University of Pisa
Research Doctorate School in Biological
and Molecular Sciences
Program: Biology
Analysis of genetic and epigenetic
alterations in candidate genes in
thoracic aortic aneurysm
Supervisors:
Student:
Dr. Maria Grazia Andreassi
Paola Panesi
Dr. Ilenia Foffa
Academic Year 2014/2015 BIO/11
INDEX
ABSTRACT
Pag. 1
INTRODUCTION
Pag. 3
THORACIC AORTIC ANEURYSM (TAA)
Pag. 3
PATHOGENESIS OF TAA
Pag. 3
KEY STRUCTURAL COMPONENTS OF
THE AORTA WALL AND
EXTRACELLULAR MATRIX
Pag. 5
CELLULAR AND BIOCHEMICAL
REGULATORY PATHWAYS IN TAA
Pag. 8
GENETICS OF TAA
Pag. 15
EPIGENETIC OF TAAS
Pag. 22
AIM
Pag. 24
MATERIALS AND METHODS
Pag. 25
MUTATIONAL SEQUENCING ANALYSIS
Pag. 25
Study population
Pag. 25
DNA extraction
Pag. 26
DNA quantitative assessment
Pag. 26
Gene amplification
Pag. 26
Agarose gel electrophoresis
Pag. 27
Purification of DNA fragments from PCR
Spectrophotometric measurement of the PCR product
Pag. 28
Gene sequencing
Pag. 29
In silico analysis
Pag. 31
Analysis of aberrantly spliced transcripts
Pag. 31
Analysis of mRNA expression levels
Pag. 34
LTBPs EXPRESSION
Pag. 34
Study population
Pag. 34
RNA Isolation and cDNA Synthesis
Pag. 35
Analysis of LTBPs expression levels
Pag. 35
Statistical analysis
Pag. 36
SMALL RNAs SEQUENCING
Pag. 36
Study population
Pag. 36
Small RNA Library Preparation
Pag. 37
In silico analyses
Pag. 39
RESULTS
Pag. 41
MUTATIONAL SEQUENCING ANALYSIS
Pag. 41
Transforming Growth Factor–β Receptor 1 (TGFBR1)
Pag. 42
Transforming Growth Factor–β Receptor 2 (TGFBR2)
Pag. 42
Mothers against decapentaplegic homolog 3 (SMAD3)
Pag. 44
LTBPs EXPRESSION
Pag. 44
DISCUSSION
Pag. 48
MUTATIONAL SEQUENCING ANALYSIS
Pag. 48
LTBPs EXPRESSION
Pag. 51
SMALL RNAs SEQUENCING
Pag. 53
CONCLUSIONS
Pag. 56
1
ABSTRACT
Aortic aneurysm and dissections account for 1% to 2% of all deaths in the Western countries, representing the 15th leading cause of death in individuals older than 65 years. Despite progressive dilatation, thoracic aortic aneurysm (TAA) usually remain asymptomatic until dissection or rupture occurs. Because of this, the detection and monitoring of TAAs are absolutely crucial, and studies on potential biomarkers are underway. While size is currently the main aortic parameter/criterion by which to predict complications, other characteristics of TAAs, such as mechanical properties and genes and molecular aspects are being investigated as potential additional criteria for the future.
Gene defect identification in human thoracic aortic aneurysm conditions is proceeding at a rapid pace and the integration of pathogenesis-based management strategies in clinical practice is an emerging reality. Despite the remarkable progress of the past decade, the pathogenesis of TAA remains unclear. The main molecular hypothesis of TAA formation appears to be an altered signaling pathway of the transforming growth factor-β (TGF-β). Several studies showed that mutations in transforming growth factor-β receptor 1 (TGFBR1), receptor 2 (TGFBR2) and (SMAD3) genes play a key role in the onset of syndromic and familial TAA. Moreover, an altereted expression of this signaling components, caused by microRNA (miRNAs), could lead to TAA formation.
The purpose of this study was to investigate the potential contribution of germline mutations in TGFBR1, TGFBR2 and SMAD3 genes in a cohort of Italian patients with familial TAA. Moreover, we evaluated the different expression levels of latent transforming growth factor-β binding proteins (LTBPs) and the role of miRNAs, in the development and progression of the TAA in patients with bicuspid (BAV) or tricuspid aortic valve (TAV). We performed a direct sequencing of all coding regions and untranslated reregions of TGFBR1, TGFBR2 and SMAD3 genes in 10 Italian patients with familial TAA.
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The LTBPs levels were examinated by qRT-PCR, while the miRNA profile using next generation sequencing platform (MiSeq). As regards the genetic screening, a novel TGFBR2 mutation in the 5’ untranslated region (c.-59 C>T) was identified in a 31-year-old male who referred for an emergent operative repair of Stanford type A aortic dissection. Bioinformatics tools showed that c.-59 C>T variant was predicted to affect exonic splicing enhancer and was validated by quantitative real-time RT-PCR, revealing a six-fold increase of TGFBR2 mRNA level in aneurysmatic aortic tissue of patient harboring mutation, compared to aortic samples from patients without the mutation. Moreover, we found a previously described missense mutation, p.E239K, in the MH2 domain of the SMAD3 gene in a 60-year-old man who presented diffuse aneurysms involving various arteries.
As regard gene expression pattern of LTBP-isoforms, we found a significantly down expression of LTBP4L mRNA levels in BAV group compared with TAV. Conversely, we found any significant difference in the expression of other TGF-β-related factors. The miRNAs profile revealed several miRNAs differentially expressed between BAV and TAV, in particular 2 miRNAs were up-regulated and 10 were down-regulated in BAV compared to TAV. A preliminary analysis with DIANA tool revealed the role of 4 specific miRNAs (miR-424-3p, miR-3158-3p, hsa-miR-3688-3p, hsa-miR-486-3p) in the regulation of 12 genes involved in several cellular mechanisms previously described to be important in the pathogenesis of TAA, such as apoptosis, angiogenesis extracellular matrix neogenesis, and osteoblast differentiation. In conclusion, our results confirm the key role played by TGF-β pathway in the etiopathogenesis of TAA and support the hypothesis that TAA in BAV and TAV patients is linked to different molecular mechanisms.
3
INTRODUCTION
THORACIC AORTIC ANEURYSM (TAA)
Aortic aneurysm and dissections account for 1% to 2% of all deaths in the Western countries (Lindsay and Dietz, 2011), representing the 15th leading cause of death in individuals older than 65 years (Ruddy et al. 2013). Despite progressive dilatation, aortic aneurysms usually remain asymptomatic until dissection or rupture occurs. Because of this, the detection and monitoring of TAAs are absolutely crucial, and studies on potential biomarkers are underway. While size is currently the main aortic parameter/criterion by which to predict complications, other characteristics of TAAs, such as mechanical properties and genes and molecular aspects are being investigated as potential additional criteria for the future.
