Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing

Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis

The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system’s dynamics using a discrete, semi-quantitative Petri net model.

It is still a challenge to develop mechanistic models for big molecular systems without the knowledge of enough kinetic parameters of sufficient quality. At the same time, more qualitative and semi-quantitative data have been produced in increasing numbers, e.g., by new high-throughput technologies. This has generated demands for new concepts at appropriate abstraction levels. The Petri net formalism enables the integration of qualitative as well as quantitative data and provides algorithms and methods for model verification and model simulation. Moreover, Petri nets exhibit a clear and coherent visualization. Here, we modeled the molecular switches between cell survival, apoptosis, and necroptosis induced by tumor necrosis factor 1. We were interested not only in an exhaustive exploration of all possible signaling pathways, but also in finding the system’s checkpoints. Our Petri net model comprises 118 biochemical entities, 130 reactions, and 299 edges. We found 279 pathways that describe signal flows from receptor activation to cellular response.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. We applied in silico knockout analyses to the Petri net model and could identify important checkpoints of the tumor necrosis factor 1 signaling pathway.

NF-κB memory coordinates transcriptional responses to dynamic inflammatory stimuli

Many scenarios in cellular communication require cells to interpret multiple dynamic signals. It is unclear how exposure to inflammatory stimuli alters transcriptional responses to subsequent stimulus. Using high-throughput microfluidic live-cell analysis, we systematically profile the NF-κB response to different signal sequences in single cells. We find that NF-κB dynamics store the short-term history of received signals: depending on the prior pathogenic or cytokine signal, the NF-κB response to subsequent stimuli varies from no response to full activation. Using information theory, we reveal that these stimulus-dependent changes in the NF-κB response encode and reflect information about the identity and dose of the prior stimulus. Small-molecule inhibition, computational modeling, and gene expression profiling show that this encoding is driven by stimulus-dependent engagement of negative feedback modules. These results provide a model for how signal transduction networks process sequences of inflammatory stimuli to coordinate cellular responses in complex dynamic environments.

New Insights in the Interplay Between African Swine Fever Virus and Innate Immunity and Its Impact on Viral Pathogenicity

The continuous spread of African swine fever virus (ASFV) in Europe and Asia represents a major threat to livestock health, with billions of dollars of income losses and major perturbations of the global pig industry. One striking feature of African swine fever (ASF) is the existence of different forms of the disease, ranging from acute with mortality rates approaching 100% to chronic, with mild clinical manifestations. These differences in pathogenicity have been linked to genomic alterations present in attenuated ASFV strains (and absent in virulent ones) and differences in the immune response of infected animals. In this mini-review, we summarized current knowledge on the connection between ASFV pathogenicity and the innate immune response induced in infected hosts, with a particular focus on the pathways involved in ASFV detection. Indeed, recent studies have highlighted the key role of the DNA sensor cGAS in ASFV sensing. We discussed what other pathways may be involved in ASFV sensing and inflammasome activation and summarized recent findings on the viral ASFV genes involved in the modulation of the interferon (IFN) and nuclear factor kappa B (NF-κB) pathways.

Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS

Background: Despite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we aimed to develop mathematical models to explore theoretical cellular states underpinning the empirically observed responses of macrophages following lipopolysaccharide (LPS) challenge.

Methods: We obtained empirical data following primary and secondary responses to LPS in two in vitro cellular models (bone marrow-derived macrophages or BMDMs, and RAW 264.7 cells) and single-cell protein measurements for four key inflammatory mediators: TNF, IL-6, pro-IL-1β, and NOS2, and used mathematical modelling to understand heterogeneity.

Results: For these four factors, we showed that macrophage community AIH is dependent on LPS dose and that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical three-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a four-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS.

Conclusions: Overall, we provide a mathematical model for studying macrophage ecology and community dynamics that can be used to elucidate the role of phenotypically negative macrophage populations in AIH and, primary and secondary responses to LPS.

A chemogenetic platform for controlling plasma membrane signaling and synthetic signal oscillation

Chemogenetic methods enabling the rapid translocation of specific proteins to the plasma membrane (PM) in a single protein-single ligand manner are useful tools in cell biology. We recently developed a technique, in which proteins fused to an Escherichia coli dihydrofolate reductase (eDHFR) variant carrying N-terminal hexalysine residues are recruited from the cytoplasm to the PM using the synthetic myristoyl-d-Cys-tethered trimethoprim (m^DcTMP) ligand. However, this system achieved PM-specific translocation only when the eDHFR tag was fused to the N terminus of proteins, thereby limiting its application. In this report, we engineered a universal PM-targeting tag for m^DcTMP-induced protein translocation by grafting the hexalysine motif into an intra-loop region of eDHFR. We demonstrate the broad applicability of the new loop-engineered eDHFR tag and m^DcTMP pair for conditional PM recruitment and activation of various tag-fused signaling proteins with different fusion configurations and for reversibly and repeatedly controlling protein localization to generate synthetic signal oscillations.

SUMOylation of Dorsal attenuates Toll/NF-κB signaling

In Drosophila, Toll/NF-κB signaling plays key roles in both animal development and in host defense. The activation, intensity, and kinetics of Toll signaling are regulated by posttranslational modifications such as phosphorylation, SUMOylation, or ubiquitination that target multiple proteins in the Toll/NF-κB cascade. Here, we have generated a CRISPR-Cas9 edited Dorsal (DL) variant that is SUMO conjugation resistant. Intriguingly, embryos laid by dlSCR mothers overcome dl haploinsufficiency and complete the developmental program. This ability appears to be a result of higher transcriptional activation by DLSCR. In contrast, SUMOylation dampens DL transcriptional activation, ultimately conferring robustness to the dorso-ventral program. In the larval immune response, dlSCR animals show an increase in crystal cell numbers, stronger activation of humoral defense genes, and high cactus levels. A mathematical model that evaluates the contribution of the small fraction of SUMOylated DL (1-5%) suggests that it acts to block transcriptional activation, which is driven primarily by DL that is not SUMO conjugated. Our findings define SUMO conjugation as an important regulator of the Toll signaling cascade, in both development and host defense. Our results broadly suggest that SUMO attenuates DL at the level of transcriptional activation. Furthermore, we hypothesize that SUMO conjugation of DL may be part of a Ubc9-dependent mechanism that restrains Toll/NF-κB signaling.

