Altered MicroRNA Profile in Osteoporosis Caused by Impaired WNT Signaling

Altered MicroRNA Profile in Osteoporosis Caused by Impaired WNT Signaling Abstract Context WNT signaling is fundamental to bone health, and its aberrant activation leads to skeletal pathologies. The heterozygous missense mutation p.C218G in WNT1, a key WNT pathway ligand, leads to severe early-onset and progressive osteoporosis with multiple peripheral and spinal fractures. Despite the severe skeletal manifestations, conventional bone turnover markers are normal in mutation-positive patients. Objective This study sought to explore the circulating microRNA (miRNA) pattern in patients with impaired WNT signaling. Design and Setting A cross-sectional cohort study at a university hospital. Participants Altogether, 12 mutation-positive (MP) subjects (median age, 39 years; range, 11 to 76 years) and 12 mutation-negative (MN) subjects (35 years; range, 9 to 59 years) from two Finnish families with WNT1 osteoporosis due to the heterozygous p.C218G WNT1 mutation. Methods and Main Outcome Measure Serum samples were screened for 192 miRNAs using quantitative polymerase chain reaction. Findings were compared between WNT1 MP and MN subjects. Results The pattern of circulating miRNAs was significantly different in the MP subjects compared with the MN subjects, with two upregulated (miR-18a-3p and miR-223-3p) and six downregulated miRNAs (miR-22-3p, miR-31-5p, miR-34a-5p, miR-143-5p, miR-423-5p, and miR-423-3p). Three of these (miR-22-3p, miR-34a-5p, and miR-31-5p) are known inhibitors of WNT signaling: miR-22-3p and miR-34a-5p target WNT1 messenger RNA, and miR-31-5p is predicted to bind to WNT1 3′UTR. Conclusions The circulating miRNA pattern reflects WNT1 mutation status. The findings suggest that the WNT1 mutation disrupts feedback regulation between these miRNAs and WNT1, providing insights into the pathogenesis of WNT-related bone disorders. These miRNAs may have potential in the diagnosis and treatment of osteoporosis. Bone health is maintained by precisely balanced bone formation and resorption. In addition to key regulatory pathways and endocrine factors, microRNAs (miRNAs) have recently emerged as integral modulators of bone metabolism (1, 2). miRNAs are short, noncoding RNA fragments that regulate target gene expression by posttranscriptional silencing or repression of protein translation and serve important functions in various biological processes (3). In bone, miRNAs regulate both osteogenesis during fetal development and maintenance of bone health postnatally (3–6). In vitro studies further demonstrated their direct role in the regulation of osteoblast and osteoclast development, maturation, and function (7–11). Furthermore, some clinical studies suggest that expression levels of different miRNAs are associated with idiopathic and postmenopausal osteoporosis, correlate with bone mineral density (BMD), and differentiate between patient cohorts (12–16). On the basis of these observations, miRNAs are anticipated to have applications in the diagnosis and treatment of bone diseases, including osteoporosis (12, 16, 17). The canonical WNT/β-catenin pathway is a key regulator of bone metabolism, and defective WNT signaling underlies several monogenic skeletal disorders with low or high bone mass, such as osteoporosis-pseudoglioma syndrome, van Buchem disease, and sclerosteosis (18–20). In 2013, our research group identified WNT1 as a major regulator of bone mass, as the heterozygous missense mutation p.C218G in WNT1 was shown to decrease activity of the WNT/β-catenin pathway in bone; this resulted in low bone turnover with a decreased number of bone cells and impaired bone formation and consequently low bone mass and skeletal fragility (21). The mutation was first reported in a large Finnish family exhibiting severe early-onset osteoporosis, multiple peripheral and spinal compression fractures, and subsequent loss in adult height (21, 22). Despite the low BMD and major skeletal pathology, the conventional bone biomarkers currently in clinical use, such as alkaline phosphatase, the bone formation marker N-terminal propeptide of type I procollagen, and the bone resorption marker type I collagen cross-linked N-telopeptide, did not differ between WNT1 mutation-positive and mutation-negative individuals (21, 22). Recent in vitro studies showed that specific miRNAs regulate WNT signaling by binding to the pathway’s key components and inhibitors: miR-152-3p and miR-335 to Dickkopf-1; miR-30e-5p to low-density lipoprotein-receptor 6; and miR-27a-3p, miR-142, and miR-135a to adenomatous polyposis coli (23–28). However, data on miRNA expression and circulating miRNA levels in genetic bone diseases are still scarce (29). In patients with altered WNT signaling, the miRNA profiles, the potential roles of the miRNAs in disease pathogenesis, the clinical diagnostics, and the follow-up remain unknown and unexplored. To gain more insight into the clinical relevance of miRNAs in inherited bone diseases with defective WNT signaling, we assessed miRNA profiles in subjects with a heterozygous WNT1 mutation and their mutation-negative family members. Subjects and Methods Subjects We previously identified two large Finnish families with autosomal dominant WNT1 osteoporosis (21, 22). The first family (Family A) was identified in 2013 when the heterozygous missense mutation p.C218G in WNT1 was determined by linkage analysis and targeted sequencing to be the cause of severe early-onset osteoporosis in 10 affected family members (21). We subsequently offered genetic screening to all first-degree relatives at risk, and, since then, 21 additional mutation-positive individuals in Family A have thus far been identified. When the whole WNT1 gene was Sanger sequenced for >250 other Finnish index patients with osteoporosis, a second family (Family B) was found to harbor the exact same point mutation. In this family, four subjects have so far been found to harbor the same mutation. Hence to date, altogether 25 mutation-positive subjects with the heterozygous p.C218G have been identified in Finland. For the study, we offered all mutation-positive subjects from Family A and Family B (n = 25) the opportunity to participate in research study concerning miRNAs in a search for new bone biomarkers associated with the heterozygous WNT1 mutation. A control group with similar age and sex distributions was formed by also offering the mutation-negative subjects in Family A (n = 29) and Family B (n = 3) the opportunity to participate. Altogether, 17 mutation-positive and 18 mutation-negative individuals consented. All participants filled out a questionnaire with the following information: other diagnosed diseases, all previous fractures, all previous surgeries, previous bisphosphonate or other osteoporosis medication, other medications, and calcium and vitamin D supplementation. On the basis of these anamnestic data, we aimed to exclude subjects with fractures or orthopedic surgeries in the past 12 months or with possible confounding illnesses or medications. Genetic evaluations We screened all participating study subjects for the heterozygous missense mutation c.652T>G (p.C218G) in exon 4 of WNT1 (The National Center for Biotechnology Information Reference Sequence NM_005430.3). We performed genetic validations on DNA extracted from peripheral blood as previously described (22). Serum samples We collected all serum samples during the spring season (January to April) in 2017 to avoid bias from variable sunlight exposure. All samples were taken after a 12-hour fast, in the morning between 0800 and 0900 hours. Venous blood was first collected into normal serum tubes and left to stand at room temperature for 30 to 60 minutes to allow clotting. The tubes were then centrifuged at 2500 × g for 10 minutes at room temperature, and the supernatant was transferred to 1.5-mL tubes in 250-µL aliquots. The serum was immediately stored at −80°C until analysis. Blood biochemistry We assessed biochemical values from separate peripheral blood samples for serum calcium, phosphate, alkaline phosphatase, 25-hydroxyvitamin D (assessed with immunochemiluminometry), and 1,25-dihydroxyvitamin D (assessed with immunochemiluminometry). Results were compared with the laboratory’s reference values. Serum parathyroid hormone and collagen type 1 cross-linked C-telopeptide (a bone resorption marker) were analyzed with an IDS-iSYS fully automated immunoassay system (Immunodiagnostic Systems, Ltd., Bolton, UK) with chemiluminescence detection and were compared with the manufacturer’s reference values. Serum intact fibroblast growth factor 23 was assessed by manual enzyme-linked immunosorbent assay (Immutopics International, San Clemente, CA, and Kainos Laboratories, Tokyo, Japan); the manufacturers’ reference values were used. miRNA analysis We isolated RNA from the serum samples using the miRNeasy Mini Kit (Qiagen, Hilden, Germany). Serum samples were thawed on ice and centrifuged at 12,000g for 5 minutes to remove any cellular debris. For each sample, 200 µL of serum was mixed with 1000 µL of Qiazol and 1 µL of synthetic spike-ins (Exiqon, Vedbaek, Denmark). After a 10-minute incubation at room temperature, 200 µL of chloroform was added to the lysates followed by cooled