimulated mostly by LPS and Zymosan A at 24H (Fig. 4), but showed little response to the other cytokines, thus indicating that these genes responded to conditions triggering the “classical” activation of macrophages.

Our analysis has identified Twistnb as another cis-eQTL gene whose expression level correlated with LVM in our panel of RIS mice. Its locus in on chr12, and corresponded to that of a minor QTL linked to LVM. As for some of our other candidate genes, there is very little information concerning its possible functions. In humans, this gene is located in a region that, if deleted,

64 is linked to the Saethre-Chotzen syndrome, which is characterized by a distinct learning disability and premature fusion of certain skull bones 81. In protein databases, the corresponding protein product is also known as “DNA- directed RNA polymerase I subunit RPA43”, and is involved in ribosomal RNA synthesis. However, as the 2 genes described above, BioGPS revealed that its highest levels of expression was in macrophages and myeloid progenitors. In the course of preliminary experiments, we verified that it was indeed expressed at high levels in our primary cultures of mouse macrophages, and that its expression was further increased upon activation with Zymosan A.

To explore the feasibility of knocking down the expression of specific genes in primary macrophages, we performed pilot experiments using D-siRNA against Fastkd3 and Twistnb. These experiments allowed us to establish that better knockdown was obtained by transfection with lipofectamine than with electroporation. For each gene, we also tested 3 different D-siRNA preparations. In each case, it was possible to identify at least one D-siRNA that knocked downed the expression of each gene by ± 50%, either in control conditions or after stimulation with a macrophage activator (Table 4).

One may question how the expression of genes in cells of myeloid origin may relate to the LVM phenotype. However, cardiomyocytes constitute only about 50% of cardiac mass, other cells including fibroblasts, endothelial cells, vascular smooth muscle cells, macrophages and mast cells 43. It is believed that interactions between cardiomyocytes and non- cardiomyocytes play important roles in cardiac ventricular remodeling 82. In the case of macrophages, there is evidence that these cells can contribute to cardiac ventricular remodeling in the course of pressure overload 44 or after myocardial infarction 45,46. Other studies have shown the important roles played by mast cells in left ventricular remodeling 83. Beyond cells of myeloid

65 origin, others have shown that T lymphocytes can contribute to cardiac remodeling as well 84.

In most examples listed above, the contributions of non- cardiomyocytes to cardiac remodeling have been studied in the course of pathologic conditions. Although little is currently known about the possible contributions of non-cardiomyocytes to differences in LVM under basal conditions, the above examples show that genes can potentially contribute to overall LVM via their expression in such cells.

By obtaining a denser genetic map, it became possible to perform more precise alignments between eQTLs and the LVM linked phenotypic QTL on chr 13. Strikingly, this analysis revealed a cluster of 10 eQTLs within the confidence interval of the LVM linked phenotypic QTL, all of which showing significant correlation with the values of LVM in the RIS. In keeping with our previous preliminary results, many of these eQTL/QTT genes were expressed mostly in cells of myeloid origin (Fig. 12): according to BioGPS, Ctsl and Ccrk are expressed almost exclusively in macrophages, and these cells were among those that expressed 1110018J18Rik and Tgfbi most abundantly. Likewise, 4930486L24RIK (a paralog of Ctsl) is reported to be expressed mostly by mast cells (another cell from the myeloid lineage), and our own experiments show that it is expressed abundantly in primary cultures of macrophages (see above). Fastkd3 remained a potential candidate gene, but according to the new genetic linkage map, its locus was slightly outside of the confidence interval of the LVM QTL (Fig. 11).

In addition to their genomic position, many of these candidate genes have the additional interest that they have been related to cardiac ventricular remodeling. Ctsl is a lysosomal cysteine proteinase belonging to the family of papain-like cysteine proteinases. Ctsl knockout mice show dilated cardiomyopathy 66,85 as well as impaired cardiac remodelling after myocardial infarction 86. Conversely, transgenic overexpression of Ctsl

66 protects against cardiac hypertrophy 87,88. Ccrk belongs to the family of cyclin-dependent protein kinase-activating kinases. It has been shown that heart contains a splice variant of CCRK exist that differs from the generic isoform at the level of protein-protein interactions, substrate specificity and regulation of the cell cycle. This splice variant promotes cardiac cell growth and survival, but is down-regulated in the course of heart failure 67. Finally, Tgfbi (a paralog of periostin) is an extracellular matrix (ECM) protein that is associated with other ECM proteins and functions as a ligand for various types of integrins 89. It is induced upon stimulation by TGF-β, which is expressed almost exclusively by macrophages and is known to play pivotal roles in the murine pathological response to sustained pressure overload 90.

