Bioinformatic identification of disease driver networks using functional profiling data

Show simple item record

dc.contributor Helsingin yliopisto, lääketieteellinen tiedekunta, tutkimusohjelmayksikkö fi
dc.contributor Helsingfors universitet, medicinska fakulteten, forskningsprogramenheten sv
dc.contributor University of Helsinki, Faculty of Medicine, Research Programs Unit en
dc.contributor Institute for Molecular Medicine Finland, FIMM en
dc.contributor.author Szwajda, Agnieszka fi
dc.date.accessioned 2018-01-11T05:56:36Z
dc.date.available 2018-01-23 fi
dc.date.available 2018-01-11T05:56:36Z
dc.date.issued 2018-02-02 fi
dc.identifier.uri URN:ISBN:978-951-51-3960-3 fi
dc.identifier.uri http://hdl.handle.net/10138/230923
dc.description.abstract Genomics-based drug discovery utilizing sequencing data for elucidation of candidate targets has led to the development of a number of successful treatments in the last decades. However, the molecular driver signals for many complex diseases cannot be easily derived from genome sequencing. Functional profiling studies, such as those involving the detection of protein interaction networks or the effects of perturbations with small molecules or siRNAs on cellular phenotypes, offer a complementary approach for the identification of molecular vulnerabilities that can be exploited in the development of new treatment strategies. The goal of this thesis was to develop computational systems biology methods for supporting such functional endeavors, and through their application use cases, to elucidate novel disease driver signals in cancer and Alzheimer’s disease networks. The availability of functional profiling data (such as biochemical target selectivity information or efficacy readouts) for numerous small molecule compounds has enabled building interaction network models to predict cancer addictions i.e. genes that are essential for disease progression but are not necessarily mutated. In this work, network-based computational methods (such as kinase inhibition sensitivity score – KISS) were developed to infer disease addictions (either single genes or sub-networks) using functional data from high-throughput drug sensitivity screens, and applied in breast cancer cell lines. Further extension of the KISS method, named combinatorial KISS, was introduced as a novel approach to predict synergistic drug combinations and their underlying co-essential target pairs. Driver deconvolution from drug response profiles relies on extensive and reliable drug-target interaction networks. Therefore, a systematic evaluation of target selectivity profiles was performed among recently published large-scale biochemical assays of kinase inhibitors, combined with data reported in the drug-target databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among various bioactivity types, including IC50, Ki, and Kd. To make better use of the complementary information captured by the various bioactivity types, we developed a model-based integration approach, termed KIBA, and demonstrated how it can be used to classify kinase inhibitor targets. As a result, we created kinome-level, quantitative drug–target interaction network for further modeling studies. Besides the analysis of drug responses, another way to find novel disease drivers or molecular vulnerabilities is to explore the interaction partners of known oncogenes, since the existence of a protein-protein interaction suggests their involvement in the same biological pathway and thereby in the same biological process. However, one of the major challenges in the protein-protein interaction screens is the identification of functionally relevant interactions from the long hit list, in particular when their functional annotations are missing. This motivated the development of Relevance Rank Platform (RRP) approach that can suggest the candidate proteins from the high throughput screens that most likely contribute to the function of the bait protein. The method predicts functionally similar candidate interactors regardless of either the reliability of the mass spectrometry-based identification, or the knowledge of the biological function of the putative interactor. RRP was applied and validated in PIN1 (Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1) and PME-1 (Protein Phosphatase Methylesterase 1) interaction networks in prostate cancer. Finally, we carried out functional comparison of nitrosylated proteins in the brain synaptosomes of Alzheimer’s disease (AD) mouse models and their healthy controls, with the aim to reveal the disease processes in which this posttranslational modification plays a role. We also elucidated the amyloid precursor protein (APP) - centered Alzheimer’s disease network of differentially nitrosylated proteins that are likely to be implicated in this neurological disorder. Taken together, this thesis work introduces novel experimental-computational strategies for the deconvolution or prioritization of potential disease drivers, either single proteins or their subnetworks. These methods are applicable to various cell lines or patient-derived samples. They can provide directly druggable therapeutic targets for personalized treatment applications and may be used in the development of novel therapeutic options. en
dc.description.abstract Genomisekvenssointiin perustuva lääkeainekehitys missä lähdetään liikkeelle potentiaalisista tautigeeneistä on tuottanut uudensukupolven kohdennettuja hoitomuotoja eri sairauksiin, etenkin syöpäsairauksiin, joiden kehitys pohjaa tunnettuihin syöpägeeneihin. On kuitenkin useita sairauksia, sekä myös syövän eri alimuotoja, joiden syntymekanismit eivät ilmene tai ole helposti löydettävissä genomisekvenssin tasolla. Funktionaaliset profiloinnit, jotka perustuvat proteiinimolekyylien välisiin vuorovaikutussuhteisiin tai geenien ilmenemisen hiljentämiseen joko RNA-interferenssi-tekniikalla tai lääkemolekyylien avulla mahdollistavat vaihtoehtoisen tavan etsiä uusia kohdemolekyylejä sairauksien kohdennettuun hoitoon. Tämän väitöskirjatyön tavoitteena oli kehittää laskennallisia, systeemibiologian menetelmiä, joiden avulla funktionaalisten profilointien tuloksia voidaan paremmin käyttää etsittäessä eri syöpämuotojen sekä Alzheimer-taudin mekanismeja sekä niihin kohdennettuja hoitokeinoja. fi
dc.format.mimetype application/pdf fi
dc.language.iso en fi
dc.publisher Helsingin yliopisto fi
dc.publisher Helsingfors universitet sv
dc.publisher University of Helsinki en
dc.relation.isformatof URN:ISBN:978-951-51-3959-7 fi
dc.relation.isformatof Unigrafia Oy, 2018 fi
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. sv
dc.subject fi
dc.title Bioinformatic identification of disease driver networks using functional profiling data en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Doktorsavhandling (sammanläggning) sv
dc.ths Aittokallio, Tero fi
dc.ths Wennerberg, Krister fi
dc.opn Nykter, Matti fi
dc.type.dcmitype Text fi

Files in this item

Total number of downloads: Loading...

Files Size Format View
BIOINFOR.pdf 1.208Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record