PATHOGENESIS OF TAA
Aneurysm refers to an undue enlargement of the aorta. For large aneurysms, it is intuitively clear that the aorta has exceeded the normal contours of its structure. Although the “normal” size of the aorta varies with sex and body size, most would agree that a normal aorta should not exceed 4 cm in diameter. Another proposed definition for aneurysm depends on a diameter of the affected segment that is more than 1½ times larger than normal. The specific etiology of the aortic disease formation is still unclear, the progression of aortic dilatation in the chronic phase probably results from a combination of hemodynamic stress, aortic injury, chronic inflammation, genetic propensity, and epidemiologic risk factors. Histologically, TAAs are characterized by smooth muscle cells (SMCs) loss, fragmentation and depletion of elastic fibers, and accumulation of proteoglycans and glycosaminoglycans within cell-depleted areas of the aortic media (He et al. 2006). The aorta is a heterogeneous structure with varying structural, biochemical, and genetic influences above and below the diaphragm (Ruddy et al. 2008). The aortic wall is divided into three layers: the inner tunica intima, the tunica media and the outer tunica adventitia (Figure 1). Each layer has a specific composition and distinct roles in development,
4
homeostasis, and pathologic degradation. The intima consists of a single layer of endothelial cells, sub‐endothelial connective tissue and a lamina elastica interna. This layer forms the smooth, friction reducing lining of the lumen. The tunica media, or media, is composed of elastic fibers and vascular smooth muscle cells (VSMCs) interconnected with collagen fibers, proteoglycans, glycosaminoglycans, and various adhesive proteins (Figure 2). All of these elements form the extracellular matrix (ECM) and are important for imparting elasticity and tensile strength, sequestering growth factors, and forming structural interactions between ECM components (Lander AD, 1999). So its degradative remodeling is known to be responsible for aneurysm formation. Finally, the adventitia is made from fibroblast cells in a loose collagen‐rich connective tissue. The function of this layer is to anchor and provide rigidity to the blood vessel. As this layer also contains small blood vessels (vasa vasorum) and nerves, it also functions as a supply of oxygen and nutrients for the cells inside the vessel wall and for innervations of the smooth muscle cells present in the media.
Figure 1: An overview of the aortic wall, that display the three layers and
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KEY STRUCTURAL COMPONENTS OF THE AORTA WALL AND EXTRACELLULAR MATRIX
VSMCs
Vascular smooth muscle cells (VSMCs) are embedded in parallel layers of elastic fibers, which form concentric layers of lamellar units in the media. VSMCs possess a contractile apparatus composed of thin and thick filaments. The contractile apparatus enables the cells to generate mechanical force (contraction) and hence to maintain vascular tone and resistance (Schwartz et al. 1997). The VSMCs are characterized by a marked plasticity, switching from one phenotypic state to another in response to environmental stress and vascular injury. The most abundant protein in vascular smooth muscle cells is smooth muscle α‐actin (SM α‐actin), encoded by the ACTA2 gene (Jondeau and Boileau, 2012). The thick filaments of the contractile apparatus are made of myosin molecules, encoded by the MYH11 gene. Arterial contraction, driven by the contraction of vascular smooth muscle cells, is the result of interacting actin and myosin filaments (Kim et al. 2008). This link mediates and regulates many processes, including cell adhesion and migration (Berrier and Yamada, 2007).
Elastin
Elastin is the major protein of the ECM, provides elastic properties to aorta and is important for vascular morphogenesis (Karnik et al. 2003). Binding of elastin to integrin avb3 (Rodgers and Weiss, 2004) and elastin binding protein (Mochizuki et al. 2002) promotes cell adhesion and regulates cell proliferation. Disruption of the interaction between elastin and other cellular constituents during morphogenesis results in uncontrolled VSMCs proliferation (Wu et al. 2013).
6 Microfibrils
Microfibrils are widely distributed filamentous, extracellular multimolecule structures with tissue specific architecture, comprise 10% of elastic fibers and provide extensibility to the aortic wall (Kielty et al. 2002). Microfibrils are composed of fibrillin and several microfibril associated proteins, such as latent transforming growth factor beta (TGF-β) binding proteins (LTBP 1 e 4) (Isogai et al. 2003), elastin microfibril interface-located protein 1 (EMILIN-1), microfibril associated glycoproteins (MAGP-1 and -2), and fibulins (Wu et al. 2013). Fibrillin-1 (FBN-1) and fibrillin-2 are two well-characterized isoforms that are important for aortic function. FBN-1 is a major structural component of microfibrils in the mature aorta and regulates tissue homeostasis by sequestering growth factors such as TGF-β (Neptune et al. 2003). Fibrillin-2 is primarily expressed in the embryonic period and is important for aortic morphogenesis (Ritty et al. 2003). Mice deficient in the FBN-1 gene (Fbn1-/-) recapitulate the human aortic Marfan syndrome phenotype, present thin and fragmented elastic fibers, and are prone to aortic aneurysm and dissection (Bax et al. 2003).
Fibulins
The fibulins are a group of seven ECM proteins that are important for aortic wall homeostasis. Fibulin-1 is commonly located within the elastin core (Roark et al. 1995). Fibulin-2 and -4 are located at the interface between the elastin core and microfibrils, and fibulin-5 is associated with microfibrils (Wagenseil and Mecham, 2009). Fibulin-4 (FBLN-4) is required for lysyl oxidase (LOX) recruitment to cross-link elastin molecules (Horiguchi et al. 2009). Studies of aortic tissue from humans and mice with FBLN-4/Fbln-4 deficiency show increased TGF-β signaling, which may result from impaired LOX-mediated TGF-β repression (Atsawasuwan et al. 2008; Maki et al. 2002). Fibulin-5 is essential for lamellar unit formation (Yanagisawa et al. 2002), and Fbln-5-deficient mice have disrupted elastic fiber assembly (Wang et al. 2005), as well as less compliant aortas (Spencer et al. 2005). These findings suggest that fibulin-4 and -5 have key roles in elastic fiber
7
assembly; deficiency of either one may predispose patients to aortic dissection (Wu et al. 2013).
Collagen
Collagen contributes both the structural and the functional properties of the aorta. Collagen type 1 and type 3 are the most abundant collagen fibers in the aortic wall and are important for tensile strength. From a functional perspective, collagen is important for sequestering cytokines and mediating cell proliferation by binding to integrins (Pozzi et al. 1998). Therefore, mutations in collagen-encoding genes may lead to structural and functional alterations and to aneurysms formation (Wu et al. 2013).
Figure 2: Key structural components of the aorta. Adapted from Wu et al.
2013.
LTBPs
The Latent TGF-β Binding Proteins (LTBPs) are secreted glycoproteins structurally very similar to fibrillins (Figure 3) and, for this reason, they may exert similar biological functions. The LTBP family counts 4 proteins, LTBP1-4. LTBP-1, -2 and -4 are able to bind to fibrillin-1 (Ono et al. 2009; Taipale et al. 1996; Dallas et al. 2000; Sinha et al. 2002) and only LTBP-1, and -4 can sequester TGF-β playing an important role in regulating TGF-β availability (Isogai et al. 2003; Todorovic et al. 2005). LTBP-2 cannot
8
interact with TGF-β, but it regulates targeting of fibulin-5 to microfibrils during elastogenesis (Hirai et al. 2007).
Figure 3: Representation of LTBP family domain organization with
isoforms and known splice variants. Adapted from Robertson et al. 2010.
CELLULAR AND BIOCHEMICAL REGULATORY PATHWAYS IN TAA
The extracellaular matrix of the aortic wall is a highly dynamic environment. Changes in the structural components of the ECM influence the cellular and molecular signaling pathways that regulate aortic wall homeostasis, as well as aortic function. The ongoing of imbalanced and disorganized healing process in the aortic wall probably play a role in the etiology of TAA. These molecular pathways may be either triggered or reinforced by environmental and genetic factors.