centrifugation at 12,000g for 15 minutes at 4°C. Precisely 650 µL of the upper aqueous phase were mixed with 7 µL of glycogen (50 mg/mL) to enhance precipitation. Samples were transferred to an miRNeasy mini column, and RNA was precipitated with 750 µL of ethanol followed by washing with RPE and RWT buffer. RNA was eluted in 30 µL of nuclease-free water and stored at −80°C until further analysis. Starting from total RNA samples, we synthesized complementary DNA using the Universal cDNA Synthesis Kit II (Exiqon). We chose reaction conditions according to recommendations of the manufacturer. The protocol was modified in that 4 µL of total RNA was used per 10 µL reverse transcription (RT) reaction. To monitor RT efficiency and the presence of impurities with inhibitory activity, a synthetic RNA spike-in (cel-miR-39-3p) was added to the RT reaction. Polymerase chain reaction amplification was performed in a 384-well plate format using custom Pick-&-Mix plates (Exiqon) in a Roche LC480 II instrument (Roche, Mannheim, Germany) and EXiLENT SYBR Green Master Mix (Exiqon) with the following settings: 95°C for 10 minutes, 45 cycles of 95°C for 10 seconds and 60°C for 60 seconds, followed by melting curve analysis. To calculate the cycle of quantification (Cq)-values, the second derivative method was used. Cq-values were normalized to the mean Cq-value in each sample (global mean normalization) by subtracting the individual miRNA Cq-value from the Cq average calculated for that sample. Experimental design, platform description, and raw as well as normalized data were submitted to the Gene Expression Omnibus according to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines (accession number GSE103473). Statistical analysis We used ClustVis to perform exploratory data analysis (30). Principal component analysis and hierarchical clustering analyses were performed using the default settings (i.e., singular value decomposition with imputation, correlation as the distance metric, and average distance for clustering). Group-wise differential expression analysis based on global mean normalized Δ Cq-values under the assumption of normal distribution was performed, which was assessed visually using two-sided t tests. Receiver operator characteristic analysis was performed to evaluate the power of selected circulating miRNAs to distinguish WNT1 MP subjects from MN subjects. Receiver operator characteristic analysis was performed using MedCalc Statistical Software version 18 (Ostend, Belgium; http://www.medcalc.org; 2018) to compute area-under-the-curve values. Study approval All genetic and clinical studies were approved by the Research Ethics Board of Helsinki University Hospital. All subjects gave written informed consent before participation in the study. Results Clinical and biochemical characteristics We recruited study subjects from two families with autosomal dominant WNT1 osteoporosis. On the basis of pedigree analysis, we selected 12 mutation-positive and 12 mutation-negative (hereafter MP and MN, respectively) family members for the analyses (Fig. 1; Table 1), aiming at comparable age and sex distribution in the MP and MN groups. The MP cohort thus represented approximately half of all Finnish subjects identified with a WNT1 mutation. The selected subjects were negative for all four (22 subjects) or for three (two subjects) of the following exclusion criteria: (1) a fracture in the past 12 months, (2) use of bisphosphonate or other osteoporosis medication in the past 12 months, (3) hypothyroidism, and (4) use of aspirin or other possible confounding medications. Figure 1. View largeDownload slide Pedigrees of the families with a heterozygous p.C218G WNT1 mutation. Squares represent males, and circles represent females. Black symbols represent mutation-positive family members, white symbols represent mutation-negative family members, and gray symbols represent mutation-negative healthy controls participating in this study. Slashes represent deceased family members. The pedigree has been altered to ensure anonymity. Figure 1. View largeDownload slide Pedigrees of the families with a heterozygous p.C218G WNT1 mutation. Squares represent males, and circles represent females. Black symbols represent mutation-positive family members, white symbols represent mutation-negative family members, and gray symbols represent mutation-negative healthy controls participating in this study. Slashes represent deceased family members. The pedigree has been altered to ensure anonymity. Table 1. Biochemical Findings in 12 MP Subjects With the Heterozygous WNT1 Mutation p.C218G and in 12 MN Subjects Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  Normal ranges according to HUSLAB Laboratory (females/males): S-Ca-ion: 1.16–1.3 mmol/L. P-Pi: 2–12 y, 1.2–1.8 mmol/L; 13–16 y, 1.1–1.8 mmol/L; 17 y, 0.8–1.4 mmol/L; females >18 y, 0.76–1.41 mmol/L; males 18–49 y, 0.71–1.53 mmol/L; males >50 y, 0.71–1.23 mmol/L. P-ALP: 8–9 y, 115–345 U/L; 10–11 y, 115–435/115–335 U/L; 12–13 y, 90–335/125–405 U/L; 14–15 y, 80–210/80–445 U/L; 16–18 y, 35–125/55–330 U/L; >18 y, 35–105 U/L. S-D-25: >50 nmol/L. Normal range for D-1,25 (1,25-dihydroxyvitamin D) according to United Medix Laboratories Ltd: 48–190 pmol/L. Normal ranges for iFGF23 according to Immutopics International and Kainos Laboratories: 8.2–54.3 pg/mL. Normal ranges for iPTH and CTX according to IDS-iSYS Kit Manual (Immunodiagnostic Systems, Ltd., Bolton, UK): iPTH: adults, 11.5–78.4 pg/mL; CTX-1: premenopausal females, 0.034–0.635 ng/mL; postmenopausal females, 0.034–1.037 ng/mL; males, 0.038–0.724 ng/mL. Supranormal values are underlined, and subnormal values are in bold. Abbreviations: CTX, collagen type 1 cross-linked C-telopeptide; F, female; iFGF23, intact fibroblast growth factor 23; iPTH, intact parathyroid hormone; M, male; P-ALP, alkaline phosphatase; P-Pi, phosphate; S-Ca-ion, calcium; S-D-25, 25-hydroxyvitamin D. View Large Table 1. Biochemical Findings in 12 MP Subjects With the Heterozygous WNT1 Mutation p.C218G and in 12 MN Subjects Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  Normal ranges according to HUSLAB Laboratory (females/males): S-Ca-ion: 1.16–1.3 mmol/L. P-Pi: 2–12 y, 1.2–1.8 mmol/L; 13–16 y, 1.1–1.8 mmol/L; 17 y, 0.8–1.4 mmol/L; females >18 y, 0.76–1.41 mmol/L; males 18–49 y, 0.71–1.53 mmol/L; males >50 y, 0.71–1.23 mmol/L. P-ALP: 8–9 y, 115–345 U/L; 10–11 y, 115–435/115–335 U/L; 12–13 y, 90–335/125–405 U/L; 14–15 y, 80–210/80–445 U/L; 16–18 y, 35–125/55–330 U/L; >18 y, 35–105 U/L. S-D-25: >50 nmol/L. Normal range for D-1,25 (1,25-dihydroxyvitamin D) according to United Medix Laboratories Ltd: 48–190 pmol/L. Normal ranges for iFGF23 according to Immutopics International and Kainos Laboratories: 8.2–54.3 pg/mL. Normal ranges for iPTH and CTX according to IDS-iSYS Kit Manual (Immunodiagnostic Systems, Ltd., Bolton, UK): iPTH: adults, 11.5–78.4 pg/mL; CTX-1: premenopausal females, 0.034–0.635 ng/mL; postmenopausal females, 0.034–1.037 ng/mL; males, 0.038–0.724 ng/mL. Supranormal values are underlined, and subnormal values are in bold. Abbreviations: CTX, collagen type 1 cross-linked C-telopeptide; F, female; iFGF23, intact fibroblast growth factor 23; iPTH, intact parathyroid hormone; M, male; P-ALP, alkaline phosphatase; P-Pi, phosphate; S-Ca-ion, calcium; S-D-25, 25-hydroxyvitamin D. View Large The MP group comprised seven females and five males (median age, 39 years; range, 11 to 76 years), and the MN group comprised five females and seven males (median age, 34.5 years; range 9 to 59 years) (Fig. 1; Table 1). The MP subjects had dual-energy X-ray absorptiometry-measured BMD values ranging from normal to osteoporosis (z score less than −2.5) and a history of multiple fractures and other skeletal manifestations, but none had had fractures or orthopedic surgeries in the past 12 months and none had other confounding illnesses (Supplemental Table 1). Regarding medications, three MP subjects had received osteoporosis medication >12 months before the study, whereas one subject (MP-4) had received the most recent dose of low-dose pamidronate treatment (1 mg/kg once every 4 months) 3 months before the study. Another male subject (MP-13) used aspirin. Serum 25-hydroxy-vitamin D concentration in the MP group was slightly higher than that in the MN group (90 nmol/L vs 68 nmol/L), as the MP subjects also received, on average, higher vitamin D supplementation (53 μg/d vs 9 μg/d). Other parameters of calcium homeostasis and bone turnover, including 1,25-dihydroxyvitamin D, parathyroid hormone, type 1 cross-linked C-telopeptide, and intact fibroblast growth factor 23, were similar in the MP and MN groups (Supplemental Fig. 1; Table 1). In the MN group, seven of 12 subjects had previously been assessed with dual-energy X-ray absorptiometry or spinal magnetic resonance imaging with no signs of osteoporosis or spinal compression fractures (Supplemental Table 1) (21, 31). Quality control of miRNA quantitation We used a previously described analytical workflow to screen 192 distinct miRNA species and controls in the 12 MP and 12 MN subjects (14). Spike-in controls added before RNA extraction, RT, and quantitative polymerase chain reaction amplification were used to assess the technical variance of the workflow (Supplemental Fig. 2) and