Reference : Systems Biology Approaches for Identification of Molecular Mechanisms in Brain Disorders
Dissertations and theses : Doctoral thesis
Life sciences : Biochemistry, biophysics & molecular biology
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/36435
Systems Biology Approaches for Identification of Molecular Mechanisms in Brain Disorders
English
Androsova, Ganna mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
22-Aug-2018
University of Luxembourg, ​Belval, ​​Luxembourg
Docteur en Biologie
276
Schneider, Reinhard mailto
Glaab, Enrico mailto
Schneider, Jochen mailto
Andrade, Miguel mailto
von Mering, Christian mailto
[en] systems biology ; brain disorder ; molecular mechanism ; network analysis ; postoperative delirium ; epileptogenesis ; machine learning ; antiepileptic drug
[en] One out of four people are affected by a brain disorder at some stage in their life. Depending on the symptoms and the underlying molecular mechanisms, brain disorders can be classified into neurological and cognitive disorders. Complex disorders typically have a multifactorial pathogenesis. Epilepsy and postoperative delirium (POD) exemplifying neurological and cognitive disorders are no exception. Research efforts contributed to the understanding of molecular mechanisms of these diseases by discovering associations between clinical and genomic information and disease phenotypes. These findings, although necessary, are not sufficient to reconstruct the complete map of system-level interactions. To achieve a system-level understanding of a biological system, one can integrate diverse data sources by a network-based approach. Network analysis methods characterise interactions within and between molecular systems and can identify candidate biomarkers in various biological contexts. Specifically, correlation networks can reveal condition-dependent molecular patterns whose functional enrichment points to the altered molecular mechanisms of the phenotype. A molecular signature of a phenotype can be determined by machine learning algorithms for supervised classification as a set of molecules accurately discriminating between disease and healthy state. The primary aim of this dissertation is to identify altered biological pathways and functionally relevant molecules of epileptogenesis and postoperative delirium.

This cumulative dissertation is composed of six chapters. Chapter 1provides the background information on brain disorders and the systems biology methods to study their molecular mechanisms. Chapter 2 was motivated by the fact that current anti-epilepsy treatments focus on minimisation of the symptoms and epileptic seizures, while no definitive cure exists. The understanding of molecular events triggering the development of epilepsy (also called epileptogenesis) can yield therapies halting the onset of epilepsy. We identified proteomic alterations in the animal model of epileptogenesis by a network-based method and validated our results by external data set and immunohistochemical staining. The functional annotation of molecular expression patterns revealed biological pathways not yet described in the context of epileptogenesis. Next, we identified the gap in a comparative analysis of available antiepileptic drugs for mesial temporal lobe epilepsy due to hippocampal sclerosis. Chapter 3 retrospectively compares retention, efficacy and tolerability of antiepileptic drugs in the large epilepsy pharmacogenomics database. Chapter 4 is focused on the identification of molecular alterations in postoperative delirium. Overlaying postmortem brain expression data with locations of functional networks disturbed in POD, we identified several gene expression patterns with relevant biological enrichment. Moreover, same biological functions were altered in the blood of POD patients. Previously described POD markers such as acetylcholinesterase, alpha-synuclein and protein C appeared in the identified clusters. In Chapter 5, I focused on the identification of a molecular signature discriminating POD patients before they undergo surgery. Having ranked preoperative expression levels of mRNAs and miRNAs by their ability to detect patients with POD, I identified a set of discriminatory features that achieved high accuracy, sensitivity and specificity in the training set. The trained model had a good generalisability on the unseen data set but its performance decreased on the test set not matched by age and gender. The final Chapter 6 summarises the main outcomes of the presented studies and concludes with an outlook.
Researchers ; Students ; General public
http://hdl.handle.net/10993/36435

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