NExT: Integration of Thermodynamic Constraints and Metabolomics Data into a Metabolic Network

ABSTRACTNew technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.In conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.AUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

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Metabolic network changes that are strongly associated with Dementia with Lewy Bodies as determined through signed distance and partial correlation analysis

10.1101/2021.10.16.21265003 ◽

2021 ◽

Author(s):

Miroslava Cuperlovic-Culf ◽

Ali Yilmaz ◽

Sumeyya Akyol ◽

Sangeetha Vishweswaraiah ◽

David Stewart ◽

...

Keyword(s):

Correlation Analysis ◽

Metabolic Network ◽

Dementia With Lewy Bodies ◽

Metabolic Networks ◽

Lewy Bodies ◽

Deep Understanding ◽

Interaction Network ◽

Major Change ◽

Metabolomics Data ◽

Concentration Changes

INTRODUCTION Determining significant metabolic changes in Dementia with Lewy Bodies (DLB), a complex and multifactorial neurodegenerative disease, requires, in addition to the analysis of concentration changes, a deep understanding of functional modifications in the context of metabolic networks. METHODS Brain metabolomics data from DLB patients and controls was explored using novel correlation analysis approaches to identify metabolites with the largest changes in their network in the disease state. RESULTS Novel clustering and correlation network analysis shows major change in the metabolic network in DLB brain relative to matching controls with the largest interaction network alterations for fructose, propylene-glycol, pantothenate and O-acetylcarnitine in spite of no statistically significant changes in their concentrations. DISCUSSION Network and correlation analyses indicate major changes in the purine degradation pathway, propanoate and b-alanine metabolism as well as an increased role of fructose and reduced significance of glucose in DLB affected brain.

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Network-based metabolite ratios for an improved functional characterization of genome-wide association study results

10.1101/048512 ◽

2016 ◽

Cited By ~ 2

Author(s):

Jan Krumsiek ◽

Ferdinand Stückler ◽

Karsten Suhre ◽

Christian Gieger ◽

Tim D. Spector ◽

...

Keyword(s):

Metabolic Network ◽

Quantitative Traits ◽

Association Studies ◽

Small Sample ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Metabolomics Data ◽

Association Tests ◽

Genome Wide ◽

Study Results

AbstractGenome-wide association studies (GWAS) with metabolite ratios as quantitative traits have successfully deepened our understanding of the complex relationship between genetic variants and metabolic phenotypes. Usually all ratio combinations are selected for association tests. However, with more metabolites being detectable, the quadratic increase of the ratio number becomes challenging from a statistical, computational and interpretational point-of-view. Therefore methods which select biologically meaningful ratios are required.We here present a network-based approach by selecting only closely connected metabolites in a given metabolic network. The feasibility of this approach was tested on in silico data derived from simulated reaction networks. Especially for small effect sizes, network-based metabolite ratios (NBRs) improved the metabolite-based prediction accuracy of genetically-influenced reactions compared to the ‘all ratios’ approach. Evaluating the NBR approach on published GWAS association results, we compared reported ‘all ratio’-SNP hits with results obtained by selecting only NBRs as candidates for association tests. Input networks for NBR selection were derived from public pathway databases or reconstructed from metabolomics data. NBR-candidates covered more than 80% of all significant ratio-SNP associations and we could replicate 7 out of 10 new associations predicted by the NBR approach.In this study we evaluated a network-based approach to select biologically meaningful metabolite ratios as quantitative traits in GWAS. Taking metabolic network information into account facilitated the analysis and the biochemical interpretation of metabolite-gene association results. For upcoming studies, for instance with case-control design, large-scale metabolomics data and small sample numbers, the analysis of all possible metabolite ratios is not feasible due to the correction for multiple testing. Here our NBR approach increases the statistical power and lowers computational demands, allowing for a better understanding of the complex interplay between individual phenotypes, genetics and metabolic profiles.

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