Comparative pathway and network analysis of brain transcriptome changes during adult aging and in Parkinson's disease

in Neurobiology of Disease (2015), 74

Aging is considered as one of the main factors promoting the risk for Parkinson's disease (PD) and common mechanisms of dopamine neuron degeneration in aging and PD have been proposed in recent years ... [more ▼]

Aging is considered as one of the main factors promoting the risk for Parkinson's disease (PD) and common mechanisms of dopamine neuron degeneration in aging and PD have been proposed in recent years. Here, we use a statistical meta-analysis of human brain transcriptomics data to investigate potential mechanistic relationships between adult brain aging and PD pathogenesis at the pathway and network level. The analyses identify statistically significant shared pathway and network alterations in aging and PD and an enrichment in PD-associated sequence variants from genome-wide association studies among the jointly deregulated genes. We find robust discriminative patterns for groups of functionally related genes with potential applications as combined risk biomarkers to detect aging- and PD-linked oxidative stress, e.g. a consistent over-expression of metallothioneins matching with findings in previous independent studies. Interestingly, analyzing the regulatory network and mouse knockout expression data for the transcription factor NR4A2, previously associated with rare mutations in PD and here found as the most significantly under-expressed gene in PD among the jointly altered genes, suggests that aging-related NR4A2 expression changes may increase PD risk by producing downstream effects similar to disease-linked mutations and to expression changes observed in sporadic PD. Overall, the analyses suggest mechanistic explanations for the age-dependence of PD risk, reveal significant and robust shared process alterations in aging and PD, and examine their potential for biomarker applications in pre-symptomatic risk assessment or early-stage diagnosis. [less ▲]

Inference of longevity-related genes from a robust co-expression network of seed maturation identifies new regulators linking seed storability to biotic defense-related pathways

in Plant Cell (2015), 27(10), 2692-2708

Seed longevity, the maintenance of viability during storage is a crucial factor to preserve genetic resources and to ensure proper seedling establishment and high crop yield. A systems biology approach ... [more ▼]

Seed longevity, the maintenance of viability during storage is a crucial factor to preserve genetic resources and to ensure proper seedling establishment and high crop yield. A systems biology approach was used to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes of seed developmental time courses obtained under five growth environments, a robust, stable co-expression network (MatNet) was generated, thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a co-expression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis between maturation processes in Medicago and A. thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light and auxin. Characterization of defense-related wrky3 and nfxl1 mutants demonstrated that these genes regulate part of the network nodes and exhibit impaired longevity acquisition during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating activation of defense against pathogens. [less ▲]

The Mouse Brain Metabolome: Region-Specific Signatures and Response to Excitotoxic Neuronal Injury

in American Journal of Pathology (2015), 185(6), 1699-1712

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems' analyses, or omics approaches, have become an important tool in characterizing ... [more ▼]

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems' analyses, or omics approaches, have become an important tool in characterizing this process. Although RNA and protein profiling made their entry into this field a couple of decades ago, metabolite profiling is a more recent addition. The metabolome represents a large part or all metabolites in a tissue, and gives a snapshot of its physiology. By using gas chromatography coupled to mass spectrometry, we analyzed the metabolic profile of brain regions of the mouse, and found that each region is characterized by its own metabolic signature. We then analyzed the metabolic profile of the mouse brain after excitotoxic injury, a mechanism of neurodegeneration implicated in numerous neurological diseases. More important, we validated our findings by measuring, histologically and molecularly, actual neurodegeneration and glial response. We found that a specific global metabolic signature, best revealed by machine learning algorithms, rather than individual metabolites, was the most robust correlate of neuronal injury and the accompanying gliosis, and this signature could serve as a global biomarker for neurodegeneration. We also observed that brain lesioning induced several metabolites with neuroprotective properties. Our results deepen the understanding of metabolic changes accompanying neurodegeneration in disease models, and could help rapidly evaluate these changes in preclinical drug studies. [less ▲]

ROS-management in Parkinson’s disease: Dynamic blue-print domino-based model and design principles study. Dynamic modelling of ROS management and ROS-induced mitophagy

Poster (2014, October)

Integrating Pathways of Parkinson's Disease in a Molecular Interaction Map

in Molecular Neurobiology (2014)

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is ... [more ▼]

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map . [less ▲]

Meta-omic Analyses of the Microbiome in a Family Study of Diabetes Mellitus

Poster (2013, October)

Network deregulation analysis in complex diseases via the pairwise elastic net

in Proc 8th BeNeLux Bioinformatics Conference (2013)

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network ... [more ▼]

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel Pairwise Elastic Net penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on microarray gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods. [less ▲]

Network analysis for systems biology

in Prokop, Aleš; Csukás (Eds.) Springer book in Systems Biology, Vol.1: Systems Biology:, Integrative Biology and Simulation Tools (2013)

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways, which involve DNA, RNA proteins and metabolites as key elements, coordinate most aspects of cellular functioning. Cellular processes, which are dependent on the structure and dynamics of gene regulatory networks, can be studied by employing a network representation of molecular interactions. In this chapter we describe several types of networks and how combination of different analytic approaches can be used to study diseases. We provide a list of selected tools for visualization and network analysis. We introduce protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks. We then define concepts underlying network representation of cellular processes and molecular interactions. We finally discuss how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular network type. [less ▲]

On different aspects of network analysis in systems biology

in Systems Biology (2013), 1

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type. © Springer Science+Business Media Dordrecht 2013. All rights are reserved. [less ▲]

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data

in PLoS ONE (2012), 7(7), 39932-39932

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find ... [more ▼]

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL’s classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes. [less ▲]