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Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing 100191, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing 100191, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing 100191, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing 100191, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing 100191, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing 100191, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing 100191, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing 100191, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing 100191, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing 100191, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing 100191, China
Are there any differences between conjoined twin fetuses at the molecular level?
Design
Skin tissues were collected from thoracopagus conjoined twins at 15+4 weeks of gestation. The skin tissues were collected from the thigh side of conjoined twins after the abortion procedure. All specimens were obtained after written informed patient consent and were fully anonymized. All relevant ethical regulations were followed. Every specimen underwent multiomics sequencing analysis to determine associations among the DNA methylome, transcriptome and mutations in the exon regions in the conjoined twins.
Results
The global methylation pattern was similar in the two fetuses of conjoined twins, while significant differences were seen in local regions such as CpG islands (P = 0.026), enhancers (P < 0.001) and various repetitive elements (P < 0.05), which showed significant differences. The conjoined twins also differed in genes related to growth and development, cellular component morphogenesis and cellular stress, both in terms of DNA methylation levels and gene expression levels. Exon data analysis revealed that the common mutations in conjoined twins mainly occurred in neural development, lipid metabolism and microtubule morphogenesis. Specific mutations were associated with cellular component biosynthesis, behaviour and germ cell development.
Conclusion
Conjoined twins were similar to each other globally, but there were significant differences related to growth and development, cellular component morphogenesis and cellular stress. The current study reveals the molecular features of conjoined twins for the first time, laying the foundation for future exploration of the mechanism of conjoined twins.
Conjoined twins are one of the most serious and rarest complications of twin pregnancy, accounting for 1.5 per 100,000 births. More than 50% of conjoined twin pregnancies end in miscarriage or stillbirth, and a proportion die shortly after birth (
). The joint site can be ventral or dorsal. Based on their joint anatomy, conjoined twins can be classified into eight types: thoracopagus (the most common type, accounting for 75%), cephalogapus, omphalopagus, ischiopagus, parapagus, craniopagus, pygopagus and rachipagus (
). Due to the increased perinatal morbidity and mortality, prenatal evaluation of conjoined twins in the first trimester is very important. In the second trimester, ultrasound assessment and magnetic resonance imaging can further improve and confirm the diagnosis by viewing the detailed anatomy and shared parts of the two fetuses. The prenatal ultrasound diagnosis characteristics of conjoined twins include a single placenta without septum; fetuses in the same constant position with head and body parts at the same level; inseparable body and skin contours; fetuses facing each other with cervical spine hyperflexion, sharing organs and a single umbilical cord with over three blood vessels; fewer limbs than normal twins; and abnormal flexion of the spine (
The prognosis of conjoined twins is mainly affected by the conjoined parts and related malformations. The prognosis is generally poor, and the survival rate is low. Only 25% of live births survive long enough for surgery (
). The surgical approach to separating conjoined twins is complex, expensive and involves a multidisciplinary process, so diagnostic tools are crucial to provide anatomical information for the surgical segmentation of conjoined twins. Surgical separation can be performed in omphalopagus, pygopagus and some craniopagus and thoracopagus twins, but not in cephalopagus, parapagus or rachipagus twins. The most common type is thoracopagus twins, although surgical treatment of this form is difficult due to the frequent presence of a shared heart (
). Complex ethical issues can also arise when separation involves the unequal sharing of limbs and organs or when separation results in the death of one of the conjoined twins (
). There are two well-known theories to explain the emergence of conjoined twins: fission and fusion. The fission theory states that the aberrant fission of the primitive streak development after the embryonic period of 13–15 days results in conjoined twins. Fission fails to account for asymmetrically conjoined twins. In these instances, the secondary association of two separate mono-ovulated embryonic discs proposed by the fusion theory is more plausible (
). These theories remain at the stage of speculation and still lack experimental evidence.
Due to the extremely low incidence of conjoined twins and difficulty in obtaining twin specimens, the molecular features of conjoined twins have not been studied, and the malformation pathogenesis of conjoined twins remains poorly understood. Through integration analysis of DNA methylation, gene expression and exome mutation, this study found that the global pattern was similar in the two fetuses of conjoined twins, but there were significant differences in local regions. The two conjoined twins differed in both the methylation levels and expression levels of genes related to growth and development, cellular component morphogenesis and cellular stress. The whole genome exome sequencing data indicated that the common mutations of conjoined twins mainly occurred in neural development, glycerolipid metabolism processes and microtubule morphogenesis. Mutations specific to conjoined twins were associated with cellular component biosynthesis, behaviour and germ cell development. This comprehensive analysis reveals for the first time the epigenomic similarities and differences between the two fetuses of conjoined twins and may serve as a valuable resource for researchers in future studies of the pathogenesis of conjoined twins.
