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Molecular Endocrinology 21 (2): 574-580
Copyright © 2007 by The Endocrine Society

Research Resource

Implications for Molecular Mechanisms of Glycoprotein Hormone Receptors Using a New Sequence-Structure-Function Analysis Resource

Gunnar Kleinau, Mara Brehm, Urs Wiedemann, Dirk Labudde, Ulf Leser and Gerd Krause

Leibniz-Institut für Molekulare Pharmakologie (G.K., U.W., D.L., G.K.), 13125 Berlin, Germany; FH-Giessen (M.B.), 35390 Giessen, Germany; and Institute for Computer Science (U.L.), Humboldt-Universität Berlin, 10099 Berlin, Germany

Address all correspondence and requests for reprints to: Gerd Krause, Leibniz-Institut für Molekulare Pharmakologie, Robert-Rössle-Strasse 10, D-13125 Berlin, Germany. E-mail: GKrause{at}FMP-Berlin.de.

ABSTRACT

Comparison between wild-type and mutated glycoprotein hormone receptors (GPHRs), TSH receptor, FSH receptor, and LH-chorionic gonadotropin receptor is established to identify determinants involved in molecular activation mechanism. The basic aims of the current work are 1) the discrimination of receptor phenotypes according to the differences between activity states they represent, 2) the assignment of classified phenotypes to three-dimensional structural positions to reveal 3) functional-structural hot spots and 4) interrelations between determinants that are responsible for corresponding activity states. Because it is hard to survey the vast amount of pathogenic and site-directed mutations at GPHRs and to improve an almost isolated consideration of individual point mutations, we present a system for systematic and diversified sequence-structure-function analysis (http://www.fmp-berlin.de/ssfa). To combine all mutagenesis data into one set, we converted the functional data into unified scaled values. This at least enables their comparison in a rough classification manner. In this study we describe the compiled data set and a wide spectrum of functions for user-driven searches and classification of receptor functionalities such as cell surface expression, maximum of hormone binding capability, and basal as well as hormone-induced G{alpha}s/G{alpha}q mediated cAMP/inositol phosphate accumulation. Complementary to known databases, our data set and bioinformatics tools allow functional and biochemical specificities to be linked with spatial features to reveal concealed structure-function relationships by a semiquantitative analysis. A comprehensive discrimination of specificities of pathogenic mutations and in vitro mutant phenotypes and their relation to signaling mechanisms of GPHRs demonstrates the utility of sequence-structure-function analysis. Moreover, new interrelations of determinants important for selective G protein-mediated activation of GPHRs are resumed.

THE TSH RECEPTOR (TSHR), FSH receptor (FSHR), and LH-chorionic gonadotropin (CG) receptor (LHCGR) belong to the family of glycoprotein hormone receptors (GPHRs). These receptors are members of the seven-transmembrane receptors (7TMRs), and they are evolutionary classified as rhodopsin-like receptors of family A. TSH regulates growth and function of thyroid follicular cells, and the gonadotropins LH/CG and FSH play an important role in human reproduction (1, 2, 3, 4). Therefore, this class of receptors is central to medical, pharmaceutical, and biological research.

Investigating relationships between structure and function based on analysis and comparison of mutant phenotypes is a useful strategy to identify epitopes or cores of high functional importance (5). This information is a prerequisite for understanding molecular mechanisms such as signal initiation, intramolecular signal triggering and transmission, and intermolecular signal transduction to the G protein subtypes, as well as molecular reasons for dysfunctions occurring in mutation-related diseases (6, 7). Much of pathogenic and site-directed mutation data on GPHRs has been published as functional effects of mutant phenotypes. Several reviews have considered one or two GPHRs (8, 9, 10, 11, 12, 13), but rarely all three GPHR subtypes together (7). The large body of functional data is only poorly exploited for structure-function considerations of GPHRs.

