Composite Annotation for                   Heart Development                        Tariq Abdulla1, Ryan Imms1,           ...
Outline Heart Development – what happens?     Anatomy, Tissue, Cell, Protein   Multiscale Modelling   Pre-composition:...
Heart Development: what happens?3
AnatomyRear View
Tissue      Cardiac Jelly                      EndocardiumMyocardium
Cell                 Protein       Hign Delta,       Low Notch       Hign Notch,       Low Delta
Multiscale Modelling10
Compucell3D and an SBML Solver•BionetSolver                  CC3D     •Concentration of a subcellular species (SBML)     d...
<Plugin Name="CellType">       <CellType TypeName="Medium"     TypeId="0"/>       <CellType     TypeName="EndocardiumNotch...
Gene Ontology16
Mammalian Phenotype Ontology
Phenotype and Trait Ontology          (PATO)20
Human Developmental Anatomy          (EHDA) 21
Conclusions Gene to phenotype annotation tends to use a surgical or anatomical  perspective – but does not directly inclu...
Ackowledgements•Randy Heiland•Maciej Swat•Lucile Houyel•Jean-Marc Schleich•Ron Summers•Fanny Bajolle•Dan Cook•John Gennari...
OBOintersection_of:PATO:0001163 ! decreased concentrationintersection_of: inheres_in PR:000015308 ! SNAI1intersection_of: ...
In vitro EMTWildtype       Notch1                                         BMP2                 L. Luna-zurita et al. “Inte...
CPM ModelE   J ( ( x )), ( ( x )) (1   ( ( x )), ( ( x )) )  s (s  S ) 2  v (v  V ) 2      x, x     ...
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
Abdulla, ICBO2011, Composite annotation for heart development
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Abdulla, ICBO2011, Composite annotation for heart development


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  • 1) At an early stage the heart is a tube. It has no valves but is somehow pumping blood in one direction, perhaps using impedance pumping. In the third and fourth week of human development, heart looping takes place, and we end up with the four chambered double pump. Let’s look at this in more detail.2) One of the key things that happens is heart looping and wedging. There’s a lot of jargon here, so let’s break it down a little bit. The long thing, which is looping behind is known as the Outflow Tract (OFT) or conotruncus. You can divide it into two parts the conus and the truncus. Now while it is doing this looping, the Atrioventricular canal is septating (this means a wall is growing to divide it). Also, a septum is growing in the conotruncus. Because the conus is looping, the shape of this septum is spiral. When the OFT wedges into the AV canal, in normal development the conal septum lines up with the AV septum. This allows blood to flow from right ventricle to pulmonary artery (to the lungs) and from left ventricle to the aorta and around the body.3) In this bottom image we see two Scanning Electron Micrograph (SEM) images of mouse hearts during looping. The first one has looped in the correct direction – clockwise, while the second one has looped in the direction – anticlockwise. In this case, we have genetically induced situsinversus – where the organs develop on the opposite side of the body. Images: [1][2] Kirby et al. Cardiac Development[3] J. Schleich, C. Almange, J. Dillenseger, and J. Coatrieux, &quot;Understanding normal cardiac development using animated models,&quot; IEEE Computer Graphics and Applications, vol. 22, 2002, pp. 14-19.
  • The motivation for looking in more detail at heart development is to understand how congenital heart defects occur.Many different mechanisms can interact and contribute towards several different types of congenital heart defect.To really understand the risk factors behind CHD, and provide early warning, it is not sufficient just to identify genes associated with CHD. We need to understand how each of these processes work.(click)Each of these processes are complex in their own right – and the work presented here focuses only on the EMT.
  • This slide shows what is happening in the heart between embryonic day 24 and embryonic day 32.Much of the inner structure of the heart is the result of the growth of endocardial cushions.These grow in the Outflow tract and the Atrioventricular canal by EMT. In both locations, they fuse by Day 32.Because the Outflow tract rotates, the fusing cushions form a helical shaped septum, which divides the aorta from the pulmonary artery.In the AV canal. Upper and lower cushions fuse to form the AV septum. They also contribute flaps to the Mitral and Tricuspid valves, as shown.
