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. Author manuscript; available in PMC: 2026 Jan 21.
Published in final edited form as: Hum Genet. 2025 Jun 11;144(7):761–773. doi: 10.1007/s00439-025-02754-w

Expanding the phenotypic spectrum of PROK2/PROKR2: a recall-by-genotype study

Maria I Stamou 1, Crystal J Chiu 1, Shreya V Jadhav 1, Kathryn B Salnikov 1, Lacey Plummer 1, Stephanie B Seminara 1, Ravikumar Balasubramanian 1
PMCID: PMC12817191  NIHMSID: NIHMS2131583  PMID: 40498399

Abstract

Rare variants in prokineticin 2 pathway genes (PROK2; PROKR2), cause isolated hypogonadotropic hypogonadism (IHH) in humans, leading to pubertal failure and infertility. In addition to reproduction, this pathway is also implicated in cardiovascular, metabolic, and inflammatory regulation. The role of naturally occurring PROK2/R2 variants in the general population remains unknown. Thus, we aimed to investigate the role of PROK2/R2 variants in the overall human health. We performed a recall-by-genotype study in rare PROK2/R2 variant carriers and non-carrier controls from a large hospital dataset [Massachusetts General Brigham Biobank (MGBB)]. All recalled participants underwent medical history, physical exam, completed detailed questionnaires and laboratory evaluation including a frequently sampled intravenous glucose tolerance test. Continuous and categorical variables were analyzed with a t-test/non-parametric Wilcoxon rank sum test and a Fisher’s exact test, respectively. Twenty-five rare PROKR2 variant carriers (11 males and 14 females, mean age 45.6 years ± SD 11.7) and 24 non-carrier controls (16 males and 8 females, mean age 44.8 years ± SD 10) were recruited. Male variant carriers were more likely to seek fertility evaluation compared to non-carrier controls (p = 0.03) and carriers of the founder PROKR2 (p.L173R) variant (44% of the cohort) in both sexes were more likely to be diagnosed with lower gastrointestinal phenotypes compared to controls (p = 0.02). This novel clinical association is in line with the reported role of prokineticin 2 in intestinal smooth muscle function in preclinical models. Rare heterozygous PROK2/R2 variants contribute to known reproductive and novel gastrointestinal phenotypes within a hospital-based population cohort.

Introduction

Human genetic studies have primarily focused on phenotype-first approaches, wherein individuals presenting with a specific disease state are assessed for shared genetic variants (Wilczewski et al. 2023). Even though this has led to significant gene discoveries, this approach may have an ascertainment bias that can affect our understanding of the full phenotypic spectrum, the penetrance and pathogenicity of the detected variants (Wilczewski et al. 2023). In sharp contrast, Recall-by-Genotype (RbG) studies utilize a genotype-first approach and provide a unique opportunity to address such limitations by selecting participants based on genomic variants of interest and assessing their phenotypic outcomes across a broader unbiased medical phenome (Corbin et al. 2018). Utilizing a RbG approach, in this study, we examined the impact of rare human deleterious variants in prokineticin 2 and its receptor [PROK2 (OMIM:607002) and PROKR2 (OMIM: 607123)] in a hospital-based population cohort.

The prokineticin 2 signaling pathway is a compelling candidate for RbG studies for several reasons. Rare human PROK2/PROKR2 deleterious variants cause the Mendelian disorder of Isolated Hypogonadotropic Hypogonadism (IHH) which results from defects in Gonadotropin Releasing Hormone (GnRH) neuronal development and leads to pubertal failure and infertility (Abreu et al. 2008; Dode et al. 2006; Pitteloud et al. 2007). Those variants are inherited with an inconsistent mode of inheritance, and IHH patients with homozygous, compound heterozygous, and heterozygous rare PROK2/PROKR2 (PROK2/R2) variants have been identified (Abreu et al. 2008; Dode et al. 2006; Martin et al. 2011; Balasubramanian et al. 2011). Further, pedigrees harboring these variants have variable expressivity and phenotypic penetrance (Martin et al. 2011; Balasubramanian et al. 2011). Thus, RbG studies in a hospital-based population cohort will provide further insights into the variant expressivity in an unbiased setting. Furthermore, among all rare variants known to cause IHH, PROKR2 p.L173R is an ancient founder variant with an age of 9000 years (Avbelj Stefanija et al. 2012). This variant’s age has been previously estimated by haplotype mapping and decay model analysis in 22 unrelated patients with IHH and 30 first-degree family members. The same ~ 123 kb haplotype was identified in 13/22 unrelated IHH patients suggesting that the PROKR2 p.L173R variant represents a founder variant (Avbelj Stefanija et al. 2012). The persistence of such a variant known to cause reproductive dysregulation is paradoxical and remains an enigma (Avbelj Stefanija et al. 2012). We posit that phenotyping of “healthy” individuals with the PROKR2 p.L173R will provide insight into its impact on phenotypic expression and elucidate any phenotypic advantages that it may provide. Finally, in addition to the known effect of human rare PROK2/PROKR2 variants in the hypothalamic regulation of reproduction, preclinical studies have described the prokineticin 2 pathway’s effect on cardiovascular development, metabolic regulation, and motility of the gastrointestinal tract, suggestive of a pleiotropic nature of PROK2 and its receptor (Watson et al. 2012; Nebigil 2009, 2017; Mortreux et al. 2019; Amodeo et al. 2023a; Gardiner et al. 2010; Vincenzi et al. 2023). These preclinical observations warrant validation in human cohorts.

The aim of our study was to address an important knowledge gap with regards to relevance of the role of the prokineticin pathway beyond reproduction. Hence our primary hypothesis was that an unbiased genotype-first approach will help decipher the role of human variants involved in this pathway across all organ systems. To test this hypothesis, we utilized a detailed phenotypic approach, including review of ICD-10 diagnosis codes and electronic medical records (EMR). Given that the review of EMR may miss cryptic or previously underappreciated diagnoses, we also designed and collected dedicated clinical questionnaires and performed in-person medical history, physical exam, laboratory evaluation, and a frequently sampled intravenous glucose tolerance test (FSIGT). These procedures were conducted on carriers of rare deleterious variants in PROK2/PROKR2 and non-carrier controls from the large hospitalwide cohort of the Massachusetts General Brigham Biobank (MGBB). In addition to examining all rare variant carriers, we also investigated the role of the PROKR2 founder variant (PROKR2 p.L173R) to discern whether the associated phenotypes at a population level provide insights into the putative reasons for the persistence of this variant despite its association with reproductive dysfunction.

