Mutations in human DNA methyltransferase DNMT1 induce specific genome-wide epigenomic and transcriptomic changes in neurodevelopment.

DNA methyltransferase type 1 (DNMT1) is a major enzyme involved in maintaining the methylation pattern after DNA replication. Mutations in DNMT1 have been associated with autosomal dominant cerebellar ataxia, deafness and narcolepsy (ADCA-DN). We used fibroblasts, induced pluripotent stem cells (iPSCs) and induced neurons (iNs) generated from patients with ADCA-DN and controls, to explore the epigenomic and transcriptomic effects of mutations in DNMT1. We show cell type-specific changes in gene expression and DNA methylation patterns. DNA methylation and gene expression changes were negatively correlated in iPSCs and iNs. In addition, we identified a group of genes associated with clinical phenotypes of ADCA-DN, including PDGFB and PRDM8 for cerebellar ataxia, psychosis and dementia and NR2F1 for deafness and optic atrophy. Furthermore, ZFP57, which is required to maintain gene imprinting through DNA methylation during early development, was hypomethylated in promoters and exhibited upregulated expression in patients with ADCA-DN in both iPSC and iNs. Our results provide insight into the functions of DNMT1 and the molecular changes associated with ADCA-DN, with potential implications for genes associated with related phenotypes.

Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600.

In the original publication [...].

Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events.

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

Network Effects of the 15q13.3 Microdeletion on the Transcriptome and Epigenome in Human-Induced Neurons.

BACKGROUND: The 15q13.3 microdeletion is associated with several neuropsychiatric disorders, including autism and schizophrenia. Previous association and functional studies have investigated the potential role of several genes within the deletion in neuronal dysfunction, but the molecular effects of the deletion as a whole remain largely unknown. METHODS: Induced pluripotent stem cells, from 3 patients with the 15q13.3 microdeletion and 3 control subjects, were generated and converted into induced neurons. We analyzed the effects of the 15q13.3 microdeletion on genome-wide gene expression, DNA methylation, chromatin accessibility, and sensitivity to cisplatin-induced DNA damage. Furthermore, we measured gene expression changes in induced neurons with CRISPR (clustered regularly interspaced short palindromic repeats) knockouts of individual 15q13.3 microdeletion genes. RESULTS: In both induced pluripotent stem cells and induced neurons, gene copy number change within the 15q13.3 microdeletion was accompanied by significantly decreased gene expression and no compensatory changes in DNA methylation or chromatin accessibility, supporting the model that haploinsufficiency of genes within the deleted region drives the disorder. Furthermore, we observed global effects of the microdeletion on the transcriptome and epigenome, with disruptions in several neuropsychiatric disorder-associated pathways and gene families, including Wnt signaling, ribosome function, DNA binding, and clustered protocadherins. Individual gene knockouts mirrored many of the observed changes in an overlapping fashion between knockouts. CONCLUSIONS: Our multiomics analysis of the 15q13.3 microdeletion revealed downstream effects in pathways previously associated with neuropsychiatric disorders and indications of interactions between genes within the deletion. This molecular systems analysis can be applied to other chromosomal aberrations to further our etiological understanding of neuropsychiatric disorders.

Epigenetic signatures of attachment insecurity and childhood adversity provide evidence for role transition in the pathogenesis of perinatal depression.

Early life adversity and insecure attachment style are known risk factors for perinatal depression. The biological pathways linking these experiences, however, have not yet been elucidated. We hypothesized that overlap in patterns of DNA methylation in association with each of these phenomena could identify genes and pathways of importance. Specifically, we wished to distinguish between allostatic-load and role-transition hypotheses of perinatal depression. We conducted a large-scale analysis of methylation patterns across 5 × 106 individual CG dinucleotides in 54 women participating in a longitudinal prospective study of perinatal depression, using clustering-based criteria for significance to control for multiple comparisons. We identified 1580 regions in which methylation density was associated with childhood adversity, 3 in which methylation density was associated with insecure attachment style, and 6 in which methylation density was associated with perinatal depression. Shorter telomeres were observed in association with childhood trauma but not with perinatal depression or attachment insecurity. A detailed analysis of methylation density in the oxytocin receptor gene revealed similar patterns of DNA methylation in association with perinatal depression and with insecure attachment style, while childhood trauma was associated with a distinct methylation pattern in this gene. Clinically, attachment style was strongly associated with depression only in pregnancy and the early postpartum, whereas the association of childhood adversity with depression was time-invariant. We concluded that the broad DNA methylation signature and reduced telomere length associated with childhood adversity could indicate increased allostatic load across multiple body systems, whereas perinatal depression and attachment insecurity may be narrower phenotypes with more limited DNA methylation signatures outside the CNS, and no apparent association with telomere length or, by extension, allostatic load. In contrast, the finding of matching DNA methylation patterns within the oxytocin receptor gene for perinatal depression and attachment insecurity is consistent with the theory that the perinatal period is a time of activation of existing attachment schemas for the purpose of structuring the mother-child relationship, and that such activation may occur in part through specific patterns of methylation of the oxytocin receptor gene.

GAD2 Alternative Transcripts in the Human Prefrontal Cortex, and in Schizophrenia and Affective Disorders.

Genetic variation and early adverse environmental events work together to increase risk for schizophrenia. γ-aminobutyric acid (GABA), the major inhibitory neurotransmitter in adult mammalian brain, plays a major role in normal brain development, and has been strongly implicated in the pathobiology of schizophrenia. GABA synthesis is controlled by two glutamic acid decarboxylase (GAD) genes, GAD1 and GAD2, both of which produce a number of alternative transcripts. Genetic variants in the GAD1 gene are associated with increased risk for schizophrenia, and reduced expression of its major transcript in the human dorsolateral prefrontal cortex (DLPFC). No consistent changes in GAD2 expression have been found in brains from patients with schizophrenia. In this work, with the use of RNA sequencing and PCR technologies, we confirmed and tracked the expression of an alternative truncated transcript of GAD2 (ENST00000428517) in human control DLPFC homogenates across lifespan besides the well-known full length transcript of GAD2. In addition, using quantitative RT-PCR, expression of GAD2 full length and truncated transcripts were measured in the DLPFC of patients with schizophrenia, bipolar disorder and major depression. The expression of GAD2 full length transcript is decreased in the DLPFC of schizophrenia and bipolar disorder patients, while GAD2 truncated transcript is increased in bipolar disorder patients but decreased in schizophrenia patients. Moreover, the patients with schizophrenia with completed suicide or positive nicotine exposure showed significantly higher expression of GAD2 full length transcript. Alternative transcripts of GAD2 may be important in the growth and development of GABA-synthesizing neurons as well as abnormal GABA signaling in the DLPFC of patients with schizophrenia and affective disorders.