The ups and downs of p53: understanding protein dynamics in single cells

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies' increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination

Entrainment to an external stimulus enables a synchronized oscillatory response across a population of cells, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are intrinsic to the individual cell, rather than dependent on coherence across a population of cells. Using a combination of mathematical modeling, time-lapse fluorescence microscopy, and single-cell tracking, we demonstrated that p53 oscillations triggered by DNA double-strand breaks (DSBs) can be entrained with a periodic damage stimulus, despite such synchrony not known to function in effective DNA damage responses. Surprisingly, p53 oscillations were experimentally entrained over a wider range of DSB frequencies than predicted by an established computational model for the system. We determined that recapitulating the increased range of entrainment frequencies required, non-intuitively, a less robust oscillator and wider steady-state valley on the energy landscape. Further, we show that p53 entrainment can lead to altered expression dynamics of downstream targets responsible for cell fate in a manner dependent on target mRNA stability. Overall, this study demonstrates that entrainment can occur in a biological oscillator despite the apparent lack of an evolutionary advantage conferred through synchronized responses and highlights the potential of externally entraining p53 dynamics to reduce cellular variability and synchronize cell-fate responses for therapeutic outcomes.

Reinforcement Learning Data-Acquiring for Causal Inference of Regulatory Networks

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.

Long-term p21 and p53 dynamics regulate the frequency of mitosis events and cell cycle arrest following radiation damage

Radiation exposure of healthy cells can halt cell cycle temporarily or permanently. In this work, we analyze the time evolution of p21 and p53 from two single cell datasets of retinal pigment epithelial cells exposed to several levels of radiation, and in particular, the effect of radiation on cell cycle arrest. Employing various quantification methods from signal processing, we show how p21 levels, and to a lesser extent p53 levels, dictate whether the cells are arrested in their cell cycle and how frequently these mitosis events are likely to occur. We observed that single cells exposed to the same dose of DNA damage exhibit heterogeneity in cellular outcomes and that the frequency of cell division is a more accurate monitor of cell damage rather than just radiation level. Finally, we show how heterogeneity in DNA damage signaling is manifested early in the response to radiation exposure level and has potential to predict long-term fate.

Elucidating the Implications of Diverse Dynamical Responses in p53 Protein

p53 is a well-known tumor suppressor gene that acts as a transcription factor to exhibit a variety of dynamical responses by sensing different types and extent of stress conditions causing DNA damage in Mammalian cells. Mathematical modeling has played a crucial role to correlate cell fate decision-making with some of these dynamic p53 regulations. However, it is extremely challenging to explain the various cell-type and stimulus-specific p53 protein dynamics under different stress conditions by using a single mathematical model. In this article, we propose a simple mathematical model of p53 regulation based on a generic p53 regulatory network that elucidates a range of p53 dynamical responses. By employing bifurcation analysis along with deterministic and stochastic simulations, we explain an array of p53 dynamics by correlating it with the corresponding cell fate regulations in a cell type-specific and stimulus-dependent manner. Moreover, our model makes experimentally testable predictions to fine-tune p53 dynamics under various DNA damage conditions and can be systematically used and improved to analyze complex p53 dynamics in the future.

Encoding and Decoding of p53 Dynamics in Cellular Response to Stresses

In the cellular response to stresses, the tumor suppressor p53 is activated to maintain genomic integrity and fidelity. As a transcription factor, p53 exhibits rich dynamics to allow for discrimination of the type and intensity of stresses and to direct the selective activation of target genes involved in different processes including cell cycle arrest and apoptosis. In this review, we focused on how stresses are encoded into p53 dynamics and how the dynamics are decoded into cellular outcomes. Theoretical modeling may provide a global view of signaling in the p53 network by coupling the encoding and decoding processes. We discussed the significance of modeling in revealing the mechanisms of the transition between p53 dynamic modes. Moreover, we shed light on the crosstalk between the p53 network and other signaling networks. This review may advance the understanding of operating principles of the p53 signaling network comprehensively and provide insights into p53 dynamics-based cancer therapy.

The role of mitosis in generating fitness heterogeneity

Cancer cells have heterogeneous fitness, and this heterogeneity stems from genetic and epigenetic sources. Here, we sought to assess the contribution of asymmetric mitosis (AM) and time on the variability of fitness in sister cells. Around one quarter of sisters had differences in fitness, assessed as the intermitotic time (IMT), from 330 to 510 min. Phenotypes related to fitness, such as ERK activity (herein referring to ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively), DNA damage and nuclear morphological phenotypes were also asymmetric at mitosis or turned asymmetric over the course of the cell cycle. The ERK activity of mother cell was found to influence the ERK activity and the IMT of the daughter cells, and cells with ERK asymmetry at mitosis produced more offspring with AMs, suggesting heritability of the AM phenotype for ERK activity. Our findings demonstrate how variabilities in sister cells can be generated, contributing to the phenotype heterogeneities in tumor cells.

Optimal Recursive Expert-Enabled Inference in Regulatory Networks

Accurate inference of biological systems, such as gene regulatory networks and microbial communities, is a key to a deep understanding of their underlying mechanisms. Despite several advances in the inference of regulatory networks in recent years, the existing techniques cannot incorporate expert knowledge into the inference process. Expert knowledge contains valuable biological information and is often reflected in available biological data, such as interventions made by biologists for treating diseases. Given the complexity of regulatory networks and the limitation of biological data, ignoring expert knowledge can lead to inaccuracy in the inference process. This paper models the regulatory networks using Boolean network with perturbation. We develop an expert-enabled inference method for inferring the unknown parameters of the network model using expert-acquired data. Given the availability of information about data-acquiring objectives and expert confidence, the proposed method optimally quantifies the expert knowledge along with the temporal changes in the data for the inference process. The numerical experiments investigate the performance of the proposed method using the well-known p53-MDM2 gene regulatory network.

