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Stimulus-selective crosstalk via the NF-κB signaling system reinforces innate immune response to alleviate gut infection

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Cite this article as: eLife 2015;4:e05648 doi: 10.7554/eLife.05648

Abstract

Tissue microenvironment functions as an important determinant of the inflammatory response elicited by the resident cells. Yet, the underlying molecular mechanisms remain obscure. Our systems-level analyses identified a duration code that instructs stimulus specific crosstalk between TLR4-activated canonical NF-κB pathway and lymphotoxin-β receptor (LTβR) induced non-canonical NF-κB signaling. Indeed, LTβR costimulation synergistically enhanced the late RelA/NF-κB response to TLR4 prolonging NF-κB target gene-expressions. Concomitant LTβR signal targeted TLR4-induced newly synthesized p100, encoded by Nfkb2, for processing into p52 that not only neutralized p100 mediated inhibitions, but potently generated RelA:p52/NF-κB activity in a positive feedback loop. Finally, Nfkb2 connected lymphotoxin signal within the intestinal niche in reinforcing epithelial innate inflammatory RelA/NF-κB response to Citrobacter rodentium infection, while Nfkb2−/− mice succumbed to gut infections owing to stromal defects. In sum, our results suggest that signal integration via the pleiotropic NF-κB system enables tissue microenvironment derived cues in calibrating physiological responses.

https://doi.org/10.7554/eLife.05648.001

eLife digest

The innate immune system is the body's first line of defense against infection and disease. Innate immune cells are found in every tissue type, poised to respond immediately to damaged, stressed, or infected host cells. When innate immune cells recognize any injury or infection, one of the first things they do is trigger the inflammatory response. Fluid and other immune cells then move from the blood into the injured tissues. This movement can cause redness and swelling. But the response helps to establish a physical barrier against the spread of infection, promotes the elimination of both invading microbes and damaged host cells, and encourages the repair of the tissue.

Inflammation is tightly controlled. If the response is too weak, it could leave an individual prone to serious infection. On the other hand, excessive inflammation can severely damage healthy cells and tissues. Inflammation is regulated differently in different tissue types, and the environment within the tissue itself influences the activity of local innate immune cells and the inflammatory response. However, the molecular mechanisms responsible for receiving and interpreting the signals derived from the host tissue remain unknown.

Now, Banoth et al., have revealed that the integration of inflammation-provoking signals, such as injury or infection and cues from the tissue environment occurs via the so-called ‘NF-κB signaling system’. NF-κB is a protein found in almost all cell types, and when activated it is able to switch on the expression of many different genes. Banoth et al. explain that signal integration via the NF-κB system enables cues from the tissue environment to tune a cell's responses. Further experiments confirmed the importance of this signal integration by showing how a signal coming from intestinal tissue can influence the activity of innate immune cells and inflammation in the gut.

These findings suggest that a breakdown in the NF-κB signaling system's ability to integrate multiple signals, including those derived from the tissue environment, may be responsible for many inflammatory disorders, and in particular those that involve the gut. Future work is now needed to explore this possibility.

https://doi.org/10.7554/eLife.05648.002

Introduction

Tight regulation of inflammatory responses is important; uncontrolled inflammation underlies various human ailments, while insufficient responses limit host defense to pathogens. Tissue-resident cells those that participate in inflammatory immune activation also exhibit functional differences by adapting to the repertoire of cell-differentiating cues present in distinct microenvironments. Indeed, macrophages and dendritic cells present in different anatomic niche display heterogeneity in inflammatory signatures (Iwasaki and Kelsall, 1999; Stout and Suttles, 2004). Likewise, a requirement for CD40, primarily involved in B-cell maturation, in inflammatory gene expressions in endothelial cells was documented (Pluvinet et al., 2008). Similarly, lymph node inducing lymphotoxin-β receptor (LTβR) was shown to be critical for innate immune responses (Spahn et al., 2004; Wang et al., 2010). Yet, the cellular circuitry that functions at the intersection of tissue microenvironment derived signals and those impinged upon by pro-inflammatory cytokines or pathogen-derived substances remains obscure.

The NF-κB family of transcription factors plays an essential role in activating pathogen-responsive gene-expression program in tissue-resident cells. In the canonical NF-κB pathway, inflammatory cues engage NEMO-IKK2 (NEMO-IKKβ) kinase complex to phosphorylate inhibitory IκB proteins, the major isoform being IκBα, bound to the cytoplasmic RelA:p50 NF-κB dimers. Signal-induced phosphorylation leads to proteasomal degradation of IκBs and release of RelA:p50 dimers into the nucleus. The nuclear RelA dimers activate the expressions of pro-inflammatory chemokine and cytokine genes as well as its own inhibitor IκBα, which ensures proper attenuation of inflammatory responses in a negative feedback loop. In contrast to canonical signaling, the non-canonical pathway transduces signals from cell-differentiating cues those engage BAFFR, CD40, or LTβR. Non-canonical signaling involves NIK and IKK1 (NIK-IKKα) mediated phosphorylation of Nfkb2 encoded precursor p100 bound to RelB (Sun, 2012). Subsequent proteasomal processing removes the C-terminal inhibitory domain of p100 from RelB:p100 complex to generate RelB:p52 NF-κB dimer, which mediates the expressions of organogenic chemokine genes in the nucleus (Bonizzi et al., 2004).

Molecular interaction between the non-canonical signal transducer p100 and RelA has also been charted. In its homo-oligomeric form, termed IκBδ, p100 was shown to utilize its inhibitory domain to sequester a subpopulation of the RelA:p50 dimer (Basak et al., 2007; Savinova et al., 2009). LTβR through non-canonical NIK-IKK1 signal inactivates IκBδ to induce a weak yet sustained RelA:p50 activity. Conversely, RelA-induced synthesis of p100 and consequent accumulation of inhibitory IκBδ was shown to exert negative feedback limiting canonical RelA activity (de Wit et al., 1998; Legarda-Addison and Ting, 2007; Shih et al., 2009). In addition, an alternate RelA:p52 dimer has been reported which is thought to constitute a minor kappaB DNA binding activity (Hoffmann et al., 2003). Crosstalk between apparently distinct cell signaling pathways is known to offer diversity in cellular responses. Despite these connectivities, a plausible role of signal integration via the NF-κB system in regulating inflammatory RelA NF-κB responses has not been investigated.

In a multidisciplinary study combining biochemistry, genetics, and mathematical modeling, here, we characterized a duration code that determines stimulus-specific crosstalk between canonical and non-canonical signaling in fine-tuning inflammatory RelA NF-κB activity. Through such crosstalk, LTβR sustained TLR4 triggered RelA NF-κB responses by supplementing RelA:p52 NF-κB dimer in a positive feedback loop. Finally, we established the physiological significance of crosstalk control of RelA in intestinal epithelial cells (IECs), where, the NF-κB system integrates gut microenvironment derived lymphotoxin signals through Nfkb2 to calibrate innate immune responses to Citrobacter rodentium.

Results

A duration code controlling crosstalk between canonical and non-canonical NF-κB signaling

Given the interconnectedness of the canonical and non-canonical arms (see Introduction and Figure 1A), we asked if signal integration via the NF-κB system would allow cell-differentiating cues to modulate inflammatory RelA NF-κB responses. Mathematical reconstruction of dynamic networks illuminates emergent properties, such as crosstalk (Basak et al., 2012). To explore crosstalk control, we developed a mathematical model, which we termed the NF-κB Systems Model v1.0 (Appendix-1), basing on previously published single NF-κB dimer model versions (Hoffmann et al., 2002; Basak et al., 2007). In our mathematical model, however, we depicted nuclear activation of both the major RelA:p50 dimer and RelA:p52 dimer, which is thought to constitute a minor RelA NF-κB activity. As described in the preceding single dimer models (Hoffmann et al., 2002; Basak et al., 2007), signal-responsive degradation and resynthesis of IκBα, IκBβ, IκBε, and inhibitory p100/IκBδ dynamically controlled RelA activity. The model was parameterized based on literature, our own measurements (Appendix-1, Appendix figures 1–5, Supplementary files 1–3), and fitting procedures. Simulating individual TNFR or LTβR regime, we could recapitulate experimentally observed strong, but temporally controlled, activation of RelA NF-κB complexes during canonical IKK2 signaling or the weak induction of RelA dimers during non-canonical NIK-IKK1 signaling, respectively (Figure 1B, Figure 1—figure supplement 1).

Figure 1 with 3 supplements see all
Computational simulations predicting a duration code underlying crosstalk control.

(A) A current model for RelA NF-κB activation via the canonical (IKK2) or the non-canonical (NIK-IKK1) pathways, respectively. RelA dimers represent both RelA:p50 and RelA:p52. Regulation of RelA NF-κB activities through crosstalk between these two pathways has not been addressed. (B) Computational simulations of nuclear RelA NF-κB induction by TNFR-induced IKK2 (top, magenta) or LTβR-induced NIK-IKK1 signals (bottom, green). (C) A theoretical library of 356 distinct kinase activity profiles. (D) A schematic describing in silico crosstalk studies. The kinase inputs were fed into the model through the IKK2 or NIK-IKK1 or both the arms. The RelA NF-κB responses, quantified as baseline corrected total area under the respective activity curves, were used for computing crosstalk indexes. (E) Based on their respective crosstalk indexes, top 10% combinations of theoretical IKK2 and NIK1 activity profiles were identified and duration as well as amplitude of the associated crosstalk-proficient IKK2 (top) or NIK-IKK1 (bottom) profiles were plotted. (F and G) The IKK2 (F) or NIK-IKK1 (G) activities were monitored by incubating GST-IκBα or GST-IκBδ with NEMO or NIK co-immunoprecipitates derived from MEFs treated with IL-1β, LPS (F, left and right panels), or αLTβR (G), respectively. For IKK2 assay, co immunoprecipitated IKK1 and for NIK-IKK1 assay, actin present in the cell extracts was used as loading controls. (H) Computational simulations predicting augmented RelA activity in LPS+αLTβR (right panel) and a lack of crosstalk in IL-1+αLTβR (left) co-treatment regimes.

https://doi.org/10.7554/eLife.05648.003

Next, we examined potential crosstalk between IKK2 and NIK-IKK1 inputs in augmenting RelA NF-κB response in silico. To this end, we generated a theoretical library (Shih et al., 2009) of 356 kinase activity profiles, where each member possesses distinct peak onset time, peak amplitude, and duration (Figure 1C, Figure 1—figure supplement 2A). To screen for permissive crosstalk conditions, we fed this library into the model through IKK2 or NIK-IKK1 or both the arms and iteratively simulated respective RelA activities. Then, we computed RelA responses in the co-treatment regime relative to individual cell stimulations to assign crosstalk indexes to different IKK2 and NIK-IKK1 combinations (Figure 1D). Plotting the dynamic features of the crosstalk-proficient kinase inputs, we could reveal a critical duration threshold; where IKK2 activities sustained for more than 2 hr were more likely to engage into crosstalk for varied peak amplitudes and inputs with shorter duration were crosstalk inefficient (Figure 1E and Figure 1—figure supplement 2B). Illustrating a similar but more elaborate duration control, NIK-IKK1 activities longer than 8 hr selectively participated into crosstalk with the canonical pathway.

Inflammatory mediators activate canonical IKK2 with disparate temporal controls. Consistent to the prior report (Werner et al., 2008), our kinase assay (‘Materials and methods’) revealed that IL-1β, an important pro-inflammatory cytokine, only transiently activates IKK2 in mouse embryonic fibroblasts (MEFs) (left panel, Figure 1F). In contrast, bacterial LPS through TLR4-induced IKK2 activity that persisted above the basal level even at 24 hr post-stimulation (right panel, Figure 1F, Figure 1—figure supplement 3B) (Covert et al., 2005). Mimicking prolonged signaling during cell-differentiation processes, LTβR engagement using agonistic αLTβR antibody led to sustained activation of the non-canonical NIK-IKK1 (Figure 1G and Figure 1—figure supplement 3A,B). Using these experimental kinase activities as inputs, our computational simulations revealed insulation of IL-1R signaling from LTβR-mediated crosstalk (left panel, Figure 1H), but robust crosstalk between TLR4 and LTβR that amplified late RelA response upon costimulation (right panel, Figure 1H). Therefore, our mathematical analyses predicted that a duration code selectively engages long lasting canonical kinase activities into crosstalk with LTβR induced NIK signal to impart stimulus specificity.

Stimulus-specific crosstalk allows LTβR signal to prolong TLR4 induced RelA NF-κB response

To experimentally verify stimulus specificity of crosstalk control, we measured nuclear RelA activities induced in MEFs by canonical or non-canonical inducers or co-treatment regime that concomitantly activated both the pathways. IL-1R signal, in parallel to transient IKK2 activation, elicited strong RelA activity at 30 min in EMSA that was largely attenuated within 1 hr, whereas, non-canonical LTβR signal only weakly induced RelA and RelB dimers those persisted even at 24 hr (Figure 2A). Indeed, we were unable to detect any significant enhancement of RelA activity, relative to IL-1 induced peak, upon costimulation (Figure 2A). In comparison, canonical TLR4 induced a temporally distinct RelA NF-κB activity with an early peak at 1 hr, subsequent descend and a progressively weakened late phase between 8 hr and 24 hr (Figure 2B). Corroborating our mathematical prediction, concomitant LTβR signal sustained NF-κB response triggered by TLR4 (Figure 2B). Signal integration via the NF-κB system synergistically enhanced TLR4-induced late RelA activity at 24 hr in the costimulation regime with mostly unaltered RelB response relative to solitary LTβR engagement (Figure 2B, quantification and Figure 2C). Sequentially engaging MyD88 and Trif, TLR4 was shown to produce extended IKK2 activity (Covert et al., 2005). To determine if the observed stimulus specificity of crosstalk is indeed due to the duration of IKK2, we utilized Trif-deficient MEFs that only transiently activated IKK2 upon LPS treatment (Figure 2D). Despite a functional non-canonical pathway (Figure 2—figure supplement 1), LTβR was restricted from crosstalk with TLR4 in Trif-deficient cells (Figure 2E), thereby, suggesting a Trif-dependent mechanism that relies on the duration of canonical IKK2 in imparting stimulus specificity of crosstalk control.

Figure 2 with 1 supplement see all
LTβR signal sustains TLR4, but not IL-1R, induced RelA NF-κB response.

