OP038: Novel whole methylome automated data analysis tool for investigation of unsolved diagnostic odyssey cases

Abstract

The study of epigenetics has changed the lens in which we view both health and disease. Emerging tools in this field have brought new discoveries to the forefront of preventive, diagnostic and therapeutic markers. DNA methylation is an essential process that regulates chromatin structure and gene expression, and therefore affects many cellular functions including gene imprinting and cell differentiation. Using the power of molecular techniques and bioinformatics, we can now gain insight into methylation patterns at a single nucleotide level throughout the genome. In this study, we developed a methylation automated data analyzer to investigate putative multi-locus methylation defects in diagnostic odyssey cases, and to further appreciate differences in methylation patterns across the lifespan. Individual pathway analyses for genes DNMT3A, NLRP5, NLRP7, NPM2, PADI6 and ZFP42 were conducted. These genes were chosen because they are either known methyltransferases or may cause hypermethylation of H19 and/or hypomethylation of LIT1 . Additional genes from this analysis were chosen based on known direct interactions with the original input gene and co-expression with the input gene resulting in a final list of 15 genes of interest. Next, we filtered through the history of our exome sequencing (ES) cases from our clinical laboratory with high impact variants in these 15 genes of interest. Cases were chosen based on impact of the variant and allele frequency in gnomAD, which resulted in 11 cases, 10 of which were unsolved by ES. Methylation profiling was performed on all 11 cases using the Infinium MethylationEPIC 850K array. Using Python3.7, we developed our own unique Methylation Automation Data Analyzer (MADA). Using MADA, each case was analyzed and compared to either 74 female or 75 male healthy controls of various ages spanning from newborn to 81 years. Probes with a difference greater than or equal to 0.5 between the patient beta value and the mean beta value of the controls were selected. This final list of probes was investigated further based on genomic region, gene content, proximity to a CpG island and the individual patient’s phenotype. To investigate methylation patterns of various genomic regions across the lifespan, a separate algorithm within MADA was constructed. This analysis uses our cohort of healthy individuals (74 males and 75 females) and calculates the mean beta value for each age for each probe and thus allows for the observation of changes in methylation over time in healthy individuals from age newborn to 81 years. MADA provided insight into methylation of genomic regions that had otherwise been overlooked by conventional genetic testing in diagnostic odyssey cases. Here, we highlight three patients. Patient #1, a 13-year old male, had a de novo point variant in DNMT1 c.4636C>G; p.Pro1546Ala (NM_001130823.2) by ES. MADA produced a final list of 13 probes for which the genomic regions were all significantly hypermethylated compared to controls. This patient had additional clinical features that were atypical for the DNMT1 spectrum of phenotypes and MADA revealed other interesting findings that could potentially be contributing to the additional features. Patient #2, a 7-year old male, had a heterozygous maternally inherited variant in KHDC3L c.31_39dup9 p.Val11_Leu13dup (NM_001017361.3) by ES. MADA identified 11 probes of interest of which four regions covered by these probes were hypomethylated (including LARS2) and seven were hypermethylated as compared to the controls. This patient’s phenotype includes abnormal facial shape, autistic behavior delayed speech and language development, developmental regression, hyperpigmentation of the skin, intellectual disability, neurodevelopmental abnormality, and abnormal gait. Patient #3, a 10-year-old male with a paternally inherited variant NLRP7 c.2243_2249del7 (NM_001127255.1) identified through ES. The patient’s phenotype included absent speech, anxiety, aplasia cutis congenita of scalp, autistic behavior, brachycephaly, cortical dysplasia, dental crowding, intellectual disability, thin ear helix and progressive language deterioration. MADA identified 13 hits, including AFF2 and LARS2 both of which are hypomethylated in the patient compared to the controls. The second analysis investigated methylation patterns of various genomic regions across the lifespan. This tool allows for the input of any gene covered by the Illumina Infinium platform to be analyzed. We observed that there are critical points in an individual’s lifespan during which methylation of various genomic regions is altered that have not been previously described. These periods are related to important developmental stages such as infancy and puberty, among others. MADA can hence be used as a tool to further understand epigenomic changes during these critical periods throughout the lifespan, and the consequences of deviations from normal methylation patterns. There is growing evidence of “episignatures” or DNA methylation biomarkers for over 40 rare disorders in association with more than 60 genes. This study describes the development of a novel whole methylome automated data analysis tool, MADA, for the investigation of unsolved diagnostic odyssey cases. MADA offers an easy, streamlined approach for analysis of the methylome in undiagnosed ES cases and for uncovering potential episignatures in rare disorders.