Immune cells activate in binary, switch-like fashion via large protein assemblies known as signalosomes, but the molecular mechanism of the switch is not yet understood. Here, we employed an in-cell biophysical approach to dissect the assembly mechanism of the CARD-BCL10-MALT1 (CBM) signalosome, which governs nuclear transcription factor-κB activation in both innate and adaptive immunity. We found that the switch consists of a sequence-encoded and deeply conserved nucleation barrier to ordered polymerization by the adaptor protein BCL10. The particular structure of the BCL10 polymers did not matter for activity. Using optogenetic tools and single-cell transcriptional reporters, we discovered that endogenous BCL10 is functionally supersaturated even in unstimulated human cells, and this results in a predetermined response to stimulation upon nucleation by activated CARD multimers. Our findings may inform on the progressive nature of age-associated inflammation, and suggest that signalosome structure has evolved via selection for kinetic rather than equilibrium properties of the proteins.
Signalosomes are multi-protein complexes that transduce signals by sequentially assembling filamentous oligomers. Here, Rodriguez-Gama and colleague present their exciting finding in which the BCL10 adaptor in the CBM signalosome acts as an analogue-to-digital converter, resulting in binary activation of immune cells.https://doi.org/10.7554/eLife.79826.sa0
The innate immune system is the body’s first line of defence against pathogens. Although innate immune cells do not recognize specific disease-causing agents, they can detect extremely low levels of harmful organisms or substances. In response, they activate signals that lead to inflammation, which tells other cells that there is an infection. Innate immune cells are turned on in a switch-like fashion, becoming active very quickly after interacting with a pathogen. This is due to the action of signalosomes, large complexes made up of several proteins that clump together to form long chains that activate the cell.
But how do these large protein complexes assemble quick enough to create the switch-like activation observed in innate immune cells? To answer this question, Rodríguez Gama et al. focused on the CBM signalosome, which is involved in triggering inflammation through the activation of a protein called NF-kB.
First, Rodríguez Gama et al. used genetic tools to determine that activating the CBM signalosome drives a switch-like activation of NF-kB in cells. This means that individual cells in a population either become fully activated or not at all in response to minute amounts of harmful substances.
Once they had established this, Rodríguez Gama et al. wanted to know which protein in the CBM signalosome was responsible for the switch. They found that one of the proteins in the signalosome, called BCL10, has a ‘nucleation barrier’ encoded in its sequence. This means that it is very hard for BCL10 to start clumping together, but once it does, the clumps grow on their own. The nucleation barrier describes exactly how hard it is for these clumps to get started, and is determined by how disorganized the protein is.
When a pathogen ‘stimulates’ an immune cell, a tiny template is formed that lowers the nucleation barrier so that BCL10 can then aggregate itself together, leading to the switch-like behaviour observed. The nucleation barrier allows there to be more than enough BCL10 present in the cell at all times – ready to clump together at a moment’s notice – and this permits the cell to detect very low levels of a pathogen.
Rodríguez Gama et al. then tested whether BCL10 from other animals also has a nucleation barrier. They found that this feature is conserved from cnidarians, such as corals or jellyfish, to mammals, including humans. This suggests that the use of nucleation barriers to regulate innate immune signalling has existed for a long time throughout evolution.
The work by Rodríguez Gama et al. broadens our understanding of how the innate immune system senses and responds to extremely low levels of pathogens. That BCL10 is always ready to clump together suggests it may be a driving force for chronic and age-associated inflammation. Additionally, the findings of Rodríguez Gama et al. also offer insights into how other signalosomes may become activated, and offer the possibility of new drugs aimed at modifying nucleation barriers.
Cells of both the innate and adaptive immune systems respond immediately and decisively to danger through the formation of large cytosolic protein complexes, known as signalosomes. Signalosomes function to coordinate the detection of pathogen- or danger-associated molecular patterns with protective transitions in cell state, such as programmed cell lysis or activation of nuclear transcription factor-κB (NF-κB) (Kellogg et al., 2015; Liu et al., 2014; Matyszewski et al., 2018; Wu and Fuxreiter, 2016). NF-κB directs the transcription of genes encoding pro-inflammatory cytokines and growth factors that are essential to both innate and adaptive immune responses, and its improper regulation contributes to cancer, chronic inflammation, and autoimmune diseases. Although NF-κB is known to respond in switch-like fashion to certain stimuli (Kingeter et al., 2010; Muñoz et al., 2019; Tay et al., 2010; Figure 1A), the thermodynamic basis of bistability has not been determined.
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The CARD-BCL10-MALT1 (CBM) signalosome mediates NF-κB activation in lymphoid, myeloid, and certain nonimmune cell lineages. Named for its three protein constituents, the CBM signalosome is composed of: (1) a Caspase Activation and Recruitment Domain (CARD)-coiled coil (-CC) family member (either CARD9, 10, 11, or 14); (2) B-cell lymphoma 10 (BCL10); and (3) mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) (Gehring et al., 2018; Lu et al., 2019). The CARD-CC proteins have tissue-specific expression that places CBM formation under the control of specific cell surface receptors. CARD11 expression in the lymphoid lineage controls B- and T-cell activation upon antigen recognition by B- and T-cell receptors (Egawa et al., 2003; Wang et al., 2002). CARD9 expression in the myeloid lineage links the antifungal response of monocytes to fungal carbohydrate detection by C-type lectin receptors (Gross et al., 2006; Hsu et al., 2007; Strasser et al., 2012). CARD10 and CARD14 are expressed in nonhematopoietic cells including intestinal and skin epithelia, respectively (Ruland and Hartjes, 2019). Multiple clinically significant mutations in CARD proteins have been found to compromise immune system homeostasis. For instance, mutations that relieve the normally autoinhibited state of CARD11 promote lymphoid cell proliferation leading to lymphoma (Compagno et al., 2009; Lenz et al., 2008). Several mutations in CARD9 cause familial hypersusceptibility to fungal infections (Glocker et al., 2009; Lanternier et al., 2015; Lanternier et al., 2013). CARD10 mutations are associated with risk of primary open angle glaucoma (Zhou et al., 2016), while CARD14 gain of function mutations cause psoriasis (Fuchs-Telem et al., 2012; Jordan et al., 2012). Once activated, CARD-CC proteins recruit the adaptor protein BCL10 and its binding partner, MALT1, resulting in the activation of downstream effectors that ultimately allow NF-κB to translocate to the nucleus and induce transcription of its target genes. As the integrators of cell type-specific CARD-CC signaling, BCL10 and MALT1 are expressed ubiquitously across cell types. Point mutations and translocations involving BCL10 and MALT1 cause immunodeficiencies, testicular cancer, and lymphomas (Juilland and Thome, 2018; Kuper-Hommel et al., 2013; Ruland and Hartjes, 2019).
Signalosome assembly involves homotypic polymerization by protein modules in the death domain (DD), Toll/interleukin-1 receptor (TIR), and RIP homotypic interaction motif (RHIM) families (Nanson et al., 2019; Rodríguez Gama et al., 2021; Wu and Fuxreiter, 2016). Some of these polymers, including those of BCL10, exhibit ‘prion-like’ self-templating activity in vitro or when overexpressed (Cai et al., 2014; Franklin et al., 2014; Holliday et al., 2019; Hou et al., 2011; Kajava et al., 2014; Latty et al., 2018; Lu et al., 2014; Matyszewski et al., 2018; Mompeán et al., 2018; O’Carroll et al., 2020; Qiao et al., 2013). Prion-like behavior is made possible by a structurally encoded kinetic barrier to de novo assembly, a process known as ‘nucleation’ (Khan et al., 2018; Rodríguez Gama et al., 2021; Serio et al., 2000). Nucleation involves a rare thermodynamic fluctuation whereby order spontaneously emerges from disorder (Vekilov, 2012). The nucleation barrier describes the rarity of that fluctuation in a given window of time and space.
For a signalosome protein whose activity is coupled to ordered polymerization, a sufficiently large nucleation barrier could allow cells to express the protein to concentrations that exceed its thermodynamic solubility limit, that is become supersaturated, in anticipation of pathogen exposure. Supersaturation would then provide a thermodynamic driving force for subsequently activating all of the excess molecules, thereby reducing the fraction of total molecules that need to be activated by directly interacting with – or ‘sensing’ – the pathogen. We posit that nucleation barriers could theoretically therefore allow immune systems to detect vanishingly small stimuli (perhaps even a single viral RNA; Jiang, 2019; Zeng et al., 2010). This mechanism would require (1) that the signalosome protein retains a nucleation barrier in the cellular milieu; (2) that its endogenous unstimulated intracellular concentration is supersaturating; and (3) that its polymerization can be triggered solely by the appearance of a nucleating particle in the cell, that is without the cell upregulating the protein or lowering its solubility in any way (e.g., via post-translational modifications or binding factors). These criteria have not yet been assessed for any signalosome.
In this study, we used a single-cell reporter of NF-κB activity to reveal that the CBM signalosome activates NF-κB in a binary fashion. We then used our recently developed biophysical approach, Distributed Amphifluoric FRET (DAmFRET; Khan et al., 2018; Venkatesan et al., 2019), to dissect the mechanism of CBM signalosome formation. This effort uncovered a structurally encoded nucleation barrier specifically in the adaptor protein, BCL10. We found that pre-existing CARD-CC protein multimers undergo a stimulus-dependent reorientation of their CARDs to create a template for BCL10 polymer nucleation. We further developed an optogenetic approach to nucleate BCL10 independently of upstream factors, revealing that BCL10 is indeed supersaturated even in resting cells. Finally, we showed that BCL10 activity does not depend on its polymer structure, implying that the structure is primarily a consequence of natural selection acting on the nucleation barrier. We found that the nucleation barrier is conserved from cnidaria to humans. Altogether, our work indicates that the preassembled CBM signalosome has evolved to store energy analogously to a spring-loaded mousetrap that allows cells to constitutively anticipate and respond to the slightest provocation.
To explore the link between signalosome nucleation and signaling kinetics, we first developed a single-cell reporter of NF-κB activity (Figure 1—figure supplement 1A). This transcriptional reporter contains four copies of the core NF-κB response element followed by the coding sequence of the fluorescent protein, T-Sapphire (Wilson et al., 2013). We used human embryonic kidney 293T cells and THP-1 monocytic cells to measure the activation of NF-κB with increasing concentrations of CBM signalosome stimulation. To activate the CBM signalosome in THP-1 monocytes, we stimulated CARD9 using the yeast cell wall component, β-glucan. To activate the CBM signalosome in HEK293T cells, we stimulated CARD10 (the only CARD-CC expressed in HEK293T cells) using the protein kinase C (PKC) activator, PMA (Staal et al., 2021). Using flow cytometry, we found that the percentage of T-Sapphire-positive cells increased in a dose-dependent manner for both HEK293T and THP-1 cells (Figure 1B,C). The distribution of T-Sapphire fluorescence was bimodal, with cells distributing between nonfluorescent and uniformly fluorescent populations. Increasing doses increased the fraction of cells in the fluorescent population, but did not influence the level of fluorescence within that population, even across multiple stimulation concentrations spanning several orders of magnitude (Figure 1B,C insets and Figure 1—figure supplement 1B, C). To ensure that activation was occurring solely through the CBM signalosome, we used CRISPR-Cas9 to disrupt exon 1 of BCL10 in both HEK293T and THP-1 cells (Figure 1—figure supplement 2A–C). As expected, cells lacking BCL10 failed to respond to stimulation (Figure 1B,C).
This all-or-none switch from inactive to active NF-κB at the cellular level could be explained by a nucleation-limited transition of one or more signaling components to a stable assembly. We sought structural evidence of such a transition using size exclusion chromatography of lysates from THP-1 monocytic cell lines either with or without β-glucan stimulation, followed by immunodetection of both BCL10 and CARD9. We found that CARD9 (62 kDa) ran as an oligomer that, upon stimulation, shifted uniformly to a higher molecular weight (Figure 1F). BCL10 (26 kDa) also populated a large (>500 kDa) complex upon stimulation, although in this case, the size distribution was bimodal, with a fraction of the protein remaining monomeric. Because the protein does not populate intermediate-sized species, this result suggests an underlying phase transition.
To determine if the large multimers involve BCL10 polymerization, we knocked out BCL10 in THP-1 cells and reconstituted it with either wild-type (WT) BCL10-mEos3.2 or polymerization-deficient mutant (E53R; David et al., 2018), expressed from a doxycycline-inducible promoter (Figure 1—figure supplement 2D). We then induced the expression of the WT and mutant proteins to similar levels, and then used microscopy to analyze the proteins’ distributions in the presence or absence of β-glucan stimulation. In untreated cells, BCL10 fluorescence was entirely dispersed, consistent with the expected soluble form of the protein (Figure 1D). Conversely, in cells treated with β-glucan, the WT protein formed puncta in 70% of cells (Figure 1E), while the E53R mutant protein remained dispersed.
We also evaluated NF-κB activation in these reconstituted THP-1 BCL10-KO cells. To do so, we used an antibody against the NF-κB subunit, p65, to observe the protein’s distribution between the nucleus and cytoplasm. We found that β-glucan-induced p65 nuclear translocation for cells reconstituted with WT, but not E53R mutant, BCL10-mEos3.2. Moreover, the degree of p65 nuclear translocation in the reconstituted cells resembled that of WT THP-1 cells treated with β-glucan (Figure 1—figure supplement 3A, B). Together, these results suggest that the CBM signalosome exerts binary control over NF-κB activation in monocytes.
We next sought to identify the specific protein(s) responsible for the switch. The assembly of certain innate immune signalosomes has been shown to involve prion-like self-templated polymerization of death fold domains (Cai et al., 2014; Franklin et al., 2014). CBM has a death fold domain in each of the three proteins – a CARD in CARD9/10/11/14, a CARD in BCL10, and a DD in MALT1 (Figure 2A). To determine whether one or more of these polymerizes in a switch-like fashion, we used DAmFRET to detect nucleation barriers for the full-length (FL) proteins (Khan et al., 2018; Venkatesan et al., 2019). In this method, a protein of interest is expressed as a genetic fusion to a photoconvertible fluorescent protein, mEos3.1, from a genetic construct that varies in copy number between cells, so as to sample the broadest possible range of intracellular protein concentrations. An empirically optimized dose of blue light then photoconverts a fraction of the molecules in each cell to a FRET acceptor form, while unconverted molecules remain in the FRET donor form. Protein self-assembly increases FRET signal, which is evaluated as a function of the protein’s concentration in each cell. We chose yeast cells as the reaction vessel because they contain neither death folds nor the associated signaling networks, which could otherwise obscure the exogenous proteins’ sequence-encoded assembly properties. We found that neither CARD9 nor MALT1 formed self-templating polymers. The former formed low AmFRET multimers across the full range of concentrations (Figure 2B). These corresponded to the protein’s accumulation into a single irregular punctum in each cell (Figure 2—figure supplement 1A). There was no discontinuity in DAmFRET and hence no observable nucleation barrier to the formation of these puncta at the cellular level. MALT1 remained completely monomeric (Figure 3D, and Figure 2—figure supplement 1C). BCL10, in contrast, produced a clear bimodal distribution wherein cells partitioned between a no-AmFRET population and a high-AmFRET population, respectively, comprised of cells containing only monomeric BCL10 or highly polymerized BCL10 (Figure 2C and Figure 2—figure supplement 1B). The two populations overlapped in their expression levels, indicating that BCL10 can accumulate to supersaturating concentrations while remaining monomeric, but then polymerizes following a stochastic nucleation event. The paucity of cells between the two populations, that is with moderate levels of AmFRET, suggests that BCL10 polymerizes rapidly to a new steady state following a single nucleation event in each cell (Khan et al., 2018; Posey et al., 2021).