Our system of mouse primary macrophage cultures proved useful to provide some additional preliminary information about these 10 candidate genes. Firstly, we could indeed confirm that all genes reported by GPS as being macrophage-specific (i.e. Ctsl and Ccrk) were indeed expressed robustly in macrophage primary cultures. For a second class of genes form the cluster (including Cplx2, Habp2 and Cdc14b), we found no evidence of expression within primary macrophages. Finally, two other genes (i.e. 4930486L24RIK and GOLM1) were strongly expressed within primary macrophages, despite the fact that BioGPS did not provide information in this regard. Nonetheless, 4930486L24RIK (a paralog of Ctsl) is reported by BioGPS to be expressed mostly mast cells, which belong (as macrophages) to the myeloid cell lineage. Golm1 (also known as Golph2 or GP73) codes for a protein that cycle between the Golgi and the endosomal compartments 91. This particular intracellular pathway has been reported to play an important role in orchestrating the differential secretion of cytokines during inflammation by macrophages 92.

Genes that behave as cis-eQTL presumably carry in the vicinity of their locus a mutation that affects their level of expression. To further validate this notion, we compared the expression levels of Ctsl,

67 4930486L24RIK, Ccrk and Golm1 in primary cultures of macrophages from A/J and C57BL/6J mice. We also used macrophages from Balb/c mice to obtain some indication of the overall level of expression of these genes in macrophages from an unrelated strain. For all 4 genes, we observed that their level of expression (as well as their response to LPS exposure) was different in macrophages from A/J and C57BL/6J mice (Fig. 13). Although more experiments are required, these data are at least compatible with the notion that differences in the genetic background of A/J and C57BL/6J mice may lead to cell-autonomous differences in the expression of these 4 candidate genes.

Variation in gene expression levels is both widespread and governed by heritable genetic elements, the latter being a combination of cis- eQTLs (proximal eQTLs) and trans-eQTLS (distal eQTLs). Although abundances of mRNA transcripts are generally polygenic traits, distal-acting eQTLs generally have smaller effect sizes, and are also much harder to detect than cis-eQTLs. In contrast, mutations in cis elements are more likely to affect the regulation of only one gene, and thus may have less effect on phenotypic traits. Interestingly, trans-eQTLs have not been found to be randomly distributed across chromosomes, but often concentrate in so called “eQTL hotspots”, which might harbor “master regulators” that affect the expression of many genes 33.

Our current data are compatible with the above concept. Nonetheless, it is intriguing to observe that 10 cis-eQTLs not only cluster within a narrow 10 MB window, but also all correlate significantly with values of LVM in the RIS strains. One possible explanation for the correlation is that gene order in eukaryotes appears not to be entirely random, and that chromosome regions contain clusters of functionally related genes 93,94. However, the clustering of 10 cis-eQTLs is more difficult to explain. If changes in expression levels result from variants in cis-regulatory regions, it

68 is unclear why such functional variants would occur in 10 consecutive genes within a given genome region.

One alternative possibility would be that of a hotspot of 10 closely spaced genes all being under the control of a common regulator. Accordingly, we found that the center of this relatively gene-poor region contains the gene of the miR-23b-27b-24-1 miRNA cluster. Although additional experiments are required to test the possible roles of these miRNA, we have already observed that miR-23b and miR-27b show differences in abundance in primary macrophages from A/J and C57BL/6J mice. Moreover, polymorphisms have been reported within the promoter region of this gene at the level of transcription factor binding sites.

It is interesting to consider miRNAs as common regulators for several reasons. In recent years, these molecules have been shown to play important roles in the regulation of cardiac pathophysiology, including hypertrophy. Alteration of miRNA processing by dicer knockout hearts causes severe DCM leading to heart failure 95. Additional experiments on specific miRNA products have shown that these molecules: 1) have been shown to be regulated differentially during cardiac hypertrophy and failure in both rodent and human heart 96-102; 2) perform regulatory roles during cardiac development and in the course of DCM and in mouse 103,104; 3) play a role in promotion of heart failure 105,106. On a more general note, there is evidence that miRNAs participate to the coordinate regulation of genes either belonging to a biologic network of functionally associated molecules or responding collectively to a stressful stimulus 107,108.