Materials and methods
Sample collection
Skin tissue was collected from the thigh side of thoracopagus conjoined twins at 15+4 weeks of gestation, following the abortion procedure (Supplementary Figure 1). Twin specimens were obtained after written informed patient consent and were fully anonymized. The Peking University Third Hospital Medical Science Research Ethics Committee approved the study (research licence 2022-455-02; approval date 1 September 2022). All relevant ethical regulations were followed. A summary of the methodology is shown in Figure 1.
Figure 1Schematic illustration of the study. Skin tissue collected from the thigh side of 15+4 weeks’ conjoined twins after the abortion procedure. Subsequent multiomics sequencing analysis to determine associations among the transcriptome, DNA methylome and mutations in the exon regions in the conjoined twins. BS-Seq = bisulfite sequencing; RNA-seq = RNA sequencing; TES = transcription end site; TSS = transcription start site; WES = whole-exome sequencing.
The skin tissue from conjoined twins was digested and cleaved to extract RNA. A total of at least 1 μg RNA was used as input material for the RNA sample preparations. Sequencing libraries were constructed using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs, Ipswich, MA, USA) following the vendor's protocol. Briefly, mRNA purification from total RNA was performed using Poly-T Oligo-Attached Magnetic Beads (ThermoFisher, US). After fragmentation, first-strand and second-strand cDNA were synthesized. The purified double-stranded cDNA was end-repaired, A-tailed and connected to the sequencing adapter. Then, 250–300 bp cDNA enriched by AMPure XP Beads (Beckman Coulter, US) was screened to perform polymerase chain reaction (PCR) amplification and purification. The libraries were constructed and subjected to 150 bp paired-end sequencing on the Illumina NovaSeq platform.
Whole-genome DNA methylation sequencing
Approximately 0.5 ng unmethylated lambda DNA was spiked into a total amount of 100 ng genomic DNA. Covaris S220 randomly interrupted the mixture DNA to 200–300 bp fragments. The DNA fragments were end-repaired, tailed with deoxyadenosine and ligated with sequencing adapters. These DNA fragments were treated with bisulfite using an EZ DNA Methylation-Gold™ Kit (Zymo Research, CA). The libraries were constructed and subjected to paired-end sequencing on the Illumina platform.
Whole-exome sequencing
Extracted genomic DNA (0.4 μg) was randomly fragmented by a Covaris S220 sonicator into an average size of 180–280 bp. These DNA fragments were end-repaired, phosphorylated, A-tailed and ligated at the 3’ ends with paired-end adaptors (Illumina). Then, the ligated DNA fragments were selectively enriched in a PCR. After PCR, the libraries were hybridized with a biotin-labelled probe, and the exons of genes were captured with streptomycin magnetic beads. Then, index tags for sequencing were added to the captured libraries. Products were purified using the AMPure XP system. Finally, the DNA library was subjected to 150 bp paired-end sequencing on the Illumina platform.
Processing of the transcriptome data
First, Illumina adapter sequences, amplification primers, poly(A) tail sequences and reads of low-quality bases (n > 10%) were removed from the raw RNA sequencing (RNA-seq) data. Then, the processed sequences were aligned to the hg19 human reference genome (UCSC) using STAR software with the default settings. Then, the uniquely mapped reads were counted using the HTSeq package. Subsequently, the expression level of each gene was calculated using Cufflinks. The expression level of each gene was converted to log2(fpkm + 1) for downstream analysis.
DNA methylation data processing
First, the sequencing adapters, amplification primers and low-quality bases were removed from the raw data. Then, the clean reads were aligned to the hg19 human reference genome (UCSC) using BS-Seeker2 (v2.1.1) in end-to-end alignment mode. The unaligned reads were realigned to the hg19 genome in local alignment mode, and the low confidence alignments were discarded. Next, Picard tools (v1.119) were applied to remove PCR duplicates. The ratio of the number of reads with methylated C to that of total reads (methylated and unmethylated) was defined as the DNA methylation level.