We next evaluated nucleation barriers of the isolated death fold domains of CARD9, BCL10, and MALT1. MALT1DD remained monomeric at all concentrations (Figure 2—figure supplement 1F). CARD9CARD was monomeric up to a threshold concentration of approximately 100 µM, above which it readily polymerized with only a slight discontinuity (Figure 2—figure supplement 1D). In contrast, BCL10CARD switched from monomer to polymer with a large discontinuity (Figure 2—figure supplement 1E) resembling that of FL BCL10, suggesting that the large nucleation barrier of the FL protein, and by extension the switch-like assembly of the CBM assembly, can be attributed to the CARD of BCL10.
The structural basis of nucleation barriers that allow for certain death fold domains to supersaturate in cells is not completely understood. We reasoned that, as for amyloid-based prions, it may result from a conformational change required for subunit polymerization. To investigate this possibility, we took advantage of previously solved structures for all three of the CBM death fold domains. By computationally superposing the structures of the monomer and polymer forms for each domain, and calculating the root mean square deviation (RMSD) between backbone C-alpha atoms, we found that BCL10CARD undergoes a greater conformational change to polymerize than does either CARD9CARD or MALT1DD (Figure 2E–G; RMSD 3.581, 1.655, and 0.956 Å, respectively). Although more sophisticated analyses are required to relate these differences to nucleation barriers, they are consistent with the nucleation barrier of BCL10CARD resulting from a particularly unfavorable conformational fluctuation.
If the monomeric fold of BCL10 functions to restrict nucleation, then mutations that relax that fold can be expected to pathogenetically activate the CBM signalosome. The R58G mutation of BCL10 was first described in a germ cell line tumor and shown to hyperactivate NF-κB (Willis et al., 1999). We tested via DAmFRET if this can be attributed to a reduction in the sequence-encoded nucleation barrier. The EC50 and ẟ (delta) statistics obtained by fitting DAmFRET to a Weibull function serve as crude proxies for the inter- and intramolecular free energies of nucleation, respectively (Figure 2—figure supplement 1G). More specifically, they describe the median concentration at which nucleation occurs, and the independence of nucleation on concentration, respectively, where conformationally limited nucleation has higher ẟ values (Khan et al., 2018). Indeed, relative to WT, R58G reduced both ẟ and EC50 (Figure 2H,I). In contrast, mutation R36E, which disrupts polymer interface IIa (David et al., 2018), increased both ẟ and EC50 (Figure 2I). Consistent with previous observations, mutant E53R, which disrupts polymer interface IIIb (David et al., 2018), blocked nucleation at all tested concentrations (Figure 2H). We next investigated the evolutionary conservation of R58. We found via multiple sequence alignment that position 58 is an arginine in mammals but a glutamine in lamprey, cartilaginous fish, and amphibians (Figure 2—figure supplement 2A). We therefore evaluated the nucleation barrier when R58 is substituted with Gln or with a hydrophobic residue of comparable size (Leu). As expected, R58Q resembled WT, whereas R58L lowered the nucleation barrier (Figure 2—figure supplement 2B, C). These data suggest that Arg or Gln residues at position 58 functionally oppose nucleation and thereby prevent precocious activation of BCL10 in the germ cell lineage. Altogether, we conclude that the native monomeric structure of BCL10 creates a physiological nucleation barrier that allows for switch-like self-assembly and all-or-none NF-κB activation.
CARD-CC proteins form oligomers that promote BCL10 polymerization (David et al., 2018; Holliday et al., 2019). Somewhat paradoxically, however, CARD-CC proteins are believed to reside in an oligomeric state even in the absence of stimulation. The active configuration is prevented from forming within these oligomers due to an inhibitory interaction between the CARD and the adjacent coiled-coil region (Holliday et al., 2019; Sommer et al., 2005). That interaction is released by post-translational modifications induced by pathway activation, leading to CBM assembly. The very low affinity of CARD9CARD for itself (Figure 2—figure supplement 1D and Holliday et al., 2019) suggests that nucleation is unlikely to occur at physiological concentrations without assistance from coiled coil-mediated multimerization. This in turn necessitates that the coiled-coil interactions support a stoichiometry higher than the dimer that we had previously observed for CARD91–142 (Holliday et al., 2019), as a dimer has too few interfaces to template the four-start helical polymer of BCL10 (Qiao et al., 2013). Our observation that FL CARD9 forms a punctum at all expression levels, whereas the CARD itself forms either monomers (at low expression) or polymers (at high expression), is consistent with an essential role of non-CARD interactions in both autoinhibition and higher-order assembly.
To further dissect the relationship between multimerization, autoinhibition, and nucleation, we first used DAmFRET to assess CARD9 multimerization in the absence of its CARD. Remarkably, this truncated protein aggregated just as robustly as FL (Figure 3—figure supplement 1B, C), suggesting that coiled-coil interactions are predominantly if not entirely responsible for its multimerization in the inactive state. Next, we progressively truncated the non-CARD region of the protein (Figure 3—figure supplement 1A). We found that even the shortest variant tested (1–142), retaining only 44 residues of additional sequence beyond the CARD, multimerized at all concentrations (Figure 3—figure supplement 1B, C). Unlike longer versions of the protein, however, this variant populated a lower AmFRET state and exclusively soluble oligomers, consistent with the reduced valency of the truncated coiled-coil region (Figure 3—figure supplement 1D). Importantly, none of these constructs formed the higher AmFRET state or fibrillar puncta that would be indicative of polymerization. This tight relationship between multimerization and inhibition can be rationalized by the previously solved structure of the autoinhibited dimer formed by the short variant, wherein each CARD packs against the broad face of the immediately adjacent dimeric coiled-coil, making extensive contacts with both helices (Holliday et al., 2019).
We had previously demonstrated that point mutations in the coiled-coil-CARD interface release autoinhibition (Holliday et al., 2019). We therefore asked if the best characterized such mutation, I107E, has any effect on multimerization. Relative to WT, the mutant I107E achieved higher AmFRET at low expression and rendered the punctum less spherical (Figure 3—figure supplement 1E, F). Together with the prior demonstration of functional activity, the present findings suggest that multimerization itself is not inhibitory to nucleation and that once autoinhibition is released, CARD–CARD interactions stabilize the aggregated state. We therefore speculate that pre-multimerization driven by the multivalent coiled-coil expedites the response to stimulation, perhaps by allowing stimulus-dependent post-translational modifications (Zhong et al., 2018) to occur cooperatively on multiple CARD9 subunits in close proximity. This would reduce the entropic cost of CARD9 activation relative to the subunits oligomerizing de novo.
We noticed that high expression levels of CARD9CARD-mCardinal sufficed to nucleate BCL10-mEos3.1, whereas mCardinal itself had no such activity (Figure 3—figure supplement 2A,C), suggesting that polymers of CARD9CARD itself can nucleate BCL10. To test if a high local concentration suffices for CARD9CARD subunits to organize into BCL10 polymer nuclei, we substituted the coiled-coil region with an inert and well-characterized homomultimeric domain, μNS (Schmitz et al., 2009). This domain forms stable condensates that drive its fusion partner to very high local concentration (Figure 3A) without affecting global expression levels (Figure 3—figure supplement 2D), and thereby eliminates intermolecular entropic contributions to nucleation barriers even at low expression (Kandola et al., 2021). Expressing the CARD9CARD-μNS-mCardinal fusion should trigger the high-AmFRET state of any yeast cell expressing either CARD9CARD- or BCL10-mEos3.1 above their respective solubility thresholds. As hypothesized, CARD9CARD-μNS-mCardinal shifted all BCL10-mEos3.1 yeast cells to the high-FRET population (Figure 3B), confirming that CARD9CARD indeed forms a polymer seed when condensed. Moreover, the data indicate that in the absence of a template, BCL10 is supersaturated even at the lowest levels of expression (nanomolar). In striking contrast, CARD9CARD-μNS-mCardinal had no effect on the DAmFRET profile of CARD9CARD-mEos3.1, suggesting that CARD9CARD instead forms labile polymers with no observable nucleation barrier at the cellular level (Figure 3B).
We next asked if the other CARD-CC family members – CARD10, 11, and 14 – function in the same fashion. These proteins have the same domain architecture as CARD9: a CARD followed by coiled-coil domains. We performed DAmFRET on their CARD and found that all polymerized comparably to that of CARD9 (Figure 3—figure supplement 2E), that is only at high concentrations (the most stable polymers were formed by CARD10, but even then only above approximately 40 µM), and with negligible nucleation barriers. We then asked if they too, when artificially condensed by fusion to μNS-mCardinal, suffice to nucleate BCL10. Indeed all exhibited this activity (Figure 3—figure supplement 2F). These findings suggest a shared mechanism of action by all four CARD-CC proteins.
Several mutations in CARD9CARD cause susceptibility to fungal infections in humans (Figure 2A). We noted that these residues are conserved within the CARD-CC proteins (Figure 3—figure supplement 3A). To determine their mode(s) of action, we first asked if the mutants can form the polymer structure using DAmFRET and fluorescence microscopy. Whereas WT CARD9CARD assembled into polymers at high concentration, the pathogenic mutants R18W, R35Q, R57H, and G72S failed to do so (Figure 3C and Figure 3—figure supplement 3B, C). Remarkably, however, the R70W mutant not only retained the ability to polymerize, but did so at lower concentrations than WT (Figure 3C and Figure 3—figure supplement 3B, C), implying that its defect instead lies downstream of multimerization. We then assessed the ability of the CARD9 pathogenic mutations to nucleate BCL10, by expressing the CARD9CARD mutants as fusions to μNS in yeast cells simultaneously expressing BCL10-mEos3.1. As expected CARD9CARD WT nucleated BCL10-mEos3.1 at all expression levels. Using confocal fluorescence microscopy, we observed the BCL10-mEos3.1 filaments emanating directly from the CARD9CARD-μNS-mCardinal puncta. In contrast, none of the pathogenic mutants induced BCL10-mEos3.1 nucleation nor observable filaments (Figure 3E,F). Collectively, these data reveal that R18W, R35Q, R57H, and G72S are structurally incompatible with the polymeric configuration, whereas R70W specifically disrupts the interaction of CARD9 polymers with BCL10 at interface IIb (Figure 3—figure supplement 3E).
To investigate downstream consequences of the CARD mutations, and in the context of the FL protein, we next transiently transfected our HEK293T NF-κB reporter cells with FL versions of CARD9, all harboring the I107E mutation to eliminate autoinhibition. We first confirmed that CARD9 I107E strongly activated NF-κB, and that a construct lacking the CARD did not (Figure 3D). We next introduced each of the pathogenic mutants into this construct and found that every one of them eliminated the protein’s ability to activate NF-κB (Figure 3D). All variants expressed to similar levels as WT (Figure 3—figure supplement 3D). Altogether, these results suggest that CARD9 and its orthologs function to nucleate BCL10 by forming a polymeric structure stabilized by multivalent coiled-coil interactions, and that disrupting that activity compromises innate immunity.
We next asked if switch-like BCL10 polymerization results in binary activation of NF-κB in human cells. To do so, we monitored NF-κB activation with respect to polymerization by CBM signalosome components. We transfected constructs of proteins fused to mEos3.2 into HEK293T cells containing the transcriptional reporter (T-Sapphire) of NF-κB activation (Figure 4A). Using DAmFRET, we confirmed that all of the proteins behaved the same way in HEK293T cells as they did in yeast. Specifically, FL CARD9 formed irregular higher-order assemblies at all concentrations (Figure 4—figure supplement 1A,C); CARD9CARD polymerized only above a high threshold expression level; MALT1 was entirely monomeric (Figure 4—figure supplement 1A); and BCL10 became supersaturated with respect to a polymerized form (Figure 4B). Neither CARD9 nor MALT1 expression activated NF-κB (Figure 4—figure supplement 1A,B). BCL10 expression, in contrast, robustly activated NF-κB (Figure 4C). Importantly, this activation only occurred in cells that contained BCL10 polymers, and not monomers, irrespective of the expression level of BCL10 (Figure 4C, Figure 4—figure supplement 1D, E). The frequency of activation, but not the level of activation within individual cells, was reduced or eliminated for the polymer-inhibiting mutants R36E and E53R, respectively (Figure 4—figure supplement 1D, E). Mutant R58G increased the fraction of cells with activated NF-κB (Figure 4—figure supplement 2A–C), as expected from its reduced nucleation barrier. Importantly, the intensity of NF-κB response at the cellular level did not differ between the mutants (Figure 4—figure supplement 1E and Figure 4—figure supplement 2A–C), suggesting a specific effect on nucleation and not downstream signaling. To determine if activation was specific to BCL10 polymers, we also performed the experiment with ASC, the death fold-containing adaptor protein of a different signalosome (the inflammasome). As expected (Cai et al., 2014), ASC formed prion-like polymers (Figure 4B), but did not activate the NF-κB reporter (Figure 4C). This experiment suggests that polymers of BCL10, specifically, and independently of upstream components or physiological stimuli, suffice to activate downstream components of the pathway, and that an intrinsic nucleation barrier to their formation causes activation to occur in a binary fashion.
While the results obtained thus far indicate that BCL10 can function as a switch via nucleation-limited polymerization, we next asked whether it indeed does so even at endogenous levels of expression, that is in the absence of overexpression to promote BCL10 assembly. This distinction is very important because it determines if the nucleation barrier is responsible for preventing NF-κB activation in the absence of stimulation. To determine if the protein is constitutively supersaturated, we designed the experiment to eliminate any upregulation of BCL10 transcription or translation that might occur upon stimulation (Yan et al., 2008). Specifically, we reconstituted our BCL10-deficient HEK293T cells with BCL10-mScarlet expressed from a doxycycline-inducible promoter. After isolating a single clone with uniform expression, we performed a doxycycline titration and used capillary protein immunodetection to compare the expression level of reconstituted BCL10-mScarlet to that of endogenous BCL10 in unstimulated HEK293T. We found that 1 μg/ml doxycycline-induced BCL10-mScarlet to approximately the same level as endogenous BCL10 (Figure 4—figure supplement 3A–C). We next compared BCL10 expression levels in HEK293T cells to that of THP-1 cells and primary human fibroblasts, and found comparable expression levels across all three cell lines (Figure 4—figure supplement 3D, E), consistent with the known ubiquity of the CBM signalosome. Finally, we analyzed BCL10 expression at the single-cell level using immunofluorescence and flow cytometry, which revealed that the reconstituted expression of BCL10-mScarlet has the same median intensity of BCL10 antibody binding as that of endogenous BCL10 in unmodified HEK293T cells (Figure 4—figure supplement 3F).
