A systems level analysis of epileptogenesis-associated proteome alterations.

Despite intense research efforts, the knowledge about the mechanisms of epileptogenesis and epilepsy is still considered incomplete and limited. However, an in-depth understanding of molecular pathophysiological processes is crucial for the rational selection of innovative biomarkers and target candidates. Here, we subjected proteomic data from different phases of a chronic rat epileptogenesis model to a comprehensive systems level analysis. Weighted Gene Co-expression Network analysis identified several modules of interconnected protein groups reflecting distinct molecular aspects of epileptogenesis in the hippocampus and the parahippocampal cortex. Characterization of these modules did not only further validate the data but also revealed regulation of molecular processes not described previously in the context of epilepsy development. The data sets also provide valuable information about temporal patterns, which should be taken into account for development of preventive strategies in particular when it comes to multi-targeting network pharmacology approaches. In addition, principal component analysis suggests candidate biomarkers, which might inform the design of novel molecular imaging approaches aiming to predict epileptogenesis during different phases or confirm epilepsy manifestation. Further studies are necessary to distinguish between molecular alterations, which correlate with epileptogenesis versus those reflecting a mere consequence of the status epilepticus

Comparative effectiveness of antiepileptic drugs in juvenile myoclonic epilepsy

Abstract Objective To study the effectiveness and tolerability of antiepileptic drugs (AEDs) commonly used in juvenile myoclonic epilepsy (JME). Methods People with JME were identified from a large database of individuals with epilepsy, which includes detailed retrospective information on AED use. We assessed secular changes in AED use and calculated rates of response (12-month seizure freedom) and adverse drug reactions (ADRs) for the five most common AEDs. Retention was modeled with a Cox proportional hazards model. We compared valproate use between males and females. Results We included 305 people with 688 AED trials of valproate, lamotrigine, levetiracetam, carbamazepine, and topiramate. Valproate and carbamazepine were most often prescribed as the first AED. The response rate to valproate was highest among the five AEDs (42.7\%), and significantly higher than response rates for lamotrigine, carbamazepine, and topiramate; the difference to the response rate to levetiracetam (37.1\%) was not significant. The rates of ADRs were highest for topiramate (45.5\%) and valproate (37.5\%). Commonest ADRs included weight change, lethargy, and tremor. In the Cox proportional hazards model, later start year (1.10 [1.08-1.13], P < 0.001) and female sex (1.41 [1.07-1.85], P = 0.02) were associated with shorter trial duration. Valproate was associated with the longest treatment duration; trials with carbamazepine and topiramate were significantly shorter (HR [CI]: 3.29 [2.15-5.02], P < 0.001 and 1.93 [1.31-2.86], P < 0.001). The relative frequency of valproate trials shows a decreasing trend since 2003 while there is an increasing trend for levetiracetam. Fewer females than males received valproate (76.2 vs 92.6\%, P = 0.001). Significance In people with JME, valproate is an effective AED; levetiracetam emerged as an alternative. Valproate is now contraindicated in women of childbearing potential without special precautions. With appropriate selection and safeguards in place, valproate should remain available as a therapy, including as an alternative for women of childbearing potential whose seizures are resistant to other treatments

Systems Biology Approaches for Identification of Molecular Mechanisms in Brain Disorders

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

Network-based Approach Enabling Drug Repositioning for the Treatment of Myocardial Infarction

Despite a notable reduction in incidence of acute myocardial infarction (MI), patients who experience it remain at risk for premature death and cardiac malfunction. The human cardiomyocytes are not able to achieve extensive regeneration upon MI. Remarkably, the adult zebrafish is able to achieve complete heart regeneration following amputation, cryoinjury or genetic ablation. This raises new potential opportunities on how to boost the heart healing capacity in humans. The objective of our research is to characterize the transcriptional network of the zebrafish heart regeneration, to describe underlying regulatory mechanisms, and to identify potential drugs capable to boost heart regeneration capacity. Having identified the gene co-expression patterns in the data from a zebrafish cryoinjury model, we constructed a weighted gene co-expression network. To detect candidate functional sub-networks (modules), we used two different network clustering approaches: a density-based (ClusterONE) and a topological overlap-based (Dynamic Hybrid) algorithms. We identified eighteen distinct modules associated with heart recovery upon cryoinjury. Functional enrichment analysis displayed that the modules are involved in different cellular processes crucial for heart regeneration, including: cell fate specification (p-value < 0.006) and migration (p-value < 0.047), cardiac cell differentiation (p-value < 3E-04), and various signaling events (p-value < 0.037). The visualization of the modules’ expression profiles confirmed the relevance of these functional enrichments. Among the candidate hub genes detected in the network, there are genes relevant to atherosclerosis treatment and inflammation during cardiac arrest. Among the top candidate drugs, there were drugs already reported to play therapeutic roles in heart disease, though the majority of the drugs have not been considered yet for myocardial infarction treatment. In conclusion, our findings provide insights into the complex regulatory mechanisms involved during heart regeneration in the zebrafish. These data will be useful for modeling specific network-based responses to heart injury, and for finding sensitive network points that may trigger or boost heart regeneration in the zebrafish, and possibly in mammals