Methods

Genotypic basis of case selection

Case selection and recruitment of rare PROK2/PROKR2 variant carriers and non-carrier controls from the MGBB

The Mass General Brigham Biobank (MGBB) (N = 65274, 29214 males and 36058 females) was used to identify PROK2/PROKR2 variant carriers and non-carrier participants. Genotyping data and exome sequencing data were available in 65,106 (29144 males and 35960 females) and 53,382 (23675 males and 29705 females) MGBB participants, respectively (Fig. 1). Participants with rare single nucleotide variants (SNVs) in PROK2/PROKR2 were prioritized. Rare SNVs were defined by a minor allele frequency (MAF) of less than 1% in the control database—gnomAD (Karczewski et al. 2020). Those rare SNVs included protein-truncating variants (i.e., nonsense, frameshift, essential splice site) and missense variants. The functional effect of these PROK2/R2 variants was evaluated using in silico tools and through previously published in vitro functional analyses. Given that PROK2 binds to its receptor PROKR2 and activates different G-protein subtypes (Gq, Gs and Gi/o) (Libri et al. 2014; Sbai et al. 2014) for intracellular signaling, we reviewed in-vitro data first reported by international groups (Libri et al. 2014; Sbai et al. 2014) and more recently by our group and collaborators (Wang et al. 2023; Cox et al. 2018). In silico analysis included the utilization of the following prediction programs: CADD (Kircher et al. 2014), Polyphen 2 (Adzhubei et al. 2010), SIFT (Ng and Henikoff 2003), Mutation Taster (Schwarz et al. 2014), MutPred Score (Pejaver et al. 2020), MutationAssessor (Reva et al. 2011), PrimateAI (Sundaram et al. 2018), Eigen (IonitaLaza et al. 2016), REVEL (Ioannidis et al. 2016), and Eve (Frazer et al. 2021). All variants were evaluated based on the American College of Medical Genetics and Genomics (ACMG) criteria (Richards et al. 2015). Age and BMI matched controls without PROK2/PROKR2 variants were also recruited from the MGBB. The age, gender, ethnicity, and race of the PROK2/R2 rare variant carriers and the non-carrier controls are provided in Supplementary Tables 1 and the in-vitro functional prediction and ACMG classification of the detected rare variants is shown in Table 1 and Supplementary Table 2. For the recruitment, a recontact letter was sent to patients from the Biobank staff, co-signed by the Biobank PI and the PI of this study, along with an opt-in or opt-out letter. Ten business days after the Biobank sent the recontact letters to patients, each patient who did not opt-out was contacted for recruitment. Participants were pre-screened based on: (i) their willingness to participate in both study visits; (ii) willingness of female participants to wash-out of combined oral contraceptives (COCs), given that COCs may interfere with reproductive hormone measurements; and (iii) absence of any bleeding or thromboembolic disorder that may interfere with blood sampling and laboratory evaluation.

Fig. 1.

Fig. 1

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Recall-by-genotype enrollment pipeline.

The Mass General Brigham Biobank (MGBB) (N = 65274, 29214 males and 36058 females) was used to identify PROK2/PROKR2 variant carriers and non-carrier participants. Genotyping data and exome sequencing data were available in 65,106 (29144 males and 35960 females) and 53,382 (23675 males and 29705 females) MGBB participants, respectively. A total of 546 PROK2/R2 (228 males and 318 females) variant carriers and 573 (308 males and 265 females) non-variant carrier controls were identified and contacted through the MGBB. Screening visits were scheduled and completed in 25 carriers (11 males and 14 females) and 24 non-carrier controls (16 males and 8 females)

Table 1.

Genetic change details, functional effect and prior association with IHH of the rare PROK2/R2 variants harbored by the MGBB participants

Genetic change Functional Effect in vitro
 MAF in gnomAD
 SNV previously seen in IHH
Effect on Gq Effect on Gs Effect on Gi/o 2.25E-03
PROKR2 p.Ala51Thr Impaired signaling Impaired signaling Impaired signaling 2.82E-04 Yes
PROKR2 p.Arg85Gly Impaired signaling Impaired signaling Impaired signaling 4.30E-04 Yes
PROKR2 p.Arg85Cys Impaired signaling Impaired signaling Non-significant effect 1.15E-03 Yes
PROKR2 p.Arg85His Impaired signaling Impaired signaling Impaired signaling 3.29E-04 Yes
PROKR2 p.Arg85Leu Impaired signaling Impaired signaling Impaired signaling 1.36E-05 Yes
PROKR2 p.Met111Arg Impaired signaling Impaired signaling Impaired signaling 3.16E-03 Yes
PROKR2 p.Leu173Arg Impaired signaling Impaired signaling Impaired signaling 1.86E-06 Yes
PROKR2 p.Ala189Ser Impaired signaling Impaired signaling Non-significant effect 3.02E-04 Yes
PROKR2 p.Ser202Gly Non-significant effect No significant effect Impaired signaling 2.04E-05 Yes
PROKR2 p.Ile206Asn Not assessed Not assessed Not assessed 4.65E-04 No
PROKR2 p.Val297Ile Impaired signaling Impaired signaling Non-significant effect 1.16E-05 Yes
PROK2 p.Asn79Lys Not assessed Not assessed Not assessed 2.37E-04 No
PROK2 p. Gly100TrpfsTer22 Not assessed Not assessed Not assessed 9.67E-05 No
PROK2 p.Arg101Trp Not assessed Not assessed Not assessed 2.25E-03 No
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Table 1 shows all rare PROK2/R2 variants harbored by the recruited participants, their effect on PROKR2 intracellular pathway signaling as previously described (Cox et al. 2018; Libri et al. 2014; Sbai et al. 2014; Wang et al. 2023), the minor allele frequency (MAF) in gnomAD (Karczewski et al. 2020), and whether were previously seen in IHH patients. Most variants impaired the signaling of the pathway and were previously seen in IHH patients allowing us to examine the effect of the rare disease-associated variants in a cohort of participants recruited from a hospital-based dataset

Age, sex, ancestry and socio-economic considerations

The MGBB was founded in 2008 as an enterprise-wide initiative to drive medical discovery across Mass General Brigham (MGB). MGB is a large, integrated healthcare system located primarily in the Boston area that serves 1.5 million patients each year and includes several teaching hospitals, including Massachusetts General Hospital (MGH) and Brigham and Women’s Hospital (BWH). From the MGBB, we enrolled participants from both sexes for our RbG studies to understand any sex-specific effects. This is particularly relevant since IHH, a phenotype already linked to the prokineticin pathway is more prevalent in men (Laitinen et al. 2011). Similarly, PROKR2 mutations in IHH have been shown to have different prevalence in certain ancestries (Sarfati et al. 2013). Since 2014, the MGBB began recruitment at community health centers associated with MGH and BWH, improving the diversity of the cohort. For example, between 2015 and 2017, 1015 participants were recruited at Chelsea Community Health Center. This cohort is primarily Hispanic/Latino, reflecting the community at Chelsea. Thus, the multi-ethnic MGBB biobank that includes individuals from various ancestries provided an appropriate biobank population for this study (Boutin et al. 2022). In addition, given the metropolitan nature of the Great Boston area, the MGBB participants hail from a spectrum of socio-economic backgrounds, thus minimizing any overt socio-economic bias.

Phenotypic analysis in RbG participants

RbG participants were invited for 2 study visits:

