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86 PART | I Overview
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passive components of the modeled system. Nodes of the second type are transitions and correspond to active components
of the system. The arcs can connect only places with transitions or transitions with places. They model causal relationships
present in system [7,8].
There are also components of one additional type in Petri nets, which are responsible for one of their crucial properties, i.e., their dynamics. These components are tokens and they reside in places. Each of the places can carry an arbitrary
number of tokens, which correspond to an amount of a passive component represented by a given place. The distribution of
tokens among all places of the net corresponds to a state of the modeled system. They can flow from one place to another
via transitions. This corresponds to a flow of substances, information, etc., in the modeled system. The flow of tokens is
governed by a simple transition firing rule. According to it a transition is active if, in all places that directly precede it, the
number of tokens is equal to at least a weight of an arc connecting such a place with the transition. An active transition
can be fired, which means that tokens from the places preceding it flows to places that directly follows the transition. The
number of tokens flowing through a given arc is equal to a label of this arc [7].
Petri nets have a very intuitive graphical representation. In this representation, places are denoted as circles, transitions
as rectangles or bars, arcs as arrows, and tokens as dots or numbers in places. This representation is very helpful in an
understanding the structure of the modeled system and in a simulation of the model. However, it is not very well suited for
an analysis of formal properties of the net. So, for this purpose another representation, called an incidence matrix, is used.
In such a matrix rows correspond to places and columns to transitions. An entry in an i-th row and a j-th column contains
a number that is equal to a difference between the numbers of tokens residing in an i-th place before and after firing a j-th
transition [7].
When Petri net–based models of biological systems are analyzed, especially important are t-invariants (i.e., transition invariants) of the net. Such an invariant is vector x being a solution to the equation A·x = 0, where A is an incidence
matrix of the analyzed net. To each t-invariant there corresponds a set of transitions called its support. The support
contains those transitions that correspond to positive entries of the invariant. If every transition from the support is
fired, the number of times equal to the corresponding entry of the invariant the state of the modeled system does not
change. If the analysis of the net is based on t-invariants, usually the net should be covered by them, which means
that every transition should belong to at least one support of a t-invariant. In the case of models of biological systems
t-invariants correspond to subprocesses occurring in the analyzed system. Analyzing relations among these subprocesses, it is possible to discover some previously unknown properties of the system. These relations can be analyzed
by looking for similarities among t-invariants. Because every transition corresponds to some elementary process and
the subprocesses modeled by t-invariants are composed of these elementary processes, similarities between t-invariants (or their supports) mean that these subprocesses contain some common elementary processes. These processes
are reasons of possible interactions between the subprocesses, which in turn may lead to some important properties
of the system. Because the number of t-invariants can be very large to find similarities among them they usually are
grouped into t-clusters using standard clustering algorithms. It is not an easy task because there are many such algorithms and similarity measures, which are bases for calculation of the clusters. Also the number of the resulting clusters is a parameter of the method. All of them, i.e., the algorithm, the similarity measure and the number of clusters
should be carefully chosen and can be dependent on the analyzed biological system. Each of the obtained t-clusters
usually corresponds to some functional block of the biological system and the similarities are looked for within the
t-clusters [9–11].
Moreover, also transitions can be grouped into sets corresponding to some functional blocks called maximum common transition (MCT) sets. Each of them contains transitions being elements of supports of exactly the same t-invariants
[10,11].
PETRI NET–BASED MODEL OF AORTIC ANEURYSM PROGRESSION
The Petri net–based model of aortic aneurysm progression is presented in Fig. 7.1. The net is composed of 61 places (p) and
82 transitions (t) and has been drawn using the Snoopy software [12].
The net is covered by 5190 t-invariants and 1 sur-t-invariant (i.e., vector x being a solution of the inequality A·x ≥ 0).
There are 17 nontrivial (i.e., containing more than one transition) MCT sets. All the invariants have been grouped into 10
t-clusters using Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm and Euclidean measure of
similarity. The biggest clusters (c6 and c7) wherein there are 1932 and 3223 t-invariants, respectively, revealed that the pathways that create the studied phenomenon are closely connected and it is very difficult to separate individual subprocesses.
The biological meaning of the t-clusters is given in Table 7.1, whereas the meaning of the MCT sets is provided in Table 7.2.
