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

Macrophages initiate inflammatory responses via the transcription factor NFκB. The temporal pattern of NFκB activity determines which genes are expressed and thus, the type of response that ensues. Here, we examined how information about the stimulus is encoded in the dynamics of NFκB activity. We generated an mVenus-RelA reporter mouse line to enable high-throughput live-cell analysis of primary macrophages responding to host- and pathogen-derived stimuli. An information-theoretic workflow identified six dynamical features-termed signaling codons-that convey stimulus information to the nucleus. In particular, oscillatory trajectories were a hallmark of responses to cytokine but not pathogen-derived stimuli. Single-cell imaging and RNA sequencing of macrophages from a mouse model of Sjögren's syndrome revealed inappropriate responses to stimuli, suggestive of confusion of two NFκB signaling codons. Thus, the dynamics of NFκB signaling classify immune threats through six signaling codons, and signal confusion based on defective codon deployment may underlie the etiology of some inflammatory diseases.

Tonic TNF conditioning of macrophages safeguards stimulus-specific inflammatory responses.

Tumor necrosis factor (TNF) is a key inflammatory cytokine that warns recipient cells of a nearby infection or tissue damage. Acute exposure to TNF activates characteristic oscillatory dynamics of the transcription factor NFκB and induces a characteristic gene expression program; these are distinct from the responses of cells directly exposed to pathogen-associated molecular patterns (PAMPs). Here, we report that tonic TNF exposure is critical for safeguarding TNF's specific functions. In the absence of tonic TNF conditioning, acute exposure to TNF causes (i) NFκB signaling dynamics that are less oscillatory and more like PAMP-responsive NFκB dynamics, (ii) immune gene expression that is more similar to the Pam3CSK4 response program, and (iii) broader epigenomic reprogramming that is characteristic of PAMP-responsive changes. We show that the absence of tonic TNF signaling effects subtle changes to TNF receptor availability and dynamics such that enhanced pathway activity results in non-oscillatory NFκB. Our results reveal tonic TNF as a key tissue determinant of the specific cellular responses to acute paracrine TNF exposure, and their distinction from responses to direct exposure to PAMPs.

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.

Quantifying information accumulation encoded in the dynamics of biochemical signaling.

Cellular responses to environmental changes are encoded in the complex temporal patterns of signaling proteins. However, quantifying the accumulation of information over time to direct cellular decision-making remains an unsolved challenge. This is, in part, due to the combinatorial explosion of possible configurations that need to be evaluated for information in time-course measurements. Here, we develop a quantitative framework, based on inferred trajectory probabilities, to calculate the mutual information encoded in signaling dynamics while accounting for cell-cell variability. We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. Our analyses also suggest specific temporal phases during which information distinguishing threats becomes available to immune response genes; one specific phase could be mapped to the functionality of the IκBα negative feedback circuit. The framework is generally applicable to single-cell time series measurements, and enables understanding how temporal regulatory codes transmit information over time.

NF-κB dynamics determine the stimulus specificity of epigenomic reprogramming in macrophages.

The epigenome of macrophages can be reprogrammed by extracellular cues, but the extent to which different stimuli achieve this is unclear. Nuclear factor κB (NF-κB) is a transcription factor that is activated by all pathogen-associated stimuli and can reprogram the epigenome by activating latent enhancers. However, we show that NF-κB does so only in response to a subset of stimuli. This stimulus specificity depends on the temporal dynamics of NF-κB activity, in particular whether it is oscillatory or non-oscillatory. Non-oscillatory NF-κB opens chromatin by sustained disruption of nucleosomal histone-DNA interactions, enabling activation of latent enhancers that modulate expression of immune response genes. Thus, temporal dynamics can determine a transcription factor's capacity to reprogram the epigenome in a stimulus-specific manner.

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

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

An NFκB Activity Calculator to Delineate Signaling Crosstalk: Type I and II Interferons Enhance NFκB via Distinct Mechanisms.

Nuclear factor kappa B (NFκB) is a transcription factor that controls inflammation and cell survival. In clinical histology, elevated NFκB activity is a hallmark of poor prognosis in inflammatory disease and cancer, and may be the result of a combination of diverse micro-environmental constituents. While previous quantitative studies of NFκB focused on its signaling dynamics in single cells, we address here how multiple stimuli may combine to control tissue level NFκB activity. We present a novel, simplified model of NFκB (SiMoN) that functions as an NFκB activity calculator. We demonstrate its utility by exploring how type I and type II interferons modulate NFκB activity in macrophages. Whereas, type I IFNs potentiate NFκB activity by inhibiting translation of IκBα and by elevating viral RNA sensor (RIG-I) expression, type II IFN amplifies NFκB activity by increasing the degradation of free IκB through transcriptional induction of proteasomal cap components (PA28). Both cross-regulatory mechanisms amplify NFκB activation in response to weaker (viral) inducers, while responses to stronger (bacterial or cytokine) inducers remain largely unaffected. Our work demonstrates how the NFκB calculator can reveal distinct mechanisms of crosstalk on NFκB activity in interferon-containing microenvironments.