identify potential outliers. We observed low technical variance in spike-in controls (11% to 23% coefficient of variation). To exclude a potential bias in our data due to hemolysis, we calculated a hemolysis index on the basis of the ratio of miR-451a/miR-23a-3p (32, 33). None of the samples exhibited a ratio >7, which would indicate hemolysis. The overall sensitivity of the analysis was very good. Missing values for low abundant miRNAs were observed in only six of the 24 total samples. Of the 192 analyzed miRNAs, the maximum number of missing miRNA values observed in any sample was two (Supplemental Data). Exploratory data analysis We performed unsupervised exploratory data analyses in the form of principal component analysis and hierarchical clustering to assess the overall impact of the WNT1 mutation on circulating miRNA patterns in MP subjects compared with MN subjects. Using data from the 50 most variable miRNAs (sorted by coefficient of variation %), we observed that overall circulating miRNA levels were not significantly determined by WNT1 mutation status, sex, or subfamily division (Fig. 2A; Supplemental Fig. 3). However, group-wise comparison of circulating miRNA levels between MP and MN subjects revealed a balanced number of up- and downregulated miRNAs (Fig. 2B). In total, after application of a low-stringent P value cutoff of <0.1 to reduce the number of false-negatives (Table 2), this screening identified 16 putative miRNAs that together enabled good discrimination between MP and MN subjects (Fig. 3A). Figure 2. View largeDownload slide (A) Hierarchical clustering and heat map representation of 187 circulating microRNAs, with labeling of genotype, sex, and subfamily status in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. Expression information for all 187 microRNAs was used as input for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B) Volcano plot highlighting circulating miRNA regulation in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. For every analyzed miRNA (dots), the observed fold difference (log2 transformed) between MP and MN subjects (x-axis) is shown in combination with the P value derived from parametric t statistics. MicroRNAs P < 0.05 are labeled. Figure 2. View largeDownload slide (A) Hierarchical clustering and heat map representation of 187 circulating microRNAs, with labeling of genotype, sex, and subfamily status in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. Expression information for all 187 microRNAs was used as input for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B) Volcano plot highlighting circulating miRNA regulation in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. For every analyzed miRNA (dots), the observed fold difference (log2 transformed) between MP and MN subjects (x-axis) is shown in combination with the P value derived from parametric t statistics. MicroRNAs P < 0.05 are labeled. Table 2. Differential Expression for Putative MicroRNA Biomarker Candidates With P Value <0.1 in 12 MP Subjects With the Heterozygous Missense Mutation p.C218G in WNT1 and in 12 MN Subjects miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNAs in bold show P values <0.05. AUC, area-under-the-curve from ROC analysis; Cq, quantification value; let, lethal-gene; ROC, receiver operator characteristic. View Large Table 2. Differential Expression for Putative MicroRNA Biomarker Candidates With P Value <0.1 in 12 MP Subjects With the Heterozygous Missense Mutation p.C218G in WNT1 and in 12 MN Subjects miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNAs in bold show P values <0.05. AUC, area-under-the-curve from ROC analysis; Cq, quantification value; let, lethal-gene; ROC, receiver operator characteristic. View Large Figure 3. View largeDownload slide Heat map with hierarchical clustering and scatterplots. (A) The top 16 miRNAs (sorted according to P value with P < 0.1) in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1 were used for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B–I) Normalized (global mean) Δ Cq-values are shown for seven significantly regulated miRNAs (P < 0.05) and miR-31-5p (P = 0.053). The parametric t test was applied. Figure 3. View largeDownload slide Heat map with hierarchical clustering and scatterplots. (A) The top 16 miRNAs (sorted according to P value with P < 0.1) in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1 were used for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B–I) Normalized (global mean) Δ Cq-values are shown for seven significantly regulated miRNAs (P < 0.05) and miR-31-5p (P = 0.053). The parametric t test was applied. Two of these miRNAs (miR-18a-3p and miR-223-3p) were significantly upregulated (P < 0.043), whereas five miRNAs (miR-22-3p, miR-34a-5p, miR-423-5p, miR-423-3p, and miR-143-5p) were significantly downregulated (P < 0.045) in MP subjects (Fig. 3B–3I). Two miRNAs, miR-22-3p and miR-34a-5p, exhibited good classification performance with area-under-the-curve values reaching 0.896 and 0.868, respectively. Correlation between the significantly regulated miRNAs was observed at varying moderate degrees, suggesting that some single miRNAs might contain information derived from different phenotypic characteristics caused by the WNT1 mutation (Supplemental Table 2). Ongoing low-dose bisphosphonate treatment in one MP female (MP-4) had no effect on miRNA values (Supplemental Fig. 4) In silico prediction of miRNA targets We used Targetscan release 7.1 to identify predicted miRNA binding sites in the 3′UTR of the human WNT1 gene (34). The tool reported putative binding sites for three miRNAs—miR-22-3p, miR-34a-5p, and miR-31-5p—which we found to be downregulated in the serum of MP subjects. For miR-34a-5p and miR-22-3p, binding to and regulation of WNT1 has been experimentally validated (35–38). For miR-31-5p, experimental confirmation of a direct interaction with WNT1 has not been reported, although its interference with the WNT signaling ligand frizzled 3 has been observed (9). Discussion This study reports on miRNA profiles in subjects with a monogenic bone disease due to defective WNT signaling. All of our MP subjects harbored the heterozygous missense mutation p.C218G in WNT1, which was previously shown to lead to decreased WNT signaling, low bone formation, low BMD, and fractures (21, 22), whereas the MN subjects had normal BMD and no fractures (21, 31). We screened a custom-designed panel of 192 miRNAs and compared the results from 12 MP subjects with those from 12 MN subjects. Our results show that a unique profile of eight miRNAs differentiated between MP and MN subjects and that three of these miRNAs, miR-22-3p, miR-34a-5p, and miR-31-5p, were downregulated in the serum of MP subjects (P = 0.001, P = 0.003, and P = 0.053, respectively). All three miRNAs are known inhibitors of WNT signaling, and miR-22-3p and miR-34a-5p have been shown to target WNT1 specifically (35–38). To the best of our knowledge, no previously published data have reported a direct association between miR-31-5p and WNT1. Recent research found that circulating miRNAs are promising new markers in various diseases, including malignancies, as many conventional biomarkers have shown limitations in diagnostics and in evaluating treatment outcomes (29). Regarding bone health and disease, current conventional metabolic bone markers are inadequate in reflecting bone health status, predicting future fracture risk, or monitoring treatment efficacy (12, 39, 40). Circulating miRNAs show promise as future bone markers, as specific miRNAs that discriminate [e.g., patients with manifest osteoporosis (14, 16, 41)] have also influenced bone metabolism in vitro (1, 13, 14, 24, 42‒44) and in vivo (16, 41). This suggests that circulating miRNA-based biomarkers might have causal links to the disease phenotype, as miRNAs packaged in extracellular vesicles or in protein particles can be taken up by recipient cells in an auto-, para-, or even endocrine manner (45). Of the eight miRNAs identified in our study as discriminating between MP and MN subjects, seven have reportedly influenced bone metabolism (Table 3). Interestingly, three of the downregulated miRNAs in MP subjects were confirmed or predicted to directly target WNT signaling and specifically WNT1 messenger RNA. One of these, miR-22-3p, negatively regulates osteogenesis and osteoblastogenesis through WNT signaling by targeting the coding region and suppressing the expression of β-catenin, which inhibits formation of calcium nodules during osteoblast differentiation (10). miR-22-3p also targets and decreases the levels of Tcf7 and Ep300, key transcriptional proteins for target gene expression in the WNT pathway (57). Furthermore, the 3′UTR of WNT1 contains a binding site for miR-22-3p, and miR-22-3p directly targets WNT1 (35). Secondly, miR-34a-5p interacts with WNT pathway components (58) and also directly with WNT1 to regulate its messenger RNA expression and posttranscriptional translation (36–38). Lastly, Weilner et al. (9) showed that miR-31-5p interacts with the WNT pathway component frizzled-3 and suppresses WNT signaling, whereas Xi et al. (59) showed that miR-31-5p targets WNT pathway antagonists Dkk-1 and DACT3 in lung cancer cells. However, unlike with miR-22-3p and miR-34a-5p, there are no reports