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Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

ABSTRACT: Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states.We present a systematic analysis of phenotypic information of 5047 perturbations of single genes in mice, 4766 human diseases and 1666 drugs that examines the relationships between different gene properties and the phenotypic impact at the organ system level in mammalian organisms. We observe that while single gene perturbations and alterations of nonessential, tissue-specific genes or those with low betweenness centrality in protein-protein interaction networks often show organ-specific effects, multiple gene alterations resulting e.g. from complex disorders and drug treatments have a more widespread impact. Interestingly, certain cellular localizations are distinctly associated to systemic effects in monogenic disease genes and mouse gene perturbations, such as the lumen of intracellular organelles and transcription factor complexes, respectively. In summary, we show that the broadness of the phenotypic effect is clearly related to certain gene properties and is an indicator of the severity of perturbations. This work contributes to the understanding of gene properties influencing the systemic effects of diseases and drugs.

SUBMITTER: Vogt I

PROVIDER: S-EPMC4609011 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

Vogt Ingo I Prinz Jeanette J Worf Karolina K Campillos Monica M

Bioinformatics (Oxford, England) 20140723 21

<h4>Motivation</h4>Diseases and adverse drug reactions are frequently caused by disruptions in gene functionality. Gaining insight into the global system properties governing the relationships between genotype and phenotype is thus crucial to understand and interfere with perturbations in complex organisms such as diseases states.<h4>Results</h4>We present a systematic analysis of phenotypic information of 5047 perturbations of single genes in mice, 4766 human diseases and 1666 drugs that examin ...[more]

PMID: 25061072

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Project description:Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the 'search space' for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html.

Dataset Information

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

Publications

Systematic analysis of gene properties influencing organ system phenotypes in mammalian perturbations.

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