Whole-exome sequencing in Chinese Tibetan patients with VSD

Introduction

Congenital heart disease (CHD) refers to cardiovascular malformations caused by abnormal development of cardiac vessels during the fetal period, which is the most common congenital dysplasia and also the main cause of non-infectious death in newborns and infants.1 CHD includes atrial septal defect (ASD), ventricular septal defect (VSD), pulmonary atresia (PA), patent ductus arteriosus (PDA), tetralogy of Fallot (TOF) and other cardiac malformations, among which the incidence of VSD is the highest, especially isolated VSD, and 30% of cases are combined with other malformations.2 The incidence of isolated muscular VSD has been reported to be 5.7% in preterm infants and 1.1–5.3% in term infants.3 While many VSD can close spontaneously, if they do not close, large defects can lead to deleterious complications such as pulmonary arterial hypertension (PAH), ventricular dysfunction, and an increased risk of arrhythmias.4 The low pressure and hypoxia in the Qinghai-Tibet Plateau can cause altitude sickness, and the incidence of CHD will increase with the increase of altitude.5 Therefore, it is inferred that the hypoxic and low pressure environment at plateau is closely related to the pathogenesis of CHD. According to epidemiology, the incidence of neonatal CHD in high-altitude areas is about 20 times higher than that in low-altitude areas.6 In the Columbia region, rate of CHD has also been found to be much lower in plains than that in high-altitude regions.7 Ethnic and regional differences have significant roles in the occurrence of CHD, and the data have indicated that there may be ethnic differences in the prevalence of CHD in China.8 Malg et al have found that in China, the incidence of CHD in plain areas is not as high as that in Huangnan Tibetan Autonomous Prefecture and Yushu Tibetan Autonomous Prefecture in Qinghai Province.9 The above studies have shown that CHD in the Qinghai-Tibet Plateau area in China is characterized by high incidence and high mortality. The pathogenesis of CHD is not yet clear, and researchers think that there are two main factors for its occurrence, environmental factors and genetic factors. Among the genetic factors, gene mutation plays an important role in the occurrence of VSD. The study by Basson et al has demonstrated for the first time that TBX5 mutations is associated with the development of hereditary CHD.10 Subsequently, some gene mutations, including NOTCH1, CITED2, NDRG4, etc. were found to be related to VSD.11–13 Ji et al have reported that NOTCH1 rs6563G > A variant may affect the regulation of miR-3691-3p on the NOTCH1 gene, thereby influencing the susceptibility to VSD.11 The study by Zheng et al has found that functional changes caused by variants in the promoter region of the CITED2 gene may affect a set of downstream genes and pathways and eventually lead to VSD.12 Peng et al have revealed that the p.T256M variant in NDRG4 is a pathogenic variant associated with the impaired proliferation of human cardiomyocytes and cell cycle arrest, which may be involved in the pathogenesis of VSD.13 Taken together, genetic factors are crucial in the occurrence of VSD. However, some genetic variants associated with VSD in the Chinese Tibetan population remain to be further identified. At present, there are few studies on molecular mechanisms of VSD in the high-altitude environment.

Whole-exome sequencing (WES) can detect low-frequency variants in the coding regions of a patient’s genes, rare variants with different effects on disease, and disease-related genetic variants. This study intended to perform WES analysis on 20 Tibetan subjects with VSD, and use bioinformatics technology to screen rare or low-frequency variants in VSD genes and annotate them with pathogenicity, so as to further provide accurate molecular diagnosis for VSD.

Materials and Methods

Study Participants

All participants under the age of 18 years in this study were diagnosed with CHD in the Second People’s Hospital of Tibet Autonomous Region, and a total of 20 subjects with congenital heart type VSD were screened. This study fully followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Xizang Minzu University (201808). Informed consent form was obtained from parental/legal guardian of all participants. The clinical data of all subjects are shown in Table 1.

