Date of Award


Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)


Biology/Molecular, Cellular, and Organismal Biology

First Advisor

Adán Colón-Carmona

Second Advisor

Kenneth C. Kleene

Third Advisor

Helen C. Poynton


Polycyclic aromatic hydrocarbons (PAH) are a family of carcinogenic and toxic byproducts of carbon-based fuel combustion. As these pollutants are widely distributed in the environment, it is essential to monitor and remediate their accumulation. Plants take up PAHs and respond with molecular, biochemical, as well as physiological changes, and these processes are valuable in the detection and remediation of PAH environmental pollution.

To better understand the molecular physiology involved in the bioremediation of PAHs, we performed microarray experiments on Arabidopsis thaliana treated continuously for 21 days with 0.25 mM phenanthrene, a low molecular weight PAH. The microarray data identified 1031 altered transcripts, indicating large perturbations in the oxidative stress, abiotic stress, and photosynthetic networks. Comparative analysis with published Arabidopsis microarray datasets revealed surprisingly strong similarities to the biotic stress responses from microbial pathogens. Collectively, these stresses may be limiting the ability to take up, metabolize, and detoxify the xenobiotic, and the microarray data suggests a number of research directions towards the improved bioremediation of environmental PAHs.

Towards the biomonitoring of PAHs, we hypothesized that highly-selective transcriptional biomarkers of PAH exposure could be computationally identified through the data mining of microarray datasets representing PAH and non-PAH stress responses. To test that hypothesis, we utilized the k-top scoring pair algorithm, which identified three transcript pairs that accurately discriminate between phenanthrene and non-phenanthrene classes. External validation with a large, disjoint set of microarray data indicated that the classifier performed accurately with a low rate of false positive responses to non-phenanthrene conditions. The classifier was further validated in vivo through experiments under treatment conditions chosen to mimic the physiology of PAH stress, and the responses were measured by quantitative reverse transcription PCR (qRT-PCR). These results support the hypothesis that phenanthrene exposure can be recognized from a small number of transcriptional biomarkers and measured with qRT-PCR. Moreover, the computational approach to finding these biomarkers is general and may be applied in other contexts, for arbitrary pollutants in arbitrary organisms.


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