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Interpreting metabolomic profiles using unbiased pathway models.

ABSTRACT: Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.

SUBMITTER: Deo RC

PROVIDER: S-EPMC2829050 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Interpreting metabolomic profiles using unbiased pathway models.

Deo Rahul C RC Hunter Luke L Lewis Gregory D GD Pare Guillaume G Vasan Ramachandran S RS Chasman Daniel D Wang Thomas J TJ Gerszten Robert E RE Roth Frederick P FP

PLoS computational biology 20100226 2

Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to e ...[more]

PMID: 20195502

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Project description:BackgroundBreast cancer is the most common malignancy among women worldwide. Despite advances in treating breast cancer over the past decades, drug resistance and adverse effects remain challenging. Recent therapeutic progress has shifted toward using drug combinations for better treatment efficiency. However, with a growing number of potential small-molecule cancer inhibitors, in silico strategies to predict pharmacological synergy before experimental trials are required to compensate for time and cost restrictions. Many deep learning models have been previously proposed to predict the synergistic effects of drug combinations with high performance. However, these models heavily relied on a large number of drug chemical structural fingerprints as their main features, which made model interpretation a challenge.ResultsThis study developed a deep neural network model that predicts synergy between small-molecule pairs based on their inhibitory activities against 13 selected key proteins. The synergy prediction model achieved a Pearson correlation coefficient between model predictions and experimental data of 0.63 across five breast cancer cell lines. BT-549 and MCF-7 achieved the highest correlation of 0.67 when considering individual cell lines. Despite achieving a moderate correlation compared to previous deep learning models, our model offers a distinctive advantage in terms of interpretability. Using the inhibitory activities against key protein targets as the main features allowed a straightforward interpretation of the model since the individual features had direct biological meaning. By tracing the synergistic interactions of compounds through their target proteins, we gained insights into the patterns our model recognized as indicative of synergistic effects.ConclusionsThe framework employed in the present study lays the groundwork for future advancements, especially in model interpretation. By combining deep learning techniques and target-specific models, this study shed light on potential patterns of target-protein inhibition profiles that could be exploited in breast cancer treatment.

Project description:Bovine Respiratory Disease (BRD) is a major source of economic loss within the agricultural industry. Vaccination against BRD-associated viruses does not offer complete immune protection and vaccine failure animals present potential routes for disease spread. Serological differentiation of infected from vaccinated animals (DIVA) is possible using antigen-deleted vaccines, but during virus outbreaks DIVA responses are masked by wild-type virus preventing accurate serodiagnosis. Previous work by the authors has established the potential for metabolomic profiling to reveal metabolites associated with systemic immune responses to vaccination. The current study builds on this work by demonstrating for the first time the potential to use plasma metabolite profiling to differentiate between vaccinated and non-vaccinated animals following infection-challenge. Male Holstein Friesian calves were intranasally vaccinated (Pfizer RISPOVAL®PI3+RSV) and subsequently challenged with Bovine Parainfluenza Virus type-3 (BPI3V) via nasal inoculation. Metabolomic plasma profiling revealed that viral challenge led to a shift in acquired plasma metabolite profiles from day 2 to 20 p.i., with 26 metabolites identified whose peak intensities were significantly different following viral challenge depending on vaccination status. Elevated levels of biliverdin and bilirubin and decreased 3-indolepropionic acid in non-vaccinated animals at day 6 p.i. may be associated with increased oxidative stress and reactive oxygen scavenging at periods of peak virus titre. During latter stages of infection, increased levels of N-[(3α,5β,12α)-3,12-dihydroxy-7,24-dioxocholan-24-yl]glycine and lysophosphatidycholine and decreased enterolactone in non-vaccinated animals may reflect suppression of innate immune response mechanisms and progression to adaptive immune responses. Levels of hexahydrohippurate were also shown to be significantly elevated in non-vaccinated animals from days 6 to 20 p.i. These findings demonstrate the potential of metabolomic profiling to identify plasma markers that can be employed in disease diagnostic applications to both differentially identify infected non-vaccinated animals during disease outbreaks and provide greater information on the health status of infected animals.

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Interpreting metabolomic profiles using unbiased pathway models.

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Interpreting metabolomic profiles using unbiased pathway models.

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