This cell line enables us to monitor NF-κB activity with respect to the assembly state of BCL10 at endogenous unstimulated levels of expression. To achieve this, we introduced a fluorescently tagged NF-κB subunit, EYFP-p65, and performed time-lapse fluorescence microscopy following the addition of PMA. Prior to stimulation, both BCL10 and p65 were diffusely distributed throughout the cytoplasm. BCL10 became punctate within 50 min after PMA addition, and the puncta could be observed to elongate with time (Figure 4D,E and Video 1). Concomitantly, p65 was observed to translocate to the nucleus (Figure 4E). This result suggests that BCL10 is indeed supersaturated prior to stimulation.
Two key predictions of our hypothesis are that (1) BCL10 will be unable to respond to stimulation when its concentration falls below a threshold (corresponding to its solubility limit), and (2) it will activate spontaneously even in the absence of stimulation when its concentration far exceeds that threshold. To test the first prediction, we induced BCL10-mScarlet to subphysiological levels using 0.05 μg/ml doxycycline, stimulated the cells with PMA, and then used confocal microscopy to quantify the nucleocytoplasmic distribution of EYFP-p65 as a function of BCL10-mScarlet intensity. As expected, EYFP-p65 failed to translocate to the nucleus in cells with BCL10-mScarlet intensity below an apparent threshold (Figure 4—figure supplement 4A, B). To test the second prediction, we induced BCL10-mScarlet to superphysiological levels by administering 1 μg/ml doxycycline for extended durations, while withholding PMA. By 24 hr of doxycycline induction, a small fraction of cells (1.1%) had spontaneously acquired BCL10 puncta. This fraction increased to 5.1% at 48 hr and 7.8% at 72 hr (Figure 4—figure supplement 4C, D). As expected, the cells containing BCL10 puncta also showed EYFP-p65 translocation (Figure 4—figure supplement 4C). We conclude that BCL10 supersaturation is necessary to activate NF-κB, while deep supersaturation is sufficient.
As a final test that BCL10 is physiologically supersaturated, we next devised an optogenetic approach to nucleate BCL10 independently of overexpression and independently of potential orthogonal cellular response to stimulation, such as post-translational modifications or changes in the binding of other proteins to BCL10 that could lower its solubility limit. Cry2clust is a variant of a plant photoreceptor that reversibly homo-oligomerizes in response to blue light excitation (Park et al., 2017). We expressed CARD9CARD as a fusion to miRFP670-Cry2clust (CARD9CARD-Cry2) in the HEK293T BCL10-KO cells with BCL10-mScarlet reconstituted to endogenous levels (Figure 5A), and used a brief (1 s) pulse of 488 nm laser light to activate Cry2. Prior to blue light exposure, both CARD9CARD-Cry2 and BCL10-mScarlet were diffusely distributed throughout the cells (Figure 5B and Video 2). Following the pulse, however, both proteins assembled into puncta. By measuring the kinetics of CARD9CARD and BCL10 assembly (as the coefficient of variation in pixel intensity over time), we found that CARD9CARD clustering peaked at approximately 5-min post-excitation, and then those clusters disassembled to background levels by approximately 20 min (Figure 5C). Within 6 min following the pulse, BCL10 began to form filamentous puncta that initially colocalized with those of CARD9CARD (Figure 5B). Remarkably, the puncta continued to elongate even after the CARD9CARD clusters had dissolved (Figure 5B and inset), revealing that BCL10 is driven toward a polymeric steady state even in the absence of CARD9 templates. To test the specificity of the CARD9-BCL10 interaction, we also performed the experiment with the R18W and R70W mutants of CARD9CARD-Cry2, whereupon BCL10 did not polymerize despite comparable kinetics of CARD9CARD-Cry2 clustering (Figure 5—figure supplement 1A–D).
We then asked if the optogenetically nucleated BCL10 polymers sufficed to induce the nuclear translocation of EYFP-p65. In order to avoid repeated stimulation of Cry2 oligomerization with the 514 nm laser, we only imaged EYFP-p65 at the beginning and end (40 min following the blue light pulse) of the experiment. We observed that blue light-induced EYFP-p65 to translocate comparably to stimulation with PMA (Figure 5D,E). Additionally, as with the different doses of PMA, the degree of translocation within cells did not depend on the duration of the blue light pulse. This again highlights the all-or-none nature of signaling from BCL10. We found that the polymerization-incompetent mutant of BCL10, E53R, failed to form puncta or activate NF-κB in response to blue light (Figure 5—figure supplement 2A–C). Altogether these data indicate that BCL10 is endogenously supersaturated and physiologically restrained from activation by the kinetic barrier associated with polymer nucleation, which collectively allow for switch-like CBM signalosome formation and NF-κB activation.
Throughout our experiments with both THP-1 and HEK293T cells – whether analyzing BCL10 puncta formation, NF-κB transcriptional activity, or p65 translocation – we observed that 25–30% of cells failed to respond to stimulation even at saturating levels of β-glucan or PMA (Figure 1B,C,E and Figure 4E). The size of this population did not depend on whether BCL10 was endogenously or exogenously expressed, and dropped to 10% in our optogenetic experiments that bypassed upstream factors (Figure 5—figure supplement 1E), together suggesting that recalcitrance results at least in part from an increase in the BCL10 nucleation barrier due to cell-to-cell heterogeneity in upstream factors. While the origin of this phenomenon remains to be determined, it demonstrates that cells may regulate their sensitivity to stimulation by raising or lowering the nucleation barrier.
That BCL10 function derives from a nucleation barrier implies that the structure of the active state has evolved toward increased order relative to the inactive state, irrespective of its interactions with downstream signaling components. If so, the well-ordered nature of BCL10 polymers (as distinct from merely a high density of subunits) should be irrelevant for MALT1 activation. To determine if there is any specific requirement of the BCL10 polymer structure for downstream signaling, we asked if MALT1 can be activated simply by increasing its local proximity in the absence of an ordered scaffold. To do so, we used optogenetic approaches to directly dimerize or multimerize MALT1 lacking its death fold domain. Specifically, we fused MALT1126-824 to either the blue light-dependent dimerization module, VfAU1-LOV, or the blue light-dependent multimerization module, Cry2clust, followed by the fluorescent protein miRFP670nano (Figure 6A,B). When expressed in HEK293T cells containing EYFP-p65 and illuminated with blue light, the dimerizing module failed to induce nuclear translocation, while the multimerizing module did so robustly, and to the same levels as had been achieved for BCL10-mediated activation using HEK293T WT treated with PMA (Figure 6D). As a control that the constructs were each functioning as intended, we also performed experiments with the analogous region of CASP8 (180–479) fused to either VfAU1-LOV or Cry2clust. CASP8 has previously been shown to activate upon dimerization (Demarco et al., 2020). Indeed, both constructs triggered rampant cell death upon blue light illumination (Figure 6—figure supplement 1A, B). Together these results reveal that MALT1 is activated by proximity rather than structural order of the adaptor protein polymers, consistent with BCL10 having a kinetic rather than a specific structural function. The fact that MALT1 activation requires higher-order multimerization rather than just dimerization may relate to its function in concentrating the ubiquitin ligase TRAF6, a critical intermediary in the activation of NF-κB (Ginster et al., 2017).
If the binary response of NF-κB to CBM stimuli is indeed conferred by BCL10, then it should switch to a graded response when MALT1 is activated optogenetically. To test this prediction, we expressed the Cry2clust- and VfAU1-LOV MALT1126-824 fusions in our NF-κB reporter cell line, and examined the population-level distribution of NF-κB activity in single cells exposed to continuous blue light for different durations. As expected, the intensity of T-Sapphire fluorescence increased in a monotonic fashion with the duration of blue light stimulation (Figure 6E,F). When we instead treated these cells with PMA, the NF-κB reporter did not activate (Figure 6E). This result indicates that BCL10 polymers formed upon PMA treatment are unable to activate MALT1 when it lacks its DD, ruling out any unanticipated role of the polymers in structuring MALT1126-824. These data are consistent with BCL10 conferring the switch-like activation of NF-κB in WT cells upon physiological CBM stimulation.
The CBM signalosome originated in the ancient ancestor of cnidarians and bilaterians (Staal et al., 2018; Figure 7A). To determine if binarization is a conserved ancestral function of this signaling pathway, we investigated the structure and assembly properties of BCL10 from the model cnidarian, Nematostella vectensis (Nv). Using AlphaFold2 (Jumper et al., 2021), we found that the overall structure of the CARD, predicted with high confidence, is highly similar to that of human BCL10, and retains key residues of the polymer interface, such as E53 (Figure 7—figure supplement 1A–C).
We next expressed each of the Nv CBM components as mEos3.2 fusions in human cells. We found that the DAmFRET profiles for all three proteins are strikingly similar to those of their human counterparts. Specifically, the CARD of CARD-CC, the CARD9 homolog, polymerized with a high saturating concentration and negligible nucleation barrier (Figure 7B). Nv BCL10 exhibited a discontinuous, nucleation-limited transition between monomer and polymer, and the death fold from Nv PCASP1, the MALT1 homolog, remained entirely monomeric (Figure 7B).
Finally, we asked if NvBCL10 can functionally replace human BCL10 with respect to binary activation of NF-κB. We expressed the protein in HEK293T BCL10-KO cells containing the transcriptional reporter (T-Sapphire) of NF-κB activation. By measuring T-Sapphire fluorescence within a DAmFRET experiment, we found that indeed, the population of cells with NvBCL10 polymers also contained activated NF-κB, while cells expressing the same concentration of NvBCL10 in its monomeric form contained inactive NF-κB (Figure 7C). Together, these results suggest that the function of human BCL10 as a kinetic determinant of cellular decisions has been retained for over 600 million years of animal evolution.
In this study, we investigated the influence of CBM signalosome assembly on the binary kinetics of transcription factor NF-κB activation, a crucial effector of immune signaling. Using biophysical tools and a single-cell reporter of NF-κB activity, we revealed that the all-or-none response of NF-κB has a physical origin in the CBM signalosome, and that it specifically results from an ancient sequence-encoded nucleation barrier in the CARD of the adaptor protein, BCL10. Our discovery that human CBM mutations that are responsible for pathogen susceptibility and cancer, respectively, increase or decrease the barrier suggests that it has been evolutionary tuned for optimum immune system function.
This barrier allows BCL10 molecules to exist in a physiologically supersaturated state. In this state, the protein molecules are soluble and inactive, yet poised for mass activation when even a tiny fraction of them are templated to the polymer conformation by stimulated oligomers of a CARD-CC protein. We characterized in depth one of those proteins – CARD9 – and showed that its long coiled-coil region drives it into large multimers that facilitate nucleation by lowering the entropic cost to assembling multiple CARD subunits into a nucleating configuration. We reiterate that the role of upstream CARD-CC proteins is primarily kinetic in nature and culminates with the initial BCL10-nucleating event, as evidenced by our finding that BCL10 continues to polymerize even after the experimental dissolution of CARD nuclei. Nevertheless, our data do not imply that the frequency or duration of CBM formation cannot be regulated. Indeed, our observations of cells recalcitrant to stimulation show that the nucleation barrier can differ between cells. We suspect that some of the known regulatory modifications to CBM and upstream proteins function to tune the nucleation barrier. Others represent negative feedback that eventually allows signaling to terminate (Ruland and Hartjes, 2019; Thome and Weil, 2007).
CBM is one of multiple immune signalosomes featuring ordered polymers that, collectively, have been conserved since the dawn of multicellular immune systems (Dyrka et al., 2020; Essuman et al., 2018; Saupe, 2020; Wu and Fuxreiter, 2016). The prevailing notions for the functions of these polymers, which can be broadly categorized as ultrasensitivity (threshold responsiveness) and proximity-dependent effector activation (Matyszewski et al., 2018; Nanson et al., 2019; Vajjhala et al., 2017; Wu and Fuxreiter, 2016) do not encompass bistability. The former activities emerge from the equilibrium properties of phase separation, whereas the latter necessarily involves a kinetic property. We previously showed that nucleation barriers large enough to partition identically stimulated cells into discrete states only occur when phase separation is coupled to a conformational transition (Khan et al., 2018; Posey et al., 2021; Rodríguez Gama et al., 2021). The preponderance of ordered polymers in immunity signalosomes is therefore consistent with their functioning to confer bistability rather than threshold sensitivity, consistent with the need for immune systems to respond decisively to vastly substoichiometric stimulation. This is further evidenced by the fact that (1) proximity-dependent effector activation does not require any specific polymer structure, as shown presently for MALT1 and previously for caspases-1 and -8 (Boucher et al., 2018; Shen et al., 2018; Würstle et al., 2010); and (2) polymers have finite structural nuclei that limit cooperativity (Khan et al., 2018; Vekilov, 2012) whereas phase separation allows for theoretically infinite cooperativity (O’Flynn and Mittag, 2021; Rodríguez Gama et al., 2021). Indeed, signaling networks commonly feature liquid–liquid phase separation without any apparent transition in structural order (Dignon et al., 2020; Gibson et al., 2019; Riback et al., 2017; Yoo et al., 2019).
In our view, signalosome polymers are evolutionary spandrels (Gould and Lewontin, 1979; Manhart and Morozov, 2015) – byproducts rather than primary targets of natural selection. They are not well described by the classical protein structure–function paradigm, that is, that a protein’s function emerges directly from a particular three-dimensional structure (Redfern et al., 2008; Branden and Tooze, 2012; Papoian, 2008). This is because BCL10 function emerges from a kinetic property of its collective that can be conferred by any sufficiently restrictive entropic bottleneck. That activated BCL10 is an ordered polymer appears to be a consequence of selection for that bottleneck more than for some activity of the polymer itself. This is plausibly why signalosomes have evolved from at least three structurally distinct polymer scaffolds, including bona fide amyloids that in most other contexts cause protein dysfunction (Rodríguez Gama et al., 2021). Any sequence variant that flattens the nucleation barrier for example by preordering the monomers or disordering the polymers, is likely to reduce the sensitivity and executive functionality of the signalosome. As opposed to liquid-like non-membrane-bound organelles, extant signalosomes have therefore evolved such a high level of structural order that they do not form spontaneously (or only rarely so) despite a thermodynamic driving force to do so. In short, our results imply that a protein’s structure can evolve via selection for kinetic rather than equilibrium properties of its collective.
Nevertheless, nucleation is inherently probabilistic and, with enough time, may occur even in the absence of stimulation. In embracing supersaturation to power immune signaling, cells may have also predetermined their fates. That BCL10 and potentially other pro-inflammatory signalosome proteins are constitutively supersaturated implies that cells are thermodynamically predisposed to inflammation, and therefore hints at an ultimate physical basis for the centrality of inflammation in progressive and age-associated diseases (Furman et al., 2019; López-Otín et al., 2013; Taniguchi and Karin, 2018). This fact should guide ongoing efforts to control inflammation for the betterment of human health.
