MARGEN-00264; No of Pages 11 Marine Genomics xxx (2014) xxx–xxx

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Marine Genomics journal homepage: www.elsevier.com/locate/margen

Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus) Annalaura Mancia a,b,⁎, Luigi Abelli a, John R. Kucklick c, Teresa K. Rowles d, Randall S. Wells e, Brian C. Balmer f, Aleta A. Hohn g, John E. Baatz b, James C. Ryan f a

Department of Life Sciences and Biotechnology, University of Ferrara, 44121 Ferrara, Italy Marine Biomedicine and Environmental Science Center, Medical University of South Carolina, Hollings Marine Laboratory, Charleston, SC 29412, USA National Institute of Standards and Technology, Hollings Marine Laboratory, Charleston, SC 29412, USA d NOAA, National Marine Fisheries Service, Office of Protected Species, Silver Spring, MD 20910, USA e Chicago Zoological Society, c/o Mote Marine Laboratory, Sarasota, FL 34236, USA f NOAA, National Ocean Service, Hollings Marine Laboratory, Charleston, SC 29412, USA g NOAA, National Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort, NC 28516, USA b c

a r t i c l e

i n f o

Article history: Received 15 September 2014 Received in revised form 7 November 2014 Accepted 7 November 2014 Available online xxxx Keywords: Transcriptome Common bottlenose dolphin Polychlorinated biphenyls Ocean health

a b s t r a c t It is increasingly common to monitor the marine environment and establish geographic trends of environmental contamination by measuring contaminant levels in animals from higher trophic levels. The health of an ecosystem is largely reflected in the health of its inhabitants. As an apex predator, the common bottlenose dolphin (Tursiops truncatus) can reflect the health of near shore marine ecosystems, and reflect coastal threats that pose risk to human health, such as legacy contaminants or marine toxins, e.g. polychlorinated biphenyls (PCBs) and brevetoxins. Major advances in the understanding of dolphin biology and the unique adaptations of these animals in response to the marine environment are being made as a result of the development of celllines for use in in vitro experiments, the production of monoclonal antibodies to recognize dolphin proteins, the development of dolphin DNA microarrays to measure global gene expression and the sequencing of the dolphin genome. These advances may play a central role in understanding the complex and specialized biology of the dolphin with regard to how this species responds to an array of environmental insults. This work presents the creation, characterization and application of a new molecular tool to better understand the complex and unique biology of the common bottlenose dolphin and its response to environmental stress and infection. A dolphin oligo microarray representing 24,418 unigene sequences was developed and used to analyze blood samples collected from 69 dolphins during capture-release health assessments at five geographic locations (Beaufort, NC, Sarasota Bay, FL, Saint Joseph Bay, FL, Sapelo Island, GA and Brunswick, GA). The microarray was validated and tested for its ability to: 1) distinguish male from female dolphins; 2) differentiate dolphins inhabiting different geographic locations (Atlantic coasts vs the Gulf of Mexico); and 3) study in detail dolphins resident in one site, the Georgia coast, known to be heavily contaminated by Aroclor 1268, an uncommon polychlorinated (PCB) mixture. The microarray was able to distinguish dolphins by sex, geographic location, and corroborate previously published health irregularities for the Georgia dolphins. Genes involved in xenobiotic metabolism, development/differentiation and oncogenic pathways were found to be differentially expressed in GA dolphins. The report bridges the advancements in dolphin genome sequencing to the first step towards providing a cost-effective means to screen for indicators of chemical toxin exposure as well as disease status in top level predators. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The response of the dolphin to environmental stressors is neither well characterized nor understood, especially when compared to what is known in species such as human or mouse. Genomic technologies ⁎ Corresponding author at: Annalaura Mancia, University of Ferrara, via L. Borsari 46, Ferrara, Italy. Tel.: +39 0532455704. E-mail address: [email protected] (A. Mancia).

and functional genomics offer the opportunity to study molecular responses on a broad ecological scale through the deployment of gene microarrays as transcriptomic biosensors (Almeida et al., 2005; Gracey and Cossins, 2003; Sherf et al., 2000). The underlying paradigm of the transcript profiling approach is that all stimuli impinging on a cell will affect both gene and protein expression in that cell. Transcript profiling yields a snapshot of an entire expressed genome and is now an established technique in the biomedical models of human physiology and disease (Stauton et al., 2001; Weinstein, 2000). Thousands of

http://dx.doi.org/10.1016/j.margen.2014.11.002 1874-7787/© 2014 Elsevier B.V. All rights reserved.

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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A. Mancia et al. / Marine Genomics xxx (2014) xxx–xxx