Mathematical Modeling of Aortic Aneurysm Progression Chapter | 7 87
_t48_
_p40_
_t39_
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_t31_
_p38_
_t44_
_t77_
_p28_
_t45_
_p54_
_t63_
_t16_
_p0_
_t32_
_t62_
_p35_
_t12_
_p30_
_p34_
_t38_
_p31_
_t68_
_p47_
_t56_
_t81_
_p60_
_t37_
_p12_
_p18_
_t21_
_p17_
_t67_
_t54_
_p48_
_t65_
_p55_
_t17_
_p37_
_p39_
_p22_
_t10_
_t20_
_t46_
_t28_
_p22_
_p6_
_t75_
_p50_
_p56_
_t43_
_t64_
_p36_
_t55_
_t66_
_t18_
_p28_
_p10_
_p11_
_t11_
_p13_
_p57_
_p53_
_t73_
_t57_
_p33_
_t30_
_p27_
_t71_
_p1_
_t76_
_t33_
_t80_
_p16_
_t19_
_p49_
_t72_
_p59_
_t42_
_p52_
_p32_
_t2_
_t61_
_p46_
_t60_
_t40_
_p42_
_t6_
_p6_
_t13_
_p14_
_t1_
_p3_
_p23_
_t29_
_p51_
_t69_
_t47_
_p38_
_t70_
_t58_
_t27_
_p24_
_p26_
_p5_
_t4_
_p4_
_t3_
_p2_
_t0_
_p42_
_t59_
_t25_
_p19_
_t36_
_p20_
_t5_
_t8_
_p9_
_t14_
_p41_
_p19_
_t22_
_p7_
_p55_
_p8_
_t7_
_t49_
_p21_
_t26_
_p37_
_t9_
_t35_
_p29_
_t34_
_p30_
_t24_
_p25_
_t23_
_p10_
_t15_
_p15_
_t41_
_p34_
_p37_
_p26_
_p42_
_p39_
_t74_
_p22_
_p58_
_t78_
_t52_
_p45_
_t50_
_p44_
_t51_
_p43_
_t53_
_t79_
The analysis of the calculated t-invariants revealed that three transitions, i.e., t80 (aortic aneurysm rupture), t20
(plaque rupture process), and t11 (extracellular matrix degradation) may be found very often together in a support of one
t-invariant with transitions that correspond to the oxidative stress and inflammatory processes (t-invariants grouped in
c3, c6, c7, c9).
The results of our study show that for the formation of aneurysm and then its rupture, extracellular matrix degradation,
inflammatory processes, and oxidative stress must coexist together.
FIGURE 7.1 The Petri net–based model of aortic aneurysm progression.
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TABLE 7.1 The List of T-clusters of the Model
T-cluster Biological Interpretation
c
1
Dimethylarginine dimethylaminohydrolases (DDAH), enzymes metabolizing asymmetrical dimethylarginine (ADMA)
pathway regulates vascular endothelial growth factor (VEGF), mediated processes
c
2
c
3
c
4
c
5
c
6
Processes involving proteolysis
Protein–arginine methyl transferase (PRMTs)–DDAH–ADMA axis affected by oxidative stress
ADMA urinary excretion affected by chronic kidney disease (CKD)
NG-monomethylarginine regulation
PRMTs–DDAH–ADMA axis affected by oxidative stress with the influence of iron and Fenton’s reaction and macrophages
action with processes leading to aortic aneurysm rupture
c
7
c
8
c
9
c
10
Oxidative stress and the strong influence of the regulation of inflammatory processes leading to aortic aneurysm rupture
Activation of the antioxidative mechanisms
Superoxide anion generation
Both ADMA and NG-monomethylarginine urinary excretion affected by CKD
TABLE 7.2 The List of Maximum Common Transition (MCT) Sets of the Model
MCT Set Biological Interpretation
m
1
m
2
m
3
m
4
m
5
m
6
m
7
m
8
m
9
m
10
m
11
m
12
m
13
Aneurysm rupture affected by atherosclerosis complication
Expression and action of scavenger receptors
Pentose-phosphate regulation pathway
Monocyte chemoattractant protein-1 (MCP-1)—CC chemokine receptor 2 (CCR2) system
Monocytes binding to endothelial cells by intercellular adhesion molecule 1 (ICAM-1) or selectin E
Hydroxyl ion generation affected by Fenton reaction
Proteolysis
Activation of intimal macrophages via macrophage colony-stimulating factor (MCF1)
Macrophages foam cells affected by endothelial injury
Radiolysis
Local leukocytes infiltration leading to atherosclerosis progression
Regulation of asymmetrical dimethylarginine (ADMA) urinary excretion
Dimethylarginine dimethylaminohydrolases (DDAH)–vascular endothelial growth factor (VEGF) axis (nitric oxide synthase
independent)
m
14
m
15
m
16
m
17
Protein–arginine methyl transferases (PRMTs) path
Regulation of NG-monomethylarginine urinary excretion
DDAH enzymes influenced by oxidative stress
Angiotensin II importance
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ACKNOWLEDGMENTS
This research has been partially supported by the Polish National Science Centre, grant No. 2012/07/B/ST6/01537.