(A) Nuclear NF-κB activities induced in MEFs by IL-1β or αLTβR or co-treatment were resolved in EMSA using a κB site containing DNA probe. The faster migrating complex, indicated with an arrowhead, consists of RelB and the slower migrating complex activated by both canonical or non-canonical signaling, denoted with an arrow, consists of RelA dimers. The compositions of the DNA binding complexes were ascertained in Figure 2C. Right, signal corresponding to RelA NF-κB activities were quantified and graphed relative to the respective IL-1 induced peak value. Data were expressed as mean of 3 quantified biological replicates ± SEM. (B) EMSA result, representative of three independent biological replicates, revealing augmented late NF-κB activities in the co-treatment regime as compared to cell treatment with LPS or αLTβR alone. Right, signal corresponding to RelA NF-κB activities were similarly quantified and graphed relative to LPS induced peak value. Note, late RelA activities in the LPS+αLTβR co-treatment regime were significantly augmented from of the LPS induced activities. (C) Supershift analysis distinguishing RelA and RelB dimers induced in MEFs treated with LPS (L) or αLTβR (B) or both (LB) for 24 hr. (D) Kinase assay revealing transient IKK2 activities in response to LPS in Trif-deficient MEFs. (E) EMSA data, representative of three independent experiments, revealing a lack of NF-κB crosstalk between TLR4 and LTβR in Trif-deficient MEFs.

https://doi.org/10.7554/eLife.05648.007

Signal integration via the NF-κB system amplifies the late expressions of TLR4-induced pro-inflammatory genes

Long-lasting kinase activities are expected to elicit sustained RelA responses. Then what might be the significance of signal integration via the NF-κB system? Interestingly, computational simulations demonstrated only a muted increment in the RelA activity with increasing duration of IKK2 (Figure 3A). LTβR induced NIK-IKK1 signal relieved this saturation to fully unravel the NF-κB activation potential of long duration IKK2 signals upon crosstalk. We postulated that the difference in the RelA responses induced by long-lasting IKK2 in the presence or absence of non-canonical signal would decode into differential gene activities.

LTβR signal augments the late expressions of TLR4-induced NF-κB target genes.

(A) Computational simulation revealing total RelA activities induced by IKK2 inputs of various durations, in the absence or presence of LTβR induced NIK-IKK1. (B) Quantitative RT-PCR measuring early (1 hr) and late (24 hr) expressions of chemokine and cytokine genes in WT MEFs by IL-1R or LTβR or costimulation. (C) Gene-expression analyses similarly revealing early (3 hr) and late (24 hr) expressions of chemokine and cytokine genes in WT MEFs by TLR4 or LTβR or costimulation. In (B) and (C), data are expressed as mean of 3 quantified biological replicates ± SEM. The statistical significance was determined using two-tailed Student's t-test. (D) LPS-induced genes, identified in representative microarray experiments at 24 hr post-stimulation, were ranked based on their normalized crosstalk score (bottom panels), which reflects synergistic gene activation in the co-treatment regime as compared to individual cell treatments for a positive value. GSEA demonstrated statistically significant enrichment of NF-κB targets (top), with enrichment score of 0.44 for WT MEFs, among genes positively controlled through crosstalk. Hits (middle) indicate NF-κB response genes.

https://doi.org/10.7554/eLife.05648.009

To evaluate the potential gene-effect of crosstalk, we measured the expressions of several known RelA target chemokine and cytokine genes using quantitative RT-PCR. Our analyses revealed that IL-1 treatment rapidly induces the expressions of TNF, IP-10, and MIP-1α mRNAs within 1 hr with residual expressions at 24 hr post-stimulation (Figure 3B). The levels of IL-1β and RANTES mRNAs were insensitive to IL-1 treatment. Also, weak LTβR signal alone did not significantly induce the expressions of these pro-inflammatory genes in MEFs. Indeed, LTβR costimulation was ineffective in augmenting IL-1 induced early or late expressions of the chemokine and cytokine genes (Figure 3B). Solitary LPS treatment not only robustly induced the expressions of TNF, IP-10, and MIP-1α mRNAs, but also led to late accumulation of IL-1β and RANTES mRNAs at 24 hr (Figure 3C). Consistent to our hypothesis, LTβR costimulation prolonged TLR4-induced gene expressions with further augmented late, but not early, expressions of IL-1β, IP-10, MIP-1α, and RANTES mRNAs. TNF mRNA levels were insensitive to crosstalk regulation (Figure 3C).

Furthermore, we used microarray analyses to compare global gene expressions activated by TLR4 or LTβR or both at 24 hr post-stimulation. Estimating normalized crosstalk scores (bottom panel, Figure 3D, Figure 3—source data 1, ‘Materials and methods’), we could reveal a synergistic effect of LTβR on TLR4 stimulated late gene expressions in WT MEFs. Out of 943 LPS induced genes, however, a select set of 114 genes was further upregulated upon costimulation. Strikingly, gene set enrichment analysis (GSEA) (Subramanian et al., 2005) (Figure 3—source data 2, ‘Materials and methods’) demonstrated an enrichment of NF-κB targets among genes positively controlled through crosstalk (middle and top, Figure 3D). We have also noted downregulation of several LPS-induced genes in the costimulation regime those appeared less likely to be NF-κB targets in GSEA. Taken together, these analyses substantiated an important function of prolonged RelA activity in crosstalk-amplification of TLR4-induced late expressions of NF-κB target genes, particularly those encode pro-inflammatory chemokines and cytokines.

Non-canonical signal transducer Nfkb2 supplements RelA:p52 dimer to sustain canonical RelA NF-κB responses

To determine the mechanism underlying signal integration via the NF-κB system, we individually perturbed 105 model parameters and quantified relative changes in the crosstalk index (‘Materials and methods’). Our parameter sensitivity analysis identified the rate constant associated with the NF-κB-induced transcription of Nfkb2 as the most critical parameter underlying crosstalk control of RelA NF-κB activity (Figure 4A). As such, Nfkb2 encodes for both NF-κB inhibitor and NF-κB precursor functions. Computationally simulating individual RelA:p50 and RelA:p52 nuclear activities in the LPS, αLTβR, or costimulation regimes, we could further suggest that the Nfkb2 precursor function in generating RelA:p52 dimer is important for augmenting RelA NF-κB activity during crosstalk in WT system (Figure 4B). Consistently, our modeling analyses predicted complete abrogation of crosstalk in Nfkb2−/− cells (Figure 4B).

Figure 4 with 1 supplement see all
Signal generation of RelA:p52 NF-κB dimer underlies a pro-synergistic function of Nfkb2.

(A) Local sensitivity analysis revealing the effect of perturbation of the individual biochemical parameters on crosstalk between TLR4 and LTβR. (B) Computational simulations of total RelA:p50 and RelA:p52 activities between 8 and 24 hr in response to LPS, αLTβR, or both in WT or Nfkb2 deficient systems. (C) Immunoblot charting cellular abundance of NF-κB/IκB proteins during signaling. Right, signal corresponding to p100 and p52 levels at 24 hr post-stimulation were quantified and graphed. (D) Immunoblot of RelA co-immunoprecipitates, normalized for the RelA content, obtained using whole cells extracts derived from MEFs treated with LPS (L) or αLTβR (B) or both (LB) for 24 hr. The quantified data demonstrates the level of RelA-p100 or RelA-p52 complexes at 24 hr post-stimulation. (E) Supershift analysis revealing the composition of the RelA dimers induced upon indicated cell treatments for 24 hr. Right, signal corresponding to RelA:p50 or RelA:p52 NF-κB activities were quantified and graphed. (F) Representative immunoblot demonstrating an increase in the RelA protein level in MEFs, in parallel to phospho-IκBα accumulation, upon proteasome inhibition using MG-132. (G) EMSA revealing RelA activities induced in WT or Nfkb2−/− MEFs upon indicated cell-stimulations by supershifting RelB. Below, quantified late (24 hr) RelA activities were plotted for different genotypes subsequent to normalizing against the respective LPS induced early 1 hr peak. (H) mRNA analyses comparing late (24 hr) expressions of chemokines/cytokines in Relb−/− (top) and Nfkb2−/− (bottom) MEFs upon indicated cell stimulations. Quantified data for both biochemical and gene-expression analyses presented in this figure are expressed as mean of 3 biological replicates ± SEM.

https://doi.org/10.7554/eLife.05648.012

Our biochemical studies revealed that LPS induces RelA NF-κB-driven transcription of Nfkb2 to produce p100 (top panel, Figure 4C and Figure 4—figure supplement 1A), which was shown to oligomerize as NF-κB inhibitory IκBδ (Savinova et al., 2009; Shih et al., 2009; Tao et al., 2014). Concomitant LTβR signal instead utilized TLR4-induced, newly synthesized p100 to potently generate p52, thereby, neutralizing the inhibitory p100 function (Figure 4C). p50 levels were not discernibly altered in these stimulation regimes in our experiments. Our immunoprecipitation based analysis demonstrated that NIK-IKK1 signal relieves RelA from p100/IκBδ-mediated inhibition during crosstalk (Figure 4D). Intriguingly, LTβR costimulation of MEFs for 24 hr also produced ∼fourfold more RelA:p52 NF-κB dimer as compared to solitary LPS treatments (Figure 4D). Limited transcriptional up-regulation of Nfkb2 by weak LTβR signal was correlated with only the modest p52 and RelA:p52 generation (Figure 4C,D). In supershift assay, we could ascertain that RelA:p52 dimer generated upon LTβR costimulation appears as a strong nuclear DNA binding activity at 24 hr (Figure 4E) to supplement to the TLR4-induced RelA NF-κB responses. An important role of dimerization in stabilizing NF-κB monomers from degradation has been reported earlier (Fusco et al., 2008). Interestingly, RelA protein rapidly accumulated in cells upon proteasome inhibition (Figure 4F) suggesting a robust constitutive degradation mechanism that offsets basal synthesis of RelA monomers in maintaining the steady-state level. Our study indicated that this enduring flux ensures copious supply of RelA to bind to de novo generated p52, produced from the newly synthesized p100 during crosstalk.

Next, our genetic analyses revealed that canonical RelA:p50 response to TLR4 signal, primarily controlled through classical IκBs, is largely intact in Nfkb2−/− with early induction and diminished late activities comparable to WT MEFs (Figure 4G and Figure 4—figure supplement 1B). Consistent to the prediction based on computational modeling studies, a lack of RelA:p52 dimer generation in Nfkb2−/− cells, however, ablated LTβR-mediated enhancement of TLR4-induced late RelA DNA binding activity (Figure 4G) as well as crosstalk amplification of RelA target pro-inflammatory gene expressions (bottom panel, Figure 4H). LTβR costimulation not only enhanced TLR4 induced RelA DNA binding but also activated RelB dimers through the non-canonical pathway. Prior reports have indicated cell-type specific inhibitory as well as activating role of RelB in chemokine gene expressions (Weih et al., 1996; Shih et al., 2012). Importantly, costimulation of Relb−/− MEFs led to similar hyperactivation of LPS-induced late expressions of IL-1β, IP-10, and RANTES mRNAs as in WT cells (compare top panel, Figure 4H with Figure 3C). Although, the crosstalk effect on MIP-1α expressions was somewhat muted owing to prolonged expression of this gene in Relb−/− MEFs in response to solitary LPS treatment. Therefore, our analyses confirmed that the precursor function encoded by Nfkb2 in generating RelA:p52 NF-κB dimer is critical for integrating lymphotoxin derived signals to the pro-inflammatory RelA NF-κB pathway. Our analyses also suggested that LTβR costimulation led to the hyperactivation of LPS-induced expressions of chemokine and cytokine genes in an Nfkb2-dependent manner with only a minor, if any, role for Relb.

Inducible synthesis of Nfkb2 by canonical signal triggers a positive feedback loop during crosstalk

Given the computational prediction for an important role of NF-κB-induced transcription of Nfkb2, we compared the inducible expression of the crosstalk mediator Nfkb2 in response to LPS or IL-1 to understand the molecular basis of stimulus-specific control. As opposed to rapid expression of Nfkbia mRNA, which encodes IκBα, LPS induced Nfkb2 mRNA with a delay (top panel, Figure 5A). Similarly, chronic TNF treatment induced Nfkb2 mRNA in WT MEFs with an explicit 1 hr delay (Figure 5B) that was also observed earlier and incorporated in both the previous (Basak et al., 2007) as well as the current mathematical model versions. Analogous time lags were observed in the expression of several inflammatory genes those require additional chromatin modifications for the initiation of RelA-induced transcription (Natoli et al., 2005). When Nfkb2 transgene was stably expressed in Nfkb2−/− MEFs from an exogenous NF-κB responsive promoter, Nfkb2 mRNA was readily induced by TNF without a delay (Figure 5B). Remarkably, IL-1 treatment was ineffective in activating the expression of Nfkb2 mRNA in WT MEFs, despite the early induction of Nfkbia (bottom, Figure 5A). Only upon eliminating the transcriptional delay, our mathematical model could simulate Nfkb2 mRNA induction by IL-1 treatment (Figure 5C). Indeed, we could also experimentally rescue the defect in Nfkb2 mRNA induction by IL-1 treatment in the engineered Nfkb2−/− cell line, which expresses Nfkb2 transgene from the NF-κB-responsive promoter without the delay (Figure 5D). Consistent to our computational identification that NF-κB inducible transcription of Nfkb2 is important, disruption of NF-κB-inducible synthesis by expressing p100 from a constitutive promoter in Nfkb2−/− MEFs abrogated crosstalk amplification of TLR4-induced late NF-κB activity by concomitant LTβR signal (Figure 5E and Figure 5—figure supplement 1A). While the NF-κB-responsive expression of Nfkb2 transgene in Nfkb2−/− cells restored the crosstalk effect at the level of RelA NF-κB activation (Figure 5E) by potentiating RelA:p52 induction in LPS+αLTβR costimulation regime (Figure 5F, Figure 5—figure supplement 1B).

Figure 5 with 1 supplement see all
Induction of Nfkb2 expressions by canonical signal is required for crosstalk.

