Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Mutations in the gene of human type IIb sodium-phosphate cotransporter SLC34A2

Type IIb sodium-phosphate cotransporter (NaPi2b) provides phosphate intake in the cells of some epithelial tissues, osteoblasts and odontoblasts. Abnormal expression of NaPi2b has been detected in some types of epithelial tumors. An alteration in NaPi2b activity, caused by mutations in transporter gene SLC34A2, has been recently revealed in patients with pulmonary alveolar microlithiasis, an autosomal recessively inherited disease, characterized by deposition of calcium-phosphate precipitates in the lungs. In the present study we have combined the information about all mutations found to date in the coding sequence of SLC34A2 and its transcript, compiled their map, and analysed their relevance to the function of NaPi2b

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.MethodsSeveral machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.ResultsOf 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.ConclusionsThis model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics

Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry.MethodsSeveral machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12-18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index.ResultsOf 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers.ConclusionsThis model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics

ANXUR receptor-like kinases coordinate cell wall integrity with growth at the pollen tube tip via NADPH oxidases

It has become increasingly apparent that the extracellular matrix (ECM), which in plants corresponds to the cell wall, can influence intracellular activities in ways that go far beyond their supposedly passive mechanical support. In plants, growing cells use mechanisms sensing cell wall integrity to coordinate cell wall performance with the internal growth machinery to avoid growth cessation or loss of integrity. How this coordination precisely works is unknown. Previously, we reported that in the tip-growing pollen tube the ANXUR receptor-like kinases (RLKs) of the CrRLK1L subfamily are essential to sustain growth without loss of cell wall integrity in Arabidopsis. Here, we show that over-expression of the ANXUR RLKs inhibits growth by over-activating exocytosis and the over-accumulation of secreted cell wall material. Moreover, the characterization of mutations in two partially redundant pollen-expressed NADPH oxidases coupled with genetic interaction studies demonstrate that the ANXUR RLKs function upstream of these NADPH oxidases. Using the H₂O₂-sensitive HyPer and the Ca²⁺-sensitive YC3.60 sensors in NADPH oxidase-deficient mutants, we reveal that NADPH oxidases generate tip-localized, pulsating H₂O₂ production that functions, possibly through Ca²⁺ channel activation, to maintain a steady tip-focused Ca²⁺ gradient during growth. Our findings support a model where ECM-sensing receptors regulate reactive oxygen species production, Ca²⁺ homeostasis, and exocytosis to coordinate ECM-performance with the internal growth machinery

Discovery of stimulation-responsive immune enhancers with CRISPR activation

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa) to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (TH17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs

LibraryCarpentry/lc-shell: Library Carpentry: Introduction to the Shell for librarians, June 2019

Library Carpentry lesson to learn how to use the Shell