Peripheral blood leukocyte (PBL) samples were collected from 151 bottlenose dolphins (Tursiops truncatus) in the course of capture/ release health evaluation studies at 4 different sites: Charleston Harbor SC, Indian River Lagoon FL, Sarasota Bay, FL and St Joseph Bay FL, USA. RNA was extracted and hybridized to a first-generation dolphin microarray. We tested the hypothesis that individual dolphins could be assigned (by using artificial neural networks, ANNs, a machine-learning approach) to their home regions using only their transcriptomic signatures as classifiers. ANNs could correctly classify  dolphins according to sex, with an accuracy depending on the number of genes used and the geographical location. Thus, male and female populations at each location were considered separately. ANNs was able to correctly classify dolphins according to their site of sampling with a high degree of confidence. The basis for this result may lie in genetic differences between populations, or in environmental factors (such as diet, infection, exposure to toxins or contaminants etc) or in combinations of these factors. These results suggest that a combina- tion of microarrays and machine-learning analytical approaches will be a powerful approach to understanding the interaction of marine environment/organism interactions.

The Power of the transcriptome: Prognostic tool for populations of free-ranging bottlenose dolphins, Tursiops truncatus

MANCIA, Annalaura;
2008

Abstract

Peripheral blood leukocyte (PBL) samples were collected from 151 bottlenose dolphins (Tursiops truncatus) in the course of capture/ release health evaluation studies at 4 different sites: Charleston Harbor SC, Indian River Lagoon FL, Sarasota Bay, FL and St Joseph Bay FL, USA. RNA was extracted and hybridized to a first-generation dolphin microarray. We tested the hypothesis that individual dolphins could be assigned (by using artificial neural networks, ANNs, a machine-learning approach) to their home regions using only their transcriptomic signatures as classifiers. ANNs could correctly classify  dolphins according to sex, with an accuracy depending on the number of genes used and the geographical location. Thus, male and female populations at each location were considered separately. ANNs was able to correctly classify dolphins according to their site of sampling with a high degree of confidence. The basis for this result may lie in genetic differences between populations, or in environmental factors (such as diet, infection, exposure to toxins or contaminants etc) or in combinations of these factors. These results suggest that a combina- tion of microarrays and machine-learning analytical approaches will be a powerful approach to understanding the interaction of marine environment/organism interactions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2053812
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