Degradation-driven protein level oscillation in the yeast Saccharomyces cerevisiae

Generating robust, predictable perturbations in cellular protein levels will advance our understanding of protein function and enable the control of physiological outcomes in biotechnology applications. Timed periodic changes in protein levels play a critical role in the cell division cycle, cellular stress response, and development. Here we report the generation of robust protein level oscillations by controlling the protein degradation rate in the yeast Saccharomyces cerevisiae. Using a photo-sensitive degron and red fluorescent proteins as reporters, we show that under constitutive transcriptional induction, repeated triangular protein level oscillations as fast as 5-10 min-scale can be generated by modulating the protein degradation rate. Consistent with oscillations generated though transcriptional control, we observed a continuous decrease in the magnitude of oscillations as the input modulation frequency increased, indicating low-pass filtering of input perturbation. By using two red fluorescent proteins with distinct maturation times, we show that the oscillations in protein level is largely unaffected by delays originating from functional protein formation. Our study demonstrates the potential for repeated control of protein levels by controlling the protein degradation rate without altering the transcription rate.

[The role of the response to DNA damage in granulomatous diseases]

Granulomas are organized aggregates of immune cells, which are formed in response to a persistent stimulus and are found in various rheumatic diseases, including sarcoidosis, rheumatoid arthritis and granulomatosis with polyangiitis. The core of granulomas contains a multitude of different macrophage subtypes, including multinucleated macrophages and foam cells. The mechanisms which induce the formation of granulomas are not well understood; however, recent data show that the DNA damage response regulates granuloma macrophage differentiation.

Dual Role of Reactive Oxygen Species and their Application in Cancer Therapy

Reactive oxygen species (ROS) play a dual role in the initiation, development, suppression, and treatment of cancer. Excess ROS can induce nuclear DNA, leading to cancer initiation. Not only that, but ROS also inhibit T cells and natural killer cells and promote the recruitment and M2 polarization of macrophages; consequently, cancer cells escape immune surveillance and immune defense. Furthermore, ROS promote tumor invasion and metastasis by triggering epithelial-mesenchymal transition in tumor cells. Interestingly, massive accumulation of ROS inhibits tumor growth in two ways: (1) by blocking cancer cell proliferation by suppressing the proliferation signaling pathway, cell cycle, and the biosynthesis of nucleotides and ATP and (2) by inducing cancer cell death via activating endoplasmic reticulum stress-, mitochondrial-, and P53- apoptotic pathways and the ferroptosis pathway. Unfortunately, cancer cells can adapt to ROS via a self-adaption system. This review highlighted the bidirectional regulation of ROS in cancer. The study further discussed the application of massively accumulated ROS in cancer treatment. Of note, the dual role of ROS in cancer and the self-adaptive ability of cancer cells should be taken into consideration for cancer prevention.

The guardian's choice: how p53 enables context-specific decision-making in individual cells

p53 plays a central role in defending the genomic integrity of our cells. In response to genotoxic stress, this tumour suppressor orchestrates the expression of hundreds of target genes, which induce a variety of cellular outcomes ranging from damage repair to induction of apoptosis. In this review, we examine how the p53 response is regulated on several levels in individual cells to allow precise and context-specific fate decisions. We discuss that the p53 response is not only controlled by its canonical regulators but also controlled by interconnected signalling pathways that influence the dynamics of p53 accumulation upon damage and modulate its transcriptional activity at target gene promoters. Additionally, we consider how the p53 response is diversified through a variety of mechanisms at the promoter level and beyond to induce context-specific outcomes in individual cells. These layers of regulation allow p53 to react in a stimulus-specific manner and fine-tune its signalling according to the individual needs of a given cell, enabling it to take the right decision on survival or death.

Deep learning in cancer pathology: a new generation of clinical biomarkers

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.

Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.

Single-Cell Transcriptome Analysis of Colon Cancer Cell Response to 5-Fluorouracil-Induced DNA Damage

DNA damage often induces heterogeneous cell-fate responses, such as cell-cycle arrest and apoptosis. Through single-cell RNA sequencing (scRNA-seq), we characterize the transcriptome response of cultured colon cancer cell lines to 5-fluorouracil (5FU)-induced DNA damage. After 5FU treatment, a single population of colon cancer cells adopts three distinct transcriptome phenotypes, which correspond to diversified cell-fate responses: apoptosis, cell-cycle checkpoint, and stress resistance. Although some genes are regulated uniformly across all groups of cells, many genes showed group-specific expression patterns mediating DNA damage responses specific to the corresponding cell fate. Some of these observations are reproduced at the protein level by flow cytometry and are replicated in cells treated with other 5FU-unrelated genotoxic drugs, camptothecin and etoposide. This work provides a resource for understanding heterogeneous DNA damage responses involving fractional killing and chemoresistance, which are among the major challenges in current cancer chemotherapy.

Oscillatory behavior of p53-Mdm2 system driven by transcriptional and translational time delays

Biological experiments clarify that p53-Mdm2 module is the core of tumor network and p53 oscillation plays an important role in determining the tumor cell fate. In this paper, we investigate the effect of time delay on the oscillatory behavior induced by Hopf bifurcation in p53-Mdm2 system. First, the stability of the unique positive equilibrium point and the existence of Hopf bifurcation are investigated by using the time delay as the bifurcation parameter and by applying the bifurcation theory. Second, the explicit criteria determining the direction of Hopf bifurcation and the stability of bifurcating periodic solutions are developed based on the normal form theory and the center manifold theorem. In addition, the combination of numerical simulation results and theoretical calculation results indicates that time delays in p53-Mdm2 system are critical for p53 oscillations. The results may help us to better understand the biological functions of p53 pathway and provide clues for treatment of cancer.

