Identification of maternal serum microRNAs as novel non-invasive biomarkers for prenatal detection of fetal congenital heart defects

Clinica Chimica Acta 424 (2013) 66–72

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Identification of maternal serum microRNAs as novel non-invasive biomarkers for prenatal detection of fetal congenital heart defects Shasha Zhu a,1, Li Cao b,1, Jingai Zhu a, Liping Kong a, Junxia Jin a, Lingmei Qian c, Chun Zhu a, Xiaoshan Hu a, Mengmeng Li a, Xirong Guo a, Shuping Han a,⁎, Zhangbin Yu a,⁎ a b c

State Key Laboratory of Reproductive Medicine, Department of Pediatrics, Nanjing Maternity and Child Health Care Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China State Key Laboratory of Reproductive Medicine, Department of Ultrasound, Nanjing Maternity and Child Health Care Hospital Affiliated to Nanjing Medical University, Nanjing 210029, China Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China

a r t i c l e

i n f o

Article history: Received 11 March 2013 Received in revised form 13 May 2013 Accepted 14 May 2013 Available online 21 May 2013 Keywords: Maternal serum miR-19b miR-22 miR-29c miR-375 Biomarker

a b s t r a c t Background: Congenital heart defects (CHD) are the most common form of malformation during embryonic development. Circulating miRNAs have recently been shown to serve as diagnostic/prognostic biomarkers in patients with cancers. Our current study focused on the altered expression of maternal serum miRNAs and their correlation with fetal CHD. Methodology/principle findings: We systematically performed SOLiD sequencing followed by individual quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assays to identify and validate the expression of maternal serum miRNAs at 18–22 weeks of gestation. Four miRNAs (miR-19b, miR-22, miR-29c and miR-375) were found to be significantly up-regulated in pregnant women with fetal CHD, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79, 0.671, 0.767 and 0.693, respectively. Furthermore, the combination of the four miRNAs using multiple logistic regression analysis showed a larger AUC (0.813) that was more efficient for the early detection of fetal CHD. Conclusions/significance: We identified and validated a class of four maternal serum miRNAs which could act as novel non-invasive biomarkers for the prenatal detection of fetal CHD. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Congenital heart defects (CHD) are the most common major congenital malformation, comprising numerous structural and functional abnormalities of the heart and great vessels, with a prevalence of approximately 8 in every 1000 newborn infants [1]. Although details of the underlying causes of CHD have been well studied [2,3], such defects remain a serious problem, accounting for approximately 40% of perinatal deaths and more than one fifth of deaths in the first month of life [4]. Multiple previous studies have reported that in the fetal deaths, the incidence of CHD is associated with the gestational age of fetal loss. The main causes of the earliest deaths are the presence of complex CHDs [5]. Furthermore, it is proved that the earlier the diagnosis of CHD, the better the prognosis [6]. Thus, the prenatal detection of fetal CHD is a key to the decrease of the mortality and the improvement of the prognosis of individuals with CHD.

⁎ Corresponding authors at: Department of Pediatrics, Nanjing Maternity and Child Health Care Hospital of Nanjing Medical University, No. 123 Tian Fei Xiang, Mo Chou Road, Nanjing 21004, China. E-mail addresses: [email protected] (S. Han), [email protected] (Z. Yu). 1 The authors have contributed equally to this study and they should be regarded as joint first authors. 0009-8981/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cca.2013.05.010

Even with the advent of fetal echocardiography as a screening tool for CHD, cardiac abnormalities are still overlooked during routine prenatal care, with disappointing detection rates ranging from 6% to 35% [7–9]. In addition, results of ultrasound examinations vary from center to center due to a lack of standardization [10]. Several studies have reported that many factors influence the accuracy of prenatal sonographic investigations to detect CHD, such as the experience of operators, the quality of the ultrasound equipment, the lesion type, different departmental policies and guidelines [11–15]. Recently, biomarkers have been found to correlate with CHD in utero, including elevated levels of nuchal translucency (NT), free beta-human chorionic gonadotropin (β-hCG) and lowered levels of pregnancy-associated plasma protein-A (PAPP-A) in the first trimester[16–21]. However, differences in these CHD biomarkers are not specific enough to be used as biomarkers for fetal CHD screening [22]. MicroRNAs (miRNAs) are a class of small non-coding RNAs which are 19–23 nt in size. To date, more than 800 miRNAs have been identified in animal cells and have been reported to be involved in various biological processes, including cell growth, the modulation on differentiation, cell proliferation and apoptosis [23–25]. An increasing number of studies have shown that circulating miRNA levels could have great potential as novel prognostic and predictive biomarkers for many cancers or to assess treatment outcomes. They have unique merits for these roles, including their abundance, stability, ease of

