Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

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)

Prospects & Overviews Bet hedging or not? A guide to proper classification of microbial survival strategies

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

How mathematical modelling elucidates signalling in Bacillus subtilis

P>Appropriate stimulus perception, signal processing and transduction ensure optimal adaptation of bacteria to environmental challenges. In the Gram-positive model bacterium Bacillus subtilis signalling 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, sigma B-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

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

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.

Heterochronic Phosphorelay Gene Expression as a Source of Heterogeneity in Bacillus subtilis Spore Formation▿ †

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

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

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

Methotrexate Polyglutamates Exposure – Response Modeling in a Large Cohort of Rheumatoid Arthritis Patients Starting Methotrexate

Methotrexate polyglutamates (MTX-PG) concentrations in red blood cells (RBCs) have been suggested as a biomarker of response in patients with rheumatoid arthritis (RA) receiving low-dose MTX therapy. We investigated the association and interpatient variability between RBC-MTX-PG3-5-exposure and response in patients with RA starting MTX. Data of three prospective cohorts were available. The relationship between exposure and Disease Activity Score in 28 joints (DAS28) was analyzed using a population pharmacokinetic-pharmacodynamic model. Relevant covariates were tested using full covariate modeling and backward elimination. From 395 patients, 3,401 MTX-PG concentrations and 1,337 DAS28 measurements were available between 0 and 300 days after MTX treatment onset. The developed model adequately described the time course of MTX-PG3-5 and DAS28. The median MTX-PG3-5 level at month 1 was 30.9 nmol/L (interquartile range (IQR): 23.6–43.7; n = 41) and at month 3: 69.3 nmol/L (IQR: 17.9–41.2; n = 351). Clearance of MTX-PG3-5 from RBCs was 28% lower (95% confidence interval (CI): 23.6–32.8%) in a woman and 10% lower (95% CI: 7.7–12.4%) in a 65-year-old compared with a 35-year-old patient. MTX-PG3-5 concentrations associated with DAS28: half-maximal effective concentration (EC50) was 9.14 nmol/L (95% CI: 4.2 nmol/L-14.1 nmol/L). EF at 80% (EC80) above 47 nmol/L was regarded as the optimal response. Independent of the MTX-PG 3–5 – response association, co-administration of disease-modifying antirheumatic drugs and corticosteroids improved response (additive effect on maximum effect (Emax)), whereas smoking, high body mass index and low albumin decreased Emax. In patients with RA starting MTX, RBC-MTX-PG3-5 was associated with clinical response. A dose increase is suggested when MTX-PG3-5 at month 1 is below 9.15 nmol/L, continued with the same dose when the concentration is above 47 nmol/L, and consider other treatment options above 78 nmol/L from 3 months onwards.</p