The transcription factor network of E. coli steers global responses to shifts in RNAP concentration.

The robustness and sensitivity of gene networks to environmental changes is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the known topology and logic of the transcription factor network (TFN) of Escherichia coli. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs). Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. Our results suggest that the global topological traits of the TFN of E. coli shape which gene cohorts respond to genome-wide stresses.

Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli.

In Escherichia coli, under optimal conditions, protein aggregates associated with cellular aging are excluded from midcell by the nucleoid. We study the functionality of this process under sub-optimal temperatures from population and time lapse images of individual cells and aggregates and nucleoids within. We show that, as temperature decreases, aggregates become homogeneously distributed and uncorrelated with nucleoid size and location. We present evidence that this is due to increased cytoplasm viscosity, which weakens the anisotropy in aggregate displacements at the nucleoid borders that is responsible for their preference for polar localisation. Next, we show that in plasmolysed cells, which have increased cytoplasm viscosity, aggregates are also not preferentially located at the poles. Finally, we show that the inability of cells with increased viscosity to exclude aggregates from midcell results in enhanced aggregate concentration in between the nucleoids in cells close to dividing. This weakens the asymmetries in aggregate numbers between sister cells of subsequent generations required for rejuvenating cell lineages. We conclude that the process of exclusion of protein aggregates from midcell is not immune to stress conditions affecting the cytoplasm viscosity. The findings contribute to our understanding of E. coli's internal organisation and functioning, and its fragility to stressful conditions.

Robustness of the Process of Nucleoid Exclusion of Protein Aggregates in Escherichia coli.

UNLABELLED: Escherichia coli segregates protein aggregates to the poles by nucleoid exclusion. Combined with cell divisions, this generates heterogeneous aggregate distributions in subsequent cell generations. We studied the robustness of this process with differing medium richness and antibiotics stress, which affect nucleoid size, using multimodal, time-lapse microscopy of live cells expressing both a fluorescently tagged chaperone (IbpA), which identifies in vivo the location of aggregates, and HupA-mCherry, a fluorescent variant of a nucleoid-associated protein. We find that the relative sizes of the nucleoid's major and minor axes change widely, in a positively correlated fashion, with medium richness and antibiotic stress. The aggregate's distribution along the major cell axis also changes between conditions and in agreement with the nucleoid exclusion phenomenon. Consequently, the fraction of aggregates at the midcell region prior to cell division differs between conditions, which will affect the degree of asymmetries in the partitioning of aggregates between cells of future generations. Finally, from the location of the peak of anisotropy in the aggregate displacement distribution, the nucleoid relative size, and the spatiotemporal aggregate distribution, we find that the exclusion of detectable aggregates from midcell is most pronounced in cells with mid-sized nucleoids, which are most common under optimal conditions. We conclude that the aggregate management mechanisms of E. coli are significantly robust but are not immune to stresses due to the tangible effect that these have on nucleoid size. IMPORTANCE: Escherichia coli segregates protein aggregates to the poles by nucleoid exclusion. From live single-cell microscopy studies of the robustness of this process to various stresses known to affect nucleoid size, we find that nucleoid size and aggregate preferential locations change concordantly between conditions. Also, the degree of influence of the nucleoid on aggregate positioning differs between conditions, causing aggregate numbers at midcell to differ in cell division events, which will affect the degree of asymmetries in the partitioning of aggregates between cells of future generations. Finally, we find that aggregate segregation to the cell poles is most pronounced in cells with mid-sized nucleoids. We conclude that the energy-free process of the midcell exclusion of aggregates partially loses effectiveness under stressful conditions.

In silico analysis of division times of Escherichia coli populations as a function of the partitioning scheme of non-functional proteins.

Recent evidence suggests that cells employ functionally asymmetric partitioning schemes in division to cope with aging. We explore various schemes in silico, with a stochastic model of Escherichia coli that includes gene expression, non-functional proteins generation, aggregation and polar retention, and molecule partitioning in division. The model is implemented in SGNS2, which allows stochastic, multi-delayed reactions within hierarchical, transient, interlinked compartments. After setting parameter values of non-functional proteins' generation and effects that reproduce realistic intracellular and population dynamics, we investigate how the spatial organization of non-functional proteins affects mean division times of cell populations in lineages and, thus, mean cell numbers over time. We find that division times decrease for increasingly asymmetric partitioning. Also, increasing the clustering of non-functional proteins decreases division times. Increasing the bias in polar segregation further decreases division times, particularly if the bias favors the older pole and aggregates' polar retention is robust. Finally, we show that the non-energy consuming retention of inherited non-functional proteins at the older pole via nucleoid occlusion is a source of functional asymmetries and, thus, is advantageous. Our results suggest that the mechanisms of intracellular organization of non-functional proteins, including clustering and polar retention, affect the vitality of E. coli populations.