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detection and disease-specific nature [26–29]. A relationship between miRNAs and cardiogenesis has been identified [30,31]. Moreover, specific miRNAs that relate to fetal CHD can be found in placental tissues from fetuses with CHD, and it has been shown that miRNAs of placental origin can also be detected in the peripheral blood of pregnant women [32,33]. Herein, we hypothesized that maternal serum miRNA could act as candidate biomarkers for the prenatal detection of fetal CHD in relatively early pregnancy. This present study applied SOLiD sequencing to the screening of maternal serum miRNAs systematically and performed individual quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays to validate them. 2. Materials and methods 2.1. Study design and patient samples This multistage nested case-control study was designed to identify and confirm that maternal serum miRNA levels could detect fetal CHD in the second trimester. 98 participants in this study were recruited at the Nanjing Maternity and Child Health Care Hospital of Nanjing Medical University between July 2011 and June 2012. Women with a history of heart disease, a family history of cardiovascular disease, pregnancy complications and multiple pregnancies were excluded (35 cases). In addition, if the affected fetuses have a diagnosis that may have caused the CHD (e.g., Down's, 22q11DS, CHARGE, etc), the pregnant women were also subtracted (3 cases). At last, a total number of 60 pregnant women were selected as the subjects of the study. Thirty pregnant women bearing a single fetus with a CHD were chosen as cases, while the other thirty women bearing a single normal fetus were defined as controls. All the thirty cases were fetuses with ventricular septal defect (VSD), atrial septal defect (ASD) and tetralogy of Fallot (TOF). The diagnosis of fetal CHD was confirmed with fetal echocardiography. In order to reduce heterogeneity, controls were matched with cases on the basis of gestational age and maternal age. The study protocol was approved by Nanjing Medical University and Nanjing Maternity and Child Health Care Hospital of Nanjing Medical University, and was conducted according to the tenets of the Declaration of Helsinki. Clinical information for each subject was collected from their obstetric medical records, and written informed consent was also obtained from all the participants before their enrollment in this research. The study was divided into two stepwise phases. In phase I (biomarker discovery), we randomly pooled the maternal serum of three pregnant women with fetal CHD and three matched controls, respectively, to identify the differential miRNAs profile by SOLiD sequencing between these two groups. The three cases represented the three specific CHDs. By comparing the relative expression level of maternal serum miRNAs, significantly up- or down-regulated miRNAs were preliminarily selected for further analysis in the next phase. In phase II, we subsequently conducted quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on the rest 27 women who were pregnant with a child with fetal CHD and 27 women with healthy fetuses to validate the miRNAs which was initial screened. Based on the results of this phase, receiver operating characteristic (ROC) curve-based risk assessment analysis was conducted to assess the sensitivity and specificity of maternal serum for predicting fetal CHD. 2.2. Serum preparation and RNA extraction A venous blood sample of up to 5 ml from each participant in the case and control groups was collected at the routine 22–28 week obstetric examination. Each sample was aliquoted into a procoagulant drying tube. The whole blood was allowed to stand for 30–50 min at room temperature before centrifugation at 4000 rpm for 10 min

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to separate them into serum and cellular fractions. The supernatant serum was aliquoted into 1.5-ml Eppendorf tubes and stored at −80 °C until the further analysis. Total RNA, including miRNAs, was purified from 400 μl serum by using the mirVana PARIS Kit (Ambion, Foster City, USA), according to the manufacturer's protocol with some minor modifications [34]. As there is currently no consensus on sample normalization for the qRT-PCR analysis, we spiked in a synthetic Caenorhabditis elegans miR-39, (cel-miR-39; Invitrogen, CA) at a final concentration of 10−4 pmol/μl for each serum sample after the addition of 2X Denaturing Solution, which was provided in the kit. Owing to the absence of homologous sequences in humans, cel-miR-39 could control sampleto-sample variations in the RNA extraction and/or purification procedures as a housekeeping miRNA [35,36]. Then, the aqueous phase was pipetted onto the Filter Cartridge after organic isolation. The RNA pellet was washed and dried by spinning the assembly for 1 min according to the manufacturer's instructions. Then, we dissolved the RNA in 20 μl of preheated (95 °C) Elution Solution. RNA was measured using a NanoDrop spectrophotometer (NanoDrop, Wilmington, DE) to assess its quantity and quality, and stored at −80 °C. In general, we simultaneously performed RNA extraction and cDNA transcription for all subjects. Furthermore, the serum must be stored under the same conditions and added in equal volumes during every experiment step to reduce any potential bias. 2.3. SOLid sequencing In the discovery stage, we screened differentially expressed miRNAs with the use of the SOLiD version 2 sequencing system (Applied Biosystems). Briefly, we took equal mass of total RNA from each sample to hybridize and ligate overnight with 5′ and 3′ adaptors, reverse-transcribed, RNase H-treated and PCR amplified. Subsequently, we cleaned up the PCR products and selected them on agrose gels by size of 105–150 bp. Template bead preparation, emulsion PCR, and deposition were conducted in order. After the completed sequencing run, mapping of SOLiD reads were analyzed by SOLiD System Small RNA Analysis Pipeline Tool (RNA2MAP, version 0.5.0). We decoded the barcodes. If the reads matched a barcode uniquely, they were used for mapping. Following the calculation of length distribution and mapping to other small RNAs reference of rRNA, snRNA, snoRNA and tRNA, reads were compared with miRBase (release 15.0 at http://microRNA.sanger.ac.uk/). Finally, we mapped reads to the database of human genome. Since low copy number was less liable, only the miRNAs with more than 10 copies were picked up. Fold changes were calculated based on the expression profiles which were normalized to the total counts of 1,000,000. Based on the SOLiD sequencing results, 11 significantly varied miRNAs were initially selected for the validation stage. 2.4. Reverse transcription (RT) and quantitative PCR (qPCR) The total RNA was reverse-transcribed to cDNA with the TaqMan MicroRNA reverse transcription kit and miRNA-specific stem-loop primers (Applied Biosystems Inc.). The 15-μl reaction mix consisted of 0.15-μl of 100 mM dNTP mix, 1 μl of Multiscribe RT enzyme (5 U/μl), 1.5 μl of 10 × RT Buffer, 0.19 μl of RNase Inhibitor (20 U/μl), 7.16 μl of nuclease-free water, 3 μl of TaqMan RT primer and 2 μl total RNA. Reverse transcription was initiated at 16 °C for 30 min, 42 °C for 30 min, 85 °C for 5 min, and held at 4 °C. Subsequently, real-time PCR was performed in triplicate for each maternal serum sample and no-template negative controls included. For the final volume of 20 μl reaction, 1 μl synthesized cDNA was mixed with 8 μl diethylpyrocarbonate (DEPC)-treated water, 10 μl TaqMan Gene Expression Master Mix and 1 μl TaqMan MicroRNA Assay (Applied Biosystems). The MicroRNA Assay IDs was shown in Table 3. The mixture was incubated at 50 °C for 2 min, 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and

