Imke de Jong

About Imke de Jong:
No description specified

SEEK ID: https://fairdomhub.org/people/281

Location:  Netherlands

Expertise: Microbiology, Genetics, Molecular Biology, Bacillus subtilis, translational control of gene expression, sporulation, phenotypic heterogeneity, bistability, gene regulation, stress responses, Signal transduction in Gram-negative bacteria; Synthetic Microbiology; Single cell gene expression; Regulatory networks; biochemistry; histidine ki..., regulation of gene expression, Systems Biology

Tools: Microbiology, Genetics, Molecular Biology, Genetic analysis, Genetic modification, Model organisms, Single Cell analysis, PCR, Fluorecence based reporter gene analyses/single cell analyses, Molecular biology techniques (RNA/DNA), time lapse microscopy, Time-lapse fluorescence microscopy Flow cytometry

ORCID: Not specified

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Project positions

BaCell-SysMO (University of Groningen)

Related items

SysMO

SysMO is a European transnational funding and research initiative on "Systems Biology of Microorganisms".

The goal pursued by SysMO was to record and describe the dynamic molecular processes going on in unicellular microorganisms in a comprehensive way and to present these processes in the form of computerized mathematical models.

Systems biology will raise biomedical and biotechnological research to a new quality level and contribute markedly to progress in understanding. Pooling European research ...

Projects: BaCell-SysMO, COSMIC, SUMO, KOSMOBAC, SysMO-LAB, PSYSMO, SCaRAB, MOSES, TRANSLUCENT, STREAM, SulfoSys, SysMO DB, SysMO Funders, SilicoTryp, Noisy-Strep

Web page: http://sysmo.net/

BaCell-SysMO

BaCell-SysMO 2 Modelling carbon core metabolism in Bacillus subtilis – Exploring the contribution of protein complexes in core carbon and nitrogen metabolism.

Bacillus subtilis is a prime model organism for systems biology approaches because it is one of the most advanced models for functional genomics. Furthermore, comprehensive information on cell and molecular biology, physiology and genetics is available and the European Bacillus community (BACELL) has a well-established reputation for applying ...

Programme: SysMO

Public web page: http://www.sysmo.net/index.php?index=53

Single cell analysis of gene expression patterns during carbon starvation in Bacillus subtilis reveals large phenotypic variation
Noisy-Strep

Abstract (Expand)

How cells dynamically respond to fluctuating environmental conditions depends on the architecture and noise of the underlying genetic circuits. Most work characterizing stress pathways in the model … bacterium Bacillus subtilis has been performed on bulk cultures using ensemble assays. However, investigating the single cell response to stress is important since noise might generate significant phenotypic heterogeneity. Here, we study the stress response to carbon source starvation and compare both population and single cell data. Using a top-down approach, we investigate the transcriptional dynamics of various stress-related genes of B. subtilis in response to carbon source starvation and to increased cell density. Our data reveal that most of the tested gene-regulatory networks respond highly heterogeneously to starvation and cells show a large degree of variation in gene expression. The level of highly dynamic diversification within B. subtilis populations under changing environments reflects the necessity to study cells at the single cell level.

Editor:

Date Published: 4th Oct 2012

Publication Type: Not specified

Live Cell Imaging of <em>Bacillus subtilis</em> and <em>Streptococcus pneumoniae</em> using Automated Time-lapse Microscopy
Noisy-Strep

Abstract (Expand)

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not representative of the behavior, status or phenotype of … single cells. Due to this new insight the number of single cell studies rises continuously (for recent reviews see (1,2,3)). However, many of the single cell techniques applied do not allow monitoring the development and behavior of one specific single cell in time (e.g. flow cytometry or standard microscopy). Here, we provide a detailed description of a microscopy method used in several recent studies (4, 5, 6, 7), which allows following and recording (fluorescence of) individual bacterial cells of Bacillus subtilis and Streptococcus pneumoniae through growth and division for many generations. The resulting movies can be used to construct phylogenetic lineage trees by tracing back the history of a single cell within a population that originated from one common ancestor. This time-lapse fluorescence microscopy method cannot only be used to investigate growth, division and differentiation of individual cells, but also to analyze the effect of cell history and ancestry on specific cellular behavior. Furthermore, time-lapse microscopy is ideally suited to examine gene expression dynamics and protein localization during the bacterial cell cycle. The method explains how to prepare the bacterial cells and construct the microscope slide to enable the outgrowth of single cells into a microcolony. In short, single cells are spotted on a semi-solid surface consisting of growth medium supplemented with agarose on which they grow and divide under a fluorescence microscope within a temperature controlled environmental chamber. Images are captured at specific intervals and are later analyzed using the open source software ImageJ.