(A) Relative levels of Nfkb2 mRNA and Nfkbia mRNA, which encodes IκBα, in WT MEFs during LPS or IL-1 signaling. (B) TNF induced delayed expression of Nfkb2 mRNA in WT MEFs and rapid production in an engineered Nfkb2−/− cell-line from an exogenous NF-κB dependent promoter. Data presented in (A) and (B) are expressed as mean of 3 quantified biological replicates ± SEM. (C) Simulations comparing IL-1-induced Nfkb2 mRNA expressions in the presence or absence of transcriptional delay. (D) Quantitative RT-PCR revealing IL-1 induced expression of Nfkb2 and Nfkbia mRNAs in the engineered Nfkb2−/− cell line with transgenic expressions of Nfkb2 from the NF-κB inducible promoter. Data are expressed as mean of 3 quantified biological replicates ± SEM. (E) EMSA comparing NF-κB activities induced in Nfkb2−/− MEFs expressing Nfkb2 from either a constitutive (lane 1–7) or an NF-κB responsive (lane 2–14) transgenic (Tg) promoter. (F) Supershift analyses comparing nuclear abundance of different NF-κB dimers activated upon costimulation with LPS+αLTβR for 24 hr in these two engineered Nfkb2−/− cell lines. (G) The engineered Nfkb2−/− cell line, which expresses Nfkb2 from the NF-κB-inducible promoter, was pretreated for 8 hr with αLTβR and subsequently treated with IL-1. Supershifting RelB, representative RelA activities were captured in EMSA. (H) A graphical depiction of the proposed crosstalk control; two negative feedback loops coordinately attenuate TLR4 responses. However, one of these negative feedback loops is converted into a positive feedback loop by non-canonical signals to generate crosstalk at the level of RelA NF-κB activation. Magenta and green arrows indicate canonical IKK2 and non-canonical NIK-IKK1 inputs, respectively, and line thickness signifies relative strength of feedbacks.

https://doi.org/10.7554/eLife.05648.014

Our results confirmed that signal induction of Nfkb2 is important for crosstalk and suggested that a promoter intrinsic delay necessitates persistent canonical signal for RelA-mediated induction of pro-synergistic Nfkb2. Such delay encoding insulated IL-1R signaling, which transiently activates IKK2 and RelA by restricting Nfkb2 mRNA expressions, and accounted for the abrogated crosstalk in Trif-deficient MEFs that transiently activated the NF-κB pathway. Our studies also explained the requirement for the long-duration NIK-IKK1 signals in targeting this late acting p100 Nfkb2 feedback for RelA:p52 dimer generation. In contrast, IL-1 signal led to early induction of Nfkb2 mRNA expressions in the engineered cells, which inducibly express Nfkb2 transgene without the delay (Figure 5D). Pretreatment of these engineered cells with αLTβR for 8 hr and subsequent IL-1 stimulation that effectively converged the non-canonical signal to IL-1-induced Nfkb2 feedback, potentiated p52 production (Figure 5—figure supplement 1C) and prolonged IL-1-induced RelA response (Figure 5G and Figure 5—figure supplement 1D). Correlating with the early onset of Nfkb2 mRNA induction in response to IL-1 treatment, observed crosstalk effects were indeed obvious within 1 hr of IL-1 treatment in these engineered cells.

In sum, we elucidate a crosstalk mechanism that discriminates between TLR4 engagement and concomitant cell activation through TLR4 and LTβR (Figure 5H). Negative feedbacks by IκBα and p100/IκBδ coordinately terminate canonical TLR4 response. But, Nfkb2 functions pro-synergistically upon costimulation; in a positive feedback loop, non-canonical LTβR signal targets the newly synthesized p100, abundantly produced by TLR4, to potently generate p52 and RelA:p52 dimers in sustaining inflammatory RelA NF-κB responses. Importantly, RelA:p50 and RelA:p52 heterodimers were shown to share DNA binding and gene-expression specificities (Siggers et al., 2012; Zhao et al., 2014). Our experimental data also indicated that RelA:p52 dimer has comparable efficiency in inducing the expression of Nfkb2 mRNA as the RelA:p50 dimer (Appendix-1, Appendix figure 4C). Although, emergent crosstalk is expected to be controlled by several biochemical constrains, the transcriptional delay intrinsic to the Nfkb2 promoter appears to be critical for the duration code and thereby the stimulus specificity.

Nfkb2 integrates lymphotoxin signal within intestinal niche to reinforce epithelial NF-κB responses to C. rodentium

In addition to its role in lymph node development during embryogenesis, recent studies have illustrated a requirement for LTβR in innate immune responses in adult mice. Disruption of LTβR signal using LTβR-Ig fusion protein was shown to compromise innate immune responses upon subsequent infection with C. rodentium, a natural mouse enteric pathogen that led to mortality (Spahn et al., 2004; Wang et al., 2010). IEC-specific deletion of LTβR similarly obliterated bacterial clearance (Wang et al., 2010). The engagement of ligand-expressing RORγt+ innate lymphoid cell is thought to provide the critical lymphotoxin signal in colon during the course of bacterial infection (Upadhyay and Fu, 2013). A requirement of epithelial RelA activity in the chemokine gene expressions has been documented earlier (Alcamo et al., 2001). Given our identification of a costimulatory function of LTβR in inflammatory RelA activation, we asked if signal integration via the NF-κB system could explain the epithelial requirement of LTβR in innate immune responses in vivo.

First, a functional non-canonical pathway downstream of LTβR (Figure 6—figure supplement 1A) augmented the RelA activity induced by pathogen sensing TLR4 in otherwise hypo-responsive MSIE colon epithelial cell-line (Figure 6A). Next, we biochemically analyzed NF-κB activation in IECs derived from WT mice intraperitoneally injected with antagonistic LTβR-Ig or a control-Ig 1 day prior to oral infection with C. rodentium. Upon colonization, Citrobacter initially triggered epithelial accumulation of p100 that was fully processed into p52 by day5 (Figure 6B) generating RelA:p52 dimer (Figure 6C) in control-Ig, but not LTβR-Ig, treated mice. Bacterial infection elicited RelA DNA binding activity in IECs that gradually accumulated in the nucleus (Figure 6D,E) with substantial contribution from RelA:p52 dimer along with RelA:p50 dimer at day5 post-infection (Figure 6—figure supplement 1B). Our supershift analyses further confirmed complete absence of RelB containing NF-κB DNA binding activity in IECs derived from infected mice (Figure 6E). Perturbing LTβR signal attenuated RelA NF-κB activation with more obvious defects at day5 (Figure 6D). Likewise, pathogen-responsive RelA activation in IECs derived from Nfkb2−/− mice was severely weakened at day5 (Figure 6F) that led to significantly reduced expressions of the RelA target chemokines encoding KC and MIP-2α as compared to WT mice (Figure 6G). Indeed, infected Nfkb2−/− mice exhibited diminished neutrophil recruitment in the lamina propria, as revealed by anti-myeloperoxidase immunostaining of the colon sections (Figure 6H). Sustained epithelial RelA activity that relies on LTβR mediated processing of pathogen-induced p100 into p52, therefore, mirrored our MEF-based analyses depicting crosstalk between canonical and non-canonical signaling. Collectively, our results connected the previously reported epithelial requirement of LTβR (Wang et al., 2010) and NIK (Shui et al., 2012) in innate immune response to the NF-κB system in reinforcing RelA activity through Nfkb2 mediated crosstalk control. Subdued epithelial NF-κB activation, and not hyper-induction, in IECs from infected Nfkb2−/− mice also suggested that a dominant precursor function of p100 supplying RelA:p52 dimer prolongs RelA response within the intestinal niche.

Figure 6 with 1 supplement see all
Nfkb2 dependent LTβR crosstalk prolongs RelA NF-κB response in the colon of Citrobacter rodentium-infected mice.

(A) EMSA data, representing two independent experiments, revealing LPS-induced total RelA NF-κB activities induced in MSIE colon epithelial cell line at 12 hr in the absence or presence of 1 μg/ml of αLTβR. (B), (C), and (D) WT mice were injected with control-Ig or LTβR-Ig (n = 2) 1 day prior to infection with C. rodentium. IECs were isolated at day3 and day5 post-infection and analyzed for p52 and p100 levels by immunoblotting (B), RelA:p52 complex formation by immunoprecipitation-based assay (C) or NF-κB DNA binding activities in EMSA (D). OCT1 DNA binding activity served as loading control. (E) Supershift analyses revealing that exclusively RelA/NF-κB dimer are activated in IECs derived from mice infected with C. rodentium. (F) NF-κB activities induced in IECs derived from infected WT and Nfkb2−/− mice (n = 2) were similarly measured. (G) Epithelial expressions of KC and MIP-2a mRNA derived from WT and Nfkb2−/− mice (n = 5) at day5 post-infection. Data are expressed as mean of 3 quantified biological replicates ± SEM. The statistical significance was determined using two-tailed Student's t-test. (H) Representative data showing antimyeloperoxidase staining of neutrophils in colons of WT and Nfkb2−/− mice at day4 post-infection. Colon sections from three animals per set and five fields/section were used for quantification and presented as mean ± SEM. The panels with 40× magnification have been presented using scale bars that represent 200 μm.

https://doi.org/10.7554/eLife.05648.016

Stromal expression of Nfkb2 is required for limiting C. rodentium infection

While WT mice efficiently eliminated infections, increased fecal excretion of bacteria at day10 post-infection in Nfkb2−/− mice (Figure 7A) indicated an inadequacy in limiting local infection, thereby, correlating with the observed defects in the early innate inflammatory response in this knockout (Figure 6). Histological analysis of the shrunken colons (Figure 7—figure supplement 1A,B) derived from the infected Nfkb2−/− mice further revealed exacerbated damage with signatures of submucosal leukocyte infiltration (Figure 7B). Breach in the intestinal barrier was accompanied by systemic bacterial dissemination with increased count in blood (left panel, Figure 7C) and liver (right, Figure 7C). Finally, bacterial colitis induced in Nfkb2−/− mice resulted in significant body weight loss (Figure 7D) and onset of mortality as early as day10 post-infection (Figure 7E). Next, we performed reciprocal bone marrow transfer experiments between WT and Nfkb2−/− mice to ensure that the observed sensitivity was not due to previously reported hematopoietic defects in Nfkb2−/− mice (Caamaño et al., 1998). WT bone marrow cells in Nfkb2−/− recipients (Figure 7—figure supplement 1C) were unable to prevent the infection-related colon pathology (Figure 7F), reductions in the body weight (Figure 7G) and morality (Figure 7H). In contrast, WT recipients receiving either WT or Nfkb2−/− bone marrow resolved infections with comparable efficiencies (Figure 7F–H).

Figure 7 with 1 supplement see all
A protective role of pro-synergistic Nfkb2 in the non-hematopoietic compartment to Citrobacter infection.

(A) and (C) Bacterial titers in the fecal homogenate (A), blood (C, left panel) or spleen and liver homogenate (C, right) derived from WT and Nfkb2−/− mice (n = 4) at day10 post-infection. Data are expressed as mean ± SEM. The statistical significance was determined using two-tailed Student's t-test. (B) and (F) H&E staining of the representative colon sections derived from WT and Nfkb2−/− mice at day10 (n = 4, five fields/section) (B) or indicated bone marrow chimeras at day7 (n = 2, five fields/section) (F) after inoculation. The panel shows 20× magnification with scale bars representing 200 μm. (D) and (G) Average change in the body weight of WT and Nfkb2−/− mice (n = 7) (D) or indicated bone marrow chimeras (n = 4) (G) upon infection. Data are expressed as mean ± SEM. The statistical significance was determined using two-tailed Student's t-test. (E) and (H) Survival rates of WT and Nfkb2−/− mice (n = 7) (E) or indicated bone marrow chimeras (n = 4) (H) infected with C. rodentium. The statistical significance was determined using log rank (Mantel–Cox) test. (I) A model depicting the proposed regulatory role of crosstalk in sustaining colonic inflammatory immune responses.

https://doi.org/10.7554/eLife.05648.018

In addition to modulating RelA activity through crosstalk, Nfkb2 also mediates RelB:p52 activation in response to singular non-canonical stimuli. Heightened RelB:p52 activation in B-cells was shown to strengthen host defense in Otu7b−/− mice (Hu et al., 2013). Our inability to rescue the infection-related mortality in Nfkb2−/− mice using WT hematopoietic cells indicated that the protective function of Nfkb2 lies within stromal cells. Despite the presence of mesenteric lymph nodes (Lo et al., 2006), additional stromal defects in Peyer's patches in Nfkb2−/− mice (Yilmaz et al., 2003) could impair mucosal IgA responses. Although IgM levels were comparable, very low IgA levels at day7, prior to the onset of mortality in Nfkb2−/− recipients, precluded bona fide comparisons in the radiation chimeras. Interestingly, adult mice, similar to those utilized in our study, lacking IgA or its transporter pIgR efficiently resolved Citrobacter infections (Maaser et al., 2004). μMT mice depleted of peripheral B-cells presented few mucosal changes in the first 2 weeks of infection and were completely free of mortality (Simmons et al., 2003). Finally, Rag1−/− mice succumbed to Citrobacter only late during infections in the 4th week (Wang et al., 2010). Though, our study does not rule out crosstalk independent engagement of the Nfkb2 pathway in activating RelB:p52 dimer in the hematopoietic compartment at a later stage of infection, epithelial RelA NF-κB activation defects coupled to aggravated early colon pathology and early onset of mortality in Nfkb2−/− mice suggested that the stromal requirement of Nfkb2, at least in part, lies within the intestinal epithelial cells in the initial events controlling early innate immunity and involves crosstalk regulation of RelA NF-κB activity (Figure 7I, also discussed later).