Table 1 Sample Information of Enrolled Patients

Inclusion criteria for the subjects in this study: (1) Three family generations of all enrolled subjects were free from VSD, CHD and other genetic disease. (2) Subjects with clinically diagnosed VSD. (3) Subjects themselves and their parents who signed informed consent and underwent WES examination. Exclusion criteria: (1) Subjects with other CHD. (2) Subjects with FCHD, FPAH, respiratory system-related PAH or other familial hereditary diseases. (3) Any other cardiovascular disease, chronic anemia, thyroid disease, electrolyte imbalance, systemic immune disease, malignant tumor, or other diseases that may cause VSD.

Collection of Blood Samples and WES

With the consent of subjects and their guardians, about 5-mL of fasting peripheral venous blood was collected in anticoagulation tube and stored at ultra-low temperature refrigerator (−80 degrees C). DNA extraction kit was used to extract genomic DNA from blood samples, and Nanodrop 2000/Qubit was carried out to detect the quality and concentration of genomic DNA (the total amount of samples ≥ 1.5 μg, the concentration ≥ 50 ng/μL and OD260/280 = 1.8~2.0). The genomic DNA was fragmented using Covaris, so that most of the genomic DNA was between 100 and 500 bp in length. DNA libraries were prepared by adding “A” bases to the 3’ ends of DNA fragments. Whole-exome capture was performed using the SureSelectXT Reagent kit and probe hybridization was performed with the library using the SureSelectXT Human All Exon Kit V6. Concentration (> 5 ng/μL). The fragment length (300~400 bp) of libraries were detected using Qubit and Agilent 2100 Bioanalyzer. Additionally, high-throughput sequencing was conducted on the Illumina Hiseq platform using 2×150 bp paired-end sequencing mode to obtain FastQ data.

WES Data Analysis

The raw data of each sample was compared with the human reference genome (UCSC, hgdownload.soe.ucsc.edu/downloads.html) using the mem algorithm of BWA (bio-bwa.sourceforge.net/), and preliminary alignment results in BAM format (samtools.sourceforge.net/) were obtained. Picard software (broadinstitute.github.io/picard/) was used to count the alignment results of each sample, including the number and ratio of sequences on the alignment, Q20 and Q30 sequence ratio, average coverage depth, etc. In order to accurately identify SNVs and InDels, we used the GATK standard procedure (software.broadinstitute.org/gatk/best-practices/) to correct base quality, repeat sequences caused by PCR amplification, and misalignments generated by InDels. The SNV/InDel of each sample was detected using the GATK HaplotypeCaller method (software.broadinstitute.org/gatk/best-practices/) and filtered according to the screening protocol recommended by the software. In order to quickly find the most biologically meaningful SNV/InDel loci from a large amount of variant information under the premise of ensuring the accuracy of research data, we used ANNOVAR (annovar.openbioinformatics.org/en/latest/) to compare all SNV/InDel sites with these in the latest published population databases (dbSNP, 1000 Genomes, esp6500, ExAC03, ExAC03_EAS, gnomAD_exome_EAS), disease databases (HGMD, InterVar, MGI, HPO) and other known information for alignment analysis, so as to evaluate the mutation frequencies, functional characteristics, conservation, pathogenicity of these SNV/InDel sites, etc. Conservation and protein hazard were predicted by SIFT, PolyPhen-2, Mutation Taster, Cadd, Dann to analyze whether the mutation would affect the structure and function of the protein. In detail, SIFT prediction results can be divided into two types: D (damaging) and T (tolerated); PolyPhen prediction results can be divided into three types: B (benign), P (possibly damaging), and D (probably damaging); Mutation Taster prediction results can be divided into four types: A (disease causing automatic), D (disease causing), N (polymorphism), and P (polymorphism automatic). Cadd and Dann predictions were used to calculate SNV/InDel risk scores. And the higher the score, the higher the probability that the mutation site is deleterious.