of miR-31-5p directly targeting WNT1. Table 3. Previously Reported Data on the Role of the Seven Discriminative miRNAs in Bone Metabolism in WNT1 MP Subjects miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  View Large Table 3. Previously Reported Data on the Role of the Seven Discriminative miRNAs in Bone Metabolism in WNT1 MP Subjects miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  View Large Intriguingly, our findings suggest that decreased WNT signaling due to mutated WNT1 leads to downregulation of miRNAs that exhibit suppressive action on the WNT pathway. This could be regarded as an attempt to normalize WNT signaling in a situation where WNT1-related WNT signaling is impaired. The molecular and genetic feedback mechanisms governing balanced bone metabolism, including gene regulation by miRNAs, are inadequately understood. To the best of our knowledge, miR-423-3p, which was significantly downregulated in our MP subjects, has not been previously linked to bone metabolism or WNT signaling. Wang et al. (60) previously reported that miR-423-3p expression changed with age in the microvesicles of mouse bone marrow mesenchymal stem cells, but no evidence for its effect on osteogenesis was shown. Also, other studies reported that miR-423-3p was linked to myocardial tissue and heart diseases (61, 62), but no specific heart phenotype or increased prevalence of myocardial disease was observed in our cohort or in other WNT1 MP subjects (21, 22). Therefore, the suggestion of an association between miR-423-3p and the WNT pathway is notable. Prior or ongoing osteoporosis medication use could potentially alter an individual’s miRNA expression, although the mechanisms and exact consequences are still unclear (63). Altogether, our cohort included three subjects with previous osteoporosis medication use and one subject with ongoing low-dose pamidronate treatment at the time of the study. A separate analysis showed that bisphosphonate treatment had no effect on the expression of the eight specific miRNAs in our cohort; therefore, the observed differences between MP and MN groups are likely not related to the patients’ medical therapy. We previously showed that conventional bisphosphonates had little to no effect on BMD status in WNT1 MP patients (22). The therapeutic potential in miRNAs has been contemplated and experimentally tested in previous studies. Krzeszinski et al. (46) were able to attenuate postmenopausal osteoporosis in ovariectomized mice with the administration of systemic miR-34; Wang et al. (64) showed the anabolic potential of anti‒miR-214 in stimulating bone formation in mice; and Wang et al. (42) demonstrated protection against glucocorticoid-induced bone loss with miR-29a in rats. In a wider context, miRNAs are used clinically in cancer treatment as replacement therapies, to sensitize tumors to chemotherapy, and to treat drug-resistant malignancies (65). Whether the miRNAs upregulated or downregulated in our study could have therapeutic potential—as drug targets or exogenously administered medications—in WNT1 or other forms of osteoporosis should be explored in future experimental settings. Our study provides data on miRNA expression levels in WNT-related bone disease. The study could have been strengthened by larger cohort sizes and with only subjects with no confounding factors, such as use of osteoporosis medication. However, we did exclude subjects with recent fractures and showed that subjects with previous or ongoing osteoporosis medication use did not differ from the others in miRNA patterns. Further, to minimize the effect of other confounding genetic factors, we recruited MN subjects from the same families as MP subjects. We also considered the premenopausal and postmenopausal status of female subjects as a possible confounding factor. However, the two cohorts were very similar in age and sex distribution, alleviating possible bias. Furthermore, there was no overlap with the miRNAs reported in this study and those previously reported to have alternative expression as a result of changes in estradiol concentration in Finnish subjects (65, 66). The small sample size may have prevented us from observing some important differences between the groups, leading to a high false-negative rate. We tried to account for this by allowing relatively high type-I error (P < 0.1) for initial selection. An independent cohort of WNT1 MP/MN subjects would be required to validate miR-22, miR-34a, and miR-31 findings. However, because of the scarcity of subjects with confirmed WNT1 or other WNT pathway-related osteoporosis, such a study was not possible. Despite these limitations and acknowledging the worldwide rarity of WNT1 osteoporosis and WNT1 mutation-positive individuals and the overall scarcity of miRNA data in monogenic bone disorders, we consider our study’s setting and findings valid and valuable. We concluded that a unique miRNA profile was observed in WNT1 mutation-positive individuals compared with healthy individuals. These observations provide valuable information about the molecular pathways involved in WNT1 osteoporosis and the effect of aberrant WNT signaling on miRNA expression. Our data also support an association between WNT1 and miR-31-5p and miR-423-3p expression. The specific miRNAs highlighted in this study could serve as circulating metabolic bone markers in WNT1 osteoporosis to evaluate bone health, fracture healing, and treatment efficacy in affected individuals. Future studies are encouraged to further explore these specific miRNAs in other WNT pathway-related skeletal diseases, their response to antiosteoporosis treatment, and their potential utilization in the development of osteoporosis treatment. Abbreviations: Abbreviations: BMD bone mineral density Cq cycle of quantification miRNA microRNA MN mutation-negative MP mutation-positive RT reverse transcription Acknowledgments We thank Päivi Turunen and Kirsi Mäkelä-Kvist for their help with collecting subject and sample data and Susanna Skalicky for excellent technical support. Financial Support: This study was financially supported by the Sigrid Jusélius Foundation (to O.M.); the Folkhälsan Research Foundation (to O.M.); the Academy of Finland (to O.M.); the Foundation for Pediatric Research (to O.M.); Helsinki University Research Funds (to R.E.M.); the Swedish Research Council (to O.M.); the Novo Nordisk Foundation (to O.M.); Helsinki University and Helsinki University Hospital through the Doctoral Programme in Clinical Research (to R.E.M.); the Finnish Medical Foundation (to R.E.M.); the Jalmari and Rauha Ahokas Foundation (to R.E.M.); the Swedish Childhood Cancer Foundation (to O.M.); and the Stockholm County Council (ALF project; to O.M.). Author Contributions: Study design: R.E.M., M.H., R.N., J.G., and O.M. Study conduct: R.E.M., M.H., J.G., and O.M. Data collection: R.E.M., M.H., R.N., S.K., and J.G. Data analysis: R.E.M., M.H., and J.G. Drafting of the manuscript: R.E.M., M.H., and J.G. Revising of the manuscript content: all authors. 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Google Scholar CrossRef Search ADS PubMed  Copyright © 2018 Endocrine Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Clinical Endocrinology and Metabolism Oxford University Press

Altered MicroRNA Profile in Osteoporosis Caused by Impaired WNT Signaling

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Publisher
Endocrine Society
Copyright
Copyright © 2018 Endocrine Society
ISSN
0021-972X
eISSN
1945-7197
D.O.I.
10.1210/jc.2017-02585
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Abstract

Abstract Context WNT signaling is fundamental to bone health, and its aberrant activation leads to skeletal pathologies. The heterozygous missense mutation p.C218G in WNT1, a key WNT pathway ligand, leads to severe early-onset and progressive osteoporosis with multiple peripheral and spinal fractures. Despite the severe skeletal manifestations, conventional bone turnover markers are normal in mutation-positive patients. Objective This study sought to explore the circulating microRNA (miRNA) pattern in patients with impaired WNT signaling. Design and Setting A cross-sectional cohort study at a university hospital. Participants Altogether, 12 mutation-positive (MP) subjects (median age, 39 years; range, 11 to 76 years) and 12 mutation-negative (MN) subjects (35 years; range, 9 to 59 years) from two Finnish families with WNT1 osteoporosis due to the heterozygous p.C218G WNT1 mutation. Methods and Main Outcome Measure Serum samples were screened for 192 miRNAs using quantitative polymerase chain reaction. Findings were compared between WNT1 MP and MN subjects. Results The pattern of circulating miRNAs was significantly different in the MP subjects compared with the MN subjects, with two upregulated (miR-18a-3p and miR-223-3p) and six downregulated miRNAs (miR-22-3p, miR-31-5p, miR-34a-5p, miR-143-5p, miR-423-5p, and miR-423-3p). Three of these (miR-22-3p, miR-34a-5p, and miR-31-5p) are known inhibitors of WNT signaling: miR-22-3p and miR-34a-5p target WNT1 messenger RNA, and miR-31-5p is predicted to bind to WNT1 3′UTR. Conclusions The circulating miRNA pattern reflects WNT1 mutation status. The findings suggest that the WNT1 mutation disrupts feedback regulation between these miRNAs and