Design of Tunable Oscillatory Dynamics in a Synthetic NF-κB Signaling Circuit

Small transcriptional differences among cell clones lead to distinct NF-κB dynamics

Transcription factor dynamics is fundamental to determine the activation of accurate transcriptional programs and yet is heterogeneous at a single-cell level, even within homogeneous populations. We asked how such heterogeneity emerges for the nuclear factor κB (NF-κB). We found that clonal populations of immortalized fibroblasts derived from a single mouse embryo display robustly distinct NF-κB dynamics upon tumor necrosis factor ɑ (TNF-ɑ) stimulation including persistent, oscillatory, and weak activation, giving rise to differences in the transcription of its targets. By combining transcriptomics and simulations we show how less than two-fold differences in the expression levels of genes coding for key proteins of the signaling cascade and feedback system are predictive of the differences of the NF-κB dynamic response of the clones to TNF-ɑ and IL-1β. We propose that small transcriptional differences in the regulatory circuit of a transcription factor can lead to distinct signaling dynamics in cells within homogeneous cell populations and among different cell types.

Ubiquitination-mediated Golgi-to-endosome sorting determines the toxin-antidote duality of fission yeast wtf meiotic drivers

Killer meiotic drivers (KMDs) skew allele transmission in their favor by killing meiotic progeny not inheriting the driver allele. Despite their widespread presence in eukaryotes, the molecular mechanisms behind their selfish behavior are poorly understood. In several fission yeast species, single-gene KMDs belonging to the wtf gene family exert selfish killing by expressing a toxin and an antidote through alternative transcription initiation. Here we investigate how the toxin and antidote products of a wtf-family KMD gene can act antagonistically. Both the toxin and the antidote are multi-transmembrane proteins, differing only in their N-terminal cytosolic tails. We find that the antidote employs PY motifs (Leu/Pro-Pro-X-Tyr) in its N-terminal cytosolic tail to bind Rsp5/NEDD4 family ubiquitin ligases, which ubiquitinate the antidote. Mutating PY motifs or attaching a deubiquitinating enzyme transforms the antidote into a toxic protein. Ubiquitination promotes the transport of the antidote from the trans-Golgi network to the endosome, thereby preventing it from causing toxicity. A physical interaction between the antidote and the toxin enables the ubiquitinated antidote to translocate the toxin to the endosome and neutralize its toxicity. We propose that post-translational modification-mediated protein localization and/or activity changes may be a common mechanism governing the antagonistic duality of single-gene KMDs.

Biphasic amplitude oscillator characterized by distinct dynamics of trough and crest

Biphasic amplitude dynamics (BAD) of oscillation have been observed in many biological systems. However, the specific topology structure and regulatory mechanisms underlying these biphasic amplitude dynamics remain elusive. Here, we searched all possible two-node circuit topologies and identified the core oscillator that enables robust oscillation. This core oscillator consists of a negative feedback loop between two nodes and a self-positive feedback loop of the input node, which result in the fast and slow dynamics of the two nodes, thereby achieving relaxation oscillation. Landscape theory was employed to study the stochastic dynamics and global stability of the system, allowing us to quantitatively describe the diverse positions and sizes of the Mexican hat. With increasing input strength, the size of the Mexican hat exhibits a gradual increase followed by a subsequent decrease. The self-activation of input node and the negative feedback on input node, which dominate the fast dynamics of the input node, were observed to regulate BAD in a bell-shaped manner. Both deterministic and statistical analysis results reveal that BAD is characterized by the linear and nonlinear dependence of the oscillation trough and crest on the input strength. In addition, combining with computational and theoretical analysis, we addressed that the linear response of trough to input is predominantly governed by the negative feedback, while the nonlinear response of crest is jointly regulated by the negative feedback loop and the self-positive feedback loop within the oscillator. Overall, this study provides a natural and physical basis for comprehending the occurrence of BAD in oscillatory systems, yielding guidance for the design of BAD in synthetic biology applications.

Enabling technology and core theory of synthetic biology

Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.