Screening visit

All participants were evaluated with: (i) Review of their electronic medical records prior to presentation at clinic; (ii) In person collection of a detailed medical history including reproductive, HEENT, Respiratory, Cardiovascular, Gastrointestinal, Hepatic, Urinary, Endocrine/Metabolic, Neurologic, Hematologic/Lymphatic, Musculoskeletal, Immune/Inflammatory, Psychiatric, and Dermatologic history. History of medications and allergies, social history, and surgical history were collected; (iii) Physical exam including anthropometric measurements, i.e., weight, height, and body mass index (BMI) using simple measurement tools. Examination of the following organ systems was also conducted: HEENT, Chest, Cardiovascular, Abdominal, Respiratory, Peripheral Vascular, Skin, Musculoskeletal, and Genitourinary. Tanner staging for breast development (females), pubic hair (both sexes), and genitals (both sexes) was documented, and testicular volume was measured with a Prader orchidometer; (iv) Collection of a blood sample for a detailed laboratory evaluation: CBC, CMP, lipids, C-reactive protein, sex steroid hormones, HgbA1c, pituitary gonadotropins, prolactin, and TSH. At the end of the screening visit, all participants completed dedicated reproductive questionnaires that were built using the PhenX toolkit, a web-based catalog of validated measurement protocols that allowed discovery of previously underappreciated self-reported reproductive phenotypes (Hamilton et al. 2011). Male participants answered questions about their birth history, presence of genitourinary defects at birth (hypospadias, cryptorchidism), pubertal timing (age at which voice changes, age at which penis started increasing in size, age at which testicles started increasing in size, age at which they developed pubic hair, and age they entered puberty). They also answered questions about libido and erectile function, including questions on: (a) difficulty achieving an erection, (b) ejaculating too early, (c) difficulty ejaculating, and (d) lack of interest in sex. Participants’ answers were graded as follows: Almost never = 1, Sometimes = 2, Often = 3, and Almost always = 4. Male participants also answered questions on prior evaluation for low testosterone, history of achieving a pregnancy, history of infertility, and prior infertility evaluation. Female participants answered questions about their age of menarche, age of pubarche, age of thelarche, cycle length, irregular cycles, age at first and last menstrual period, usage of birth control methods, history of eating disorders, history of high-intensity exercise, history of pregnancies, live births, miscarriages, hirsutism, hair loss, acne, history of infertility, and prior infertility evaluation.

Metabolic study visit

To assess the metabolic health of participants, individuals who met the following criteria underwent a frequently sampled intravenous glucose tolerance test (FSIGT) and body composition measurements by DEXA (Lunar Prodigy version 8.50): (i) No history of bleeding or thromboembolic disorders (i.e., thrombocytopenia, deep vein thrombosis, pulmonary embolism, cerebrovascular disease, warfarin treatment, hypercoagulability syndromes); (ii) No history of illicit drug or heavy alcohol use (> 4 gms of alcohol per day) (iii) Stable weight for previous three months (iv) Not currently pregnant; all female participants were administered a urinary pregnancy test to rule out pregnancies; (v) Serum Hemoglobin ≥ 10 g/dL and baseline hematocrit ≥ 38% (vi) No current or previous diagnosis of type 1 or type 2 diabetes as defined by the American Diabetes Association criteria (American Diabetes Association Professional Practice 2024): fasting glucose greater than 126 mg/dL or random blood glucose greater than 200 mg/dL on two occasions; (vii) Not currently taking medications that may influence glucose metabolism (e.g., corticosteroids, thiazide diuretics). The FSIGT was conducted as previously described (Stamou et al. 2024). The FSIGT began with baseline sampling for plasma insulin and glucose at −10 and −1 min. These measurements were followed by the administration of 0.3 g/kg bolus of glucose within 2 min. Subsequent venous blood samples were obtained at 1, 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 20, 22, 24, 26, 28, 30, 40, 50, 60, 70, 80, 90, 100, 120, 160, and 180 min. At 20 min, participants received a 0.03 U/kg infusion of regular human insulin (HumilinR– Eli Lilly and Company) over 45 s to help assess the impact of insulin on glucose uptake. The test was stopped if the subject experienced severe neuroglycopenic symptoms. Any participant with screening hemoglobin levels below their sex-specific reference range but ≥ 10 g/dL was given their 50-day course of iron tablets prior to being discharged. The MinMod Millennium software was utilized for this analysis(Pacini and Bergman 1986; Saad et al. 1994). Indices of insulin resistance were estimated with fasting insulin and C-peptide levels, the glucose response to its own mediated action and insulin infusion based on the area under the curve (AUCglucose) during the FSIGT, the insulin sensitivity (SI) and glucose effectiveness (SG) based on the minimal model analysis, and the homeostatic model assessment of insulin resistance and insulin sensitivity (HOMAIR and HOMA-SI) (Levy et al. 1998; Matthews et al. 1985). The indices of pancreatic β-cell function (insulin secretory function) were estimated based on the first- and second-phase of insulin and C-peptide (AUCinsulin and AUCCpeptide) during the first 10 min and 12–20 min after glucose bolus (prior to exogenous insulin administration), the acute insulin response to glucose (AIRg) and the Disposition index (DI), that is calculated based on the minimal model analysis and reflects measures of insulin secretion and sensitivity, and the homeostatic model assessment (HOMA) of β-cell function(Matthews et al. 1985).

Rare variant association (PROKR2 p.L173R) analysis with ICD10 codes across the entire MGBB

Given that the founder PROKR2 c.518T > G p.L173R [rs74315416, chromosomal coordinates chr20:g.5302677 A > C (GRCh38)] is seen in the general population with a minor allele frequency (MAF) of 0.3156%, a separate phenome-wide analysis was conducted for this rare SNV in the MGBB. Through the MGBB portal, we identified that genotyping data on this particular variant was available in 64,953 participants: 487 rare variant carriers (i.e., participants heterozygous for the A and C allele) and 64,446 non-variant controls (i.e. participants homozygous for the A allele). The associations of this variant with the following phenotypes were evaluated: Crohn’s disease (ICD10:K50, N = 2301), Ulcerative Colitis (ICD10:K51, N = 2559), Type 2 Diabetes (ICD10:E11, N = 14239), Obesity (ICD10:E66.0, N = 9785), Female Infertility (ICD10:N97, N = 3482), and Male Infertility (ICD10:N46, N = 1713). The number of MGBB participants with each ICD10 diagnosis is shown in Supplementary Table 3.

Statistical analysis

For continuous variables (variables listed in Supplementary Tables 4–7), distributions of the outcomes and clinical characteristics were reported using mean and standard deviation (SD) for normally distributed values and median and 1st and 3rd quartile for non-normally distributed values. Statistical comparisons were performed with a t-test and non-parametric Wilcoxon rank sum test for normally and non-normally distributed values, respectively (Stata-Corp. 2023. Stata Statistical Software: Release 18. College Station). For categorical variables measured in male participants (i.e., evaluation for infertility, infertility, therapy for infertility, cryptorchidism, and whether they fathered a pregnancy as seen in Fig. 2), female participants (included in Supplementary Table 5) and the differences in proportions with MGBB carriers and non-carrier controls with ICD10 diagnoses (as shown in Supplementary Table 3), a Fisher exact test was used. Given the descriptive nature of the study and since genotypes of interest are rare variants, a priori power calculations were not performed for this recall by genotype study. Since cases and controls were enrolled matching for age, sex, and BMI, statistical significance was set to unadjusted significance of p < 0.05.

Fig. 2.

Fig. 2

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Reproductive phenotypes of PROK2/R2 male rare variant carriers and non-carrier male control.

