We propose to carry out a large scale meta-analysis of global transcriptional profiles from multiple species exposed to oil. The goal of this project is to leverage the considerable resources expended by GoMRI and the NRDA process to identify 1) conserved patterns of response across multiple species that will allow for generalizable hypotheses to be drawn about the effects of oil to GoM fish species, 2) identification of subtle effects resulting from sublethal exposure to oil that may have implications for future fisheries management practices, and 3) identification of novel hypotheses regarding the effects of oil on ecologically important fish species in the Gulf of Mexico.
|Series||RNAseq samples||Microbiome samples|
We propose three Aims that will guide us in our investigations. Aim 1. Identification of conserved, ecologically meaningful, transcriptional responses in teleosts following oil exposure. Aim 2. Interrogating fish transcriptomes to identify synergistic effects of environmental stressors. Aim 3. Evaluating multiple teleost transcriptomes for evidence of impairment of basic molecular functions resulting from oil exposure.
The resources available for this project are substantial. As part of their GoMRI-II funded project Griffitt and Sepulveda produced 78 RNAseq libraries (36 from Cyprinodon variegatus, 42 from Fundulus grandis) that cover multiple exposure conditions and age classes in fully balanced designs. Further, PI Griffitt has 52 southern flounder RNAseq libraries from NRDA funded work that have not been fully analyzed. PI Griffitt is also in possession of tissue samples taken from three ecologically important pelagic species (Red snapper, Atlantic croaker, and Red drum) that we propose to sequence as well (24 from each, 72 additional RNAseq libraries). These contain both lab-exposed and field collected samples, and so represent, in combination with the GoMRI funded datasets, a very fertile area of investigation. This is obviously a substantial dataset (130 complete RNAseq libraries), and an in-depth analysis of all possible comparisons was not possible in the duration of the previous projects. We propose to have a significant and in-depth bioinformatics analysis of the collected samples that will allow us to maximize the data yield and knowledge gained from the resources already invested in this research.