NFAT and NF-κB dynamically co-regulate TCR and CAR signaling responses in human T cells

While it has been established that the responses of T cells to antigens are combinatorially regulated by multiple signaling pathways, it remains elusive what mechanisms cells utilize to quantitatively modulate T cell responses during pathway integration. Here, we show that two key pathways in T cell signaling, calcium/nuclear factor of activated T cells (NFAT) and protein kinase C (PKC)/nuclear factor κB (NF-κB), integrate through a dynamic and combinatorial strategy to fine-tune T cell response genes. At the cis-regulatory level, the two pathways integrate through co-binding of NFAT and NF-κB to immune response genes. Pathway integration is further regulated temporally, where T cell receptor (TCR) and chimeric antigen receptor (CAR) activation signals modulate the temporal relationships between the nuclear localization dynamics of NFAT and NF-κB. Such physical and temporal integrations together contribute to distinct modes of expression modulation for genes. Thus, the temporal relationships between regulators can be modulated to affect their co-targets during immune responses, underscoring the importance of dynamic combinatorial regulation in cellular signaling.

Coupled oscillator cooperativity as a control mechanism in chronobiology

Control of dynamical processes is vital for maintaining correct cell regulation and cell-fate decisions. Numerous regulatory networks show oscillatory behavior; however, our knowledge of how one oscillator behaves when stimulated by two or more external oscillatory signals is still missing. We explore this problem by constructing a synthetic oscillatory system in yeast and stimulate it with two external oscillatory signals. Letting model verification and prediction operate in a tight interplay with experimental observations, we find that stimulation with two external signals expands the plateau of entrainment and reduces the fluctuations of oscillations. Furthermore, by adjusting the phase differences of external signals, one can control the amplitude of oscillations, which is understood through the signal delay of the unperturbed oscillatory network. With this we reveal a direct amplitude dependency of downstream gene transcription. Taken together, these results suggest a new path to control oscillatory systems by coupled oscillator cooperativity.

Systematic analysis of negative and positive feedback loops for robustness and temperature compensation in circadian rhythms

Temperature compensation and robustness to biological noise are two key characteristics of the circadian clock. These features allow the circadian pacemaker to maintain a steady oscillation in a wide range of environmental conditions. The presence of a time-delayed negative feedback loop in the regulatory network generates autonomous circadian oscillations in eukaryotic systems. In comparison, the circadian clock of cyanobacteria is controlled by a strong positive feedback loop. Positive feedback loops with substrate depletion can also generate oscillations, inspiring other circadian clock models. What makes a circadian oscillatory network robust to extrinsic noise is unclear. We investigated four basic circadian oscillators with negative, positive, and combinations of positive and negative feedback loops to explore network features necessary for circadian clock resilience. We discovered that the negative feedback loop system performs the best in compensating temperature changes. We also show that a positive feedback loop can reduce extrinsic noise in periods of circadian oscillators, while intrinsic noise is reduced by negative feedback loops.

Network design principle for robust oscillatory behaviors with respect to biological noise

Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus, a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive autoregulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive autoregulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive autoregulation—improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

Insights on the NF-κB System Using Live Cell Imaging: Recent Developments and Future Perspectives

The transcription factor family of nuclear factor kappa B (NF-κB) proteins is widely recognized as a key player in inflammation and the immune responses, where it plays a fundamental role in translating external inflammatory cues into precise transcriptional programs, including the timely expression of a wide variety of cytokines/chemokines. Live cell imaging in single cells showed approximately 15 years ago that the canonical activation of NF-κB upon stimulus is very dynamic, including oscillations of its nuclear localization with a period close to 1.5 hours. This observation has triggered a fruitful interdisciplinary research line that has provided novel insights on the NF-κB system: how its heterogeneous response differs between cell types but also within homogeneous populations; how NF-κB dynamics translate external cues into intracellular signals and how NF-κB dynamics affects gene expression. Here we review the main features of this live cell imaging approach to the study of NF-κB, highlighting the key findings, the existing gaps of knowledge and hinting towards some of the potential future steps of this thriving research field.