Authors: , Katrin Beilharz, ,

Date Published: 16th Aug 2011

Publication Type: Not specified

Bet hedging or not? A guide to proper classification of microbial survival strategies
BaCell-SysMO

Abstract (Expand)

Bacteria have developed an impressive ability to survive and propagate in highly diverse and changing environments by evolving phenotypic heterogeneity. Phenotypic heterogeneity ensures that a subpopulation … is well prepared for environmental changes. The expression bet hedging is commonly (but often incorrectly) used by molecular biologists to describe any observed phenotypic heterogeneity. In evolutionary biology, however, bet hedging denotes a risk-spreading strategy displayed by isogenic populations that evolved in unpredictably changing environments. Opposed to other survival strategies, bet hedging evolves because the selection environment changes and favours different phenotypes at different times. Consequently, in bet hedging populations all phenotypes perform differently well at any time, depending on the selection pressures present. Moreover, bet hedging is the only strategy in which temporal variance of offspring numbers per individual is minimized. Our paper aims to provide a guide for the correct use of the term bet hedging in molecular biology.

Authors: , Patsy Haccou,

Date Published: 21st Jan 2011

Publication Type: Not specified

How mathematical modelling elucidates signalling in B. subtilis
BaCell-SysMO

Abstract (Expand)

Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling …g networks and molecular interactions therein are well-studied, making this species a suitable candidate for the application of mathematical modelling. Here, we review systems biology approaches, focusing on chemotaxis, sporulation, σB-dependent general stress response and competence. Processes like chemotaxis and Z-ring assembly depend critically on the subcellular localization of proteins. Environmental response strategies, including sporulation and competence, are characterized by phenotypic heterogeneity in isogenic cultures. The examples of mathematical modelling also include investigations that have demonstrated how operon structure and signalling dynamics are intricately interwoven to establish optimal responses. Our review illustrates that these interdisciplinary approaches offer new insights into the response of B. subtilis to environmental challenges. These case studies reveal modelling as a tool to increase the understanding of complex systems, to help formulating hypotheses and to guide the design of more directed experiments that test predictions.

Editor:

Date Published: 1st Jul 2010

Publication Type: Not specified

Heterochronic phosphorelay gene expression as a source of heterogeneity in Bacillus subtilis spore formation
BaCell-SysMO

Abstract (Expand)

In response to limiting nutrient sources and cell density signals, Bacillus subtilis can differentiate and form highly resistant endospores. Initiation of spore development is governed by the master … regulator Spo0A, which is activated by phosphorylation via a multicomponent phosphorelay. Interestingly, only part of a clonal population will enter this developmental pathway, a phenomenon known as sporulation bistability or sporulation heterogeneity. How sporulation heterogeneity is established is largely unknown. To investigate the origins of sporulation heterogeneity, we constructed promoter-green fluorescent protein (GFP) fusions to the main phosphorelay genes and perturbed their expression levels. Using time-lapse fluorescence microscopy and flow cytometry, we showed that expression of the phosphorelay genes is distributed in a unimodal manner. However, single-cell trajectories revealed that phosphorelay gene expression is highly dynamic or "heterochronic" between individual cells and that stochasticity of phosphorelay gene transcription might be an important regulatory mechanism for sporulation heterogeneity. Furthermore, we showed that artificial induction or depletion of the phosphorelay phosphate flow results in loss of sporulation heterogeneity. Our data suggest that sporulation heterogeneity originates from highly dynamic and variable gene activity of the phosphorelay components, resulting in large cell-to-cell variability with regard to phosphate input into the system. These transcriptional and posttranslational differences in phosphorelay activity appear to be sufficient to generate a heterogeneous sporulation signal without the need of the positive-feedback loop established by the sigma factor SigH.

Editor:

Date Published: 12th Feb 2010

Publication Type: Not specified

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