Viral-mediated noisy gene expression reveals biphasic E2f1 response to MYC

A unified approach to dissecting biphasic responses in cell signaling

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signaling and post-translational modification systems and represent safeguards against overactivation or overexpression of species. In this paper, we provide a unified theoretical synthesis of biphasic responses in cell signaling systems, by assessing signaling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other. By using analytical and computational approaches applied to a range of systems across levels (described by broadly employed models), we reveal (i) design principles enabling the presence of biphasic responses, including in almost all instances, an explicit characterization of the parameter space (ii) structural factors which preclude the possibility of biphasic responses (iii) different combinations of the presence or absence of enzyme-biphasic and substrate-biphasic responses, representing safeguards against overactivation and overexpression, respectively (iv) the possibility of broadly robust biphasic responses (v) the complete alteration of signaling behavior in a network due to biphasic interactions between species (biphasic regulation) (vi) the propensity of different co-existing biphasic responses in the Erk signaling network. These results both individually and in totality have a number of important consequences for systems and synthetic biology.

miRNAs responsive to the diabetic microenvironment in the human beta cell line EndoC-βH1 may target genes in the FOXO, HIPPO and Lysine degradation pathways

Altered expression of miRNAs is evident in the islets of diabetic human donors, but the effects of specific aspects of the diabetic microenvironment and identity of gene ontology pathways demonstrating target gene enrichment in response to each is understudied. We assessed changes in the miRNA milieu in response to high/low glucose, hypoxia, dyslipidaemia and inflammatory factors in a humanised EndoC-βH1 beta cell culture system and performed miRPath analysis for each treatment individually. The 10 miRNAs demonstrating the greatest dysregulation across treatments were then independently validated and Gene Set Enrichment Analysis to confirm targeted pathways undertaken. 171 of 392 miRNAs displayed altered expression in response to one or more cellular stressors. miRNA changes were treatment specific, but their target genes were enriched in conserved pathways. 5 miRNAs (miR-136-5p, miR299-5p, miR-454-5p, miR-152 and miR-185) were dysregulated in response to multiple stressors and survived validation in independent samples (p = 0.008, 0.002, 0.012, 0.005 and 0.024 respectively). Target genes of dysregulated miRNAs were clustered into FOXO1, HIPPO and Lysine degradation pathways (p = 0.02, p = 5.84 × 10^-5 and p = 3.00 × 10^-3 respectively). We provide evidence that the diabetic microenvironment may induce changes to the expression of miRNAs targeting genes enriched in pathways involved in cell stress response and cell survival.

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.

Mechanistic models provide valuable insights, but large-scale simulations are computationally expensive. Here, the authors show that it is possible to explore the dynamics of a mechanistic model over a large set of parameters by training an artificial neural network on a smaller set of simulations.

Temporal order and precision of complex stress responses in individual bacteria

Sudden stress often triggers diverse, temporally structured gene expression responses in microbes, but it is largely unknown how variable in time such responses are and if genes respond in the same temporal order in every single cell. Here, we quantified timing variability of individual promoters responding to sublethal antibiotic stress using fluorescent reporters, microfluidics, and time‐lapse microscopy. We identified lower and upper bounds that put definite constraints on timing variability, which varies strongly among promoters and conditions. Timing variability can be interpreted using results from statistical kinetics, which enable us to estimate the number of rate‐limiting molecular steps underlying different responses. We found that just a few critical steps control some responses while others rely on dozens of steps. To probe connections between different stress responses, we then tracked the temporal order and response time correlations of promoter pairs in individual cells. Our results support that, when bacteria are exposed to the antibiotic nitrofurantoin, the ensuing oxidative stress and SOS responses are part of the same causal chain of molecular events. In contrast, under trimethoprim, the acid stress response and the SOS response are part of different chains of events running in parallel. Our approach reveals fundamental constraints on gene expression timing and provides new insights into the molecular events that underlie the timing of stress responses.

