Scalable in vitro production of defined mouse erythroblasts

Mouse embryonic stem cells (mESCs) can be manipulated in vitro to recapitulate the process of erythropoiesis, during which multipotent cells undergo lineage specification, differentiation and maturation to produce erythroid cells. Although useful for identifying specific progenitors and precursors, this system has not been fully exploited as a source of cells to analyse erythropoiesis. Here, we establish a protocol in which characterised erythroblasts can be isolated in a scalable manner from differentiated embryoid bodies (EBs). Using transcriptional and epigenetic analysis, we demonstrate that this system faithfully recapitulates normal primitive erythropoiesis and fully reproduces the effects of natural and engineered mutations seen in primary cells obtained from mouse models. We anticipate this system to be of great value in reducing the time and costs of generating and maintaining mouse lines in a number of research scenarios

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Chromosome errors, or aneuploidy, affect an exceptionally high number of human conceptions, causing pregnancy loss and congenital disorders. Here, we have followed chromosome segregation in human oocytes from females aged 9 to 43 years and report that aneuploidy follows a U-curve. Specific segregation error types show different age dependencies, providing a quantitative explanation for the U-curve. Whole-chromosome nondisjunction events are preferentially associated with increased aneuploidy in young girls, whereas centromeric and more extensive cohesion loss limit fertility as women age. Our findings suggest that chromosomal errors originating in oocytes determine the curve of natural fertility in humans. [Abstract copyright: Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Influence of socioeconomic deprivation on interventions and outcomes for patients admitted with COVID-19 to critical care units in Scotland: a national cohort study

Background: Coronavirus disease 2019 (COVID-19) can lead to significant respiratory failure with between 14% and 18% of hospitalised patients requiring critical care admission. This study describes the impact of socioeconomic deprivation on 30-day survival following critical care admission for COVID-19, and the impact of the COVID-19 pandemic on critical care capacity in Scotland. Methods: This cohort study used linked national hospital records including ICU, virology testing and national death records to identify and describe patients with COVID-19 admitted to critical care units in Scotland. Multivariable logistic regression was used to assess the impact of deprivation on 30-day mortality. Critical care capacity was described by reporting the percentage of baseline ICU bed utilisation required. Findings: There were 735 patients with COVID-19 admitted to critical care units across Scotland from 1/3/2020 to 20/6/2020. There was a higher proportion of patients from more deprived areas, with 183 admissions (24.9%) from the most deprived quintile and 100 (13.6%) from the least deprived quintile. Overall, 30-day mortality was 34.8%. After adjusting for age, sex and ethnicity, mortality was significantly higher in patients from the most deprived quintile (OR 1.97, 95%CI 1.13, 3.41, p=0.016). ICUs serving populations with higher levels of deprivation spent a greater amount of time over their baseline ICU bed capacity. Interpretation: Patients with COVID-19 living in areas with greatest socioeconomic deprivation had a higher frequency of critical care admission and a higher adjusted 30-day mortality. ICUs in health boards with higher levels of socioeconomic deprivation had both higher peak occupancy and longer duration of occupancy over normal maximum capacity. Funding: None

STAT3 regulated ARF expression suppresses prostate cancer metastasis.

Prostate cancer (PCa) is the most prevalent cancer in men. Hyperactive STAT3 is thought to be oncogenic in PCa. However, targeting of the IL-6/STAT3 axis in PCa patients has failed to provide therapeutic benefit. Here we show that genetic inactivation of Stat3 or IL-6 signalling in a Pten-deficient PCa mouse model accelerates cancer progression leading to metastasis. Mechanistically, we identify p19(ARF) as a direct Stat3 target. Loss of Stat3 signalling disrupts the ARF-Mdm2-p53 tumour suppressor axis bypassing senescence. Strikingly, we also identify STAT3 and CDKN2A mutations in primary human PCa. STAT3 and CDKN2A deletions co-occurred with high frequency in PCa metastases. In accordance, loss of STAT3 and p14(ARF) expression in patient tumours correlates with increased risk of disease recurrence and metastatic PCa. Thus, STAT3 and ARF may be prognostic markers to stratify high from low risk PCa patients. Our findings challenge the current discussion on therapeutic benefit or risk of IL-6/STAT3 inhibition.Lukas Kenner and Jan Pencik are supported by FWF, P26011 and the Genome Research-Austria project “Inflammobiota” grants. Helmut Dolznig is supported by the Herzfelder Family Foundation and the Niederösterr. Forschungs-und Bildungsges.m.b.H (nfb). Richard Moriggl is supported by grant SFB-F2807 and SFB-F4707 from the Austrian Science Fund (FWF), Ali Moazzami is supported by Infrastructure for biosciences-Strategic fund, SciLifeLab and Formas, Zoran Culig is supported by FWF, P24428, Athena Chalaris and Stefan Rose-John are supported by the Deutsche Forschungsgemeinschaft (Grant SFB 877, Project A1and the Cluster of Excellence --“Inflammation at Interfaces”). Work of the Aberger lab was supported by the Austrian Science Fund FWF (Projects P25629 and W1213), the European FP7 Marie-Curie Initial Training Network HEALING and the priority program Biosciences and Health of the Paris-Lodron University of Salzburg. Valeria Poli is supported by the Italian Association for Cancer Research (AIRC, No IG13009). Richard Kennedy and Steven Walker are supported by the McClay Foundation and the Movember Centre of Excellence (PC-UK and Movember). Gerda Egger is supported by FWF, P27616. Tim Malcolm and Suzanne Turner are supported by Leukaemia and Lymphoma Research.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms873