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60 °C for 1 min. Amplification was run using the 96-well on the ABI 7500 Real-Time PCR system (Applied Biosystems) according to the manufacturer's instructions. We performed RT-PCR for the target miRNAs and cel-miR-39 simultaneously to normalize the serum miRNA levels. The relative expression of target miRNA was determined with the comparative cycle threshold (CT) (2-△△CT) method, in which △△CT was calculated by △CT of cases minus mean △CT of controls and △CT = CT sample−CT cel-miR-39. 2.5. Statistical analysis All statistical analyses were performed with SPSS software version 13.0 (SPSS, Inc, Chicago, USA). Differences in the demographic and clinical characteristics were estimated by the Student's t-test between different groups. The Mann–Whitney U test was adopted to compare the relative expression level of the maternal serum miRNAs between the fetal CHD group and controls. For each miRNA, a ROC curve was plotted to evaluate its discriminating effects. The sensitivity and specificity of detecting cases and controls were assessed by the area under the ROC curve (AUC) and 95% confidence interval (CI). To improve the diagnostic rates of CHD, we combined the expression levels of the four miRNAs using multiple logistic regression analysis to perform the above risk assessment. All P-values less than 0.05 were considered statistically significant, and all the statistical tests were two-sided. 3. Results 3.1. Subject characteristics A total number of 60 participants were eventually selected into the study, including 30 pregnant women who were carrying fetuses with CHD and 30 subjects with normal pregnancies. There were 12 VSDs, 4 ASDs and 11 TOFs among the cases. The diagnosis of fetal CHD was confirmed by fetal echocardiography in both the discovery and validation cases. As is shown in Table 1, the controls were well matched with cases in terms of age and weeks of gestation. 3.2. Phase I: Biomarker discovery The purpose of this study was to screen maternal serum miRNAs as biomarkers for fetal CHD. SOLiD sequencing was employed to identify differentially expressed miRNAs in maternal serum samples from three pregnant women with fetal CHD and their corresponding controls who were selected randomly. We calculated the mean expression level and employed fold changes of sequenced miRNAs. On the basis of both scientific and applicable consideration, miRNAs that were > 2-fold up-regulated or b0.5-fold down-regulated changes in the fetal CHD group were selected as the candidates for the next stage of validation. Following preliminary screening, we found nine up-regulated miRNAs (miR-19b, miR-26a, miR-375, let-7a, miR-22,