WNT1, providing insights into the pathogenesis of WNT-related bone disorders. These miRNAs may have potential in the diagnosis and treatment of osteoporosis. Bone health is maintained by precisely balanced bone formation and resorption. In addition to key regulatory pathways and endocrine factors, microRNAs (miRNAs) have recently emerged as integral modulators of bone metabolism (1, 2). miRNAs are short, noncoding RNA fragments that regulate target gene expression by posttranscriptional silencing or repression of protein translation and serve important functions in various biological processes (3). In bone, miRNAs regulate both osteogenesis during fetal development and maintenance of bone health postnatally (3–6). In vitro studies further demonstrated their direct role in the regulation of osteoblast and osteoclast development, maturation, and function (7–11). Furthermore, some clinical studies suggest that expression levels of different miRNAs are associated with idiopathic and postmenopausal osteoporosis, correlate with bone mineral density (BMD), and differentiate between patient cohorts (12–16). On the basis of these observations, miRNAs are anticipated to have applications in the diagnosis and treatment of bone diseases, including osteoporosis (12, 16, 17). The canonical WNT/β-catenin pathway is a key regulator of bone metabolism, and defective WNT signaling underlies several monogenic skeletal disorders with low or high bone mass, such as osteoporosis-pseudoglioma syndrome, van Buchem disease, and sclerosteosis (18–20). In 2013, our research group identified WNT1 as a major regulator of bone mass, as the heterozygous missense mutation p.C218G in WNT1 was shown to decrease activity of the WNT/β-catenin pathway in bone; this resulted in low bone turnover with a decreased number of bone cells and impaired bone formation and consequently low bone mass and skeletal fragility (21). The mutation was first reported in a large Finnish family exhibiting severe early-onset osteoporosis, multiple peripheral and spinal compression fractures, and subsequent loss in adult height (21, 22). Despite the low BMD and major skeletal pathology, the conventional bone biomarkers currently in clinical use, such as alkaline phosphatase, the bone formation marker N-terminal propeptide of type I procollagen, and the bone resorption marker type I collagen cross-linked N-telopeptide, did not differ between WNT1 mutation-positive and mutation-negative individuals (21, 22). Recent in vitro studies showed that specific miRNAs regulate WNT signaling by binding to the pathway’s key components and inhibitors: miR-152-3p and miR-335 to Dickkopf-1; miR-30e-5p to low-density lipoprotein-receptor 6; and miR-27a-3p, miR-142, and miR-135a to adenomatous polyposis coli (23–28). However, data on miRNA expression and circulating miRNA levels in genetic bone diseases are still scarce (29). In patients with altered WNT signaling, the miRNA profiles, the potential roles of the miRNAs in disease pathogenesis, the clinical diagnostics, and the follow-up remain unknown and unexplored. To gain more insight into the clinical relevance of miRNAs in inherited bone diseases with defective WNT signaling, we assessed miRNA profiles in subjects with a heterozygous WNT1 mutation and their mutation-negative family members. Subjects and Methods Subjects We previously identified two large Finnish families with autosomal dominant WNT1 osteoporosis (21, 22). The first family (Family A) was identified in 2013 when the heterozygous missense mutation p.C218G in WNT1 was determined by linkage analysis and targeted sequencing to be the cause of severe early-onset osteoporosis in 10 affected family members (21). We subsequently offered genetic screening to all first-degree relatives at risk, and, since then, 21 additional mutation-positive individuals in Family A have thus far been identified. When the whole WNT1 gene was Sanger sequenced for >250 other Finnish index patients with osteoporosis, a second family (Family B) was found to harbor the exact same point mutation. In this family, four subjects have so far been found to harbor the same mutation. Hence to date, altogether 25 mutation-positive subjects with the heterozygous p.C218G have been identified in Finland. For the study, we offered all mutation-positive subjects from Family A and Family B (n = 25) the opportunity to participate in research study concerning miRNAs in a search for new bone biomarkers associated with the heterozygous WNT1 mutation. A control group with similar age and sex distributions was formed by also offering the mutation-negative subjects in Family A (n = 29) and Family B (n = 3) the opportunity to participate. Altogether, 17 mutation-positive and 18 mutation-negative individuals consented. All participants filled out a questionnaire with the following information: other diagnosed diseases, all previous fractures, all previous surgeries, previous bisphosphonate or other osteoporosis medication, other medications, and calcium and vitamin D supplementation. On the basis of these anamnestic data, we aimed to exclude subjects with fractures or orthopedic surgeries in the past 12 months or with possible confounding illnesses or medications. Genetic evaluations We screened all participating study subjects for the heterozygous missense mutation c.652T>G (p.C218G) in exon 4 of WNT1 (The National Center for Biotechnology Information Reference Sequence NM_005430.3). We performed genetic validations on DNA extracted from peripheral blood as previously described (22). Serum samples We collected all serum samples during the spring season (January to April) in 2017 to avoid bias from variable sunlight exposure. All samples were taken after a 12-hour fast, in the morning between 0800 and 0900 hours. Venous blood was first collected into normal serum tubes and left to stand at room temperature for 30 to 60 minutes to allow clotting. The tubes were then centrifuged at 2500 × g for 10 minutes at room temperature, and the supernatant was transferred to 1.5-mL tubes in 250-µL aliquots. The serum was immediately stored at −80°C until analysis. Blood biochemistry We assessed biochemical values from separate peripheral blood samples for serum calcium, phosphate, alkaline phosphatase, 25-hydroxyvitamin D (assessed with immunochemiluminometry), and 1,25-dihydroxyvitamin D (assessed with immunochemiluminometry). Results were compared with the laboratory’s reference values. Serum parathyroid hormone and collagen type 1 cross-linked C-telopeptide (a bone resorption marker) were analyzed with an IDS-iSYS fully automated immunoassay system (Immunodiagnostic Systems, Ltd., Bolton, UK) with chemiluminescence detection and were compared with the manufacturer’s reference values. Serum intact fibroblast growth factor 23 was assessed by manual enzyme-linked immunosorbent assay (Immutopics International, San Clemente, CA, and Kainos Laboratories, Tokyo, Japan); the manufacturers’ reference values were used. miRNA analysis We isolated RNA from the serum samples using the miRNeasy Mini Kit (Qiagen, Hilden, Germany). Serum samples were thawed on ice and centrifuged at 12,000g for 5 minutes to remove any cellular debris. For each sample, 200 µL of serum was mixed with 1000 µL of Qiazol and 1 µL of synthetic spike-ins (Exiqon, Vedbaek, Denmark). After a 10-minute incubation at room temperature, 200 µL of chloroform was added to the lysates followed by cooled centrifugation at 12,000g for 15 minutes at 4°C. Precisely 650 µL of the upper aqueous phase were mixed with 7 µL of glycogen (50 mg/mL) to enhance precipitation. Samples were transferred to an miRNeasy mini column, and RNA was precipitated with 750 µL of ethanol followed by washing with RPE and RWT buffer. RNA was eluted in 30 µL of nuclease-free water and stored at −80°C until further analysis. Starting from total RNA samples, we synthesized complementary DNA using the Universal cDNA Synthesis Kit II (Exiqon). We chose reaction conditions according to recommendations of the manufacturer. The protocol was modified in that 4 µL of total RNA was used per 10 µL reverse transcription (RT) reaction. To monitor RT efficiency and the presence of impurities with inhibitory activity, a synthetic RNA spike-in (cel-miR-39-3p) was added to the RT reaction. Polymerase chain reaction amplification was performed in a 384-well plate format using custom Pick-&-Mix plates (Exiqon) in a Roche LC480 II instrument (Roche, Mannheim, Germany) and EXiLENT SYBR Green Master Mix (Exiqon) with the following settings: 95°C for 10 minutes, 45 cycles of 95°C for 10 seconds and 60°C for 60 seconds, followed by melting curve analysis. To calculate the cycle of quantification (Cq)-values, the second derivative method was used. Cq-values were normalized to the mean Cq-value in each sample (global mean normalization) by subtracting the individual miRNA Cq-value from the Cq average calculated for that sample. Experimental design, platform description, and raw as well as normalized data were submitted to the Gene Expression Omnibus according to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines (accession number GSE103473). Statistical analysis We used ClustVis to perform exploratory data analysis (30). Principal component analysis and hierarchical clustering analyses