Panel A: Reproductive phenotypes of PROK2/R2 male rare variant carriers (N = 11, red) and non-carrier controls (N = 16, grey). Male carriers were more likely to undergo evaluation for fertility (p = 0.03); Panel B: Male carriers of the PROKR2 founder variant p.L173R (N = 6, red) and non-carrier controls (N = 16, grey). Male carriers of the founder variant were more likely to undergo evaluation for fertility (p = 0.04), be diagnosed with infertility (p = 0.03) and receive therapy for infertility (p = 0.04). A Fisher exact test was utilized for the statistical analysis. P values < 0.05 were considered statistically significant

Results

Characteristics of the enrolled participants

A total of 546 PROK2/R2 (228 males and 318 females) variant carriers and 573 (308 males and 265 females) non-variant carrier controls were identified and contacted through the MGBB. All MGBB participants were contacted over the phone 10 business days following receiving the MGBB letter and screening visits were scheduled for participants who expressed interest and met pre-screening eligibility criteria (see Methods). Screening visits were scheduled and completed in 25 carriers (11 males and 14 females) and 24 non-carrier controls (16 males and 8 females) (ClinVar accession No. SUB14706469, Fig. 1 & Supplementary Table 1). None of the PROK2/R2 variant carriers harbored other causal or putative pathogenic variants in additional 18 well validated genes previously implicated in IHH (Supplementary Table 8) (Stamou et al. 2022). No differences in age and BMI were observed between the carriers and controls. The mean age of the carriers was 45.6 years compared to 44.8 years of the controls (p = 0.80). Similarly, the BMI of the variant carriers was 26.2 compared to 27.4 in the controls (p = 0.38). All carriers harbored rare heterozygous SNVs in either PROK2 or PROKR2 genes and most of these SNVs were deemed to be deleterious alleles based on prior functional work conducted by our group and collaborators (Cox et al. 2018; Wang et al. 2023) (Table 1 & Supplementary Table 2). Eleven participants (44% of the enrolled cases, 6 males and 5 females) harbored the rare deleterious founder variant PROKR2 p.L173R. Of note, most variants detected in the MGBB carriers were previously seen in IHH patients and were previously shown to affect the intracellular signaling of PROKR2 (Table 1) (Libri et al. 2014; Sbai et al. 2014; Cox et al. 2018; Wang et al. 2023), allowing us to examine the effect of the rare disease-associated variants in the general population.

Naturally occurring PROK2/PROKR2 SNVs affect the reproductive health of human carriers

Since PROK2/PROKR2 are both encoded by autosomal genes, we enrolled participants from both sexes to assess the reproductive health of individuals with rare PROK2/PROKR2 variants. However, since reproductive phenotypes may affect sexes through distinct mechanisms, we undertook sex-specific analyses.

Male analysis

We first evaluated the effect of rare PROK2/PROKR2 SNVs in male carriers from the MGBB compared to non-carrier controls. Eleven males with rare PROK2/PROKR2 variants and 16 non-carrier controls completed the screening visit of this study. Males with rare PROK2/PROKR2 variants were more likely to seek evaluation for infertility compared to controls (5 out of 11 [~ 50% of the carriers] compared to 1/16 [~ 8% of the controls], p = 0.03, Fig. 2A). Among the 5 male carriers reporting infertility, further information was available in 2 subjects both of whom carried the founder variant PROKR2 p.L173R: while 1 carrier was reported to have oligospermia, the other subject underwent assisted reproduction. In addition, across the male cohort, the carriers had a higher prevalence of neonatal signs of GnRH deficiency (i.e., cryptorchidism and hypospadias) compared to controls (18% in cases vs. 0% in controls, p = 0.07, Fig. 2A). All men were well-virilized at exam (Tanner V state for genitalia and pubic hair). Male carriers also reported a later age of different landmarks of pubertal development, such as age at voice breaking, penile and testicular growth, and pubic hair growth, but the differences were not statistically significant (Supplementary Table 4). Similarly, male carriers had a lower testicular volume compared to controls, but still in normal adult range (18.1 ml in cases compared to 21.4 ml in the controls, p = 0.07, Supplementary Table 4). No differences in erectile function and libido were noted (Supplementary Table 4).

Sub-analysis of males with PROKR2 founder mutation

When the analysis was restricted to carriers of the founder PROKR2 p.L173R only, 33% of male variant carriers were diagnosed with infertility compared to no controls (p = 0.03). Among the 5 male carriers reporting infertility, further information was available in 2 subjects both of whom carried the founder variant PROKR2 p.L173R: while 1 carrier was reported to have oligospermia, the other subject underwent assisted reproduction. They were also more likely to seek medical advice and therapy for fertility (50% of the cases compared to 8% of the controls, p = 0.04, Fig. 2B). However, there was no difference in the number of pregnancies fathered in those with the founder mutations compared to controls.

Female analysis

14 females with rare PROK2/PROKR2 variants and 8 non-carrier controls completed the screening visit of this study. No differences were observed for the age of menarche, breast growth, and growth spurt. Results remained unchanged when the analysis was restricted to carriers of the PROKR2 founder variant. PROK2/PROKR2 female carriers demonstrated a higher number of pregnancies per person [2.7 (± 1.7) vs. 1.2 (± 0.4), p = 0.08] and live births per person [1.8 (± 0.7) vs. 0.8 (± 0.4), p = 0.01] compared to controls (Supplementary Table 5). Females in both groups received fertility advice, with 2 out of 14 female carriers receiving fertility advice and treatment compared to 1 out of 8 controls (p = 0.79). Of note, both female carriers who sought fertility advice harbored the founder PROKR2 variant p.L173R, with one participant requiring ovulation induction therapy and artificial insemination to achieve a pregnancy.

Founder variant PROKR2 p.L173R is associated with higher prevalence of Gastrointestinal phenotypes and inflammatory bowel disease

To assess the pleiotropic effect of rare human naturally-occurring variants in PROK2/PROKR2, we analyzed the data obtained from the medical history and examined the prevalence of diseases across different organ systems in the PROK2/PROKR2 variant carriers compared to non-carrier controls. No differences were observed between the two groups when PROK2/PROKR2 carriers of all variants were analyzed (Fig. 3). In addition, no differences in the laboratory results between the two groups were noted (Supplementary Table 6). However, participants with the founder PROKR2 variant (p. L173R) demonstrated lower gastrointestinal phenotypes (N = 4, including 2 participants including inflammatory bowel disease– IBD, 1 with diverticulitis/inflammatory bowel syndrome and 1 with anal fissures) at a higher frequency (37%) compared to controls (N = 1 with prior episode of diarrhea, 4%, p value 0.02) (Fig. 3). To further evaluate the role of this founder variant in IBD, we also evaluated the prevalence of IBD among carriers of the founder variant within 64,953 MGBB biobank participants with genetic information at this variant site. Of note, 487 participants with the founder variant were identified, and we found that 5.3% of the carriers were diagnosed with Crohn’s disease compared to 3.5% of the participants with the homozygous reference allele (p = 0.03). However, no statistical differential association was noted for ulcerative colitis (3.9% vs. 4.7%, p = 0.35).

Fig. 3.

Fig. 3

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Clinical phenotypes across all organ systems of PROK2/R2 rare variant carriers (red), PROKR2 founder variant (p.L173R) carriers (blue) and non-carrier controls.

The prevalence of diseases across different organ systems was investigated via medical histories and review of electronic medical records in PROK2/PROKR2 variant carriers (reds), PROK2 p.L172R carriers (blue), and non-carrier controls (grey). No differences were observed between the two groups when PROK2/PROKR2 carriers of all variants were analyzed. However, participants with the founder PROKR2 variant (p. L173R) demonstrated lower gastrointestinal/bowel phenotypes (N = 4, including 2 participants including inflammatory bowel disease– IBD, 1 with diverticulitis/inflammatory bowel syndrome and 1 with anal fissures) at a higher frequency (37%) compared to controls (N = 1 with prior episode of diarrhea, 4%, p value 0.02). A Fisher exact test was utilized for the statistical analysis. P values < 0.05 were considered statistically significant

PROK2/PROKR2 SNVs are not associated with metabolic phenotypes

Given that prior studies had previously implicated the prokineticin 2 signaling in metabolic health (Mortreux et al. 2019), we then focused on assessing the metabolic health of rare PROK2/PROKR2 carriers compared to non-carrier controls. Seventeen carriers and 15 controls were deemed eligible (see methods) to complete a frequently sampled intravenous glucose tolerance test (FSIGT) as previously described (Stamou et al. 2024). We observed no differences in insulin sensitivity and β-cell function between the 2 groups (Fig. 4 & Supplemental Table 7).

