Structure and repair of replication-coupled DNA breaks.

Using CRISPR-Cas9 nicking enzymes, we examined the interaction between the replication machinery and single-strand breaks, one of the most common forms of endogenous DNA damage. We show that replication fork collapse at leading-strand nicks generates resected single-ended double-strand breaks (seDSBs) that are repaired by homologous recombination (HR). If these seDSBs are not promptly repaired, arrival of adjacent forks creates double-ended DSBs (deDSBs), which could drive genomic scarring in HR-deficient cancers. deDSBs can also be generated directly when the replication fork bypasses lagging-strand nicks. Unlike deDSBs produced independently of replication, end resection at nick-induced seDSBs and deDSBs is BRCA1-independent. Nevertheless, BRCA1 antagonizes 53BP1 suppression of RAD51 filament formation. These results highlight distinctive mechanisms that maintain replication fork stability.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

Heterozygosity alters Msh5 binding to meiotic chromosomes in the baker's yeast.

Meiotic crossovers are initiated from programmed DNA double-strand breaks. The Msh4-Msh5 heterodimer is an evolutionarily conserved mismatch repair-related protein complex that promotes meiotic crossovers by stabilizing strand invasion intermediates and joint molecule structures such as Holliday junctions. In vivo studies using homozygous strains of the baker's yeast Saccharomyces cerevisiae (SK1) show that the Msh4-Msh5 complex associates with double-strand break hotspots, chromosome axes, and centromeres. Many organisms have heterozygous genomes that can affect the stability of strand invasion intermediates through heteroduplex rejection of mismatch-containing sequences. To examine Msh4-Msh5 function in a heterozygous context, we performed chromatin immunoprecipitation and sequencing (ChIP-seq) analysis in a rapidly sporulating hybrid S. cerevisiae strain (S288c-sp/YJM789, containing sporulation-enhancing QTLs from SK1), using SNP information to distinguish reads from homologous chromosomes. Overall, Msh5 localization in this hybrid strain was similar to that determined in the homozygous strain (SK1). However, relative Msh5 levels were reduced in regions of high heterozygosity, suggesting that high mismatch densities reduce levels of recombination intermediates to which Msh4-Msh5 binds. Msh5 peaks were also wider in the hybrid background compared to the homozygous strain (SK1). We determined regions containing heteroduplex DNA by detecting chimeric sequence reads with SNPs from both parents. Msh5-bound double-strand break hotspots overlap with regions that have chimeric DNA, consistent with Msh5 binding to heteroduplex-containing recombination intermediates.