White, M. L. | Search bioRxiv

bioRxiv2022

MraZ is a transcriptional inhibitor of cell division in Bacillus subtilis

The bacterial division and cell wall (dcw) cluster is a highly conserved region of the genome which encodes several essential cell division factors including the central divisome protein FtsZ. Understanding the regulation of this region is key to our overall understanding of the division process. mraZ is found at the 5 end of the dcw cluster and previous studies have described MraZ as a sequence-specific DNA binding protein. In this article, we investigate MraZ to elucidate its role in Bacillus subtilis. Through our investigation, we demonstrate that increased levels of MraZ result in lethal filamentation due to repression of its own operon (mraZ-mraW-ftsL-pbpB). We observe rescue of filamentation upon decoupling ftsL expression, but not other genes in the operon, from MraZ control. Furthermore, through timelapse microscopy we were able to identify that overexpression of mraZ, results in de-condensation of the FtsZ ring (Z-ring). This is likely due to depletion of FtsL, and thus, we believe the precise role of FtsL is likely in Z-ring maturation and promotion of subsequent treadmilling. Our data suggests that regulation of the mra operon may be an alternative way for cells to quickly arrest cytokinesis potentially during entry into stationary phase and in the event of DNA replication arrest.

microbiology↗

bioRxiv2021

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq Data

Single-cell RNA sequencing (scRNA-seq) can reveal accurate and sensitive RNA abundance in a single sample, but robust integration of multiple samples remains challenging. Large-scale scRNA-seq data generated by different workflows or laboratories can contain batch-specific systemic variation. Such variation challenges data integration by confounding sample-specific biology with undesirable batch-specific systemic effects. Therefore, there is a need for guidance in selecting computational and experimental approaches to minimize batch-specific impacts on data interpretation and a need to empirically evaluate the sources of systemic variation in a given dataset. To uncover the contributions of experimental variables to systemic variation, we intentionally perturb four potential sources of batch-effect in five human peripheral blood samples. We investigate sequencing replicate, sequencing depth, sample replicate, and the effects of pooling libraries for concurrent sequencing. To quantify the downstream effects of these variables on data interpretation, we introduced a new scoring metric, the Cell Misclassification Statistic (CMS), which identifies losses to cell type fidelity that occur when merging datasets of different batches. CMS reveals an undesirable overcorrection by popular batch-effect correction and data integration methods. We show that optimizing gene expression matrix normalization and merging can reduce the need for batch-effect correction and minimize the risk of overcorrecting true biological differences between samples.

systems biology↗