Inflammation
SMCs apoptosis and ECM destruction in the aortic wall are accompanied by an increased degree of inflammation, as evidenced by the presence of T
9
lymphocytes, macrophages, mast cells, and neutrophils (Figure 4). This suggests that inflammation participates in the pathogenesis of aortic aneurysm formation by regulating aortic wall homeostasis. T lymphocytes and macrophages can be found diffusely throughout the media or in focal accumulations between SMC layers and inside the wall of vasa vasorum, suggesting their possible migration from the adventitia to the media of the aortic wall (He et al. 2006). Several cytokines and chemokines that promote the recruitment of inflammatory cells to the aortic wall, such as tumor necrosis factor a, interferon (IFN)-g, interleukin (IL)-1, IL-2, IL-6, and IL-8, are upregulated in TAA (del Porto et al. 2010). This increased expression of cytokines is accompanied by an increased expression of other chemokines, of which are linked to inflammatory processes. These inflammatory cells are potential sources of proteases that can degrade the ECM and weaken the aortic wall. For example, matrix metalloproteinase (MMP)-9, elastase, and collagenase secreted from macrophages and neutrophils are capable of directly degrading the ECM and may also contribute to the detachment of SMCs from the ECM, leading to cell death (Allaire et al. 2009). The distribution of inflammatory cells varies within the layers of the aortic wall and may contribute to different inflammatory mechanisms of vascular degeneration (Zhao et al. 2005). For instance, at the adventitia-media border, activated dendritic cells can produce chemokines that trigger the recruitment of macrophages and CD4+T cells (Galle et al. 2005). Once activated, these cells undergo clonal expansion and secrete cytokines, including IFN-g, involved in the regulation of macrophage differentiation and function (Xiong et al. 2004). The ultimate consequence of these inflammatory activities is elastic fiber fragmentation, as well as activation of repair mechanisms, such as cell proliferation and angiogenesis. Recently, Kessler et al. showed that some angiogenic factors were more greatly increased in the degenerative form of TAA compared with BAV and Marfan forms. Thus, despite the fact that different forms of TAA present many common features, each etiology can have its own particularity (Kessler et al. 2014).
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Figure 4: Aortic inflammation. Injured or stressed SMCs produce
chemokines such as monocyte chemoattractant protein 1 (MCP-1) to promote the recruitment of inflammatory cells to the aortic wall. Elastin fragments are also strong monocyte chemoattractants. Adapted from Wu et al. 2013.
Matrix metalloproteinases in TAA
MMPs are proteases that can act on a variety of extracellular protein substrates, activating some and degrading others (Ra et al. 2007). In the context of TAA, however, MMPs mainly act as proteases on ECM components. Several studies confirm the increased expression of MMPs in TAA cases, especially MMP-1, -2, -7, and -9 (Koullias et al. 2004; Sangiorgi et al. 2006). Like the variety of substrates that MMPs can act on, these proteases can be regulated through various mechanisms (Zhang et al. 2009). These include cysteine switch, allosteric activation, furin activation, activation by other MMPs, plasmin, reactive oxygen species and in particular by tissue inhibitors of metalloproteinase (TIMPs). Infact, it has been suggested that an imbalance between MMPs and TIMPs expression is responsible for the shift toward a proteolytic state.
11 Hypoxia and increased oxidative stress
Hypoxia and increased oxidative stress also probably contribute to medial degeneration. In support of this hypothesis, research conducted in TAA patients shows increased levels of the oxidative stress markers and decreased levels of the extracellular superoxide dismutase, an antioxidative enzyme (Liao et al. 2008). Increased oxidative stress from vasa vasorum alters the elastic and collagen fibers in the outer media, stiffens the aortic wall and increases circumferential wall stress. This irregular arrangement may contribute to the reduced functional properties of the aortic wall and could promote aortic aneurysm formation and progression (Nagashima et al. 2001).
TGF-β signaling
During the past 2 decades, several studies revealed that the transforming growth factor (TGF)-β signaling pathway is a key culprit in the pathogenesis of TAA, suggesting the dysregulation of TGF-β signaling as the common final pathway in aortic aneurysm development.
TGF-β‐related molecules are a superfamily of pleiotropic cytokines, which includes TGF-βs, bone morphogenic proteins (BMPs), growth and differentiation factors (GDFs), activins/inhibins, and nodal molecules. These cytokines regulate a broad range of biological processes, including embryogenesis, wound healing, angiogenesis, tumorigenesis, and organization of the extracellular matrix (Blobe et al. 2000). Usually, TGF-β is commonly known for its role in matrix synthesis. However, TGF-β signaling is also involved in the degradation of the ECM through the upregulation of MMP-2 and -9 leading to TAA formation (Chung et al. 2008). TGF-β upregulates multiple factors involved in ECM synthesis, including fibronectin; collagens I, III, V, and VI, proteoglycans, tenascin (Blobe et al. 2000). Additionally, TGF-β contributes to cell-matrix adhesion by upregulating integrin receptors for collagen, fibronectin, laminin, and vitronectin (Blobe et al. 2000). It is well known that TGF-β is stored, in the extracellular matrix as a small latent complex (SLC) in an inactive form bound to latent TGF-β binding proteins (LTBPs), in particular
LTBP-1,-3,-12
4, forming a large latent complex (LLC), which is secreted from the cell (Shi M, 2011). The TGF-β activation requires several steps. In the first step the LLC is released from the microfibrils through proteolytic enzymes, such as elastase, that are able to degrade microfibrils, releasing a specific internal fragment. This fragment interacts strongly with the N‐terminal region of full‐length fibrilin‐1, inhibiting the association of fibrillin‐1 with LTBP-1 and releasing the LLC from the matrix (Chaudhry et al. 2007). Subsequently, the SLC needs to be released from the LLC, which can be achieved by proteases such as MMPs. Finally the mature TGF-β needs to be liberated from latency-associated peptide (LAP). Several mechanisms exist to release TGF-β from LAP such as plasmin, and MMPs, that can release TGF-β by targeting either the hinge region of LTBP, acting on the extracellular environment, or cleaving LAP (Yu and Stamenkovic, 2000) (Figure 5).
Figure 5: Schematic overview of the synthesis and matrix binding (a), and
activation and receptor binding (b) of TGF-β. Adapted from ten Dijke et al. 2007.
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TGF-β mature normally binds to the TGFBR2 homodimer, activating it, which in turn activates the TGFBR1 homodimer (Figure 6). The activation of the type I receptor leads to the propagation of signalling by at least two seemingly independent routes: the SMAD-dependent canonical pathway and the SMAD-independent or non-canonical pathway, both involved in the onset of TAA. In the canonical pathway, the active complex proceeds to phosphorylate SMAD-2 and SMAD-3 proteins, which recruit SMAD4 to form a SMAD2/3-SMAD4 complex. This complex translocates to the nucleus, activating the transcription of TGF-β target genes that affect cell proliferation, growth, development, and death (Moustakas and Heldin 2009). The regulation of the canonical pathway is key to normal biological function. When the SMAD2/3-SMAD4 complex activates the transcription of TGF-β target genes, it also promotes the transcription of SMAD6 and SMAD7 proteins, that antagonize TGF-β signaling; the SMAD7 protein specifically binds to TGFBR1 and inhibits the phosphorylation of SMAD2 and SMAD3 proteins (Moustakas and Heldin 2005; Guo and Wang 2009). These pathways have been shown to contribute to the progression of aortic aneurysms in a mouse model of Marfan Syndrome and may also have some relevance for TAA formation (Goumans et al. 2002). In the non-canonical pathway other proteins mediate TGF-β signaling. Infact the SMAD pathway may not be viewed at a unique mean for TGF-β to regulate cellular functions. Other signaling pathways including the mitogen-activated protein kinase (MAPK), the TNF-β or PI3 kinase/AKT pathways, can either be induced by TGF-β or can modulate the outcome of TGF-β -induced SMAD signaling (Massagué and Chen, 2000; Lutz and Knaus, 2002; Derynck and Zhang, 2003). This complex network of crosstalks with other signaling pathways that largely contribute to modify the initial Smad signal, may allow the pleiotropic activities of TGF-β. Furthermore, the TGF-β pathway can also communicate with the Wnt, Hedgehog, Notch, Interleukines, and IFNγ pathways (Guo and Wang 2009).
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Figure 6: Canonical and non-canonical TGF-β signaling pathways.
(Adapted from Derynck and Zhang, 2003).