Adaptive Particle Filtering for Fault Detection in Partially-Observed Boolean Dynamical Systems

We propose a novel methodology for fault detection and diagnosis in partially-observed Boolean dynamical systems (POBDS). These are stochastic, highly nonlinear, and derivativeless systems, rendering difficult the application of classical fault detection and diagnosis methods. The methodology comprises two main approaches. The first addresses the case when the normal mode of operation is known but not the fault modes. It applies an innovations filter (IF) to detect deviations from the nominal normal mode of operation. The second approach is applicable when the set of possible fault models is finite and known, in which case we employ a multiple model adaptive estimation (MMAE) approach based on a likelihood-ratio (LR) statistic. Unknown system parameters are estimated by an adaptive expectation-maximization (EM) algorithm. Particle filtering techniques are used to reduce the computational complexity in the case of systems with large state-spaces. The efficacy of the proposed methodology is demonstrated by numerical experiments with a large gene regulatory network (GRN) with stuck-at faults observed through a single noisy time series of RNA-seq gene expression measurements.

Frequency switching between oscillatory homeostats and the regulation of p53

Homeostasis is an essential concept to understand the stability of organisms and their adaptive behaviors when coping with external and internal assaults. Many hormones that take part in homeostatic control come in antagonistic pairs, such as glucagon and insulin reflecting the inflow and outflow compensatory mechanisms to control a certain internal variable, such as blood sugar levels. By including negative feedback loops homeostatic controllers can exhibit oscillations with characteristic frequencies. In this paper we demonstrate the associated frequency changes in homeostatic systems when individual controllers -in a set of interlocked feedback loops- gain control in response to environmental changes. Taking p53 as an example, we show how Per2, ATM and Mdm2 feedback loops -interlocked with p53- gain individual control in dependence to the level of DNA damage, and how each of these controllers provide certain functionalities in their regulation of p53. In unstressed cells, the circadian regulator Per2 ensures a basic p53 level to allow its rapid up-regulation in case of DNA damage. When DNA damage occurs the ATM controller increases the level of p53 and defends it towards uncontrolled degradation, which despite DNA damage, would drive p53 to lower values and p53 dysfunction. Mdm2 on its side keeps p53 at a high but sub-apoptotic level to avoid premature apoptosis. However, with on-going DNA damage the Mdm2 set-point is increased by HSP90 and other p53 stabilizers leading finally to apoptosis. An emergent aspect of p53 upregulation during cell stress is the coordinated inhibition of ubiquitin-independent and ubiquitin-dependent degradation reactions. Whether oscillations serve a function or are merely a by-product of the controllers are discussed in view of the finding that homeostatic control of p53, as indicated above, does in principle not require oscillatory homeostats.

p53 pulse modulation differentially regulates target gene promoters to regulate cell fate decisions

The p53 tumor suppressor regulates distinct responses to cellular stresses. Although different stresses generate different p53 dynamics, the mechanisms by which cells decode p53 dynamics to differentially regulate target genes are not well understood. Here, we determined in individual cells how canonical p53 target gene promoters vary in responsiveness to features of p53 dynamics. Employing a chemical perturbation approach, we independently modulated p53 pulse amplitude, duration, or frequency, and we then monitored p53 levels and target promoter activation in individual cells. We identified distinct signal processing features—thresholding in response to amplitude modulation, a refractory period in response to duration modulation, and dynamic filtering in response to frequency modulation. We then showed that the signal processing features not only affect p53 target promoter activation, they also affect p53 regulation and downstream cellular functions. Our study shows how different promoters can differentially decode features of p53 dynamics to generate distinct responses, providing insight into how perturbing p53 dynamics can be used to generate distinct cell fates.

Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients

Although tumor invasiveness is known to drive glioblastoma (GBM) recurrence, current approaches to treatment assume a fairly simple GBM phenotype transition map. We provide new analyses to estimate the likelihood of reaching or remaining in a phenotype under dynamic, physiologically likely perturbations of stimuli ("phenotype stability"). We show that higher stability values of the motile phenotype (Go) are associated with reduced patient survival. Moreover, induced motile states are capable of driving GBM recurrence. We found that the Dormancy and Go phenotypes are equally represented in advanced GBM samples, with natural transitioning between the two. Furthermore, Go and Grow phenotype transitions are mostly driven by tumor-brain stimuli. These are difficult to regulate directly, but could be modulated by reprogramming tumor-associated cell types. Our framework provides a foundation for designing targeted perturbations of the tumor-brain environment, by assessing their impact on GBM phenotypic plasticity, and is corroborated by analyses of patient data.

Multi-scale modeling identifies the role of p53-Gys2 negative feedback loop in cellular homeostasis

The transcription factor p53 is a tumor suppressor and strictly controlled p53 protein abundance coordinates cellular outcomes in response to various stresses. The glycogen synthase 2 (Gys2) and p53 generates a novel negative feedback circuit in which p53 represses Gys2 expression whereas Gys2 can stabilize p53 by competitive binding with MDM2. However, the dynamic role of p53-Gys2 negative feedback is still elusive. In current work, we recapitulated the main experimental findings using multi-scale modeling and emphasized the pivotal role of p53-Gys2 negative feedback loop to main cellular homeostasis. The multi-scale modeling strategy was used to simulate both in vitro and in vivo experimental findings. We found that expression of a key oncoprotein HBx may facilitate cancer progression. Gys2 overexpression can inhibit hepatocellular carcinoma progression whereas Gys2 knockdown advanced cancer development. We also applied oscillatory and impulse disturbance to p53 signaling pathway and the results showed that optimal p53-Gys2 negative feedback loop was highly resistant to oscillatory or impulse disturbances. Instead, the canonical p53-MDM2 negative feedback circuit can significantly affect the dynamics of p53 and therefore effectively shaped pulsatile patterns. Therefore, the dual negative feedback loops in p53 signaling can provide features of both robustness and tunability. These dynamic features are critical for cellular homeostasis against tumor progression in p53 signaling pathway.

