Using the INTACT method to study PICKLE in individual cell types

Cell differentiation is an essential part of development in multicellular organisms. Cells with identical genomic DNA are able to differentiate into a variety of tissues due to selective expression and repression of genes. This tissue-specific gene expression is enabled in part by proteins called chromatin remodelers, which can move, remove, or restructure histone proteins to restrict or allow physical access to genomic DNA. PICKLE (PKL) is a member of the CHD family of ATP-dependent chromatin remodelers that promotes cellular identity in the plant model organism Arabidopsis thaliana. PKL promotes cell identity by silencing embryonic genes during seed germination by promoting the repressive epigenetic modification trimethylation of lysine 27 on histone H3 (H3K27me3). However, the contributions of PKL to H3K27me3 and gene expression have only been studied on an organism-wide scale. Due to the wide variety of tissues that comprise a plant, the specific role of PKL in a given cell type cannot be determined by examining levels of gene expression and epigenetic modifications as averaged across the organism. Through use of the INTACT (isolating nuclei tagged in specific cell types) method, nuclei of two different cell types will be tagged and purified from both wild-type Arabidopsis and Arabidopsis lacking functional PKL. Isolating nuclei from one cell type at a time will allow us to study the function of PKL at a much higher resolution. This will provide both a better understanding of PKL function and a precedent for studies of how CHD chromatin remodelers regulate gene expression in other organisms

Retrospective Data Filter

In a target detection communication system, apparatus and method for determining the presence of probable targets based on contacts (which can indicate the presence of a target, noise, chatter, or objects not of interest) detected within a predefined position sector or sectors over a specified number of scans. The position of each detected contact, as a contact of interest, is compared with the positions of contacts detected at previous times or scans. Velocity profiles indicate which previous contacts support the likelihood that the contact of interest represents a target having a velocity within a defined band. The likelihood, which can be represented by a quality value, may be a function of number of contacts, timing of contacts, or both the number and timing of contacts in a given velocity profile. A preselected threshold value, which is related to false alarm rate, is compared to the most likely, or highest quality, velocity profile associated with a contact of interest. If the highest quality value exceeds the threshold value, an output is provided indicating that the contact of interest represents a probable target having a velocity within the band defined by the highest quality velocity profile

Shared heritability and functional enrichment across six solid cancers

Correction: Nature Communications 10 (2019): art. 4386 DOI: 10.1038/s41467-019-12095-8Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r(g) = 0.57, p = 4.6 x 10(-8)), breast and ovarian cancer (r(g) = 0.24, p = 7 x 10(-5)), breast and lung cancer (r(g) = 0.18, p = 1.5 x 10(-6)) and breast and colorectal cancer (r(g) = 0.15, p = 1.1 x 10(-4)). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.Peer reviewe

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 hospitalization2–4 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