Within the miRNA cluster, miR-27b is of particular interest. Accordingly, out of 13 miRs that have previously been reported to be associated with late-stage pressure-overload induced hypertrophy and heart failure, miR-27b is one out of only four that are induced during early hypertrophic growth 109. More recently, it has been shown that cardiac-

69 specific overexpression of miR-27b was sufficient to induce cardiac hypertrophy and dysfunction, whereas silencing of miR-27b with antagomirs attenuated hypertrophy induced by pressure overload 110. Two well- characterized molecular targets of miR-27b are TGFb and PPARg 111. Interestingly, Tgfbi is one of the genes within our cluster that correlates with values of LVM. Likewise, PPARg has been shown to play important roles within macrophages, and to modulate the contributions of these cells to cardiac fibrosis 112,113.

70 VIII CONCLUSION

We identified a group of 10 candidate genes within a 10 MB region on chr 13 that lies within the confidence interval of a phenotypic QTL linked to LVM in a mouse RIS panel. Since several of these genes have been reported to be expressed preferentially in macrophages, we have used primary cultures of mouse macrophages to: 1) verify that these genes were indeed expressed in such cells; 2) test whether their expression was modulated by conditions known to affect the phenotypic properties of macrophages; 3) and test whether their expression can be knocked down by transfection with D-siRNA constructs. These experiments establish the feasibility of using these cells for further biologic validation of the roles and/or properties of these candidate genes.

One intriguing observation was that 10 cis-eQTLs all clustered within the same regions as our LVM QTL and all correlated with levels of LVM in the panel of RIS. As this prompted us to search for a possible common regulator, miR-27b emerged as one plausible candidate. As a follow-up to the experiments presented in this body of work, our laboratory will test whether miR-27b antagomirs regulate the abundance of mRNA transcripts of candidate genes in mouse primary macrophages. Additional experiments will be necessary to test to which extent down-regulation of these genes (either individually or collectively) affects the properties of macrophages. In the future, it will also be possible to test whether knockdown of these genes in macrophages in vivo is sufficient to cause alterations of the cardiac phenotype.

71 IX REFERENCES (Literature Review)

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80 APPENDIX

Improvements done in the thesis according to the suggestions by External Examiner:

1) Chapter III should include a section describing the statistical analysis used through out the thesis, e.g. which test and statistical package was used in Figure 4, 7, 8? Correction: Added a section 6 statistical analysis to chapter III “Materials and Methods” indicating that the analysis was statistically detected by one-way ANOVA analysis, followed by Tukey’s multiple comparison test and also included the same explanation to the legends of fig. 4, 7, 8 and 13.

2) It is not entirely clear the contribution of Ms Gupta to Figures 2, 3, 10 and 11. These figures appear in the results sections however there are no references attached nor the methods (statistical analysis) are described in the methods section. Correction: Explained in the section 1.1 “Experimental Model” of chapter II “Introduction” and also in the legends of fig. 2, 3, 10 and 11 that this piece of work was conducted by Ms Marie-Pier Scott-Boyer, and are part of her PhD project in Department of Bioinformatics at University of Montreal.

3) The blue captions in Figure 3 are unreadable. The font should be increased. Correction: Mentioned in the legend of fig. 3 that for the purpose of current project the important information has been magnified in the figure on left from figure on right.

4) Indicate the source of the screen shot in Figure 14.

81 Correction: Mentioned in the legend of fig. 14 that the source of the screen shot is US Santa Cruz Genome Browser.

5) Figure 15, indicate how the alignment was obtained and what represent the green boxes surrounding CDxA, Sry and Ik-2. Correction: Indicated in section 2 “Biologic Validation Of Genomic Results (Second Phase)” of chapter IV “Results” and also in the legends of fig. 15 that the sequence alignments was obtained using the Blast2 tool from NCBI followed by use of DBD Transcription Factor Prediction Database (www.transcriptionfactor.org) to predict the transcription factor binding sites and the transcription factor highlighted in green boxes (CDxA, Sry and Ik-2), have perturbed gene sequences in between two mouse strains.

6) It is not entirely clear how many genes were tested in total. It seems that only those showing macrophage expression are shown. It would be useful if Table 5 could be shown earlier in the text indicating which genes have been studied. Correction: Added a column “Genes Tested” to Table 5 indicating the figures in which genes have been tested. Also, table 5 have been moved to the very beginning of chapter IV “Results”.

Swati Gupta