title: Novel computational identification of highly selective biomarkers of pollutant exposure.
abstract: The use of in vivo biosensors to acquire environmental pollution data is an emerging and promising paradigm. One major challenge is the identification of highly specific biomarkers that selectively report exposure to a target pollutant, while remaining quiescent under a diverse set of other, often unknown, environmental conditions. This study hypothesized that a microarray data mining approach can identify highly specific biomarkers, and, that the robustness property can generalize to unforeseen environmental conditions. Starting with Arabidopsis thaliana microarray data measuring responses to a variety of treatments, the study used the top scoring pair (TSP) algorithm to identify mRNA transcripts that respond uniquely to phenanthrene, a model polycyclic aromatic hydrocarbon. Subsequent in silico analysis with a larger set of microarray data indicated that the biomarkers remained robust under new conditions. Finally, in vivo experiments were performed with unforeseen conditions that mimic phenanthrene stress, and the biomarkers were assayed using qRT-PCR. In these experiments, the biomarkers always responded positively to phenanthrene, and never responded to the unforeseen conditions, thereby supporting the hypotheses. This data mining approach requires only microarray or next-generation RNA-seq data, and, in principle, can be applied to arbitrary biomonitoring organisms and chemical exposures.
authors.affiliation: Department of Biology, University of Massachusetts Boston, Boston, Massachusetts 02125, USA.
authors.forename: David
authors.initials: D
authors.lastname: Weisman
authors.forename: Hong
authors.initials: H
authors.lastname: Liu
authors.forename: Jessica
authors.initials: J
authors.lastname: Redfern
authors.forename: Liya
authors.initials: L
authors.lastname: Zhu
authors.forename: Adán
authors.initials: A
authors.lastname: Colón-Carmona
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D015415:Biomarkers / Q000032:analysis*; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000032:analysis*; D018506:Gene Expression Regulation, Plant; D017343:Genes, Plant / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D012015:Reference Standards; D020133:Reverse Transcriptase Polymerase Chain Reaction
chemical_list: D015415:Biomarkers; D004785:Environmental Pollutants