were performed using the default settings (i.e., singular value decomposition with imputation, correlation as the distance metric, and average distance for clustering). Group-wise differential expression analysis based on global mean normalized Δ Cq-values under the assumption of normal distribution was performed, which was assessed visually using two-sided t tests. Receiver operator characteristic analysis was performed to evaluate the power of selected circulating miRNAs to distinguish WNT1 MP subjects from MN subjects. Receiver operator characteristic analysis was performed using MedCalc Statistical Software version 18 (Ostend, Belgium; http://www.medcalc.org; 2018) to compute area-under-the-curve values. Study approval All genetic and clinical studies were approved by the Research Ethics Board of Helsinki University Hospital. All subjects gave written informed consent before participation in the study. Results Clinical and biochemical characteristics We recruited study subjects from two families with autosomal dominant WNT1 osteoporosis. On the basis of pedigree analysis, we selected 12 mutation-positive and 12 mutation-negative (hereafter MP and MN, respectively) family members for the analyses (Fig. 1; Table 1), aiming at comparable age and sex distribution in the MP and MN groups. The MP cohort thus represented approximately half of all Finnish subjects identified with a WNT1 mutation. The selected subjects were negative for all four (22 subjects) or for three (two subjects) of the following exclusion criteria: (1) a fracture in the past 12 months, (2) use of bisphosphonate or other osteoporosis medication in the past 12 months, (3) hypothyroidism, and (4) use of aspirin or other possible confounding medications. Figure 1. View largeDownload slide Pedigrees of the families with a heterozygous p.C218G WNT1 mutation. Squares represent males, and circles represent females. Black symbols represent mutation-positive family members, white symbols represent mutation-negative family members, and gray symbols represent mutation-negative healthy controls participating in this study. Slashes represent deceased family members. The pedigree has been altered to ensure anonymity. Figure 1. View largeDownload slide Pedigrees of the families with a heterozygous p.C218G WNT1 mutation. Squares represent males, and circles represent females. Black symbols represent mutation-positive family members, white symbols represent mutation-negative family members, and gray symbols represent mutation-negative healthy controls participating in this study. Slashes represent deceased family members. The pedigree has been altered to ensure anonymity. Table 1. Biochemical Findings in 12 MP Subjects With the Heterozygous WNT1 Mutation p.C218G and in 12 MN Subjects Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  Normal ranges according to HUSLAB Laboratory (females/males): S-Ca-ion: 1.16–1.3 mmol/L. P-Pi: 2–12 y, 1.2–1.8 mmol/L; 13–16 y, 1.1–1.8 mmol/L; 17 y, 0.8–1.4 mmol/L; females >18 y, 0.76–1.41 mmol/L; males 18–49 y, 0.71–1.53 mmol/L; males >50 y, 0.71–1.23 mmol/L. P-ALP: 8–9 y, 115–345 U/L; 10–11 y, 115–435/115–335 U/L; 12–13 y, 90–335/125–405 U/L; 14–15 y, 80–210/80–445 U/L; 16–18 y, 35–125/55–330 U/L; >18 y, 35–105 U/L. S-D-25: >50 nmol/L. Normal range for D-1,25 (1,25-dihydroxyvitamin D) according to United Medix Laboratories Ltd: 48–190 pmol/L. Normal ranges for iFGF23 according to Immutopics International and Kainos Laboratories: 8.2–54.3 pg/mL. Normal ranges for iPTH and CTX according to IDS-iSYS Kit Manual (Immunodiagnostic Systems, Ltd., Bolton, UK): iPTH: adults, 11.5–78.4 pg/mL; CTX-1: premenopausal females, 0.034–0.635 ng/mL; postmenopausal females, 0.034–1.037 ng/mL; males, 0.038–0.724 ng/mL. Supranormal values are underlined, and subnormal values are in bold. Abbreviations: CTX, collagen type 1 cross-linked C-telopeptide; F, female; iFGF23, intact fibroblast growth factor 23; iPTH, intact parathyroid hormone; M, male; P-ALP, alkaline phosphatase; P-Pi, phosphate; S-Ca-ion, calcium; S-D-25, 25-hydroxyvitamin D. View Large Table 1. Biochemical Findings in 12 MP Subjects With the Heterozygous WNT1 Mutation p.C218G and in 12 MN Subjects Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  Subject Code  Age (y)  Sex  S-Ca-ion (mmol/L)  P-Pi 
(mmol/L)  P-ALP 
(U/L)  S-D-25 
(nmol/L)  S-D-1,25 
(pmol/L)  S-FGF23, I 
(pg/mL)  S-iPTH 
(pg/mL)  S-CTX-1 
(ng/mL)  MP Subjects (n = 12)  MP-1  11  F  1.26  1.35  258  90  173.0  42.00  22.0  1.792  MP-2  13  M  1.25  1.66  247  112  94.3  77.27  7.6  2.946  MP-3  13  F  1.32  1.23  122  75  140.0  66.36  23.8  1.349  MP-4  17  F  1.25  1.12  77  116  96.4  75.45  31.4  0.562  MP-5  19  M  1.27  0.82  83  31  168.0  32.90  22.6  0.452  MP-6  34  F  1.24  1.25  84  105  44.70  63.77  40.0  0.147  MP-7  44  F  1.32  0.89  51  82  66.8  51.04  24.9  0.102  MP-9  52  M  1.24  0.86  50  74  172.0  54.68  24.3  0.196  MP-12  54  F  1.22  1.34  74  136  64.1  46.14  30.8  0.710  MP-13  63  M  1.24  1.09  52  146  114.0  66.10  29.3  0.107  MP-15  71  F  1.24  1.39  72  88  124.0  41.17  18.2  0.199  MP-17  76  M  1.28  0.85  84  89  87.6  54.94  47.9  0.161  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  MN Subjects (n = 12)  MN-2  9  F  1.28  1.55  257  68  165.0  49.45  33.0  1.685  MN-3  10  M  1.32  1.42  313  70  192.0  37.03  12.6  1.636  MN-6  24  M  1.24  1.53  67  39  144.0  43.93  26.2  0.861  MN-7  27  F  1.27  1.38  60  70  127.0  51.56  36.6  0.731  MN-8  30  M  1.24  0.88  104  54  117.0  56.49  38.6  0.376  MN-11  32  M  1.27  0.51  67  41  92.8  65.84  29.8  0.332  MN-12  37  F  1.27  1.13  52  60  134.0  39.52  33.4  0.213  MN-13  43  M  1.22  0.84  61  68  74.1  74.16  35.6  0.284  MN-14  49  F  1.22  1.05  63  90  82.9  43.66  46.1  0.152  MN-15  53  M  1.28  1.09  50  73  57.2  61.17  56.4  0.268  MN-16  57  F  1.26  1.08  83  39  109.0  41.17  49.7  0.624  MN-17  59  M  1.28  1.02  94  21  71.1  77.79  35.8  0.223  Normal ranges according to HUSLAB Laboratory (females/males): S-Ca-ion: 1.16–1.3 mmol/L. P-Pi: 2–12 y, 1.2–1.8 mmol/L; 13–16 y, 1.1–1.8 mmol/L; 17 y, 0.8–1.4 mmol/L; females >18 y, 0.76–1.41 mmol/L; males 18–49 y, 0.71–1.53 mmol/L; males >50 y, 0.71–1.23 mmol/L. P-ALP: 8–9 y, 115–345 U/L; 10–11 y, 115–435/115–335 U/L; 12–13 y, 90–335/125–405 U/L; 14–15 y, 80–210/80–445 U/L; 16–18 y, 35–125/55–330 U/L; >18 y, 35–105 U/L. S-D-25: >50 nmol/L. Normal range for D-1,25 (1,25-dihydroxyvitamin D) according to United Medix Laboratories Ltd: 48–190 pmol/L. Normal ranges for iFGF23 according to Immutopics International and Kainos Laboratories: 8.2–54.3 pg/mL. Normal ranges for iPTH and CTX according to IDS-iSYS Kit Manual (Immunodiagnostic Systems, Ltd., Bolton, UK): iPTH: adults, 11.5–78.4 pg/mL; CTX-1: premenopausal females, 0.034–0.635 ng/mL; postmenopausal females, 0.034–1.037 ng/mL; males, 0.038–0.724 ng/mL. Supranormal values are underlined, and subnormal values are in bold. Abbreviations: CTX, collagen type 1 cross-linked C-telopeptide; F, female; iFGF23, intact fibroblast growth factor 23; iPTH, intact parathyroid hormone; M, male; P-ALP, alkaline phosphatase; P-Pi, phosphate; S-Ca-ion, calcium; S-D-25, 25-hydroxyvitamin D. View Large The MP group comprised seven females and five males (median age, 39 years; range, 11 to 76 years), and the MN group comprised five females and seven males (median age, 34.5 years; range 9 to 59 years) (Fig. 1; Table 1). The MP subjects had dual-energy X-ray absorptiometry-measured BMD values ranging from normal to osteoporosis (z score less than −2.5) and a history of multiple fractures and other skeletal manifestations, but none had had fractures or orthopedic surgeries in the past 12 months and none had other confounding illnesses (Supplemental Table 1). Regarding medications, three MP subjects had received osteoporosis medication >12 months before the study, whereas one subject (MP-4) had received the most recent dose of low-dose pamidronate treatment (1 mg/kg once every 4 months) 3 months before the study. Another male subject (MP-13) used aspirin. Serum 25-hydroxy-vitamin D concentration in the MP group was slightly higher than that in the MN group (90 nmol/L vs 68 nmol/L), as the MP subjects also received, on average, higher vitamin D supplementation (53 μg/d vs 9 μg/d). Other parameters of calcium homeostasis and bone turnover, including 1,25-dihydroxyvitamin D, parathyroid hormone, type 1 cross-linked C-telopeptide, and intact fibroblast growth factor 23, were similar in the MP and MN groups (Supplemental Fig. 1; Table 1). In the MN group, seven of 12 subjects had previously been assessed with dual-energy X-ray absorptiometry or spinal magnetic resonance imaging with no signs of osteoporosis or spinal compression fractures (Supplemental Table 1) (21, 31). Quality control of miRNA quantitation We used a previously described analytical workflow to screen 192 distinct miRNA species and controls in the 12 MP and 12 MN subjects (14). Spike-in controls added before RNA extraction, RT, and quantitative polymerase chain reaction amplification were used to assess the technical variance of the workflow (Supplemental Fig. 2) and identify potential outliers. We observed low technical variance in spike-in controls (11% to 23% coefficient of variation). To exclude a potential