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.

A nucleation barrier spring-loads the CBM signalosome for binary activation

Immune cells activate in binary, switch-like fashion via large protein assemblies known as signalosomes, but the molecular mechanism of the switch is not yet understood. Here, we employed an in-cell biophysical approach to dissect the assembly mechanism of the CARD-BCL10-MALT1 (CBM) signalosome, which governs nuclear transcription factor-κB activation in both innate and adaptive immunity. We found that the switch consists of a sequence-encoded and deeply conserved nucleation barrier to ordered polymerization by the adaptor protein BCL10. The particular structure of the BCL10 polymers did not matter for activity. Using optogenetic tools and single-cell transcriptional reporters, we discovered that endogenous BCL10 is functionally supersaturated even in unstimulated human cells, and this results in a predetermined response to stimulation upon nucleation by activated CARD multimers. Our findings may inform on the progressive nature of age-associated inflammation, and suggest that signalosome structure has evolved via selection for kinetic rather than equilibrium properties of the proteins.

The innate immune system is the body’s first line of defence against pathogens. Although innate immune cells do not recognize specific disease-causing agents, they can detect extremely low levels of harmful organisms or substances. In response, they activate signals that lead to inflammation, which tells other cells that there is an infection. Innate immune cells are turned on in a switch-like fashion, becoming active very quickly after interacting with a pathogen. This is due to the action of signalosomes, large complexes made up of several proteins that clump together to form long chains that activate the cell.

But how do these large protein complexes assemble quick enough to create the switch-like activation observed in innate immune cells? To answer this question, Rodríguez Gama et al. focused on the CBM signalosome, which is involved in triggering inflammation through the activation of a protein called NF-kB.

First, Rodríguez Gama et al. used genetic tools to determine that activating the CBM signalosome drives a switch-like activation of NF-kB in cells. This means that individual cells in a population either become fully activated or not at all in response to minute amounts of harmful substances.

Once they had established this, Rodríguez Gama et al. wanted to know which protein in the CBM signalosome was responsible for the switch. They found that one of the proteins in the signalosome, called BCL10, has a ‘nucleation barrier’ encoded in its sequence. This means that it is very hard for BCL10 to start clumping together, but once it does, the clumps grow on their own. The nucleation barrier describes exactly how hard it is for these clumps to get started, and is determined by how disorganized the protein is.

When a pathogen ‘stimulates’ an immune cell, a tiny template is formed that lowers the nucleation barrier so that BCL10 can then aggregate itself together, leading to the switch-like behaviour observed. The nucleation barrier allows there to be more than enough BCL10 present in the cell at all times – ready to clump together at a moment’s notice – and this permits the cell to detect very low levels of a pathogen.

Rodríguez Gama et al. then tested whether BCL10 from other animals also has a nucleation barrier. They found that this feature is conserved from cnidarians, such as corals or jellyfish, to mammals, including humans. This suggests that the use of nucleation barriers to regulate innate immune signalling has existed for a long time throughout evolution.

The work by Rodríguez Gama et al. broadens our understanding of how the innate immune system senses and responds to extremely low levels of pathogens. That BCL10 is always ready to clump together suggests it may be a driving force for chronic and age-associated inflammation. Additionally, the findings of Rodríguez Gama et al. also offer insights into how other signalosomes may become activated, and offer the possibility of new drugs aimed at modifying nucleation barriers.

Temperature Controls Onset and Period of NF-κB Oscillations and can Lead to Chaotic Dynamics

The transcription factor NF-κB plays a vital role in the control of the immune system, and following stimulation with TNF-α its nuclear concentration shows oscillatory behaviour. How environmental factors, in particular temperature, can control the oscillations and thereby affect gene stimulation is still remains to be resolved question. In this work, we reveal that the period of the oscillations decreases with increasing temperature. We investigate this using a mathematical model, and by applying results from statistical physics, we introduce temperature dependency to all rates, resulting in a remarkable correspondence between model and experiments. Our model predicts how temperature affects downstream protein production and find a crossover, where high affinity genes upregulates at high temperatures. Finally, we show how or that oscillatory temperatures can entrain NF-κB oscillations and lead to chaotic dynamics presenting a simple path to chaotic conditions in cellular biology.

Enhanced transcriptional heterogeneity mediated by NF-κB super-enhancers

The transcription factor NF-κB, which plays an important role in cell fate determination, is involved in the activation of super-enhancers (SEs). However, the biological functions of the NF-κB SEs in gene control are not fully elucidated. We investigated the characteristics of NF-κB-mediated SE activity using fluorescence imaging of RelA, single-cell transcriptome and chromatin accessibility analyses in anti-IgM-stimulated B cells. The formation of cell stimulation-induced nuclear RelA foci was abolished in the presence of hexanediol, suggesting an underlying process of liquid-liquid phase separation. The gained SEs induced a switch-like expression and enhanced cell-to-cell variability in transcriptional response. These properties were correlated with the number of gained cis-regulatory interactions, while switch-like gene induction was associated with the number of NF-κB binding sites in SE. Our study suggests that NF-κB SEs have an important role in the transcriptional regulation of B cells possibly through liquid condensate formation consisting of macromolecular interactions.

NF-κB produces an all-or-none activation response upon the activation of B cell receptors. These dynamics modulate the amplitude and frequency of target mRNA induction in cell populations. In this research, we performed single-cell assessment of chromatin accessibility and RNA expression, coupled with fluorescence imaging to characterize the nuclear dynamics of NF-κB proteins in B cell upon receptor stimulation. We found that upon cellular activation, NF-κB-mediated long-range activation of enhancers cooperatively evoked RNA production. In addition, predicted DNA contacts brought by open chromatin led to the high heterogeneity of RNA levels in cell populations. Stimuli-dependent NF-κB foci formation was further inhibited by 1,6-hexanediol (liquid-liquid phase separation inhibitor) and JQ1 (coactivator protein BRD4 inhibitor). We thus propose that nuclear NF-κB plays an important role in the transcriptional regulation of B cell development possibly through the formation of liquid condensates.

