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Matching Receptome Genes with Their Ligands for Surveying Paracrine/Autocrine Signaling Systems

Division of Reproductive Biology, Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California 94305-5317

Address all correspondence and requests for reprints to: Aaron J. W. Hsueh, Department of Obstetrics and Gynecology, Stanford University Medical Center, Boswell A344, Stanford University School of Medicine, Stanford, California 94305-5317. E-mail: aaron.hsueh{at}stanford.edu.

ABSTRACT

Sequencing of genomes from diverse organisms facilitates studies on the repertoire of genes involved in intercellular signaling. Extending previous efforts to annotate most human plasma membrane receptors in the Human Plasma Membrane Receptome database, we matched cognate ligands with individual receptors by surveying the published literature. In the updated online database we called "liganded receptome," users can search for individual ligands or receptors to reveal their pairing partners and browse through receptor or ligand families to identify relationships between ligands and receptors in their respective families. Because local signaling systems are prevalent in diverse normal and diseased tissues, we used the liganded receptome knowledgebase to interrogate DNA microarray datasets for genome-wide analyses of potential paracrine/autocrine signaling systems. In addition to viewing ligand-receptor coexpression based on precomputed DNA microarray data, users can submit their own microarray data to perform online genome-wide searches for putative paracrine/autocrine signaling systems. Investigation of transcriptome data based on liganded receptome allows the discovery of paracrine/autocrine signaling for known ligand-receptor pairs in previously uncharacterized tissues or developmental stages. The present annotation of ligand-receptor pairs also identifies orphan receptors and ligands without known interacting partners in select families. Because hormonal ligands within the same family usually interact with paralogous receptors, this genomic approach could also facilitate matching of orphan receptors and ligands. The liganded receptome is accessible at http://receptome.stanford.edu.

HORMONAL SYSTEMS EVOLVED in eukaryotes and facilitated intercellular communication (1). In addition to steroid hormones and their nuclear receptors, plasma membrane receptors and their ligands constitute the main means of crosstalks between cells in multicellular organisms. The emergence of metazoan complexity during evolution is associated with an impressive diversification of ligands and plasma membrane receptors that allowed both redundancy and promiscuity. These receptor and polypeptide ligand families with multiple paralogous genes evolved over time from common ancestral genes by segmental, chromosomal, and whole genomic duplication as well as by domain shuffling. In the postgenomic era, it is possible to group receptors and their polypeptide ligands into protein families by tracing their phylogenetic relatedness. Based on the evolutionary genomic principle, we categorized most of the plasma membrane receptors in the human genome and established the Human Plasma Membrane Receptome (HPMR) database (2).

Most publicly accessible primary databases, such as those sponsored by the National Center for Biotechnology Information, were designed to serve users with a wide range of research interests, and the intended lack of specificity limits their value for researchers in particular fields. As a result, there is the need for secondary databases such as the HPMR wherein researchers interested in hormone-receptor interactions can access text and sequence information directly related to ligand-receptor relationships. In contrast to our attempts to categorize all plasma membrane receptors based on genomic and evolutionary perspectives, several existing databases with different emphases are available. These include databases for receptor tyrosine kinase receptors (3), G protein-coupled receptors (4), olfactory receptors (5), thyrotropin receptor mutations (6), nuclear receptors (7, 8), and endocrine disruptor receptors (9). In addition, the Alliance for Cellular Signaling/Nature Signaling Gateway (http://www.signaling-gateway.org) facilitates the understanding of genes involved in cellular signaling, whereas the Database of Ligand-Receptor Partners (http://dip.doe-mbi.ucla.edu/dip/DLRP.cgi) contains subgroups of receptors for chemokines, TNF, fibroblast growth factor (FGF), and TGFß ligands. Although the Alliance for Cellular Signaling database contains extensive information on many signaling genes, it does not arrange genes based on their phylogenetic relationship, nor does it match known interacting ligands and receptors into a searchable format. Likewise, the reactome database (http://www.reactome.org/) (10) and the Human Protein Reference Database (http://www.hprd.org/) (11) represent curated resources of protein-protein interactions for core pathways and reactions in human biology but do not emphasize ligand-receptor pairing.