Antibodies used in this study include: BCL10 Rabbit polyclonal Ab (Cell Signaling Technology, C78F1), CARD9 mouse monoclonal Ab (Santa Cruz Biotechnology, A-8: sc-374569), and MALT1 mouse monoclonal Ab (Santa Cruz Biotechnology, B-12: sc-46677). β-Actin mouse monoclonal Ab (Santa Cruz Biotechnology, C4: sc-47778). mEos3.1 Rabbit polyclonal Ab (Halfmann laboratory, #4530). Alexa-488 Goat anti-Rabbit IgG (Thermo Fisher Scientific, A32731). Anti-mouse IgG Ab HRP (Cell Signaling Technology, 7076), mouse anti-rabbit IgG-HRP (Santa Cruz Biotechnology, sc-2357). Other reagents include: FuGENE HD (promega, E2311), β-glucan peptide (Invivogen, tlrl-bn, ant-zn-05), Hygromycin B (Invivogen, ant-hg-1), Penicillin–Streptomycin (Thermo Fisher, 1514014 gp), LPS (Sigma-Aldrich, L2880), PMA (BioVision, 1544–5), Halt protease inhibitor (Thermo Fisher, 78439), Puromycin (Invivogen, ant-pr-1), and Zeocin (Invivogen, ant-zn-05).
A list of plasmids used in this study can be found in Supplementary file 1. Yeast expression plasmids were made as previously described in Khan et al., 2018. Briefly, we used a Golden Gate cloning-compatible vector V08 which contains inverted BsaI sites followed by 4x(EAAAR) (a rigid linker to minimize potential impacts of mEos3.1 on the solution phase ensemble of the protein of interest). V08 vector drives expression of proteins from a GAL promoter and contains the auxotrophic marker URA3. The m1 mammalian expression vector was constructed in two steps from mEos3.2-N1 (Addgene #54525). First an existing BsaI site in mEos3.2-N1 was removed by introducing a point mutation at nucleotide position (3719) G to A. Second, the existing multiple cloning site in mEos3.2-N1 was replaced via Gibson assembly with a fragment containing inverted BsaI sites followed by 4x(EAAAR) to create the Golden Gate compatible vector, m1. In this vector protein expression is controlled by a CMV promoter. m1 was then used to create the m1_C1 vector in two steps. First, inverted BsaI sites upstream of mEos3.1 were removed. Second, using Gibson assembly, a fragment containing 4x(EAAAR) followed by inverted BsaI was inserted in frame after mEos3.2. The mCardinal vector was made via Gibson by replacing the mEos3.2 in m1 with a synthetic fragment encoding mCardinal. Vector m16 was made via Gibson by replacing the mEos3.2 in m1 with a synthetic fragment encoding mScarlet-I. The m25 backbone was made by replacing the mCherry sequence in mCherry-CRY2clust (Addgene #105624) with an insert containing inverted BsaI sited followed by 4x(EAAAR) and miRFP670nano.
Lentivirus vectors were made as follows. The lentivirus vector containing the NF-κB reporter, phage_NFkB-TSapp, was made by replacing the luciferase sequences with the fluorescent protein T-Sapphire in pHAGE NFKB-TA-LUC-UBC-dTomato-W (Addgene #49335). Additionally, we replaced the marker tdTomato in Addgene #49,335 with the resistance cassette for HygromycinB. The lentivirus vector for expression of EYFP-p65, pLV_EYFP-p65 was made by replacing the Hygromycin B resistance cassette in pLV-EF1a-IRES-Hygro (Addgene #85134) with a Zeocin resistance cassette and inserting the p65 coding sequence before the IRES site. To create lentiviral vectors for the expression of CARD9 fused with miRFP670 and Cry2, we inserted via Gibson the respective inserts from m25 into pLV-EF1a-IRES-Hygro. Finally, for the doxycycline controlled lentiviral vectors, we cloned the respective coding sequences from BCL10 in m1 or m16 vectors into pCW57.1 (Addgene #41393).
Inserts were ordered as GeneArt Strings (Thermo Fisher) flanked by Type IIs restriction sites for ligation between BsaI sites in V08, V12, m1, and other vectors derived from m1. All other inserts were cloned into respective vectors via Gibson assembly between the promoter and respective fluorescent or tag marker. All plasmids were verified by Sanger sequencing.
For DAmFRET experiments we employed strain rhy1713, which was made as previously described in Khan et al., 2018. To create artificial intracellular seeds of CARD9 variants, CARD10, CARD11, and CARD14, sequences were fused to a constitutive condensate-forming protein, μNS (471–721), hereafter ‘μNS’. Yeast strain rhy2153 was created by replacing the HO locus in rhy1734 with a cassette consisting of: natMX followed by the tetO7 promoter followed by counterselectable URA3 ORFs derived from C. albicans and K. lactis, followed by µNS-mCardinal. To create strains expressing the fusion CARD9-mCardinal-μNS and others, AseI digests of yeast expression plasmids containing desired proteins were transformed into rhy2153 to replace the counterselectable URA3 ORFs with the gene of interest. The resulting strains express the proteins of interest fused to μNS-mCardinal, under the control of a doxycycline-repressible promoter. Transformants were selected for 5-FOA resistance and validated by PCR. See Supplementary file 2 for a list of all strains used in this study.
For measuring nucleating interactions diploid strains were maintained on doxycycline (40 mg/ml) until initial culture for DAmFRET assay.
HEK293T (CRL-3216) cells and THP-1 (TIB-202) cells were purchased from ATCC. HEK293T cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) with L-glutamine, 10% fetal bovine serum (FBS), and PenStrep 100 U/ml. THP-1 cells were grown in Roswell Park Memorial Institute (RPMI) medium 1,640 with L-glutamine and 10% FBS. All cells were grown at 37°C in a 5% CO2 atmosphere incubator. Cell lines were regularly tested for mycoplasma using the Universal mycoplasma detection kit (ATCC, #30-1012K).
HEK293T cells were plated in 6-well culture plates at 8.0 × 105 cells/well in DMEM. The next day, 2.0 µg DNA of expression plasmids were mixed 150 μl Opti-MEM and transfected using FuGENE HD (Promega) at a DNA to FuGENE HD ratio of (1:3). Cells were treated with either PMA or Dimerizer ligand at described concentrations 24 hr after transfection. Cells were harvested for flow cytometry analysis after a total of 48 hr. For microscopy experiments, cells were plated directly into 35 mm glass-bottom dishes (iBidi, μ-Dish 35 mm, high Glass Bottom) at a concentration of 0.8 × 105 cells/dish and transfected as previously mentioned.
For generation of stable cell lines, constructs were packaged into lentivirus in a 10 cm plate 60% confluent of HEK293T cells using the TransIT-LT1 (Mirus Bio, MIR2300) transfection reagent and 7 μg of the vector, 7 μg psPAX2, and 1 μg pVSV-G. After 48 hr, media was collected and centrifuged at 3000 × g to remove cell debris. At this point, media containing lentivirus was used or stored at −80°C. For HEK293T adherent cell transduction, cells were plated in 6-well culture plates at 50% density. Next day, media was replaced with media containing lentivirus at a multiplicity of infection of 1, supplemented with 5 μg/ml Polybrene (Sigma-Aldrich, TR-1003-G). For THP-1 suspension cell transduction, cells were spinfected with virus containing media for 1 hr at 1000 × g at room temperature supplemented with 5 μg/ml Polybrene. For transduction of phageNF-κB-TSapp, THP-1 and HEK293T cells were selected with Hygromycin B (350 mg/ml) for 14 days and used for further analysis or complementary transductions. For transduction of pCW57.1_BCL10-mScarlet, pwtBCL10-mScarlet, HEK293T cells were selected with Puromycin (1 μg/mL) for 7 days. After this time, cells were sorted for positive expression of mScarlet and expanded in continuing selection with puromycin. For transduction of expression clones tagged with mEos3.1, THP-1 cells were selected with Puromycin (1 μg/ml) for 7 days. Cells were sorted for positive expression of mEos3.1 and expanded for further experiments with continued selection in puromycin. For transduction of pLV_EYFP-p65, HEK293T BCL10-KO cells reconstituted with pBCL10-mScarlet, were transduced and selected with Zeocin (300 μg/ml). Positive cells expressing EYFP and mScarlet were sorted and expanded in selection media containing drugs. Plasmid pCARD9-CARD_miRFP-Cry2 was transduced into HEK293T cells. Cells were selected with Hygromycin B (350 μg/ml) for 7 days and then sorted for positive expression of miRFP670nano.
To generate HEK293T BCL10-KO and MALT KO cell line, cells were nucleofected (Neon Transfection system, Thremo) with a ribonucleoprotein (RNP) mix of sgRNA for BCL10 exon1 or MALT1 exon1 and Cas9 protein. After this, cells were pooled and submitted for targeted deep sequencing to find the insertion/deletion (InDel) ratio. Subsequently, cells were prepared for single-cell sorting into four 96-well plates and expanded to obtain a duplicate for each well. At this stage cells were lysed and submitted for deep sequencing to find the genome edited cells. Selected wells were further expanded to validate via protein immuno detection. To generate THP-1 BCL10-KO cells, sgRNA targeting BCL10 exon1 was cloned into the lentiCRISPR v2-Blast (Addgene #83480). This vector was packaged into lentivirus as described in generation of stable cell lines section. THP-1 cells were transduced via spinfection with lentivirus and supplemented with polybrene. 24 hr after spinfection, media was replaced. 48 hr after spinfection, cells were selected with blasticidin (1 μg/ml). After 10 days of blasticidin selection, single-cell clonal expansion was done by serial dilution of resistant cells to achieve complete knockouts. Selected wells were analyzed by immuno detection to confirm the absence of BCL10 protein.
We performed DAmFRET analysis as previously described in Khan et al., 2018. Expression plasmids were transformed into specified strains following a standard lithium acetate protocol (see Supplementary file 1 for list of expression plasmids). Yeast transformants were selected in SD-URA plates. Briefly, single transformant colonies were inoculated in 200 μl of SD-URA in a microplate well and incubated in a Heidolph Titramax platform shaker at 30°C, 1350 RPM overnight without presence of Dox. Cells were washed with sterile water, resuspended in galactose-containing media, and allowed to continue incubating for approximately 16 hr. Microplates were then illuminated for 25 min with 320–500 nm violet light to photoconvert an empirically optimized fraction of mEos3.1 molecules from a green (516 nm) form to a red form (581 nm). At this point, cells were either used to collect microscopy data or continue the DAmFRET protocol.
We performed DAmFRET in HEK293T cells as previously described (Venkatesan et al., 2019). Briefly, cells were transfected as described in the methods section on transient transfection. 48 hr after transfection, cells were trypsinized and washed in phosphate-buffered saline (PBS) supplemented with 10 mM ethylenediamine tetraacetic acid (EDTA). Then, samples were fixed in 4% paraformaldehyde (PFA) for 5 min with constant shaking. Cells were washed two additional times with PBS + EDTA and transferred to a 96-well plate. The sample-containing plate was photoconverted as previously described for yeast samples. After photoconversion, samples were analyzed in a ZE5 cell analyzer cytometer.
DAmFRET data were collected on a ZE5 cell analyzer cytometer or an ImageStreamx MkII imaging cytometer (Amnis) at ×60 magnification as previously described (Khan et al., 2018). Autofluorescence was detected with 405 nm excitation and 460/22 nm emission; Side Scatter (SSC) and Forward Scatter (FSC) were detected with 488 nm excitation and 488/10 nm emission. Donor and FRET fluorescence were detected with 488 nm excitation and 525/35 nm or 593/52 nm emission, respectively. Acceptor fluorescence was excited with 561 nm excitation and 589/15 nm emission. Approximately 500,000 events were collected per sample. Data compensation was done in the built-in tool for compensation (Everest software V1.1) on single-color controls: non-photoconverted mEos3.1 and dsRed2 (as a proxy for the red form of mEos3.1). For nucleating interactions, all regular channels for DAmFRET were collected with the addition of mCardinal intensity by 561 nm excitation and 670/30 nm emission.
DAmFRET coupled with the NF-κB reporter data was collected as previously mentioned with the following modifications. HEK293T cells were treated as previously described in the DAmFRET protocol. In addition to the regular channel for DAmFRET data acquisition, T-Sapphire expression was detected using 405 nm excitation and 525/50 nm emission.
Data were processed on FCS Express Plus 6.04.0015 software (De Novo). Events were gated for single unbudded cells by FSC versus SSC, followed by gating of live cells with low autofluorescence and donor positive. Live gate was then selected for double positives (donor and acceptor). Plots represent the distribution of AmFRET (FRET intensity/acceptor intensity) versus Acceptor intensity (protein expression). For DAmFRET data in mammalian cells, cells were first gated for single cells followed by gating double positive expressing cells donor, acceptor. In the cases for DAmFRET coupled to NF-κB reporter, an additional gating was performed on the DAmFRET plot to quantify the intensity of T-Sapphire.
The data were fit to a Weibull function as previously described (Khan et al., 2018). Briefly, cells exhibiting assemblies were identified by their exclusion from a negative DAmFRET gate created from the monomeric mEos3.1 distribution. DAmFRET plots were divided into 64 logarithmically spaced bins and, within each, the fraction of cells with AmFRET higher than the monomer control was calculated. We applied this approach to all DAmFRET plots. This analysis provides a gross percentage of the fraction of cells containing assembled protein. The fit of these data to the Weibull function produced the EC50 and ẟ statistics.
Imaging of yeast and mammalian cells was done in an LSM 780 microscope with a ×63 Plan-Apochromat (NA = 1.40) objective. T-Sapphire excitation was done with a 405 nm laser. mEos3.1 and mScarlet-I excitation were made with a 488 and 561 nm laser, respectively. To quantify the percentage of cells containing polymers, images were subjected to an in-house Fiji (https://imagej.net/software/fiji/) adapted implementation of Cellpose (https://www.nature.com/articles/s41592-020-01018-x, https://www.cellpose.org/) analysis for cellular segmentation for both whole cell contour and nucleus. The Cellpose generated regions of interest (ROIs) were used to measure specified imaging channels. For each experiment, more than 200 cells were quantified in an unbiased way. Cells were defined as positive for containing polymers if the coefficient of variation (CV, standard deviation divided by the mean intensity) for the fluorescent channel was above 0.8 as defined by the distribution of values.
For time-lapse imaging, fluorescence images were taken using a spinning-disk confocal microscope (Nikon, CSU-W1) with a ×60 Plan Apochromat objective (NA = 1.40) and a Flash 4 sCMOS camera (Hamamatsu). Samples were maintained at 37°C and 5% CO2 with a stage top incubator (Okolab). Prior to stimulation, cells were treated with Hoechst 33,342 (0.5 μg/ml) for 30 min. 405 nm laser at 20% was used to collect emission of Hoechst for 50 ms per frame. 514 and 561 nm laser were used at 25% to collect EYFP and m-Scarlet emission for 300 ms, respectively. Images were captured every 10 min for p65 nuclear translocation experiments. ROIs were generated by Cellpose segmentation algorithm around each cell contour and nucleus on the EYFP and Hoechst image, respectively. These ROIs were then used to measure the area, mean, standard deviation, and integrated density of each cell on the EYFP and mScarlet fluorescence channels. For each cell, we computed the nuclear to cytoplasmic ratio of integrated density for the EYFP channel. The same ROIs were used to compute the CV of the mScarlet-I fluorescence.