Whole-exome sequencing processing
First, low-quality and adapter-contaminated reads were removed from raw data using trim galore. Then, the clean reads were aligned to the hg19 human reference genome (UCSC) through BWA. Next, Picard tools (v1.119) were applied to remove PCR duplicates. To improve the alignment accuracy, the Genome Analysis Toolkit (version 3.8.1) was used to process binary alignment map (BAM) files through steps including local realignment around high-confidence insertion and deletions, and base quality recalibration.
Global DNA methylation and RNA expression level correlation coefficient calculations
For global DNA methylation correlation, the whole genome was divided into 1000 tiles, and the DNA methylation level of CpG sites with coverage greater than 3 in each tile was calculated. Then, the Pearson correlation of each tile in conjoined fetal samples was calculated. For RNA expression correlation, the log2(fpkm + 1) formula was used to define the relative expression levels of genes, and Pearson correlation coefficients were calculated for the relative expression levels of each gene in two conjoined fetal samples.
DNA methylation levels of different genomic regions
Genomic region annotations in the UCSC genome table and other databases were downloaded, including promoters, CpG islands (CGI), enhancers, intergenic regions, intragenic regions and repetitive regions. The DNA methylation level of these genomic regions was estimated on the basis of the average methylation level of CpG sites with coverage greater than 3 within these regions.
Identification of differentially methylated regions
The CpG sites covered by both samples were used for further differentially methylated region (DMR) analyses. Then, the DNA methylation levels of 100 bp tiles (with three or more CpG) of each sample were calculated. DMR with a P-value <0.05, an absolute value of log2(fold change) >1.5, and a methylation level difference between the two samples >0.5 were accepted. Bedtools was used to intersect the DMR with genomic regions. GO analysis was performed using https://metascape.org/gp/index.html#/ (
Each gene was normalized using read counts, and differentially expressed genes (DEG) were analysed using edgeR. The magnitude and statistical significance of differential gene expression was assessed using the ‘exactTest’ method in edgeR. Genes with an absolute value of log2(fold change) >1.5 and P-value <0.05 were used for downstream analysis.
Correlation calculation between DNA methylation and RNA expression
Each gene was divided into 100 windows from the transcription start site (TSS) to the transcription end sites (TES). The upstream 15 kb flanking regions of the TSS and downstream 15 kb flanking region of TES were divided into 50 windows. For every window of each gene in two samples, the pairwise Spearman correlation between the DNA methylation levels and RNA expression values was calculated.
Categorization and annotation of variants in genome sequences
A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118.
) was used to predict putative effects of DNA variants based on genomic location. For the analysis, a binary database file was built in snpEff using the hg19 reference genome in fasta format and the annotation file in gff3 format. SNP and indels in a variant call format (VCF) file were then annotated with the snpEff program using default parameters. Variants in intergenic regions and introns were defined as modifier impacts and did not affect gene coding. The variants located in coding genic regions generated low, moderate and high impact. Low-impact variants (e.g. synonymous variants) were considered largely harmless or unlikely to change protein behaviour, while variants with moderate impact (e.g. missense variants and in-frame deletions) might change protein effectiveness. High-impact variants (e.g. stop-gain and frameshift variants) might result in protein truncation or loss of function.
Results
Conjoined twin fetuses display similar methylation overall but also show local differences
To systematically study the similarities and differences in DNA methylation levels in conjoined fetuses, the similarity in genome-wide methylation levels between conjoined fetuses was initially compared. The overall methylation level of the two samples was 79%, and the Pearson correlation coefficient between the two was as high as 0.91, indicating that conjoined fetuses are very similar in DNA methylation (Figure 2A).
Figure 2DNA methylation features of conjoined twins. (A) Global methylation level correlation within conjoined twins. Data points represent the DNA methylation levels in each window across the whole genome. The darker the blue, the denser the data points. (B) DNA methylation levels across gene body, including upstream and downstream 15 kb flanking regions. (C) DNA methylation levels among different genomic regions. The box plot shows the median, 25th and 75th percentiles and the range. (D) DMR identified in two specimens. (E) GO terms of differentially methylated promoters. Functional enrichment analysis of genes falling in the differential methylation promoter regions. GO analysis was performed using Metascape (https://metascape.org/). (F) Violin plot showing DNA methylation levels in the promoter regions of typical genes in two samples from conjoined fetuses. Levels calculated as number of reads with methylated C from total number of reads. *P < 0.05, **P < 0.01, ***P < 0.001. ALR = alpha-like repeats; Alu = a SINE element; CGI = CpG islands; DMR = differentially methylated regions; GO = gene ontology; HCP = high CpG density promoter; ICP = intermediate CpG density promoter; LCP = low CpG density promoter; LINE = long interspersed nuclear element; LTR = long terminal repeats; SINE = short interspersed nuclear element; TES = transcription end site for each gene; TSS = transcription start sites.