  • At the tissue level, the embryonic heart is composed of two layers – outer myocardium (heart muscle) inner endocardium (an epithilium). These are seperated by an extracellular matrix termed cardiac jelly. In the
  • At the cell level, we need to consider changes in cell type – or state. Notch signalling creates a feedback loop whereby cells either highly express notch or a ligand for notch (eg delta). So one state is whether they are primary or secondary. In the myocardium notch has a secondary effect on expression of VEGF. We might have cell level states like the level of adhesiveness between different types of cell. These can be preferential, so you may need to specify every possible combination. Here we are looking at the same thing, but closer up. We can see the intracellular signalling that takes place within a single cell.So to properly understand these processes, we really do need to start linking between the scales. Only making a model of what the cells do, doesn’t really explain why they do it. Likewise, a protein interaction model of the pathways involved, doesn’t tell you a lot about EMT on its own, because In the Endocardium... Notch signalling has pre-sepecified cells that may undergo EMT. In combination with BMP and TGF-beta signalling from the myocardium, this increases the expression of Snail protein, which in turn reduces the expression of VE-Cadherin. This causes endocardial cells to lose their adhesiveness and undergo EMT, which leads to growth of the cushions.We will return to this later, when I talk about multiscale modelling.
  • Without going into too much detail, part of the explanation for this is that Notch is downstream of BMP2, as illustrated here.(click)So the endocardial cells also lose their adhesion to one another, but something else also happens to induce them to invade the collagen gel. The hypothesis tested in these initial simulations is that this could be due to a simultaneous increase in the adhesion between the endocardial cells and the collagen gel / cadiac jelly.
  • We apply this framework for multiscale modelling to heart development – which is illustrated here for endocardial cushion growth. We are interested in processes at the nanometer scale (protein interaction) which take place within each cell, as well as the cell behaviour and tissue interaction at the micrometer scale, and the overall development of the heart at the milimeter scale.There are different modelling approaches applicable to each level of scale, and for this initial work, we are really interested in the middle level (cell-tissue level) (click)
  • So... For example, here is a part of the Mammalian Phenotype Ontology (MP). Everything in the MP is a phenotype that mammals can have – hance the root node is “Mammalian Phenotype”. Everything under this is a subtype of “Mammalian Phenotype”. The arrows here are “is_a” relations. This means we go from broader to narrower terms. Shown here are some phenotypes that are types of “abnormal heart development”. Some child nodes are hidden to keep it tidier. We have phenotypes like “abnormal looping morphogenesis”, which could be delayed heart looping, abnormal direction, or failure of looping.We have “abnormal outflow tract development” and in this case, the only child nodes are abnormal septation or ‘transposition of great arteries’ – as we have seen already this can mean many different things. And then there are things like dextrocardia – where the heart develops to the right, or abnormal EMT. And under abnormal endocardial cushion morphology, well they can be absent, decreased in size increased in size thin – or there can be a failure to close.Now this may be called a pre-composed phenotype ontology. The curators attempt to populate it with all the phenotype terms that might ever be applicable, and in some circumstances this can be helpful. But there are always going to be situations where you just don’t have terms to the required specificity. Getting new terms added to an ontology might be a somewhat cumbersome process. For example... When this ontology defines ‘decreased size of endocardial cushion’ are we talking about the endocardial cushions in the AVC or in the OFT? This is a real problem, sometimes an induced genetic mutation leads to an increase in one set of endocardial cushions, but not in the other.A better approach might be post-composition. We can see that terms in the MP consists of a relation between an anatomical entity – say ‘endocardial cushion’ and a quality “decreased size”Ontology view from:
  • We have noted that one genetic disruption may lead to multiple types of CHD, and one type of CHD might be caused by many different genetic mutations. With this image we can gain further insight as to how CHD sit on a spectrum. We have different disease classifications corresponding to different degrees of rotation.In the normal situation the OFT should rotate about 150 degrees, clockwise.So... Diseases are usually classified by their symptoms, but in reality diseases can have common causes, and even be overlapping. The classification of Double Outlet Right Ventricle (DORV) overlaps with that of Tetralogy of Fallot (TOF) with rotation varying from about 90 to 140 degrees.Image: after L.F. Donnelly, &quot;Adapting disease concepts to changes in imaging modalities in complex congenital heart disease Imaging and staging of Wilms ’ tumour,&quot; Pediatric Radiology, vol. 27, 1997, pp. 284-285.
