Genome-wide analysis of differential DNA methylation in Silver-Russell syndrome

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

Genome-wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.

Patients with PWS and related syndromes display differentially methylated regions involved in neurodevelopmental and nutritional trajectory

Background

Prader–Willi syndrome is a rare genetic neurodevelopmental disorder caused by a paternal deficiency of maternally imprinted gene expression located in the chromosome 15q11–q13 region. Previous studies have demonstrated that several classes of neurodevelopmental disorders can be attributed to either over- or under-expression of specific genes that may lead to impairments in neuronal generation, differentiation, maturation and growth. Epigenetic changes that modify gene expression have been highlighted in these disorders. One recent study focused on epigenetic analysis and compared patients with PWS with patients with other imprinting disorders. No study, however, has yet focused on epigenetics in patients with PWS specifically by comparing the mutations associated with this syndrome.

Objective

This study investigated the epigenetic modifications in patients with PWS and patients with PWS-related disorders caused by inactivation of two genes of the PWS chromosomal region, SNORD116 and MAGEL2. Our approach also aimed to compare the epigenetic modifications in PWS and PWS-related disorders.

Methods

We compared genome-wide methylation analysis (GWAS) in seven blood samples from patients with PWS phenotype (five with deletions of the PWS locus, one with a microdeletion of SNORD116 and one with a frameshift mutation of MAGEL2 presenting with Schaaf–Yang syndrome), as well as two control patients. Controls were infants that had been studied for suspicion of genetic diseases that was not confirmed by the genetic analysis and the clinical follow-up.

Results

The analysis identified 29,234 differentially methylated cytosines, corresponding to 5,308 differentially methylated regions (DMRs), which matched with 2,280 genes. The DMRs in patients with PWS were associated with neurodevelopmental pathways, endocrine dysfunction and social and addictive processes consistent with the key features of the PWS phenotype. In addition, the separate analysis for the SNORD116 and MAGEL2 deletions revealed that the DMRs associated with the SNORD116 microdeletion were found in genes implicated in metabolic pathways and nervous system development, whereas MAGEL2 mutations mostly concerned genes involved in macromolecule biosynthesis.

Conclusion

The PWS is associated with epigenetic modifications with differences in SNORD116 and MAGEL2 mutations, which seem to be relevant to the different associated phenotypes.

Genome-wide methylation analysis in Silver-Russell syndrome, Temple syndrome, and Prader-Willi syndrome

Background

Imprinting disorders (IDs) show overlapping phenotypes, particularly in Silver–Russell syndrome (SRS), Temple syndrome (TS14), and Prader–Willi syndrome (PWS). These three IDs include fetal and postnatal growth failure, feeding difficulty, and muscular hypotonia as major clinical features. However, the mechanism that causes overlapping phenotypes has not been clarified. To investigate the presence or absence of methylation signatures associated with overlapping phenotypes, we performed genome-wide methylation analysis (GWMA).

Results

GWMA was carried out on 36 patients with three IDs (SRS [n = 16], TS14 [n = 7], PWS [n = 13]) and 11 child controls using HumanMethylation450 BeadChip including 475,000 CpG sites across the human genome. To reveal an aberrantly methylated region shared by SRS, TS14, and PWS groups, we compared genome-wide methylation data of the three groups with those of control subjects. All the identified regions were known as SRS-, TS14-, and PWS-related imprinting-associated differentially methylated regions (iDMRs), and there was no hypermethylated or hypomethylated region shared by different ID groups. To examine the methylation pattern shared by SRS, TS14, and PWS groups, we performed clustering analysis based on GWMA data. The result focusing on 620 probes at the 62 known iDMRs (except for SRS-, TS14-, and PWS-related iDMRs) classified patients into two categories: (1) category A, grossly normal methylation patterns mainly consisting of SRS group patients; and (2) category B, broad and mild hypermethylation patterns mainly consisting of TS14 and PWS group patients. However, we found no obvious relationship between these methylation patterns and phenotypes of patients.

Conclusions

GWMA in three IDs found no methylation signatures shared by SRS, TS14, and PWS groups. Although clustering analysis showed similar mild hypermethylation patterns in TS14 and PWS groups, further study is needed to clarify the effect of methylation patterns on the overlapping phenotypes.

A pilot study of expanded newborn screening for 573 genes related to severe inherited disorders in China: results from 1,127 newborns

Background

Newborn screening (NBS) in China is mainly aimed at detecting biochemical levels of metabolites in the blood, which may generate false-positive/negative results. Current biochemical NBS includes tandem mass spectrometry (MS/MS) screening for metabolites as well as phenylalanine (Phe), thyroid-stimulating hormone (TSH), 17-α-hydroxyprogesterone (17-OHP), and glucose-6-phosphate dehydrogenase (G6PD) test. This study intended to explore whether next-generation sequencing (NGS) for dried blood spots combining with biochemical screening could improve the current screening efficiency and to investigate the carrier frequencies of mutations in causative genes related to amino acid metabolism, organic acid metabolism, and fatty acid oxidation in this cohort.