Fig. 4.

Fig. 4

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Glucose and insulin levels of the PROK2/R2 rare variant carriers and non-carriers controls during the frequently sampled intravenous glucose tolerance test (FSIGT).

PROK2/R2 rare variant carriers (N = 17, red) and non-carrier controls (N = 15, grey) underwent a frequently sampled intravenous tolerance test (FSIGT). During the 4-hour FSIGT, baseline sampling for plasma glucose occurred at −10 and −1 min. A bolu of 0.3 g/kg of glucose was administered at time 0. At 20 min, participants received a 0.03-U/kg infusion of regular human insulin over 45 s to enhance the insulin level to better help assess the effect of insulin on glucose uptake. Variant carriers demonstrated similar glucose and insulin levels to non-carrier as calculated by the area under the curve (AUC) for glucose and insulin levels during the FSIGT

Discussion

Rare PROK2/R2 variants contribute to a broad phenotypic spectrum in a hospital-based population

Detailed phenotyping is required to deepen our understanding of the spectrum and mechanisms underlying genotype-phenotype associations. While phenotype-first approaches have helped to decipher the phenotypes connected to certain genetic variants, genotype-first approaches provide a unique opportunity to evaluate the full phenotypic spectrum, the penetrance and pathogenicity of the discovered variants (Wilczewski et al. 2023). Recall-by-genotype (RbG) studies have so far investigated the role of multiple, common variants in population settings or rare variants in family members of affected individuals with rare diseases (Corbin et al. 2018; Alver et al. 2019). In this study, we utilized a novel approach of recalling individuals from the MGBB dataset who carry rare deleterious single nucleotide variants (SNVs) in the genes of PROK2 and its receptor PROKR2. We showed that rare variants implicated in the rare Mendelian disease of IHH can also impart variable phenotypes in a population setting.

Prokineticin signaling affects reproductive function in variant carriers

Utilizing a detailed, systematic approach of reviewing electronic health records, designing dedicated questionnaires, and performing in-person medical histories and physical exams, we found that male carriers of rare PROK2/PROKR2 heterozygous variants from the general population are more likely to be evaluated for infertility. None of those individuals were previously diagnosed with IHH but were found to have had milder phenotypes despite harboring variants previously identified in IHH. Of note, most of these reproductive phenotypes were not previously documented in their EMRs, highlighting the importance of in-person and dedicated phenotyping of individuals enrolled in the RbG studies. These observations further confirm the previous reports of variable expressivity and incomplete penetrance of PROK2/PROKR2 genetic variation. Specifically, PROK2/PROKR2 variants cause severe reproductive disease (IHH) when inherited in a homozygous/compound heterozygous state compared to a heterozygous state (Abreu et al. 2008; Dode et al. 2006). This is also consistent with the fact that homozygous mice lacking Prokr2 demonstrate severe hypothalamic defects of their reproductive axis while heterozygous mice are unaffected (Matsumoto et al. 2006). While the variants harbored by the MGBB participants are also often seen in IHH individuals (e.g. PROKR2 p.L173R), the observed differential phenotypic outcome in the presence of identical variants is likely to reflect the presence of additional synergistic genetic modifiers that lead to more severe phenotypes i.e., IHH. Indeed, IHH is well recognized as an oligogenic disorder and oligogenic pedigrees involving the prokineticin pathway have been previously described (Sykiotis et al. 2010; Sarfati et al. 2013). While mild reproductive phenotypes were observed in male carriers from the general population, females in the general population with heterozygous variants in PROK2/PROKR2 lacked any reproductive phenotypic association. The increased number of live births per person that was observed in female carriers compared to controls could be linked to the small sample size of the cohort studied and the large variance in the reproductive history. Hence, future studies with larger cohorts of variant carriers will be required for more in-depth phenotypic investigation tp confirm or refute these findings. The precise basis of this sex-difference in phenotypic penetrance relating to PROK2/PROKR2 variants in the general population remains unclear. However, this observation is consistent with the fact that IHH is more common in males compared to females (Laitinen et al. 2011). Thus, the findings in this report raise the possibility that female carriers with PROK2/PROKR2 variants in the general population may have a higher resilience to reproductive dysfunction. In addition, tissue-specific expression factors may be contributing to this observation. Prior studies in male and female mouse brains have revealed a sexually dimorphic distribution of neurons expressing Prokr2. In female mice, such neurons are mainly located in the medial preoptic area, ventromedial nucleus of the hypothalamus, arcuate nucleus, medial amygdala and lateral parabrachial nucleus. In contrast, in male mice, the distribution differs with most neurons being located in the amygdalo-hippocampal area. This sexually dimorphic pattern of Prokr2 expression points to potential differential roles in reproductive function between sexes (Mohsen et al. 2017). Further studies will be required to fully dissect these sex-specific phenotypic penetrance/expressivity differences.

PROKR2 p.L173R founder mutation is associated with Gastrointestinal phenotypes

Our detailed systematic phenotypic evaluation of all carriers and controls revealed that founder variant PROKR2 c.518T > G p.L173R carriers were more likely to be diagnosed with lower gastrointestinal phenotypes, including Crohn’s disease. The significant impact of this variant is in line with its deleterious effect on the intracellular signaling of PROKR2 (Cox et al. 2018; Libri et al. 2014). While this association has not been previously described in patients with IHH, it raises the possibility that such phenotypes may have been unascertained in IHH patients. Notably, IHH patients are usually evaluated at a very young age due to pubertal failure (earlier than the average age of the recruited MGBB participants of this study (45.6 years)] and Crohn’s disease may be a later health sequelae. Thus, IHH patients may require close follow up to monitor for the development of IBD related symptoms as they age and additional studies in larger IHH and general population cohorts are required to confirm this finding. This novel gastrointestinal finding is in line with the notion that both prokineticin 1 and 2 are named for their role in intestinal smooth muscle function and gut motility, and their role in IBD has also been recently explored in other studies (Watson et al. 2012; Amodeo et al. 2023a; Vincenzi et al. 2023; Li et al. 2001). Both prokineticin 1 and prokineticin 2 and their receptors are widely distributed in human and rodent tissues, including gastrointestinal tract, and are upregulated during inflammatory states (Amodeo et al. 2023). In addition, preclinical studies report an upregulation of prokineticin 2 and its receptor in the colon of Crohn’s disease rodent models along with pro-inflammatory cytokine overexpression (Amodeo et al. 2023a, b).

The role of Gastrointestinal and reproductive phenotypes in explaining the PROKR2 founder mutation paradox

To our knowledge, this is the first report of rare human PROKR2 variants identified in individuals with IBD. A prior pathway enrichment analysis of known Mendelian diseases (conducted via de-identified electronic medical records), showed a significant association of the prokineticin 2 signaling pathway with inflammatory bowel disease (IBD) (Han et al. 2018). This latter observation is in keeping with our study results and provides additional support for the role of the prokineticin 2 pathway signaling in IBD. Prior studies have suggested that inflammatory bowel diseases have persisted through centuries due to their putative importance in modifying pathogen defense (Brinkworth and Barreiro 2014). In keeping with this notion, common polymorphisms linked to IBD have been shown to have balancing selection effects (Jostins et al. 2012) and the heterozygous PROKR2 p.L173R variant was previously postulated to allow for a selective advantage due to increased protection from Trypanosoma cruzi infection (Lattanzi et al. 2021). Additionally, PROK2/PROKR2 variant carrier females also had higher number of pregnancies per person and live births per person compared to controls. This observation suggests that reproductive fitness advantage in females harboring the founder mutation may also contribute to the selective heterozygous advantage. Taken together, our findings: (i) lend credence to the importance of prokineticin 2 signaling in mediating host-pathogen interactions and further support the pathway’s candidacy as a putative drug target (Vincenzi et al. 2023); and (ii) provide a possible explanation for the elusive puzzle relating to the persistence of the PROKR2 p.L173R founder allele despite its association with IHH, which reduces reproductive fitness.

