Philippe Nghe

Stochasticity of metabolism and growth at the single-cell level

Elucidating the role of molecular stochasticity in cellular growth is central to understanding phenotypic heterogeneity and the stability of cellular proliferation. The inherent stochasticity of metabolic reaction events should have negligible effect, because of averaging over the many reaction events contributing to growth. Indeed, metabolism and growth are often considered to be constant for fixed conditions. Stochastic fluctuations in the expression level of metabolic enzymes could produce variations in the reactions they catalyse. However, whether such molecular fluctuations can affect growth is unclear, given the various stabilizing regulatory mechanisms, the slow adjustment of key cellular components such as ribosomes, and the secretion and buffering of excess metabolites. Here we use time-lapse microscopy to measure fluctuations in the instantaneous growth rate of single cells of Escherichia coli, and quantify time-resolved cross-correlations with the expression of lac genes and enzymes in central metabolism. We show that expression fluctuations of catabolically active enzymes can propagate and cause growth fluctuations, with transmission depending on the limitation of the enzyme to growth. Conversely, growth fluctuations propagate back to perturb expression. Accordingly, enzymes were found to transmit noise to other unrelated genes via growth. Homeostasis is promoted by a noise-cancelling mechanism that exploits fluctuations in the dilution of proteins by cell-volume expansion. The results indicate that molecular noise is propagated not only by regulatory proteins but also by metabolic reactions. They also suggest that cellular metabolism is inherently stochastic, and a generic source of phenotypic heterogeneity.

Single-Cell Dynamics Reveals Sustained Growth during Diauxic Shifts

Stochasticity in gene regulation has been characterized extensively, but how it affects cellular growth and fitness is less clear. We study the growth of E. coli cells as they shift from glucose to lactose metabolism, which is characterized by an obligatory growth arrest in bulk experiments that is termed the lag phase. Here, we follow the growth dynamics of individual cells at minute-resolution using a single-cell assay in a microfluidic device during this shift, while also monitoring lac expression. Mirroring the bulk results, the majority of cells displays a growth arrest upon glucose exhaustion, and resume when triggered by stochastic lac expression events. However, a significant fraction of cells maintains a high rate of elongation and displays no detectable growth lag during the shift. This ability to suppress the growth lag should provide important selective advantages when nutrients are scarce. Trajectories of individual cells display a highly non-linear relation between lac expression and growth, with only a fraction of fully induced levels being sufficient for achieving near maximal growth. A stochastic molecular model together with measured dependencies between nutrient concentration, lac expression level, and growth accurately reproduces the observed switching distributions. The results show that a growth arrest is not obligatory in the classic diauxic shift, and underscore that regulatory stochasticity ought to be considered in terms of its impact on growth and survival.

High shear rheology of shear banding fluids in microchannels

We characterize heterogeneous flows of a wormlike micelles solution in microchannels. Combining a pressure resistant microfabrication technology and a performant particle image velocimetry setup, we succeed in determining the nonlinear rheology of this fluid over 4 decades in shear rate and in particular more than 1 decade beyond the end of the stress plateau. We performed an independent measurement of the slip length with 1 μm resolution.

Prebiotic network evolution: six key parameters

The origins of life likely required the cooperation among a set of molecular species interacting in a network. If so, then the earliest modes of evolutionary change would have been governed by the manners and mechanisms by which networks change their compositions over time. For molecular events, especially those in a pre-biological setting, these mechanisms have rarely been considered. We are only recently learning to apply the results of mathematical analyses of network dynamics to prebiotic events. Here, we attempt to forge connections between such analyses and the current state of knowledge in prebiotic chemistry. Of the many possible influences that could direct primordial network, six parameters emerge as the most influential when one considers the molecular characteristics of the best candidates for the emergence of biological information: polypeptides, RNA-like polymers, and lipids. These parameters are viable cores, connectivity kinetics, information control, scalability, resource availability, and compartmentalization. These parameters, both individually and jointly, guide the aggregate evolution of collectively autocatalytic sets. We are now in a position to translate these conclusions into a laboratory setting and test empirically the dynamics of prebiotic network evolution.

Generation and filtering of gene expression noise by the bacterial cell cycle

BackgroundGene expression within cells is known to fluctuate stochastically in time. However, the origins of gene expression noise remain incompletely understood. The bacterial cell cycle has been suggested as one source, involving chromosome replication, exponential volume growth, and various other changes in cellular composition. Elucidating how these factors give rise to expression variations is important to models of cellular homeostasis, fidelity of signal transmission, and cell-fate decisions.ResultsUsing single-cell time-lapse microscopy, we measured cellular growth as well as fluctuations in the expression rate of a fluorescent protein and its concentration. We found that, within the population, the mean expression rate doubles throughout the cell cycle with a characteristic cell cycle phase dependent shape which is different for slow and fast growth rates. At low growth rate, we find the mean expression rate was initially flat, and then rose approximately linearly by a factor two until the end of the cell cycle. The mean concentration fluctuated at low amplitude with sinusoidal-like dependence on cell cycle phase. Traces of individual cells were consistent with a sudden two-fold increase in expression rate, together with other non-cell cycle noise. A model was used to relate the findings and to explain the cell cycle-induced variations for different chromosomal positions.ConclusionsWe found that the bacterial cell cycle contribution to expression noise consists of two parts: a deterministic oscillation in synchrony with the cell cycle and a stochastic component caused by variable timing of gene replication. Together, they cause half of the expression rate noise. Concentration fluctuations are partially suppressed by a noise cancelling mechanism that involves the exponential growth of cellular volume. A model explains how the functional form of the concentration oscillations depends on chromosome position.