Angiotensin II signaling
Angiotensin II (Ang II) is another signaling pathway that is interrelated with TGF-β signaling and may have a role in TAA formation. Angiotensin II can affect the vascular wall through direct vasoconstriction and the modulation of the function of multiple adhesion molecules and growth factors that may be responsible for cell proliferation, hypertrophy, fibrosis, and inflammation (Qin Z, 2008). Through Ang II receptor 1 (AT-1), Ang II can enhance TGF-β signaling, as well as SMAD and mitogen-activated protein kinase signaling in a TGF-β independent manner (Habashi et al. 2011). Infusing Ang II into apolipoprotein E deficient (ApoE_/_) mice results in aneurysm and dissection formation (Daugherty et al. 2000). These findings suggest that blockade of Ang II signaling may attenuate TAA formation and
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progression. However, the role of the Ang II receptor 2 (AT-2) in aneurysm formation remains less clear. AT-2 is known to inhibit AT-1-induced TGF-β signaling and to attenuate both canonical and non-canonical TGF-β signaling (Habashi et al. 2011). AT-2 is also known to induce VSMCs apoptosis (Tan et al. 2009). Thus, AT-2 may have a role in inducing or inhibiting dissection formation.
GENETICS OF TAA
Thoracic aortic aneurysm is a multifactorial pathology which involves a complex interaction between genetic and haemodinamics factors. Disease of the wall of the thoracic aorta has many causes: inflammation, infection and atherosclerosis are the most common ‘acquired’ causes, but even these have genetic predispositions. Infact, current evidence suggests that they have an even stronger genetic component, with 15% of patients having a positive family history (Albornoz G, 2006). TAAs more commonly, occur in the absence of a clear syndromic constellation, i.e. non-syndromic TAAs. Infact, TAAs are divided into two broad categories: syndromics (associated with abnormalities of other organ systems) and non-syndromics (with manifestations restricted to the aorta) (El-Hamamsy and Yacoub, 2009). Syndromic aortic aneurysms occur in patients with: Marfan Syndrome (MFS), Loeys-Dietz Syndrome (LDS), Aneurysm Osteoarthritis Syndrome (AOS), Arterial Tortuosity Syndrome (ATS), Ehlers-Danlos Syndrome (EDS), Bicuspid Aortic Valve (BAV), and TGF-β mutations (Milewicz et al. 2008). Many of the syndromic aneurysms can be diagnosed by their characteristic dysmorphic features and gene testing (Figure 7).
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Figure 7: Genes associated to syndromic and non-syndromic TAAs.
Adapted from Pomianowski and Elefteriades, 2013.
Syndromic TAAs
Marfan Syndrome (MFS)
The Marfan Syndrome (MFS) is an autosomal dominant connective tissue disorder, with a prevalence of 2 to 3 per 10000 individuals (Gillis et al. 2013). It is a multisystemic disorder affecting the skeletal (disproportionate overgrowth, joint laxity, vertebral column deformity), ocular (lens dislocation and myopia), and cardiovascular system (aortic root aneurysm and dissection, mitral valve disease) (Loeys et al. 2004).The cardiovascular manifestations cause the most important morbidity and mortality in patients with MFS. In the majority of MFS patients, the disease is caused by mutations in FBN-1 gene on chromosome 15q21.1, which encodes fibrillin-1, an important component of the ECM. FBN-1 molecules assemble into microfibrils, which have an important structural function, both in the aortic wall and in the ciliary apparatus supporting the ocular lens (Neptune et al. 2003). Investigation of the developmental impairment of the pulmonary alveolar septation in the Fbn-1tm1Rmz mouse model (alias Fbn-1mgΔ) demonstrated that fibrillin-1 deficiency alters matrix sequestration of the TGF-β latent complex, leading to uncontrolled release of TGF-β and with a consequence activation of the TGF-β pathway (Neptune et al. 2003).
17 Loeys-Dietz syndrome (LDS)
The Loeys-Dietz Syndrome (LDS) is an autosomal dominant disorder of connective tissue with multisystem involvement (El-Hamamsy et al. 2009). The natural history of both types of LDS is marked by early and rapid onset of thoracic aortic aneurysms (originating at the level of the sinuses of Valsalva) and death at an early age (mean age reported at 26.1 years) (Van Hemelrijk et al. 2010). LDS is caused by a heterozygous mutation in one of the transforming growth factor beta receptor genes (TGFβR1 or TGFβR2).
TGFβR2 mutations on chromosome 3 (3p24.1) account for the majority of
LDS mutations (75%), while TGFβR1 mutations on chromosome 9 (9q22.33) account for the remaining 25% (Van Hemelrijk et al. 2010). Both
TGFβR1 and TGFβR2 gene mutations are thought to result in the overall
up-regulation of TGF-β signaling. The classical TGF-β signaling pathway involving the SMAD-mediated cascade has been characterized by signals that not only induce extracellular matrix (ECM) deposition but also matrix degradation. This implicates the pathway as being critical to the structure and composition of ECM (Jones et al. 2009).
Aneurysm Osteoarthritis Syndrome (AOS)
Aneurysm Osteoarthritis Syndrome (AOS) is a newly described autosomal dominant syndrome with variable expression. The frequency of AOS is 2% among TAA patients and is caused by mutations in the SMAD3 gene on chromosome 15 (15q22.33), which encodes a critical protein for cellular signaling downstream of the TGF-β receptors (Regalado et al. 2011).
Arterial Tortuosity Syndrome (ATS)
Arterial Tortuosity Syndrome (ATS) is an autosomal recessive disorder caused by mutations in the SLC2A10 gene on chromosome 20 (20q13.1), which encodes the facilitative glucose transporter GLUT10 (Coucke et al. 2006). GLUT10 is localized to the gene promoter region of decorin, a natural inhibitor of TGF-β. Mutations in the SLC2A10 gene are thought to result in down-regulation of decorin and thereby up-regulation of TGF-β signaling. The clinical spectrum of ATS includes: arachnodactyly, joint
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laxity or contractions, hypertelorism, cleft palate, bifid uvula, micrognathia, down-slanting palpebral fissures, blepharophimosis and arterial tortuosity with aneurysm formation (Coucke et al. 2006).
Ehlers-Danlos Syndrome Type IV (EDS IV)
TAA can also be an important complication of different subtypes of Ehlers– Danlos Syndrome (EDS). EDS is a heterogeneous group of disorders with cutaneous (skin hyperextensibility, atrophic scars), skeletal (joint hypermobility and luxations), and vascular features (vascular rupture, easy bruising). The most life-threatening form is the vascular type of EDS, the type IV (EDS IV), which is an autosomal dominant disorder caused by highly penetrant mutations in COL3A1 (2q32.2) (Germain DP, 2007).
Bicuspid Aortic Valve (BAV)
Bicuspid Aortic Valve (BAV) is the most common congenital heart defect in adults, affecting 2% of the population worldwide, and is responsible for more deaths and complications than the combined effects of all the other congenital heart defects (Siu and Silversides, 2010). Although aortic stenosis and regurgitation are the most common complications of BAV, dilatation of any or all segments of the proximal aorta from the aortic root to the aortic arch, called bicuspid aortopathy, is also present in approximately 50% of affected persons (Siu and Silversides, 2010; Girdauskas et al. 2011). It is poorly understand the molecular mechanism about the development of BAV and BAV-associated TAA, demonstrating the complexity of the disease. Although several linked loci have been identified (5q, 13q, 18q) (Martin et al. 2007) and a set of candidate genes/proteins (ACTA2, TGFBR2, nitric oxide synthase 3, ubiquitin fusion degradation 1-like, etc)(Lee et al. 2000; Mohamed et al. 2005) have been proposed, mutations in only a single gene, NOTCH1, have been consistently associated with BAV (Garg et al. 2005; Foffa et al. 2013). NOTCH1 is a key component of the Notch signaling pathway, involved in transcriptional regulation, in cell growth and differentiation, epithelial–mesenchymal transition during cardiac development (Timmerman et al. 2004). Several studies have shown that
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TAA pathogenesis in BAV and TAV is different, resulting from the activation of different mechanisms and pathways (Tadros et al. 2009). In particular, BAV and TAV patients presented different mRNA expression profiles, as well as alternative splicing of genes involved in the TGF-β pathway or an increased TGF-β activity (Kjellqvist et al. 2013).