Biomarkers of postoperative delirium and cognitive dysfunction

Elderly surgical patients frequently experience postoperative delirium (POD) and the subsequent development of postoperative cognitive dysfunction (POCD). Clinical features include deterioration in cognition, disturbance in attention and reduced awareness of the environment and result in higher morbidity, mortality and greater utilization of social financial assistance. The aging Western societies can expect an increase in the incidence of POD and POCD. The underlying pathophysiological mechanisms have been studied on the molecular level albeit with unsatisfying small research efforts given their societal burden. Here, we review the known physiological and immunological changes and genetic risk factors, identify candidates for further studies and integrate the information into a draft network for exploration on a systems level. The pathogenesis of these postoperative cognitive impairments is multifactorial; application of integrated systems biology has the potential to reconstruct the underlying network of molecular mechanisms and help in the identification of prognostic and diagnostic biomarkers

Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states

PRUNET interface.

<p>The graphical user interface is organized in tabs, namely a welcome tab (<b>A</b>), input tab (<b>B</b>), options tab (<b>C</b>) and results tab (D). Both the input and results tab include a network visualizer to facilitate the interaction of the user with the program.</p

Comparison of PRUNET with similar available software.

<p>Scores represent the match between predicted and real phenotypes; a score of 1 represents a full agreement between training set and model predictions, while a score of 0.5 reflects a correct description of 50% of the training set.</p><p>Comparison of PRUNET with similar available software.</p

Validation of PRUNET using four biological examples.

<p>The scores obtained after the application of PRUNET to four biological examples, namely epithelial to mesenchymal transition (EMT), Th1-Th2 transdifferentiation (Th1-Th2), induced pluripotent stem cells (iPSC) and cardiomyocyte differention of human embryonic stem cells (hESC-cardiomyocyte), were compared with the scores obtained by a population of randomly generated subnetworks from the prior knowledge network. Green, orange and red lines represent the cumulative frequencies of scores obtained with training sets of a half, an intermediate number (different for each case) and all of the genes respectively, whereas the blue line represents the scores obtained from the population of subnetworks randomly generated from the prior knowledge network. The separation between blue and the other lines represents the contribution of PRUNET to the prior knowledge network in terms of describing the known phenotypes of different training set sizes.</p

A comprehensive integrative analysis of the transcriptional network underlying the zebrafish heart regeneration

Despite a notable reduction in incidence of acute myocardial infarction (MI), patients who experienced it remain at risk for premature death and cardiac malfunction. The human cardiomyocytes are not able to achieve extensive regeneration upon MI. Remarkably, the adult zebrafish is able to achieve complete heart regeneration following amputation, cryoinjury or genetic ablation. This raises new potential opportunities on how to boost heart healing capacity in humans. The objective of our research is to characterize the transcriptional network of the zebrafish heart regeneration and underlying regulatory mechanisms. To conduct our investigation, we used microarray data from zebrafish at 6 post-cryoinjury time points (4 hours, and 1, 3, 7, 14 and 90 days) and control samples. We thereon looked for the gene co-expression patterns in the data and, based on that, constructed a weighted gene co-expression network. To detect candidate functional sub-networks (modules), we used two different network clustering approaches: a density-based (ClusterONE) and a topological overlap-based (Hybrid Dynamic Branch Cut) algorithms. The visualization of the expression changes of the candidate modules reflected the dynamics of the recovery process. Also we aimed to identify candidate “hub” genes that might regulate the behavior of the biological modules and drive the regeneration process. We identified eighteen distinct modules associated with heart recovery upon cryoinjury. Functional enrichment analysis displayed that the modules are involved in different cellular processes crucial for heart regeneration, including: cell fate specification (p-value < 0.006) and migration (p-value < 0.047), ribosome biogenesis (p-value < 0.004), cardiac cell differentiation (p-value < 3E-04), and various signaling events (p-value < 0.037). The visualization of the modules’ expression profiles confirmed the relevance of these functional enrichments. For instance, the genes of the module involved in regulation of endodermal cell fate specification were up-regulated upon injury until 3 days. Among the candidate hub genes detected in the network, there are genes relevant to atherosclerosis treatment and inflammation during cardiac arrest. These and other findings are currently undergoing deeper computational analyses. The top promising targets will be independently validated using our zebrafish (in vivo) model. In conclusion, our findings provide insights into the complex regulatory mechanisms involved during heart regeneration in the zebrafish. These data will be useful for modelling specific network-based responses to heart injury, and for finding sensitive network points that may trigger or boost heart regeneration