Cellular heterogeneity: yeast-side story

A major challenge for cells lies in their ability to detect, respond and adapt to changing environments that may threaten their survival. Among the numerous evolutionary strategies, cell-to-cell heterogeneity allows the emergence of different phenotypes within a population. This variability in cellular behaviors can be essential for a small fraction of cells to adapt and survive in various environments. Analyses at the single-cell level have allowed to highlight the great variability that is present between cells within an isogenic population. Numerous molecular mechanisms have been uncovered, allowing to understand the emergence and the role of cellular heterogeneity. These attempts at identifying the source of cellular noise have also provided clues for strategies needed to control heterogeneity. In this review, S. cerevisiae is used as an example to illustrate the different factors leading to cell heterogeneity, ranging from intracellular processes to environmental constraints. In addition, some recent strategies developed to modulate cell-to-cell variability are discussed.     

Predictive power of cell-to-cell variability

Much of our current knowledge of biology has been constructed based on population-average measurements. However, advances in single-cell analysis have demonstrated the omnipresent nature of cell-to-cell variability in any population. On one hand, tremendous efforts have been made to examine how such variability arises, how it is regulated by cellular networks, and how it can affect cell-fate decisions by single cells. On the other hand, recent studies suggest that the variability may carry valuable information that can facilitate the elucidation of underlying regulatory networks or the classification of cell states. To this end, a major challenge is determining what aspects of variability bear significant biological meaning. Addressing this challenge requires the development of new computational tools, in conjunction with appropriately chosen experimental platforms, to more effectively describe and interpret data on cell-cell variability. Here, we discuss examples of when population heterogeneity plays critical roles in determining biologically and clinically significant phenotypes, how it serves as a rich information source of regulatory mechanisms, and how we can extract such information to gain a deeper understanding of biological systems.     

Actin-based motility of intracellular bacteria, and polarized surface distribution of the bacterial effector molecules

Several intracellular bacterial pathogens, including species of Listeria, Rickettsia, Shigella, Mycobacteria, and Burkholderia, have evolved mechanisms to exploit the actin polymerization machinery of their hosts to induce actin-based motility, enabling these pathogens to spread between host cells without exposing themselves to the extracellular milieu. Efficient cell-to-cell spread requires directional motility, which the bacteria may achieve by concentrating the effector molecules at one pole of their cell body, thereby restricting polymerization of monomeric actin into actin tails to this pole. The study of the molecular processes involved in the initiation of actin tail formation at the bacterial surface, and subsequent actin-based motility, has provided much insight into the pathogenesis of infections caused by these bacteria and into the cell biology of actin dynamics. Concomitantly, this field of research has provided an opportunity to understand the mechanisms whereby bacteria can achieve a polarized distribution of surface proteins. This review will describe the process of actin-based motility of intracellular bacteria, and the mechanisms by which bacteria can obtain a polarized distribution of their surface proteins.     

Modeling stochasticity and variability in gene regulatory networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.     

Contributions of Cell Growth and Biochemical Reactions to Nongenetic Variability of Cells

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a particular molecule. It quantifies the individual contributions made by processes associated with cell growth, biochemical reactions, and their control. We decompose the growth contribution further into variance contributions of random partitioning of molecules at cell division, mother-cell heterogeneity, and variation in cell-cycle progression. The contribution made by biochemical reactions is expressed in variance generated by molecule synthesis, degradation, and their regulation. We use this theory to study the influence of different growth and reaction-related processes, such as DNA replication, variable molecule-partitioning probability, and synthesis bursts, on stochastic cell-to-cell variability. Using simulations, we characterize the impact of noise in the generation-time on cell-to-cell variability. This article offers a widely-applicable theory on the influence of biochemical reactions and cellular growth on the phenotypic variability of growing, isogenic cells. The theory aids the design and interpretation of experiments involving single-molecule counting or real-time imaging of fluorescent reporter constructs.     