Signaling oscillations in embryonic development

Tight spatiotemporal control of cellular behavior and cell fate decisions is paramount to the formation of multicellular organisms during embryonic development. Intercellular communication via signaling pathways mediates this control. Interestingly, these signaling pathways are not static, but dynamic and change in activity over time. Signaling oscillations as a specific type of dynamics are found in various signaling pathways and model systems. Functions of oscillations include the regulation of periodic events or the transmission of information by encoding signals in the dynamic properties of a signaling pathway. For instance, signaling oscillations in neural or pancreatic progenitor cells modulate their proliferation and differentiation. Oscillations between neighboring cells can also be synchronized, leading to the emergence of waves traveling through the tissue. Such population-wide signaling oscillations regulate for example the consecutive segmentation of vertebrate embryos, a process called somitogenesis. Here, we outline our current understanding of signaling oscillations in embryonic development, how signaling oscillations are generated, how they are studied and how they contribute to the regulation of embryonic development.

A universal microfluidic approach for integrated analysis of temporal homocellular and heterocellular signaling and migration dynamics

Microfluidics offers precise and dynamic control of microenvironments for the study of temporal cellular responses. However, recent research focusing solely on either homocellular (single-cell, population) or heterocellular response may yield insufficient output, which possibly leads to partial comprehension about the underlying mechanisms of signaling events and corresponding cellular behaviors. Here, a universal microfluidic approach is developed for integrated analysis of temporal signaling and cell migration dynamics in multiple cellular contexts (single-cell, population and coculture). This approach allows to confine the desired number or mixture of specific cell sample types in a single device. Precise single cell seeding was achieved manually with bidirectional controllability. Coupled with time-lapse imaging, temporal cellular responses can be observed with single-cell resolution. Using NIH3T3 cells stably expressing signal transducer and activator of transcription 1/2 (STAT1/2) activity biosensors, temporal STAT1/2 activation and cell migration dynamics were explored in isolated single cells, populations and cocultures stimulated with temporal inputs, such as single-pulse and continuous signals of interferon γ (IFNγ) or lipopolysaccharide (LPS). We demonstrate distinct dynamic responses of fibroblasts in different cellular contexts. Our presented approach facilitates a multi-dimensional understanding of STAT signaling and corresponding migration behaviors.

Novel Nexus with NFκB, β-catenin, and RB1 empowers PSMD10/Gankyrin to counteract TNF-α induced apoptosis establishing its oncogenic role

NFκB is a critical rapid-acting transcription factor that protects cancer cells from programmed cell death induced by stress or therapy. While NFκB works in nexus with non-classical oncoproteins such as STAT3 and AKT under a variety of conditions, it is a major antiapoptotic factor activated by TNF-α of the tumor microenvironment. Therefore, it is surprising that PSMD10, an oncoprotein overexpressed in several cancers and a marker of poor prognosis, is reported to inhibit the NFκB pathway. In this study, we explore the role of PSMD10 in cancer cells exposed to TNF-α. We screen several breast and colon cancer cell lines and select SW480, a colon cancer cell line highly resistant to TNF-α, and demonstrate that PSMD10 knockdown sensitizes these cells to TNF-α induced cell death. One of the mechanisms involves transcriptional regulation of β-catenin and RB1, two key colon cancer cell specific anti-apoptotic factors. Surprisingly, we find that PSMD10 is required for optimal phosphorylation and transcriptional activation of NFκB (RELA). Thus, upon PSMD10 knockdown, there is significant downregulation of anti-apoptotic NFκB target genes TNFAIP3 (A20), BIRC2 (cIAP1), BIRC3 (cIAP2), and XIAP. Our study, for the first time, shows that PSMD10 is required for the activation of the pro-survival arm via NFκB transcriptional activation to prevent cancer cells from succumbing to TNF-induced cell death. In addition by transcriptional regulation of two major antiapoptotic players RB1 and β-catenin, PSMD10 proves to be a coveted oncoprotein with a key role in tumorigenesis.

Functional Hallmarks of Healthy Macrophage Responses: Their Regulatory Basis and Disease Relevance

Macrophages are first responders for the immune system. In this role, they have both effector functions for neutralizing pathogens and sentinel functions for alerting other immune cells of diverse pathologic threats, thereby initiating and coordinating a multipronged immune response. Macrophages are distributed throughout the body-they circulate in the blood, line the mucosal membranes, reside within organs, and survey the connective tissue. Several reviews have summarized their diverse roles in different physiological scenarios and in the initiation or amplification of different pathologies. In this review, we propose that both the effector and the sentinel functions of healthy macrophages rely on three hallmark properties: response specificity, context dependence, and stimulus memory. When these hallmark properties are diminished, the macrophage's biological functions are impaired, which in turn results in increased risk for immune dysregulation, manifested by immune deficiency or autoimmunity. We review the evidence and the molecular mechanisms supporting these functional hallmarks.

NfκB signaling dynamics and their target genes differ between mouse blood cell types and induce distinct cell behavior

Cells can use signaling pathway activity over time (i.e., dynamics) to control cell fates. However, little is known about the potential existence and function of signaling dynamics in primary hematopoietic stem and progenitor cells (HSPCs). Here, we use time-lapse imaging and tracking of single murine HSPCs from GFP-p65/H2BmCherry reporter mice to quantify their nuclear factor κB (NfκB) activity dynamics in response to TNFα and IL1β. We find response dynamics to be heterogeneous between individual cells, with cell type specific dynamics distributions. Transcriptome sequencing of single cells physically isolated after live dynamics quantification shows activation of different target gene programs in cells with different dynamics. Finally, artificial induction of oscillatory NfκB activity causes changes in GMP behavior. Thus, HSPC behavior can be influenced by signaling dynamics, which are tightly regulated during hematopoietic differentiation and enable cell type specific responses to the same signaling inputs.

