Dataset Information

Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network.

ABSTRACT:

Background

Most cases of idiopathic autism spectrum disorder (ASD) likely result from unknown environmental triggers in genetically susceptible individuals. These triggers may include maternal exposure of a fetus to minute concentrations of pharmaceuticals, such as carbamazepine (CBZ), venlafaxine (VNX) and fluoxetine (FLX). Unmetabolized pharmaceuticals reach drinking water through a variety of routes, including ineffectively treated sewage. Previous studies in our laboratory examined the extent to which gene sets were enriched in minnow brains treated with pharmaceuticals. Here, we tested the hypothesis that genes in fish brains and human cell cultures, significantly enriched by pharmaceuticals, would have distinct characteristics in an ASD-associated protein interaction network. We accomplished this by comparing these groups using 10 network indices.

Results

A network of 7212 proteins and 33,461 interactions was generated. We found that network characteristics for enriched gene sets for particular pharmaceuticals were distinct from each other, and were different from non-enriched ASD gene sets. In particular, genes in fish brains, enriched by CBZ and VNX 1) had higher network importance than that in the overall network, and those enriched by FLX, and 2) were distinct from FLX and non-enriched ASD genes in multivariate network space. Similarly, genes in human cell cultures enriched by pharmaceutical mixtures (at environmental concentrations) and valproate (at clinical dosages) had similar network signatures, and had greater network importance than genes in the overall ASD network.

Conclusions

The results indicate that important gene sets in the ASD network are particularly susceptible to perturbation by pharmaceuticals at environmental concentrations.

SUBMITTER: Kaushik G

PROVIDER: S-EPMC4423768 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network.

Kaushik Gaurav G Thomas Michael A MA Aho Ken A KA

BMC bioinformatics 20150423

<h4>Background</h4>Most cases of idiopathic autism spectrum disorder (ASD) likely result from unknown environmental triggers in genetically susceptible individuals. These triggers may include maternal exposure of a fetus to minute concentrations of pharmaceuticals, such as carbamazepine (CBZ), venlafaxine (VNX) and fluoxetine (FLX). Unmetabolized pharmaceuticals reach drinking water through a variety of routes, including ineffectively treated sewage. Previous studies in our laboratory examined t ...[more]

PMID: 25952302

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Project description:BackgroundProtein-protein interactions mediate a wide range of cellular functions and responses and have been studied rigorously through recent large-scale proteomics experiments and bioinformatics analyses. One of the most important findings of those endeavours was the observation that 'hub' proteins participate in significant numbers of protein interactions and play critical roles in the organization and function of cellular protein interaction networks (PINs) 12. It has also been demonstrated that such hub proteins may constitute an important pool of attractive drug targets.Thus, it is crucial to be able to identify hub proteins based not only on experimental data but also by means of bioinformatics predictions.ResultsA hub protein classifier has been developed based on the available interaction data and Gene Ontology (GO) annotations for proteins in the Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens genomes. In particular, by utilizing the machine learning method of boosting trees we were able to create a predictive bioinformatics tool for the identification of proteins that are likely to play the role of a hub in protein interaction networks. Testing the developed hub classifier on external sets of experimental protein interaction data in Methicillin-resistant Staphylococcus aureus (MRSA) 252 and Caenorhabditis elegans demonstrated that our approach can predict hub proteins with a high degree of accuracy.A practical application of the developed bioinformatics method has been illustrated by the effective protein bait selection for large-scale pull-down experiments that aim to map complete protein-protein interaction networks for several species.ConclusionThe successful development of an accurate hub classifier demonstrated that highly-connected proteins tend to share certain relevant functional properties reflected in their Gene Ontology annotations. It is anticipated that the developed bioinformatics hub classifier will represent a useful tool for the theoretical prediction of highly-interacting proteins, the study of cellular network organizations, and the identification of prospective drug targets - even in those organisms that currently lack large-scale protein interaction data.

Dataset Information

Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network.

Background

Results

Conclusions

Publications

Psychoactive pharmaceuticals as environmental contaminants may disrupt highly inter-connected nodes in an Autism-associated protein-protein interaction network.

Similar Datasets

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