Microfluidic-Enabled Multi-Cell-Densities-Patterning and Culture Device for Characterization of Yeast Strains’ Growth Rates under Mating Pheromone

Yeast studies usually focus on exploring diversity in terms of a specific trait (such as growth rate, antibiotic resistance, or fertility) among extensive strains. Microfluidic chips improve these biological studies in a manner of high throughput and high efficiency. For a population study of yeast, it is of great significance to set a proper initial cell density for every strain under specific circumstances. Herein, we introduced a novel design of chip, which enables users to load cells in a gradient order (six alternatives) of initial cell density within one channel. We discussed several guidelines to choose the appropriate chamber to ensure successful data recording. With this chip, we successfully studied the growth rate of yeast strains under a mating response, which is crucial for yeasts to control growth behaviors for prosperous mating. We investigated the growth rate of eight different yeast strains under three different mating pheromone levels (0.3 μM, 1 μM, and 10 μM). Strains with, even, a six-fold in growth rate can be recorded, with the available data produced simultaneously. This work has provided an efficient and time-saving microfluidic platform, which enables loading cells in a pattern of multi-cell densities for a yeast population experiment, especially for a high-throughput study. Besides, a quantitatively analyzed growth rate of different yeast strains shall reveal inspiring perspectives for studies concerning yeast population behavior with a stimulated mating pheromone.

Independent control of amplitude and period in a synthetic oscillator circuit with modified repressilator

Synthetic Biology aims to create predictable biological circuits and fully operational biological systems. Although there are methods to create more stable oscillators, such as repressilators, independently controlling the oscillation of reporter genes in terms of their amplitude and period is only on theoretical level. Here, we introduce a new oscillator circuit that can be independently controlled by two inducers in Escherichia coli. Some control components, including σECF11 and NahR, were added to the circuit. By systematically tuning the concentration of the inducers, salicylate and IPTG, the amplitude and period can be modulated independently. Furthermore, we constructed a quantitative model to forecast the regulation results. Under the guidance of the model, the expected oscillation can be regulated by choosing the proper concentration combinations of inducers. In summary, our work achieved independent control of the oscillator circuit, which allows the oscillator to be modularized and used in more complex circuit designs.

Zhang et al. create an oscillator circuit that can be orthogonally controlled by two inducers in Escherichia coli and by systematically tuning the concentration of the inducers, the amplitude and period can be modulated independently. This design allows the oscillator to be modularized and used in more complex circuit designs, which can be a valuable contribution to the Synthetic Biology field.

The Systems and Synthetic Biology of Auxin

Auxin biology as a field has been at the forefront of advances in delineating the structures, dynamics, and control of plant growth networks. Advances have been enabled by combining the complementary fields of top-down, holistic systems biology and bottom-up, build-to-understand synthetic biology. Continued collaboration between these approaches will facilitate our understanding of and ability to engineer auxin's control of plant growth, development, and physiology. There is a need for the application of similar complementary approaches to improving equity and justice through analysis and redesign of the human systems in which this research is undertaken.

Computational analysis of GAL pathway pinpoints mechanisms underlying natural variation

Quantitative traits are measurable phenotypes that show continuous variation over a wide phenotypic range. Enormous effort has recently been put into determining the genetic influences on a variety of quantitative traits with mixed success. We identified a quantitative trait in a tractable model system, the GAL pathway in yeast, which controls the uptake and metabolism of the sugar galactose. GAL pathway activation depends both on galactose concentration and on the concentrations of competing, preferred sugars such as glucose. Natural yeast isolates show substantial variation in the behavior of the pathway. All studied yeast strains exhibit bimodal responses relative to external galactose concentration, i.e. a set of galactose concentrations existed at which both GAL-induced and GAL-repressed subpopulations were observed. However, these concentrations differed in different strains. We built a mechanistic model of the GAL pathway and identified parameters that are plausible candidates for capturing the phenotypic features of a set of strains including standard lab strains, natural variants, and mutants. In silico perturbation of these parameters identified variation in the intracellular galactose sensor, Gal3p, the negative feedback node within the GAL regulatory network, Gal80p, and the hexose transporters, HXT, as the main sources of the bimodal range variation. We were able to switch the phenotype of individual yeast strains in silico by tuning parameters related to these three elements. Determining the basis for these behavioral differences may give insight into how the GAL pathway processes information, and into the evolution of nutrient metabolism preferences in different strains. More generally, our method of identifying the key parameters that explain phenotypic variation in this system should be generally applicable to other quantitative traits.