3.3. Phase II: MiRNA validation The validation of the 11 putative markers identified from the marker discovery phase was performed on a large set of maternal serum from the left 27 pairs of pregnant women with fetal CHD and controls using qRT-PCR. Using cel-miR-39 as normalization control, the statistics demonstrated that four out of the 11 miRNAs (miR-19b, miR-22, miR-29c, miR-375) in maternal serum were more significantly up-regulated in women with a fetal CHD-affected pregnancy than healthy controls (P = 0.001, 0.031, 0.001, 0.015 for miR-19b, miR-22, miR-29c and miR-375, respectively) (Fig. 1). The circulating levels of the other 7 miRNAs (miR-15b, miR-27b, miR-24, miR-26a, miR-21, let-7a and miR-221) were not different between the two groups (P = 0.749, 0.392, 0.126, 0.762, 174, 0.647 and 0.099, respectively) (Table 3). As shown in Fig. 1, medians of the four miRNAs are strongly dependent of 1 or 2 extreme high values of fold change. We performed a subanalysis by excluding such extreme values. Consequently, the difference of the reported fold change remained significant and the validity of the data was dependent of such subanalysis. In addition, VSD, ASD and TOF of the case group were compared with controls, respectively, in order to find miRNAs over and down-regulated in such defects. We found only two significantly up-regulated miRNAs in VSD (miR-19b and miR-29c), and three in ASD (miR-19b, miR-29c and miR-375), while all the four miRNAs were validated significantly different in TOF (Table 4). 3.4. Diagnostic value of maternal serum miRNAs for fetal CHD To evaluate the sensitivity and specificity of the maternal serum miRNA signature individually and in combination for the prediction of fetal CHD, we further established ROC curves and calculated the AUC in each case (Fig. 2). We assessed the discriminating effect of miR-19b, miR-29c, miR-375 and miR-22 at the cut-off values of 1.49, 2.61, 1.33 and 1.08, respectively, at which the largest Youden's index (sensitivity + specificity − 1) was defined as the optimal diagnostic point. It is found that the sensitivity and specificity of miR-19b, miR-29c, miR-22 and miR-375 were 74.1% and 77.8%, 63% and 88.9%, 70.4% and 66.7%, and 55.6% and 85.2%, respectively (detailed data not shown). When we subjected the four miRNAs to multiple logistic regulation analysis, the AUC increased to 81.3% (95%CI: 0.695–0.931) (Fig. 3). The results revealed that the combination of the four differentially expression miRNA which yielded the largest AUC proved to be a more efficient biomarker tool for the detection of fetal CHD. 4. Discussion A number of previous studies have demonstrated the association between CHD and both neonatal morbidity and mortality [37].

Table 1 Characteristics of the study population. Classification

miR-21, miR-221, miR-29c and miR-15b) and two down-regulated miRNAs (miR-27b and miR-24) (Table 2). Then, the eventual validation of the 11 miRNAs was performed in phase II.

Age (year) N

mean

SD

All cases Control

27 27

27.74 28.48

4.68 3.09

Specific CHDs# VSD ASD Tetralogy of Falot

12 4 11

27.75 25.75 29.27

4.88 2.50 4.34

P

Gestationl weeks

P*

mean

SD

0.496

24.99 25.20

1.29 1.09

0.517

0.574 0.104 0.529

25.38 25.25 24.47

0.88 0.96 1.64

0.623 0.933 0.115

*Student's t-test. #Specific CHDs (VSD, ASD, and TOF) compared with control, too.

Table 2 The results of SOLiD sequencing. miRNA

Fold changea

miRNA

Fold Changea

hsa-miR-27b hsa-miR-24 hsa-let-7a hsa-miR-15b hsa-miR-26a hsa-miR-29c

0.296 0.437 2.667 2.667 3 3.375

hsa-miR-21 hsa-miR-221 has-miR-19b hsa-miR-22 hsa-miR-375

3.97 4.049 2.222 2.741 3.029

a The relative expression of case/the relative expression of control. Up-regulated miRNAs (change > 2-fold as a cut off level); down-regulated miRNAs (change b 0.5-fold as a cut off level).

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Fig. 1. Relative expression levels of 11 miRNAs in 27 pairs of pregnant women with fetal CHD and control subjects in the phase II. (A–D) Box plots of maternal serum miR-19b, miR-22, miR-29c, miR-375 in 27 pairs of women with a fetal CHD-affected pregnancy and healthy controls. (E–K) Box plots of the other 7 maternal serum miRNAs between the two groups. All the Box–whisker Plots represented the relative expression levels of miRNAs which were normalized against the spiked-in cel-miR-39 (A–K). The bottom and top of the box were the 5th and 95th percentiles, and the band in the middle of the box was the 50th percentile of the relative expression levels of miRNAs. Any data beyond these whiskers were shown as cycles and stars.

However, no gold standard for the detection of fetal CHD has been determined to date. In this two-phase study, we drew peripheral blood from pregnant women with and without fetal CHD in order to identify maternal serum miRNAs that could be used as novel promising non-invasive biomarkers for the early detection of fetal CHD. Four significantly up-regulated miRNAs (miR-19b, miR-22, miR-29c and miR-375) were identified and validated through phase I and phase II, with a high degree of sensitivity. In addition, these four miRNAs in combination were more efficient in detecting fetal CHD, with a larger AUC value than individual miRNAs. The distribution of the four miRNAs in specific CHDs was different. Only two significantly up-regulated miRNAs (miR-19b, miR-29c) were found to bear correlation with VSD. We also verified three miRNAs over regulated in ASD and all of the four in TOF. It followed that miR-22 may be

specifically up-regulated in TOF. To the best of our knowledge, it is the first study that explores the dynamic change of maternal serum miRNAs in cases of fetal CHD and their clinical value. Since the discovery of lin-4 as the first miRNA [38], increasing numbers of studies have reported that there is a close correlation between serum/plasma miRNAs and various diseases, especially in human malignancies [22–24,32,33,37,38,39]. Song et al. demonstrate the aberrant expression of serum miR-221, miR-376c and miR-744 in gastric cancer (GC) patients and their increasing trend during GC development [39]. The correlation between several circulating miRNAs (miR-486, miR-30d, miR-1 and miR-499) and non-small-cell lung cancer (NSCLC) survival rates has also been observed [40]. Ng et al. discover that miR-92 is significantly elevated in the plasma of patients with colorectal cancer (CRC) [41], while patients with coronary

Table 3 Results of the maternal serum miRNAs during the validation stage.