Novel insights into the potential mechanisms underlying carbendazim-induced hepatorenal toxicity in rats

Carbendazim (CBZ) is a common environmental pollutant that can contaminate food and water and severely damage human health. Some studies revealed the adverse effect of CBZ on different organs, but its detailed toxicity mechanism has not been elucidated yet. Thus, the present study aims to clarify the mechanisms of CBZ-induced hepatorenal toxicity in rats. Therefore, we partitioned 40 male Wistar rats into four groups (n = 10): a negative control group and three treatment groups, which received 100, 300, and 600 mg/kg of CBZ. All rats received the treatment daily by oral gavage. We collected blood and organ samples (liver and kidney) at 14 and 28 days postdosing. CBZ caused extensive pathological alterations in both the liver and kidneys, such as cellular degeneration and necrosis accompanied by severe inflammatory reactions in a dose- and time-dependent manner. All the CBZ-treated groups displayed strong tumor necrosis factor-α and nuclear factor-κB (NF-κB) immunopositivity. Additionally, CBZ dose-dependently elevated the alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, urea, and creatinine serum levels and reduced the serum albumin levels. Furthermore, CBZ-induced apoptosis, as indicated by the observed Bax gene upregulation and Bcl-2 gene downregulation in both organs. All these changes may be related to oxidative stress, as indicated by the increase in malondialdehyde levels and the decrease in total antioxidant capacity. Our results demonstrate that CBZ-induced dose- and time-dependent hepatorenal damage through oxidative stress, which activated both the NF-κB signaling pathway and Bcl-based programmed cell death.

Principles, mechanisms and functions of entrainment in biological oscillators

Entrainment is a phenomenon in which two oscillators interact with each other, typically through physical or chemical means, to synchronize their oscillations. This phenomenon occurs in biology to coordinate processes from the molecular to organismal scale. Biological oscillators can be entrained within a single cell, between cells or to an external input. Using six illustrative examples of entrainable biological oscillators, we discuss the distinctions between entrainment and synchrony and explore features that contribute to a system's propensity to entrain. Entrainment can either enhance or reduce the heterogeneity of oscillations within a cell population, and we provide examples and mechanisms of each case. Finally, we discuss the known functions of entrainment and discuss potential functions from an evolutionary perspective.

Temporal signaling, population control, and information processing through chromatin-mediated gene regulation

Chromatin regulation is a key pathway cells use to regulate gene expression in response to temporal stimuli, and is becoming widely used as a platform for synthetic biology applications. Here, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations. Chromatin regulators can silence genes in an all-or-none fashion at the single-cell level, with individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We integrate this mode of regulation with classical gene regulatory motifs, such as autoregulatory and incoherent feedforward loops, to determine the types of responses achievable with duration-dependent signaling. We demonstrate that repressive regulators without long-term epigenetic memory can filter out high frequency noise, and as part of an autoregulatory loop can precisely tune the fraction of cells in a population that expresses a gene of interest. Additionally, we find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced and, when included in a feed forward loop, enable perfect adaptation. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Altogether, we show that chromatin-mediated gene control enables a repertoire of complex cell population responses to temporal signals and can transmit higher information levels than previously measured in gene regulation.

Phenotypic variability, not noise, accounts for most of the cell-to-cell heterogeneity in IFN-γ and oncostatin M signaling responses

Cellular signaling responses show substantial cell-to-cell heterogeneity, which is often ascribed to the inherent randomness of biochemical reactions, termed molecular noise, wherein high noise implies low signaling fidelity. Alternatively, heterogeneity could arise from differences in molecular content between cells, termed molecular phenotypic variability, which does not necessarily imply imprecise signaling. The contribution of these two processes to signaling heterogeneity is unclear. Here, we fused fibroblasts to produce binuclear syncytia to distinguish noise from phenotypic variability in the analysis of cytokine signaling. We reasoned that the responses of the two nuclei within one syncytium could approximate the signaling outcomes of two cells with the same molecular content, thereby disclosing noise contribution, whereas comparison of different syncytia should reveal contribution of phenotypic variability. We found that ~90% of the variance in the primary response (which was the abundance of phosphorylated, nuclear STAT) to stimulation with the cytokines interferon-γ and oncostatin M resulted from differences in the molecular content of individual cells. Thus, our data reveal that cytokine signaling in the system used here operates in a reproducible, high-fidelity manner.

Viscoelasticity, Like Forces, Plays a Role in Mechanotransduction

Viscoelasticity and its alteration in time and space has turned out to act as a key element in fundamental biological processes in living systems, such as morphogenesis and motility. Based on experimental and theoretical findings it can be proposed that viscoelasticity of cells, spheroids and tissues seems to be a collective characteristic that demands macromolecular, intracellular component and intercellular interactions. A major challenge is to couple the alterations in the macroscopic structural or material characteristics of cells, spheroids and tissues, such as cell and tissue phase transitions, to the microscopic interferences of their elements. Therefore, the biophysical technologies need to be improved, advanced and connected to classical biological assays. In this review, the viscoelastic nature of cytoskeletal, extracellular and cellular networks is presented and discussed. Viscoelasticity is conceptualized as a major contributor to cell migration and invasion and it is discussed whether it can serve as a biomarker for the cells’ migratory capacity in several biological contexts. It can be hypothesized that the statistical mechanics of intra- and extracellular networks may be applied in the future as a powerful tool to explore quantitatively the biomechanical foundation of viscoelasticity over a broad range of time and length scales. Finally, the importance of the cellular viscoelasticity is illustrated in identifying and characterizing multiple disorders, such as cancer, tissue injuries, acute or chronic inflammations or fibrotic diseases.

Managing digital knowledge for ensuring business efficiency and continuity

Purpose

Today many firms are pushed towards digitalization to ensure business continuity and their survival due to COVID-19. Therefore, this study aims to investigate the emerging knowledge management models in the era of digitalization and disruption.

Design/methodology/approach

The authors have adopted a semi-structured approach composed of qualitative data collection from 37 business executives from India representing different industry sectors. The authors adopted a three-layer coding process (axial, open and selective) to develop a framework grounded in organizational information processing theory.

Findings

Scanning the business environment leads to understand the status of current and potential business through intelligence of information, whereas better planning and execution can be achieved through employing and using the information intelligently that fits to the overall and strategic objective of the business. Overall, the business continuity can be obtained by information prosperity across the business by engaging diverse stakeholders. According to the findings, these aspects lead to the effective implementation of digital knowledge to ensure business continuity in uncertain business environment.

Practical implications

The study offers the insights for managing and executing the knowledge in digital platforms, where they can think of developing a system architecture on the basis of degree of uncertainty and information processing requirements for combining the knowledge.

Originality/value

The present study is unique, where it offers the meaningful visions to the designers and users of virtual knowledge management systems.