Immune activation by DNA damage predicts response to chemotherapy and survival in oesophageal adenocarcinoma.

OBJECTIVE: Current strategies to guide selection of neoadjuvant therapy in oesophageal adenocarcinoma (OAC) are inadequate. We assessed the ability of a DNA damage immune response (DDIR) assay to predict response following neoadjuvant chemotherapy in OAC. DESIGN: Transcriptional profiling of 273 formalin-fixed paraffin-embedded prechemotherapy endoscopic OAC biopsies was performed. All patients were treated with platinum-based neoadjuvant chemotherapy and resection between 2003 and 2014 at four centres in the Oesophageal Cancer Clinical and Molecular Stratification consortium. CD8 and programmed death ligand 1 (PD-L1) immunohistochemical staining was assessed in matched resection specimens from 126 cases. Kaplan-Meier and Cox proportional hazards regression analysis were applied according to DDIR status for recurrence-free survival (RFS) and overall survival (OS). RESULTS: A total of 66 OAC samples (24%) were DDIR positive with the remaining 207 samples (76%) being DDIR negative. DDIR assay positivity was associated with improved RFS (HR: 0.61; 95% CI 0.38 to 0.98; p=0.042) and OS (HR: 0.52; 95% CI 0.31 to 0.88; p=0.015) following multivariate analysis. DDIR-positive patients had a higher pathological response rate (p=0.033), lower nodal burden (p=0.026) and reduced circumferential margin involvement (p=0.007). No difference in OS was observed according to DDIR status in an independent surgery-alone dataset.DDIR-positive OAC tumours were also associated with the presence of CD8+ lymphocytes (intratumoural: p<0.001; stromal: p=0.026) as well as PD-L1 expression (intratumoural: p=0.047; stromal: p=0.025). CONCLUSION: The DDIR assay is strongly predictive of benefit from DNA-damaging neoadjuvant chemotherapy followed by surgical resection and is associated with a proinflammatory microenvironment in OAC.This work was supported by the Gastrointestinal Cancer Research Charitable Fund administered by the Belfast Health and Social Care Trust, the Cancer Research UK Experimental Cancer Medicine Centre Initiative, Invest Northern Ireland and Almac Diagnostics. Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) was funded by a programme grant from Cancer Research UK (RG66287). We would like to thank the Human Research Tissue Bank, which is supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre from Addenbrooke’s Hospital. Additional infrastructure support was provided from the CRUK funded Experimental Cancer Medicine Centre. RF has programmatic funding from the Medical Research Council and infrastructure support from the NIHR Biomedical Research Centre and the Cambridge Experimental Medicine Centre. Tissue samples used in this research were received from the Northern Ireland Biobank, which is funded by HSC Research and Development Division of the Public Health Agency in Northern Ireland and Cancer Research UK through the Belfast Cancer Research UK Centre and the Northern Ireland Experimental Cancer Medicine Centre; additional support was received from the Friends of the Cancer Centre. The Northern Ireland Molecular Pathology Laboratory has received funding from Cancer Research UK, the Friends of the Cancer Centre and the Sean Crummey Foundation. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 721906. The OCCAMS Study Group is a multicentre UK collaboration

Predicting Phospholipidosis Using Machine Learning

Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the importance of computational approaches to the problem has been well documented. Previous work on predictive methods for phospholipidosis showed that state of the art machine learning methods produced the best results. Here we extend this work by looking at a larger data set mined from the literature. We find that circular fingerprints lead to better models than either E-Dragon descriptors or a combination of the two. We also observe very similar performance in general between Random Forest and Support Vector Machine models.</p

Random forest models to predict aqueous solubility

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r(2) = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.</p