Table 4 Results of the maternal serum miRNAs in specific CHDs.

miRNA

hsa-mir-15b hsa-mir-22 hsa-mir-27b hsa-mir-19b hsa-mir-24 hsa-mir-26a hsa-mir-21 hsa-mir-375 hsa-mir-29c hsa-let-7a hsa-mir-221

Assay-ID

ID:000390 ID:000398 ID:000409 ID:000396 ID:000402 ID:000405 ID:000397 ID:000564 ID:000587 ID:000377 ID:000524

CHD

NO CHD

N

Median

27 27 27 27 27 27 27 27 27 27 27

0.9 1.53 1.1 2.75 1.34 0.88 1.41 1.44 2.96 1.12 0.79

#

N

median

27 27 27 27 27 27 27 27 27 27 27

0.99 0.96 1.04 0.95 1 0.9 1.16 0.84 1.28 1.15 1.04

Fold Change

0.91 1.59 1.06 2.89 1.34 0.98 1.22 1.71 2.31 0.97 0.76

P⁎

0.749 0.031 0.392 0.001 0.126 0.762 0.174 0.015 0.001 0.647 0.099

*Mann–Whitney U test. # The distribution of the data which was calculated by the comparative cycle threshold (CT) (2-△△CT) method was skewed. We chose the median to represent the feature of the data. △△CT = △CT(case) - △ C T ðcontrolÞ , △CT = CT sample − CT cel-miR-39.

miRNA

hsa-mir-15b hsa-mir-22 hsa-mir-27b hsa-mir-19b hsa-mir-24 hsa-mir-26a hsa-mir-21 hsa-mir-375 hsa-mir-29c hsa-let-7a hsa-mir-221

VSD

ASD

Tetralogy of Fallot

N

Median#

P⁎

N

median

P

N

median

P

12 12 12 12 12 12 12 12 12 12 12

0.87 1.27 1.07 2.14 1.27 0.91 1.14 0.91 2.39 1.47 0.89

0.715 0.191 0.584 0.015 0.287 0.831 0.692 0.26 0.033 0.543 0.503

4 4 4 4 4 4 4 4 4 4 4

0.86 1.64 1.23 2.86 1.20 0.65 1.70 1.77 4.73 1.53 0.82

0.517 0.346 0.637 0.006 0.409 0.556 0.377 0.029 0.034 0.953 0.409

11 11 11 11 11 11 11 11 11 11 11

1.07 1.69 1.16 2.86 1.39 0.96 1.80 1.45 2.96 1.08 0.40

0.885 0.035 0.449 0.007 0.204 0.987 0.126 0.032 0.004 0.809 0.055

*Mann–Whitney U test; the results compared with NO CHD group in Table 3. # The distribution of the data which was calculated by the comparative cycle threshold (CT) (2-△△CT) method was skewed. We chssose the median to represent the feature of the data. △△CT = △CT(case) -△ C T ðcontrolÞ , △CT = CT sample − CT cel-miR-39.

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Fig. 2. ROC curve analysis using four miRNAs to discriminate 24 women with fetal CHD-affected pregnancies from 24 healthy controls. (A) miR-19b, (B) miR-22, (C) miR-29c, (D) miR-375.

artery disease have reduced levels of miR-126, miR-17, miR-92a and miR-155[42]. Furthermore, Zhao et al. demonstrate the utility of maternal serum miR-29a, miR-222 and miR-132 levels as biomarkers for predicting women who will affected by gestational diabetes mellitus (GDM) [43]. For CHD, Bruneau et al. describe how cardiac development dysregulation underlies each type of CHD [2]. Recently, accumulating evidence has revealed the key roles that miRNAs play in the growth, development, function and stress responsiveness of the heart [44]. Xu et al. find that rs11614913 of the miR-196a2 sequence could influence the susceptibility of a fetus to developing CHD by altering mature miR-196a expression and target mRNA binding [45]. Other groups have identified an inverse correlation of miR-1 and miR-133 expression with cardiac hypertrophy in murine models and in human disease states

Fig. 3. ROC curve of the combination of the 4 miRNAs using multiple logistic regression analysis. The combination of the four miRNAs (miR-19b, miR-22, miR-29c and miR-375) yielded the largest area under the ROC curve (AUC).

