Biology subjects

Edwards, M.

Publications and source records attributed to Edwards, M..

4 recordsLinked to original sources

bioRxiv2018

IDH1R132H acts as a tumor suppressor in glioma via epigenetic upregulation of the DNA damage response

One sentence summaryMutant IDH1 acts as a tumor suppressor when co-expressed together with TP53 and ATRX inactivating mutations in glioma, inducing genomic stability, DNA repair and resistance to genotoxic therapies.\n\nAbstractGlioma patients whose tumors carry a mutation in the Isocitrate Dehydrogenase 1 (IDH1R132H) gene are younger at the time of diagnosis and survive longer. The molecular glioma subtype which we modelled, harbors IDH1R132H, tumor protein 53 (TP53) and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss. The impact of IDH1R132H on genomic stability, DNA damage response (DDR) and DNA repair in this molecular glioma subtype is unknown. We discovered that IDH1R132H expression in the genetic context of ATRX and TP53 inactivation: (i) increases median survival (MS), (ii) enhances DDR activity via epigenetic upregulation of Ataxia-telangiectasia mutated (ATM) signaling, and (iii) elicits tumor radioresistance. Pharmacological inhibition of ATM or checkpoint kinase 1 and 2 (CHK1/2), two essential kinases in the DDR pathways, restored tumors radiosensitivity. Translation of these findings for mlDH1 glioma patients could significantly improve the therapeutic efficacy of radiotherapy, and thus have a major impact on patient survival.

cancer biology↗

bioRxiv2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

neuroscience↗

bioRxiv2017

Comparison of reprogramming factor targets reveals both species-specific and conserved mechanisms in early iPS cells

Both human and mouse fibroblasts can be reprogrammed to pluripotency with Oct4, Sox2, Klf4, and c-Myc (OSKM) transcription factors. While both systems generate pluripotency, human reprogramming takes considerably longer than mouse. To assess additional similarities and differences, we sought to compare the binding of the reprogramming factors between the two systems. In human fibroblasts, the OSK factors initially target many more closed chromatin sites compared to mouse. Despite this difference, the intra- and intergenic distribution of target sites, target genes, primary binding motifs, and combinatorial binding patterns between the reprogramming factors are largely shared. However, while many OSKM binding events in early mouse cell reprogramming occur in syntenic regions, only a limited number is conserved in human. In summary, these findings suggest similar general effects of OSKM binding across these two species, even though the detailed regulatory networks have diverged significantly.

genomics↗

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