The major aims of our collection of receptor phenotypes and the developed bioinformatics tools were to provide a basis for: 1) phenotype analysis by extraction and linking of functional with spatial specificities to reveal concealed structure-function relationships; 2) mapping of interrelations between sequence, structure and functions, and visualization of functional similarities and differences between GPHRs at certain sequence positions and structural loci; 3) contributing to new hypotheses concerning intramolecular interrelations and molecular signaling mechanisms; 4) evaluating data availability (or lack of information).

Faced with the options of either completely abandoning a wealth of functional data or enabling at least a rough data comparison, we converted the functional data into classifiable unified values. The functional data (cell surface expression level, maximum of hormone binding capability, basal and hormone-induced G{alpha}s/G{alpha}q-mediated signaling) are expressed as percentages of the corresponding wild-type values (= 100%) for each experiment. We implemented a filtering and analyzing strategy that filters functional significance by user-specified queries or query combinations. Therefore, our semiquantitative tool is designed to consider the functional data in rough distant values and by a two-class separation mode. To improve the data management information concerning structural locations [two- and three-dimensional (2D and 3D)], experimental conditions, and corresponding positions at homologous GPHRs are also provided and searchable. Our SSFA tool allows the comparison and analysis of functionalities from pathogenic and site-directed mutations of TSHR, LHCGR, and FSHR. Using our data set we give examples for SSFA usage by comparing and extracting signaling pathway-specific determinants of all three GPHR subclasses.

RESULTS AND DISCUSSION

SSFA Concept and Modules
Experimentally identified inactivating and constitutive activating mutations of GPHRs provide clues about the involvement of particular residues in forming conformations defining different receptor activity states (14, 15, 16, 17, 18). Receptor phenotypes and their biological characterizations represent information about differences between the activity states (Ro to R*) (19, 20). To improve the almost isolated consideration of point mutations for structure-function studies for the deduction of molecular activation mechanisms of GPHR, we present a retrieval system for a systematic and diversified analysis of GPHR mutation data. The basic concept of our SSFA is outlined in Fig. 1Go, and the modules are shown in Fig. 2Go. A semiquantitative classification of mutant phenotypes, according to the different activity states they represent, and mapping of classified phenotypes to the 3D-structures of the receptors reveal interrelations between determinants of the corresponding activity state. More specifically, hormones and mutations give rise to different phenotypes that represent levels of various activation states, e.g. constitutive or basal activity (Fig. 1Go). Our compiled set of GPHR mutant phenotypic data (Fig. 2Go) and their percentage values enables the use of the SSFA search and filtering tools to compare and discriminate the phenotypes according to the difference ({Delta}) between the respective activation states (Fig. 1Go). This allows a semiquantitative, user-driven functional classification of mutant phenotypes. Output tools (Fig. 2Go) allow the assignment and mapping of spatial locations for similar and different functionalities as 2D-(table) and 3D-(model) outputs. This yields determinants that are responsible for forming conformations of corresponding activity states and finally 3D patterns of activation mechanisms.


Figure 1
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  		Fig. 1. Basic Concepts of SSFA for GPHRs

Starting points are the sequences of wild-type and mutation phenotypes of GPHR. Different phenotypes that are generated by mutations (black bold arrows) like constitutive activating mutations (CAMs) and activation by hormones (gray dashed arrows) represent levels of different activation states (Ro, abolished basal activity; R*1basal, basal activity, R*x... y, intermediate active states, R*, complete induced activation) of the receptor (function of the protein). Search and filtering tools of SSFA enable a semiquantitative user-driven functional classification of mutant phenotypes that discriminates according to the difference {Delta} (gray arrows) between the respective activation states. Assignment and mapping of spatial locations for similar and different functionalities to 3D structural models (structure) lead to determinants that are responsible for forming conformations of corresponding activity states and finally to 3D patterns of activation mechanisms. Example at TSHR serpentine domain model: mutations inactivating the G{alpha}s- and G{alpha}q-mediated signaling simultaneously are clustered at the transmembrane core at TMH6 and TMH7 (dark balls, dashed circle) and indicate that wild-type residues at these positions are significantly involved in formation of a general active conformation enabling activation of G{alpha}s and G{alpha}q as well. On the other hand, amino acids selectively responsible only for the G{alpha}q signaling cascade are localized (gray balls) mainly in ECL3 and intracellular loop 2 (ICL2).

