Biology subjects

White, K.

Publications and source records attributed to White, K..

4 recordsLinked to original sources

bioRxiv2018

Real-time analysis of the diphtheria outbreak in forcibly displaced Myanmar nationals in Bangladesh

BackgroundBetween August and December 2017, more than 625,000 Rohingya from Myanmar fled into Bangladesh, settling in informal makeshift camps in Coxs Bazar district, joining 212,000 Rohingya already present. In early November, a diphtheria outbreak was reported in the camps, with 440 cases being reported during the first month. A rise in cases during early December led to a collaboration between teams from Medecins sans Frontieres - who were running a provisional diphtheria treatment centre - and the London School of Hygiene & Tropical Medicine with the goal to use transmission dynamic models to forecast the potential scale of the outbreak and the resulting resource needs.\n\nMethodsWe first adjusted for delays between symptoms onset and case presentation using the observed distribution of reporting delays from previously reported cases. We then fit a compartmental transmission model to the adjusted incidence stratified by age-group and location. Model forecasts with a lead-time of two weeks were issued on 12th, 20th, 26th and 30th December and communicated to decision-makers.\n\nResultsThe first forecast estimated that the outbreak would peak on 16th December in Balukhali camp with 222 (95% prediction interval 126-409) cases and would continue to grow in Kutupalong camp, requiring a bed capacity of 200 (95% PI 142-301). On 16th December, a total of 70 cases were reported, lower than forecasted. Subsequent forecasts were more accurate: on 20th December we predicted a total of 701 cases (95% PI 477-901) and 105 (95% PI 72-135) hospitalizations until the end of the year, with 616 cases actually reported during this period.\n\nConclusionsReal-time modelling enabled feedback of key information about the potential scale of the epidemic, resource needs, and mechanisms of transmission to decision-makers at a time when this information was largely unknown. By December 20th, the model generated reliable forecasts and helped support decision-making on operational aspects of the outbreak response, such as hospital bed and staff needs, and with advocacy for control measures. Although modelling is only one component of the evidence base for decision-making in outbreak situations, suitable analysis and forecasting techniques can be used to gain insights into an ongoing outbreak.

epidemiology↗

bioRxiv2018

A Cut/cohesin axis alters the chromatin landscape to facilitate neuroblast death

Precise control of cell death in the nervous system is essential for development. Spatial and temporal factors activate the death of Drosophila neural stem cells (neuroblasts) by controlling the transcription of multiple cell death genes through a shared enhancer, enh1. The activity of enh1 is controlled by abdominalA and Notch, but additional inputs are needed for proper specificity. Here we show that the Cut DNA binding protein is required for neuroblast death, acting downstream of enh1. In the nervous system, Cut promotes an open chromatin conformation in the cell death gene locus, allowing cell death gene expression in response to abdominalA. We demonstrate a temporal increase in global H3K27me3 levels in neuroblasts, which is enhanced by cut knockdown. Furthermore, cut regulates the expression of the cohesin subunit Stromalin in the nervous system. The cohesin components Stromalin and NippedB are required for neuroblast death, and knockdown of Stromalin increases repressive histone modifications in neuroblasts. Thus Cut and cohesin regulate apoptosis in the developing nervous system by altering the chromatin landscape.\n\nSummary statementCut regulates the programmed death of neural stem cells by altering cohesin levels and promoting a more open chromatin conformation to allow cell death gene expression.

developmental biology↗

bioRxiv2017

Human iPSC-derived RPE and retinal organoids reveal impaired alternative splicing of genes involved in pre-mRNA splicing in PRPF31 autosomal dominant retinitis pigmentosa

Mutations in pre-mRNA processing factors (PRPFs) cause 40% of autosomal dominant retinitis pigmentosa (RP), but it is unclear why mutations in ubiquitously expressed PRPFs cause retinal disease. To understand the molecular basis of this phenotype, we have generated RP type 11 (PRPF31-mutated) patient-specific retinal organoids and retinal pigment epithelium (RPE) from induced pluripotent stem cells (iPSC). Impaired alternative splicing of genes encoding pre-mRNA splicing proteins occurred in patient-specific retinal cells and Prpf31+/- mouse retinae, but not fibroblasts and iPSCs, providing mechanistic insights into retinal-specific phenotypes of PRPFs. RPE was the most affected, characterised by loss of apical-basal polarity, reduced trans-epithelial resistance, phagocytic capacity, microvilli, and cilia length and incidence. Disrupted cilia morphology was observed in patient-derived-photoreceptors that displayed progressive features associated with degeneration and cell stress. In situ gene-editing of a pathogenic mutation rescued key structural and functional phenotypes in RPE and photoreceptors, providing proof-of-concept for future therapeutic strategies.\n\neTOCPRPF31 is a ubiquitously expressed pre-mRNA processing factor that when mutated causes autosomal dominant RP. Using a patient-specific iPSC approach, Buskin and Zhu et al. show that retinal-specific defects result from altered splicing of genes involved in the splicing process itself, leading to impaired splicing, loss of RPE polarity and diminished phagocytic ability as well as reduced cilia incidence and length in both photoreceptors and RPE.\n\nHighlightsO_LISuccessful generation of iPSC-derived RPE and photoreceptors from four RP type 11 patients\nC_LIO_LIRPE cells express the mutant PRPF31 protein and show the lowest expression of wildtype protein\nC_LIO_LIPRPF31 mutations result in altered splicing of genes involved in pre-mRNA splicing in RPE and retinal organoids\nC_LIO_LIPrpf31 haploinsufficiency results in altered splicing of genes involved in pre-mRNA splicing in mouse retina\nC_LIO_LIRPE cells display loss of polarity, reduced barrier function and phagocytosis\nC_LIO_LIPhotoreceptors display shorter and fewer cilia and degenerative features\nC_LIO_LIRPE cells display most abnormalities suggesting they might be the primary site of pathogenesis\nC_LIO_LIIn situ gene editing corrects the mutation and rescues key phenotypes\nC_LI

genetics↗

bioRxiv2017

A Guide To Analysis And Reconstruction Of Serial Block Face Scanning Electron Microscopy Data

Serial block face scanning electron microscopy (SBF-SEM) is a relatively new technique that allows the acquisition of serially sectioned, imaged and digitally aligned ultrastructural data. There is a wealth of information that can be obtained from the resulting image stacks but this presents a new challenge for researchers - how to computationally analyse and make best use of the large data sets produced. One approach is to reconstruct structures and features of interest in 3D. However the software programs can appear overwhelming, time consuming and not intuitive for those new to image analysis. There are a limited number of published articles that provide sufficient detail on how to do this type of reconstruction. Therefore the aim of this paper is to provide a detailed step-by-step protocol, videos and explanation on the types of analysis and programs that can be used. To showcase the programs, skeletal muscle from fetal and adult guinea pigs were used. The tissue was processed using the heavy metal protocol developed specifically for SBFSEM. Trimmed resin blocks were placed into a Zeiss Sigma SEM incorporating the Gatan 3View and the resulting image stacks were analysed in 3 different programs, Fiji, Amira and MIB, using a range of tools available for segmentation. The results from the image analysis comparison show that the analysis tools are often more suited to a type of structure. For example larger structures, such as nuclei and cells, can be segmented using interpolation, which speeds up analysis; single contrast structures, such as the nucleolus, can be segmented using the contrast-based thresholding tools. Knowing the nature of the tissue and its specific structures (complexity, contrast, if there are distinct membranes, size) will help to determine the best method for reconstruction and thus maximising output from valuable tissue.

cell biology↗