Controlling Depth of Cellular Quiescence by an Rb-E2F Network Switch

Quiescence is a non-proliferative cellular state that is critical to tissue repair and regeneration. Although often described as the G0 phase, quiescence is not a single homogeneous state. As cells remain quiescent for longer durations, they move progressively deeper and display a reduced sensitivity to growth signals. Deep quiescent cells, unlike senescent cells, can still re-enter the cell cycle under physiological conditions. Mechanisms controlling quiescence depth are poorly understood, representing a currently underappreciated layer of complexity in growth control. Here, we show that the activation threshold of a Retinoblastoma (Rb)-E2F network switch controls quiescence depth. Particularly, deeper quiescent cells feature a higher E2F-switching threshold and exhibit a delayed traverse through the restriction point (R-point). We further show that different components of the Rb-E2F network can be experimentally perturbed, following computer model predictions, to coarse- or fine-tune the E2F-switching threshold and drive cells into varying quiescence depths.

Alteration in microRNA-17-92 dynamics accounts for differential nature of cellular proliferation

MicroRNAs associated with the mir-17-92 cluster are crucial regulators of the mammalian cell cycle, as they inhibit transcription factors related to the E2F family that tightly control decision-making events for a cell to commit for active cellular proliferation. Intriguingly, in many solid cancers, these mir-17-92 cluster members are overexpressed, whereas in some hematopoietic cancers they are down-regulated. Our proposed model of the Myc/E2F/mir-17-92 network demonstrates that the differential expression pattern of mir-17-92 in different cell types can be conceived due to having a contrasting E2F dynamics induced by mir-17-92. The model predicts that by explicitly altering the mir-17-92-related part of the network, experimentally it is possible to control cellular proliferation in a cell type-dependent manner for therapeutic intervention.

Crosstalk between E2F1 and P53 transcription factors in doxorubicin-induced DNA damage: evidence for preventive/protective effects of silymarin

Objectives

To study the effects of silymarin in various forms of applications on the molecular mechanism(s) of doxorubicin-induced testicular toxicity in male rats.

Methods

Following DOX administration with or without SMN in male rats, sperm quality assays were conducted. Moreover, total antioxidant capacity and nitric oxide content of testis were determined. Expression profile of p53 and E2F1 was analysed by PCR technique. Ultimately, the rate of DNA fragmentation in the testes was quantitatively measured.

Key findings

P53 and E2F1 expression in DOX-received animals at mRNA level showed a revers profile of an up- and down-regulation, respectively. Administration of SMN in preventive and protective forms resulted in a significant (P < 0.05) reduction in DOX-induced sperm abnormalities, DNA fragmentation, nitric oxide concentration and a marked increase in total antioxidant power, rate of sperm motility and viability. SMN lowered the DOX-up-regulated expression of p53 at mRNA level.

Conclusions

DOX-induced testicular toxicity was characterized by lowering sperm quality values, induction of oxidative and nitrosative stress and DNA fragmentation. Preventive and protective effects of SMN on DOX-induced testicular toxicity may attribute to its antioxidant property. DOX-induced testicular damages and SMN preventive/protective effects might be mediated via up- and down-regulation of p53 and E2F1 transcription factors.

Expression level is a key determinant of E2F1-mediated cell fate

The Rb/E2F network has a critical role in regulating cell cycle progression and cell fate decisions. It is dysfunctional in virtually all human cancers, because of genetic lesions that cause overexpression of activators, inactivation of repressors, or both. Paradoxically, the downstream target of this network, E2F1, is rarely strongly overexpressed in cancer. E2F1 can induce both proliferation and apoptosis but the factors governing these critical cell fate decisions remain unclear. Previous studies have focused on qualitative mechanisms such as differential cofactors, posttranslational modification or state of other signaling pathways as modifiers of the cell fate decisions downstream of E2F1 activation. In contrast, the importance of the expression levels of E2F1 itself in dictating the downstream phenotypes has not been rigorously studied, partly due to the limited resolution of traditional population-level measurements. Here, through single-cell quantitative analysis, we demonstrate that E2F1 expression levels have a critical role in determining the fate of individual cells. Low levels of exogenous E2F1 promote proliferation, moderate levels induce G1, G2 and mitotic cell cycle arrest, and very high levels promote apoptosis. These multiple anti-proliferative mechanisms result in a strong selection pressure leading to rapid elimination of E2F1-overexpressing cells from the population. RNA-sequencing and RT-PCR revealed that low levels of E2F1 are sufficient to induce numerous cell cycle-promoting genes, intermediate levels induce growth arrest genes (i.e., p18, p19 and p27), whereas higher levels are necessary to induce key apoptotic E2F1 targets APAF1, PUMA, HRK and BIM. Finally, treatment of a lung cancer cell line with a proteasome inhibitor, MLN2238, resulted in an E2F1-dependent mitotic arrest and apoptosis, confirming the role of endogenous E2F1 levels in these phenotypes. The strong anti-proliferative activity of moderately overexpressed E2F1 in multiple cancer types suggests that targeting E2F1 for upregulation may represent an attractive therapeutic strategy in cancer.