Both conjoined fetuses showed obvious hypomethylation in the TSS region. CpG methylation increased from TSS to TES regions and decreased sharply after TES (Figure 2B). Conjoined fetuses displayed distinct DNA methylation patterns on different genomic elements (Figure 2C). Significant differences occurred in most genomic regions, such as CGI (P = 0.026), enhancer (P < 0.001), intergenic (P = 0.0075) and intragenic regions (P = 0.04), and several repetitive elements, such as SINE (P < 0.001), LINE (P < 0.001), LTR (P < 0.001), ALR (P = 0.041), Alu (P < 0.001) and LCP (P = 0.036), among which twin_1 was always hypermethylated compared with twin_2 (Figure 2C).
Next, to better show the differences between conjoined twins, the chromosome copy number of the specimen was calculated, identifying a series of DMR. The results showed that the conjoined twin samples were euploid boys with no copy number abnormalities (Supplementary Figure 2). In addition, 265 and 259 hypermethylated regions were found in twin_1 and twin_2 specimens, respectively, where about 10% of the hypermethylated regions fell on the promoter and 40% on the gene body (Figure 2D). By annotating the differentially methylated promoter regions, it was found that the hypermethylated regions of twin_1 were mainly related to regulation of developmental growth, cellular component morphogenesis, and cellular response to growth factor stimulus. The hypermethylated region of twin_2 was mainly enriched in cellular calcium ion homeostasis and cellular responses to stress (see Figure 2E). It was observed that the promoter region of NKD1, which negatively regulates the Wnt signalling pathway, was hypermethylated (P = 0.031) in twin_1. Promoters for both microRNA MIR1–1 and MIR199B showed hypermethylation trends in twin_1. KEL, which encodes a type II transmembrane glycoprotein, showed high methylation in the promoter of twin_2. The promoter for a modulator of stress responses, FKBP5, was hypermethylated in twin_2. MAP3K5 promoter, an important factor in the MAPK signalling pathway, was also hypermethylated in twin_2 (Figure 2F).
Combined transcriptome and DNA methylome analysis reveals differences in the growth and development of conjoined twins
To understand the similarities and differences of the conjoined twin samples at the transcriptome level, the gene expression of the two samples was analysed; the Pearson correlation coefficient of the two samples at the global expression level was up to 0.985, which was more similar than that at the DNA methylation level (Figure 3A). To further reveal the differences, DEG between the two pairs of samples in conjoined twins were identified. Six up-regulated and 94 down-regulated genes were found in twin_1 compared to twin_2 (Figure 3B). The down-regulated genes in twin_1 were mainly enriched in signalling pathways related to NABA core matrisome, muscle structure development, cellular response to growth factor stimulus and cellular component morphogenesis (Figure 3C).
Figure 3Combined analysis of transcriptome and DNA methylation. (A) Correlation coefficients of RNA expression in conjoined fetal pairs. Data points represent the RNA expression levels of all genes in two samples from conjoined fetuses. The darker the blue, the denser the data points. (B) Number of DEG, which were analysed using edgeR. (C) GO terms of DEG. Functional enrichment analysis of DEG. GO analysis was performed using Metascape (https://metascape.org/). (D) Spearman correlations between RNA expression levels and DNA methylation levels across gene body (including 15 kb flanking regions) in conjoined fetal pairs. (E) DNA methylation levels in the promoter regions and RNA expression values of typical genes in two samples from conjoined fetuses. The log2(fpkm + 1) formula was used to define the relative expression levels of genes. DEG = differentially expressed gene; GO = gene ontology.
To systematically understand the features of conjoined twins at the level of DNA methylation and transcriptomics, a joint analysis of multiomics data was carried out, which found that twin_2 was more correlated with DNA methylation and transcriptome than twin_1 (the correlation coefficient of twin_2 was higher than twin_1), both negatively correlated with promoter region and positively correlated with gene body region (Figure 3D), which revealed that DNA methylome and transcriptomics were more tightly regulated in twin_2. Key genes involved in early growth (CILP, MYOM2, MYOZ1 and WFIKKN2) were expressed at low levels in twin_1, but their promoter region was hypermethylated (Figure 3E).
