Budding yeast as a model organism to study the effects of age

Although a budding yeast culture can be propagated eternally, individual yeast cells age and eventually die. The detailed knowledge of this unicellular eukaryotic species as well as the powerful tools developed to study its physiology makes budding yeast an ideal model organism to study the mechanisms involved in aging. Considering both detrimental and positive aspects of age, we review changes occurring during aging both at the whole-cell level and at the intracellular level. The possible mechanisms allowing old cells to produce rejuvenated progeny are described in terms of accumulation and inheritance of aging factors. Based on the dynamic changes associated with age, we distinguish different stages of age: early age, during which changes do not impair cell growth; intermediate age, during which aging factors start to accumulate; and late age, which corresponds to the last divisions before death. For each aging factor, we examine its asymmetric segregation and whether it plays a causal role in aging. Using the example of caloric restriction, we describe how the aging process can be modulated at different levels and how changes in different organelles might interplay with each other. Finally, we discuss the beneficial aspects that might be associated with ag

Response to comment on textquoteleftInitiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanismtextquoteright

Last year we published an article (Witz et al., 2019) in which we used time-lapse microscopy in combination with microfluidics to measure growth, division and replication in single E. coli cells on the one hand, and developed a new statistical analysis method to calculate the ability of different cell cycle models to capture the correlation structure observed in the data on the other hand. This led us to propose a new model of cell cycle control in E. coli which we called the double-adder model. Recently Le Treut et al. published a comment (Le Treut et al., 2020) on our article which made a number of highly critical claims, including allegations that our own data support a different model than the one we proposed, and that our model cannot reproduce the ‘adder phenotype’ observed in the data. We here show that all these allegations are false and based on basic analysis errors. Although our focus is on explaining the errors in the analysis of Le Treut et al, we have attempted to make the presentation of interest to a broader scientific audience by discussing the issues in the context of what our current understanding is of the bacterial cell cycle, and to what extent recent data either support or reject various proposed models

Localization of protein aggregation in Escherichia coli is governed by diffusion and nucleoid macromolecular crowding effect

Aggregates of misfolded proteins are a hallmark of many age-related diseases. Recently, they have been linked to aging of Escherichia coli (E. coli) where protein aggregates accumulate at the old pole region of the aging bacterium. Because of the potential of E. coli as a model organism, elucidating aging and protein aggregation in this bacterium may pave the way to significant advances in our global understanding of aging. A first obstacle along this path is to decipher the mechanisms by which protein aggregates are targeted to specific intercellular locations. Here, using an integrated approach based on individual-based modeling, time-lapse fluorescence microscopy and automated image analysis, we show that the movement of aging-related protein aggregates in E. coli is purely diffusive (Brownian). Using single-particle tracking of protein aggregates in live E. coli cells, we estimated the average size and diffusion constant of the aggregates. Our results evidence that the aggregates passively diffuse within the cell, with diffusion constants that depend on their size in agreement with the Stokes-Einstein law. However, the aggregate displacements along the cell long axis are confined to a region that roughly corresponds to the nucleoid-free space in the cell pole, thus confirming the importance of increased macromolecular crowding in the nucleoids. We thus used 3d individual-based modeling to show that these three ingredients (diffusion, aggregation and diffusion hindrance in the nucleoids) are sufficient and necessary to reproduce the available experimental data on aggregate localization in the cells. Taken together, our results strongly support the hypothesis that the localization of aging-related protein aggregates in the poles of E. coli results from the coupling of passive diffusion- aggregation with spatially non-homogeneous macromolecular crowding. They further support the importance of "soft" intracellular structuring (based on macromolecular crowding) in diffusion-based protein localization in E. coli.Comment: PLoS Computational Biology (2013

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

Fluorescence flow cytometry is increasingly being used to quantify single-cell expression distributions in bacteria in high-throughput. However, there has been no systematic investigation into the best practices for quantitative analysis of such data, what systematic biases exist, and what accuracy and sensitivity can be obtained. We investigate these issues by measuring the same E. coli strains carrying fluorescent reporters using both flow cytometry and microscopic setups and systematically comparing the resulting single-cell expression distributions. Using these results, we develop methods for rigorous quantitative inference of single-cell expression distributions from fluorescence flow cytometry data. First, we present a Bayesian mixture model to separate debris from viable cells using all scattering signals. Second, we show that cytometry measurements of fluorescence are substantially affected by autofluorescence and shot noise, which can be mistaken for intrinsic noise in gene expression, and present methods to correct for these using calibration measurements. Finally, we show that because forward- and side-scatter signals scale non-linearly with cell size, and are also affected by a substantial shot noise component that cannot be easily calibrated unless independent measurements of cell size are available, it is not possible to accurately estimate the variability in the sizes of individual cells using flow cytometry measurements alone. To 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 debris and for estimating true biological expression means and variances from the fluorescence signal. The package is available at https://github.com/vanNimwegenLab/E-Flow

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter's sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network