Combination of Gas Plasma and Radiotherapy Has Immunostimulatory Potential and Additive Toxicity in Murine Melanoma Cells in Vitro

Despite continuous advances in therapy, malignant melanoma is still among the deadliest types of cancer. At the same time, owing to its high plasticity and immunogenicity, melanoma is regarded as a model tumor entity when testing new treatment approaches. Cold physical plasma is a novel anticancer tool that utilizes a plethora of reactive oxygen species (ROS) being deposited on the target cells and tissues. To test whether plasma treatment would enhance the toxicity of an established antitumor therapy, ionizing radiation, we combined both physical treatment modalities targeting B16F10 murine melanoma cell in vitro. Repeated rather than single radiotherapy, in combination with gas plasma-introduced ROS, induced apoptosis and cell cycle arrest in an additive fashion. In tendency, gas plasma treatment sensitized the cells to subsequent radiotherapy rather than the other way around. This was concomitant with increased levels of TNFα, IL6, and GM-CSF in supernatants. Murine JAWS dendritic cells cultured in these supernatants showed an increased expression of cell surface activation markers, such as MHCII and CD83. For PD-L1 and PD-L2, increased expression was observed. Our results are the first to suggest an additive therapeutic effect of gas plasma and radiotherapy, and translational tumor models are needed to develop this concept further.

p53 dynamics in single cells are temperature-sensitive

Cells need to preserve genome integrity despite varying cellular and physical states. p53, the guardian of the genome, plays a crucial role in the cellular response to DNA damage by triggering cell cycle arrest, apoptosis or senescence. Mutations in p53 or alterations in its regulatory network are major driving forces in tumorigenesis. As multiple studies indicate beneficial effects for hyperthermic treatments during radiation- or chemotherapy of human cancers, we aimed to understand how p53 dynamics after genotoxic stress are modulated by changes in temperature across a physiological relevant range. To this end, we employed a combination of time-resolved live-cell microscopy and computational analysis techniques to characterise the p53 response in thousands of individual cells. Our results demonstrate that p53 dynamics upon ionizing radiation are temperature dependent. In the range of 33 °C to 39 °C, pulsatile p53 dynamics are modulated in their frequency. Above 40 °C, which corresponds to mild hyperthermia in a clinical setting, we observed a reversible phase transition towards sustained hyperaccumulation of p53 disrupting its canonical response to DNA double strand breaks. Moreover, we provide evidence that mild hyperthermia alone is sufficient to induce a p53 response in the absence of genotoxic stress. These insights highlight how the p53-mediated DNA damage response is affected by alterations in the physical state of a cell and how this can be exploited by appropriate timing of combination therapies to increase the efficiency of cancer treatments.

Effective Combined Photodynamic Therapy with Lipid Platinum Chloride Nanoparticles Therapies of Oral Squamous Carcinoma Tumor Inhibition

Encapsulating cisplatin (CDDP) into liposomes to form lipid-platinum-chloride nanoparticles (LPC NPs) has shown a promising anticancer effect in melanoma, bladder, and liver cancer models. This promising anticancer effect of LPC NPs challenges us to study its implications in combination with photodynamic therapy (PDT). Herein, we report the therapeutic efficacy of PDT+LPC on a xenograft model of oral squamous cell carcinoma (OSCC). Results showed that PDT+LPC significantly reduced the tumor volume by up to ~112%. Meanwhile, LPC, PDT+CDDP, or the CDDP group showed ~98.8%, ~73.1%, or ~39.5% volume reductions, respectively. Histological examination suggests that PDT+LPC or LPC treatment showed minimal side effects on renal damage compared to either CDDP or the PDT+CDDP group. Immunohistochemistry staining (IHC) staining on Ki-67, CD31, cleaved caspase-3, TUNEL assays, and western blots of tumor suppressor p53 confirmed consistent results. Most importantly, PDT+LPC prolonged tumor growth inhibition, which leads to minimum chemotherapy treatment administrations. Results suggest that PDT cytotoxicity provided a potent additive effect towards chemotherapy efficacy. Therefore, combined PDT with LPC NPs enhanced the therapeutic outcome in human OSCC.

Bioactivity and modulatory functions of Napoleona vogelii on oxidative stress-induced micronuclei and apoptotic biomarkers in mice

Napoleona vogelii is used in traditional medicine for the management of pain, inflammatory conditions and cancer. This study was conducted to investigate the modulatory mechanisms of methanol stem bark extract of N. vogelii on induction of micronuclei, apoptotic biomarkers and in vivo antioxidant enzymes in mice. Forty male albino mice were randomly divided into eight groups (n = 5) and were administered distilled water (DW, 5 mL/kg) as negative control, 100, 200 or 400 mg/kg of the extract respectively for 28 days before the injection of cyclophosphamide (CP, 40 mg/kg) i.p. on the 28th day. The remaining groups were administered 100, 200 or 400 mg/kg of the extract only for 28 days. Twenty four hours after injection of CP or administration of the last dose of extract, animals were euthanized by cervical dislocation and blood samples collected for determination of in vivo antioxidants, the spleen harvested for immunohistochemical expression of NFκB, Bcl-2, Bax and p53. Bone marrow smears were also made for the micronucleus assay. Treatment with the extract resulted in a significant (p < 0.0001) reduction in frequency of micronucleated polychromatic erythrocytes (MNPCEs) compared to CP exposed control conferring protection of 75.09, 94.74 and 96.84% at 100, 200 or 400 mg/kg respectively. In extract and CP exposed animals, there were significant (p < 0.05) increases in GSH, GST and SOD with a corresponding significant (p < 0.05) reduction in MDA. In addition, the extract significantly downregulated cytoplasmic levels of NFκB and Bcl-2 and upregulated Bax and p53. These findings demonstrate that N. vogelli may serve as an interesting lead for chemo-preventive drug development.

