Cells use a limited number of receptors to sense and process molecular information from their environment. In the classical view of signaling, receptor-ligand affinities determine binding kinetics, on timescales of diffusion, where their time-averaged contact duration regulates rapid cytoplasmic signaling events to coordinate cellular responses. For some cytokines, single receptor-ligand binding events can initiate large multiprotein complexes in the cytoplasm that assemble over tens of minutes, raising the question of how cytokine affinity influences the sensitivity and strength of signaling. Here, we leverage naturally occurring variations of interleukin (IL)-1β from multiple species to determine the impact of affinity on human IL-1 receptor signaling. Using experiments and models, we investigate single receptor complexes activated by ligands that vary across multiple orders of magnitude in affinity. Our results show that while the receptor-ligand affinity establishes cytokine response sensitivity, activated IL-1 receptor complexes signal as discrete, quantized packets of signaling flux independent of affinity.

Temporal properties of molecules within signaling networks, such as sub-cellular changes in protein abundance, encode information that mediate cellular responses to stimuli. How dynamic signals relay and process information is a critical gap in understanding cellular behaviors. In this work, we investigate transmission of information about changing extracellular cytokine concentrations from receptor-level supramolecular assemblies of IKK kinases downstream to the NF-κB transcription factor. In a custom robot-controlled microfluidic cell culture, we simultaneously measure input-output encoding of IKK-NF-κB in dual fluorescent-reporter cells. When compared with single cytokine pulses, dose-conserving pulse trains prolong IKK assemblies and lead to disproportionately enhanced retention of nuclear NF-κB. Using particle swarm optimization, we demonstrate that a mechanistic model does not recapitulate this emergent property. By contrast, invoking mechanisms for NF-κB-dependent chromatin remodeling to the model recapitulates experiments, showing how temporal dosing that prolongs IKK assemblies facilitates switching to permissive chromatin that sequesters nuclear NF-κB. Remarkably, using simulations to resolve single-cell receptor data accurately predicts same-cell NF-κB time courses for more than 80% of our single cell trajectories. Our data and simulations therefore suggest that cell-to-cell heterogeneity in cytokine responses are predominantly due to mechanisms at the level receptor-associated protein complexes.

Recently, there is a rise in studies that recognize the importance of targeting ubiquitin and related molecular machinery in various therapeutic contexts. Here we briefly discuss the history of ubiquitin, its biological roles in protein degradation and beyond, as well as the current state of ubiquitin-targeting therapeutics across diseases. We conclude that targeting ubiquitin machinery is approaching a renaissance, and tapping its full potential will require embracing a wholistic perspective of ubiquitin's multifaceted roles.

Visualization of mRNA molecules in single cells has revealed their core mechanisms as well as sources of cell-to-cell and spatiotemporal heterogeneity. Here, we describe a protocol to label, visualize, and quantify mRNA molecules by time-lapse imaging with the capability of resolving mRNA molecules over durations of hours to days. We provide links to mRNA-labeling plasmids as well as free software for a semi-automated image analysis pipeline. For complete details on the use and execution of this protocol, please refer to Guo and Lee (2022) and Kowalczyk et al. (2021).

Single-cell imaging of individual mRNAs has revealed core mechanisms of the central dogma. However, most approaches require cell fixation or have limited sensitivity for live-cell applications. Here, we describe SunRISER (SunTag-based reporter for imaging signal-enriched mRNA), a computationally and experimentally optimized approach for unambiguous detection of single mRNA molecules in living cells. When viewed by epifluorescence microscopy, SunRISER-labeled mRNAs show strong signal to background and resistance to photobleaching, which together enable long-term mRNA imaging studies. SunRISER variants, using 8× and 10× stem-loop arrays, demonstrate effective mRNA detection while significantly reducing alterations to target mRNA sequences. We characterize SunRISER to observe mRNA inheritance during mitosis and find that stressors enhance diversity among post-mitotic sister cells. Taken together, SunRISER enables a glimpse into living cells to observe aspects of the central dogma and the role of mRNAs in rare and dynamical trafficking events.

A myriad of inflammatory cytokines regulates signaling pathways for normal cellular physiology. The IKK complex is an integration hub for extracellular cytokines that govern NF-κB signaling. In response to inflammation, IKK is activated through recruitment to receptor-associated protein assemblies. How and what information IKK complexes transmit about the milieu are open questions. Here we track dynamics of IKK complexes and nuclear NF-κB to identify upstream signaling features that determine same-cell responses. Quantitative imaging experiments and computational modeling of single complexes reveals that their size, number, and timing relays cytokine-specific information with feedback control that is independent of transcription. Our results provide evidence for variable-gain stochastic pooling, a noise-reducing motif that enables parsimonious and cytokine-specific signaling. We argue that emergent properties of stochastic pooling motif are general principles of receptor signaling, and we present new modules of regulation of NF-kB signaling in response to cytokine stimulation.