Kinetic Modeling of ABCG2 Transporter Heterogeneity: A Quantitative, Single-Cell Analysis of the Side Population Assay

The side population (SP) assay, a technique used in cancer and stem cell research, assesses the activity of ABC transporters on Hoechst staining in the presence and absence of transporter inhibition, identifying SP and non-SP cell (NSP) subpopulations by differential staining intensity. The interpretation of the assay is complicated because the transporter-mediated mechanisms fail to account for cell-to-cell variability within a population or adequately control the direct role of transporter activity on staining intensity. We hypothesized that differences in dye kinetics at the single-cell level, such as ABCG2 transporter-mediated efflux and DNA binding, are responsible for the differential cell staining that demarcates SP/NSP identity. We report changes in A549 phenotype during time in culture and with TGFβ treatment that correlate with SP size. Clonal expansion of individually sorted cells re-established both SP and NSPs, indicating that SP membership is dynamic. To assess the validity of a purely kinetics-based interpretation of SP/NSP identity, we developed a computational approach that simulated cell staining within a heterogeneous cell population; this exercise allowed for the direct inference of the role of transporter activity and inhibition on cell staining. Our simulated SP assay yielded appropriate SP responses for kinetic scenarios in which high transporter activity existed in a portion of the cells and little differential staining occurred in the majority of the population. With our approach for single-cell analysis, we observed SP and NSP cells at both ends of a transporter activity continuum, demonstrating that features of transporter activity as well as DNA content are determinants of SP/NSP identity.

Crosstalk between E2f1 and c-Myc mediates hepato-protective effect of royal jelly on taxol-induced damages

Previous histopathological studies have shown the hepatotoxicity of paclitaxel (TXL). However, there is little known about the molecular pathway(s) of TXL-induced hepatotoxicity. Therefore, this study aimed to uncover the role of two transcription factors in the TXL-induced hepatotoxicity. Moreover, the hepato-protective effect of royal jelly (RJ) on TXL-induced toxicity was investigated. Wistar rats were divided into control and test groups. The test groups along with TXL received various doses of RJ (0, 50, 100 and 150 mg/kg, body weight). Biochemical hepatic functional assays, histopathological studies and hepatic superoxide dismutase level were determined. Additionally, the expression of E2f1 and cellular-myelocytomatosis (c-Myc) at messenger RNA (mRNA) level in the liver was evaluated. The hepatic functional biomarkers showed a significant ( p < 0.05) elevation in the TXL-received animals, while RJ administration for 28 days resulted in a remarkable reduction in TXL-elevated alkaline phosphatase, alanine transaminase and lactate dehydrogenase levels. The TXL-treated animals showed a significant ( p < 0.05) up-regulation of E2f1 and down-regulation of c-Myc at mRNA level, respectively. RJ lowered the expression of E2f1 while enhanced the expression of c-Myc in a dose-dependent manner. Our data suggest the hepato-protective effects of RJ on TXL-induced toxicity, which may attribute to a clear crosstalk between E2f1 and c-Myc as two regulators of liver growth.