REFERENCES
[1] Golledge J, Tsao PS, Dalman RL, Norman PE. Circulating markers of abdominal aortic aneurysm presence and progression. Circulation
2008;118:2382–92.
[2] Ward MR, Pasterkamp G, Yeung AC, Borst C. Arterial remodeling: mechanisms and clinical implications. Circulation 2000;102:1186–91.
[3] Klipp E, Liebermeister W, Wierling C, Kowald A, Lehrach H, Herwig R. Systems biology: a textbook. Weinheim: Wiley-VCH; 2009.
[4] Szallasi Z, Stelling J, Perival V. System modeling in cellular biology: from concepts to nuts and bolts. Cambridge (Massachusetts): The MIT Press;
2006.
[5] Taubes CH. Modeling differential equations in biology. Cambridge: Cambridge University Press; 2008.
[6] Koch I, Reisig W, Schreiber F, editors. Modeling in systems biology: the Petri net approach. London: Springer; 2011.
[7] Murata T. Petri nets: properties, analysis and applications. Proc IEEE 1989;90:541–80.
[8] David R, Alla H. Discrete, continuous and hybrid Petri nets. Berlin, Heidelberg: Springer-Verlag; 2010.
[9] Grafahrend-Belau E, Schreiber F, Heiner M, Sackmann A, Junker BH, Grunwald S, et al. Modularization of biochemical networks based on clas-
sification of Petri net t-invariants. BMC Bioinform 2008:90.
[10] Formanowicz D, Sackmann A, Kozak A, Błażewicz J, Formanowicz P. Some aspects of the anemia of chronic disorders modeled and analyzed by
petri net based approach. Bioprocess Biosyst Eng 2011;34:581–95.
[11] Formanowicz D, Kozak A, Głowacki T, Radom M, Formanowicz P. Hemojuvelin-hepcidin axis modeled and analyzed using Petri nets. J Biomed
Inform 2013;46:1030–43.
[12] Heiner M, Herajy M, Liu F, Rohr C, Schwarick M. Snoopy – a unifying Petri net tool. Lect Notes Comp Sci 2012;7347:398–407.
Chapter 8
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Genetic Basis of Aortic Disease
John Mark Redmond
Our Lady’s Children’s Hospital Crumlin, Mater Misericordiae University Hospital, Dublin, Ireland
Chapter Outline
Syndromic Thoracic Aortic Aneurysms and Dissections 91
Marfan Syndrome 91
Genetic Basis and Molecular Mechanisms 91
Loeys–Dietz Syndrome 92
Genetic Basis and Molecular Mechanisms 92
Ehlers–Danlos Syndromes 93
Genetic Basis and Molecular Mechanisms 93
Arterial Tortuosity Syndrome 93
Cutis Laxa 94
Turner Syndrome 94
Noonan Syndrome 94
Shprintzen–Goldberg Syndrome 94
MASS Phenotype 94
Nonsyndromic Thoracic Aortic Aneurysms and Dissections 94
Familial TAAD 94
Genetic Basis and Molecular Mechanisms 95
Bicuspid Aortic Valve With Thoracic Aortic Aneurysm 97
Genetic Basis and Molecular Mechanisms 97
Persistent Patent Ductus Arteriosus With Thoracic Aortic
Aneurysm 97
Conclusions 97
References 98
Further Reading 100
SYNDROMIC THORACIC AORTIC ANEURYSMS AND DISSECTIONS
Marfan Syndrome
Marfan syndrome (MFS) is a systemic connective tissue disorder. It is the most common syndromic form of thoracic aortic
aneurysm leading to aortic dissection (TAAD) with a prevalence of about 1/5000 of the population. Diagnosis is made using
revised Ghent nosology [1]. The cardinal manifestations include the ocular, skeletal, and cardiovascular systems. The major
causes of morbidity and early mortality in MFS relate to the cardiovascular system including dilatation of the aorta at the level
of the sinuses of Valsalva and resultant predisposition to aortic dissection and rupture, mitral valve prolapse and regurgitation, tricuspid valve, and pulmonary valve regurgitation. Ascending aortic dilatation and dissection were the primary cause
of premature death before 1970 [2]. However, the life expectancy of MFS has improved over the last 30 years [3], as a result
of medical therapy and close surveillance of the aortic root diameter to facilitate prophylactic aortic root replacement. As a
consequence, other cardiovascular complications such as type B dissections are becoming increasingly problematic.
