Abstract
Decapod crustaceans are important animal models for neurobiologists due to their relatively simple nervous systems with well-defined neural circuits and extensive neuromodulation by a diverse set of signaling peptides. However, biochemical characterization of these endogenous neuropeptides is often challenging due to limited sequence information about these neuropeptide genes and the encoded preprohormones. By taking advantage of sequence homology in neuropeptides observed in related species using a home-built crustacean neuropeptide database, we developed a semi-automated sequencing strategy to characterize the neuropeptidome of Panulirus interruptus, an important aquaculture species, with few known neuropeptide preprohormone sequences. Our streamlined process searched the high mass accuracy and high-resolution data acquired on a LTQ-Orbitrap with a flexible algorithm in ProSight that allows for sequence discrepancy from reported sequences in our database, resulting in the detection of 32 neuropeptides, including 19 novel ones. We further improved the overall coverage to 51 neuropeptides with our multidimensional platform that employed multiple analytical techniques including dimethylation-assisted fragmentation, de novo sequencing using nanoliquid chromatography-electrospray ionization-quadrupole-time-of-flight (nanoLC-ESI-Q-TOF), direct tissue analysis, and mass spectrometry imaging on matrix-assisted laser desorption/ionization (MALDI)-TOF/TOF. The high discovery rate from this unsequenced model organism demonstrated the utility of our neuropeptide discovery pipeline and highlighted the advantage of utilizing multiple sequencing strategies. Collectively, our study expands the catalog of crustacean neuropeptides and more importantly presents an approach that can be adapted to exploring neuropeptidome from species that possess limited sequence information.