 

Figure 2
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  		Fig. 2. Scheme of Modules and Service Tools

Data set: specific and general phenotype data of GPHR mutations are compiled as unified percentage values allowing comparable analysis of basal and hormone-induced cAMP and inositol phosphate level data and surface expression and hormone binding throughout all mutants (~850 at 9 November 2006). Apart from the functional data, diverse side chain properties of wild-type and mutation, location at structural regions and features, used cell type and hormone type, citations and comments, different numbering systems, etc. are also enclosed as searchable attributes for each mutation. Search: The classification of functional data according to freely adjustable percent ranges is the essential part of an advanced search to filter common, different, or even contrary phenotype data. Output: Further filtering and discrimination of distant functional values by two color modes. Assignment to structural location given as 2D (table) and 3D (model) output. hum, Human; wt, wild type.

 
Data Set and SSFA Tools
Data Set.
Using the focused influence of single-side chain substitutions on structure-functional effects, only point mutational data are extracted from the literature. About 317 mutations containing specific and basic information for human TSHR are included in the data set at time, 192 for human LHCGR, 179 for human FSHR, and about 197 for rat LHCG and 11 for rat FSHR.

The data from the following standard assays for wild-type and mutant phenotypes are included: 1) cell surface expression level; 2) hormone binding capability (maximum); 3) basal G{alpha}s-mediated activity (cAMP accumulation); 4) basal G{alpha}q-mediated activity (inositol phosphate accumulation); 5) G{alpha}s-mediated activity after hormone treatment (maximum); and 6) G{alpha}q-mediated activity after hormone treatment (maximum). The original data of each publication and assay are scaled to unified percentage values that are calibrated to the corresponding wild-type values of 100% and rounded up or down to the closest decimal place. Results that are presented as diagrams in publications are only partially readable for the extraction of absolute values. Therefore, when data are extracted from studies presenting results as diagrams, the values are strongly rounded or not specified. These mutations are marked in the comment field. However, in contrast to known mutation databases with 7TMRs (21, 22) or GPHRs exclusively (Ref.23 ; and http://www.leipzig.de/innere/TSH), we provide a functional data set from mutation studies compiled for a semiquantitative SSFA.

In addition to the functional data, we also included experimental conditions such as the cell system used (e.g. COS, HEK) or the type of hormone used for each experiment to enable comparison of data from the same or different experimental conditions. Additional features are the comparison of corresponding amino acids between all three GPHR subclasses, the numbering using the three most popular numbering systems, and the citation of the original study. Keywords in a "comment" field are searchable via the "Advanced Search," e.g. pathogenic mutations (see also supplemental data published on The Endocrine Society’s Journals Online web site at http://mend.endojournals.org) or mutations causing promiscuous hormone binding.

SSFA Tools.
For a SSFA the mutation data set is combined with different tools for focused searches and generating different output formats (see also supplemental data: Search Functions and Output Options). Regardless of type of sequence numbering, a multiple sequence alignment with a unified numbering system allows an easy localization of any residue number. This not only enables a helpful overview of which residues occur at corresponding positions in homologous GPHRs but also whether mutational data are available (supplemental Fig. 1 published on The Endocrine Society’s Journals Online web site). The queries and outputs are designed with regard to: 1) the GPHR subtypes TSHR (human), LHCGR (human and rat), and FSHR (human and rat) used mostly for mutagenesis studies; 2) the three different established sequence-numbering systems [starting with the first amino acid of the sequence (Num1]; the Ballesteros-Weinstein numbering-system (24); numbering by the G protein-coupled receptor database (GPCRDB); 3) structural epitopes or domains; 4) specified amino acid or mutation properties; 5) type of mutation (change of and to specific residue properties), 6) functional characteristics of mutations that allow a combinatorial search for various parameters; and 6) different comments, e.g. pathogenic mutations (see also supplemental data).