Genetic Basis and Molecular Mechanisms
Fibrillin-1 is a ubiquitous protein and an essential component of the elastin-associated microfibrils in connective tissue.
MFS results from mutations in the fibrillin-1 gene, FBN1, which encodes fibrillin-1. Molecular genetic testing of FBN1
detects 70%–93% of probands. Inheritance is autosomal dominant. One-third of individuals have a de novo mutation of
this gene [4].
Proposed underlying mechanisms for MFS result from genetically engineered murine models of MFS, in which
multiple phenotypic manifestations, including aortic aneurysm, correlate with enhanced transforming growth factor beta
(TGFβ) signaling, whereas treatment with either TGFβ neutralizing antibody (NAb) or the angiotensin II type 1 receptor (AT1R) blocker losartan can ameliorate these phenotypes, in association with evidence of reduced TGFβ signaling
[4–7] (see Fig. 8.1).
New Approaches to Aortic Diseases from Valve to Abdominal Bifurcation. http://dx.doi.org/10.1016/B978-0-12-809979-7.00008-0
Copyright © 2018 Elsevier Inc. All rights reserved.
91
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FIGURE 8.1 Putative molecular mechanisms of aneurysm in murine models of Marfan syndrome and Loeys–Dietz syndrome (drugs which
ameliorate aneurysm progression are shown in red). DAG, diacylglyerol; IP3, inositol triphosphate; LAP, latency associated peptide; LTBP, latent
TGFβ binding protein; pERK1/2, phosphorylated extracellular signal-regulated kinases 1 and 2; PKCβ, protein kinase C beta; PLCγ, phospholipase C
gamma; TGFβ, transforming growth factor β.
Both canonical (Smad2/3) and noncanonical (extracellular signal regulated kinase or ERK1/2) TGFβ-dependent
signaling cascades have been shown to be activated in the aortas of Marfan mice, whereas the selective inhibition of
ERK1/2 activation using RDEA119 (refametinib, selective inhibitor of MEK1/2) rescues aortic growth and aortic wall
architecture in Marfan mice [8,9].
Marfan mice treated with blood pressure–lowering calcium channel blockers (CCBs) show accelerated aneurysm expansion, rupture, and premature death. This effect is both ERK1/2 dependent and AT1R dependent. Protein kinase C beta (PKCβ)
appears to be a critical mediator of this pathway and the PKCβ inhibitor enzastaurin and the clinically available antihypertensive agent hydralazine both normalize aortic growth in Marfan mice, in association with reduced PKCβ and ERK1/2 activation. Clinically, patients with MFS and other forms of inherited thoracic aortic aneurysm taking CCBs display increased risk
of aortic dissection and need for aortic surgery compared with patients on other antihypertensive agents [10].
Loeys–Dietz Syndrome
Loeys–Dietz syndrome (LDS) is an autosomal dominant connective tissue disorder closely related to MFS, predisposing patients
to TAAD [11]. It is characterized by vascular findings (cerebral, thoracic, abdominal arterial aneurysms, and/or dissections) and
skeletal manifestations (pectus carinatum or excavatum, scoliosis, joint laxity, arachnodactyly, talipes equinovarus). Among the
original 52 families described, 75% of affected individuals had LDS type I with craniofacial manifestations (widely spaced eyes,
bifid uvula/cleft palate, craniosynostosis); 25% of individuals had LDS type II with systemic manifestations of LDS type I but
minimal or absent craniofacial features. The natural history of LDS is characterized by aggressive arterial aneurysms (mean age
at death 26.1 years) and a high incidence of pregnancy-related complications, including uterine rupture and death.
