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Original Paper

Free Access

Computational Analysis of Missense Variants of G Protein-Coupled Receptors Involved in the Neuroendocrine Regulation of Reproduction

Min L. · Nie M. · Zhang A. · Wen J. · Noel S.D. · Lee V. · Carroll R.S. · Kaiser U.B.

Author affiliations

Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass., USA

Corresponding Author

Le Min

Division of Endocrinology, Diabetes and Hypertension

Brigham and Women's Hospital

221 Longwood Avenue, Boston, MA 02115 (USA)

E-Mail lmin1@partners.org.

Related Articles for ""

Neuroendocrinology 2016;103:230-239

Abstract

Introduction: Many missense variants in G protein-coupled receptors (GPCRs) involved in the neuroendocrine regulation of reproduction have been identified by phenotype-driven or large-scale exome sequencing. Computational functional prediction analysis is commonly performed to evaluate their impact on receptor function. Methods: To assess the performance and outcome of functional prediction analyses for these GPCRs, we performed a statistical analysis of the prediction performance of SIFT and PolyPhen-2 for variants with documented biological function as well as variants retrieved from Ensembl. We obtained missense variants with documented biological function testing from patients with reproductive disorders from a comprehensive literature search. Missense variants from individuals with known reproductive disorders were retrieved from the Human Gene Mutation Database. Missense variants from the general population were retrieved from the Ensembl genome database. Results: The accuracies of SIFT and PolyPhen-2 were 83 and 85%, respectively. The performance of both prediction tools was greater in predicting loss-of-function variants (SIFT: 92%; PolyPhen-2: 95%) than in predicting variants that did not affect function (SIFT: 54%; PolyPhen-2: 57%). Concordance between SIFT and PolyPhen-2 did not improve accuracy. Surprisingly, approximately half of the variants retrieved from Ensembl were predicted as loss-of-function variants by SIFT (47%) and PolyPhen-2 (54%). Conclusion: Our findings provide new guidance for interpreting the results and limitations of computational functional prediction analyses for GPCRs and will help to determine which variants require biological function testing. In addition, our findings raise important questions regarding the link between genotype and phenotype in the general population.

© 2015 S. Karger AG, Basel


Introduction

Phenotype-driven exome sequencing [1] and large-scale next-generation DNA sequencing of the general population [2] have revealed numerous rare nonsynonymous missense variants in protein-coding DNA sequences. The identification of causal missense variants that alter human phenotypes, in particular to induce disease states, is one of the fundamental goals of human genetics, with the objective of providing crucial insights into the biology connecting genotype and phenotype and potentially facilitating the prediction of disease onset. Performing biological testing to determine the effect of a missense variant on the function of the encoded protein usually produces reliable results but is laborious and time-consuming. In this context, the search for alternative and reliable methods for assigning effects of novel variants on protein function is of primary importance. There are various computational in silico prediction tools available for predicting the function of variants, using information derived from sequence similarity [3] and phylogenetic profiles [4]. However, the performance of available functional prediction tools varies between proteins in different functional categories [5,6,7,8]. Delineating the effects of missense variants identified in G protein-coupled receptors (GPCRs), a large family of receptors involved in signal transduction and cellular response to outside signals and the most common drug targets (the targets account for 27% of all FDA-approved drugs) [9], faces this challenge. Sequence alignment of 94 GPCRs revealed many highly conserved amino acids [10], suggestive of potentially damaging effects if mutations occur in these highly conserved amino acids. Bioinformatics approaches have been used to predict intrinsically disordered regions of GPCRs - regions lacking a stable three-dimensional structure and playing a role in intra- and extracellular plasticity and protein-protein interactions of GPCRs - and regions predicting G protein coupling specificity [11,12]. In the GPCR family, gonadotropin-releasing hormone receptor (GnRHR), kisspeptin receptor (KISS1R), prokineticin receptor 2 (PROKR2), and tachykinin receptor 3 (TACR3) have been found to play key roles in the central neuroendocrine regulation of reproductive function [13]. In these GPCRs, more than 300 missense variants have been found, either from patients with phenotypic reproductive disorders or from large-scale genomic sequencing of general populations. Some of the variants identified in patients with reproductive disorders have undergone in vitro biological function testing [13,14], which has aided in the interpretation of the pathogenic relationship between genotype and phenotype. Computational prediction results from Sorting Intolerant from Tolerant (SIFT) and Polymorphism Phenotyping-2 (PolyPhen-2) are available for most of the variants, but the correlation between in silico prediction and in vitro biological function for these GPCR missense variants has not been well established.

Materials and Methods

Data and Materials

The missense variants of GnRHR, KISS1R, PROKR2, and TACR3 identified in patients with known phenotypes were acquired from The Human Gene Mutation Database (HGMD) (http://www.hgmd.org). The overall missense variants in representative populations were obtained from the Ensembl genome database (http://useast.ensembl.org/index.html). The data from HGMD and Ensembl were retrieved in July 2014. The missense variants with documented biological function tests were retrieved through a comprehensive literature search in PubMed of articles published between 1997 and July 2014.

Computational Analysis

Computational functional prediction results for each variant with documented biological function testing were obtained using SIFT [8] and PolyPhen-2 [15] analyses. We defined the function predicted by SIFT and PolyPhen-2 following the programs' parameters. For SIFT, the predicted function of a variant is characterized as tolerated or deleterious based on the SIFT score (0-1). A score ≤0.05 is defined as deleterious, and a score >0.05 is defined as tolerated. For PolyPhen-2, the predicted function of a variant is characterized as benign (score 0-0.5), possibly damaging (score >0.5-0.9), or probably damaging (score >0.9) based on a score scale of 0-1. For in vitro biological function tests, we characterized the function as normal (maximum response of a variant at least 80% of the maximum response of the corresponding wild-type receptor), partial loss of function (maximum response of a variant between 20 and 80% of the maximum response of the wild-type receptor), or complete loss of function (maximum response <20% of the maximum response of the wild-type receptor).

