Living cells proliferate by completing and coordinating two cycles, a division cycle controlling cell size, and a DNA replication cycle controlling the number of chromosomal copies. It remains unclear how bacteria such as E. coli tightly coordinate those two cycles across a wide range of growth conditions. Here, we used time-lapse microscopy in combination with microfluidics to measure growth, division and replication in single E. coli cells in slow and fast growth conditions. To compare different phenomenological cell cycle models, we introduce a statistical framework assessing their ability to capture the correlation structure observed in the data. In combination with stochastic simulations, our data indicate that the cell cycle runs from one initiation event to the next rather than from birth to division and is controlled by two adder mechanisms: the added volume since the last initiation event determines the timing of both the next division and replication initiation events.
Images of growth channels and MoMA segmentations have been deposited on Zenodo.
Analysis of division and replication cycles in E. coli using time-lapse microscopy, microfluidics and the MoMA softwareZenodo, doi:10.5281/zenodo.3149097.
- Guillaume Witz
- Erik van Nimwegen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Michael T Laub, Massachusetts Institute of Technology, United States
© 2019, Witz et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM.
Imprinting is a critical part of normal embryonic development in mammals, controlled by defined parent-of-origin (PofO) differentially methylated regions (DMRs) known as imprinting control regions. Direct nanopore sequencing of DNA provides a means to detect allelic methylation and to overcome the drawbacks of methylation array and short-read technologies. Here, we used publicly available nanopore sequencing data for 12 standard B-lymphocyte cell lines to acquire the genome-wide mapping of imprinted intervals in humans. Using the sequencing data, we were able to phase 95% of the human methylome and detect 94% of the previously well-characterized, imprinted DMRs. In addition, we found 42 novel imprinted DMRs (16 germline and 26 somatic), which were confirmed using whole-genome bisulfite sequencing (WGBS) data. Analysis of WGBS data in mouse (Mus musculus), rhesus monkey (Macaca mulatta), and chimpanzee (Pan troglodytes) suggested that 17 of these imprinted DMRs are conserved. Some of the novel imprinted intervals are within or close to imprinted genes without a known DMR. We also detected subtle parental methylation bias, spanning several kilobases at seven known imprinted clusters. At these blocks, hypermethylation occurs at the gene body of expressed allele(s) with mutually exclusive H3K36me3 and H3K27me3 allelic histone marks. These results expand upon our current knowledge of imprinting and the potential of nanopore sequencing to identify imprinting regions using only parent-offspring trios, as opposed to the large multi-generational pedigrees that have previously been required.