Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins
<p>The purpose of this thesis was to study mitosis in Drosophila, from a network biology perspective. The primary aim was to develop and test a network-based prediction model that could integrate available data in public databases (like Flybase) and, based on that, predict potential mitotic pr...
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| Iaith: | English |
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2013
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| _version_ | 1826280844815237120 |
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| author | Khan, FF |
| author2 | Deane, C |
| author_facet | Deane, C Khan, FF |
| author_sort | Khan, FF |
| collection | OXFORD |
| description | <p>The purpose of this thesis was to study mitosis in Drosophila, from a network biology perspective. The primary aim was to develop and test a network-based prediction model that could integrate available data in public databases (like Flybase) and, based on that, predict potential mitotic proteins.</p> <p>The approach taken to design the protein interaction network included the use of a priori knowledge about the microtubule composition of the mitotic spindle and the higher likelihood of microtubule-associated proteins (MAPs) to have a putative mitotic function. The design also included the integration of different complementary datasets, from gene expression and functional RNAi screens to cross species conservation of MAPs for fitting a network-based model for predicting mitotic proteins.</p> <p>I begin with the creation of the MAP interactome based on a MAP dataset in Drosophila. This initial network was extended by transferring homologs and interologues of MAP datasets from four other species, i.e. human, mouse, rat and Arabidopsis. These proteins were then used as seed proteins to conduct a virtual pull-down experiment, by adding indirect interactors into the network, i.e. proteins that directly bind to two or more MAPs within the network, which completed the MAP interactome. Data from genome-wide studies in Drosophila were gathered for each node in the MAP interactome. These ‘layers’ of data were then used as features to fit a prediction model that could score each node in the network, based on the likelihood of its role in mitosis. The final model performed with 96% accuracy after 10-fold cross validation and was used to rank all the proteins in the MAP interactome.</p> <p>By analysing the top 100 high scoring predicted mitotic proteins, a highly connected cluster of 33 proteins was identified that was subject to experimental validation in the lab. The first approach was to conduct an in vitro analysis using an RNAi screen to test for any spindle, chromosome or centrosome phenotypes upon gene knockdown. After two independent RNAi screens, around 80% of the proteins produced mutant mitotic phenotypes strongly supporting the results of the MAP prediction model.</p> <p>The second approach was to conduct an in vivo analysis by expressing GFP- fusion constructs of selected genes from the subcluster. These were expressed in Drosophila early embryos to study their subcellular localization during interphase and mitosis. A variety of localizations were observed ranging from chromatin and microtubules to more generic cytoplasmic localizations. These results suggested not all predicted proteins were co-localizing with microtubules, and therefore might not necessarily be microtubule associated proteins but can possibly be functioning as microtubule associated regulator proteins. Proteomics analysis of a subset of these genes showed a large proportion of false positive interactions but also picked new interactions between member proteins that highlighted a module within the subcluster.</p> <p>The RNAi hits from the in vitro analysis and the members of the module within subcluster-16 from the in vivo analysis provide interesting subjects for further characterization.</p> |
| first_indexed | 2024-03-07T00:19:50Z |
| format | Thesis |
| id | oxford-uuid:7c27b09b-08c3-43f5-93c3-0580cf5fd103 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:19:50Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:7c27b09b-08c3-43f5-93c3-0580cf5fd1032022-03-26T20:55:11ZDesign, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteinsThesishttp://purl.org/coar/resource_type/c_db06uuid:7c27b09b-08c3-43f5-93c3-0580cf5fd103Cell Biology (see also Plant sciences)Bioinformatics (life sciences)BiologyEnglishOxford University Research Archive - Valet2013Khan, FFDeane, CWakefield, J<p>The purpose of this thesis was to study mitosis in Drosophila, from a network biology perspective. The primary aim was to develop and test a network-based prediction model that could integrate available data in public databases (like Flybase) and, based on that, predict potential mitotic proteins.</p> <p>The approach taken to design the protein interaction network included the use of a priori knowledge about the microtubule composition of the mitotic spindle and the higher likelihood of microtubule-associated proteins (MAPs) to have a putative mitotic function. The design also included the integration of different complementary datasets, from gene expression and functional RNAi screens to cross species conservation of MAPs for fitting a network-based model for predicting mitotic proteins.</p> <p>I begin with the creation of the MAP interactome based on a MAP dataset in Drosophila. This initial network was extended by transferring homologs and interologues of MAP datasets from four other species, i.e. human, mouse, rat and Arabidopsis. These proteins were then used as seed proteins to conduct a virtual pull-down experiment, by adding indirect interactors into the network, i.e. proteins that directly bind to two or more MAPs within the network, which completed the MAP interactome. Data from genome-wide studies in Drosophila were gathered for each node in the MAP interactome. These ‘layers’ of data were then used as features to fit a prediction model that could score each node in the network, based on the likelihood of its role in mitosis. The final model performed with 96% accuracy after 10-fold cross validation and was used to rank all the proteins in the MAP interactome.</p> <p>By analysing the top 100 high scoring predicted mitotic proteins, a highly connected cluster of 33 proteins was identified that was subject to experimental validation in the lab. The first approach was to conduct an in vitro analysis using an RNAi screen to test for any spindle, chromosome or centrosome phenotypes upon gene knockdown. After two independent RNAi screens, around 80% of the proteins produced mutant mitotic phenotypes strongly supporting the results of the MAP prediction model.</p> <p>The second approach was to conduct an in vivo analysis by expressing GFP- fusion constructs of selected genes from the subcluster. These were expressed in Drosophila early embryos to study their subcellular localization during interphase and mitosis. A variety of localizations were observed ranging from chromatin and microtubules to more generic cytoplasmic localizations. These results suggested not all predicted proteins were co-localizing with microtubules, and therefore might not necessarily be microtubule associated proteins but can possibly be functioning as microtubule associated regulator proteins. Proteomics analysis of a subset of these genes showed a large proportion of false positive interactions but also picked new interactions between member proteins that highlighted a module within the subcluster.</p> <p>The RNAi hits from the in vitro analysis and the members of the module within subcluster-16 from the in vivo analysis provide interesting subjects for further characterization.</p> |
| spellingShingle | Cell Biology (see also Plant sciences) Bioinformatics (life sciences) Biology Khan, FF Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title | Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title_full | Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title_fullStr | Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title_full_unstemmed | Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title_short | Design, implementation and experimental validation of a network-based model to predict mitotic microtubule regulating proteins |
| title_sort | design implementation and experimental validation of a network based model to predict mitotic microtubule regulating proteins |
| topic | Cell Biology (see also Plant sciences) Bioinformatics (life sciences) Biology |
| work_keys_str_mv | AT khanff designimplementationandexperimentalvalidationofanetworkbasedmodeltopredictmitoticmicrotubuleregulatingproteins |