Statistical Analysis

In order to perform statistical analyses, we simplified the characterization to be either benign or damaging for both computational prediction tests and biological function test results. As such, benign was defined as tolerated in SIFT, benign in PolyPhen-2, and normal (>80% of wild type) in biological function tests. Damaging was defined as deleterious in SIFT, either possibly damaging or probably damaging in PolyPhen-2, and either partial or complete loss of function (<80% of wild type) in biological function tests. Concordance for the in silico analyses was defined as both computational prediction tools having the same functional prediction outcome. Statistical analyses, including true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (AC), and Matthews correlation coefficient (MCC), were calculated using the formulas presented below.

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Results

Summary of Variants in GnRHR, KISS1R, PROKR2, and TACR3 with Documented Biological Function Test Results and Their Correlation with the Computational Functional Prediction Tools

A total of 52 missense variants with documented biological function testing were identified in GnRHR, KISS1R, PROKR2, and TACR3through a literature search in PubMed. All of the variants with biological function tests were identified in patients with reproductive disorders. Table 1 summarizes the nucleotide (position and exon) and amino acid (position and receptor domain) changes, computational function prediction results by SIFT and PolyPhen-2, and biological function test results. Nineteen of 52 variants were identified in GnRHR, 7 in KISS1R, 17 in PROKR2, and 9 in TACR3. Ten of the variants had normal biological function testing, 21 had partial loss of function, and 21 had complete loss of function (table 1). All 10 variants reported with normal biological function were either PROKR2 or TACR3 variants, except one in KISS1R.

Table 1

Summary of missense variants with documented biological function tests retrieved from a literature search

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To evaluate the performance of the individual computational prediction tools, we analyzed the outcome and compared the findings with the results of biological function tests. Figure 1 summarizes the outcome of SIFT and PolyPhen-2 computational prediction testing and the comparison with biological function test results. Among variants that were predicted by SIFT to be tolerated, 53% had normal results on biological function testing (fig. 1a), while 57% of the variants predicted by PolyPhen-2 to be benign had normal results on biological function testing (fig. 1b). When SIFT predicted the variants to be deleterious, 93% of them had impaired function, based on documented in vitro biological testing (fig. 1a). Similarly, among variants predicted by PolyPhen-2 to be possibly or probably damaging, 100 and 94% had impaired function based on in vitro biological test results (partial or complete loss of function), respectively (fig. 1b). Overall, both tools performed better in predicting loss-of-function variants. In contrast, the rate for correctly predicting a variant to have normal function was only slightly above 50% for both prediction programs.

Fig. 1

Comparison of computational predictions of the function of GPCR variants by SIFT and PolyPhen-2 with documented biological function test findings. SIFT (a) and PolyPhen-2 (b) were used to predict the function of 52 variants in GnRHR, KISS1R, PROKR2, and TACR3, for which the results of biological function tests are available. SIFT categorized variants as tolerated or deleterious; PolyPhen-2 categorized variants as benign, possibly damaging, or probably damaging. These predictions were compared with the results of in vitro biological function testing for each variant. Biological function was categorized as normal, partial loss of function, or complete loss of function. There were 10 variants with normal function, 21 with partial loss of function, and 21 with complete loss of function. The percentage of mutations predicted by each program in each category was then calculated.

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Concordance between the Prediction Tools

Since there is no single perfect tool that can guarantee correct computational prediction of biological function of a GPCR variant, many scientists tend to use more than one prediction tool with the expectation that concordance between or among the prediction tools increases the accuracy of the computational predictions. To evaluate whether the use of more than one prediction tool improved the computational prediction tool performance, we analyzed the concordance between the prediction tools assessed and matched the concordance rate with the results of biological function testing of the variants. Figure 2 shows the results of the concordance analysis. The concordance rates were calculated as the number of the variants predicted by SIFT and PolyPhen-2 to have the same functional outcome, divided by the total 52 variants with verified in vitro biological function. The overall concordance rate was 98%, but decreased to 83% after matching the computational predictions for concordance with biological function test results (fig. 2). PolyPhen-2 and SIFT concordantly predicted 25% of the 52 variants as benign. This decreased to 13.5% for concordance between the two computational programs and the documented biological function test results (fig. 2). Both tools concordantly predicted 73% of the total 52 variants as damaging. This rate again decreased, to 62.8%, upon matching the concordance with biological function test results (fig. 2).

Fig. 2

Concordance rates between predictions by SIFT and PolyPhen-2 and between the in silico prediction programs and in vitro biological function testing. The concordance between SIFT and PolyPhen-2 in predicting variants to be either benign or damaging was calculated and represented as the percentage of the total number of variants. The variants with concordant computational predictions were then compared with their in vitro biological function test results. The concordance between the two programs and the biological function test results was then calculated as the percentage of the total 52 variants with known biological function.

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Statistical Analyses of the Performance of the Computational Prediction Tools Compared to Biological Function Test Outcomes

We analyzed sensitivity, specificity, PPV, NPV, accuracy, and MCC to further evaluate the performance of the prediction programs. Of note, damaging (possibly or probably) or deleterious was defined as positive (loss of function), while benign or tolerated was defined as negative (normal function). Table 2 summarizes the results of these statistical analyses. The concordant prediction group is defined as the group in which both in silico tools concordantly predict the variants to have the same function. Among 42 variants with documented impaired biological function based on in vitro testing, each prediction program alone, as well as the combined prediction from both tools, predicted 36 variants to be damaging. Among 10 variants with normal biological function test results, PolyPhen-2 predicted 8 to be benign, while SIFT predicted 7 to be benign; a concordant prediction from both computational tools also correctly predicted 7 to be benign. One variant with normal biological function tests had discordance in functional prediction by the two in silico prediction tools.