Discussion

The NF-κB system transduces signals from a variety of cell-activating stimuli. Our study suggested that such pleiotropic system enables tuning of cellular response to an instructive signal by microenvironment-derived additional costimulatory signals. However, a duration code selectively integrates costimulatory LTβR signal with the TLR4 pathway, insulating transient cytokine signaling, secondary to microbial infections, from crosstalk amplifications. Recent studies have identified important positive feedback regulations underlying dose threshold control of NF-κB response in B-cells (Shinohara et al., 2014) or in myeloid cells (Sung et al., 2014). Our crosstalk study illuminated a role of a positive feedback loop in sustaining NF-κB response; where non-canonical LTβR signal prolonged canonical TLR4 response by targeting RelA induced p100 for the generation of RelA:p52 NF-κB dimer. Indeed, a dominant precursor function of p100 in producing RelA:p52 led to ablated crosstalk in Nfkb2−/− cells. As such, non-canonical signal generates RelB:p52 by removing the C-terminal domain of p100 present in the preexisting RelB:p100 dimeric complex (Sun, 2012) or liberates RelA:p50 dimer from the p100/IκBδ-inhibited complexes (Basak et al., 2007). Signal generation of the RelA:p52 dimer requires both canonical induction of p100/Nfkb2 expressions and concomitant processing of the newly synthesized p100 into p52 by non-canonical signal. Previous report demonstrating that p100 could be efficiently processed cotranslationally (Mordmüller et al., 2003) explained the requirement of canonical signals in amply synthesizing nascent p100 as a substrate for the abundant production of p52 subunit during crosstalk. More so, mature p52 could readily dimerize with RelA, despite a preference of full-length p100 for RelB binding (Fusco et al., 2008). These observations along with our current study, thereby, elaborated a requirement of convergence of canonical and non-canonical signals in synergistically generating and activating RelA:p52 dimer. Nevertheless, consistent to the observed gene-effect of crosstalk in our study, a significant overlap between RelA:p50 and RelA:p52 dimers in DNA binding (Siggers et al., 2012) and pro-inflammatory gene expressions (Hoffmann et al., 2003) was reported earlier. Of note, our results rely on bulk measurements of signaling intermediates and deterministic modeling approaches. Given studies documenting cell-to-cell variations in signal-induced NF-κB responses (Lee et al., 2009), it would be interesting to examine the potential implication of signal integration at the single cell level in determining cellular heterogeneity.

TLR4 activation of epithelial RelA was implicated in the chemokine gene expressions and neutrophil recruitment upon bacterial infections (Khan et al., 2006). Yet, epithelial LTβR (Wang et al., 2010) was also important for effective innate immune responses to Citrobacter. In our proposed model (Figure 7I), we could clarify that LTβR provides a critical costimulatory signal through Nfkb2 to sustain RelA NF-κB response to pathogens in otherwise hyporesponsive colonic epithelial cells. Such signal integration ameliorated innate immune functions by enhancing pro-inflammatory gene expressions. Interestingly, p100−/− mice, which lacked the expression of p100, but aberrantly produced p52, revealed hyperplasia of gastric epithelial cells and elevated expressions of RelA target genes (Ishikawa et al., 1997). In Nlrp12−/− mice, robust p52 generation in stromal cells through the non-canonical pathway led to colon cancer associated inflammation (Allen et al., 2012), a hallmark for aberrant RelA activity. These studies indeed support a possible role of Nfkb2 in mucosal epithelial cells in strengthening RelA activity. LTβR engagement in the dendritic cells within colonic patches was shown to trigger IL-22 production by innate lymphoid cells involving cell–cell communications to potentiate gut immunity (Tumanov et al., 2011). Although, a defect in the colonic patches in Nfkb2−/− mice could impair IL-22 mediated protective responses, our cell-intrinsic crosstalk model explained that the reported epithelial requirement of LTβR (Wang et al., 2010) is in sustaining RelA NF-κB response during bacterial infection. In future, tissue-specific knockouts may help to further distinguish between innate immune functions of Nfkb2 in different cell types.

The underlying mechanism and biological functions of RelB:p52 dimer activated by non-canonical signal is well established. From the perspective of signaling crosstalk, our study offers a significant revision of our understanding of non-canonical signaling in amplifying canonical RelA responses. It extends the intriguing possibility that cell-differentiating cues present in the tissue microenvironment may play a more direct role, separate from merely determining the differentiation states of the resident cells, in calibrating innate immune responses by engaging into cell-autonomous signaling crosstalks. Future studies ought to further examine the signal integration via Nfkb2 in potentiating immune responses against other microbial pathogens. More so, the potential involvement of the deregulated crosstalk control in the pathophysiology of inflammatory disorders, particularly those involving gut, remains to be addressed.

Materials and methods

Mice, cells and recombinant DNA

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Wild-type or gene-deficient C57BL/6 mice were housed at NII small animal facility and used in accordance with the IAEC guidelines. Primary MEFs were generated from E12.5–14.5 embryos. Late passage Trif-deficient and NIK-deficient MEFs have been described (Basak et al., 2007). MSIE cell line was a gift from R. Whitehead, Ludwig Cancer Research. Mouse Nfkb2 was stably expressed from a promoter containing five tandem kappaB sites from HRS.puro or from a constitutive promoter from pBabe.puro retroviral constructs.

Biochemical analyses

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Cells were stimulated using 0.3 μg/ml αLTβR (a gift from J Browning and A Papandile, Biogen, Cambridge, MA, USA), 100 ng/ml recombinant LTα1β2 (Sigma, St. Louis, MO, USA), 1 ng/ml TNF (Roche, BASEL, Switzerland), 1 ng/ml IL-1β (Biosource, Carlsbad, CA, USA), or 1 μg/ml LPS (Enzo, NY, USA), either individually or in combination. EMSA, immunoblot analyses, and IKK assay have been described earlier (Basak et al., 2007). Recombinant GST-IκBα (1-54aa) used in IKK assay was from BioBharati Life Sciences, Kolkata, India. NIK was immunoprecipitated (Cell Signaling Technology, Danvers, MA, USA) from cytoplasmic extracts and immunopellets were examined for kinase activity using GST-IκBδ as substrate (GST-p100406–899, BioBharati Life Sciences, Kolkata, India). The gel images were acquired using PhosphorImager (GE, Amersham, UK) and quantified in ImageQuant. Immunoblotting of immunoprecipitates was done using TrueBlot (eBioscience, San Diego, CA, USA).

Gene expression studies

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Total RNA was isolated using RNeasy Kit (Qiagen, Venlo, Netherlands). For microarray analysis, labeling, hybridization of RNA samples to the Illumina MouseRef-8 v2.0 Expression BeadChip, data processing and quantile normalization was performed by Sandor Pvt. Ltd (Hyderabad, India). We have considered genes that are induced at least 1.3 fold by LPS at 24 hr in representative data sets and has a detection p-value < 0.05 for LPS, αLTβR and co-treatment regimes. Next, LPS response genes in WT MEFs were ranked based on the merit of their normalized crosstalk scores, which is defined below and also described earlier (Zhu et al., 2006).

Crosstalk score = [(Δco-treatment − (ΔLPS + ΔαLTβR))/0h_int], where 0h_int indicates the signal intensity of a given gene in untreated cells and Δtreatment signifies the differences in signal intensities between treated and untreated cells,

Normalized crosstalk score = Crosstalk Score * [{(ΔL + ΔB))/0h_int/|(ΔL + ΔB))/0h_int|],

As implied, positive crosstalk scores signify hyperactivation, whereas negative crosstalk scores imply diminished gene expressions in the co-treatment regime as compared to cell treatment with the individual stimuli. The ordered gene set was examined in GSEA v2.0.12 (Broad Institute at MIT) (Subramanian et al., 2005). The MIAME version of the microarray data set discussed in this publication are available on NCBI Gene Expression Omnibus (accession number GSE62301). For quantitative RT-PCR, total RNA was reverse transcribed with Transcriptor cDNA kit and amplified using Sybr Green PCR mix (Roche, Mannheim, Germany) in ABI7500 cycler. The relative gene expressions were quantified using ΔΔCT method upon normalizing to β-actin mRNA level. Absolute quantification was done using plasmid DNA constructs encoding respective genes as standards and normalized to express as gene/actin mRNA level.

Murine infection model

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Sex matched, 8 to 10 week old mice, fasting for 8 hr, were orally gavaged with 1.2 × 1010 cfu of C. rodentium strain DBS100 (ATCC 51459). In certain instances, mice were intraperitoneally injected with 200 μg of murine LTβR-IgG1 fusion protein or MOPC21 isotype control (Biogen Idec) 1 day prior to infection, as described (Wang et al., 2010). IECs, isolated following published procedure (Greten et al., 2004), were utilized for biochemical analyses. For histology, dissected colons were fixed in 10% neutral buffered formalin. Paraffin-embedded tissue sections were stained with anti-myeloperoxidase antibody (Pierce, Waltham, Massachusetts, USA) for neutrophil recruitment or with Hematoxylin and Eosin (H&E) for tissue pathology evaluation. Fecal samples were weighed, homogenized, and serially diluted homogenates were plated on MacConkey agar (HiMedia, Mumbai, India) to score for C. rodentium. Similarly, spleens and livers were aseptically removed and assessed for bacterial load. For bone marrow chimera experiment, recipient WT or Nfkb2−/− mice were lethally irradiated and marrow cells from the indicated donor mice were transferred. After 6–8 weeks, mice were infected.

Computational modeling

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The NF-κB Systems Model v1.0 was simulated in Matlab (v. 2012b, Mathworks, Natick, MA, USA) using the ode15 s (Basak et al., 2007). A detailed description of the model has been provided in the Appendix-1. To estimate crosstalk sensitivity, each parameter values were individually increased and decreased by 10%, euclidean distances were used to determine the resultant changes in the crosstalk indexes as compared to the unperturbed system, averaged for a given parameter and normalized to nominal crosstalk index.

Statistical analysis

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Data are expressed as mean of 3–5 quantified biological replicates ± SEM. Statistical significance was calculated by two-tailed Student's t-test. For survival curves, log rank (Mantel–Cox) test was conducted.

Additional files

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This article additionally contains (i) 10 figure supplements associated with the main text, (ii) three Supplementary tables (Supplementary files 1–3) and five Appendix figures associated with the an Appendix file (Appendix-1), which provides a detailed description of the mathematical model, as well as a file describing Matlab source codes.

Appendix-1

Appendix-1 includes, a description of the mathematical model, supporting references, and associated Appendix figures 1–5, as well as Supplementary files 1–3.

(i) Description of the mathematical model

The NF-κB Systems Model Version 1.0 builds upon a previously constructed computational model (Basak et al., 2007). In the preceding version, canonical IKK2 signals degraded classical IκBs, while non-canonical NIK-IKK1 targeted IκBδ to liberate the RelA:p50 NF-κB dimers, represented as a singular species, into the nucleus. Although, biochemical analyses defined IκBδ as an oligomer of p100, the previous versions represented IκBδ as a composite species that is produced directly from an NF-κB responsive gene. In the current version, we have elaborated biochemical reactions associated with p100 in the model. We have explicitly described p100 as a species that is generated from the transcripts encoded by NF-κB responsive Nfkb2. Monomer p100 was subjected to (i) constitutive degradation or (ii) oligomerization to form IκBδ or (iii) NIK-IKK1-dependent processing that generated RelA:p52 dimer as a composite species. In contrast, RelA:p50 dimer was constitutively produced as a singular species. As described in the preceding single dimer model, the activation dynamics of these two RelA NF-κB dimers was coordinately regulated by signal responsive degradation and resynthesis of IκBα, IκBβ, IκBε, and IκBδ during inflammatory signaling. The newly built model consists of 34 species, 138 reactions with 105 parameters; those describe the synthesis and degradation of NF-κB and IκBs, association and dissociation of IκB:NF-κB complexes, nuclear import and export of NF-κB and IκB species as well as processing of p100 (see a detailed wiring in Appendix figure 1). Constrains generated based on our quantitative biochemical analyses (Appendix figures 2–4) was used along with published literature and fitting procedures for parameterizing the model (see the description of the parameters in Supplementary files 1, 2). The following assumptions were implicit in the wiring.

  1. RelA forms two distinct dimers RelA:p50 and RelA:p52;

  2. RelA:p50 dimers are regulated at the level of nuclear translocation;

  3. NIK-IKK1 induced processing of p100 generates p52 that forms RelA:p52, which is then regulated by IκBs akin to RelA:p50 dimer.

Appendix figure 1
A detailed wiring diagram.

A wiring diagram depicting the biochemical connectivities between the molecular species included in the NF-κB Systems Model v1.0 to capture signal responsive activation of RelA:p50 (Ap50) and RelA:p52 (Ap52) dimers. The number adjacent to each reaction arrow is the corresponding parameter number, as described in the associated Supplementary files 1, 2. For the ease of presentation, three classical IκBs, IκBα, -β and -ε, were presented as IκBs on certain occasions. Similarly, RelA:p50 and RelA:p52 dimers were collectively depicted as A:NFκB.

https://doi.org/10.7554/eLife.05648.023
Appendix figure 2
Nfkb2 mRNA analyses in unstimulated cells and during signaling.

(A) The NF-kB independent constitutive transcription of Nfkbia mRNA, which encodes IκBα, and Nfkb2 mRNA in the late passage MEFs was measured using quantitative RT-PCR. The mRNA levels of Nfkbia and Nfkb2 were reduced to 10% and 25% in NF-κB deficient (Rela−/−Rel−/−Relb−/−) cells as compared to WT cells. Transcript level of b-actin was used for normalization. (B) Quantitative RT-PCR to measure half-life of Nfkb2 mRNA. WT cells were treated with 5 mg/ml of transcription inhibitor actinomycin-D in a time-course, harvested, absolute mRNA levels of Nfkb2 and b-actin were determined and presented as Nfkb2 mRNA copy numbers per thousand copies of b-actin mRNA. The curve fitting was done assuming first-order decay of Nfkb2 mRNA to arrive onto a half-life of ∼10.5 hr. (C) LTβR induced expression of Nfkbia and Nfkb2 mRNAs was analyzed in a time course. Due to weak NF-κB activation, LTβR activation using 0.3 mg/ml agonist antibody only subtly altered the levels of these two NF-κB target genes in MEFs.

https://doi.org/10.7554/eLife.05648.024
Appendix figure 3
Analyzing p52 and p100 protein encoded by Nfkb2 in unstimulated cells and during signaling.

(A) Constitutive degradation of NF-κB bound (left panel) or free (right) IκBα (top panel) and p100/IκBδ (middle) were evaluated by immunoblotting respective extracts derived from WT (left) or NF-κB deficient (right) cells treated with 10 mg/ml of protein synthesis inhibitor cycloheximide. As compared to the stable NF-κB bound form, free IκBα degraded rapidly with a half-life ∼5 min. In contrast, both NF-κB bound p100/IκBδ present in WT cells (top) and free p100/IκBδ in NF-κB-deficient cells (bottom) were stable with a half-life > 12 hr. Actin (bottom panel) was used as loading control. The data represents two biological replicates. (B) Immunoblot revealing TNFR-induced IKK2 mediated degradation of NF-κB bound IκBα in WT (left panel, top) or free IκBα in NF-κB-deficient cells (right, top). Based on these data, IKK2-mediated degradation rates of the free and NF-κB bound classical IκBs were assigned comparable values in the model. Both in its bound (left, middle) or free (right, middle) form, p100/IκBδ were insensitive to IKK2 signals. (C) Immunoblot showing LTβR-induced NIK-IKK1 mediated degradation of NF-κB bound (top panel) and free (bottom) p100/IκBδ in WT and NF-κB deficient cells, respectively. Importantly, signal induced p52 accumulation was evident at 24 hr in WT, but not in NF-κB deficient, cells suggesting that newly generated p52 requires other NF-κB monomers for mutual stabilization. (D) Immunoblot of RelA co-immunoprecipitates revealing liberation of RelA from inhibitory p100/IκBδ (top panel) and generation of RelA:p52 complexes (middle) during LTβR signaling. Nfkb2−/− cell extracts were used as control (bottom).

https://doi.org/10.7554/eLife.05648.025
Appendix figure 4
Comparing RelA:p50 and RelA:p52 NF-κB dimers.

