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. 2023 Jul 12;26(8):107309.
doi: 10.1016/j.isci.2023.107309. eCollection 2023 Aug 18.

A ligand-receptor interactome atlas of the zebrafish

Milosz Chodkowski  1 , Andrzej Zielezinski  1 , Savani Anbalagan  1
Affiliations

A ligand-receptor interactome atlas of the zebrafish

Milosz Chodkowski et al. iScience. .

Abstract

Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signaling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptors, ligands, and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins and created repositories of zebrafish ligands, membrane receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is based on both the physical interaction data of zebrafish proteome and existing human ligand-receptor pair databases. The resources are available as R and Python scripts. DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome. DanioTalk methodology can be applied to build and explore the ligand-receptor atlas of other non-mammalian model organisms.

Keywords: Cell biology; Omics; Specialized functions of cells.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Prediction of cellular localization of zebrafish reference proteome (A) Graphic describing the DeepLoc 2.0-based prediction of cellular location of the reference zebrafish proteome. (B) Zebrafish reference proteome cellular localization prediction. Donut chart showing the major DeepLoc 2.0 predictions of zebrafish proteome. The number of proteins (B) and protein-coding genes (B′) are shown. (C) Upset plot comparing the number of genes with existing extracellular annotation records and DeepLoc 2.0-based 'Extracellular' predictions. CC & GO indicates cellular localization and GO term records, respectively, available in UniprotKB database. (D) Venn diagram comparing genes with existing cell membrane localization annotation records and DeepLoc-based 'cell membrane' (CM) predictions. CC & GO indicates cellular localization and GO term records, respectively, available in UniprotKB database.
Figure 2
Figure 2
Curated zebrafish ligands and membrane receptors (A) Scheme describing the steps taken for the creation of curated zebrafish ligands and membrane receptors list. (B) Donut chart showing the number and percentage of zebrafish genes coding for curated receptor- and ligand-coding genes. (C) Venn diagram comparing curated ligands with DeepLoc 2.0 extracellular predicted proteins and previously reported Matrisome dataset. (D) Conservation between zebrafish and human ligand- and membrane receptor-coding genes based on ZFIN records. (E) Venn diagram comparing the number of curated ligand- and receptor-coding genes with zebrafish orthologs of human disease-linked genes in the OMIM database. (F) Dot plot showing the diseases associated with human orthologs of some of the zebrafish ligands and membrane receptors. (G) Venn diagram showing the number and percentage of zebrafish membrane receptors and ligands potentially targetable by drugs listed in the DrugCentralDB. (H) Donut chart showing the number and percentage of drugs that can potentially target zebrafish membrane receptors and/or ligands.
Figure 3
Figure 3
Zebrafish Ligand-Receptor Interactome (A) Scheme describing steps undertaken to map the zebrafish ligands and membrane receptors interactome (A). Circle plot showing the top 25 ranked ligand-receptor pairs (A′). (B) Orphan status of ligands. Upset plot of genes coding for ligands with or without potential receptors in the zebrafish STRING PPI database (High or Medium cutoff) or orthologous receptors in the human ligand-receptor database. (C) Orphan status of receptors. Upset plot of genes coding for receptors with or without potential ligands in the zebrafish STRING PPI database (High or Medium cutoff) or orthologous ligands in the human ligand-receptor database. (D) Upset plot of DanioTalk ligand-receptor pair database genes derived from zebrafish STRING PPI database (High or Medium cutoff) or orthologous ligand-receptor pairs in the human ligand-receptor databases. (E) Zebrafish ligand-receptor interactome stats. Column chart comparing average number of ligand/receptor or receptor/ligand based on zebrafish STRING PPI database or orthologs in the human ligand-receptor database.
Figure 4
Figure 4
Application of DanioTalk (A) Receptors for Nodal-related Tgfβ ligands. Circle plot showing the top-ranked receptors for Ndr2, Ndr1, and Spaw ligands. The top 50 ranked interactions at medium confidence (>400) cutoff are plotted. (B) Receptors for Wnt ligands. Circle plot showing the top-ranked receptors for Wnt ligands. The top 50 ranked interactions at high confidence (>700) cutoff are plotted. (C) Scheme showing synaptosome-PSD from adult zebrafish brain. Mass spectrometry data were analyzed.. (D) Circle plot showing the top-ranked ligand-receptor interactions between the synaptosome ligands and PSD receptors. Interactions were ranked based on quasi-percentile of peptide expression. The top 50 ranked interactions at medium confidence (>400) cutoff are plotted. Ribbon width indicates interaction scores. (E) Circle plot showing the top-ranked ligand-receptor interactions between the PSD ligands and synaptosome receptors. Interactions were ranked based on quasi-percentile of peptide expression. The top 50 ranked interactions at medium confidence (>400) cutoff are plotted. Ribbon width indicates interaction scores. (F) Scheme showing axo-glial interaction between oxytocin neuron and glial pituicytes. Adult oxytocin neuron cluster pseudo-bulk data from scRNA-seq data and adult glial pituicyte bulk RNA-seq data were analyzed.,. (G) Circle plot showing top-ranked ligand-receptor interactions between oxytocin neurons and glial pituicytes. Interactions were ranked based on quasi-percentile of gene expression. The top 50 ranked interactions at highest confidence cutoff (>900) are plotted.
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