Transient compartmentalization of RNA replicators prevents extinction due to parasites

Beating the curse of the parasite The evolution of molecular replicators was a critical step in the origin of life. Such replicators would have suffered from faster-replicating “molecular parasites” outcompeting the parental replicator. Compartmentalization of replicators inside protocells would have helped ameliorate the effect of parasites. Matsumura et al. show that transient compartmentalization in nonbiological materials is sufficient to tame the problem of parasite takeover. They analyzed viral replication in a droplet-based microfluidic system, which revealed that as long as there is selection for a functional replicator, the population is not overwhelmed by the faster-replicating parasite genomes. Science, this issue p. 1293 Temporary compartmentalization in water drops prevents molecular replicators from being swamped by faster-replicating parasitic mutants. The appearance of molecular replicators (molecules that can be copied) was probably a critical step in the origin of life. However, parasitic replicators would take over and would have prevented life from taking off unless the replicators were compartmentalized in reproducing protocells. Paradoxically, control of protocell reproduction would seem to require evolved replicators. We show here that a simpler population structure, based on cycles of transient compartmentalization (TC) and mixing of RNA replicators, is sufficient to prevent takeover by parasitic mutants. TC tends to select for ensembles of replicators that replicate at a similar rate, including a diversity of parasites that could serve as a source of opportunistic functionality. Thus, TC in natural, abiological compartments could have allowed life to take hold.

Evolutionary constraints in variable environments, from proteins to networks

Environmental changes can not only trigger a regulatory response, but also impose evolutionary pressures that can modify the underlying regulatory network. Here, we review recent approaches that are beginning to disentangle this complex interplay between regulatory and evolutionary responses. Systematic genetic reconstructions have shown how evolutionary constraints arise from epistatic interactions between mutations in fixed environments. This approach is now being extended to more complex environments and systems. The first results suggest that epistasis is affected dramatically by environmental changes and, hence, can profoundly affect the course of evolution. Thus, external environments not only define the selection of favored phenotypes, but also affect the internal constraints that can limit the evolution of these phenotypes. These findings also raise new questions relating to the conditions for evolutionary transitions and the evolutionary potential of regulatory networks.

Microfabricated polyacrylamide devices for the controlled culture of growing cells and developing organisms.

The ability to spatially confine living cells or small organisms while dynamically controlling their aqueous environment is important for a host of microscopy applications. Here, we show how polyacrylamide layers can be patterned to construct simple microfluidic devices for this purpose. We find that polyacrylamide gels can be molded like PDMS into micron-scale structures that can enclose organisms, while being permeable to liquids, and transparent to allow for microscopic observation. We present a range of chemostat-like devices to observe bacterial and yeast growth, and C. elegans nematode development. The devices can integrate PDMS layers and allow for temporal control of nutrient conditions and the presence of drugs on a minute timescale. We show how spatial confinement of motile C. elegans enables for time-lapse microscopy in a parallel fashion.

Information-theoretic analysis of the directional influence between cellular processes

Inferring the directionality of interactions between cellular processes is a major challenge in systems biology. Time-lagged correlations allow to discriminate between alternative models, but they still rely on assumed underlying interactions. Here, we use the transfer entropy (TE), an information-theoretic quantity that quantifies the directional influence between fluctuating variables in a model-free way. We present a theoretical approach to compute the transfer entropy, even when the noise has an extrinsic component or in the presence of feedback. We re-analyze the experimental data from Kiviet et al. (2014) where fluctuations in gene expression of metabolic enzymes and growth rate have been measured in single cells of E. coli. We confirm the formerly detected modes between growth and gene expression, while prescribing more stringent conditions on the structure of noise sources. We furthermore point out practical requirements in terms of length of time series and sampling time which must be satisfied in order to infer optimally transfer entropy from times series of fluctuations.

The molecular basis of genetic interaction diversity in a metabolic pathway

Metabolic imbalances underlie a large spectrum of diseases, spanning congenital and chronic conditions and cancer. Our ability to explain and predict such imbalances remains severely limited by the diversity of underlying mutation effects and their dependence on the genetic background and environment, but it is unclear whether these complicating factors can be reduced to simple quantitative rules. Here, we characterise their interplay in determining cell physiology and fitness by systematically quantifying almost 4,000 interactions between expression variants of two genes from a classical sugar-utilisation pathway containing a toxic metabolite in the model bacterium, Escherichia coli, in different environments. We detect a remarkable variety of types and trends of intergenic interaction in this linear pathway, which cannot be reliably predicted from the effects of each variant in isolation, along with a dependence of this epistasis on the environment. Despite such apparent complexity, the fitness consequences of interactions between alleles and environment are explained by a mechanistic model accounting for catabolic flux and toxic metabolite concentration. Our findings reveal how, contrary to a common assumption, the nature of fitness interactions is governed by more than just the topology of the molecular network underlying a selected trait. Our prospects of predicting disease and evolution will therefore improve by expanding our knowledge of the links among proteome, metabolome and physiology.Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4,000 fitness interactions between expression variants of two metabolic genes, in different environments. We detect a remarkable variety of environment-dependent interactions, and demonstrate they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.