bias in our data due to hemolysis, we calculated a hemolysis index on the basis of the ratio of miR-451a/miR-23a-3p (32, 33). None of the samples exhibited a ratio >7, which would indicate hemolysis. The overall sensitivity of the analysis was very good. Missing values for low abundant miRNAs were observed in only six of the 24 total samples. Of the 192 analyzed miRNAs, the maximum number of missing miRNA values observed in any sample was two (Supplemental Data). Exploratory data analysis We performed unsupervised exploratory data analyses in the form of principal component analysis and hierarchical clustering to assess the overall impact of the WNT1 mutation on circulating miRNA patterns in MP subjects compared with MN subjects. Using data from the 50 most variable miRNAs (sorted by coefficient of variation %), we observed that overall circulating miRNA levels were not significantly determined by WNT1 mutation status, sex, or subfamily division (Fig. 2A; Supplemental Fig. 3). However, group-wise comparison of circulating miRNA levels between MP and MN subjects revealed a balanced number of up- and downregulated miRNAs (Fig. 2B). In total, after application of a low-stringent P value cutoff of <0.1 to reduce the number of false-negatives (Table 2), this screening identified 16 putative miRNAs that together enabled good discrimination between MP and MN subjects (Fig. 3A). Figure 2. View largeDownload slide (A) Hierarchical clustering and heat map representation of 187 circulating microRNAs, with labeling of genotype, sex, and subfamily status in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. Expression information for all 187 microRNAs was used as input for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B) Volcano plot highlighting circulating miRNA regulation in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. For every analyzed miRNA (dots), the observed fold difference (log2 transformed) between MP and MN subjects (x-axis) is shown in combination with the P value derived from parametric t statistics. MicroRNAs P < 0.05 are labeled. Figure 2. View largeDownload slide (A) Hierarchical clustering and heat map representation of 187 circulating microRNAs, with labeling of genotype, sex, and subfamily status in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. Expression information for all 187 microRNAs was used as input for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B) Volcano plot highlighting circulating miRNA regulation in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1. For every analyzed miRNA (dots), the observed fold difference (log2 transformed) between MP and MN subjects (x-axis) is shown in combination with the P value derived from parametric t statistics. MicroRNAs P < 0.05 are labeled. Table 2. Differential Expression for Putative MicroRNA Biomarker Candidates With P Value <0.1 in 12 MP Subjects With the Heterozygous Missense Mutation p.C218G in WNT1 and in 12 MN Subjects miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNAs in bold show P values <0.05. AUC, area-under-the-curve from ROC analysis; Cq, quantification value; let, lethal-gene; ROC, receiver operator characteristic. View Large Table 2. Differential Expression for Putative MicroRNA Biomarker Candidates With P Value <0.1 in 12 MP Subjects With the Heterozygous Missense Mutation p.C218G in WNT1 and in 12 MN Subjects miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNA-ID
Group  Average 
(Normalized Δ Cq)  Standard Deviation
 (Cq-Value)  Fold Change 
(log2-Transformed)  Fold Change (Linear)  Parametric 
t Test
P Value  ROC Analysis
AUC Value  MP  MN  MP  MN  MP vs MN  MP vs MN  miR-22-3p  1.31  1.82  0.32  0.34  −0.51  0.70  0.001  0.896  miR-34a-5p  −2.92  −1.95  0.63  0.76  −0.97  0.51  0.003  0.868  miR-423-5p  1.04  1.32  0.28  0.3  −0.27  0.83  0.039  0.743  miR-18a-3p  −2.05  −2.47  0.49  0.43  0.43  1.35  0.042  0.757  miR-423-3p  1.14  1.45  0.35  0.31  −0.30  0.81  0.042  0.715  miR-223-3p  9.17  8.82  0.4  0.37  0.35  1.27  0.043  0.757  miR-143-5p  −7.24  −5.88  1.91  0.95  −1.37  0.39  0.045  0.726  miR-31-5p  −5.36  −4.56  0.94  0.89  −0.80  0.57  0.053  0.729  miR-425-3p  −0.96  −1.27  0.27  0.42  0.31  1.24  0.053  0.774  miR-874-3p  −2.19  −1.68  0.67  0.54  −0.51  0.70  0.061  0.736  miR-200b-3p  −2.87  −3.39  0.55  0.73  0.52  1.43  0.071  0.688  miR-330-3p  −4.01  −4.55  0.42  0.91  0.55  1.46  0.086  0.684  miR-103a-3p  4.69  5.01  0.39  0.44  −0.32  0.80  0.089  0.729  let-7g-5p  3.83  4.06  0.31  0.3  −0.23  0.85  0.092  0.722  miR-128-3p  −1.78  −1.54  0.33  0.31  −0.24  0.85  0.092  0.698  let-7d-5p  0.47  0.74  0.31  0.39  −0.26  0.84  0.095  0.705  miRNAs in bold show P values <0.05. AUC, area-under-the-curve from ROC analysis; Cq, quantification value; let, lethal-gene; ROC, receiver operator characteristic. View Large Figure 3. View largeDownload slide Heat map with hierarchical clustering and scatterplots. (A) The top 16 miRNAs (sorted according to P value with P < 0.1) in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1 were used for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B–I) Normalized (global mean) Δ Cq-values are shown for seven significantly regulated miRNAs (P < 0.05) and miR-31-5p (P = 0.053). The parametric t test was applied. Figure 3. View largeDownload slide Heat map with hierarchical clustering and scatterplots. (A) The top 16 miRNAs (sorted according to P value with P < 0.1) in 12 MP subjects with the heterozygous missense mutation p.C218G in WNT1 were used for hierarchical clustering. Average linkage and correlation were used as distance metrics. (B–I) Normalized (global mean) Δ Cq-values are shown for seven significantly regulated miRNAs (P < 0.05) and miR-31-5p (P = 0.053). The parametric t test was applied. Two of these miRNAs (miR-18a-3p and miR-223-3p) were significantly upregulated (P < 0.043), whereas five miRNAs (miR-22-3p, miR-34a-5p, miR-423-5p, miR-423-3p, and miR-143-5p) were significantly downregulated (P < 0.045) in MP subjects (Fig. 3B–3I). Two miRNAs, miR-22-3p and miR-34a-5p, exhibited good classification performance with area-under-the-curve values reaching 0.896 and 0.868, respectively. Correlation between the significantly regulated miRNAs was observed at varying moderate degrees, suggesting that some single miRNAs might contain information derived from different phenotypic characteristics caused by the WNT1 mutation (Supplemental Table 2). Ongoing low-dose bisphosphonate treatment in one MP female (MP-4) had no effect on miRNA values (Supplemental Fig. 4) In silico prediction of miRNA targets We used Targetscan release 7.1 to identify predicted miRNA binding sites in the 3′UTR of the human WNT1 gene (34). The tool reported putative binding sites for three miRNAs—miR-22-3p, miR-34a-5p, and miR-31-5p—which we found to be downregulated in the serum of MP subjects. For miR-34a-5p and miR-22-3p, binding to and regulation of WNT1 has been experimentally validated (35–38). For miR-31-5p, experimental confirmation of a direct interaction with WNT1 has not been reported, although its interference with the WNT signaling ligand frizzled 3 has been observed (9). Discussion This study reports on miRNA profiles in subjects with a monogenic bone disease due to defective WNT signaling. All of our MP subjects harbored the heterozygous missense mutation p.C218G in WNT1, which was previously shown to lead to decreased WNT signaling, low bone formation, low BMD, and fractures (21, 22), whereas the MN subjects had normal BMD and no fractures (21, 31). We screened a custom-designed panel of 192 miRNAs and compared the results from 12 MP subjects with those from 12 MN subjects. Our results show that a unique profile of eight miRNAs differentiated between MP and MN subjects and that three of these miRNAs, miR-22-3p, miR-34a-5p, and miR-31-5p, were downregulated in the serum of MP subjects (P = 0.001, P = 0.003, and P = 0.053, respectively). All three miRNAs are known inhibitors of WNT signaling, and miR-22-3p and miR-34a-5p have been shown to target WNT1 specifically (35–38). To the best of our knowledge, no previously published data have reported a direct association between miR-31-5p and WNT1. Recent research found that circulating miRNAs are promising new markers in various diseases, including malignancies, as many conventional biomarkers have shown limitations in diagnostics and in evaluating treatment outcomes (29). Regarding bone health and disease, current conventional metabolic bone markers are inadequate in reflecting bone health status, predicting future fracture risk, or monitoring treatment efficacy (12, 39, 40). Circulating miRNAs show promise as future bone markers, as specific miRNAs that discriminate [e.g., patients with manifest osteoporosis (14, 16, 41)] have also influenced bone metabolism in vitro (1, 13, 14, 24, 42‒44) and in vivo (16, 41). This suggests that circulating miRNA-based biomarkers might have causal links to the disease phenotype, as miRNAs packaged in extracellular vesicles or in protein particles can be taken up by recipient cells in an auto-, para-, or even endocrine manner (45). Of the eight miRNAs identified in our study as discriminating between MP and MN subjects, seven have reportedly influenced bone metabolism (Table 3). Interestingly, three of the