Microbes adopt elaborate strategies for the preferred uptake and use of nutrients to cope with complex and fluctuating environments. As a result, yeast strains originating from different ecological niches show significant variation in the way they induce genes in the galactose metabolism (GAL) pathway in response to nutrient signals. To identify the mechanistic sources of this variation, we built a mathematical model to simulate the dynamics of the galactose metabolic regulation network, and studied how parameters with different biological implications contributed to the natural variation. We found that variations in the behavior of the galactose sensor Gal3p, the negative feedback node Gal80p, and the hexose transporters HXT were critical elements in the GAL pathway response. Tuning single parameters in silico was sufficient to achieve phenotype switching between different yeast strains. Our computational approach should be generally useful to help pinpoint the genetic and molecular bases of natural variation in other systems.

Deep learning for bioimage analysis in developmental biology

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

Time-Resolved Effect of Interferon-Alpha 2a on Activities of Nuclear Factor Kappa B, Pregnane X Receptor and on Drug Disposition Genes

Interferon-alpha (IFN-α) is suggested to cause pharmacokinetic drug interactions by lowering expression of drug disposition genes through affecting the activities of nuclear factor kappa B (NF-ĸB) and pregnane X receptor (PXR). The time-resolved impact of IFN-α 2a (1000 U/mL; 5000 U/mL; 2 h to 30 h) on the activities of NF-ĸB and PXR and mRNA expression (5000 U/mL; 24 h, 48 h) of selected drug disposition genes and on cytochrome P450 (CYP3A4) activity in LS180 cells (5000 U/mL; 24 h, 48 h) was evaluated using luciferase-based reporter gene assays, reverse transcription polymerase chain reaction, and luminescence-based CYP3A4 activity assays. The cross-talk between NF-ĸB activation and PXR suppression was evaluated by NF-ĸB blockage (10 µM parthenolide). IFN-α 2a initially (2 h, 6 h) enhanced NF-ĸB activity 2-fold and suppressed PXR activity by 30%. mRNA of CYP3A4 was halved, whereas UGT1A1 was increased (1.35-fold) after 24 h. After 48 h, ABCB1 expression was increased (1.76-fold). CYP3A4 activity remained unchanged after 24 h, but was enhanced after 48 h (1.35-fold). IFN-α 2a demonstrated short-term suppressive effects on PXR activity and CYP3A4 mRNA expression, likely mediated by activated NF-ĸB. Longer exposure enhanced CYP3A4 activity. Clinical trials should evaluate the relevance by investigating the temporal effects of IFN-α on CYP3A4 using a sensitive marker substrate.

Programmable protein circuit design

A fundamental challenge in synthetic biology is to create molecular circuits that can program complex cellular functions. Because proteins can bind, cleave, and chemically modify one another and interface directly and rapidly with endogenous pathways, they could extend the capabilities of synthetic circuits beyond what is possible with gene regulation alone. However, the very diversity that makes proteins so powerful also complicates efforts to harness them as well-controlled synthetic circuit components. Recent work has begun to address this challenge, focusing on principles such as orthogonality and composability that permit construction of diverse circuit-level functions from a limited set of engineered protein components. These approaches are now enabling the engineering of circuits that can sense, transmit, and process information; dynamically control cellular behaviors; and enable new therapeutic strategies, establishing a powerful paradigm for programming biology.

A tale of two rhythms: Locked clocks and chaos in biology

The fundamental mechanisms that control and regulate biological organisms exhibit a surprising level of complexity. Oscillators are perhaps the simplest motifs that produce time-varying dynamics and are ubiquitous in biological systems. It is also known that such biological oscillators interact with each other-for instance, circadian oscillators affect the cell cycle, and somitogenesis clock proteins in adjacent cells affect each other in developing embryos. Therefore, it is vital to understand the effects that can emerge from non-linear interaction between oscillations. Here, we show how oscillations typically arise in biology and take the reader on a tour through the great variety in dynamics that can emerge even from a single pair of coupled oscillators. We explain how chaotic dynamics can emerge and outline the methods of detecting this in experimental time traces. Finally, we discuss the potential role of such complex dynamical features in biological systems.

