Katharina Heil

Katharina Heil


Research Interests

During my PhD I am interested in gaining a better understanding of Parkinson's Disease. This is done by a systems biological approach, using graph analysis and dynamic modeling techniques. In particular the pre-synaptic compartment of the synapse will be analyzed using the rule-based modeling language "kappa".

Publications:
2017
  Community detection identifies a subnetwork of the synaptic proteome associated with differences in educational attainment
Robertson, G, McLean, C, Sorokina, O, C, D, Wysocka, E, Heil, K, Hill, WD, Simpson, T, Armstrong, JD & Deary, I 2017, 'Community detection identifies a subnetwork of the synaptic proteome associated with differences in educational attainment' Edinburgh Neuroscience Day 2017, Edinburgh, United Kingdom, 22/03/17, .
The human synaptic proteome is a complex structure composed of over 5000 interacting proteins. Disruptions of these proteins have been associated with over 100 brain disorders making them of considerable interest to researchers examining the molecular antecedents of these disorders. The structure of the human synaptic proteome can be modelled as a network with each protein a vertex and each interaction an edge. A property of complex networks is community structure. Vertices form tightly interconnected groups (communities) with sparser connections between communities.

Previous studies have shown associations between communities detected in a subset of the synaptic proteome and cellular functions. The community detection methods used previously perform poorly at the scale of the complete synaptic proteome. We use a recently developed algorithm that scales well with increasing network size, to detect communities in a curated database of synaptic protein interactions. We test whether the communities have an enriched association with educational attainment using Gene Set Analysis.
General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Robertson, Grant, McLean, Colin, Sorokina, Oksana, C, David, Wysocka, Emilia, Heil, Katharina, Hill, W David, Simpson, Thomas, Armstrong, J. Douglas & Deary, Ian.
Publication Date: 22 Mar 2017
Publication Information
Category: Poster
Original Language: English
2016
  Systems biological approach to Parkinson's disease
Heil, KF, Sorokina, O, Armstrong, JD & Hellgren-kotaleski, J 2016, 'Systems biological approach to Parkinson's disease: Uncovering effects of PD on the synapse' 20th International Congress of Parkinson's Disease and Movement Disorders, Germany, 19/06/16 - 23/06/16, .
Traditional models of Parkinson’s disease (PD) focus on events at the neuronal synapse and although recently, non-synaptic pathways have been linked to PD, most therapies still target the synapse. Large-scale Genome Wide Association Studies (GWAS), mRNA array analyses and neuronal proteomics give new insights into causal and pathological mechanisms at all levels: genes – transcripts – proteins. Here we examined correlations amongst different datasets, specifically looking for evidence of synaptic mechanisms involved in PD.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Heil, K.F., Sorokina, O., Armstrong, J. D. & Hellgren-kotaleski, J.
Keywords: (Multidisciplinary Approach. )
Publication Date: 19 Jun 2016
Publication Information
Category: Abstract
Original Language: English
  Synaptic Protein-Protein Interaction Networks unravel insights into Parkinson's Disease
Heil, K, Sorokina, O & Armstrong, JD 2016, 'Synaptic Protein-Protein Interaction Networks unravel insights into Parkinson's Disease' International Conference on Systems Biology, Barcelona, Spain, 16/09/16 - 21/09/16, .
Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the
Western World. Dysfunction and subsequent degeneration of dopaminergic neurons leads to classic symptoms featuring disorders in motor function followed by other non-motor symptoms including depression and cognitive decline. However the disease is complex. Modern high-throughput analysis in recent years has identified affected pathways and proteins including mitochondrial dysfunction, abnormalities in the ubiquitin pathway and disrupted vesicle transport.
Synaptic dysfunction is one of the models for the early stages of the disease and hence we looked at how these datasets relate to the molecular complexes found at the human
synapse. Proteomic and protein-protein interaction datasets were used to generate
molecular maps of the synapse. We extracted evidence for proteins and genes associated with PD from a variety of sources including GWAS studies, databases and literature. We found that the synaptic datasets were significantly enriched with subset of PD-related proteins. In order to identify structural clusters within the network we used a spectral community algorithm. We then overlaid genetic association data and molecular function annotations from the Gene Ontology (GO). Hypergeometric testing was used to detect enrichment of molecular clusters for both disease and functional class associations. We have identified clear molecular clusters within both pre- and post synapses that were specifically enriched in proteins linked to Parkinson’s Disease. In addition, in the presynapse, significant enrichment for GO terms related to synaptic vesicle cycling is associated with PD enriched clusters. In summary our analysis reveals that despite the complex nature of integrating disease associations from many sources, when applied onto a molecular model of the synapse specific substructures and mechanisms clearly emerge.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Heil, Katharina, Sorokina, Oksana & Armstrong, J. Douglas.
Publication Date: 16 Sep 2016
Publication Information
Category: Poster
Original Language: English
2015
  A rule-based model of Clathrin Mediated Endocytosis: Gaining a better understanding of Parkinson's Disease
Heil, K, Sorokina, O, Hellgren-kotaleski, J & Armstrong, JD 2015, 'A rule-based model of Clathrin Mediated Endocytosis: Gaining a better understanding of Parkinson's Disease' International Synthetic and Systems Biology Summer School, Taormina, Italy, 5/07/15 - 9/07/15, .
Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the modern world. Nevertheless we are far from understanding disease causing dysfunctions and identifying early disease stages.
Through a variety of experimental and analytical approaches a variety of proteins are known to be associated with Parkinson’s Disease. There is experimental evidence for about 30 well-known proteins to cause PD or play a crucial role in the disease progression. An additional 200 proteins have been implicated in the disease based on Genome Wide Association Studies and Microarray Analysis.
Based on network analysis studies we see that PD-causing proteins are enriched in specific synaptic departments, such as the vesicle cycle. We identified clathrin mediated endocytosis (CME) as a potentially measurable variable to distinguish healthy against disease conditions. CME is a process that regulates (synaptic) plasma membrane turnover, e.g. internalization of receptors and lipids, as well as the uptake of extracellular material like neurotransmitters, nutrients and growth factors. As a pre-synaptic pathway it hosts many proteins that trigger the
development of Parkinson’s Disease and is linked to further PD-causing proteins. Hence we are highly interested in gaining further insights into this process.
Due to the large number of proteins required for an extended model of CME, rule-based modelling was chosen. Based on the formulation of rather generic rules instead of very specific equations, this allows us to build a dynamic model of a larger system. It avoids the combinatorial explosion, often occurring when considering growing protein models and is easily extendible when needed.
We can currently model part of the endocytosis process and are looking forward to simulating a more complete system in the near future. Preliminary insights into the endocytosis process and aberrations under disease conditions will be presented and give hope to further advances in the understanding of Parkinson's Disease.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Heil, Katharina, Sorokina, O., Hellgren-kotaleski, J & Armstrong, J. D..
Publication Date: 5 Jul 2015
Publication Information
Category: Poster
Original Language: English
2014
  A systems biological approach to Parkinson's disease
Heil, KF, Sorokina, O & Armstrong, JD 2014, 'A systems biological approach to Parkinson's disease' Society for Neuroscience (SfN) 2014, Washington DC, United States, 15/11/14 - 19/11/14, .
Parkinson’s Disease (PD) is the second most common neurodegenerative disorder with an average prevalence of approximately 0.3%, with numbers rising as populations age (1%/4% prevalence for over 60/80 year olds respectively). Initial symptoms feature disorders in motor functions followed by non-motor symptoms such as depression, amongst others. The major characteristic of the disease is the degeneration of dopaminergic neurons. Nevertheless the underlying mechanisms cannot yet be fully explained. Various molecular approaches identified key genes/proteins involved e.g. LRRK2, SNCA and PARK7. This suite of implicated genes spans multiple functional pathways, explaining the complexity of the disease. Oxidative stress, altered mitochondrial, proteasomal and lysosomal function, inflammatory changes and excitotoxicity all play a role in the disease and its pathology (Dexter, 2013). Nevertheless the whole picture in particular, the differences between causal and symptomic pathways are far from being understood. Systems biology provides a unique view of complex biological systems and gives a framework for integrating available experimental data. Several modelling approaches have been taken with respect to PD, mainly focusing on the impaired dopamine metabolism. However, few current models, give insights into molecular mechanisms of the disease. We developed a protein-protein interaction model describing all proteins implicated in PD. The candidate protein list is based on literature and the latest human genetics studies data (hand-curated). Molecular interactions have been retrieved from public databases (Hippie, Intact, etc.). The network’s community structure was obtained by applying clustering techniques (Newman, 2006). As expected, we find many PD proteins associated with presynaptic compartments. However we can see specific enrichment in subsets of proteins associated with synaptic vesicle cycling and other subsets more closely associated with post-synaptic complexes in glutamatergic neurons each of which may represent different mechanisms involved in the disease pathology.
REFERENCES: Parkinson Disease: from pathology to molecular disease mechanisms, Dexter D, et al., (2013), Free Radical Bio Med, 62, 132 Modularity and community structure in networks, Newman M, (2006), PNAS, 103, 8577
General Information
Organisations: Edinburgh Neuroscience.
Authors: Heil, K.F., Sorokina, O & Armstrong, J. D..
Keywords: (Parkinson's Disease, Synapse, Modeling and Graph Analysis. )
Publication Date: Nov 2014
Publication Information
Category: Poster
Original Language: English
  Understanding synaptic pathways - modelling Parkinson's Disease
Heil, KF, Sorokina, O, Hellgren-kotaleski, J & Armstrong, JD 2014, 'Understanding synaptic pathways - modelling Parkinson's Disease' The 1st HBP Education Workshop on New Frontiers in Neuroscience and Methods of Transdisciplinary Education, Tel Aviv, Israel, 18/06/14 - 20/06/14, .
General Information
Organisations: Edinburgh Neuroscience.
Authors: Heil, K F, Sorokina, O, Hellgren-kotaleski, J & Armstrong, JD.
Publication Date: 18 Jun 2014
Publication Information
Category: Poster
Original Language: English
  A Systems Biological Approach to Parkinson's Disease
Heil, K, Sorokina, O & Armstrong, D 2014, 'A Systems Biological Approach to Parkinson's Disease' SysBio2014: Advanced Lecture Course on Systems Biology, Innsbruck, Austria, 2/03/14 - 8/03/14, .
Parkinson’s Disease (PD) is the second most common neurodegenerative disorder in industrialized countries. Its average prevalence is approximately 0.3%, with numbers rising as populations age (1% prevalence for over 60 year olds). Initial symptoms feature disorders in motor function followed by non-motor symptoms such as depression, amongst others.