A myriad of inflammatory cytokines regulate signaling pathways to maintain cellular homeostasis. The IκB kinase (IKK) complex is an integration hub for cytokines that govern nuclear factor κB (NF-κB) signaling. In response to inflammation, IKK is activated through recruitment to receptor-associated protein assemblies. How and what information IKK complexes transmit about the milieu are open questions. Here, we track dynamics of IKK complexes and nuclear NF-κB to identify upstream signaling features that determine same-cell responses. Experiments and modeling of single complexes reveal their size, number, and timing relays cytokine-specific control over shared signaling mechanisms with feedback regulation that is independent of transcription. Our results provide evidence for variable-gain stochastic pooling, a noise-reducing motif that enables cytokine-specific regulation and parsimonious information transfer. We propose that emergent properties of stochastic pooling are general principles of receptor signaling that have evolved for constructive information transmission in noisy molecular environments.

MOTIVATION:Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images.RESULTS:The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data.AVAILABILITY AND IMPLEMENTATION:The data and software are freely available online at https://github.com/recleelab.SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach that uses Lasso to perform selection at the level of reaction modules is applied to the NF-κB signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.

Immune and stromal cells communicate through cytokines to initiate intracellular signaling, gene expression, and a multifaceted immune response. Cytokine signaling coordinates the dynamics of intracellular pathways that regulate inflammation. Immune and stromal cells both produce and respond to tumor necrosis factor-alpha (TNF), mediating inflammation and enabling communication between these compartments. The inflammatory role of TNF, activating the nuclear factor-κB (NF-κB) signaling and mitogen-activated protein kinase (MAPK) pathways, is well known. However, the effect of varying the time and amplitude patterns of TNF production in neighboring immune and stromal cells has not been systematically studied. Moreover, little is known about how time and amplitude patterns of an inflammatory signal input impact the nature of inflammatory responses. To study this relationship between production and response to TNF and its role in the dynamics of TNF-dependent NF-κB and MAPK signaling, we are using a systems immunology approach. Specifically, we are experimentally determining the patterns of TNF produced by human macrophages stimulated with heat-killed bacteria and characterizing the effect of these TNF stimulation patterns in the dynamics of NF-κB and MAPK responses in human cell lines. Additionally, we will use these experimental results to calibrate our deterministic 2 feedback with competitor (D2FC) mathematical model and model the dynamics of NF-κB signaling. Findings from this research will likely apply to other cytokine and signaling pathways, contributing to understanding the integration of time varying cytokine stimuli.

Cellular microenvironments are dynamic. When exposed to extracellular cues, such as changing concentrations of inflammatory cytokines, cells activate signaling networks that mediate fate decisions. Exploring responses broadly to time-varying microenvironments is essential to understand the information transmission capabilities of signaling networks and how dynamic milieus influence cell fate decisions. Here, we present a gravity-driven cell culture and demonstrate that the system accurately produces user-defined concentration profiles for one or more dynamic stimuli. As proof of principle, we monitor nuclear factor-κB activation in single cells exposed to dynamic cytokine stimulation and reveal context-dependent sensitivity and uncharacterized single-cell response classes distinct from persistent stimulation. Using computational modeling, we find that cell-to-cell variability in feedback rates within the signaling network contributes to different response classes. Models are validated using inhibitors to predictably modulate response classes in live cells exposed to dynamic stimuli. These hidden capabilities, uncovered through dynamic stimulation, provide opportunities to discover and manipulate signaling mechanisms.

The Wnt/β-catenin pathway is one of the most conserved signaling pathways across species with essential roles in development, cell proliferation, and disease. Wnt signaling occurs at the protein level and via β-catenin-mediated transcription of target genes. However, little is known about the underlying mechanisms regulating the expression of the key Wnt ligand Wnt3a or the modulation of its activity. Here, we provide evidence that there is significant cross-talk between the dopamine D2 receptor (D2R) and Wnt/β-catenin signaling pathways. Our data suggest that D2R-dependent cross-talk modulates Wnt3a expression via an evolutionarily-conserved TCF/LEF site within the WNT3A promoter. Moreover, D2R signaling also modulates cell proliferation and modifies the pathology in a renal ischemia/reperfusion-injury disease model, via its effects on Wnt/β-catenin signaling. Together, our results suggest that D2R is a transcriptional modulator of Wnt/β-catenin signal transduction with broad implications for health and development of new therapeutics.