Cellular Interrogation: Exploiting Cell-to-Cell Variability to Discriminate Regulatory Mechanisms in Oscillatory Signalling

The molecular complexity within a cell may be seen as an evolutionary response to the external complexity of the cell’s environment. This suggests that the external environment may be harnessed to interrogate the cell’s internal molecular architecture. Cells, however, are not only nonlinear and non-stationary, but also exhibit heterogeneous responses within a clonal, isogenic population. In effect, each cell undertakes its own experiment. Here, we develop a method of cellular interrogation using programmable microfluidic devices which exploits the additional information present in cell-to-cell variation, without requiring model parameters to be fitted to data. We focussed on Ca²⁺ signalling in response to hormone stimulation, which exhibits oscillatory spiking in many cell types and chose eight models of Ca²⁺ signalling networks which exhibit similar behaviour in simulation. We developed a nonlinear frequency analysis for non-stationary responses, which could classify models into groups under parameter variation, but found that this question alone was unable to distinguish critical feedback loops. We further developed a nonlinear amplitude analysis and found that the combination of both questions ruled out six of the models as inconsistent with the experimentally-observed dynamics and heterogeneity. The two models that survived the double interrogation were mathematically different but schematically identical and yielded the same unexpected predictions that we confirmed experimentally. Further analysis showed that subtle mathematical details can markedly influence non-stationary responses under parameter variation, emphasising the difficulty of finding a “correct” model. By developing questions for the pathway being studied, and designing more versatile microfluidics, cellular interrogation holds promise as a systematic strategy that can complement direct intervention by genetics or pharmacology.

We have developed a cellular interrogation methodology that combines programmable microfluidics, fluorescence microscopy and mathematical analysis and have used it to discriminate between models of repetitive Ca²⁺ spiking in HeLa cells. Our approach exploits the natural variability in response of individual cells in a clonal population and the non-steady state behavior of the response in each cell, thereby providing more powerful discrimination. Interrogation consists of steps or pulses of histamine of fixed concentration and width but varying frequency. Eight mathematical models of repetitive Ca²⁺ spiking were chosen from the literature and methods of nonlinear frequency and nonlinear amplitude analysis were developed which ruled out all but two of the models, without having to fit the models to the data. Further analysis of the remaining models yielded predictions that were experimentally confirmed. Cellular interrogation offers a general approach to ruling out competing hypotheses about molecular mechanisms, which is complementary to traditional methods of genetics and biochemistry.

MYC, Metabolic Synthetic Lethality, and Cancer

The MYC oncogene plays a pivotal role in the development and progression of human cancers. It encodes a transcription factor that has broad reaching effects on many cellular functions, most importantly in driving cell growth through regulation of genes involved in ribosome biogenesis, metabolism, and cell cycle. Upon binding DNA with its partner MAX, MYC recruits factors that release paused RNA polymerases to drive transcription and amplify gene expression. At physiologic levels of MYC, occupancy of high-affinity DNA-binding sites drives 'house-keeping' metabolic genes and those involved in ribosome and mitochondrial biogenesis for biomass accumulation. At high oncogenic levels of MYC, invasion of low-affinity sites and enhancer sequences alter the transcriptome and cause metabolic imbalances, which activates stress response and checkpoints such as p53. Loss of checkpoints unleashes MYC's full oncogenic potential to couple metabolism with neoplastic cell growth and division. Cells that overexpress MYC, however, are vulnerable to metabolic perturbations that provide potential new avenues for cancer therapy.

Addressing biological uncertainties in engineering gene circuits

Synthetic biology has grown tremendously over the past fifteen years. It represents a new strategy to develop biological understanding and holds great promise for diverse practical applications. Engineering of a gene circuit typically involves computational design of the circuit, selection of circuit components, and test and optimization of circuit functions. A fundamental challenge in this process is the predictable control of circuit function due to multiple layers of biological uncertainties. These uncertainties can arise from different sources. We categorize these uncertainties into incomplete quantification of parts, interactions between heterologous components and the host, or stochastic dynamics of chemical reactions and outline potential design strategies to minimize or exploit them.

Stochastic sensitivity analysis and kernel inference via distributional data

Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.

Basal p21 controls population heterogeneity in cycling and quiescent cell cycle states

Phenotypic heterogeneity within a population of genetically identical cells is emerging as a common theme in multiple biological systems, including human cell biology and cancer. Using live-cell imaging, flow cytometry, and kinetic modeling, we showed that two states--quiescence and cell cycling--can coexist within an isogenic population of human cells and resulted from low basal expression levels of p21, a Cyclin-dependent kinase (CDK) inhibitor (CKI). We attribute the p21-dependent heterogeneity in cell cycle activity to double-negative feedback regulation involving CDK2, p21, and E3 ubiquitin ligases. In support of this mechanism, analysis of cells at a point before cell cycle entry (i.e., before the G1/S transition) revealed a p21-CDK2 axis that determines quiescent and cycling cell states. Our findings suggest a mechanistic role for p21 in generating heterogeneity in both normal tissues and tumors.