MATCHING RECEPTORS WITH THEIR COGNATE LIGANDS

To allow a better understanding of the hormonal signaling systems, we annotated the HPMR database by matching individual receptors with their cognate ligands. When relevant, the coreceptors involved in signal transduction were also included. The major effort of our annotation is based on literature extraction, analysis, and interpretation. The starting point in each case was individual receptor genes in the HPMR database, and every entry was manually annotated by searching literature using Google Scholar (http://scholar.google.com/) and Information Hyperlinked Over Proteins (http://www.ihop-net.org/), followed by PubMed. Information about name, synonym, and related information for individual polypeptide ligands were obtained from Entrez and Online Mendelian Inheritance in Man. For each receptor-ligand interaction, at least one PubMed literature link is provided to support the annotation. For each interaction submission, two independent annotators confirmed the relationship. Like receptors, polypeptide ligands were categorized based on their phylogenetic relationships, and each gene has synonyms, Entrez Gene Summary, and other information. It also contains links to Entrez Gene and Online Mendelian Inheritance in Man.

After the matching of ligands for individual receptor genes in the receptome, we identified multiple "orphan receptors" with no known interactions and some receptors with multiple ligands. Excluding the olfactory receptors, the current online version of the liganded HPMR includes more than 900 receptor genes grouped into more than 20 superfamilies and more than 100 subfamilies. For these receptors, approximately 600 of them have known ligands, whereas approximately 300 of them are still "orphans" with no interactions. The database also lists more than 600 ligands, with more than 500 ligands showing interactions with at least one receptor. In addition to the prevalent one ligand-one receptor pairs accounting for most interactions, about one third of receptors interact with more than one ligand. For example, several type II and type I serine/threonine kinase receptors as well as several chemokine and FGF receptors interact with seven to nine ligands. Likewise, ephrin type EPHA4, epidermal growth factor (EGF), opioid , tachykinin, and leukemia inhibitory factor receptors interact with five to six ligands. In select families, coreceptors [e.g. RET (receptor tyrosine kinase proto-oncogene), RAMPs (receptor activity-modifying proteins), GP130 (glycoprotein 130), etc.] interact with multiple paralogous ligands.

Users can access the liganded receptome database using the SEARCH option based on gene names for individual receptors or ligands. Once a receptor or ligand is selected, a web page displays paired ligand-receptor genes together with additional interaction partners, including coreceptors, additional ligands, or receptors. Users can then access the PubMed reference link supporting the ligand-receptor relationship and view interactions of all ligand-receptor pairs within the specific ligand or receptor family based on the initial search. Alternatively, the users can use the BROWSE option to find all receptors within a given family, followed by viewing the interactions of ligand-receptor pairs in a specific family of interest. In addition, orphan receptors in the same family can be identified. From the family pages, users can access family description and phylogenetic relationship of receptors within the same family. Users can also view all receptor-ligand interactions within the family or navigate to individual ligand-receptor interaction pages. An online tutorial for navigating through HPMR is provided (supplemental data published on The Endocrine Society’s Journals Online web site at http://mend.endojournals.org) (http://receptome.stanford.edu/HPMR/info/present1.asp).

Ligands in HPMR can be divided into three major categories (Table 1). The most prevalent ligands are gene-encoded ligands for protein and peptide hormones. In this category, there are more than 150 domains found for polypeptide ligands with polypeptide hormones containing chemokine CXC, FGF, EGF/EGF-like, and TGF-ß domains representing 40, 28, 27, and 25 ligand genes, respectively. The second category of gene-dependent hormones consists of products of different enzymes, including amino acid derivatives, phospholipid metabolites, steroid hormones, nucleic acids, and others. For this group, we annotated the penultimate enzymes involved in the biosynthesis of individual ligands. For the third category of gene-independent ligands, such as calcium and proton, specific ligand molecules are listed.

Comparative genomic analyses indicated that, during evolution, the paracrine/juxtacrine/autocrine type of cellular communication, stemming from close communication between adjacent cells, gave rise to the long-range endocrine system in which given ligands, produced in remote tissues, travel by blood circulation to act on target cells (2, 12). Indeed, paracrine and autocrine networks outnumber endocrine interactions in extant vertebrates. To maintain homeostasis of body functions, paracrine/autocrine regulation is the common mechanism and is prevalent during early embryonic development, whereas endocrine regulation only involves a subgroup of ligands acting on remote target cell receptors.

