Neuropeptides are small signaling molecules that regulate an organism’s nervous system and behavior. The American lobster, Homarus americanus, is an excellent model organism to study how neuropeptides modulation and control rhythmic movements because the nervous system is simple and can function when isolated from the animal. Active neuropeptides are typically formed from larger, inactive precursor proteins that undergo a series of posttranslational processing steps to produce a mature, active neuropeptide. Processing steps following enzymatic cleavage to smaller peptides may include C-terminal amidation, N-terminal pyroglutamate formation, production of disulfide bonds, and sulfation. Mass spectrometry and de novo sequencing has played an essential role in the identification of mature neuropeptides. More recently, in-silico predictions based upon genetic information has been used to propose the structures of mature neuropeptides. In this study, we report on the use of top-down and bottom-up mass spectrometric strategies to determine both neuropeptides and neuropeptide precursor-related peptides (PRPs) found in the brain of the American lobster, H. americanus. In this work, the structures of neuropeptides and PRPs have been predicted using data-mining techniques and in-silico processing models applied to recently generated transcriptomes for neural tissues from the American lobster, which have yielded 194 distinct neuropeptide sequences. Because many sequences for PRPs are too large to be identified using conventional neuropeptide sequencing strategies, we have applied bottom-up proteomics techniques to neuropeptide identification. In this report, we describe how tissue extraction, with and without reduction, alkylation, and digestion of the tissue extract, have been analyzed by a chip-based nanoESI Q-TOF-MS/MS analysis to characterize mature and unprocessed neuropeptides present in H. americanus brain tissues. This large scale study sets the stage for comparative analyses with other nervous system tissues.

Top-down and bottom-up proteomics for the identification of crustacean neuropeptides and precursor-related peptides predicted by transcriptomics

253rd National Meeting (2017)

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