genes can be examined simultaneously resulting in large amounts of information that are characteristic of the sum total of the environmental impacts an organism is experiencing (Chapman et al., 2010), thus making it not only informative of basic mechanisms, but also a valuable diagnostic tool for health and disease. The effects of exposure to persistent pollutants in humans and in marine organisms are typically chronic rather than acute. In sessile aquatic organisms, such as bivalves, which have been monitored routinely for decades in the US, the highest POPs concentrations are associated with industrial areas or urbanization (O'Connor, 2002). It is also widely known that POPs bioaccumulate in fish, marine mammals and seabirds, which are often at the top of marine food webs (Borga et al., 2012; Letcher et al., 2009; Ueno et al., 2004; Gauthier et al., 2009; Pulster et al., 2005). As long-lived, top-level predators, the bottlenose dolphin (Tursiops truncatus) can be subject to bioaccumulation and biomagnification of chemicals present in the marine ecosystem. Because of their high trophic status, dolphins can accumulate very high levels of contaminants from prey items, hence are subject to substantial toxicological stress (Wells et al., 2005; Wilson et al., 2012; Kucklick et al., 2011). The presence of environmental contaminants in dolphin blubber and the effects of the exposure and accumulation have been reported previously (Kucklick et al., 2011; Houde et al., 2006; Hansen et al., 2004). Accumulation of POPs in dolphins is influenced by age, sex, and reproductive status (Tanabe et al., 1998; Law et al., 1995). Generally, levels of POPs tend to increase with age; in females, however, this holds true until they reach sexual maturity. Females then transfer, and thus reduce, their POPs body burden during gestation and lactation, with the latter considered as the major route for lipophilic contaminant offloading (up to 80% of the POPs from the mother to the calf) (Cockcroft et al., 1989). Polychlorinated biphenyls (PCBs) are POPs used since 1930 for a variety of industrial purposes (electrical capacitors and transformers, flame retardants, ink solvents, plasticizers, etc.) because of their chemical stability, and have been banned in most parts of the world for decades. Nevertheless, PCBs are still found in high concentrations in marine organisms, particularly those that occupy upper trophic positions, such as marine mammals (Kucklick et al., 2011; Houde et al., 2006; Hansen et al., 2004; Hickie et al., 2007; Letcher et al., 2009). While PCB-related health effects have been well documented in some mammals, studies among cetaceans are limited. Prior studies have suggested an association between high PCB concentrations and increased risk of infectious disease suggesting a causal link between POPs exposure, immune function and susceptibility to disease (Aguilar and Borrel, 1994; Lahvis et al., 1995; Geraci, 1989). Although direct cause– effect relationships are difficult to demonstrate in free-ranging animals, Sormo et al. (2009) used T-/B-cell mitogen assays to show that PCBs, and dioxin-like PCBs in particular, modulate lymphocyte function in free-ranging gray seals. Similarly, Schwacke et al. (2011) showed T-lymphocyte proliferation and indices of innate immunity decreased with blubber PCB concentration, suggesting an increased susceptibility to infectious disease in a population of common bottlenose dolphins near Brunswick, Georgia. In addition to the hematological effects, they found severely decreased serum thyroid hormones (total triiodothyronine, TT3 and free thyroxine, FT4) with a highly significant negative correlation with measured blubber PCB concentration, consistent with the strong evidence of PCB-related thyroid hormone disruption confirmed by a transcriptomic analysis of the same dolphins in our previous published study (Mancia et al., 2014) and also in studies of other marine mammals (Debier et al., 2005; Tabuchi et al., 2006) as well as laboratory studies of rats dosed with a mixture of PCB congeners (Byrne et al., 1987). Extreme concentrations of PCBs (up to 2900 mg kg−1 lipid, Kucklick et al., 2011) were documented in common bottlenose dolphins along the southern coast of Georgia (GA), United States of America (USA) (Pulster and Maruya, 2008; Balmer et al., 2011). The PCBs were represented by an uncommon technical PCB mixture (highly

chlorinated octa- through deca-chlorobiphenyl congeners), characteristic of Aroclor 1268. Aroclor 1268 was used at a former chlor-alkali facility, Linden Chemicals and Plastics (LCP) located on the banks of the Turtle/Brunswick River Estuary, near Brunswick, GA, USA. High concentrations of PCBs characteristic of Aroclor 1268 were also reported in soil, marsh sediments and marine biota sampled in close proximity to the LCP site (Kannan et al., 1997, 1998). The PCB concentrations reported in common bottlenose dolphins are among the highest ever reported in wildlife (Balmer et al., 2011; Kucklick et al., 2011). Moreover, dolphins sampled approximately 40 km northeast near the Sapelo Island National Estuarine Research Reserve were also found to have PCB concentrations at or above the highest levels reported for dolphins near urban centers such as Biscayne Bay, Florida, and Charleston, South Carolina (Hansen et al., 2004; Litz et al., 2007). In this study we show how informative the transcriptome can be using a newly developed dolphin oligo microarray representing 24,418 unigene sequences when compared to traditional measurement of environmental quality (e.g. relative abundance of species, the pollutants load in the environment, the frequency and severity of harmful algal blooms etc.…) which do not provide enough sensitive information on the effects/interactions of multiple stressors on marine life in the coastal ecosystems. Here we show the utility of transcriptome analysis and its applicability; not only can we distinguish animals from different geographic locations (which is the result of a group of parameters such as genetics — e.g. microsatellite marker analysis, sex, and contaminants) but we can report how one of these parameters (contaminant, PCBs) can impact global gene expression, comparing wild dolphin blood transcriptomic profiles of animals from different geographic locations of the southeast US to the transcriptomic profiles of dolphins inhabiting the most polluted areas of all, Sapelo and Brunswick, Georgia. 2. Methods 2.1. The dolphin microarray The microarray is a species-specific, custom 4X44K Agilent oligo array. The 60-mer oligos representing 24,418 unigene sequences were selected after assembling/cleaning/filtering the dolphin expressed sequence tags (ESTs) collection. The dataset used to generate the unigene selection is publicly available at the National Center for Biotechnology Information (NCBI). We used 1) 726 nucleotide sequences; 2) five datasets from the short read archive (SRA; 230,909 from SRR027945.fastq; 205,652 from SRR027946.fastq; 189,094 from SRR027947.fastq; 195,357 from SRR027948.fastq; 266,416 from SRR027949.fastq), 3) 75 mRNA sequences and 4) 2971 Sanger-style ESTs from NCBI. The 2971 Sanger-style ESTs used were (for the most part) from cDNA libraries originated from dolphin peripheral blood lymphocytes (PBL), liver, kidney, spleen, muscle and skin. The dolphin libraries originated at the Hollings Marine Laboratories (Charleston, SC, USA) and were sequenced at Baylor College of Medicine (Baylor, Texas, USA). All the ESTs used are now publicly available at NCBI. The fastq files, e.g. text based format for storing a biological sequence and its quality scores, were converted to fasta and quality files. For the ‘cleaning’ step, the program ‘seqclean’ (http://compbio. dfci.harvard.edu/tgi/software/) was used to remove sequences that had significant homology (96% identity) to any of the vector sequences from NCBI's UniVec.nt vector database. Additionally, sequences that were less than 30 base pairs (bp) after trimming of vector, low quality regions, low complexity regions and polyA/T tails were also removed). After cleaning we had a total of 672,044 sequences with an average length of 138 bp (minimum length 30 bp to maximum length 3312 bp) and total of 92,591,866 bases, represented by 28.57% A, 25.38% T, 23.8% G, 22.2% C and a 0.05% of N. Average size of contig length (N50) was 194 bases, where a contig is a set of contiguous, overlapping DNA fragments that together represent a consensus region of DNA. Assembled sequences are then grouped