Quantification and cell-to-cell variation of vascular endothelial growth factor receptors

The vascular endothelial growth factor receptors (VEGFR) play a significant role in angiogenesis, the formation of new blood vessels from existing vasculature. Systems biology offers promising approaches to better understand angiogenesis by computational modeling the key molecular interactions in this process. Such modeling requires quantitative knowledge of cell surface density of pro-angiogenic receptors versus anti-angiogenic receptors, their regulation, and their cell-to-cell variability. Using quantitative fluorescence, we systematically characterized the endothelial surface density of VEGFRs and neuropilin-1 (NRP1). We also determined the role of VEGF in regulating the surface density of these receptors. Applying cell-by-cell analysis revealed heterogeneity in receptor surface density and VEGF tuning of this heterogeneity. Altogether, we determine inherent differences in the surface expression levels of these receptors and the role of VEGF in regulating the balance of anti-angiogenic or modulatory (VEGFR1) and pro-angiogenic (VEGFR2) receptors.     

Contributions of Cell Growth and Biochemical Reactions to Nongenetic Variability of Cells

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a particular molecule. It quantifies the individual contributions made by processes associated with cell growth, biochemical reactions, and their control. We decompose the growth contribution further into variance contributions of random partitioning of molecules at cell division, mother-cell heterogeneity, and variation in cell-cycle progression. The contribution made by biochemical reactions is expressed in variance generated by molecule synthesis, degradation, and their regulation. We use this theory to study the influence of different growth and reaction-related processes, such as DNA replication, variable molecule-partitioning probability, and synthesis bursts, on stochastic cell-to-cell variability. Using simulations, we characterize the impact of noise in the generation-time on cell-to-cell variability. This article offers a widely-applicable theory on the influence of biochemical reactions and cellular growth on the phenotypic variability of growing, isogenic cells. The theory aids the design and interpretation of experiments involving single-molecule counting or real-time imaging of fluorescent reporter constructs.     

Modeling of Cell-to-Cell Communication Processes with Petri Nets Using the Example of Quorum Sensing

The understanding of the molecular mechanism of cell-to-cell communication is fundamental for system biology. Up to now, the main objectives of bioinformatics have been reconstruction, modeling and analysis of metabolic, regulatory and signaling processes, based on data generated from high-throughput technologies. Cell-to-cell communication or quorum sensing (QS), the use of small molecule signals to coordinate complex patterns of behavior in bacteria, has been the focus of many reports over the past decade. Based on the quorum sensing process of the organism Aliivibrio salmonicida, we aim at developing a functional Petri net, which will allow modeling and simulating cell-to-cell communication processes. Using a new editor-controlled information system called VANESA (http://vanesa.sf.net), we present how to combine different fields of studies such as life-science, database consulting, modeling, visualization and simulation for a semi-automatic reconstruction of the complex signaling quorum sensing network. We show how cell-to-cell communication processes and information-flow within a cell and across cell colonies can be modeled using VANESA and how those models can be simulated with Petri net network structures in a sophisticated way.     

Cell-to-cell variability in cell death: can systems biology help us make sense of it all?

One of the most common observations in cell death assays is that not all cells die at the same time, or at the same treatment dose. Here, using the perspective of the systems biology of apoptosis and the context of cancer treatment, we discuss possible sources of this cell-to-cell variability as well as its implications for quantitative measurements and computational models of cell death. Many different factors, both within and outside of the apoptosis signaling networks, have been correlated with the variable responses to various death-inducing treatments. Systems biology models offer us the opportunity to take a more synoptic view of the cell death process to identify multifactorial determinants of the cell death decision. Finally, with an eye toward ‘systems pharmacology'', we discuss how leveraging this new understanding should help us develop combination treatment strategies to compel cancer cells toward apoptosis by manipulating either the biochemical state of cancer cells or the dynamics of signal transduction.     

