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A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

ABSTRACT: In this article we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as "ponds," of progressively more contaminated water. We model the expected expression of a gene in a pond as the sum of the mean of the same gene in the previous pond plus a gene-pond specific difference. We incorporate a variable selection mechanism for the identification of the differential expressions, with a prior distribution on the probability of a change that accounts for the available information on the concentration of chemical compounds present in the water. We carry out posterior inference via MCMC stochastic search techniques. In the application, we reduce the complexity of the data by grouping genes according to their functional characteristics, based on the KEGG pathway database. This also increases the biological interpretability of the results. Our model successfully identifies a number of pathways that show differential expression between consecutive purification stages. We also find that changes in the transcriptional response are more strongly associated to the presence of certain compounds, with the remaining contributing to a lesser extent. We discuss the sensitivity of these results to the model parameters that measure the influence of the prior information on the posterior inference.

SUBMITTER: Cassese A

PROVIDER: S-EPMC4880373 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

Cassese Alberto A Guindani Michele M Antczak Philipp P Falciani Francesco F Vannucci Marina M

Biometrics 20150313 3

In this article we propose a Bayesian hierarchical model for the identification of differentially expressed genes in Daphnia magna organisms exposed to chemical compounds, specifically munition pollutants in water. The model we propose constitutes one of the very first attempts at a rigorous modeling of the biological effects of water purification. We have data acquired from a purification system that comprises four consecutive purification stages, which we refer to as "ponds," of progressively ...[more]

PMID: 25771699

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Project description:Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

Project description:BACKGROUND:Radiation exposure due to the detonation of an improvised nuclear device remains a major security concern. Radiation from such a device involves a combination of photons and neutrons. Although photons will make the greater contribution to the total dose, neutrons will certainly have an impact on the severity of the exposure as they have high relative biological effectiveness. RESULTS:We investigated the gene expression signatures in the blood of mice exposed to 3 Gy x-rays, 0.75 Gy of neutrons, or to mixed field photon/neutron with the neutron fraction contributing 5, 15%, or 25% of a total 3 Gy radiation dose. Gene ontology and pathway analysis revealed that genes involved in protein ubiquitination pathways were significantly overrepresented in all radiation doses and qualities. On the other hand, eukaryotic initiation factor 2 (EIF2) signaling pathway was identified as one of the top 10 ranked canonical pathways in neutron, but not pure x-ray, exposures. In addition, the related mTOR and regulation of EIF4/p70S6K pathways were also significantly underrepresented in the exposures with a neutron component, but not in x-ray radiation. The majority of the changed genes in these pathways belonged to the ribosome biogenesis and translation machinery and included several translation initiation factors (e.g. Eif2ak4, Eif3f), as well as 40S and 60S ribosomal subunits (e.g. Rsp19, Rpl19, Rpl27). Many of the differentially downregulated ribosomal genes (e.g. RPS19, RPS28) have been causally associated with human bone marrow failure syndromes and hematologic malignancies. We also observed downregulation of transfer RNA processes, in the neutron-only exposure (p?<?0.005). Ingenuity Pathway Analysis (p?<?0.05) of differentially expressed genes predicted significantly suppressed activity of the upstream regulators c-Myc and Mycn, transcription factors known to control ribosome biogenesis. CONCLUSIONS:We describe the gene expression profile of mouse blood following exposure to mixed field neutron/photon irradiation. We have discovered that pathways related to protein translation are significantly underrepresented in the exposures containing a neutron component. Our results highlight the significance of neutron exposures that even the smallest percentage can have profound biological effects that will affect medical management and treatment decisions in case of a radiological emergency.

Dataset Information

A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

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A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.

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