  • So here is a view of part of PATO – PATO deals only with qualities – things like size and all the many subtypes of size.This can only really be used in a post-composition approach – in which we combine these terms with terms from other ontologies such as anatomical ontologies. “decreased size” doesn’t make sense unless we are talking about the decreased size of something.Note that we have more options here. The MP ontology stopped at “decreased size of endocardial cushion” – but if we were to use this ontology, in a post-composition approach we could specify whether it was hypoplastic (fewer cells) or hyopotrophic (smaller cells). The same is true of increased size with hyperplastic and hypertrophic).Ontology view from:
  • Here is a view of a species-specific developmental anatomy ontology – the EHDA. Note that this is primarily orgainsied along part_of relationships, as this is generally easier from an anatomical perspective. We also have starts_at and ends_at relationships, which link to entities representing the Carnegie Stages (CS) of human development. The heart starts developing at CS06 and ends at CS20. The AV canal and OFT start at CS10.
  • Now here we demonstrate an example of post-composition . We define a new term as an intersection entity that has_quality of being “mislocalisedradially” and inheres_in the outflow tract.Because the “aortic component” is part of the “outflow tract”, an automatic reasoner could infer that “outflow tract”+”mislocalisedradially” has a part “aortic component”+”mislocalisedradially”.Now even from this simple example, we can see that we are starting to work across multiple scales,
  • Now... So far we have talked about models being stored in XML, which allows for easy organisation with ontologies. But results and data, can also be stored as XML, in web accessible databases. These include both results of biomedical measurements, and the results of simulations of biomedical models.Along the left we have data sources at different levels of scale. These can be annotated, for example with segmentation of MRI images using the FMA. Doing this allows automatic generation of realistic 3d models of organs. Histochemical data can be annoted with the Protein Ontology and the CellularComponent (GO-CC) in which they are located. Gel electrophoresis data can be annoted with GO-Molecular Function and the Protein Ontology.We can build composite annotations. These same composite annotations can then be used for annotating Biosimulation variables, parameters and modules. Finally there are disease classifictions, such as “Ventricular Septal Defect” which might be inferred from a decreased volume of the membranous portion of the cardiac septum.
  • …we can tell what diseases are related to particular genetic mutations but know very little about the causes or mechanisms.
  • Because tissue ends up in quite different places, a defect in one place, at one stage of development leads to defects in different places later on. The endocardial cushions in the AVC end up as the atrioventicular valves (blue). Those in the conotruncus end up as the semilunar valves and the membranous septum (yellow). This is the most common location for a ventricular septal defect. Some of these names things have real boundaries, and others have only fiat boundaries. The names and locations are quite disputed, and this is one case in which information modelling – and more specifically ontologies – will come into play.Image:D. Srivastava and E.N. Olson, &quot;A genetic blueprint for cardiac development.,&quot; Nature, vol. 407, 2000, pp. 221-6.
  • So... This re-modelling of the Outflow Tract is a crucial part of heart development... Among CHD this is the most common thing that can go wrong. But there are several mechanisms that can disrupt this remodelling – either individually or in combination. Contribution from the Second heart field can lead to a shortened OFT, which causes shorter rotation. Neural crest cells migrate from the neural tube to the heart tube, and abnormal migration of these lead to septation defects. The cardiac muscle (myocardium) itself may have some defect, which causes incorrect rotation. Finally the endocardium may undertake abnormal Epithelial-Mesenchymal Transisition. This will affect the growth of the endocardial cushions in the OFT and the AVC, causing septation and valve defects.Image:F. Bajolle, S. Zaffran, and D. Bonnet, &quot;Genetics and embryological mechanisms of congenital heart diseases.,&quot; Archives of cardiovascular diseases, vol. 102, 2009, pp. 59-63.
  • We base our simulations on this in vitro study – which induced EMT in mouse endocardial cells. They cells were taken from mouse ventricles, so are not disposed to undertake EMT. In their wildtype state, they remain in a monolayer floating on the collagen gel.By the introduction of Notch protein, the endocardial cells lose their adhesion as expected. However this is not sufficient to induce a full EMT – instead cells only scatter on the surface of the collagen gel, without invading it.(click)With the introduction of BMP2 – a protein which is secreted by the myocardium in the cushion forming regions – cells both scatter on the surface of the gel, and invade into it.(click)
  • The simulations we used are a type of Cellular Potts model. Cells are defined as being multiple adjacent sites on a lattice. We decide the “surface energy parameter” J between different types of cells. Larger J means less adhesion between the cells.The simulation uses a Monte Carlo algorithm so there is some randomness. It goes through each pixel and attempts to update it, with an adjacent cell. Whether it updates depends on whether this is a valid attempt (the tow pixels belong to two different cells) and the change in energy which would result. If the energy is reduced (change in E less than zero) then it will definitely accept the change (p=1). If it increases the energy, it is accepted with probability e^-/\\E/kT – where kT is a constant.