Cry2clust optogenetic experiments were conducted on an LSM 780 microscope with a ×63 Plan-Apochromat (NA = 1.40) objective in the integration mode. Samples were maintained at 37°C and 5% CO2 with a microscope incubator. To stimulate Cry2clust we used the 488 nm laser at a power setting of 50% for a pulse of 1 s, which is the amount of time it took to scan the user generated region of interest, unless indicated otherwise. 561 and 633 nm lasers were used for imaging mScarlet-I and miRFP670nano, respectively. To track BCL10 nucleation dependent on CARD9-Cry2 we imaged cells before and after stimulation every 2 min. For the experiments to quantify the nuclear translocation of p65 to the nucleus, EYFP was imaged using the 514 nm laser. Imaging EYFP with the 514 laser resulted in undesired Cry2clust restimulation. To avoid additional Cry2clust stimulation in our experiments, EYFP was only imaged at the beginning of the experiment and in the last frame. We computed the nuclear to cytoplasmic ratio of EYFP as previously described in the section above. Similarly, we calculated the CV for mScarlet-I intensity as mentioned above.
To perform immunostaining of NF-κB subunit, p65, first THP-1 BCL10-KO cells reconstituted with either BCL10-mScarlet or BCL10(E53R)-mScarlet were seeded on glass coverslips (Neuvitro, GG-18-PLL) placed on 12-well plates at a density of 3.0×105 cells/well. PMA was added at a concentration of 10 ng/ml for 24 hr. This allowed cells to attach on the coverslips. After withdrawal of PMA, cells rested for 24 hr and then BCL10-mScarlet protein expression was induced with doxycycline (0.8 μg/ml) for 16 hr. Dox was removed from the wells and replaced with fresh media. Samples were then treated with β-glucan (10 μg/ml) for 12 hr. Prior primary antibody incubation, cells attached to the coverslips were washed with PBS and then fixed with PFA 4% for 15 min. PFA was then washed away with PBS rinse and added PBS + Triton 0.05% to permeabilize cells. Cells were then washed three times with PBS and blocked with bovine serum albumin 1% for 30 min at room temperature. Primary antibody incubation ocurred overnight with p65 antibody (Proteintech, 10745-1-AP) at a dilution of 1:400. Primary antibody was washed three times with PBS. After this, cells were incubated with anti-rabbit Alexa Fluor 647 secondary antibody (Thermo Fisher, A-31573) at a 1:400 dilution for 4 hr at RT. Following secondary antibody wash steps, cells were stained with DAPI (0.1 μg/ml) for 10 min. Subsequently, cells were mounted on glass slides using ProLong Gold (Thermo Fisher, P36965). Images were collected in LSM 780 microscope with a ×63 Plan-Apochromat (NA = 1.40) objective.
The Wes platform (ProteinSimple) was used to perform capillary based protein immunodetection. We followed the recommended protocol from the manufacturer. Briefly, cell lysates were diluted with 0.1× sample buffer. Subsequently, four parts of diluted sample were combined with one part 5× Fluorescent Master Mix (containing 5× sample buffer, 5× fluorescent standard, and 200 mM dithiothreitol (DTT)) then boiled at 95°C for 5 min. Our sample final protein concentration for each capillary was 1 μg/ml. After the denaturing step, an assay plate was filled with samples, blocking reagent, primary antibodies (1:50 dilution for mEos3.1, MALT1 and actin, 1:10 dilution for BCL10, and 1:150 dilution for CARD9), HRP-conjugated secondary antibodies and chemiluminescent substrate. A biotinylated ladder provided molecular weight standards for each assay. Once the assay plate was set up, electrophoretic protein separation and immunodetection were carried out in the fully automated capillary system. Data were processed using the open-source software Compass (https://www.proteinsimple.com/compass/downloads/) to extract the intensities for the peaks corresponding to the expected molecular weight of proteins of interest.
For two sample comparisons, two-sided Student’s t-tests were used for significance testing unless stated otherwise. The graphs represent the means ± standard deviation of independent biological experiments unless stated otherwise. Statistical analysis was performed using GraphPad Prism 9 and R packages.
Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1675.
Stowers Original Data RepositoryID libpb-1675. A nucleation barrier spring-loads the CBM signalosome for binary activation.
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Jungsan SohnReviewing Editor; Johns Hopkins University School of Medicine, United States
Volker DötschSenior Editor; Goethe University, Germany
In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
[Editors' note: this paper was reviewed by Review Commons.]https://doi.org/10.7554/eLife.79826.sa1
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In this manuscript, Gama et al. use a biophysical assay DAmFRET, structural analysis, and optogenetic tools to uncover the nucleation mechanism of CBM signalosome. They performed experiments first in yeast cells that lack death folds or related signaling networks, then confirmed their discoveries in human cells. The results presented here are clear and convincing. The paper is very well presented and clearly written.
They found it is the CARD domain of BCL10 that acts as a molecular switch that drives all-or-none activation of NF-κB. Monomeric BCL10 possesses an unfavorable conformation and serves as a nucleation barrier, keeping BCL10 in a supersaturated inactive state that allows for binary activation upon stimulation.
They also characterized CARD9 CARD domain and a coiled-coil region. They reasoned that CARD9CARD functions as a polymer seed to nucleate BCL10, and that the coiled-coil region has multimerization ability to facilitate nucleation. Furthermore, they characterized that MALT1 activation doesn't depend on BCL10 polymers but its own proximity. And MALT1 induces graded NF-κB activation, thus further demonstrating the binary activation is conferred by BCL10.
1. Figure S1D and E, the authors used TNF-a to activate NF-κB independent of CBM signalosome and found the activation in each cell increased with dose. In contrast, CBM activation led to bimodal cell activation. The authors claim that this is evidence that positive feedback upstream of NF-κB. We do not believe this claim can be made from this comparative experiment alone. We agree that positive feedback is important for activating an NF-κB response, but the comparison between CBM and TNFa is inaccurate and glosses over published data. Specifically, there is published data that TNF-a does activate a 'switch-like' or digital response, as defined by the translocation of p65 (see (Tay et al. 2010) among other studies that have examined p65 translocation at the single-cell level). The difference in T-sapphire expression between CBM and TNF activation is most likely due to TNFa induced oscillations of p65 translocation (although this is speculation on our part). Therefore we suggest to the authors that the TNF-a data (Figure S1D and E) should be omitted, as the claim of switch or not-switch as pertains to TNF signaling is more complex and nuanced than presented here. We believe omitting this data will strengthen the manuscript and avoid confusion in the field. The bimodal expression of the T-sapphire NF-κB reporter driven by the CBM signalosome activation is sufficient to claim an all-or-none response.
We thank the reviewer for this suggestion. We acknowledge that the activation of NF-κB by TNF-ɑ is more complex than we had presented, and agree that the differences in T-Sapphire reporter output could be attributed to p65 oscillations. We had not previously considered this interesting possibility - which is not addressed by the present data - believe it is worth future investigation. As suggested by the reviewer, we have now omitted the TNF-a data, and agree that this change does not impact the overall claims of the paper.
2. Figure 3B, the authors introduced CARD9CARD-µNS as a stable condensed seed for BLC10. However, considering CARD9CARD can form polymers at high concentration (Figure 3B and S3D), are these high expression levels of CARD9CARD able to induce BCL10-mEos3.1 assembly (as measured by DamFRET in yeast cells)? Can the authors examine BCL10 FRET at these high expression level of CARD9CARD? We assume that BCL10 will be assembled in these cells. This would provide a valuable control experiment and support the author's conclusions.
Indeed, this question is amenable to DAmFRET. Accordingly, we have now performed DAmFRET of yeast cells expressing Bc10-mEos3.1 in the presence of either CARD9CARD-mCardinal or mCardinal itself (see new Figure S6A and B, and associated Results section). We confirmed that cells with high CARD9CARD-mCardinal expression had higher FRET on average than cells with low expression. Importantly, cells expressing high or low levels of mCardinal itself had the same FRET level (Figure S6).
3. Figure 3C, the text said "Whereas WT CARD9CARD assembled into polymers at high concentration, the pathogenic mutants R18W, R35Q, R57H, and G72S failed to do so (Figure 3C and S7B,C), explaining why they cannot nucleate BCL10". This claim that these mutants can not nucleate BCL10 does not have a figure call out or a reference. The authors then show the results in Figure 3E which supports this claim. Even though they were done in the context of full-length CARD, all proteins contain the I107E mutation that releases autoinhibition. For clarity, the authors should consider rearranging the text to avoid explaining a phenomenon and making conclusions before showing the results.
We have now rearranged this section to match the figures and claims.
4. Figure 4D, E and Video 1, the authors showed the nucleation of BCL10 into puncta within live cells is followed by p65 translocation to the nucleus. The authors claim that 'this result suggests that BCL10 is indeed supersaturated prior to stimulation' (paragraph 2 section titled BCL10 is endogenously supersaturated'). We fail to understand how this live-cell experiment leads to the conclusion BCL10 is supersaturated before stimulation. We think this text should be deleted from the text, or put into context with the DAmFRET data that lead the authors to make this claim. It would be interesting for the authors to define in discussion what are the golden criteria to claim a protein exists in a supersaturated state with live cells (by microscopy or other methods)? Adaptor protein assembly into puncta and the subsequent nuclear translocation of transcription factors is a common phenomenon across signalling pathways. Not all these pathways rely on signaling adaptors existing in a supersaturated state. The field of cell signaling (and cell biology in general) would benefit from a detailed definition of how these physical-chemical definitions of proteins are supported by experimental data. We believe that this paper will become a seminal paper in the field, and future work will benefit from a clear definition of how a claim of supersaturation is derived from the data.
We appreciate that the concept of supersaturation will be foreign to many biologists, and welcome this opportunity to elaborate. We have now rephrased the corresponding Results section for figure 4D, E, and have added new evidence to support our claim that BCL10 is supersaturated, as had been requested by reviewer 2 (see below in response to point 1). Supersaturation, as we (correctly) use the term, occurs when the concentration of a protein in solution exceeds its equilibrium solubility for the given conditions. The term is also sometimes used to describe global protein “concentrations” in excess of the solubility limit, even if a dense phase has already formed and potentially depleted the effective concentration (in solution) to the solubility limit. This is a key distinction, as only the former implies a high-energy out-of-equilibrium scenario that predetermines a future change -- release of the excess energy via phase separation.
How does one experimentally determine if a protein is supersaturated? In theory, one may conclude that a protein is supersaturated if its assembly causes a net loss of energy from the system (i.e. exothermic). Unfortunately, it is likely not yet possible to perform such measurements with sufficient sensitivity inside a living cell. However, it is possible to infer that a protein is supersaturated if assembly can be shown to occur without a net input of energy to the system, i.e. without any change in thermodynamic control parameters such as temperature, pH, post-translational modifications, concentration of the protein, or concentration of any interacting factor. To do this, one introduces a substoichiometric amount of pre-assembled protein to the system. This manipulation will trigger assembly if the protein is supersaturated. If the protein is instead subsaturated, assembly will not occur and the exogenously added assemblies will simply dissolve. This phenomenon, known as “seeding” in the prion field, is considered a golden criterion sufficient to conclude that a protein has prion behavior. However, because bona fide prions additionally require a means for dissemination between cells, seeding analyzed at the cellular rather than population level is more appropriately considered a sufficient criterion for supersaturation (which is a prerequisite for classical prion behavior (Khan et al. 2018)). Our CARD9CARD-Cry2 experiment was designed to test this criterion. Specifically, it allowed us to introduce a seed independently of receptor activation, thereby precluding any orthogonal cellular response that might lower Bcl10 solubility through e.g. a post-translational change. That the seeds were substoichiometric is evidenced by the fact that Bcl10 polymerized homotypically following stimulation (i.e. it didn’t just bind to the CARD9CARD puncta, but went on to deposit onto itself).
How does assembly under this scenario differ in principle from the many examples of puncta formed by other signaling proteins that occur upon stimulation of their respective pathways? Puncta formation that is induced by a thermodynamic change in the cell cannot be said to have resulted from pre-existing supersaturation. Rather, the stimulus may have caused some change that either increases the effective concentration of the protein (e.g. upregulates its expression, induces a post-translational change that activates it, or releases an inhibitory factor) or reduces solvent activity (e.g. change in pH).
An additional requirement (necessary but not sufficient) is that the assembly must be regular with respect to some order parameter. That is to say, it must be a bona fide “phase”. At a minimum, this implies a uniform density. Additionally, for supersaturation to persist over biological timescales under physiological conditions and confinement volumes, the assembly (once formed) must also have structural repetition in at least two dimensions, i.e. crystallinity (Rodríguez Gama et al. 2021; Zhang and Schmit 2016). We know this to be true for Bcl10.
Rodríguez Gama A, Miller T, Halfmann R. 2021. Mechanics of a molecular mousetrap-nucleation-limited innate immune signaling. Biophys J 120:1150–1160. doi:10.1016/j.bpj.2021.01.007
Khan, T., Kandola, T.S., Wu, J., Venkatesan, S., Ketter, E., Lange, J.J., Rodríguez Gama, A., Box, A., Unruh, J.R., Cook, M., et al. (2018). Quantifying nucleation in vivo reveals the physical basis of prion-like phase behavior. Mol. Cell 71, 155-168.e7.
Zhang L, Schmit JD. 2016. Pseudo-one-dimensional nucleation in dilute polymer solutions. Phys Rev E 93:060401. doi:10.1103/PhysRevE.93.060401
5. Regarding the supersaturated state of BCL10, the authors convincingly use optogenetics to show how transient assemblies of CARD-Cry2 can template BCL10 assembly. This is a convincing experiment that shows templated nucleation of BCL10. To strengthen the claim that BCL10 is supersaturated endogenously we suggest the author quantify the expression of BCL10-mScarlet and CARD-Cry2 and ideally show that this phenomenon can be observed at expression levels equivalent to endogenous.
As stated above, that BCL10-mScarlet formed polymers that we observed to elongate homotypically off of the CARD9CARD seeds indicates that the protein was supersaturated under the conditions of the experiment. The concentration of CARD9 is not a relevant parameter in this case. We had already compared the expression of BCL10-mScarlet to endogenous BCL10 in 293T, THP-1, and human fibroblast cells by quantitative immunodetection (Figure S10D), revealing that the expression level of our BCL10-mScarlet constructs matched that of endogenous BCL10, which was approximately the same in all cell lines. We also compared the distribution of expression levels of BCL10-mScarlet versus that of endogenous BCL10 using antibody staining followed by flow cytometry, which confirmed that the range of expression levels of BCL10-mScarlet falls within that of endogenous BCL10 in 293T cells (Figure S10F). Hence, we believe our data suffice to conclude that Bcl10 is supersaturated at endogenous levels of expression.
1. Special character "δ" is not displayed in the text (instead only a space).
This error occurred upon exporting the manuscript from our text editor to a PDF. We now have made sure all special characters are present in the PDF version.
2. Several cell lines including mouse, human, and yeast lines were used across this manuscript. It would be clearer and more helpful if the exact cell type of the line could be indicated. Such as, "BCL10-mEos3.1 yeast cells" instead of "BCL10-mEos3.1 cells", "BCL10-mScarlet HEK293T cells" instead of "BCL10-mScarlet cells".
We have now modified all instances to indicate the origin of the cell lines tested.
3. Figure 5B, the authors indicated that BCL10 colocalized with CARD9CARD, then please show the merged image as well.
We have now included the merged image to indicate colocalization in the inset images.
4. Figure 6E, authors claimed that cells were stimulated with blue light for the indicated durations. The longest duration is 12 hours. Please specify if it was continuous exposure or several rounds of exposure in the indicated durations.
We have now specified in the figure legends, text, and methods section, that this specific experiment used a continuous exposure of blue light.