Recent advances in DNA microarray studies have enabled genome-wide analyses of gene expression in diverse tissues (13, 14). The availability of transcriptome data based on DNA microarrays, coupled with the present matching of most plasma membrane receptors and cognate ligands, provide a unique opportunity for a genome-wide survey of the expression of paracrine/autocrine signaling systems in tissues of interest. In the present database, we provide three preplotted DNA microarray datasets, representing differential gene expression in different pituitary adenomas in man, during multiple stages of testis development in mice and at different early embryonic developmental phases. For human tumor samples, we plotted the expression of ligand-receptor pairs based on a published dataset (15). Each figure compared gene expression among normal pituitary, pituitary adenomas secreting GH, prolactin, or ACTH, and nonfunctioning pituitary adenoma. For murine testis microarray data, we plotted the expression of ligand-receptor pairs in the testis during the progression of spermatogenesis based on another published dataset (16). Transcriptome analyses of 11 stages were plotted based on expression in whole testes of BL/6–129 mice from birth through adulthood (d 0, 3, 6, 8, 10, 14, 18, 20, 30, 35, and 56 postpartum). These data provide insight into genes implicated in the maturation, maintenance, and function of testis and the integrated process of spermatogenesis. The third dataset deals with DNA microarray analyses of gene expression during development from oocyte to blastocyst in mice. This dataset includes data on oocytes at germinal vesicle and metaphase II stages, zygotes, and early embryos from 2-cell, 4-cell, 8-cell, and 16-cell to early, middle, and late blastocyst stages (17). By comparing the expression of individual ligand-receptor pairs from these datasets, users can identify putative paracrine/autocrine signaling systems in these tissues and cells.

The HPMR database also allows users to submit their own DNA microarray data from private or public source for the identification of potential paracrine/autocrine signaling systems. After submitting DNA microarray data in the Excel format (Microsoft, Seattle, WA) containing unique gene identifiers and linked gene expression levels, users can access their own DNA microarray data using passwords. Transcriptome data can be generated on-the-fly by querying directly the liganded receptome to reveal potential coexpression of ligand-receptor pairs in tissues of interest. The users can also search for coexpression of ligands and receptors in a particular family. When appropriate, reports on different expression criteria (presence vs. absence, increase vs. decrease, etc.) within individual families of receptors or ligands can also be generated based on the need of individual users.

The availability of liganded receptome to survey DNA microarray datasets provides the unique opportunity to perform genome-wide analyses of paracrine/autocrine systems. Although many ligand-receptor pairs are known to be expressed in particular tissues for paracrine/autocrine signaling, the same ligand-receptor pairs could play unique and previously uncharacterized paracrine/autocrine functions in additional tissues. For example, our use of the liganded receptome indicated the coexpression of brain-derived neurotrophic factor (BDNF)-TrkB ligand-receptor pairs in the ovary (18). Subsequent functional studies revealed that, in addition to their well-known roles in neuronal differentiation and survival (19), the BDNF-TrkB system is important for the nuclear and cytoplasmic maturation of the oocyte. In response to the preovulatory gonadotropin surge, ovarian granulosa cells produce BDNF as a paracrine factor to promote oocyte development, leading to the enhancement of early embryonic development. The same genome-wide searches further suggested a paracrine role of the ligand TWEAK (tumor necrosis factor-related weak inducer of apoptosis) and its receptor FN14 (fibroblast growth factor-inducible-14) in the ovary (20). After gonadotropin induction of ovulation, FN14 is induced and the TWEAK/FN14 paracrine signaling system could protect preovulatory follicles from excessive luteinization. These findings indicate that the TWEAK/FN14 signaling system plays important intraovarian functions in addition to its extensively documented paracrine roles in the immune system (21). As an example of the utility of HPMR to discover known ligand-receptor pairs in a previously uncharacterized tissue, users can search for BDNF to uncover its expression at multiple stages of testicular development based on the preplotted DNA microarray datasets. Of interest, the BDNF receptor TrkB [NTRK2 (neurotrophic tyrosine kinase receptor type 2)] and the coreceptor TNFRSF1B [for TNF receptor superfamily member 1B (NGFRAP1, for nerve growth factor receptor associated protein 1)] are also expressed in these testis DNA microarrays, suggesting the existence of a paracrine/autocrine system. Although data on the coreceptor TNFRSF1B were missing in the preplotted early embryonic DNA microarray dataset, users can find high expression of both BDNF and its receptor TrkB (NTRK2) throughout early embryonic development, suggesting potential paracrine/autocrine roles of this hormone/receptor system. Based on the interrogation of DNA microarray transcriptome data to reveal potential ligand-receptor pairs in a given tissue, investigators can then study protein production, cell types of expression, and downstream signaling pathways to validate the existence of paracrine/autocrine signaling systems.

