Chromatin accessibility reveals insights into androgen receptor activation and transcriptional specificity

BACKGROUND: Epigenetic mechanisms such as chromatin accessibility impact transcription factor binding to DNA and transcriptional specificity. The androgen receptor (AR), a master regulator of the male phenotype and prostate cancer pathogenesis, acts primarily through ligand-activated transcription of target genes. Although several determinants of AR transcriptional specificity have been elucidated, our understanding of the interplay between chromatin accessibility and AR function remains incomplete. RESULTS: We used deep sequencing to assess chromatin structure via DNase I hypersensitivity and mRNA abundance, and paired these datasets with three independent AR ChIP-seq datasets. Our analysis revealed qualitative and quantitative differences in chromatin accessibility that corresponded to both AR binding and an enrichment of motifs for potential collaborating factors, one of which was identified as SP1. These quantitative differences were significantly associated with AR-regulated mRNA transcription across the genome. Base-pair resolution of the DNase I cleavage profile revealed three distinct footprinting patterns associated with the AR-DNA interaction, suggesting multiple modes of AR interaction with the genome. CONCLUSIONS: In contrast with other DNA-binding factors, AR binding to the genome does not only target regions that are accessible to DNase I cleavage prior to hormone induction. AR binding is invariably associated with an increase in chromatin accessibility and, consequently, changes in gene expression. Furthermore, we present the first in vivo evidence that a significant fraction of AR binds only to half of the full AR DNA motif. These findings indicate a dynamic quantitative relationship between chromatin structure and AR-DNA binding that impacts AR transcriptional specificity

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Discrete element method modelling of a centrifugal fertiliser spreader

Experimental testing and calibration of new machines and fertiliser can be costly and time consuming. Standard centrifugal fertiliser spreaders are designed to achieve a uniform distribution across the entire field. Fruit trees, however, are planted in rows and it would be advantageous to concentrate the fertiliser closer to the tree trunk and hence the root system. This could reduce the amount of fertiliser needed and will also reduce the negative environmental impact. A discrete element method (DEM) model of a centrifugal fertiliser spreader was developed. A sensitivity study was used to determine the DEM parameters by comparing the results to experimental results. The effects of the disc speed, feed position, feed rate and vane angle on the spread pattern were investigated experimentally and compared to the DEM results. A deflector plate was designed to separate the flow into two paths along the tree lines. The DEM results of the deflector were compared to the experimental results. It was shown that the DEM model can be used to make both qualitative and quantitative predictions of the spread pattern under different spreader settings. The model could also accurately predict the effect of the deflector on the spread pattern. This indicates that DEM can be a powerful tool in the development of new spreader concepts. © 2011 IAgrE.Articl

Naturally occurring phytopathogens enhance biological control of water hyacinth (Eichhornia crassipes) by Megamelus scutellaris (Hemiptera: Delphacidae), even in eutrophic water.

Insect biological control agents directly damage target weeds by removal of plant biomass, but herbivorous insects have both direct and indirect impacts on their host plants and can also facilitate pathogen infection. Megamelus scutellaris Berg (Hemiptera: Delphacidae) was recently released into South Africa to help control invasive water hyacinth (Eichhornia crassipes, Pontederiaceae). We compared the impact of fungicide surface-sterilised and unsterilised M. scutellaris individuals and water hyacinth leaves on growth of the weed at two nutrient levels. The survival and reproduction of adult M. scutellaris was not reduced by sterilisation. Under high nutrient conditions, unsterilised M. scutellaris with unsterilised leaves reduced water hyacinth daughter plant production by 32%, length of the second petiole by 15%, chlorophyll content by 27% and wet weight biomass by 48%, while also increasing leaf chlorosis 17-fold, in relation to control plants under the same nutrient regime. Surface sterilisation of the insect and/or plant surfaces led to a general reduction in these impacts on water hyacinth growth and health. This contrast was less evident under low nutrient conditions. Megamelus scutellaris facilitated infection by fungal and other pathogens, thus its biology is compatible with pathogens that could be developed into mycoherbicides. This integrated approach may be ideal for management of infestations of water hyacinth in eutrophic water systems where control has been problematic, both in South Africa and elsewhere

La filologia classica e umanistica di Remigio Sabbadini

Understanding the regulatory landscape of the human genome is a central question in complex trait genetics. Most single-nucleotide polymorphisms (SNPs) associated with cancer risk lie in non-protein-coding regions, implicating regulatory DNA elements as functional targets of susceptibility variants. Here, we describe genome-wide annotation of regions of open chromatin and histone modification in fallopian tube and ovarian surface epithelial cells (FTSECs, OSECs), the debated cellular origins of high-grade serous ovarian cancers (HGSOCs) and in endometriosis epithelial cells (EECs), the likely precursor of clear cell ovarian carcinomas (CCOCs). The regulatory architecture of these cell types was compared with normal human mammary epithelial cells and LNCaP prostate cancer cells. We observed similar positional patterns of global enhancer signatures across the three different ovarian cancer precursor cell types, and evidence of tissue-specific regulatory signatures compared to non-gynecological cell types. We found significant enrichment for risk-associated SNPs intersecting regulatory biofeatures at 17 known HGSOC susceptibility loci in FTSECs (P = 3.8 × 10(-30)), OSECs (P = 2.4 × 10(-23)) and HMECs (P = 6.7 × 10(-15)) but not for EECs (P = 0.45) or LNCaP cells (P = 0.88). Hierarchical clustering of risk SNPs conditioned on the six different cell types indicates FTSECs and OSECs are highly related (96% of samples using multi-scale bootstrapping) suggesting both cell types may be precursors of HGSOC. These data represent the first description of regulatory catalogues of normal precursor cells for different ovarian cancer subtypes, and provide unique insights into the tissue specific regulatory variation with respect to the likely functional targets of germline genetic susceptibility variants for ovarian cancer

Cell-type-specific enrichment of risk-associated regulatory elements at ovarian cancer susceptibility loci

Contains fulltext : 154725.pdf (publisher's version ) (Closed access)Understanding the regulatory landscape of the human genome is a central question in complex trait genetics. Most single-nucleotide polymorphisms (SNPs) associated with cancer risk lie in non-protein-coding regions, implicating regulatory DNA elements as functional targets of susceptibility variants. Here, we describe genome-wide annotation of regions of open chromatin and histone modification in fallopian tube and ovarian surface epithelial cells (FTSECs, OSECs), the debated cellular origins of high-grade serous ovarian cancers (HGSOCs) and in endometriosis epithelial cells (EECs), the likely precursor of clear cell ovarian carcinomas (CCOCs). The regulatory architecture of these cell types was compared with normal human mammary epithelial cells and LNCaP prostate cancer cells. We observed similar positional patterns of global enhancer signatures across the three different ovarian cancer precursor cell types, and evidence of tissue-specific regulatory signatures compared to non-gynecological cell types. We found significant enrichment for risk-associated SNPs intersecting regulatory biofeatures at 17 known HGSOC susceptibility loci in FTSECs (P = 3.8 x 10(-30)), OSECs (P = 2.4 x 10(-23)) and HMECs (P = 6.7 x 10(-15)) but not for EECs (P = 0.45) or LNCaP cells (P = 0.88). Hierarchical clustering of risk SNPs conditioned on the six different cell types indicates FTSECs and OSECs are highly related (96% of samples using multi-scale bootstrapping) suggesting both cell types may be precursors of HGSOC. These data represent the first description of regulatory catalogues of normal precursor cells for different ovarian cancer subtypes, and provide unique insights into the tissue specific regulatory variation with respect to the likely functional targets of germline genetic susceptibility variants for ovarian cancer