Biology subjects

van Nimwegen, E. J.

Publications and source records attributed to van Nimwegen, E. J..

6 recordsLinked to original sources

bioRxiv2022

No evidence that synonymous mutations in yeast genes are mostly deleterious

In a recent paper1, Shen et al. reported that most mutations in the coding regions of 21 yeast genes were strongly deleterious, and that the distributions of fitness effects were similar for synonymous and nonsynonymous mutations. Taken at face value, these results would conflict with well-established findings from a broad range of fields and approaches. Here, we argue that these results arose from a lack of appropriate controls for the impacts of background genetic effects in edited strains. A re-examination of the data in Shen et al. strongly suggests that it is entirely consistent with the expectation that most nonsynonymous and nearly all synonymous mutations have no detectable effects on fitness. We present analyses which show that the data is inconsistent with the proposed explanation that pervasive fitness effects of synonymous mutations arise from their effects on mRNA levels, that the sequence-based fitness assay overestimates fitness effects compared to the growth-based fitness assay, and that the observed wide fitness distributions for nonsense mutations are consistent with off-target effects or other uncontrolled sources of biological variation contributing to measured fitness. We conclude by discussing the essential controls and other experimental design considerations that are required to produce interpretable results regarding the fitness effects of mutations in large-scale screens.

genetics↗

bioRxiv2022

Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs

We present RCRUNCH, an end-to-end solution to CLIP data analysis for identification of binding sites and sequence specificity of RNA-binding proteins. RCRUNCH can analyze not only reads that map uniquely to the genome, but also those that map to multiple genome locations or across splice boundaries, and can consider various types of background in the estimation of read enrichment. By applying RCRUNCH to the eCLIP data from the ENCODE project, we have constructed a comprehensive and homogeneous resource of in vivo-bound RBP sequence motifs. RCRUNCH automates the reproducible analysis of CLIP data, enabling studies of post-transcriptional control of gene expression.

bioinformatics↗

bioRxiv2019

Bayesian inference of the gene expression states of single cells from scRNA-seq data

In spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters. Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

bioinformatics↗

bioRxiv2019

Noise propagation shapes condition-dependent gene expression noise in Escherichia coli

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoters sequence, little is known about the variation in transcriptional noise across growth conditions. Using flow cytometry we here quantify transcriptional noise in E. coli genome-wide across 8 growth conditions, and find that noise and gene regulation are intimately coupled. Apart from a growth-rate dependent lower bound on noise, we find that individual promoters show highly condition-dependent noise and that condition-dependent expression noise is shaped by noise propagation from regulators to their targets. A simple model of noise propagation identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. The overall correlation structure of sequence and expression properties of E. coli genes uncovers that genes are organized along two principal axes, with the first axis sorting genes by their mean expression and evolutionary rate of their coding regions, and the second axis sorting genes by their expression noise, the number of regulatory inputs in their promoter, and their expression plasticity.

systems biology↗

bioRxiv2019

Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria

Fluorescence flow cytometry is a highly attractive technology for quantifying single-cell expression distributions in bacteria in high-throughput. However, so far there has been no systematic investigation of the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We here investigate these issues by systematically comparing flow cytometry measurements of fluorescent reporters in E. coli with measurements of the same strains in microscopic setups and develop a method for rigorous quantitative analysis of fluorescence flow cytometry data.\n\nWe find that forward and side scatter cannot be used to reliably estimate cell size in bacteria. Second, we show that cytometry measurements contain a large shot noise component that can be easily mistaken for intrinsic noise in gene expression, and show how calibration measurements can be used to correct for this measurement shot noise.\n\nTo aid other researchers with quantitative analysis of flow cytometry expression data in bacteria, we distribute E-Flow, an open-source R package that implements our methods for filtering cells based on forward and side scatter, and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow.

bioinformatics↗

bioRxiv2019

Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism

Living cells proliferate by completing and coordinating two essential cycles, a division cycle that controls cell size, and a DNA replication cycle that controls the number of chromosomal copies in the cell. Despite lacking dedicated cell cycle control regulators such as cyclins in eukaryotes, bacteria such as E. coli manage to tightly coordinate those two cycles across a wide range of growth conditions, including situations where multiple nested rounds of replication progress simultaneously. Various cell cycle models have been proposed to explain this feat, but it has been impossible to validate them so far due to a lack of experimental tools for systematically testing their different predictions. Recently new insights have been gained on the division cycle through the study of the structure of fluctuations in growth, size, and division in individual cells. In particular, it was found that cell size appears to be controlled by an adder mechanism, i.e. the added volume between divisions is held approximately constant and fluctuates independently of growth rate and cell size at birth. However, how replication initiation is regulated and coupled to cell size control remains unclear, mainly due to scarcity of experimental measurements on replication initiation at the single-cell level. Here, we used time-lapse microscopy in combination with microfluidics to directly measure growth, division and replication in thousands of single E. coli cells growing in both slow and fast growth conditions. In order to compare different phenomenological models of the cell cycle, we introduce a statistical framework which assess their ability to capture the correlation structure observed in the experimental data. Using this in combination with stochastic simulations, our data indicate that, instead of thinking of the cell cycle as running from birth to division, the cell cycle is controlled by two adder mechanisms starting at the initiation of replication: the added volume since the last initiation event controls the timing of both the next division event and the next replication initiation event. Interestingly the double-adder mechanism identified in this study has recently been found to explain the more complex cell cycle of mycobacteria, suggesting shared control strategies across species.

systems biology↗