Single-cell RNA profiling identifies diverse cellular responses to EWSR1/FLI1 downregulation in Ewing sarcoma cells

BACKGROUND

The EWSR1/FLI1 gene fusion is the most common rearrangement leading to cell transformation in Ewing sarcoma (ES). Previous studies have indicated that expression at the cellular level is heterogeneous, and that levels of expression may oscillate, conferring different cellular characteristics. In ES the role of EWSR1/FLI1 in regulating subpopulation dynamics is currently unknown.

METHODS

We used siRNA to transiently suppress EWSR1/FLI1 expression and followed population dynamics using both single cell expression profiling, CyTOF and functional assays to define characteristics of exponentially growing ES cells and of ES cells in which EWSR1/FLI1 had been downregulated. Novel transcriptional states with distinct features were assigned using random forest feature selection in combination with machine learning. Cells isolated from ES xenografts in immune-deficient mice were interrogated to determine whether characteristics of specific subpopulations of cells in vitro could be identified. Stem-like characteristics were assessed by primary and secondary spheroid formation in vitro, and invasion/motility was determined for each identified subpopulation. Autophagy was determined by expression profiling, cell sorting and immunohistochemical staining.

RESULTS

We defined a workflow to study EWSR1/FLI1 driven transcriptional states and phenotypes. We tracked EWSR1/FLI1 dependent proliferative activity over time to discover sources of intra-tumoral diversity. Single-cell RNA profiling was used to compare expression profiles in exponentially growing populations (si-Control) or in two dormant populations (D1, D2) in which EWSR1/FLI1 had been suppressed. Three distinct transcriptional states were uncovered contributing to ES intra-heterogeneity. Our predictive model identified ~1% cells in a dormant-like state and ~ 2-4% cells with stem-like and neural stem-like features in an exponentially proliferating ES cell line and in ES xenografts. Following EWSR1/FLI1 knockdown, cells re-entering the proliferative cycle exhibited greater stem-like properties, whereas for those cells remaining quiescent, FAM134B-dependent dormancy may provide a survival mechanism.

CONCLUSIONS

We show that time-dependent changes induced by suppression of oncogenic EWSR1/FLI1 expression induces dormancy, with different subpopulation dynamics. Cells re-entering the proliferative cycle show enhanced stem-like characteristics, whereas those remaining dormant for prolonged periods appear to survive through autophagy. Cells with these characteristics identified in exponentially growing cell populations and in tumor xenografts may confer drug resistance and could potentially contribute to metastasis.

Using average transcription level to understand the regulation of stochastic gene activation

Gene activation is a random process, modelled as a framework of multiple rate-limiting steps listed sequentially, in parallel or in combination. Together with suitably assumed processes of gene inactivation, transcript birth and death, the step numbers and parameters in activation frameworks can be estimated by fitting single-cell transcription data. However, current algorithms require computing master equations that are tightly correlated with prior hypothetical frameworks of gene activation. We found that prior estimation of the framework can be facilitated by the traditional dynamical data of mRNA average level M(t), presenting discriminated dynamical features. Rigorous theory regarding M(t) profiles allows to confidently rule out the frameworks that fail to capture M(t) features and to test potential competent frameworks by fitting M(t) data. We implemented this procedure for a large number of mouse fibroblast genes under tumour necrosis factor induction and determined exactly the 'cross-talking n-state' framework; the cross-talk between the signalling and basal pathways is crucial to trigger the first peak of M(t), while the following damped gentle M(t) oscillation is regulated by the multi-step basal pathway. This framework can be used to fit sophisticated single-cell data and may facilitate a more accurate understanding of stochastic activation of mouse fibroblast genes.

Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS

Background: Despite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we aimed to develop mathematical models to explore theoretical cellular states underpinning the empirically observed responses of macrophages following lipopolysaccharide (LPS) challenge. Methods: We obtained empirical data following primary and secondary responses to LPS in two in vitro cellular models (bone marrow-derived macrophages or BMDMs, and RAW 264.7 cells) and single-cell protein measurements for four key inflammatory mediators: TNF, IL-6, pro-IL-1β, and NOS2, and used mathematical modelling to understand heterogeneity. Results: For these four factors, we showed that macrophage community AIH is dependent on LPS dose and that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical three-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a four-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS. Conclusions: Overall, we provide a mathematical model for studying macrophage ecology and community dynamics that can be used to elucidate the role of phenotypically negative macrophage populations in AIH and, primary and secondary responses to LPS.

Systems approaches to investigate the role of NF-κB signaling in aging

The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.

Petri nets and ODEs as complementary methods for comprehensive analysis on an example of the ATM-p53-NF-[Formula: see text]B signaling pathways

Intracellular processes are cascades of biochemical reactions, triggered in response to various types of stimuli. Mathematical models describing their dynamics have become increasingly popular in recent years, as tools supporting experimental work in analysis of pathways and regulatory networks. Not only do they provide insights into general properties of these systems, but also help in specific tasks, such as search for drug molecular targets or treatment protocols. Different tools and methods are used to model complex biological systems. In this work, we focus on ordinary differential equations (ODEs) and Petri nets. We consider specific methods of analysis of such models, i.e., sensitivity analysis (SA) and significance analysis. So far, they have been applied separately, with different goals. In this paper, we show that they can complement each other, combining the sensitivity of ODE models and the significance analysis of Petri nets. The former is used to find parameters, whose change results in the greatest quantitative and qualitative changes in the model response, while the latter is a structural analysis and allows indicating the most important subprocesses in terms of information flow in Petri net. Ultimately, both methods facilitate finding the essential processes in a given signaling pathway or regulatory network and may be used to support medical therapy development. In the paper, the use of dual modeling is illustrated with an example of ATM/p53/NF-[Formula: see text]B pathway. Each method was applied to analyze this system, resulting in finding different subsets of important processes that might be prospective targets for changing this system behavior. While some of the processes were indicated in each of the approaches, others were found by one method only and would be missed if only that method was applied. This leads to the conclusion about the complementarity of the methods under investigation. The dual modeling approach of comprehensive structural and parametric analysis yields results that would not be possible if these two modeling approaches were applied separately. The combined approach, proposed in this paper, facilitates finding not only key processes, with which significant parameters are associated, but also significant modules, corresponding to subsystems of regulatory networks. The results provide broader insight into therapy targets in diseases in which the natural control of intracellular processes is disturbed, leading to the development of more effective therapies in medicine.