Bioinformatics Analysis

Firstly, the filtering criteria for low-frequency functional mutations included population database frequency filtering (1000 Genome ≤ 0.01, ExAC03_EAS ≤ 0.01, gnomAD_exome_EAS ≤ 0.01) and functional mutation filtering (retaining mutations located in exons or splice regions, nonsynonymous SNV, stopgain, and other non-synonymous SNV types of mutations). The second step was to prioritize these SNV/InDel sites (divided into First1, First2, Second, Third, according to the reliability from high to low). Finally, the screening strategy for candidate mutations was generated as follows. (1) Only keep mutations with priority class First1. (2) Screening of known pathogenic genes. According to the standard English name of the disease, the gene and disease phenotype databases (HGMD, HPO, MGI, ClinVar) were used to check whether there is a known causative gene. (3) The loci with lower frequency and stronger pathogenicity were preferentially selected. Combined with information such as the incidence of disease, only the variant sites with the frequency screening threshold < 0.01 were retained according to the database frequency. Reference frequency databases included Freq_Alt (1000g), ExAC03_EAS, esp6500, gnomAD_exome_EAS, etc. Meanwhile, combining with the predicted of pathogenicity software (score of D and Dann ≥ 0.93), the loci were screened.

Results

Whole-Exome Sequencing Data and the Reference Sequence Alignment

The raw data of 20 VSD samples were obtained through the Illumina HiSeq sequencing platform. The statistical results of WES data are shown in Table S1, and a total of 1,237,197,436 reads were obtained. The clean reads of each sample had high Q20 and Q30, and the average proportion of Q30 was 95.83%, indicating good sequencing quality of sequencing data. The quality of the paired-end sequenced bases of all samples met the standard (Figure S1). As shown in Figure S2, qualified DNA will form a library mainly distributed between 300 and 400 bp in size by randomizing fragments. The comparison results of each sample were counted by Picard software (Table S2), and it was found that after removing low-quality reads, the proportion of bases with a sequencing depth of 10X was > 90%. When the base coverage depth of a locus reached more than 10X, the SNV detected at that locus was considered to be relatively credible.

Variant Detection

By using the GATK HaplotypeCaller method, we found a total of 225,735 mutation sites, including 196,458 SNV mutation sites and 29,277 InDel sites. According to the classification statistics of SNV/InDel loci relative to the genome, the distribution ratio of SNV/InDel loci in different regions was obtained, as shown in Figure 1, mainly including intronic mutations (53.74%), exonic mutations (22.92%), splicing mutations (4.91%), etc. Classification statistics were performed according to the functional types of SNV/InDel sites, as shown in Figure 2. Function types mainly includes missense mutation or nonsynonymous mutation (50.43%), synonymous mutation (44.3%), unknown (0.99%) etc. We further counted the genotypes (homozygous/heterozygous) of SNV/InDel loci according to genotype classification, and the number of SNV/InDel loci of different genotypes in 20 patients is shown in Table 2.

Table 2 Number of SNV/InDel Loci for Different Genotypes

Figure 1 Distribution ratio of SNV/InDel sites in different regions.

Figure 2 Distribution ratio of SNV/InDel sites for different functional types.