  • This is an illustration of the iteration of lattice sites, with both invalid attepmtps (same cell) and valid attempts (different cells). Valid attempts are either rejected or accepted depending on the equation shown before.
  • So our simulations use a 3D lattice, at the bottom you can see a cross section. In the base case, our simulated endocardial cells float on the collagen gel in a monolayer.
  • By increasing the surface energy parameter between endocardial cells (e.g. Reducing their cohesion) we have a 2D scattering on the surface of the simulated collage gel.
  • By simulating a simultaneous reduction in endocardial – to – endocardial adhesion, and an increase in endocardial – collagen gel adhesion, we observe fully invasive behavior. This supports the hypothesis that the mesenchymal phenotype is achieved in part by an increase in adhesion to the extracellular matrix.
  • Abdulla, ICBO2011, Composite annotation for heart development

    1. 1. Composite Annotation for Heart Development Tariq Abdulla1, Ryan Imms1, Jean-Marc Schleich2, Ron Summers1 ICBO 20111.Dept Electronic & Electrical Engineering, Loughborough University, UK2. LTSI, University of Rennes 1,
    2. 2. Outline Heart Development – what happens?  Anatomy, Tissue, Cell, Protein Multiscale Modelling Pre-composition: GO, MP Post-composition: PATO, OPB Conclusions
    3. 3. Heart Development: what happens?3
    4. 4. AnatomyRear View
    5. 5. Tissue Cardiac Jelly EndocardiumMyocardium
    6. 6. Cell Protein Hign Delta, Low Notch Hign Notch, Low Delta
    7. 7. Multiscale Modelling10
    8. 8. Compucell3D and an SBML Solver•BionetSolver CC3D •Concentration of a subcellular species (SBML) determines cell type (CC3D)•CC3D BionetSolver •Cell type (CC3D) determines value for rate parameters in the subcellular model (SBML)12
    9. 9. <Plugin Name="CellType"> <CellType TypeName="Medium" TypeId="0"/> <CellType TypeName="EndocardiumNotch" TypeId="1" /> <!--CellType TypeName="Mesenchymal" TypeId="2" /--> <CellType TypeName="CardiacJelly" TypeId="2" /> </Plugin> <Plugin Name="Contact"> <Energy Type1="Medium" Type2="Medium">0</Energy> <Energy Type1="EndocardiumNotch" Type2="EndocardiumNotch">2</Energy> […] </Plugin>13
    10. 10. 14
    11. 11. Pre-composition15
    12. 12. Gene Ontology16
    13. 13. Mammalian Phenotype Ontology
    14. 14. Post-composition19
    15. 15. Phenotype and Trait Ontology (PATO)20
    16. 16. Human Developmental Anatomy (EHDA) 21
    17. 17. Post-composition22
    18. 18. Conclusions Gene to phenotype annotation tends to use a surgical or anatomical perspective – but does not directly include mechanism or causes By including cell and protein level annotations, causes and mechanisms are more explicit Post-composition enables more flexible annotation. But it is more difficult for annotators. The two strategies can be combined, but some post- composition seems necessary for multiscale and development research  In development, we can’t ignore the structure of cells  For multiple scales, there are too many combinations to pre-compose them all Lightweight reference ontologies are more manageable, but repositories of post-composed annotations are more challenging for reasoning
    19. 19. Ackowledgements•Randy Heiland•Maciej Swat•Lucile Houyel•Jean-Marc Schleich•Ron Summers•Fanny Bajolle•Dan Cook•John Gennari•Ryan Roper
    20. 20. Questions?
    21. 21. OBOintersection_of:PATO:0001163 ! decreased concentrationintersection_of: inheres_in PR:000015308 ! SNAI1intersection_of: contained_in CL:0002350 ! endocardial cellOWLEquivalentTo:PATO:0001163and (inheres_in some PR:000015308)and (contained_in some CL:0002350)
    22. 22. 30
    23. 23. In vitro EMTWildtype Notch1 BMP2 L. Luna-zurita et al. “Integration of a Notch-dependent mesenchymal gene program and Bmp2-driven cell invasiveness regulates murine cardiac valve formation,” The Journal of Clinical Investigation, vol. 120, 2010.
    24. 24. CPM ModelE   J ( ( x )), ( ( x )) (1   ( ( x )), ( ( x )) )  s (s  S ) 2  v (v  V ) 2 x, x Compucell3D