A combined strategy of neuropeptide prediction and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone Nematostella vectensis

Abstract

Neuropeptides are a class of bioactive peptides shown to be involved in various physiological processes, including metabolism, development, and reproduction. Although neuropeptide candidates have been predicted from genomic and transcriptomic data, comprehensive characterization of neuropeptide repertoires remains a challenge owing to their small size and variable sequences. De novo prediction of neuropeptides from genome or transcriptome data is difficult and usually only efficient for those peptides that have identified orthologs in other animal species. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, reliable identification of naturally occurring peptides using a conventional tandem mass spectrometry approach, scanning spectra against a protein database, remains difficult because a large search space must be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of in silico prediction of candidate neuropeptides followed by peptide-spectrum matching. This approach enables highly sensitive and reliable neuropeptide identification, as the search space for peptide-spectrum matching is highly reduced. Nematostella vectensis is a basal eumetazoan with one of the most ancient nervous systems. We scanned the Nematostella protein database for sequences displaying structural hallmarks typical of eumetazoan neuropeptide precursors, including amino- and carboxyterminal motifs and associated modifications. Peptide-spectrum matching was performed against a dataset of peptides that are cleaved in silico from these putative peptide precursors. The dozens of newly identified neuropeptides display structural similarities to bilaterian neuropeptides including tachykinin, myoinhibitory peptide, and neuromedin-U/pyrokinin, suggesting these neuropeptides occurred in the eumetazoan ancestor of all animal species.

Fig 1. Schema of the neuropeptide identification strategy using a combination of peptide-spectrum matching against a dataset of in silico cleaved neuropeptide sequences extracted from putative neuropeptide precursors from the Nematostella protein database.

Amino- and carboxyterminal motifs were used to scan the Nematostella protein database for neuropeptide precursor candidates, from which the peptides sequences were cleaved in silico. Peptide sequences were then exported into a target database for MS/MS spectral searching.

Fig 2. Representative fragment spectra of identified peptides.

Fragmentation spectra of the peptide “QPPYLDLTPSYFHIRa” (A) and “MPEQDANPQTRFDa” (B). The dotted lines indicate fragment ions assigned. Ion labeled with * means loss of NH3.

Fig 3. Primary structures of neuropeptide precursor proteins.

The location of the detected neuropeptides is indicated by full lines. Numbers correspond to the ID in Table 1. Predicted neuropeptides that were not detected in this study are indicated by dotted lines. Signal peptides predicted by SignalP are highlighted in red.

Fig 4. Structural similarities of identified neuropeptides in Nematostella vectensis and other species.

A: HIRamides and Tachykinin related peptides[,–52]. B: PRGamides and PRXamide related peptides [–57]. C: QWamides, myoinhibitory peptide (MIP) and allatostatin type B [–64]. Conserved amino acid residues are shown in red.

Fig 5. WISH staining of juvenile polyps of Nematostella vectensis (8 days post fertilization).

The figure shows localized expression of HIRamide, PRGamide RFamide, and VRHamide genes at low (upper panels) and high magnification (lower panels). Scale bars, 100 μm (upper) and 50 μm (lower). Neural processes are indicated by red arrows.