Reviewer #1 (Significance (Required)):
This work used a combination of FRET and optogenetic tools to engineer CBM signaling and visualize the effects. They incorporated knowledge from structure biology, together with their results from mutations and truncations, dissected the significance of each protein in CBM signalosome, and demonstrated in detail how higher-order assemblies make all-or-none cellular decisions. We believe this paper will be a seminal paper in the field of cell signalling and cytoplasmic organization. It defines a new paradigm of macromolecules assembly of signalling complexes as being dependent on protein existing in a supersaturated state. Importantly this paper opens up new questions regarding macromolecular signaling complexes (found in many innate immune signaling pathways): How is protein supersaturation maintained and used throughout evolution to construct biochemical signalling switches?
This paper will be of particular interest to scientists working on immunity and cell signalling, especially in the field of higher-order assemblies. However, we feel the impact of this paper goes beyond these fields, and we believe this manuscript will be of broad interest to the cell biology and biophysics communities. For reference, our expertise is in innate immunity and cell biology.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In their manuscript entitled "A nucleation barrier springloads…" Rodriguez-Gama et al. dissect the assembly mechanism of the signalosome, composed of the proteins CARD9, BCL10 and MALT1, using a novel in-cell biophysical approach (DAmFRET). They first overexpressed fluorescently tagged versions of the proteins to promote their assembly in yeast and mammalian cells, finding that CARD9 forms higher order assemblies across a wide range of concentrations with no discontinuity in the DAmFRET profile. In contrast, the DAmFRET profile of BCL10 showed a clear separation between monomers and higher order assemblies, which started to form spontaneously only at higher BCL10 concentrations. Furthermore, at the two states of the proteins co-exist at all concentrations. These observations imply that there is a nucleation barrier to forming BCL10 assemblies. MALT1 showed no change in FRET regardless of its expression level. These observations, alongside fluorescence microscopy of the assemblies, and previous structural studies, suggest that BCL10 forms self-templating polymers that act as a switch for an all-or-nothing immune response, assayed in this case by monitoring the nuclear translocation of the NF-κB subunit p65. The authors also assessed the effects of known disease-causing mutations on the nucleation barrier, showing that changes in the strength of the nucleation barrier can have major effects on signalosome function. Finally, they used optogenetic methods to trigger assembly of individual signalosome components, providing insight into the minimal components/conditions required for signalosomes to work.
Overall, the experiments by Rodriguez-Gama et al. offer convincing evidence that there is a nucleation barrier to BCL10 polymerisation, and that a CARD9 template is sufficient to overcome the barrier. Although the existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise. The optogenetic experiments are a nice sufficiency test for their ideas.
We feel there are a few key points to address before publication.
1) One of the main conclusions is that spring-loading the nucleation barrier with high super-saturating BCL10 concentrations allows a decisive response. Although much of the data strongly imply this conclusion, the dependence of the immune response on BCL10 concentration was not tested directly. A key prediction of the nucleation barrier is that at concentrations below saturation, BCL10 should not be able to induce an all-or-nothing response when stimulated. At saturated/super-saturated concentrations BCL10 should be able to induce a response. At deeply super-saturated concentrations the response should start to be activated spontaneously in the absence of an external stimulus. These predictions could be tested using the doxycycline-inducible BCL10 system (Figure S2D), without establishing major new experimental avenues. We feel that such an experiment would strengthen the main conclusion. It might also help to shed light on whether being highly supersaturated enables a more decisive response than being just saturated.
This is a great idea. As the reviewer suggested, our Doxycycline-inducible BCL10 system enables us to induce and track the state of BCL10 over time. We have now performed the requested experiments (Figure S9D, E) and incorporated the results into the relevant section of the text. In short, our new analyses show that BCL10 indeed has a concentration threshold for activation by stimulation, and that it can also nucleate spontaneously when overexpressed. Note that our original analyses in Figure 4B and C also demonstrate spontaneous BCL10 activation at high concentrations. With this new evidence and the orthogonal approaches used in Figure 5, we believe our data definitively support our conclusion that BCL10 is supersaturated.
2) Intuitively, readers might expect that if BCL10 is supersaturated then, once nucleated, it would rapidly assemble at the nucleation sites. In Figure 5B, CARD9CARD-miRFP670nano-Cry2 assemblies are optically induced throughout the cell. However, BCL10 appears to nucleate at just a few sites with a few minutes delay. More widespread nucleation and growth of BCL10 polymers seems to take longer (20-40 minutes, Figures 5B and 5C), after CARD9CARD-miRFP670nano-Cry2 has disassembled. Furthermore, in Figures 4D and 4E, very few BCL10 assemblies are visible/quantifiable after 70 minutes PMA exposure, but p65 has clearly entered the nucleus. It looks like BCL10 assembly slightly lags behind p65 nuclear entry. Can the authors provide a more detailed explanation of these kinetics?
We do note that the number of CARD9CARD clusters formed upon opto-stimulation exceeds the apparent number of BCL10 nucleation sites. We believe this is consistent with nucleation-limited kinetics, where the clustering of CARD9-CARD increases the local probability of nucleation. As nuclei form and grow, they lower the probability of subsequent nucleation elsewhere in the cell. Additionally, it is possible that our artificial seeds do not perfectly mimic the native CARD9 seeds that form upon natural stimulation (e.g. due to potential steric interference from the fluorophore and Cry2). We also acknowledge that there is a slight delay in the visible appearance of BCL10 polymers relative to p65 nuclear translocation. We expect that MALT1 activates already when the polymers are still too small to see (sub-resolution), whereas the polymers only become microscopically visible once they’ve grown quite a bit more.
3) Related to point 2 above, in Figure 5D, the leftmost cell in the field of view clearly contains CARD9CARD assemblies but there are no BCL10 assemblies and p65 is not imported into the nucleus (in contrast to the central cell in the field of view). How often does CARD9CARD optogenetic assembly lead to BCL10 assembly? In other words, can the authors quantify the cell-to-cell variability in this experiment?
Throughout our experiments, whether analyzing BCL10 puncta formation, NF-κB transcriptional activity, or p65 translocation, we observed a persistent nonresponsive fraction of cells even at saturating levels of stimulation. Specifically, approximately 30% of THP-1 cells failed to acquire T-Sapphire fluorescence or form BCL10-mEos3.2 puncta when stimulated with high levels of β-glucan (Figure 1B and E, respectively), and approximately 25% of 293T cells failed to acquire T-Sapphire fluorescence or exhibit p65 nuclear translocation when stimulated with high levels of PMA (Figure 1C and Figure 4E, respectively). Because these numbers did not depend on whether BCL10 was endogenously or exogenously expressed, we know that the underlying cell-to-cell heterogeneity involves factors upstream of BCL10. Indeed, the fraction of recalcitrant cells drops to 10% in our optogenetic experiments that bypass upstream factors (Figure S11E). Possible sources of heterogeneity include different physiological states of the cells or fluctuations in the expression levels of any upstream factor in the signaling pathway. We believe that this phenomenon is not unique to the CBM signalosome, as we (unpublished) and others (Fernandes-Alnemri T et al., 2009, Dick M et al., 2016) have similarly observed a fraction of non-responding cells upon activation of the inflammasome, which involves nucleation-limited polymerization of the adaptor protein ASC. While this phenomenon is interesting and may be important to our understanding of the full complexity of signalosomes in vivo, we believe that identifying the source of heterogeneity would be outside the scope of the present manuscript. We now describe this phenomenon in the final paragraph of the “Endogenous BCL10 is constitutively supersaturated” section.
Fernandes-Alnemri, T., Yu, JW., Datta, P. et al. AIM2 activates the inflammasome and cell death in response to cytoplasmic DNA. Nature 458, 509–513 (2009). https://doi.org/10.1038/nature07710
Dick, M., Sborgi, L., Rühl, S. et al. ASC filament formation serves as a signal amplification mechanism for inflammasomes. Nat Commun 7, 11929 (2016). https://doi.org/10.1038/ncomms11929
While the work is scientifically well done, the text reads as though it is meant for experts rather than a broad audience. This is a pity because it risks alienating readers. We suggest that some adjustments to the text (mainly additional explanations and not ruling out alternative interpretations of the data) would widen the audience and increase the impact of this important study. Below are some suggestions that might help.
1. In the first Results section, the authors write: 'This suggests that Bcl10 but not CARD9 assembly occurs in a highly cooperative fashion that could, in principle (Koch, 2020), underlie the feed forward mechanism.' It isn't obvious how Figure 1 leads to this statement. Could the authors give a more detailed explanation?
We have now revised the text to elaborate on this interpretation.
2. One limitation of DAmFRET is that it can only detect a nucleation barrier where there is a difference in FRET between the monomer and the assembled form of the protein. However, it can't necessarily detect when there is not a nucleation barrier i.e. if there's no difference in FRET. The text seems to suggest that CARD9 and MALT1 don't have nucleation barriers to their assembly. While this might not be intentional, it would be helpful to explicitly state that CARD9 and MALT1 could also possess such barriers that are not detectable by this method. This wouldn't detract from the finding that BCL10 has a barrier that plays an important function.
The reviewer is correct that DAmFRET would not be able to detect a nucleation barrier if the assembled phase does not condense the fluorophore to a sufficiently high density for FRET to occur. In our experience, this is only a concern for very large proteins whose bulk “dilutes” the fluorophores within the assembly. Death domains, on the other hand, are only ~ 3 nm in diameter, and FRET occurs within a range of ~10 nm; hence we think it very unlikely that the death domains could be forming cryptic polymers that escape our detection. In any case, when assembly does produce a change in FRET, we can with confidence determine how strongly that form of assembly is governed by concentration. Hence, for CARD9, which does produce a FRET signal upon assembly, we can say that assembly has a smaller intrinsic nucleation barrier than that of BCL10. We further eliminated the possibility of multi-step nucleation (which would reduce the apparent nucleation barrier relative to the one-step ideal case) for CARD9 by showing that artificial condensates of the protein expressed in trans do not influence the concentration-dependence of FRET (Figure 4 B). Finally, under all conditions where CARD9 lacked FRET, it also lacked signaling activity, suggesting there is not a cryptic functional assembly that evades our assay. Likewise MALT1, which lacked FRET at all concentrations, was entirely unable to activate NF-κB upon overexpression (Figure S8 A and B), suggesting that it too is not forming a cryptic functional assembly that evades our assay. We therefore feel confident in our conclusion that CARD9 and MALT1 lack nucleation barriers of a magnitude comparable to that of BCL10. Note that our claim is not that they entirely lack a nucleation barrier (CARD9 after all does form a multi-dimensionally ordered polymer), but rather that we fail to observe a nucleation barrier and hence any barrier that may exist is insufficient to manifest at the cellular level.
3. In the final Results section, the idea that MALT1 activation doesn't depend on BCL10 polymer structure doesn't necessarily follow from the data. An alternative interpretation is that optogenetic clustering of MALT1 causes it to recruit BCL10 and form BCL10-MALT1 filaments (structure solved by Schlauderer et al., 2018). Also, the optogenetic clustering of MALT1 may mimic some structure found in the BCL10 cluster. Therefore, we are neither convinced that the data unambiguously show that MALT1 activation strictly depends on multi-valency rather than an ordered structure of BCL10 polymers nor that this conclusion is truly necessary for the paper.
We agree that the reviewer’s alternative interpretation of this experiment is possible. However, we consider it unlikely because we performed the experiment with MALT1 lacking its Death Domain (residues 126-824), which mediates its interaction with BCL10 (Schlauderer et al., 2018). Our experiments then suggest that MALT1 clustering is sufficient for activation independent of any structuring mediated by BCL10. Nevertheless, we have now performed an additional control in which we treated these cells with PMA to induce BCL10 polymerization. As expected, the NF-κB transcriptional reporter utterly failed to activate in this condition, indicating that MALT1 does not interact with BCL10 polymers when it lacks its death domain. This aspect has been further elaborated in our response to reviewer 3 point 5.
4. What optical density do the yeast cells reach during the 16h induction in galactose? If they are in stationary phase, this could affect the assembly status of the proteins being expressed, as the cytoplasm becomes glassy when cells are starved, and this coincides with widespread protein aggregation/assembly (Joyner et al., 2016; Munder et al., 2016).
In our DAmFRET strategy, we first dilute an overnight culture and regrow the cells to log phase prior to resuspending them in galactose media. Our strain is engineered to undergo cell cycle arrest upon protein induction, hence exponential growth is prevented and the cells do not deplete galactose during the 16 hr induction. We have also performed many time courses of DAmFRET following induction and generally find no qualitative difference between early and late times (unpublished). Early time points simply have lower expression and correspondingly fewer cells in the high FRET state. Importantly, all comparisons between proteins are made with the same 16 hr induction.
5. Although these experiments show that thermodynamically lowering the BCL10 nucleation barrier (e.g. by post-translational modifications or protein expression levels) isn't required for a response, they don't rule it out. It would be good to state this in the discussion, as cells may have multiple mechanisms of switching on the signalosome.
We thank the reviewer for this suggestion and have now explicitly stated in the discussion that our experiments do not argue against possible thermodynamic tuning of the nucleation barrier.
6. The discussion compares signalosomes with condensates formed by liquid-liquid phase separation. This is an interesting comparison but it suggests that disordered assemblies would not be capable of performing signalosome-like functions. This needs to be explained more clearly. For example, non-amyloid prions seem to form gel-like assemblies with a high nucleation barrier that are capable of driving heritable traits, likely through self-templating (Chakravarty et al., 2020). Such examples could represent disordered assemblies with signalosome switch-like behaviour. Furthermore, there are examples of condensates that are induced by environmental changes e.g. Pab1 and Ded1 condensates (Riback et al., 2017; Iserman et al., 2020). This potentially allows the proteins to reach high concentrations and remain un-condensed until a change in heat or pH overcomes a nucleation barrier required for condensate formation. Although the condensates aren't self-templating, they seem to require energy for their disassembly. Combined, this also allows switch-like behaviour, where the switch is flipped back to the uncondensed off state once conditions return to normal. In general, crossing a phase boundary can represent a switch-like response. Finally, recent electron-tomography experiments show that ASC puncta comprise clusters of filaments (Liu et al., 2021, biorxiv). CARD9/BCL10 assemblies may have similar ultrastructures and liquid-liquid phase separation may well play a role in their assembly.
Indeed, we explicitly maintain that liquid phases cannot themselves perform signalosome-like functions. Chakravarty et al. 2020 did not observe amyloids associated with their phenomena, but the relevant experiments were not designed to exhaustively exclude an underlying ordered phase. To the extent that gelation is involved, their observations are fully consistent with ours. IUPAC defines a “gel” as a colloidal network involving a solid phase and a dispersed phase. The existence of a solid phase necessarily implies an underlying disorder-to-order transition, even if limited to small length scales. In the case of gelation associated with liquid-liquid phase separation, nucleation of the ordered phase simply occurs in two steps (first condensation, then ordering). Note also that a liquid phase could in principle give rise to a heritable phenotype if it activates a positive feedback in a molecular biological process involving the protein of interest (e.g. upregulation of its expression or a change in interacting factors). Chakravarty et al. did not exclude such phenomena (it would be very difficult to do so); hence it cannot be concluded that phase separation is responsible for the sustained phenotypic changes.