Various molecular approaches have been taken to gain further understanding of the disease phenotype. In line with this, different studies have identified key genes/proteins involved e.g. LRRK2, SNCA, PARK. The suite of implicated genes seems to be linked to multiple functional pathways, explaining the complexity of the disease. Oxidative stress, altered mitochondrial, proteasomal and lysosomal function, inflammatory changes and excitotoxicity all play a role in the disease and its pathology (Dexter, 2013). Nevertheless the whole picture is far from being understood.

Systems biology provides a unique view of complex biological systems and gives a framework for integrating available experimental data. Several modelling approaches have been taken with respect to PD, mainly focusing on the impaired dopamine metabolism. However, few current models, give insights into molecular mechanisms of the disease.

We have started the generation of a static model focussing on a full interaction map of proteins involved in PD. The candidate protein list is based on literature and the latest GWAS data. Molecular interactions have been retrieved from public databases (Hippie, Intact, etc.). The network’s community structure was obtained by applying clustering techniques (Newman, 2006). We find clusters enriched with pre- and post-synaptic proteins, each of which may correspond to different mechanistic aspects of PD. The main known PD proteins appear to be located in a presynaptic compartment, and in particular in specific subsets associated with synaptic vesicle cycling.

Our aims are now to use richer systems biology modelling methods, such as dynamic and rule based approaches (Sekar, 2012) to analyse how these proteins might interact with one another and known signalling pathways to better understand the disease mechanism.


REFERENCES:
Parkinson Disease: from pathology to molecular disease mechanisms, Dexter D, et al., (2013), Free Radical Bio Med, 62, 132
Rule-Based Modeling of Signal Transduction: A Primer, Sekar J, et al., (2012), Methods Mol Biol, 880, 139
Modularity and community structure in networks, Newman M, (2006), PNAS, 103, 8577

General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Heil, Katharina, Sorokina, Oksana & Armstrong, Douglas.
Publication Date: 2014
Publication Information
Category: Poster
Original Language: English

Projects:
A systems biological approach to Parkinson’s Disease (PhD)