Data Analysis.
The Advanced Search functions allow combinations of queries for specific assays used for characterization of receptor phenotypes. This enables a precise definition of queries under inclusion and/or exclusion of user-driven data ranges of normalized standard assay values (Fig. 2Go). We implemented an analyzing strategy that filters distant values. Therefore, our semiquantitative tool is designed to classify the functional data into two rough classes. A freely adjustable coloring system allows easy discrimination of similar and different functional effects of mutations and their corresponding properties by color coding-tabulated results (supplemental Fig. 2 published on The Endocrine Society’s Journals Online web site). Colored balls at C-{alpha} positions enable the visualization of results from our approach also on one 3D structure per receptor as well as the loci for combinations of point mutations with similar and different or even opposed phenotypes. The color(s) can be chosen according to the discrimination of phenotype classes. This enables the identification, not only of clusters of loci generating common phenotypes, but also of spatially close wild-type residues that might be feasible for interaction and whose modification or disruption by mutation might therefore cause this phenotype (supplemental Fig. 3 published on The Endocrine Society’s Journals Online web site).

Signaling Specificities and Interrelated Determinants of GPHRs Revealed by SSFA
To quantify functional similarities and differences between wild-type and mutations to distinguish between several receptor activity states, the available information must be summarized and retrieved. In the following section examples for data retrieval and analysis are given for in vitro identified mutations. (See supplemental data for statistics for in vivo pathogenic mutations and examples for constitutive activating mutations.)

Interacting Counterpart Residues Constrain Wild-Type Conformation.
Two examples of our work illustrate how 3D assignment of comparable mutant phenotypes can be implemented to reveal potential interaction partners successfully.

In a previous study we suggested the roles of D633 and N676 at TSHR as interacting residues to be of general importance for all G{alpha} subtype activations (40). We reanalyzed all presently available, well-expressed transmembrane mutations of decreased G{alpha}q and G{alpha}s activity. Apart from the confirmation of the interacting residues, D633 and N676, by other mutations (45), finally three residues of equal phenotypes are clustered closely together and are very likely to interact with each other (see Fig. 3Go).


Figure 3
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  		Fig. 3. Amino Acids of High Importance for the Hormone-Induced Receptor Conformation

SSFA query revealing mutations that exclusively affect hormone-induced G{alpha}q- and G{alpha}s-mediated signaling. The natural amino acids at these positions are explicit signaling determinants, and mutations either prevent the formation of the active state upon hormone binding or might stabilize the inactive state and block further activation. Such a query reveals 12 TSHR mutations with less than 50% hormone induced mediated signaling activity compared with the wild type. Further query specifications (filtering) are: cell surface expression and maximum binding capability greater than 50%; hormone independent basal activity between 50–140% compared with the wild type. Used borderlines are extracted by analysis of inactivating pathogenic mutations (iPMs) of GPHRs. For most of characterized iPMs the receptor functionalities are less than 50% compared with the wild type. Three mutations (TMH6 D633R; TMH7 N670A, N674A) are simultaneously important for both G{alpha}q- and G{alpha}s-mediated signaling. In conclusion, these three amino acids are fundamental for both signaling pathways, and nine mutations exclusively impair the G{alpha}q-mediated signaling capability.

 
Other experimental studies, on the pathogenic constitutive activating mutation, V509A, located in transmembrane helix (TMH)3 of TSHR, confirmed A593 as an predicted interaction partner that is constraining the wild-type basal active conformation (43). The interaction partner was predicted at this time by a previous offline version of SSFA tools, which found A593 at TMH5 to be located closely to V509 at TMH3 and that this position had already been reported as a constitutive active TSHR mutation [A593N (44)].