Table 2

Statistical analysis of missense variants with documented biological function testing

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The sensitivity (the ability to identify variants with loss of function) was 86% in all groups. The specificity (the ability to identify variants with normal function) was 70, 80 and 78% for the SIFT, PolyPhen-2, and concordant prediction groups, respectively. The PPVs (confidence that the prediction of a variant to be damaging in biological testing was correct) were 92, 95, and 95% for the SIFT, PolyPhen-2, and concordant prediction groups, respectively. The NPVs (confidence that a prediction of a variant to be benign was correct) were 54, 57, and 54%, respectively. The prediction accuracies were 83, 85, and 84% for SIFT, PolyPhen-2, and the concordant prediction groups, respectively. The MCC was 0.5, 0.6, and 0.6, respectively.

Proportion and Distribution of Missense Variants with Documented or Unknown Phenotype and Biological Function, and Functional Prediction Results

We retrieved nonsynonymous missense variants in GNRHR, KISS1R, PROKR2, and TACR3from Ensembl, one of the largest databases for human variants. After excluding duplicates and the variants with known pathogenic clinical significance, there were a total of 322 missense variants derived from the Ensembl database. Among the variants, 16% (52), 12% (40), 30% (98), and 41% (132) were identified in GnRHR, KISS1R, PROKR2, and TACR3, respectively. In comparison, 95 variants identified by phenotype-driven sequencing were retrieved from HGMD. Among phenotype-related variants retrieved from HGMD, 36% (34), 13% (12) 38% (36), and 14% (13) were identified in GnRHR, KISS1R, PROKR2, and TACR3, respectively. Among 52 variants identified by phenotype-driven sequencing with documented in vitro biological function test results, 37% (19), 13% (7) 33% (17), and 17% (9) were identified in GnRHR, KISS1R, PROKR2, and TACR3, respectively (table 3).

Table 3

Summary of all missense variants retrieved from Ensembl, HGMD, and the literature search

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We took advantage of the availability of SIFT and PolyPhen-2 prediction results in Ensembl and analyzed the computational functional predictions of the nonsynonymous missense variants retrieved from Ensembl. Interestingly, SIFT and PolyPhen-2 predicted 53% (171/322) and 46% (146/316) of the variants to be benign, respectively, with the other 47% (151/322) and 54% (170/316) as deleterious or damaging (table 4). PolyPhen-2 prediction results for 6 variants were not available. We compared the concordance between the two prediction programs. Among 169 variants that were predicted to be benign (tolerated) by SIFT, 72% (122/169) were concordantly predicted to be benign by PolyPhen-2. Among 150 variants that SIFT predicted to be damaging (deleterious), 145 (97%) were predicted to be damaging by PolyPhen-2 as well. In turn, when we calculated the concordance based on the PolyPhen-2 predictions, 86% (126/146) and 82% (155/170) were concordantly predicted by SIFT to be benign (tolerated) and damaging (deleterious), respectively (online suppl. table S1; for all suppl. material, see www.karger.com/doi/10.1159/000435884).

Table 4

In silico prediction of the variants derived from the Ensembl database

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Discussion

With the advances in genetic sequencing methodologies, the list of new nonsynonymous missense variants continues to increase rapidly. Accurate determination of the effect of each missense variant on protein function is a fundamental step to provide the connection between genotype and phenotype. Biological function testing is an effective way to determine the pathogenicity of a newly identified missense variant, which can provide revolutionary advances in our knowledge of human physiology and pathophysiology. For example, after a novel Leu148Ser variant of KISS1R was identified in a patient with hypogonadotropic hypogonadism (HH), our laboratory performed biological function testing and confirmed that the variant caused loss of function of KISS1R [16]. This finding revealed the key role of KISS1R and its ligand kisspeptin in the regulation of reproductive function and advanced our understanding of reproductive control upstream of GnRH. Measuring changes in second messengers involved in GPCR-mediated signal transduction is an effective way to assess the impact of variants on receptor function. GnRHR, KISS1R, PROKR2, and TACR3are Gq-coupled receptors; activation of these receptors is reflected by changes in inositol phosphate production (IP), intracellular calcium concentrations ([Ca2+]i), and ERK phosphorylation. The assays for measurement of these second messengers have been well established [17,18,19]. However, biological function testing can be costly, labor-intensive, and time-consuming. As shown in this study, only 50% of the missense variants identified to date from phenotype-driven sequencing have had biological function tests (table 3). As an alternative, computational functional prediction tools have been used widely to predict the impact of missense variants on protein function, in an attempt to identify disease-causing variants. However, the accuracy of each prediction tool, while impressive, is not yet ideal. In a recent comprehensive analysis, the accuracies of nine widely used prediction methods were in the range of 60-82% [5].

In this study, we analyzed the performance of two frequently used prediction tools, SIFT and PolyPhen-2, in predicting the functional effects of missense variants of GPCRs involved in the central neuroendocrine regulation of reproduction. Another important rationale for our selection of these two prediction programs for this study is that the results of computational analyses of GPCR variants by SIFT and PolyPhen-2 are available in the Ensembl database. Analysis of the performance of these two programs will aid investigators in the proper interpretation of the results of computational analysis of variants retrieved from Ensembl. There are additional computational prediction programs available, such as Panther (http://www.pantherdb.org/tools/csnpScoreForm.jsp) and Mutation Taster (http://www.mutationtaster.org). Computational prediction programs principally use several attributes related to protein structure, evolutionary conservation, phylogeny, biophysical characteristics of the substitution, secondary structural information, and chain flexibility. The programs share some similarities but also have some distinct features [5]. SIFT makes inferences from sequence similarity using mathematical operations [8], while PolyPhen2 employs a combination of sequence- and structure-based attributes for the description of an amino acid substitution, and the effect of a mutation is predicted by a naive Bayesian classifier [15,20].

