Radford, E. J. | Search bioRxiv

bioRxiv2025

TET3 protects the Dlk1-Dio3 Imprinted Locus from DNA hypomethylation during adult NSC Reprogramming

Genomic imprinting is an epigenetic mechanism that drives monoallelic gene expression depending on parental origin. Loss of imprinting (LOI) is associated with human imprinting disorders, fetal development, and cancer progression. Imprinted genes, organized in clusters, are regulated by methylation at imprint control regions (ICRs), differentially methylated regions (DMRs) between parental chromosomes. Somatic cell reprogramming into induced pluripotent stem cells (iPSCs) is a valuable tool for studying pluripotency and holds promise for patient-specific therapies. Discerning whether genomic imprinting changes during reprogramming represent epigenetic abnormalities or essential adaptations linked to pluripotency is crucial. Here, we perform RNA-seq and MeDIP-seq analysis on mouse iPSCs derived from neural stem cells (NSCs). Our findings reveal that ICRs undergo DNA hypomethylation, confirming widespread LOI in pluripotent cells. However, the IG-DMR within the Dlk1-Dio3 imprinted cluster resists hypomethylation, a hallmark of successful pluripotency acquisition. We also identify a non-canonical role of TET3 in IG-DMR methylation protection through transcriptional regulation of Oct4 and Trim28. These findings highlight genomic imprinting as a key mechanism of gene dosage control in pluripotency acquisition and maintenance.

cell biology↗

bioRxiv2021

Variant Library Annotation Tool (VaLiAnT): an oligonucleotide library design and annotation tool for Saturation Genome Editing and other Deep Mutational Scanning experiments

MotivationRecent advances in CRISPR/Cas9 technology allow for the functional analysis of genetic variants at single nucleotide resolution whilst maintaining genomic context (Findlay et al., 2018). This approach, known as saturation genome editing (SGE), is a distinct type of deep mutational scanning (DMS) that systematically alters each position in a target region to explore its function. SGE experiments require the design and synthesis of oligonucleotide variant libraries which are introduced into the genome by homology-directed repair (HDR). This technology is broadly applicable to diverse research fields such as disease variant identification, drug development, structure-function studies, synthetic biology, evolutionary genetics and the study of host-pathogen interactions. Here we present the Variant Library Annotation Tool (VaLiAnT) which can be used to generate saturation mutagenesis oligonucleotide libraries from user-defined genomic coordinates and standardised input files. This software package is intentionally versatile to accommodate diverse operability, with species, genomic reference sequences and transcriptomic annotations specified by the user. Genomic ranges, directionality and frame information are considered to allow perturbations at both the nucleotide and amino acid level. ResultsCoordinates for a genomic range, that may include exonic and/or intronic sequence, are provided by the user in order to retrieve a corresponding oligonucleotide reference sequence. A user-specified range within this sequence is then subject to systematic, nucleotide and/or amino acid saturating mutator functions, with each discrete mutation returned to the user as a separate sequence, building up the final oligo library. If desired, variant accessions from genetic information repositories, such as ClinVar and gnomAD, that fall within the user-specified ranges, will also be incorporated into the library. For SGE library generation, base reference sequences can be modified to include PAM (Protospacer Adjacent Motif) and protospacer protection edits that prevent Cas9 from cutting incorporated oligonucleotide tracts. Mutator functions modify this protected reference sequence to generate variant sequences. Constant regions are designated for non-editing to allow specific adapter annealing for downstream cloning and amplification from the library pool. A metadata file is generated, delineating annotation information for each variant sequence to aid computational analysis. In addition, a library file is generated, which contains unique sequences (any exact duplicate sequences are removed) ready for submission to commercial synthesis platforms. A VCF file listing all variants is also generated for analysis and quality control processes. The VaLiAnT software package provides a novel means to systemically retrieve, mutate and annotate genomic sequences for oligonucleotide library generation. Specific features for SGE library generation can be employed, with other diverse applications possible. Availability and ImplementationVaLiAnT is a command line tool written in Python. Source code, testing data, example library input and output files, and executables are available at https://github.com/cancerit/VaLiAnT. A user manual details step by step instructions for software use, available at https://github.com/cancerit/VaLiAnT/wiki. The software is freely available for non-commercial use (see Licence for more details, https://github.com/cancerit/VaLiAnT/blob/develop/LICENSE).

genomics↗

bioRxiv2018

Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

There are thousands of rare human disorders caused by a single deleterious, protein-coding genetic variant 1. However, patients with the same genetic defect can have different clinical presentation 2-4, and some individuals carrying known disease-causing variants can appear unaffected 5. What explains these differences? Here, we show in a cohort of 6,987 children with heterogeneous severe neurodevelopmental disorders expected to be almost entirely monogenic that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome wide common variant burden by showing that it is over-transmitted from parents to children in an independent sample of 728 trios from the same cohort. Our common variant signal is significantly positively correlated with genetic predisposition to fewer years of schooling, decreased intelligence, and risk of schizophrenia. We found that common variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, suggesting that common variant risk is not confined to patients without a monogenic diagnosis. In addition, previously published common variant scores for autism, height, birth weight, and intracranial volume were all correlated with those traits within our cohort, suggesting that phenotypic expression in individuals with monogenic disorders is affected by the same variants as the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in disorders typically considered to be monogenic.

genetics↗