Aortic dissection has been observed in infancy and early childhood and at aortic dimensions that do not confer the risk
of dissection or rupture in other aortopathies. Compared with MFS, the arterial involvement is more diffuse with arterial
tortuosity present in most individuals, affecting the head and neck vessels. Arterial aneurysms have been observed in the
subclavian, renal, hepatic, superior mesenteric, and the coronary arteries.
As with MFS, valve-sparing aortic root replacement has proven effective surgical therapy. For children with severe
manifestations of LDS, aortic root repair should be considered when the maximal diameter reaches the 99th percentile and
the aortic annulus reaches 1.8–2.0 cm ensuring sufficient graft size to accommodate somatic growth. For adolescents and
adults, surgical repair should be considered once maximal diameter exceeds 4.0 cm.
Genetic Basis and Molecular Mechanisms
The diagnosis of LDS is based on characteristic clinical findings in the proband and family members and molecular genetic
testing for mutations of TGFBR1 and TGFBR2 (encoding transforming growth factor beta receptor 1 and 2), SMAD3
(mothers against decapentaplegic, Drosophila, homolog of 3), and TGFB2 genes. No differences in phenotype are observed
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between individuals with a mutation of TGFBR1 and TGFBR2. The phenotype of individuals with SMAD3 mutations demonstrates significant overlap with LDS. These have, however, a greater predilection for osteoarthritis. For those individuals
with TGFB2 mutation, the phenotype is milder with common features, including club feet and mitral valve disease. A de
novo mutation in TGFB3 gene has been identified in an individual with a syndrome with clinical features overlapping with
MFS and LDS [12].
LDS is most often caused by heterozygous missense mutations in the gene encoding either TGFBR1 or TGFBR2 subunit
(TβRI and TβRII, respectively) [13,14]. Upon TGFβ ligand binding to TβRII, TβRI is recruited to and phosphorylated by
TβRII. This event activates TβRI, which propagates signaling through phosphorylation of the receptor-regulated SMADS,
SMAD2 and SMAD3 [15,16]. SMADS are intracellular proteins that transduce extracellular signals from TGFβ ligands
to the nucleus where they activate downstream gene transcription. The SMAD pathway is the canonical signaling pathway
through which TGFβ family members signal (see Fig. 8.1).
A role for TGFβ signaling in aortic aneurysm in LDS has been substantiated by the discovery that heterozygous
inactivating mutations in genes encoding both positive (SMAD3 and TGFB2) and negative effectors (SKI) of this pathway
cause aneurysmal disorders very similar to LDS [17–19]. These findings have raised controversy whether excessive or
diminished activation of the TGFβ pathway is the primary driver of aortic root aneurysm [20,21]. Heterozygous missense
mutations in either TGFβ receptor gene (TGFBR1 or TGFBR2) would be predicted to result in diminished TGFβ signaling;
however, aortic surgical samples from patients with LDS show evidence of paradoxically increased TGFβ signaling.
In murine LDS models, knock-in mouse strains with LDS mutations in either Tgfbr1 or Tgfbr2 and transgenic mouse
overexpressing mutant Tgfbr2, but not haploinsufficient animals, recapitulate the LDS phenotype [21]. Although heterozygous mutant cells have diminished signaling in response to exogenous TGFβ in vitro, they maintain normal levels of Smad2
phosphorylation under steady-state culture conditions, suggesting a chronic compensation. Analysis of TGFβ signaling in the
aortic wall in vivo reveal progressive upregulation of Smad2 phosphorylation and TGFβ target gene output, which parallel the
worsening of aneurysm pathology and coincide with the upregulation of TGFβ1 ligand expression. Importantly, suppression
of Smad2 phosphorylation and TGFβ1 expression correlates with the therapeutic efficacy of the angiotensin II type 1 receptor
antagonist, losartan. These data suggest that increased TGFβ signaling contributes to postnatal aneurysm progression in LDS.
Ehlers–Danlos Syndromes
These are a heterogeneous group of connective tissue disorders classified according to the Villefranche nosology [22]. The
former numbered classification system has been replaced by one that describes the major clinical findings. The former types
I/II, III, IV, and VI are now called classic, hypermobile, vascular, and kyphosclerotic type of Ehlers–Danlos syndrome (EDS),
respectively.