(A) EMSA revealing TNF activation of RelA:p50 (left panel) or RelA:p52 (right) dimers in Relb−/−Nfkb2−/− or Relb−/−Nfkb1−/− cells, respectively. Quantification of the band intensities (bottom panel) revealed that basal and TNF induced RelA:p52 activities in Relb−/−Nfkb1−/− are ∼10% of the corresponding RelA:p50 activities in Relb−/−Nfkb2−/−. These values were used as constrains to describe constitutive RelA:p52 dimer generation in WT cells in the model. Both RelA:p50 and RelA:p52 dimers were induced within 30 min of TNF treatment indicating that NEMO-IKK mediated degradation rates of IκBs bound these RelA heterodimers are comparable. Of note, TNF induced fold changes in the activity of the respective RelA dimers were comparable in Relb−/−Nfkb2−/− and Relb−/−Nfkb1−/− cells. The result represents three biological replicates. (B) NF-κB responsive expression of Nfkbia mRNA, encoding IκBα, induced by either RelA:p50 or RelA:p52 dimers in Relb−/−Nfkb2−/− or Relb−/−Nfkb1−/− cells upon TNF treatment, respectively. Comparable mRNA fold induction along with our EMSA data suggested similar DNA binding and transcriptional proficiency of RelA:p50 and RelA:p52. Data are expressed as mean of 3 quantified biological replicates ± SEM. (C) Gene-expression analyses revealing comparable induction of Nfkb2 mRNA upon LPS treatment in WT MEFs, those mostly activate RelA:p50 dimer and Relb−/−Nfkb1−/− MEFs, which exclusively activates RelA:p52 dimer. Our results confirm that RelA:p52 dimer is capable of inducing the expressions of Nfkb2 mRNA as effectively as RelA:p50 dimers. (D) Biochemical analyses indicating weaker IκBα binding to the RelA:p52 dimer. Briefly, nuclear extracts derived from Relb−/−Nfkb2−/− or Relb−/−Nfkb1−/− cells treated with TNF was used as a source of free RelA:p50 or RelA:p52, respectively, and normalized to obtain comparable DNA binding activities. Nuclear RelA dimers were incubated with a gradient of recombinant IκBα for 30 min to facilitate the formation of IκB:NF-κB complex, which does not bind DNA in EMSA. The abundance of unbound NF-κB dimers was then scored using EMSA. Incubation with 25 nM IκBα was sufficient to completely inhibit RelA:p50 DNA binding. While, more than 200 nM of IκBα was required to inhibit RelA:p52 and RelA:RelA DNA binding, owing to weaker IκBα binding by these dimers. The data represents three experimental replicates.

https://doi.org/10.7554/eLife.05648.026

Based on the existing literature and our own analyses (Appendix figures 2–4), we have assumed that RelA:p50 and RelA:p52 dimers exhibit mostly similar biochemical properties, except for IκBα binding (note Appendix figure 4D). By generating RelA:p52 dimer from p100 upon processing, we also captured the mutual stabilization of NF-κB monomers, where, de novo synthesized p52 rescued RelA monomer from degradation to generate RelA:p52 dimer in response to NIK-IKK1 signal. We have summarized the new reactions incorporated into the model version published in Basak et al. (2007) to arrive onto the Systems Model v1.0 and provided justification for the parameter values in Supplementary file 3.

Simulations were performed in MatLab version 2012b (Mathworks) using the built-in ode15 s solver at default settings. We have introduced a time delay of 65 and 37 min in the NF-κB-induced transcription of Nfkb2 and those encoding IκBε, respectively, to recapitulate experimental observations. The transcriptional delay function has been executed in the Systems Model v1.0 as follows: for an explicit delay of t1 min, a given NF-κB dependent transcription reaction assumes a NF-κB concentration at basal level up to t1 min. Subsequently, nuclear concentration of NF-κB has been used as such from (t1 + Δt1) min and onwards for computing transcription rates. As previously described, an equilibrium phase was introduced prior to the onset of stimulation phase (Basak et al., 2007). Matlab model codes have been provided as an additional source code file with this manuscript. As such, the NF-κB Systems Model version 1.0 was able to reproduce temporal profiles of nuclear RelA:p50 and RelA:p52 dimer activities (Appendix figure 5), as measured experimentally in response to TNFR as well as LTβR stimulation (Figure 1—figure supplement 1).

Appendix figure 5
Simulating RelA:p50 and RelA:p52 activation in response to canonical and non-canonical signals.

Computational simulations of nuclear RelA:p50 (grey) and RelA:p52 (black) induction by TNFR-induced IKK2 (top) or LTβR-induced NIK-IKK1 signals (bottom). Our computational data effectively recapitulate relative abundance of these two RelA dimers during canonical and non-canonical signaling, as experimentally observed and demonstrated in Figure 1—figure supplement 1.

https://doi.org/10.7554/eLife.05648.027

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Decision letter

  1. Arup K Chakraborty
    Reviewing Editor; Massachusetts Institute of Technology, United States

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The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

Summary:

In this manuscript, the authors describe a potentially novel synergistic effect of LTβR signaling on signaling from LPS-TLR4 that contributes to enhanced NF-kB activity at later time points after stimulation (∼24 hours). As LPS signaling to NF-kB proceeds through the canonical pathway and leads to p50:RelA and p50:c-Rel complexes, while LTβR signaling proceeds through NIK-IKK1 to promote processing of p100 to p52, which then associates with RelB (or is preassociated with it) to form activating p52:RelB dimers, the demonstration that LTβR-induced p52:RelA enhances the canonical pathway, is interesting. As the activation phase of the canonical NF-kB activity is determined by negative feedback through newly synthesized IκBα and IκBδ (as well as molecules such as A20), the ability of LTβR signaling to somehow overcome the observed reduction of canonical NF-kB activity, is also an interesting observation. Some of the data presented are indeed exciting, as is the progression of analysis from computational models to physiology.

The shortcomings of this paper pertain to important deficiencies in quantitation and statistical analysis of results, the lack of some key experimental controls, and there are some questions about the model. The following specific points need to be addressed in order to be considered further for publication.

Specific points:

1) Could the authors base their model more closely on an established model, for example their own Basak et al. 2007, or the recent Yilmaz et al. 2014 and then identify very explicitly the reaction and parameters that are new (in separate tables) and justify those values within experimentally determined ranges?

2) Panel 1E is unclear. It seems to indicate that both early and late peaks of NEMO-IKK2 engage in crosstalk, contrary to the claim. This needs clarification.

3) Whereas 1B distinguishes between RelA:p50 and RelA:p52 the later figures do not seem to distinguish them, though this seems to be an important distinction later. The model should be used to make that distinction.

4) Panels within this figure (and others) should be arranged in order or presentation.

5) In Figure 2, experimental data is provided to test the computational predictions. Quantitated line or bar graphs should be shown. As effects are not obvious, statistical significance should be established.

Also, it is not clear why authors use the Ifnr1-/- MEFs. They previously stated that prolonged NFκB is dues to autocrine TNF. If they are interested in testing this they should use TNF or TNFR, and then the interferon knockouts may be a useful contrast.

Is it possible to show quantitation of late RelA induction in the presence of both signals from biological replicates? This should be included in Figure 2.

The error bars between 3 biological replicates look surprisingly small in Figure 2–figure supplement 1. Why is this?

6) Figure 3 shows transcriptomic analysis, but the analysis is not transparent. The presentation focuses on a crosstalk score, and says that NF-κB target genes are over-represented. How about the genes that are not amplified by the crosstalk? The authors only show LPS here, and it would be more complete in line with the biochemistry if the authors also showed IL1. They then show that this amplification is maintained in Relb-/- but it is not clear that these are the same genes. Also the negative result with Relb-/- MEFs demands a positive control where there is a change, such as Nfkb2-/-. Later the authors introduce the Nfkb2-/- MEFs, so maybe the Relb-/- data could be moved to that figure?

7) In Figure 4, the authors present a one-dimensional sensitivity analysis, but the reliability of this prediction depends on the initial parameterization. Too little information is provided on how the parameterization was achieved and how the error/confidence intervals of experimental data translates to alternative parameter sets, which in turn may affect the results of the sensitivity analysis. Here, given the known regulation by p100 described in the Introduction the authors need no further justification for examining p100 in subsequent panels. Panel B should be quantitated and the three conditions should be run on the same gel, and statistically evaluated. The quantitative analysis could be restricted to the late time point(s). Panels C and D are important, and a quantitation of p50 vs p52 association with RelA should be provided. In B, could the authors add a p50 immunoblot? In C, using both p50 and p52 antibodies should result in ablation of the shift but this control is missing. Basically, we would expect an experimental counterpart for the computational panel F. Panel G is quantitated but a statistical confidence evaluation should be provided. Panel H directly relates to Figure 3 and presumably these experiments were done in parallel. Have the authors also tried to use microarrays and undertaken the analysis shown in Figure 3?

8) Figure 5 is not easy to understand. It would be helpful if the following issues were addressed:

A) Why does panel B show the response to TNF, but everywhere else LPS and IL1 are compared?

B) Why is the Nfkb2 mRNA fold induction different in panels A and B?

It is not clear what delay-null cells are. If they are simply the transgenic Nfkb2 described in B, it would be more straightforward to use the same terminology. Panels E/F contain critically important information. They need to examine three conditions: LPS alone, LPS and LTβR, and LTβR alone. Also, there are really 4 cell types that are of interest: WT, Nfkb2-/-. and the two transgenes. Distinction should be drawn between RelAp50 and RelAp52. Panel G mentions a positive feedback loop, but the figure does not show any data related to p52. And strictly speaking no data was presented that RelAp52 produces more RelAp52. Finally, synergistic effects shown in Figure 5F occur at very early time points, and are therefore difficult to relate to duration synergy that happens at >8h.

The top diagram seems to be one stimulation condition resulting in the green curve, and the bottom diagram related to the pink curve. Yet the top diagram also contains pink arrows. What makes things difficult to follow is that the canonical and non-canonical arrows come in from different directions, and it is unclear where the curved arrows are pointing.

9) Figure 6 shows exciting data which puts the previous fibroblast studies in physiological context.

The following control might strengthen results (if possible): distinguish between RelAp50 and RelAp52 for panel E, similar to the analysis in 4D, and quantitate the levels of each.

10) Figure 7 suggests that p100 KO's are more susceptible to Citronella infection, but that does not really prove that p52 complexes in LTβR induced synergy are responsible, and instead the p52 complexes might be acting on their own. Also as p100 KO's do not display any general sensitivity to infection, it is unclear whether the Citronella results are unique to these bacteria.

11) The observed effects in Figure 6G and Figure 7 could be 'simply' due to lack of non-canonical NF-κB activation in response to LTβR in the NFκB2-/- strain. The crux of the duration model is that extended RelA activity is RelB-independent. The mouse experiments neither address whether RelA function is involved in pathogen clearance nor do they show that the effects are RelB-independent. How might this question be addressed?

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled “Stimulus-selective crosstalk via the NF-κB signaling system reinforces innate immune response to alleviate gut infection” for further consideration at eLife. Your revised article has been favorably evaluated by Detlef Weigel (Senior editor), a Reviewing editor and one reviewer. The manuscript has been improved but there are some remaining issues pertinent to clarity of presentation that need to be addressed before acceptance, as outlined below:

1) By calling the model NF-κB system model v. 1 it is not clear that the model is based on (and justified by) previous models. The authors should pick a different name (or is there a need for a name given that a model is generally cited with the paper reference?). Similarly the Table should be more explicit. Many values are described as “adapted from [REF]”. If the values are the same, that should be stated. If they were modified then a justification should be given. For some values the authors state “assumed to be similar to [another]”. They should state if they are the same, or when different a justification should be provided. The diagram in Appendix figure 1 could be confusing with many different shapes, colors and shadings. Why not adopt the normal conventions for kinetic model diagrams and indicate the species in the diagram itself rather than the legend? It is hard to keep track of the colors, and indeed it seems that the pink/orange shading in the legend became an orange/red shading in the diagram.

2) In Figure 1E the authors are focused on points that are missing in the range of <2hrs. It is not evident from this figure what curves are missing, and so the figure is difficult to understand. They should find another way to make this point. Why not show the actual crosstalk score in a matrix of IKK2 and IKK1 profiles? That would avoid the inherent problems of applying a threshold (crosstalk proficient) which can also lead to misleading conclusions.

3) The quantitation added to Figure 2 seems surprising in some cases: for example in 2B LPS, the 3 and 5 h points are very different in the gel, but very similar in the graph with minimal error bars. There are several other points that do not transparently seem to square with the gel data. Because the graph is the result of 3 gels, it will not match precisely but the error bars should indicate the true standard deviation in the data. Some clarification of this point is required.

4) Figure 2D is still rather obscure. Why not show the genome-wide data in a more direct manner (e.g. heatmap) so that the quality of it can be assessed. Figure 4H requires a wt control. Does RelB play no role, a minor role?

https://doi.org/10.7554/eLife.05648.028

Author response

1) Could the authors base their model more closely on an established model, for example their own Basak et al. 2007, or the recent Yilmaz et al. 2014 and then identify very explicitly the reaction and parameters that are new (in separate tables) and justify those values within experimentally determined ranges?

The computational model presented in this manuscript builds on a previously published model (Basak et. al., Cell 2007) to add new details to the pathway involving RelA:p52 dimer with a parameterization supported by additional data (presented in Appendix-1, Appendix figure 2-4). We would like to point out that we have already provided a detailed description of the model parameters in the Supplementary file 1-2. As evident from these presentations, the majority of the parameter values are derived from the model versions previously published in Basak et al., 2007 (Cell. 2007 Jan 26;128(2):369-81) and to some extent, from Shih et al., 2009 (Proc Natl Acad Sci U S A. 2009 Jun16;106(24):9619-2) as well as Shih et al., 2012 (Nat Immunol. 2012 Dec;13(12):1162-70).