TGFβ2 mutation
TGFβ2 (1q41) is the most recently identified gene as a cause of TAA.
Mutations identified, frame-shift and nonsense mutations, are predicted to cause haplo-insufficiency. Clinical features in TGFβ2 mutation carriers are similar to those of other syndromes causing thoracic aortic disease including: cardiovascular [aortic root aneurysm (74%), cerebrovascular disease, and arterial tortuosity], skeletal (pectus deformity, joint hyperflexibility, scoliosis, and arachnodactyly), cutaneous (striae, herniations), and pulmonary (pneumothorax, dural ectasia) (Boileau et al. 2012).
Non-syndromic TAAs
Non-syndromic aortic aneurysms are thus divided into familial thoracic aortic aneurysms (FTAAs), where more than one person in the family is affected, and sporadic TAAs, where only a single person in the family is known to have an aneurysm. FTAAs typically occur earlier during the life than sporadic aneurysms, have a higher annual growth rate and are genetically heterogeneous (Albornoz et al. 2006). Mutations in five genes (MYH11, TGFβR1, TGFβR2, MYLK, and ACTA2) have been identified and showed an autosomal dominant inheritance.
Mutations in ACTA2 gene are the most common mutations resulting in aortic aneurysms and account for 10-15% of all FTAA mutations. In particular individuals affected by the ACTA2 mutation can present dissection at a diameter smaller than usual criteria for intervention
(Pomianowski and Elefteriades et al. 2013). The ACTA2 gene is localized to
chromosome 10 (10q23.31). Mutations in ACTA2 are heterozygous mutations with a dominant negative mechanism with reduced penetrance
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and variable expressivity. For this reason, early surgical intervention should be considered even when minimal changes in aortic diameter are recognized.
Mutations in the MYH11 gene (chromosome 16p13.11) account for 2% of non-syndromic TAAs, are often associated with patent ductus arteriosus (PDA) and are predominantly splice-site mutations resulting in in-frame deletions and missense mutations (Pomianowski and Elefteriades, 2013).
MYLK mutations were found in a minority of FTAA cases (<1%) (Kamm et
al. 2001). Loss of MYLK function could lead to alteration of its calcium-calmodulin-binding capacity, which is required for MYLK activation and phosphorylation of myosin. Patients with MYLK mutations often develop aneurysms in the ascending thoracic aorta, which is the part of the aorta exposed to the highest biomechanical force. It has been suggested that MYLK haploinsufficiency may decrease the VSMC contractile function and that, as a consequence, the aorta will not be able to withstand the biomechanical forces for long (Wang et al. 2010). Also, the absence of other VSMC problems in patients with mutations in components of the VSMC contractile apparatus demonstrates that the VSMC itself is not majorly affected and that aneurysms mainly develop at locations where the biomechanical forces are greatest. This may mean that one working allele might be sufficient for smooth muscle cells contraction but that it leaves the aortic wall in a weaker position.
Because of the identification of TGFBR1 and TGFBR2 mutations in aortic aneurysm syndromes such as LDS, considerable attention has been devoted to the role that TGF-β may plays in FTAA pathogenesis. Several families with TAA without other features of MFS were subsequently screened leading to the identification of TGFBR2 mutations (Hasham et al. 2003). Moreover a clinical spectrum of mutations in TGFBR1 has been reported to be similar to that of mutations in TGFBR2, but these mutation are even more rare (Loeys et al. 2005). Previous studies have described four missense mutations that effect the kinase domain of TGFBR1, causing familial TAAs (Tran-Fadulu et al. 2009), while for TGFBR2, two recurrent missense mutations (Arg460Cys and Arg460His) can lead to familial TAAs (Pannu et
21
al. 2005), without Marfan Syndrome. All these mutations effect the kinase domain, and implicate an important role for TGF-β signaling in the pathophysiology of TAA. These mutations occur in the intraceullular kinase domain of the protein, which is important in function, and thus they could lead to missing function. This has been demonstrated by functional analysis of mutant TGFBR2 in cell lines (Inamoto et al. 2010). In aortic tissue of TAA patients, the TGF-β pathway can be upregulated with TGFBR1 and
TGFBR2 mutations (Loeys et al. 2005), whilst the precise function of the
abnormal gene product is still unclear.
Sporadic TAAs (STAAs)
The genetic basis for STAAs has been explored as well. A first genome-wide association study was performed, which, surprisingly, identified a region of linkage disequilibrium that contained the FBN-1 gene. Thus, it seems that common variation at the FBN-1 locus may explain part of the susceptibility to aneurysm formation in STAA (Lemaire et al. 2011). Genome-wide copy number variant analysis in a large number of STAA cases further suggested the involvement of VSMCs adhesion and contraction in the development of TAA (Prakash et al. 2010; Kuang et al. 2011).
EPIGENETIC OF TAAS
Epigenetics is the term used to define the properties of the genome that are not explained by the primary sequence, but are due to the modifications of DNA and/or associated proteins. Epigenetic processes modulate the chromosomal organisation without altering the actual DNA sequence, and thereby contribute to the modulation of gene expression (Goldberg et al. 2007). Epigenetic control of gene expression involves chromatin modification processes such as DNA methylation and several histone modifications and the microRNAs (Goldberg et al. 2007).
Currently the contribution of epigenetic factors to the development of thoracic aortic aneurysm has been little investigated. However, several studies showed a implication of acetylation and methylation (Gomez et al.
22
2011) in the onset of the disease and, in particular, an key role of microRNAs (Liao et al. 2011).
MicroRNAs (miRNAs)
The microRNAs (miRNAs) are a group of highly conserved, small noncoding RNAs of 21 to 25nt, which usually negatively modulate gene expression at the post-transcriptional level by incomplete or complete complementary binding to target sequences within the 3’ untranslated region (3’ UTR) of mRNA (Chitwood et al. 2007). Recent works have identified that changes in miRNAs expression may contribute to the pathogenesis of aortic aneurysm or dissection and revealed that the identified miRNAs have as putative targets genes involved in several pathways including SMC phenotypic change and disease, TGF-β pathways, ECM remodeling (Jones JA, 2012, Liao M,2011). In the vasculature, miRNAs are known to control apoptosis, angiogenesis and vessel remodeling (Ohtani et al. 2011). Several miRNAs have been shown to be up- or down-regulated in aneurysmatic aortic walls (Liao et al. 2011). In particular a study of TAA patients, in which 18 miRNAs upregulated and 56 downregulated, showed that the miRNA-29 and miRNA-30 families are likely to play a role in the regulation of the focal adhesion and the mitogen-activated protein kinase (MAPK) signaling pathways, respectively (Liao et al. 2011). Moreover in a group of TAA patients, a significant relationship between miRNAs expression levels (miRNAs -1, -21, -29a, and -133a) and aortic diameter was identified such that miRNA expression decreased as aortic diameter increased. In the same study, an inverse relationship between miRNA-29a and total MMP-2 was also identified. This suggests that the loss of specific miRNAs expression may allow for the elaboration of specific MMPs capable of driving aortic remodeling during TAA development (Jones et al 2011). These data may suggest that dysregulated miRNAs expression is a common feature of vascular disease and that targeting the regulation of miRNAs expression/function may provide significant therapeutic advantages. Indeed, the miRNAs expression profiles may provide significant insight into the identification of potential upstream mediators and
23
it may reveal diagnostic or prognostic bioassays with the potential to define phases of disease progression, informing of the best time for surgical intervention or even indicating the potential risk for aortic rupture. In addition, the potential use of circulating miRNAs as disease biomarkers promises further improvement of the currently available diagnostic tools. In fact, recent studies have shown that miRNAs can be found in the circulation in a surprisingly stable form (Turkinovich et al. 2011).