We do not fully follow the reviewer’s logic concerning the relevance of Pab1 and Ded1 condensates. These proteins only condense when their respective phase boundaries fall below the endogenous protein concentration, as upon thermal stress. The proteins are not supersaturated in the absence of such conditions (for example, they cannot be seeded), and it is incorrect to characterize the change in heat or pH as overcoming a pre-existing nucleation barrier. The concept of a nucleation barrier only applies under conditions where a phase is thermodynamically favored. It is also misleading to state that the Ded1 and Pab1 condensates require energy for disassembly. Rather, they require energy to disassemble rapidly. Unless the assemblies have accessed a more ordered phase as described above (two step nucleation), involving a lower phase boundary, they will inevitably dissolve after the conditions return to normal.
We have much prior experience with ASC. Although it has not been explicitly shown, that it forms ordered polymers and can behave as a prionoid in vivo suggests that it very likely operates the same way as BCL10 (i.e. is physiologically supersaturated). That full-length ASC forms clusters of filaments is not relevant (in our view) to the mechanism shown here, which only requires that filaments are indeed formed. Formally, the size of the relevant nucleus determines the minimum length scale at which ordering must manifest in our mechanism. Based on the structure of death domain filaments, this could be as small as tetramers or hexamers (a minimal but structurally complete “polymer”).
As stated above, and now elaborated in the discussion, our data do not exclude a role of thermodynamic regulation, as could lead to liquid-liquid phase separation, in tuning the nucleation barrier of Bcl10. What they do exclude is that such changes are required for Bcl10 to activate in the first place.
7. Can the authors comment on the loss of BCL10 in Echinodermata, Anthropoda, Nematoda? Is there another protein that plays a similar role? Could a CARD or PCASP protein possess self-templating properties? Could other methods of control be at play e.g. protein expression?
This is a very interesting question! We think the reviewer’s suggested explanations for the loss of BCL10 in those lineages are valid and worthy of future exploration. Nematodes such as C. elegans have lost multiple components of innate immunity. They have very few pathogen recognition receptors and also lack NF-κB! They do, however, have other adaptor proteins that the literature and our unpublished data suggest may have self-templating ability, such as TIR-1. Drosophila also encodes multiple TIR-containing proteins that are essential for innate immunity. In short, it is possible that other proteins have acquired the hypothetically essential role of supersaturation and nucleation-limited signaling in these organisms.
8. Figures 1B/1C: Can the authors comment on why the active cells plateau at about 70-75%? This is a striking feature of the plots, but the explanation may not be obvious to readers.
See our response to major point 3, above.
9. Figures 1D/1E: What was the concentration of B-glucan used in this experiment? This could be included in the figure legend. If greater than 1ug/ml this means that the % of active cells in Figure 1B matches the % of cells with BCL10 assemblies in Figures 1D/1E, which is potentially an important point.
We thank the reviewer for bringing this point to our attention. We have now indicated in the figure legend the concentration of B-glucan used in this experiment (10 μg/ml). That the percentage of active cells in Figure 1B matches that of cells containing BCL10 polymers in Figure 1D and E indeed strengthens the stated relationship between BCL10 assembly and NF-κB activation in THP-1 cells subjected to a relatively physiological stimulus. Additionally, we have performed experiments to measure the levels of p65 translocation in THP-1 cells treated with B-glucan that express BCL10-mEos3.2. This data is shown in Figures S1D and E in response to reviewer 3.
10. Use of both 'BCL10' and 'Bcl10' when referring to the protein.
We have now replaced all instances where Bcl10 was used to follow guidelines for gene and protein name conventions.
Bruford EA, Braschi B, Denny P, Jones TEM, Seal RL, Tweedie S. Guidelines for human gene nomenclature. Nat Genet. 2020;52(8):754-758. doi:10.1038/s41588-020-0669-3
11. In the supplementary figures there are some formatting problems/missing words in the figure legends. In Figure S11 there is a black box covering the lower part of the figure.
We have now fixed these instances.
References used in this review
Chakravarty, A.K. et al. (2020) "A Non-amyloid Prion Particle that Activates a Heritable Gene Expression Program," Molecular Cell, 77(2), pp. 251-265.e9. doi:10.1016/j.molcel.2019.10.028.
Iserman, C. et al. (2020) "Condensation of Ded1p Promotes a Translational Switch from Housekeeping to Stress Protein Production," Cell, 181, pp. 818-831.e19. doi:10.1016/j.cell.2020.04.009.
Joyner, R.P. et al. (2016) "A glucose-starvation response regulates the diffusion of macromolecules," eLife, 5. doi:10.7554/eLife.09376.
Munder, M.C. et al. (2016) "A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy," eLife, 5(MARCH2016). doi:10.7554/eLife.09347.
Riback, J.A. et al. (2017) "Stress-Triggered Phase Separation Is an Adaptive, Evolutionarily Tuned Response," Cell, 168(6), pp. 1028-1040.e19. doi:10.1016/j.cell.2017.02.027.
Schlauderer, F. et al. (2018) "Molecular architecture and regulation of BCL10-MALT1 filaments," Nature Communications 2018 9:1, 9(1), pp. 1-12. doi:10.1038/s41467-018-06573-8.
Reviewer #2 (Significance (Required)):
The existence of a nucleation barrier had already been postulated, based on structural and other studies (referenced by the authors), it had lacked a rigorous demonstration. This work provides that demonstration, which is important for the signalosome field and more broadly applicable to researchers studying cellular decision making. The study further demonstrates that DaMFRET is an excellent to study protein assembly processes in their native environment, allowing the authors to tackle a question that would have been technically very difficult to address otherwise.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The study by Rodriguez Gama et al. addresses the molecular function of CBM complex-forming proteins CARD9, BCL10 and MALT1 in the activation of myeloid cells, using optogenetic tools, transcriptional reporters and biochemical approaches. It is known from previous studies that Bcl10 oligomerizes into filamentous oligomeric structures incorporating Malt1, and that these structures are nucleated by receptor-induced activation of CARD proteins such as CARD11 (in lymphocytes) or CARD9 (in myeloid cells), but the mechanism underlying the assembly of the resulting CBM complexes remain incompletely understood.
The authors develop beautiful optogenetic tools to address this question, and convincingly demonstrate that CARD9-mediated nucleation of BCL10 triggers a binary cellular NF-κB response in a spring-load-like fashion, and identify mutants of BCL10 and CARD9 that impact this capacity. Unfortunately, however, the authors do not do a good job to simplify this complex problem so it can be easily understood. In particular, the choices of mutants, models and experiments are not consistent between figures, and some data seem to be arbitrarily added or omitted. Complex hybrid constructs are also used, without assessing whether these are indeed functional in the corresponding ko cells. The paper would therefore benefit from a major overhaul. We also noticed that the literature is often not cited adequately and have included a (non-exhaustive) list of examples of wrong, incomplete, or erroneous citations below.
1. The initial observations of binary signaling are derived from a reporter system. Although there are controls to show that the reporter used does not function intrinsically cooperatively, it would be nice to see additional data to show that cooperativity occurs also at the level of endogenous response systems, for instance by qPCR-based assessment of a natural NF-κB target gene (induced for example by TNFa versus B-glucan in THP-1 cells, and by TNFa versus PMA in 293T cells).
As detailed in the introduction, NF-κB has been shown by multiple labs to activate in a binary fashion. Our manuscript shows that NF-κB activation occurs in a binary fashion both at the level of transcription and at the level of nuclear translocation (upstream of any transcriptional output). While we do agree that additional data could further illustrate the biological significance of our findings, we do not feel it is necessary for our conclusions. Note also that because NF-κB activation occurs in a binary fashion per cell, a simple qPCR experiment would not suffice to extend our findings to the broader NF-κB regulon. Instead, one would have to use e.g. RNA-FISH or single cell RNA-seq, nontrivial experiments that would take months to complete.
2. The cell lines in Figures 1D-E (and also some of the BCL10 mutants used later on) would have been better run in the assays in the early parts of Figure 1. The final conclusion prior to the section The adaptor protein BCL10 is a nucleation-mediated switch is otherwise not justified. This is a central tenet of the paper, that is referred to again, with some other ancillary data to support it. These mutants reappear later in the paper, but it would have been better, and easier to make rescue lines of BCL10 KO in Figure 1, otherwise the logic is lost, and the models seem chosen arbitrarily.
The choice of experiments in different panels of Figure 1 resulted from a chronological progression of reagent construction as the project evolved. We do appreciate that switching between the assays may lead readers to doubt one or the other. Therefore, we have now immunostained for endogenous p65 in the same experiment as for Figure 1D and confirmed that p65 translocated to the nucleus only in THP-1 BCL10-KO cells that have been reconstituted with WT BCL10-mEos3.2, but not E53R. We think this additional evidence along with our orthogonal measurements in other reporter systems confirms our findings that BCL10 nucleation determines NF-κB activity.
3. Expression with microNS is not well controlled and gives little real evidence for what is occurring. It is unclear what the concentration of the protein expressed was, but certainly the relative expression of the CARD9(CARD) and the microNS version should be assessed.
We believe these concerns result from a misunderstanding. We assume the reviewer is referring to the experiment in Figure 3B. Expression of muNS on its own has no effect on the DAmFRET of other proteins, and we have previously used it in exactly the same way as here (Holliday M et al. 2019 and Kandola T et al. 2021). Please note that muNS fusion proteins in our experiment have an orthogonal fluorescent protein whose spectra do not significantly overlap with those of mEos3.1. The experiment evaluates a protein’s ability, when condensed via its fusion to muNS, to nucleate an mEos3.1-fused protein that is expressed in trans. Fusion of proteins to muNS does not affect their expression levels, as we now show for CARD9CARD-muNS-mCardinal versus CARD9CARD-mCardinal (Figure S6D).
Also, the AmFRET profile of CARD9CARD looks very weird, it cannot be compared to BCL10.
We are unsure in what way the AmFRET profile of CARD9CARD is “weird”. It is fully consistent with expectations and has been thoroughly explained in the text. We suspect the reviewer was bothered by the sharp acquisition of FRET at approximately 100 uM. As explained in the text, this represents the phase boundary, also known as the solubility line, for CARD9CARD polymers, which we previously showed in vitro (Holliday M et al. 2019). Above this concentration, the protein self-assembles without a nucleation barrier, hence the sharp but continuous change in FRET. BCL10 plots, in contrast, show a discontinuous acquisition of FRET, which indicates a nucleation barrier. In order to highlight that the CARD9CARD transition is understood and expected, we have also now added a line to the plot to demarcate the phase boundary.
4. We are not convinced of the usefulness of the introduction of a slew of disease-causing CARD9 mutations that may or may not be relevant to the authors' point. The fact that they do or do not function in a specific sub portion of an assay that may or may not be relevant to biological activity seems to be of interest but without biochemical understanding, little is clear.
While several reports have shown the clinical importance of these CARD9 mutations on susceptibility to fungal infections, little was known about the molecular mechanism underlying their effects. The inclusion of the disease-causing mutants to this paper is justified for the following reasons. First, they demonstrate the relevance of our work to disease. Second, they build off our findings to provide an otherwise unknown molecular mechanism of these mutants. We showed using independent methods that CARD9CARD mutations disrupt the ability to nucleate BCL10, via two different mechanisms. Finally, validating the disease-causing mutations allowed us to use them as controls for subsequent experiments demonstrating that BCL10 is supersaturated.
5. The Optogenetic experiments are interesting, but difficult to interpret without evidence that these MALT1 constructs are indeed still functional when expressed in MALT1-deficient THP-1 cells. We do not therefore think that this experiment shows a necessity for clustering to signal, just a sufficiency, and in a highly artificial construct.
We welcome the opportunity to elaborate on the optogenetic experiments. Since BCL10 and MALT1 are expressed ubiquitously across cell types, the validity of our findings should not depend on the cell type used. Indeed, much of what we already know about innate immunity signalosomes comes from work in HEK293T cells. Our optogenetic experiments using MALT1 were performed in 293T MALT1-KO cells in Figures 6E and F, and employed two distinct functional assays (p65 nuclear translocation and a transcriptional reporter). While our approach employs light to control clustering, similar approaches using (no less-artificial) chemically induced dimerization domains have been used to study caspase activation (Oberst A et al., 2010, Boucher D et al., 2018). Our use of light affords higher specificity, reversibility, and spatial and temporal control over MALT1 assembly than does chemically induced dimerization.
To demonstrate the necessity of clustering, we have now performed an experiment with MALT1(126-824)-miRFP670-Cry2 expressed in 293T MALT1 KO cells that contain a transcriptional reporter of NF-κB ,as in figures 6E and F. We added PMA to the cells and found that it failed to activate NF-κB (Figure 6), confirming that the interaction of MALT1 (via its death domain) with polymerized BCL10 is required for activation. Note that MALT1 and BCL10 exist as a soluble heterodimer prior to BCL10 polymerization; hence it is polymerization, rather than the interaction itself, that activates MALT1. That artificial clustering rescues this defect strongly suggests that the effect of polymerization can be attributed to increased proximity rather than some allosteric effect communicated from BCL10 polymers through the MALT1 DD to its caspase-like domain.
Oberst, A., Pop, C., Tremblay, A.G., Blais, V., Denault, J.-B., Salvesen, G.S., and Green, D.R. (2010). Inducible dimerization and inducible cleavage reveal a requirement for both processes in caspase-8 activation. J. Biol. Chem. 285, 16632–16642.
Boucher, D., Monteleone, M., Coll, R.C., Chen, K.W., Ross, C.M., Teo, J.L., Gomez, G.A., Holley, C.L., Bierschenk, D., Stacey, K.J., et al. (2018). Caspase-1 self-cleavage is an intrinsic mechanism to terminate inflammasome activity. J. Exp. Med. 215, 827–840.
6. In the introduction and other parts of the paper, there are numerous instances where the previous literature in the field is not adequately cited. Examples include:
- In the introduction, it is weird to cite one original paper (a MALT1 ko study by Ruland et al., 2001; there are several other studies of ko papers for CBM components that would merit being citated along with this study) together with two reviews on that topic (Ruland and Hartjes 2019 and Gehring et al. 2018)
- In the introduction, the original study by Wang et al., 2002 should be cited together with Rebeaud et al., 2002; the two studies on the same topic were published back-to-back
- In the introduction, the statement "CARD10 and CARD14 are expressed in nonhematopoietic cells including intestinal and skin epithelia, respectively" should be supported by citations.
- Still in the introduction, the 2 references for the statement "… CARD14 gain of function mutations cause psoriasis (Howes et al., 2016; Jordan et al., 2012)" are not appropriate. There are several reports of patients with CARD14 mutations (the study by Jordan et al. is only one of them) and several CARD14 mouse models that provoke a psoriasis-like phenotype, which would merit being cited.
- In the following sentence: "Point mutations and translocations involving BCL10 and MALT1 cause immunodeficiencies (Ruland and Hartjes, 2019), testicular cancer (Kuper-Hommel et al., 2013), and lymphomas (Zhang et al., 1999).", the citation style also seems completely random, combining the citation of a single original paper for lymphomas (Zhang et al. 1999) (there are several other important original studies on that topic or recent reviews that could be cited instead), together with a review on immunodeficiencies (Ruland and Hartjes, 2019) and then another single example for a role of BCL10 and MALT1 in carcinoma (the study by Kuper-Hommel et al. is one, but several other original publications exist on the latter topic, showing for example a role in breast carcinoma or glioblastoma).