Prokineticin signaling does not impact metabolic health in the general population

In this study, no differences were seen in the metabolic health of carriers of rare deleterious PROK2/PROKR2 variants compared to controls, in contrast to prior studies that have shown common PROK2 variant associations with type 2 diabetes (Mortreux et al. 2019). One potential explanation for the lack of metabolic dysregulation in the carriers of rare variants in the PROK2/PROKR2 is that the majority of those carriers harbored variants in the genes encoding for the receptor (N = 22) and fewer with variants in the gene encoding the ligand (N = 3). Our observations are in line with prior studies that showed metabolic phenotypes in animal models with ligand (Prok2) defects. Depletion of Prok2 in rats leads to altered feeding patterns and increased food intake and thus, prokineticin has been proposed to have anorexigenic properties (Gardiner et al. 2010). In addition, Prok2 under-expression in the olfactory bulb of mice induces insulin resistance compared to scrambled shRNA-injected mice (Mortreux et al. 2019). Additionally, there was a significantly lower mean concentration of plasma PROK2 in people with T2D than in those with normoglycemia (Mortreux et al. 2019). Moreover, prok2 appears to control food intake and fat tissue expansion by acting upon prokineticin receptor 1 (prokr1), which is highly expressed in the NPY/AgRP and POMC/CART neurons (Gardiner et al. 2010; Nebigil 2017). Thus, the discordance between the findings of this study and prior reports could be attributed to a ligand-specific effect that may be mediated by the PROKR1 receptor rather than PROKR2. Furthermore, given that the average age of the PROK2/PROKR2 carriers was 45.6 years and that T2D usually emerges late in adult life, long term follow-up of the rare variant carriers is required to investigate whether those individuals are at high risk of developing T2D as they age. Future longitudinal studies are required to investigate the role of the prokineticin 2 signaling pathway in the prevalence of T2D in the general population.

Study strengths and limitations

This study has several strengths. This is the first RbG in carriers of rare variants in genes implicated in the prokineticin 2 signaling pathway. This RbG study allowed detailed in-person prospective phenotypic evaluation of the recruited participants, and it was not limited to a review of already established diagnoses linked to the participants electronic medical records. In addition, to control for any bias due to socioeconomic status, we selected cases and controls from the same Biobank that serves the greater Boston metropolitan area, which includes participants across a broad socioeconomic background. Since our RbG approach included carriers of rare variants [minor allele frequency (MAF) < 1%], the number of participants recruited was comparable to similar rare variant based RbG previously published studies, wherein the number of participants with rare variants was ranging between 1 and 37 per gene (Wilczewski et al. 2023). Our enrollment rate of 5% of participants from a healthy population cohort also highlights the challenges for clinical translational investigation in reverse (i.e. genotype first) phenotypes studies that do not focus on participants with a certain disease or health family members of diseased probands who may express interest in participating in such studies. Due to the relatively low enrollment rate, our downstream sex-specific analyses also further reduce statistical power, and this may explain why some phenotypic differences (for e.g. testicular volume, libido) did not reach statistical significance. The rarity of the variants and the size limitations of the cohorts enrolled could also be contributing to those negative results. For these reasons, our study observations will merit further replication in larger cohorts to both confirm and unravel novel associations.

Conclusions

In conclusion, this RbG study showed rare PROK2/PROKR2 variants may impart a variety of phenotypes across multiple organ systems. Our findings expand the role of known Mendelian genes beyond their initial phenotypic associations and provide an investigatory framework for conducting similar RbG studies in population cohorts.

Supplementary Material

supplementary tables

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s00439-025-02754-w.

Acknowledgements

We thank the Mass General Brigham Biobank for providing genomic data and health information data. The project described was supported by Grant Number 1UL1TR001102 & 1UL1TR002541–01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center For Research Resources, the National Center for Advancing Translational Science or the National Institutes of Health.

Funding

This work was supported by the following grants: S.B.S: NICHD P50 HD104224 (The MGH Harvard Center for Reproductive Medicine), NICHD R37 HD043341, and FDA R01 FD007843; R.B.: NIDCR R01 DE031452 and NICHD R01 HD096324; and M.I.S: NICHD F32 HD108873.

Footnotes

Declarations

Ethics approval and consent to participate The analysis of rare variant carriers and non-carrier controls was reviewed and approved by the Massachusetts General Brigham (MGB) Institutional Review Board (IRB Protocol 2009P002349). All participants provided written informed consent. Participants were recruited from the Mass General Brigham Biobank (MGBB), which has been reviewed and approved by the MGB IRB (Protocol 2009P002312). A variant association analysis with secondary use of clinical/research data under the MGH IRB Protocol 2020P000762, which has been reviewed and approved by the Massachusetts General Brigham Institutional Review Board, was also conducted.

Competing interests The authors declare no competing interests.

Data availability

Data and materials will be made available by the authors individually upon request subject to the data sharing plan and consent provided by the study participants.