24
AIMS
The overall goal of this thesis was to obtain advances in genetic and molecular knowledge of thoracic aortic aneurysm.
In order to reach this goal, the specific aims were:
1. To evaluate the incidence and the potential contribution of germline mutations in TGFBR1, TGFBR2 and SMAD3 genes, by automated gene-sequencing, in a cohort of Italian patients with familial TAA.
2. To evaluate the different expression levels of LTBPs proteins in aortic media of TAA patients associated with BAV or TAV.
3. To investigate the role of epigenetic mechanisms, in particular the function of miRNAs, in the development and progression of the aneurysm, in aortic media of TAA patients associated with BAV or TAV.
25
MATERIALS AND METHODS
MUTATIONAL SEQUENCING ANALYSIS
Study population
A cohort of 10 unrelated Italian patients (10 males; age = 53,4 ± 9,6 years) with familial TAA were recruited for this study. A detailed family history was obtained for each proband. Familial TAA was defined if 2 or more affected relative had presumptive or proven TAA. Informed consent and a blood sample (5 cc) were obtained from all subjects and available family members. Clinical characteristics are shown in table 1.
Proband Sex Age Diagnosis Aneurysmal Diameter
(mm)
Familial History
A M 31 TAAD 60 Paternal
B M 52 BAV / TAA 74 Paternal
C M 54 TAA 53 Maternal D M 66 TAAD 55 Maternal E M 58 TAA 52 Paternal F M 49 TAA 48 Paternal G M 55 TAA 54 Paternal H M 55 TAA, IA, AAA 55 Maternal I M 64 TAA 54 Paternal J M 50 TAA 48 Maternal
26 DNA extraction
Genomic DNA was extracted from whole-blood samples through Biorobot
EZ1 (Qiagen) that allows to extract DNA, automatically, from both blood
and tissue, from 6 together samples in a single step: Sample lysis,
DNA binding to magnetic particles, Washing and elution of the DNA. DNA quantitative assessment
We have prepared DNA elutions 1:250 on sterilized water for spectrophotometric quantification at 260 nm and 280 nm. The spectrophotometric analysis determines the quantitative concentration of the DNA and qualitative information. In fact, the ratio of the absorbance at 260 nm and 280 nm represent a good index of the sample purity. For the DNA, the well index is 1.7-1.9. The formula for the final concentration of the sample was:
FINAL CONCENTRATION= λ260 x dilution factor.
Gene amplification
All coding exons and flanking intronic regions of TGFBR1, TGFBR2 and
SMAD3 were amplified by polymerase chain reaction (PCR) using specific
primers previously described in literature (Santiago-Sim et al. 2009, Baetens et al. 2011, ). Approximately, PCR amplification was performed with a volume of 50 μl mixture, containing 1x PCR Buffer, 100ng of DNA, primers, 1.5mM of MgCl2, 0.2 mM of dNTPs and 1.25 of Hot Start Taq Polymerase (Euroclone, Milano, Italy). The primers sequences and the related conditions of PCR are shown in tables 2.
27
Gene Exon Code Primer Primer F (5’-3’) Primer R (5’-3’) T° Annealing TGFBR1 1 Pr 1 cagacacacccatcgctcta gcgccatgtttgagaaagag TD 72-62° 2 Pr 2 ctacacaatctttctctttttcc gtttttcttgtagtatctagg TD 68-58° 3 Pr 3 ctgacagagctggtgtgcat ggagctgacttattgattcgc TD 66-56° 4 Pr 4 ccctctagcaggagtttctgg aggaatgctatcaagagtcaaga 59° 5 Pr 5 ttgcagtgtgtgactcagga gatgcggttttgtcatgttg 62° 6 Pr 6 gtgggctgaaatgctttgat attttctggaagggcaacct 57° 7 Pr 7 agatcatgaggcagatagtgtg gcctttgttttctctggcac TD 70-59° 8 Pr 8 ggaagtggcttgtggatacag aaaggccactgcaaatgttc 60° 9 Pr 9 tccagaccaatggaaaatggtg cccaggagcagatctgaag 63° TGFBR2 1 Pr 1 ggactcctgtgcagcttcc cacaatccctgcagctacg TD 64-54° 2 Pr 1a aactttgaagaaaacatattgacca aagcagtgagggagcatgac TD 67-57° 3 Pr 2 tgaaattgcataacatcttcagg ggaaagggaaatggaacagg 56° 4 Pr 3 cagattgcctttctgtctgga ccacaggaggaatgtgctct 58° 5 Pr 4a gcacttgcatccctgaaataa acctcagcaaagcgacctt TD 65-55° Pr 4b ggaagatgaccgctctgaca actgtggaggtgagcaatcc TD 65-55° Pr 4c ggggaaacaatactggctga ttccctagaccagtgtccaga 58° 6 Pr 5 aggggccaccatcagcta ccctggaataatgctcgaag TD 65-55° 7 Pr 6 agccaggcatctcaccat cagggccatagaacacaatg 61° 8 Pr 7 gaccctgcttgcactcacta tctgcttatccccacagctt TD 65-55°
SMAD3 1 Pr 1 gcgccctccccagccatgtc actccctccctctctctccctcttc TD 70-60° 4 Pr 4 ttccctctctttctgcccctc agaccagcaccaaagtcccc TD 64-54° 5 Pr 5 agccacctctgctctgtctcc agcctgtgccgcccacgtgc TD 69-59° 7 Pr 7 ggccttttaacagaccaccttcc ataagagcatgaccctgcatgac TD 64-54° 8 Pr 8 ggttttctttctgctgtgttggg tagagggggttccagttgtgtg TD 69-59° 9 Pr 9 ccagtagcccaccctgtgtcc gggcggggaatggagccacc TD 70-60° 10 Pr 10 cggcagtgcccatttcccctac ctaatccaatcacctccagatt TD 70-60° 11 Pr 11 tataaatgaggctggtctaggg ctgtacggatgatgctggcatc TD 70-60° 12 Pr 12 gtttaactctttaaagtcgact acagctgttcataacatccacc TD 70-60°
Table 2: Primers sequences and PCR conditions.
Agarose gel electrophoresis
PCR reaction was evaluated in the 1.5% agarose gel, stained with ethidium bromide 10 mg/ml at final concentration of 0.3%. We have loaded the wells of the gel with 10 µl of PCR product with 2 µl of “loading” buffer (L.B.: 0.25% bromphenol blue, 0.25% cyanol xylene, 15% glycerol) and a DNA marker of 100 bp (PRIME). The electrophoresis occurred at 100 V in TBE 1X buffer (Tris-base 4 mM, 0.9 M boric acid, 50 mM EDTA, pH 8).
28 Purification of DNA fragments from PCR
All PCR products were purified with "EuroGOLD Cycle Pure Kit" in order to remove salt, free nucleotides, oligonucleotides and polymerases. The DNA is so ready for further applications like the sequencing. This procedure consist of three steps:
1) reversible binding of DNA to the silica matrix through Binding Buffer XP1
2) a rapid wash step, salts, free nucleotides and oligonucleotides are removed by SPW Wash Buffer
3) DNA elution with Elution Buffer.
Spectrophotometric measurement of the PCR product
Quantity of DNA amplification products was misured using the QubitTM Fluorometer (Invitrogen), that generates concentration data based on the relationship between the two Standards used in calibration. This is a quantization system relying on dyes that only fluoresce when bound to specific molecules, such as dsDNA. The instrument was calibrated with the Quant-iT dsDNA BR Assay (declared assay range between 2–1000 ng; sample starting concentration between 100 pg/µl and µg/µl), according to the manufacturer's instructions. The QubitTM fluorometer gives values in ng/mL. This value corresponds to the concentration after the sample was diluted into the assay tube, calculated using the following equation:
C= QF x 100/X where:
QF = the value given by the Qubit® fluorometer
100 = dilution factor
x = the number of microliters of sample added to the assay
29 Gene sequencing
Gene sequencing was performed using the high-throughput platform “GenomeLab GeXP Genetic Analysis System” (Beckman) (Figure 8).