IL-1Ra protects hematopoietic cells from chemotoxicity through p53-induced quiescence

The protection of constantly proliferating gut epithelia and hematopoietic tissues from cytotoxicity could improve conventional chemotherapy efficacy and widen its therapeutic window. Previously, we reported that, in mouse models, pretreatment of recombinant human IL-1 receptor antagonist (rhIL-1Ra) protected both types of vulnerable tissues from chemotherapeutics. Here, we showed that rhIL-1Ra treatment up-regulated the protein levels of phosphorylated p38, p53, and p21 and induced transient hematopoietic stem/progenitor cell (HS/PC) quiescence. Knockout of IL-1 receptor I (IL-1RI), p53, or p21 alleles and pharmacological inactivation of p38 mapped the rhIL-1Ra pathway in the induction of HS/PC quiescence. Therefore, rhIL-1Ra administration before but not after chemotherapy alleviated 5-fluorouracil-induced neutropenia. In addition, in vivo and in vitro cell proliferation assays revealed that the rhIL-1Ra treatment did not affect cancer cell proliferation or chemosensitivity. Lastly, we propose an IL-1/IL-1Ra pathway (IL-1RI → p38 → p53 → p21), which regulates HS/PC quiescence. The rhIL-1Ra may provide a new route for p53-based cyclotherapy, which spares normal cells but kills cancer cells during chemotherapy.-Ye, H., Qian, L., Zhu, S., Deng, S., Wang, X., Zhu, J., Chan, G. L., Yu, Y., Han, W. IL-1Ra protects hematopoietic cells from chemotoxicity through p53-induced quiescence.

Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning

Control of gene regulatory networks (GRNs) to shift gene expression from undesirable states to desirable ones has received much attention in recent years. Most of the existing methods assume that the cost of intervention at each state and time point, referred to as the immediate cost function, is fully known. In this paper, we employ the Partially-Observed Boolean Dynamical System (POBDS) signal model for a time sequence of noisy expression measurement from a Boolean GRN and develop a Bayesian Inverse Reinforcement Learning (BIRL) approach to address the realistic case in which the only available knowledge regarding the immediate cost function is provided by the sequence of measurements and interventions recorded in an experimental setting by an expert. The Boolean Kalman Smoother (BKS) algorithm is used for optimally mapping the available gene-expression data into a sequence of Boolean states, and then the BIRL method is efficiently combined with the Q-learning algorithm for quantification of the immediate cost function. The performance of the proposed methodology is investigated by applying a state-feedback controller to two GRN models: a melanoma WNT5A Boolean network and a p53-MDM2 negative feedback loop Boolean network, when the cost of the undesirable states, and thus the identity of the undesirable genes, is learned using the proposed methodology.

A Boolean network control algorithm guided by forward dynamic programming

Control problem in a biological system is the problem of finding an interventional policy for changing the state of the biological system from an undesirable state, e.g. disease, into a desirable healthy state. Boolean networks are utilized as a mathematical model for gene regulatory networks. This paper provides an algorithm to solve the control problem in Boolean networks. The proposed algorithm is implemented and applied on two biological systems: T-cell receptor network and Drosophila melanogaster network. Results show that the proposed algorithm works faster in solving the control problem over these networks, while having similar accuracy, in comparison to previous exact methods. Source code and a simple web service of the proposed algorithm is available at http://goliaei.ir/net-control/www/.

Single-Cell Omics Analyses Enabled by Microchip Technologies

Single-cell omics studies provide unique information regarding cellular heterogeneity at various levels of the molecular biology central dogma. This knowledge facilitates a deeper understanding of how underlying molecular and architectural changes alter cell behavior, development, and disease processes. The emerging microchip-based tools for single-cell omics analysis are enabling the evaluation of cellular omics with high throughput, improved sensitivity, and reduced cost. We review state-of-the-art microchip platforms for profiling genomics, epigenomics, transcriptomics, proteomics, metabolomics, and multi-omics at single-cell resolution. We also discuss the background of and challenges in the analysis of each molecular layer and integration of multiple levels of omics data, as well as how microchip-based methodologies benefit these fields. Additionally, we examine the advantages and limitations of these approaches. Looking forward, we describe additional challenges and future opportunities that will facilitate the improvement and broad adoption of single-cell omics in life science and medicine.

Single Cell Omics of Breast Cancer: An Update on Characterization and Diagnosis

Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell's state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.

An experimental design framework for Markovian gene regulatory networks under stationary control policy

BACKGROUND

A fundamental problem for translational genomics is to find optimal therapies based on gene regulatory intervention. Dynamic intervention involves a control policy that optimally reduces a cost function based on phenotype by externally altering the state of the network over time. When a gene regulatory network (GRN) model is fully known, the problem is addressed using classical dynamic programming based on the Markov chain associated with the network. When the network is uncertain, a Bayesian framework can be applied, where policy optimality is with respect to both the dynamical objective and the uncertainty, as characterized by a prior distribution. In the presence of uncertainty, it is of great practical interest to develop an experimental design strategy and thereby select experiments that optimally reduce a measure of uncertainty.

RESULTS

In this paper, we employ mean objective cost of uncertainty (MOCU), which quantifies uncertainty based on the degree to which uncertainty degrades the operational objective, that being the cost owing to undesirable phenotypes. We assume that a number of conditional probabilities characterizing regulatory relationships among genes are unknown in the Markovian GRN. In sum, there is a prior distribution which can be updated to a posterior distribution by observing a regulatory trajectory, and an optimal control policy, known as an "intrinsically Bayesian robust" (IBR) policy. To obtain a better IBR policy, we select an experiment that minimizes the MOCU remaining after applying its output to the network. At this point, we can either stop and find the resulting IBR policy or proceed to determine more unknown conditional probabilities via regulatory observation and find the IBR policy from the resulting posterior distribution. For sequential experimental design this entire process is iterated. Owing to the computational complexity of experimental design, which requires computation of many potential IBR policies, we implement an approximate method utilizing mean first passage times (MFPTs) - but only in experimental design, the final policy being an IBR policy.

CONCLUSIONS

Comprehensive performance analysis based on extensive simulations on synthetic and real GRNs demonstrate the efficacy of the proposed method, including the accuracy and computational advantage of the approximate MFPT-based design.