Although the mechanisms of prediction differ between these two programs, both performed well in predicting loss-of-function variants (fig. 1). In contrast, however, the capability for precisely identifying normal function variants was inferior for both SIFT and PolyPhen-2, and as such, more than 40% of variants with demonstrated impaired receptor function by biological testing were falsely predicted to be benign by each method (fig. 1). This is an important consideration because investigators may stop pursuing biological function tests if in silico programs predict a novel variant to be benign. This statement is supported by our finding that only a small proportion (13/52 in SIFT, 14/52 in PolyPhen-2) of variants with documented biological function testing were predicted to be benign (table 1), whereas half of all missense variants from Ensembl were predicted to be benign by both programs (table 4). Alternatively, this difference in the proportion of variants predicted to be benign in the two datasets may be because variants in the first set were identified in patients with reproductive phenotypes, and were therefore more likely to be damaging, whereas variants from large-scale sequencing were found in general populations with unknown phenotypes. Since HH and central precocious puberty are rare, variants in these populations are unlikely to come from individuals with an underlying reproductive phenotype, so the variants in this population should be more likely to be benign. This tendency for investigators to stop pursuing biological function tests if in silico programs predict a novel variant to be benign may also explain why the benign variants with documented biological function reported were primarily in PROKR2 and TACR3, but not in GnRHR, since most of the studies of GnRHR were done earlier than the more recent studies of PROKR2 and TACR3, at a time when it was less accepted to report benign variants. In addition to loss-of-function mutations reported in these GPCRs in patients with GnRH deficiency, a gain-of-function mutation, R386P KISS1R, has been reported in association with central precocious puberty [21]. However, neither SIFT nor PolyPhen-2 is designed to predict variants with gain of function.

To increase the accuracy of computational functional prediction analysis, a common approach is to apply more than one prediction tool. Interestingly, in this study, when the concordance between SIFT and PolyPhen-2 predictions was matched with biological function testing outcomes, the performance of the concordant predictions was not superior to the performance of a single program (table 2). Evidently, both programs can concordantly predict the incorrect function in a variant since concordance rates were always higher for the two in silico prediction tests than the rates when concordance of the prediction tests was further matched with biological function testing outcomes, although these differences did not reach statistical significance (fig. 2). It is even more difficult to interpret the function of a variant if the programs' predictions are discordant.

In our study, sensitivity represents the ability to identify loss-of-function variants. Both in silico prediction tools had the same sensitivity using biological function testing as the gold standard (table 2). Although even biological testing may not be perfect, there is no other, better method to be used than the gold standard. In contrast, our statistical analysis showed that the ability to identify variants with normal function by in silico prediction (specificity and NPV) was inferior to the ability to identify loss-of-function variants (sensitivity and PPV). Given the fact that more than 40% of variants predicted to be benign were actually deleterious/damaging in biological function testing, if a novel GPCR variant is predicted to be benign by in silico prediction tools, a biological function test may nonetheless still be indicated, particularly if there is a strong phenotype correlation. On the other hand, performance of biological function testing may not be necessary if a variant is predicted to be damaging or deleterious, as more than 90% of variants predicted to be damaging were true loss-of-function variants in biological function testing assays (fig. 1; table 2). The interpretation of concordant prediction analysis is complex. After excluding one variant with discordant predictions, the combined prediction analysis by SIFT and PolyPhen-2 had comparable performance outcomes when compared with the single-program predictions (table 2). The accuracies range from 83 to 86% for SIFT and PolyPhen-2, respectively, which, interestingly, was better than in an earlier comprehensive analysis [5].

The MCC [22] is an important statistical parameter as it is unaltered by differing proportions of benign and damaging variants, while PPV and NPV may be affected by the prevalence in varied populations [23]. Because of its insensitivity to variation in sample size, the MCC gives a more balanced assessment of performance than the other performance measures [24]. In light of the reference range (-1 to 1), a coefficient of +1 represents a perfect prediction, 0 equals random prediction, and −1 resembles complete discrepancy between prediction and observation. Compared with an earlier study [5], the MCC in this study (table 2) indicates a fair performance of SIFT, PolyPhen-2, and concordant predictions, without any differences between the different analyses.

Ensembl is one of several well-known genome browsers for the retrieval of genomic information. We chose Ensembl to retrieve missense variants of GPCRs involved in the neuroendocrine regulation of reproductive function because it also provides information on the functional prediction results by SIFT and PolyPhen-2. Analysis of the computational prediction results revealed a surprising finding that half of the variants were predicted to be deleterious/damaging by SIFT and PolyPhen-2 (table 4). Interpretation of the results of these prediction analyses in this setting is even more difficult since the majority of the missense variants were found in large-scale genomic sequencing of general, unphenotyped populations rather than from phenotype-driven sequencing (table 3). In view of the fact that both SIFT and PolyPhen-2 accurately predict more than 90% of variants with documented impaired function as deleterious/damaging (fig. 1; table 2), it is likely that most variants retrieved from Ensembl and predicted by SIFT and PolyPhen-2 to be deleterious/damaging do have impaired function. Linking these genotype findings to phenotype is difficult since the majority of the variants from Ensembl were not identified by phenotype-driven sequencing. In the general population, the prevalence of HH is extremely low, estimated to be about 0.01-0.025% [25]. Individuals harboring loss-of-function variants may have a subclinical phenotype or no phenotype at all, in light of diverse pathogenetic mechanisms and inheritance patterns (autosomal recessive [26], autosomal dominant [14], haploinsufficiency, digenic inheritance [27,28], and oligogenic inheritance [29]) of GPCR loss-of-function variants. We speculate that if a variant co-segregates with an HH phenotype in a family, one might expect that it is more likely to be deleterious, but we did not specifically test this hypothesis in this study.

In conclusion, we have found that the performance of the computational prediction programs SIFT and PolyPhen-2 is effective in predicting loss of function for variants of GPCRs involved in the neuroendocrine regulation of reproduction. In contrast, both programs were less effective for predicting benign variants - more than 40% of variants predicted to be benign showed loss of function in biological function tests. Based on these findings, we recommend performing biological function testing even for variants predicted to be benign, especially if there is a close phenotype correlation. The surprising finding that a significant number of the variants identified in large-scale sequencing were predicted to be loss-of-function variants creates an immense challenge for the interpretation of their clinical significance.