Figure 8: The CEQ8800 Genetic Analysis Systems (Beckman);
GenomeLab DTCS Quick Start kit and an example of a sequence obtained.
The genetic analysis is fully automated and capable of determining the base sequence and fragment length of DNA samples that have been prepared with dye-labeled reagents. Each row of eight samples (sample set), containing labeled DNA fragments, is automatically denatured and then separated by capillary electrophoresis. Detection is by laser-induced fluorescence in four spectral channels. Dye spectra values are calculations of the relative emission intensities of each dye in each of the four detection channels. These values are calculated during each analysis, and are used to transform the four electropherogram channels to dye signal traces for each of the dye labels present in the sample. The samples sequences are compared to known reference sequences. For each comparison, one or two new sequence results, in either orientation, are used to form a consensus for the comparison. A software, based on Gene Bank BLAST (Basic Local Alignment Search Tool), automatically determines and matches the
30
orientation of the sequences, forms the consensus, aligns the consensus with the reference sequence, and provides a detailed report of the differences. The sequencing PCR reactions were performed with a volume of 20 μl mixture containing: H2O 0-9 µl DNA Template 0.5-10.0 µl Sequncing Primer F (1.6µM) 1.8 µl Sequencing Primer R (1.6µM) 1.8 µl DTCSQuick Start Master Mix 6.0 µl
The amount of DNA in µl to be used is determined based on the data of the fluorometer QubitTM.
The PCR condition involved:
96°C 20 sec. 50°C 20 sec. 60°C 4 min.
for 30 cycles followed by holding at 4°C
The next step was to precipitate DNA from the sequencing reaction through a “Stop Solution” containing 2 µl of 3M NaOAc (pH 5.2), 2 µl of 100 mM NA2-EDTA (pH 8.0), and 1 µl of 20 mg/ml of glycogen, that was added to any PCR sample and three washes with ice cold 95% ethanol and ice cold 70% ethanol. At last the pellet is resuspended by adding a Sample Loading Solution (SLS).
31 In silico analysis
To evaluate the effects of synonymous, intronic and 3’-5’ UTR variants on the splicing regulation we used Human Splicing Finder (HSF) version 2.4.1 (www.umd.be/HSF/) which evaluate splicing signal present in any human gene by using matrices to predict 5’ and 3’ splice sites and splice regulatory sites using different algorithms (for example MaxEnt, HSF matrix, ESEfinder, and so on); default parameter setting were used in all analyses and in accordance with other studies (Desmet et al. 2009).
Analysis of aberrantly spliced transcripts
RNA samples were extracted from aortic tissue of patients underwent to aorta replacement using the miRNeasy Mini kit (Qiagen).
The miRNeasy Mini Kit combines phenol/guanidine-based lysis of samples and silicamembrane–based purification of total RNA including miRNA and other small RNA molecules. Tissue samples are homogenized in QIAzol Lysis Reagent, that is a monophasic solution of phenol and guanidine thiocyanate, designed to facilitate lysis of tissues, to inhibit RNases, and also to remove most of the cellular DNA and proteins from the lysate by organic extraction. After addition of chloroform, the homogenate is separated into aqueous and organic phases by centrifugation. The RNA partitions is the upper, aqueous phase, that is extracted, and ethanol is added to provide appropriate binding conditions for all RNA molecules from 18 nucleotides upwards. The sample is then applied to the RNeasy Mini spin column, where the total RNA binds to the membrane, phenol and other contaminants are efficiently washed away and RNA is then eluted in RNase-free water. RNA quality was determined by photometrical analysis using 1 μL of RNA sample. RNA integrity was confirmed by denaturing RNA gel electrophoresis with 1% agarose. RNA concentration and purity were determined on a spectrophotometer by calculating the optical density ratio at a wavelength ratio of 260/280 nm. The RNA concentration is calculated as follows:
32
where :
DF = dilution factor
40 = molar extinction factor
Complementary DNA (cDNA) was synthesized from 1 μg of total RNA in a 20 μL reaction volume using QuantiTect Reverse Transcription Kit. The QuantiTect Reverse Transcription Kit provides a fast and convenient procedure for efficient reverse transcription and comprises 2 main steps: elimination of genomic DNA and reverse transcription. The elimination of genomic DNA consists in an incubation at 42°C of the purified RNA sample in gDNA Wipeout Buffer (table 3). After genomic DNA elimination, the RNA sample is ready for reverse transcription using a master mix prepared from Quantiscript Reverse Transcriptase, Quantiscript RT Buffer, and RT Primer Mix (table 4). The entire reaction takes place at 42°C for 30 min and then is inactivated at 95°C for 3 min.
33
Component Volume
gDNA Wipeout Buffer, 7x
2 µl
Template RNA Variable
(up to 1 µg) RNase-free
water
Variable
Total volume 14 µl
Table 3: Genomic DNA elimination reaction components.
Component Volume Reverse-transcription master mix Quantiscript Reverse Transcriptase 1 µl Quantiscript RT Buffer, 5x 4 µl RT Primer Mix 1 µl Template RNA
Entire genomic DNA elimination reaction
14 µl
Total volume 20 µl
Table 4: Reverse-transcription reaction components.
Reverse-transcription PCR (RT-PCR) primers targeting the mutation flanking region have been designed on the cDNA sequences available in GenBank for TGFBR2 screening using Primer3 (v. 0.4.0) software (http://frodo.wi.mit.edu/primer3/). The RT-PCR products were separated on a 1.5% agarose gel (previously described). The primers sequences and PCR conditions were:
Forward: 5’-CGC GGA GGC GCA GCC AG-3’
Reverse: 5’-TAT GTC TCA GTG GAT GGG C-3’
34 Analysis of mRNA expression levels
Quantitative Real-Time PCR (qPCR) was performed using 384-well CFX RT-PCR System (Bio-Rad) to identified the expression level of TGFBR2 using 5 µl of SYBR-green Supermix (Bio-Rad), 1.25 µl of each primers and 2.5 µl of cDNA, in a 10 μl reaction volume. qPCR primers were previously described in literature (Zheng et al. 2012).
All reactions were run in triplicates and β actin and GAPDH were chosen as standard reference genes for the normalization assay. The primers sequences and the related conditions of PCR are shown in table 5.
Gene Primer F (5’-3’) Primer R (5’-3’) T° Annealing TGFBR2 accaccagggcatccaga tgaagcgttctgccacaca 55°
β ACTIN gcatgggtcagaaggattcct tcgtcccagttggtgacgat 55°
GAPDH cgtcttcaccaccatggaga cggccatcacgccacagttt 55°
Table 5: Primer sequences and PCR conditions used for RT-PCR of
TGFBR2.
LTBPS EXPRESSION
Study population
Aortic specimens were collected from 22 TAV and 28 BAV patients both with TAA (table 6). Aortic samples from patients with aneurysms secondary to genetic syndromes or with other cardiovascular diseases, cancer, or chronic diseases were excluded from this study.
35 Variables BAV (n=28) TAV (n=22) p value Age (years ± SD) 52.3±15.8 64.6±11.3 0.004 Male (n,%) 23 (82%) 11 (50%) 0.02 Diameter (mm) 48.9±4.7 52 ±5.5 0.04 Stenosis (n,%) 15 (53%) 2 (9%) 0.001 Insufficiency (n,%) 7 (25%) 16 (73%) 0.001 Hypertension (n,%) 12 (43%) 14 (64%) 0.2 Smoking (n,%) 9 (32%) 10 (45%) 0.3 Diabetes (n,%) 1(3.5%) 2 (9%) 0.3 Dyslipidaemia (n,%) 3 (11%) 5 (23%) 0.3
Table 6: Clinical and demographic characteristics of study population.