associated with cardiac hypertrophy [31]. Cordes et al. summarize the recent discoveries regarding the regulation of miRNAs during cardiovascular development [46], including the function of miR-1 and miR-133a during cardiogenesis [47,48], the regulation of cardiac patterning by miR-138 [49], and the regulation of angiogenesis by miR-126 [50]. In addition, ongoing studies have also reported that miRNAs originating from the placenta, some of which are correlated with fetal CHD [30], could be identified in peripheral blood samples from pregnant women [29]. All these studies suggest that maternal miRNAs could be a promising class of biomarkers for the prenatal detection of fetal CHD. What distinguishes the current study from the previous ones are: 1) we did not focus on the roles that maternal serum miRNAs play in fetal CHD, but the relationship between them; and 2) we compared maternal serum miRNA levels to detect fetal CHD directly during pregnancy. Among the four maternal serum miRNAs identified in our study, previous studies have reported that miR-19b-1 could inhibit angiogenesis by targeting mRNA that corresponds to the pro-angiogenic protein, FGFR2, and blocks the cell cycle progression of endothelial cells [51]. It has been shown that the up-regulation of miR-22 in the aging heart contributed at least partly to accelerated cardiac fibroblast senescence and increased migratory activity [52]. For miR-375 and miR-29c, there are several studies about their expression pattern, physiological function and relationship with carcinogenesis, including esophageal squamous cell carcinoma [53], Barrett's esophageal carcinogenesis [54], nasopharyngeal carcinoma [55] and malignant pleural mesothelioma [56]. However, no study regarding the four maternal serum miRNAs that we identified as being involved in CHD has been reported to date. Our study has several strengths. Firstly, we paid attention to the prenatal diagnosis of fetal CHD. The use of maternal blood samples to assess miRNA expression levels enabled us to detect fetal CHD. It shed light on the objective, reliable and convenient effects compared

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to fetal echocardiography. Secondly, we chose ABI SOLiD System that was one of the next generation sequencing technology to identify the differential miRNAs profile. Nowadays, there are two principal methods to analyze the expression profile of miRNA: RT-PCR and microarray [57,58]. As the gold standard way to quantify the expression of miRNAs, RT-PCR only can detect the limited kinds of miRNAs one time, which probably makes it better as a validation rather than a discovery tool [59]. Microarray has some difficulties to distinguish members of miRNA families and could only detect those known miRNAs [60]. Recently, the next generation sequencing technology had been developed and widely applied. One main advantage over the two methods above is that SOLiD sequencing can detect unknown genes without no sequence specific probe and showed more concern for its highest throughput per run [61,62]. Lastly, we used cel-miR-39 as a normalization strategy in phase II to replace the traditional tissue approach (RNU6), which may have led to more accurate and reliable expression data regarding serum miRNAs. However, it needs to be pointed out that this study is subjects to limitations. During phase I, we pooled the serum of three cases and three controls, respectively, to perform SOLiD sequencing. If these had been separated, a more typical microRNA panel for the validation stage might have been identified by calculating the P-value. Furthermore, the detailed mechanism and roles of these miRNAs as cardiac development regulators remains unknown, and more fundamental studies are required. Finally, more samples are needed for further confirmation of our findings in a larger prospective study. Maybe we can classify the miRNAs according to the type of CHD. In conclusion, we identified and validated a four maternal serum miRNAs (miR-19b, miR-22, miR-29c and miR-375) that acted as novel non-invasive biomarkers for the prenatal detection of fetal CHD. Further investigation and optimization are still needed for the clinical application of these serum miRNAs in the diagnosis of fetal CHD. Conflict of interest statement No conflict of interest was declared. Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (Grant No. 81070500), the Key Medical Personnel Foundation of Jiangsu Province (Grant No. RC2011021), Nanjing Medical Science and Technique Development Foundation, and the Science and Technology Development Foundation of Nanjing Medical University (Grant No. 2011NJMU209). References [1] Meberg A, Lindberg H, Thaulow E. Congenital heart defects: the patients who die. Acta Paediatr 2005;94:1060–5. [2] Bruneau BG. The developmental genetics of congenital heart disease. Nature 2008;451:943–8. [3] Olson EN. Gene regulatory networks in the evolution and development of the heart. Science 2006;313:1922–7. [4] Trojnarska O, Grajek S, Katarzynski S, Kramer L. Predictors of mortality in adult patients with congenital heart disease. Cardiol J 2009;16:341–7. [5] Hoffman JI. Incidence of congenital heart disease: II. Prenatal incidence; 1995 . [6] Sadeck LS, Azevedo R, Barbato AJ, et al. Clinical-epidemiologic indications for echocardiographic assessment in the neonatal period. Value of risk groups. Arq Bras Cardiol 1997;69:301–7. [7] Jaeggi ET, Sholler GF, Jones OD, Cooper SG. Comparative analysis of pattern, management and outcome of pre- versus postnatally diagnosed major congenital heart disease: a population-based study. Ultrasound Obstet Gynecol 2001;17: 380–5. [8] Garne E, Stoll C, Clementi M. Evaluation of prenatal diagnosis of congenital heart diseases by ultrasound: experience from 20 European registries. Ultrasound Obstet Gynecol 2001;17:386–91. [9] Sharland GK, Allan LD. Screening for congenital heart disease prenatally. Results of a 2 1/2-year study in the South East Thames Region. Br J Obstet Gynaecol 1992;99:220–5.