Genetic Basis and Molecular Mechanisms
EDS classic type is caused by mutations in the genes encoding for type V collagen (COL5A1, COL5A2), tenascin X (TNX),
and type I collagen (COL1A1). Although a proportion of individuals with EDS classic and EDS hypermobile type have
aortic root enlargement, progression of aortic dilatation or acute dissection has not been described.
EDS vascular type is characterized by thin, translucent skin, easy bruising, characteristic facial appearance, arterial, intestinal, and uterine fragility. Vascular rupture or dissection and gastrointestinal perforation are present in 70% of
these individuals. Arterial rupture may be preceded by aneurysm or may occur spontaneously. The median age of death
is 48 years. Inheritance is autosomal dominant. The diagnosis of EDS vascular type is based on clinical findings and confirmed by abnormal type III collagen biosynthesis and/or identification of a mutation in COL3A1, the only gene known to
be associated with EDS vascular type.
EDS valvular type is characterized by joint hypermobility, skin hyperextensibility, and severe cardiac valvular defects.
It is caused by mutations of COL1A2, an autosomal recessive form of EDS.
EDS kyphoscoliotic type is characterized by kyphoscoliosis, joint laxity, with muscle hypotonia. Affected individuals are
at risk for rupture of medium-sized arteries. Aortic dilatation and rupture can also occur. These individuals have a deficient
activity of the enzyme procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 (PLOD1 or lysyl hydroxylase 1). Mutations in
PLOD1, the gene encoding the enzyme lysyl hydroxylase 1, are causative. Inheritance is autosomal recessive [23].
Arterial Tortuosity Syndrome
Arterial tortuosity syndrome is a rare autosomal recessive connective tissue disorder characterized by severe tortuosity,
stenosis, and aneurysmal dilatation of the aorta and major branches [24].
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The underlying genetic defect is loss-of-function mutations in SLC2A10 the gene encoding for the glucose transporter
GLUT10. Studies have confirmed the upregulation of the TGFβ signaling pathway [25], consistent with the pathophysiology
of MFS and LDS.
Cutis Laxa
Cutis laxa is a group of rare connective tissue disorders in which skin becomes inelastic and characteristically hangs in
folds. Autosomal recessive cutis laxa (ARCL) is associated with high morbidity and mortality as a consequence of aortic
aneurysms and pulmonary emphysema.
Mutations in EFEMP2, the gene encoding fibulin-4, cause ARCL with arterial tortuosity and a predilection for aortic
aneurysm and aortic dissections [26], whereas mutations in FBLN5, the gene encoding fibulin-5, cause ARCL with typical
cutaneous and pulmonary disease along with arterial tortuosity but not aneurysmal dilatation [27].
Mutations in the elastin gene ELN cause autosomal dominant cutis laxa [28], which has been considered a strictly
cutaneous disorder. However, it is known that individuals with mutations of ELN gene have aortic root aneurysms that may
rupture in early childhood and require prophylactic aortic replacement, similar to MFS patients.
Turner Syndrome
The phenotypic characteristics of this syndrome, caused by the loss of one of the X chromosomes (45,X), include short
stature, neck webbing, gonadal dysgenesis, with renal anomalies. The associated cardiovascular abnormalities include
bicuspid aortic valve, aortic coarctation, and thoracic aortic aneurysms. Surveillance computed tomography or magnetic
resonance imaging scanning is recommended as aortic root dilatation is observed in up to 40% of women with this
condition.
Noonan Syndrome
Congenital heart disease occurs in 50%–80% of individuals in this condition, characterized by short stature, webbed neck,
pectus deformities, low-set nipples, cryptorchidism, and characteristic facies. A variable degree of developmental delay,
mild intellectual disability, and lymphatic dyspasias are also associated with this syndrome. Although pulmonary valve
stenosis and hypertrophic cardiomyopathy are most common, aortic aneurysms are described.
Up to 50% of individuals with Noonan Syndrome have a mutation PTPN11, whereas mutations in KRAS, SOS1, RAF1,
and NRAS have also been demonstrated in affected individuals. Inheritance is autosomal dominant.
Shprintzen–Goldberg Syndrome
This condition is characterized by distinctive craniofacial features, craniosynostosis, skeletal anomalies (arachnodactyly,
joint hypermobility), brain abnormalities (Chiari 1 malformation) with intellectual disability. It is associated with aortic
root dilatation, along with other cardiac anomalies including aortic and mitral regurgitation.