Exome sequencing identifies mutations between two conjoined twin fetuses
To identify sites of variation on exons in conjoined twin samples, whole-genome exon sequencing was performed on both twin pair samples. First, by comparing the variants in the database from the 1000 Genomes Project, it was found that the variants in the two conjoined twin samples were similar. Mutations were mainly concentrated in SNP and rarely in deletions and insertions (Figure 4A). In addition, mutations were mainly enriched in introns and transcripts, with a low proportion in other regions of the genome (Figure 4B).
Figure 4Variants in the exons of conjoined fetuses. (A) Ratio of different variant types in two conjoined fetuses across whole exons. (B) The proportion of variations that fell on different genomic regions in two conjoined fetuses. (C) The proportion of variations with different degrees of impact in two conjoined fetuses. High: likely protein truncation or loss of function; Low: considered largely harmless or unlikely to change protein behaviour; Moderate: might change protein effectiveness; Modifier: variants in intergenic regions and introns. (D) Number of high-impact variants separated by twin. (E) GO terms of high-impact variants with intersection in two specimens of conjoined fetuses. GO analysis was performed using Metascape (https://metascape.org/). (F) GO terms of specific high-impact variants in two specimens of conjoined fetuses. (G) Examples of typical high-impact variants in two samples from conjoined fetuses. The red bases represent the sequence on the reference genome, and the blue bases represented the altered sequence. GO = gene ontology; SNP = single nucleotide polymorphism.
According to the classification of the harmfulness of the mutation sites, the variation of the conjoined twin samples was mainly the modifier variation, and the relative number of high-impact coding variants (e.g. stop-gain and frameshift variants) was very low (Figure 4C). Because high-hazard variants have the greatest impact on gene function, the focus here was on high-impact variants between conjoined twin samples. It was found that the two twin samples had a large number of common high-impact variants (3065), while only 23 were unique to twin_1 and 37 were unique to twin_2 (Figure 4D). The common high-impact variants between the two twin samples mainly affected the functions of genes such as neural development, head development, glycerolipid metabolic processes and microtubule morphogenesis (Figure 4E).
The high-impact variations of twin_1 were highly related to cellular component biogenesis and behaviour, while the high-impact variations of twin_2 were related to germ cell development (Figure 4F). Furthermore, this study looked at several typical variants in two samples of conjoined twins. A frameshift mutation occurred in exon 1 of the ROCK2 gene in the twin_1 sample. Specifically, the AGCCCG sequence was inserted at A of 11,484,462 on chromosome 2. In this region, the frameshift mutation of ROCK2 occurred only in three-quarters of the reads of twin_1 but not in twin_2. A frameshift mutation with a base deletion occurred in the 16,646,194 region of chromosome 11 in twin_2. The TCCCGC of this region became T and appeared in three-quarters of the reads, while twin_1 was perfectly normal. The mutation was located in exon 1 of the PANX1 gene (Figure 4G).
Discussion
It has long been assumed that monozygotic twins are genetically identical, but a wealth of data suggest otherwise (
reported that monozygotic twins differed by an average of 5.2 early developmental mutations, and approximately 15% of monozygotic twins had a high number of early developmental mutations specific to one fetus. This was consistent with the current conclusion, that there are differences between conjoined fetuses in terms of DNA methylome, transcriptome and exome.
The current results show that conjoined twins had significant differences in DNA methylation levels in certain regions of the whole genome, such as the repetitive elements Alu, SINE, LINE, etc. Global and locus-specific differences in DNA methylation and histone acetylation of a large cohort of monozygotic twins have been revealed. The results showed that early in life, there were slight epigenetic differences in identical twins, especially in the Alu repeat region, and these differences were amplified as monozygotic twins grow older, which may have significant effects on gene expression (
). The data shows that this difference was already present in conjoined fetuses at 15 weeks, but at that time it was relatively subtle. The findings also supported a role for epigenetic diversity in the discordant disease onset frequency of monozygotic twins (
Epigenome-wide DNA methylation analysis in siblings and monozygotic twins discordant for sporadic Parkinson's disease revealed different epigenetic patterns in peripheral blood mononuclear cells.
