Quantitative proteomics in teleost fish: insights and challenges for neuroendocrine and neurotoxicology research

Abstract

Neuroendocrine systems integrate both extrinsic and intrinsic signals to regulate virtually all aspects of an animal's physiology. In aquatic toxicology, studies have shown that pollutants are capable of disrupting the neuroendocrine system of teleost fish, and many chemicals found in the environment can also have a neurotoxic mode of action. Omics approaches are now used to better understand cell signaling cascades underlying fish neurophysiology and the control of pituitary hormone release, in addition to identifying adverse effects of pollutants in the teleostean central nervous system. For example, both high throughput genomics and proteomic investigations of molecular signaling cascades for both neurotransmitter and nuclear receptor agonists/antagonists have been reported. This review highlights recent studies that have utilized quantitative proteomics methods such as 2D differential in-gel electrophoresis (DIGE) and isobaric tagging for relative and absolute quantitation (iTRAQ) in neuroendocrine regions and uses these examples to demonstrate the challenges of using proteomics in neuroendocrinology and neurotoxicology research. To begin to characterize the teleost neuroproteome, we functionally annotated 623 unique proteins found in the fish hypothalamus and telencephalon. These proteins have roles in biological processes that include synaptic transmission, ATP production, receptor activity, cell structure and integrity, and stress responses. The biological processes most represented by proteins detected in the teleost neuroendocrine brain included transport (8.4%), metabolic process (5.5%), and glycolysis (4.8%). We provide an example of using sub-network enrichment analysis (SNEA) to identify protein networks in the fish hypothalamus in response to dopamine receptor signaling. Dopamine signaling altered the abundance of proteins that are binding partners of microfilaments, integrins, and intermediate filaments, consistent with data suggesting dopaminergic regulation of neuronal stability and structure. Lastly, for fish neuroendocrine studies using both high-throughput genomics and proteomics, we compare gene and protein relationships in the hypothalamus and demonstrate that correlation is often poor for single time point experiments. These studies highlight the need for additional time course analyses to better understand gene-protein relationships and adverse outcome pathways. This is important if both transcriptomics and proteomics are to be used together to investigate neuroendocrine signaling pathways or as bio-monitoring tools in ecotoxicology.

Fig. 1

Most abundant ontologies for proteins detected in the neuroendocrine brain for (A) biological process (B) molecular function (C) cellular compartment. Fractionation of peptides was achieved using strong cation exchange followed by reverse phase HPLC. Peptides were detected by LC MS/MS using a QSTAR XL (Applied Biosystems) over a 2 h gradient (details in reference 19). Gene ontology categories depicted in the figure represent the ten most abundant categories and their relative percentages within the top ten. For biological processes, the most abundant categories comprised 37% of all GO terms, while molecular function and cellular compartment comprised 38% and 52%, respectively.

Fig. 2

Regression analyses of mRNA steady state levels (generated through microarray data) and protein abundance (generated by iTRAQ) in the hypothalamus of female teleost fish. The transcripts and proteins used in the analysis showed significant changes in steady state levels. It is important to point out that not all proteins had corresponding probes on the LMB microarray and not all significantly altered proteins had corresponding changes in mRNA levels.

Fig. 3

Recombinant protein strategies can be used to reconstruct the protein for column affinity purification and initial screening for putative protein–protein interactions using immobilization beads. This approach will provide information on regulatory interactions amongst proteins and insight into the relationships among corresponding transcripts.