Acknowledgments

This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health (NICHD/NIH), through cooperative agreement U54 HD28138 as part of the Specialized Cooperative Centers Program in Reproduction and Infertility Research and by R01 HD19938 (to U.B.K.), and by NICHD/NIH K08 HD070957 (to L.M.).

Disclosure Statement

The authors have nothing to disclose.


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  28. Pitteloud N, Quinton R, Pearce S, Raivio T, Acierno J, Dwyer A, Plummer L, Hughes V, Seminara S, Cheng YZ, Li WP, Maccoll G, Eliseenkova AV, Olsen SK, Ibrahimi OA, Hayes FJ, Boepple P, Hall JE, Bouloux P, Mohammadi M, Crowley W: Digenic mutations account for variable phenotypes in idiopathic hypogonadotropic hypogonadism. J Clin Invest 2007;117:457-463.
  29. Silveira LF, Trarbach EB, Latronico AC: Genetics basis for GNRH-dependent pubertal disorders in humans. Mol Cell Endocrinol 2010;324:30-38.
  30. Costa EM, Bedecarrats GY, Mendonca BB, Arnhold IJ, Kaiser UB, Latronico AC: Two novel mutations in the gonadotropin-releasing hormone receptor gene in Brazilian patients with hypogonadotropic hypogonadism and normal olfaction. J Clin Endocrinol Metab 2001;86:2680-2686.
  31. Beranova M, Oliveira LM, Bedecarrats GY, Schipani E, Vallejo M, Ammini AC, Quintos JB, Hall JE, Martin KA, Hayes FJ, Pitteloud N, Kaiser UB, Crowley WF Jr, Seminara SB: Prevalence, phenotypic spectrum, and modes of inheritance of gonadotropin-releasing hormone receptor mutations in idiopathic hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2001;86:1580-1588.
  32. Bedecarrats GY, Linher KD, Janovick JA, Beranova M, Kada F, Seminara SB, Michael Conn P, Kaiser UB: Four naturally occurring mutations in the human GNRH receptor affect ligand binding and receptor function. Mol Cell Endocrinol 2003;205:51-64.
  33. Soderlund D, Canto P, de la Chesnaye E, Ulloa-Aguirre A, Mendez JP: A novel homozygous mutation in the second transmembrane domain of the gonadotrophin releasing hormone receptor gene. Clin Endocrinol (Oxf) 2001;54:493-498.
  34. Tello JA, Newton CL, Bouligand J, Guiochon-Mantel A, Millar RP, Young J: Congenital hypogonadotropic hypogonadism due to GNRH receptor mutations in three brothers reveal sites affecting conformation and coupling. PLoS One 2012;7:e38456.
  35. Antelli A, Baldazzi L, Balsamo A, Pirazzoli P, Nicoletti A, Gennari M, Cicognani A: Two novel GnRHR gene mutations in two siblings with hypogonadotropic hypogonadism. Eur J Endocrinol 2006;155:201-205.
  36. Maya-Nunez G, Janovick JA, Aguilar-Rojas A, Jardon-Valadez E, Leanos-Miranda A, Zarinan T, Ulloa-Aguirre A, Conn PM: Biochemical mechanism of pathogenesis of human gonadotropin-releasing hormone receptor mutants Thr104Ile and Tyr108Cys associated with familial hypogonadotropic hypogonadism. Mol Cell Endocrinol 2011;337:16-23.
  37. de Roux N, Young J, Misrahi M, Genet R, Chanson P, Schaison G, Milgrom E: A family with hypogonadotropic hypogonadism and mutations in the gonadotropin-releasing hormone receptor. N Engl J Med 1997;337:1597-1602.
  38. Caron P, Chauvin S, Christin-Maitre S, Bennet A, Lahlou N, Counis R, Bouchard P, Kottler ML: Resistance of hypogonadic patients with mutated GNRH receptor genes to pulsatile GNRH administration. J Clin Endocrinol Metab 1999;84:990-996.
  39. Pralong FP, Gomez F, Castillo E, Cotecchia S, Abuin L, Aubert ML, Portmann L, Gaillard RC: Complete hypogonadotropic hypogonadism associated with a novel inactivating mutation of the gonadotropin-releasing hormone receptor. J Clin Endocrinol Metab 1999;84:3811-3816.
  40. Karges B, Karges W, Mine M, Ludwig L, Kuhne R, Milgrom E, de Roux N: Mutation Ala(171)Thr stabilizes the gonadotropin-releasing hormone receptor in its inactive conformation, causing familial hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2003;88:1873-1879.
  41. de Roux N, Young J, Brailly-Tabard S, Misrahi M, Milgrom E, Schaison G: The same molecular defects of the gonadotropin-releasing hormone receptor determine a variable degree of hypogonadism in affected kindred. J Clin Endocrinol Metab 1999;84:567-572.
  42. Layman LC, Cohen DP, Jin M, Xie J, Li Z, Reindollar RH, Bolbolan S, Bick DP, Sherins RR, Duck LW, Musgrove LC, Sellers JC, Neill JD: Mutations in gonadotropin-releasing hormone receptor gene cause hypogonadotropic hypogonadism. Nat Genet 1998;18:14-15.
  43. Meysing AU, Kanasaki H, Bedecarrats GY, Acierno JS Jr, Conn PM, Martin KA, Seminara SB, Hall JE, Crowley WF Jr, Kaiser UB: GnRHR mutations in a woman with idiopathic hypogonadotropic hypogonadism highlight the differential sensitivity of luteinizing hormone and follicle-stimulating hormone to gonadotropin-releasing hormone. J Clin Endocrinol Metab 2004;89:3189-3198.