Bioinformatics Analysis of Identified Gene Variants

We obtained a total of 4793 variant loci (including 4168 SNVs, 557 InDels and 68 unknown loci) and 2566 genes, of which 3232 belonged to heterozygous mutations, 62 belonged to homozygous mutations, 24 belonged to heterozygous and homozygous mutations, and all variant loci were filtered by frequency in population database and mutations’ function. Confidence site classifications were obtained according to the criteria of site priority classification: First1 (1396 sites), First2 (1218 sites), Second (1891 sites) and Third (288 sites). According to the candidate mutation screening strategy, a total of five predicted deleterious genes and five mutation sites were screened, and they were all located in the first1 with priority. The frequencies of the five genes in the population database were all far below 0.01, indicating that the selected genes were rare in normal people, and the predicted results of software were all harmful genes (Table 3). The predicted deleterious genes, including five genes related to VSD, were located in the exon region with nonsynonymous mutation SNVs, namely NOTCH2 (c.1396C >A:p.Gln466Lys), ATIC (c.235C >T:p.Arg79Cys), MRI1 (c.629G >A:p.Arg210Gln), SLC6A13 (c.1138G >A:p.Gly380Arg), and ATP13A2 (c.1363C >T:p.Arg455Trp). In the gene inheritance pattern, NOTCH2 was autosomal dominant; ATIC and ATP13A2 were autosomal recessive (Table 4). Subsequently, we further studied the functions of SNP through the RegulomeDB database. The results showed that rs141935585, rs367615313, rs141094096 and rs377179026 could affect transcription factor (TF) binding and DNase. And rs371445094 was associated with TF binding, motif and DNase (Table 4). According to the results of function annotation of predicted disease-associated genes, it was found that NOTCH2 was associated with VSD, and ATIC was associated with ASD and ventricular disease. MRI1 was annotated as pathogenic (Table 5).

Table 3 Predicted Gene Frequencies and Harmful Prediction Tables

Table 4 Genetic Information for Predicted Genes

Table 5 Predicting Gene Disease Functional Annotation

Discussion

In this study, the genetic etiology of VSD subjects in the Chinese Tibetan population was studied by WES technology, and rare or low-frequency gene mutations related to VSD, namely five disease-causing genes with heterozygous mutations were found. This study is the first to identify pathogenic genes associated with VSD in the Chinese Tibetan population. According to the results of function annotation of predicted disease-associated genes, it was found that NOTCH2 was associated with VSD, and ATIC was associated with ASD. Meanwhile, MRI1 was annotated as pathogenic.

The NOTCH receptor family is highly conserved and belongs to the membrane protein receptor family, including 4 homologous receptors in mammals, namely Notch1~4. Many studies have shown that NOTCH2 plays an extremely important role in cardiac development, and its deletion may lead to diseases such as reduced cardiac compaction, cardiac hypertrophy, and VSD, due to its involvement in cardiomyocyte apoptosis, proliferation, and differentiation and other important cellular biological processes.14–16 For example, miR-29b-3p inhibits cardiomyocyte proliferation through NOTCH2, which demonstrates the important role of NOTCH2 in cardiac development.14 Some studies have indicated that NOTCH2 is essential for the correct formation of the cardiac outflow tract in the proliferation of cardiac neural crest-derived smooth muscle cells.17 Besides, another studied have showed that the use of Cre-lox technology to specifically inhibit NOTCH signaling in neural crest cells (NCCs) results in outflow tract defects, such as VSD, pulmonary stenosis, ASD, and OA.18 Moreover, mutations in NOTCH2 cause alagille syndrome, pulmonary artery stenosis and CHD.19 Taken together, NOTCH2 mutations may play an important role in the occurrence of VSD. Through a comprehensive review of the literature, we found that the NOTCH signaling pathway related to the NOTCH2 gene has been reported in VSD. NOTCH signaling is an evolutionarily conserved pathway whose aberrations contribute to the development of cardiac malformations,20 such as bicuspid aortic valve disease, heart valve calcification, alagille syndrome, and VSD.21 Mutations in the NOTCH receptors and their ligands have been identified as the cause of CHD in humans, suggesting the importance of NOTCH signaling during cardiac development.17 Taken the above, we speculated that NOTCH2 could affect VSD through NOTCH signaling pathway.