71

[10] Friedberg MK, Silverman NH, Moon-Grady AJ, et al. Prenatal detection of congenital heart disease.J Pediatr 2009;155:26–31 [31]. [11] Fernandez CO, Ramaciotti C, Martin LB, Twickler DM. The four-chamber view and its sensitivity in detecting congenital heart defects. Cardiology 1998;90: 202–6. [12] Stumpflen I, Stumpflen A, Wimmer M, Bernaschek G. Effect of detailed fetal echocardiography as part of routine prenatal ultrasonographic screening on detection of congenital heart disease. Lancet 1996;348:854–7. [13] Stoll C, Alembik Y, Dott B, et al. Evaluation of prenatal diagnosis of congenital heart disease. Prenat Diagn 1998;18:801–7. [14] Isaksen CV, Eik-Nes SH, Blaas HG, Tegnander E, Torp SH. Comparison of prenatal ultrasound and postmortem findings in fetuses and infants with congenital heart defects. Ultrasound Obstet Gynecol 1999;13:117–26. [15] Allan L. Antenatal diagnosis of heart disease. Heart 2000;83:367. [16] O'Leary P, Breheny N, Dickinson JE, et al. First-trimester combined screening for Down syndrome and other fetal anomalies. Obstet Gynecol 2006;107:869–76. [17] Souter VL, Nyberg DA, El-Bastawissi A, Zebelman A, Luthhardt F, Luthy DA. Correlation of ultrasound findings and biochemical markers in the second trimester of pregnancy in fetuses with trisomy 21. Prenat Diagn 2002;22:175–82. [18] Jelliffe-Pawlowski LL, Walton-Haynes L, Currier RJ. Using second trimester ultrasound and maternal serum biomarker data to help detect congenital heart defects in pregnancies with positive triple-marker screening results. Am J Med Genet A 2008;146A:2455–67. [19] Makrydimas G, Sotiriadis A, Ioannidis JP. Screening performance of first-trimester nuchal translucency for major cardiac defects: a meta-analysis. Am J Obstet Gynecol 2003;189:1330–5. [20] Michailidis GD, Economides DL. Nuchal translucency measurement and pregnancy outcome in karyotypically normal fetuses. Ultrasound Obstet Gynecol 2001;17:102–5. [21] Hyett J, Perdu M, Sharland G, Snijders R, Nicolaides KH. Using fetal nuchal translucency to screen for major congenital cardiac defects at 10–14 weeks of gestation: population based cohort study. BMJ 1999;318:81–5. [22] Jelliffe-Pawlowski LL, Walton-Haynes L, Currier RJ. Identification of second trimester screen positive pregnancies at increased risk for congenital heart defects. Prenat Diagn 2009;29:570–7. [23] Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004;116:281–97. [24] Ambros V. The functions of animal microRNAs. Nature 2004;431:350–5. [25] Michael MZ, O' CS, van Holst PN, Young GP, James RJ. Reduced accumulation of specific microRNAs in colorectal neoplasia. Mol Cancer Res 2003;1:882–91. [26] Huang Z, Huang D, Ni S, Peng Z, Sheng W, Du X. Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer 2010;127: 118–26. [27] Brase JC, Johannes M, Schlomm T, et al. Circulating miRNAs are correlated with tumor progression in prostate cancer. Int J Cancer 2011;128:608–16. [28] Chen X, Ba Y, Ma L, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Res 2008;18: 997–1006. [29] Weber DG, Casjens S, Rozynek P, et al. Assessment of mRNA and microRNA stabilization in peripheral human blood for multicenter studies and biobanks. Biomark Insights 2010;5:95–102. [30] Thum T, Catalucci D, Bauersachs J. MicroRNAs: novel regulators in cardiac development and disease. Cardiovasc Res 2008;79:562–70. [31] Catalucci D, Latronico MV, Condorelli G. MicroRNAs control gene expression: importance for cardiac development and pathophysiology. Ann N Y Acad Sci 2008;1123:20–9. [32] Chim SS, Shing TK, Hung EC, et al. Detection and characterization of placental microRNAs in maternal plasma. Clin Chem 2008;54:482–90. [33] Liang Y, Ridzon D, Wong L, Chen C. Characterization of microRNA expression profiles in normal human tissues. BMC Genomics 2007;8:166. [34] Kroh EM, Parkin RK, Mitchell PS, Tewari M. Analysis of circulating microRNA biomarkers in plasma and serum using quantitative reverse transcription-PCR (qRT-PCR). Methods 2010;50:298–301. [35] Mitchell PS, Parkin RK, Kroh EM, et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 2008;105: 10513–8. [36] Mahn R, Heukamp LC, Rogenhofer S, von Ruecker A, Muller SC, Ellinger J. Circulating microRNAs (miRNA) in serum of patients with prostate cancer. Urology 2011;77:1265–9. [37] Campbell M. Incidence of cardiac malformations at birth and later, and neonatal mortality. Br Heart J 1973;35:189–200. [38] Wightman B, Ha I, Ruvkun G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 1993;75:855–62. [39] Song MY, Pan KF, Su HJ, et al. Identification of serum microRNAs as novel non-invasive biomarkers for early detection of gastric cancer. PLoS One 2012;7: e33608. [40] Hu Z, Chen X, Zhao Y, et al. Serum microRNA signatures identified in a genome-wide serum microRNA expression profiling predict survival of non-small-cell lung cancer. J Clin Oncol 2010;28:1721–6. [41] Ng EK, Chong WW, Jin H, et al. Differential expression of microRNAs in plasma of patients with colorectal cancer: a potential marker for colorectal cancer screening. Gut 2009;58:1375–81. [42] Fichtlscherer S, De Rosa S, Fox H, et al. Circulating microRNAs in patients with coronary artery disease. Circ Res 2010;107:677–84. [43] Zhao C, Dong J, Jiang T, et al. Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus. PLoS One 2011;6:e23925.