ELUCIDATION OF LIGAND-RECEPTOR RELATIONSHIPS AMONG PHYLOGENETICALLY RELATED LIGANDS AND RECEPTORS TO REVEAL NOVEL LIGAND-RECEPTOR INTERACTIONS

Because receptor genes in the HPMR database are grouped based on their phylogenetic relationship, annotation of known ligand-receptor interactions within receptor families reveals parallel and coordinated evolution of phylogenetically related receptors and polypeptide ligands with similar evolutionary roots. Many receptors and ligands remain orphans because their interacting partners have not been identified. Because polypeptide ligands belonging to the same family usually interact with receptors with the same phylogenetic roots as the result of gene duplication, the present analyses could facilitate the identification of receptors for orphan ligands and cognate ligands for orphan receptors.

A comprehensive bioinformatic analysis of the evolution of several polypeptide ligand-receptor families was performed (22), including the TGFß ligand/TGFß receptor system, the chemokine/chemokine receptors, and the vascular EGF/vascular EGF receptor family. Investigation of these ligand-receptor systems suggested that analyses of interacting protein families could limit the number of candidate genes as potential binding partners. Previously, systematic pairing of orphan cytokines and cytokine receptors with their cognate partners based on phylogenetic and other information led to a marked acceleration in the elucidation of biological functions for cytokines (23).

Based on the coevolution of paralogous ligand and receptor genes, we identified type I and type II receptors for growth differentiation factor 9 (GDF9), members of the bone morphogenetic protein (BMP)/TGFß ligand family (24, 25). After genomic analyses of all paralogous type II and type I receptors for TGFß family ligands, we found no remaining orphan receptors from the serine/threonine kinase receptor family in the human genome. One can hypothesize that the ancestral TGFß ligand interacted with one type I and one type II serine/threonine kinase receptors. During evolution, gene duplication led to the generation of more than 30 human ligands with sequence homology to TGFß, whereas only five type II and seven type I serine/threonine kinase receptors evolved in man. Assuming that newly evolved TGFß paralogs interact with the limited number of paralogous serine/threonine kinase receptors, one can further hypothesize that GDF9 likely shares kinase receptors with other TGFß family ligands. Based on phylogenetic prediction followed by experimental verification, we demonstrated that BMP receptor II and activin receptor-line kinase 5 (ALK5) are type II and type I receptors for GDF9, respectively (24, 25). In addition, GDF6, GDF7, and BMP10 were found to interact with type II receptors BMP receptor II and activtin receptor IIA, as well as type I receptors ALK3 and ALK6 (26). This genomic paradigm to match paralogous polypeptide ligands with a limited number of evolutionarily related receptors highlights the importance of establishing databases to group specific families of paralogous ligand and receptor genes in completely sequenced genomes.

Although many polypeptide ligands and their corresponding receptors have been characterized, some receptors could interact with more than one ligand, and the exact ligand-receptor relationship for a given tissue of interest might not be fully elucidated. Using the massive DNA array data, one could identify specific ligand-receptor pairs important for tissue-specific paracrine regulation based on their coexpression pattern. Using gene expression profiling, an algorithm was set up to detect autocrine ligand-receptor loops based on the hypothesis that, for some autocrine pathways, the ligand and receptor are regulated by coupled mechanisms, and ligand-receptor pairs comprising such a loop show correlated mRNA expression (27). With this approach, several examples of ligand-receptor pairs exhibiting correlated expression were identified in five cancer-based gene expression datasets (27).

CONCLUSIONS

The establishment of an online liganded receptome database (HPMR, available at http://receptome.stanford.edu) provides the opportunity for users to identify previously uncharacterized paracrine/autocrine signaling systems in diverse normal and tumor tissues. The matching of known ligand-receptor pairs within individual receptor and ligand families further reveals orphan ligands and orphan receptors to facilitate future elucidation of their interacting partners. The present liganded receptome allows investigation of subgenomes containing phylogenetically related receptors and ligands. Coupled with the online analyses of transcriptome data from large DNA microarray datasets, this bioinformatic approach could facilitate studies on the physiological and pathophysiological roles of diverse paracrine/autocrine signaling systems in different mammalian tissues.

ACKNOWLEDGMENTS

We thank Sabine Mazerbourg, Jennifer M. Chen, and Eric Luo for help with annotation. We also thank S. Y. Hsu for suggestions on this manuscript.

FOOTNOTES

The authors have nothing to disclose.

First Published Online June 5, 2007

Abbreviations: ALK, Activin receptor-like kinase; BDNF, brain-derived neurotrophic factor; BMP, bone morphogenetic protein; EGF, epidermal growth factor; FGF, fibroblast growth factor; GDF9, growth differentiation factor 9; HPMR, Human Plasma Membrane Receptome.

Received for publication February 15, 2007. Accepted for publication May 16, 2007.

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