Exome and targeted Sanger sequencing of individuals with unaffected parents reveal de novo missense mutations in
the SKI gene encoding the protein SKI. This protein suppresses TGFβ signaling by binding to SMAD proteins, linking the
condition to LDS.
MASS Phenotype
This condition characterized by mitral valve prolapse, myopia, nonprogressive aortic enlargement has skin and skeletal
findings that overlap with those seen in MFS. Heterozygous mutations of FBN1 are causative; inheritance is autosomal
dominant.
NONSYNDROMIC THORACIC AORTIC ANEURYSMS AND DISSECTIONS
Familial TAAD
Familial TAAD (FTAAD) is diagnosed based on the presence of dilatation and/or dissection/rupture of the thoracic aorta,
the absence of MFS or other connective tissue disorders, and the presence of a positive family history.
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Cardiovascular manifestations are usually the only findings. The onset and rate of aortic dilatation are highly variable. Probands with familial TAAD present with aortic complications at a mean age of 56.8 years, which is younger
than those with sporadic TAAD (64.3 years) but much older than those with MFS (24.8 years) [29].
Up to 20% of patients with TAAD have similarly affected first-degree relatives, indicating a significant genetic predisposition to TAAD in patients without a genetic syndrome. TAAD in these families is typically inherited in an autosomal dominant manner, with decreased penetrance in women [30,31]. The vascular features of the disease expressed
in these individuals are variable in relation to the age of onset of the aortic disease, the involvement of the aortic root,
ascending aorta or both, the risk of dissection for a given ascending aortic diameter, and the prevalence of type B dissection. There is variation also in the presence of other clinical features that segregate with TAAD such as congenital
defects such as bicuspid aortic valve, intracranial aneurysms, early onset coronary artery disease, and cerebrovascular
disease [32–34].
Characterization of the specific genes mutated in FTAAD is important as the underlying gene mutated in a family
appears to predict aortic disease presentation including risk for dissection for a range of aortic diameters and risk for subsequent nonaortic vascular diseases.
Genetic Basis and Molecular Mechanisms
At least 10 genes predisposing to autosomal dominant TAAD have been identified. These genes can be divided into two
categories. The genes FBN1, TGFBR1, TGFBR2, TGFB2, and SMAD3 encoding proteins in the TGFβ pathway have
been identified in 6%–8% of FTAAD families, predisposing the probands to TAAD (for molecular mechanisms see
MFS and LDS sections). These individuals have no features of connective tissue disorders such as MFS or LDS, but the
identification of these genes suggests a similar pathogenesis for aneurysm development between syndromic TAAD and
FTAAD.
The second category includes vascular smooth muscle cell (SMC) contractile genes ACTA2, MYH11, MYLK, and
PRKG1 and accounts for up to 12%–14% of FTAAD families, see Fig. 8.2.
Vascular SMCs express smooth muscle-specific isoforms of α-actin and myosin encoded by ACTA2 and MYH11,
respectively. SMCs are arranged circumferentially in multiple layers in the tunica media of the arterial wall and
interact in a dynamic multidirectional process with the microenvironment of the extracellular matrix (ECM) proteins
including collagen, fibronectin, and proteoglycans. ECM factors activate biochemical and mechano-transduction signaling pathways, which modulate SMC contraction, stiffness, survival, growth, cytokine production, and migration,
whereas SMCs are involved in collagen fibrillogenesis, fibronectin assembly, proteolysis, and cross-linking to regulate ECM form and structure.
Fibronectin assembly is initiated within the ECM with the binding of fibronectin dimers onto an integrin receptor and
syndecan, which possibly acts as a coreceptor, on the SMC interface. Integrins and syndecan are also involved in the assembly of the actin cytoskeleton within the SMC. Fibronectin fibrillogenesis is promoted by the integrin/cytoskeleton complex
by SMC contractility, which induces conformational changes in fibronectin, allowing the association of multiple fibronectin molecules and formation of fibrils. Fibrillin-1 C-terminal association and formation of beadlike structures depends on
these fibronectin fibrils. Fibronectin fibrils are required for the sequestration of latent TGFβ, which is subsequently transferred onto the fibrillin-1 microfibrils, which are part of the elastic fibers [35,36].
