Functional genomics analyses of an organism’s transcriptome can be informative of the interaction of genetic, disease and environmental factors. Here we used a combination of microarrays and machine- learning analytical approaches to understand the impact of environmental infection and stress on marine organisms. In particular, we studied marine mammals because they are considered an ideal model for the assessment of immunological responses to pathogens and contaminants. In fact, as mammals that live their entire life (or most of it) in the sea, they act as integrators of the stressors present in the marine environment. Marine mammals may have the potential to predict contaminant effects on health, and to be an indicator of infectious disease that may impact humans who have contact with the marine ecosystem through residence, work, or recreation near the coast.
We tested the hypotheses that 1) individual wild dolphins could be assigned to their home regions and 2) individual sea lions could be assigned to a specific disease status category, using only their blood transcriptomic signatures as classifiers. The tools used were a dolphin peripheral blood leukocyte (PBL) cDNA microarray specifically designed for studies of immune function and stress reactions and a custom oligonucleotide microarray generated from cross-hybridization probing of a canine microarray. Microarray data of 151 wild dolphin PBL samples and 73 sea lion blood samples were analyzed using a machine-learning approach. Artificial neural networks (ANNs) were able to correctly classify dolphins according to their site of sampling and sea lions according to their diseased status. These results suggest that a combination of microarrays and machine-learning analytical approaches would significantly improve the knowledge about the marine environment/organism interactions