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

A. Mancia et al. / Marine Genomics xxx (2014) xxx–xxx

into clusters: we used default parameters with the program ‘wcd’ (http://bioinformatics.oxfordjournals.org/cgi/content/full/24/13/ 1542) for pre-clustering of cleaned reads. ‘Cap3’ (http://seq.cs. iastate.edu/cap3.html, http://pbil.univ-lyon1.fr/cap3.php) was then used to assemble each cluster individually. Default parameters were used. This was run on Clemson University's high-performance computing cluster Palmetto http://citi.clemson.edu/palmetto). The results were 28,564 contigs with 6408 singlets, which were combined into a single unigene collection. The unigene collection resulted in 28,564 sequences, with an average length of 261 bp (minimum length 86 bp to max length 4817 bp) and a total of 7,455,680 bases, represented by 28.57% A, 26.43% T, 23.61% G, 21.36% C and 0.03% of N. Average size of contig length (N50) was 257 bases. The unigene was then blasted against ExPASy Swissprot and NCBI EST databases with e-value of 1e−6, also using the Palmetto cluster. Blast hits were combined from both databases and the best hit was provided for each contig/singlet. To reduce the amount of cross-hybridization that might occur under microarray hybridization conditions, only one contig from each of the clusters was kept. Further sequences with the potential for crosshybridizing probes were removed using the Agilent eArray software. This process left 24,418 unigenes to be used for the microarray and included 13 lung-specific genes individually cloned and added to the unigene list: AbcA3_ATP-binding cassette sub-family A member 3; AQP5_Aquaporin5; CC10 or SCGB1a1_Clara cell 10 or secretoglobin; CDH5_Cadherin5; FOXF1_Forkhead box protein F1; HIF-1a_Hypoxia-inducible factor 1-alpha; HIF 2-alpha_Hypoxia-inducible factor 2-alpha; MUC1_Mucin-1; NKX2 or TTF-1_NK-2 homeobox or Thyroid transcription factor; PDPN1_Podoplanin/T1alpha; SFTPA1_Surfactant protein A; SFTPB_Surfactant protein B; and SFTPC_Surfactant protein C. Thirty control genes for the array were selected from the unigene list based on previously published work (Zheng et al., 2006; Ortiz-Pineda et al., 2009). Each of the probes for these genes was printed five times in a random fashion to monitor hybridization consistency across the array (Table 1). Sixty-mer unigene probes and internal controls (custom and Agilent) were printed in a 4X44K format using the Agilent eArray interface. The total number of probes printed on the custom species-specific dolphin array were: 1) 24,418 unigene sequences (singlets, contigs, targeted sequences), 2) 150 controls (30 genes spotted 5 times each), and 3) 1417 Agilent internal controls. After all probes were assigned for printing, most unigene probes were printed in duplicate to fill the remaining spaces of the 44 K format. 2.2. Dolphin samples Common bottlenose dolphin sampling was conducted during summer months over a five year period (2005–2010) in Sapelo Island and Brunswick, GA (n = 26; 14 males and 12 females, 2009), Beaufort, North Carolina, NC (n = 8; 2 males and 6 females, 2006), Sarasota Bay, Florida, FL (n = 19; 7 males and 12 females, 2005 and 2010) and Saint Joseph Bay, Florida, FL (n = 16; 7 males and 9 females, 2005 and 2006) (Table 2). Tables 3 and 4 show the estimated age group of the male and female dolphins used in the study. Age was determined by reading dentinal and cemental growth layer groups. Age class (used to estimate if dolphins are sexually mature) was determined by both age and length, where possible (Schwacke et al., 2010). Eight subadults, twenty adults and 2 calf comprise the male group and the females are made up of 21 subadults, 14 adults and 3 calves, 1 not determined (ND). Adults are sexually mature individuals. Subadults are independent of their mothers, but they are not yet sexually mature. Calves are still closely associated with their mothers, N 50% of the time. Methods of dolphin capture-release for health assessment have been previously described (Schwacke et al., 2010; Wells et al., 2004). Blood samples were collected from 69 dolphins in PAXgene™ Blood tubes (Qiagen, Valencia, CA, USA), mixed immediately to stabilize the

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Table 1 Control genes selected and printed 5x/gene on the dolphin microarray.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Gene ID

Probe ID

Eukaryotic translation initiation factor 3 subunit C Eukaryotic translation initiation factor 3 subunit I Albumin ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit 1 Splicing factor, arginine-serine-rich 3 Mitochondrial ribosomal protein L19 Ubiquitin carboxyl-terminal hydrolase Mitochondrial ribosomal protein L13 Polypyrimidine tract binding protein 1 Ubiquitin associated protein Cytochrome c oxidase subunit VIa polypeptide 1 Thymosin beta 10 Actin gamma 1 Isoleucyl-tRNA synthetase Dynein, cytoplasmic 1, intermediate chain 2 Lysosomal-associated membrane protein 2 Mitochondrial ribosomal protein L11 DNA directed RNA polymerase II polypeptide A Prefoldin subunit 1 Mitochondrial ribosomal protein S14 Mitochondrial ribosomal protein S18 Aldolase A Beta-2-microglobulin H2A histone family, member Y Fibrillarin Enolase 1 NADH dehydrogenase-ubiquinone-1 alpha subcomplex 2 Tubulin beta Eukaryotic initiation factor 4A-II Glyceraldehyde-3-phosphate dehydrogenase

AM-3876 AM-11728 AM-32762 AM-13308 AM-13442 AM-13614 AM-15507 AM-1594 AM-16572 AM-1689 AM-2201 AM-2219 AM-233 AM-23700 AM-2383 AM-28208 AM-29138 AM-30030 AM-30095 AM-7888 AM-5095 AM-31743 AM-3513 AM-3706 AM-5742 AM-6228 AM-6336 AM-8988 AM-14526 AM-29126

Details on sequences used are available on the Tursiops truncatus platform at the GEO database (www.ncbi.nlm.nih.gov/geo/).

