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. 2019 Apr 16;116(16):7847-7856.
doi: 10.1073/pnas.1816640116. Epub 2019 Apr 1.

Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides

Akira Shiraishi  1 , Toshimi Okuda  1 , Natsuko Miyasaka  1 , Tomohiro Osugi  1 , Yasushi Okuno  2 , Jun Inoue  3 , Honoo Satake  4
Affiliations

Repertoires of G protein-coupled receptors for Ciona-specific neuropeptides

Akira Shiraishi et al. Proc Natl Acad Sci U S A. .

Abstract

Neuropeptides play pivotal roles in various biological events in the nervous, neuroendocrine, and endocrine systems, and are correlated with both physiological functions and unique behavioral traits of animals. Elucidation of functional interaction between neuropeptides and receptors is a crucial step for the verification of their biological roles and evolutionary processes. However, most receptors for novel peptides remain to be identified. Here, we show the identification of multiple G protein-coupled receptors (GPCRs) for species-specific neuropeptides of the vertebrate sister group, Ciona intestinalis Type A, by combining machine learning and experimental validation. We developed an original peptide descriptor-incorporated support vector machine and used it to predict 22 neuropeptide-GPCR pairs. Of note, signaling assays of the predicted pairs identified 1 homologous and 11 Ciona-specific neuropeptide-GPCR pairs for a 41% hit rate: the respective GPCRs for Ci-GALP, Ci-NTLP-2, Ci-LF-1, Ci-LF-2, Ci-LF-5, Ci-LF-6, Ci-LF-7, Ci-LF-8, Ci-YFV-1, and Ci-YFV-3. Interestingly, molecular phylogenetic tree analysis revealed that these receptors, excluding the Ci-GALP receptor, were evolutionarily unrelated to any other known peptide GPCRs, confirming that these GPCRs constitute unprecedented neuropeptide receptor clusters. Altogether, these results verified the neuropeptide-GPCR pairs in the protochordate and evolutionary lineages of neuropeptide GPCRs, and pave the way for investigating the endogenous roles of novel neuropeptides in the closest relatives of vertebrates and the evolutionary processes of neuropeptidergic systems throughout chordates. In addition, the present study also indicates the versatility of the machine-learning-assisted strategy for the identification of novel peptide-receptor pairs in various organisms.

Keywords: G protein-coupled receptor; deorphanization; machine learning; neuropeptide; peptide descriptor.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Overview of PD-incorporated SVM. (A) Regular expression-based PDs were generated by concatenating five elements, which represent amino acid categories, and regular expressions for these fragments were calculated. Ambiguous residue categories for regular expressions are listed in SI Appendix, Table S2. (B) Conventional prediction methods include computation of kernels, which are necessary for SVM to learn and predict interaction pairs, from all of the elements of a GPCR and ligand descriptors. In contrast, the proposed method includes a step for GAFS. In GAFS, descriptor sets were selected to improve the prediction performance with the AUC, which was measured by LOSO cross-validation.
Fig. 2.
Fig. 2.
Boxplots of SSs for GPCRs and peptides. (A) SSs for GPCRs against other GPCRs in the same subsets (humans, mice, other vertebrates, and nonascidian invertebrates), (B) SSs for GPCRs against other GPCRs in other subsets, (C) SSs for peptides against other peptides in the same subsets, and (D) SSs for peptides against other peptides in other subsets. *P < 0.05.
Fig. 3.
Fig. 3.
The original PD-incorporated SVM showed sufficient prediction performance for neuropeptide–GPCR pairs of various species. (A) AUCs of model composed from original PDs and the resultant model from the second-round GAFS are shown with error bars representing the SEM of five repeated experiments with independently generated negative data. *P < 0.05. (B and C) Prediction results for C. intestinalis CPIs using (B) the model resulting from original PDs and (C) the model resulting from the second-round GAFS are shown as a heat map. The color gradient represents predicted values for individual peptide–GPCR interactions. The known pairs are outlined in yellow.
Fig. 4.
Fig. 4.
GPCRs for Ci-GALP, Ci-LF-2, Ci-NTLP-2, and Ci-YFV-1 were identified by signaling assays based on the prediction by PD-incorporated SVM. (A) Prediction results for C. intestinalis CPIs are shown as a heat map. The color gradient represents the predicted value for each interaction between peptide and GPCR. Only GPCRs that were predicted to interact with at least one peptide with prediction scores higher than 0.7 are shown. Dose-dependent responses of (B) Ci-GALP, (C) Ci-NTLP-2, (D) Ci-LF-2, and (E) Ci-YFV-1 in Sf-9 cells expressing each receptor were assessed with intracellular Ca2+ mobilization, and sigmoid curves were calculated using Prism 3.03. Error bars represent the SEM of more than three experiments.
Fig. 5.
Fig. 5.
Data feedback of experimentally validated results of four Ciona neuropeptide–GPCR pairs, leading to the identification of eight additional pairs. (A) Prediction results for C. intestinalis CPIs are shown as a heat map. The color gradient represents the predicted value for each interaction between peptide and GPCR. Only GPCRs that were predicted to interact with at least one peptide with prediction scores higher than 0.7 are shown. Dose-dependent responses of (B and D) Ci-LF-1, (E) Ci-LF-5, (C and F) Ci-LF-6, (G) Ci-LF-7, (H) Ci-LF-8, and (I) Ci-YFV-3 in Sf-9 cells expressing each receptor were assessed with intracellular Ca2+ mobilization, and sigmoid curves were calculated using Prism 3.03. Error bars represent the SEM of more than three experiments.
Fig. 6.
Fig. 6.
Demonstration that Ci-NTLP-2-R, Ci-LF-Rs, and Ci-YFV-Rs are not evolutionarily related to any known neuropeptide GPCRs using gene trees of (A) Ci-GALP-R, (B) Ci-NTLP-2-R, (C) Ci-LF-Rs, and (D) Ci-YFV-Rs. Phylogenic trees of ligand-identified Ciona GPCRs were constructed using the ML and NJ methods (SI Appendix, Fig. S5 A–D, 612 sites, 450 sites, 492 sites, and 394 sites, respectively) and resultant topologies were confirmed by estimating ML trees based solely on TM domains. Each schematic of gene trees was constructed based on the three molecular phylogenetic trees using ORTHOSCOPE 1.0.1 (58). The monophyly-supported and -unsupported gene clades were indicated by closed triangles and open triangles, respectively. Clade names indicate inferred functions of ancestral genes based on clade members with experimental data. The number at each branch node represents the percentage given by 100× bootstrap replicates (ML/NJ). Ciona GPCRs characterized herein are shown in red.
Fig. 7.
Fig. 7.
Gene expression profiles for Ciona neuropeptide GPCRs. Relative expression of the Ciona neuropeptide receptors to Ciona GAPDH in the indicated tissues was confirmed by real-time PCR. Data are shown as the means of three independent experiments ± SE.
Fig. 8.
Fig. 8.
PD-incorporated SVM cycle: the prediction-experimental validation-data feedback is a powerful procedure for deorphanization of GPCRs for novel neuropeptides. The original prediction model was constructed by learning positive data (red dots) and negative data (blue dots) for known neuropeptide–GPCR pairs, followed by cell-based signaling assays of each predicted pair; predicted GPCR–peptide pairs are green dots, positive matches are yellow dots, and false positives are purple dots. The feedback of experimentally validated neuropeptide–GPCR pairs updated the prediction model, which enabled the prediction of more positive GPCR–peptide pairs. This improved prediction model indicated that repeated prediction-experimental validation-feedback cycles make the PD-incorporated SVM more “intelligent” and improve the prediction performance.
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