Transcription Profiling in Environmental Diagnostics: Health Assessments in Columbia River Basin Steelhead (Oncorhynchus mykiss)

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• 作者：Richard E. Connon ; Leandro S. D鈥橝bronzo ; Nathan J. Hostetter ; Alireza Javidmehr ; Daniel D. Roby ; Allen F. Evans ; Frank. J. Loge ; Inge Werner
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2012
• 出版时间：June 5, 2012
• 年：2012
• 卷：46
• 期：11
• 页码：6081-6087
• 全文大小：320K
• 年卷期：v.46,no.11(June 5, 2012)
• ISSN：1520-5851

文摘

The health condition of out-migrating juvenile salmonids can influence migration success. Physical damage, pathogenic infection, contaminant exposure, and immune system status can affect survival probability. The present study is part of a wider investigation of out-migration success in juvenile steelhead (Oncorhynchus mykiss) and focuses on the application of molecular profiling to assess sublethal effects of environmental stressors in field-collected fish. We used a suite of genes in O. mykiss to specifically assess responses that could be directly related to steelhead health condition during out-migration. These biomarkers were used on juvenile steelhead captured in the Snake River, a tributary of the Columbia River, in Washington, USA, and were applied on gill and anterior head kidney tissue to assess immune system responses, pathogen-defense (NRAMP, Mx, CXC), general stress (HSP70), metal-binding (metallothionein-A), and xenobiotic metabolism (Cyp1a1) utilizing quantitative polymerase chain reaction (PCR) technology. Upon capture, fish were ranked according to visual external physical conditions into good, fair, poor, and bad categories; gills and kidney tissues were then dissected and preserved for gene analyses. Transcription responses were tissue-specific for gill and anterior head kidney with less significant responses in gill tissue than in kidney. Significant differences between the condition ranks were attributed to NRAMP, MX, CXC, and Cyp1a1 responses. Gene profiling correlated gene expression with pathogen presence, and results indicated that gene profiling can be a useful tool for identifying specific pathogen types responsible for disease. Principal component analysis (PCA) further correlated these responses with specific health condition categories, strongly differentiating good, poor, and bad condition ranks. We conclude that molecular profiling is an informative and useful tool that could be applied to indicate and monitor numerous population-level parameters of management interest.