RNA, tubes were maintained in a field cooler with a cold pack to simulate room temperature incubation for a minimum of 2 h and maximum of 6 h, then slowly frozen at −20 °C for 24 h and transferred to −80 °C for long term storage. 2.3. Chemical analysis Fifty-five PCB congeners or congener groups were measured in blubber as detailed in Litz et al. (2007) and Kucklick et al. (2011). Briefly, PCBs in blubber were determined using pressurized fluid extraction (PFE), size exclusion, and alumina solid phase extraction cleanup followed by gas chromatography mass spectrometry (GC/MS) using two different capillary columns and two different ion sources. The measured values of the 55 congeners of PCBs were normalized to lipid present in the blubber for each animal as previously described in Mancia et al. (2014) and Kucklick et al. (2011). 2.4. Dolphin samples and the microarray: the questions The microarray was tested for its ability to 1) distinguish between male and female dolphins; 2) differentiate dolphins by geographic locations: dolphins from the Atlantic coasts and dolphins from the Gulf of Mexico; and 3) study in detail one population: the Georgia dolphin

Table 2 Dolphin samples and sites of collection along the coast of US Southeastern coast. Sampling site

Males

Females

All

1. 2. 3. 4.

2 14 7 7 30

6 12 12 9 39

8 26 19 16 69

Beaufort (BEA), NC Sapelo Island (SAP)–Brunswick (BRN), GA Sarasota Bay (SAR), FL Saint Joseph Bay (SJB), FL

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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A. Mancia et al. / Marine Genomics xxx (2014) xxx–xxx

Table 3 Male dolphins used in the microarray analysis.

Table 4 Female dolphins used in the microarray analysis.

Site

ID

Age

Length

Age class

Year of sampling

Site

ID

Age

Length

Age class

Year of sampling

BEA BEA BRN BRN BRN BRN BRN BRN BRN SAP SAP SAP SAP SAP SAP SAP SAR SAR SAR SAR SAR SAR SAR SJB SJB SJB SJB SJB SJB SJB

460 464 Z16 Z20 Z24 Z26 Z18 Z14 Z22 Z12 Z00 Z06 Z02 Z08 Z10 Z04 232 220 218 260O 20T 258V 118I X02 X08 X06 X00 X24 X20 X18

ND ND 15 ND 22 ND 22 ND 32 17 16 18 ND 27 ND 16 2.5 6 6 2 21 17 13 32 24 ND 2 8 20 4

236 258 238 250 248 243 224 254 251 251 242 239 231 257 257 241 195 211 216 202 273 254 249 274 247 178 192 209 238 210

Subadult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Adult Subadult Adult Adult Adult Subadult Subadult Subadult Calf Adult Adult Adult Adult Adult Calf Subadult Subadult Adult Subadult

2006 2006 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2005 2005 2005 2010 2010 2010 2005 2005 2005 2005 2005 2006 2006 2006

BEA BEA BEA BEA BEA BEA BRN BRN BRN BRN BRN GEO SAP SAP SAP SAP SAP SAP SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SAR SJB SJB SJB SJB SJB SJB SJB SJB SJB

461 467 465 457 449 443 Z25 Z19 Z23 Z29 Z21 Z17 Z03 Z13 Z01 Z07 Z11 Z09 75 187 135 54S 231P 229Q 227W 225X 223Y 211N 151U 113R X01 X11 X07 X13 X03 X27 X21 X15 X009

ND ND ND ND ND ND 29 ND 22 ND ND ND ND ND ND 36 10 ND 31 1.5 5 39 26 2 5 ND 8.5 5 10 14 18 24 ND 9 9 ND ND ND ND

244 265 186 237 217 210 226 221 251 238 226 218 239 217 233 245 221 220 253 191 214 245 249 180 225 249 230 231 231 230 243 235 202 218 229 194 228 198 ND

Adult Adult Calf Subadult Subadult Subadult Adult Subadult Adult Subadult Subadult Subadult Subadult Subadult Subadult Adult Adult Subadult Adult Calf Subadult Adult Adult Calf Subadult Adult Subadult Subadult Adult Adult Adult Adult Subadult Subadult Subadult Subadult Subadult Subadult ND

2006 2006 2006 2006 2006 2006 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2009 2005 2005 2005 2010 2010 2010 2010 2010 2010 2010 2010 2010 2005 2005 2005 2005 2005 2006 2006 2005 2005

SAR, Sarasota, FL; BEA, Beaufort, FL; SJB, Saint Joseph Bay, FL; SAP, Sapelo Isalnd, GA; Brunswick, GA. Lip ΣPCBs are measured in μg/g. Age was determined by reading dentinal and cemental growth layer groups (GLGs): Adult, A ≥ 10 GLS; 2 ≤ Subadult, SA b 10 GLGs; Calf, C: b2 GLGs (described in Schwacke et al., 2010). Age class was determined by both age and length, where possible. PCBs load and Lipid % for each animal used in the analysis are shown in Mancia et al. (2014), Table 2. Animals in Italic don't have contaminant information. ND, no data.

population. RNA from all 69 blood samples was interrogated to answer questions 1) and 2). To answer question 3) we used samples (n = 25) with contaminant associated data (Mancia et al., 2014). This included 21 animals from GA, 14 males (13 subadults and 1 adult) and 11 females (7 subadults). Due to the offloading of PCBs to offspring, the 4 adult females we had contaminant data (Z07, Z11, Z23 and Z25, Table 4) were excluded from all the PCB correlated gene expression analysis.

SAR, Sarasota, FL; BEA, Beaufort, FL; SJB, Saint Joseph Bay, FL; SAP, Sapelo Isalnd, GA; Brunswick, GA. L, Low; H, High; Lip ΣPCBs are measured in μg/g. Age was determined by reading dentinal and cemental growth layer groups (GLGs): Adult ≥10 GLS; 2 ≤ Subadult b 10 GLGs; C: b2 GLGs (described in Schwacke et al., 2010). Age class was determined by both age and length, where possible. PCBs load and Lipid % for each animal used in the analysis are shown in Mancia et al. (2014), Table 3. Animals in italic don't have contaminant. ND, no data.