Toward a translationally independent RNA-based synthetic oscillator using deactivated CRISPR-Cas

In synthetic circuits, CRISPR-Cas systems have been used effectively for endpoint changes from an initial state to a final state, such as in logic gates. Here, we use deactivated Cas9 (dCas9) and deactivated Cas12a (dCas12a) to construct dynamic RNA ring oscillators that cycle continuously between states over time in bacterial cells. While our dCas9 circuits using 103-nt guide RNAs showed irregular fluctuations with a wide distribution of peak-to-peak period lengths averaging approximately nine generations, a dCas12a oscillator design with 40-nt CRISPR RNAs performed much better, having a strongly repressed off-state, distinct autocorrelation function peaks, and an average peak-to-peak period length of ∼7.5 generations. Along with free-running oscillator circuits, we measure repression response times in open-loop systems with inducible RNA steps to compare with oscillator period times. We track thousands of cells for 24+ h at the single-cell level using a microfluidic device. In creating a circuit with nearly translationally independent behavior, as the RNAs control each others' transcription, we present the possibility for a synthetic oscillator generalizable across many organisms and readily linkable for transcriptional control.

A Quantitative Modular Modeling Approach Reveals the Effects of Different A20 Feedback Implementations for the NF-kB Signaling Dynamics

Signaling pathways involve complex molecular interactions and are controled by non-linear regulatory mechanisms. If details of regulatory mechanisms are not fully elucidated, they can be implemented by different, equally reasonable mathematical representations in computational models. The study presented here focusses on NF-κB signaling, which is regulated by negative feedbacks via IκBα and A20. A20 inhibits NF-κB activation indirectly through interference with proteins that transduce the signal from the TNF receptor complex to activate the IκB kinase (IKK) complex. A number of pathway models has been developed implementing the A20 effect in different ways. We here focus on the question how different A20 feedback implementations impact the dynamics of NF-κB. To this end, we develop a modular modeling approach that allows combining previously published A20 modules with a common pathway core module. The resulting models are fitted to a published comprehensive experimental data set and therefore show quantitatively comparable NF-κB dynamics. Based on defined measures for the initial and long-term behavior we analyze the effects of a wide range of changes in the A20 feedback strength, the IκBα feedback strength and the TNFα stimulation strength on NF-κB dynamics. This shows similarities between the models but also model-specific differences. In particular, the A20 feedback strength and the TNFα stimulation strength affect initial and long-term NF-κB concentrations differently in the analyzed models. We validated our model predictions experimentally by varying TNFα concentrations applied to HeLa cells. These time course data indicate that only one of the A20 feedback models appropriately describes the impact of A20 on the NF-κB dynamics in this cell type.

Network Topologies That Can Achieve Dual Function of Adaptation and Noise Attenuation

Many signaling systems execute adaptation under circumstances that require noise attenuation. Here, we identify an intrinsic trade-off existing between sensitivity and noise attenuation in the three-node networks. We demonstrate that although fine-tuning timescales in three-node adaptive networks can partially mediate this trade-off in this context, it prolongs adaptation time and imposes unrealistic parameter constraints. By contrast, four-node networks can effectively decouple adaptation and noise attenuation to achieve dual function without a trade-off, provided that these functions are executed sequentially. We illustrate ideas in seven biological examples, including Dictyostelium discoideum chemotaxis and the p53 signaling network and find that adaptive networks are often associated with a noise attenuation module. Our approach may be applicable to finding network design principles for other dual and multiple functions.

Signaling Crosstalk Mechanisms That May Fine-Tune Pathogen-Responsive NFκB

Precise control of inflammatory gene expression is critical for effective host defense without excessive tissue damage. The principal regulator of inflammatory gene expression is nuclear factor kappa B (NFκB), a transcription factor. Nuclear NFκB activity is controlled by IκB proteins, whose stimulus-responsive degradation and re-synthesis provide for transient or dynamic regulation. The IκB-NFκB signaling module receives input signals from a variety of pathogen sensors, such as toll-like receptors (TLRs). The molecular components and mechanisms of NFκB signaling are well-understood and have been reviewed elsewhere in detail. Here we review the molecular mechanisms that mediate cross-regulation of TLR-IκB-NFκB signal transduction by signaling pathways that do not activate NFκB themselves, such as interferon signaling pathways. We distinguish between potential regulatory crosstalk mechanisms that (i) occur proximal to TLRs and thus may have stimulus-specific effects, (ii) affect the core IκB-NFκB signaling module to modulate NFκB activation in response to several stimuli. We review some well-documented examples of molecular crosstalk mechanisms and indicate other potential mechanisms whose physiological roles require further study.