In vivo kinetics of segregation and polar retention of MS2-GFP-RNA complexes in Escherichia coli.

The cytoplasm of Escherichia coli is a crowded, heterogeneous environment. From single cell live imaging, we investigated the spatial kinetics and heterogeneities of synthetic RNA-protein complexes. First, although their known tendency to accumulate at the cell poles does not appear to introduce asymmetries between older and newer cell poles within a cell lifetime, these emerge with cell divisions. This suggests strong polar retention of the complexes, which we verified in their history of positions and mean escape time from the poles. Next, we show that the polar retention relies on anisotropies in the displacement distribution in the region between midcell and poles, whereas the speed is homogeneous along the major cell axis. Afterward, we establish that these regions are at the border of the nucleoid and shift outward with cell growth, due to the nucleoid's replication. Overall, the spatiotemporal kinetics of the complexes, which is robust to suboptimal temperatures, suggests that nucleoid occlusion is a source of dynamic heterogeneities of macromolecules in E. coli that ultimately generate phenotypic differences between sister cells.

Robustness of the division symmetry in Escherichia coli and functional consequences of symmetry breaking.

The morphological symmetry of the division process of Escherichia coli is well-known. Recent studies verified that, in optimal growth conditions, most divisions are symmetric, although there are exceptions. We investigate whether such morphological asymmetries in division introduce functional asymmetries between sister cells, and assess the robustness of the symmetry in division to mild chemical stresses and sub-optimal temperatures. First, we show that the difference in size between daughter cells at birth is positively correlated to the difference between the numbers of fluorescent protein complexes inherited from the parent cell. Next, we show that the degree of symmetry in division observed in optimal conditions is robust to mild acidic shift and to mild oxidative stress, but not to sub-optimal temperatures, in that the variance of the difference between the sizes of sister cells at birth is minimized at 37 °C. This increased variance affects the functionality of the cells in that, at sub-optimal temperatures, larger/smaller cells arising from asymmetric divisions exhibit faster/slower division times than the mean population division time, respectively. On the other hand, cells dividing faster do not do so at the cost of morphological symmetry in division. Finally we show that at suboptimal temperatures the mean distance between the nucleoids increases, explaining the increased variance in division. We conclude that the functionality of E. coli cells is not immune to morphological asymmetries at birth, and that the effectiveness of the mechanism responsible for ensuring the symmetry in division weakens at sub-optimal temperatures.

SGNS2: a compartmentalized stochastic chemical kinetics simulator for dynamic cell populations.

MOTIVATION: Cell growth and division affect the kinetics of internal cellular processes and the phenotype diversity of cell populations. Since the effects are complex, e.g. different cellular components are partitioned differently in cell division, to account for them in silico, one needs to simulate these processes in great detail. RESULTS: We present SGNS2, a simulator of chemical reaction systems according to the Stochastic Simulation Algorithm with multi-delayed reactions within hierarchical, interlinked compartments which can be created, destroyed and divided at runtime. In division, molecules are randomly segregated into the daughter cells following a specified distribution corresponding to one of several partitioning schemes, applicable on a per-molecule-type basis. We exemplify its use with six models including a stochastic model of the disposal mechanism of unwanted protein aggregates in Escherichia coli, a model of phenotypic diversity in populations with different levels of synchrony, a model of a bacteriophage's infection of a cell population and a model of prokaryotic gene expression at the nucleotide and codon levels. AVAILABILITY: SGNS2, instructions and examples available at www.cs.tut.fi/~lloydpri/sgns2/ (open source under New BSD license). CONTACT: jason.lloyd-price@tut.fi. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening.

Accurate quantification of drug effects is crucial for identifying pharmaceutically actionable cancer vulnerabilities. Current cell viability-based measurements often lead to biased response estimates due to varying growth rates and experimental artifacts that explain part of the inconsistency in high-throughput screening results. We developed an improved drug scoring model, normalized drug response (NDR), which makes use of both positive and negative control conditions to account for differences in cell growth rates, and experimental noise to better characterize drug-induced effects. We demonstrate an improved consistency and accuracy of NDR compared to existing metrics in assessing drug responses of cancer cells in various culture models and experimental setups. Notably, NDR reliably captures both toxicity and viability responses, and differentiates a wider spectrum of drug behavior, including lethal, growth-inhibitory and growth-stimulatory modes, based on a single viability readout. The method will therefore substantially reduce the time and resources required in cell-based drug sensitivity screening.

UBR5 Is Coamplified with MYC in Breast Tumors and Encodes an Ubiquitin Ligase That Limits MYC-Dependent Apoptosis.