E2F1 is a novel fibrogenic gene that regulates cholestatic liver fibrosis through the Egr-1/SHP/EID1 network

E2F transcription factor 1 (E2F1) is an important regulator of metabolic diseases; however, its role in liver function remains elusive. This study unraveled a regulatory cascade involving E2F1, early growth response-1 (Egr-1), nuclear receptor small heterodimer partner (SHP, NR0B2), and EIA-like inhibitor of differentiation 1 (EID1) in cholestatic liver fibrosis. Liver E2F1 messenger RNA (mRNA) and protein expression was strongly up-regulated in human nonalcoholic steatohepatitis (NASH) and alcohol cirrhosis; the latter was inversely correlated with diminished SHP expression. E2F1 was also highly induced by 3,5-diethoxycarbonyl-1, 4-dihydrocollidine (DDC) feeding and bile-duct ligation (BDL) in mice. E2F1-/- mice exhibited reduced biliary fibrosis by DDC as determined by Masson Trichrome and Picro Sirius red staining, and decreased serum bile acid (BA), BA pool size, and fecal BA excretion. In addition, cholestatic liver fibrosis induced by BDL, as determined by immunohistochemistry analysis of a1 collagen expression, was increased in SHP-/- mice but attenuated in hepatocyte SHP-overexpressed transgenic (STG) mice. Egr-1 exhibited marked induction in livers of SHP-/- mice compared to the wild-type mice in both sham and BDL groups, and reduction in STG livers. Egr-1 promoter was activated by E2F1, and the activation was abrogated by expression of SHP and its co-repressor EID1 in hepatoma cells Huh7, Hepa1, and stellate cells LX2. Chromatin immunoprecipitation assays further confirmed the association of E2F1, SHP, and EID1 proteins with the Egr-1 promoter, and their direct protein interactions were determined by glutathione S-transferase pull-down assays. Interestingly, E2F1 activated Egr-1 expression in a biphasic fashion as described in both human and mouse hepatocytes.

CONCLUSION

E2F1 is a fibrogenic gene and could serve as a potential new diagnostic marker for nonalcoholic and alcoholic liver fibrosis/cirrhosis.

Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control

A body of evidence has shown that the control of E2F transcription factor activity is critical for determining cell cycle entry and cell proliferation. However, an understanding of the precise determinants of this control, including the role of other cell-cycle regulatory activities, has not been clearly defined. Here, recognizing that the contributions of individual regulatory components could be masked by heterogeneity in populations of cells, we model the potential roles of individual components together with the use of an integrated system to follow E2F dynamics at the single-cell level and in real time. These analyses reveal that crossing a threshold amplitude of E2F accumulation determines cell cycle commitment. Importantly, we find that Myc is critical in modulating the amplitude, whereas cyclin D/E activities have little effect on amplitude but do contribute to the modulation of duration of E2F activation, thereby affecting the pace of cell cycle progression.

Systematic reverse engineering of network topologies: a case study of resettable bistable cellular responses

A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.

Linear population allocation by bistable switches in response to transient stimulation

Many cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g. OFF and ON). While extensive studies have been carried out to analyze various bistable networks, they are primarily focused on responses of bistable networks to sustained input signals. In this work, we investigate the response characteristics of bistable networks to transient signals, using both theoretical analysis and numerical simulation. We find that bistable systems exhibit a common property: for input signals with short durations, the fraction of switching cells increases linearly with the signal duration, allowing the population to integrate transient signals to tune its response. We propose that this allocation algorithm can be an optimal response strategy for certain cellular decisions in which excessive switching results in lower population fitness.

Phenotypic signatures arising from unbalanced bacterial growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.