It was observed that the promoter region of NKD1, which negatively regulates the Wnt signalling pathway, showed different methylation levels between the two fetus pairs. The Wnt signalling pathway has been shown to play an increasingly important role in growth and development (
). Pathway analyses based on the nearest genes of hypermethylated DMR in monozygotic twins were most strongly enriched for genes involved in cell adhesion, Wnt signalling and the planar cell polarity pathway (
). Therefore, the Wnt signal raised the possibility that developmental growth might be involved in the conjoined twin twinning process.
Tracking genome-wide DNA methylation changes in monozygotic twins from birth to adolescence, scientists identified a series of hypervariable genes stable over time, and these genes were associated with several diseases and disorders, developmental processes and cellular mechanisms. This suggested that these highly variable stable genes might respond to the immediate environment in a stable trait manner throughout life. One of these genes, MYOM2, was identified as a highly variable stable gene involved in cell death and survival pathways (
Identification of MYOM2 as a candidate gene in hypertrophic cardiomyopathy and Tetralogy of Fallot, and its functional evaluation in the Drosophila heart.
). The MYOM2 gene showed an inverse correlation between promoter region DNA methylation and expression levels in the current data, further indicating that this gene may play a stabilizing role in the lifetime response of conjoined twins to environmental stimuli.
This study investigated the molecular characteristics of conjoined twins using multiomics techniques. Through the DNA methylome, it was found that the global methylation pattern was similar in the two samples of conjoined twins. This is comparable to the methylation level of monozygotic twins reported in a previous article (
). But there were significant differences in local regions, such as CGI, enhancers and repetitive elements. At the same time, the two conjoined twins also differed in the promoter regions of genes particularly related to growth and development, cellular component morphogenesis and cellular stress. From the transcriptome analysis, the results showed that the differences in gene expression between the two conjoined twins were small but not without differences. For example, conjoined twins differed in the expression levels of the cellular response to growth factor stimulation and cellular component morphogenetic-related genes, echoing DNA methylation analysis. The whole genome exome sequencing data indicated that the common mutations of conjoined twins mainly occurred in neural development, glycerolipid metabolism, microtubule morphogenesis and other pathways. Mutations specific to conjoined twins were associated with cellular component biosynthesis, behaviour and germ cell development. For the first time, this work revealed the molecular features of conjoined twins, which may provide clues for twin research laboratories and lay the foundation for future exploration of the mechanism of conjoined twins. Of course, this study also had limitations. Because the incidence of conjoined twins is only 1.5 per 100,000 births, it is difficult to collect specimens of conjoined twins. Therefore, the sample size in this study is small, and a larger sample size is needed in the future to support understanding of the pathologic mechanism of conjoined twins.
Data availability
Data will be made available on request.
Acknowledgements
We would like to thank the donor who participated in the study, and Novogene Corporation for the library building and next-generation sequencing. We are also thankful to the Computing Platform of the Center for Life Science for data analysis.
This project was funded by the National Natural Science Foundation of China [82071721], the Beijing Natural Science Foundation [7232203] and Key Clinical Projects of Peking University Third Hospital [BYSYZD2022029].
Author roles
This project was conceived and coordinated by Jin Huang. Yidong Chen conducted all the studies and performed the bioinformatics analysis with the help of Wei Chen. Yun Wang and Yuan Wei collected the samples. Yidong Chen and Jin Huang wrote the manuscript, with contributions from all of the authors.
Identification of MYOM2 as a candidate gene in hypertrophic cardiomyopathy and Tetralogy of Fallot, and its functional evaluation in the Drosophila heart.
A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118.
Epigenome-wide DNA methylation analysis in siblings and monozygotic twins discordant for sporadic Parkinson's disease revealed different epigenetic patterns in peripheral blood mononuclear cells.
Yidong Chen is an assistant research fellow at the Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, China. She has been working in the field of obstetrics and gynecology for many years and has published many Science Citation Index papers with high scores.
Key message
Conjoined twins are globally similar by multiomics sequencing but display striking differences in growth and development and cellular stress signalling pathways. This study reveals the molecular features of conjoined twins from multiple dimensions for the first time, laying the foundation for future exploration of the mechanism of conjoined twins.
Article info
Publication history
Published online: March 06, 2023
Accepted:
March 2,
2023
Received in revised form:
February 20,
2023
Received:
October 19,
2022
Declaration: The authors report no financial or commercial conflicts of interest.
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