Limits...
Metabolic phenotyping and systems biology approaches to understanding neurological disorders.

Dumas ME, Davidovic L - F1000Prime Rep (2013)

Bottom Line: The development of high-throughput metabolic profiling and the study of the metabolome are particularly important in brain research where small molecules or metabolites play fundamental signalling roles: neurotransmitters, signalling lipids, osmolytes and even ions.Metabolic profiling has shown that metabolic perturbations in the brain go beyond alterations of neurotransmission and that variations in brain metabolic homeostasis are associated with neurological disorders.Also, we will highlight the necessity of implementing systems biology approaches to integrate metabolic data and tackle the structural and functional complexity of the brain in normal and pathological conditions.

View Article: PubMed Central - PubMed

Affiliation: Imperial College London, Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ UK.

ABSTRACT
The development of high-throughput metabolic profiling and the study of the metabolome are particularly important in brain research where small molecules or metabolites play fundamental signalling roles: neurotransmitters, signalling lipids, osmolytes and even ions. Metabolic profiling has shown that metabolic perturbations in the brain go beyond alterations of neurotransmission and that variations in brain metabolic homeostasis are associated with neurological disorders. In this report, we will focus on recent developments in the field of metabolic phenotyping that have contributed to unravelling the pathophysiology of neurological diseases. Also, we will highlight the necessity of implementing systems biology approaches to integrate metabolic data and tackle the structural and functional complexity of the brain in normal and pathological conditions.

No MeSH data available.


Related in: MedlinePlus

Principles of integrated metabolome and interactome mapping (iMIM): a biomolecular GPS to navigate the underlying signalling networks and understand metabolic signaturesDisease-causing candidate genes (A) are analysed in relation to patterns of significant metabolites (B) via protein-protein and metabolite-protein interactions (C). In iMIM, network metrics are used to define shortest paths and pivotal proteins between metabolites and disease-causing genes (D).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3672944&req=5

fig-002: Principles of integrated metabolome and interactome mapping (iMIM): a biomolecular GPS to navigate the underlying signalling networks and understand metabolic signaturesDisease-causing candidate genes (A) are analysed in relation to patterns of significant metabolites (B) via protein-protein and metabolite-protein interactions (C). In iMIM, network metrics are used to define shortest paths and pivotal proteins between metabolites and disease-causing genes (D).

Mentions: Once metabolic biomarkers of a disease of interest have been assigned and adequately quantified, systems biology can then be applied to enhance the biological interpretation of metabolic signatures. For example, mapping metabolic biomarkers onto biological networks enhances the understanding of complex metabolic signatures at the pathway level. The combination of metabonomics with systems biology in the field of neurological diseases is only beginning but has a very promising future. First, metabolite-set enrichment analysis (MSEA, [32]), an extension of the gene-set enrichment analysis (GSEA) [33], can be used to identify which metabolic pathways are enriched in the metabolic signature and highlight biologically relevant pathways. We used MSEA to highlight alterations of glutamate metabolism and oxidative stress response in the cortex of a fragile X syndrome mouse model [3]. However, to enhance our understanding of the mechanisms linking the disease-causing gene (Figure 2A) to the metabolic signature of fragile X syndrome, we developed a novel alternative strategy, named “integrated metabolome and interactome mapping” (iMIM). iMIM is based on mapping metabolic phenotypes (Figure 2B) directly onto protein-protein interactions, metabolite-enzyme interactions and ligand-receptor interactions networks extracted from literature and databases (Figure 2C) [3]. As the estimated number of protein-protein interactions approaches 650,000 [34], it has become necessary to analyse the topology of the resulting network to identify key proteins regulating the metabolic phenotype associated with the disease-causing genes (Figure 2D). In this type of network, the shortest paths between causal genes and the downstream metabolic consequences are computed and compiled to identify the central proteins (i.e. the busiest proteins) in the interaction network [3]. This is achieved using network statistics such as the “betweenness” [35], which can be used to highlight key proteins that relay the signal between the causal genes and the metabolites. Further developments of the iMIM methodology could introduce weighting factors integrating other “omics” data, for example, taking into account variations in transcriptomic or proteomic profiles could help in refining the bioinformatic predictions of key proteins and key pathways and increase biological relevancy towards the studied disease.