Following the suggestions by the reviewers, we have now explicitly identified the reactions and parameters that are new in a separate table (Supplementary file 3) and provided justification for each of the parameter values. As presented in Table 3, the parameter values describing the biochemical reactions involving newly described RelA:p52 species was assumed to be similar to that of RelA:p50 dimer. In case of a deviation, such as those involving composite species (NFκB1) or RelA:p52 association with IkBs, additional experimental evidence has been provided and also indicated in the table. We hope that our new table will adequately capture the rigor in our model building efforts. Of note, Yilmaz et al. (2014), in their study mostly focused on interdependent p50 and p52 generation and did not model p52 containing DNA binding complexes.

2) Panel 1E is unclear. It seems to indicate that both early and late peaks of NEMO-IKK2 engage in crosstalk, contrary to the claim. This needs clarification.

Consistent to our claim, our analyses indeed indicated that NEMO-IKK2 activities with less than 2h of duration are unlikely to engage into crosstalk. Of note, the library used in our experiment consisted of 46 IKK2 inputs with duration ≤ 2h. Yet, none of these short-duration IKK2 activities engaged into signaling crosstalk as reflected in Figure 1E (top panel). In Figure 1–figure supplement 2B, we have further verified our claim by using representative short-duration NEMO-IKK2 inputs. Although, the duration threshold for NEMO-IKK2 was somewhat less elaborate as compared to NIK-IKK1, which exhibited a duration threshold of ∼ 8h (bottom panel, Figure 1E). We suspect that this difference may have attributed to the undesired confusion undermining NEMO-IKK2 duration control. Notably, this rather narrow time scale in the NEMO-IKK2 duration threshold was effective in discriminating between IL-1R and TLR4 signaling with respect to crosstalk. In the revised text, we have further restructured the pertinent section to highlight the differences between NEMO-IKK2 and NIK-IKK1 activities in the quantum of the duration threshold (in the subsection headed “A duration code controlling crosstalk between canonical and non-canonical NF-κB signaling”).

3) Whereas 1B distinguishes between RelA:p50 and RelA:p52 the later figures do not seem to distinguish them, though this seems to be an important distinction later. The model should be used to make that distinction.

We thank the reviewers for pointing out the apparent logical gap in the figure presentation that may have generated distraction in Figure 1. Although RelA:p52 dimers accounted for only a minor RelA/NF-κB nuclear activity during canonical or non-canonical signaling, the distinction between RelA:p50 and RelA:p52 dimer was made for merely validating the faithfulness of the model in recapitulating experimental data. In the revised draft, instead we have presented the simulation data for total nuclear RelA/NF-κB activity in Figure 1B. We agree that such distinction is largely peripheral for confirming the effectiveness of the mathematical model. As also suggested by the reviewers, such distinction by mathematical model instead provided powerful argument in Figure 4 in revealing the plausible mechanism underlying crosstalk control. As discussed later (point [7]), we have now used the mathematical modeling data revealing enhanced level of nuclear RelA:p52 in the crosstalk settings (Figure 4F in the earlier version) in Figure 4B to suggest the involvement of Nfkb2-derived RelA:p52 in crosstalk. Accordingly, we have made alterations in the text in the Results section; those did not adversely impact the major conclusions, but offered more streamlined logical progress in the revised manuscript.

4) Panels within this figure (and others) should be arranged in order or presentation.

While apologizing for the unintended confusion, we have now arranged all the panels in order of presentation in the respective figures in the revised manuscript.

5) In Figure 2, experimental data is provided to test the computational predictions. Quantitated line or bar graphs should be shown. As effects are not obvious, statistical significance should be established.

Our experimental data demonstrated that late RelA NF-κB response to the co-treatment regime is augmented as compared to solitary LPS treatment. Although, the effect on amplitude may appear less dramatic, LTβR cotreatment led to chronically elevated RelA NF-κB activity between 8 -24h as compared to diminishing RelA NF-κB response in solitary TLR4 regime. We argue that the subtitle for the pertinent subsection “Stimulus specific crosstalk allows LTβR signal to prolong TLR4 induced RelA NF-κB response” aptly reflects this effect of LTβ R cotreatment in prolonging TLR4 induced RelA NF-κ B response. As recommended by the reviewers, we have now included the quantitated bar graphs along with statistical tests in Figure 2 in the revised manuscript to further strengthen our claim. These bar diagrams were part of the Figure 2–figure supplement 1 in the initial submission, and now they have been included in the main text. We have also removed the panel merely showing a longer exposure of the EMSA gel in Figure 2A in our revised manuscript to accommodate newer panels in the main figure.

Also, it is not clear why authors use the Ifnr1-/- MEFs. They previously stated that prolonged NFκB is dues to autocrine TNF. If they are interested in testing this they should use TNF or TNFR, and then the interferon knockouts may be a useful contrast.

Although, the contribution of Trif in prolonging IKK2 activity downstream of TLR4 is well established, whether the autocrine TNF is solely responsible for mediating the effects of Trif remains an active area of research. Indeed, recent studies have established a direct role of Trif in engaging NEMO-IKK2 complex for NF-κ B activation (reviewed in Kawai and Akira, Nature Immunol, 2010). In the revised text, we have now restructured the relevant sentence (in the subsection headed “A duration code controlling crosstalk between canonical and non-canonical NF-κB signaling”) to omit the section unnecessarily emphasizing on TNF autocrine. Given the lack of clarity on function of TNF autocrine, we choose to use Trif-/-, and not Tnfr1-/-, MEFs in our biochemical analyses those only transiently induced NEMO-IKK2 activity downstream of TLR4. However, we agree with the reviewers’ perspective that Ifnr1-/- does not provide for a powerful contrast with Trif-/-. As the reviewers have also indicated, we concur that Ifnr1-/- data appears to be superfluous for establishing the duration code and actually could be distracting. Therefore, we have removed the relevant figure panel (Figure 2F in the earlier version) and restructured the text in the revised manuscript (in the subsection headed “Stimulus specific crosstalk allows LTβR signal to prolong TLR4 induced RelA NF-κB response”).

Is it possible to show quantitation of late RelA induction in the presence of both signals from biological replicates? This should be included in Figure 2.

As discussed earlier, we have included the quantitated bar graphs along with statistical tests in Figure 2 in the revised manuscript.

The error bars between 3 biological replicates look surprisingly small in Figure 2–figure supplement 1. Why is this?

Here, we have examined RelA NF-κB response in the costimulation regime in relation to those induced by solitary TLR4 or LTβ R signals. Given the complex biological question, we have normalized the EMSA signals against the respective IL-1 induced (0.5h) or LPS induced (1h) peak values. As one could anticipate, differences in the normalized data intensities were less obvious, in contrast to the variations in the raw signal intensities normally observed (and we also noted) in different experiments. We have now placed the data quantification (previously presented in Figure 2–figure supplement 1) in Figure 2A and Figure 2B in main text, respectively, in the revised manuscript. In the respective figure legends, we have clearly mentioned the data normalization modalities.

6) Figure 3 shows transcriptomic analysis, but the analysis is not transparent. The presentation focuses on a crosstalk score, and says that NF-κB target genes are over-represented. How about the genes that are not amplified by the crosstalk? The authors only show LPS here, and it would be more complete in line with the biochemistry if the authors also showed IL1. They then show that this amplification is maintained in Relb-/- but it is not clear that these are the same genes. Also the negative result with Relb-/- MEFs demands a positive control where there is a change, such as Nfkb2-/-. Later the authors introduce the Nfkb2-/-MEFs, so maybe the Relb-/- data could be moved to that figure?

We deeply regret that our transcriptomic analysis did not appear adequately transparent to the reviewers, perhaps owing to the lapses in appropriately communicating the data. We would like to point out that the gene set enrichment analysis (GSEA) is a correlation study, which offered global confirmation for a role of NF-κB in the heightened expressions of LPS induced genes in the costimulation regime. We have provided a detailed description of the gene-expression analyses those include methodologies used for estimating crosstalk scores (included in the Materials and methods in the main text in the revised draft), crosstalk scores of the individual genes (Figure 3–source data 1 in the revised draft) as well as identity of the genes belonging to the NF- κB target gene set used for GSEA (Figure 3–source data 2 in the revised draft). As indicated by the reviewers, we have also noted that several genes were actually downregulated in the costimulation regime. But specific enrichment of NF-κB targets among the hyper-induced genes in GSEA conversely validated that these downregulated genes were less likely to be NF-κB targets. TLR4 signaling, however, activates multiple other transcription factors those include members of ATF, IRF as well as STAT family. At this point, it remains unclear to us if the observed downregulation of gene-expressions is attributed by any of these transcription factors. Owing to a lack of bona fide target gene sets validated through experimental analyses, we were unable to further test the possible engagement of other transcription factors in GSEA. Instead restricting the scope of our conclusion, we could narrowly infer that the augmented RelA NF-κB activity observed in the nucleus in the costimulation regime leads to heightened NF-κB dependent gene-expressions. We have also performed quantitative RT-PCR analyses confirming heightened expressions of several NF-κB target chemokine/cytokine genes in the costimulation regime.

Concurring to the suggestions by the reviewers, we have now performed additional quantitative RT-PCR analyses revealing that LTβR costimulation did not alter IL-1 induced expressions of known NF-κB target chemokine or cytokine genes. This additional data now has been incorporated in Figure 3B in the revised manuscript (in the subsection headed “Signal integration via the NF-κB system amplifies the late expressions of TLR4 induced pro-inflammatory genes”).

Agreeing to the reviewers’ suggestions, we have placed the quantitative RT-PCR data using Relb-/- MEFs in Figure 4H in the revised manuscript to contrast with the Nfkb2-/-data (in the subsection headed “Non-canonical signal transducer Nfkb2 supplements RelA:p52 dimer to sustain canonical RelA NF-κB responses”). Given our quantitative RT-PCR analyses revealed an abrogated crosstalk control for several pro-inflammatory, NF-κB target genes, we did not pursue global-scale gene analyses using Nfkb2-/- MEFs. Of note, GSEA for NF-κB targets is expected to be largely inconsequential in the absence of heightened expressions of genes in the costimulation regime. As indicated later, to maintain the consistency in the data presentation, we have also excluded the microarray data obtained using Relb-/- MEFs in the revised manuscript and accordingly limited the scope of our conclusion in the revised text.

Despite these changes, the major conclusion that “LTβR costimulation led to the hyperactivation of LPS-induced known RelA target chemokine/cytokine genes in a RelB independent, but Nfkb2 dependent manner” was preserved. Instead, the revised Figure 4H offered a powerful contrast between the requirement for Relb and Nfkb2 in NF-κB driven gene-expressions during crosstalk.

7) In Figure 4, the authors present a one-dimensional sensitivity analysis, but the reliability of this prediction depends on the initial parameterization. Too little information is provided on how the parameterization was achieved and how the error/confidence intervals of experimental data translates to alternative parameter sets, which in turn may affect the results of the sensitivity analysis. Here, given the known regulation by p100 described in the Introduction the authors need no further justification for examining p100 in subsequent panels.

In response to point [1], we have already provided a detailed description of the model parameters in the Supplementary file 1-2. As evident from the table, the majority of the parameter values are derived from the model versions previously published in Basak et al., 2007 (Cell. 2007 Jan 26;128(2):369-81) and to some extent, from Shih et al., 2009 (Proc Natl Acad Sci U S A. 2009 Jun16;106(24):9619-2) as well as Shih et al., 2012 (Nat Immunol. 2012 Dec;13(12):1162- 70). More so, we have now explicitly identified the reaction and parameters that are new in a separate table (Supplementary file 3) and provided justification for the parameter values.

Nevertheless, we apologize that we have not effectively clarified the new findings in our data, which, we submit, are far from being expected. Our sensitivity analyses not only suggested that p100 is important, but also indicated that NF-κB induced transcription of Nfkb2 might be critical for crosstalk control. We have experimentally validated the importance of p100 in crosstalk control in Figure 4, while we have further examined the significance of NF-κB induced transcription of Nfkb2 in regulating crosstalk in Figure 5. Importantly, a requirement for NF-κB induced transcription of Nfkb2 also explained the duration code.

Following the reviewers’ suggestion (point [3]), however, we have restructured the figure panel and used the mathematical modeling data revealing the enhanced level of nuclear RelA:p52 in the crosstalk settings (Figure 4F in the earlier version) along with our sensitivity analyses in Figure 4B and 4A, respectively. Following this alteration, we could now emphasize in the revised draft that our modeling studies not only predicted an important role of NF-κB induced transcription of Nfkb2, but also suggested that Nfkb2 derived RelA:p52 contributes in augmenting RelA/NF-κB activity during crosstalk (in the subsection headed “Non-canonical signal transducer Nfkb2 supplements RelA:p52 dimer to sustain canonical RelA NF-κB responses”).

Panel B should be quantitated and the three conditions should be run on the same gel, and statistically evaluated. The quantitative analysis could be restricted to the late time point(s).

Following the reviewers’ suggestion, we have repeated the experiment, analyzed these conditions (untreated and treated for 24h with either LPS or LPS+αLTβR or αLTβR) together in the same gel, and quantitated the relevant band intensities. A plot revealing data quantitation and statistical analyses has now been incorporated in the revised manuscript in Figure 4C, while one of the representative immunoblot with all three conditions in the same gel has been provided for the reviewers’ eyes (Author response image 1).

Author response image 1

Comparing p100 and p52 levels in the same gel using immunoblot analysis. The data, representative of three biological replicates, consistently reflects augmented accumulation of p52 protein in the costimulation regime as compared to cell treatment with LPS or αLTβR alone for 24h.

https://doi.org/10.7554/eLife.05648.030

Panels C and D are important, and a quantitation of p50 vs p52 association with RelA should be provided.

As suggested by the reviewers, we have included the quantification of p50 vs. p52 association with RelA in LPS or LPS+αLT βR or αLTβR treatment regimes in Figure 4D and Figure 4E in the revised manuscript. Our quantification data, which represents 3 biological replicates, consistently reveal a dominant role of p52 Nfkb2 pathway in crosstalk.

In B, could the authors add a p50 immunoblot?

In the revised draft, we have included a p50 immunoblot in Figure 4C. Further supporting our hypothesis that p52 Nfkb2 pathway plays a dominant role in crosstalk, we did not notice any discernible change in the p50 level during crosstalk.