72

S. Zhu et al. / Clinica Chimica Acta 424 (2013) 66–72

[44] van Rooij E, Olson EN. MicroRNAs: powerful new regulators of heart disease and provocative therapeutic targets. J Clin Invest 2007;117:2369–76. [45] Xu J, Hu Z, Xu Z, et al. Functional variant in microRNA-196a2 contributes to the susceptibility of congenital heart disease in a Chinese population. Hum Mutat 2009;30:1231–6. [46] Cordes KR, Srivastava D, Ivey KN. MicroRNAs in cardiac development. Pediatr Cardiol 2010;31:349–56. [47] Kwon C, Han Z, Olson EN, Srivastava D. MicroRNA1 influences cardiac differentiation in Drosophila and regulates Notch signaling. Proc Natl Acad Sci U S A 2005;102:18986–91. [48] Zhao Y, Samal E, Srivastava D. Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature 2005;436:214–20. [49] Morton SU, Scherz PJ, Cordes KR, Ivey KN, Stainier DY, Srivastava D. microRNA-138 modulates cardiac patterning during embryonic development. Proc Natl Acad Sci U S A 2008;105:17830–5. [50] Wang S, Aurora AB, Johnson BA, et al. The endothelial-specific microRNA miR-126 governs vascular integrity and angiogenesis. Dev Cell 2008;15:261–71. [51] Yin R, Bao W, Xing Y, Xi T, Gou S. MiR-19b-1 inhibits angiogenesis by blocking cell cycle progression of endothelial cells. Biochem Biophys Res Commun 2012;417: 771–6. [52] Jazbutyte V, Fiedler J, Kneitz S, et al. MicroRNA-22 increases senescence and activates cardiac fibroblasts in the aging heart. Age (Dordr) 2013;35:747–62. [53] Komatsu S, Ichikawa D, Takeshita H, et al. Prognostic impact of circulating miR-21 and miR-375 in plasma of patients with esophageal squamous cell carcinoma. Expert Opin Biol Ther 2012;12(Suppl. 1):S53–9.

[54] Leidner RS, Ravi L, Leahy P, et al. The microRNAs, MiR-31 and MiR-375, as candidate markers in Barrett's esophageal carcinogenesis. Genes Chromosomes Cancer 2012;51:473–9. [55] Zeng X, Xiang J, Wu M, et al. Circulating miR-17, miR-20a, miR-29c, and miR-223 combined as non-invasive biomarkers in nasopharyngeal carcinoma. PLoS One 2012;7:e46367. [56] Pass HI, Goparaju C, Ivanov S, et al. hsa-miR-29c* is linked to the prognosis of malignant pleural mesothelioma. Cancer Res 2010;70:1916–24. [57] Zhao H, Shen J, Medico L, Wang D, Ambrosone CB, Liu S. A pilot study of circulating miRNAs as potential biomarkers of early stage breast cancer. PLoS One 2010;5:e13735. [58] Wang GK, Zhu JQ, Zhang JT, et al. Circulating microRNA: a novel potential biomarker for early diagnosis of acute myocardial infarction in humans. Eur Heart J 2010;31:659–66. [59] Git A, Dvinge H, Salmon-Divon M, et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 2010;16:991–1006. [60] Coppee JY. Do DNA, microarrays have their future behind them? Microbes Infect 2008;10:1067–71. [61] Tian G, Yin X, Luo H, et al. Sequencing bias: comparison of different protocols of microRNA library construction. BMC Biotechnol 2010;10:64. [62] Johansen SD, Karlsen BO, Furmanek T, et al. RNA deep sequencing of the Atlantic cod transcriptome. Comp Biochem Physiol Part D Genomics Proteomics 2011;6: 18–22.