Search for: All records

Abstract CANVAS is a recently characterized repeat expansion disease, most commonly caused by homozygous expansions of an intronic (A2G3)n repeat in the RFC1 gene. There are a multitude of repeat motifs found in the human population at this locus, some of which are pathogenic and others benign. In this study, we conducted structure-functional analyses of the pathogenic (A2G3)n and nonpathogenic (A4G)n repeats. We found that the pathogenic, but not the nonpathogenic, repeat presents a potent, orientation-dependent impediment to DNA polymerization in vitro. The pattern of the polymerization blockage is consistent with triplex or quadruplex formation in the presence of magnesium or potassium ions, respectively. Chemical probing of both repeats in vitro reveals triplex H-DNA formation by only the pathogenic repeat. Consistently, bioinformatic analysis of S1-END-seq data from human cell lines shows preferential H-DNA formation genome-wide by (A2G3)n motifs over (A4G)n motifs. Finally, the pathogenic, but not the nonpathogenic, repeat stalls replication fork progression in yeast and human cells. We hypothesize that the CANVAS-causing (A2G3)n repeat represents a challenge to genome stability by folding into alternative DNA structures that stall DNA replication. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.