In C, using both p50 and p52 antibodies should result in ablation of the shift but this control is missing.

The reviewers have asked for an important control. Indeed, on a few occasions, we had used both the antibodies that led to complete ablation of DNA binding activities. In the revised draft, we have included a representative panel in Figure 4E to demonstrate complete shift-ablation upon using both the antibodies. The corresponding full EMSA gel panel has been provided for the reviewers’ eyes (Author response image 2).

Author response image 2

Using both p50 and p52 antibodies along with RelB antibody, complete ablation of RelA NF-κB DNA binding activity was achieved in our supershift analyses. The last three lane was used as a panel within Figure 4E.

https://doi.org/10.7554/eLife.05648.031

Basically, we would expect an experimental counterpart for the computational panel F. Panel G is quantitated but a statistical confidence evaluation should be provided.

We have already provided a statistical test for the data presented in Figure 4G in Figure 2B. Adhering to the reviewers’ suggestions, our revised draft now incorporates several quantitative data analyses as well as statistical tests in Figure 4 those significantly helped in fortifying the claim that p52 Nfkb2 pathway is important for mediating crosstalk effects.

Panel H directly relates to Figure 3 and presumably these experiments were done in parallel. Have the authors also tried to use microarrays and undertaken the analysis shown in Figure 3?

Given our EMSA as well as quantitative RT-PCR analyses consistently revealed an abrogated crosstalk control in Nfkb2-/- MEFs, we did not further pursue global scale analyses of NF-κB target gene-expressions in the crosstalk settings using Nfkb2-/- MEFs. Of note, GSEA for NF-κB targets would be largely inconsequential in the absence of differential gene-expressions. Following the reviewers’ suggestion (point [6]), however, we have restructured the figure panels to include the quantitative RT- PCR data obtained using Relb-/-MEFs (previously in Figure 3) in Figure 4 in the revised draft to contrast with the Nfkb2-/-MEF data. This new figure arrangement strongly supports our conclusion that the crosstalk-amplification of LPS induced NF-κB target gene-expressions requires p52 Nfkb2 but circumvents the requirement for RelB NF-κB transcription factor. To maintain the consistency in the data presentation, we have removed the microarray data obtained using Relb-/- MEFs in the revised manuscript. Accordingly, we have stringently focused our conclusion in revised text to suggest that RelB is not required for the heightened induction of pro-inflammatory cytokines in the crosstalk setting. Our conclusion is in line with the overall proposal that crosstalk potentiates innate immune response to bacterial pathogens by prolonging pro-inflammatory gene-expressions through RelA.

8) Figure 5 is not easy to understand. It would be helpful if the following issues were addressed:

A) Why does panel B show the response to TNF, but everywhere else LPS and IL1 are compared?

We sincerely apologise for the lack of clarity in describing Figure 5 and we have implemented several changes in the relevant result section in the text to offer further clarity.

TNFR1 induced NF-κB signaling has been traditionally considered as a bona fide representative of canonical signaling. Indeed, we have used examples of TNFR1 singling in Figure 1B to claim that our model was able to recapitulate NF-κB activation via the canonical pathway. Although, we have discovered an important role of the promoter intrinsic delay in Nfkb2 mRNA induction in crosstalk regulations, the delayed induction per se was not a novel finding. In this context, we have presented the TNF data and referred to the earlier research, which originally articulated the delay function (Basak et al., Cell 2007). In the revised text, we have attempted to further clarify this issue (in the subsection entitled “Inducible synthesis of Nfkb2 by canonical signal triggers a positive feedback loop during crosstalk”).

B) Why is the Nfkb2 mRNA fold induction different in panels A and B?

We suspect that individual doses of LPS or TNF used in this study may have resulted in dissimilar induction levels of Nfkb2 mRNA. Of note, we have routinely used 1µg/ml of LPS and 1ng/ml of TNF those led to unequal levels of nuclear NF-κ B activity. It is also possible that the observed stimulus specific variations are attributed by other mechanisms involving chromatin modifications.

It is not clear what delay-null cells are. If they are simply the transgenic Nfkb2 described in B, it would be more straightforward to use the same terminology.

Our computational simulations predicted that NF-κB inducible synthesis of Nfkb2 is important for mediating crosstalk between TLR4 and αLTβR signals. To experimentally validate the prediction, we have engineered Nfkb2-/- cell-line for transgenic (retroviral) expression of Nfkb2 from either constitutive or NF-κB responsive promoter (Figure 5). Unlike the observed delay in the inducible synthesis from the endogenous promoter, Nfkb2 mRNA was readily induced when expressed from the NF-κB responsive exogenous promoter. Indeed, the delay null cells represent the engineered Nfkb2-/- cells, which inducibly express Nfkb2 mRNA without any delay. Following reviewers’ suggestion, we have now unambiguously denoted these two cell-lines as Nfkb2-/- cell-lines expressing Nfkb2-Tg either constitutively or inducibly. We have now indicated in the text that inducible transgene expresses Nfkb2 mRNA without any delay (in the subsection “Inducible synthesis of Nfkb2 by canonical signal triggers a positive feedback loop during crosstalk”). We thank the reviewers, as these changes are likely to bring more clarity in the text.

Panels E/F contain critically important information. They need to examine three conditions: LPS alone, LPS and LTβR, and LTβR alone. Also, there are really 4 cell types that are of interest: WT, Nfkb2-/-, and the two transgenes. Distinction should be drawn between RelAp50 and RelAp52.

Following the reviewers’ suggestions, we have performed additional experiments to compare solitary LPS treatment, solitary αLTβR treatment and LPS+ αLTβR costimulation conditions. In Figure 5, we have compared two engineered Nfkb2-/- cell-lines, those express Nfkb2 transgene from either constitutive or NF-κB responsive promoter, respectively. First, we confirmed a lack of LPS inducible expression of Nfkb2 from the constitutive promoter, but 3.5 fold induced expression from the inducible promoter (Figure 5–figure supplement 1A, additional data). Second, additional studies could convincingly demonstrate that constitutive expression of Nfkb2 abrogates crosstalk-amplification of TLR4 induced late NF-κB activity by concomitant LTβR signal and that NF-κB inducible expression of Nfkb2 is required (Figure 5E in the revised draft, additional data). Of note, LTβR response per se was not considerably different in these two cell-lines.

Furthermore, our supershift analyses revealed that inducible expression of Nfkb2 transgene augments RelA/NF-κB activation in the crosstalk settings by potentiating RelA:p52 dimer induction (Figure 5F in the revised draft, additional data). Indeed, these additional analyses supported our hypothesis that NF-κB inducible synthesis of Nfkb2 potentiates RelA:p52 induction to generate crosstalk at the level of RelA/NF-κB activation. We have dedicated Figure 4 to rigorously compare WT and Nfkb2-/- cells.

Panel G mentions a positive feedback loop, but the figure does not show any data related to p52. And strictly speaking no data was presented that RelAp52 produces more RelAp52.

In panel G (panel H in the resubmitted draft), we have summarized the insight obtained from our mechanistic studies (Figure 1-5) in a schematic presentation. We have revealed an involvement of Nfkb2 derived p52 in crosstalk in Figure 4. In a suitably sub-titled result section, we have further examined the requirement for inducible synthesis of Nfkb2 in crosstalk in Figure 5. In response to reviewers’ concern, we have now incorporated additional experimental evidence in the revised manuscript demonstrating a role of RelA:p52 in crosstalk in Figure 5F.

Several recent studies have strongly suggested that RelA containing heterodimers share DNA binding and gene-expression specificities (Siggers et al., Nature Immunol 2011; Zhao et al., Cell Reports 2014). We have also shown that RelA:p50 and RelA:p52 dimers were equally efficient in inducing Nfkbia mRNA expressions during TNFR1 signaling (Appendix-1, Appendix figure 4B). Now in the revised manuscript, we further reveal comparable efficiency of the RelA:p52 dimer in inducing Nfkb2 mRNA during TLR4 signaling using Relb-/-Nfkb1-/- MEFs (Appendix-1, Appendix figure 4C).

Finally, synergistic effects shown in Figure 5F occur at very early time points, and are therefore difficult to relate to duration synergy that happens at >8h.

TLR4 signal induces Nfkb2 mRNA expressions in WT MEFs, albeit with a delay (Figure 5A). Our studies indicated that NIK-IKK1 signal targets the late acting p100 Nfkb2 feedback for generating crosstalk effect at 8-24h in LPS+αLT βR costimulation regime (Figure 2B). In the Discussion section, we have elaborated a requirement for the convergence of canonical and non-canonical signals in mediating crosstalk.

The above-mentioned scenario is truly different from crosstalk observed in the IL-1 regime in the engineered cell-line. Unlike late induction of Nfkb2 mRNA in WT MEFs by LPS, IL-1 signal led to early induction in the engineered cells, which inducibly express Nfkb2 transgene without the delay (Figure 5D). Pretreatment of these engineered cells with αLTβR for 8h and subsequent IL -1 treatment effectively converged the non-canonical signal to IL-1 induced Nfkb2 feedback, thereby, potentiating p52 production (Figure 5–figure supplement 1C) and prolonging IL-1 induced RelA response (Figure 5G and Figure 5–figure supplement 1D). Correlating with the early onset of Nfkb2 mRNA induction in response to IL-1 treatment, observed crosstalk effects were obvious within 1h of cotreatment in these cells. In the revised manuscript, we have attempted to further clarify this issue (please see the subsection entitled “Inducible synthesis of Nfkb2 by canonical signal triggers a positive feedback loop during crosstalk”).

The top diagram seems to be one stimulation condition resulting in the green curve, and the bottom diagram related to the pink curve. Yet the top diagram also contains pink arrows. What makes things difficult to follow is that the canonical and non-canonical arrows come in from different directions, and it is unclear where the curved arrows are pointing.

We thank the reviewers for pointing out the apparent confusions in the graphical presentation in Figure 5H. In the revised manuscript, we have now used gray color to depict biomolecular species and intracellular biochemical reactions those engage during canonical or non-canonical signaling. We have also used magenta color to depict canonical signaling inputs, green color to represent non-canonical inputs. In the revised manuscript including in Figure 5H, we have now consistently presented the cellular responses to solitary canonical signals using magenta, those to singular non-canonical inducers in green and cellular activities in response to costimulation regimes were articulated using black. Replacing the curved arrows, we have now used pointed arrows to connect RelA to the respective temporal activity profiles representing NF-κB responses to either solitary canonical signal or costimulation.

9) Figure 6 shows exciting data which puts the previous fibroblast studies in physiological context.

The following control might strengthen results (if possible): distinguish between RelAp50 and RelAp52 for panel E, similar to the analysis in 4D, and quantitate the levels of each.

We thank the reviewers for their excitement and support. Following the reviewers’ suggestion, we have repeated the supershift analyses using nuclear extracts derived from IECs. Our additional experiments revealed considerable nuclear accumulation of RelA:p52 dimer, along with RelA:p50, in the enterocytes at day5 post-infection (Figure 6–figure supplement 1B in the revised manuscript, see revised text in the subsection headed “Nfkb2 integrates lymphotoxin signal within intestinal niche to reinforce epithelial NF-κB responses to Citrobacter rodentium”). As pointed out later, it also seems that our previous supplementary data showing the absence of RelB containing NF-κB dimers in enterocytes was not adequately noticed. Therefore, we have presented a supershift panel in the main text in Figure 6E revealing that nuclear NF-κB activity is exclusively composed of RelA dimers in IECs.

10) Figure 7 suggests that p100 KO's are more susceptible to Citronella infection, but that does not really prove that p52 complexes in LTβR induced synergy are responsible, and instead the p52 complexes might be acting on their own.

Solitary LTβR stimulation generates RelB:p52 dimer, while our mechanistic studies (Figure 1-5) suggested that convergence of LTβR and TLR4 signals generates an additional RelA:p52 activity in a synergistic manner. We assume that the reviewers may have enquired about the possible engagement of RelB:p52 complexes, which is generated independent of crosstalk regulations, in pathogen response. Using colon derived MSIE cells, we could show that solitary LTβR stimulation only weakly activates NF-κB signaling (Figure 6A). But as a co-stimulus, LTβR signal significantly augmented TLR4 induced RelA activity in these intestinal epithelial cells. Furthermore, our supershift analyses clearly demonstrated a complete absence of RelB DNA binding complexes in the enterocytes derived from infected WT mice. Given the importance of the question, we have repeated the supershift analyses and incorporated the supershift data in the revised main text in Figure 6E revealing that nuclear NF-κB activity is exclusively composed of RelA dimers in IECs. Our additional experiments also revealed considerable nuclear accumulation of RelA:p52 dimer along with RelA:p50 in the enterocytes (Figure 6–figure supplement 1B). Taken together, these results suggested that the observed defect in the innate immune response to Citrobacter infection (Figure 6) in Nfkb2- /- mice unlikely involve epithelial RelB activity. Notably, our analyses also confirmed a critical requirement of Nfkb2 in the non-hematopoietic cells in imparting resilience to pathogen infection. Although, we agree that the observed mortality (Figure 7) could be, in fact, a consequence of deficiencies in the several other cell compartments and may include crosstalk independent Nfkb2 functions. As discussed in the Results section (please see the subheading “Stromal expression of Nfkb2 is required for limiting C. rodentium infection”) and in the Discussion, we also do not rule out crosstalk-independent engagement of the Nfkb2 pathway in other cell-types at a later stage of infection, those may contribute to the observed mortality in Nfkb2-/- mice. However, epithelial RelA NF-κB activation defects coupled to aggravated early colon pathology and early onset of mortality in Nfkb2-/- mice suggested that the stromal requirement of Nfkb2, at least in part, lies within the intestinal epithelial cells in the initial events controlling early innate immunity and involves crosstalk regulations. In the revised manuscript, we have subtly altered the text in the relevant Results section discussing Figure 7 to address the point raised by the reviewers.

Also as p100 KO's do not display any general sensitivity to infection, it is unclear whether the Citronella results are unique to these bacteria.

In an accompanying unpublished study, we are addressing the cell-type specificity of signaling crosstalk between LTβR and TLR4. Our analyses established that such crosstalk regulations are less likely to be important in myelomonocytic cells. A related manuscript articulating the mechanism underlying cell-type specificity of crosstalk control is currently in preparation. While these results offer an explanation for the apparent lack of general sensitivity in Nfkb2-/- mice, additional studies will be important to further examine the immune responses in Nfkb2-/- mice to other microbial pathogens, particularly those infected through the gut. We have now discussed this issue in the Discussion.