Mutations in ACTA2 or MYH11 will result in an incorrect assembly of the actin or myosin filaments, leading to a
defect in fibronectin fibrillogenesis and resulting in an incorrect fibrillin-1 assembly into microfibrils and loss of the
ability to sequestrate latent TGFβ. Latent TGFβ that is not incorporated into the matrix is more prone to activation and
can bind to its receptors, initiating TGFβ signaling, characterized by phosphorylation of the receptor Smads (RSmad),
binding to the Co-Smad. This complex is then translocated to the nucleus and together with coregulators can initiate the
transcription of target genes [37,38].
Mutations in ACTA2 lead to early onset aortic complications, whereas over 20% of mutation carriers have no aortic
events (dissection or aneurysm repair) at advanced ages. In a sentinel study by Milewicz et al. of 277 individuals with 41
various ACTA2 mutations, aortic events occurred in over 48% of individuals, the vast majority presenting with thoracic
aortic dissections (88%) associated with the sudden death in 25%. Of these, over 54% were type A dissections presenting at the median age of 36 years, whereas 21% are type B dissections occurring at even younger ages, median 27 years.
This indicates that patients with ACTA2 are even more likely to present with a life-threatening acute aortic dissection than
patients with MFS. The overall cumulative risk of an aortic event at age 85 years was 0.76 (95% confidence interval 0.64,
0.86), indicating that environmental factors or genetic factors, such as modifier genes may play a role in the expression of
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FIGURE 8.2 Proposed molecular mechanism of MYH11 and ACTA2 mutations in aneurysm formation. Normally fibronectin assembly is
initiated with the binding of fibronectin dimers onto an integrin receptor and syndecan, acting as a coreceptor. Integrins and syndecan are also
involved in assembly of the actin cytoskeleton. Fibronectin fibrillogenesis is facilitated by the integrin/cytoskeleton complex by cell contractility, which induces conformational changes in fibronectin, promoting the association of multiple fibronectin molecules and formation of fibrils.
Fibrillin-1 C-terminal association and formation of beadlike structures depends on these fibronectin fibrils. Fibronectin fibrils are required for the
sequestration of latent TGFβ, which is subsequently transferred onto the fibrillin-1 microfibrils, which are part of the elastic fibers. Mutations in
MYH11 or ACTA2 will result in an improper assembly of the actin or myosin filaments, resulting in a defect in fibronectin fibrillogenesis and incorrect fibrillin-1 assembly into microfibrils, and therefore loss of the ability to sequestrate latent TGFβ. Latent TGFβ that is not incorporated into the
matrix is more prone to activation and can bind to its receptors, initiating TGFβ signaling, characterized by phosphorylation of the receptor Smads
(RSmad), binding to the Co-Smad. This complex is then translocated to the nucleus and together with coregulators can initiate the transcription of
target genes. FBN1, fibrillin-1; FN, fibronectin; LAP, latency associated peptide; LTBP, latent TGFβ binding protein; MMP2 and 9, matrix metal-
loproteinases 2 and 9; R-Smad, receptor Smad; TGFβ, transforming growth factor β; TGFβR1, transforming growth factor β receptor 1; TGFβR2,
transforming growth factor β receptor 2.
aortic pathology in individuals with ACTA2 mutations. Ongoing evaluation of the cohort of patients with ACTA2 mutations
has demonstrated that one-third of these individuals dissected at ascending aortic diameters less than 5 cm, suggesting that
prophylactic repair of TAAs in probands with ACTA2 mutations should be considered when the aorta reaches 4.5 cm in
diameter [39].
MYLK and PRKG1 encode kinases that control SMC contraction and relaxation, respectively. MYLK encodes for myo-
sin light chain kinase. This kinase is involved in smooth muscle cell contractility. Loss-of-function mutations of MYLK
identified so far have an effect on kinase activity or calmodulin-binding properties of the protein, clinically leading to acute
aortic dissection with minimal or no aortic enlargement prior to dissection [40].
PRKG1 encodes type 1 cyclic guanosine monophosphate (cGMP)-dependent protein kinase (PKG-1), which is activated upon binding of cGMP and controls SMC relaxation. The altered PKG-1 is constitutively active even in the absence
of binding cGMP. The increased PKG-1 activity leads to decreased phosphorylation of the myosin regulatory light chain.
Gain of function mutations in PRKG1 have been found to cause TAAD [41].
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