References

  1. Abreu AP, Trarbach EB, de Castro M, Frade Costa EM, Versiani B, Matias Baptista MT, Garmes HM, Mendonca BB, Latronico AC (2008) Loss-of-function mutations in the genes encoding prokineticin-2 or prokineticin receptor-2 cause autosomal recessive Kallmann syndrome. J Clin Endocrinol Metab 93:4113–4118 [DOI] [PubMed] [Google Scholar]
  2. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR (2010) A method and server for predicting damaging missense mutations. Nat Methods 7:248–249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alver M, Palover M, Saar A, Lall K, Zekavat SM, Tonisson N, Leitsalu L, Reigo A, Nikopensius T, Ainla T, Kals M, Magi R, Gabriel SB, Eha J, Lander ES, Irs A, Philippakis A, Marandi T, Natarajan P, Metspalu A, Kathiresan S, Esko T (2019) Recall by genotype and cascade screening for Familial hypercholesterolemia in a population-based biobank from Estonia. Genet Med 21:1173–1180 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. American Diabetes Association Professional Practice, Committee (2024) 2. Diagnosis and classification of diabetes: standards of care in Diabetes-2024. Diabetes Care 47:S20–S42 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Amodeo G, Franchi S, Galimberti G, Riboldi B, Sacerdote P (2023a) ‘The Prokineticin System in Inflammatory Bowel Diseases: A Clinical and Preclinical Overview’. Biomedicines 11 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Amodeo G, Galimberti G, Sacerdote P, Franchi S (2023b) Characterization of prokineticin system in Crohn’s disease pathophysiology and pain, and its modulation by alcohol abuse: A preclinical study. Biochim Biophys Acta Mol Basis Dis 1869:166791. [DOI] [PubMed] [Google Scholar]
  7. Avbelj Stefanija M, Jeanpierre M, Sykiotis GP, Young J, Quinton R, Abreu AP, Plummer L, Au MG, Balasubramanian R, Dwyer AA, Florez JC, Cheetham T, Pearce SH, Purushothaman R, Schinzel A, Pugeat M, Jacobson-Dickman EE, Ten S, Latronico AC, Gusella JF, Dode C, Crowley WF Jr., Pitteloud N (2012) An ancient founder mutation in PROKR2 impairs human reproduction. Hum Mol Genet 21:4314–4324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Balasubramanian R, Plummer L, Sidis Y, Pitteloud N, Martin C, Zhou QY, Crowley WF Jr (2011) The puzzles of the prokineticin 2 pathway in human reproduction. Mol Cell Endocrinol 346:44–50 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boutin NT, Schecter SB, Perez EF, Tchamitchian NS, Cerretani XR, Gainer VS, Lebo MS, Mahanta LM, Karlson EW and J. W. Smoller (2022) ‘The Evolution of a Large Biobank at Mass General Brigham’. J Pers Med 12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Brinkworth JF, Barreiro LB (2014) The contribution of natural selection to present-day susceptibility to chronic inflammatory and autoimmune disease. Curr Opin Immunol 31:66–78 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Corbin LJ, Tan VY, Hughes DA, Wade KH, Paul DS, Tansey KE, Butcher F, Dudbridge F, Howson JM, Jallow MW, John C, Kingston N, Lindgren CM, O’Donavan M, O’Rahilly S, Owen MJ, Palmer CNA, Pearson ER, Scott RA, van Heel DA, Whittaker J, Frayling T, Tobin MD, Wain LV, Smith GD, Evans DM, Karpe F, McCarthy MI, Danesh J, Franks PW, Timpson NJ (2018) Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference. Nat Commun 9:711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cox KH, Oliveira LMB, Plummer L, Corbin B, Gardella T, Balasubramanian R, Crowley WF (2018) Modeling mutant/wild-type interactions to ascertain pathogenicity of PROKR2 missense variants in patients with isolated GnRH deficiency. Hum Mol Genet 27:338–350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. 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 (2006) Kallmann syndrome: mutations in the genes encoding prokineticin-2 and prokineticin receptor-2. PLoS Genet 2:e175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Frazer J, Notin P, Dias M, Gomez A, Min JK, Brock K, Gal Y and Marks DS (2021) ‘Disease variant prediction with deep generative models of evolutionary data’. Nature 599:91–95 [DOI] [PubMed] [Google Scholar]
  15. Gardiner JV, Bataveljic A, Patel NA, Bewick GA, Roy D, Campbell D, Greenwood HC, Murphy KG, Hameed S, Jethwa PH, Ebling FJ, Vickers SP, Cheetham S, Ghatei MA, Bloom SR, Dhillo WS (2010) Prokineticin 2 is a hypothalamic neuropeptide that potently inhibits food intake. Diabetes 59:397–406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, Hammond JA, Huggins W, Jackman D, Pan H, Nettles DS, Beaty TH, Farrer LA, Kraft P, Marazita ML, Ordovas JM, Pato CN, Spitz MR, Wagener D, Williams M, Junkins HA, Harlan WR, Ramos EM, Haines J (2011) The phenx toolkit: get the most from your measures. Am J Epidemiol 174:253–260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Han L, Maciejewski M, Brockel C, Afzelius L, Altman RB (2018) Mendelian disease associations reveal novel insights into inflammatory bowel disease. Inflamm Bowel Dis 24:471–481 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, Powell IJ, Cussenot O, Cancel-Tassin G, Giles GG, MacInnis RJ, Maier C, Hsieh CL, Wiklund F, Catalona WJ, Foulkes WD, Mandal D, Eeles RA, Kote-Jarai Z, Bustamante CD, Schaid DJ, Hastie T, Ostrander EA, Bailey-Wilson JE, Radivojac P, Thibodeau SN, Whittemore AS, Sieh W (2016) REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet 99:877–885 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ionita-Laza I, McCallum K, Xu B, Buxbaum JD (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48:214–220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, Lee JC, Schumm LP, Sharma Y, Anderson CA, Essers J, Mitrovic M, Ning K, Cleynen I, Theatre E, Spain SL, Raychaudhuri S, Goyette P, Wei Z, Abraham C, Achkar JP, Ahmad T, Amininejad L, Ananthakrishnan AN, Andersen V, Andrews JM, Baidoo L, Balschun T, Bampton PA, Bitton A, Boucher G, Brand S, Buning C, Cohain A, Cichon S, D’Amato M, De Jong D, Devaney KL, Dubinsky M, Edwards C, Ellinghaus D, Ferguson LR, Franchimont D, Fransen K, Gearry R, Georges M, Gieger C, Glas J, Haritunians T, Hart A, Hawkey C, Hedl M, Hu X, Karlsen TH, Kupcinskas L, Kugathasan S, Latiano A, Laukens D, Lawrance IC, Lees CW, Louis E, Mahy G, Mansfield J, Morgan AR, Mowat C, Newman W, Palmieri O, Ponsioen CY, Potocnik U, Prescott NJ, Regueiro M, Rotter JI, Russell RK, Sanderson JD, Sans M, Satsangi J, Schreiber S, Simms LA, Sventoraityte J, Targan SR, Taylor KD, Tremelling M, Verspaget HW, De Vos M, Wijmenga C, Wilson DC, Winkelmann J, Xavier RJ, Zeissig S, Zhang B, Zhang CK, Zhao H, Silverberg IBD, Annese V, Hakonarson H, Brant SR, Radford-Smith G, Mathew CG, D. J, Rioux EE, Schadt MJ, Daly A, Parkes Franke M., Vermeire S, Barrett JC, and Cho JH (2012) ‘Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease’. Nature 491:119–24 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, Walters RK, Tashman K, Farjoun Y, Banks E, Poterba T, Wang A, Seed C, Whiffin N, Chong JX, Samocha KE, Pierce-Hoffman E, Zappala Z, O’Donnell-Luria AH, Minikel EV, Weisburd B, Lek M, Ware JS, Vittal C, Armean IM, Bergelson L, Cibulskis K, Connolly KM, Covarrubias M, Donnelly S, Ferriera S, Gabriel S, Gentry J, Gupta N, Jeandet T, Kaplan D, Llanwarne C, Munshi R, Novod S, Petrillo N, Roazen D, Ruano-Rubio V, Saltzman A, Schleicher M, Soto J, Tibbetts K, Tolonen C, Wade G, Talkowski ME (2020) Nature 581:434–443 ‘The mutational constraint spectrum quantified from variation in 141,456 humans’ [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Laitinen EM, Vaaralahti K, Tommiska J, Eklund E, Tervaniemi M, Valanne L, Raivio T (2011) Incidence, phenotypic features and molecular genetics of Kallmann syndrome in Finland. Orphanet J Rare Dis 6:41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lattanzi R, Maftei D, Fullone MR, Miele R (2021) Trypanosoma Cruzi trans-sialidase induces STAT3 and ERK activation by prokineticin receptor 2 binding. Cell Biochem Funct 39:326–334 [DOI] [PubMed] [Google Scholar]