Systems biology primer: the basic methods and approaches

Systems biology is an integrative discipline connecting the molecular components within a single biological scale and also among different scales (e.g. cells, tissues and organ systems) to physiological functions and organismal phenotypes through quantitative reasoning, computational models and high-throughput experimental technologies. Systems biology uses a wide range of quantitative experimental and computational methodologies to decode information flow from genes, proteins and other subcellular components of signaling, regulatory and functional pathways to control cell, tissue, organ and organismal level functions. The computational methods used in systems biology provide systems-level insights to understand interactions and dynamics at various scales, within cells, tissues, organs and organisms. In recent years, the systems biology framework has enabled research in quantitative and systems pharmacology and precision medicine for complex diseases. Here, we present a brief overview of current experimental and computational methods used in systems biology.

Interferon-stimulated gene 15 enters posttranslational modifications of p53

The tumor suppressor protein p53 is a central governor of various cellular signals. It is well accepted that ubiquitination as well as ubiquitin-like (UBL) modifications of p53 protein is critical in the control of its activity. Interferon-stimulated gene 15 (ISG15) is a well-known UBL protein with pleiotropic functions, serving both as a free intracellular molecule and as a modifier by conjugating to target proteins. Initially, attentions have historically focused on the antiviral effects of ISG15 pathway. Remarkably, a significant role in the processes of autophagy, DNA repair, and protein translation provided considerable insight into the new functions of ISG15 pathway. Despite the deterministic revelation of the relation between ISG15 and p53, the functional consequence of p53 ISGylation appears somewhat confused. More important, more recent studies have hinted p53 ubiquitination or other UBL modifications that might interconnect with its ISGylation. Here, we aim to summarize the current knowledge of p53 ISGylation and the differences in other significant modifications, which would be beneficial for the development of p53-based cancer therapy.

Dose and Schedule Determine Distinct Molecular Mechanisms Underlying the Efficacy of the p53-MDM2 Inhibitor HDM201

Activation of p53 by inhibitors of the p53-MDM2 interaction is being pursued as a therapeutic strategy in p53 wild-type cancers. Here, we report distinct mechanisms by which the novel, potent, and selective inhibitor of the p53-MDM2 interaction HDM201 elicits therapeutic efficacy when applied at various doses and schedules. Continuous exposure of HDM201 led to induction of p21 and delayed accumulation of apoptotic cells. By comparison, high-dose pulses of HDM201 were associated with marked induction of PUMA and a rapid onset of apoptosis. shRNA screens identified PUMA as a mediator of the p53 response specifically in the pulsed regimen. Consistent with this, the single high-dose HDM201 regimen resulted in rapid and marked induction of PUMA expression and apoptosis together with downregulation of Bcl-xL in vivo Knockdown of Bcl-xL was identified as the top sensitizer to HDM201 in vitro, and Bcl-xL was enriched in relapsing tumors from mice treated with intermittent high doses of HDM201. These findings define a regimen-dependent mechanism by which disruption of MDM2-p53 elicits therapeutic efficacy when given with infrequent dosing. In an ongoing HDM201 trial, the observed exposure-response relationship indicates that the molecular mechanism elicited by pulse dosing is likely reproducible in patients. These data support the clinical comparison of daily and intermittent regimens of p53-MDM2 inhibitors.Significance: Pulsed high doses versus sustained low doses of the p53-MDM2 inhibitor HDM201 elicit a proapoptotic response from wild-type p53 cancer cells, offering guidance to current clinical trials with this and other drugs that exploit the activity of p53. Cancer Res; 78(21); 6257-67. ©2018 AACR.

CRISPR-Based Editing Reveals Edge-Specific Effects in Biological Networks

Unraveling the properties of biological networks is central to understanding both normal and disease cellular phenotypes. Networks consist of functional elements (nodes) that form a variety of diverse connections (edges), with each node being a hub for multiple edges. Herein, in contrast to node-centric network perturbation and analysis approaches, we present a high-throughput CRISPR-based methodology for delineating the role of network edges. Ablation of network edges using a library targeting 93 miRNA target sites in 71 genes reveals numerous edges that control, with variable importance, cellular growth and survival under stress. To compare the impact of removing nodes versus edges in a biological network, we dissect a specific p53-microRNA pathway. We show that removal of the miR-34a target site from the anti-apoptotic gene BCL2 desensitizes the cell to ectopic delivery of miR-34a in a p53-dependent manner. In summary, we demonstrate that network edges are critical to the function and stability of biological networks. Our results introduce a novel genetic screening opportunity via edge ablation and highlight a new dimension in biological network analysis.

Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

Dynamical analysis of cellular ageing by modeling of gene regulatory network based attractor landscape

Ageing is a natural phenomenon that is inherently complex and remains a mystery. Conceptual model of cellular ageing landscape was proposed for computational studies of ageing. However, there is a lack of quantitative model of cellular ageing landscape. This study aims to investigate the mechanism of cellular ageing in a theoretical model using the framework of Waddington's epigenetic landscape. We construct an ageing gene regulatory network (GRN) consisting of the core cell cycle regulatory genes (including p53). A model parameter (activation rate) is used as a measure of the accumulation of DNA damage. Using the bifurcation diagrams to estimate the parameter values that lead to multi-stability, we obtained a conceptual model for capturing three distinct stable steady states (or attractors) corresponding to homeostasis, cell cycle arrest, and senescence or apoptosis. In addition, we applied a Monte Carlo computational method to quantify the potential landscape, which displays: I) one homeostasis attractor for low accumulation of DNA damage; II) two attractors for cell cycle arrest and senescence (or apoptosis) in response to high accumulation of DNA damage. Using the Waddington's epigenetic landscape framework, the process of ageing can be characterized by state transitions from landscape I to II. By in silico perturbations, we identified the potential landscape of a perturbed network (inactivation of p53), and thereby demonstrated the emergence of a cancer attractor. The simulated dynamics of the perturbed network displays a landscape with four basins of attraction: homeostasis, cell cycle arrest, senescence (or apoptosis) and cancer. Our analysis also showed that for the same perturbed network with low DNA damage, the landscape displays only the homeostasis attractor. The mechanistic model offers theoretical insights that can facilitate discovery of potential strategies for network medicine of ageing-related diseases such as cancer.