Target-centric drug development strategies prioritize single-target potency in vitro and do not account for connectivity and multi-target effects within a signal transduction network. Here, we present a systems biology approach that combines transcriptomic and structural analyses with live-cell imaging to predict small molecule inhibitors of TNF-induced NF-κB signaling and elucidate the network response. We identify two first-in-class small molecules that inhibit the NF-κB signaling pathway by preventing the maturation of a rate-limiting multiprotein complex necessary for IKK activation. Our findings suggest that a network-centric drug discovery approach is a promising strategy to evaluate the impact of pharmacologic intervention in signaling.

Cell-to-cell differences in protein expression in normal tissues and tumors are a common phenomenon, but the underlying principles that govern this heterogeneity are largely unknown. Here, we show that in monolayer cancer cell-line cultures, the expression of the five metabolic enzymes of serine-glycine synthesis (SGS), including its rate-limiting enzyme, phosphoglycerate dehydrogenase (PHGDH), displays stochastic cell-to-cell variation. By contrast, in cancer cell line-derived three-dimensional (3D) microtumors PHGDH expression is restricted to the outermost part of the microtumors' outer proliferative cell layer, while the four other SGS enzymes display near uniform expression throughout the microtumor. A mathematical model suggests that metabolic stress in the microtumor core activates factors that restrict PHGDH expression. Thus, intracellular enzyme expression in growing cell ecosystems can shift to spatially ordered patterns in 3D structured environments due to emergent cell-cell communication, with potential implications for the design of effective anti-metabolic cancer therapies.

Noisy gene expression generates diverse phenotypes, but little is known about mechanisms that modulate noise. Combining experiments and modeling, we studied how tumor necrosis factor (TNF) initiates noisy expression of latent HIV via the transcription factor nuclear factor κB (NF-κB) and how the HIV genomic integration site modulates noise to generate divergent (low-versus-high) phenotypes of viral activation. We show that TNF-induced transcriptional noise varies more than mean transcript number and that amplification of this noise explains low-versus-high viral activation. For a given integration site, live-cell imaging shows that NF-κB activation correlates with viral activation, but across integration sites, NF-κB activation cannot account for differences in transcriptional noise and phenotypes. Instead, differences in transcriptional noise are associated with differences in chromatin state and RNA polymerase II regulation. We conclude that, whereas NF-κB regulates transcript abundance in each cell, the chromatin environment modulates noise in the population to support diverse HIV activation in response to TNF.

Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://github.com/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.

This chapter provides a brief introduction to the theory and practice of spatial stochastic simulations. It begins with an overview of different methods available for biochemical simulations highlighting their strengths and limitations. Spatial stochastic modeling approaches are indicated when diffusion is relatively slow and spatial inhomogeneities involve relatively small numbers of particles. The popular software package MCell allows particle-based stochastic simulations of biochemical systems in complex three dimensional (3D) geometries, which are important for many cell biology applications. Here, we provide an overview of the simulation algorithms used by MCell and the underlying theory. We then give a tutorial on building and simulating MCell models using the CellBlender graphical user interface, that is built as a plug-in to Blender, a widely-used and freely available software platform for 3D modeling. The tutorial starts with simple models that demonstrate basic MCell functionality and then advances to a number of more complex examples that demonstrate a range of features and provide examples of important biophysical effects that require spatially-resolved stochastic dynamics to capture.

Antigen stimulation of the T cell receptor in a naïve cell triggers a complex network of signaling pathways that collectively determine whether it adopts a tolerogenic, Treg, or immunogenic, Th, phenotype. The complexity of this network presents a major barrier to the development of computational models, but such efforts hold the promise for increased understanding and the development of therapies based on selective T cell differentiation. We previously developed a Boolean model of T cell differentiation that predicted an optimal TCR signal duration for the production of Tregs, a strategy that we implemented and confirmed using an in vitro system. This model suggested that the dynamics of PTEN, a lipid phosphatase that opposes the activation the Akt/mTOR signaling pathway, were predictive of cell fate. Subsequent experiments revealed a critical positive feedback loop for Akt activation acting through the transcription factor FoxO1, a substrate of Akt that regulates PTEN. Here, we present a detailed computational model of Akt signaling during T cell differentiation that tracks the dynamics of numerous protein phosphoforms and complexes. The model is developed using the BioNetGen modeling language and simulated using ordinary differential equations. The calibrated model reproduces dose-dependent kinetics of phosphorylation of Akt at T308 and S473 measured experimentally, and predicts the existence of a sharp threshold in signal strength, which we have confirmed experimentally using Nur77-GFP reporter mice. The model also predicts a reduction in this threshold with reduction in PTEN expression, which we confirmed in mice heterozygous for PTEN deficiency.

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.