RNA Isolation and cDNA Synthesis
Total RNA was extracted from TAA samples with miRNeasy Mini Kit (Qiagen). Complementary DNA (cDNA) was synthesized from 1 μg of total RNA in a 20 μL reaction volume using QuantiTect Reverse Transcription Kit (Qiagen) as previously described.
Analysis of LTBPs expression levels
Quantitative Real-Time PCR (qPCR) was performed using 384-well CFX RT-PCR System (Bio-Rad) to identified the expression levels of LTBP1, LTBP4S, LTBP4L, LTBP4Δ using 10 µl of SYBR-green Supermix (Bio-Rad), 2.5 µl of each primers and 5 µl of cDNA, in a 20 μl reaction volume. qPCR primers were previously described in literature (Mangasser-Stephan K, 2001). All reactions were run in triplicates and β actin, and GAPDH were chosen as standard reference genes for the normalization assay. Primers sequencing and related PCR condition are shown in table 7.
36 Gene Primer F (5’-3’) Primer R (5’-3’) T° Annealing LTBP1 cccagcagatacattctcaag ggcaaattaccactcggaag 60° LTBP4S agacttaggtccaccttac ctcatcacactcgttgatgtc 55° LTBP4L agcgttgctgtttgtcgctg ttgagggacacctgtctcttc 55° LTBP4Δ aacactgacggctcctttg tcatggggtcaaactcttc 55°
Table 7: Primer sequences and PCR conditions used for RT-PCR of LTBP
isoforms.
Statistical analysis
Statistical analysis for the expression proteins levels was performed using the statistic software Statview, version 5.0.1 [Abacus Concepts, Berkeley, CA, USA]. Data are expressed as the mean ± SD. The two groups (BAV and TAV) were compared with t-test using a threshold of 95% (p-value < 0.05).
SMALL RNAS SEQUENCING
Study population
A subgroup of the population used for the analysis of expression of LTBPs (7 BAV and 6 TAV) was employed for the smallRNAs sequencing. Clinical characteristics are shown in Table 8.
37
Proband Morfologia valvola
Sex Age Aneurysm Diameter
(mm)
Stenosis Insufficiency
P1 BAV M 60 48 yes yes
P2 TAV F 60 43 yes no
P3 TAV M 67 50 no yes
P4 BAV M 36 50 yes yes
P5 TAV F 71 47 no no
P6 BAV M 34 45 yes yes
P7 TAV M 70 53 no yes
P8 BAV M 47 50 yes yes
P9 TAV M 59 46 no yes
P10 TAV M 43 52 no yes
P11 BAV M 71 47 yes no
P12 BAV M 69 55 no no
P13 BAV M 77 47 yes yes
Table 8: Clinical and demographic characteristics of study population.
Small RNA Library Preparation
Total RNA was extracted as previously described and after DNase treatment, RNA was quantified using the Nanodrop 2000. Small RNA library preparation and sequencing were carried out according to the manufacturer instruction (Illumina) using Illumina MiSeq platform (Figure 9).
38
Figure 9: The Illumina MiSeq platform.
100 ng of total RNA of each sample were subjected to miRNA library construction using the NEBNext® Multiplex Small RNA Library Prep Set
for Illumina®. The 3’ SR Adaptor
(5’-rAppAGATCGGAAGAGCACACGTCT-NH2-3’) was ligated to the RNA pool in the presence of 3’ Ligation Reaction Buffer (2X) and 3’ Ligation Enzyme Mix, that contain the T4 RNA Ligase 1, 1 hour at 25°C. Then a an hybridation step between the excess of 3´ SR Adaptor and RT primer was carried out, adding nuclease-free water and SR RT Primer to each sample. The mixture was incubated for 5 minutes at 75°C, transferred to 37°C for 15 minutes, followed by 15 minutes at 25°C. The 5’ SR Adaptor (5’-GrUrUrCrArGrArGrUrUrCrUrArCrArGrUrCrCrGrArCrGrArUrC-3’) was subsequently ligated to the samples in the presence of 5’ Ligation Reaction Buffer (10X) and 5’ Ligation Enzyme Mix for 1 hour at 25°C. The RNA was converted to single stranded cDNA using ProtoScript II Reverse Transcriptase (NEB), following the manufacturer’s instructions. The resulting cDNA was PCR-amplified with LongAmp Taq 2X Master Mix, SR Primer and Index Primer in 15 cycles. PCR conditions are the following:
39
First cycle
Initial Denaturation 94° 30 sec
15 cycles
Denaturation 94° 15 sec
Annealing 62° 30 sec
Extension 70° 15 sec
Last cycle
Final Extension 70° 5 min
Hold 4° forever
The library quality control and the size selection were performed using 6% PolyAcrylamide Gel. The 140-150 nucleotide bands correspond to adapter-ligated constructs derived from the 21-30 nucleotide RNA fragments. The bands of interest were laid in a gel breaker tube and centrifuged for 2 minutes at room temperature. The DNA was eluted by rotating the tube at room temperature for at least 2 hours. Then the pellet was precipitated adding glycogen, 3M NaOAc and 100% EtOH. After washing with 70% EtOH, the pellet was allowed to air dry and resuspended in TE buffer. One microliter of purified PCR product was uploaded on a 2100 Bioanalyzer (Agilent), using a DNA-specific chip, to check the size, purity, and concentration of the sample. At last the concentration of each sample library was normalized to 2 nM for sequencing on the Illumina MiSeq platform.
In silico analyses
The smallRNA-Seq analysis was performed using iMir (Giurato et al. 2013), a pipeline that allows the identification of miRNAs, to perform differential expression analysis and to predict the corresponding mRNA targets. Moreover we carried out a preliminary functional analysis using DIANA tools (Vlachos et al. 2012), that provide algorithms, databases and software for interpreting and archiving data. DIANA-miRPath v1 (Vlachos
40
et al. 2012) was one of the first available applications focused on the enrichment analysis of predicted target genes, capable of detecting pathways targeted by single or multiple miRNAs. The new DIANA-miRPath v2.0 interface has been designed to be highly adaptable to different use-case scenarios and to provide results in real time. In order to perform the analysis, the user can select one or more microRNAs identified. Optionally, a list of expressed genes can be also loaded. Subsequently, the server presents the significantly enriched pathways, the targeted genes in each pathway and the number of miRNAs with positively identified targets for each pathway in the form of an interactive table.
41
RESULTS
MUTATIONAL SEQUENCING ANALYSIS
Mutational analysis was performed on a cohort of 10 unrelated Italian patients (10 males; age = 55,3 ± 7,4 years) with familial TAA. We identified several genetic variants summarized in table 7. TGFBR1, TGFBR2 and
SMAD3 were analyzed by direct gene sequencing of all coding exons
including adjacent intronic regions as well as 5’ and 3’ UTRs sequences.
Gene Position Nucleotide Variation Number of patients
TGFBRI Exon 1 c.69_77delGGCGGCGGC rs11466445 5/10
IVS5+75 c.973+75 G>T rs150964122 1/10
IVS7+24 c.1255+24 A>G rs334354 4/10
TGFBRII IVS1-59 c.-59 C>T New 1/10
IVS3+7 c.338+7 A>G rs1155705 4/10
Exon 4 c.458 delA rs79375991 10/10
IVS5-4 c.530-4 A>T rs11466512 6/10
SMAD3 Exon 2 c.309 A>G rs1065080 10/10
IVS2+59 c.400+59 G>C rs2289261 5/10
Exon 3 c.508 A>G rs35874463 2/10
Exon 6 c.715 G>A ESP 1/10
Exon 6 c.870 C>T rs117185005 1/10
IVS 7-95 c.872-95 T>C rs2289790 4/10