Hypothermia modulates the DNA damage response to ionizing radiation in human peripheral blood lymphocytes

PURPOSE

Low temperature at exposure has been shown to act in a radioprotective manner at the level of cytogenetic damage. It was suggested to be due to an effective transformation of DNA damage to chromosomal damage at low temperature. The purpose of the study was to analyze the kinetics of aberration formation during the first hours after exposing human peripheral blood lymphocytes to ionizing radiation at 0.8 °C and 37 °C.

MATERIALS AND METHODS

To this end, we applied the technique of premature chromosome condensation. In addition, DNA damage response was analyzed by measuring the levels of phosphorylated DNA damage responsive proteins ATM, DNA-PK and p53 and mRNA levels of the radiation-responsive genes BBC3, FDXR, GADD45A, XPC, MDM2 and CDKN1A.

RESULTS

A consistently lower frequency of chromosomal breaks was observed in cells exposed at 0.8 °C as compared to 37 °C already after 30 minutes postexposure. This effect was accompanied by elevated levels of phosphorylated ATM and DNA-PK proteins and a reduced immediate level of phosphorylated p53 and of the responsive genes.

CONCLUSIONS

Low temperature at exposure appears to promote DNA repair leading to reduced transformation of DNA damage to chromosomal aberrations.

Optimal Fault Detection and Diagnosis in Transcriptional Circuits Using Next-Generation Sequencing

We propose a methodology for model-based fault detection and diagnosis for stochastic Boolean dynamical systems indirectly observed through a single time series of transcriptomic measurements using Next Generation Sequencing (NGS) data. The fault detection consists of an innovations filter followed by a fault certification step, and requires no knowledge about the possible system faults. The innovations filter uses the optimal Boolean state estimator, called the Boolean Kalman Filter (BKF). In the presence of knowledge about the possible system faults, we propose an additional step of fault diagnosis based on a multiple model adaptive estimation (MMAE) method consisting of a bank of BKFs running in parallel. Performance is assessed by means of false detection and misdiagnosis rates, as well as average times until correct detection and diagnosis. The efficacy of the proposed methodology is demonstrated via numerical experiments using a p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.

Loss of p53-mediated cell-cycle arrest, senescence and apoptosis promotes genomic instability and premature aging

Although p53-mediated cell cycle arrest, senescence and apoptosis are well accepted as major tumor suppression mechanisms, the loss of these functions does not directly lead to tumorigenesis, suggesting that the precise roles of these canonical activities of p53 need to be redefined. Here, we report that the cells derived from the mutant mice expressing p53^3KR, an acetylation-defective mutant that fails to induce cell-cycle arrest, senescence and apoptosis, exhibit high levels of aneuploidy upon DNA damage. Moreover, the embryonic lethality caused by the deficiency of XRCC4, a key DNA double strand break repair factor, can be fully rescued in the p53^3KR/3KR background. Notably, despite high levels of genomic instability, p53^3KR/3KRXRCC4^−/− mice, unlike p53^−/− XRCC4^−/− mice, are not succumbed to pro-B-cell lymphomas. Nevertheless, p53^3KR/3KR XRCC4^−/− mice display aging-like phenotypes including testicular atrophy, kyphosis, and premature death. Further analyses demonstrate that SLC7A11 is downregulated and that p53-mediated ferroptosis is significantly induced in spleens and testis of p53^3KR/3KRXRCC4^−/− mice. These results demonstrate that the direct role of p53-mediated cell cycle arrest, senescence and apoptosis is to control genomic stability in vivo. Our study not only validates the importance of ferroptosis in p53-mediated tumor suppression in vivo but also reveals that the combination of genomic instability and activation of ferroptosis may promote aging-associated phenotypes.

BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems

Background

Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis.

Results

BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package.

Conclusions

We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays.

ALS-linked mutant SOD1 proteins promote Aβ aggregates in ALS through direct interaction with Aβ

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive degeneration of motor neurons. Aggregation of ALS-linked mutant Cu/Zn superoxide dismutase (SOD1) is a hallmark of a subset of familial ALS (fALS). Recently, intracellular amyloid-β (Aβ) is detected in motor neurons of both sporadic and familial ALS. We have previously shown that intracellular Aβ specifically interacts with G93A, an ALS-linked SOD1 mutant. However, little is known about the pathological and biological effect of this interaction in neurons. In this study, we have demonstrated that the Aβ-binding region is exposed on the SOD1 surface through the conformational changes due to misfolding of SOD1. Interestingly, we found that the intracellular aggregation of Aβ is enhanced through the direct interaction of Aβ with the Aβ-binding region exposed to misfolded SOD1. Ultimately, increased Aβ aggregation by this interaction promotes neuronal cell death. Consistent with this result, Aβ aggregates was three-fold higher in the brains of G93A transgenic mice than those of non Tg. Our study provides the first direct evidence that Aβ, an AD-linked factor, is associated to the pathogenesis of ALS and provides molecular clues to understand common aggregation mechanisms in the pathogenesis of neurodegenerative diseases. Furthermore, it will provide new insights into the development of therapeutic approaches for ALS.

Identification of translationally controlled tumor protein in promotion of DNA homologous recombination repair in cancer cells by affinity proteomics

Translationally controlled tumor protein(TCTP) has been implicated in the regulation of apoptosis, DNA repair and drug resistance. However, the underlying molecular mechanisms are poorly defined. To better understand the molecular mechanisms underlying TCTP involved in cellular processes, we performed an affinity purification-based proteomic profiling to identify proteins interacting with TCTP in human cervical cancer HeLa cells. We found that a group of proteins involved in DNA repair are enriched in the potential TCTP interactome. Silencing TCTP by short hairpin RNA in breast carcinoma MCF-7 cells leads to the declined repair efficiency for DNA double-strand breaks on the GFP-Pem1 reporter gene by homologous recombination, the persistent activation and the prolonged retention of γH2AX and Rad51 foci following ionizing radiation. Reciprocal immunoprecipitations indicated that TCTP forms complexes with Rad51 in vivo, and the stability maintenance of Rad51 requires TCTP in MCF-7 cells under normal cell culture conditions. Moreover, inactivation of TCTP by sertraline treatment enhances UVC irradiation-induced apoptosis in MCF-7 cells, and causes sensitization to DNA-damaging drug etoposide and DNA repair inhibitor olaparib. Thus, we have identified an important role of TCTP in promoting DNA double-stand break repair via facilitating DNA homologous recombination processes and highlighted the great potential of TCTP as a drug target to enhance conventional chemotherapy for cancer patients with high levels of TCTP expression.