Metabolic phenotyping and systems biology approaches to understanding neurological disorders.

Dumas ME, Davidovic L - F1000Prime Rep (2013)

Principles of integrated metabolome and interactome mapping (iMIM): a biomolecular GPS to navigate the underlying signalling networks and understand metabolic signaturesDisease-causing candidate genes (A) are analysed in relation to patterns of significant metabolites (B) via protein-protein and metabolite-protein interactions (C). In iMIM, network metrics are used to define shortest paths and pivotal proteins between metabolites and disease-causing genes (D).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3672944&req=5

fig-002: Principles of integrated metabolome and interactome mapping (iMIM): a biomolecular GPS to navigate the underlying signalling networks and understand metabolic signaturesDisease-causing candidate genes (A) are analysed in relation to patterns of significant metabolites (B) via protein-protein and metabolite-protein interactions (C). In iMIM, network metrics are used to define shortest paths and pivotal proteins between metabolites and disease-causing genes (D).
Mentions: Once metabolic biomarkers of a disease of interest have been assigned and adequately quantified, systems biology can then be applied to enhance the biological interpretation of metabolic signatures. For example, mapping metabolic biomarkers onto biological networks enhances the understanding of complex metabolic signatures at the pathway level. The combination of metabonomics with systems biology in the field of neurological diseases is only beginning but has a very promising future. First, metabolite-set enrichment analysis (MSEA, [32]), an extension of the gene-set enrichment analysis (GSEA) [33], can be used to identify which metabolic pathways are enriched in the metabolic signature and highlight biologically relevant pathways. We used MSEA to highlight alterations of glutamate metabolism and oxidative stress response in the cortex of a fragile X syndrome mouse model [3]. However, to enhance our understanding of the mechanisms linking the disease-causing gene (Figure 2A) to the metabolic signature of fragile X syndrome, we developed a novel alternative strategy, named “integrated metabolome and interactome mapping” (iMIM). iMIM is based on mapping metabolic phenotypes (Figure 2B) directly onto protein-protein interactions, metabolite-enzyme interactions and ligand-receptor interactions networks extracted from literature and databases (Figure 2C) [3]. As the estimated number of protein-protein interactions approaches 650,000 [34], it has become necessary to analyse the topology of the resulting network to identify key proteins regulating the metabolic phenotype associated with the disease-causing genes (Figure 2D). In this type of network, the shortest paths between causal genes and the downstream metabolic consequences are computed and compiled to identify the central proteins (i.e. the busiest proteins) in the interaction network [3]. This is achieved using network statistics such as the “betweenness” [35], which can be used to highlight key proteins that relay the signal between the causal genes and the metabolites. Further developments of the iMIM methodology could introduce weighting factors integrating other “omics” data, for example, taking into account variations in transcriptomic or proteomic profiles could help in refining the bioinformatic predictions of key proteins and key pathways and increase biological relevancy towards the studied disease.

Bottom Line: The development of high-throughput metabolic profiling and the study of the metabolome are particularly important in brain research where small molecules or metabolites play fundamental signalling roles: neurotransmitters, signalling lipids, osmolytes and even ions.Metabolic profiling has shown that metabolic perturbations in the brain go beyond alterations of neurotransmission and that variations in brain metabolic homeostasis are associated with neurological disorders.Also, we will highlight the necessity of implementing systems biology approaches to integrate metabolic data and tackle the structural and functional complexity of the brain in normal and pathological conditions.

View Article: PubMed Central - PubMed

Affiliation: Imperial College London, Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ UK.

ABSTRACT
The development of high-throughput metabolic profiling and the study of the metabolome are particularly important in brain research where small molecules or metabolites play fundamental signalling roles: neurotransmitters, signalling lipids, osmolytes and even ions. Metabolic profiling has shown that metabolic perturbations in the brain go beyond alterations of neurotransmission and that variations in brain metabolic homeostasis are associated with neurological disorders. In this report, we will focus on recent developments in the field of metabolic phenotyping that have contributed to unravelling the pathophysiology of neurological diseases. Also, we will highlight the necessity of implementing systems biology approaches to integrate metabolic data and tackle the structural and functional complexity of the brain in normal and pathological conditions.

No MeSH data available.


Related in: MedlinePlus