Molecular Engineering of the TGF-β Signaling Pathway

Transforming growth factor beta (TGF-β) is an important growth factor that plays essential roles in regulating tissue development and homeostasis. Dysfunction of TGF-β signaling is a hallmark of many human diseases. Therefore, targeting TGF-β signaling presents broad therapeutic potential. Since the discovery of the TGF-β ligand, a collection of engineered signaling proteins have been developed to probe and manipulate TGF-β signaling responses. In this review, we highlight recent progress in the engineering of TGF-β signaling for different applications and discuss how molecular engineering approaches can advance our understanding of this important pathway. In addition, we provide a future outlook on the opportunities and challenges in the engineering of the TGF-β signaling pathway from a quantitative perspective.

Techniques for Studying Decoding of Single Cell Dynamics

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

Lipidation-independent vacuolar functions of Atg8 rely on its noncanonical interaction with a vacuole membrane protein

The ubiquitin-like protein Atg8, in its lipidated form, plays central roles in autophagy. Yet, remarkably, Atg8 also carries out lipidation-independent functions in non-autophagic processes. How Atg8 performs its moonlighting roles is unclear. Here we report that in the fission yeast Schizosaccharomyces pombe and the budding yeast Saccharomyces cerevisiae, the lipidation-independent roles of Atg8 in maintaining normal morphology and functions of the vacuole require its interaction with a vacuole membrane protein Hfl1 (homolog of human TMEM184 proteins). Crystal structures revealed that the Atg8-Hfl1 interaction is not mediated by the typical Atg8-family-interacting motif (AIM) that forms an intermolecular β-sheet with Atg8. Instead, the Atg8-binding regions in Hfl1 proteins adopt a helical conformation, thus representing a new type of AIMs (termed helical AIMs here). These results deepen our understanding of both the functional versatility of Atg8 and the mechanistic diversity of Atg8 binding.

NF-κB, the Importance of Being Dynamic: Role and Insights in Cancer

In this review, we aim at describing the results obtained in the past years on dynamics features defining NF-κB regulatory functions, as we believe that these developments might have a transformative effect on the way in which NF-κB involvement in cancer is studied. We will also describe technical aspects of the studies performed in this context, including the use of different cellular models, culture conditions, microscopy approaches and quantification of the imaging data, balancing their strengths and limitations and pointing out to common features and to some open questions. Our emphasis in the methodology will allow a critical overview of literature and will show how these cutting-edge approaches can contribute to shed light on the involvement of NF-κB deregulation in tumour onset and progression. We hypothesize that this “dynamic point of view” can be fruitfully applied to untangle the complex relationship between NF-κB and cancer and to find new targets to restrain cancer growth.

Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources

Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects. In this study, based on a whole-cell model simulation of translation in S. cerevisiae, we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes. We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S. cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S. cerevisiae mRNAs oscillate. We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes. Furthermore, we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy (e.g. ribosomal “traffic jam”) induces the largest impact on the translation of the S. cerevisiae genome.

The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes.

Each cell contains a limited number of macromolecules and factors that participate in the gene expression process. These expression resources are shared between the different molecules that encode the genetic code, resulting in non-trivial couplings and competitions between the different gene expression stages. Such competitions should be considered when analyzing the cellular economy of the cell, the genome evolution, and the design of synthetic expression circuits. Here we study the effect of couplings and competitions for ribosomes by performing a whole-cell simulation of translation of S. cerevisiae, with parameters estimated from experimental data. We demonstrate that by periodically changing the mRNA levels of a single gene (endogenous or heterologous) or a set of genes, the translation of all S. cerevisiae genes are affected in a periodic manner. We numerically estimate the exact impact of the mRNA levels periodicity on the translation process dynamics, as well as on the dynamics of the free ribosomal pool and the way it is affected by parameters such as the codon composition of the oscillating gene, its initiation rate and mRNA levels. Furthermore, we show that the codon compositions of synthetically highly expressed heterologous genes that are expected to oscillate must be carefully considered. For example, synonymous mutations resulting in “traffic jams” of ribosomes along the fluctuated mRNAs may cause significant fluctuations of up to 50% in the steady-state translation rates of all genes.

Tunable NF-κB Oscillations in Yeast

Studies of genetic networks in situ are confounded by unknown interactions with native networks. In Zhang et al. (2017), the authors capitalize on the fact that yeast lacks the NF-κB pathway to study the human NF-κB pathway in isolation and develop a predictive model.