5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (ATIC) is a novel rate-limiting enzyme in the purine biosynthesis pathway, catalyzing the last 2 reactions of the purine synthesis pathway.22 The abnormal expression of ATIC is closely related to the prognosis of hepatocellular carcinoma patients and can cause significant changes in the proliferation and migration of hepatocellular carcinoma cells.23 Other studies have also shown that the abnormal expression of ATIC is closely related to the occurrence of various diseases, including multiple myeloma,24 lung cancer,25 lymphoma,26 etc. We found that ATIC gene was associated with VSD in the Chinese Tibetan population, and it was associated with ASD, ventricular disease. It has been suggested that synthetase deficiency may lead to pregnancy complications through reducing purine synthesis and cell proliferation.27 There is an undeniable relationship between the incidence of VSD and the developmental status of ATIC in the mother, and it is inferred that this enzyme may affect the pathogenesis of VSD through purine synthesis.

The MRI1 gene, encoding the translation initiation factor eIF-2B subunit alpha/beta/delta-like protein 2, belongs to a little-known protein family in eukaryotes, the eIF2B-related protein.28 The MRI1 gene serves as a candidate locus for infantile epilepsy with severe cerebral degeneration,29 and it is also significant in the differential gene expression of peripheral blood DNA methylation in infants with maternal asthma.30 The role of this gene in VSD has not been found, but is pathogenic in the functional annotation of the disease-related genes. Therefore, we speculated that MRI1 gene may play an important role in VSD risk, which needs further validation.

SLC6A13, also known as GAT2, belongs to the gene encoding a high-affinity GABA transporter.31 It is mainly expressed in the liver, kidney and other peripheral tissues such as testis, retina and lung. Currently, there are few studies on the SLC6A13 gene, not mention to the studies on its expression in the heart.32 We detected SLC6A13 gene in VSD for the first time, but its specific underlying mechanism needs further exploration.

ATPase cation transporting 13A2 (ATP13A2) is a late endolysosomal transporter that encodes a lysosomal transmembrane P5B-type ATPase,33 and it is genetically linked to a range of neurodegenerative diseases, for instance, Kufor-Rakeb syndrome, early-onset PD, neuronal ceroid lipofuscinosis, juvenile parkinsonism, and complex hereditary spastic paraplegia.34,35 We detected ATP13A2 gene in VSD patients for the first time. ATP13A2 deficiency causes mitochondrial and lysosomal damage in various models,36 including mitochondrial fragmentation, increased oxygen consumption, and mitochondrial DNA damage.37 Hypoxia stimulates ATP13A2 transcription through HIF1α, and hypoxia signaling plays an important role in regulating ATP13A2 gene expression.38 The subjects in this study live in a plateau hypoxic environment for a long time, so we may speculate that hypoxia regulates the expression of ATP13A2 gene to control the occurrence of VSD.

This study only carried out a preliminary screening test, and did not verify key genes and pathways and elucidate the mechanism of VSD through cell culture or animal models, which is still to be improved in the future. In future research work, we will continue to expand the sample size of the included VSDs to conduct more comprehensive researches.

Conclusion

In this study, WES biological analysis was performed on Tibetan subjects with VSD, and five pathogenic genes with low-frequency mutations were found: NOTCH2, ATIC, MRI1, SLC6A13, ATP13A2. It provides a bioinformatics basis for exploring the relationship between gene mutations and the pathogenesis of VSD, and provides clues for the molecular mechanism of VSD and new treatment strategies.

Data Sharing Statement

The datasets used or analyzed during the current study are available from the corresponding author of Tianbo Jin on reasonable request.

Ethical Approval and Consent to Participate

This study fully followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Xizang Minzu University (201808). All participants under the age of 18 years of age, parental/legal guardian consent was obtained.

Acknowledgments

We are very grateful to all the volunteers, clinicians and hospital staff who participated in this study.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work were supported by National Natural Science Fund (81860600), Open Fund for Key Laboratory of Plateau Hypoxia Environment and Life and Health (XZMU-2022M-H06), Scientific research project of Xizang Minzu University (2022MDY011), Natural Science Foundation of Tibet Autonomous Region (XZ2018ZRG-79(Z)), Postgraduate research innovation and practice project of Xizang Minzu University (Y2022099).

Disclosure

The authors declare that they have no competing interests.

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