Methods

We designed a panel of 573 genes related to severe inherited disorders and performed NGS in 1,127 individuals who had undergone biochemical NBS. The NGS screening results of neonates were used to compare with the biochemical results.

Results

NGS screening results revealed that all the four newborns with abnormal G6PD values carried hemizygous G6PD mutations, which were consistent with the decreased G6PD enzymatic activity. The NGS results revealed an individual with compound heterozygous mutations of SLC22A5, who was biochemically negative in 2016. The MS/MS screening results in 2019 showed free carnitine deficiency, which was consistent with the genetic findings. The top five genes with the highest carrier frequencies of mutations in these newborns were PAH (1:56, 1.79%), ETFDH (1:81, 1.23%), MMACHC (1:87, 1.15%), SLC25A13 (1:102, 0.98%), and GCDH (1:125, 0.80%).

Conclusions

Our study highlighted that combining NGS screening with biochemical screening could improve the current NBS efficiency. This is the first study to investigate carrier frequencies of mutations in 77 genes causing inherited metabolic diseases (IMDs) in China.

The role of maternal methylation in the association between prenatal meteorological conditions and neonatal H19/H19-DMR methylation

Meteorological conditions during pregnancy can affect birth outcome, which has been linked to the H19/H19-differentially methylated region (DMR). However, the detailed mechanisms underlying this association are unclear. This was investigated in the present study to provide epidemiological evidence for elucidating the pathogenesis of adverse birth outcomes. A total of 550 mother-newborn pairs were recruited in Zhengzhou, China from January 2010 to January 2012. Meteorological data including temperature (T), relative humidity (RH), and sunshine duration (SSD) were obtained from the China Meteorological Data Sharing Service System. Bisulfite sequencing PCR was performed to determine the methylation levels of H19/H19-DMR using genomic DNA extracted from maternal peripheral and umbilical cord blood. The results showed that H19-DMR methylation status in cord blood was positively associated with that in maternal blood. Neonatal H19-DMR methylation was negatively associated with T and RH during the first trimester and positively associated with these variables during the third trimester. There was a positive correlation between neonatal H19-DMR methylation and SSD during the second trimester and a negative correlation during the third trimester. Similar associations were observed between maternal H19-DMR methylation and prenatal meteorological conditions. We also observed significant interaction effects of maternal H19/H19-DMR methylation and most prenatal meteorological factors on neonatal methylation, and found that changes in the methylation status of maternal H19-DMR were responsible for the effects of prenatal meteorological conditions on neonatal methylation. In summary, neonatal H19-DMR methylation was significantly associated with prenatal meteorological conditions, which was modified and mediated by maternal H19-DMR methylation changes. These findings provide insights into the relationship between meteorological factors during pregnancy and adverse birth outcomes or disease susceptibility in offspring, and can serve as a reference for environmental policy-making.

eRAM: encyclopedia of rare disease annotations for precision medicine

Rare diseases affect over a hundred million people worldwide, most of these patients are not accurately diagnosed and effectively treated. The limited knowledge of rare diseases forms the biggest obstacle for improving their treatment. Detailed clinical phenotyping is considered as a keystone of deciphering genes and realizing the precision medicine for rare diseases. Here, we preset a standardized system for various types of rare diseases, called encyclopedia of Rare disease Annotations for Precision Medicine (eRAM). eRAM was built by text-mining nearly 10 million scientific publications and electronic medical records, and integrating various data in existing recognized databases (such as Unified Medical Language System (UMLS), Human Phenotype Ontology, Orphanet, OMIM, GWAS). eRAM systematically incorporates currently available data on clinical manifestations and molecular mechanisms of rare diseases and uncovers many novel associations among diseases. eRAM provides enriched annotations for 15 942 rare diseases, yielding 6147 human disease related phenotype terms, 31 661 mammalians phenotype terms, 10,202 symptoms from UMLS, 18 815 genes and 92 580 genotypes. eRAM can not only provide information about rare disease mechanism but also facilitate clinicians to make accurate diagnostic and therapeutic decisions towards rare diseases. eRAM can be freely accessed at http://www.unimd.org/eram/.

DNA methylation signatures in mendelian developmental disorders as a diagnostic bridge between genotype and phenotype

Epigenetic and genetic mechanisms regulate the establishment and maintenance of gene expression in its proper context. Recent genome-wide mapping approaches have identified DNA methylation (DNAm) signatures in patients clinically diagnosed with syndromes manifesting as developmental disabilities with intellectual impairments. Here, we review recent studies in which these DNA methylation signatures have enabled highly sensitive and specific screening of such individuals and have clarified ambiguous cases where subjects present with genetic sequence variants of unknown clinical significance (VUS). We propose that these episignatures be considered as echoes and/or legacies of the initiating mutational events within proteins of the so-called epigenetic machinery. As well, we discuss approaches to directly confirm the functional consequences and the implications of these episignatures to patient management and treatment.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

RDAD: A Machine Learning System to Support Phenotype-Based Rare Disease Diagnosis

DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.

PedAM: a database for Pediatric Disease Annotation and Medicine

There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/.