The measurement of bacterial growth in batch cultures is a routine practice in microbiology. In these cultures, bacterial growth rates drastically fluctuate over time due to the continuously changing growth environment: changing population size, accumulation of waste products, and depletion of nutrients. Such “unbalanced” growth is normally considered undesirable, which has led to the design of methods to achieve balanced growth environments (i.e., chemostats). However, we have discovered that unbalanced growth dynamics contain rich information that can be exploited to deduce regulatory functions or to classify cell strains or growth conditions. We further show that this approach is generally applicable to temporal gene expression data. Taken together, our method and results have broad implications for system identification, experimental design, and the study of cellular growth.

Modulating the frequency and bias of stochastic switching to control phenotypic variation

Mechanisms that control cell-to-cell variation in gene expression ('phenotypic variation') can determine a population's growth rate, robustness, adaptability and capacity for complex behaviours. Here we describe a general strategy (termed FABMOS) for tuning the phenotypic variation and mean expression of cell populations by modulating the frequency and bias of stochastic transitions between 'OFF' and 'ON' expression states of a genetic switch. We validated the strategy experimentally using a synthetic fim switch in Escherichia coli. Modulating the frequency of switching can generate a bimodal (low frequency) or a unimodal (high frequency) population distribution with the same mean expression. Modulating the bias as well as the frequency of switching can generate a spectrum of bimodal and unimodal distributions with the same mean expression. This remarkable control over phenotypic variation, which cannot be easily achieved with standard gene regulatory mechanisms, has many potential applications for synthetic biology, engineered microbial ecosystems and experimental evolution.

E2F1 acts as a negative feedback regulator of c-Myc‑induced hTERT transcription during tumorigenesis

Since induction of hTERT expression and subsequent telomerase activation play a critical role in the multistep process of tumorigenesis, a better understanding of hTERT regulation may provide not only a rationale for the molecular basis of cancer progression but also a path to the development of cancer prevention. The c-Myc oncoprotein can function effectively in activating the transcriptional expression of hTERT through E-box elements on its promoter. E2F transcription factor 1 (E2F1) was found to be a repressor of hTERT transcription by directly binding to its promoter, thereby inhibiting hTERT protein expression. For the extensively crosstalk between c-Myc and E2F1 signals, which is now known to be vital to cell fate, we speculated that E2F1 may play a negative regulatory role in c-Myc-induced hTERT transcription. In the present study, we chose to use human embryonic fibroblast cells as an experimental model system, and present evidence that the E2F1 transcription factor constitutes a negative regulatory system to limit c-Myc transcriptional activation of hTERT in normal cells. Furthermore, we demonstrated that upregulation of the miR-17-92 cluster (miR-20a/miR-17-5p) is involved in the regulation of E2F1-mediated negative feedback of the c-Myc/hTERT pathway. Our results not only reveal novel insights into how normal cells control the transmission of c-Myc-mediated oncogenic signals, but also further establish E2F1 as an important molecular target for cancer therapy.

Vitamin E improved cypermethrin-induced damages in the ovary of rats; evidence for angiogenesis and p53 involvement

This study aimed to investigate the protective effect of vitamin E (VitE) on cypermethrin (CPM)-induced damages in the ovary. Wistar rats were divided into seven groups (n=6) including; control-sham (c), CPM-received (CPM, 75 mg/kg, i.p.), and CPM and VitE-treated (VitE, 150 mg/kg, orally) for 7, 14 and 24 days. The antioxidant status determination and hormonal assays along with histological and immunofluorescent assessments were performed. The expression of p53 at mRNA level was also examined. The CPM administration affected the ovarian structure and functions as it elevated the follicular atresia and significantly (P<0.05) lowered the estradiol level, time dependently. VitE administration enhanced the CPM-reduced antioxidant capacity, gonadotropins and estradiol levels. Co-administration of VitE and CPM remarkably attenuated the CPM-induced RNA damage in granulosa and theca cells and elevated the deranged angiogenesis. The CPM-reduced micro and macro vessels distribution was significantly (P<0.05) elevated in the VitE-received animals. Expression of p53 at mRNA level was down regulated in the VitE-treated groups completely and relatively following 7 and 14 days, respectively. Our data showed that the CPM-induced biochemical and histological damages could be prevented by VitE. Moreover, protective effects of VitE attribute to its potency in enhancing the antioxidant capacity and promoting the gonadotropins secretion, which resulted in down regulation of p53 overexpression and RNA damage in follicular cells accomplished with improved angiogenesis.

Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network

Drug resistance is a major cause of deaths from cancer. E2F1 is a transcription factor involved in cell proliferation, apoptosis. and metastasis through an intricate regulatory network, which includes other transcription factors like p73 and cancer-related microRNAs like miR-205. To investigate the emergence of drug resistance, we developed a methodology that integrates experimental data with a network biology and kinetic modeling. Using a regulatory map developed to summarize knowledge on E2F1 and its interplay with p73/DNp73 and miR-205 in cancer drug responses, we derived a kinetic model that represents the network response to certain genotoxic and cytostatic anticancer drugs. By perturbing the model parameters, we simulated heterogeneous cell configurations referred to as in silico cell lines. These were used to detect genetic signatures characteristic for single or double drug resistance. We identified a signature composed of high E2F1 and low miR-205 expression that promotes resistance to genotoxic drugs. In this signature, downregulation of miR-205, can be mediated by an imbalance in the p73/DNp73 ratio or by dysregulation of other cancer-related regulators of miR-205 expression such as TGFβ-1 or TWIST1. In addition, we found that a genetic signature composed of high E2F1, low miR-205, and high ERBB3 can render tumor cells insensitive to both cytostatic and genotoxic drugs. Our model simulations also suggested that conventional genotoxic drug treatment favors selection of chemoresistant cells in genetically heterogeneous tumors, in a manner requiring dysregulation of incoherent feedforward loops that involve E2F1, p73/DNp73, and miR-205.

A synthetic biology approach to understanding cellular information processing

The survival of cells and organisms requires proper responses to environmental signals. These responses are governed by cellular networks, which serve to process diverse environmental cues. Biological networks often contain recurring network topologies called "motifs". It has been recognized that the study of such motifs allows one to predict the response of a biological network and thus cellular behavior. However, studying a single motif in complete isolation of all other network motifs in a natural setting is difficult. Synthetic biology has emerged as a powerful approach to understanding the dynamic properties of network motifs. In addition to testing existing theoretical predictions, construction and analysis of synthetic gene circuits has led to the discovery of novel motif dynamics, such as how the combination of simple motifs can lead to autonomous dynamics or how noise in transcription and translation can affect the dynamics of a motif. Here, we review developments in synthetic biology as they pertain to increasing our understanding of cellular information processing. We highlight several types of dynamic behaviors that diverse motifs can generate, including the control of input/output responses, the generation of autonomous spatial and temporal dynamics, as well as the influence of noise in motif dynamics and cellular behavior.

Cooperative binding of transcription factors promotes bimodal gene expression response

In the present work we extend and analyze the scope of our recently proposed stochastic model for transcriptional regulation, which considers an arbitrarily complex cis-regulatory system using only elementary reactions. Previously, we determined the role of cooperativity on the intrinsic fluctuations of gene expression for activating transcriptional switches, by means of master equation formalism and computer simulation. This model allowed us to distinguish between two cooperative binding mechanisms and, even though the mean expression levels were not affected differently by the acting mechanism, we showed that the associated fluctuations were different. In the present generalized model we include other regulatory functions in addition to those associated to an activator switch. Namely, we introduce repressive regulatory functions and two theoretical mechanisms that account for the biphasic response that some cis-regulatory systems show to the transcription factor concentration. We have also extended our previous master equation formalism in order to include protein production by stochastic translation of mRNA. Furthermore, we examine the graded/binary scenarios in the context of the interaction energy between transcription factors. In this sense, this is the first report to show that the cooperative binding of transcription factors to DNA promotes the "all-or-none" phenomenon observed in eukaryotic systems. In addition, we confirm that gene expression fluctuation levels associated with one of two cooperative binding mechanism never exceed the fluctuation levels of the other.