Signal Amplification in Highly Ordered Networks Is Driven by Geometry

Here, we hypothesize that, in biological systems such as cell surface receptors that relay external signals, clustering leads to substantial improvements in signaling efficiency. Representing cooperative signaling networks as planar graphs and applying Euler’s polyhedron formula, we can show that clustering may result in an up to a 200% boost in signaling amplitude dictated solely by the size and geometry of the network. This is a fundamental relationship that applies to all clustered systems regardless of its components. Nature has figured out a way to maximize the signaling amplitude in receptors that relay weak external signals. In addition, in cell-to-cell interactions, clustering both receptors and ligands may result in maximum efficiency and synchronization. The importance of clustering geometry in signaling efficiency goes beyond biological systems and can inform the design of amplifiers in nonbiological systems.

Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.

Using Microfluidics and Live Cell Reporters to Dissect the Dynamics of TGF-β Signaling in Mouse Embryonic Stem Cells

The TGF-β pathway is known to behave as a classical morphogen, meaning that it can dictate cell fate decisions in a dose-dependent manner. Recent observations however showed that in addition to the absolute value of morphogen concentration, cells could also extract information from its temporal variations. In the present article we describe how to use automated microfluidics cell culture to stimulate cells with precisely defined temporal profiles of morphogens and how to engineer mouse embryonic stem cells with fluorescent reporters of pathway activity to record in real time their response to the applied stimulations. The combination of automated cell culture and of live cell reporter provides a complete toolbox to study how cells encode the information carried by time-varying TGF-β signals.

Chemical-Biology-derived in vivo Sensors: Past, Present, and Future

To understand the complex biochemistry and biophysics of biological systems, one needs to be able to monitor local concentrations of molecules, physical properties of macromolecular assemblies and activation status of signaling pathways, in real time, within single cells, and at high spatio-temporal resolution. Here we look at the tools that have been / are being / need to be provided by chemical biology to address these challenges. In particular, we highlight the utility of molecular probes that help to better measure mechanical forces and flux through key signalling pathways. Chemical biology can be used to both build biosensors to visualize, but also actuators to perturb biological processes. An emergent theme is the possibility to multiplex measurements of multiple cellular processes. Advances in microscopy automation now allow us to acquire datasets for 1000's of cells. This produces high dimensional datasets that require computer vision approaches that automate image analysis. The high dimensionality of these datasets are often not immediately accessible to human intuition, and, similarly to 'omics technologies, require statistical approaches for their exploitation. The field of biosensor imaging is therefore experiencing a multidisciplinary transition that will enable it to realize its full potential as a tool to provide a deeper appreciation of cell physiology.

Signalling dynamics in embryonic development

In multicellular organisms, cellular behaviour is tightly regulated to allow proper embryonic development and maintenance of adult tissue. A critical component in this control is the communication between cells via signalling pathways, as errors in intercellular communication can induce developmental defects or diseases such as cancer. It has become clear over the last years that signalling is not static but varies in activity over time. Feedback mechanisms present in every signalling pathway lead to diverse dynamic phenotypes, such as transient activation, signal ramping or oscillations, occurring in a cell type- and stage-dependent manner. In cells, such dynamics can exert various functions that allow organisms to develop in a robust and reproducible way. Here, we focus on Erk, Wnt and Notch signalling pathways, which are dynamic in several tissue types and organisms, including the periodic segmentation of vertebrate embryos, and are often dysregulated in cancer. We will discuss how biochemical processes influence their dynamics and how these impact on cellular behaviour within multicellular systems.

Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning

We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.

Progress and challenges in understanding the regulation and function of p53 dynamics

The dynamics of p53 expression provide a mechanism to increase differentiation between cellular stresses and specificity in appropriate responses. Here, we review recent advances in our understanding of the molecular mechanisms regulating p53 dynamics and the functions of the dynamics in the regulation of p53-dependent cell stress responses. We also compare dynamic encoding in the p53 system with that found in other important cell signaling systems, many of which can interact with the p53 network. Finally, we highlight some of the current challenges in understanding dynamic cell signaling within a larger cellular network context.

Computer vision reveals hidden variables underlying NF-κB activation in single cells

Individual cells are heterogeneous when responding to environmental cues. Under an external signal, certain cells activate gene regulatory pathways, while others completely ignore that signal. Mechanisms underlying cellular heterogeneity are often inaccessible because experiments needed to study molecular states destroy the very states that we need to examine. Here, we developed an image-based support vector machine learning model to uncover variables controlling activation of the immune pathway nuclear factor κB (NF-κB). Computer vision analysis predicts the identity of cells that will respond to cytokine stimulation and shows that activation is predetermined by minute amounts of “leaky” NF-κB (p65:p50) localization to the nucleus. Mechanistic modeling revealed that the ratio of NF-κB to inhibitor of NF-κB predetermines leakiness and activation probability of cells. While cells transition between molecular states, they maintain their overall probabilities for NF-κB activation. Our results demonstrate how computer vision can find mechanisms behind heterogeneous single-cell activation under proinflammatory stimuli.

Correspondence insights into the role of genes in cell functionality. Comments on "The gene: An appraisal" by K. Baverstock

One of the most important goals of the post-genomic era is to understand the different sources of molecular information that regulate the functional and structural architecture of cells. In this regard, Prof. K. Baverstock underscores in his recent article "The gene: An appraisal" (Baverstock, 2021) that genes are not the leading elements in cellular functionality, inheritance and evolution. As a consequence, the theory of evolution based on the Neo-Darwinian synthesis, is inadequate for today's scientific evidence. Conversely, the author contends that life processes viewed on the basis of thermodynamics, complex system dynamics and self-organization provide a new framework for the foundations of Biology. I consider it necessary to comment on some essential aspects of this relevant work, and here I present a short overview of the main non-genetic sources of biomolecular order and complexity that underline the molecular dynamics and functionality of cells. These sources generate different processes of complexity, which encompasses from the most elementary levels of molecular activity to the emergence of systemic behaviors, and the information necessary to sustain them is not contained in the genome.