  25. Levy JC, Matthews DR, Hermans MP (1998) Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21:2191–2192 [DOI] [PubMed] [Google Scholar]
  26. Li M, Bullock CM, Knauer DJ, Ehlert FJ, Zhou QY (2001) Identification of two prokineticin cDNAs: Recombinant proteins potently contract Gastrointestinal smooth muscle. Mol Pharmacol 59:692–698 [DOI] [PubMed] [Google Scholar]
  27. Libri DV, Kleinau G, Vezzoli V, Busnelli M, Guizzardi F, Sinisi AA, Pincelli AI, Mancini A, Russo G, Beck-Peccoz P, Loche S, Crivellaro C, Maghnie M, Krausz C, Persani L, Bonomi M, Hypogonadism Italian Study Group on Idiopathic Central (2014) Germline prokineticin receptor 2 (PROKR2) variants associated with central hypogonadism cause differental modulation of distinct intracellular pathways. J Clin Endocrinol Metab 99:E458–E463 [DOI] [PubMed] [Google Scholar]
  28. Martin C, Balasubramanian R, Dwyer AA, Au MG, Sidis Y, Kaiser UB, Seminara SB, Pitteloud N, Zhou QY, Crowley WF, Jr (2011) The role of the prokineticin 2 pathway in human reproduction: evidence from the study of human and murine gene mutations. Endocr Rev 32:225–246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Matsumoto S, Yamazaki C, Masumoto KH, Nagano M, Naito M, Soga T, Hiyama H, Matsumoto M, Takasaki J, Kamohara M, Matsuo A, Ishii H, Kobori M, Katoh M, Matsushime H, Furuichi K, Shigeyoshi Y (2006) Abnormal development of the olfactory bulb and reproductive system in mice lacking prokineticin receptor PKR2. Proc Natl Acad Sci U S A 103:4140–4145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419 [DOI] [PubMed] [Google Scholar]
  31. Mohsen Z, Sim H, Garcia-Galiano D, Han X, Bellefontaine N, Saunders TL, Elias CF (2017) Sexually dimorphic distribution of Prokr2 neurons revealed by the Prokr2-Cre mouse model. Brain Struct Funct 222:4111–4129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Mortreux M, Foppen E, Denis RG, Montaner M, Kassis N, Denom J, Vincent M, Fumeron F, Kujawski-Lafourcade M, Andreelli F, Balkau B, Marre M, Roussel R, Magnan C, Gurden H, MigrenneLi S (2019) New roles for prokineticin 2 in feeding behavior, insulin resistance and type 2 diabetes: studies in mice and humans. Mol Metab 29:182–196 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nebigil CG (2009) Prokineticin receptors in cardiovascular function: foe or friend? Trends Cardiovasc Med 19:55–60 [DOI] [PubMed] [Google Scholar]
  34. Nebigil CG (2017) Prokineticin is a new linker between obesity and cardiovascular diseases. Front Cardiovasc Med 4:20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Pacini G, Bergman RN (1986) MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Comput Methods Programs Biomed 23:113–122 [DOI] [PubMed] [Google Scholar]
  37. Pejaver V, Urresti J, Lugo-Martinez J, Pagel KA, Lin GN, Nam HJ, Mort M, Cooper DN, Sebat J, Iakoucheva LM, Mooney SD, Radivojac P (2020) Inferring the molecular and phenotypic impact of amino acid variants with MutPred2. Nat Commun 11:5918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pitteloud N, Zhang C, Pignatelli D, Li JD, Raivio T, Cole LW, Plummer L, Jacobson-Dickman EE, Mellon PL, Zhou QY, Crowley WF, Jr (2007) Loss-of-function mutation in the prokineticin 2 gene causes Kallmann syndrome and normosmic idiopathic hypogonadotropic hypogonadism. Proc Natl Acad Sci U S A 104:17447–17452 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Reva B, Antipin Y, Sander C (2011) Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 39:e118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, Acmg Laboratory Quality Assurance Committee (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American college of medical genetics and genomics and the association for molecular pathology. Genet Med 17:405–424 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Saad MF, Anderson RL, Laws A, Watanabe RM, Kades WW, Chen YD, Sands RE, Pei D, Savage PJ, Bergman RN (1994) ‘A comparison between the minimal model and the glucose clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin Resistance Atherosclerosis Study’. Diabetes 43:1114–21 [DOI] [PubMed] [Google Scholar]
  42. Sarfati J, Fouveaut C, Leroy C, Jeanpierre M, Hardelin JP, Dode C (2013) Greater prevalence of PROKR2 mutations in Kallmann syndrome patients from the Maghreb than in European patients. Eur J Endocrinol 169:805–809 [DOI] [PubMed] [Google Scholar]
  43. Sbai O, Monnier C, Dode C, Pin JP, Hardelin JP, Rondard P (2014) Biased signaling through G-protein-coupled PROKR2 receptors harboring missense mutations. FASEB J 28:3734–3744 [DOI] [PubMed] [Google Scholar]
  44. Schwarz JM, Cooper DN, Schuelke M, Seelow D (2014) Mutation-Taster2: mutation prediction for the deep-sequencing age. Nat Methods 11:361–362 [DOI] [PubMed] [Google Scholar]
  45. Stamou MI, Brand H, Wang M, Wong I, Lippincott MF, Plummer L, Crowley WF, Talkowski M, Seminara S, Balasubramanian R (2022) Prevalence and phenotypic effects of copy number variants in isolated hypogonadotropic hypogonadism. J Clin Endocrinol Metab 107:2228–2242 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Stamou MI, Chiu CJ, Jadhav SV, Lopes VF, Salnikov KB, Plummer L, Lippincott MF, Lee H, Seminara SB, Balasubramanian R (2024) Defective FGFR1 signaling disrupts glucose regulation: evidence from humans with FGFR1 mutations. J Endocr Soc 8:bvae118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. StataCorp (2023) Stata statistical software: release 18. College station. StataCorp LLC, TX [Google Scholar]
  48. Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, Fritzilas N, Hakenberg J, Dutta A, Shon J, Xu J, Batzoglou S, Li X and Farh KK (2018) ‘Predicting the clinical impact of human mutation with deep neural networks’. Nat Genet 50:1161–1170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Sykiotis GP, Plummer L, Hughes VA, Au M, Durrani S, Nayak-Young S, Dwyer AA, Quinton R, Hall JE, Gusella JF, Seminara SB, Crowley WF Jr., Pitteloud N (2010) Oligogenic basis of isolated gonadotropin-releasing hormone deficiency. Proc Natl Acad Sci U S A 107:15140–15144 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Vincenzi M, Kremic A, Jouve A, Lattanzi R, Miele R, Benharouga M, Alfaidy N, Migrenne-Li S, Kanthasamy AG, Porcionatto M, Ferrara N, Tetko IV, Desaubry L, Nebigil CG (2023) Therapeutic potential of targeting prokineticin receptors in diseases. Pharmacol Rev 75:1167–1199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wang X, Chen D, Zhao Y, Men M, Chen Z, Jiang F, Zheng R, Stamou MI, Plummer L, Balasubramanian R, Li JD (2023) A functional spectrum of PROKR2 mutations identified in isolated hypogonadotropic hypogonadism. Hum Mol Genet 32:1722–1729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Watson RP, Lilley E, Panesar M, Bhalay G, Langridge S, Tian SS, McClenaghan C, Ropenga A, Zeng F, Nash MS (2012) Increased prokineticin 2 expression in gut inflammation: role in visceral pain and intestinal ion transport. Neurogastroenterol Motil 24:65–75e12 [DOI] [PubMed] [Google Scholar]
  53. Wilczewski CM, Obasohan J, Paschall JE, Zhang S, Singh S, Maxwell GL, Similuk M, Wolfsberg TG, Turner C, Biesecker LG, Katz AE (2023) Genotype first: clinical genomics research through a reverse phenotyping approach. Am J Hum Genet 110:3–12 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplementary tables
NIHMS2131583-supplement-supplementary_tables.xlsx (36KB, xlsx)

Data Availability Statement

Data and materials will be made available by the authors individually upon request subject to the data sharing plan and consent provided by the study participants.

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