2017 WONOEP appraisal: Studying epilepsy as a network disease using systems biology approaches

Integrated Proteomics and Protein Co-expression Network Analysis Identifies Novel Epileptogenic Mechanism in Mesial Temporal Lobe Epilepsy

Over 50 million people worldwide are affected by epilepsy, a common neurological disorder that has a high rate of drug resistance and diverse comorbidities such as progressive cognitive and behavioural disorders, and increased mortality from direct or indirect effects of seizures and therapies. Despite extensive research with animal models and human studies, limited insights have been gained into the mechanisms underlying seizures and epileptogenesis, which has not translated into significant reductions in drug resistance, morbidities, or mortality. To better understand the molecular signaling networks associated with seizures in MTLE patients, we analyzed the proteome of brain samples from MTLE and control cases using an integrated approach that combines mass spectrometry-based quantitative proteomics, differential expression analysis, and co-expression network analysis. Our analyses of 20 human brain tissues from MTLE patients and 20 controls showed the organization of the brain proteome into a network of 9 biologically meaningful modules of co-expressed proteins. Of these, 6 modules are positively or negatively correlated to MTLE phenotypes with hub proteins that are altered in MTLE patients. Our study is the first to employ an integrated approach of proteomics and protein co-expression network analysis to study patients with MTLE. Our findings reveal a molecular blueprint of altered protein networks in MTLE brain and highlight dysregulated pathways and processes including altered cargo transport, neurotransmitter release from synaptic vesicles, synaptic plasticity, proteostasis, RNA homeostasis, ion transport and transmembrane transport, cytoskeleton disorganization, metabolic and mitochondrial dysfunction, blood micro-particle function, extracellular matrix organization, immune response, neuroinflammation, and cell signaling. These insights into MTLE pathogenesis suggest potential new candidates for future diagnostic and therapeutic development.

A complex systems view on the current hypotheses of epilepsy pharmacoresistance

Drug-resistant epilepsy remains to this day as a highly prevalent condition affecting around one-third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities-related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.

Proteomic differences in the hippocampus and cortex of epilepsy brain tissue

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioural disorders, and increased mortality from direct (e.g. sudden unexpected death in epilepsy, accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signalling networks associated with seizures in epilepsy with a broad range of aetiologies, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry was performed on the hippocampal cornu ammonis 1-3 region (CA1-3), frontal cortex and dentate gyrus microdissected from epilepsy and control cases (n = 14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1 - 3 region, 296 proteins in the frontal cortex and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signalling and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein subunit beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signalling and G-protein-coupled receptors in epilepsy. Our results provide insights into common molecular mechanisms underlying epilepsy across various aetiologies, which may allow for novel targeted therapeutic strategies.

Precision Medicine: Academic dreaming or clinical reality?

Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.