Amino Acids of High Importance for the Conformation of Hormone-Induced Receptor Activation.
SSFA queries for mutations that only affect hormone-induced G{alpha}q- and G{alpha}s-mediated signaling can reveal positions that are explicit signaling determinants. These mutations either prevent the formation of the active state upon hormone binding or might stabilize the inactive state and block further activation.

Such queries showed 12 TSHR mutations with less than 50% hormone-induced G{alpha}q-mediated signaling compared with wild type. Three of these mutations (TMH6-D633R (40); TMH7-N670A (40), N674A (40) show inactivation simultaneously for G{alpha}s as well. Therefore, nine mutations selectively affect G{alpha}q-mediated signaling (Fig. 3Go). These nine amino acids are localized in the extracellular loops ECL1 (39), ECL3 (42), the junction of ECL3/TMH7 (42), and the intracellular loop 2 (41). Together with positions of selective mutations initiating constitutive G{alpha}q activity (see supplemental data) these mutations can be considered as determinants responsible for forming a G{alpha}q-selective conformation. LHCGR and FSHR mutants with common phenotypes could not be retrieved. Thus, at present, only G{alpha}q-mediated signaling-selective determinants could be identified. The amino acids of high importance for both G{alpha}q- and G{alpha}s-mediated signaling are located in the center of the TM domain between TMH6 and TMH7. These positions are key players both for the G{alpha}q- and G{alpha}s-mediated signaling process.

The compilation of unified percentage values of the mutational effects allowed, for the first time, exact discrimination between common loci/positions that are important for maintaining basal activity and for generation constitutive activity (as exemplarily shown in Fig. 3Go). Moreover, the simultaneous consideration of different assay data enables us to distinguish between loci/positions that are, in general, important for all G{alpha} subtype signaling pathways and additionally to discriminate positions important to determine conformations either for G{alpha}q- or for G{alpha}s subtype signaling pathways (Fig. 3Go). Apart from expected positions at the intracellular loops, our results show that a conformation for G{alpha}q-selective signaling is also dependent on a defined spatial cluster of positions at the extracellular loops that constrain a G{alpha}q-selective conformation. These results provide new implications toward understanding the molecular cascade of G{alpha} subtype-dependent signal transduction through the transmembrane domain.

In summary, we have developed a system of bioinformatics tools combined with experimental, biochemical, and structural data that allows the analysis and comparison between GPHR subtypes. This system may open new possibilities, on the one hand, for more focused studies and, on the other hand, for broad survey of GPHRs. From comparison of GPHR mutation data utilizing the SSFA resource, we hypothesize that the level of basal activity of a receptor may prejudice the capability for constitutive activation caused by mutation. Furthermore, extracting common and selective constitutive activating mutations and inactivating mutations for G{alpha}s- and G{alpha}q-mediated cAMP and inositol phosphate accumulation revealed relevant hot spots, which are involved in the signaling process for one or both G protein subtypes.

MATERIALS AND METHODS

Alignment
The multiple sequence alignment of human TSHR, human LHCGR, rat LHCGR, human FSHR, and rat FSHR is completed by combining automatic multiple sequence alignment techniques using CLUSTAL W (35) with a manual refinement (e.g. structural corresponding extracellular cysteines aligned to each other, no gaps allowed within the TM region according to the x-ray structure of rhodopsin).

The amino acids are linked to structural features (location, structure, substructure). Potential N-glycosylation sites are indicated in brown; extracellular cysteines and predicted helices are yellow and gray, respectively. The first amino acids of leucine-rich repeats (LRRs) and the amino acids that are highly conserved in the helices of GPCR class A are marked in bold.