  44. Tenenbaum-Rakover Y, Commenges-Ducos M, Iovane A, Aumas C, Admoni O, de Roux N: Neuroendocrine phenotype analysis in five patients with isolated hypogonadotropic hypogonadism due to a L102P inactivating mutation of GPR54. J Clin Endocrinol Metab 2007;92:1137-1144.
  45. Semple RK, Achermann JC, Ellery J, Farooqi IS, Karet FE, Stanhope RG, O'Rahilly S, Aparicio SA: Two novel missense mutations in G protein-coupled receptor 54 in a patient with hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2005;90:1849-1855.
  46. Teles MG, Trarbach EB, Noel SD, Guerra-Junior G, Jorge A, Beneduzzi D, Bianco SD, Mukherjee A, Baptista MT, Costa EM, De Castro M, Mendonca BB, Kaiser UB, Latronico AC: A novel homozygous splice acceptor site mutation of KISS1R in two siblings with normosmic isolated hypogonadotropic hypogonadism. Eur J Endocrinol 2010;163:29-34.
  47. Nimri R, Lebenthal Y, Lazar L, Chevrier L, Phillip M, Bar M, Hernandez-Mora E, de Roux N, Gat-Yablonski G: A novel loss-of-function mutation in GPR54/KISS1R leads to hypogonadotropic hypogonadism in a highly consanguineous family. J Clin Endocrinol Metab 2011;96:E536-E545.
  48. Brioude F, Bouligand J, Francou B, Fagart J, Roussel R, Viengchareun S, Combettes L, Brailly-Tabard S, Lombes M, Young J, Guiochon-Mantel A: Two families with normosmic congenital hypogonadotropic hypogonadism and biallelic mutations in KISS1R (KISS1 receptor): clinical evaluation and molecular characterization of a novel mutation. PLoS One 2013;8:e53896.
  49. Abreu AP, Trarbach EB, de Castro M, Frade Costa EM, Versiani B, Matias Baptista MT, Garmes HM, Mendonca BB, Latronico AC: Loss-of-function mutations in the genes encoding prokineticin-2 or prokineticin receptor-2 cause autosomal recessive Kallmann syndrome. J Clin Endocrinol Metab 2008;93:4113-4118.
  50. Abreu AP, Noel SD, Xu S, Carroll RS, Latronico AC, Kaiser UB: Evidence of the importance of the first intracellular loop of prokineticin receptor 2 in receptor function. Mol Endocrinol 2012;26:1417-1427.
  51. Dode C, Teixeira L, Levilliers J, Fouveaut C, Bouchard P, Kottler ML, Lespinasse J, Lienhardt-Roussie A, Mathieu M, Moerman A, Morgan G, Murat A, Toublanc JE, Wolczynski S, Delpech M, Petit C, Young J, Hardelin JP: Kallmann syndrome: mutations in the genes encoding prokineticin-2 and prokineticin receptor-2. PLoS Genet 2006;2:e175.
  52. Cole LW, Sidis Y, Zhang C, Quinton R, Plummer L, Pignatelli D, Hughes VA, Dwyer AA, Raivio T, Hayes FJ, Seminara SB, Huot C, Alos N, Speiser P, Takeshita A, Van Vliet G, Pearce S, Crowley WF Jr, Zhou QY, Pitteloud N: Mutations in prokineticin 2 and prokineticin receptor 2 genes in human gonadotrophin-releasing hormone deficiency: molecular genetics and clinical spectrum. J Clin Endocrinol Metab 2008;93:3551-3559.
  53. Monnier C, Dode C, Fabre L, Teixeira L, Labesse G, Pin JP, Hardelin JP, Rondard P: PROKR2 missense mutations associated with Kallmann syndrome impair receptor signalling activity. Hum Mol Genet 2009;18:75-81.
  54. McCabe MJ, Gaston-Massuet C, Gregory LC, Alatzoglou KS, Tziaferi V, Sbai O, Rondard P, Masumoto KH, Nagano M, Shigeyoshi Y, Pfeifer M, Hulse T, Buchanan CR, Pitteloud N, Martinez-Barbera JP, Dattani MT: Variations in PROKR2, but not PROK2, are associated with hypopituitarism and septo-optic dysplasia. J Clin Endocrinol Metab 2013;98:E547-E557.
  55. Gianetti E, Tusset C, Noel SD, et al: TAC3/TACR3 mutations reveal preferential activation of gonadotropin-releasing hormone release by neurokinin B in neonatal life followed by reversal in adulthood. J Clin Endocrinol Metab 2010;95:2857-2867.
  56. Topaloglu AK, Reimann F, Guclu M, Yalin AS, Kotan LD, Porter KM, Serin A, Mungan NO, Cook JR, Ozbek MN, Imamoglu S, Akalin NS, Yuksel B, O'Rahilly S, Semple RK: TAC3 and TACR3 mutations in familial hypogonadotropic hypogonadism reveal a key role for neurokinin B in the central control of reproduction. Nat Genet 2009;41:354-358.
  57. Guran T, Tolhurst G, Bereket A, Rocha N, Porter K, Turan S, Gribble FM, Kotan LD, Akcay T, Atay Z, Canan H, Serin A, O'Rahilly S, Reimann F, Semple RK, Topaloglu AK: Hypogonadotropic hypogonadism due to a novel missense mutation in the first extracellular loop of the neurokinin B receptor. J Clin Endocrinol Metab 2009;94:3633-3639.
  58. Francou B, Bouligand J, Voican A, Amazit L, Trabado S, Fagart J, Meduri G, Brailly-Tabard S, Chanson P, Lecomte P, Guiochon-Mantel A, Young J: Normosmic congenital hypogonadotropic hypogonadism due to TAC3/TACR3 mutations: characterization of neuroendocrine phenotypes and novel mutations. PLoS One 2011;6:e25614.