2.5. Dolphin microarray hybridization

2.6. Dolphin microarray data analysis

RNA labeling and microarray hybridizations were previously described in Mancia et al. (2014). For gene expression profiling, total RNA from the 69 blood samples from wild dolphins was extracted using the RNAeasy Kit according to the manufacturer (Qiagen, Valencia, CA), quantified using a NanoDrop ND-1000 (Wilmington, DE) and qualified on an Agilent 2100 Bioanalyzer (Foster City, CA). Five hundred nanograms (ng) of RNA was amplified and fluorescently labeled with Cy3 dye with an Agilent Quick Amp labeling kit. This amplification product was measured for quantity and dye incorporation using the Nanodrop 1000. For each dolphin, 1600 ng of fluorescently labeled RNA was hybridized to the microarray at 65 °C in a rotating oven for 17 h then washed (Mancia et al., 2014). Microarrays were imaged on an Agilent microarray scanner, extracted with Agilent Feature Extraction software version A8.5.3, and the data was analyzed with Rosetta Resolver 7.0 gene expression analysis system (Rosetta Informatics, Seattle, WA). The microarray build and hybridization data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSM1226273–GSM1226329, GSM1226332– GSM1226335, GSM1226337–GSM1226345 (www.ncbi.nlm.nih.gov/ geo/).

One-color gene expression array analysis was performed as previously described in Mancia et al. (2014). Differences were in initial filter of the ratio data; here was used a p b 0.0001 and 1.5 fold expression cutoff for significant differential expression between and within groups.

2.7. Quantitative real time PCR (qPCR) Real time PCR (qPCR) of 13 selected genes for thirty different animals was used to validate the microarray results: eukaryotic translation initiation factor 4E family member 2 (EIF4E2) and GTP binding protein 5 (GTPBP5) which were both used as controls; X inactivespecific transcript (XIST), son of sevenless homolog 2 (SOS2), decorin precursor (DCN), interleukin 23 (IL23), thyroid hormone receptor interact 11 (TRIP11), DNA-damage binding protein 1 (DDB1); sialic acid binding Ig-like lectin (SIGLEC1), Jumonji AT Rich Interactive Domain 1A (JARID 1A), chromodomain helicase DNA binding protein (CHD4), ino80 complex (INO80), oxidation resistance 1 (OXR1), growth arrest and DNA damage protein (GADD) and the eukaryotic translation initiation factor 1A, Y-linked (EIF1AY).

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

A. Mancia et al. / Marine Genomics xxx (2014) xxx–xxx

Relative levels of mRNA were determined using qPCR on an ABI7500 (Applied Biosystems, Carlsbad, CA) with gene-specific primers deigned using T. truncatus sequences on the arrays and deposited in the NCBI database (Table S1). Each gene primer set was optimized for efficiency and specificity by running standard curves on pooled cDNA samples from four animals resulting in a correlation coefficient R2 N 96.5% and efficiency = 90%–110% (Amp = 1.91–2.04) (Table S1). Quantitative RT-PCR efficiencies were calculated using the equation: m = (1 / logE), where m is the slope of the line and E is the efficiency (Dhar et al., 2009). Five hundred ng of total RNA was reverse transcribed to cDNA for each of the 30 samples using an RNA-to-cDNA Kit (Applied Biosystems, Carlsbad, CA) according to the manufacturer's instructions. Optimized qPCR parameters for each gene were determined using diluted (1:5) cDNA reverse transcribed from 1 μg of total RNA from 4 animals (2 adult females [Sarasota Bay, 135 and Saint Joseph Bay, X11] and 2 adult males [Sapelo Island, Z04 and Brunswick, Z24]) and 12.5 μL SyBr GreenMaster Mix (Applied Biosystems, Carlsbad, CA) in a final volume of 25 μL and were run and analyzed as previously described in Mancia et al., 2014. 3. Results and discussion 3.1. Validation of the dolphin microarray 3.1.1. Sex and dolphin populations The microarray was tested for its capacity to differentiate between male and female dolphins using blood transcriptome information. Seven genes were identified for their ability to perfectly separate dolphins by sex (Fig. 1). Five genes were up-regulated in all females and down-regulated in all males. Of these five, all but one (the ESF1, nucleolar pre-rRNA processing protein, gene 5 in Fig. 1) are X-chromosome related genes (X inactive-specific transcript XIST, non-coding RNA; DNA sequence from clone CH242-76N16 on chromosome X; X inactive-specific transcript XIST; X-inactivation center region, Jpx and Xist genes; genes 1, 2, 3, 4 in Fig. 1, respectively). The two genes upregulated in males and down-regulated in females were eukaryotic translation initiation factor 1A Y-linked (E1F1AY) and an ‘unknown function’ gene (genes 6 and 7 in Fig. 1, respectively). The expression of XIST (gene 2) and of the E1F1AY (gene 6) was confirmed by qPCR, which was performed on 30 animals, 17 females and 13 males. Results are shown in the histogram of Fig. 2. The amplification curves revealed a striking visual difference in sex during the real time PCR runs (Fig. 2). 3.1.2. Geographic location and dolphin populations The microarray was also tested for its ability to differentiate between dolphin populations. This analysis resulted in the selection of 153 differentially expressed genes (Table S2, A) between two major groups: the Gulf of Mexico (Sarasota Bay and Saint Joseph Bay, FL) and the Atlantic coast (Beaufort, NC and Georgia). The genes selected were used to generate a principle components analysis (PCA) plot (Fig. 3) showing the separation between the 2 geographic locations. When we separated the animals by sex, this difference was able to show a finer scale geographical difference and there was a clear difference in the transcripts of the Georgia dolphins. Fig. 4A shows the PCA plot generated using only male dolphins with the correspondent 69 male-specific genes selected (Table S2, B). In Fig. 4B, the Georgia male dolphins are highlighted. We had previously shown that differences in gene expression between the sexes could bias discrimination of dolphins from Charleston Harbor, South Carolina, St. Joseph Bay, and Sarasota Bay, FL (Mancia et al., 2010). If sample size is fairly large (N30) and the sex ratios within contrasting groups are relatively stable it is unlikely that this complication will influence the results; however, if sample sizes are small, as in this study, unequal sampling of the sexes could bias the results (Mancia et al., 2010). Furthermore, contaminant concentrations have been shown to be significantly lower in reproductively

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mature female dolphins due to offloading through parturition and lactation (Wells et al., 2005). All of the variables such as genotype, health history, prior contaminant exposures as well as known variables such as age and sex can potentially confound the data. For this reason, the approach that follows was conducted for each sex independently, before being applied to both males and females together, to better understand if certain molecular pathways respond preferentially or similarly in terms of sex. 3.2. Application of the dolphin microarray: the case of Georgia dolphins After the validation which resulted in successful answers to our first two questions, e.g. the ability of the array to discriminate dolphin 1) sex and 2) geographic location, on a large scale, we used the microarray to 3) study, in detail, the Georgia dolphin population specifically to probe why these animals appear to have such different gene expression profiles relative to the other locations considered in this study. 3.2.1. Georgia and other geographic locations The clear separation of the dolphins from Georgia revealed in the PCA plot prompted us to separate the animals from Georgia (GA) from all other geographic locations (OGL) for further analysis between GA and OGL, and within the GA group itself. The comparison between GA and OGL dolphins for each sex resulted in sets of significantly (t-test, p ≤ 0.05) differentially expressed genes, (69 in males (M) and 46 in females (F) (Table S3), that were used to generate the Venn diagram shown in Fig. 5). Of the 69 differentially expressed genes in males, 36 have unknown function. The remaining genes fall into 4 different categories 1) Development & Differentiation: e.g., Son of Sevenless homolog 1 (SOS1) is a membrane-bound guanine nucleotide-binding protein (RAS genes) involved in the signaling pathway to maintain cell growth and survival in thyroid cells; 2) Wound healing & anti-tumorigenic: Decorin (DCN), is a small cellular or pericellular matrix proteoglycan, component of connective tissue, binds to type I collagen fibrils, and plays a role in matrix assembly, it has been shown to either enhance or inhibit the activity of TGF-beta and its function involves regulation during the cell cycle: its expression is altered in follicular thyroid carcinoma. 3) Inflammatory Response: interleukin-23 (IL23) is a protein produced by dendritic cells and macrophages that plays an important role in the inflammatory response against infection. It promotes up-regulation of the metalloproteases, increases angiogenesis and reduces T-cell infiltration. 4) Xenobiotic metabolism: Oxidation Resistance 1 (OXR1) is a protein involved in protection from oxidative damage. Of the 46 differentially expressed genes in GA females, 11 have unknown function and 23 fall into 4 functional categories: 1) Transcription/Translation: Maf1 homolog, responds to changes in the cellular environment and represses pol-III transcription; 2) Immune response: tyrosine–protein kinase JACK1, involved in the IFN-alpha/beta/gamma signaling pathway; 3) Development/cell growth: prostate androgenregulated protein (PAR), BB1, a protein involved in tumor development and metastatic potential; thyroid hormone receptor interactor 11 (TRIP11), interacting with thyroid hormone receptor beta (THRB) in the presence of triiodothyronine T3 enhancing transcription. TRIP11 also interacts with the aryl hydrocarbon receptor nuclear translocator (ARNT) for the transcriptional response to both dioxin and hypoxia; and 4) Xenobiotic metabolism: Oxidation Resistance 1 (OXR1), is a protein involved in protection from oxidative damage. The up-regulation of DCN in GA males and the up-regulation of TRIP11 in the GA females were confirmed with qPCR (Fig. 6A and B). The genes that were found to be representative of the Georgia location are genes involved in the activation of an immune response, which may be linked to the contaminated environment of the Georgia coast. Also, genes correlated to development, differentiation and cell growth were predominant, which is not surprising given prior work showing how changes in thyroid hormones and growth are significantly

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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1 2 3 4 5 6 7

Fig. 1. Hierarchical clustering showing the differential expression of 7 genes in dolphin blood. The 7 genes were the minimum number of genes that could allow a 100% stratification of female and male dolphins. Cluster on top, genes differentially expressed; cluster on the left, animals. Red, up-regulated genes; green, down-regulated genes. Pink box on top, female dolphins. Blue box on the bottom, male dolphins. From 1 to 7: 1) X inactive-specific transcript XIST; 2) DNA sequence from clone CH242-76N16 on chromosome X; 3) X inactive-specific transcript XIST; 4) X-inactivation center region, Jpx and Xist genes; 5; ESF1, nucleolar pre-rRNA processing protein; 6) eukaryotic translation initiation factor 1A Y-linked (E1F1AY) and 7) ‘unknown function’ gene. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

correlated to PCBs exposure in wild dolphins resident in the Georgia locations (Schwacke et al., 2012). This is noteworthy considering the balanced nature of the array, which is not biased towards toxicresponse genes. Thus, the next logical step was to correlate the contaminant load in the blubber with the transcriptomic profiles between dolphins within the GA population.

3.2.2. Georgia dolphins and PCBs Before examining the correlation of contaminant load in the blubber with transcriptomic changes in GA dolphins, we had to address the possibility that GA dolphins from Sapelo Island and Brunswick were one population, and not genetically different. The comparisons between the transcriptomes of the animals from Sapelo (13 samples, 7 males, 6

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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Fig. 2. Quantification of the expression of 2 genes in male and female dolphins. Left, EIF1AY gene; right, XIST1 gene. Striped bars, Males (13). Full bars, Females (17). Histogram on top, quantification of real time PCR data. Bottom, Ct curves during amplification. Black arrow indicates the expression in Males.

females, Tables 3 and 4) vs Brunswick (12 samples, 7 males, 5 females, Tables 3 and 4), showed only 6 genes as differentially expressed, 5 of which had a small fold difference (b1.5) suggesting that the two groups are genetically similar. Previously published data on dolphins' mitochondrial DNA and contaminant burdens are consistent with long term fidelity to the Brunswick/Turtle River estuary and the Southern Georgia Estuarine System Stock (Balmer et al., 2011). The boundaries are subject to change but it seems that dolphins inhabiting these two areas belong to 2 stocks. The lack of differentiation that we observe probably reflects the fact that we are examining genes under selection and the selective pressures between these two areas are similar enough that we see little to no differentiation between them. With so few differences, we considered the dolphins inhabiting the two areas as belonging to one location, the Georgia location. Of the six genes, four were down-regulated while two were up-regulated (Fig. S1). Two of the four down-regulated genes have unknown function. The two with a known function are 1) the cell division protein kinase 5 (Cdk5), an essential

kinase in sensory pathways (e.g., pain), involved in the processes of neuronal maturation and migration, and 2) THO complex subunit 2, a component of the THO subcomplex of the transcription export (TREX) complex, which is essential for the export of Kaposi's sarcomaassociated herpesvirus (KSHV), intronless mRNAs and infectious virus production. All the down-regulated genes showed a small fold change in expression with the highest being Cdk5 (1.38 fold, still below our cutoff). We used a fold change N1.5 as the cutoff for significant differential expression between samples in a comparison. For the up-regulated genes, eukaryotic translation initiation factor, whose function is to help regulate overall protein production (synthesis), had a very low fold change while the 2) adipose differentiation-related protein (ADIRF) is the only one with a fold difference of 1.5 between dolphins from the two sites. ADIRF plays a role in fat cell development and promotes adipogenic differentiation and stimulates transcription initiation of master adipogenesis factors like PPARG and CEBPA at early stages of preadipocyte differentiation.

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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Fig. 5. Venn diagram of genes in GA vs OGL comparisons in male and female dolphins. Each circle represents a GA vs OGL comparison with unique and shared differentially expressed genes. Blue, females; green, males; yellow, male and females. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. 2-D cluster (PCA) showing all animals clustered by similarity of blood transcriptomes. Dots represented animals, colors represent geographical locations; Beaufort, purple, n = 8; Georgia, blue, n = 26; Saint Joseph Bay, green, n = 16; Sarasota Bay, yellow, n = 19. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

We know that Georgia dolphins live in areas near the LCP site, so we measured the level of PCBs contaminants in dolphin blubber and correlated the measured values to blood transcriptomic profiles, as previously described (Mancia et al., 2014). Measured PCBs concentration in blubber were used to divide dolphins into H (high load of measured PCBs in blubber, H N50 mg/kg) and L (low load of measured PCBs in blubber, L b 50 mg/kg) contamination groups. We previously showed

it was possible to separate dolphins by PCB loads using a suite of genes as a classifier (Mancia et al., 2014): the genes were selected only using the most heavily (highest, Ht, 5 for the males and 4 for the females) and the least (lowest, Lt, 5 for the males and 4 for the females) contaminated dolphins of the 47 samples from all locations, which was meant to be representative of the South-East coast of the US. Those genes were then used as a gene set to classify all the remaining animals (29 total, 13 males and 16 females) as PCBs contaminated or not contaminated. Here, we carried out a deeper analysis on the PCB correlation looking only within the Georgia group (of course networks and pathways involved were similar to what was previously shown, but here the analysis is focused on this one group of animals instead of all four). We separated the 25 Georgia animals into only males, then males and juvenile females (subadults). Due to the offloading of PCBs to offspring, adult females (Z07, Z11, Z23 and Z25, Table 4) were excluded from all the PCB correlated gene expression analysis. 3.2.3. Georgia males and PCBs The comparisons between males with H (4 samples, x axis in the scatter plot Fig. S2) vs L (5 samples, y axis in the scatter plot Fig. S2),

Fig. 4. 2-D cluster (PCA) showing all male dolphins clustered by similarity of blood transcriptomes. Dots represented animals, colors represent geographical locations. Beaufort, purple, n = 2; Georgia, blue, n = 14; Saint Joseph Bay, green, n = 7; Sarasota Bay, yellow, n = 7. B. Georgia, blue, n = 14; Others, red, n = 16. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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Fig. 6. qPCR of selected genes in Georgia dolphins compared to dolphins from other geographic locations. Real time PCR data for decorin (DCN), a selected gene showing differential expression in Georgia vs Others Males. RNA from 13 different male dolphins was individually reverse transcribed, and each gene was amplified in triplicate from the cDNA. Striped bars, Georgia (n = 6); full bars, Others (n = 7). B. RNA from 17 different male dolphins was individually reverse transcribed, and each gene was amplified in triplicate from the cDNA. Striped bars, Georgia (n = 8); full bars, Others (n = 9). Data were normalized to EIF4E2, a gene that showed no variation in the microarray analysis. ΔCt values of animals belonging to the same group were pooled. Below the histograms in A and B the Bioanalyzer run result of the qPCR product showing the specificity of the amplicons.

revealed 34 differentially expressed genes. Of the 34, 20 have unknown function. The remaining 14 (genes with a positive fold value are higher, up-regulated, in H; genes with a negative fold are lower, downregulated in H) are shown in Table S4. Of interest, showing a downregulation in H samples are the signal transducer and activator of transcription 1 (STAT1), the nuclear receptor subfamily 3 (NR3C1) and the complement component 1q (C1QB). In response to cytokines and growth factors, STAT family members are phosphorylated by the receptor associated kinases, and then form homo- or heterodimers that translocate to the cell nucleus where they act as transcription activators. STAT1 can be activated by various ligands including interferon-alpha, interferon-gamma, EGF, PDGF and IL6. The STAT protein mediates the expression of a variety of genes, and is thought to be important for cell viability in response to different cell stimuli and pathogens. NR3C1 encodes glucocorticoid receptor, which can function both as a transcription factor that binds to glucocorticoid response elements in the promoters of glucocorticoid responsive genes to activate their transcription, and as a regulator of other transcription factors. C1QB encodes a major constituent of the complement subcomponent C1q which associates with C1r and C1s in order to yield the first component of the serum complement system.

3.2.4. Georgia males, juvenile females and PCBs Next, juvenile female transcriptomic profiles were added to the analysis. The comparison between H and L samples resulted in eight genes that were differentially expressed (two with unknown function). The four genes down-regulated in H samples (Table S5) are a mitochondrial inner membrane protein (IMMT), a DNA damage-binding protein (DDB1), collagen alpha-1 (COL14A1) and a methyltransferase-like protein 20-like (METTL20). DDB1 is a protein complex that is responsible for repair of UV-damaged DNA, and its fold difference is almost 1.4. Down-regulated in H dolphins is also the collagen type XIV COL14A1 with a difference of 2.5 fold (versus L, low, PCB samples).

The genes found to be differentially expressed in GA dolphins are not known to directly respond to PCBs exposure. However, they can be important factors in the prominent health issues seen in this population such as anemia, hypothyroidism and immune suppression as reported by Schwake et al. (2012). In males with the highest contaminant load, the most down-regulated gene is the activin A receptor type II-like 1, ACVRL1 (Table S3), a receptor in the TGF beta signaling pathway. Members of the TGF-β superfamily, which include TGF-βs, activins, GDFs and bone morphogenetic proteins (BMPs), are known regulators of late stage erythropoiesis (Maguer-Satta et al., 2003; Söderberg et al., 2009; Rider and Mulloy, 2010). In a recent study, Suragani et al. (2014), demonstrated that uncoupling of activin type IIB signaling through a ligand trap corrects EPO resistant anemia. In fact, this ligand trap, under the trade name Luspatercept, is now in late stage clinical trials as a therapeutic agent for anemia in patients with rare blood disorders. If taken together with the down-regulation of the complement subcomponent C1q (CIQB) this information strengthens the results previously obtained by Schwacke et al. (2012), describing the anemia and immunosuppression of the dolphin sampled in the Georgia Superfund location. CIQB associates with C1r and C1s in order to yield the first component of the serum complement system and malfunctioning of this gene is correlated with the lupus erythematosus, a well-known autoimmune disease. The most up-regulated gene in male dolphins with the highest contaminant load encodes a mitochondrial acetyl-CoA synthetase enzyme essential for energy expenditure under ketogenic conditions (Fujino et al., 2001), and the activation of this gene could be a consequence of the anemia and immunosuppressive health status of the animal. Another interesting gene found to be up-regulated in male and juvenile female females with high contaminant load is a member of the perilipin family, perilipin 2 (PLIN2), Perilipins coat intracellular lipid storage droplets with a surface membrane material, and may be involved in development and maintenance of adipose tissue. This could be the result of immunosuppression, as we know that marine mammals in poor health conditions have a thinner blubber tissue.

Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

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The most up-regulated gene in both males and females highly exposed to PCB is dual-specificity phosphatase 2, DUSP2. DUSP is an emerging subclass of the protein tyrosine phosphatase gene superfamily involved in cell proliferation, cancer and the immune response, and DUSP2 in particular is predominantly expressed in hematopoietic tissues. Wei et al. (2013) suggested the potential use of DUSP2 as a biomarker and therapeutic target in diseases such as different types of cancer and immune-related diseases. This hypothesis could result in a very practical solution for monitoring dolphin health status in future investigation. 4. Conclusion With sequencing of the dolphin genome, we have advanced in the evolution of using transcriptional profiling to capture a comprehensive view of its response to the environment. The common bottlenose dolphins used in this study are estuarine residents and their health status reflects the status of their environment. The development and application of this comprehensive gene microarray to monitor global gene expression of the whole blood has been very useful in the identification of geographical location, which, in this specific example, reflects the high level of contamination of Superfund sites on the Georgia coast of the US. This is not only informative of the health status of the dolphin itself but also of their prey, which in many cases include species also consumed by humans. Since PCBs persist in the environment for decades, this information can be important to understand the long term effects of exposure for mammals residing in coastal areas, both humans and dolphins. With this sensitive tool, we may be able to detect the buildup of contaminants on our coasts before they require Superfund designation, and spare coastal inhabitants of all types from toxic exposure to legacy chemicals. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.margen.2014.11.002. Acknowledgments Samples from Sarasota Bay dolphins were collected through the support of Dolphin Quest, Inc., NOAA's National Marine Fisheries Service, Morris Animal Foundation's Betty White Wildlife Rapid Response Fund, the Chicago Zoological Society, and Mote Marine Laboratory. The Sarasota Bay samples were collected under National Marine Fisheries Service (NMFS) Scientific Research Permit No. 522-1785 to Wells. The North Carolina samples were collected under NMFS Permit No. 779-1681-00 to the Southeast Fisheries Science Center. The Georgia and Saint Joseph Bay samples were collected under NMFS Permit No. Permit No. 932-1489-05 and 932-1905/MA-009526 issued to Dr Teresa Rowles. Protocols were reviewed and approved by a NOAA/NMFS ad hoc Institutional Animal Care and Use Committee (IACUC). We thank Frances M. Van Dolah and Lori H. Schwacke (NOAA, National Ocean Service, Hollings Marine Laboratory, Charleston, SC, 29412, USA) and Patricia E. Rosel (National Marine Fisheries Service, Southeast Fisheries Science Center, Lafayette, LA, 70506, USA) for the collaboration on the interpretation of the data. The capture-release field work requires the participation of many individuals and we appreciate their contribution to safe collection of samples and data. References Aguilar, A., Borrell, A., 1994. Abnormally high polychlorinated biphenyl levels in striped dolphins (Stenella coeruleoalba) affected by the 1990–92 Mediterranean epizootic. Sci. Total Environ. 154, 237–247. Almeida, J.S., McKillen, D.J., Chen, Y.A., Gross, P.S., Chapman, R.W., Warr, G., 2005. Design and calibration of microarrays as universal transcriptomic environmental biosensors. Comp. Funct. Genom. 6 (3), 132–137. Balmer, B.C., Schwacke, L.H., Wells, R.S., George, R.C., Hoguet, J., Kucklick, J.R., Lane, S.M., Martinez, A., McLellan, W.A., Rosel, P.E., Rowles, T.K., Sparks, K., Speakman, T., Zolman, E.S., Pabst, D.A., 2011. Relationship between persistent organic pollutants

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Please cite this article as: Mancia, A., et al., Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus), Mar. Genomics (2014), http://dx.doi.org/10.1016/j.margen.2014.11.002

Microarray applications to understand the impact of exposure to environmental contaminants in wild dolphins (Tursiops truncatus).

It is increasingly common to monitor the marine environment and establish geographic trends of environmental contamination by measuring contaminant le...
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