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Jason Pearson
Jason Pearson

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The common bottlenose dolphin (Tursiops truncatus) is a global marine mammal species for which some populations, due to their coastal accessibility, have been monitored diligently by scientists for decades. Health assessment examinations have developed a comprehensive knowledge base of dolphin biology, population structure, and environmental or anthropogenic stressors affecting their dynamics. Bottlenose dolphin health assessments initially started as stock assessments prior to acquisition. Over the last four decades, health assessments have evolved into essential conservation management tools of free-ranging dolphin populations. Baseline data enable comparison of stressors between geographic locations and associated changes in individual and population health status. In addition, long-term monitoring provides opportunities for insights into population shifts over time, with retrospective application of novel diagnostic tests on archived samples. Expanding scientific knowledge enables effective long-term conservation management strategies by facilitating informed decision making and improving social understanding of the anthropogenic effects. The ability to use bottlenose dolphins as a model for studying marine mammal health has been pivotal in our understanding of anthropogenic effects on multiple marine mammal species. Future studies aim to build on current knowledge to influence management decisions and species conservation. This paper reviews the historical approaches to dolphin health assessments, present day achievements, and development of future conservation goals.




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This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.


Skin lesions among bottlenose dolphins are geographically widespread and can affect a large proportion of a population. Also, lesion types may be differentially distributed among populations. In the current study, the prevalence of skin lesions was significantly different among dolphins from the three study sites, and differences in the occurrence of lesion types were also observed. The findings suggest that skin disease can vary by population, and that certain disease types may be geographically distinct. These geographic differences may be due to seasonal or environmental fluctuations, exposure to anthropogenic influences, or differences in population demographics; however, more research in these areas is needed to confirm this. This study demonstrates that images from photo-id surveys can be used as a non-invasive and cost-effective approach to study lesion occurrence in wild cetacean populations, and while many skin lesions do not appear to be fatal [2], [12], [21], [57], [58], lesions detected on free-ranging animals may serve as an indication of other underlying health concerns or environmental threats.


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Recent advances in instrumentation, reagents, and techniques for high throughput proteomics are making it possible to simultaneously identify and compare disease, development, and treatment-related changes to the level of protein expression [1]. In most cases, these techniques rely on two dimensional gel electrophoresis (2DGE) or liquid chromatography (LC) to separate proteins or peptides by charge, mass, or other chemical properties followed by identification using mass spectrometry (MS). For gel-based techniques, quantitation is performed by comparing the intensity of associated spots in gel images or by comparing the intensity of signals from appropriately excited Cyanine reactive dyes used to tag the samples being compared [2]. For techniques using liquid chromatography, methods for quantitating expression can be grouped into two broad classes: differential labeling, and label-free LC-MS [3]. Labeling methods such as SILAC, ICAT, and iTRAQ use isotopic or isobaric tags to differentially label the samples being compared and paired reporter ion or isotopic peaks provide estimates of the expression ratio for identified peptides. Label free methods estimate peptide abundance from a chromatographic elution profile, integrated across retention time or from the frequency with which a peptide is selected from an MS scan for MSMS analysis, termed spectral counting. In the former case, the integrated profile is taken to be indicative of the abundance of the associated peptide and thus a measure of associated protein abundance. In spectral counting, the methods compute protein abundance indices from the average of the associated peptide abundances based on these frequencies.


Each iTRAQ experiment produces tens of thousands of spectra (several gigabytes of data), several thousand identified peptides, and hundreds of identified proteins. Bioinformatic tools and statistical methods are essential to the interpretation of these data. Several data management and analysis tools have been developed to analyze these data, such as ProQuant, and ProteinPilot, software supplied by the manufacturer of the iTRAQ reagents, and a number of freely available tools. The software packages i-Tracker [6] and TandTRAQ [7] support the analysis of iTRAQ-generated quantitation data and the integration of that analysis with search results from Mascot, Sequest, and X!Tandem. The i-Tracker software performs reporter ion peak area calculations, isotopic impurity correction, threshold checking, and spectrum-level expression ratio calculations and links quantitations to peptide identities provided by Sequest or Mascot. TandTRAQ additionally provides a method for combining spectrum-level quantitation data from i-Tracker with peptide identifications from X!Tandem. Another iTRAQ data analysis tool, Multi-Q, provides instrument-independent processing, extracts reporter ion peak intensities, eliminates redundant peptides, and compensates for reporter ion saturation and variations in spectrum quality [8, 9]. Multi-Q additionally provides both graphical- and web-based user interfaces. Quant, a MATLAB-based software package for iTRAQ data analysis, provides protein-level relative expression estimates and associated uncertainty measures using error propagation techniques [10]. Data management tools such as the Yale Protein Expression Database (YPED) facilitate biological interpretation through the capture, display, and linking of data from proteomic experiments using a variety of experimental techniques including iTRAQ [11]. Extensions to the Proteomics Identifications Database (PRIDE) and mzData standards have been proposed to permit the storage of iTRAQ reporter ion intensities [12, 13]. Tools such as these for data manipulation, analysis, and storage are essential to the application of iTRAQ.


iQuantitator is available as an installable package for the freely-available R statistical computing environment and, through scripting, can be tailored to a variety of iTRAQ study designs. The package includes a collection of R functions for structuring input files, a Gibbs sampler designed for this application, and an R/latex script used to construct hypertext-linked reports. To make use of the package, users create an R script that specifies the input files, defines the experiment, gives the statistical model and the comparison of interest, and specifies the results file names. The software is intended for use by statisticians and analysts familiar with experimental design and statistical modeling. The processing flow of a typical iQuantitator application is illustrated in Figure 2


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