Author Contacts

Le Min

Division of Endocrinology, Diabetes and Hypertension

Brigham and Women's Hospital

221 Longwood Avenue, Boston, MA 02115 (USA)

E-Mail lmin1@partners.org.


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: April 01, 2015
Accepted: June 10, 2015
Published online: June 18, 2015
Issue release date: May 2016

Number of Print Pages: 10
Number of Figures: 2
Number of Tables: 4

ISSN: 0028-3835 (Print)
eISSN: 1423-0194 (Online)

For additional information: http://www.karger.com/NEN


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  26. Sarfati J, Guiochon-Mantel A, Rondard P, et al: A comparative phenotypic study of Kallmann syndrome patients carrying monoallelic and biallelic mutations in the prokineticin 2 or prokineticin receptor 2 genes. J Clin Endocrinol Metab 2010;95:659-669.
  27. Quaynor SD, Kim HG, Cappello EM, Williams T, Chorich LP, Bick DP, Sherins RJ, Layman LC: The prevalence of digenic mutations in patients with normosmic hypogonadotropic hypogonadism and Kallmann syndrome. Fertil Steril 2011;96:1424-1430.e1426.
  28. Pitteloud N, Quinton R, Pearce S, Raivio T, Acierno J, Dwyer A, Plummer L, Hughes V, Seminara S, Cheng YZ, Li WP, Maccoll G, Eliseenkova AV, Olsen SK, Ibrahimi OA, Hayes FJ, Boepple P, Hall JE, Bouloux P, Mohammadi M, Crowley W: Digenic mutations account for variable phenotypes in idiopathic hypogonadotropic hypogonadism. J Clin Invest 2007;117:457-463.
  29. Silveira LF, Trarbach EB, Latronico AC: Genetics basis for GNRH-dependent pubertal disorders in humans. Mol Cell Endocrinol 2010;324:30-38.
  30. Costa EM, Bedecarrats GY, Mendonca BB, Arnhold IJ, Kaiser UB, Latronico AC: Two novel mutations in the gonadotropin-releasing hormone receptor gene in Brazilian patients with hypogonadotropic hypogonadism and normal olfaction. J Clin Endocrinol Metab 2001;86:2680-2686.
  31. Beranova M, Oliveira LM, Bedecarrats GY, Schipani E, Vallejo M, Ammini AC, Quintos JB, Hall JE, Martin KA, Hayes FJ, Pitteloud N, Kaiser UB, Crowley WF Jr, Seminara SB: Prevalence, phenotypic spectrum, and modes of inheritance of gonadotropin-releasing hormone receptor mutations in idiopathic hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2001;86:1580-1588.
  32. Bedecarrats GY, Linher KD, Janovick JA, Beranova M, Kada F, Seminara SB, Michael Conn P, Kaiser UB: Four naturally occurring mutations in the human GNRH receptor affect ligand binding and receptor function. Mol Cell Endocrinol 2003;205:51-64.
  33. Soderlund D, Canto P, de la Chesnaye E, Ulloa-Aguirre A, Mendez JP: A novel homozygous mutation in the second transmembrane domain of the gonadotrophin releasing hormone receptor gene. Clin Endocrinol (Oxf) 2001;54:493-498.
  34. Tello JA, Newton CL, Bouligand J, Guiochon-Mantel A, Millar RP, Young J: Congenital hypogonadotropic hypogonadism due to GNRH receptor mutations in three brothers reveal sites affecting conformation and coupling. PLoS One 2012;7:e38456.
  35. Antelli A, Baldazzi L, Balsamo A, Pirazzoli P, Nicoletti A, Gennari M, Cicognani A: Two novel GnRHR gene mutations in two siblings with hypogonadotropic hypogonadism. Eur J Endocrinol 2006;155:201-205.
  36. Maya-Nunez G, Janovick JA, Aguilar-Rojas A, Jardon-Valadez E, Leanos-Miranda A, Zarinan T, Ulloa-Aguirre A, Conn PM: Biochemical mechanism of pathogenesis of human gonadotropin-releasing hormone receptor mutants Thr104Ile and Tyr108Cys associated with familial hypogonadotropic hypogonadism. Mol Cell Endocrinol 2011;337:16-23.
  37. de Roux N, Young J, Misrahi M, Genet R, Chanson P, Schaison G, Milgrom E: A family with hypogonadotropic hypogonadism and mutations in the gonadotropin-releasing hormone receptor. N Engl J Med 1997;337:1597-1602.
  38. Caron P, Chauvin S, Christin-Maitre S, Bennet A, Lahlou N, Counis R, Bouchard P, Kottler ML: Resistance of hypogonadic patients with mutated GNRH receptor genes to pulsatile GNRH administration. J Clin Endocrinol Metab 1999;84:990-996.
  39. Pralong FP, Gomez F, Castillo E, Cotecchia S, Abuin L, Aubert ML, Portmann L, Gaillard RC: Complete hypogonadotropic hypogonadism associated with a novel inactivating mutation of the gonadotropin-releasing hormone receptor. J Clin Endocrinol Metab 1999;84:3811-3816.
  40. Karges B, Karges W, Mine M, Ludwig L, Kuhne R, Milgrom E, de Roux N: Mutation Ala(171)Thr stabilizes the gonadotropin-releasing hormone receptor in its inactive conformation, causing familial hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2003;88:1873-1879.
  41. de Roux N, Young J, Brailly-Tabard S, Misrahi M, Milgrom E, Schaison G: The same molecular defects of the gonadotropin-releasing hormone receptor determine a variable degree of hypogonadism in affected kindred. J Clin Endocrinol Metab 1999;84:567-572.
  42. Layman LC, Cohen DP, Jin M, Xie J, Li Z, Reindollar RH, Bolbolan S, Bick DP, Sherins RR, Duck LW, Musgrove LC, Sellers JC, Neill JD: Mutations in gonadotropin-releasing hormone receptor gene cause hypogonadotropic hypogonadism. Nat Genet 1998;18:14-15.