- In the first section of the results, the reference cited for endogenous CARD10 expression in 293T cells (Ruland et al., 2001) is wrong, no endogenous CARD10 expression was assessed in that study
We have now revised the citations mentioned above and other instances to ensure adequate citations in each case.
Reviewer #3 (Significance (Required)):
The paper deals with a complex question, namely how the CBM signalosome assembles and functions to stimulate NF-κB signaling. This question is important to the understanding of pro-inflammatory immune responses and basic life sciences in general. As the focal point of the paper is complex, and tools to study such phenomena are at the limit of technical capabilities, this further increases the potential impact of the work.
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
The characterization of open-ended signalosomes in a number of innate-immunity and cell-death pathways, in particular formed by domains from the death-fold family, has led to the suggestions that these complexes allow a switch-like signalling response suitable for these pathways. It appears that this has been widely accepted. However, these suggestions are based largely on indirect observations and speculation.
Rodriguez-Gama and coworkers have decided to test these suggestions more directly. Their results confirm the suggestions. Based on my own experience, papers that validate widely adopted suggestions are often not considered seriously by top journals, who are looking for hot topics/paradigm-changing/surprising type results. I would urge the editors to consider seriously work such as in this paper, which directly tests important suggestions and does so at a technically high standard. The authors use a range of ingenious approaches, both with recombinant proteins and in cells, and including proteins from organisms from different parts of the evolutionary tree, to support their interpretations, so it is an extensive and high-quality study. I am impressed that so many different fusion proteins with fluorescent tags continued to function as expected, but I guess the authors controlled for this as much as they could.
Having said all this, I do get the feeling the authors are "over-selling" the nucleation barrier aspect of these signalling mechanisms. It is clearly an important and critical aspect of signalling in many systems, but then it is not the only important aspect; a number of other regulatory inputs play a role in different systems. So the statement "Our findings introduce a novel structure-function paradigm" in my view is overstretching things somewhat. Further in the Discussion section, the authors state "Existing explanations for the preponderance of ordered polymers in immune cell signalosomes have centered on the functions of multivalency at steady state, such as scaffolding and sensitivity enhancement resulting from the cooperativity of homo-oligomerization". They cite a small (and non-exhaustive) number of papers discussing this topic; all these include "seeding" or "nucleation" as an important part of the proposed mechanism. So I suggest the authors provide a more balanced discussion of this aspect. Different pathways appear to display a different level of switch-like behaviour, and one thing that the current version of the manuscript is missing is more discussion of other death fold-based systems and how the results on the CBM signalosome apply to these, and also other systems such as TIR domain-based ones, which currently get no mention whatsoever. In the CBM system, there seems to be one main nucleation barrier; can there be more than one in others?
We appreciate the reviewer’s perspective and have now acknowledged in the introduction and discussion additional prior literature that has paved the way for our study. Nevertheless, we maintain -- as now stated in the abstract -- that “our results defy the usual protein structure/function paradigm, and demonstrate that protein structure can evolve via selection for energetic maxima in addition to minima”. We have elaborated in the introduction and discussion how immune signaling provides the functional context in which such a paradigm can evolve, and how our findings uniquely support the paradigm.
One other aspect I need to express some criticism about is attention to detail – especially with a paper focusing on the physics behind biological processes, I would expect a higher standard of getting the terminology and units correct – see specific examples below. This can obviously be fixed easily.
Specific points are listed below. No page or line numbers are provided so I have done my best to make it clear what the comments refer to.
1. Abstract line 6 and throughout: in "NF-κB", the "k" is supposed to be "kappa" (Greek letter) – it stands for "nuclear factor kappa-light-chain-enhancer of activated B cells", not fully defined in the manuscript as far as I can see. Occasionally, small k is also used instead of the small cap K or whatever the authors used most of the time, but I don't think any of them use the Greek letter.
We had indeed used a version of the small “kappa” κ. We have now fixed the cases where we mistakenly used k instead of κ.
2. Page 2 (Introduction) paragraph 2 line 9: period missing at the end of sentence. Same Page 4 (Results: Assembly) paragraph 4 line 3.
This is now fixed.
3. Page 2 (Introduction) paragraph 2 line 15 and throughout: in long sentences, more commas can help help readability, for example before "leading" here. Similar page 15 paragraph 2 line 3 after "Additionally", paragraph 4 line 2 before "which".
We have now included more commas and tried to improve readability throughout.
4. Page 4 (Results: Assembly) paragraph 2 line 2: is "positive feedback" different from "cooperativity"? Is it a broader term that includes cooperativity, nucleation and other mechanisms? It may be useful to introduce some of these terms to avoid confusion by the readers.
“Positive feedback” is the broadest term as it is agnostic to mechanism. “Nucleation” refers to the initiation of a first order phase transition, which is one mechanism of positive feedback. Nucleation involves “cooperativity”, in that a higher order species is more stable than smaller species. However, cooperativity can occur for oligomers of finite size, whereas nucleation is reserved for phase transitions to species of infinite size. We appreciate that the use of so many related terms may have created more confusion than necessary. Hence, we have now revised the text to omit the more general terms -- “positive feedback” and “cooperativity” where possible.
5. Page 4 (Results: Assembly) paragraph 2 line 3: please define "TNF".
We have now fixed this and other acronyms.
6. Page 4 (Results: Assembly) paragraph 3 line 2: the use of size-exclusion chromatography to follow the size of complexes would assume that they are irreversible or very stable. It appears this may be the case here, but some discussion may be warranted.
We have now explained that SEC is appropriate for this experiment because large nucleation barriers generally imply stable assemblies.
7. Page 4 (Results: Assembly) paragraph 3 line 4 and throughout: the symbol for "kilodalton" is "kDa".
We have now fixed this mistake.
8. Page 4 (Results: Assembly) paragraph 3: I am not sure how the results discussed in this paragraph demonstrate that assembly occurs in cooperative fashion – just that there is a change in oligomeric states upon stimulation.
Cooperativity is implied by the absence of oligomer sizes between monomer and the large assembly. Nevertheless, we realized this can only be concluded in the case of homotypic assembly, which we cannot yet assume at this point in the paper. Therefore, we have revised this paragraph to say that the distribution is “consistent with” an underlying phase transition (which we then go on to prove).
9. Page 4 (Results: Assembly) paragraph 4 line 2: "WT" is not defined. Wild-type what? I presume "protein"?
We refer here to the wild-type protein. We have now fixed this mistake.
10. Page 4 (Results: Assembly) paragraph 4: it may be worth pointing out here the wild-type and mutant proteins expressed at similar levels; clearly the outcomes will depend on protein concentration in the cell. I believe the supplementary figure shows this to a large extent.
Indeed, our supplementary figure shows that the WT and mutant protein express to comparable levels. We have now pointed this out in the text.
11. Page 4 (Results: The adaptor) paragraph 1 line 4: "CARD domain" would stand for "caspase activation and recruitment domain domain". Please check throughout (including Supplementary Material).
We have fixed this mistake.
12. Page 4 (Results: The adaptor) paragraph 1 line 9: "expressed over a range of concentrations in cells" – this would imply the authors controlled expression – please rephrase to explain what exactly was done.
We have now rephrased this sentence to indicate that the range of expression results from the use of a genetic construct with cell-to-cell variation in copy number.
13. Page 5 (Results: The adaptor) paragraph 2 line 3 and throughout (including Supplementary Material): please use the Greek letter rather that "u" for micro.
We have now fixed this mistake.
14. Page 5 (Results: The adaptor) paragraph 3: this analysis is rather simplistic, it is not just the RMSD value, it is the nature of conformational change that is important? Please elaborate, I would think the papers presenting structural work have already discussed this to some extent?
The reviewer is correct; it is the nature of the conformational change that is most important. We are unsure how to accurately estimate the energy barrier separating the two conformations for each protein. However, we have now undertaken a collaboration to attempt to do so via FAST molecular simulations (Zimmerman and Bowman 2015). In lieu of the results of these ongoing studies, we have modified the text to acknowledge that RMSD does not necessarily relate to nucleation barriers.
Maxwell I. Zimmerman and Gregory R. Bowman. Journal of Chemical Theory and Computation, 2015, 11 (12), 5747-5757 DOI: 10.1021/acs.jctc.5b00737
15. Page 5 (Results: The adaptor) paragraph 4 line 5 and further in this section: some symbol(s) do not show in the pdf – before "(δ)", next page line 3-5 after "higher" and "both".
We have fixed this issue that resulted from exporting to a PDF file from our text editor.
16. Page 6 (Results: The adaptor) paragraph 4: interface IIa and IIIb are not introduced, and there is not even any reference provided here.
We have now added a reference for these mutations and elaborated on the interfaces IIa and IIIb.
17. Page 6 (Results: Pathogenic) paragraph 1 line 12: "FL" is not introduced.
We have now fixed this mistake.
18. Page 8 (Results: Pathogenic) paragraph 7: the text "absent the pathogenic mutations" is missing something.
We have now reworded this section.
19. Page 10 (Results: BCL10) paragraph 3: why does CARD9 CARD clustering peak and then disassemble (I guess "clustering" doesn't disassemble, please rewrite as well).
We have now fixed this mistake.
20. Page 11 (Results: MALT1) paragraph 1: I presume dimerization doesn't achieve the same level of proximity as higher-order multimerization?
Our interpretation here is that for MALT1, activation requires close proximity of more than two molecules. Although our dimerization module did not activate the caspase-like domain of MALT1, we know that it achieves close enough proximity to activate the caspase domain of CASP8. Hence we believe the MALT1 mechanism has a stoichiometry requirement in addition to a proximity requirement. This is, of course, consistent with the fact that activation normally occurs in the context of polymers rather than dimers.
21. Page 11 (Results: Ancient) paragraph 1 line 4: is this AlphaFold2?
That is correct, we used AlphaFold2. We have added that detail.
22. Page 12 (Discussion) paragraph 4: not sure if "molecular examples of evolutionary spandrels" will be clear to most readers.
We have now explained what evolutionary spandrels are, and elaborated on the relationship to our findings.
23. Page 14 (Materials: Plasmid) line 2 and throughout: "Golden Gate" is usually capitalized. Similar for "Gibson" further in the paragraph. The English in this paragraph is not up to standard in general; for example "Then placing…" is not a complete sentence, and a number of sentences ending with "via gibson" need to be rewritten.
We have now rewritten this paragraph.
24. Page 16 (Materials: Cell) line 4 and throughout: "2" in "CO2" should be subscripted.
This is now fixed.
25. Page 16 (Materials: Transient) line 6 and throughout (including Supplementary Material): please use a space between number and unit ("35 mm").
This is now fixed.
26. Page 16 (Materials: Generation) line 4 and throughout: to distinguish from "gram", please italicize "g" and/or use "x g".
We have now fixed this.
27. Page 17 (Materials: Yeast) line 3: please specify which table is "table X".
We have now fixed this mistake.
28. Page 17 (Materials: Mammalian) line 1: please provide full reference. Same next paragraph line 2.
We have now fixed this.
29. Page 17 (Materials: DAmFRET) line 3: "SSC" and "FSC" are not defined.
We have now fixed this.
30. Page 18 (Materials: Fluorescence) line 10: "Coefficient" does not have to be capitalized. It does not have to be defined again in the next paragraph.
We have now fixed this.
31. Page 19 (Materials: Optogenetic) line 1: "performed" rather than "made"?
We have now fixed this.
32. Page 19 (Materials: Protein) line 12: the Compass software doesn't have a reference?
We have now added the reference to the software.
33. References: please make format consistent: articles titles in sentence or title case.
We have now formatted all references to be consistent.
34. Legend to Figure 1: I suggest "Schematic diagram"; and "h" rather than "hrs"; please check throughout (including Supplementary Material).
We agree with this suggestion.
35. Legend to Figure S1: is "TNF-a" supposed to be "TNF-α"?
We have fixed this.
36. Legend to Figure S7: please capitalize "Figure 2H".
We have fixed this.
37. Legend to Figure S10F: please move "Dox" behind the concentration.
We have fixed this.
38. Figure S14B: the colours in the superposition make it difficult to see the differences.
We have used a different color now.
39. Legend to Figure S14: I suggest "structure…predicted by AlphaFold" (2?) and include the reference.
We agree with this suggestion.
Reviewer #4 (Significance (Required)):
As argued above, the significance of this paper is that it tests directly important hypotheses proposed or assumed previously, and does so at a technically high standard. No published report has done so to a similar extent.
The paper should be of interest to a broad audience from cell biologists and immunologists to biochemists, biophysicists and structural biologists.
My expertise is in structural biology or systems similar to the one studied here.
- Randal Halfmann
- Alejandro Rodriguez Gama
- Randal Halfmann
- Jay R Unruh
- Randal Halfmann
The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
We thank Xiaoqing Song for assistance with immunostaining experiments, and members of the Halfmann lab for critical reading of the manuscript. We are also grateful to the very constructive anonymous reviewers of Review Commons. This work was performed to fulfill, in part, requirements for ARG’s thesis research in the Graduate School of the Stowers Institute for Medical Research. This work was supported by the National Institute of General Medical Sciences (Award Number R01GM130927, to RH) and the National Institute on Aging (Award Number F99AG068511, to ARG) of the National Institutes of Health, the American Cancer Society (RSG-19-217-01-CCG to RH), and the Stowers Institute for Medical Research. The funders had no role in study design, data collection and analysis, or manuscript preparation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at http://www.stowers.org/research/publications/libpb-1675.
- Volker Dötsch, Goethe University, Germany
- Jungsan Sohn, Johns Hopkins University School of Medicine, United States
© 2022, Rodriguez Gama 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.
Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood-borne immune cell types (naive B, naive CD4+ and CD8+ T cells, granulocytes, monocytes, and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousands of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1β) and REST transcription factor (TF) motifs, respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to TF motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.
Infection with Influenza A virus (IAV) causes the well-known symptoms of the flu, including fever, loss of appetite, and excessive sleepiness. These responses, mediated by the brain, will normally disappear once the virus is cleared from the system, but a severe respiratory virus infection may cause long-lasting neurological disturbances. These include encephalitis lethargica and narcolepsy. The mechanisms behind such long lasting changes are unknown. The hypothalamus is a central regulator of the homeostatic response during a viral challenge. To gain insight into the neuronal and non-neuronal molecular changes during an IAV infection, we intranasally infected mice with an H1N1 virus and extracted the brain at different time points. Using single-nucleus RNA sequencing (snRNA-seq) of the hypothalamus, we identify transcriptional effects in all identified cell populations. The snRNA-seq data showed the most pronounced transcriptional response at 3 days past infection, with a strong downregulation of genes across all cell types. General immune processes were mainly impacted in microglia, the brain resident immune cells, where we found increased numbers of cells expressing pro-inflammatory gene networks. In addition, we found that most neuronal cell populations downregulated genes contributing to the energy homeostasis in mitochondria and protein translation in the cytosol, indicating potential reduced cellular and neuronal activity. This might be a preventive mechanism in neuronal cells to avoid intracellular viral replication and attack by phagocytosing cells. The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings.