Six distinct NFκB signaling codons convey discrete information to distinguish stimuli and enable appropriate macrophage responses

Biological computations: Limitations of attractor-based formalisms and the need for transients

Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, for e.g. via cell surface receptors or sensory organs. By integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Stimulus-response signaling dynamics characterize macrophage polarization states

The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.

Dynamical and combinatorial coding by MAPK p38 and NFκB in the inflammatory response of macrophages

Macrophages sense pathogens and orchestrate specific immune responses. Stimulus specificity is thought to be achieved through combinatorial and dynamical coding by signaling pathways. While NFκB dynamics are known to encode stimulus information, dynamical coding in other signaling pathways and their combinatorial coordination remain unclear. Here, we established live-cell microscopy to investigate how NFκB and p38 dynamics interface in stimulated macrophages. Information theory and machine learning revealed that p38 dynamics distinguish cytokine TNF from pathogen-associated molecular patterns and high doses from low, but contributed little to information-rich NFκB dynamics when both pathways are considered. This suggests that immune response genes benefit from decoding immune signaling dynamics or combinatorics, but not both. We found that the heterogeneity of the two pathways is surprisingly uncorrelated. Mathematical modeling revealed potential sources of uncorrelated heterogeneity in the branched pathway network topology and predicted it to drive gene expression variability. Indeed, genes dependent on both p38 and NFκB showed high scRNAseq variability and bimodality. These results identify combinatorial signaling as a mechanism to restrict NFκB-AND-p38-responsive inflammatory cytokine expression to few cells.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

NF-κB subunits RelA and c-Rel selectively control CD4+ T cell function in multiple sclerosis and cancer

The outcome of cancer and autoimmunity is often dictated by the effector functions of CD4+ conventional T cells (Tconv). Although activation of the NF-κB signaling pathway has long been implicated in Tconv biology, the cell-autonomous roles of the separate NF-κB transcription-factor subunits are unknown. Here, we dissected the contributions of the canonical NF-κB subunits RelA and c-Rel to Tconv function. RelA, rather than c-Rel, regulated Tconv activation and cytokine production at steady-state and was required for polarization toward the TH17 lineage in vitro. Accordingly, RelA-deficient mice were fully protected against neuroinflammation in a model of multiple sclerosis due to defective transition to a pathogenic TH17 gene-expression program. Conversely, Tconv-restricted ablation of c-Rel impaired their function in the microenvironment of transplanted tumors, resulting in enhanced cancer burden. Moreover, Tconv required c-Rel for the response to PD-1-blockade therapy. Our data reveal distinct roles for canonical NF-κB subunits in different disease contexts, paving the way for subunit-targeted immunotherapies.

Therapeutic effect and mechanism of action of pterostilbene nano drugs in dry eye models

Dry eye disease is a multifactorial dysfunction of the tear film and ocular surface, with etiology involving inflammation and oxidative stress on the ocular surface. Pterostilbene (PS) is a secondary metabolite extracted from plants, which possesses remarkable anti-inflammatory and antioxidant effects. However, its application is limited by light instability and very poor water solubility. We modified fat-soluble PS into a biparental pterostilbene-glutaric anhydride-arginine-glycine-aspartic acid (PS-GA-RGD) nanomedicine by prodrug ligation of functional peptides. The aim of this study was to explore the protective effect and potential mechanism of PS-GA-RGD on dry eye disease in vitro and in vivo. We demonstrated good long-term biocompatibility of PS-GA-RGD through rabbit eye stimulation test. Lipopolysaccharide (LPS) was used to induce murine macrophages RAW 264.7 to establish an inflammation and oxidative stress model. In this model, PS-GA-RGD effectively reduced the production of ROS and 8-OHdG, enhancing the expression of antioxidant factor Nrf2 and antioxidant enzyme heme oxygenase-1. In addition, the expression of NF-κB inflammatory pathway significantly increased in LPS-induced RAW 264.7 cells, while PS-GA-RGD could significantly reduce this pathway. Hypertonic saline was utilized to establish a hypertonic model of human corneal epithelial cells. PS-GA-RGD was found to significantly reduce the production of ROS and NLRP3 inflammasomes in this model, exhibiting superior efficacy compared to PS. Experimental dry eye animal models were co-induced with subcutaneous injection of scopolamine and an intelligently controlled environmental system. We demonstrated that PS-GA-RGD nano drugs can prevent and reduce corneal epithelial cell defects and apoptosis, protect conjunctival goblet cells, and have an excellent anti-inflammatory effect. Finally, we demonstrated that RGD sequence in PS-GA-RGD can enhance cellular uptake, corneal retention, and penetration, thereby increasing their bioavailability and efficacy by a cell uptake assay and rabbit corneal drug retention experiment. Overall, this study highlights the potential of PS-GA-RGD nanomedicines in the treatment of dry eyes.

Co-imaging of RelA and c-Rel reveals features of NF-κB signaling for ligand discrimination

Individual cell sensing of external cues has evolved through the temporal patterns in signaling. Since nuclear factor κB (NF-κB) signaling dynamics have been examined using a single subunit, RelA, it remains unclear whether more information might be transmitted via other subunits. Using NF-κB double-knockin reporter mice, we monitored both canonical NF-κB subunits, RelA and c-Rel, simultaneously in single macrophages by quantitative live-cell imaging. We show that signaling features of RelA and c-Rel convey more information about the stimuli than those of either subunit alone. Machine learning is used to predict the ligand identity accurately based on RelA and c-Rel signaling features without considering the co-activated factors. Ligand discrimination is achieved through selective non-redundancy of RelA and c-Rel signaling dynamics, as well as their temporal coordination. These results suggest a potential role of c-Rel in fine-tuning immune responses and highlight the need for approaches that will elucidate the mechanisms regulating NF-κB subunit specificity.

Open questions in the NF-κB field

A variety of stress signals leads to activation of the inducible transcription factor NF-κB, one of the master regulators of the innate immune response. Despite a wealth of information available on the NF-κB core components and its control by different activation pathways and negative feedback loops, several levels of complexity hamper our understanding of the system. This has also contributed to the limited success of NF-κB inhibitors in the clinic and explains some of their unexpected effects. Here we consider the molecular and cellular events generating this complexity at all levels and point to a number of unresolved questions in the field. We also discuss potential future experimental and computational strategies to provide a deeper understanding of NF-κB and its coregulatory signaling networks.

Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

Molecular mechanism of lncRNAs in pathogenesis and diagnosis of auto-immune diseases, with a special focus on lncRNA-based therapeutic approaches

Autoimmune diseases are a diverse set of conditions defined by organ damage due to abnormal innate and acquired immune system responses. The pathophysiology of autoimmune disorders is exceedingly intricate and has yet to be fully understood. The study of long non-coding RNAs (lncRNAs), non-protein-coding RNAs with at least 200 nucleotides in length, has gained significant attention due to the completion of the human genome project and the advancement of high-throughput genomic approaches. Recent research has demonstrated how lncRNA alters disease development to different degrees. Although lncRNA research has made significant progress in cancer and generative disorders, autoimmune illnesses are a relatively new research area. Moreover, lncRNAs play crucial functions in differentiating various immune cells, and their potential relationships with autoimmune diseases have received growing attention. Because of the importance of Th17/Treg axis in auto-immune disease development, in this review, we discuss various molecular mechanisms by which lncRNAs regulate the differentiation of Th17/Treg cells. Also, we reviewed recent findings regarding the several approaches in the application of lncRNAs in the diagnosis and treatment of human autoimmune diseases, as well as current challenges in lncRNA-based therapeutic approaches to auto-immune diseases.

Antigen perception in T cells by long-term Erk and NFAT signaling dynamics

Immune system threat detection hinges on T cells' ability to perceive varying peptide-major histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs but diverge only over longer (9+ h) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception and establish a framework for understanding T cell responses under diverse contexts.

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

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

A guide to ERK dynamics, part 2: downstream decoding

Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.

Processing stimulus dynamics by the NF-κB network in single cells

Cells at the site of an infection experience numerous biochemical signals that vary in amplitude, space, and time. Despite the diversity of dynamic signals produced by pathogens and sentinel cells, information-processing pathways converge on a limited number of central signaling nodes to ultimately control cellular responses. In particular, the NF-κB pathway responds to dozens of signals from pathogens and self, and plays a vital role in processing proinflammatory inputs. Studies addressing the influence of stimulus dynamics on NF-κB signaling are rare due to technical limitations with live-cell measurements. However, recent advances in microfluidics, automation, and image analysis have enabled investigations that yield high temporal resolution at the single-cell level. Here, we summarize the recent research which measures and models the NF-κB response to pulsatile and fluctuating stimulus concentrations, as well as different combinations and sequences of signaling molecules. Collectively, these studies show that the NF-κB network integrates external inflammatory signals and translates these into downstream transcriptional responses.

Information-Theoretic Analysis of a Model of CAR-4-1BB-Mediated NFκB Activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of NFκB signaling initiated by the CAR following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of variability in protein concentrations. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.Kindly check and confirm whether the corresponding affiliation is correctly identified.this is correct.

Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells

Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.

NF-κB subunits direct kinetically distinct transcriptional cascades in antigen receptor-activated B cells

The nuclear factor kappa B (NF-κB) family of transcription factors orchestrates signal-induced gene expression in diverse cell types. Cellular responses to NF-κB activation are regulated at the level of cell and signal specificity, as well as differential use of family members (subunit specificity). Here we used time-dependent multi-omics to investigate the selective functions of Rel and RelA, two closely related NF-κB proteins, in primary B lymphocytes activated via the B cell receptor. Despite large numbers of shared binding sites genome wide, Rel and RelA directed kinetically distinct cascades of gene expression in activated B cells. Single-cell RNA sequencing revealed marked heterogeneity of Rel- and RelA-specific responses, and sequential binding of these factors was not a major mechanism of protracted transcription. Moreover, nuclear co-expression of Rel and RelA led to functional antagonism between the factors. By rigorously identifying the target genes of each NF-κB subunit, these studies provide insights into exclusive functions of Rel and RelA in immunity and cancer.

Inflammasomes as regulators of non-infectious disease

Inflammasomes are cytoplasmic organelles that stimulate inflammation upon cellular detection of infectious or non-infectious stress. While much foundational work has focused on the infection-associated aspects of inflammasome activities, recent studies have highlighted the role of inflammasomes in non-infectious cellular and organismal functions. Herein, we discuss the evolution of inflammasome components and highlight characteristics that permit inflammasome regulation of physiologic processes. We focus on emerging data that highlight the importance of inflammasome proteins in the regulation of reproduction, development, and malignancy. A framework is proposed to contextualize these findings.

Are physiological oscillations physiological?

Despite widespread and striking examples of physiological oscillations, their functional role is often unclear. Even glycolysis, the paradigm example of oscillatory biochemistry, has seen questions about its oscillatory function. Here, we take a systems approach to argue that oscillations play critical physiological roles, such as enabling systems to avoid desensitization, to avoid chronically high and therefore toxic levels of chemicals, and to become more resistant to noise. Oscillation also enables complex physiological systems to reconcile incompatible conditions such as oxidation and reduction, by cycling between them, and to synchronize the oscillations of many small units into one large effect. In pancreatic β-cells, glycolytic oscillations synchronize with calcium and mitochondrial oscillations to drive pulsatile insulin release, critical for liver regulation of glucose. In addition, oscillation can keep biological time, essential for embryonic development in promoting cell diversity and pattern formation. The functional importance of oscillatory processes requires a re-thinking of the traditional doctrine of homeostasis, holding that physiological quantities are maintained at constant equilibrium values, a view that has largely failed in the clinic. A more dynamic approach will initiate a paradigm shift in our view of health and disease. A deeper look into the mechanisms that create, sustain and abolish oscillatory processes requires the language of nonlinear dynamics, well beyond the linearization techniques of equilibrium control theory. Nonlinear dynamics enables us to identify oscillatory ('pacemaking') mechanisms at the cellular, tissue and system levels.

Antigen perception in T cells by long-term Erk and NFAT signaling dynamics

Immune system threat detection hinges on T cells' ability to perceive varying peptide major-histocompatibility complex (pMHC) antigens. As the Erk and NFAT pathways link T cell receptor engagement to gene regulation, their signaling dynamics may convey information about pMHC inputs. To test this idea, we developed a dual reporter mouse strain and a quantitative imaging assay that, together, enable simultaneous monitoring of Erk and NFAT dynamics in live T cells over day-long timescales as they respond to varying pMHC inputs. Both pathways initially activate uniformly across various pMHC inputs, but diverge only over longer (9+ hrs) timescales, enabling independent encoding of pMHC affinity and dose. These late signaling dynamics are decoded via multiple temporal and combinatorial mechanisms to generate pMHC-specific transcriptional responses. Our findings underscore the importance of long timescale signaling dynamics in antigen perception, and establish a framework for understanding T cell responses under diverse contexts.

SIGNIFICANCE STATEMENT

To counter diverse pathogens, T cells mount distinct responses to varying peptide-major histocompatibility complex ligands (pMHCs). They perceive the affinity of pMHCs for the T cell receptor (TCR), which reflects its foreignness, as well as pMHC abundance. By tracking signaling responses in single living cells to different pMHCs, we find that T cells can independently perceive pMHC affinity vs dose, and encode this information through the dynamics of Erk and NFAT signaling pathways downstream of the TCR. These dynamics are jointly decoded by gene regulatory mechanisms to produce pMHC-specific activation responses. Our work reveals how T cells can elicit tailored functional responses to diverse threats and how dysregulation of these responses may lead to immune pathologies.

Single-Cell Heterogeneity in Snake Venom Expression Is Hardwired by Co-Option of Regulators from Progressively Activated Pathways

The ubiquitous cellular heterogeneity underlying many organism-level phenotypes raises questions about what factors drive this heterogeneity and how these complex heterogeneous systems evolve. Here, we use single-cell expression data from a Prairie rattlesnake (Crotalus viridis) venom gland to evaluate hypotheses for signaling networks underlying snake venom regulation and the degree to which different venom gene families have evolutionarily recruited distinct regulatory architectures. Our findings suggest that snake venom regulatory systems have evolutionarily co-opted trans-regulatory factors from extracellular signal-regulated kinase and unfolded protein response pathways that specifically coordinate expression of distinct venom toxins in a phased sequence across a single population of secretory cells. This pattern of co-option results in extensive cell-to-cell variation in venom gene expression, even between tandemly duplicated paralogs, suggesting this regulatory architecture has evolved to circumvent cellular constraints. While the exact nature of such constraints remains an open question, we propose that such regulatory heterogeneity may circumvent steric constraints on chromatin, cellular physiological constraints (e.g., endoplasmic reticulum stress or negative protein-protein interactions), or a combination of these. Regardless of the precise nature of these constraints, this example suggests that, in some cases, dynamic cellular constraints may impose previously unappreciated secondary constraints on the evolution of gene regulatory networks that favors heterogeneous expression.

Proofreading does not result in more reliable ligand discrimination in receptor signaling due to its inherent stochasticity

Kinetic proofreading (KPR) has been used as a paradigmatic explanation for the high specificity of ligand discrimination by cellular receptors. KPR enhances the difference in the mean receptor occupancy between different ligands compared to a nonproofread receptor, thus potentially enabling better discrimination. On the other hand, proofreading also attenuates the signal and introduces additional stochastic receptor transitions relative to a nonproofreading receptor. This increases the relative magnitude of noise in the downstream signal, which can interfere with reliable ligand discrimination. To understand the effect of noise on ligand discrimination beyond the comparison of the mean signals, we formulate the task of ligand discrimination as a problem of statistical estimation of the receptor affinity of ligands based on the molecular signaling output. Our analysis reveals that proofreading typically worsens ligand resolution compared to a nonproofread receptor. Furthermore, the resolution decreases further with more proofreading steps under most commonly biologically considered conditions. This contrasts with the usual notion that KPR universally improves ligand discrimination with additional proofreading steps. Our results are consistent across a variety of different proofreading schemes and metrics of performance, suggesting that they are inherent to the KPR mechanism itself rather than any particular model of molecular noise. Based on our results, we suggest alternative roles for KPR schemes such as multiplexing and combinatorial encoding in multi-ligand/multi-output pathways.

Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework

The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

High-throughput co-culture system for analysis of spatiotemporal cell-cell signaling

Study of spatial and temporal aspects of signaling between individual cells is essential in understanding development, the immune response, and host-pathogen interactions. We present an automated high-throughput microfluidic platform that chemically stimulates immune cells to initiate cytokine secretion, and controls the formation of signal gradients that activate neighboring cell populations. Furthermore, our system enables controlling the cell type and density based on distance, and retrieval of cells from different regions for gene expression analysis. Our device performs these tasks in 192 independent chambers to simultaneously test different co-culture conditions. We demonstrate these capabilities by creating various cellular communication scenarios between macrophages and fibroblasts in vitro. We find that spatial distribution of macrophages and heterogeneity in cytokine secretion determine spatiotemporal gene expression responses. Furthermore, we describe how gene expression dynamics depend on a cell's distance from the signaling source. Our device addresses key challenges in the study of cell-to-cell signaling, and provides high-throughput and automated analysis over a wide range of co-culture conditions.

An optimized reporter of the transcription factor hypoxia-inducible factor 1α reveals complex HIF-1α activation dynamics in single cells

Immune cells adopt a variety of metabolic states to support their many biological functions, which include fighting pathogens, removing tissue debris, and tissue remodeling. One of the key mediators of these metabolic changes is the transcription factor hypoxia-inducible factor 1α (HIF-1α). Single-cell dynamics have been shown to be an important determinant of cell behavior; however, despite the importance of HIF-1α, little is known about its single-cell dynamics or their effect on metabolism. To address this knowledge gap, here we optimized a HIF-1α fluorescent reporter and applied it to study single-cell dynamics. First, we showed that single cells are likely able to differentiate multiple levels of prolyl hydroxylase inhibition, a marker of metabolic change, via HIF-1α activity. We then applied a physiological stimulus known to trigger metabolic change, interferon-γ, and observed heterogeneous, oscillatory HIF-1α responses in single cells. Finally, we input these dynamics into a mathematical model of HIF-1α-regulated metabolism and discovered a profound difference between cells exhibiting high versus low HIF-1α activation. Specifically, we found cells with high HIF-1α activation are able to meaningfully reduce flux through the tricarboxylic acid cycle and show a notable increase in the NAD+/NADH ratio compared with cells displaying low HIF-1α activation. Altogether, this work demonstrates an optimized reporter for studying HIF-1α in single cells and reveals previously unknown principles of HIF-1α activation.

Quantifying stimulus-response specificity to probe the functional state of macrophages

Immune sentinel macrophages initiate responses to pathogens via hundreds of immune response genes. Each immune threat demands a tailored response, suggesting that the capacity for stimulus-specific gene expression is a key functional hallmark of healthy macrophages. To quantify this property, termed "stimulus-response specificity" (SRS), we developed a single-cell experimental workflow and analytical approaches based on information theory and machine learning. We found that the response specificity of macrophages is driven by combinations of specific immune genes that show low cell-to-cell heterogeneity and are targets of separate signaling pathways. The "response specificity profile," a systematic comparison of multiple stimulus-response distributions, was distinctly altered by polarizing cytokines, and it enabled an assessment of the functional state of macrophages. Indeed, the response specificity profile of peritoneal macrophages from old and obese mice showed characteristic differences, suggesting that SRS may be a basis for measuring the functional state of innate immune cells. A record of this paper's transparent peer review process is included in the supplemental information.

The NF-κB multidimer system model: A knowledge base to explore diverse biological contexts

The nuclear factor κB (NF-κB) system is critical for various biological functions in numerous cell types, including the inflammatory response, cell proliferation, survival, differentiation, and pathogenic responses. Each cell type is characterized by a subset of 15 NF-κB dimers whose activity is regulated in a stimulus-responsive manner. Numerous studies have produced different mathematical models that account for cell type-specific NF-κB activities. However, whereas the concentrations or abundances of NF-κB subunits may differ between cell types, the biochemical interactions that constitute the NF-κB signaling system do not. Here, we synthesized a consensus mathematical model of the NF-κB multidimer system, which could account for the cell type-specific repertoires of NF-κB dimers and their cell type-specific activation and cross-talk. Our review demonstrates that these distinct cell type-specific properties of NF-κB signaling can be explained largely as emergent effects of the cell type-specific expression of NF-κB monomers. The consensus systems model represents a knowledge base that may be used to gain insights into the control and function of NF-κB in diverse physiological and pathological scenarios and that describes a path for generating similar regulatory knowledge bases for other pleiotropic signaling systems.

NFκB signaling in T cell memory

Memory T cells play an essential role in protecting against infectious diseases and cancer and contribute to autoimmunity and transplant rejection. Understanding how they are generated and maintained in the context of infection or vaccination holds promise to improve current immune-based therapies. At the beginning of any immune response, naïve T cells are activated and differentiate into cells with effector function capabilities. In the context of infection, most of these cells die once the pathogenic antigen has been cleared. Only a few of them persist and differentiate into memory T cells. These memory T cells are essential to host immunity because they are long-lived and can perform effector functions immediately upon re-infection. How a cell becomes a memory T cell and continues being one for months and even years past the initial infection is still not fully understood. Recent reviews have thoroughly discussed the transcriptional, epigenomic, and metabolic mechanisms that govern T cell memory differentiation. Yet much less is known of how signaling pathways that are common circuitries of multiple environmental signals regulate T cell outcome and, precisely, T cell memory. The function of the NFκB signaling system is perhaps best understood in innate cells. Recent findings suggest that NFκB signaling plays an essential and unique role in generating and maintaining CD8 T cell memory. This review aims to summarize these findings and discuss the remaining questions in the field.

NF-κB in monocytes and macrophages - an inflammatory master regulator in multitalented immune cells

Nuclear factor κB (NF-κB) is a dimeric transcription factor constituted by two of five protein family members. It plays an essential role in inflammation and immunity by regulating the expression of numerous chemokines, cytokines, transcription factors, and regulatory proteins. Since NF-κB is expressed in almost all human cells, it is important to understand its cell type-, tissue-, and stimulus-specific roles as well as its temporal dynamics and disease-specific context. Although NF-κB was discovered more than 35 years ago, many questions are still unanswered, and with the availability of novel technologies such as single-cell sequencing and cell fate-mapping, new fascinating questions arose. In this review, we will summarize current findings on the role of NF-κB in monocytes and macrophages. These innate immune cells show high plasticity and dynamically adjust their effector functions against invading pathogens and environmental cues. Their versatile functions can range from antimicrobial defense and antitumor immune responses to foam cell formation and wound healing. NF-κB is crucial for their activation and balances their phenotypes by finely coordinating transcriptional and epigenomic programs. Thereby, NF-κB is critically involved in inflammasome activation, cytokine release, and cell survival. Macrophage-specific NF-κB activation has far-reaching implications in the development and progression of numerous inflammatory diseases. Moreover, recent findings highlighted the temporal dynamics of myeloid NF-κB activation and underlined the complexity of this inflammatory master regulator. This review will provide an overview of the complex roles of NF-κB in macrophage signal transduction, polarization, inflammasome activation, and cell survival.

Bow-tie architectures in biological and artificial neural networks: Implications for network evolution and assay design

Modern artificial neural networks (ANNs) have long been designed on foundations of mathematics as opposed to their original foundations of biomimicry. However, the structure and function of these modern ANNs are often analogous to real-life biological networks. We propose that the ubiquitous information-theoretic principles underlying the development of ANNs are similar to the principles guiding the macro-evolution of biological networks and that insights gained from one field can be applied to the other. We generate hypotheses on the bow-tie network structure of the Janus kinase - signal transducers and activators of transcription (JAK-STAT) pathway, additionally informed by the evolutionary considerations, and carry out ANN simulation experiments to demonstrate that an increase in the network's input and output complexity does not necessarily require a more complex intermediate layer. This observation should guide novel biomarker discovery-namely, to prioritize sections of the biological networks in which information is most compressed as opposed to biomarkers representing the periphery of the network.

Utility of monocyte HLA-DR and rationale for therapeutic GM-CSF in sepsis immunoparalysis

Sepsis, a heterogeneous clinical syndrome, features a systemic inflammatory response to tissue injury or infection, followed by a state of reduced immune responsiveness. Measurable alterations occur in both the innate and adaptive immune systems. Immunoparalysis, an immunosuppressed state, associates with worsened outcomes, including multiple organ dysfunction syndrome, secondary infections, and increased mortality. Multiple immune markers to identify sepsis immunoparalysis have been proposed, and some might offer clinical utility. Sepsis immunoparalysis is characterized by reduced lymphocyte numbers and downregulation of class II human leukocyte antigens (HLA) on innate immune monocytes. Class II HLA proteins present peptide antigens for recognition by and activation of antigen-specific T lymphocytes. One monocyte class II protein, mHLA-DR, can be measured by flow cytometry. Downregulated mHLA-DR indicates reduced monocyte responsiveness, as measured by ex-vivo cytokine production in response to endotoxin stimulation. Our literature survey reveals low mHLA-DR expression on peripheral blood monocytes correlates with increased risks for infection and death. For mHLA-DR, 15,000 antibodies/cell appears clinically acceptable as the lower limit of immunocompetence. Values less than 15,000 antibodies/cell are correlated with sepsis severity; and values at or less than 8000 antibodies/cell are identified as severe immunoparalysis. Several experimental immunotherapies have been evaluated for reversal of sepsis immunoparalysis. In particular, sargramostim, a recombinant human granulocyte-macrophage colony-stimulating factor (rhu GM-CSF), has demonstrated clinical benefit by reducing hospitalization duration and lowering secondary infection risk. Lowered infection risk correlates with increased mHLA-DR expression on peripheral blood monocytes in these patients. Although mHLA-DR has shown promising utility for identifying sepsis immunoparalysis, absence of a standardized, analytically validated method has thus far prevented widespread adoption. A clinically useful approach for patient inclusion and identification of clinically correlated output parameters could address the persistent high unmet medical need for effective targeted therapies in sepsis.

NF-κB dynamics in the language of immune cells

Biological discovery has been driven by advances in throughput and resolution of analysis technologies. They have also created an indelible bias for snapshot-based knowledge. Even though recent methods such as multi-omics single-cell assays have empowered immunological investigations, they still provide snapshots of cellular behaviors and thus, have inherent limitations in reconstructing unsynchronized dynamic events across individual cells. Here, we present a rationale for how NF-κB may convey specificity of contextual information through subtle quantitative features of its signaling dynamics. The next frontier of predictive understanding should involve functional characterization of NF-κB signaling dynamics and their immunological implications. This may help solve the apparent paradox that a ubiquitously activated transcription factor can shape accurate responses to different immune challenges.

A comprehensive investigation discovered the novel methyltransferase METTL24 as one presumably prognostic gene for kidney renal clear cell carcinoma potentially modulating tumor immune microenvironment

Background

Recently, an increasing number of studies have uncovered the aberrant expression of methyltransferase-like family (METTL) plays an important role in tumorigenesis, such as METTL3 (an m6A writer). In our recent work, we discovered METTL24 expression was highly associated with the hazard ratio (HR) of kidney renal clear cell carcinoma (KIRC) compared to other tumors, implying a special function of METTL24 in KIRC carcinogenesis. Until now, the functions and mechanisms of METTL24 in KIRC have remained mostly unknown.

Methods

The mRNA expression of METTL24 in KIRC was analyzed using the TIMER 2.0, GEPIA, and UALCAN databases. The immunohistochemical assay was performed to validate METTL24 expression in our self-built Chinese cohort (n tumor = 88, n normal = 85). The gene set enrichment analysis (GSEA) was used to investigate the biological processes in which METTL24 might be engaged. The Spearman analysis was used to evaluate the expression correlations between METTL24 and a range of immunological variables, and the effects of METTL24 on the infiltration levels of multiple immune cells were explored using TCGA data. The upstream transcription factors of METTL24 were screened through a multi-omics analysis.

Results

METTL24 expression in KIRC tissues was significantly decreased compared to normal adjacent kidney tissues, which was associated with the lower survival rate of KIRC patients. METTL24 potentially participated in the immune-relevant biological processes such as cytokine binding, NF-kappa B binding, MHC protein complex, and interleukin-12 action. Besides, METTL24 expression was linked to a number of immune checkpoints, cytokines, chemokines, and chemokine receptors, and also correlated with the infiltration levels of 10 types of immune cells in KIRC. Meanwhile, METTL24 expression differently affected the overall survival rates (OS) of KIRC patients with high or low levels of immune infiltration. Finally, CTCF and EP300 were discovered to be the probable transcription factors of METTL24 in KIRC.

Conclusion

This study revealed that METTL24 might serve as a prognostic marker in KIRC and as one immune-relevant target for clinical treatment.

Post-transcriptional regulatory feedback encodes JAK-STAT signal memory of interferon stimulation

Immune cells fine tune their responses to infection and inflammatory cues. Here, using live-cell confocal microscopy and mathematical modelling, we investigate interferon-induced JAK-STAT signalling in innate immune macrophages. We demonstrate that transient exposure to IFN-γ stimulation induces a long-term desensitisation of STAT1 signalling and gene expression responses, revealing a dose- and time-dependent regulatory feedback that controls JAK-STAT responses upon re-exposure to stimulus. We show that IFN-α/β1 elicit different level of desensitisation from IFN-γ, where cells refractory to IFN-α/β1 are sensitive to IFN-γ, but not vice versa. We experimentally demonstrate that the underlying feedback mechanism involves regulation of STAT1 phosphorylation but is independent of new mRNA synthesis and cognate receptor expression. A new feedback model of the protein tyrosine phosphatase activity recapitulates experimental data and demonstrates JAK-STAT network’s ability to decode relative changes of dose, timing, and type of temporal interferon stimulation. These findings reveal that STAT desensitisation renders cells with signalling memory of type I and II interferon stimulation, which in the future may improve administration of interferon therapy.

Spatiotemporal NF-κB dynamics encodes the position, amplitude, and duration of local immune inputs

Infected cells communicate through secreted signaling molecules like cytokines, which carry information about pathogens. How differences in cytokine secretion affect inflammatory signaling over space and how responding cells decode information from propagating cytokines are not understood. By computationally and experimentally studying NF-κB dynamics in cocultures of signal-sending cells (macrophages) and signal-receiving cells (fibroblasts), we find that cytokine signals are transmitted by wave-like propagation of NF-κB activity and create well-defined activation zones in responding cells. NF-κB dynamics in responding cells can simultaneously encode information about cytokine dose, duration, and distance to the cytokine source. Spatially resolved transcriptional analysis reveals that responding cells transmit local cytokine information to distance-specific proinflammatory gene expression patterns, creating "gene expression zones." Despite single-cell variability, the size and duration of the signaling zone are tightly controlled by the macrophage secretion profile. Our results highlight how macrophages tune cytokine secretion to control signal transmission distance and how inflammatory signaling interprets these signals in space and time.

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.

Quantifying the phenotypic information in mRNA abundance

Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca²⁺ signaling network and the dynamic Ca²⁺ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca²⁺ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca²⁺ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.

Stochastic models of nucleosome dynamics reveal regulatory rules of stimulus-induced epigenome remodeling

The genomic positions of nucleosomes are a defining feature of the cell's epigenomic state, but signal-dependent transcription factors (SDTFs), upon activation, bind to specific genomic locations and modify nucleosome positioning. Here we leverage SDTFs as perturbation probes to learn about nucleosome dynamics in living cells. We develop Markov models of nucleosome dynamics and fit them to time course sequencing data of DNA accessibility. We find that (1) the dynamics of DNA unwrapping are significantly slower in cells than reported from cell-free experiments, (2) only models with cooperativity in wrapping and unwrapping fit the available data, (3) SDTF activity produces the highest eviction probability when its binding site is adjacent to but not on the nucleosome dyad, and (4) oscillatory SDTF activity results in high location variability. Our work uncovers the regulatory rules governing SDTF-induced nucleosome dynamics in live cells, which can predict chromatin accessibility alterations during inflammation at single-nucleosome resolution.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

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.

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.

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.

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer.

The Role of lncRNAs in Regulating the Intestinal Mucosal Mechanical Barrier

lncRNA is a transcript that is more than 200 bp in length. Currently, evidence has shown that lncRNA is of great significance in cell activity, involved in epigenetics, gene transcription, chromatin regulation, etc. The existence of an intestinal mucosal mechanical barrier hinders the invasion of pathogenic bacteria and toxins, maintaining the stability of the intestinal environment. Serious destruction or dysfunction of the mechanical barrier often leads to intestinal diseases. This review first summarizes the ability of lncRNAs to regulate the intestinal mucosal mechanical barrier. We then discussed how lncRNAs participate in various intestinal diseases by regulating the intestinal mucosal mechanical barrier. Finally, we envision its potential as a new marker for diagnosing and treating intestinal inflammatory diseases.

Peripherally-driven myeloid NFkB and IFN/ISG responses predict malignancy risk, survival, and immunotherapy regime in ovarian cancer

Background

Tumors can influence peripheral immune macroenvironment, thereby creating opportunities for non-invasive serum/plasma immunobiomarkers for immunostratification and immunotherapy designing. However, current approaches for immunobiomarkers’ detection are largely quantitative, which is unreliable for assessing functional peripheral immunodynamics of patients with cancer. Hence, we aimed to design a functional biomarker modality for capturing peripheral immune signaling in patients with cancer for reliable immunostratification.

Methods

We used a data-driven in silico framework, integrating existing tumor/blood bulk-RNAseq or single-cell (sc)RNAseq datasets of patients with cancer, to inform the design of an innovative serum-screening modality, that is, serum-functional immunodynamic status (sFIS) assay. Next, we pursued proof-of-concept analyses via multiparametric serum profiling of patients with ovarian cancer (OV) with sFIS assay combined with Luminex (cytokines/soluble immune checkpoints), CA125-antigen detection, and whole-blood immune cell counts. Here, sFIS assay’s ability to determine survival benefit or malignancy risk was validated in a discovery (n=32) and/or validation (n=699) patient cohorts. Lastly, we used an orthotopic murine metastatic OV model, with anti-OV therapy selection via in silico drug–target screening and murine serum screening via sFIS assay, to assess suitable in vivo immunotherapy options.

Results

In silico data-driven framework predicted that peripheral immunodynamics of patients with cancer might be best captured via analyzing myeloid nuclear factor kappa-light-chain enhancer of activated B cells (NFκB) signaling and interferon-stimulated genes' (ISG) responses. This helped in conceptualization of an ‘in sitro’ (in vitro+in situ) sFIS assay, where human myeloid cells were exposed to patients’ serum in vitro, to assess serum-induced (si)-NFκB or interferon (IFN)/ISG responses (as active signaling reporter activity) within them, thereby ‘mimicking’ patients’ in situ immunodynamic status. Multiparametric serum profiling of patients with OV established that sFIS assay can: decode peripheral immunology (by indicating higher enrichment of si-NFκB over si-IFN/ISG responses), estimate survival trends (si-NFκB or si-IFN/ISG responses associating with negative or positive prognosis, respectively), and coestimate malignancy risk (relative to benign/borderline ovarian lesions). Biologically, we documented dominance of pro-tumorigenic, myeloid si-NFκB response^HIGHsi-IFN/ISG response^LOW inflammation in periphery of patients with OV. Finally, in an orthotopic murine metastatic OV model, sFIS assay predicted the higher capacity of chemo-immunotherapy (paclitaxel–carboplatin plus anti-TNF antibody combination) in achieving a pro-immunogenic peripheral milieu (si-IFN/ISG response^HIGHsi-NFκB response^LOW), which aligned with high antitumor efficacy.

Conclusions

We established sFIS assay as a novel biomarker resource for serum screening in patients with OV to evaluate peripheral immunodynamics, patient survival trends and malignancy risk, and to design preclinical chemo-immunotherapy strategies.

The soluble cytoplasmic tail of CD45 regulates T-cell activation via TLR4 signaling

The soluble cytoplasmic tail of CD45 (ct-CD45) is a cleavage fragment of CD45, that is generated during the activation of human phagocytes. Upon release to the extracellular space, ct-CD45 binds to human T cells and inhibits their activation in vitro. Here, we studied the potential role of TLR4 as a receptor for ct-CD45. Treatment of Jurkat TLR4/CD14 reporter cells with ct-CD45 induced the upregulation of the reporter gene NFκB-eGFP and could be blocked by inhibitors of TLR4 signaling. Conversely, ct-CD45 did not promote the NFκB-controlled eGFP induction in reporter cells expressing TLR1, TLR2, and TLR6 transgenes and did not lead to the activation of the transcription factors NFκB, AP-1, and NFAT in a Jurkat reporter cell line expressing endogenous TLR5. Moreover, ct-CD45 binds to recombinant TLR4 in an in vitro assay and this association was reduced in the presence of oxidized 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine. Blockade of TLR4 with mAb HTA125 partially reversed the ct-CD45-mediated inhibition of T-cell proliferation. Interestingly, targeting of TLR4 with mAb W7C11 also suppressed T-cell proliferation. In summary, the results of this study demonstrate that ct-CD45 acts via a noncanonical TLR4 activation pathway on T cells, which modulates TCR signaling.