A dynamical framework for complex fractional killing

When chemotherapy drugs are applied to tumor cells with the same or similar genotypes, some cells are killed, while others survive. This fractional killing contributes to drug resistance in cancer. Through an incoherent feedforward loop, chemotherapy drugs not only activate p53 to induce cell death, but also promote the expression of apoptosis inhibitors which inhibit cell death. Consequently, cells in which p53 is activated early undergo apoptosis while cells in which p53 is activated late survive. The incoherent feedforward loop and the essential role of p53 activation timing makes fractional killing a complex dynamical challenge, which is hard to understand with intuition alone. To better understand this process, we have constructed a representative model by integrating the control of apoptosis with the relevant signaling pathways. After the model was trained to recapture the observed properties of fractional killing, it was analyzed with nonlinear dynamical tools. The analysis suggested a simple dynamical framework for fractional killing, which predicts that cell fate can be altered in three possible ways: alteration of bifurcation geometry, alteration of cell trajectories, or both. These predicted categories can explain existing strategies known to combat fractional killing and facilitate the design of novel strategies.

Single-Molecule Analysis of Bacterial DNA Repair and Mutagenesis

Ubiquitous conserved processes that repair DNA damage are essential for the maintenance and propagation of genomes over generations. Then again, inaccuracies in DNA transactions and failures to remove mutagenic lesions cause heritable genome changes. Building on decades of research using genetics and biochemistry, unprecedented quantitative insight into DNA repair mechanisms has come from the new-found ability to measure single proteins in vitro and inside individual living cells. This has brought together biologists, chemists, engineers, physicists, and mathematicians to solve long-standing questions about the way in which repair enzymes search for DNA lesions and form protein complexes that act in DNA repair pathways. Furthermore, unexpected discoveries have resulted from capabilities to resolve molecular heterogeneity and cell subpopulations, provoking new questions about the role of stochastic processes in DNA repair and mutagenesis. These studies are leading to new technologies that will find widespread use in basic research, biotechnology, and medicine.

Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis

Stochastic fluctuations, termed "noise," in the level of biological molecules can greatly impact cellular functions. While biological noise can sometimes be detrimental, recent studies have provided an increasing number of examples in which biological noise can be functionally beneficial. Rather than provide an exhaustive review of the growing literature in this field, in this review, we focus on single-cell studies based on quantitative microscopy that have generated a deeper understanding of the sources, characteristics, limitations, and benefits of biological noise. Specifically, we highlight studies showing how noise can help coordinate the expression of multiple downstream target genes, impact the channel capacity of signaling networks, and interact synergistically with oscillatory dynamics to enhance the sensitivity of signal processing. We conclude with a discussion of current challenges and future opportunities.

Modeling amplified p53 responses under DNA-PK inhibition in DNA damage response

During DNA double strand breaks (DSBs) repair, coordinated activation of phosphatidylinositol 3-kinase (PI3K)-like kinases can activate p53 signaling pathway. Recent findings have identified novel interplays among these kinases demonstrating amplified first p53 pulses under DNA-PK inhibition. However, no theoretical model has been developed to characterize such dynamics. In current work, we modeled the prolonged p53 pulses with DNA-PK inhibitor. We could identify a dose-dependent increase in the first pulse amplitude and width. Meanwhile, weakened DNA-PK mediated ATM inhibition was insufficient to reproduce such dynamic behavior. Moreover, the information flow was shifted predominantly to the first pulse under DNA-PK inhibition. Furthermore, the amplified p53 responses were relatively robust. Taken together, our model can faithfully replicate amplified p53 responses under DNA-PK inhibition and provide insights into cell fate decision by manipulating p53 dynamics.

Transcription factor oscillations in neural stem cells: Implications for accurate control of gene expression

Naturally occurring oscillations in glucocorticoids induce a cyclic activation of the glucocorticoid receptor (GR), a well-characterized ligand-activated transcription factor. These cycles of GR activation/deactivation result in rapid GR exchange at genomic response elements and GR recycling through the chaperone machinery, ultimately generating pulses of GR-mediated transcriptional activity of target genes. In a recent article we have discussed the implications of circadian and high-frequency (ultradian) glucocorticoid oscillations for the dynamic control of gene expression in hippocampal neural stem/progenitor cells (NSPCs) (Fitzsimons et al., Front. Neuroendocrinol., 2016). Interestingly, this oscillatory transcriptional activity is common to other transcription factors, many of which regulate key biological functions in NSPCs, such as NF-kB, p53, Wnt and Notch. Here, we discuss the oscillatory behavior of these transcription factors, their role in a biologically accurate target regulation and the potential importance for a dynamic control of transcription activity and gene expression in NSPCs.

Dynamics of p53: A Master Decider of Cell Fate

Cellular stress-induced temporal alterations—i.e., dynamics—are typically exemplified by the dynamics of p53 that serve as a master to determine cell fate. p53 dynamics were initially identified as the variations of p53 protein levels. However, a growing number of studies have shown that p53 dynamics are also manifested in variations in the activity, spatial location, and posttranslational modifications of p53 proteins, as well as the interplay among all p53 dynamical features. These are essential in determining a specific outcome of cell fate. In this review, we discuss the importance of the multifaceted features of p53 dynamics and their roles in the cell fate decision process, as well as their potential applications in p53-based cancer therapy. The review provides new insights into p53 signaling pathways and their potentials in the development of new strategies in p53-based cancer therapy.