Protein kinase sensors: an overview of new designs for visualizing kinase dynamics in single plant cells

Protein kinase dynamics play key roles in regulation of cell differentiation, growth, development and in diverse cell signaling networks. Protein kinase sensors enable visualization of protein kinase activity in living cells and tissues in time and space. These sensors have therefore become important and powerful molecular tools for investigation of diverse kinase activities and can resolve long-standing and challenging biological questions. In the present Update, we review new advanced approaches for genetically encoded protein kinase biosensor designs developed in animal systems together with the basis of each biosensor's working principle and components. In addition, we review recent first examples of real time plant protein kinase activity biosensor development and application. We discuss how these sensors have helped to resolve how stomatal signal transduction in response to elevated CO2 merges with abscisic acid signaling downstream of a resolved basal SnRK2 kinase activity in guard cells. Furthermore, recent advances, combined with the new strategies described in this Update, can help deepen the understanding of how signaling networks regulate unique functions and responses in distinct plant cell types and tissues and how different stimuli and signaling pathways can interact.

High-throughput RNA sequencing of paraformaldehyde-fixed single cells

Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.

High-throughput RNA sequencing of paraformaldehyde-fixed single cells

Single-cell transcriptomic studies that require intracellular protein staining, rare cell sorting, or inactivation of infectious pathogens are severely limited. This is because current high-throughput single-cell RNA sequencing methods are either incompatible with or necessitate laborious sample preprocessing for paraformaldehyde treatment, a common tissue and cell fixation and preservation technique. Here we present FD-seq (Fixed Droplet RNA sequencing), a high-throughput method for droplet-based RNA sequencing of paraformaldehyde-fixed, permeabilized and sorted single cells. We show that FD-seq preserves the RNA integrity and relative gene expression levels after fixation and permeabilization. Furthermore, FD-seq can detect a higher number of genes and transcripts than methanol fixation. We first apply FD-seq to analyze a rare subpopulation of cells supporting lytic reactivation of the human tumor virus KSHV, and identify TMEM119 as a potential host factor that mediates viral reactivation. Second, we find that infection with the human betacoronavirus OC43 leads to upregulation of pro-inflammatory pathways in cells that are exposed to the virus but fail to express high levels of viral genes. FD-seq thus enables integrating phenotypic with transcriptomic information in rare cell subpopulations, and preserving and inactivating pathogenic samples.

Current high-throughput single-cell transcriptomic methods are incompatible with paraformaldehyde, a common cell fixation technique. Here the authors present FD-seq, a method for droplet-based RNA sequencing of paraformaldehyde-fixed, stained and sorted single cells.

Stimulus-specific responses in innate immunity: Multilayered regulatory circuits

Immune sentinel cells initiate immune responses to pathogens and tissue injury and are capable of producing highly stimulus-specific responses. Insight into the mechanisms underlying such specificity has come from the identification of regulatory factors and biochemical pathways, as well as the definition of signaling circuits that enable combinatorial and temporal coding of information. Here, we review the multi-layered molecular mechanisms that underlie stimulus-specific gene expression in macrophages. We categorize components of inflammatory and anti-pathogenic signaling pathways into five layers of regulatory control and discuss unifying mechanisms determining signaling characteristics at each layer. In this context, we review mechanisms that enable combinatorial and temporal encoding of information, identify recurring regulatory motifs and principles, and present strategies for integrating experimental and computational approaches toward the understanding of signaling specificity in innate immunity.

High-throughput injection molded microfluidic device for single-cell analysis of spatiotemporal dynamics

Single-cell level analysis of various cellular behaviors has been aided by recent developments in microfluidic technology. Polydimethylsiloxane (PDMS)-based microfluidic devices have been widely used to elucidate cell differentiation and migration under spatiotemporal stimulation. However, microfluidic devices fabricated with PDMS have inherent limitations due to material issues and non-scalable fabrication process. In this study, we designed and fabricated an injection molded microfluidic device that enables real-time chemical profile control. This device is made of polystyrene (PS), engineered with channel dimensions optimized for injection molding to achieve functionality and compatibility with single cell observation. We demonstrated the spatiotemporal dynamics in the device with computational simulation and experiments. In temporal dynamics, we observed extracellular signal-regulated kinase (ERK) activation of PC12 cells by stimulating the cells with growth factors (GFs). Also, we confirmed yes-associated protein (YAP) phase separation of HEK293 cells under stimulation using sorbitol. In spatial dynamics, we observed the migration of NIH 3T3 cells (transfected with Lifeact-GFP) under different spatiotemporal stimulations of PDGF. Using the injection molded plastic devices, we obtained comprehensive data more easily than before while using less time compared to previous PDMS models. This easy-to-use plastic microfluidic device promises to open a new approach for investigating the mechanisms of cell behavior at the single-cell level.

Inhibitory feedback control of NF-κB signalling in health and disease

Cells must adapt to changes in their environment to maintain cell, tissue and organismal integrity in the face of mechanical, chemical or microbiological stress. Nuclear factor-κB (NF-κB) is one of the most important transcription factors that controls inducible gene expression as cells attempt to restore homeostasis. It plays critical roles in the immune system, from acute inflammation to the development of secondary lymphoid organs, and also has roles in cell survival, proliferation and differentiation. Given its role in such critical processes, NF-κB signalling must be subject to strict spatiotemporal control to ensure measured and context-specific cellular responses. Indeed, deregulation of NF-κB signalling can result in debilitating and even lethal inflammation and also underpins some forms of cancer. In this review, we describe the homeostatic feedback mechanisms that limit and ‘re-set’ inducible activation of NF-κB. We first describe the key components of the signalling pathways leading to activation of NF-κB, including the prominent role of protein phosphorylation and protein ubiquitylation, before briefly introducing the key features of feedback control mechanisms. We then describe the array of negative feedback loops targeting different components of the NF-κB signalling cascade including controls at the receptor level, post-receptor signalosome complexes, direct regulation of the critical ‘inhibitor of κB kinases’ (IKKs) and inhibitory feedforward regulation of NF-κB-dependent transcriptional responses. We also review post-transcriptional feedback controls affecting RNA stability and translation. Finally, we describe the deregulation of these feedback controls in human disease and consider how feedback may be a challenge to the efficacy of inhibitors.