Tension and Robustness in Multitasking Cellular Networks

Cellular networks multitask by exhibiting distinct, context-dependent dynamics. However, network states (parameters) that generate a particular dynamic are often sub-optimal for others, defining a source of “tension” between them. Though multitasking is pervasive, it is not clear where tension arises, what consequences it has, and how it is resolved. We developed a generic computational framework to examine the source and consequences of tension between pairs of dynamics exhibited by the well-studied RB-E2F switch regulating cell cycle entry. We found that tension arose from task-dependent shifts in parameters associated with network modules. Although parameter sets common to distinct dynamics did exist, tension reduced both their accessibility and resilience to perturbation, indicating a trade-off between “one-size-fits-all” solutions and robustness. With high tension, robustness can be preserved by dynamic shifting of modules, enabling the network to toggle between tasks, and by increasing network complexity, in this case by gene duplication. We propose that tension is a general constraint on the architecture and operation of multitasking biological networks. To this end, our work provides a framework to quantify the extent of tension between any network dynamics and how it affects network robustness. Such analysis would suggest new ways to interfere with network elements to elucidate the design principles of cellular networks.

Multitasking pervades our daily lives. For example, the technological devices that we increasingly rely upon are now engineered with such multifunctionality or “integration” in mind. Similarly, cellular networks also multitask in that they generate multiple, distinct dynamics according to their operating context. Here we show that differences in parameter spaces that underlie different dynamics thus cause a “tension”, which ultimately constrains network operation. In particular, our analysis reveals that tension negatively impacts robustness by reducing accessibility of parameters able to accomplish two tasks and reduces their ability to withstand perturbations. The presence of tension and its negative impact on network robustness represents a fundamental, generic constraint on the operation of different multitasking networks.

Dynamics of protein noise can distinguish between alternate sources of gene-expression variability

Within individual cells, two molecular processes have been implicated as sources of noise in gene expression: (i) Poisson fluctuations in mRNA abundance arising from random birth and death of individual mRNA transcripts or (ii) promoter fluctuations arising from stochastic promoter transitions between different transcriptional states. Steady-state measurements of variance in protein levels are insufficient to discriminate between these two mechanisms, and mRNA single-molecule fluorescence in situ hybridization (smFISH) is challenging when cellular mRNA concentrations are high. Here, we present a perturbation method that discriminates mRNA birth/death fluctuations from promoter fluctuations by measuring transient changes in protein variance and that can operate in the regime of high molecular numbers. Conceptually, the method exploits the fact that transcriptional blockage results in more rapid increases in protein variability when mRNA birth/death fluctuations dominate over promoter fluctuations. We experimentally demonstrate the utility of this perturbation approach in the HIV-1 model system. Our results support promoter fluctuations as the primary noise source in HIV-1 expression. This study illustrates a relatively simple method that complements mRNA smFISH hybridization and can be used with existing GFP-tagged libraries to include or exclude alternate sources of noise in gene expression.

Network calisthenics: control of E2F dynamics in cell cycle entry

Stimulation of quiescent mammalian cells with mitogens induces an abrupt increase in E2F1-3 expression just prior to the onset of DNA synthesis, followed by a rapid decline as replication ceases. This temporal adaptation in E2F facilitates a transient pattern of gene expression that reflects the ordered nature of DNA replication. The challenge to understand how E2F dynamics coordinate molecular events required for high-fidelity DNA replication has great biological implications. Indeed, precocious, prolonged, elevated or reduced accumulation of E2F can generate replication stress that culminates in either arrest or death. Accordingly, temporal characteristics of E2F are regulated by several network modules that include feedforward and autoregulatory loops. In this review, we discuss how these network modules contribute to "shaping" E2F dynamics in the context of mammalian cell cycle entry.

Origin of bistability underlying mammalian cell cycle entry

Precise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R-point). It has long been recognized that the R-point is tightly regulated by the Rb-E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb-E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb-E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed-forward loop coupled with a mutual-inhibition feedback loop, which forms an AND-gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb-E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R-point.

Using noisy gene expression mediated by engineered adenovirus to probe signaling dynamics in mammalian cells

Perturbations from environmental, genetic, and pharmacological sources can generate heterogeneous biological responses, even in genetically identical cells. Although these differences have important consequences on cell physiology and survival, they are often subsumed in measurements that average over the population. Here, we describe in detail how variability in adenoviral-mediated gene expression provides an effective means to map dose responses of signaling pathways. Cell-cell variability is inherent in gene delivery methods used in cell biology, which makes this approach adaptable to many existing experimental systems. We also discuss strategies to quantify biologically relevant inputs and outputs.