downregulated miRNAs in MP subjects were confirmed or predicted to directly target WNT signaling and specifically WNT1 messenger RNA. One of these, miR-22-3p, negatively regulates osteogenesis and osteoblastogenesis through WNT signaling by targeting the coding region and suppressing the expression of β-catenin, which inhibits formation of calcium nodules during osteoblast differentiation (10). miR-22-3p also targets and decreases the levels of Tcf7 and Ep300, key transcriptional proteins for target gene expression in the WNT pathway (57). Furthermore, the 3′UTR of WNT1 contains a binding site for miR-22-3p, and miR-22-3p directly targets WNT1 (35). Secondly, miR-34a-5p interacts with WNT pathway components (58) and also directly with WNT1 to regulate its messenger RNA expression and posttranscriptional translation (36–38). Lastly, Weilner et al. (9) showed that miR-31-5p interacts with the WNT pathway component frizzled-3 and suppresses WNT signaling, whereas Xi et al. (59) showed that miR-31-5p targets WNT pathway antagonists Dkk-1 and DACT3 in lung cancer cells. However, unlike with miR-22-3p and miR-34a-5p, there are no reports of miR-31-5p directly targeting WNT1. Table 3. Previously Reported Data on the Role of the Seven Discriminative miRNAs in Bone Metabolism in WNT1 MP Subjects miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  View Large Table 3. Previously Reported Data on the Role of the Seven Discriminative miRNAs in Bone Metabolism in WNT1 MP Subjects miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  miRNA-ID  Finding in This Study  Role in Bone Metabolism  Target Proteins in Bone  References  miR-22-3p  Downregulated  Negatively regulates osteogenesis and osteoblastogenesis  WNT1, Tcf7, Ep300  (10, 35)  miR-34a-5p  Downregulated  Inhibits osteoblast differentiation and proliferation, increases osteoclast differentiation, elevates resorption, and leads to decreased bone mineralization  JAG1, WNT1  (7, 36, 46)  miR-423-5p  Downregulated  Serum levels correlate negatively with fracture risk and bone quality  —  (47)  miR-18a-3p  Upregulated  Upregulated in osteosarcoma tissue  —  (48)  miR-223-3p  Upregulated  Regulates osteoclast differentiation, modulates expression of osteoclast marker genes NF-kB, TNF-α, and osteoprotegerin  NFIA, FGFR2, IKKα,  (49–52)  miR-143-5p  Downregulated  Suppresses osteogenic differentiation; downregulated in osteosarcoma tissue  Osx  (53, 54)  miR-31-5p  Downregulated  Inhibits osteogenesis and osteogenic differentiation of mesenchymal stem cells; increases osteoclastogenesis  FZD3, RhoA, SATB2, RUNX2, Osterix  (8, 9, 55, 56)  View Large Intriguingly, our findings suggest that decreased WNT signaling due to mutated WNT1 leads to downregulation of miRNAs that exhibit suppressive action on the WNT pathway. This could be regarded as an attempt to normalize WNT signaling in a situation where WNT1-related WNT signaling is impaired. The molecular and genetic feedback mechanisms governing balanced bone metabolism, including gene regulation by miRNAs, are inadequately understood. To the best of our knowledge, miR-423-3p, which was significantly downregulated in our MP subjects, has not been previously linked to bone metabolism or WNT signaling. Wang et al. (60) previously reported that miR-423-3p expression changed with age in the microvesicles of mouse bone marrow mesenchymal stem cells, but no evidence for its effect on osteogenesis was shown. Also, other studies reported that miR-423-3p was linked to myocardial tissue and heart diseases (61, 62), but no specific heart phenotype or increased prevalence of myocardial disease was observed in our cohort or in other WNT1 MP subjects (21, 22). Therefore, the suggestion of an association between miR-423-3p and the WNT pathway is notable. Prior or ongoing osteoporosis medication use could potentially alter an individual’s miRNA expression, although the mechanisms and exact consequences are still unclear (63). Altogether, our cohort included three subjects with previous osteoporosis medication use and one subject with ongoing low-dose pamidronate treatment at the time of the study. A separate analysis showed that bisphosphonate treatment had no effect on the expression of the eight specific miRNAs in our cohort; therefore, the observed differences between MP and MN groups are likely not related to the patients’ medical therapy. We previously showed that conventional bisphosphonates had little to no effect on BMD status in WNT1 MP patients (22). The therapeutic potential in miRNAs has been contemplated and experimentally tested in previous studies. Krzeszinski et al. (46) were able to attenuate postmenopausal osteoporosis in ovariectomized mice with the administration of systemic miR-34; Wang et al. (64) showed the anabolic potential of anti‒miR-214 in stimulating bone formation in mice; and Wang et al. (42) demonstrated protection against glucocorticoid-induced bone loss with miR-29a in rats. In a wider context, miRNAs are used clinically in cancer treatment as replacement therapies, to sensitize tumors to chemotherapy, and to treat drug-resistant malignancies (65). Whether the miRNAs upregulated or downregulated in our study could have therapeutic potential—as drug targets or exogenously administered medications—in WNT1 or other forms of osteoporosis should be explored in future experimental settings. Our study provides data on miRNA expression levels in WNT-related bone disease. The study could have been strengthened by larger cohort sizes and with only subjects with no confounding factors, such as use of osteoporosis medication. However, we did exclude subjects with recent fractures and showed that subjects with previous or ongoing osteoporosis medication use did not differ from the others in miRNA patterns. Further, to minimize the effect of other confounding genetic factors, we recruited MN subjects from the same families as MP subjects. We also considered the premenopausal and postmenopausal status of female subjects as a possible confounding factor. However, the two cohorts were very similar in age and sex distribution, alleviating possible bias. Furthermore, there was no overlap with the miRNAs reported in this study and those previously reported to have alternative expression as a result of changes in estradiol concentration in Finnish subjects (65, 66). The small sample size may have prevented us from observing some important differences between the groups, leading to a high false-negative rate. We tried to account for this by allowing relatively high type-I error (P < 0.1) for initial selection. An independent cohort of WNT1 MP/MN subjects would be required to validate miR-22, miR-34a, and miR-31 findings. However, because of the scarcity of subjects with confirmed WNT1 or other WNT pathway-related osteoporosis, such a study was not possible. Despite these limitations and acknowledging the worldwide rarity of WNT1 osteoporosis and WNT1 mutation-positive individuals and the overall scarcity of miRNA data in monogenic bone disorders, we consider our study’s setting and findings valid and valuable. We concluded that a unique miRNA profile was observed in WNT1 mutation-positive individuals compared with healthy individuals. These observations provide valuable information about the molecular pathways involved in WNT1 osteoporosis and the effect of aberrant WNT signaling on miRNA expression. Our data also support an association between WNT1 and miR-31-5p and miR-423-3p expression. The specific miRNAs highlighted in this study could serve as circulating metabolic bone markers in WNT1 osteoporosis to evaluate bone health, fracture healing, and treatment efficacy in affected individuals. Future studies are encouraged to further explore these specific miRNAs in other WNT pathway-related skeletal diseases, their response to antiosteoporosis treatment, and their potential utilization in the development of osteoporosis treatment. Abbreviations: Abbreviations: BMD bone mineral density Cq cycle of quantification miRNA microRNA MN mutation-negative MP mutation-positive RT reverse transcription Acknowledgments We thank Päivi Turunen and Kirsi Mäkelä-Kvist for their help with collecting subject and sample data and Susanna Skalicky for excellent technical support. Financial Support: This study was financially supported by the Sigrid Jusélius Foundation (to O.M.); the Folkhälsan Research Foundation (to O.M.); the Academy of Finland (to O.M.); the Foundation for Pediatric Research (to O.M.); Helsinki University Research Funds (to R.E.M.); the Swedish Research Council (to O.M.); the Novo Nordisk Foundation (to O.M.); Helsinki University and Helsinki University Hospital through the Doctoral Programme in Clinical Research (to R.E.M.); the Finnish Medical Foundation (to R.E.M.); the Jalmari and Rauha Ahokas Foundation (to R.E.M.); the Swedish Childhood Cancer Foundation (to O.M.); and the Stockholm County Council (ALF project; to O.M.). Author Contributions: Study design: R.E.M., M.H., R.N., J.G., and O.M. Study conduct: R.E.M., M.H., J.G., and O.M. Data collection: R.E.M., M.H., R.N., S.K., and J.G. Data analysis: R.E.M., M.H., and J.G. Drafting of the manuscript: R.E.M., M.H., and J.G. Revising of the manuscript content: all authors. 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