Plasma LOX-Products and Monocyte Signaling Is Reduced by Adjunctive Cyclooxygenase-2 Inhibitor in a Phase I Clinical Trial of Tuberculosis Patients

Introduction

Eicosanoids and intracellular signaling pathways are potential targets for host-directed therapy (HDT) in tuberculosis (TB). We have explored the effect of cyclooxygenase 2 inhibitor (COX-2i) treatment on eicosanoid levels and signaling pathways in monocytes.

Methods

Peripheral blood mononuclear cells isolated from TB patients included in a randomized phase I clinical trial of standard TB treatment with (n=21) or without (n=18) adjunctive COX-2i (etoricoxib) were analyzed at baseline, day 14 and day 56. Plasma eicosanoids were analyzed by ELISA and liquid chromatography-mass spectrometry (LC-MS), plasma cytokines by multiplex, and monocyte signaling by phospho-flow with a defined set of phospho-specific antibodies.

Results

Lipoxygenase (LOX)-derived products (LXA4 and 12-HETE) and pro-inflammatory cytokines were associated with TB disease severity and were reduced during TB therapy, possibly accelerated by adjunctive COX-2i. Phosphorylation of p38 MAPK, NFkB, Erk1/2, and Akt in monocytes as well as plasma levels of MIG/CXCL9 and procalcitonin were reduced in the COX-2i group compared to controls.

Conclusion

COX-2i may reduce excess inflammation in TB via the LOX-pathway in addition to modulation of phosphorylation patterns in monocytes. Immunomodulatory effects of adjunctive COX-2i in TB should be further investigated before recommended for use as a HDT strategy.

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Analyzing signaling activity and function in hematopoietic cells

Cells constantly sense their environment, allowing the adaption of cell behavior to changing needs. Fine-tuned responses to complex inputs are computed by signaling pathways, which are wired in complex connected networks. Their activity is highly context-dependent, dynamic, and heterogeneous even between closely related individual cells. Despite lots of progress, our understanding of the precise implementation, relevance, and possible manipulation of cellular signaling in health and disease therefore remains limited. Here, we discuss the requirements, potential, and limitations of the different current technologies for the analysis of hematopoietic stem and progenitor cell signaling and its effect on cell fates.

TNF stimulation primarily modulates transcriptional burst size of NF-κB-regulated genes

Cell-to-cell heterogeneity is a feature of the tumor necrosis factor (TNF)-stimulated inflammatory response mediated by the transcription factor NF-κB, motivating an exploration of the underlying sources of this noise. Here, we combined single-transcript measurements with computational models to study transcriptional noise at six NF-κB-regulated inflammatory genes. In the basal state, NF-κB-target genes displayed an inverse correlation between mean and noise characteristic of transcriptional bursting. By analyzing transcript distributions with a bursting model, we found that TNF primarily activated transcription by increasing burst size while maintaining burst frequency for gene promoters with relatively high basal histone 3 acetylation (AcH3) that marks open chromatin environments. For promoters with lower basal AcH3 or when AcH3 was decreased with a small molecule drug, the contribution of burst frequency to TNF activation increased. Finally, we used a mathematical model to show that TNF positive feedback amplified gene expression noise resulting from burst size-mediated transcription, leading to a subset of cells with high TNF protein expression. Our results reveal potential sources of noise underlying intercellular heterogeneity in the TNF-mediated inflammatory response.

A variable-gain stochastic pooling motif mediates information transfer from receptor assemblies into NF-κB

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.

Visualization and modeling of inhibition of IL-1β and TNF-α mRNA transcription at the single-cell level

IL-1β and TNF-α are canonical immune response mediators that play key regulatory roles in a wide range of inflammatory responses to both chronic and acute conditions. Here we employ an automated microscopy platform for the analysis of messenger RNA (mRNA) expression of IL-1β and TNF-α at the single-cell level. The amount of IL-1β and TNF-α mRNA expressed in a human monocytic leukemia cell line (THP-1) is visualized and counted using single-molecule fluorescent in-situ hybridization (smFISH) following exposure of the cells to lipopolysaccharide (LPS), an outer-membrane component of Gram-negative bacteria. We show that the small molecule inhibitors MG132 (a 26S proteasome inhibitor used to block NF-κB signaling) and U0126 (a MAPK Kinase inhibitor used to block CCAAT-enhancer-binding proteins C/EBP) successfully block IL-1β and TNF-α mRNA expression. Based upon this single-cell mRNA expression data, we screened 36 different mathematical models of gene expression, and found two similar models that capture the effects by which the drugs U0126 and MG132 affect the rates at which the genes transition into highly activated states. When their parameters were informed by the action of each drug independently, both models were able to predict the effects of the combined drug treatment. From our data and models, we postulate that IL-1β is activated by both NF-κB and C/EBP, while TNF-α is predominantly activated by NF-κB. Our combined single-cell experimental and modeling efforts show the interconnection between these two genes and demonstrates how the single-cell responses, including the distribution shapes, mean expression, and kinetics of gene expression, change with inhibition.

Gene Expression at a Single Molecule Level: Implications for MDS and AML

Non-genetic heterogeneity, or gene expression stochasticity, is an important source of variability in biological systems. With the advent and improvement of single molecule resolution technologies, it has been shown that transcription dynamics and resultant transcript number fluctuations generate significant cell-to-cell variability that has important biological effects and may contribute substantially to both tissue homeostasis and disease. In this respect, the pathophysiology of stem cell-derived malignancies such as AML and MDS, which has historically been studied at the ensemble level, may require re-evaluation. To that end, it is our aim in this review to highlight the results of recent single-molecule, biophysical, and systems studies of gene expression dynamics, with the explicit purpose of demonstrating how the insights from these basic science studies may help inform and progress the field of leukemia biology and, ultimately, research into novel therapies.

A forecast for large-scale, predictive biology: Lessons from meteorology

Quantitative systems biology, in which predictive mathematical models are constructed to guide the design of experiments and predict experimental outcomes, is at an exciting transition point, where the foundational scientific principles are becoming established, but the impact is not yet global. The next steps necessary for mathematical modeling to transform biological research and applications, in the same way it has already transformed other fields, is not completely clear. The purpose of this perspective is to forecast possible answers to this question-what needs to happen next-by drawing on the experience gained in another field, specifically meteorology. We review here a number of lessons learned in weather prediction that are directly relevant to biological systems modeling, and that we believe can enable the same kinds of global impact in our field as atmospheric modeling makes today.