For maximal oncogenic activity, cellular MYC protein levels need to be tightly controlled so that they do not induce apoptosis. Here, we show how ubiquitin ligase UBR5 functions as a molecular rheostat to prevent excess accumulation of MYC protein. UBR5 ubiquitinates MYC and its effects on MYC protein stability are independent of FBXW7. Silencing of endogenous UBR5 induced MYC protein expression and regulated MYC target genes. Consistent with the tumor suppressor function of UBR5 (HYD) in Drosophila, HYD suppressed dMYC-dependent overgrowth of wing imaginal discs. In contrast, in cancer cells, UBR5 suppressed MYC-dependent priming to therapy-induced apoptosis. Of direct cancer relevance, MYC and UBR5 genes were coamplified in MYC-driven human cancers. Functionally, UBR5 suppressed MYC-mediated apoptosis in p53-mutant breast cancer cells with UBR5/MYC coamplification. Furthermore, single-cell immunofluorescence analysis demonstrated reciprocal expression of UBR5 and MYC in human basal-type breast cancer tissues. In summary, UBR5 is a novel MYC ubiquitin ligase and an endogenous rheostat for MYC activity. In MYC-amplified, and p53-mutant breast cancer cells, UBR5 has an important role in suppressing MYC-mediated apoptosis priming and in protection from drug-induced apoptosis. SIGNIFICANCE: These findings identify UBR5 as a novel MYC regulator, the inactivation of which could be very important for understanding of MYC dysregulation on cancer cells. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/7/1414/F1.large.jpg.

Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.

An Overview of Network-Based and -Free Approaches for Stochastic Simulation of Biochemical Systems.

Stochastic simulation has been widely used to model the dynamics of biochemical reaction networks. Several algorithms have been proposed that are exact solutions of the chemical master equation, following the work of Gillespie. These stochastic simulation approaches can be broadly classified into two categories: network-based and -free simulation. The network-based approach requires that the full network of reactions be established at the start, while the network-free approach is based on reaction rules that encode classes of reactions, and by applying rule transformations, it generates reaction events as they are needed without ever having to derive the entire network. In this study, we compare the efficiency and limitations of several available implementations of these two approaches. The results allow for an informed selection of the implementation and methodology for specific biochemical modeling applications.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

Robustness and information propagation in attractors of Random Boolean Networks.

Attractors represent the long-term behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (I(A)), relates to the robustness of the attractor to perturbations (R(A)). We find that the dynamical regime of the network affects the relationship between I(A) and R(A). In the ordered and chaotic regimes, I(A) is anti-correlated with R(A), implying that attractors that are highly robust to perturbations have necessarily limited information propagation. Between order and chaos (for so-called "critical" networks) these quantities are uncorrelated. Finite size effects cause this behavior to be visible for a range of networks, from having a sensitivity of 1 to the point where I(A) is maximized. In this region, the two quantities are weakly correlated and attractors can be almost arbitrarily robust to perturbations without restricting the propagation of information in the network.

Regulation of mean and noise of the in vivo kinetics of transcription under the control of the lac/ara-1 promoter.

The kinetics of transcription initiation in Escherichia coli depend on the duration of two rate-limiting steps, the closed and the open complex formation. In a lac promoter variant, P(lac/ara-1), the kinetics of these steps is controlled by IPTG and arabinose. From in vivo single-RNA measurements, we find that induction affects the mean and normalized variance of the intervals between consecutive RNA productions. Transcript production is sub-Poissonian in all conditions tested. The kinetics of each step is independently controlled by a different inducer. We conclude that the regulatory mechanism of P(lac/ara-1) allows the stochasticity of gene expression to be environment-dependent.

In vivo kinetics of transcription initiation of the lar promoter in Escherichia coli. Evidence for a sequential mechanism with two rate-limiting steps.

BACKGROUND: In Escherichia coli the mean and cell-to-cell diversity in RNA numbers of different genes vary widely. This is likely due to different kinetics of transcription initiation, a complex process with multiple rate-limiting steps that affect RNA production. RESULTS: We measured the in vivo kinetics of production of individual RNA molecules under the control of the lar promoter in E. coli. From the analysis of the distributions of intervals between transcription events in the regimes of weak and medium induction, we find that the process of transcription initiation of this promoter involves a sequential mechanism with two main rate-limiting steps, each lasting hundreds of seconds. Both steps become faster with increasing induction by IPTG and Arabinose. CONCLUSIONS: The two rate-limiting steps in initiation are found to be important regulators of the dynamics of RNA production under the control of the lar promoter in the regimes of weak and medium induction. Variability in the intervals between consecutive RNA productions is much lower than if there was only one rate-limiting step with a duration following an exponential distribution. The methodology proposed here to analyze the in vivo dynamics of transcription may be applicable at a genome-wide scale and provide valuable insight into the dynamics of prokaryotic genetic networks.