The nature of systems biology

The advent of functional genomics has enabled the molecular biosciences to come a long way towards characterizing the molecular constituents of life. Yet, the challenge for biology overall is to understand how organisms function. By discovering how function arises in dynamic interactions, systems biology addresses the missing links between molecules and physiology. Top-down systems biology identifies molecular interaction networks on the basis of correlated molecular behavior observed in genome-wide "omics" studies. Bottom-up systems biology examines the mechanisms through which functional properties arise in the interactions of known components. Here, we outline the challenges faced by systems biology and discuss limitations of the top-down and bottom-up approaches, which, despite these limitations, have already led to the discovery of mechanisms and principles that underlie cell function.     

Cell death goes LIVE: Technological advances in real-time tracking of cell death

Cell population can be viewed as a quantum system, which like Schrödinger’s cat exists as a combination of survival- and death-allowing states. Tracking and understanding cell-to-cell variability in processes of high spatio-temporal complexity such as cell death is at the core of current systems biology approaches. As probabilistic modeling tools attempt to impute information inaccessible by current experimental approaches, advances in technologies for single-cell imaging and omics (proteomics, genomics, metabolomics) should go hand in hand with the computational efforts. Over the last few years we have made exciting technological advances that allow studies of cell death dynamically in real-time and with the unprecedented accuracy. These approaches are based on innovative fluorescent assays and recombinant proteins, bioelectrical properties of cells, and more recently also on state-of-the-art optical spectroscopy. Here, we review current status of the most innovative analytical technologies for dynamic tracking of cell death, and address the interdisciplinary promises and future challenges of these methods.     

Homogenizing bacterial cell factories: Analysis and engineering of phenotypic heterogeneity

In natural habitats, microbes form multispecies communities that commonly face rapidly changing and highly competitive environments. Thus, phenotypic heterogeneity has evolved as an innate and important survival strategy to gain an overall fitness advantage over cohabiting competitors. However, in defined artificial environments such as monocultures in small- to large-scale bioreactors, cell-to-cell variations are presumed to cause reduced production yields as well as process instability. Hence, engineering microbial production toward phenotypic homogeneity is a highly promising approach for synthetic biology and bioprocess optimization.In this review, we discuss recent studies that have unraveled the cell-to-cell heterogeneity observed during bacterial gene expression and metabolite production as well as the molecular mechanisms involved. In addition, current single-cell technologies are briefly reviewed with respect to their applicability in exploring cell-to-cell variations. We highlight emerging strategies and tools to reduce phenotypic heterogeneity in biotechnological expression setups. Here, strain or inducer modifications are combined with cell physiology manipulations to achieve the ultimate goal of equalizing bacterial populations. In this way, the majority of cells can be forced into high productivity, thus reducing less productive subpopulations that tend to consume valuable resources during production. Modifications in uptake systems, inducer molecules or nutrients represent valuable tools for diminishing heterogeneity.Finally, we address the challenge of transferring homogeneously responding cells into large-scale bioprocesses. Environmental heterogeneity originating from extrinsic factors such as stirring speed and pH, oxygen, temperature or nutrient distribution can significantly influence cellular physiology. We conclude that engineering microbial populations toward phenotypic homogeneity is an increasingly important task to take biotechnological productions to the next level of control.     

独脚金内酯信号感知揭示配体-受体作用新机制

植物激素在调控细胞与细胞及细胞与环境的相互作用中起着至关重要的作用。作为一种信号分子, 植物激素如何被植物细胞感知一直是植物生物学研究的热点。与底物-酶相互作用的结果不同, 激素分子与受体结合后会触发信号转导, 但激素分子一般不会被受体修饰, 信号转导起始后激素分子通常会从复合体中释放出来被重新利用或降解。近期, 我国科学家通过对独脚金内酯及其受体复合体(AtD14-D3-ASK1)的结构学解析, 发现独脚金内酯的生物活性分子CLIM (covalently linked intermediate molecule)是独脚金内酯被其受体水解后得到的中间分子。研究表明, CLIM与受体AtD14的催化中心以共价键相结合, 进而激活其信号转导。该研究揭示了一种全新的“底物-酶-活性分子-受体”激素识别机制。这种配体-受体作用新机制的发现为植物激素研究开拓了新的视野。