Search bioRxiv⌕ Search

Biology subjects

Galbusera, L.

Publications and source records attributed to Galbusera, L..

4 recordsLinked to original sources

Transient transcription factor depletions explain diverse single-cell responses of LexA target promoters to mild DNA damage

In bacteria, the effects of transcription factors (TFs) on the expression of their target genes are highly stochastic at the single-cell level. Not only do TF concentrations fluctuate in time and from cell to cell, in each cell the actual binding and unbinding of TFs to promoters is also stochastic. However, how this noise propagation from TFs to their targets determines the expression fluctuations in target genes of the same TF has so far not been quantitatively characterized. Here we use fluorescence time-lapse microscopy in combination with microfluidics and automated image analysis to quantitatively track the single-cell growth and gene expression dynamics of different target promoters of the LexA SOS response regulator in E. coli under mild DNA damage induced by the antibiotic ciprofloxacin (CIP). We find that, at the single-cell level, LexA target promoters exhibit stochastic bursts in protein production with specific quantitative characteristics. First, the durations of protein production bursts are short, identical for all promoters, and independent of CIP levels. Second, the heights of the production peaks are exponentially distributed for each promoter, and independent of CIP level, but with different average heights for different promoters. Third, the frequency of the production bursts increases with CIP level for all promoters, but differs across promoters. Importantly, we show that these observations are inconsistent with popular equilibrium models of bacterial gene regulation that assume that the dynamics of binding and unbinding of TFs is fast relative to the time-scale of TF concentration fluctuations. Instead, all observations can be quantitatively fit by a relatively simple model in which DNA damage events lead to short transient dips in LexA concentration, and an expression burst occurs in a cell whenever LexA unbinds from the target promoter during such a short dip in LexA concentration. Moreover, because these transient dips in LexA concentration occur on the same time-scale as the stochastic unbinding of LexA from promoters, the response of a target promoter to an individual DNA damage event is stochastic and depends in a complex manner on the strengths of its LexA binding sites and the duration of the event. Consequently, the expression levels of different LexA target genes in a given cell reflect distinct statistics of the frequency and durations of DNA damage events in the recent history of that cell. Our results show that, even in the simple scenario where a single TF is induced, different target promoters can exhibit distinct stochastic responses that depend on the precise kinetics of the induction events.

systems biology↗

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↗

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↗

A prize-collecting Steiner tree application for signature selection to stratify diffuse large B-cell lymphoma subtypes

BackgroundWith the explosion of high-throughput data available in biology, the bottleneck is shifted to effective data interpretation. By taking advantage of the available data, it is possible to identify the biomarkers and signatures to distinguish subtypes of a specific cancer in the context of clinical trials. This requires sophisticated methods to retrieve the information out of the data, and various algorithms have been recently devised.\n\nResultsHere, we applied the prize-collecting Steiner tree (PCST) approach to obtain a gene expression signature for the classification of diffuse large B-cell lymphoma (DLBCL). The PCST is a network-based approach to capture new insights about genomic data by incorporating an interaction network landscape. Moreover, we adopted the ElasticNet incorporating PCA as a classification method. We used seven public gene expression profiling datasets (three for training, and four for testing) available in the literature, and obtained 10 genes as signature. We tested these genes by employing ElasticNet, and compared the performance with the DAC algorithm as current golden standard. The performance of the PCST signature with ElasticNet outperformed the DAC in distinguishing the subtypes. In addition, the gene expression signature was able to accurately stratify DLBCL patients on survival data.\n\nConclusionsWe developed a network-based optimization technique that performs unbiased signature selection by integrating genomic data with biological networks. Our classifier trained with the obtained signature outperformed the state-of-the-art method in subtype distinction and survival data stratification in DLBCL. The proposed method is a general approach that can be applied on other classification problems.

bioinformatics↗