Numbering
To achieve a unique identification of residues and a transparent navigation within the sequence numbering, we implemented the available different sequence numbering schemes according to the following alignment. 1) The sequence for each specific GPHR is provided separately, including the signal peptide (Num1). Published mutagenesis data for FSHR and LHCGR, given in a numbering excluding the signal peptide sequence, are translated into the numbering system including the signal peptide. To facilitate analysis of the comparative relationship between the receptors, two widely spread GPCR residue indexing systems are also offered: 2) the GPCRDB numbering system; and 3) Ballesteros-Weinstein (Ba-We) nomenclature (24). A highly conserved residue at each helix is used as a reference common for all GPCRs in family A. For example, the highly conserved N at TMH1 is defined as 1.50, and the highly conserved P from the NPxxY motif of TMH7 is defined as 7.50.

Structures and Structural Templates for GPHR Models
Bovine rhodopsin, the only currently available x-ray crystal structure (36) of 7TMRs is well established as a structural template for homologous 7TMR structural models (serpentine domain and helix 8). The procedure for generating homologous serpentine domain models for human TSHR and human LHCGR and their specific differences was described elsewhere (37). Models for rat LHCGR, human FSHR, and rat FSHR were generated in the same manner. Common to all GPHRs is a large extracellular N-terminal ectodomain containing the hormone-binding site in the LRR domain. An x-ray crystal structure (38) of the FSHR LRR domain-FSH complex is available as a structural template for the N-terminal ectodomain excluding the hinge region. Waiving the hormone structure, this template was used for LRR models of human TSHR, human LHCGR, rat LHCGR, and rat FSHR. They were generated using the same procedure as described previously for the modeling of GPHR LRR domains including Cysteine-box 1 (15). Because the templates of the models are originally based either on the inactive rhodopsin or on the hormone-bound LRR conformation and do not (and can not) represent single mutations (or even multiple mutations), conformations of different activity states (inactive, basal decreased, basal active, partial active, complete active) of the overall receptor, they are shown only as C-{alpha} atom trace representations at the wild-type receptor model in our system. In our current version of the system we deliberately excluded a side chain representation to avoid, on one hand, false predictions of side chain interactions notably for combinations of mutations and, on the other hand, to avoid the impression generating conformations of respective activity states of the overall receptor, which, apart from residues replacement and rotamer selection, would demand much more complex modeling studies.

Database Technology
Data are stored in a MySQL database (http://www.mysql.com). The web interface was implemented using PHP (http://www.php.net). Additionally, functions for internal database administrations have been developed, but access is restricted (for data insertion and updating). The molecular structures/models are displayed using the Jmol software (http://www.jmol.org).

ACKNOWLEDGMENTS

We thank Frank Eisenmenger for system administration and information technology support. We thank Susanne Neumann, Stefano Costanzi, Stanislav Engel (National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Disease, Bethesda, MD), Ralf Paschke and Holger Jäschke (III. Department of Medicine, Leipzig, Germany), and Silke Trissl (Humboldt Universität zu Berlin) for helpful discussions. We thank Victoria Higman and Elisabeth Dowler for critical reading of the manuscript and their constructive suggestions.

FOOTNOTES

This work was supported by the Deutsche Forschungsgemeinschaft (Grant KR 1273/1-1).

Disclosure Statement: The authors have nothing to disclose.

First Published Online November 16, 2006

Abbreviations: CG, Choriogonadotropin; 2D and 3D, two- and three-dimensional, respectively; ECL, extracellular loop; FSHR, FSH receptor; GPCR, G protein-coupled receptor; GPCRDB, GPCR database; GPHRs, glycoprotein hormone receptors; LHCGR, luteinizing hormone-choriogonadotropin receptor; LRR, leucine-rich repeat; SSFA, Sequence-Structure-Function Analysis; TMH, transmembrane helix; 7TMR, seven-transmembrane spanning receptor; TSHR, TSH receptor.

Received for publication July 31, 2006. Accepted for publication November 8, 2006.

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