Biology subjects

Gosden, M.

Publications and source records attributed to Gosden, M..

3 recordsLinked to original sources

bioRxiv2019

Dissection of the 4D chromatin structure of the α-globin locus through in vivo erythroid differentiation with extreme spatial and temporal resolution

Mammalian gene expression patterns are controlled by regulatory elements, which interact within Topologically Associating Domains (TADs). The relationship between activation of regulatory elements, formation of structural chromatin interactions and gene expression during development is unclear. We developed Tiled-C, a low-input Chromosome Conformation Capture (3C) approach, to study chromatin architecture at high spatial and temporal resolution through in vivo mouse erythroid differentiation. Integrated analysis of matched chromatin accessibility and single-cell expression data shows that regulatory elements gradually become accessible within pre-existing TADs during early differentiation. This is followed by structural re-organization within the TAD and formation of specific contacts between enhancers and promoters. In contrast to previous reports, our high-resolution data show that these enhancer-promoter interactions are not established prior to gene expression, but formed gradually during differentiation, concomitant with progressive upregulation of gene activity. Together, these results provide new insight into the close, interdependent relationship between chromatin architecture and gene regulation during development.

genomics↗

bioRxiv2019

Modelling erythropoiesis in congenital dyserythropoietic anaemia type I (CDA-I)

We employ and extensively characterise an ex vivo culture system to study terminal erythroid maturation of CD34+ progenitors from the peripheral blood of normal individuals and patients with Congenital Dyserythropoietic Anaemia type 1 (CDA-I). Using morphological analysis, FACS analysis and the proteomic approach CyTOF, we analysed patient-derived erythroblasts stage-matched with those from healthy donors during the expansion phase and into early differentiation. In patient cells, aspects of disordered erythropoiesis manifest midway through differentiation, including increased proliferation and changes in the DNA accessibility profile. We also show that cultured erythroblasts from CDA-I patients recapitulate the pathognomic feature of this erythroid disorder with up to 40% of the cells having abnormal spongy chromatin morphology by electron microscopy, as well as upregulation of GDF15, a marker of ineffective erythropoiesis. In the tertiary phase of culture, patient cells show significantly less enucleation and there is persistence of earlier erythroid precursors. Furthermore, the enucleation defect appears to be more severe in patients with mutations in C15orf41, as compared to the other known causative gene CDAN1, indicating a genotype/phenotype correlation in CDA-I. Such erythroblasts are a valuable resource for investigating the pathogenesis of this disease and provide the opportunity for streamlining diagnosis for CDA-I patients and ultimately other forms of unexplained anaemia.

genetics↗

bioRxiv2019

DeepC: Predicting chromatin interactions using megabase scaled deep neural networks and transfer learning.

Understanding 3D genome structure requires high throughput, genome-wide approaches. However, assays for all vs. all chromatin interaction mapping are expensive and time consuming, which severely restricts their usage for large-scale mutagenesis screens or for mapping the impact of sequence variants. Computational models sophisticated enough to grasp the determinants of chromatin folding provide a unique window into the functional determinants of 3D genome structure as well as the effects of genome variation.\n\nA chromatin interaction predictor should work at the base pair level but also incorporate large-scale genomic context to simultaneously capture the large scale and intricate structures of chromatin architecture. Similarly, to be a flexible and generalisable approach it should also be applicable to data it has not been explicitly trained on. To develop a model with these properties, we designed a deep neuronal network (deepC) that utilizes transfer learning to accurately predict chromatin interactions from DNA sequence at megabase scale. The model generalizes well to unseen chromosomes and works across cell types, Hi-C data resolutions and a range of sequencing depths. DeepC integrates DNA sequence context on an unprecedented scale, bridging the different levels of resolution from base pairs to TADs. We demonstrate how this model allows us to investigate sequence determinants of chromatin folding at genome-wide scale and to predict the importance of regulatory elements and the impact of sequence variations.

genomics↗