11) The observed effects in Figure 6G and Figure 7 could be 'simply' due to lack of non-canonical NF-κB activation in response to LTβR in the NFκB2-/- strain.

The point [11] raised by the reviewers is not very different from the point [10] raised earlier. Unlike the mechanistic studies in the cell culture system (Figure 1-5), directly linking Nfkb2 dependent RelA activity (Figure 6) with the mouse phenotype (Figure 7) is somewhat daunting. We could ascertain in Figure 6 that crosstalk amplification of epithelial RelA response by Nfkb2 potentiates the expressions of neutrophil-attracting chemokine and cytokine genes. We have gladly noted that the reviewers have lauded our experimental approach and agreed with the interpretation of the data [point 9]. In Figure 6G (Figure 6H in the revised draft), we have simply concluded that defective epithelial expressions of chemokine and cytokine genes may have led to diminished neutrophil recruitment.

We would also like to also point out that prior analysis with Nfkb2-/- mice revealed only mild lymph node phenotype (Lo et al., Blood 2006), unlike a complete lack of lymph nodes in ltbr--/- mice. Subsequent mechanistic studies demonstrated that constitutive RelB:p50 activity largely compensates for the absence of LTβR induced RelB:p52 activity in this knockout. These results suggest that the defect in non- canonical RelB:p52 activation in response to LTβR is tolerated in Nfkb2-/- system. Although, we do not rule out crosstalk-independent engagement of the Nfkb2 pathway in other cell-types at a later stage of infection, those may contribute to the observed mortality in Nfkb2-/- mice. In the revised text, we have attempted to further clarify this point and toned down the interpretation of our mortality data (in the subsection headed “Stromal expression of Nfkb2 is required for limiting C. rodentium infection”).

The crux of the duration model is that extended RelA activity is RelB-independent. The mouse experiments neither address whether RelA function is involved in pathogen clearance nor do they show that the effects are RelB-independent. How might this question be addressed?

In general, a critical role of RelA/NF-κB in the pro-inflammatory gene-expressions is firmly established. Indeed, genetic studies have confirmed a requirement of RelA in the epithelial expressions of chemokine and cytokine genes and consequent neutrophil recruitment in LPS induced pneumonia (Alcamo et al., J. Immunol., 2001). In the revised manuscript, we have now cited this important contribution in the subsection “Nfkb2 integrates lymphotoxin signal within intestinal niche to reinforce epithelial NF-κB responses to Citrobacter rodentium”. Furthermore, TLR4 activated RelA was implicated in the chemokine gene expressions and neutrophil recruitment upon bacterial infections (Khan et al., Infect. Immunity., 2006, discussed in the second paragraph of the Discussion section). Consistent to these reports, our study revealed a correlation between diminished RelA activity in IECs and the inability of Nfkb2-/- mice in limiting bacterial infection. Furthermore, our supershift analyses clearly demonstrated a complete absence of RelB DNA binding complexes in the enterocytes derived from infected WT mice (Figure 6E). These results strongly suggested that epithelial NF-κ B response to pathogen infection is mediated by RelA in a RelB independent manner. Of note, pre-existing conditions in Relb -/-mice with signatures of multiorgan inflammation and a complete lack of secondary lymph nodes (Weih et al, Cell 1995) deterred us from further analyzing these mice in Citrobacter infection experiments. In light of these constructive criticisms and concerns, however, we have felt the necessity to moderate the interpretation of our in vivo data. Accordingly, we have emphasized the correlative nature of our argument in the aforementioned subsection and indicated a possible role of RelB in other cell-types in pathogen response.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

1) By calling the model NF-κB system model v. 1 it is not clear that the model is based on (and justified by) previous models. The authors should pick a different name (or is there a need for a name given that a model is generally cited with the paper reference?).

We are fully aware of elegant model building efforts of other NF-κB research groups. While apologizing, we certainly did not intend to suppress that the core circuitry as well as the parameter space of our current NF-κB multidimer model was largely based upon previously published single dimer model versions (Basak et al., 2007; Shih et al., 2009). However, we would like to point out that the scope and the detailed wiring of our current model is significantly different. As such, the NF-κB Systems model v.1 describes two different RelA NF-κB heterodimers, but as composite species. In an iterative model building effort, my laboratory has now implemented another advanced NF-κB model (The NF-κB systems model v.2), which depicts dimerization of the NF-κB monomers forming various heterodimers. In the future, we hope to expand the scope of the model to arrive at the NF-κB systems model v.3, which would address issues pertinent to homodimeric NF-κB species. We feel that it is somewhat important to name these model versions generated in a research program to maintain a catalogue of these incremental advances. Although a detailed description with relevant references has been presented in Appendix-1, we have now also referred to those preceding model versions at the first occurrence of the NF-κB systems Model v.1 to avoid further confusion (in the subsection entitled “A duration code controlling crosstalk between canonical and non-canonical NF-κB signaling”).

Similarly the Table should be more explicit. Many values are described asadapted from [REF]. If the values are the same, that should be stated. If they were modified then a justification should be given. For some values the authors stateassumed to be similar to [another]. They should state if they are the same, or when different a justification should be provided.

Indeed, the terminology “adapted from” indicates that the parameter values were derived from another model version, but were subjected to minor modifications (fitting) for adapting to the newer wiring in the NF-κB Systems Model version 1.0. Concurring to the suggestion, we have now modified the table presented in Supplementary file 2 to indicate the quantum of modification with proper justification. Also in the modified table presented in Supplementary file 3, we have now explicitly mentioned if the values were identical. In case of a deviation, we have provided justification using our biochemical data. We have now included a summary note in Supplementary file 2 to highlight the changes.

As such, out of the total 105-parameter values (Supplementary file 2), 34 were identical to those published in earlier model versions. Moreover, 20 were derived from the published literature, but were subjected to a minor < 3 fold modification for adapting to the NF-κB Systems Model version 1.0. Another 4 parameters were modified < 5 fold for fitting. For additional 12 parameters, further experimental evidence was provided to justify the alterations. Furthermore, another 27 parameters related to the newly described RelA:p52 dimer (Supplementary file 2 and Supplementary file 3) were assumed to be identical to those of the RelA:p50 dimer, and the assumption was justified using our own experimental measurements and literature. An exception was being made for the association rates underlying RelA:p52-IkB complex formation (a total of 8 parameters) basing on our experimental analyses.

The diagram in Appendix figure 1 could be confusing with many different shapes, colors and shadings. Why not adopt the normal conventions for kinetic model diagrams and indicate the species in the diagram itself rather than the legend? It is hard to keep track of the colors, and indeed it seems that the pink/orange shading in the legend became an orange/red shading in the diagram.

Accepting the constructive criticism, we have now presented the diagram adhering to the formalism followed in Figure 5H. Not only, we have eliminated the color scheme, but also specified the species names in the diagram.

2) In Figure 1E the authors are focused on points that are missing in the range of <2hrs. It is not evident from this figure what curves are missing, and so the figure is difficult to understand. They should find another way to make this point. Why not show the actual crosstalk score in a matrix of IKK2 and IKK1 profiles? That would avoid the inherent problems of applying a threshold (crosstalk proficient) which can also lead to misleading conclusions.

For the past few years, we have been discussing the possible mode of presentation of the duration code data with several of our colleagues. As such, for a given duration of IKK2 or IKK1 (a+b), there are multiple kinase activities within the library those differ with respect to peak amplitude (h) and peak onset time (a) (see Figure 1–figure supplement 2A). For a specific IKK2 activity explicitly defined through peak duration, amplitude and onset time, again all possible IKK1 activities must be considered for evaluating its crosstalk potency. We argue that hierarchical clustering of kinase profiles in a given axis would be even more arbitrary. Therefore, we contest that it is not possible to present crosstalk score in a matrix of IKK2 and IKK1 profiles in a two-dimensional plot as suggested. Unfortunately, even a three-dimensional plot would be insufficient. To remain connected to a broader audience (the manuscript describes bacterial colitis studies), we did not consider other multidimensional plotting modalities. Instead, we have attempted to reduce the complexity of the data by analyzing top 10% crosstalk proficient IKK2 or IKK1 profiles separately for peak duration or amplitude. In Figure 1–figure supplement 2B, we have further utilized candidate kinase activity profiles in a case study to confirm fidelity of our interpretation. Moreover, use of experimentally derived long-duration or short-duration kinase activities consistently confirmed the presence of a duration code (Figure 1H). With these arguments, we hope to be able to convince you of our rigor in ensuring that threshold intrinsic issues did not lead to erroneous conclusions in our study.

3) The quantitation added to Figure 2 seems surprising in some cases: for example in 2B LPS, the 3 and 5 h points are very different in the gel, but very similar in the graph with minimal error bars. There are several other points that do not transparently seem to square with the gel data. Because the graph is the result of 3 gels, it will not match precisely but the error bars should indicate the true standard deviation in the data. Some clarification of this point is required.

As described in Materials and methods, gel images were acquired using PhosphorImager (GE, Amersham, UK) and quantified in ImageQuant software. We agree that the reduction in NFκB activity at 3h was less obvious in the quantitation panel as compared to the presented gel picture. As suggested by the reviewers, the quantitation panel reflects an average of 3 experiments. Note error bar for the 3h time-point is largest among all LPS time-points that indicate variations in the 3h data point in replicate experiments. As explained earlier in response to reviewers’ comments, we have normalized EMSA signals against respective IL-1 induced (0.5h) or LPS induced (1h) peak values. As one could anticipate, our normalization procedure led to lesser-pronounced error bars, including those for 3h LPS data point. In the revised draft, we have now separately indicated data normalization procedure for LPS regime (see Figure legend to Figure 2). As such, 3h LPS time-point is also peripheral to our principal claim that LTβR costimulation augments LPS induced late NFκB activity.

4) Figure 2D is still rather obscure. Why not show the genome-wide data in a more direct manner (e.g. heatmap) so that the quality of it can be assessed.

We would like to point out that our experimental settings compare three different cell-conditions, viz. LPS stimulated, αLTβR treated and costimulated. We would also like to emphasize that the goal of our genome-wide analyses was to identify a set of genes those are synergistically activated, and not merely hyperinduced, in the costimulation regime. Although suitable for presenting differential gene-expressions for a given stimulation regime, traditional heatmaps, with very small dynamic range, are inept in capturing synergistic gene-activations that require simultaneous and complex comparisons across multiple stimulation regimes. Therefore, we have adapted a previously published methodology (see ref. Zhu et. al., 2006), which enables comparison of multiple stimulation regimes. Indeed, our crosstalk score based analyses identified 114 genes those are not only hyperinduced, but synergistically activated in the costimulation regime as compared to solitary LPS or α LTβR treatments (see bottom panel, Figure 3D). More so, heatmap based analyses are not expected to disclose if synergistically activated genes are also likely to be NF-κB targets. Based on the literature survey and our discussions with several experts, we trust that GSEA (see ref. Subramanian et. al., 2005) offers best possible solution to the complex problem, which we have addressed. Note, we have referred to the bottom panel of Figure 3D in the text while concluding synergistic gene-effects (please see “Signal integration via the NF-κB system amplifies the late expressions of TLR4 induced pro-inflammatory genes”). In essence, we submit that Figure 3D is as such more informative, in conjunction with the detailed methodologies, than a mere heatmap presentation.

Figure 4H requires a wt control. Does RelB play no role, a minor role?

As you may recall, we initially had both WT and Relb-/- gene-expression data in Figure 3. Following reviewers’ suggestions, we moved Relb-/- gene-expression data in Figure 4H in the revised manuscript to offer a contrast with gene-expressions scored in Nfkb2-/-. Careful inspection of our WT (Figure 3C) and Relb-/- gene-expression data (Figure 4H) revealed that crosstalk-amplification of LPS induced gene-expressions are largely intact for IL1β, RANTES and IP-10. However, we have noted that crosstalk effects on MIP-1α expressions are rather muted in Relb- /- MEFs owing to prolonged expressions of this gene in response to solitary LPS treatment in the absence of RelB. While insisting that our Figure 3C provides for a control for Figure 4H as far as the role of RelB is concerned, we have now revised the text to indicate a possible minor (although indirect) role of RelB (please see the end of the subsection headed “Non-canonical signal transducer Nfkb2 supplements RelA:p52 dimer to sustain canonical RelA NF-κB responses”).

https://doi.org/10.7554/eLife.05648.029

Article and author information

Author details

  1. Balaji Banoth

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    BB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  2. Budhaditya Chatterjee

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    BC, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  3. Bharath Vijayaragavan

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    BV, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  4. MVR Prasad

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    MVRP, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  5. Payel Roy

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    PR, Acquisition of data, Analysis and interpretation of data
    Competing interests
    The authors declare that no competing interests exist.
  6. Soumen Basak

    Systems Immunology Laboratory, National Institute of Immunology, New Delhi, India
    Contribution
    SB, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    sobasak@nii.ac.in
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome Trust (DBT India Alliance, intermediate fellowship)

  • Soumen Basak

National Institute of Immunology (Core Funding)

  • Soumen Basak

Council of Scientific and Industrial Research (CSIR) (Graduate Student Fellowship)

  • Balaji Banoth
  • Payel Roy

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Alexander Hoffmann, UCLA for insightful discussions, Gouri Ghosh, UCSD and Satyajit Rath, NII for critical reading of the manuscript. We thank P Nagarajan, SAF and V Kumar, SIL for technical help. This study was supported by an intermediate fellowship to SB from the Wellcome Trust DBT India Alliance and funding from NII-Core. BB and PR thank CSIR for research fellowships.

Ethics

Animal experimentation: Wild-type or gene-deficient C57BL/6 mice were housed at NII small animal facility and used strictly in accordance with the Institutional Animal Ethics Committee guidelines of the institute. The protocol was approved by the committee with the approved protocol no: IAEC#258/11 (for embryonic fibroblast cell collection) and IAEC#313/13 (for infection related studies).

Reviewing Editor

  1. Arup K Chakraborty, Massachusetts Institute of Technology, United States

Publication history

  1. Received: November 19, 2014
  2. Accepted: April 22, 2015
  3. Accepted Manuscript published: April 23, 2015 (version 1)
  4. Version of Record published: May 15, 2015 (version 2)

Copyright

© 2015, Banoth et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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