  43. Meysing AU, Kanasaki H, Bedecarrats GY, Acierno JS Jr, Conn PM, Martin KA, Seminara SB, Hall JE, Crowley WF Jr, Kaiser UB: GnRHR mutations in a woman with idiopathic hypogonadotropic hypogonadism highlight the differential sensitivity of luteinizing hormone and follicle-stimulating hormone to gonadotropin-releasing hormone. J Clin Endocrinol Metab 2004;89:3189-3198.
  44. Tenenbaum-Rakover Y, Commenges-Ducos M, Iovane A, Aumas C, Admoni O, de Roux N: Neuroendocrine phenotype analysis in five patients with isolated hypogonadotropic hypogonadism due to a L102P inactivating mutation of GPR54. J Clin Endocrinol Metab 2007;92:1137-1144.
  45. Semple RK, Achermann JC, Ellery J, Farooqi IS, Karet FE, Stanhope RG, O'Rahilly S, Aparicio SA: Two novel missense mutations in G protein-coupled receptor 54 in a patient with hypogonadotropic hypogonadism. J Clin Endocrinol Metab 2005;90:1849-1855.
  46. Teles MG, Trarbach EB, Noel SD, Guerra-Junior G, Jorge A, Beneduzzi D, Bianco SD, Mukherjee A, Baptista MT, Costa EM, De Castro M, Mendonca BB, Kaiser UB, Latronico AC: A novel homozygous splice acceptor site mutation of KISS1R in two siblings with normosmic isolated hypogonadotropic hypogonadism. Eur J Endocrinol 2010;163:29-34.
  47. Nimri R, Lebenthal Y, Lazar L, Chevrier L, Phillip M, Bar M, Hernandez-Mora E, de Roux N, Gat-Yablonski G: A novel loss-of-function mutation in GPR54/KISS1R leads to hypogonadotropic hypogonadism in a highly consanguineous family. J Clin Endocrinol Metab 2011;96:E536-E545.
  48. Brioude F, Bouligand J, Francou B, Fagart J, Roussel R, Viengchareun S, Combettes L, Brailly-Tabard S, Lombes M, Young J, Guiochon-Mantel A: Two families with normosmic congenital hypogonadotropic hypogonadism and biallelic mutations in KISS1R (KISS1 receptor): clinical evaluation and molecular characterization of a novel mutation. PLoS One 2013;8:e53896.
  49. Abreu AP, Trarbach EB, de Castro M, Frade Costa EM, Versiani B, Matias Baptista MT, Garmes HM, Mendonca BB, Latronico AC: Loss-of-function mutations in the genes encoding prokineticin-2 or prokineticin receptor-2 cause autosomal recessive Kallmann syndrome. J Clin Endocrinol Metab 2008;93:4113-4118.
  50. Abreu AP, Noel SD, Xu S, Carroll RS, Latronico AC, Kaiser UB: Evidence of the importance of the first intracellular loop of prokineticin receptor 2 in receptor function. Mol Endocrinol 2012;26:1417-1427.
  51. Dode C, Teixeira L, Levilliers J, Fouveaut C, Bouchard P, Kottler ML, Lespinasse J, Lienhardt-Roussie A, Mathieu M, Moerman A, Morgan G, Murat A, Toublanc JE, Wolczynski S, Delpech M, Petit C, Young J, Hardelin JP: Kallmann syndrome: mutations in the genes encoding prokineticin-2 and prokineticin receptor-2. PLoS Genet 2006;2:e175.
  52. Cole LW, Sidis Y, Zhang C, Quinton R, Plummer L, Pignatelli D, Hughes VA, Dwyer AA, Raivio T, Hayes FJ, Seminara SB, Huot C, Alos N, Speiser P, Takeshita A, Van Vliet G, Pearce S, Crowley WF Jr, Zhou QY, Pitteloud N: Mutations in prokineticin 2 and prokineticin receptor 2 genes in human gonadotrophin-releasing hormone deficiency: molecular genetics and clinical spectrum. J Clin Endocrinol Metab 2008;93:3551-3559.
  53. Monnier C, Dode C, Fabre L, Teixeira L, Labesse G, Pin JP, Hardelin JP, Rondard P: PROKR2 missense mutations associated with Kallmann syndrome impair receptor signalling activity. Hum Mol Genet 2009;18:75-81.
  54. McCabe MJ, Gaston-Massuet C, Gregory LC, Alatzoglou KS, Tziaferi V, Sbai O, Rondard P, Masumoto KH, Nagano M, Shigeyoshi Y, Pfeifer M, Hulse T, Buchanan CR, Pitteloud N, Martinez-Barbera JP, Dattani MT: Variations in PROKR2, but not PROK2, are associated with hypopituitarism and septo-optic dysplasia. J Clin Endocrinol Metab 2013;98:E547-E557.
  55. Gianetti E, Tusset C, Noel SD, et al: TAC3/TACR3 mutations reveal preferential activation of gonadotropin-releasing hormone release by neurokinin B in neonatal life followed by reversal in adulthood. J Clin Endocrinol Metab 2010;95:2857-2867.
  56. Topaloglu AK, Reimann F, Guclu M, Yalin AS, Kotan LD, Porter KM, Serin A, Mungan NO, Cook JR, Ozbek MN, Imamoglu S, Akalin NS, Yuksel B, O'Rahilly S, Semple RK: TAC3 and TACR3 mutations in familial hypogonadotropic hypogonadism reveal a key role for neurokinin B in the central control of reproduction. Nat Genet 2009;41:354-358.
  57. Guran T, Tolhurst G, Bereket A, Rocha N, Porter K, Turan S, Gribble FM, Kotan LD, Akcay T, Atay Z, Canan H, Serin A, O'Rahilly S, Reimann F, Semple RK, Topaloglu AK: Hypogonadotropic hypogonadism due to a novel missense mutation in the first extracellular loop of the neurokinin B receptor. J Clin Endocrinol Metab 2009;94:3633-3639.
  58. Francou B, Bouligand J, Voican A, Amazit L, Trabado S, Fagart J, Meduri G, Brailly-Tabard S, Chanson P, Lecomte P, Guiochon-Mantel A, Young J: Normosmic congenital hypogonadotropic hypogonadism due to TAC3/TACR3 mutations: characterization of neuroendocrine phenotypes and novel mutations. PLoS One 2011;6:e25614.
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