La théorie de la sélection naturelle présentée par Darwin et Wallace

En cette année Darwin (cent cinquantenaire de la parution de L’Origine des espèces, 1859), les écrits présentés ici, datant d’une année auparavant, aident à comprendre la vision en gestation de Darwin. La théorie de la sélection naturelle y est présentée, notamment à partir des fameux « pinsons de Darwin » ; la comparaison avec l’évolution en géologie est esquissée. Bien qu'ayant proposé une théorie solide de l'origine et de la diversité des espèces, Darwin a posé à ses successeurs quantité de nouvelles questions. Comment mesurer les effets de la sélection, notamment comment changent-ils au cours du temps ainsi qu'en fonction de la taille de la population ? Quels sont les liens entre l'impact des variations (les mutations) et leur taux d'occurrence ? Comment la sélection opère-t-elle à chaque niveau d'organisation d'un système dit « complexe » ? Le XXIe siècle arrivant avec son lot d'innovations technologiques permettra sûrement d'aller encore un peu plus loin dans notre compréhension de ces phénomènes, en suivant la voie ouverte autour de Darwin.In the year marking the 150th anniversary of the publication of The Origin of Species (1859), the texts presented here, dating from the previous year, help us to understand Darwin’s burgeoning vision. The theory of natural selection is presented in these texts, notably via the famous “Darwin’s finches”, while the comparison with geological evolution is sketched out. Though he proposed a sound theory for the origin and diversity of species, Darwin left his successors with countless new questions. How can the effects of selection be measured, namely how do they change over time as well as in relation to population size ? What are the links between the impact of variations (mutations) and their occurrence ratio ? How does selection operate at each level of the organisation of a “complex” system ? The 21st century, with its array of technological innovations, will no doubt enable scientists to advance our understanding of these phenomena a little further, following the path opened up around Darwin

Faster Growth Reduces the Sensitivity of Gene Circuits to Environmental Signals

Non UBCUnreviewedAuthor affiliation: University of BaselOthe

Evolution, compétition et coopération dans les populations bactériennes

The interplay of environment, heritability, and stochasticity results in the development of different individuals starting from a given genotype. This phenotypic variability affects how natural selection acts on genetic variability. From a general perspective, I aim at studying the impact of phenotypic variability on adaptive dynamics. In the first chapter, I report on the design of an evolutionary experiment in a structured environment using Escherichia coli. The trait under selection is resistance to high temperature. In particular, we study the effects of high temperature on chemotaxis, as well as the impact of acclimation on growth and survival at high temperature. The second chapter is about the development of a microbial population measurement device dedicated to diluted populations. This continuous, non-invasive measurement has a low detection limit that depends on the species. For the model species Escherichia ecoli, the limit is ca. 5000/mL which represents a 100-fold improvement compared to classical photometric methods. In the third chapter, we study the distribution of pyoverdine between individuals of a clonal population of Pseudomonas aeruginosa. The variability of the concentration of this siderophore is much greater than expected. Although pyoverdine is considered to be a public good, neither spatial heterogeneity nor heritability provide a meaningful description of the variability. Instead we characterize rapid fluctuations in pyoverdine concentration, and propose a model based on a phenotypic switch in pyoverdine metabolism that is in good agreement with the experimental data.Les différents facteurs que sont l'environnement, l'héritabilité et la stochasticité contribuent au développement d'individus différents à partir d'une information génétique donnée. Cette variabilité phénotypique modifie l'action de la sélection naturelle sur la variabilité génétique. Un fil conducteur de ce travail est l'étude de l'impact de la variabilité phénotypique sur les dynamiques d'adaptation. Le premier chapitre expose la conception d'une expérience d'évolution de Escherichia coli dans un environnement structuré. Le trait sélectionné est la resistance aux hautes températures. En particulier, nous étudions les effets de la température sur le chimiotactisme ainsi que l'impact de l'acclimatation sur la croissance et la survie à haute température. Le deuxième chapitre porte sur la réalisation d'un dispositif de mesure de population microbienne à basse concentration. Cette mesure est continue et non invasive et sa limite de détection varie selon l'espèce. Pour l'espèce modèle Escherichia ecoli, la limite est environ 5000/mL soit une amélioration d'un facteur 100 par rapport à la photométrie classique. Dans le troisième chapitre, nous étudions la distribution de la pyoverdine entre les individus d'une population clonale de Pseudomonas aeruginosa. La variabilité de la concentration de ce sidérophore considéré comme un "bien commun'' est beaucoup plus grande que celle attendue et ne peut être expliquée en terme de répartition spatiale ou d'héritabilité. Après avoir caractérisé des fluctuations rapides de la concentration en pyoverdine, nous proposons un modèle de switch phénotypique dans le métabolisme de la pyoverdine qui est en très bonne adéquation avec les observations

Bacteria grow swiftly and live thriftily

Bacteria have evolved numerous strategies to use resources efficiently. However, bacterial economies depend on both the physiological context of the organisms as well as their growth state - whether they are growing, non-growing or reinitiating growth. In this essay, we discuss some of the features that make bacteria efficient under these different conditions and during the transitions between them. We also highlight the many outstanding questions regarding the physiology of non-growing bacterial cells. Lastly, we examine how efficiency is apparent in both the mode and tempo of bacterial evolution

Budding yeast as a model organism to study the effects of age

ISSN:0168-6445ISSN:1574-697